Improving irrigation and evapotranspiration simulation by incorporating seasonal irrigation map into a land surface model

Abstract Irrigation is essential for maintaining the agricultural production and supporting India’s growing population. Land surface models provide an effective approach to estimate irrigation requirements and hydrological fluxes. However, the irrigation modeling tends to be affected by uncertainties related to the spatiotemporal uncertainty in irrigation map, frequency and irrigation factor (related to the target soil water content). To address these uncertainties, the irrigation and hydrological fluxes over India were reconstructed by simulation experiments with the Community Land Model (CLM). Results show that using a season-specific irrigation map improved the transpiration-total evapotranspiration ratio (T/ET) by up to 30% in the pre-monsoon season, implying higher irrigation efficiency. The remote sensing-based evapotranspiration products were used to compare with simulated model results, showing a similar increasing ET-trend in the pre-monsoon season as the irrigation induced CLM. Furthermore, the results show that higher irrigation frequency leads to increased irrigation amounts, evapotranspiration, and surface runoff. These findings demonstrate that incorporating seasonally varying irrigation maps significantly improves irrigation efficiency estimates and reduces overestimation of irrigation amounts and runoff, highlighting the need for dynamic irrigation representation in land surface modeling over regions with pronounced cropping seasonality.