Use of Chosen Methods for Determination of the USLE Soil Erodibility Factor on the Example of Loess Slope

Kruk, Edyta

doi:10.12911/22998993/128861

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…Phosphorus is lost in the form of particles in water reservoirs owing to soil erosion. The universal soil loss equation is applied to evaluate the paththrough rate of total phosphorus in this study [21].…”

Section: Construction Of the Ptrffm 221 Conceptualization Of Five Major Parametersmentioning

confidence: 99%

Development and Assessment of a New Framework for Agricultural Nonpoint Source Pollution Control

Zhou

Geng

2021

Water

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The transport of agricultural nonpoint source (NPS) pollutants in water pathways is affected by various factors such as precipitation, terrain, soil erosion, surface and subsurface flows, soil texture, land management, and vegetation coverage. In this study, based on the transmission mechanism of NPS pollutants, we constructed a five-factor model for predicting the path-through rate of NPS pollutants. The five indices of the hydrological processes, namely the precipitation index (α), terrain index (β), runoff index (TI), subsurface runoff index (LI), and buffer strip retention index (RI), are integrated with the pollution source data, including the rural living, livestock and farmland data, obtained from the national pollution source census. The proposed model was applied to the headwater of the Miyun Reservoir watershed for identifying the areas with high path-through rates of agricultural NPS pollutants. The results demonstrated the following. (1) The simulation accuracy of the model is acceptable in mesoscale watersheds. The total nitrogen (TN) and total phosphorus (TP) agriculture loads were determined as 705.11 t and 3.16 t in 2014, with the relative errors of the simulations being 19.62% and 24.45%, respectively. (2) From the spatial distribution of the agricultural NPS, the TN and TP resource loads were mainly distributed among the upstream of Dage and downstream of Taishitun, as well as the towns of Bakshiying and Gaoling. The major source of TN was found to be farmland, accounting for 47.6%, followed by livestock, accounting for 37.4%. However, the path-through rates of TP were different from those of TN; rural living was the main TP source (65%). (3) The path-through rates of agricultural NPS were the highest for the towns of Wudaoying, Dage, Tuchengzi, Anchungoumen, and Huodoushan, where the path-through rate of TN ranged from 0.17 to 0.26. As for TP, it was highest in Wudaoying, Kulongshan, Dage, and Tuchengzi, with values ranging from 0.012 to 0.019. (4) A comprehensive analysis of the distribution of the NPS pollution load and the path-through rate revealed the towns of Dage, Wudaoying, and Tuchengzi as the critical source areas of agricultural NPS pollutants. Therefore, these towns should be seriously considered for effective watershed management. In addition, compared with field monitoring, the export coefficient model, and the physical-based model, the proposed five-factor model, which is based on the path-through rate and the mechanism of agricultural NPS pollutant transfer, cannot only obtain the spatial distribution characteristics of the path-through rate on a field scale but also be applicable to large-scale watersheds for estimating the path-through rates of NPS pollutants.

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Section: Construction Of the Ptrffm 221 Conceptualization Of Five Major Parametersmentioning

confidence: 99%

Development and Assessment of a New Framework for Agricultural Nonpoint Source Pollution Control

Zhou

Geng

2021

Water

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“…factor(Williams and Renard, 1983) as cited in[99] and similar equation in 638[104,105].639 K = (0.2 + 0.3 * exp(−0.0256 * S a * (1 − s i 100 ))) * ( S i c L +s i ) 0.3 * (1 −0.25c c+exp(3.72−2.95c) ) * 640 (1 − 0.7S N S N +exp(−5.51+22.9S N ) ) 641 where S a = sand (%); S i = silt (%); C L = clay (%); S N = 1 − (S a /100); C = organic 642 carbon 643 The K-factor that was tested at soil condition of the Philippine [100]: 644 K = 0.043 * pH + 0.62 OM + 0.0082 * S − 0.0062 * C * Si 645 where pH = pH of the soil; OM = organic matter (%); S = sand content (%); C = 646 clay ratio =% clay / (% sand + % silt); Si = silt content = % silt /100 647 The K-factor that was originally developed at volcanic soil of Hawaii, USA (El-648 Swaify and Dangler, 1976) as cited in[20]: 649 K = −0.03970 + 0.00311 * x 1 + 0.00043x 2 + 0.00185x 3 + 0.00258x 4 − 0.00823x 5 650 where x 1 : unstable aggregate size fraction (< 0.250mm)(%); x 2 = modified silt 651 (0.002-0.1 mm) (%) * modified sand (0.1-2 mm) (%); x 3 : % base saturation; x 4 : silt 652 fraction (0.002-0.050 mm) (%); x 5 : modified sand fraction (0.1-2 mm) (%).…”

mentioning

confidence: 99%

Estimating the Best Exponent of the Modified Universal Soil Loss Equation and Regionalizing the Modified Universal Soil Loss Equation Under Hydro-Climatic Condition of Ethiopia

