Analysis by Topic

Future Climate

Climate change is one of the external drivers in Willamette Envision. Daily weather data, such as temperature and precipitation, become input variables for biophysical and human systems modeling components as they simulate land and water system changes over the 21st century. Although every projection made by global climate models indicates a warmer climate, the range of plausible projections is wide, and how they will play out in the Willamette River Basin climate is unknown. To account for the large uncertainty in projected climate, the WW2100 project selected three representative scenarios (High Climate Change, Reference, and Low Climate Change) spanning the range of projections in temperature while including variability in precipitation changes. The WW2100 team then used the daily weather conditions predicted by these three scenarios as model forcings for WW2100 simulations. This page describes the process that we, as the WW2100 climate team, used to select and process data from global climate models so that it could be used as input variables for Willamette Envision. It also summarizes key characteristics of future climate conditions predicted by these models.

Our approach involved the following steps:

  • Assessment of the best available climate models for the Pacific Northwest.
  • Selection of greenhouse gas emissions scenarios.
  • Selection of three representative climate scenarios using a sensitivity approach.
  • Tailoring resulting data to the Willamette River Basin by employing an approach called downscaling.

Our analysis indicates that by 2100, the Willamette River Basin will be between 1° C (2° F) to 7° C (13° F) warmer than today. WW2100’s three representative climate scenarios closely span the spread of this uncertainty and range from 6° C (10.5° F) warming for the High Climate Change scenario, to 1° C (2° F) warming for the Low Climate Change scenario. The Reference Case scenario represents the middle of this range with 4° C (7.5° F) warming over the century.

Climate Methods in Brief

Climate Model Evaluation

The World Climate Research Programme’s Coupled Model Intercomparison Project (CMIP) is a worldwide effort to establish a set of standard experimental protocols for the use of general circulation computer models (also called global climate models), or GCMs, in the development of climate scenarios. Essentially, the CMIP project is an attempt by the world’s climate modelers to improve the performance of GCMs, standardize methods of model evaluation, and make GCM outputs directly comparable.

Results from the CMIP project’s latest phase (Phase 5 or CMIP5) began to be available around the time of the launch of WW2100. CMIP5 ushered in a new wave of GCMs and resulting climate data, and was seen by the WW2100’s climate modeling team as an opportunity to both use the new CMIP5 GCMs in WW2100’s work and assess the performance of the CMIP5 models for the United States Pacific Northwest as a whole.

To that end, the WW2100 climate team assessed 41 GCMs from CMIP5 for their ability to simulate various aspects of climate in the Pacific Northwest. The team’s results were published in the Geophysical Research Letters: Atmospheres, where the researchers’ full results and methods can be reviewed. What follows is a summary of the methods employed and how it aided WW2100.

As the WW2100 team stated in their paper, their goal in evaluating the CMIP5 models was “to evaluate model performance in order to make informed recommendations to those who may use these model outputs” (Rupp et al., 2013). The researchers determined these “downstream” users, including resource managers and other scientists assessing climate impacts, would be best served by GCMs that gave the best statistical fit to the observed climate of the Pacific Northwest. (The team defined the Pacific Northwest as the area within longitude 124.5° and 110.5° W and in latitude 41.5° and 49.5° N, or roughly Oregon, Washington, Idaho, and western Montana.)

To find the best fit, the team evaluated the CMIP5 GCMs according to their ability to re-create in computer simulations the observed historical climate of the 20th century. This hindcasting ran from 1850-2005 and focused on temperature and precipitation. Observed climate data were taken from five gridded datasets of monthly means. A suite of statistics, or metrics, were calculated from both the hindcasts and observations and then compared. These metrics included mean seasonal values, interannual variability, amplitude of the seasonal cycle, consistency in spatial patterns, and sensitivity to the El Niño Southern Oscillation, among others. The researchers used two methods for ranking the performance of the GCMs based on these metrics: The first method assigned equal weight to each of the metrics. The second method excluded those metrics that were not considered robust. This involved ranking the metrics under the assumption that certain metrics might be more important in the assessment process. It also included an attempt to avoid redundancy, given that not all metrics are independent of one another.

The result was a ranking of the models according to metrics that led to a subset of GCMs that the team’s methods determined were the best statistical fit for the climate of the Pacific Northwest.

Illustration of CMIP5 Climate Models for the PNW.

Figure 1. A depiction of the climate model assessment work done for WW2100. Models are listed at the bottom. On the left are meteorological measures, including temperature and precipitation. The graph depicts a relative error, in this case how well the models compare relative to each other when matched against actual historical measures for the Northwest. Here warm colors depict higher degrees of error and cooler colors less error. The models are organized from left (least error) to right (most error). (Image Source: Rupp et al., 2013)

Emissions Scenarios Selection

The biggest certainty in climate science is that increasing greenhouse gas (GHG) concentrations, especially carbon dioxide, are heating the Earth’s atmosphere. Precisely how much regional climate temperatures will increase in response to a given rise in GHGs is not known, which is why climate researchers examine more than one GCM. However, the biggest uncertainty about future climate by the end of the 21st century stems from not knowing just how much GHG human industry will continue to emit.

With their chosen models, the WW2100’s climate team used GCM output (available from the CMIP5 project) for two emission scenarios to incorporate a range of GHG concentration uncertainty. These emission scenarios are known as Representative Concentration Pathways, or RCPs, a category created by researchers convened to support the work of the United Nation’s Intergovernmental Panel on Climate Change (IPCC).

RCPs are the new standard for modeling emission uncertainty. There are four RCPs representing different concentrations of greenhouse gases, or different possible futures based on how much GHGs human industry might emit. These scenarios are: RCP 8.5, RCP 6, RCP 4.5, and RCP 2.6. Here, higher numbers represent a greater degree of radiative forcing (in terms of W m2 over preindustrial levels; e.g., 8.5 = 8.5 W m2) that the scenarios are expected to produce by 2100 (RCP 8.5 is bigger than RCP 6 and so on) and, hence, represents a future scenario with more emissions.

For WW2100,  we employed two RCPs: RCP 8.5 (the high emissions scenario that assumes human industry will continue to emit greenhouse gases at a growing rate) and RCP 4.5 (a middle-of-the-road scenario in which emissions will be curbed starting in the middle of this century).

Selecting Representative Climate Scenarios Using a Sensitivity Approach

Resource managers and other downstream users of GCM data often lack the ability to process data from multiple climate models and scenarios, which requires considerable computing resources. This is especially true when considering other types of future scenarios in their impacts work, such as economics, demographics, and land uses, which require still more computing resources. What is often done instead is to select a subset of representative models and scenarios. For WW2100,  we selected a subset of three “representative” scenarios, that is scenarios that are representative of GCMs run with the RCPs, the data from which could then be fed into WW2100’s latter modeling.

To find their subset of scenarios, the team conducted a sensitivity analysis in the Willamette River Basin that allowed them to select three representative GCMs from the 33 CMIP5 climate models for which future climate scenarios were available (from the 41 GCMs evaluated in the first step).

The sensitivity analysis used simple perturbation experiments to estimate how changes in temperature and precipitation affect summertime streamflow in the Willamette River Basin. More specifically, using the Variable Infiltration Capacity (VIC) Macroscale Hydrologic Model, we simulated streamflow using historical weather data from 1975-2004. Then, we ran a perturbation: Using the same set up, we ran the hydrologic model again, but with incremental increases in temperature. The percent in which summertime streamflow changes in these perturbation experiments provides an estimate for how sensitive it will be in a warmer climate. The same type of perturbation experiments were then repeated, but this time for incremental precipitation increases and decreases.

From this, we then used these derived sensitivities to draw contours of constant summertime streamflow change on a scatter plot of temperature and precipitation changes in GCM output. With the contours as guides, the team selected GCMs and accompanying RCPs with the objective of spanning a wide range of warming (high, middle, and low) while also spanning a wide range of hydrological impact. Where multiple models were available to choose from in each category (high, middle, low), the team chose one of the better performing GCMs according to the model ranking discussed above.

Representative scenarios selection plot for summertime streamflow change in the Willamette River basin.

Figure 2. Representative scenarios selection plot for summertime streamflow change in the Willamette River Basin. Contours represent constant change in streamflow calculated from perturbation experiments. GCM precipitation and temperature changes are based on differences between 1970-1999 and projected changes for the period 2041-2070. Climate models are listed on the right. Models were run using the emissions scenarios RCP 4.5 (low scenario) and RCP 8.5 (high scenario), shown in blue and red respectively. (Image Source: Vano et al., 2015)

From this analysis, three scenarios, now a combination of GCMs that had been run with the two separate RCPs, were ultimately selected.

The final representative selections are: the High Climate Change scenario (the HadGEM2−ES climate model run with RCP 8.5); the Reference Case scenario (the MIROC5 climate model run with RCP 8.5); and the Low Climate Change scenario (the GFDL−ESM2M climate model run with RCP 4.5). Note: GFDL-ESM2M was not ranked highly by performance, but it was selected nonetheless to represent the Low Climate Change scenario, as none of the lowest-warming models were ranked highly. Hereafter the scenarios will be referred to as HighClim, Reference, and LowClim.

The LowClim scenario represents a small temperature increase and small decrease in summertime streamflow (lowest impact). The HighClim scenario represents a large temperature increase and large decrease in summertime streamflows (highest projected impacts). The Reference scenario lies between the two extremes.

It’s worth noting that an additional constraint was placed on the selection process: that all requisite data for a given GCM was available for the downscaling procedure (see section on downscaling below). This constraint ultimately limited the team to choosing from among 20 GCMs.

Downscaling

In order to feed data from the HighClim, Reference, and LowClim scenarios into WW2100’s later modeling work, the team performed a process called downscaling. Downscaling is a means to convert or translate the coarse resolution of GCM grids (which are as large as 375 km, roughly 233 miles, to a side) down to a finer resolution, which for WW2100 was about 4 km, roughly 2.5 miles. This is done to account for the details of local topography and local climate. This adjustment is needed in the Pacific Northwest, which has a complex mountainous topography that does not appear in a detailed form in the GCMs.

For the WW2100, we downscaled data from the HighClim, Reference, and LowClim scenarios for the Willamette Valley. The method used was the Multivariate Adaptive Constructed Analogs (MACA), a downscaling method developed by University of Idaho (UI) researcher John Abatzoglou. Resulting data from the downscaling was then fed into the Envision model’s component models.

 

Select Findings from Climate Analysis

By 2100, the Willamette River Basin is projected to be between 1° Celsius (2° Fahrenheit) and 7° C (13° F) warmer than in the recent past (1950-2005). Here we summarize climate projections for the Willamette River Basin determined from analysis and downscaling of global climate models and provide context for the three WW2100 climate scenarios.
 

Temperature

  • By 2100, the Willamette River Basin is projected to be between 1° C (2° F) and 7° C (13° F) warmer than today. This conclusion is based on two greenhouse gas (GHG) concentration pathways, also called emissions scenarios, with output from 20 global climate models (GCM) from the Coupled Model Intercomparison Project Phase 5.

  • WW2100’s three representative climate scenarios closely span the spread of this uncertainty and range from 6° C (10.5° F) warming for the High Change Climate (HighClim) scenario to 4° C (7.5° F) warming for the Reference Case scenario to 1° C (2° F) warming for the Low Change Climate (LowClim) scenario.

  • Warming from increasing anthropogenic GHG concentrations dominates the long-term variability in temperature. Projected temperature increases on the decadal scale (or decades-long scale) exceed natural variability such that the Willamette River Basin does not experience the climate of the latter 20th century during any decade from the present through 2100 (and beyond).

  • The summer months of July through September, already the warmest months of the year, are projected to warm most under climate change, by about 2° C °(3.6° F) more than in winter.

showing the four RCPs and their emissions trajectories over the 21st century

Figure 3. The four RCPs and their emissions trajectories over the 21st century. The largest uncertainty in climate modeling is how much greenhouse gas (GHG) humans will continue to emit into the atmosphere and thus how much trapped energy from the sun will continue to heat the atmosphere. To account for this uncertainty, the United Nation’s Intergovernmental Panel on Climate Change (IPCC) has created four emissions scenarios, called the Representative Concentration Pathways (RCPs), to help streamline climate modeling. (RCPs replace the older Special Report on Emissions Scenarios [SRES].) The RCPs are: RCP 8.5, RCP 6, RCP 4.5, and RCP 2.6. The four RCPs represent different GHG concentrations. Higher numbers represent a greater degree of additional radiative forcing above preindustrial levels by 2100 (RCP 8.5 is bigger than RCP 6 and so on). For WW2100,  we employed two RCPs: RCP 8.5 (the high emissions scenario, which assumes human industry will continue to emit greenhouse gases at a growing rate) and RCP 4.5 (a middle-of-the-road scenario in which emissions will be curbed starting in the middle of the 21st century).

Differences in annual temperature for 1950-2100 from a historical baseline (mean of 1950-2005).

Figure 4. Temperature projections for the Willamette Basin with WW2100 scenarios. Differences in annual temperature for 1950-2100 from a historical baseline (mean of 1950-2005). Results are from 40 downscaled climate simulations, employing 20 CMIP5 GCMs and two GHG concentration pathways (RCP 4.5 and RCP 8.5). The simulated historical temperatures (with known GHG concentrations) are shown in gray. Future projections with assumed GHG concentrations are color-coded: yellow for the lower GHG concentration pathway (RCP 4.5) and red for the high GHG concentration pathway (RCP 8.5); orange denotes where two RCPs intersect. Overlaying this are WW2100’s representative climate scenarios: HighClim, Reference, andLowClim. The representative scenarios are combinations of three climate models (HadGEM2−ES, MIROC5, and GFDL−ESM2M) run with RCP 4.5 and RCP 8.5: The HighClim scenario was run with RCP 8.5; the Reference scenario was run with RCP 8.5; and the LowClim scenario was run with RCP 4.5. (See Fig. 1 for explanation of RCP emissions scenarios.) Note: the three representative scenarios track closely the range of uncertainty resulting from the multi-model ensemble runs.

Changes in mean temperature by month for the period 2050-2099 from the historical period 1950-1999 for the Willamette River basin

Figure 5. Change in mean temperature. Changes in mean temperature by month for the period 2050-2099 from the historical period 1950-1999 for the Willamette River Basin. The October-to-October timeframe shows the water year, which runs from 1 October to 30 September of any given year. Note: WW2100’s representative climate scenarios largely span the range of the uncertainty of the projected temperature changes at the high, medium, and low ends of the temperature distribution. HighClim is represented in blue; Reference Case in represented in purple; LowClim is represented in pink.

Precipitation

  • The majority of climate scenarios show a general trend of wetter winters and drier summers in the Willamette River Basin. However, unlike with temperature projections that uniformly show temperatures will rise, climate models do not unanimously simulate either a drier or wetter future.
  • Increases in winter precipitation stem mainly from heavier precipitation during wet periods, not an increase in the frequency of precipitation.
  • Natural variability will remain large relative to the greenhouse gas response, even at the decadal scale, so that yearly and decadal precipitation both above and below the historical averages should still be expected.
  • Due to rising temperatures, precipitation is increasingly likely to fall as rain instead of as snow, resulting in a decreased snowpack. The snowpack (measured as snow water equivalent: SWE) as a proportion of cumulative water-year precipitation (P) is expected to decline markedly across the region. A parallel study by members of the WW2100 team shows that those sub-basins that historically receive the most snow, such as North Santiam, have projected winter (December, January, and February) declines of one-quarter to two-thirds in SWE/P by about the mid-21st century. Sub-basins with little snow currently, such as Middle Willamette, are projected to receive virtually no snow in the future. The small projected increases in total winter precipitation provide little offset to the loss in snow due to projected warming
  • For every 1° C (~2° F) increase in annual mean temperature, there is a roughly 15 percent decrease in summer flow in the lower Willamette River Basin. However, as temperatures get significantly higher than the historical average, the spring snowpack is essentially absent. Thus, additional temperature increases have only a marginal effect on streamflow.

Projected differences (as percentages) in annual precipitation for 1950-2100 from a historical baseline (mean of 1950-2005).

Figure 6. Projected differences (as percentages) in annual precipitation for 1950-2100 from a historical baseline (mean of 1950-2005). Here the zero line represents historical climate; changes above and below the line represent more or less precipitation respectively. Note: The majority of climate models run for WW2100 are projecting changes to annual precipitation that do not deviate largely from our region’s historical climate. This means natural variability is expected to play a larger role in precipitation trends into the future than forcing from GHGs. However, the majority of models are trending toward wetter winters and drier summers (See Fig. 7). The simulated historical precipitation (with known GHG concentrations) is shown in gray. Future projections with assumed GHG concentrations are color-coded: pale blue for the lower emissions scenario (RCP 4.5) and dark blue for the high emissions scenario (RCP 8.5); medium blue denotes where two RCPs intersect. Overlaying this are WW2100’s representative climate scenarios: HighClim, Reference, and LowClim. The representative scenarios are combinations of three climate models (HadGEM2−ES, MIROC5, and GFDL−ESM2M) run with RCP 4.5 and RCP 8.5: The HighClim scenario was run with RCP 8.5; the Reference scenario was run with RCP 8.5; and the LowClim scenario was run with RCP 4.5. Note that the three representative scenarios include both drier and wetter than average periods.

Changes in mean monthly precipitation for the period 2050-2099 from the period 1950-1999.Figure 7. Changes in mean monthly precipitation for the period 2050-2099 from the period 1950-1999. Output for the majority of climate models run done for WW2100 are showing a tendency toward wetter winters and drier summers. The October-to-October time frame shows the water year, which runs from 1 October to 30 September of any given year. Here, the zero line represents historical climate. Note: the tendency to rise above the zero line (get wetter) in winter and to drop below the zero line in summer (get drier).

