Evaluation of daily gridded meteorological datasets for hydrological modeling in data-sparse basins of the largest lake in Southeast Asia

Abstract

Study regionTonle Sap Lake (TSL) Basin in Cambodia. Study focusThe development and application of hydrological models for data-sparse basins are hindered by the limited hydro-meteorological data. Although gridded meteorological products are alternatively considered in many studies, the validation of the products with limited point observations overlooks the original spatiotemporal characteristics, thus leading to a selection of datasets with high uncertainty. Here, we evaluated seven gridded meteorological datasets of rainfall and air temperature covering the data-sparse Tonle Sap Lake Basin by employing the statistical approach based on the bilinear-interpolation method and hydrological approach using the SWAT model, which ensures the reliable estimates of streamflow and evapotranspiration. New hydrological insights for the regionThe results of the statistical approach indicate that APHRODITE, ERA5, TRMM and IMERG-based precipitation and CPC and SA-OBS-based air temperature performed comparably well (R ≥ 0.75) with the gauged data. However, ERA5-based streamflow performed relatively poor, while SWAT driven by APHRODITE underestimated satellite-based evapotranspiration, indicating the underestimation of basin-wide precipitation by APHRODITE. Although TRMM and IMERG provide more reliable estimation of streamflow and evapotranspiration, slightly better performance and a higher spatial resolution of IMERG dataset suggest that IMERG precipitation is superior for basin-wide hydrological modeling and impact assessment. These findings showed that statistical comparisons with gauge-data and hydrological evaluation of streamflow are not enough to justify the reliability of each gridded dataset.