Effect of temporal resolution on rainfall erosivity estimates in zones of precipitation caused by frontal systems

Lobo, Gabriel P.; Bonilla, Carlos A.

doi:10.1016/j.catena.2015.08.002

Cited by 13 publications

(3 citation statements)

References 22 publications

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“…In Chile's central region, Bonilla and Vidal [47] obtained an RE range of 50 to 6000 MJ • mm • ha −1 • h −1 with an increase from north to south. Moreover, Lobo and Bonilla [60] based on the hourly precipitation from AWS estimates the RE at a point level with a range of 68 to 3520 MJ • mm • ha −1 • h −1 . In addition, they highlighted that the use of rainfall at a higher temporal resolution results in a nonlinear decrease in the RE.…”

Section: Introductionmentioning

confidence: 99%

Rainfall Erosivity in Peru: A New Gridded Dataset Based on GPM-IMERG and Comprehensive Assessment (2000–2020)

Gutierrez,

Huerta,

Sabino

et al. 2023

Remote Sensing

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In soil erosion estimation models, the variables with the greatest impact are rainfall erosivity (RE), which is the measurement of precipitation energy and its potential capacity to cause erosion, and erosivity density (ED), which relates RE to precipitation. The RE requires high temporal resolution records for its estimation. However, due to the limited observed information and the increasing availability of rainfall estimates based on remote sensing, recent research has shown the usefulness of using observed-corrected satellite data for RE estimation. This study evaluates the performance of a new gridded dataset of RE and ED in Peru (PISCO_reed) by merging data from the IMERG v06 product, through a new calibration approach with hourly records of automatic weather stations, during the period of 2000–2020. By using this method, a correlation of 0.94 was found between PISCO_reed and RE obtained by the observed data. An average annual RE for Peru of 7840 MJ · mm · ha−1· h−1 was estimated with a general increase towards the lowland Amazon regions, and high values were found on the North Pacific Coast area of Peru. The spatial identification of the most at risk areas of erosion was evaluated through a relationship between the ED and rainfall. Both erosivity datasets will allow us to expand our fundamental understanding and quantify soil erosion with greater precision.

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Section: Introductionmentioning

confidence: 99%

Rainfall Erosivity in Peru: A New Gridded Dataset Based on GPM-IMERG and Comprehensive Assessment (2000–2020)

Gutierrez,

Huerta,

Sabino

et al. 2023

Remote Sensing

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“…Vários autores comentam da dificuldade de obter esses dados, tanto no Brasil como em outros países de todas as regiões do globo (MELLO et al, 2007, BESKOW et al, 2009LI, 2015;LOBO;BONILLA, 2015). Yin et al (2015) comentam que mesmo nas redes de estações automáticas de países desenvolvidos, a resolução temporal dos dados não permite o cálculo de forma direta da energia cinética da chuva.…”

