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Creative Commons License BY 4.0.
This report is for the watershed with an outlet near 20°30'00"N, 85°49'01"E, or (20.500, 85.817), with a drainage area of around 133,000 km².
The watershed is entirely within India, and in five states, as shown in Table 2.
Table 2. States in the watershed.
| State | Area (km²) | Percent of watershed |
|---|---|---|
| Odisha | 56,900 | 43% |
| Maharashtra | 351 | 0% |
| Chhattisgarh | 75,600 | 57% |
| Jharkhand | 246 | 0% |
| Madhya Pradesh | 278 | 0% |
Data on political boundaries comes from Natural Earth, https://www.naturalearthdata.com.
The watershed has an estimated population of 36,600,000 in the year 2020. Figure 1 shows how population has changed from 1990 to 2020. The population grew at an average rate of 1.6% per year over this time period.
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).
The most common land cover type in the watershed is cropland, covering 46,100 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.
Table 3. Land cover in the watershed.
| Land Cover Type | Area in 2000, km² | Area in 2020, km² | Change |
|---|---|---|---|
| Cropland | 39,800 | 46,100 | 15% |
| Tree cover | 42,000 | 42,300 | 0% |
| Dense short vegetation | 45,700 | 37,400 | -18% |
| Built-up | 1,590 | 3,540 | 122% |
| Open surface water | 2,170 | 3,290 | 51% |
| Semi-arid | 935 | 822 | -12% |
| Wetland + dense short vegetation | 1,030 | 213 | -79% |
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.
The average annual precipitation over the watershed is 1,358 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 617 mm/year. The basin climatology, or the monthly average precipitation and evapotranspiration, is shown in Figure 4.
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).
The GRACE satellites provide information about changes in the amount of water over different locations on the Earth. Figure 3 shows the average terrestrial water storage anomaly over the watershed.
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 upwards at a rate of 0.9 cm per decade (P = 0.54). 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 Terrestrial Water Storage Anomaly data is from the Center for Space Research at the University of Texas, Austin (Save et al. 2016, 2022).
The watershed had about 13779.6 km² of land equipped for irrigation in 2005. This is about 10.3% 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).
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.
This watershed contains 403 dams identified in the Global Dam Watch database, with a total storage capacity of 18,000 million m³.
Information on these dams is listed in Table 4. The number and size of dams in a watershed is one measure of hydromodification, or how much the natural hydrologic cycle is influenced by human activities.
Table 4. Dams in the watershed. Unknown or missing data shown with a dash.
| Dam Name | Reservoir Name | River | Main Use | Year | Dam Height (m) | Capacity (10⁶ m³) | Latitude | Longitude | URL |
|---|---|---|---|---|---|---|---|---|---|
| – | – | – | – | – | 22.456 | 82.055 | – | ||
| – | – | – | – | – | 22.301 | 82.083 | – | ||
| – | – | – | – | – | 22.288 | 82.087 | – | ||
| – | – | – | – | – | 22.258 | 82.094 | – | ||
| – | – | – | – | – | 22.059 | 82.331 | – | ||
| – | – | – | – | – | 22.025 | 82.331 | – | ||
| – | – | – | – | – | 21.954 | 82.325 | – | ||
| – | – | – | – | – | 21.939 | 82.317 | – | ||
| – | – | – | – | – | 21.917 | 82.306 | – | ||
| – | – | – | – | – | 21.951 | 83.391 | – | ||
| – | – | – | – | – | 21.916 | 82.929 | – | ||
| – | – | – | – | – | 21.913 | 83.406 | – | ||
| – | – | – | – | – | 21.888 | 83.400 | – | ||
| – | – | – | – | – | 21.880 | 82.314 | – | ||
| – | – | – | – | – | 21.861 | 82.339 | – | ||
| – | – | – | – | – | 21.864 | 82.927 | – | ||
| – | – | – | – | – | 21.844 | 82.926 | – | ||
| – | – | – | – | – | 21.844 | 82.351 | – | ||
| – | – | – | – | – | 21.824 | 82.359 | – | ||
| – | – | – | – | – | 21.796 | 82.372 | – | ||
| – | – | – | – | – | 21.795 | 82.920 | – | ||
| – | – | – | – | – | 21.772 | 82.379 | – | ||
| – | – | – | – | – | 21.780 | 82.808 | – | ||
