Estimating evapotranspiration (ET) is crucial for understanding changes in the water cycle, yet it remains a challenge due to its dynamic nature. Numerous gridded global ET products exist, but no single product serves as a definitive benchmark. Validation of these large-scale ET products against point data is difficult since ET can vary significantly over space unless atmospheric conditions are stable and land-surface cover is uniform. In this study, six widely used global gridded ET products were first cross-compared and then validated using selected Eddy-covariance towers. The products included: ERA5 (Reanalysis based), GLEAM (Remote sensing based), WGHM (Hydrological model based), Fluxcom (Machine learning based) and KF-ET, WA-ET (Water balance based). The comparison is conducted at a 1˚ spatial and monthly temporal resolution from 2003-2016. Significant differences were observed among the ET datasets, with products performing similarly in some regions but varying considerably in others. Specifically, the Amazon, Southern Africa, Eastern India, and Southern China exhibited large discrepancies, characterized by low correlation (R) and high RMSE values. KF-ET closely resembled Fluxcom in R and RMSE, except in the Amazon and Eastern India, regions where the Fluxcom network is sparse. A dedicated network of Eddy-covariance towers is essential for optimal validation, covering at least a few 1˚ grid cells. To ensure fair comparison, 24 Fluxnet sites were selected from 267 global sites of Fluxnet 2015 dataset based on similarity between the site’s land-use/land-cover (LULC) class and the dominant LULC class within the corresponding 1˚ grid. Further the sites with atleast 30% usable data from 2003-2016 were chosen. Analysis revealed that Fluxcom, ERA5, and KF-ET performed similarly, with Fluxcom outperforming at some sites. However, as Fluxcom is directly derived from Fluxnet sites, its superior performance is expected. WGHM, incorporating various irrigation scenarios, proved reliable among hydrological models. The dominant LULC class percentage within a grid influenced the NSE value between Fluxnet sites and gridded products. The linear relationship between NSE and the dominant LULC class percentage underscores the challenge of comparing site-scale and 1˚ grid-scale data.

Increased irrigation due to agricultural intensification has profound impacts on the surface water and energy balance at regional to local scales. Recent updates of the state of the art Land Surface Models (LSMs) include the impacts of irrigation on surface hydrology. The Indo-Gangetic Plain (IGP) is one of the global hotspots of irrigation water applications. However, the direct application of these models to Indian basins has certain limitations. The commonly employed flood irrigation technique is often indiscriminate and unmanaged, unlike the state-of-the-art models’ estimation of crop water use based on soil moisture conditions. The primary crop in the IGP is paddy, cultivated in inundated fields with quite distinct water and energy partitioning mechanisms represented in very few models. Here, we developed an improved irrigation module to simulate the Indian agricultural practices for the widely used Variable Infiltration Capacity (VIC) model. We incorporated the crop-specific water use for flood irrigation, calculated based on previously reported field studies. The water and energy balance processes are modified by incorporating the ponded paddy fields with proper parameterization. We achieved a substantial improvement in the simulated evapotranspiration and soil moisture of the IGP, particularly in the non-monsoon seasons with the updated model. We found that evapotranspiration and soil moisture are more sensitive to the irrigation techniques than the interval of irrigation application. Runoff strongly responded to irrigation technique as well as the interval of application. We emphasize accurate representation of irrigation practices in the LSMs, specifically when applied to the human-natural hydrological system.