scholarly journals Spatial Distribution of Annual and Monthly Rainfall Erosivity in the Jaguarí River Basin

2017 ◽  
Vol 41 (0) ◽  
Author(s):  
Lucas Machado Pontes ◽  
Marx Leandro Naves Silva ◽  
Diêgo Faustolo Alves Bispo ◽  
Fabio Arnaldo Pomar Avalos ◽  
Marcelo Silva de Oliveira ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
River Basin ◽  
Monthly Rainfall ◽  
Rainfall Erosivity

Mapping monthly rainfall erosivity for the Lazio Region (Italy)

2020 ◽  
Author(s):  
C. Mineo ◽  
E. Ridolfi ◽  
B. Moccia ◽  
F. Napolitano
Keyword(s):  
Monthly Rainfall ◽  
Rainfall Erosivity ◽  
Lazio Region

scholarly journals Estimativa da erosividade da chuva na bacia hidrográfica do rio Juma com base em dados do satélite TRMM / Estimation of rain erosion in the Juma river basin based on TRMM satellite data

2019 ◽  
Vol 29 (56) ◽  
pp. 45
Author(s):  
Miqueias Lima Duarte ◽  
Eliomar Pereira da Silva Filho
Keyword(s):  
River Basin ◽  
Soil Degradation ◽  
Prevention And Control ◽  
Rainfall Erosivity ◽  
Territorial Planning ◽  
Surface Data ◽  
Historical Series ◽  
Erosivity Index ◽  
And Control ◽  
Rain Erosion

Conhecer o potencial da chuva em causar erosão do solo é de fundamental importância para a compreensão da fragilidade de uma região, essas informações podem ser utilizadas na prevenção e controle da degradação do solo, auxiliando o planejamento territorial. Este estudo tem por objetivo estimar a erosividade da chuva na bacia hidrográfica do rio Juma, no sul do estado do Amazonas. Foram utilizados dados mensais pluviométricos do produto 3B42-V7 do sensor TRMM obtidos na plataforma Giovanni e comparados com dados de superfície, para a série histórica de 1998 a 2016. O índice de erosividade da chuva foi obtido a partir de um modelo proposto por Oliveira Jr e Medina (1990) desenvolvido para a região. Os resultados obtidos apontam que a variação espacial do índice de erosividade da chuva ao longo da bacia do rio Juma foi pequena (média de 11,66 MJ.mm.ha-1.h-1.ano-1), as maiores variações estão relacionadas a sazonalidade regional, sendo que o mês de julho apresenta o menor índice de erosividade médio (47,74 MJ.mm.ha-1.h-1.ano-1), enquanto que o mês de fevereiro apresentou o maior índice de erosividade (145,73 MJ.mm.ha-1.h-1.ano-1).Palavras–chave: Potencial erosivo da chuva, Degradação do solo, Sensor orbital.Abstract Knowing the potential of rain to cause soil erosion is of fundamental importance to understand the fragility of a region, this information can be used in the prevention and control of soil degradation, assisting the territorial planning. This study aims to estimate the rainfall erosivity in the river basin of the Juma, in the south of the state of Amazonas. Monthly rainfall data from the 3B42-V7 TRMM sensor product obtained from the Giovanni platform and compared with surface data were used for the historical series from 1998 to 2016. The rainfall erosivity index was obtained from a model proposed by Oliveira Jr and Medina (1990) developed for the region. The results indicate that the spatial variation of the rainfall erosivity index along the Juma river basin was small (mean of 11.66 MJ.mm.ha-1.h-1.year-1), the most significant variations are related to regional seasonality, and the month of July It has the lowest mean erosivity index (47.74 MJ.mm.ha-1.h-1.year-1), while February presented the highest erosivity index (145.73 MJ.mm.ha-1.h-1.year-1).Keywords: Erosive potential of rain, Soil degradation, Orbital Sensor.

scholarly journals Study on the spatial distribution of water resource value in the agricultural system of the Yellow River Basin

Water Policy ◽  
2021 ◽  
Author(s):  
Huiliang Wang ◽  
Shuoqiao Huang ◽  
Danyang Di ◽  
Yu Wang ◽  
Fengyi Zhang
Keyword(s):  
Spatial Distribution ◽  
Water Resources ◽  
River Basin ◽  
Water Resource ◽  
Yellow River ◽  
Yellow River Basin ◽  
The Yellow River ◽  
Agricultural System ◽  
Resource Value ◽  
The Yellow River Basin

Abstract To analyze the spatial distribution characteristics of water resource value in the agricultural system of the Yellow River Basin, this paper takes the Yellow River Basin as its research object and studies the spatial distribution characteristics and influencing factors of water resource value in the agricultural system using the emergy theory and method, the spatial autocorrelation analysis method, and the spatial regression model. The results show that (1) the value of water resources in the agricultural system ranges from 0.64 to 0.98$/m3, and the value in the middle and lower reaches of the basin is relatively high; (2) the Moran index of the water resource value in the agricultural system is 0.2772, showing a positive spatial autocorrelation feature. Here, ‘high-high (high value city gathering)’ is the main aggregation mode, which is mainly concentrated in the middle and lower reaches of the basin. (3) The spatial error model, moreover, has the best simulation effect. The cultivated land area, total agricultural output value, agricultural labor force, and total mechanical power have a significant positive impact on the agricultural production value of water resources in the Yellow River Basin; the altitude, annual average temperature, and agricultural water consumption have a negative impact. Overall, this study shows that guiding the distribution of water resources according to their value and increasing agricultural water use in the middle and lower reaches of the basin will help improve the overall agricultural production efficiency of water resources in the basin.

scholarly journals RUSLE Model Based Annual Soil Loss Quantification for Soil Erosion Protection in Fincha Catchment, Abay River Basin, Ethiopia.

2021 ◽  
Author(s):  
Habtamu Tamiru ◽  
Meseret Wagari
Keyword(s):  
Soil Erosion ◽  
River Basin ◽  
Soil Loss ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Landsat 8 ◽  
Land Resources ◽  
Rusle Model ◽  
Factor C ◽  
Significant Factors

Abstract Background: The quantity of soil loss as a result of soil erosion is dramatically increasing in catchment where land resources management is very weak. The annual dramatic increment of the depletion of very important soil nutrients exposes the residents of this catchment to high expenses of money to use artificial fertilizers to increase the yield. This paper was conducted in Fincha Catchment where the soil is highly vulnerable to erosion, however, where such studies are not undertaken. This study uses Fincha catchment in Abay river basin as the study area to quantify the annual soil loss, where such studies are not undertaken, by implementing Revised Universal Soil Loss Equation (RUSLE) model developed in ArcGIS version 10.4. Results: Digital Elevation Model (12.5 x 12.5), LANDSAT 8 of Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Annual Rainfall of 10 stations (2010-2019) and soil maps of the catchment were used as input parameters to generate the significant factors. Rainfall erosivity factor (R), soil erodibility factor (K), cover and management factor (C), slope length and steepness factor (LS) and support practice factor (P) were used as soil loss quantification significant factors. It was found that the quantified average annual soil loss ranges from 0.0 to 76.5 t ha-1 yr-1 was obtained in the catchment. The area coverage of soil erosion severity with 55%, 35% and 10% as low to moderate, high and very high respectively were identified. Conclusion: Finally, it was concluded that having information about the spatial variability of soil loss severity map generated in the RUSLE model has a paramount role to alert land resources managers and all stakeholders in controlling the effects via the implementation of both structural and non-structural mitigations. The results of the RUSLE model can also be further considered along with the catchment for practical soil loss quantification that can help for protection practices.

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