Assessment of soil erosion, sediment yield and basin specific controlling factors using RUSLE-SDR and PLSR approach in Konar river basin, India

Rajbanshi, Joy; Bhattacharya, Sayan

doi:10.1016/j.jhydrol.2020.124935

Cited by 73 publications

(29 citation statements)

References 72 publications

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“…Several authors question the applicability of the plot-scalebased USLE to the landscape scale (e.g., Boardman, 2006;Evans, 1995;Govers, 2011), particularly as in large domains other processes such as gully erosion, bank erosion, or sediment deposition can dominate the erosion response (Govers, 2011). Multiple approaches are available from the literature that account, for instance, for the deposition of eroded material by employing concepts such as the sediment delivery ratio (e.g., Rajbanshi and Bhattacharya, 2020;Ferro and Minacapilli, 1995;Graham, 1975). While the USLE principally only accounts for the soil removal and does not consider soil deposition, Evans (2013) concludes that the USLE can be helpful in identifying the erosion potential or erosion hotspots but fails to predict the exact magnitude of soil that is eroded.…”

Section: Introductionmentioning

confidence: 99%

A systematic assessment of uncertainties in large-scale soil loss estimation from different representations of USLE input factors – a case study for Kenya and Uganda

Schürz

Mehdi

Kiesel

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. The Universal Soil Loss Equation (USLE) is the most commonly used model to assess soil erosion by water. The model equation quantifies long-term average annual soil loss as a product of the rainfall erosivity R, soil erodibility K, slope length and steepness LS, soil cover C, and support measures P. A large variety of methods exist to derive these model inputs from readily available data. However, the estimated values of a respective model input can strongly differ when employing different methods and can eventually introduce large uncertainties in the estimated soil loss. The potential to evaluate soil loss estimates at a large scale is very limited due to scarce in-field observations and their comparability to long-term soil estimates. In this work we addressed (i) the uncertainties in the soil loss estimates that can potentially be introduced by different representations of the USLE input factors and (ii) challenges that can arise in the evaluation of uncertain soil loss estimates with observed data. In a systematic analysis we developed different representations of USLE inputs for the study domain of Kenya and Uganda. All combinations of the generated USLE inputs resulted in 972 USLE model setups. We assessed the resulting distributions in soil loss, both spatially distributed and on the administrative level for Kenya and Uganda. In a sensitivity analysis we analyzed the contributions of the USLE model inputs to the ranges in soil loss and analyzed their spatial patterns. We compared the calculated USLE ensemble soil estimates to available in-field data and other study results and addressed possibilities and limitations of the USLE model evaluation. The USLE model ensemble resulted in wide ranges of estimated soil loss, exceeding the mean soil loss by over an order of magnitude, particularly in hilly topographies. The study implies that a soil loss assessment with the USLE is highly uncertain and strongly depends on the realizations of the model input factors. The employed sensitivity analysis enabled us to identify spatial patterns in the importance of the USLE input factors. The C and K factors showed large-scale patterns of importance in the densely vegetated part of Uganda and the dry north of Kenya, respectively, while LS was relevant in small-scale heterogeneous patterns. Major challenges for the evaluation of the estimated soil losses with in-field data were due to spatial and temporal limitations of the observation data but also due to measured soil losses describing processes that are different to the ones that are represented by the USLE.

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Section: Introductionmentioning

confidence: 99%

A systematic assessment of uncertainties in large-scale soil loss estimation from different representations of USLE input factors – a case study for Kenya and Uganda

Schürz

Mehdi

Kiesel

et al. 2020

Hydrol. Earth Syst. Sci.

