Spatial and temporal variability of the rainfall erosivity factor in Northern Algeria

Meddi, Mohamed; Toumi, Samir; Assani, Ali A.

doi:10.1007/s12517-015-2303-8

Cited by 43 publications

(17 citation statements)

References 27 publications

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“…In addition to a point estimate of the R factor, a spatial information (map) is often required for practical purposes. A number of such maps have been released recently at national (Lu and Yu, 2002;Yin et al, 2007;Bonilla and Vidal, 2011;Oliveira et al, 2013;Borrelli et al, 2016;Panagos et al, 2016a;Meddi et al, 2016) or larger (Panagos et al, 2015) scales.…”

Section: Introductionmentioning

confidence: 99%

The rainfall erosivity factor in the Czech Republic and its uncertainty

Hanel

Máca

Bašta

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. In the present paper, the rainfall erosivity factor (R factor) for the area of the Czech Republic is assessed. Based on 10 min data for 96 stations and corresponding R factor estimates, a number of spatial interpolation methods are applied and cross-validated. These methods include inverse distance weighting, standard, ordinary, and regression kriging with parameters estimated by the method of moments and restricted maximum likelihood, and a generalized least-squares (GLS) model. For the regression-based methods, various statistics of monthly precipitation as well as geographical indices are considered as covariates. In addition to the uncertainty originating from spatial interpolation, the uncertainty due to estimation of the rainfall kinetic energy (needed for calculation of the R factor) as well as the effect of record length and spatial coverage are also addressed. Finally, the contribution of each source of uncertainty is quantified. The average R factor for the area of the Czech Republic is 640 MJ ha−1 mm h−1, with values for the individual stations ranging between 320 and 1520 MJ ha−1 mm h−1. Among various spatial interpolation methods, the GLS model relating the R factor to the altitude, longitude, mean precipitation, and mean fraction of precipitation above the 95th percentile of monthly precipitation performed best. Application of the GLS model also reduced the uncertainty due to the record length, which is substantial when the R factor is estimated for individual sites. Our results revealed that reasonable estimates of the R factor can be obtained even from relatively short records (15–20 years), provided sufficient spatial coverage and covariates are available.

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Section: Introductionmentioning

confidence: 99%

The rainfall erosivity factor in the Czech Republic and its uncertainty

Hanel

Máca

Bašta

et al. 2016

Hydrol. Earth Syst. Sci.

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“…Afterward, rainfall and R-factor were mapped by means regression-kriging (Meusburger et al, 2012;Mello et al, 2013;Oliver and Webster, 2014;Borrelli et al, 2016;Meddi et al, 2016), with the aid of the Geographical Information System ArcGIS. In this procedure, the altitude extracted from the SRTM elevation digital model was used, and rainfall and R-factor maps were made under format raster, with 90-meter spatial resolution, using the models.…”

Section: Rainfall and R-factor Mapping Based On Regression-krigingmentioning

confidence: 99%

“…Both studies concluded that, because of the physical-environmental elements being highly and naturally variable, the models may be applied. Meddi et al (2016) incorporated longitude and altitude into the modeling of rainfall erosivity in Argelia, reporting the great importance of longitude as an explanatory variable for rainfall erosivity in that place. In the present study, the coefficients of determination were comparable to the above-mentioned studies and can be rated as good quality; especially considering that the state of Tocantins is located within a climatic transition region between the biomes of Cerrado and Amazon.…”

Section: Mapping Of Average Rainfall and R-factor For The State Of Tomentioning

confidence: 99%

Modeling of the Rainfall and R-Factor for Tocantins State, Brazil

Avanzi

Viola

Mello

et al. 2019

Rev. Bras. Ciênc. Solo

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The state of Tocantins is inserted in the new Brazilian agricultural frontier and has shown enormous potential for expansion of the agricultural lands. However, there is a lack of more elaborate scientific information for better planning and guide agricultural activities, especially regarding the soil and water conservation. Tocantins has a relevant rainfall spatial variability and, consequently, rainfall erosivity. Thus, this work aimed to develop models to estimate the mean monthly and annual rainfall and means rainfall erosivity factor (R-factor) of the Universal Soil Loss Equation (USLE) for the state of Tocantins. For that, 97 historical series of daily rainfall were studied, considering a standard period of 25 years (1985-2009). The fitting of the models adjustment datasets consists of 86 rain-gauges, out of which, 11 were randomly selected and used solely for model validation. Afterward, maps of these variables were generated based on the regression-kriging procedure. The fitted models presented precision statistical that allow characterizing them as "good quality", with a coefficient of determination higher than 0.68 for rainfall models and 0.65 for the R-factor model, besides acceptable bias. Therefore, the application of the models can be successfully carried out, aiding the agricultural planning in the state of Tocantins.

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“…During the last 30 years the Revised Universal Soil Loss Equation (RUSLE) [RENARD et al 1997;WISCHMEIER, SMITH 1978] was applied widely by hydrologist for predicting erosion risk in Africa [ADE- DIJI et al 2010;ANGIMA et al 2003;ANYS 1991;ANYS et al 1994;BENKADJA et al 2015;BONN 1998;CHEN et al 2008;FAGBOHUN et al 2016;LIGONJA, SHRESTHA 2013;MATI, VEIHE 2001;MEDDI et al 2016;SMITH 1999]. In this paper, the RUSLE model was applied to quantify soil losses and to map erosion prone areas in the Hammam Debagh watershed.…”

Section: Introductionmentioning

confidence: 99%

Mapping erosion prone areas in the Bouhamdane watershed (Algeria) using the Revised Universal Soil Loss Equation through GIS

Bouguerra

Bouanani

Khanchoul

et al. 2017

Journal of Water and Land Development

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Soil erosion by water is a major problem that the Northern part of Algeria witnesses nowadays; it reduces: the productivity of agricultural areas due to the loss of lands, and leads to the loss of storage capacity in reservoirs, the deterioration of water quality etc. The aim of this study is to evaluate the soil losses due to water erosion, and to identify the sectors which are potentially sensitive to water erosion in the Bouhamdane watershed, that is located in the northeastern part of Algeria. To this end, the Revised Universal Soil Loss Equation (RUSLE) was used. The application of this equation takes into account five parameters, namely the rainfall erosivity, topography, soil erodibility, vegetative cover and erosion control practices. The product of these parameters under GIS using the RUSLE mathematical equation has enabled evaluating an annual average erosion rate for the Bouhamdane watershed of 11.18 t·ha . Based on the estimates of soil loss in each grid cell, a soil erosion risk map with five risk classes was elaborated. The spatial distribution of risk classes was 16% very low, 41% low, 28% moderate, 12% high and 3% very high. Most areas showing high and very high erosion risk occurred in the lower Bouhamdane watershed around Hammam Debagh dam. These areas require adequate erosion control practices to be implemented on a priority basis in order to conserve soil resources and reduce siltation in the reservoir.

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Spatial and temporal variability of the rainfall erosivity factor in Northern Algeria

Cited by 43 publications

References 27 publications

The rainfall erosivity factor in the Czech Republic and its uncertainty

The rainfall erosivity factor in the Czech Republic and its uncertainty

Modeling of the Rainfall and R-Factor for Tocantins State, Brazil

Mapping erosion prone areas in the Bouhamdane watershed (Algeria) using the Revised Universal Soil Loss Equation through GIS

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