Validating daily precipitation products estimated by remote sensing with rainfall stations in the Vilcanota basin, Peru
DOI:
https://doi.org/10.24850/j-tyca-2025-03-05Keywords:
Spatio-temporal variability, MSWEP, CHIRPSAbstract
Precipitation represents one of the most important elements within the water cycle for assessing water supply in hydrographic basins. Due to inadequate station distribution, security, terrain, accessibility, etc., there is a scarcity of this data in the Andean basins of Peru. This represents one of the main challenges faced by earth scientists and climatologists in spatially and temporally representing precipitation. In recent years, technological advancements have enabled the estimation of hydrological variables through remote sensing techniques. These data need to be evaluated alongside meteorological observations. This research assessed 11 products of remotely sensed estimated precipitation (RSEP) that estimate precipitation. The evaluation of RSEP was conducted for the period 1981-2018 at daily, ten-day, and monthly time steps. Descriptive statistics were used: mean error (ME), Pearson correlation (R), root mean square error (RMSE), mean absolute error (MAE), and relative bias (BIAS). Additionally, categorical statistics were employed: Probability of Detection (POD), False Alarm Rate (FAR), Critical Success Index (CSI). The products MSWEP, CHIRPS, TRMM-3B42, PERSIANN-CDR were found to be more efficient in representing the spatial variability of daily and accumulated precipitation in the Vilcanota basin. Remote sensing data proved useful in representing the spatiotemporal variability of precipitation in the Vilcanota basin; the results suggest that remote sensing data could be used to simulate the hydrological functioning of Andean mountainous catchments with limited in-situ information.
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