Assessing Hydrological Model Representation of Irrigation and Subsurface Flow in a Heavily Anthropogenic‐Influenced Catchment

Abstract

Abstract Evaluating water availability in catchments with widespread anthropogenic influences demands a joint analysis of natural hydrological processes and anthropogenic influences. Achieving this through hydrological modeling requires proper representation of relevant processes. Beyond the application of a single model set‐up, we assess the impact of different model representations of irrigation and subsurface flow dynamics by implementing three irrigation calendars based on remote sensing data sets and two subsurface flow model concepts in the distributed hydrological model wflow_sbm applied to the Hindon River Basin, part of the larger Ganges River Basin in India, featuring intensive irrigation, groundwater abstractions, and inter‐basin water transfer. A comprehensive inter‐comparison of the model output reveals the spatio‐temporal variability in simulated water balance partitioning. Irrigation calendar has a strong dynamic influence across the monsoon and non‐monsoon seasons, producing differences of 9–43 mm/year in river discharge and 32–147 mm/year in evapotranspiration. The impact of the model concept of subsurface flow on the magnitude of the simulated fluxes (differences of 81–90 mm/year in river discharge and 30–37 mm/year in evapotranspiration) is mostly consistent across the seasons but varies spatially, having the strongest influence in areas limited in groundwater storage and near the river. Model evaluation based on observed river discharge (KGE = 0.62–0.7) and groundwater level changes (MAE = 1.6–2.0 m) indicates varying performances. The study highlights the necessity of comprehensive assessment of water balance components and reduction of uncertainties in model estimates when utilizing remote sensing data sets to constrain irrigation timing and increasing the complexity of groundwater process model representation.