Groundwater Potential Zones

Overview

All layers are read for your area on one regular grid. Terrain layers come from a 30 m elevation model: slope, a depression-filled D8 flow network for drainage density and the wetness index, and lineaments extracted automatically as elongated zones of strong one-directional curvature (or taken from your own lineament file). Moisture, vegetation and surface temperature come from the least cloudy satellite scenes of the dry season, when green or moist ground is most likely fed by shallow water.

The result is a ranking of the ground inside your area, not a measurement of water. It does not give depth, aquifer thickness or water quality; use the high zones to place electrical soundings and test wells. With wells loaded, the tool reports the success-rate curve, ROC for productive against dry wells, the frequency ratio of each class and the correlation with yield.

What you enter · What you get

How it works in the field

Method and equations

Accuracy and error limits

• The map ranks relative groundwater potential; its success is measured against wells: an area under the success curve of 0.7–0.8 is typical, and the result is shown for your wells when you enter them.
• Weights are expert judgements; regional geology and rainfall layers are coarse, and depth, yield and water quality are not predicted.
• To reduce the error: validate with as many wells as possible, test other weights, and confirm targets with resistivity soundings before drilling.

Who uses it

Standards and references

1. Saaty, T.L. (1980). The Analytic Hierarchy Process. McGraw-Hill, New York.
2. Machiwal, D., Jha, M.K. & Mal, B.C. (2011). Assessment of groundwater potential in a semi-arid region of India using remote sensing, GIS and MCDM techniques. Water Resources Management, 25, 1359–1386.
3. Jha, M.K., Chowdary, V.M. & Chowdhury, A. (2010). Groundwater assessment in Salboni Block, West Bengal (India) using remote sensing, geographical information system and multi-criteria decision analysis techniques. Hydrogeology Journal, 18, 1713–1728.
4. Chung, C.F. & Fabbri, A.G. (2003). Validation of spatial prediction models for landslide hazard mapping. Natural Hazards, 30, 451–472.
5. Barnes, R., Lehman, C. & Mulla, D. (2014). Priority-flood: an optimal depression-filling and watershed-labeling algorithm for digital elevation models. Computers & Geosciences, 62, 117–127.
6. Beven, K.J. & Kirkby, M.J. (1979). A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Bulletin, 24(1), 43–69.
7. Horn, B.K.P. (1981). Hill shading and the reflectance map. Proceedings of the IEEE, 69(1), 14–47.
8. Gao, B.-C. (1996). NDWI — a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257–266.
9. Copernicus Sentinel-2 Level-2A, USGS Landsat Collection 2 Level-2 surface temperature, Copernicus DEM GLO-30, ESA WorldCover 10 m, Macrostrat geological map (CC BY 4.0), ERA5 reanalysis via Open-Meteo; served by the Microsoft Planetary Computer.