Watershed Data Report

This report was created with the free Global Watersheds web app on . Creative Commons License BY 4.0.

Contents

  1. Political Boundaries
  2. Population
  3. Land Cover
  4. Hydrology
  5. GRACE Total Water Storage
  6. Irrigation
  7. Dams
  8. Longest River
  9. Topography
  10. References

This report is for the watershed with an outlet near 42°57'43"N, 108°26'13"W, or (42.962, -108.437), with a drainage area of around 1,080 km².

Political Boundaries

The watershed is entirely within the United States of America, in the state of Wyoming.

Data on political boundaries comes from Natural Earth, https://www.naturalearthdata.com.

Population

The watershed has an estimated population of 113 in the year 2020. Figure 1 shows how population has changed from 1990 to 2020. The population grew at an average rate of 11.0% per year over this time period.

Figure 1. Estimated watershed population from 1990 to 2020.

Population data comes from GlobPop, Global Gridded Population Estimates, created by researchers at Beijing Normal University (Liu 2024).

Human population growth can affect water quality via increased pollution from households, industry, and agriculture. Further, land use and land cover change associated with population growth can have a major impact on watersheds (see the next section of this report).

Land Cover

The most common land cover type in the watershed is semi-arid, covering 973 km² in 2020. More detailed information about land cover and how it has changed is shown in Table 3 and Figure 2 below. Pairs of bars represent the area for each land cover type in the years 2000 and 2020.

Figure 2. Land cover in the watershed.

Table 3. Land cover in the watershed.

Land Cover Type Area in 2000, km² Area in 2020, km²   Change
Semi-arid 978 973 0%
Dense short vegetation 53 56 4%
Tree cover 21 20 -1%
Built-up 7.8 10 32%
Cropland 3.3 3.3 0%
Desert 2.9 2.8 -3%

Land cover data comes from the GLAD: Global Land Cover and Land Use Change, 2000-2020 (Popatov et al. 2022). This dataset, created by researchers at the University of Maryland, is available online here. Classification is based on satellite imagery from Landsat and machine learning tools.

Land cover change can profoundly influence watershed hydrology and water quality. Urbanization and development can increase the impervious cover, causing more water to run off rather than infiltrate into the ground. This can decrease groundwater recharge and river baseflows, or the flows that occur during dry times. Deforestation and agricultural development are often accompanied by an increase in soil erosion and sediment loads. Other land use types are associated with water pollution. For example, agriculture can increase loads of pesticides and nutrients (nitrogen and phosphorus) from fertilizers. Urbanization and industrialization can cause contamination from a wide range of chemicals used in households and industry.

Hydrology

The average annual precipitation over the watershed is 251 mm/year. (Precipitation includes all forms of water, including snow and rain.) Some of this water leaves the watershed surface via evaporation and transpiration, or the loss of water from plants. Annual evapotranspiration is estimated at 213 mm/year. The basin climatology, or the monthly average precipitation and evapotranspiration, is shown in Figure 4.

Figure 4. Watershed climatology: monthly average precipitation and evapotranspiration over the watershed.

Precipitation data comes from WorldClim, a global gridded dataset by researchers at the University of East Anglia (Harris et al. 2020). This dataset is based on downscaling and bias-correcting the CRU-TS dataset (Fick and Hijmans 2017), which is based on a large collection of station observations that span 1901–2018.

Evapotranspiration is even more difficult to estimate than precipitation. Here, we use a dataset, GLEAM v3.6B, which combines modeling and remote sensing data (Martens et al. 2017; Miralles et al. 2011).

It is difficult to estimate water cycle variables over large areas. So you should keep in mind that these estimates are uncertain (not perfect).

Total Water Storage Anomaly

The GRACE satellites provide information about changes in the amount of water over different locations on the Earth. Figure 3 shows the average total water storage anomaly over the watershed.

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Figure 3. GRACE total water storage anomaly from 2002 to 2025.

The GRACE satellites make highly accurate measurements of the Earth's gravitational field, and provide measurements of changes in the mass of water on a monthly time scale. These measurements do not tell us how much water there is in a region, but rather, how it the amount of water has changed compared to a baseline. The measurement includes all forms of water, including water in rivers, lakes, and reservoirs, soil moisture, groundwater, glaciers, snow, and ice. For an introduction to GRACE, see the section of my PhD thesis on Remote Sensing of the Water Cycle.

The total amount of water in the watershed appears to be trending downwards at a rate of -0.5 cm per decade (P = 0.25). This P-value indicates that the observed trend is not statistically significant. In other words, there is insufficient evidence to conclude that there is a meaningful trend in the data.

GRACE Total Water Storage Anomaly data is from the Center for Space Research at University of Texas, Austin (Save et al. 2016, 2022).

Irrigation

The watershed had about 2.2 km² of land equipped for irrigation in 2005. This is about 0.2% of the watershed. Figure 5 shows the development of irrigated area from 1900 to 2005. These estimates are based on a global dataset published by an international team of researchers (Siebert et al. 2015).

Figure 5. Area equipped for irrigation in the watershed from 1900 to 2005

Irrigation brings many benefits for growing crops. In arid regions, it enables production where it would be otherwise impossible. In humid regions, irrigation can increase crop quality and yield, and provide more certainty against unpredictable climate. Yet, the expansion of irrigation can have impacts on watersheds that need to be carefully managed.

