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 Terrestrial Water Storage
  6. Irrigation
  7. Dams
  8. Longest River
  9. Topography
  10. References

This report is for the watershed with an outlet near 21°14'52"N, 74°56'49"E, or (21.248, 74.947), with a drainage area of around 51,700 km².

Political Boundaries

The watershed is entirely within India, and in three state or provinces, as shown in Table 2.

Table 2. state or provinces in the watershed.

state or province Area (km²) Percent of watershed
Gujarat 10 0%
Madhya Pradesh 8,670 17%
Maharashtra 43,000 83%

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

Population

The watershed has an estimated population of 16,400,000 in the year 2020. Figure 1 shows how population has changed from 1990 to 2020. The population grew at an average rate of 1.4% 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 cropland, covering 26,200 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
Cropland 24,300 26,200 7%
Dense short vegetation 18,600 16,700 -10%
Tree cover 3,320 3,310 0%
Semi-arid 4,040 3,220 -20%
Built-up 996 1,770 77%
Open surface water 343 647 88%

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 830 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 491 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).

Terrestrial 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 terrestrial water storage anomaly over the watershed.

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Figure 3. GRACE terrestrial 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 upwards at a rate of 9.4 cm per decade (P < 0.01). This P-value suggests that the observed trend is statistically significant; it is unlikely the trend is due to random chance.

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

Irrigation

The watershed had about 5400.0 km² of land equipped for irrigation in 2005. This is about 10.4% 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

