Using Satellite Observations to Support Water Management in South India

Abstract

<p>Small reservoir tank structures across South India form part of a complex ancient traditional water distribution system that has historically supplied irrigation to cropped fields during the dry-season. Despite their historical significance and the critical need for water storage in an agrarian dominated country with unpredictable rainfall, thousands of tanks have fallen into a state of disrepair. Our current understanding of these systems lacks knowledge about the functional state of these water systems. To understand tank functionality, spatially explicit and temporally dynamic frequent high-resolution surface water (SW) estimates developed in a synoptic and detailed way are needed. Building from an existing surface water monitoring approach (Vanthof and Kelly, 2019), we assess basin-scale dynamics of tank water storage state by segmenting water in multi-date and multi-sensor satellite images (Landsat-8, Sentinel-1, Sentinel-2, PlanetScope) for several years. These SW observations are converted to volumes using empirical rating curves developed for the region from Vanthof and Kelly (2019). Annually tanks were categorized by ‘tanks with water’ or ‘tanks without water’. For the ‘tanks with water’ category, an analysis was performed annually to identify spatial and temporal patterns in two indicators: temporal period of water storage and the rate of storage loss. Results show that hundreds of tanks are not able to store water despite precipitation inputs to the system. For tanks with water, further analysis reveals great variability among tanks for both indicators. As shown, this decade of Earth observation offers exciting opportunities to apply data-driven approaches to complement more traditional physically-based hydrological understanding and the ability to elucidate the role of reservoir filling strategies.</p><p>Vanthof, V., & Kelly, R. (2019). Water storage estimation in ungauged small reservoirs with the TanDEM-X DEM and multi-source satellite observations. <em>Remote Sens. of Environ.</em>, <em>235</em>, 111437.</p>