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NPS Water Balance Model

What it is and how to use it


What is a Water Balance Model?

Water balance is like a checking account for water. It tracks how much water is available to plants and animals as it moves through the landscape based on local conditions, like soil type, slope, and other factors.


Why Do We Need a Water Balance Model?

We use water balance to understand relationships between climate and natural resources. Water balance variables like soil moisture (water supply for plants), deficit (unmet water need) and evapotranspiration (water used by plants and evaporated) are typically more strongly correlated with changes in plant / animal health or population size than temperature or precipitation. Water balance variables are better because they are more physically related to the mechanisms of change than standard weather measurements.

Since 2016 we have published more than 20 peer-reviewed papers (see list at the end of this page) using the NPS water balance data, and new applications are underway. In addition to studies in parks focused on specific resources, we developed gridded water balance products (GIS layers) for the entire continental US (22 TB of data). This data covers 1980 to the present and 25 alternative futures until the year 2100 at 1 km resolution.

How the Model Works

The basic water cycle accounts for water entering the system as rain or snow. Water is then stored in the soil, or exits the modeled system via runoff as surface water, or to the atmosphere through evaporation or transpiration by plants (evapotranspiration). Temperature and humidity affect transitions of rain-snow and evapotranspiration. It is the integration of energy and water dynamics that makes water balance a (potentially much) more sensitive indicator of ecological processes than temperature or precipitation. Among the many advantages, water balance can account for the timing and duration of events more accurately. For example, a single day rain event of one inch has different ecological effects than 10 consecutive days with 0.1 inch of rain. Water balance variables reflect this difference but precipitation totals do not.

For more information you can read a non-technical explanation of water balance modeling. or more detailed technical explanations in Tercek et al. (2021, and 2023). You might also be interested in  short definitions and summary calculation methods for each variable (e.g. Actual Evapotranspiration).

modelsketch




Different Products for Different Users

To simplify the task of figuring out which NPS water balance products are best for you, we have identified 4 common user types.

  1.   Summaries in reports and high-level applications: A person wants to read about how the model has been used for management or download existing graphics and/or data summaries for a report, plan, or presentation.

    -- See how water balance has been applied to specific national parks in reports for the Grand Canyon,  Capitol Reef, and other Southwestern parks, or in the peer-reviewed articles on Great Sand Dunes NP and continental scale vegetation.

    -- Try our web-based tools that use water balance to predict streamflow and fire risk up to 30 days in advance. You can read about the Yellowstone fire model here (see page 27), and during the fire season, see its predictions. Or take a look at stream and fire forecasts for Great Sand Dunes NP.

  2. Beginning Data Exploration: A person who wants the same products as (1) but also wants access to raw data to guide their choice of products / variables by looking at spatial and temporal patterns.

    --  Use your browser to look at web-based maps of the data.

    -- Download CONUS-wide GIS layers (geotiff) that show 30-year average conditions for all the variables in the past and for future projections.

  3. Advanced Data Analysis: A person who wants to access raw data for conducting analyses, maybe  as a covariate for natural resource monitoring data.

    -- To get started, you can download water balance time series (daily or monthly csv files extracted from our gridded data) for the centroids of any CONUS national park. either  (zip files of pre-extracted data) or  matching temperature, precipitation, and relative humidity files (past and future) for the same locations You can extract and download time series from our gridded dataset for a table of arbitrary lat / lon points using python code that accesses a web api for historical data or future data.

    -- ClimateAnalyzer.org has a menu-driven GUI that lets you run the water balance model on weather station data (approximately 1500 locations in CONUS) that is updated every 24 hours. To use it, go to the weather station map and choose a station by clicking on one of the icons. Use the menus to choose tables or graphs and navigate until you get a graph or a table. Additional information on how the model is applied to weather datasets with missing values is here.

    -- Our historical gridded data is available (and can be graphed or subsetted) on the NASA AppEARS platform.

    -- All of our gridded water balance data are available as netCDF files on a THREDDS server. There are separate links for the historical THREDDS and the future THREDDS. This server will allow you to create links that serve the data as wms layers (active maps in a GIS environment), download the entire 22TB dataset in netcdf, make on-the-fly map previews of the data, and create smaller netCDf files for regions of interest.