| – | – | – | – | – | 21.803 | 82.783 | – | ||
| – | – | – | – | – | 21.755 | 82.900 | – | ||
| – | – | – | – | – | 21.771 | 82.948 | – | ||
| – | – | – | – | – | 21.714 | 82.296 | – | ||
| – | – | – | – | – | 21.569 | 83.161 | – | ||
| – | – | – | – | – | 21.495 | 81.631 | – | ||
| – | – | – | – | – | 21.478 | 81.630 | – | ||
| – | – | – | – | – | 21.508 | 83.136 | – | ||
| – | – | – | – | – | 21.482 | 81.663 | – | ||
| – | – | – | – | – | 21.442 | 82.623 | – | ||
| – | – | – | – | – | 21.275 | 82.636 | – | ||
| – | – | – | – | – | 21.213 | 82.643 | – | ||
| – | – | – | – | – | 20.774 | 83.340 | – | ||
| – | – | – | – | – | 20.378 | 82.658 | – | ||
| – | – | – | – | – | 20.388 | 82.657 | – | ||
| – | – | – | – | – | 19.719 | 82.900 | – | ||
| – | – | – | – | – | 20.022 | 83.240 | – | ||
| – | – | – | – | – | 22.393 | 82.027 | – | ||
| – | – | – | – | – | 22.397 | 82.032 | – | ||
| – | – | – | – | – | 23.292 | 82.309 | – | ||
| – | – | – | – | – | 22.748 | 83.510 | – | ||
| – | – | – | – | – | 22.560 | 81.935 | – | ||
| – | – | – | – | – | 22.533 | 82.018 | – | ||
| – | – | – | – | – | 22.518 | 82.047 | – | ||
| – | – | – | – | – | 22.533 | 83.359 | – | ||
| – | – | – | – | – | 20.956 | 80.864 | – | ||
| – | – | – | – | – | 20.899 | 80.823 | – | ||
| – | – | – | – | – | 21.965 | 82.933 | – | ||
| – | – | – | – | – | 20.453 | 85.736 | – | ||
| – | – | – | – | – | 21.859 | 82.095 | – | ||
| – | – | – | – | – | 21.867 | 82.029 | – | ||
| – | – | – | – | – | 21.837 | 82.117 | – | ||
| – | – | – | – | – | 21.847 | 82.159 | – | ||
| – | – | – | – | – | 21.820 | 81.944 | – | ||
| – | – | – | – | – | 21.826 | 82.187 | – | ||
| – | – | – | – | – | 21.806 | 81.927 | – | ||
| – | – | – | – | – | 21.780 | 82.188 | – | ||
| – | – | – | – | – | 21.771 | 81.808 | – | ||
| – | – | – | – | – | 21.761 | 81.708 | – | ||
| – | – | – | – | – | 21.758 | 82.219 | – | ||
| – | – | – | – | – | 21.750 | 81.747 | – | ||
| – | – | – | – | – | 21.754 | 82.254 | – | ||
| – | – | – | – | – | 21.725 | 82.786 | – | ||
| – | – | – | – | – | 21.725 | 82.305 | – | ||
| – | – | – | – | – | 21.748 | 82.294 | – | ||
| – | – | – | – | – | 21.714 | 82.346 | – | ||
| – | – | – | – | – | 21.716 | 82.601 | – | ||
| – | – | – | – | – | 21.689 | 83.270 | – | ||
| – | – | – | – | – | 21.663 | 81.672 | – | ||
| – | – | – | – | – | 21.628 | 81.691 | – | ||
| – | – | – | – | – | 21.625 | 82.384 | – | ||
| – | – | – | – | – | 21.574 | 81.678 | – | ||
| – | – | – | – | – | 21.494 | 82.267 | – | ||
| – | – | – | – | – | 21.292 | 82.123 | – | ||
| – | – | – | – | – | 21.873 | 82.066 | – | ||
| – | – | – | – | – | 21.539 | 81.380 | – | ||
| – | – | – | – | – | 21.797 | 82.731 | – | ||
| – | – | – | – | – | 21.621 | 81.546 | – | ||
| – | – | – | – | – | 21.609 | 81.436 | – | ||
| – | – | – | – | – | 21.614 | 81.605 | – | ||
| – | – | – | – | – | 21.580 | 81.653 | – | ||
| – | – | – | – | – | 21.476 | 81.361 | – | ||
| – | – | – | – | – | 21.429 | 81.379 | – | ||
| – | – | – | – | – | 21.367 | 81.322 | – | ||
| – | – | – | – | – | 21.116 | 81.245 | – | ||
| – | – | – | – | – | 20.970 | 81.872 | – | ||
| – | – | – | – | – | 22.124 | 82.636 | – | ||
| – | – | – | – | – | 22.031 | 82.639 | – | ||
| – | – | – | – | – | 20.736 | 81.951 | – | ||
| – | – | – | – | – | 20.709 | 81.688 | – | ||
| – | – | – | – | – | 22.030 | 83.157 | – | ||
| – | – | – | – | – | 21.973 | 82.217 | – | ||
| – | – | – | – | – | 21.988 | 82.224 | – | ||
| – | – | – | – | – | 21.956 | 82.226 | – | ||
| – | – | – | – | – | 21.903 | 82.207 | – | ||
| – | – | – | – | – | 21.860 | 82.201 | – | ||
| – | – | – | – | – | 21.883 | 82.199 | – | ||
| – | – | – | – | – | 21.842 | 82.198 | – | ||
| – | – | – | – | – | 21.566 | 82.563 | – | ||
| – | – | – | – | – | 21.505 | 82.608 | – | ||
| – | – | – | – | – | 21.460 | 81.678 | – | ||
| – | – | – | – | – | 21.395 | 81.629 | – | ||
| – | – | – | – | – | 21.410 | 81.637 | – | ||
| – | – | – | – | – | 21.300 | 81.535 | – | ||
| – | – | – | – | – | 21.250 | 81.542 | – | ||
| – | – | – | – | – | 21.058 | 81.245 | – | ||
| – | – | – | – | – | 21.045 | 81.240 | – | ||
| – | – | – | – | – | 21.054 | 81.605 | – | ||
| – | – | – | – | – | 21.012 | 80.912 | – | ||
