Spatial distribution and estimation of rainfall trends and erosivity in the Epitácio Pessoa reservoir catchment, Paraíba, Brazil

Silva, Richarde Marques da; Santos, Celso Augusto Guimarães; Silva, Jorge Flavio CazÉ Braga Da Costa; Silva, Alexandro Medeiros; Neto, Reginaldo Moura Brasil

doi:10.1007/s11069-020-03926-9

Cited by 23 publications

(4 citation statements)

References 55 publications

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“…Os maiores valores de precipitação foram observados no período de janeiro a abril, sendo o mês de março o mais chuvoso com média de 206.2 mm, e o período de julho a novembro o menos chuvoso com médias de no máximo 22.1 mm (Tabela 2). Este último período representa a estação seca no semiárido paraibano com raros eventos de precipitação (DA SILVA et al, 2020).…”

Section: Estatística Descritiva Da Precipitaçãounclassified

Análise Estatística Da Precipitação E Da Erosividade Das Chuvas No Semiárido Paraibano, Nordeste Do Brasil

Silva

Medeiros

Nascimento

et al. 2022

Rev. AIDIS ing. cienc. ambient.

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Soil erosion by water is a serious threat to many regions around the world. The impacts caused by this phenomenon are complex and, in some cases, imperceptible, slowly changing the geomorphology over the years. Despite being a natural process, erosion is intensified by anthropic action, and rainfall erosivity is one of the main factors for soil degradation in semiarid environments. In Brazil, research at local scales is still timid and limited, which makes it difficult for researchers to effectively analyze soil loss across the country. The objective of the work was analyzed using descriptive statistics and rainfall erosivity in the semiarid region of Paraíba State, northeastern Brazil. The study area corresponds to the Paraíba portion of the Piancó-Piranhas-Açu River basin (BHRPPA-PB). The homogeneity of time series data from 28 rainfall stations from 1962 to 2020 (59 years of data) was analyzed and verified, the erosivity was calculated using the rainfall erosivity index (EI30), and a descriptive statistic of the Erasure and erosivity data. From the results it was possible to observe that the average rainfall erosivity value that represents the R Factor of the USLE is 3275 MJ mm ha -1 h -1 being evaluated as moderate, and the period from January to May is the more conducive to the action of the erosivity of rains.

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Section: Estatística Descritiva Da Precipitaçãounclassified

Análise Estatística Da Precipitação E Da Erosividade Das Chuvas No Semiárido Paraibano, Nordeste Do Brasil

Silva

Medeiros

Nascimento

et al. 2022

Rev. AIDIS ing. cienc. ambient.

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“…In RUSLE2, monthly erosivity maps based on 15 min data from 3700 stations were generated (USDA-ARS, 2013). Erosivity maps based on spatial interpolation have been widely produced globally (Lu and Yu, 2002;Oliveira et al, 2012;Liu et al, 2013;Klik et al, 2015;Panagos et al, 2015a, b;Borrelli et al, 2016;Qin et al, 2016;Panagos et al, 2017;Sadeghi et al, 2017;Yin et al, 2019;Riquetti et al, 2020;Silva et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Rainfall erosivity mapping over mainland China based on high-density hourly rainfall records

Yue

Yin

Xie

et al. 2022

Earth Syst. Sci. Data

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Abstract. Rainfall erosivity quantifies the effect of rainfall and runoff on the rate of soil loss. Maps of rainfall erosivity are needed for erosion assessment using the Universal Soil Loss Equation (USLE) and its successors. To improve erosivity maps that are currently available, hourly and daily rainfall data from 2381 stations for the period 1951–2018 were used to generate new R-factor and 1-in-10-year event EI30 maps for mainland China (available at https://doi.org/10.12275/bnu.clicia.rainfallerosivity.CN.001; Yue et al., 2020b). One-minute rainfall data from 62 stations, of which 18 had a record length > 29 years, were used to compute the “true” rainfall erosivity against which the new R-factor and 1-in-10-year EI30 maps were assessed to quantify the improvement over the existing maps through cross-validation. The results showed that (1) existing maps underestimated erosivity for most of the south-eastern part of China and overestimated for most of the western region; (2) the new R-factor map generated in this study had a median absolute relative error of 16 % for the western region, compared to 162 % for the existing map, and 18 % for the rest of China. The new 1-in-10-year EI30 map had a median absolute relative error of 14 % for the central and eastern regions of China, compared to 21 % for the existing map (map accuracy was not evaluated for the western region where the 1 min data were limited); (3) the R-factor map was improved mainly for the western region, because of an increase in the number of stations from 87 to 150 and temporal resolution from daily to hourly; (4) the benefit of increased station density for erosivity mapping is limited once the station density reached about 1 station per 10 000 km2.

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“…As the number of extreme rainfall events grows globally, the spatial-temporal heterogeneity of rainfall has become the focus of study. Many in-depth researches dealt with topics as the spatial-temporal characteristics of rainfall erosion force (da Silva et al, 2020;Wang et al, 2013), the relationship between the spatial-temporal characteristics of rainfall and flood characteristics (Villarini et al, 2011), the inversion of the spatial-temporal characteristics of rainfall (Fuentes et al, 2008;Scher & Peßenteiner, 2021;Zhou et al, 2018), the trend of spatial-temporal characteristics of rainfall in a certain area (Jung et al, 2017;Mamunur Rashid et al, 2015;Varouchakis et al, 2018), and the impact of spatialtemporal resolution of rainfall on the accuracy of numerical simulation (Patil et al, 2011;Zhou et al, 2018), and so on.…”

Section: Introductionmentioning

confidence: 99%

The effect of spatial–temporal characteristics of rainfall on urban inundation processes

Chen

Hou

et al. 2022

Hydrological Processes

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The spatial-temporal characteristics of rainfall is one of the important elements affecting urban inundation. In this work, the effect of spatial-temporal characteristics of rainfall on urban inundation processes was investigated. To solve the problem that it is difficult to acquire the spatial-temporal characteristics of rainfall, the weather generator model was used to generate rainfall data in accordance with the climatic conditions of the study area. Then, precise rainfall data with 1 km 2 spatial resolution and 5 min temporal resolution were obtained through the downscaling method.

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Spatial distribution and estimation of rainfall trends and erosivity in the Epitácio Pessoa reservoir catchment, Paraíba, Brazil

Cited by 23 publications

References 55 publications

Análise Estatística Da Precipitação E Da Erosividade Das Chuvas No Semiárido Paraibano, Nordeste Do Brasil

Análise Estatística Da Precipitação E Da Erosividade Das Chuvas No Semiárido Paraibano, Nordeste Do Brasil

Rainfall erosivity mapping over mainland China based on high-density hourly rainfall records

The effect of spatial–temporal characteristics of rainfall on urban inundation processes

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