Section: Introductionunclassified

Avaliação Do Potencial Erosivo Das Chuvas De Florianópolis-Sc

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Poleto

2017

ABClima

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POLETO, Cristiano -cristiano.poleto@ufrgs.br Universidade Federal do Rio Grande do Sul /UFRS RESUMO: A erosão hídrica é um dos maiores problemas ambientais, determinando a redução da produtividade agrícola e compromete a qualidade e quantidade dos recursos hídricos. Este trabalho teve como objetivos determinar o potencial erosivo das chuvas da região de Florianópolis (SC), avaliar a variação mensal da erosividade, determinar as probabilidades de ocorrência de erosividade, e ajustar as equações para a estimativa da erosividade da chuva com base nos dados pluviométricos da região. Foram analisados os pluviogramas diários da estação meteorológica de Florianópolis do período de 1985 a 2012. O potencial erosivo foi estimado pelo índice EI30, que considera a energia cinética dos segmentos de chuva multiplicada pela intensidade máxima da chuva em 30 minutos (I30). Foram ajustadas as equações de regressão entre o índice de erosividade (EI30) e os coeficientes de chuva como variável dependente. No período de analisado registrou-se no pluviógrafo de Florianópolis a precipitação média anual 1.638,2 mm, destes 1.314,2 mm (80,2%) foram consideradas chuvas erosivas. Em janeiro e fevereiro as erosividade foram classificadas como muito altas, e em março e dezembro são classificadas como altas. Os menores valores de erosividade ocorrem nos meses de junho a agosto, classificados como muito baixas. O valor médio anual da erosividade é de 7.522 MJ mm ha -1 h -1 ano -1 , classificado como erosividade alta. As equações de regressão ajustadas foram significativas e podem ser usadas para estimar a erosividade da chuva na região de Florianópolis onde somente existem informações observadas em pluviômetros. A distribuição de Gumbel-Chow mostrou aderência aos dados observados pelo teste Kolmogorov-Smirnov. Os valores estimados com diferentes períodos de retorno podem ser usados incluindo um fator de risco na estimativa das perdas de solos por erosão.Palavras-chaves: precipitação; chuvas intensas; energia cinética. TEMPORAL EVALUATION OF THE RAINFALL EROSIVE POTENTIAL OF FLORIANÓPOLIS-SCABSTRACT: Water erosion is one of the major environmental problem, determining the reduction in agricultural productivity and compromising the quality and quantity of water resources. This study aimed to determine the erosive potential of rainfall in the region of Florianópolis (SC), assess the erosivity's monthly variation, determine the probability of occurrence of erosivity, and adjust the equations to estimate rainfall erosivity based on data rainfall in the region. Daily pluviograms of Florianópolis weather station from the period of 1985 to 2012 were analyzed. The erosive potential was estimated by the EI30 index, which considers the kinetic energy of rain segments multiplied by the maximum rainfall intensity in 30 minutes (I30). Regression equations were adjusted to estimate the erosivity index (EI30) with the rain coefficients as dependent variable. In the analyzed period were recorded in Florianopolis rain gauge the average annual rainfall of 1,638....

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“…Yang et al (2010) adopted the 10-min and hourly rainfall data of the drainage basin of the Zengwen Reservoir in southern Taiwan to compare the R j that was calculated from the two types of data. Lobo and Bonilla (2015) developed a method for calculating R by using rainfall data recorded at 1-24 h intervals in Chile because rainfall data recorded at less than hourly intervals are rare worldwide. Panagos et al (2017) argued that using annual rainfall data to calculate R could be problematic and lead to substantial biases.…”

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confidence: 99%

Estimation of the annual rainfall erosivity index based on hourly rainfall data in a tropical region

Lee¹,

Hsu²

2021

Soil Water Res.

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The annual mean rainfall erosivity (R) indicates the potential soil loss caused by the precipitation and runoff and is used to predict the soil loss from agricultural hillslopes. R is calculated from rainfall stations with continuously recording rainfall databases. However, many short-term real-time rainfall databases that also relate to the rainfall intensity are not readily available around Taiwan, with the hourly rainfall data being predominantly available. The annual mean rainfall erosivity calculated by the 10-min rainfall data accumulation converted to the 30-min rainfall data (R10_30) can be estimated using the annual mean rainfall erosivity calculated by the 10-min rainfall data accumulation convert to the hourly rainfall data (R10_60) that are calculated from the kinetic energy calculated by the 10-min rainfall data accumulation converted to the hourly rainfall data (E60j). The maximum 60-min rainfall intensity calculated by the 10-min rainfall data accumulation converted to the hourly rainfall data (I60j) has been established in rainfall stations throughout southern Taiwan. The 10-min rainfall data set consists of 15 221 storm events from 2002 to 2017 monitored by 51 rainfall stations located in the tropical regions in Taiwan. According to the results of this study, the average conversion factors of the kinetic energy (1.04), rainfall erosivity (1.47), and annual mean rainfall erosivity (1.30) could be estimated based on the 10-min rainfall data.

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Effect of temporal resolution on rainfall erosivity estimates in zones of precipitation caused by frontal systems

Cited by 13 publications

References 22 publications

Rainfall Erosivity in Peru: A New Gridded Dataset Based on GPM-IMERG and Comprehensive Assessment (2000–2020)

Rainfall Erosivity in Peru: A New Gridded Dataset Based on GPM-IMERG and Comprehensive Assessment (2000–2020)

Avaliação Do Potencial Erosivo Das Chuvas De Florianópolis-Sc

Estimation of the annual rainfall erosivity index based on hourly rainfall data in a tropical region

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