| – | – | – | – | – | 21.001 | 81.255 | – | ||
| – | – | – | – | – | 20.982 | 80.846 | – | ||
| – | – | – | – | – | 20.975 | 80.824 | – | ||
| – | – | – | – | – | 20.979 | 80.891 | – | ||
| – | – | – | – | – | 20.980 | 80.866 | – | ||
| – | – | – | – | – | 20.866 | 80.820 | – | ||
| – | – | – | – | – | 20.834 | 80.792 | – | ||
| – | – | – | – | – | 20.779 | 80.767 | – | ||
| – | – | – | – | – | 20.808 | 80.776 | – | ||
| – | – | – | – | – | 20.770 | 80.750 | – | ||
| – | – | – | – | – | 20.756 | 80.705 | – | ||
| – | – | – | – | – | 20.499 | 81.384 | – | ||
| – | – | – | – | – | 20.474 | 81.419 | – | ||
| – | – | – | – | – | 20.455 | 81.443 | – | ||
| – | – | – | – | – | 20.302 | 81.543 | – | ||
| – | – | – | – | – | 22.372 | 82.026 | – | ||
| Hirakud | Hirakud | Mahanadi | Irrigation | Built 1957 | 59.0 | 8100.0 | 21.520 | 83.855 | – |
| Bango | Bango Reservoir | Hasdeo | Hydroelectricity | Built 1990 | 87.0 | 3416.0 | 22.605 | 82.599 | – |
| Ravishankar Sagar | Ravishankar Sagar | Mahanadi | Irrigation | Built 1979 | 31.0 | 909.0 | 20.614 | 81.565 | – |
| Tandula | – | Tandula | Irrigation | Built 1923 | 25.0 | 312.3 | 20.702 | 81.198 | – |
| Dudhawa | – | Mahanadi | Irrigation | Built 1963 | 24.0 | 287.8 | 20.305 | 81.758 | – |
| Gondli Tank | – | – | – | – | 223.3 | 20.743 | 81.130 | – | |
| Sikasar | – | Local Nalla | Irrigation | Built 1977 | 32.0 | 216.5 | 20.518 | 82.319 | – |
| – | – | – | – | – | 210.8 | 20.702 | 81.214 | – | |
| Sondur | – | Local Nalla | Irrigation | Built 1988 | 38.0 | 198.0 | 20.226 | 82.098 | – |
| Kharung | – | – | Irrigation | Built before 1985 | 21.0 | 195.2 | 22.290 | 82.219 | – |
| Kharkhara Tank | – | Kharikhara | – | Built 1967 | 169.9 | 20.831 | 80.988 | – | |
| Murumsilli | Murumsilli | Silliyari | Irrigation | Built 1923 | 26.0 | 165.0 | 20.539 | 81.665 | – |
| Kodar | – | Kodar Nalla | Irrigation | Built 1981 | 23.0 | 160.3 | 21.198 | 82.182 | – |
| Maniyari | – | Maniyari | Irrigation | Built 1930 | 29.0 | 151.2 | 22.392 | 81.591 | – |
| – | – | – | – | Built 2007 | 141.2 | 20.756 | 80.664 | – | |
| – | – | – | – | – | 114.2 | 20.596 | 82.590 | – | |
| Mahanadi | – | Mahanadi | Irrigation | Built 1978 | 30.0 | 91.0 | 21.144 | 82.002 | – |
| – | – | – | – | – | 87.4 | 21.167 | 81.374 | – | |
| – | – | – | – | – | 85.0 | 22.307 | 81.977 | – | |
| Upper Jonk | – | Jonk | Irrigation | Built 1993 | 25.0 | 81.0 | 20.735 | 82.444 | – |
| – | – | – | – | Built before 1985 | 80.5 | 20.884 | 80.938 | – | |
| Hariharjore | – | Hariharjore | Irrigation | Built 1995 | 19.0 | 79.0 | 21.040 | 84.015 | – |
| – | – | – | – | – | 68.7 | 23.262 | 82.530 | – | |
| – | – | – | – | – | 67.2 | 20.340 | 84.797 | – | |
| – | – | – | – | – | 61.6 | 20.531 | 81.030 | – | |
| Chhirpani | – | Phon | Irrigation | Built 1994 | 32.0 | 51.3 | 22.202 | 81.198 | – |
| – | – | – | – | – | 51.0 | 20.806 | 82.664 | – | |
| – | – | – | – | – | 46.2 | 20.409 | 83.159 | – | |
| – | – | – | – | – | 45.2 | 20.793 | 82.024 | – | |
| – | – | – | – | – | 43.9 | 21.293 | 80.960 | – | |
| – | – | – | – | – | 41.5 | 22.345 | 84.120 | – | |
| – | – | – | – | – | 40.7 | 21.686 | 80.996 | – | |
| – | – | – | – | Built before 1985 | 39.2 | 21.534 | 82.485 | – | |
| – | – | – | – | Built 2003 | 38.3 | 22.383 | 82.124 | – | |
| – | – | – | – | – | 37.4 | 20.852 | 82.684 | – | |
| Saroda | – | Uttari Stream | Irrigation | Built 1964 | 32.0 | 37.1 | 21.981 | 81.139 | – |
| – | – | – | – | – | 36.8 | 22.427 | 83.569 | – | |
| – | – | – | – | – | 36.5 | 21.052 | 82.769 | – | |
| – | – | – | – | – | 35.9 | 21.455 | 83.298 | – | |
| Maroda-1 | – | – | – | – | 33.9 | 21.180 | 81.366 | – | |
| – | – | – | – | – | 33.1 | 20.423 | 84.340 | – | |
| – | – | – | – | – | 32.5 | 22.408 | 82.696 | – | |
| – | – | – | – | Built 1996 | 31.3 | 21.957 | 84.268 | – | |
| – | – | – | – | – | 30.6 | 21.542 | 80.929 | – | |
| – | – | – | – | Built before 1985 | 26.8 | 20.655 | 81.563 | – | |
| – | – | – | – | – | 25.1 | 21.563 | 82.973 | – | |
| – | – | – | – | – | 23.9 | 21.461 | 83.968 | – | |
| – | – | – | – | – | 23.8 | 20.453 | 85.604 | – | |
| – | – | – | – | Built 2002 | 23.4 | 21.217 | 80.952 | – | |
| – | – | – | – | Built 1990 | 22.9 | 21.731 | 81.223 | – | |
