Distributed Hydrological Models (DHMs) with the capability of simulating catchment-scale water and energy budgets as well as rainfall-runoff-inundation processes are essential tools for Integrated Water Resource Management (IWRM) as well as Water-related Disaster Risk Reduction (WDRR) under changing climate. This research developed a new DHM, the Water and Energy Budget-based Rainfall-Runoff-Inundation (WEB-RRI) model, by integrating the RRI model’s diffusive wave flow equations into a land surface model (hydro-SiB2) to incorporate water and energy budget processes, land-vegetation-atmosphere interactions, soil moisture dynamics, and 2-D lateral water flows to improve interception, evapotranspiration (ET), soil moisture, runoff, and inundation processes. The performance of the new model was assessed using river discharge data, MODIS and GLEAM ET data, and ground as well as satellite inundation extents in the Kalu (wet) and Mundeni (dry) River basins in Sri Lanka. The model was well calibrated and validated (Nash > 0.9) and confirmed to be highly capable of reproducing the long-term (~20 years) observed river discharges (Nash > 0.89) and hydrological flow regime properties for both basins. Particularly, the simulated low flow just before the flood and the peak discharges during the flood, as well as their timings, coincided well with the observed discharges in both basins, which indicates that the model is capable of reproducing soil and vegetation water storages reasonably well, and therefore it can be used for real-time and forecasting applications with the re-starting capability. The model-simulated basin averaged ET fluxes and their trends agreed better with GLEAM (RMSE: ~0.7–0.95 mm/day, correlation: ~0.35–0.39) than with MODIS (RMSE: ~0.96–1.04 mm/day, correlation: ~0.14–0.25). The simulated inundation extents were also consistent with the ground- and MODIS-driven inundation extents. The future focus of this research will be on expanding the model applicability for basin-wide IWRM and WDRR, including flood- and drought-related risk assessments, by employing the model to operational applications (e.g., flood forecasting and seasonal flow prediction) and long-term applications (e.g., catchment responses to past and future climatology, water cycle variability, hydrological extremes, and land-use changes).
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