 

Related Publications & Links

 

Contributors to WW2100 Climate Research

  • Philip Mote, Oregon State University (OSU) Oregon Climate Change Research Institute (OCCRI) and the Pacific Northwest Climate Impacts Research Consortium (CIRC) (lead)
  • David Rupp, OSU OCCRI/CIRC
  • Anne Nolin, OSU College of Earth, Ocean, and Atmospheric Sciences
  • Kathie Dello, OSU OCCRI/CIRC
  • Julie Vano, OSU OCCRI/CIRC
  • Dennis Lettenmaier, formerly University of Washington CIRC
  • John Abatzoglou, University of Idaho (UI) CIRC
  • Katherine Hegewisch, UI CIRC
  • Hamid Moradkhani, Portland State University Civil & Environmental Engineering

 

Web page authors: P. Mote, D. Rupp, J. Vano, N. Gilles
Last updated: November 2015

 

Population, Income & Land Use

Population and income are key drivers of land value for developed uses. As population and earning potential increase, demand for developed urban land also increases, transitioning land away from agricultural and forest uses while also driving up land value. Understanding future population and income growth is paramount to anticipating shifts in land use and the resulting impacts on water.

The land-use team developed modeling components within Willamette Envision to simulate how land moves among agricultural, forest, and urban uses in the Willamette Valley. Future population and income growth in the region were assumed to be determined by forces outside the model, and thus, like climate change, were external to any actions by individuals or policy makers in the basin. Our model also represented the value of land in agricultural and forest uses, which is determined by site-specific factors such as slope, elevation, and access to water for irrigation. Within the model, relative values of land in different uses determined changes in land use over time. Urban growth boundaries (UGBs), an integral part of the land-use planning system in Oregon, constrained where development can occur and were adjusted periodically as a city’s population grew.  

In the Reference Case scenario, the area of developed land increases by 54% between 2010 and 2100, and the areas of land in agriculture and forest decrease by 8% and 1%, respectively.  Much of the future development is projected to occur in the Portland Metropolitan Area.

 

Land Use Change Modeling in Brief

The land-use team simulated how land moves among agricultural, forest, and urban uses. These transitions are a function of economic returns to alternative uses, which are determined themselves by site characteristics such as farm rents, distances to cities, and population and income of cities. Returns to land and land-use transitions also are influenced by urban growth boundaries (UGBs), the land use planning lines that restrict development. Here we provide a brief explanation of land-use change modeling in Willamette Envision. For a more detailed explanation refer to Jaeger et al. (2016) and Bigelow (2015).

During a model run, Willamette Envision simulates land-use changes annually at the scale of parcels (referred to as Integrated Decision Units or IDUs within Willamette Envision). Growth in population and income increase the returns to developed uses relative to forest and farm uses, while agricultural land values are influenced by the availability of water for irrigation. We derived functions that estimate the economic returns to each land use from historical data for the Willamette Valley (Bigelow, 2015). In response to changes in the relative returns to different uses, IDUs may shift among agricultural, forest, and urban uses as the model runs. Land-use changes in the model are governed by probabilistic equations estimated with historical data from the National Resources Inventory (NRI).

To account for zoning rules under Oregon’s land-use planning system, the model treats land inside and outside of UGBs differently. Land outside of UGBs can move between undeveloped uses (i.e., ag-to-forest and forest-to-ag transitions are allowed), but transitions to developed use are not allowed. For IDUs inside of UGBs, all of the transitions are allowed, but development is treated as irreversible (i.e., once a forest or agricultural IDU is developed, it cannot return to its original use). Given the irreversibility of development, it follows that, over time, the share of developed land within each UGB will increase. To mimic the land-use planning process, we allowed for UGBs to expand once the developed share became sufficiently large. Once a specified threshold is exceeded, a UGB expansion will be triggered. UGB expansions occurred in a way that approximated rules under the land-use planning system. For example, land zoned for Exclusive Farm Use and Forest Conservation will be brought inside of UGBs only when other opportunities are exhausted. In the case of the Portland Metropolitan Area, priority is given to areas designated as urban reserves.

Population and Income Growth

Willamette Envision treats population and income growth as external drivers, forces that are determined outside of the model and can be varied for different modeling scenarios. The population and income projections adopted for the Reference Case and many other WW2100 modeling scenarios, were based on forecasts from the Oregon Office of Economic Analysis (2011; OEA) and Woods and Poole Economics, Inc. (2011). The OEA forecasts population for each county to 2050, while Woods and Poole forecast mean household total personal income to 2040 (in inflation-adjusted dollars). We used linear extrapolation to 2100, which implied diminishing growth rates over time for both variables (Fig.1). Projected county population was allocated to areas within UGBs and rural residential zones based on population percentages in the 2010 Census. Rural residential zones were allowed population growth until they reached a density of one household for every two acres of land. Once these areas were full, all new population was added within UGBs. All cities within a county were assumed to have the projected mean household income.

 

Selected Findings from Land Use Change Analysis

Population and Income

  • Over the period 2010 to 2100, population is projected to increase in every county in the Willamette Basin, although the increases in absolute terms are largest for Washington, Multnomah, and Clackamas counties (Fig. 1). The basin population is projected to increase by 3.05 million people, a gain of 111%.

  • Mean household total person income increases from a basin average of $83,893 in 2010 to an average of $233,479 in 2100 (Fig. 1). These figures are reported in constant 2005 dollars to remove the effects of inflation. Although these changes may seem large, the growth rate in income was much higher over the previous 90-year period (1920 to 2010).

Figure 1a. Population projections to 2100.

Figure 1b. Income projections to 2100.

Figure 1. Willamette Water 2100 population and income projections.

Land Use

  • In the Reference Case scenario, the area of land in developed use increases from 334,921 acres to 516,053 acres between 2010 and 2100, an increase of 54% (Fig. 2). These increases are mirrored by declines in agricultural land (117,751 acres or -8%) and forest land (63,376 acres or -1%).

  • Much of the increase in developed land is projected to occur in the Portland Metropolitan Area (Fig. 3). This reflects the relatively large increases in population forecasted for the three Metro counties.

  • The inflation-adjusted value of land in developed uses increases through the basin, but reaches the highest levels in the Portland Metropolitan Area (Fig. 4). The average value of land in agriculture declines by 8% between 2010 and 2100, and the forest value remains relatively constant.

  • Population densities (the ratio of population to developed land area) are projected to increase in Salem, but remain relatively constant in the other cities in the basin. Population growth has competing effects on densities, raising the level of population but also the area of developed land. In addition, the area of developed land consumed per household increases over time.

  • The projected area of land in developed use is higher in the High Population Growth (HighPop) scenario and the Urban Expansion (UrbExpand) scenario. The HighPop scenario includes population growth rates within UGBs that are doubled relative to the Reference scenario. The UrbExpand scenario relaxes the threshold requirement for UGB expansions and eliminates urban reserves for Portland Metro.

Projected land use change for the Reference scenario.

Figure 2. Projected land-use change in the Willamette Basin for the Reference Case scenario.

Map showing urban expansion in the Reference scenario.

Figure 3. Projected urbanization patterns for the Reference Case scenario.

Developed land values for the Reference scenario.

Figure 4. Projected values of developed land for the Reference Case scenario.

Population density by county/region for the Reference scenario.

Figure 5. Projected population densities for the Reference Case scenario.

Projected growth in developed land area for several WW2100 scenarios.

Figure 6. Projected developed land area for the Reference Case and two alternative scenarios.

Conclusions

Population and income growth in the Reference Case scenario raise the demand for urban land in the Willamette Basin, which raises the value of developed land over agricultural and forest land. This increases the area of developed land by 54% between 2010 and 2100, while decreasing agricultural and forest land areas by -8% and -1%, respectively. The land-use planning system determines where this future urbanization can occur. We project that the largest increases in developed area will occur within the Portland Metropolitan Area.

Notes, Related Links & Publications

  • Bigelow, D.P. (2015). How do population growth, land-use regulations, and precipitation patterns affect water use? A fine-scale empirical analysis of landscape change. PhD Dissertation. Oregon State University. http://hdl.handle.net/1957/56105 

  • Note: We conducted a statistical analysis of land values using data collected from assessor offices in Benton, Lane, Marion, and Washington counties. Details are provided in Bigelow (2015). The land-use transition model was adapted from the estimates in Lewis et al. (2012). 

Contributors to WW2100 Land Use Research

  • Andrew Plantinga, UC Santa Barbara - Bren School of Environmental Science & Management (lead)

  • Daniel Bigelow, PhD Student, OSU Applied Economics (graduated: 2015)

  • David Conklin, Oregon Freshwater Simulations

References

Bigelow, D.P. (2015). How do population growth, land-use regulations, and precipitation patterns affect water use? A fine-scale empirical analysis of landscape change. PhD Dissertation. Oregon State University.

Jaeger et. al. (2016). Scarcity amid abundance: Water, climate change, and the policy role of regional system models. Manuscript in preparation.

Lewis, D. J., Plantinga, A. J., Nelson, E., & Polasky, S. (2011). The efficiency of voluntary incentive policies for preventing biodiversity loss. Resource and Energy Economics, 33(1), 192-211.

Oregon Office of Economic Analysis. (2011). Forecasts of Oregon's County Populations and Components of Change, 2010 – 2050, Salem, Oregon.

Woods and Poole Economics, Inc. (2011). 2011 Idaho, Washington, and Oregon State Profile. Washington, DC. https://www.woodsandpoole.com

 

Webpage authors: D. Bigelow, A. Plantinga
Last updated: September 2016

Upland Forest Change

Today the Willamette River Basin’s vegetation is predominately a mix of grasslands and croplands in the valley floor and coniferous forest in the uplands. This vegetation mix is expected to change as rising temperatures create a less-favorable climate for existing vegetation and as forest fires increase in frequency and intensity. WW2100 upland forest modeling simulated how climate change is likely to affect forest composition, forest area burned by wildfires, and the resulting impact on timber harvest and evapotranspiration. Our results suggest that climate change will become an increasing influence on forest management decisions throughout the 21st century. In our simulations, low snowpack and hotter, drier summers lead to a two- to nine-times increase in land area burned by forest wildfires. The fires open up lands to transition to new forest types better suited to the changing climate. At high elevations, cool conifer forests replace subalpine forests. At mid-elevations, Douglas-fir and western hemlock forest types shift to mixed forest types. Increases in wildfire reduce the availability of forestland for timber harvest and affect hydrology.

 

Forest Modeling in Brief

The forest modeling team developed component models for Willamette Envision that simulate how upland forests will age and change through time, given forest type, climate conditions, and disturbance by wildfire and harvest. Here we provide a brief explanation of forest modeling in Willamette Envision. For full details on methods and results from WW2100 forest modeling studies, refer to Turner et al. (2015, 2016).

On an annual basis, Willamette Envision determines the forest type and age in each modeling polygon based on information from models of forest growth and succession (called forest state-and-transition models, STMs). The STMs determine the sequence of forest types that occur over time. In the simplest case, the forest progresses from new growth following a disturbance, through different successional stages, and ultimately to an old growth forest state. Depending on the type and timing of disturbances, forest growth and succession can follow alternate pathways specified by different STMs. When a disturbance occurs, the forest can also “reset” to a new forest type better suited to the current climate conditions. These new “potential” vegetation types were determined for the three WW2100 climate scenarios using offline runs of a dynamic global vegetation model called MC2. MC2 simulates wildfire occurrence and simulates the type of vegetation best suited to grow at a location based on climate, soil, elevation, and latitude. 

Willamette Envision simulates forest harvest on the landscape, according to criteria defined for each scenario. For example, a scenario can specify a harvest rate (the total area of forest harvested each year) for specific forest age classes and land ownership categories (e.g., private lands with forests older than 40 years). Over the simulation, Willamette Envision randomly selects modeling polygons that meet the criteria for harvest. Users can also prescribe the extent of wildfire (the forest area burned per year), and Envision places fires randomly on the landscape. In WW2100, the extent of wildfire was determined from historical observations and the offline runs of MC2 for the three WW2100 climate scenarios. MC2 takes into account factors such as air temperature, relative humidity, and ensuing forest moisture conditions, and determines the area of forests that burn each year. Hotter, drier conditions lead to more extensive wildfires.

Additional Modeling Details:

  • The initial condition of the landscape, which classifies different species of vegetation, and state and transition models (STMs) were based on work from the Integrated Landscape Assessment Project (ILAP) (Halofsky et al., 2014; INR, 2013). Boundaries for land ownership and protection status were from the US Geological Survey (GAP, 2014).

  • Harvest rates in the Reference Case scenario were based on the observed harvest rate from 1986-2010 in the Willamette River Basin (Kennedy et al., 2010; Kennedy et al., 2012). These Landsat-based observations suggested a harvest rate of 1.3% per year across all private forestland, equivalent to ~11,740 hectares per year (29,000 acres) and 0.5% per year on public lands, equivalent to ~3,240 hectares per year (8,006 acres). Harvest on public lands was limited to unreserved stands with ages between 40-80 years, since older forests are largely conserved for wildlife on public lands.

  • The initial probability of fire in the Reference Case scenario were based on observations of the Landsat record (Kennedy et al., 2012), and we did not stratify by ownership class. Over the 1986-2010 period, 0.2% per year of forestland area was burned in the Willamette River Basin. The future extent of fire was based on the MC2 results, thus capturing the increasing incidence of fire associated with a warming climate. Annual area burned was input to Willamette Envision, and fires were placed randomly on the landscape. Fire size was 22,500 ha except when only a fraction of that was needed to reach the prescribed total area burned.

  • We assigned values for Leaf Area Index (LAI), a measure of forest canopy cover, for each forest type and stand age class based on off-line runs of the Biome-BGC productivity model (Thornton et al., 2002). LAI is used to estimate forest evapotranspiration in hydrologic modeling.

 

Select Findings from Upland Forest Analysis

From the valley floor east to the crest of the Cascade Range, air temperatures decrease and precipitation increases as elevation rises (Fig. 1). These gradients drive a change from maritime conifer to cool needleleaf forest and ultimately to subalpine conifer forest. This vegetation mix is expected to change as rising temperatures create a less-favorable climate for existing vegetation and as forest fires increase in frequency and intensity. Here we highlight some of the key findings from WW2100 forest modeling. For more detailed analysis, refer to Turner et al. (2015, 2016).

Figure 1. The Willamette River Basin study domain: a) Vegetation cover and conifer age class, b) Elevation.

Figure 1. The Willamette River Basin study domain: a) Vegetation cover and conifer age class, b) Elevation. (Figure from Turner, 2015)

Wildfire

  • Under the various climate scenarios, frequency of wildfires relative to the historical period increases as temperature increases.
  • Under the Low Climate Change (LowClim) scenario, the area burned per year is only slightly below the historical rate.
  • Under the Reference Case (Reference) scenario, the simulated forest area burned per decade in the 2010-2100 period is 0.6% per year (vs. 0.2% per year in recent decades). Fire tends to be concentrated in particular years, with as much as 25% of the forested area burning in a high fire year late in the 21st century (Fig. 2).
  • The High Climate Change (HighClim) scenario (warmest temperatures) induces the largest areas of fire per year, with average area burned per year increasing by a factor of nine relative to the historical period.
  • In the Reference and HighClim scenarios, the proportion of the Willamette River Basin that is recently burned and relatively open increases. (Note: WW2100’s simulations did not include pest and pathogen disturbances, which are also likely to increase with climate warming.) More relatively open areas result in lower mean leaf area. These decreases in leaf area lead to reduced growing season evapotranspiration, despite higher evaporative demand due to higher temperatures.
  • Several of the WW2100 alternative scenarios influence the forest uplands. In the case of the Upland Wildfire Suppression (FireSuppress) scenario, the incidence of fire is maintained at the contemporary rate (0.2% of area per year). This assumption results in an increase in the proportion of the forest area with a difference (disequilibrium) between climate and potential vegetation type. In the Extreme scenario, the average area burned rises to 0.8% per year. Results in terms of vegetation change are similar to the Reference scenario. In the Managed scenario, the assumed fire rate is low relative to the Reference scenario and rotation age is reduced. These assumptions mean that the landscape is able to sustain contemporary rates of harvest on public and private lands.

Figure 2. Area burned per year in the three climate scenarios.

Figure 2. Total area burned in the Willamette Basin: a) LowClim, b) Reference, c) HighClim.

Harvest

  • Under climate change, the increased prevalence and power of forest fires is expected to affect how and if trees, such as the commercially important Douglas-fir, are harvested. The WW2100 forest team found that as temperatures increase from 2010-2100 (note: all three representative scenarios show some degree of warming), forest fires increase and mature forests available for harvest correspondingly decrease. (Worth noting in these results: Today’s forest age class distribution in the Willamette River Basin differs substantially between public and privately owned forestland. A significantly larger proportion of the forestland in older age classes are on public land, which also tends to be at higher elevations.)
  • Under the LowClim and Reference scenarios, the harvest rate on private land is stable at about 1.5% of the area per year, and the harvest rate on public lands is stable at a rate of about 0.5% per year (Fig. 3).
  • Under the more severe fire regimes in the HighClim scenario, harvestable forest area decreases, such that the harvest rate begins declining towards the end of the century on both private and public forestland (Fig. 3).

Figure 3.  Area harvested per year (public and private):  a) LowClim, b) Reference, c) HighClim.

Figure 3. Area harvested per year (public and private) in three Willamette Water 2100 modeling scenarios: a) LowClim, b) Reference, c) HighClim.  (Figure from Turner, 2015)

Vegetation Shifts

Plant species in the Northern Hemisphere are moving to higher latitudes and elevations in response to climate change. These climate-induced shifts in vegetation are due primarily to rising temperatures, as plant species migrate to areas with temperatures ranges they are adapted to. This shift in vegetation, already observed in the Northwest, is expected to continue under climate change. WW2100’s forest team produced the following findings concerning vegetation shifts and climate change for the Willamette River Basin:

  • The climate-induced shifts in potential vegetation cover type for the Willamette River Basin under the three climate scenarios are proportional to the magnitude of the climate change (Fig. 4). Under the LowClim scenario (least warming), there is little change in potential vegetation type, whereas with the HighClim scenario (highest warming) the potential vegetation cover type changes over the entire Willamette River Basin by the end of the 21st century.
  • Existing forest vegetation types tend to be replaced by other types of forest.
  • Subalpine forests, now dominated by Subalpine fir, are replaced by cool conifer forests, such as the Pacific silver fir.
  • At the mid-elevations, the maritime conifer forest, generally associated with Douglas-fir and western hemlock, shift to more mixed forest types with hardwood species such as the Big Leaf Maple, and conifer species such as the Grand fir increasing in dominance.
  • Potential vegetation cover type in the relatively low elevation Willamette Valley periphery change from maritime conifer to subtropical mixed forest in the Reference and HighClim scenarios, here driven by the coldest month temperature. The most likely broadleaf species to replace the existing Willamette Valley maritime conifer species are the Pacific madrone and tanoak. Both species currently dominate at mid-elevations in the Sierra Nevada Mountains 800 km (500 miles) to the south. These evergreen broadleaf species would benefit from the projected warmer winters and can tolerate drier summers.
  • The change in the actual vegetation type following a disturbance lags the change in potential vegetation type, meaning what vegetation type the climate could support based on the MC2 model runs, in all scenarios. By 2100 in the LowClim scenario, the area of upland vegetation in disequilibrium is 22% of the total forest area. However, that proportion rises in the two warmer climate scenarios (53% in the Reference and 56% in HighClim scenarios) by the end of the century. Much of the speckling in the actual vegetation cover by 2100 (Fig. 5b) is where stand replacing disturbances, either harvests or fire, induces a change in vegetation cover type to the underlying potential vegetation cover type (Fig. 5a).