| – | – | – | – | Built 1999 | 21.8 | 23.319 | 82.565 | – | |
| Budhabudiani | – | Budhabudiani | Irrigation | Built 1967 | 24.0 | 21.8 | 19.964 | 85.019 | – |
| – | – | – | – | Built 2001 | 20.5 | 22.653 | 83.555 | – | |
| – | – | – | – | – | 18.9 | 20.900 | 81.171 | – | |
| – | – | – | – | Built before 1985 | 18.3 | 21.264 | 81.796 | – | |
| – | – | – | – | – | 18.1 | 22.186 | 83.765 | – | |
| – | – | – | – | Built 1991 | 16.1 | 20.723 | 82.879 | – | |
| – | – | – | – | – | 16.0 | 20.415 | 81.280 | – | |
| – | – | – | – | Built before 1985 | 15.9 | 20.998 | 82.112 | – | |
| – | – | – | – | – | 15.8 | 21.554 | 81.884 | – | |
| – | – | – | – | – | 15.7 | 20.957 | 80.734 | – | |
| – | – | – | – | – | 15.5 | 21.147 | 82.550 | – | |
| – | – | – | – | – | 15.5 | 22.287 | 81.780 | – | |
| – | – | – | – | – | 15.2 | 20.315 | 83.218 | – | |
| – | – | – | – | Built 1998 | 14.9 | 20.590 | 85.293 | – | |
| – | – | – | – | – | 14.2 | 20.969 | 82.375 | – | |
| – | – | – | – | Built 1996 | 14.2 | 23.260 | 82.354 | – | |
| – | – | – | – | Built before 1985 | 13.6 | 21.648 | 82.102 | – | |
| – | – | – | – | – | 13.6 | 20.788 | 82.178 | – | |
| – | – | – | – | – | 13.6 | 20.562 | 84.966 | – | |
| – | – | – | – | – | 13.2 | 21.207 | 82.923 | – | |
| – | – | – | – | – | 13.0 | 20.452 | 82.698 | – | |
| Khapri | – | Local Nalla | – | Built 1909 | 11.0 | 13.0 | 21.015 | 81.369 | – |
| – | – | – | – | – | 12.9 | 21.178 | 82.813 | – | |
| – | – | – | – | – | 12.5 | 21.154 | 80.655 | – | |
| – | – | – | – | – | 12.4 | 21.368 | 82.001 | – | |
| – | – | – | – | Built 1996 | 12.1 | 21.473 | 83.423 | – | |
| – | – | – | – | – | 12.1 | 19.957 | 82.610 | – | |
| – | – | – | – | – | 11.9 | 21.048 | 82.273 | – | |
| – | – | – | – | Built 1999 | 11.8 | 20.573 | 82.851 | – | |
| Kumhari | Kumhari Reservoir | Banjari Nalla | Irrigation | Built 1927 | 12.0 | 11.6 | 21.493 | 81.909 | – |
| – | – | – | – | Built before 1985 | 11.6 | 21.042 | 81.792 | – | |
| – | – | – | – | – | 11.5 | 21.403 | 81.861 | – | |
| – | – | – | – | – | 11.4 | 21.280 | 82.186 | – | |
| – | – | – | – | Built before 1985 | 11.4 | 21.956 | 82.344 | – | |
| – | – | – | – | Built before 1985 | 11.3 | 21.022 | 81.489 | – | |
| – | – | – | – | Built 1989 | 10.9 | 19.426 | 82.792 | – | |
| – | – | – | – | – | 10.9 | 21.040 | 80.615 | – | |
| – | – | – | – | – | 10.8 | 21.175 | 82.509 | – | |
| – | – | – | – | Built 1997 | 10.5 | 21.935 | 81.877 | – | |
| – | – | – | – | Built 1994 | 10.4 | 21.361 | 81.226 | – | |
| – | – | – | – | – | 10.1 | 21.039 | 82.989 | – | |
| – | – | – | – | – | 10.0 | 21.206 | 81.818 | – | |
| – | – | – | – | – | 10.0 | 21.508 | 83.364 | – | |
| – | – | – | – | – | 10.0 | 21.210 | 80.665 | – | |
| – | – | – | – | – | 9.9 | 19.847 | 83.004 | – | |
| – | – | – | – | – | 9.9 | 21.121 | 82.366 | – | |
| – | – | – | – | – | 9.9 | 21.335 | 81.022 | – | |
| – | – | – | – | – | 9.8 | 21.849 | 83.128 | – | |
| – | – | – | – | Built before 1985 | 9.8 | 21.393 | 81.870 | – | |
| – | – | – | – | – | 9.4 | 21.564 | 82.323 | – | |
| – | – | – | – | – | 9.4 | 21.468 | 81.785 | – | |
| – | – | – | – | Built before 1985 | 9.1 | 20.259 | 85.158 | – | |
| – | – | – | – | Built 1991 | 9.0 | 23.066 | 82.372 | – | |
| – | – | – | – | – | 8.7 | 20.918 | 80.802 | – | |
| – | – | – | – | – | 8.6 | 21.102 | 81.777 | – | |
| – | – | – | – | Built before 1985 | 8.5 | 21.765 | 81.484 | – | |
| – | – | – | – | – | 8.5 | 21.290 | 82.349 | – | |
| – | – | – | – | – | 8.5 | 21.448 | 80.830 | – | |
| – | – | – | – | – | 8.4 | 21.964 | 83.168 | – | |
| – | – | – | – | – | 8.4 | 20.935 | 80.622 | – | |
| – | – | – | – | – | 8.3 | 22.270 | 81.943 | – | |
| – | – | – | – | Built 1994 | 8.1 | 21.246 | 82.763 | – | |
| – | – | – | – | – | 8.0 | 20.956 | 83.515 | – | |
| – | – | – | – | – | 7.9 | 22.113 | 82.450 | – | |
| – | – | – | – | – | 7.8 | 21.455 | 81.268 | – | |
| – | – | – | – | – | 7.8 | 21.027 | 81.788 | – | |
| – | – | – | – | – | 7.6 | 21.919 | 82.128 | – | |
| – | – | – | – | Built before 1985 | 7.4 | 21.265 | 81.080 | – | |
| – | – | – | – | – | 7.4 | 21.613 | 82.615 | – | |
| – | – | – | – | – | 7.4 | 21.338 | 82.434 | – | |
| – | – | – | – | Built before 1985 | 7.3 | 20.406 | 81.389 | – | |
