ISSN: 2157-7617

Jornal de Ciências da Terra e Mudanças Climáticas

Acesso livre

Nosso grupo organiza mais de 3.000 Séries de conferências Eventos todos os anos nos EUA, Europa e outros países. Ásia com o apoio de mais 1.000 Sociedades e publica mais de 700 Acesso aberto Periódicos que contém mais de 50.000 personalidades eminentes, cientistas de renome como membros do conselho editorial.

Periódicos de acesso aberto ganhando mais leitores e citações
700 periódicos e 15 milhões de leitores Cada periódico está obtendo mais de 25.000 leitores

Indexado em
  • Índice de Fonte CAS (CASSI)
  • Índice Copérnico
  • Google Scholar
  • Sherpa Romeu
  • Acesso Online à Pesquisa no Meio Ambiente (OARE)
  • Abra o portão J
  • Genâmica JournalSeek
  • JornalTOCs
  • Diretório de Periódicos de Ulrich
  • Acesso à Pesquisa Online Global em Agricultura (AGORA)
  • Centro Internacional de Agricultura e Biociências (CABI)
  • RefSeek
  • Universidade Hamdard
  • EBSCO AZ
  • OCLC – WorldCat
  • Convocação de Proquest
  • Catálogo online SWB
  • Publons
  • Euro Pub
  • ICMJE
Compartilhe esta página

Abstrato

Rainfall Runoff Estimation Using GIS and SCS-CN Method for Awash River Basin, Ethiopia

Shimelis Sishah

Understanding hydrological behavior is an important part of effective watershed management and planning. Runoff resulted from rainfall is a component of hydrological behavior that is needed for efficient water resource planning. In this paper, GIS based SCS-CN runoff simulation model was applied to estimate rainfall runoff in Awash river basin. Global Curve Number (GCN250), Maximum Soil Water Retention (S) and Rainfall was used as an input for SCS-CN runoff simulation model. The final surface runoff values for the Awash river basin were generated on the basis of total annual rainfall and maximum soil water retention potential (S) of the year 2020. Accordingly, a runoff variation that range from 83.95 mm/year to a maximum of 1,416.75 mm/year were observed in the study region. Conversely, recently developed Global Curve Number (GCN250) data was tested with Pearson correlation coefficient to be used as an input for SCS-CN runoff simulation model. The results of validation show that, predicted runoff was well correlated with observed runoff with correlation coefficient of 0.9253. Furthermore, correlation analysis was performed to explain the relationship between mean annual rainfall and surface runoff. The relationship between these two variables indicates a strong linear relationship with correlation coefficient of 0.9873.

Isenção de responsabilidade: Este resumo foi traduzido usando ferramentas de inteligência artificial e ainda não foi revisado ou verificado.