This watershed contains 277 dams identified in the Global Dam Watch database, with a total storage capacity of 4,340 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
20.921 75.494
20.925 74.800
20.927 74.809
20.996 74.867
20.479 73.989
20.941 74.474
20.936 74.849
20.929 74.829
20.927 74.580
20.922 74.630
20.916 74.663
20.921 74.648
20.910 74.684
20.911 74.694
20.488 73.923
20.910 74.708
20.906 74.738
20.503 75.166
20.565 75.000
20.502 75.099
20.546 74.479
20.530 74.343
20.498 75.086
20.546 74.033
20.530 74.373
20.528 74.398
20.529 74.423
20.544 75.127
20.528 74.252
20.523 74.287
20.522 74.087
20.517 74.146
20.566 75.148
20.520 74.808
21.815 77.771
20.519 74.204
20.833 75.424
20.894 75.455
20.508 74.042
20.962 75.509
20.865 75.446
21.186 75.000
21.062 75.796
21.073 75.759
21.061 75.847
21.482 76.723
21.432 76.432
21.444 76.633
21.397 76.332
21.263 76.191
20.888 76.930
20.962 74.859
20.968 74.860
Built 2007 622.6 20.926 75.710
Girna Girna Built 1969 44.0 609.0 20.477 74.714
Hatnur Tapi Irrigation Built 1982 26.0 388.0 21.076 75.949
Katepurna Built before 1990 191.9 20.481 77.155
Built 2011 175.7 20.944 74.452
Built before 1985 123.1 20.498 73.893
Aner Aner Irrigation Built 1978 47.0 103.3 21.312 75.149
Built 2005 89.1 21.367 77.766
66.3 20.731 76.185
62.0 20.480 74.792
59.0 20.777 75.034
Built 2009 58.4 20.631 73.877
Built 1992 54.4 20.463 76.659
Built before 1990 52.1 20.419 76.997
Suki Suki Irrigation Built 1977 42.0 50.2 21.308 75.898
49.5 20.602 76.660
Built 2005 47.0 21.335 77.392
Built 2007 46.5 20.551 77.085
Built before 1990 42.6 20.342 76.851
41.9 20.935 74.097
41.2 20.798 74.030
Built before 1990 39.9 20.602 77.394
Built before 1985 33.1 21.264 77.328
Built before 1990 32.8 21.702 78.199
Built 1993 31.6 20.348 74.605
Built 1998 31.4 21.069 74.839
29.1 20.539 76.415
Built 2007 25.3 21.371 77.465
22.1 20.893 74.726
20.9 20.731 74.685
Built 2006 19.9 20.642 76.401
19.5 20.660 74.785
Built 1999 19.4 21.185 76.774
Built 1995 19.3 20.460 76.594
Built 1999 17.1 20.697 75.390
16.9 20.585 76.956
16.8 21.323 76.002
16.8 21.092 74.100
Built before 1985 16.7 20.649 73.968
Built 1994 16.2 20.597 75.355
Built 1996 15.7 20.251 76.940
Built 2002 15.1 21.076 74.017
Built 1996 14.4 20.424 76.476
Built 1997 13.4 20.417 76.767
13.4 20.769 75.507
Built 1996 13.0 20.642 77.440
12.5 20.593 76.299
Built 2001 12.5 20.250 74.728
Built 2006 12.3 21.204 77.085
11.1 21.010 76.027
Built 2002 11.1 20.638 75.829
10.8 20.569 75.806
Built 1992 10.1 20.797 76.052
10.0 20.857 74.574
10.0 20.740 74.480
9.8 20.685 75.936
Built 1997 9.4 20.867 75.345
9.3 20.901 75.156
9.3 20.252 74.404
Built 2001 9.2 20.671 77.077
9.2 20.475 75.266
Mangrul Bhokar Irrigation Built 1996 31.0 9.0 21.337 76.040
Built before 1990 8.9 20.613 77.407
8.8 20.730 74.969
Built 1996 8.6 20.227 76.989
Built before 1991 8.2 20.497 77.201
8.1 20.414 75.070
Built 1995 8.1 20.573 73.781
8.0 20.727 75.538
7.9 20.429 74.998
7.9 21.018 74.771
7.9 20.796 75.724
Built 1993 7.8 20.643 76.635
Built 1997 7.7 20.676 75.665
Built 1993 7.7 20.656 76.530
Built 2007 7.4 20.443 75.148
Built before 1990 7.3 21.769 78.045
Built 1995 7.3 20.217 77.085
Built 1998 7.2 20.497 74.959
Built 2002 7.2 21.253 75.799
7.2 20.622 74.605
6.7 20.423 75.123
Built 2010 6.6 21.348 76.802
Built 1995 6.6 20.802 75.668
Built before 1989 6.5 20.893 75.377
Built before 1990 6.4 20.556 77.302
6.4 20.922 75.072
6.3 20.985 75.618
Built 1997 6.2 20.976 75.647
Built 1993 6.2 20.710 75.468
Built 1998 6.2 20.377 74.841
6.2 20.267 74.642
6.1 20.976 75.818
6.1 20.635 75.781
5.9 20.850 75.807
5.9 20.704 74.995
5.9 20.490 76.581
Built 1994 5.8 21.580 77.589
5.6 21.435 76.939
5.6 20.677 74.734
Built 1991 5.5 20.511 73.749
Built before 1985 5.4 21.476 76.881
Built before 1990 5.4 20.488 77.317
Built before 1990 5.4 21.682 77.680
5.4 21.510 76.810
5.2 20.552 74.780
5.2 20.508 76.617
5.1 20.510 75.760
5.1 20.543 75.338
5.0 20.403 74.313
5.0 20.202 74.757
5.0 20.360 74.985
Built 1994 4.9 21.349 76.097
4.9 21.446 76.803
Built before 1990 4.9 20.479 77.273
4.8 20.194 74.569
Built 1997 4.8 20.675 75.559
Built before 1990 4.8 21.644 77.924
4.8 20.959 75.836
4.7 21.061 76.260
4.7 20.773 75.124
4.7 20.477 75.306
4.7 20.922 74.980
Built 1998 4.6 20.591 74.870
4.6 20.467 75.331
Built 2000 4.6 20.623 75.437
Built 1997 4.5 20.877 75.936
4.5 20.510 75.297
Built before 1991 4.4 21.699 77.676
4.4 20.601 75.644
4.3 20.442 75.172
4.3 20.260 74.697
4.3 20.676 75.515
Built before 1990 4.2 20.572 77.307
Built before 1989 4.2 21.510 76.927
4.2 20.815 75.596
4.1 20.388 74.841
4.1 20.422 75.128
4.1 20.999 77.878
Built before 1991 4.1 20.438 77.270
Built before 1991 4.0 20.442 77.205
Built before 1990 4.0 21.847 78.151
4.0 20.694 75.498
Built 2000 3.9 20.573 75.870
Built before 1989 3.9 20.825 75.541
3.9 20.560 75.611
3.8 20.605 75.676
3.8 20.577 75.415
3.7 20.459 73.843
Built before 1989 3.6 20.696 75.611
3.5 21.430 76.872
Built before 1990 3.5 21.187 77.058
Built 2000 3.4 21.672 77.897
Built 1995 3.4 21.422 75.139
3.3 20.576 75.274
Built 1991 3.3 20.250 74.585
Built before 1990 3.3 21.659 77.953
Built 1997 3.3 20.539 75.556
3.3 20.606 74.386
3.3 20.580 75.892
3.2 20.579 74.930
3.2 21.463 76.835
Built before 1991 3.2 20.486 77.337
3.2 21.261 77.356
3.0 20.616 75.529
3.0 20.310 74.865
Built 1995 3.0 20.388 76.480
3.0 21.527 76.927
Built before 1990 3.0 21.647 77.980
2.9 21.057 76.238
2.9 20.434 74.154
2.8 20.418 74.081
2.8 20.237 74.766
Built 2002 2.8 20.963 77.821
Built before 1987 2.8 20.918 75.823
2.8 20.861 74.747
2.8 20.976 75.222
Built 1999 2.8 20.976 77.869
Built before 1990 2.7 20.442 76.915
2.7 20.762 74.488
2.7 20.397 75.024
Built before 1987 2.7 21.459 76.914
2.6 20.625 75.703
2.6 20.655 75.911
Built 1992 2.6 20.546 75.031
2.6 20.547 75.220
Built 1997 2.6 20.518 75.139
Built 1997 2.5 20.605 75.573
Built 2000 2.5 21.220 77.176
2.5 20.952 75.991
2.4 20.701 75.322
2.4 20.943 74.976
2.3 21.070 76.464
2.2 20.785 75.705
2.2 20.738 74.550
Built 2000 2.2 20.689 75.673
2.2 20.616 73.948
Built 1999 2.2 20.426 77.251
2.1 20.973 75.240
Built before 1990 2.0 21.785 78.192
2.0 20.877 75.506
1.9 20.305 74.799
1.9 20.601 75.764
1.8 20.963 77.887
Built before 1990 1.8 20.413 77.220
Built 1999 1.7 20.394 76.926
1.7 20.819 74.898
1.7 20.610 75.668
1.6 20.897 75.592
Built before 1985 1.5 20.438 73.938
1.4 20.950 75.806
Built before 1989 1.4 20.804 75.682
Built 1995 1.4 21.220 77.145
1.4 21.481 76.952
1.3 20.943 75.397
1.3 20.917 75.406
1.3 20.574 75.692
1.2 20.774 74.630
Built 1997 1.2 20.607 75.782
1.2 20.581 75.841
1.1 20.839 74.991
Built before 1989 1.0 20.962 74.984
Built 2000 0.7 20.604 75.748
Built before 1985 0.2 20.607 75.703

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).

Longest River

The longest river is the watershed is 535 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 740 m, and the outlet is at 129 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 130 m to 1,300 m above sea level. The average, or mean, elevation is 410 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.