  4. Power User: Somebody that wants a lot of data or needs to  code and run their own tweaked version of the model with site-specific data.

    -- You run or modify our code. We have versions in R, Python, and Excel.

    -- For large data requests  (> hundreds of locations) or advice on how to modify the code, feel free to contact us. Large data requests are more efficient if we do them in our shop and send you the results. Send an email to info@YellowstoneEcology.com for more information.


Data Versions

For most purposes, we recommend using version 1.5 of the model, which makes it possible to directly compare historical patterns to future projections. All the links on this page take you to that version of the data. If you are only interested in looking at historical patterns, you could try version 2 of the model, which incorporates a vegetation-greenness (NDVI) correction to Actual Evapotranspiration calculations. This may provide better accuracy in some places during certain times of the year. More info on that model appears in Tercek et al. (2021) and you can also refer to our table of model versions. Access to version 2 of the model is on a THREDDS or via python code.


Documents Describing Methods and Concepts

Methods used in the NPS Water Balance Model

The following two papers combined (with citations) describe all the equations and processing steps used to generate the NPS Gridded model.

Tercek et al (2021) -- also includes discussion of effects on CONUS-wide vegetation

Tercek et al. (2023)) -- also includes a discussion on biome shifts due to changes in plant growing season

Additional information on methods here:

Lutz et al.2015

Gridded WB data user manual

Conceptual background:

Stephenson (1998) classic showing the utility of AET and Deficit for describing vegetation communities

Historical view of Thornthwaite water balance models

Paper evaluating the accuracy of gridded data with implications for water balance       




Peer-reviewed Papers

Ray, A., Sepulveda, A., Hossack, B., Patla, D., Thoma, D., Al-chokhachy, R., & Litt, A. (2015). Wetlands : Can Long-term Monitoring Help Us Understand Their Future? Yellowstone Science, 23(1), 44–53.

Ray, A. M., Gould, W. R., Hossack, B. R., Sepulveda, A. J., Thoma, D. P., Patla, D. A., … Al-Chokhachy, R. (2016). Influence of climate drivers on colonization and extinction dynamics of wetland-dependent species. Ecosphere, 7(7), 1–21. https://doi.org/10.1002/ecs2.1409

Roberts, S., Thoma, D., Perkins, D., Tymkiw, E., Ladin, Z., Shriver, G., (2021). A Habitat-Based Approach to Determining the Effects of Drought on Aridland Bird Communities. Ornithology 138: 1–13. https://doi.org/10.1093/ornithology/ukab028.

Shanahan, E., Irvine, K. M., Thoma, D., Wilmoth, S., Ray, A., Legg, K., & Shovic, H. (2016). Whitebark pine mortality related to white pine blister rust, mountain pine beetle outbreak, and water availability. Ecosphere, 7(12). https://doi.org/10.1002/ecs2.1610

Tercek M, Thoma D, Gross J, Sherrill K, Kagone S, Senay G (2021). Historical changes in plant water use and need in the continental United States. PLoS ONE 16(9): e0256586. https://doi.org/10.1371/journal.pone.0256586  See summary web article

Thoma, David P, Michael T Tercek, E William Schweiger, Seth M Munson, John E Gross, and S Tom Olliff (2020). Water Balance as an Indicator of Natural Resource Condition: Case Studies from Great Sand Dunes National Park and Preserve. Global Ecology and Conservation 24: e01300. https://doi.org/10.1016/j.gecco.2020.e01300.

Thoma, D. P., Munson, S. M., & Witwicki, D. L. (2018). Landscape pivot points and responses to water balance in national parks of the southwest U.S. Journal of Applied Ecology. https://doi.org/10.1111/1365-2664.13250 See summary web article

Thoma (2020). Climate change in the Tetons: Science Spotlight. in U.S. Department of Interior, National Park Service, Grand Teton National Park & John D. Rockefeller, Jr. Memorial Parkway: Resource Report 2020, Moose, Wyoming, USA, 2021. pg 36.

