Average Soil Loss Research Articles

RUSLE and GIS Based Soil Erosion Modelling in a Micro-Watershed (1E1C4a2) in the Budgam district of Jammu and Kashmir

Abstract: A comprehensive methodology that integrates Revised Universal Soil Loss Equation (RUSLE) model and Geographic Information System (GIS) techniques was adopted to determine that soil erosion vulnerability of micro-watershed in J&K India. The spatial pattern of annual soil erosion rate was obtained by integrating geo-environmental variables in a raster based GIS method. GIS data layers including, rainfall erosivity (R). Soil erodibility (K), slope length and steepness (LS), cover management (C) and conservation Practice (P) factors were computed to determine their effect on average annual soil loss in the area. The resultant map of annual soil erosion shows maximum soil loss of 2.9246 t h Y with a close relation to grass land area, agricultural lands and orchards (with low LS) . The spatial erosion maps generated with RUSLE method and GIS can serve as effective inputs in deriving strategies for land planning management in the environmentally sensitive agricultural areas.

An artificial neural network emulator of the rangeland hydrology and erosion model

Machine learning (ML) is becoming an ever more important tool in hydrologic modeling. Previous studies have shown the higher prediction accuracy of those ML models over traditional process-based ones. However, there is another advantage of ML which is its lower computational demand. This is important for the applications such as hydraulic soil erosion estimation over a large area and at a finer spatial scale. Using traditional models like Rangeland Hydrology and Erosion Model (RHEM) requires too much computation time and resources. In this study, we designed an Artificial Neural Network that is able to recreate the RHEM outputs (annual average runoff, soil loss, and sediment yield and not the daily storm event-based values) with high accuracy (Nash-Sutcliffe Efficiency ≈ 1.0) and a very low computational time (13 billion times faster on average using a GPU). We ran the RHEM for more than a million synthetic scenarios and train the Emulator with them. We also, fine-tuned the trained Emulator with the RHEM runs of the real-world scenarios (more than 32,000) so the Emulator remains comprehensive while it works specifically accurately for the real-world cases. We also showed that the sensitivity of the Emulator to the input variables is similar to the RHEM and it can effectively capture the changes in the RHEM outputs when an input variable varies. Finally, the dynamic prediction behavior of the Emulator is statistically similar to the RHEM.

Exploring biochar addition impacts on soil erosion under natural rainfall: A study based on four years of field observations on the Loess Plateau

Biochar’s potential impact on soil erosion has gained attention, but remained inconsistent, particularly lacks the results from continuous field monitoring under diverse rainfall conditions. This study aimed to explore the long-term variation in soil erosion induced by biochar addition under distinct natural rainfalls. Six runoff plots, treated with different amounts of biochar derived from apple tree branches (0, 1%, 2.5%, 4%, 5.5% and 7%), were established. Over a four-year period from 2017 to 2020, runoff and soil loss from these plots were continuously monitored and analyzed. Among 215 recorded rainfall events, 27 events resulted in runoff and sediment production were classified into three rainfall patterns (RI, RII and RIII) using K-means clustering. RIII characterized by high mean rainfall depth (138.80 mm), duration (3443 min), and erosivity (879 MJ.mm (ha. h)−1), but infrequent occurrence, followed by RII and RI with moderate and low mean depth, duration and erosivity, but higher frequency. Biochar addition, relative to control, resulted in an average annual runoff reduction, ranging from 9% to 36%, with a mean reduction of 28%, and reduced runoff for each rainfall, ranging from 4% to 28%, with a mean reduction of 12%. Similarly, biochar addition also reduced average annual soil loss, ranging from 43% to 79%, with a mean reduction of 61%, and reduced soil loss for each rainfall, ranging from 29% to 74%, with a mean reduction of 52%. The reduction of runoff and soil loss was positively correlated with biochar addition rates. Notably, the soil loss reduction was exceeded that of runoff. Soil erosion was negatively linked to surface area of soil particles (p < 0.05), total carbon, and total organic carbon (p < 0.01), but positively correlated with sand content (p < 0.01) and bulk density (BD) (p < 0.05) under biochar addition. Comparison of the mean reduction of runoff and soil loss among three rainfall patterns, the order was as follows: RII>RI>RIII. The optimal controlling of soil erosion was observed in 7% biochar addition under RII. Runoff-soil loss relationships well-fitted by power equation, showed soil incorporated with biochar treatments exhibited heightened susceptibility to erosion under RIII with high intensity compared to RII and RI with moderate or low rainfall intensity. Thus, the findings indicate biochar is a potential soil amendment to improve a degraded or degrading sloping farmland, however, the rainfall characteristics need to be considered when it is applied to the sloping farmland prone to erosion.