Figure 4.  Time series for potential vegetation cover type proportions of the Willamette River Basin uplands: a) LowClim, b) Reference, c) HighClim.

Figure 4. Time series for potential vegetation cover type proportions of the Willamette River Basin uplands: a) LowClim, b) Reference, c) HighClim.  (Figure from Turner, 2015)

Figure 5.  Vegetation distribution in 2100 for the Reference Case scenario: a) Potential Vegetation Cover type (from MC2), a) Actual Vegetation Cover type (from Envision).

Figure 5. Vegetation distribution in 2100 for the Reference scenario: a) Potential Vegetation Cover type (from MC2), a) Actual Vegetation Cover type (from Envision).  (Figure from Turner, 2015)

Conclusions

The recent climate in the western U.S. is already warmer than in previous decades, and increases in tree mortality have been linked to climate change. Spatially explicit landscape simulation of potential and actual vegetation could be particularly effective in adaptation efforts. Using climate observations, stands in different locations could be regularly assessed for the degree to which the vegetation type is out of equilibrium with the local climate, and hence at risk for attack by pests and pathogens. The most vulnerable stands could be prioritized for thinning or harvest.

Dynamic global vegetation models (DGVMs) driven by the latest downscaled climate data could provide resource managers with guidance on what type of vegetation to replant after a disturbance. Our results support the conclusion that climate change will become an increasing influence on forest management decisions throughout the 21st century. The projected increase in the risk of fire points to investments in fire management.

Related Links & Publications

Contributors to WW2100 Forest Research

  • David Turner, OSU Forest Ecosystems & Society (lead)
  • David Conklin, Oregon Freshwater Simulations
  • John Bolte, OSU Biological & Ecological Engineering

References

GAP. (2014). US Geological Survey, Gap Analysis Program (GAP).  National Land Cover, Version 2. http://gapanalysis.usgs.gov/gaplandcover/data/

Halofsky, J. E., Creutzburg, M. K., & Hemstrom, M. A. (2014). Integrating social, economic, and ecological values across large landscapes (General Technical Report PNW-GTR-896). Corvallis, Oregon: USDA Pacific Northwest Research Station.

Halofsky, J. E., Hemstrom, M. A., Conklin, D. R., Halofsky, J. S., Kerns, B. K., & Bachelet, D. (2013). Assessing potential climate change effects on vegetation using a linked model approach. Ecological Modelling, 266, 131-143. http://dx.doi.org/10.1016/j.ecolmodel.2013.07.003

INR. (2013). Integrated Landscape Assessment Project. Retrieved October 15, 2015, from http://inr.oregonstate.edu/ilap

Kennedy, R. E., Yang, Z., & Cohen, W. B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—Temporal segmentation algorithms. Remote Sensing of Environment, 114(12), 2897-2910. http://dx.doi.org/10.1016/j.rse.2010.07.008  

Kennedy, R. E., Yang, Z., Cohen, W. B., Pfaff, E., Braaten, J., & Nelson, P. (2012). Spatial and temporal patterns of forest disturbance and regrowth within the area of the Northwest Forest Plan. Remote Sensing of Environment, 122, 117-133. http://dx.doi.org/10.1016/j.rse.2011.09.024  

Path Landscape Model. (2015). Retrieved October 15, 2015, from http://essa.com/tools/path-landscape-model/

Rogers, B. M., Neilson, R. P., Drapek, R., Lenihan, J. M., Wells, J. R., Bachelet, D., & Law, B. E. (2011). Impacts of climate change on fire regimes and carbon stocks of the US Pacific Northwest. Journal of Geophysical Research: Biogeosciences, 116(G3). http://dx.doi.org/10.1029/2011JG001695

Thornton, P. E., Law, B. E., Gholz, H. L., Clark, K. L., Falge, E., Ellsworth, D. S., … Sparks, J. P. (2002). Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agricultural and Forest Meteorology, 113, 185–222.

Turner, D. P., Conklin, D. R., Vache, K. B., Schwartz, C., Nolin, A. W., Chang, H., ... & Bolte, J. P. (2016). Assessing Mechanisms of Climate Change Impact on the Upland Forest Water Balance of the Willamette River Basin, Oregon. Ecohydrologyhttp://dx.doi.org/10.1002/eco.1776

Turner, D. P., Conklin, D. R., & Bolte, J. P. (2015). Projected climate change impacts on forest land cover and land use over the Willamette River Basin, Oregon, USA. Climatic Change, 133(2), 335-348. http://dx.doi.org/10.1007/s10584-015-1465-4

 

Web page authors: D. Turner, N. Gilles
Page last updated: September 2016

Snow

Each winter, snow accumulates in the higher elevations of the Willamette Valley. This natural reservoir serves to store a proportion of the winter precipitation, releasing into streams (and reservoirs) during the spring. Snow is the most climatically sensitive element of the annual water budget in the basin. Changes in precipitation, temperature, and forest cover will affect snow accumulation, while changes in temperature and forest cover will affect the rate of snowmelt. 

The snow modeling team examined the maximum snow water equivalent (SWE) over the period January 1-April 1 for each year for the Low Climate Change (LowClim), Reference Case (Reference), High Climate Change (HighClim), and Upland Wildfire Suppression (FireSuppress) scenarios. The LowClim scenario, in which temperatures increase only slightly and winter precipitation also slightly increases, shows an increase in seasonal maximum snow water equivalent of about 41% for elevations above 1200 m (3937 ft). Low elevation snow (500-1200 m, or 1640-3937 ft) varies from decade to decade but there is no trend over the 90-year period. The Reference scenario shows a steep decline in seasonal maximum SWE at both high and low elevations (74% and 94% losses, respectively). SWE declines even more steeply in the HighClim scenario with a loss of 90% of SWE above 1200 m (3937 ft) and a loss of 94% of SWE at the 500-1200 meter (1640-3937 ft) elevation zone. Hydrologic impacts indicate that the snowmelt contribution to spring discharge is lost as snowfall converts to rainfall in winter and the remaining snowpack melts earlier. In all but the LowClim scenario, we see an increase in winter flows as a result of these declines in SWE. While the total water storage from SWE is a relatively small proportion of the annual water budget (2.3-10.6%), the loss of snowpack has important implications for the timing of reservoir filling, spring and early summer high elevation soil moisture, forest health, and spring streamflow.

 

Snow Modeling in Brief

In the Willamette River Basin (WRB), most annual precipitation falls between November and March, with snowfall occurring mainly at elevations above about 1200 m (3937 ft). Sub-basins such as the McKenzie, North Santiam, and Middle Fork, whose headwaters contain substantial area above 1200 m (3937 ft), have more snow than those in the Coast range and elsewhere in the WRB.

We simulated the seasonal evolution of the mountain snowpack at a daily timestep within the Willamette Hydrology Model (WHM) portion of Willamette Envision. WHM is a modified version of the HBV model (Seibert, 1997) in which snow is computed using a degree-day model. Melt rate is governed by air temperature in excess of 0° C and moderated by a melt factor. Precipitation is partitioned into rain and snow by incorporating a temperature-based transition “ramp” rather than a single fixed temperature threshold thus allowing for mixed rain-snow events. The model accounts for the effects of canopy interception and snow sublimation from the canopy as a function of leaf area index (LAI), a measure of canopy cover. Last, we included a radiant energy term as a function of LAI, where both convective heat (as a function of air temperature) and radiant energy can affect snowmelt. Snowmelt is constrained by the amount of actual snow available in each IDU, and when snowmelt exceeds the water-holding capacity of the snowpack, melt is routed into the soil.

Analysis Approach

We selected three climate scenarios to examine the effects of climate and changing forest cover on snowpack. As described in the Climate section, the scenarios representing high, medium, and low levels of climate warming are referred to as HighClim, Reference, and LowClim, respectively. For each climate scenario, the WW2100 team examined snowpack across three elevation zones: 500 m (1640 ft) and below, 500-1200 m (1640-3937 ft), and 1200 m (3937 ft) and above. While April 1 SWE is a traditional metric used by streamflow forecasters, we found that under climate warming this metric does not account for snow water equivalent that melts prior to that date. A more hydrologically appropriate metric is the maximum winter snow water equivalent (MaxSWE). We computed MaxSWE for the January-April time period for elevation zones 500-1200 m (1640-3937 ft, or “low elevation snow”) and 1200 m (3937 ft) and above (“high elevation snow”). Because snow cover below 500 m (1640 ft) is only ephemeral and insignificant, we did not analyze changes in snowpack in that zone. Snowpack in individual winters typically varies from year to year, so our temporal analysis examined decadal and 30-year averages of SWE over the 90-year model run. We also analyzed correlations between snowpack, winter temperature, and winter precipitation on a yearly basis for each of the climate scenarios. In addition to the elevation zone analyses of SWE, we examined decadal-averaged SWE for sub-watersheds (HUC-12 level of detail).

Select Findings from Snow Analysis

As temperatures increase in WW2100 future scenarios (2010-2099), snow water equivalent varies in its response to temperature. SWE decreases in both the Reference and HighClim scenario, where temperatures increase 2.3 °C and 4.4 °C from the first to the ninth decade, respectively. However, SWE increases in the LowClim scenario for both low and high elevations because the temperature increase is only 0.05 °C, not enough to shift precipitation from snow to rain. There is also a slight increase in winter precipitation. Spatially, changes in snow cover mainly affect the McKenzie and North Santiam sub-basins, as well as high elevation portions of several other sub-basins (Fig. 1).

Snowpack Trends in the Three WW2100 Climate Scenarios

  • Under the LowClim scenario, snow water equivalent in the high elevation zone there is high interdecadal variability and maximum SWE increases by 41%. In the low elevation zone, there is high decadal variability but no significant increase in SWE. (Figs. 2 and 3).

  • Under the Reference and HighClim scenarios, snow water equivalent in the high elevation zone declines 74% and 90% under the Reference and HighClim scenarios, respectively (Figs. 4 and 5).

  • Snow water equivalent in the low elevation zone declines 94% under both the Reference and HighClim scenarios (Figs. 6 and 7). In the HighClim scenario, low elevation snow essentially disappears by mid-century.

  • Declines in SWE in the Reference and HighClim scenarios are driven by increases in winter temperatures rather than changes in precipitation.

Annual maximum SWE for early, middle and late 21st century decades (Figure by Nolin and Stephens)

Figure 1. Snow cover in early, mid, late decades of the study period for the Reference Case scenario.

Box plots showing decadal changes in January-April maximum snow water equivalent for the LowClim scenario, above 1200 m.

Box plots showing decadal changes in January-April maximum snow water equivalent for the LowClim scenario, 500-1200 m.

Figure 2a-b.  Box plots showing decadal changes in January-April maximum snow water equivalent for the LowClim scenario, above 1200 m or 3937 ft (top) and 500-1200 m or 1640-3937 ft (bottom).

Box plots showing decadal changes in January-April maximum snow water equivalent for the Reference scenario, above 1200 m.

Box plots showing decadal changes in January-April maximum snow water equivalent for the Reference scenario, above 500-1200m.

Figure 3a-b.  Box plots showing decadal changes in January-April maximum snow water equivalent for the Reference Case scenario, above 1200 m or 3937 ft (top) and 500-1200 m or 1640-3937 ft (bottom).

Box plots showing decadal changes in January-April maximum snow water equivalent for the HighClim scenario, above 1200 m.

Box plots showing decadal changes in January-April maximum snow water equivalent for the HighClim scenario, 500-1200 m.

Figure 4a-b.  Box plots showing decadal changes in January-April maximum snow water equivalent for the HighClim scenario, above 1200 m or 3937 ft (top) and 500-1200 m or 1640-3937 ft (bottom).

 

Table 1. Correlations between Jan-Apr Maximum SWE, temperature, and precipitation for the three scenarios. Values with an asterisk are significant at p=-0.05.

  LowClim Reference HighClim
 

>1200 m
(>3937 ft)

500-1200 m
(1640-3937 ft)
>1200 m
(>3937 ft)
500-1200 m
(1640-3937 ft)
>1200 m
(>3937 ft)
500-1200 m
(1640-3937 ft)

T vs. MaxSWE

-0.59* -0.53* -0.46* -0.36* -0.48* -0.38*
P vs. MaxSWE 0.31* 0.22 0.19 0.19 0.18 0.21

Changes in Volumetric Water Storage in Snowpack

We computed the difference in total volumetric water storage in the snowpack using the first and last decades of the model run and combining snow storage in both the high and low elevations zones. The LowClim scenario sees an increase of 324,285 ac-ft (0.40 km3), the Reference scenario sees a decrease of 2,180,818 ac-ft ( 2.70 km3), and the HighClim scenario sees a decrease of 956,642 ac-ft ( 1.17 km3) from the first to the last decade. The larger decline in the Reference scenario is because there is a single outlier year of extremely high snowpack during the first decade of the Reference scenario that doesn’t occur in the other two scenarios.

Because we did not run a counterfactual case controlling for the effects of wildfire, we are not able to tease apart the effects of climate from those of wildfire on snowpack. However, our field measurements indicate that dense, low elevation forests tend to decrease snowpack due to both canopy interception (reducing accumulation on the ground) and thermal effects that lead to faster melt. At the highest elevations, forests are lower density and colder, so canopy interception and thermal effects are lower. In burned areas, our field studies show that decreased canopy increases snow accumulation, but deposition of charred debris on snow leads to earlier snowmelt by several weeks. Thus, we speculate that forest fires will lead to increased snow accumulation but earlier melt. Forest harvest, especially thinning, may allow greater retention of snowpacks, though higher winter temperatures cause considerable declines in total snowpack at all elevations.

Conclusions

Pacific Northwest mountain snowpack is highly temperature sensitive. In recent decades, warm temperature anomalies have led to significant declines in snow water equivalent. Differences between the LowClim, Reference, and HighClim scenarios show that relatively small changes in temperature and precipitation affect both the magnitude and direction of temperature change. In the LowClim scenario, the slight temperature increase is insufficient to convert precipitation from snowfall to rainfall; the 19.5% increase in winter precipitation leads to a 41% increase SWE from the first to the last decade of the 90-year period. In contrast, the Reference and HighClim scenarios see significant increases in winter temperature but no change in winter precipitation, thus driving the large declines in SWE. The SWE decreases are especially substantial at lower elevations, which see a disappearance of nearly all snow. Climate-driven changes in snow hydrology are considerable. Less clear are the impacts of changing forest cover on snowpacks.

Related Links & Publications

  • Cooper, M. G., Nolin, A. W., & Safeeq, M. (2016). Testing the recent snow drought as an analog for climate warming sensitivity of Cascades snowpacks. Environmental Research Letters, 11(8), 084009.

  • Sproles, E. A., T. R. Roth, and A. W. Nolin. (2016). Future Snow? A Spatial-Probabilistic Assessment of the Extraordinarily Low Snowpacks of 2014 and 2015 in the Oregon Cascades, The Cryosphere Discussion, http://dx.doi.org/10.5194/tc-2016-66

  • Nolin, A. W. (2016). Remote sensing of the cryosphere, In The International Encyclopedia of Geography: People, the Earth, Environment, and Technology. D. Richardson, Ed., Wiley and Sons, http://dx.doi.org/10.1002/9781118786352

  • Gleason, K. E. and A. W. Nolin. (2016). Charred forests accelerate snow albedo decay: parameterizing the post-fire radiative forcing on snow for three years following fire, Hydrological Processes, http://dx.doi.org/10.1002/hyp.10897

  • Safeeq, M. S. Shukla, I. Arismendi, G. E. Grant, S. L. Lewis, and A. Nolin. (2015). Influence of winter season climate variability on snow-precipitation ratio in the western United States, International Journal of Climatology, http://dx.doi.org/10.1002/joc.4545

Contributors to WW2100 Snow Research

  • Anne Nolin, OSU College of Earth, Ocean, and Atmospheric Sciences (lead)

  • David Conklin, Oregon Freshwater Simulations

  • Matthew Cooper, MS Student, Geography (Graduate: 2014; now at UCLA Geography)

  • Kelly Gleason, PhD Student, OSU Geography (Graduated: 2015; now at USGS Flagstaff, AZ)

  • Travis Roth, PhD Student, OSU Water Resources Science (Graduated: 2012, now at 

  • Eric Sproles, PhD Student, OSU Geography (Graduated: 2012, now at Centro de Estudios Avanzados en Zonas Áridas, La Serena, Chile)

  • David Turner, OSU Forest Ecosystems and Society

  • Kellie Vache, OSU Biological & Ecological Engineering

References

Seibert, J. (1997) Estimation of parameter uncertainty in the HBV model. Nordic Hydrology, 28 (4/5), 247-262.

 

Web page author: A. Nolin
Last updated: December 2016

Urban Water Use

Willamette River Basin residents and businesses alike depend on a sustainable source of clean water for continued well being and livelihood. To anticipate future urban water demands, the WW2100 economics team developed a modeling component for Willamette Envision that projects residential and nonresidential urban water demand as a function of factors such as water price, income, population, and population density. Demand for each urban area is modeled in aggregate, with models based on empirical economic research studies and data from major urban areas in the basin. As Willamette Envision runs, the estimated water demand is met by diversions of water from surface and groundwater sources, consistent with existing municipal water rights. WW2100 water demand modeling suggest an increase in urban water use within the basin over the century, mainly due to population growth. The projections indicate that per capita consumption, which has been declining for the past 20 years because of price increases and a range of urban water conservation programs, will stabilize at between 80 and 100 gallons per day, before rising gradually due to growth in per capita income.