Increased irrigation usually requires more water to be withdrawn from rivers, lakes, or groundwater sources, which can reduce streamflow and alter natural flow patterns. This reduced flow can harm aquatic ecosystems and diminish the water available downstream for other uses. Irrigation often introduces fertilizers and pesticides into the watershed, and may increase soil erosion and sediment transport. When nutrients run off into rivers and streams, they can lead to nutrient pollution, algal blooms, and eutrophication. This plus chemical pesticides and herbicides negatively impacts aquatic life and water quality for human uses.

Dams

The watershed does not contain any dams that are identified in the Global Dam Watch database.

Data on dams comes from the Global Dam Watch database, published in July 2024. For more details, see the journal article by lead researchers at McGill University (Lehner et al. 2024).

Longest River

The longest river is the watershed is 109 km long. It is shown highlighted in the map in Figure 5 below.

This is the longest continuous flowline in the source dataset, MERIT-Basins. It is not necessarily the mainstem of the river, or the one with the same name. When it comes to naming rivers, historical, legal, and cultural influences are also important.

Figure 5. The longest river that flows to the watershed outlet (highlighted).

Figure 6 shows the elevation profile of the longest river reach. Elevations on the plot are in meters above mean sea level. The river begins at 2,397 m, and the outlet is at 1,511 m.

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Figure 6. Elevation profile of the longest river reach.

Elevation data for the profile plot is from MERIT-DEM (Yamazaki et al. 2017). Actual distances on the plot may be underestimated somewhat. This is because river paths are based on a grid or raster data, which simplifies the meandering path of real-world rivers.

Topography

Terrain elevations in the watershed range from 1,520.4 m to 2735.2 m above sea level. The average, or mean, elevation is 1856.9 m. Figure 7 shows a distribution of terrain elevations in the watershed.

Figure 7. Distribution of terrain elevations in the watershed.
Scale for x-axis: Linear   Log

Elevation data is provided by EarthEnv (Amatulli et al. 2021), and is based on the Global Multi-resolution Terrain Elevation Data 2010 dataset (GMTED2010). . Statistics are based on gridded elevation data with pixels that are about 1 km on a side. Because of the size of the pixels, some smoothing takes place, and so the statistics reported above may not capture the true minimum and maximum elevation.

References

Amatulli, G., Domisch, S., Tuanmu, M.-N., Parmentier, B., Ranipeta, A., Malczyk, J., & Jetz, W. (2018). A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Scientific Data, 5(1), 180040. https://doi.org/10.1038/sdata.2018.40

Fick, S.E., and R.J. Hijmans. 2017. “WorldClim 2: New 1‐km Spatial Resolution Climate Surfaces for Global Land Areas.” International Journal of Climatology 37 (12): 4302–15. https://doi.org/10.1002/joc.5086

Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data 7, 109 (2020). https://doi.org/10.1038/s41597-020-0453-3

Lehner, B., Beames, P., Mulligan, M. et al. The Global Dam Watch database of river barrier and reservoir information for large-scale applications. Scientific Data 11, 1069 (2024). https://doi.org/10.1038/s41597-024-03752-9

Liu, L., X. Cao, S. Li, and N. Jie. “A 31-Year (1990–2020) Global Gridded Population Dataset Generated by Cluster Analysis and Statistical Learning.” Scientific Data 11, no. 1 (January 24, 2024): 124. https://doi.org/10.1038/s41597-024-02913-0

Martens, B., Miralles, D.G., Lievens, H., van der Schalie, R., de Jeu, R.A.M., Fernández-Prieto, D., Beck, H.E., Dorigo, W.A., and Verhoest, N.E.C. 2017. GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geoscientific Model Development, 10, 1903–1925. https://doi.org/10.5194/gmd-10-1903-2017

Miralles, D.G., Holmes, T.R.H., de Jeu, R.A.M., Gash, J.H., Meesters, A.G.C.A., Dolman, A.J. 2011. Global land-surface evaporation estimated from satellite-based observations, Hydrology and Earth System Sciences, 15, 453–469. https://doi.org/10.5194/hess-15-453-2011

Potapov P., Hansen M.C., Pickens A., Hernandez-Serna A., Tyukavina A., Turubanova S., Zalles V., Li X., Khan A., Stolle F., Harris N., Song X.-P., Baggett A., Kommareddy I., Kommareddy A. (2022) The global 2000-2020 land cover and land use change dataset derived from the Landsat archive: first results. Frontiers in Remote Sensing. https://doi.org/10.3389/frsen.2022.856903

Save, H., S. Bettadpur, and B.D. Tapley (2016), High resolution CSR GRACE RL05 mascons, Journal of Geophysical Research Solid Earth, 121. https://doi.org/10.1002/2016JB013007

Save, H., 2020, "CSR GRACE and GRACE-FO RL06 Mascon Solutions v02." https://doi.org/10.15781/cgq9-nh24

Siebert, S., M. Kummu, M. Porkka, P. Döll, N. Ramankutty, and B. R. Scanlon. “A Global Data Set of the Extent of Irrigated Land from 1900 to 2005.” Hydrology and Earth System Sciences 19, no. 3 (March 25, 2015): 1521–45. https://doi.org/10.5194/hess-19-1521-2015.

Yamazaki, D., D. Ikeshima, R. Tawatari, T. Yamaguchi, F. O’Loughlin, J.C. Neal, C.C. Sampson, S. Kanae, & P.D. Bates. (2017). A high‐accuracy map of global terrain elevations. Geophysical Research Letters, 44, 5844–5853. https://doi.org/10.1002/2017GL072874.