Towards the development of a national drought monitoring framework for India: Reconstruction of historical droughts using CLM5

Abstract. In this study, we evaluate the Community Land Model version 5.0 (CLM5.0) as a first step toward building a National Drought Monitoring Framework – DRISHTI (Drought Risk & Impact Surveillance using Hydrometeorological, Terrestrial & Inference Models) in India. CLM5.0 was applied over India at 0.1 ° resolution (1980–2020), using India Meteorological Department (IMD) precipitation and two atmospheric forcing datasets: the Indian Monsoon Data Assimilation and Analysis (IMDAA) regional reanalysis and the fifth generation ECMWF Re-Analysis (ERA5). Model performance was assessed for soil moisture (SM), evapotranspiration (ET), and runoff under rainfed and irrigated conditions against in-situ measurements and satellite/reference datasets: Soil Moisture Active Passive (SMAP), Global Land Evaporation Amsterdam Model (GLEAM), and Global Runoff Reconstruction (GRUN).

CLM5.0 reproduced spatial and temporal hydrological variability well. Model diagnostics revealed three climate-zone biases: (1) an apparent 'Dry Bias' in humid zones (Am) characterized by low SM, runoff, and ET. This bias reflects underestimation of precipitation in IMD rather than model process deficiencies; (2) a 'Wet Bias' in semi-arid and arid zones (BSh, BWh), where the Dry Surface Layer scheme suppresses evaporation, generating excess storage and runoff; and (3) a 'Runoff Paradox' in temperate zones (Cwa, Cwb), where high surface SM coincides with low runoff. Systematic runoff underestimation in complex terrains reflects limitations in sub-grid hillslope lateral flow representation, which led to high moisture retention. Relative to ERA5-driven simulations, IMDAA reduces RMSD in SM and runoff by 10% and 2.5 %, respectively, while ERA5 improves ET estimates by 17 %. At the national scale, rainfed simulations generally outperform irrigated runs; however, irrigation leads to localized improvements - most notably over the Indo-Gangetic Plain - with a stronger influence on root-zone SM than on surface SM. Specifically, irrigation reduces errors over ~26 % of irrigated grid cells for root-zone SM compared to ~23 % for surface SM, while corresponding improvements are observed over ~33 % and ~45 % of irrigated grid cells for runoff and ET, respectively. Despite these limitations, CLM5.0 successfully reproduces historical droughts, demonstrating its utility for drought monitoring in data-scarce regions while highlighting the need for improved irrigation parameterization, targeted model calibration, and careful selection of meteorological forcing datasets.