 

 

Urban Water Use Modeling in Brief

Water in urban areas of the Willamette Basin is put to residential, commercial, and industrial uses. The amount used will depend on a range of factors including population, price, and income, as well as urban population density. This web page provides a brief introduction to urban water demand modeling in Willamette Envision; for a more detailed description, refer to Jaeger et al., 2016.

The urban water demand component of Willamette Envision consists of models of residential and nonresidential urban water demand for the Portland Metropolitan Area, Salem, Corvallis, and Eugene/Springfield, as well as a separate model for smaller urban areas. We selected model variables based on a review of the economics literature on urban water demand and on the need to use variables that could be forecasted over the entire study period as exogenous drivers (income and population growth) or as variables generated within the Willamette Envision framework (population density). The economics literature suggests that a water demand function must include marginal price of water, pricing structure, and income (Olmstead et al., 2007; Olmstead, 2009; Olmstead, 2010; Bell & Griffin, 2011; Mansur & Olmstead, 2012). Given the specific forecasting needs of the urban water component for WW2100, we also included population and population density in the model. We collected the most current information available (at the time the project was begun) on each of these variables for Portland, Salem, Corvallis, Eugene, and Springfield. We used coefficients from the literature and the averages of water quantity, price, income, population, and density for the five cities to calibrate a log-linear model and calculate the intercept term corresponding to the baseline averages. Finally, we adjusted the demand models to reflect seasonal variations in demand. Our baseline scenario, called the Reference Case (1) assumes initial prices that are commensurate with the basin’s major cities in 2010, (2) includes price increases comparable to what occurred on average from 2010-2015, and (3) assumes a 1.5% annual increase from 2016-2025 (in real, inflation-adjusted dollars), in recognition of the existing backlog of infrastructure needs and system maintenance and upgrades expected for western Oregon. After the year 2025, the Reference Case assumes prices remain constant in inflation-adjusted terms for a given size city. For more information about urban demand modeling in Willamette Envision refer to Jaeger et al. (2016).

Additional Modeling Details:

  • Many Willamette Basin metropolitan areas have multiple water providers that divert water from multiple sources. Many water providers also buy and sell water between municipalities. Willamette Envision does not model these complex arrangements and instead models water demand for each metro area in aggregate. We used water-use reports from recent years to apportion urban water demand among water rights that have been used most by each metro area. As demand grows, the model allows for additional water to be made available by diverting water from the Willamette mainstem.

  • Water supply for the Portland Metro area also includes water sources that are outside of the Willamette Basin including Bull Run (the major source for the city of Portland) and Barney and Scoggins Reservoirs (that serves Metro area customers in the Tualatin River Basin). In the case of Bull Run, we have restricted our model so that no more than two-thirds of total Portland Metro water demand comes from this dominant water right (based on recent water use data). However, the Bull Run water right has a maximum legal rate of 636 cfs, which is 50% above the highest rate of withdrawal in the reference run model (in 2100) with this restriction in place. In addition, our model does not include the mid-Willamette water supply source currently under development by the Tualatin Valley Water District. That source, to be completed in 2026, will have a capacity of 100 million gallons per day, or more than 36,000 acre-feet during the four peak summer months.

  • We adjust residential demand for seasonality by decomposing daily water use into outdoor and indoor use components, based on 24 years of daily data from Portland Water Bureau. Total predicted yearly water demand from above is divided by 365 to obtain daily use, and then multiplied by indoor and outdoor fractions to reflect seasonality. Water demands in rural residential zones, relying on groundwater, is also included in our model. It is predicted using the cost of pumping as a measure of the price of water, the population of the rural-residential area, income per household, and population density.

Select Findings from Urban Water Demand Analysis

Water Demand

  • The demand model produces an estimate of the total annual urban water demand of about 330,000 ccf/day (272 million gallons) in 2015, or 305,000 acre-feet per year. Model projections show these levels rising in coming decades for the entire basin, and especially for the Portland Metro Area, mainly due to population growth (Fig. 1).

  • Consumption per capita will stabilize at between 80 and 100 gallons per person per day, before rising gradually due to growth in per capita income (Fig. 2).

  • If urban water prices throughout the basin were 25% higher than in our Reference Case scenario, urban water demand would be 12% lower. For a 50% price hike, or a 75% price increase, the reductions in urban water demand would be 25% and 37%, respectively (Fig. 3). With these price increases, water consumption in the Portland Metro area would be expected to decline to about 70, 62, and 55 gallons per person per day, respectively.

  • In an alternative scenario with higher population growth than in the Reference Case scenario (called HighPop), urban water demand increases by almost 20% by 2030, 36% by 2060, and almost 50% by the end of the century relative to baseline projections.

  • In an alternative scenario in which income is assumed to remain constant (called NoIncGrowth), basinwide urban water demand is 3.7% lower than in the Reference Case scenario by 2030, 9% lower by 2060, and almost 14% lower by the end of the century.

  • We also considered a scenario in which both income and population are kept constant (called NoGrow). Compared to the Reference Case scenario, basinwide urban water demand is 24% lower by 2030, 44% lower by 2060, and almost 59% lower by the end of the century.

Projected basinwide water demand.

Figure 1. Projected basinwide water demand.

 

Projected per capita water demand.

Figure 2. Projected per capita water demand.

 

Projected basinwide water use for different price paths.

Figure 3. Projected basinwide water demand for different price paths.

Metro water use per capita, with additional price increases.

Figure 4. Projected per capita water demand for Metro under different price paths.

 

Expenditures on Water

  • Expenditures on water represent a small share (less than 0.5%) of household income, and this share is projected to decrease over time (Fig. 4). For low-income households, however, the cost of water will represent a more significant share of income.

  • Price increases of 25% to 75% have little effect on expenditures as a share of income because the rise in price will have an offsetting effect on consumption, resulting in a small effect on total expenditures (Fig. 5).

Expenditures on water as a share of income.

Figure 5. Expenditures on water as a share of income.

 

Expenditures on water as a share of income for different price increases over the Reference scenario.

Figure 6. Expenditures on water as a share of income for different price increases over the Reference Case scenario.

Net Change in Irrigation with Urban Expansion

As cities in the basin grow, they will to some extent displace agriculture as they expand, and this will include displacing some irrigated lands. Hence, reduced irrigation could occur as a result of urban expansion and displacement of these irrigated areas.

  • Our model predicts an increase in urban water use (summer outdoor) of 36,800 acre-feet for the six largest metropolitan areas in the basin.

  • Due to the land-use changes accompanying growth, displacement of irrigated farmland offsets forty percent of this increase. The net increase is estimated to be 21,400 acre-feet.

  • These effects vary significantly across cities in the basin, depending on the extent and direction of urban expansion, as well as on the proximity of the city boundaries to surface irrigated farmlands.

 

Conclusions

Urban water use will increase significantly due to growth in population and rising income per household. Price increases in recent years, and those that are anticipated in the coming decade, will curb urban water demand to a significant degree. However, because a large portion of urban water in the basin comes from outside sources (primarily the Bull Run watershed), and because most water is used indoors and returns to the surface water sources from where it originated, the urban consumptive use of in-basin surface water is a small fraction (only 7%) of total urban water use.

Notes, Related Publications & Links

  • Jaeger W.K, Plantinga A.J., Langpap C., Bigelow DP, Moore KM.  2017.  Water, Economics, and Climate Change in the Willamette Basin, Oregon. OSU Extension Service Publication EM 9157.

  • Note: Our projections for both urban and agricultural water use are based on the set of behavioral economic models described here and elsewhere. These models reflect and are derived from economic theory; they are spatially and temporally explicit, and take into account many factors, including the following: water price, household income, population, population density, water delivery costs, land values and farm profits, land use change, crop choice, planting date, water availability across space and time, shifts in seasonality of crop growth due to climate change, daily determination of crop evapotranspiration, urban displacement of farmlands, and utilization rates for irrigation water rights. The 2015 Statewide Long-Term Water Demand Forecast Report, prepared by the consulting firm MWH for Oregon’s Water Resources Department, also makes estimates of future water demand in Oregon. Their methodologies differs from ours in several ways. In the case of agriculture, the MWH report draws on USGS estimates (which in turn are based on USDA Census of Agriculture data) for irrigated acres by county and by crop. Irrigation water demand is then estimated based on Net Irrigation Water Requirements, which are then adjusted to reflect the effects of climate change. In the case of urban water demand forecasting, MWH relied on existing Water Management and Conservation Plans (WMCP) developed by various city governments, and these were then adjusted in proportion to estimated population growth. Changes in per capita demand were estimated by MWH from 50 of the most recent WMCPs from communities across Oregon.

Contributors to WW2100 Urban Water Use Modeling

  • Christian Langpap, OSU Applied Economics (lead)

  • William Jaeger, OSU Applied Economics

  • David Conklin, Oregon Freshwater Simulations

References

Bell, D. R., & Griffin, R. C. (2011). Urban water demand with periodic error correction. Land Economics, 87(3), 528-544.

Jaeger et. al. (2016). Scarcity amid abundance: Water, climate change, and the policy role of regional system models. Manuscript in preparation.

Mansur, E. T., & Olmstead, S. M. (2012). The value of scarce water: Measuring the inefficiency of municipal regulations. Journal of Urban Economics, 71(3), 332-346.

Olmstead, S. M. (2009). Reduced-form versus structural models of water demand under nonlinear prices. Journal of Business & Economic Statistics, 27(1), 84-94.

Olmstead, S. M. (2010). The economics of managing scarce water resources. Review of Environmental Economics and Policy, 4(2), 179-198.

Olmstead, S. M., Hanemann, W. M., & Stavins, R. N. (2007). Water demand under alternative price structures. Journal of Environmental Economics and Management, 54(2), 181-198.

 

Web page authors: C. Langpap, W. Jaeger
Last updated: September 2016

Agricultural Land & Water Use

The Willamette Valley is home to a large agriculture sector that sustains an important part of the Oregon economy. The water used in producing both irrigated and nonirrigated crops in the valley’s diverse agricultural system depends on several factors, including crop and soil type, precipitation and temperature, water rights, costs, and other factors. To understand and anticipate long-term water-use trends within the region’s agriculture industry, economic models were developed to describe the location, timing, crop choices, and irrigation decisions involved in agriculture in the Willamette River Basin (WRB). These economic models characterize land-use decisions based on economic returns to different land uses, crop choices that reflect economic returns to different crops, irrigation decisions that reflect the economics behind utilizing existing water rights, and the economics behind acquiring additional water rights, based on a range of factors that vary by location and year. These models generate estimates of daily water quantities expected to be used at each location in each subbasin in the WRB in future decades, for both surface water and groundwater. These projections show a small decline of 8% in both farmland acres overall, and also for surface and groundwater irrigated acres (a 5% reduction). These trends are mainly due to the expansion of urban land development that displace agriculture. Climate change and the resulting warmer summer temperatures are not found to have a significant effect on crop water requirements. Indeed, warmer temperatures are found to lead to earlier planting dates, which in turn give rise to earlier start and finish dates for irrigation. This means that a larger proportion of crop water demand takes place earlier in the season, when average temperatures are lower and precipitation is higher.

 

 

Agricultural Land and Water Use Methods in Brief

Agricultural water use depends on a range of factors. These include (1) the land area on which agriculture is practiced, (2) the choice of crops, (3) whether irrigation water rights are held, the availability of water from a given source, and the usage rates of those irrigation water rights. Water is consumed by plants on a daily basis, whether irrigated or not. For rain-fed agriculture crops, water demand will reduce the amount of water in soils, in groundwater reserves, and the amounts seeping to streams; for surface irrigation, the crop water demand will involve diverting water from surface flows when soil moisture is insufficient to meet crop water needs.

Agricultural Modeling

The agricultural water use models consist of several interconnected economic models including the limitations imposed by water rights and dynamic models of agricultural evapotranspiration and evolving soil moisture.  The models operate at the scale of the Willamette Envision’s map polygons, which are called Integrated Decision Units (IDUs). The four economic models determine: (1) which lands are put to agricultural uses, (2) which crops are grown on a given parcel of land, (3) whether the parcel of land has or will have an irrigation water right, and (4) whether the irrigation water right is used in a given year. These four models interact with the agricultural evapotranspiration model (crop water demand) that simulates daily evapotranspiration (ET) as a dynamic function of climate, land cover, soil water, and growth stage of each crop, from planting date, to ‘greening up,’ to harvest and dormancy. Soil water will vary as a function of precipitation, crop cover, irrigation, and seepage. These four models also interact with water rights that may impose limits on the timing and quantities of water available for irrigation. Farmland transitioned in or out of agriculture versus developed or forest land uses is described in the land use change section. This page provides a brief overview of agricultural modeling in Willamette Envision.  For a more complete explanation, refer to refer to Kalinin (2013) and Jaeger et al. (in prep).

In a given year where a particular land parcel is assigned to the agricultural land use, farmer decisions are modeled to simulate crop and irrigation decisions. Irrigation is only possible on IDUs with existing irrigation water rights. These initial decisions are then followed by daily decisions related to planting and harvesting, and (possibly) applying irrigation water. The availability of irrigation water is also subject to regulatory shutoffs in accordance with the prior appropriations seniority system under state law (discussed below).

The combination of decisions, choices, actions, and responses to other factors produces a unique pattern of crop water use, irrigation diversions, soil moisture, and groundwater contributions. It also influences economic returns to farming (annual farmland rent) at the parcel level. To the extent that irrigation water is shut off by regulators, current and expected future annual farmland rent is reduced.

The crop choice model estimates the probability of growing each of seven crop types or groups for the modeled year. The empirical model is estimated at the parcel level based on observed cropping patterns in recent years. The model estimates the crop observed as a function of IDU characteristics including soil quality (land capability class), elevation, and the presence of an irrigation water right, as well as varying attributes, crop prices and expected water availability (for those IDUs with irrigation water rights). Given the estimated probabilities for each IDU, the simulation models determine the crop for each IDU in each year with a random draw reflecting these estimated probabilities. No evidence of crop choices being correlated across years (i.e., a crop rotation schedule) were found in the data or in interviews with farmers or agricultural extension personnel. The resulting modeled values are interpreted as the probabilities for each crop to be grown. For perennial crops (orchards, vineyards, tree crops), a fixed set of IDUs is permanently assigned.

The model of irrigation decisions is based on a detailed farmer survey conducted for WW2100 by the USDA National Agricultural Statistics Service (Kalinin, 2013). Data on a six-year history of irrigation and cropping practices for a sample of fields from 530 randomly selected farmers was collected. From this, an irrigation decision model was estimated to represent the probability of irrigating a specific parcel as a function of parcel attributes (e.g., soil type, elevation), and seasonal factors (e.g., June precipitation).

The economic rent or annual profit from farming a given piece of land can play an important role in farm decisions to plant a crop, irrigate, or transition out of farming. Our estimate of farmland rent takes a “Ricardian” approach that is common in models of the economic returns to agriculture (Mendelsohn et al., 1994). Land value is assumed to equal the net present value of future rents from putting the land to its highest value use; as a result, we expect to see variation in land values and annual rents due to characteristics of the land that would influence agricultural productivity such as soil quality and precipitation or irrigation water rights. Similar to the hedonic model of crop choice, here we decompose the farmland rents associated with factors affecting agricultural productivity (see Kalinin, 2013 for more detail).

Agricultural lands in the WRB that currently do not have irrigation water rights may benefit from opportunities to acquire new water rights under federal contracts for stored water at one of the US Army Corps of Engineers reservoirs. The profitability of a new contract for stored water will depend on a comparison of the irrigation benefits (higher yields and wider range of crop choices) and the additional costs (capital investments in infrastructure, labor, and energy costs). For farmlands with existing irrigation water rights, these costs and benefits are already incorporated into the WW2100 estimates of farmland rent (annual profits) by soil class.

For new contract water rights, we would expect the irrigation premium to be the same as for existing irrigation water rights if the costs of irrigating are similar to the average costs for existing surface and groundwater rights. In the case of new water rights from stored water, we expect the costs to be somewhat higher due to a) the fee paid to the Bureau of Reclamation for the water contract, b) the extra cost for mainline conveyance to bring the water from a below-reservoir tributary to the field, and c) the extra lift required. Whether a new irrigation water right is attractive to a farmer depends on its profitability. Farmlands without irrigation water rights are given the opportunity to acquire new irrigation water rights based on stored water in the “New Irrigation Scenario,” provided that there is an economic justification for doing so.

Water Rights Modeling

Irrigation water demands compete with other water uses, include instream water rights. Because irrigation and instream water rights have the greatest potential to compete directly, we include a description of these water rights and our modeling of them here.

Water is allocated in the WRB according to Oregon water law, which operates according to the “prior appropriations doctrine” used in Oregon and most western states (Getches et al., 2015). The water rights system allocates water according to water right priority date (first date of use historically). Under Oregon law, all water is publicly owned. Water rights certified by the state are defined in terms of the timing of use, the maximum rate of diversion, and the annual volume allowed under the water right. When conflicts arise due to shortage, the more senior water right is given priority, while more junior water rights are required to curtail their water use if it conflicts with the senior water right holder. Water rights may be transferred between points of use under Oregon law when transactions are arranged by parties and approved by the OWRD (Amos, 2008), for example an irrigation right transferred from one farm to another.

Willamette Envision mimics this process: it takes account of the demand or request for water at a given point of diversion (POD) on a given day (from a farm, city, rural residential water user, or instream flow water right), and it evaluates the availability of water from the relevant streams and groundwater source. If there is sufficient water available, it withdraws water to satisfy the demand. If there is insufficient water to meet the needs of an existing water right, the request is denied. At the same time the model determines whether there is a junior water right in the same river reach or any upstream reaches that could be curtailed to make additional water available to satisfy the senior water right. A similar procedure is followed to satisfy instream water rights by protecting flows in streams where such water rights exist. The model includes instream water rights implemented as of 2010. When more than one instream water right applies at the same time to the same reach, the water rights model applies both water requirements. If an instream water right is “senior” to an irrigation water right, the irrigator may be shut off is there is insufficient water to meet both demands. Willamette Envision includes more than 15,000 irrigation water rights, 1,000 municipal water rights, and 90 instream water rights.