| – | – | – | – | Built 1998 | 7.2 | 21.133 | 82.413 | – | |
| – | – | – | – | – | 7.2 | 19.830 | 82.840 | – | |
| – | – | – | – | – | 7.1 | 20.623 | 82.960 | – | |
| – | – | – | – | – | 7.1 | 21.134 | 82.247 | – | |
| – | – | – | – | Built 1990 | 7.1 | 20.956 | 82.282 | – | |
| – | – | – | – | – | 7.0 | 22.164 | 82.481 | – | |
| – | – | – | – | Built 1991 | 6.9 | 21.010 | 81.014 | – | |
| – | – | – | – | – | 6.9 | 20.800 | 80.717 | – | |
| – | – | – | – | – | 6.8 | 21.924 | 82.423 | – | |
| – | – | – | – | – | 6.7 | 23.498 | 82.485 | – | |
| – | – | – | – | Built before 1985 | 6.7 | 21.230 | 81.085 | – | |
| – | – | – | – | – | 6.7 | 21.199 | 81.649 | – | |
| – | – | – | – | – | 6.6 | 21.941 | 83.321 | – | |
| – | – | – | – | – | 6.6 | 22.881 | 82.858 | – | |
| – | – | – | – | – | 6.5 | 22.273 | 81.403 | – | |
| – | – | – | – | Built before 1985 | 6.3 | 21.180 | 82.535 | – | |
| – | – | – | – | – | 6.2 | 22.331 | 82.435 | – | |
| – | – | – | – | – | 6.2 | 20.709 | 80.768 | – | |
| – | – | – | – | – | 6.2 | 22.039 | 81.256 | – | |
| – | – | – | – | – | 6.1 | 21.676 | 82.140 | – | |
| – | – | – | – | Built 1997 | 6.0 | 20.317 | 82.913 | – | |
| – | – | – | – | Built before 1985 | 6.0 | 21.881 | 81.235 | – | |
| – | – | – | – | – | 6.0 | 20.796 | 80.649 | – | |
| – | – | – | – | – | 5.9 | 21.219 | 83.177 | – | |
| – | – | – | – | – | 5.8 | 22.241 | 81.447 | – | |
| – | – | – | – | – | 5.8 | 22.253 | 81.530 | – | |
| – | – | – | – | – | 5.7 | 20.080 | 83.360 | – | |
| – | – | – | – | – | 5.6 | 20.775 | 82.400 | – | |
| – | – | – | – | Built before 1985 | 5.6 | 21.400 | 82.902 | – | |
| – | – | – | – | – | 5.4 | 21.407 | 80.876 | – | |
| – | – | – | – | Built before 1985 | 5.4 | 21.908 | 81.117 | – | |
| – | – | – | – | – | 5.3 | 20.562 | 81.111 | – | |
| – | – | – | – | – | 5.3 | 21.530 | 81.296 | – | |
| – | – | – | – | – | 5.2 | 21.221 | 81.200 | – | |
| – | – | – | – | Built before 1985 | 5.2 | 21.273 | 82.756 | – | |
| – | – | – | – | – | 5.2 | 21.986 | 82.200 | – | |
| – | – | – | – | – | 5.2 | 21.510 | 81.407 | – | |
| – | – | – | – | – | 5.0 | 21.263 | 81.030 | – | |
| – | – | – | – | Built 2008 | 5.0 | 21.507 | 80.865 | – | |
| – | – | – | – | – | 5.0 | 21.546 | 83.090 | – | |
| – | – | – | – | Built before 1985 | 5.0 | 21.590 | 81.163 | – | |
| – | – | – | – | – | 5.0 | 20.926 | 82.831 | – | |
| – | – | – | – | – | 5.0 | 21.279 | 80.828 | – | |
| – | – | – | – | Built before 1985 | 4.9 | 21.178 | 81.045 | – | |
| – | – | – | – | Built before 1985 | 4.9 | 21.012 | 80.836 | – | |
| – | – | – | – | – | 4.9 | 20.428 | 81.879 | – | |
| – | – | – | – | – | 4.9 | 20.410 | 81.227 | – | |
| – | – | – | – | – | 4.8 | 21.331 | 81.990 | – | |
| – | – | – | – | – | 4.8 | 21.134 | 81.826 | – | |
| – | – | – | – | Built 1992 | 4.7 | 22.231 | 83.951 | – | |
| – | – | – | – | Built 1989 | 4.7 | 21.147 | 81.776 | – | |
| – | – | – | – | – | 4.7 | 22.398 | 83.170 | – | |
| – | – | – | – | – | 4.6 | 20.298 | 85.097 | – | |
| – | – | – | – | – | 4.5 | 20.047 | 85.073 | – | |
| – | – | – | – | Built before 1985 | 4.5 | 21.035 | 80.831 | – | |
| – | – | – | – | – | 4.4 | 20.216 | 85.101 | – | |
| – | – | – | – | – | 4.4 | 21.129 | 82.443 | – | |
| – | – | – | – | Built 1993 | 4.3 | 20.248 | 83.294 | – | |
| – | – | – | – | – | 4.3 | 20.344 | 81.456 | – | |
| – | – | – | – | – | 4.2 | 22.303 | 81.739 | – | |
| – | – | – | – | – | 4.2 | 22.274 | 82.518 | – | |
| – | – | – | – | – | 4.2 | 21.194 | 83.463 | – | |
| – | – | – | – | – | 4.1 | 21.216 | 81.066 | – | |
| – | – | – | – | – | 4.1 | 20.781 | 82.226 | – | |
| – | – | – | – | – | 4.0 | 21.148 | 80.988 | – | |
| – | – | – | – | – | 4.0 | 20.896 | 80.563 | – | |
| – | – | – | – | – | 4.0 | 21.110 | 81.827 | – | |
| – | – | – | – | Built before 1985 | 3.8 | 22.389 | 82.244 | – | |
| – | – | – | – | – | 3.8 | 21.117 | 80.651 | – | |
| – | – | – | – | – | 3.7 | 19.646 | 82.590 | – | |
| – | – | – | – | – | 3.7 | 21.940 | 82.398 | – | |
| – | – | – | – | – | 3.6 | 19.880 | 83.232 | – | |
| – | – | – | – | Built before 1985 | 3.6 | 21.706 | 81.801 | – | |
| – | – | – | – | – | 3.5 | 21.870 | 83.095 | – | |
| – | – | – | – | – | 3.5 | 20.860 | 80.850 | – | |