Tsige¹,

Malcherek²,

Seleshi³

2022

Preprint

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Soil erosion and sediment transport are quite complex processes as they depend on physical, biological, mechanical, and chemical processes within a particular catchment. Therefore, it is highly essential to better explain engaged physical processes and means of accounting for site-specific conditions, for soil loss and sediment yield estimation. This paper mainly focuses on physical explanations behind soil erosion and common soil erosion models like Universal or Revised Universal Soil Loss Equation(USLE/RUSLE) and Modified Universal Soil Loss Equation(MUSLE). Based on the physical explanations and overall limitations, the MUSLE is selected for the application of sediment yield estimation. The main objective of this paper is to estimate the best exponent of the MUSLE, and to estimate the best combination of the exponent and topographic factor of the MUSLE under hydro-climatic conditions of Ethiopia. For the sake of calibration procedure, the main parameters of the MUSLE which directly affect soil erosion process such as cover, conservation practice, soil erodibility, and topographic factors are estimated based on the past experiences from literature and comparative approaches, whereas the other parameters which do not directly affect the erosion process or which have no any physical meaning (i.e coefficient a and exponent b) are estimated through calibration. It is verified that the best exponent of the MUSLE is 1 irrespective of the topographic factor, which results in the maximum performance of the MUSLE (i.e approximately 100\%). For the best combination of the exponent and topographic factor, the performance of the MUSLE is greater than or equal to 80\% for all four watersheds under our consideration, we expect the same for other watersheds of Ethiopia.

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“…The K-factor (Williams and Renard, 1983) as cited in [99] and similar equation in 628 [104,105]. The K-factor that was originally developed at volcanic soil of Hawaii, USA (El-…”

mentioning

confidence: 99%

Estimating the Best Exponent of the Modified Universal Soil Loss Equation and Regionalizing the Modified Universal Soil Loss Equation under Hydro-Climatic Condition of Ethiopia

Tsige¹,

Malcherek²,

Seleshi³

2022

Preprint

View full text Add to dashboard Cite

Soil erosion and sediment transport are quite complex processes as they depend on physical, biological, mechanical, and chemical processes within a particular catchment. Therefore, it is highly essential to better explain engaged physical processes and means of accounting for site-specific conditions, for soil loss and sediment yield estimation. This paper mainly focuses on physical explanations behind erosion and common erosion models like Universal or Revised Universal Soil Loss Equation(USLE/RUSLE) and Modified Universal Soil Loss Equation(MUSLE). Based on the physical explanations and overall limitations, the MUSLE is selected for the application of sediment yield estimation. To regionalize the MUSLE, the main parameters of the MUSLE which directly affect the erosion process such as cover, conservation practice, soil erodibility, and topographic factors are estimated based on the past experiences from literature and comparative approaches, whereas the other parameters which do not directly affect the erosion process or which do not have physical meaning (i.e coefficient a and exponent b ) are estimated through calibration. The best exponent (b) of the Modified Universal Soil Loss Equation is 1, which results in Nash-Sutcliffe efficiency of approximately 1. The regionalized MUSLE shows good performance for all four watersheds under our consideration and we expect the same for other watersheds of Ethiopia.

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Use of Chosen Methods for Determination of the USLE Soil Erodibility Factor on the Example of Loess Slope

Cited by 6 publications

References 6 publications

Development and Assessment of a New Framework for Agricultural Nonpoint Source Pollution Control

Development and Assessment of a New Framework for Agricultural Nonpoint Source Pollution Control

Estimating the Best Exponent of the Modified Universal Soil Loss Equation and Regionalizing the Modified Universal Soil Loss Equation Under Hydro-Climatic Condition of Ethiopia

Estimating the Best Exponent of the Modified Universal Soil Loss Equation and Regionalizing the Modified Universal Soil Loss Equation under Hydro-Climatic Condition of Ethiopia

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