Tercek, M.T., Gross, J.E., Thoma, D.P. 2023. Robust projections and consequences of an expanding bimodal growing season in the western United States, Ecosphere 2023;14:e4350, https://doi.org/10.1002/ecs2.4530. See summary web article

Roberts, S. G., D. P. Thoma, D. W. Perkins, E. L. Tymkiw, Z. S. Ladin, and W. G. Shriver. 2021. A habitat-based approach to determining the effects of drought on aridland bird communities. Ornithology 138(3):1–13. See summary web article


Brice, E.M., M. Halabisky, and A.M. Ray (2022). Making the leap from ponds to landscapes: integrating field-based monitoring of amphibians and wetlands with satellite observations. Ecological Indicators 135, 108559.

Gould, W. R., Patla, D. a., Daley, R., Corn, P. S., Hossack, B. R., Bennetts, R., & Peterson, C. R. (2012). Estimating Occupancy in Large Landscapes: Evaluation of Amphibian Monitoring in the Greater Yellowstone Ecosystem. Wetlands, 32(2), 379–389. https://doi.org/10.1007/s13157-012-0273-0

Gould, W., A. Ray, L. Bailey, D. Thoma, R. Daley, and K. Legg. (2019). Multistate occupancy modeling improves understanding of amphibian breeding dynamics in the Greater Yellowstone Area. Ecological Applications 29(1):e01825. Multistate occupancy modeling improves understanding of amphibian breeding dynamics in the Greater Yellowstone Area (wiley.com)

Ray, A. M., Gould, W. R., Hossack, B. R., Sepulveda, A. J., Thoma, D. P., Patla, D. A., … Al-Chokhachy, R. (2016). Influence of climate drivers on colonization and extinction dynamics of wetland-dependent species. Ecosphere, 7(7), 1–21. https://doi.org/10.1002/ecs2.1409

Lawrence, D. J., M. Tercek, A. Runyon, and J. Wright. 2024. Historical and projected climate change for Grand Canyon National Park and surrounding areas. Natural Resource Report NPS/NRSS/CCRP/NRR—2024/2615. National Park Service, Fort Collins, Colorado. https://irma.nps.gov/DataStore/Reference/Profile/2301726.

Marrs, A, T. Rautu, D. Thoma, M. Tercek, A. Rodman, and A. Ray. (2022). When the river breaks. Park Science 36(2). https://www.nps.gov/subjects/parkscience/issue-winter-2022.htm.

Thoma, David P, Michael T Tercek, E William Schweiger, Seth M Munson, John E Gross, and S Tom Olliff (2020). Water Balance as an Indicator of Natural Resource Condition: Case Studies from Great Sand Dunes National Park and Preserve. Global Ecology and Conservation 24: e01300. https://doi.org/10.1016/j.gecco.2020.e01300.

Weissinger, R., Philippi, T. E., & Thoma, D. (2016). Linking climate to changing discharge at springs in Arches National Park, Utah, USA. Ecosphere, 7(10). https://doi.org/10.1002/ecs2.1491 See summary web article

Laufenberg, David, David Thoma, Andrew Hansen, and Jia Hu. (2020). Biophysical Gradients and Performance of Whitebark Pine Plantings in the Greater Yellowstone Ecosystem. Forests 11(1). https://doi.org/10.3390/f11010119.

Ray, A. M., Sepulveda, A. J., Irvine, K. M., Wilmoth, S. K. C., Thoma, D. P., & Patla, D. A. (2019). Wetland drying linked to variations in snowmelt runoff across Grand Teton and Yellowstone national parks. Science of the Total Environment, 666, 1188–1197. https://doi.org/10.1016/j.scitotenv.2019.02.296

Shanahan, E., Irvine, K. M., Thoma, D., Wilmoth, S., Ray, A., Legg, K., & Shovic, H. (2016). Whitebark pine mortality related to white pine blister rust, mountain pine beetle outbreak, and water availability. Ecosphere, 7(12). https://doi.org/10.1002/ecs2.1610

Skovlin, B. J., Thoma, D. (2015). Interactions underfoot : The subtle influence of soil moisture on vegetation pattern, Park Science 32(2), 60–63.