| – | – | – | – | – | 3.5 | 22.347 | 81.676 | – | |
| – | – | – | – | – | 3.5 | 22.737 | 83.289 | – | |
| – | – | – | – | – | 3.4 | 21.207 | 82.417 | – | |
| – | – | – | – | – | 3.4 | 21.062 | 80.772 | – | |
| – | – | – | – | – | 3.4 | 21.298 | 83.191 | – | |
| – | – | – | – | – | 3.3 | 21.215 | 80.693 | – | |
| – | – | – | – | – | 3.3 | 20.269 | 82.685 | – | |
| – | – | – | – | – | 3.3 | 21.900 | 82.379 | – | |
| – | – | – | – | – | 3.3 | 20.440 | 81.361 | – | |
| – | – | – | – | – | 3.3 | 21.067 | 81.785 | – | |
| – | – | – | – | – | 3.2 | 21.102 | 80.877 | – | |
| – | – | – | – | – | 3.2 | 21.012 | 81.459 | – | |
| – | – | – | – | – | 3.2 | 21.851 | 83.180 | – | |
| – | – | – | – | – | 3.2 | 22.152 | 82.766 | – | |
| – | – | – | – | Built before 1985 | 3.2 | 21.086 | 80.964 | – | |
| – | – | – | – | Built before 1985 | 3.2 | 22.323 | 81.727 | – | |
| – | – | – | – | – | 3.1 | 19.766 | 82.759 | – | |
| – | – | – | – | Built 1999 | 3.1 | 21.012 | 80.814 | – | |
| – | – | – | – | – | 3.1 | 22.387 | 82.467 | – | |
| – | – | – | – | – | 3.1 | 20.368 | 84.210 | – | |
| – | – | – | – | – | 3.0 | 22.310 | 81.493 | – | |
| – | – | – | – | – | 3.0 | 22.716 | 82.449 | – | |
| – | – | – | – | – | 3.0 | 21.270 | 80.873 | – | |
| – | – | – | – | – | 3.0 | 21.630 | 80.950 | – | |
| – | – | – | – | Built 1995 | 3.0 | 21.981 | 83.227 | – | |
| – | – | – | – | – | 3.0 | 22.040 | 81.288 | – | |
| – | – | – | – | – | 3.0 | 20.971 | 80.615 | – | |
| – | – | – | – | – | 2.9 | 21.857 | 81.089 | – | |
| – | – | – | – | – | 2.9 | 22.406 | 82.309 | – | |
| – | – | – | – | Built before 1985 | 2.9 | 21.316 | 83.363 | – | |
| – | – | – | – | – | 2.8 | 23.274 | 82.606 | – | |
| – | – | – | – | – | 2.8 | 22.152 | 81.290 | – | |
| – | – | – | – | – | 2.8 | 20.964 | 80.597 | – | |
| – | – | – | – | – | 2.8 | 22.203 | 82.535 | – | |
| – | – | – | – | – | 2.8 | 22.278 | 81.819 | – | |
| – | – | – | – | – | 2.8 | 21.045 | 80.827 | – | |
| – | – | – | – | Built before 1985 | 2.7 | 20.693 | 80.869 | – | |
| – | – | – | – | – | 2.6 | 22.303 | 81.757 | – | |
| – | – | – | – | – | 2.6 | 21.164 | 82.431 | – | |
| – | – | – | – | – | 2.6 | 21.381 | 81.047 | – | |
| – | – | – | – | – | 2.6 | 21.367 | 82.846 | – | |
| – | – | – | – | – | 2.6 | 21.199 | 80.929 | – | |
| – | – | – | – | – | 2.6 | 21.227 | 81.628 | – | |
| – | – | – | – | – | 2.5 | 22.187 | 82.921 | – | |
| – | – | – | – | – | 2.4 | 20.239 | 82.647 | – | |
| – | – | – | – | – | 2.4 | 21.907 | 82.475 | – | |
| – | – | – | – | – | 2.3 | 20.627 | 81.282 | – | |
| – | – | – | – | – | 2.3 | 21.027 | 81.057 | – | |
| – | – | – | – | – | 2.3 | 21.270 | 83.758 | – | |
| – | – | – | – | – | 2.3 | 20.119 | 85.204 | – | |
| – | – | – | – | – | 2.2 | 21.963 | 81.247 | – | |
| – | – | – | – | – | 2.2 | 20.132 | 82.805 | – | |
| – | – | – | – | – | 2.2 | 22.265 | 82.840 | – | |
| – | – | – | – | – | 2.1 | 23.052 | 82.484 | – | |
| – | – | – | – | – | 2.1 | 20.175 | 82.778 | – | |
| – | – | – | – | – | 2.1 | 20.549 | 81.081 | – | |
| – | – | – | – | Built before 1985 | 2.1 | 22.818 | 82.188 | – | |
| – | – | – | – | – | 2.1 | 21.547 | 83.441 | – | |
| – | – | – | – | Built 1992 | 2.1 | 22.716 | 83.284 | – | |
| – | – | – | – | – | 2.0 | 23.343 | 82.626 | – | |
| – | – | – | – | Built 1991 | 2.0 | 21.827 | 81.341 | – | |
| – | – | – | – | Built before 1989 | 2.0 | 21.360 | 80.881 | – | |
| – | – | – | – | – | 2.0 | 20.228 | 82.743 | – | |
| – | – | – | – | – | 1.9 | 22.203 | 82.863 | – | |
| – | – | – | – | Built 1996 | 1.8 | 21.952 | 81.955 | – | |
| – | – | – | – | – | 1.8 | 21.261 | 80.905 | – | |
| – | – | – | – | – | 1.8 | 22.185 | 84.390 | – | |
| – | – | – | – | – | 1.7 | 19.864 | 83.176 | – | |
| – | – | – | – | Built 2004 | 1.6 | 22.269 | 81.868 | – | |
| – | – | – | – | – | 1.6 | 20.243 | 83.259 | – | |
| – | – | – | – | – | 1.6 | 20.562 | 81.072 | – | |
| – | – | – | – | – | 1.5 | 21.392 | 80.918 | – | |
| – | – | – | – | – | 1.5 | 21.100 | 80.978 | – | |
| Maroda Tank | – | Local Nalla | – | Built 1991 | 12.0 | 1.4 | 20.600 | 82.045 | – |
| – | – | – | – | – | 0.9 | 20.832 | 82.669 | – | |
| – | – | – | – | – | 0.8 | 21.008 | 83.599 | – | |
| – | – | – | – | – | 0.7 | 23.061 | 82.533 | – | |
