Distributed modelling of mean annual soil erosion and sediment delivery rates to surface waters

Tetzlaff, Björn; Friedrich, Klaus G.; Vorderbrügge, Thomas; Vereecken, Harry; Wendland, Frank

doi:10.1016/j.catena.2011.08.001

Cited by 38 publications

(21 citation statements)

References 11 publications

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“…The potential erosion rates (in t km −2 yr −1 ) from the mainland of India were estimated (Maji, ; NAAS, ) by employing Universal Soil Loss Equation (USLE) (Wischmeier and Smith, ) (Figure a). Despite its limitations (Trimble and Crosson, ; Kinnell, ), USLE has been widely used to estimate soil erosion rates on regional (van der Knijff et al , ; Bakker et al , ; de Vente et al , ; Kra´sa et al , ; Tetzlaff et al , ), country wide (Grimm et al , ; Rousseva et al , ; Cebecauer and Hofierka, ), and continental scales (van der Knijff et al , ; Podmanicky et al , ; Gericke, ). The USLE equation is A s = R K L S C P , where A s is average annual soil loss (in t ha −1 yr −1 ), R is the rainfall erosivity factor, K is the soil erodibility factor, L is the slope‐length factor, S is the slope‐steepness factor, C is the cover and management factor, and P is the conservation practice factor.…”

Section: Methodsmentioning

confidence: 99%

A revised soil erosion budget for India: role of reservoir sedimentation and land‐use protection measures

Sharda¹,

Ojasvi

2016

Earth Surf Processes Landf

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Among the different controls of erosion budget at basin level, the relative impact of dams and land management is yet to be investigated. In this paper, the impact of dams on sediment yield has been assessed by using a conceptual modelling framework which considers the gross erosion and the cascade of dams constructed on a river network. The sediment budget has been estimated based on the gross erosion, deposition of sediment in reservoirs, and sediment yields of 23 mainland river basins of India. The gross erosion of the country is estimated as 5.11 ± 0.4 Gt yr−1 or 1559 t km−2 yr−1, out of which 34.1 ± 12% of the total eroded soil is deposited in the reservoirs, 22.9 ± 29% is discharged outside the country (mainly to oceans), and the remaining 43.0 ± 41% is displaced within the river basins. The river basins of northern India contribute about 81% of the total sediment yield from landmass while the share of southern river basins is 19%. The components of revised sediment budget for India are prominently influenced by the sediment trapped in reservoirs and the treatment of catchment areas by soil and water conservation measures. Analysis of sediment deposition in 4937 reservoirs indicated the average annual percentage capacity loss as 1.04% though it varies from 0.8% to >2% per year in smaller dams (1–50 Mm3 capacity) and from <0.5% to 0.8% per year in larger dams (51 to >1000 Mm3 capacity). Siltation of smaller dams poses a serious threat to their ecosystem services as they cater to a wider population for domestic, agricultural, and industrial purposes. Amongst the environment controls, land use significantly impacts the gross erosion rate and specific sediment yield as compared to climatic and topographic parameters. However, to analyse their integrated effect on the complex processes of sediment fluxes in a basin, further research efforts are needed. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Methodsmentioning

confidence: 99%

A revised soil erosion budget for India: role of reservoir sedimentation and land‐use protection measures

Sharda¹,

Ojasvi

2016

Earth Surf Processes Landf

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“…In particular, sediment that is eroded from sloping areas can accumulate in the river's network, thereby affecting channel water conveyance [1]. Moreover, several problems due to soil erosion, such as the loss of fine and nutrient-rich topsoil that reduces land productivity, as well as the pollution of surface water bodies, are evident [2][3][4][5]. The study of erosion and sediment yield has long established itself as an important area of hydrological research due to the economic significance of the processes involved.…”

Section: Introductionmentioning

confidence: 99%

Principal Component and Multiple Regression Analyses for the Estimation of Suspended Sediment Yield in Ungauged Basins of Northern Thailand

Wuttichaikitcharoen

Babel

2014

Water

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Predicting sediment yield is necessary for good land and water management in any river basin. However, sometimes, the sediment data is either not available or is sparse, which renders estimating sediment yield a daunting task. The present study investigates the factors influencing suspended sediment yield using the principal component analysis (PCA). Additionally, the regression relationships for estimating suspended sediment yield, based on the selected key factors from the PCA, are developed. The PCA shows six components of key factors that can explain at least up to 86.7% of the variation of all variables. The regression models show that basin size, channel network characteristics, land use, basin steepness and rainfall distribution are the key factors affecting sediment yield. The validation of regression relationships for estimating suspended sediment yield shows the error of estimation ranging from −55% to +315% and −59% to +259% for suspended sediment yield and for area-specific suspended sediment yield, respectively. The proposed relationships may be considered useful for predicting suspended sediment yield in ungauged basins of Northern Thailand that have geologic, climatic and hydrologic conditions similar to the study area.