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“…Several works on the tropical and subtropical regions show that last several decades have brought widespread deforestation in those regions mostly followed by extensions of agricultural fields that may affect the carbon stocks immeasurably and cause irreversible damage to the environment (Nogueira, Yanai, De Vasconcelos, & Fearnside, 2017;De Sy, 2019). The Konar catchment located in the humid subtropical region of India also experienced the distinct loss of forested areas (15.1%) over the past decades while noticing an increase of about 14.5% in the wastelands (Rajbanshi & Bhattacharya, 2020;Sanyal, Densmore, & Carbonneau, 2014). The previous works were limited to quantifying the rate of LULC change and its impact on the surface runoff and soil erosion.…”

Section: Introductionmentioning

confidence: 99%

Changes in carbon stocks and its economic valuation under a changing land use pattern—A multitemporal study in Konar catchment, India

Rajbanshi

Das

2021

Land Degrad Dev

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Land use and land cover (LULC) changes influence the global terrestrial carbon pools and carbon sequestration processes to great extents. Therefore, the understanding of the soil carbon dynamics under the dire pressure of LULC changes is highly necessary. The subhumid tropical catchment of Konar, India, has endured immense LULC transitions over the years and this is likely to promote altered carbon exchanges with atmosphere and, thus, has a significant impact on climate regulation and ecosystem services. Our study analyzed the change in total carbon storage in the past (1992-2004 and 2004-2016) and predicted the future (2030) with respect to the changing land use scenario in the catchment. In addition, the monetary valuation of the carbon loss and/or gain in that area has been performed considering its economic value in the society. The Markov chain model was used in this work in order to predict the LULC map for the future period, and integrated valuation of ecosystem services and trade-offs (InVEST) model was applied to measure the changes in total carbon stocks. The results show that 129,273.75 Mg carbon has already been lost from 2004 to 2016 that is worth of $US 20.06 million ($US 212.69 ha À1 ), and another 66,050.25 Mg of carbon which is worth of $US 9.73 million ($US 103.21 ha À1 ) is expected to be lost in future by 2030. A thorough understanding of the carbon scenario of the study area may help to promote feasible mitigation actions to conserve the carbon pools.

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“…Human activities such as mining, agriculture, overgrazing and construction accelerate the soil erosion process [15,16]. These anthropogenic activities affected approximately 1,964 million hectares of soils, where 1,903 million hectares of eroded soils were related to water erosion and the remaining was due to wind erosion [17]. It is estimated that the rate of soil erosion will increase annually, and this would be one of the major environmental issues for a few decades [18].…”

Section: Introductionmentioning

confidence: 99%

A Study Case on Estimation of Soil Loss and Sediment Yield in Curtin University, Malaysia

Ngieng

Yong

Strimari

2021

Trop. Aqua. Soil Pollut.

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Because of human activities, soil erosion has been one of the most concerning issues in Malaysia in the past decades. This study aimed to estimate the amount of soil loss and sediment yield at Curtin University, Malaysia by using the Revised Universal Soil Loss Equation (RUSLE) and the Modified Universal Soil Loss Equation (MUSLE), respectively. The parameters of RUSLE include rainfall erosivity factor (R), soil erodibility factor (K), slope length factor (L), slope steepness factor (S), cover-management factor (C) and support practice factor (P). The rainfall data (10 years) from the Sarawak Meteorological Department was used to determine the R-factor. The K-factor was determined by sieve analysis, hydrometer analysis, the Standard Proctor Test (SPT), and organic content testing. The L-and S-factors were performed by measuring on site and using Google Earth. The C-and P-factors were based on the ground surface cover condition (bare soil in this study). In the MUSLE, the runoff factor comprises V and Qp, while the other parameters are the same as in the RUSLE. The runoff depth, V, is equivalent to the rainfall intensity. Rainfall intensities were recorded by using a rain gauge. The highest rainfall intensity was used for runoff depth. The Rational method has been utilized to calculate Qp. The amount of soil loss estimated was 119.97 tons/ha/year and the sediment yield amount estimated was 0.76 ton/storm event in Curtin University, Malaysia.

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Assessment of soil erosion, sediment yield and basin specific controlling factors using RUSLE-SDR and PLSR approach in Konar river basin, India

Cited by 73 publications

References 72 publications

A systematic assessment of uncertainties in large-scale soil loss estimation from different representations of USLE input factors – a case study for Kenya and Uganda

A systematic assessment of uncertainties in large-scale soil loss estimation from different representations of USLE input factors – a case study for Kenya and Uganda

Changes in carbon stocks and its economic valuation under a changing land use pattern—A multitemporal study in Konar catchment, India

A Study Case on Estimation of Soil Loss and Sediment Yield in Curtin University, Malaysia

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