Select Findings from Agricultural Analysis

Agricultural Water Use

  • The agricultural land use model estimates that total farmland will decline gradually in coming decades, by about 8% by the year 2100. Irrigated lands are projected to decline by 5% (Fig. 1).

Agricultural lands in the WRB.

Figure 1. Projected agricultural lands in the WRB for the Reference Case scenario.

 

  • Cropping patterns are expected to remain stable in coming decades for the largest crops (by acreage). Given the very large number of crops grown in the WRB, the data suggest that possible increases or decreases in the acres planted for one or several crops is unlikely to have a significant effect on total crop water use (Fig. 2).

Projected cropping patterns in the WRB.

Figure 2. Projected cropping patterns in the WRB for the Reference Case scenario.

  • The acreage irrigated in the WRB in any given year represents about 17% of the total agricultural land area of 1.5 million acres, with about half of this being surface water irrigated. There is significant year-to-year variation in the crops and acreage irrigated (Fig. 3). Diverted Irrigation amounts vary significantly year to year, but are estimated to average 435,000 ac-ft initially while declining by about 15% late in the century in the Reference Case scenario.

Irrigated crops, by acres planted.

Figure 3. Irrigated crops, by acres planted, for the Reference Case scenario.

  • Agricultural land values vary due to differences in soil type, elevation, average temperature, and precipitation, as well as with ownership of an irrigation water right (Fig. 4).  

 

WW2100 agricultural land values.

Figure 4. Agricultural land values.

  • In any given year there are a number of irrigation water rights that are shut off by regulators during the growing season due to a lack of available surface water. Consistent with Oregon water law, relatively junior water right holders may be forced to curtail irrigation to ensure the availability of water for more senior water right holders. These conflicts between water rights can involve instream or other surface irrigation water rights. For the Reference Case scenario, our model results suggest a surprising decline in the number of shutoffs in future decades. This is occurring in the model because climate change gives rise to warmer spring temperatures which begin to encourage earlier planting dates; with earlier planting comes an earlier start (and end) to irrigation. For some crops in future decades, and in particular locations, this will mean that some farmers will have completed their irrigation by the time that they would have (previously) been shut off (Figs. 5 and 6).  The model suggests a reduction of 10-30%.

Seasonal distribution of irrigation requests.

Figure 5. Seasonal distribution of irrgation requests for the Reference Case scenario.

Projected changes in irrigation shutoffs, Reference and HighClim scenarios.

Figure 6. Projected changes in irrigation shutoffs - Reference Case and HighClim scenarios.

  • For a range of alternative scenarios, the impact of varied assumptions about external factors (high population growth, high climate change, low climate change), and changes in assumptions related to sensitivity analysis (high irrigation, low irrigation), and combinations of these modified assumptions (Worst Case and Extreme scenarios), produce changes in the level and trajectory of irrigation shutoffs. The High Climate Change (HighClim) scenario produces a similar reduction in irrigation shutoffs as for the Reference Case (Fig. 6). For the other scenarios, the relative levels of irrigation shutoffs are consistent with what would be expected under these alternative assumptions (e.g., higher levels of shutoffs for “high irrigation” and for “worst case” scenarios).  

  • When non-irrigated farmlands are given the option of acquiring new irrigation water rights tied to federal stored water contracts, some previously non-irrigated parcels acquire new water rights in our model. However, because of (1) the high additional costs of conveyance to move water to a farmer’s field from one of the tributaries below a federal reservoirs, and (2) the relatively modest incremental profits or increased net revenue that would be expected from irrigation, the adoption of new irrigation from stored water rights is found to be profitable for only a small number of acres (less than 8,000). Even when making optimistic assumptions about the low costs of conveyance, less than 30,000 acres of land adopt new irrigation water rights (Fig. 7).

Locations of new irrigation water rights with low conveyance cost assumptions.

Figure 7. Location of new irrigation water rights, with low conveyance cost assumptions.

 

Notes, Related Links & Publications

  • Jaeger W.K., Plantinga A.J., Langpap C., Bigelow D.P., Moore K.M. 2017.  Water, Economics, and Climate Change in the Willamette Basin, Oregon. OSU Extension Service Publication EM 9157. https://catalog.extension.oregonstate.edu/em9157

  • Jaeger W.K., Amos A.L., Bigelow D.P., Chang H., Conklin D.R, Haggerty R., Langpap C., Moore K.M., Mote P.W., Nolin A.W., Plantina, A.J., Schwartz, C.L., Tullos, D., Turner, D.P.  2017.  Finding water scarcity amid abundance using human–natural system models. Proceedings of National Academy of Sciences. http://dx.doi.org/10.1073/pnas.1706847114

  • Kalinin, A. (2013). Right as Rain? The Value of Water in Willamette Valley Agriculture (MS Thesis). Oregon State University, Corvallis, Ore. http://hdl.handle.net/1957/42123

  • Jaeger, W. (2014, October 8). Modeling the Human Side of Water Scarcity in the Willamette Basin. WW2100 Recorded Webinar. https://media.oregonstate.edu/media/t/0_d5bbiufd  

  • Note: Our projections for both urban and agricultural water use are based on the set of behavioral economic models described here and elsewhere. These models reflect and are derived from economic theory; they are spatially and temporally explicit, and take into account many factors, including the following: water price, household income, population, population density, water delivery costs, land values and farm profits, land use change, crop choice, planting date, water availability across space and time, shifts in seasonality of crop growth due to climate change, daily determination of crop evapotranspiration, urban displacement of farmlands, and utilization rates for irrigation water rights. The 2015 Statewide Long-Term Water Demand Forecast Report, prepared by the consulting firm MWH for Oregon’s Water Resources Department, also makes estimates of future water demand in Oregon. Their methodologies differs from ours in several ways. In the case of agriculture, the MWH report draws on USGS estimates (which in turn are based on USDA Census of Agriculture data) for irrigated acres by county and by crop. Irrigation water demand is then estimated based on Net Irrigation Water Requirements, which are then adjusted to reflect the effects of climate change. In the case of urban water demand forecasting, MWH relied on existing Water Management and Conservation Plans (WMCP) developed by various city governments, and these were then adjusted in proportion to estimated population growth. Changes in per capita demand were estimated by MWH from 50 of the most recent WMCPs from communities across Oregon.

Contributors to WW2100 Agricultural Land and Water Use Modeling

  • William Jaeger, OSU Applied Economics (lead)
  • Alexey Kalinin, OSU Applied Economics (completed MS 2013, now a PhD student at the University of Wisconsin)
  • Dan Bigelow, OSU Applied Economics (completed PhD 2015)
  • Kathleen Moore, OSU Geography (completed PhD 2015; now a post-doctoral researcher, OSU Applied Economics)
  • Cynthia Schwartz, OSU Biological and Ecological Engineering
  • David Conklin, Oregon Freshwater Simulations

References

Amos, A. (2008). Freshwater Conservation in the Context of Energy and Climate Policy: Assessing Progress and Identifying Challenges in Oregon and the Western United States. University of Denver Water Law Review 12(1).

Getches, David, Sandi Zellmer, and Adell Amos. (2015). Water Law in a Nutshell, 5th. Minneapolis, Minn: West Academic.

Jaeger et. al. (2016). Scarcity amid abundance: Water, climate change, and the policy role of regional system models. Manuscript in preparation.

Kalinin, A. (2013). Right as Rain? The Value of Water in Willamette Valley Agriculture (MS thesis). Oregon State University, Corvallis, Ore. http://hdl.handle.net/1957/42123

Mendelsohn, R., Nordhaus, W. D., & Shaw, D. (1994). The impact of global warming on agriculture: a Ricardian analysis. The American Economic Review, 753-771.

 

Web page author: W. Jaeger
Last updated: September 2016

Hydrology

The goal of the WW2100 hydrologic modeling team was to develop a hydrologic model that could capture potential effects of long term changes in climate, land cover, and water use on the hydrology of the Willamette River Basin (WRB). To do this, we developed a modeling component for Willamette Envision called the Willamette Hydrology Model (WHM). WHM translates daily values of meteorological input (including precipitation, air temperature, wind speed, and radiation) into estimates of soil moisture and snowpack across the landscape, and daily average streamflow at locations throughout the Willamette Envision stream network. Importantly, WHM integrates with human systems by simulating operations of the 13 federal reservoirs that are a key feature of the Willamette system, and by simulating water diversions, including constraints imposed by Oregon water law.

We used results from the hydrologic model to develop a water budget of the annual water cycle in the WRB and explore how the water budget might respond to climate and demographic changes over the 21st century. The water budget illustrates several key features of the Willamette hydrologic system, including:

  • The highly seasonal nature of precipitation - more than 75% of annual precipitation falls between November and May.

  • The importance of natural and built reservoirs in sustaining summer flows.

  • The small size of human, out of stream water use, relative to the total amount of water moving through system annually.

  • The relatively small role that snowpack plays in the basinwide water budget; the Willamette is a rain-dominated hydrologic system.

  • The important role of upland forests as water users. Forests cover over 70% of the basin, extensive changes in forest land cover affect the overall water budget by changing evapotranspiration.

  • The proximity of river flows in late summer to minimum environmental flow requirements.

Hydrology Modeling in Brief

Within Willamette Envision, we model the storage and flux of water from rain or snow, into soils, groundwater, and streams using a sub-model developed for the project called the Willamette Hydrology Model (WHM). WHM is, in part, based on the widely-used rainfall-runoff model called HBV (Bergström and Singh 1995; Bergstrom et al., 2001; Seibert, 1997). For a more detailed explanation of the hydrologic modeling framework concept, refer to Vache et al. (in review).

WHM translates daily values of meteorological input (including precipitation, air temperature, wind speed, and radiation) into a spatially distributed estimate of water storage and release. It is run on a daily timestep over the full 90-year WW2100 modeling timeframe, and results in a dynamic estimate of the response of the hydrology to the evolving landscape. The hydrologic model is comprised of four overall elements describing key features of the system.

  • The mountain snowpack - Each winter, snow accumulates in the higher elevations of the Willamette River Basin (WRB). This natural reservoir serves to store a proportion of the winter precipitation, releasing into streams (and reservoirs) during the spring. We simulate the seasonal evolution of the mountain snowpack with a hybrid approach that includes the influence of both air temperature and radiation. The temperature- and radiation-driven melt depends directly on vegetation, so that the snowpack changes as forest disturbance and growth occurs over the scenario timeframe.

  • Watershed runoff - Incoming precipitation and snowmelt are stored within a set of conceptual reservoirs representing soil and groundwater (Fig. 1). WHM defines the spatial distribution of those reservoirs based primarily on a set of approximately 9000 sub-watersheds, defined by the National Hydrography Dataset (NHD). Water is released from the reservoirs based on algorithms developed as part of the HBV model (Bergström and Singh 1995; Bergstrom et al., 2001; Seibert, 1997). Biophysical processes such as evapotranspiration respond to changes in land cover simulated by the forest and human systems models.

  • Instream routing and reservoir storage - WHM uses a kinematic wave approach to simulate the flow of water within the stream network. The spatial pattern of the network was taken from NHD, and allows the model to estimate stream discharge throughout the watershed. Manmade reservoirs are a key feature of the Willamette river system, and WHM simulates operation of the 13 largest reservoirs – those that are managed by the U.S. Army Corp of Engineers (USACE) as the Willamette Project. Refer to the reservoir operations page for more details about reservoir modeling.

  • Water Allocation and water use - The use of water by human society is the fourth key feature of the hydrology of the Willamette. WHM models the movement of water through the stream network, while also allowing water to be added or removed at specific points in accordance with Oregon water law. Human water demand is estimated in the WW2100 economic models as the outcome of household and farm demand relationships. Refer to the agricultural land and water use and urban water use pages for more information about these models.

Diagram of conceptual reservoirs and fluxes modeled with the Willamette Hydrology Model.

Figure 1Hydrologic modeling within Willamette Envision involves movement of water through a set of conceptual reservoirs simulating soil and groundwater (diagram by K. Vache).

Evapotranspiration

Willamette Envision models both biophysical and human aspects of water use. The primary biophysical use modeled is evapotranspiration (ET) — water movement from soil and plants into the atmosphere. ET is calculated on a daily basis for all modeling polygons and responds to changes in landcover modeled by the forest and human systems modeling components. ET is modeled as follows:

  • Forest (~70% of the basin land area in 2010 starting conditions) - For forested lands, WHM models ET using a Penman-Monteith expression that includes both transpiration from vegetation and soil evaporation. The Penman-Monteith expression includes vegetation canopy, represented by leaf area index (LAI), as an independent variable. Thus, forest areas respond to changes in canopy cover simulated by the forest dynamics models. For example, as the forest in a modeling polygon matures, leaf area index (LAI) rises, which leads to a higher ET. When a fire occurs, the LAI decreases to almost zero. Refer to Turner et al. (2016) for more details about modeled vegetation shifts and modeling ET in the Willamette upland forests. Some forest IDUs (about 2% of basin area) were left out of the state-and-transition model due to gaps in the initial condition data. Those forest IDUs are modeled for ET using Penman-Monteith, an LAI of 1, and a canopy height of 10 meters.
  • Agricultural lands (~22% of the basin land area in 2010) - On agricultural lands, modeling of ET takes into account crop type and growth stage, and augmentation of soil moisture due to irrigation. The sequence for a modeling polygon is as follows. First, the human systems modeling components set the crop type and determine whether the land will be irrigated that year. Next, at a daily timestep, WHM determines crop growth stage and water requirements. The model then estimates actual crop water use (ET) by taking into account the amount of water available in the soil. Soil water can come from natural sources (e.g., precipitation) or it can be supplemented by irrigation if water is available (as determined by the water rights model). Modeling of crop water demand and ET is based on the crop cover approach as described in the Food and Agriculture Organization (FAO) Irrigation and Drainage Paper 56 (Allen, Pereira, Raes, & Smith, 1998) and further developed by Allen and Robison (2007). Refer to Jaeger et al. (in prep) for more details. 
  • Urban and other non-forested, non-agricultural lands (~8% of the basin land area in 2010) - Elsewhere, WHM approximates ET using a Penman-Monteith expression using a LAI of 1.0, and a canopy height of 10 meters, that does not change. In urban areas (~5% of the basin land area in 2010) modeling of ET also takes into account outdoor water use for lawns and gardens. Municipal water use is modeled by the human systems models as a unit for each urban growth area. A fraction of the water diverted for municipal use is added to ET. This fraction varies seasonally so that it is very small in winter and higher in summer when outdoor water use peaks. 

Model Calibration

The rainfall-runoff model within WHM has nine calibration parameters related to snowfall, runoff, and percolation. Calibration of these parameters affects the flux between the conceptual reservoirs shown in Figure 1. We used the Parameter Estimation program, PEST, to identify sets of the 9 parameter values which produce good correlations between modeled and measured datasets. The calibration period was 1980-1994 and we used the following measured data sets in the calibration process: USGS stream gage records, NRNI synthetic flow data, Oregon SNOTEL snow survey records, and measured inflow data for federal reservoirs. We ran the model for the calibration period using meteorological forcings representing simulated historical climate conditions from the MIROC5 global climate model (MACAv1-METDATA).

We divided the WRB into a number of sub-basins for calibration purposes, ultimately using 14 different sets of parameter values in different parts of the WRB. Thirteen of the parameter sets were produced by Oregon Freshwater Simulations in 2016; one parameter set was taken from prior calibration work by Eric Watson and Heejun Chang at Portland State University in 2015. Our use of PEST was an adaptation and refinement of the methods used by Watson and Chang (PSU) in 2015.

The main analysis steps within PEST are as follows: (1) PEST selects a set of parameter values, (2) it runs the WHM model, and (3) it calculates a value representing the divergence of the simulation results from the historical stream gage records. This process is repeated with different sets of parameter values in a systematic exploration of the parameter space. The process terminates when successive adjustments to the parameter values fail to reduce the divergence by a specified amount. The model may exhibit equifinality: different sets of parameter values may produce equally good simulation results.

Accurate simulation of the inflows to the WRB reservoirs is important to the success of the WW2100 model as a whole, so we identified nine reservoir drainages to be individually calibrated using PEST as the first step in our process. Parameter values for the remaining parts of the WRB were selected using a variety of methods in subsequent steps. For a more detailed description of model calibration, refer to Jaeger et al (in prep).

Because our focus in WW2100 was on water scarcity, calibration focused on low flows and reservoir inflows. The model was less able to accurately predict high flows, and users should use caution when making interpretations about flood events and winter peak flows.

Model Limitations

As of summer 2016, Willamette Envision does not include a detailed groundwater model and instead relies on the simpler approach of a conceptual groundwater reservoir that is part of HBV, and includes a calibration parameter, which represents groundwater recharge. Because of the simplified groundwater model, we do not model water scarcity for groundwater withdrawals. We simulate water withdrawals from groundwater, but the model does not impose a specific limit on them.

In addition, we do not model potential future changes in groundwater supplied by mountain springs in the High Cascades. Instead, we simulate springflow by adding constant discharge to High Cascades catchments that reflect measured spring flows (Jefferson et al., 2007; Grant & Lewis, 2016). The addition of High Cascades groundwater is held constant throughout the simulations. This assumption is based on measured flows in spring-fed mountain sub-basins.

As of summer 2016, Willamette Envision does not model water quality or water temperature. We are developing an energy balance stream temperature model and hope to incorporate it into future versions of Willamette Envision. WW2100 did include some offline field measurements and modeling of the potential effects of increased water temperature on mainstem fish population. Read about that analysis on the fish and stream temperature section of this website.

Water Budget Calculations

One of our goals for the hydrologic analysis was to determine how the flow of water into and out of the WRB might change in response to changing climate and water demands. WHM allowed us to calculate a water budget for the WRB and estimate how the inputs and outputs to that budget might change over the 21st century. Here we describe the calculations we made to determine the water budget using output from Willamette Envision.

