Premium
Rainfall estimation methods in the Brazilian semiarid region: 30‐year evaluation on a monthly scale
Author(s) -
Santos Vitor Juste,
Calijuri Maria Lúcia,
Ribeiro Júnior José Ivo,
Assis Leonardo Campos
Publication year - 2021
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.6723
Subject(s) - environmental science , context (archaeology) , estimation , scale (ratio) , climatology , satellite , meteorology , hydrology (agriculture) , geography , cartography , geology , management , archaeology , geotechnical engineering , aerospace engineering , engineering , economics
Abstract Studies about evaluations of different rainfall estimation methods are important as they are usually applied in works related to water resources availability, public supply, watering, animal water supply, hydroelectric power generation, drought episodes' evaluation, among others. Studies that cover the entire area of the Brazilian semiarid within this scope are still scarce. In this context, the objective of this paper was to evaluate seven rain estimation methods, from different approaches, with the aid of statistical analysis. The focus was on the long term, using 30 years of data in monthly scale, in order to verify the methods performance in estimating the spatiotemporal behaviour of rainfall compared to data measured in situ, coming from rainfall and meteorological stations. The evaluation process was based on the random selection of training and testing stations, with the first being used to interpolate rainfall data using different methods, and the latter used to evaluate this estimates with the aid of statistical analysis. Finally, the results were standardized, interpreted and discussed. Of all the estimation methods evaluated, CHIRPS obtained the best performance when compared to field stations data used as test. From the results obtained, there is evidence that the best performance is due to the incorporation of several distinct data sources derived from in situ stations, geostationary satellite infrared sensor estimates and physiographic predictors. This information is not fully present in other evaluated methods. The worst estimation performances were in the eastern side of the Brazilian semiarid, an area that receives moisture from the Atlantic Ocean and has significant topographic variations, due to the set of the complex relief mountains present in the northeast coast of Brazil.