Riscos potenciais de erosão do solo no centro-norte do Vietnã usando sensoriamento remoto e GIS

ABSTRACT Unsustainable exploitation activities (UEAs), combined with the increasing impacts of global climate change are the key causes that lead to soil erosion in the North-Central Vietnam. Mountainous areas in the North-Central Vietnam commonly have steep slopes and sandy clay in the surface soil layer, which contribute to enhancing the soil erosion, resulting in a serious loss of life and property. This study investigates the land cover change (LCC) across the Thanh Chuong district by combining Remote Sensing Technique (RST) data with Geographic Information System (GIS) and further, establishing erosion risk hazard maps based on the RUSLE model simulation. To achieve these objectives, Sentinel and Landsat satellite images from the period 2010_2021 were acquired. It was verified that the forest area gradually decreased from 2010_2021, and the average annual soil loss was approximately 25 t per year. The amount of erosion that led to a soil loss of up to 18% of the total land area is related to weather conditions, terrain features, and the soil texture. The decline in the vegetation cover is expected to be the main cause of increasing trends in erosion and soil loss.

An advanced global soil erodibility (K) assessment including the effects of saturated hydraulic conductivity

USLE-type models are widely used to estimate average annual soil loss at large scales, with the erodibility factor (K) being the sole component that accounts for soil's susceptibility to erosion. The factor includes the information on permeability in the equation, however, most definitions of the K factor consider the soil hydrological influence only very crudely and indirectly. Thus, the direct impact of surface runoff infiltration and drainage on soil erosion is largely neglected. The objective of this study is to incorporate soil hydraulic properties in the K factor map by merging available global-scale measured saturated hydraulic conductivity (Ksat) data with soil texture and organic carbon information into a modified K factor. To achieve this, the Wischmeier and Smith (1978) soil texture- and permeability-based equation (KWischmeier factor) was modified to include Ksat, called Kksat factor. Using the Random Forest machine learning algorithm, the KWischmeier factor and the Kksat factor were each correlated with soil and remote sensing covariates for spatial extrapolation of two independent K factor maps at 1 km spatial resolution. We noted a clear decrease in the mean value of the Kksat factor (0.023 t ha h ha−1 MJ−1 mm−1) compared to the mean value of the KWischmeier factor (0.027 t ha h ha−1 MJ−1 mm−1). The reduction in Kksat factor values was most pronounced in tropical regions reflecting the difference in soil properties (e.g., clay and iron), whereas other climate regions showed relatively minor changes in comparison to the KWischmeier factor as well as to the recent global modeling of Borrelli et al. (2017) (KGloSEM factor maps). As many studies discussed an overall overestimation of (R)USLE based erosion rates compared to measurements, this reduction in the K factor might improve modeled erosion rates in the right direction. The Kksat marks an important initial step in integrating hydraulic properties into the K factor of USLE-type models and can prove their significance in future studies.