| – | – | – | – | – | 0.2 | 20.853 | 82.224 | – |
Dams serve many useful purposes including electric generation, water supply, irrigation, flood control, and recreation. However, if not carefully planned and managed, dams can have a heavy environmental impact. Today, many governments are removing dams to bring rivers back to life.
For a detailed look at the impact of dams on people and the environment, I encourage you to read the book Silenced Rivers by Patrick McCully. To help conserve free-flowing rivers, consider supporting the nonprofit International Rivers.
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).
The longest river is the watershed is 985 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 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 389 m, and the outlet is at 13 m.
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.
Terrain elevations in the watershed range from 24 m to 1,100 m above sea level. The average, or mean, elevation is 350 m. Figure 7 shows a distribution of terrain elevations in the watershed.
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 5 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.
The watershed is home to 60 threatened freshwater species. Table 5 shows the species that are endangered, vulnerable, or near-threatened, based on assessments by the International Union for the Conservation of Nature, or IUCN. Data comes from the IUCN Red List.
For a photo of the species, hover the mouse over Scientific Name in the table, or tap on mobile. Click the link under Status to view the IUCN assessment. Here you can find out more about the reasons the species decline, view maps of the species distribution, and read about any conservation measures being taken.
Table 5. Threatened species in the watershed.
| Type | Scientific Name | Common Name | Status | Presence |
|---|---|---|---|---|
| birds | Aythya baeri | Baer’s Pochard | Critically Endangered ⧉ | Extant |
| reptiles | Gavialis gangeticus | Gharial | Critically Endangered ⧉ | Extant |
| reptiles | Pelochelys cantorii | Asian Giant Softshell Turtle | Critically Endangered ⧉ | Extant |
| reptiles | Batagur baska | Northern River Terrapin | Critically Endangered ⧉ | Extant |
| reptiles | Nilssonia leithii | Leith’s Softshell Turtle | Critically Endangered ⧉ | Extant |
| birds | Haliaeetus leucoryphus | Pallas’s Fish-eagle | Endangered ⧉ | Extant |
| fish | Amblyceps arunchalensis | Unknown | Endangered ⧉ | Extant |
| birds | Neophron percnopterus | Egyptian Vulture | Endangered ⧉ | Extant |
| fish | Silonia childreni | Unknown | Endangered ⧉ | Probably Extant |
| mammals | Bubalus arnee | Wild Water Buffalo | Endangered ⧉ | Extant |
| mammals | Platanista gangetica | Ganges River Dolphin | Endangered ⧉ | Extant |
| birds | Sterna acuticauda | Black-bellied Tern | Endangered ⧉ | Extant |
| fish | Urogymnus polylepis | Giant Freshwater Whipray | Endangered ⧉ | Extant |
| fish | Tor putitora | Unknown | Endangered ⧉ | Extant |
| reptiles | Nilssonia gangetica | Indian Softshell Turtle | Endangered ⧉ | Extant |
| reptiles | Nilssonia hurum | Indian Peacock Softshell Turtle | Endangered ⧉ | Extant |
| birds | Calidris tenuirostris | Great Knot | Endangered ⧉ | Extant |
| birds | Rynchops albicollis | Indian Skimmer | Endangered ⧉ | Extant |
| mammals | Prionailurus viverrinus | Fishing Cat | Vulnerable ⧉ | Possibly Extant |
| reptiles | Lissemys punctata | Indian Flapshell Turtle | Vulnerable ⧉ | Extant |
| birds | Sterna aurantia | River Tern | Vulnerable ⧉ | Extant |
| birds | Aythya ferina | Common Pochard | Vulnerable ⧉ | Extant |
| birds | Grus antigone | Sarus Crane | Vulnerable ⧉ | Extant |
| fish | Wallago attu | Unknown | Vulnerable ⧉ | Extant |
| birds | Pluvialis squatarola | Grey Plover | Vulnerable ⧉ | Extant |
| birds | Clanga clanga | Greater Spotted Eagle | Vulnerable ⧉ | Extant |
| reptiles | Crocodylus palustris | Mugger | Vulnerable ⧉ | Extant |
| plants | Polypleurum filifolium | Unknown | Vulnerable ⧉ | Probably Extant |
| fish | Bagarius bagarius | Unknown | Vulnerable ⧉ | Extant |
| birds | Calidris falcinellus | Broad-billed Sandpiper | Vulnerable ⧉ | Extant |
