Abstract

Rainfall Erosivity: Gap-Filling Method Differences in the Brazilian Pantanal and Cerrado Biomes

Highlights

  • Climate changes are each day more and more notable throughout the world, and based on this fact, scientific studies are being developed, having as one of the main subjects studied being rainfall and its historical series [1].84 Topics in HydrometerologyIn studying rainfall and its historical series, gaps in its data were found

  • With the need of filling these gaps, some methods were developed and are often used in studies. These methods include the artificial neural network (ANN) method, as it can be seen in [3–5], weighted likelihood method [6, 7], multiple regression method [8, 9], and weighted likelihood based on multiple regression method [1, 2]

  • In the soil conservation field, many studies have been developed about soil loss due to water erosion [11–13], which is described by the equation proposed by [14], which considers variables like the soil erodibility, topographic factor, soil use and management, conservation practices, and rainfall erosivity

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Summary

84 Topics in Hydrometerology

In studying rainfall and its historical series, gaps in its data (these gaps can occur due to equipment failure or data observer’s mistakes, which are the most common reasons) were found. These gaps can occur in hourly, monthly, or annually collected data. A continuous historical rainfall series, with filled gaps and analyzed consistency, can be applied in many studies such as urban drainage, soil conservation, and water conservation. This study was developed aiming to analyze the differences obtained in the rainfall erosivity results calculated with filled rainfall data using the methods—weighted likelihood, multiple regression, and weighted likelihood based on multiple regression—and to obtain a better correlation coefficient between different hydrological data sources (radar, satellite, and local).

Area of study
Weighted likelihood method
Multiple regression method
Weighted likelihood based on multiple regression (mixed)
Double mass method
Rainfall erosivity
Gap filling
Calculating the rainfall erosivity
Rainfall erosivity classification
Conclusions and recommendations
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