Evaluación del índice de precipitación estandarizado (SPI) con datos de alta resolución en clima histórico y futuro en el centro de México

Abstract

Introduction: Drought monitoring needs good quality meteorological data, but records frequently face problems. Therefore, satellite precipitation datasets are an alternative. Objective: Evaluation of three high-resolution datasets (compared with observed precipitation) to capture wet and dry periods. In addition, the influence of the selection of the dataset to downscale future climate simulations is evaluated in the estimation of drought indicators. Methodology: Precipitation products (NOAA, CHIRPS and PERSIANN-CDR) were compared with observations from seven meteorological stations (1983-2013). The 12-month standard precipitation index (SPI) was selected to evaluate drought conditions. Also, precipitation estimates from CanESM2 were downscaled and bias-corrected with each dataset. Results: The annual precipitation cycle is well capture, but underestimation is noted. The datasets have a good correlation, but less variability. Regarding the SPI, results show good correlation, but extremely dry events are generally underestimated with CHIRPS and PERSIANN-CDR. The NOAA dataset performs better in terms of categorical scores, especially for wet events. Similar median values were found in the drought indicators in future; however, the datasets lead to less variability than observations, especially in the drought frequency indicator. Study limitations: Limited number of good quality meteorological records. Originality: Selected precipitation datasets are tested under different climatic conditions for the first time in Mexico. Also, the influence of the reference precipitation dataset for bias correction is evaluated on future drought projections. Conclusions: Precipitation products should be tested before their use in monitoring droughts in the historical period, as well as in estimating droughts with future projections.