All calculations were performed using daily values that were then averaged over each month of the year. All values were calculated and reported as fluxes (cm3/y per cm2 of the WRB, so cm/y). Most of us are familiar with precipitation as cm/y, but all other inputs, stores, and outputs of water can be reported in the same way. This allowed us to easily compare basinwide precipitation, storage, human diversions of water, and so forth. Monthly, seasonal, and annual fluxes are all reported in the same units of cm/y. Therefore, the annual precipitation can be calculated simply as the sum of the monthly precipitation.

Precipitation is the sum of all rain and snow in the basin divided by the basin area. Evapotranspiration (ET) is all ET in the WRB. The ET related to human uses (municipal and agriculture) is included in the basinwide number and indicates the fraction of total ET based on human uses. Environmental flows are those in the Biological Opinion [U.S. Fish and Wildlife Service, 2008, Appendix B, p. 12, Table 9.2-1] for the Willamette River at Salem. These are the flow objectives for "adequate" and "abundant" water years. We did not include upstream environmental flows because these would be duplicates for the purpose of the water budget. Reservoir storage was calculated as the difference between inflow and outflow each month. Snow was calculated as the difference between snow water equivalent (SWE) on the first of each month and the first of the previous month. Municipal water use was calculated endogenously within Willamette Envision. The ET for municipal uses was calculated as the total water use each month minus the municipal indoor water use, which we estimated each year from Jan. 3 to Feb. 3. Agriculture ET was calculated within Willamette Envision. Storage of water in soil moisture and groundwater was calculated to balance the monthly water budget of the basin. The model does not include evaporation from open water such as reservoirs and lakes.

The seasons “Summer” and “Winter” were chosen to be of the same length of time, six months. No single six-month period uniquely qualifies as “Winter” or “Summer”, but we chose Winter to be defined in the water budget as November 1 through April 30. This aligns approximately with the historical snow storage season. We defined “Summer” as the other six months of the year. This seasonal definition is equivalent to “dry” and “wet” season in the WRB (Chang and Jung, 2010).

Selected Findings

Water Budget

The simulated historical water budget for the WRB is shown in Figure 2 and Table 1 and 2. An interactive version of the figure, called a Sankey diagram, is available online. This interactive version of the Sankey diagram allows users to examine the water budget for every month and season for several different scenarios.

  • Precipitation is 162 cm/y (37.9 million ac-ft) of water averaged over the WRB. This is in agreement with the best available data set, PRISM (NWACSE, 2015) because modeled precipitation was trained using the PRISM precipitation data over the historical period.
  • Snow water equivalent (SWE) accounts for 3.7 cm/y (~875,000 ac-ft, or 2.3% of total annual precipitation) in the simulated historical scenario (also called HistoricRef). Snowfall mainly occurs from November through March. This is certainly an underestimate of total snowfall in the basin, resulting from the method of accounting, which is the sum of monthly values, which are the difference between values on the first day of successive months averaged over the simulated historical period (1950-2010). In reality, each winter month (plus October and April) have significantly more snowfall than the model numbers indicate. However, the “within-month” snow does not include snowfall that subsequently melted within that month. Such within-month snow melt is not considered to contribute to winter season storage and instead it functions like rainfall runoff. Some readers may find that an estimate of 2.3% stored snow for the WRB is small. It may be. We worked to estimate the upper bound on the amount of stored snow. Nearly all precipitation that falls below 1200 m in the basin melts within a few days during winter. Therefore, 1200 m may be considered as an approximate lower limit of the snow storage ‘zone’. Furthermore, snow that falls before November 1 or after April 1 is not usually stored except at the very highest locations of the basin. The WRB receives only 10.6% of its precipitation between Nov. 1 and Apr. 1 above 1200 m. Therefore, 10.6% could be considered an absolute upper maximum on stored snow. However, many years are warmer than average, and so the stored snow line is higher. Furthermore, most years have warm periods in which much snow above 1200 m melts. Consequently, the amount of snow stored in an average winter in the WRB is probably well under 10.6% of annual precipitation.
  • The reservoirs in the simulated historical scenario have net positive storage of water from February through May. During that period, they store an average of 5.1 cm/y of water, which is approximately 1.2 million ac-ft of water. We know that the reservoirs in the Willamette Valley Project have an active storage of 1.6 million ac-ft. However, the reservoirs do not fill every year. Therefore, our simulation agrees reasonably well with the observed storage.

Annual water budget for the simulated historical scenario.

Figure 2. Average yearly WRB water budget for simulated historical scenario (1950-2009). The amount of precipitation in the basin is shown on the left, and outflow of the Willamette River is shown on the right. Water use for irrigation and municipal is an estimate of water use from in-basin sources. The blue line from soil moisture and groundwater includes an estimate of contributions from springs sourcing from the High Cascades aquifer. An interactive version of this diagram is available online where you can view the water budget month by month, and for different modeling scenarios.

  • The biggest storage of water in the model, and certainly in reality as well, is soil moisture and groundwater. Soil moisture and groundwater show net positive storage of water from September through January, totaling 36 cm/y (8.6 million ac-ft). In addition, springs sourcing from the High Cascades aquifer contribute significant discharge to the headwaters of some sub-basins such as the McKenzie. The model simulates this contribution based on measured spring flows (Jefferson et al., 2007; Grant & Lewis, 2016). In model simulations, total discharge from springs sourcing from the High Cascades aquifer is 8.1 cm/y (1.9 million ac-ft).

  • Agriculture extracts 1.2 cm/y (~280,000 ac-ft) of water in the simulated historical scenario. Agriculture receives significantly more water as precipitation, but the amount reported here is for irrigation only. Total municipal uses within the basin use a similar amount of water, 0.91 cm/y (~210,000 ac-ft). However, a significant fraction of municipal water, particularly in the Portland Metro area, is supplied from outside of the Willamette Basin (primarily Bull Run). Municipal use of water from within the WRB is only about 0.52 cm/y (~130,000 ac-ft). Of the water that agriculture and municipal together extract from in-basin sources, approximately 1.0 cm/y (~235,000 ac-ft) evapotranspire. This latter quantity can be considered the amount of water that the agricultural and municipal sectors extract without returning (consume).

  • Environmental flows at Salem require 14.8 cm/y (3.5 million ac-ft) of water in the simulated historical scenario. Environmental flows are the flow objectives for "adequate" and "abundant" water years prescribed by the Biological Opinion [U.S. Fish and Wildlife Service, 2008, Appendix B, p. 12, Table 9.2-1] for the Willamette River at Salem.

  • Outflow of the Willamette River to the Columbia is 100 cm/y (23.5 million ac-ft) of water in the simulated historical scenario. The average discharge of the basin, based on the period of record at Portland, is 102 cm/y (23.9 million ac-ft). Of that, 75% discharges from Nov. 1 to Apr. 30. To put that in perspective, urban water users in the WRB use approximately 100 gal/day/person. If only 20% of the winter discharge of the Willamette River were used for urban supply, it would be enough to supply approximately 32 million people water for one year.

Table 1. Summary of WRB water budget for the simulated historical scenario (1950-2009; also called HistoricRef). All units are cm/y averaged over the basin. Annual totals are shown in the top of each section, and monthly values are shown below that.

Time Precip Δ Snow Δ Reservoirs HighC GW Δ Soil & GW ET Ag Muni Ag+Muni ET Env Flow Outflow
Ann 161.53 0.00 -0.05 8.16 0.00 71.74 1.21 0.52 0.97 14.77 99.82
Oct 12.50 0.00 -1.74 0.68 7.93 3.82 0.01 0.04 0.01 1.73 3.32
Nov 25.27 0.08 -1.28 0.68 16.96 3.75 0.00 0.04 0.00 0.00 6.59
Dec 26.81 1.53 -0.20 0.68 8.84 3.77 0.00 0.04 0.00 0.00 13.70
Jan 22.15 1.40 -0.14 0.68 1.27 3.92 0.00 0.04 0.00 0.00 16.53
Feb 18.41 0.72 1.56 0.68 -1.03 4.34 0.00 0.03 0.00 0.00 13.64
Mar 17.18 -0.52 1.68 0.68 -3.14 6.53 0.01 0.04 0.01 0.00 13.46
Apr 13.09 -0.82 1.23 0.68 -6.16 8.45 0.06 0.04 0.05 3.41 11.22
May 10.52 -1.44 0.68 0.68 -6.88 10.78 0.17 0.04 0.14 2.96 8.21
Jun 6.39 -0.75 -0.02 0.68 -8.63 10.90 0.33 0.05 0.26 2.02 5.72
Jul 1.92 -0.19 -0.28 0.68 -8.02 8.04 0.37 0.06 0.30 1.48 3.21
Aug 2.10 -0.01 -0.47 0.68 -2.71 4.08 0.21 0.06 0.18 1.49 2.05
Sep 5.21 0.00 -1.05 0.68 1.57 3.34 0.04 0.05 0.04 1.67 2.18

 

Table 2. Summary of WRB water budget as simulated for the late 21st century Reference Case scenario (2070-2099). All units are cm/y averaged over the basin. Annual totals are shown in the top of each section, and monthly values are shown below that.

Time Precip Δ Snow Δ Reservoirs HighC GW Δ Soil & GW ET Ag Muni Ag+Muni ET Env Flow Outflow
Ann 171.89 0.00 0.00 8.16 0.00 66.71 1.34 1.12 1.16 14.77 115.97
Winter 137.63 0.29 3.14 4.08 20.52 27.31 0.08 0.47 0.06 3.41 91.76
Summer 34.26 -0.29 -3.14 4.08 -20.52 39.39 1.26 0.66 1.09 11.35 24.22
Oct 9.98 0.00 -1.69 0.68 5.63 3.50 0.01 0.09 0.02 1.73 3.43
Nov 27.64 0.00 -1.04 0.68 19.55 2.93 0.00 0.08 0.00 0.00 7.09
Dec 34.82 0.25 0.32 0.68 12.46 3.10 0.00 0.08 0.00 0.00 19.58
Jan 23.69 0.19 -0.36 0.68 -1.00 3.43 0.00 0.08 0.00 0.00 22.32
Feb 15.85 0.18 1.05 0.68 -3.56 3.92 0.00 0.07 0.00 0.00 15.15
Mar 22.00 0.00 1.95 0.68 0.46 6.01 0.02 0.08 0.01 0.00 14.48
Apr 13.63 -0.33 1.22 0.68 -7.40 7.92 0.07 0.08 0.05 3.41 13.12
May 8.44 -0.23 0.50 0.68 -9.64 10.50 0.28 0.09 0.22 2.96 8.22
Jun 5.42 -0.06 -0.14 0.68 -8.48 9.86 0.42 0.11 0.34 2.02 5.14
Jul 2.22 0.00 -0.32 0.68 -7.01 7.56 0.40 0.13 0.34 1.48 2.90
Aug 2.68 0.00 -0.48 0.68 -2.46 4.33 0.11 0.13 0.13 1.49 2.19
Sep 5.52 0.00 -1.01 0.68 1.44 3.64 0.04 0.10 0.05 1.67 2.34

 

Estimated water budget for August, for the simulated historical scenario.

Estimated water budget for August, for the Reference Case scenario.

Figure 3. August WRB water budget for simulated historical period (top) and 2070 - 2100 in Reference Case scenario (bottom). An interactive version of this is available online. Municipal extraction of water was increases and water use for irrigation declines in the 2070 – 2100 future scenarios. Snowmelt declines as a source of stored water in the 2070 – 2100 futures scenarios.

 

Plots of discharge at Portland for various WW2100 scenarios

Figure 4. Willamette River at Portland. Reference Case scenario. In bottom and right figures, shading is as follows. Light blue = climate effects (Range of HighClim, LowClim, StationaryClim; this is blue-green where overlapping with green; see also shading legend at bottom of page). Red = all scenarios with modeled changes in human systems (population, land use, etc.) Green = all possible scenarios (excludes counterfactual scenarios). The overall behavior of discharge in the Willamette River is similar in the future to what it has been in the past. In the Reference Case scenario, as in all scenarios, discharges are highest in December and January, on average. Flows begin to decline in late winter, and get particularly low in August and September. Average total annual flows in the Willamette are similar over the century to what they are today. Similar graphs of discharge at other locations in the basin are available online. (Plots by R. Haggerty)

Appendix: Shading Notes for Cascade Plots

Explanation of shading in monthly plots in Figure 5.

Legend for shading in cascade plots.

Related Links & Publications

  • Videos from a 2013 OSU graduate course on hydrologic modeling with a focus on Willamette Envision.

  • Vache, K. Bolte, J, Schwartz, C., Sulzman, J. (2016). A flexible framework to support socio-hydrological scenario analysis. Manuscript submitted for publication.

Contributors to Hydrologic Analysis

  • Roy Haggerty, OSU College of Earth, Ocean and Atmospheric Sciences (lead)
  • Kellie Vaché, OSU Biological & Ecological Engineering
  • Heejun Chang, PSU Department of Geography
  • Anne Nolin, OSU College of Earth, Ocean and Atmospheric Science
  • David Conklin, Oregon Freshwater Simulations
  • Maria Wright, OSU Institute for Water and Watersheds

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, 300(9), D05109.

Allen RG, Walter IA, Elliot RL, J Howell TA, Itenfisu D, Jensen ME, Snyder R. 2005. The ASCE standardized reference evapotranspiration equation. Report No. ASCE and American Society of Civil Engineers.

Bergström, S., Carlsson, B., Gardelin, M., Lindström, G., Pettersson, A., & Rummukainen, M. (2001). Climate change impacts on runoff in Sweden assessments by global climate models, dynamical downscaling and hydrological modelling. Climate research, 16(2), 101-112.

Bergström, S. and V. P. Singh. 1995. The HBV model. In V.P. Singh (Ed.) Computer Models of Watershed Hydrology (pp.443–76). Water Resources Publications.

Chang, H., & Jung, I. W. (2010). Spatial and temporal changes in runoff caused by climate change in a complex large river basin in Oregon. Journal of Hydrology388(3), 186-207.

Klipsche, J. D., & Hurst, M. B. (2007). HEC-ResSim: Reservoir system simulation user’s manual, v. 3.0, CPD 82. Davis: US Army Corps of Engineers Hydrologic Engineering Center, available at http://www. hec. usace. army. mil/software/hec-ressim.

Northwest Alliance for Computational Science and Engineering (NWACSE). (2015), PRISM Climate Data. http://prism.oregonstate.edu

Seibert, J. (1997). Estimation of parameter uncertainty in the HBV model.Hydrology Research, 28(4-5), 247-262.

Turner, D. P., Conklin, D. R., Vache, K. B., Schwartz, C., Nolin, A. W., Chang, H., ... & Bolte, J. P. (2016). Assessing Mechanisms of Climate Change Impact on the Upland Forest Water Balance of the Willamette River Basin, Oregon. Ecohydrology.

US. Fish and Wildlife Service, Oregon Fish and Wildlife Office (2008), Biological Opinion On the Continued Operation and Maintenance of the Willamette River Basin Project and Effects to Oregon Chub, Bull Trout, and Bull Trout Critical Habitat Designated Under the Endangered Species Act, File Number: 8330, F0224(07), Tails Number: 13420-2007-F-0024.

Web page authors: R. Haggerty, M. Wright, K. Vache, D. Conklin, H. Chang, A. Nolin
Last updated: December 2016

Reservoir Operational Performance

Streamflow in the Willamette River Basin (WRB) is regulated by 13 federally owned reservoirs. Together they operate as a system to provide flood regulation, power production, recreation, fish and wildlife conservation, irrigation, and water quality regulation. The reservoirs were constructed primarily to regulate flood flows, an objective that remains the highest priority for determining reservoir releases. The priority of other operating objectives varies by reservoir and circumstance.

The WW2100 reservoir team investigated whether reservoirs will continue to meet operational objectives, given expected hydrologic responses to climate change, population growth, and economic growth over the next century. We modeled operation of federal reservoirs by incorporating code from the US Army Corp of Engineers model called ResSim into Willamette Envision. ResSim determines water release quantities from reservoirs following a system of time and place specific operating rules.

In this analysis, we evaluated (1) the reliability of hydrologic modeling within Willamette Envision by comparing modeled reservoir inflows to observed historical data, and (2) the potential effects of climate warming on reservoir operational performance, by comparing outcomes for different WW2100 futures scenarios. Our analysis indicates that basinwide, Willamette Envision tends to underestimate inflows during the winter months but reliably produces inflows during the July to November time period. Our comparison of WW2100 futures scenarios suggests that climate warming will have a limited effect on the ability of the system to meet spring and summer flow targets. 

 

Reservoir Modeling in Brief

Reservoir modeling within Willamette Envision duplicates the rules of the US Army Corps of Engineers’ 2012 ResSim model for the Willamette River Basin (WRB). Each reservoir has operational zones, which are based on pool elevation on a particular date (Fig. 1) and rules sets associated with each zone. These rule sets are prioritized within each zone and across reservoirs, such that the model calculates release quantities and locations (e.g., Powerhouse, Regulating Outlet, Spillway) at each time step to meet the highest priority rule. Example rules are those that establish minimum or maximum flows or water elevations, such as the minimum flow targets established by the 2009 Biological Opinion (NMFS, 2008) for April to October, which vary based on the type of water year. These rules are often evaluated at important downstream locations, called control points, which are used to establish releases at reservoirs upstream. Many reservoirs can be influenced by a single control point (e.g., Salem), and many control points can be influenced by a single reservoir. For more information about reservoir modeling in Willamette Envision, refer to the supplemental materials from Jaeger et al. (2016).

Diagram of reservoir operation zones.

Figure 1. Reservoir operating zones (Lookout Point). The conservation curve (green) is the primary rule curve. The zone above the rule curve is the Flood Control Zone, while below the curve is the Conservation Zone. If the pool elevation is above the elevation demarcating the primary flood control zone (horizontal dark blue line), the zone is labeled Top of Dam Zone. If the pool elevation falls below the red line, the zone is labeled the Buffer Zone. An alternative flood control zone exists in this case, when the pool elevation is above the rule curve but below the secondary flood control line (in light blue).

