Estimativas de precipitação por satélite para definição de equações de chuvas intensas em áreas não monitoradas da mesorregião sul baiano.

Abstract

Satellite-based precipitation products can be an important alternative for developing IDF equations in ungauged regions, in search for more flood-resilient hydraulic systems. This study evaluated precipitation estimates from the product CHIRPS for developing IDF equations in the Southern Bahia Mesoregion. The statistical metrics used were Percent Bias (PBIAS), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Nash-Sutcliffe Efficiency coefficient (NSE). The results indicate that CHIRPS does not perform satisfactorily in its original state (PBIAS 21.54%; RMSE 43.56 mm/h; MAE 42.74 mm/h; NSE -2.78), but it can provide reasonable IDF equations if the data bias is corrected (PBIAS -4.75%; RMSE 10.09 mm/h; MAE 9.03 mm/h; NSE 0.78). Based on a linear regression model, a bias correction method was proposed, which can be applied, with some limitations, in ungauged areas of the studied region, using only geographic coordinates (PBIAS -3.77%; RMSE 15.46 mm/h; MAE 14.41 mm/h; NSE 0.45). Using CHIRPS data corrected by the proposed method showed higher performance when compared to IDF curves provided by the Plúvio 2.1 software (PBIAS 13.56%; RMSE 28.25 mm/h; MAE 26.84 mm/h; NSE -0.55), which is commonly used by engineers/designers. Finally, non-stationary models for frequency analysis proved to be more appropriate when a trend condition in the series is identified. Furthermore, based on the AIC, BIC criteria, and the likelihood ratio test, models with fewer parameters were preferred over more complex models, with the Gumbel distribution outperforming the GEV distribution.