Thoma D. 2022. Landscape phenology, vegetation condition, and relations with climate at Colorado National Monument, 2000–2019. Natural Resource Report. NPS/NCPN/NRR—2022/2384. National Park Service. Fort Collins, Colorado. https://doi.org/10.36967/nrr-2293476. Summary web article

Thoma, D. P., Munson, S. M., Irvine, K. M., Witwicki, D. L., & Bunting, E. L. (2016). Semi-arid vegetation response to antecedent climate and water balance windows. Applied Vegetation Science, 19, 413–429. https://doi.org/10.1111/avsc.12232 See summary web article

Thoma, D. P., Munson, S. M., & Witwicki, D. L. (2018). Landscape pivot points and responses to water balance in national parks of the southwest U.S. Journal of Applied Ecology. https://doi.org/10.1111/1365-2664.13250

Thoma, D. P., E. K. Shanahan, and K. M. Irvine. (2019). Climatic Correlates of White Pine Blister Rust Infection in Whitebark Pine in the Greater Yellowstone Ecosystem. Forests 10 (8): 666. https://doi.org/10.3390/f10080666.

Thoma, D. P., Munson, S. M., Rodman, A. W., Renkin, R., Anderson, H. M., & Wacker, S. D. (2019). Patterns of Primary Production & Ecological Drought in Yellowstone. Yellowstone Science, 27(1), 34–39.

Thoma, David P, Michael T Tercek, E William Schweiger, Seth M Munson, John E Gross, and S Tom Olliff (2020). Water Balance as an Indicator of Natural Resource Condition: Case Studies from Great Sand Dunes National Park and Preserve. Global Ecology and Conservation 24: e01300. https://doi.org/10.1016/j.gecco.2020.e01300.

Witwicki, D. L., Munson, S. M., & Thoma, D. P. (2016). Effects of climate and water balance across grasslands of varying C3 and C4 grass cover. Ecosphere, 7(11). https://doi.org/10.1002/ecs2.1577 See summary web article

Web Articles and Web Pages

NPS. 2024. Localized Drought Impacts on Northern Colorado Plateau Landbirds

Thoma D. 2022. Landscape phenology, vegetation condition, and relations with climate at Colorado National Monument, 2000–2019. Natural Resource Report. NPS/NCPN/NRR—2022/2384. National Park Service. Fort Collins, Colorado. https://doi.org/10.36967/nrr-2293476. Summary web article.

NPS CCRP. Introduction to water balance describing conceptual basis for water balance and how we use it

Northern Colorado Plateau Network. 2022. Using Remote Sensing to Help Managers Plan for Climate Change at Colorado National Monument. National Park Service.

Northern Colorado Plateau Network 2021. Monitoring From Space: Using Satellite Imagery to Measure Landscape Conditions on the Ground. National Park Service.

Thoma, D. and others 2021. How dry will parks get? Water deficit tells us. Park Science Vol 35 (1).

Thoma, D. 2021. Extraordinary Saguaro Bloom: What Happened? The Heliograph: Summer 2021.

Daw, S., D. Thoma, and E. W. Schweiger. 2020. Water Balance Underlies Natural Resource Conditions at Great Sand Dunes National Park and Preserve. National Park Service

Northern Colorado Plateau Network. 2018. Traits, Tradeoffs, and Pivot Points: How Climate, Plant, and Soil Properties Affect Vegetation Growth on the Northern Colorado Plateau.

Northern Colorado Plateau Inventory and Monitoring Program. 2017. A closer look at when grasses need a drink: soils, precipitation, and desert grasses. National Park Service

Northern Colorado Plateau Inventory and Monitoring Program. 2016. Short-term forecasting of vegetation condition: potential management uses. National Park Service.

Monitoring Briefs

2013 Water balance improves understanding vegetation condition and trend. Northern Colorado Plateau Network monitoring brief: land surface phenology monitoring

2012 Differences in recovery rates in wild and prescribed fire at Bryce Canyon. Northern Colorado Plateau Network monitoring brief: land surface phenology monitoring

2011 Mapping the monsoon via vegetation condition. Northern Colorado Plateau Network monitoring brief: land condition monitoring

2010 Vegetation condition and spring flow linked by climate Northern Colorado Plateau Network monitoring brief: land condition monitoring