High-resolution Spatial Mapping of Runoff Prediction for Micro-scale Surface Rainwater Harvesting

Spatially distributed runoff information is one of the most critical inputs to determining suitable locations of rainwater harvesting (RWH) structures. The majority of hydrological assessments for siting RWH structures rely on empirical formula, such as the Soil Conservation Service Curve Number method that combines soil type, land covers, land use practices, surface conditions, and antecedent moisture conditions with a weak basis on hydrological processes. In addition, runoff generation by topography is considered separately through the computation of flow accumulation. As a result, the current practice of determining suitable RWH locations is done using arbitrary scores rather than the actual spatiotemporal estimate of runoff. The present study employs a topography-based hydrological model, TOPMODEL, to explicitly generate runoff for an experimental catchment of 1800 ha located in Haryana, India. The catchment has been subdivided into 102 sub-catchments where sub-catchment-scale runoff was calculated using daily forcing data of 40 years (1980 - 2020) with other static inputs such as soil and topography data. For topography input, a 1-m resolution digital elevation model (DEM) collected by a Light Detection and Ranging (LiDAR) was used. The input variables of the model were calibrated using ground-based discharge values. The daily sub-catchment-scale runoff from TOPMODEL was aggregated to monthly, seasonal, and annual time scales to produce more detailed picture of water availability for harvest over wet and dry seasons. Finally, the runoff was converted to grid-based values using the flow accumulation scheme widely used on GIS tools. The final grid-based map at 1-m resolution contains the runoff information across the entire catchment at monthly, seasonal and annual time scales. The improved spatio-temporal representation of runoff using TOPMODEL in combination with flow accumulation scheme offers enhanced assistance to designing RWH structures tailored by the actual water volume available at candidate locations and its seasonal and interannual variability.