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“…Such an approach, assuming no erosion control practices are used, is the most frequent one in regional scale studies (VRIELING 2006;TETZLAFF et al 2013;TERRANOVA et al 2009;CEBECAUER and HOFIERKA 2008;DEUMLICH et al 2006). The actual soil erosion map was re-calculated and the prioritization of administrative units repeated.…”

Section: Influence Of P-factor Estimation On Erosion Risk Modelingmentioning

confidence: 99%

“…It has been used for erosion risk assessment in many European countries as well as for European-wide studies . USLE/RUSLE based high-resolution erosion risk maps for entire regions, and even countries, are also available (e.g., PRASUHN et al 2013;TETZLAFF et al 2013;MARTÍN-FERNÁ NDEZ and MARTÍNEZ-NÚ Ň EZ 2011;PARK et al 2011;DEUMLICH et al 2006). In Poland, this approach has never been applied as a tool of regional high-resolution assessment for soil erosion risks.…”

Section: Introductionmentioning

confidence: 99%

Quantitative and Qualitative Assessment of Soil Erosion Risk in Małopolska (Poland), Supported by an Object-Based Analysis of High-Resolution Satellite Images

Drzewiecki

Wężyk

Pierzchalski

et al. 2013

Pure Appl. Geophys.

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Abstract-In 2011 the Marshal Office of Małopolska Voivodeship decided to evaluate the vulnerability of soils to water erosion for the entire region. The quantitative and qualitative assessment of the erosion risk for the soils of the Małopolska region was done based on the USLE approach. The special workflow of geoinformation technologies was used to fulfil this goal. A high-resolution soil map, together with rainfall data, a detailed digital elevation model and statistical information about areas sown with particular crops created the input information for erosion modelling in GIS environment. The satellite remote sensing technology and the object-based image analysis (OBIA) approach gave valuable support to this study. RapidEye satellite images were used to obtain the essential up-to-date data about land use and vegetation cover for the entire region (15,000 km 2 ). The application of OBIA also led to defining the direction of field cultivation and the mapping of contour tillage areas. As a result, the spatially differentiated values of erosion control practice factor were used. Both, the potential and the actual soil erosion risk were assessed quantificatively and qualitatively. The results of the erosion assessment in the Małopolska Voivodeship reveal the fact that a majority of its agricultural lands is characterized by moderate or low erosion risk levels. However, high-resolution erosion risk maps show its substantial spatial diversity. According to our study, average or higher actual erosion intensity levels occur for 10.6 % of agricultural land, i.e. 3.6 % of the entire voivodeship area. In 20 % of the municipalities there is a very urgent demand for erosion control. In the next 23 % an urgent erosion control is needed. Our study showed that even a slight improvement of P-factor estimation may have an influence on modeling results. In our case, despite a marginal change of erosion assessment figures on a regional scale, the influence on the final prioritization of areas (municipalities) according to erosion control needs is visible. The study shows that, high-resolution satellite imagery and OBIA may be efficiently used for P-factor mapping and thus contribute to a refined soil erosion risk assessment.

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Distributed modelling of mean annual soil erosion and sediment delivery rates to surface waters

Cited by 38 publications

References 11 publications

A revised soil erosion budget for India: role of reservoir sedimentation and land‐use protection measures

A revised soil erosion budget for India: role of reservoir sedimentation and land‐use protection measures

Principal Component and Multiple Regression Analyses for the Estimation of Suspended Sediment Yield in Ungauged Basins of Northern Thailand

Quantitative and Qualitative Assessment of Soil Erosion Risk in Małopolska (Poland), Supported by an Object-Based Analysis of High-Resolution Satellite Images

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