In developing regions, accurate rain gauge measurements and satellite precipitation estimates that effectively capture rainfall spatial variability are promising sources of rainfall information. In this study, the latest Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research product, 3B42V7, was validated against ground measurements in the region surrounding the Dongting Lake in China. In the subsequent model-based evaluation and comparison, the two precipitation datasets were separately included as the inputs for data-driven predictive models of the daily Dongting Lake level. The results show that (i) the daily 3B42V7 agrees well with the gauge measurements (correlation coefficient: 0.64–0.73); (ii) 3B42V7 underestimates the frequency of low-intensity (0–30 mm/day) rainfall and the contribution of low-intensity rainfall to the total rainfall volume, but slightly overestimates those of more intense rainfall; (iii) the lake level models driven by rainfall data from the two sources have similar performance, highlighting the potential of using 3B42V7 in data-driven modeling and prediction of hydrological variables in data-scarce regions; and (iv) the inclusion of rainfall as the model input helps achieve a balance between underestimation and overestimation of the lake levels in terms of both magnitude and quantity.

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