Evaluation of Predicted Reservoir Inflows

We compared simulated reservoir inflows to those observed historically to evaluate the reliability of the hydrologic model in replicating the key hydrologic processes of the catchment. The three measures and their interpretation are summarized below.

  • Root mean square error (RMSE) is an absolute measure of model fit, meaning that the units are directly interpretable, such that higher values represent higher errors. RMSE is calculated as the square root of the variance of the residual. There is no absolute value for a reliable RMSE, as it must be interpreted with respect to the range of predicted values and practical application.
  • Normalized RMSE (NRMSE) is a nondimensional form of the RMSE, normalized to the mean of the observed data, which is useful for comparing between basins and across seasons with different ranges of flows. Expressed as a percentage, the NRMSE is interpreted as the error relative to the mean.
  • Nash Sutcliff Efficiency (NSE) measures the signal-to-noise ratio of hydrologic models by comparing the magnitude of model residuals to the variance in the observed data. A value of 1 represents perfect fit between observed and simulated inflows, a value of 0 represents a model that is equally well represented by the mean, and a value less than 0 indicates a model of questionable value. The reliability of NSE is compromised when extreme values are present in the dataset.

Analysis of Reservoir Operational Performance

The 13 federal reservoirs in the WRB are operated with a primary objective of flood regulation, while power production, recreation, fish and wildlife conservation, irrigation, and water quality regulation provide ancillary benefits. In this analysis, we focused on the potential effect of climate change on two high priority operational objectives: flood regulation and fish and wildlife conservation (low flow targets). We evaluated the ability of reservoirs to meet these operating objectives based on the concept of operational reliability, where reliability is defined (e.g., Hashimoto, 1982; Ray et al., 2010) as the likelihood of achieving a flow target or level of flood protection.

Selected Findings from Reservoir Performance Analysis

Observed vs. Simulated Reservoir Inflows

  • The simulated historical hydrograph of all reservoir inflows combined (Fig. 2) illustrates that the model tends to underestimate inflows during the winter months but reliably produces basinwide inflows during the July to November time period.
  • The relative model fit across reservoirs varies with the metric used to evaluate fit.

    • Based on root mean square error (RMSE; Table 1), model fit is lowest at Green Peter and Detroit reservoirs, which are among the largest reservoirs in the system.

    • Based on normalized root mean square error (NRMSE; Table 2), model fit is lowest at Blue River, Fall Creek, and Fern Ridge, likely as a result of lower flows into those reservoirs.

    • Based on Nash Sutcliff efficiency (NSE; Table 3), model fit is lowest at Blue River reservoir.

  • Model fit is generally higher for the dry summer months than for the wet winter months.

    • Based on RMSE (Table 1), model error is lower in dry summer months than wet winter months.

    • Based on NRMSE (Table 2), model fit is generally higher in dry summer months than wet winter months. Note that because this metric is normalized by mean flows, it will inherently be higher for summer months than winter months, since mean flows are higher in winter than summer.

    • Based on NSE (Table 3), model fit is generally higher for summer than winter months, with the exceptions of Cottage Grove and Fern Ridge.

  • There is no strong evidence of any trend in model fit across the tributaries with the expected degree of groundwater contributions.
  • Note that Foster and Lookout Point reservoirs were omitted from this analysis because they are located downstream of and are under the influence of reservoir operations at Green Peter and Hills Creek, respectively, complicating comparisons of historical and simulated inflows.

Total daily-averaged reservoir Inflow - observed vs. simulated 1980-2009

Figure 2. Total daily averaged reservoir inflow - observed vs. simulated for the period 1980-2009. Results were generated by taking a geometric average of daily streamflows to account for a log-normal streamflow distribution. The simulated data is from the simulated historical climate scenario (called HistoricRef), a WW2100 scenario that modeled the historical period using simulated historical weather data.

 

Table 1. Root mean square error (cms) of observed reservoir inflows vs. modeled reservoir inflows for the HistoricRef scenario, 1980-2009.

  Annual Nov-May Jun-Oct
Blue River 3.74 4.8 1.21
Cottage Grove 1.2 1.49 0.59
Cougar 4.21 5.1 2.5
Detroit 8.41 10.22 4.89
Dorena 3.82 4.9 1.2
Fall Creek 3.85 4.91 1.4
Fern Ridge 2.97 3.79 1.08
Green Peter 8.27 10.57 2.87
Hills Creek 4.06 5.11 1.76

 

Table 2. Normalized root mean square error of observed reservoir inflows vs. modeled reservoir inflows for the HistoricRef scenario, 1980-2009.

  Annual Nov-May Jun-Oct
Blue River 41% 33% 65%
Cottage Grove 24% 19% 63%
Cougar 22% 19% 27%
Detroit 16% 15% 19%
Dorena 27% 22% 44%
Fall Creek 34% 28% 52%
Fern Ridge 31% 24% 122%
Green Peter 24% 20% 34%
Hills Creek 15% 14% 13%

 

Table 3. Nash Sutcliffe efficiency of observed reservoir inflows vs. modeled reservoir inflows, for the HistoricRef scenario, 1980-2009.

  Annual Nov-May Jun-Oct
Blue River 0.71 -0.48 0.55
Cottage Grove 0.91 0.7 0.43
Cougar 0.83 0.04 0.71
Detroit 0.88 0.12 0.81
Dorena 0.88 0.46 0.8
Fall Creek 0.8 0.08 0.61
Fern Ridge 0.91 0.81 -3.26
Green Peter 0.88 0.29 0.81
Hills Creek 0.9 0.43 0.92

 

Simulated vs. Future Reservoir Inflows

  • We compared total systemwide reservoir inflows between the simulated historical scenario (HistoricRef) and two futures scenarios with different intensities of climate warming. These two scenarios were the Reference Case, which included moderate climate warming (4° C or 7.5° F) warming over the 21st century, and the High Climate Change scenario (with 6° C or 10.5° F) of warming over the 21st century. Refer to the climate page for more information about these scenarios. The analysis suggests that relative to the past, future reservoir inflows to the system will be the same or higher except for the early summer months (May-July) and late fall, primarily for late century.

Total reservoir inflow – Reference scenario

Figure 3. Total daily averaged reservoir inflows for early, middle, and late century periods of the Reference scenario. For comparison, values from the simulated historical scenario, 1980-2009, are also shown.

Total reservoir inflow – HighClim scenario

Figure 4. Total daily averaged reservoir inflows for early, middle, and late century periods of the HighClim scenario. For comparison, values from the simulated historical scenario, 1980-2009, are also shown.

Flood Regulation

  • Model results indicate that regulation of floods at Salem is very high, represented by zero days above flood stage for the simulated historical scenario. Flood regulation at Salem remains high in nearly all years for the Reference and HighClim future scenarios (Fig. 5).
  • The No Reservoirs scenario illustrates the degree of flood regulation provided by the reservoirs, the benefits of which are particularly evident in the mid- and late-century time periods when flood stage is regularly exceeded.
  • Model calibration emphasized summer flows since the project focused on water scarcity. As a result, model fit for reservoir inflows was lower in winter than in summer, introducing some uncertainty in the results regarding impacts of climate change on flooding.

Time reliability of flood regulation on the Willamette River at Salem

Figure 5. Time reliability of flood regulation on the Willamette River at Salem, Ore., for three WW2100 scenarios. The Reference scenario includes a moderate degree of climate warming (weather inputs were derived from the MIROC global climate model). The No Reservoirs scenario includes these same weather inputs, but models behavior of the river system as though no federal reservoirs were present. The High Climate Change scenario models the system with reservoirs, as in the Reference scenario, but includes more intense climate warming (weather inputs derived from the HadGEM global climate model). For more information about WW2100 modeling scenarios, refer to the scenarios page.

Spring and Summer Flow Targets

  • Model results indicate that, with a warmer climate, the number of days when summer targets are met increases in the future relative to the simulated historical scenario. This counterintuitive effect is greater for the Reference scenario than for the HighClim scenario. This reflects the increase in runoff projected for the April-May time period (Figs. 3, 4). However, future reservoir inflows are lower than the simulated historical during the July-August months when the reservoirs appear to provide higher reliability in summer targets.
  • Results for the No Reservoirs scenario also illustrate the role of reservoirs in augmenting flows from July to October. In this scenario (Fig. 6), the number of days when late summer flow targets are met is nearly zero.
  • The No Reservoirs scenario also demonstrates the limited effect of the reservoirs on providing flows for the spring and early summer months. Even without reservoirs, the scenario shows a high reliability of meeting flow targets from April to June.

Time reliability of spring and summer flow targets on the Willamette River at Salem

Figure 6. Time reliability of spring and summer flow targets on the Willamette River at Salem for the Reference, HighClim, and No Reservoirs scenarios. Results are plotted as the ratio of days above the flow target relative to the number of days in a given month.

Conclusions

  • Model fit: basinwide the model tends to underestimate inflows during the winter months but reliably produces inflows during the July to November time period.

  • Flood regulation: climate change appears to have limited to no effect on flood regulation. However, the model underpredicts the historical winter inflows to reservoirs, indicating that this finding has some associated uncertainty.

  • Spring flow targets: climate change appears to have limited to no effect on the ability of the system to meet spring flow targets.

  • Summer flow targets: climate change appears to have a positive effect on the ability of the system to meet summer flow targets in the late century, perhaps because of projected increases in spring runoff.

Related Links & Publications

Contributors to WW2100 Reservoir Modeling

  • Desiree Tullos, OSU Biological & Ecological Engineering (lead)

  • Cara Walter, OSU Biological & Ecological Engineering

  • Kathleen Moore, PhD Student, OSU Geography (Graduated: 2015; now a post-doctoral researcher, OSU Applied Economics)

  • Matt Cox, OSU Biological & Ecological Engineering (now at Interfluve, Hood River, Oregon)

References

Jaeger et. al. (2016). Scarcity amid abundance: Water, climate change, and the policy role of regional system models. Manuscript in preparation.

National Marine Fisheries Service. (2008). Continued Operation of 13 Dams & Maintenance of 43 Miles of Revetments in the Willamette Basin, OR. (PCTS Tracking No. NWR 2000-2117). Portland, OR.

Hashimoto, T., Stedinger, J. R., & Loucks, D. P. (1982). Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation. Water Resources Research, 18(1), 14-20. http://dx.doi.org/10.1029/WR018i001p00014

Ray, P. A., Vogel, R. M., & Watkins, D. W. (2010). Robust optimization using a variety of performance indices. In Proceedings of the World Environmental and Water Resources Congress, ASCE, Reston, VA.

 

Web page authors: D. Tullos, C. Walter, K. Moore
Last updated: August 2016

Reservoir Economics

Together the 13 federal reservoirs in the Willamette River Basin help to mitigate water scarcity. Although the reservoirs were primarily built for flood control, they also provide a large capacity – a combined 1.6 million acre-feet – to store water from abundant winter and spring streamflows for possible use during the summer when natural flows are low. While flood risk reduction remains the priority use of these reservoirs, stored water uses – including reservoir recreation and increasing downstream flows for endangered species, irrigated agriculture, and municipal uses – have become increasingly important. Because reservoir capacity cannot be used for flood control and water storage at the same time, these uses must be traded off during the transition from the wet to dry season.
Our modelling results indicate increasing shortfalls from full storage at the beginning of summer over the next century, particularly under warmer climate scenarios. Greater summer drawdowns are also more likely, as obligations to release water for downstream conservation flows or contracted stored water increasingly exceed natural inflow. Based on empirical evidence that fewer people visit these reservoirs for recreation when water levels are reduced, losses in annual recreational benefits are expected to increase from an average of $5 million over the first half of this century to more than $12 million toward the end of the century. These estimated losses represent approximately 5% of total estimated reservoir recreational benefits each summer. Recreational losses should, however, be considered relative to the estimated value of flood risk reduction. We estimate current flood control benefits at more than a billion dollars annually and expect these benefits to triple by 2100 with economic growth and urban expansion.

Reservoir Economics Methods in Brief

We estimated the value of stored water for recreation at the Willamette Basin federal reservoirs using monthly visitor count data obtained from the US Army Corp of Engineers (USACE) for the years 2001-2011 (period available at time of analysis), and an expected average willingness-to-pay per visitor day (Loomis, 2005). Lowered water levels can impact recreational use in various ways, including loss of boat ramp access and compromised aesthetics such as ‘bathtub rings.' Estimated losses in recreational benefits were based on WW2100 projected shortfalls in summer storage, and empirical evidence that fewer people visit the reservoirs when water levels are reduced (Moore, 2015).

We then estimated the value of reservoir capacity for flood risk reduction based on WW2100 projected land use within the Willamette River floodplain, the probability of flood events, and expected flood inundation. The extent of the floodplain was delineated according to the SLICES data layer developed by the Pacific Northwest Ecosystem Research Consortium (Hulse et al., 2002). We used a ‘bathtub’ model along with high-resolution topographic information (LiDAR) to estimate inundation associated with flood stage. Flood frequency and timing was assessed using the stream gauge record at Salem. Expected flood damages and the related value of reservoir capacity for flood risk reduction were estimated by integrating the spatially explicit estimates of structural value in the Willamette River floodplain, the modeled flood inundation, and assessed flood frequency distributions (Moore, 2015).

Select Findings from Reservoir Economics Analysis

The federal reservoirs in the Willamette River Basin serve multiple purposes, including flood risk reduction, reservoir recreation, and downstream flow augmentation. At certain times of the year, however, these uses compete with one another, and the value that society places on these competing uses should be considered in management decisions for water allocation. This section describes the expected changes in value for reservoir recreation and flood risk reduction over the next century.

Reservoir Recreation Benefits

  • Our modeling results indicate increasing shortfalls from full storage at the beginning of summer over time, particularly under warmer climate scenarios, as well as greater summer drawdowns as obligations to release water downstream for conservation flows or contracted stored water are increasingly unmet by natural inflow (Fig. 1).

  • Lost recreational benefits resulting from lowered summer (June-August) water levels are estimated to increase from an average of $5 million per year over most of this century to more than $12 million toward the end of the century under the Reference Case scenario (Fig. 2). Under the warmest climate scenario, average annual losses increase to almost $13.5 million during the last two decades of the century (Fig. 2).

Average shortfall from full summer storage by decade for the high climate scenario.

Figure 1. Average shortfall from full summer storage by decade for the High Change Climate scenario.

 

Reduction in recreational benefits due to reservoir drawdown.

Figure 2. Reduction in recreational benefits due to reservoir drawdown.

Flood Risk Reduction Benefits

  • Current benefits from flood risk reduction are estimated at more than a billion dollars annually (Fig. 3).
  • These benefits are expected to triple by 2100 under the Reference Case scenario, while under the High Population scenario, the projected benefits increase more than five times (Fig. 3).

Estimated flood risk reduction benefits from January through May under the reference and high population scenarios.

Figure 3. Estimated flood risk reduction benefits from January through May under the Reference Case and High Population scenarios.

Conclusions

Losses in recreational benefits, associated with lowered summer reservoir water levels, are expected to increase from an average of $5 million per year over the first half of this century to more than $12 million by the end of the century. However, since the reservoirs are managed for both flood control and stored water uses, these projected recreational losses should be tempered against the estimated value of flood risk reduction. We estimate current flood control benefits at more than a billion dollars annually and expect these benefits to triple by 2100 with economic growth and urban expansion. Thus, there is a strong economic rationale to keep reservoir fill low as long as flood risk is high at the beginning of each calendar year, before beginning to fill for storage.

Related Links & Publications

  • Moore, K.M. (2015, May 5). Optimizing reservoir operations to adapt to climate and social change in the Willamette River Basin, Oregon, Recorded WW2100 Webinar. https://media.oregonstate.edu/media/t/0_qrvmvk9h

  • Moore, K.M. (2015). Optimizing reservoir operations to adapt to 21st century expectations of climate and social change in the Willamette River Basin, Oregon (Doctoral dissertation). Oregon State University, Corvallis, Ore. http://hdl.handle.net/1957/56208

Contributors to WW2100 Reservoir Economics Analysis

  • Kathleen Moore, OSU Geography (completed PhD 2015; now a post-doctoral researcher, OSU Applied Economics)
  • William Jaeger, OSU Applied Economics

References

Hulse, D., S. Gregory, and J. P. Baker (2002). Willamette River Basin Planning Atlas: Trajectories of Environmental and Ecological Change. Oregon State University Press.

Loomis, J. B. (2005). Updated outdoor recreation use values on national forests and other public lands. US Department of Agriculture, Forest Service, Pacific Northwest Research Station.

Moore, K.M. (2015). Optimizing reservoir operations to adapt to 21st century expectations of climate and social change in the Willamette River Basin, Oregon (Doctoral dissertation). Oregon State University, Corvallis, Ore. http://hdl.handle.net/1957/56208

Web page authors: K. Moore, W. Jaeger
Last updated: September 2016

Fish & Stream Temperature

One of the major impacts of future climate change in the Pacific Northwest is the alteration of native fish communities, which include a substantial number of cold water species (Williams et al., 2014). Projected warming trends in the Pacific Northwest create direct physiological challenges for cold water species and indirect challenges through competition with species that are tolerant of warmer water, especially those species that are not native to the region. Currently, in the entire Willamette River Basin there are 69 species of fish—36 native species and 33 non-natives. 

The objectives of the WW2100 fisheries team were to:

  1. Determine the composition and distribution of native and non-native fish communities along the mainstem Willamette River.
  2. Determine the likelihood of occurrence for representative fish species based on the temperatures at which those species occurred in the Willamette River.

During our field sampling in summers of 2011-2013, we captured more than 36,000 fish including 22 native and 19 non-native species. Most fish captured (more than 90%) were native species. Species richness and abundance exhibited strong longitudinal patterns; higher numbers of fish were collected in the upper river, and a higher proportion of the fish captured were native species. We do not have modeled estimates of water temperature for 2100, but we projected the consequence of a potential temperature change on the likelihood of capturing representative fish species in our standard sampling protocol. Our results suggest that the likelihood of occurrence of native cold-water species, such as juvenile Chinook salmon, would decrease substantially if future river temperature increases by 2° C (3.6° F) or more. 