Mapping the vulnerability of irrigation sand traps in a tropical volcanic basin, Indonesia

Sand traps in irrigation networks are typically used in mitigating canal sedimentation. In irrigation networks located in basins of high sediment yield due to the presence of volcanoes, it is essential to assess the vulnerability of sand traps. Using sediment yield at irrigation scheme inlets, sand trap vulnerability can be evaluated. This study aims to understand the vulnerability of irrigation sand traps throughout the Progo–Opak–Serang (POS) Volcanic River Basin, Indonesia, via mapping the sediment yield distributions in the basin. We employed the Revised Universal Soil Loss Equation to estimate soil loss, where the results show that the average soil loss in the POS River Basin is 179.69 tons/ha/year that falls under the category of moderate erosion potential, while the average sediment yield for the whole basin is 51.04 tons/ha/year. Parts of the basin with high yields of more than 180 tons/ha/year were mostly found along the volcanic mountains such as Sindoro, Sumbing, Merapi, Merbabu, and Telomoyo, and the Menoreh Hills. The model demonstrated relatively high performance with R2, NSE, RMSE, and MAE of 0.89, 0.82, 0.14, and 0.11, respectively. Within the POS Basin, Badran, Kalibawang, and Blawong are the three most vulnerable irrigation sand traps, with sediment yield values of 252.83, 178.92, and 63.49 tons/ha/year, respectively; they are all located in sub-watershed outlets. The vulnerability assessment conducted in this study can be used for the decision support system to prioritize irrigation sand traps towards a more effective irrigation system development.

Assessing the importance of hypsometry for catchment soil erosion: A case study of the Yanze watershed, Rwanda

Implementing a watershed erosion control programme requires resource-intensive and time-consuming preliminary studies to prioritize such interventions and to focus on those sub-catchments where they are most likely to yield the most effective results.&#x0D; In this study, we explore and document the effectiveness of using hypsometric analysis as a method to prioritize erosion control measures and apply it to the Yanze watershed located in central Rwanda.&#x0D; Based on a 30m-resolution DEM of the watershed and using ArcGIS and R software, we made estimates of hypsometric integral values and calculated soil loss estimates through RUSLE modelling and by using data from different sources, namely the Rwanda Meteorological Agency (rainfall data), ISRIC (soil data), and Sentinel-2 images (land cover maps).&#x0D; The hypsometric integral values of the Yanze sub-catchments were high, ranging from 0.5 to 0.936. This, combined with the overall convex upward hypsometric curves, indicates that the Yanze watershed is still at a youthful stage in its erosional cycle.&#x0D; The results of the RUSLE model showed that the average potential soil loss in the Yanze watershed is 55.63 tonnes.ha-1.year-1, which is comparable to the national average estimated at 62 tonnes.ha-1.year-1.&#x0D; The correlation analysis that we conducted between the hypsometric integral values of the Yanze sub-catchments and their respective mean soil loss values revealed no correlation between the two variables. From the results of this study, we conclude that in watersheds where lithology affects soil erosion significantly, morphology can indeed indicate the potential for erosion. However, we further concluded that future studies to characterize erosion potential using morphometry should employ additional morphometric parameters in the regression model.

Identification of potential zones on the estimation of direct runoff and soil erosion for an ungauged watershed based on remote sensing and GIS techniques

An investigation of soil and water resources is essential to determine the future scenario of water management and water resources to attain food and water security. The improper management of watersheds results in a huge amount of sediment loss and surface runoff. Therefore, the present study was carried out to estimate the surface runoff and soil erosion using the Soil Conservation Service Curve Number (SCS-CN) method and RUSLE approach, respectively. These have been estimated using geospatial technologies for the ungauged Mandri river watershed from the Kanker district of Chhattisgarh State in India. The runoff potential zones, which are defined by the area's impermeable surfaces for a given quantity of precipitation were identified based on curve numbers at the sub-watershed levels. The land use data were collected from LISS IV images of 2009. The results showed that the average volume of runoff generated throughout the 16 years (2000-2015) was 14.37 million cubic meters (mM3). While average annual soil loss was found to be 17.23 tons/ha/year. Most of the eroded area was found to be around the major stream in a drainage system of Mandri River and on higher slopes of the terrain in the watershed. This study revealed that surface runoff and soil erosion are primary issues, which adversely affected the soil and water resources in this watershed. Therefore, suitable water harvesting sites and structures can be constructed based on the potential runoff zone and severity of soil erosion to conserve the soil and water in the watershed.

