Calibration and validation of the Modified Universal Soil Loss Equation for estimating sediment yield on sloping plots: A case study in Khun Satan catchment of Northern Thailand

Pongsai, S; Vogt, Dietrich Schmidt; Shrestha, Rajendra P.; Clemente, Roberto S.; Eiumnoh, Apisit

doi:10.4141/cjss09076

Cited by 24 publications

(17 citation statements)

References 33 publications

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“…Contrary to our finding, other reported very low average soil erosion rate, for instances, Medago watershed in the Northern Highlands with a rate of 9.63 t ha −1 year −1 by Brhane and Mekonen, 57 9.1 t ha −1 year −1 by Gizachew, 63 7.47 t ha −1 year −1 by Pongsai et al, 41 4.17 t ha −1 year −1 by Eshete, 64 and 4.81 t ha −1 year −1 by Tiruneh and Ayalew 7 in the highlands of Ethiopia.…”

Section: Projected Future Soil Loss Of the Study Areacontrasting

confidence: 99%

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Assessment of Current and Future Climate Change Impact on Soil Loss Rate of Agewmariam Watershed, Northern Ethiopia

Girmay¹,

Moges

Muluneh

2021

Air, Soil and Water Research

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Soil erosion is 1 of the most important environmental problems that pose serious challenges to food security and the future development prospects of Ethiopia. Climate change influences soil erosion and is critical for the planning and management of soil and water resources. This study aimed to assess the current and future climate change impact on soil loss rate for the near future (2011-2040), middle future (2041-2070), and far future (2071-2100) periods relative to the reference period (1989-2018) in the Agewmariam watershed, Northern Ethiopia. The 20 models of Coupled Model Intercomparison Project phase 5 global climate models (GCMs) under Representative Concentration Pathway (RCP) 4.5 (intermediate scenario) and 8.5 (high emissions scenario) scenarios were used for climate projection. The statistical bias correction method was used to downscale GCMs. Universal Soil Loss Equation integrated with geographic information system was used to estimate soil loss. The results showed that the current average annual soil loss rate and the annual total soil loss on the study area were found to be 25 t ha−1 year−1 and 51 403.13 tons, respectively. The soil loss has increased by 3.0%, 4.7%, and 5.2% under RCP 4.5 scenarios and 6.0%, 9.52%, and 14.32% under RCP 8.5 scenarios in the 2020s, 2050s, and 2080s, respectively, from the current soil loss rate. Thus, the soil loss rate is expected to increase on all future periods (the 2020s, 2050s, and 2080s) under both scenarios (RCP 4.5 and RCP 8.5) due to the higher erosive power of the future intense rainfall. Thus, climate change will exacerbate the existing soil erosion problem and would need for vigorous new conservation policies and investments to mitigate the negative impacts of climate change on soil loss.

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Section: Projected Future Soil Loss Of the Study Areacontrasting

confidence: 99%

“…Finally, the K factor was calculated from estimated soil properties, namely texture, organic matter, structure, and permeability. 40,41 Using the following equation (equation ( 3…”

Section: Data Sources and Analysismentioning

confidence: 99%

Assessment of Current and Future Climate Change Impact on Soil Loss Rate of Agewmariam Watershed, Northern Ethiopia

Girmay¹,

Moges

Muluneh

2021

Air, Soil and Water Research

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“…The MUSLE model is usually applied at the catchment scale; however, in some studies, it has been implemented at the plot scale (e.g., [52,53]). Q of Equation ( 9) was the runoff volume predicted by the SCS-CN method.…”

Section: Musle Equationmentioning

confidence: 99%

Modelling the Event-Based Hydrological Response of Mediterranean Forests to Prescribed Fire and Soil Mulching with Fern Using the Curve Number, Horton and USLE-Family (Universal Soil Loss Equation) Models