 

Fisheries Methods in Brief

Field Sampling Methods

We divided the mainstem Willamette River into three sections from its mouth, upstream 301 km to the confluence of the Coast and Middle Fork Willamette, based on analysis of river geomorphology. The SLICES framework served as the geomorphic basis of randomized site selection for fish community assessment. Each 1-km slice of the mainstem river or floodplain slough within a river slice represented a single sampling location. In each 1-km slice in 2011-2013, we captured fish with standardized boat and backpack electrofishing.  Sampling locations included 96 mainstem reaches and 71 sloughs. We measured environmental and habitat characteristics at each site.

We developed a Willamette River Fish Database (http://gis.nacse.org/wrfish/) to make spatially explicit data on fish communities in the mainstem Willamette River publicly available. Watershed councils, land trusts, NGOs, and state and federal agencies have full access to the data for designing projects to conserve or restore the aquatic ecosystems and floodplains of the Willamette River. The information also contributes to the collective development of a guiding vision of a future Willamette River and its floodplain for all partners.

Modeling Approach

We related possible temperature increases in the mainstem river to the likelihood of capturing various fish species in a standard sampling effort (e.g., spring Chinook salmon, coastal cutthroat trout, common carp). Specifically, we describe the consequence of a hypothetical increase (2° C or 3.6° F) in stream temperature on the likelihood of capturing representative fish species in our standard sampling protocol, based on the temperatures at which we observed them in 2011-2013. Such an increase in stream temperature is highly likely in the future during normal flow years.

The fisheries analysis is limited to the mainstem Willamette River, downstream of the major reservoirs, and was not coupled to Willamette Envision, the project's integrative water system model.

Select Findings from Fisheries Analysis

Fish Distribution

During our field sampling in summers of 2011-2013, we collected 41 fish species – 22 native and 19 non-native. Of the total of 36,586 individual fish collected, 93% were native species and 7% non-native (Williams, 2014). In mainstem habitat, 97% of the individual fish captured were native and 3% were non-native. A greater proportion of catch in slough habitats was non-native (13%), but native species comprised the majority (87%) of fish captured in the sloughs as well.

Species richness and relative abundance of fish exhibited significant longitudinal patterns (Fig. 1). Higher numbers of fish were collected in the upper river, and higher proportions of those fish were native species. In contrast, non-native species exhibited the opposite pattern, increasing in relative abundance and total number of taxa from the upper river to lower river. The 1-km standard sample reaches in the upper river contained 16-19 native fish species, but similar samples from the lower river contained only 3-10 native fish species.

Number of native fish species captured in BOAT electrofishing samples for the mainstem Willamette River for 2011 - 2013.

Figure 1. Number of native fish species captured in BOAT electrofishing samples for the mainstem Willamette River for 2011-2013.

Temperature Sensitivity

During our three-year monitoring study, we sampled both fish communities, habitat, and water quality throughout the Willamette River mainstem (Fig. 2). The green and red lines and markers in Fig. 2 represent the maximum daily temperature observed at each station in 2012 and 2013, respectively. We do not have modeled estimates of water temperature for 2100, but we projected the consequence of a potential temperature change on the likelihood of capturing representative fish species in our standard sampling protocol. If water temperatures in the Willamette River increase by 2° C (3.6° F, less than the projected air temperature), the longitudinal profile (blue line and markers) would increase to more than 26° C (78.8° F) in the lower river. Such an increase is highly likely in the future during normal flow years. As a frame of reference, the USGS gaging station in Portland recorded temperatures of 26.8° C (80.2° F) in early July 2015, a year that was extremely warm and when flows in the river were much lower than average (Fig. 3).

Longitudinal pattern of river temperature in observed in 2012 and 2013 (green and red, respectively) and longitudinal pattern of river temperature in 2100 if river temperatures increase by 2°C.

Figure 2. Longitudinal pattern of river temperature observed in 2012 and 2013 (green and red, respectively) and longitudinal pattern of river temperature in 2100 assuming an increase in river temperatures of 2° C (3.6° F).

Temperature at the USGS gaging station in Portland in summer 2015.

Figure 3.  Temperature at the USGS gaging station in Portland in summer 2015.

We estimated the likelihood of occurrence of juvenile Chinook salmon in our standard sampling protocol based on the temperatures at which we observed this species in 2011-2013. Projections for the likelihood of occurrence currently decreases from approximately 50% in the upper river to less than 30% in the lower river (Fig. 4). The projected temperature increase by 2100 would lower that likelihood to less than 40% in the upper river and roughly 15% in the lower river. Coastal cutthroat trout are a cold-water salmonid that resides in the Willamette River throughout the year. Cutthroat trout exhibited even greater sensitivity to temperature (based on the locations they were captured). The likelihood of occurrence of cutthroat trout in the lower river would be extremely low under potential future temperatures (Fig. 5). In contrast, the likelihood of occurrence of common carp, a warm-water non-native species, increase longitudinally and would increase to more than 90% in the lower river by 2100 (Fig. 6).

These projections of the likelihood of cold-water fish species occurrence at higher temperatures are likely to be overestimates because the National Marine Fisheries Service (NMFS) does not allow us to sample at temperatures higher than 18° C (64.4° F). We are extrapolating beyond observed temperatures and occurrences.  Based on the incipient lethal levels of these species, it is highly unlikely to observe Chinook salmon, cutthroat trout, or other salmonids in water warmer than 26° C (78.8° F).

Longitudinal pattern of the likelihood of capturing juvenile spring Chinook salmon in our standard sampling protocol based on the river temperatures in observed in 2012 and 2013..

Figure 4.  Longitudinal pattern of the likelihood of capturing juvenile spring Chinook salmon in our standard sampling protocol based on the river temperatures observed in 2012 and 2013 (green and red, respectively) and longitudinal pattern of river temperature in 2100 if river temperatures increase by 2° C (3.6° F).

Longitudinal pattern of the likelihood of capturing resident coastal cutthroat trout in our standard sampling protocol based on the river temperatures.

Figure 5.  Longitudinal pattern of the likelihood of capturing resident coastal cutthroat trout in our standard sampling protocol based on the river temperatures observed in 2012 and 2013 (green and red, respectively) and longitudinal pattern of river temperature in 2100 if river temperatures increase by 2°C (3.6°F).

Longitudinal pattern of the likelihood of capturing common carp, a warm-water non-native fish species, in our standard sampling protocol based on the river temperatures observed in 2012 and 2013.

Figure 6. Longitudinal pattern of the likelihood of capturing common carp, a warm-water non-native fish species, in our standard sampling protocol based on the river temperatures observed in 2012 and 2013 (green and red, respectively) and longitudinal pattern of river temperature in 2100 if river temperatures increase by 2° C (3.6° F).

Conclusions

The Willamette River has recovered greatly from past water pollution and river channel modifications, but it faces many threats in the future. Population in the region is expected to continue growing rapidly. Land development continues to see increasing demands for urban and residential lands while agricultural and forest industries are fighting to protect their land base. Much of the new development pressures are in the valley along the mainstem Willamette River and its floodplain. Streams and river temperatures already approach the lethal limits of native cold-water fish species, especially in the lower river near the major urban centers. Many miles of streams in the basin are listed by environmental agencies as water quality impaired because of water temperature. The climate in the basin is projected to warm by 1.0 to 3.4° C (2 to 6° F) by the middle of the century. Our results suggest that the likelihood of occurrence of native cold-water species, such as juvenile Chinook salmon, would decrease substantially if future river temperature increases by 2° C (3.6° F) or more.

One of the greatest challenges is to create a scientifically sound vision of the new river, a river that is changing because of its altered flow regimes and sediment supply, a river that is changing because of social changes in the towns and communities along its banks (Wallick et al., 2013). Water management authorities are facing increasing demands to store water in reservoirs and withdraw more water during low flow seasons when the needs of the aquatic ecosystem also are most acute. Flood control reservoirs already have reduced sediment transport to the mainstem by 60%, and peak flows in the river are reduced roughly 30 to 70%. The momentum of current trends and uncertainty of future changes make it critical for our region to anticipate the future Willamette River.

Related Links & Publications

Contributors to WW2100 Fisheries Research

  • Stan Gregory, OSU Fisheries and Wildlife (lead)
  • Josh Williams, MS Student, OSU Fisheries and Wildlife (Graduated: 2014) 

References

Wallick, J. R., Jones, K. L., O’Connor, J. E., Keith, M. K., Hulse, D., & Gregory, S. V. (2013). Geomorphic and Vegetation Processes of the Willamette River Floodplain, Oregon—Current Understanding and Unanswered Questions. US Geological Survey Open File Report, 1246. http://pubs.usgs.gov/of/2013/1246/pdf/ofr2013-1246.pdf

Williams, J. E., Giannico, G. R., & Withrow-Robinson, B. (2014). Field guide to common fish of the Willamette Valley floodplain. Corvallis, Or.: Extension Service, Oregon State University. http://ir.library.oregonstate.edu/xmlui/handle/1957/50100

 

Web page author: S. Gregory
Last updated: October 2015

Water Users Survey

The objective of the landowner survey component of the WW2100 project was to evaluate Willamette Valley landowner attitudes toward and perceptions of water availability, water scarcity, present and future water management policy, and present and future land use and management goals. Additional survey questions focused on perceptions of risks to quality of life in the Willamette Valley, environmental values and value orientations, sources of information used to learn about water, desirable community characteristics, household activities, participation in organizations related to natural resources, and socioeconomics. A summary of general results for the most salient items is presented here.  In survey responses, most landowners believe that the Willamette Valley has enough water currently. Of 1,402 respondents, approximately 54% strongly agreed that the Willamette Valley currently has enough water to meet the needs of people, plants, and animals, however respondents indicated greater uncertainty as time increased into the future. When asked about activities perceived to be high or moderate risk to water availability in the Willamette Valley, at least 70% of respondents indicated agriculture, drought conditions, and population growth.

 

Survey Methods in Brief

We used property tax records to create a sample of landowners for the survey. We used a geographic information system to stratify the sample in three ways:

  • Location in the Willamette Valley,

  • Residential versus agricultural property, and

  • Inside versus outside of the Urban Growth Boundaries (UGBs).

In winter and spring 2013, we sent questionnaires to 1,600 landowners in each of Lane, Marion, and Washington-Yamhill Counties. From this effort, 1,402 surveys were completed and returned. There were 430 surveys returned as undeliverable and 56 refusals. Therefore, the overall response rate was approximately 32%.

 

Table 1. Survey respondents by stratum (n=1,402).

  Lane Marion Washington-Yamhill Total

By property type and county

Residential

Agricultural

 

239

198

 

131

360

 

149

325

 

519

883

By location relative to Urban Growth Boundary and county

Inside UGB

Outside UGB

 

183

254

 

200

291

 

149

325

 

532

870

Total by county 437 491 474 1402

 

A follow-up questionnaire was sent to all landowners from the sample who did not answer the original survey. Approximately 400 questionnaires were completed. The most commonly reported reasons for not completing the survey were lack of interest in completing surveys, perceived lack of knowledge about water management, the survey was too long or complicated, and personal reasons.

Select Findings from Water Users Survey

This summary is divided into the basic sections of the survey. All results are considered preliminary until peer-review of data is completed. Because of the sampling strategy used, results are not generalizable to the entire Willamette Valley.

Sample demographics

  • Survey respondents averaged 64 years of age.

  • More than half (60%) of survey respondents were male.

  • Nearly all (99%) of survey respondents own their home.

  • Average length of residence at the current address was 23 years.

 

Perceptions of Water Availability

Most respondents reported that they think about the role of water in their life in terms of their lifetime (25%) or for future generations (57%). When asked the degree to which they believe that the Willamette Valley has enough water to meet the needs of people, plants, and animals in several time periods, respondents indicated greater uncertainty as time increased into the future (Fig. 1). No statistical difference existed between residential versus agricultural landowners.

The degree to which respondents believe that the Willamette Valley has enough water to meet the needs of people, plants, and animals for different future time periods.

Figure 1. The degree to which respondents believe that the Willamette Valley has enough water to meet the needs of people, plants, and animals for different future time periods.

Risk to Water Availability

When asked about their perceived risk of activities to availability of water in the Willamette Valley, more than 40% of respondents indicated:

  • Drought conditions and population growth to be high risk.

  • Agriculture, forest management, and industry to be moderate risk.

  • Water storage (e.g., hydro-electric dams), private wells, and historical appropriation (e.g., water rights) to be low risk.

Activities perceived to be high or moderate risk to availability of water in the Willamette Valley by at least 70% of respondents include: agriculture (74%), drought conditions (73%), and population growth (85%).

  • Marion County respondents were less likely to perceive high or moderate risk of agriculture and drought conditions than the other two locations.

  • Residential landowners were less likely to perceive high or moderate risk of agriculture than agricultural landowners.

 

Definition of Water Scarcity

A group of WW2100 stakeholders developed a definition of water scarcity:

“Water scarcity occurs when there is not an affordable, attainable, and reliable source of clean water when and where it is wanted or needed by humans and animals and plants currently and into the future.”

Landowners were asked to indicate the extent to which they perceived each underlined term to be associated with their own perception of water scarcity.

  • The greatest number of respondents indicated their perceptions to be strongly associated with “clean” and “humans.”

  • “Affordable” and “currently” were terms identified as weakly associated by the greatest number of landowners.

  • Two terms varied statistically for residential versus agricultural landowners. More residential (37%) than agricultural (26%) landowners indicated “affordable” (37% versus 26%) and “clean” (67% versus 57%) as strongly associated with their perception of scarcity.

  • Perceived association of several terms varied by location (county): attainable, reliable, clean, needed, humans, and animals and plants. Exploration of patterns in these differences is ongoing.

Landowners were asked to indicate the extent to which they perceived each underlined phrase in the water scarcity definition to be associated with their own perceptions of water scarcity.

Figure 2. Landowners were asked to indicate the extent to which they perceived each underlined phrase in the water scarcity definition to be associated with their own perceptions of water scarcity. This chart shows responses by phrase.

 

Water Regulation

Water law in Oregon currently is a “first in line, first in right” (i.e., those with the most recently acquired water rights lose them first). Most landowners (79%) believe that at least some regulation should exist related to water use and management, regardless of whether residential (81%) or agricultural (77%). Agreement with this statement was greatest in Washington-Yamhill Counties (83%), followed by Lane (80%) and Marion (74%) Counties.

Landowners were asked to indicate, in their opinion, the acceptability of different ways of distributing water among competing uses at times of limited water availability (Fig. 3). These included:

  • Current Oregon water law (see above)
  • The method that makes the most economic sense, regardless of priority
  • Users must share any excess water beyond what they need
  • Users can sell any excess water beyond what they need
  • All potential users have equal access to water that is available
  • Those who use more water pay more for its use
  • Users farther from the water source pay more for its use
  • Store enough water in reservoirs to account for all potential users
  • Give water not used by agriculture to municipal use
  • Give water not used by agriculture to biological use (e.g., more water in streams to maintain appropriate water temperature for fish)
  • Give water not used by agriculture to recreational use
  • Build more facilities for water storage and replenishment
  • Allow the state to decide allocation methods for water

Among the most acceptable distribution methods were those where excess is shared among users, those who use more water pay for its use, and water storage. Exploration of differences between residential and agricultural landowners and among counties are ongoing.

Responses from agricultural landowners about why they currently participate (or not) in selected land conservation practices.

Figure 3. Landowners were asked to indicate, in their opinion, the acceptability of different ways of distributing water among competing uses at times of limited water availability. This chart shows responses for the the options provided in the survey. Note: options are shown in the graph in the same order as the list provided above.

 

Future Use of Agricultural Land (Agricultural Landowners Only)

Agricultural landowners were asked several additional questions about water management on their land, and current and future use of their land. Eighty-seven percent (87%) reported that they currently live on their property. A majority of those who do not live on their property do live in Oregon. Ninety-three percent (93%) reported that they or an immediate member of their household manages decisions about water use on their property. Landowners were asked why they currently participate (or not) in selected land conservation practices (Fig. 4) and their interest in future participation in selected land conservation practices (Fig. 5).

Landowners were asked to indicate, in their opinion, the acceptability of different ways of distributing water among competing uses at times of limited water availability.

Figure 4. Responses from agricultural landowners about why they currently participate (or not) in selected land conservation practices.

 

Responses from agricultural landowners about their interest in participating in land conservation practices in the future.

Figure 5. Responses from agricultural landowners about their interest in participating in land conservation practices in the future.

Sixty-six percent (66%) of respondents indicated that they now have a formal, defined plan for future ownership of their land. There was no statistical difference between counties or location relative to UGB. Of those with a plan:

  • Forty-eight percent (48%) reported that the land will remain in its current use.
  • Twenty-five percent (25%) reported that a beneficiary will make any land use decisions.
  • Two percent (2%) reported either land will be divided into residential parcels or be part of a conservation easement.
  • Twenty-three percent (23%) reported “other.” Further evaluation of specific items provided by respondents is ongoing.

Landowners were asked to indicate which of the following will most likely to influence their decision in developing a plan for future ownership of their land, or potentially change an existing plan. Although there was no difference among counties, variation existed based on location relative to the UGB (Fig. 6).

 Influences on future ownership plans reported by agricultural landowners, grouped by property location relative to the Urban Growth Boundary.

Figure 6. Influences on future ownership plans reported by agricultural landowners, grouped by property location relative to the Urban Growth Boundary.

Landowners were also asked to report on factors that influence their decisions about changing the land use on their property (Fig. 7). Preliminary results suggest that differences among counties exist for demand for particular products and possession of a current water right for the property, and differences in relation to UGB exist for regulations on land management practices, and regulations on water use.

The extent to which selected factors are likley to influence agricultural landowners decisions about future land use.

Figure 7. The extent to which selected factors are likley to influence agricultural landowners decisions about future land use.

Related Links & Publications

Contributors to WW2100 Water Users Survey

  • Anita Morzillo, OSU Forest Ecosystems & Society, now at Department of Natural Resources and the Environment, University of Connecticut
  • Meagan Atkinson, MS Student, OSU Environmental Science (Graduated: 2014)

  • Stephanie Graham, MS Student, Professional Science Masters (Graduated: 2012)

 

Web page author: A. Morzillo
Last updated: October 2015