| mammals | Lutrogale perspicillata | Smooth-coated Otter | Vulnerable ⧉ | Extant |
| mammals | Aonyx cinereus | Asian Small-clawed Otter | Vulnerable ⧉ | Extant |
| birds | Aquila rapax | Tawny Eagle | Vulnerable ⧉ | Extant |
| birds | Calidris ferruginea | Curlew Sandpiper | Vulnerable ⧉ | Extant |
| reptiles | Pangshura tecta | Indian Roofed Turtle | Vulnerable ⧉ | Extant |
| fish | Schizothorax plagiostomus | Snow Trout | Vulnerable ⧉ | Extant |
| birds | Leptoptilos dubius | Greater Adjutant | Near Threatened ⧉ | Possibly Extinct |
| fish | Anguilla bicolor | Shortfin Eel | Near Threatened ⧉ | Extant |
| birds | Aythya nyroca | Ferruginous Duck | Near Threatened ⧉ | Extant |
| fish | Ompok pabo | Unknown | Near Threatened ⧉ | Probably Extant |
| birds | Numenius arquata | Eurasian Curlew | Near Threatened ⧉ | Extant |
| birds | Vanellus duvaucelii | River Lapwing | Near Threatened ⧉ | Extant |
| birds | Limosa lapponica | Bar-tailed Godwit | Near Threatened ⧉ | Extant |
| birds | Calidris alpina | Dunlin | Near Threatened ⧉ | Extant |
| birds | Esacus recurvirostris | Great Thick-knee | Near Threatened ⧉ | Extant |
| reptiles | Python molurus | Indian Rock Python | Near Threatened ⧉ | Extant |
| birds | Calidris canutus | Red Knot | Near Threatened ⧉ | Extant |
| reptiles | Varanus bengalensis | Bengal Monitor Lizard | Near Threatened ⧉ | Extant |
| birds | Pelecanus philippensis | Spot-billed Pelican | Near Threatened ⧉ | Extant |
| fish | Anguilla bengalensis | Indian Mottled Eel | Near Threatened ⧉ | Extant |
| birds | Circus macrourus | Pallid Harrier | Near Threatened ⧉ | Extant |
| birds | Ciconia episcopus | Asian Woollyneck | Near Threatened ⧉ | Extant |
| birds | Leptoptilos javanicus | Lesser Adjutant | Near Threatened ⧉ | Extant |
| birds | Icthyophaga ichthyaetus | Grey-headed Fish-eagle | Near Threatened ⧉ | Extant |
| fish | Parambassis lala | Highfin Glassy Perchlet | Near Threatened ⧉ | Probably Extant |
| fish | Ompok bimaculatus | Unknown | Near Threatened ⧉ | Probably Extant |
| birds | Limosa limosa | Black-tailed Godwit | Near Threatened ⧉ | Extant |
| birds | Calidris ruficollis | Red-necked Stint | Near Threatened ⧉ | Extant |
| birds | Arenaria interpres | Ruddy Turnstone | Near Threatened ⧉ | Extant |
| birds | Haematopus ostralegus | Eurasian Oystercatcher | Near Threatened ⧉ | Extant |
The presence of endangered species in a watershed provides important information about ecosystem health and biodiversity. Many factors can threaten endangered species within watersheds:
Water management plays an important role in protecting endangered species. This includes maintaining adequate streamflows, protecting riparian zones and wetlands, controlling pollution sources, and preserving habitat connectivity throughout the watershed.
If you're concerned about endangered species in your watershed, there are many ways you can help. Support organizations working on species conservation and habitat protection, or local watershed councils and land trusts in your area. Many watersheds have dedicated conservation groups focused on protecting local rivers, wetlands, and wildlife. Search for [your watershed name] + "conservation," "river keeper," or "watershed association."
You can also take direct action in your community. Participate in river cleanups, plant native vegetation along stream banks, reduce pesticide and fertilizer use, and support land use policies that protect riparian corridors and wetlands.
If you own property near streams or wetlands, consider conservation easements through organizations like the Land Trust Alliance.
Report wildlife sightings to citizen science platforms like iNaturalist or eBird. Your observations can contribute to scientific understanding and conservation planning. Finally, use your voice: contact local officials to support clean water regulations, habitat protection, and sustainable development practices that consider watershed health and wildlife needs.
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
The IUCN Red List of Threatened Species. (n.d.). IUCN Red List of Threatened Species. https://www.iucnredlist.org
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.