Carrà

Bombino

Lucas‐Borja

et al. 2021

Land

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The SCS-CN, Horton, and USLE-family models are widely used to predict and control runoff and erosion in forest ecosystems. However, in the literature there is no evidence of their use in Mediterranean forests subjected to prescribed fire and soil mulching. To fill this gap, this study evaluates the prediction capability for runoff and soil loss of the SCS-CN, Horton, MUSLE, and USLE-M models in three forests (pine, chestnut, and oak) in Southern Italy. The investigation was carried out at plot and event scales throughout one year, after a prescribed fire and post-fire soil mulching with fern. The SCS-CN and USLE-M models were accurate in predicting runoff volume and soil loss, respectively. In contrast, poor predictions of the modelled hydrological variables were provided by the models in unburned plots, and by the Horton and MUSLE models for all soil conditions. This inaccuracy may have been due to the fact that the runoff and erosion generation mechanisms were saturation-excess and rainsplash, while the Horton and MUSLE models better simulate infiltration-excess and overland flow processes, respectively. For the SCS-CN and USLE-M models, calibration was needed to obtain accurate predictions of surface runoff and soil loss; furthermore, different CNs and C factors must be input throughout the year to simulate the variability of the hydrological response of soil after fire. After calibration, two sets of CNs and C-factor values were suggested for applications of the SCS-CN and USLE-M models, after prescribed fire and fern mulching in Mediterranean forests. Once validated in a wider range of environmental contexts, these models may support land managers in controlling the hydrology of Mediterranean forests that are prone to wildfire risks.

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“…Measured soil loss data of 36 rainfall events from April to October 1992 were collected from testing and validating plots. The descriptive statistics testing includes average error (AE), relative error (RE), and standard error (SE), and can be calculated by the following equations [65][66][67]:…”

Section: Conservation Practice Factor (P-factor)mentioning

confidence: 99%

“…Likewise, the LS factor derived from the digital elevation model (DEM) may not be accurate due to inaccuracies in DEM [69]. In addition, the studied area is located in a tropical climate zone with extreme rainfall and heavy rainstorms which has more potential to erode surface soil [67]. Table 10 shows the impact of climate change (precipitation) on future net soil loss at the Mae Nam Nan Sub-catchment.…”

Section: Validation Of the Ruslementioning

confidence: 99%

Simulating the Impact of Future Land Use and Climate Change on Soil Erosion and Deposition in the Mae Nam Nan Sub-Catchment, Thailand

et al. 2013

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This paper evaluates the possible impacts of climate change and land use change and its combined effects on soil loss and net soil loss (erosion and deposition) in the Mae Nam Nan sub-catchment, Thailand. Future climate from two general circulation models (GCMs) and a regional circulation model (RCM) consisting of HadCM3, NCAR CSSM3 and PRECIS RCM ware downscaled using a delta change approach. Cellular Automata/Markov (CA_Markov) model was used to characterize future land use. Soil loss modeling using Revised Universal Soil Loss Equation (RUSLE) and sedimentation modeling in Idrisi software were employed to estimate soil loss and net soil loss under direct impact (climate change), indirect impact (land use change) and full range of impact (climate and land use change) to generate results at a 10 year interval between 2020 and 2040. Results indicate that soil erosion and deposition increase or decrease, depending on which climate and land use scenarios are considered. The potential for climate change to increase soil loss rate, soil erosion and deposition in future periods was established, whereas considerable decreases in erosion are projected when land use is increased from baseline periods. The combined climate and land use change analysis revealed that land use OPEN ACCESSSustainability 2013, 5 3245 planning could be adopted to mitigate soil erosion and deposition in the future, in conjunction with the projected direct impact of climate change.

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Calibration and validation of the Modified Universal Soil Loss Equation for estimating sediment yield on sloping plots: A case study in Khun Satan catchment of Northern Thailand

Cited by 24 publications

References 33 publications

Assessment of Current and Future Climate Change Impact on Soil Loss Rate of Agewmariam Watershed, Northern Ethiopia

Assessment of Current and Future Climate Change Impact on Soil Loss Rate of Agewmariam Watershed, Northern Ethiopia

Modelling the Event-Based Hydrological Response of Mediterranean Forests to Prescribed Fire and Soil Mulching with Fern Using the Curve Number, Horton and USLE-Family (Universal Soil Loss Equation) Models

Simulating the Impact of Future Land Use and Climate Change on Soil Erosion and Deposition in the Mae Nam Nan Sub-Catchment, Thailand

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