Estimation of Soil Loss using RUSLE, GIS, and Remote Sensing: A Case Study of Sangli District, Maharashtra

Accurate estimation of soil loss is essential for watershed managers and planners to identify the priority areas for soil and water conservation measures. This study was undertaken to estimate the average annual soil loss in the study area of Sangli district, Maharashtra by using the Revised Universal Soil Loss Equation (RUSLE) in conjunction with Geographic Information System (GIS) and Remote Sensing (RS) data. The five potential factors of RUSLE impacting soil erosion were estimated through remote sensing data, enabling a comprehensive and informed assessment of soil erosion. The results of the analysis revealed that the average annual soil loss from the study area varied between 0 t.ha-1.yr-1 and 202.10 t.ha-1.yr-1. Higher annual soil loss was estimated in the western part of the study area, which ranged from 15 t.ha-1.yr-1 to 25 t.ha-1.yr-1 as compared to other parts of the study area. The Sangali district, in general, can be categorised as a low erosion potential district (0-5 t.ha-1.yr-1). The generated information can be utilised for the implementation of soil and water management and conservation measures in the western part of Sangli district, where there is a large area under the forest and agricultural land.

Spatiotemporal dynamics of soil erosion response to land use land cover dynamics and climate variability in Maybar watershed, Awash basin, Ethiopia

ABSTRACT Soil erosion is a global problem that threatens sustainability and food security by washing away fertile soil, nutrients, and organic matter. Land use changes, land degradation, and soil erosion are increasing, resulting in annual losses of fertile soil and declining crop yields. The objective of this study was to examine how changes in land use and land cover affected soil erosion dynamics and determine which areas should receive priority for soil and water conservation interventions. The study employs remote sensing and Geographic information system techniques to assess the impact of land use and land cover change on soil erosion between 2004 and 2020. The RUSLE model estimates soil erosion rates. Mean annual rainfall, soil data, 2 m DEM, and satellite images are used to input data into the model to determine soil erosion. The study identifies six land use and cover types and shows that significant changes have occurred in bare land and shrubland, decreasing by 25.48% and 6.17%, respectively, from 2004–2020. On the other hand, forest land increased by 20.19%, and cultivated land increased by 3.52%. Additionally, the study evaluated the response of soil erosion to land use and land cover changes, showed that bare land, cultivated land, and grazing land have the highest annual loss. In contrast, forest land, shrubland, and water bodies have the lowest. The analysis of soil erosion risk indicated a decline in the mean annual soil loss rate at the watershed scale, with rates decreasing from 12.8t ha−1 yr-1 in 2004 to 3.21t ha-1 yr-1 in 2020. The watershed is classified into five severity classes, and the study area was divided into five priority categories based on their average annual soil losses. The study emphasized that prioritizing interventions for watershed types with high erosion risks is crucial for reducing on-site soil loss and off-site effects together with conserving water resources. The findings support the importance of long-term watershed management in reducing soil erosion. To effectively reduce soil erosion and improve environmental quality, it is recommended to encourage afforestation and reforestation, develop sustainable land use policies, strengthen soil conservation measures, and improve monitoring and evaluation of land use changes.

Identification of soil erosion-prone areas and annual average soil loss of Siddheswari River basin, Eastern India

Identification of soil erosion-prone areas and annual average soil loss of Siddheswari River basin, Eastern India

Hydric erosion rates in Raouz watershed, Morocco: RUSLE, GIS, and remote sensing

Water erosion significantly contributes to global soil degradation, inducing detrimental consequences. One of the most adverse impacts is the sedimentation buildup in reservoirs, which results in reduced water storage capacity and damage to the agro-pedological environment. In Morocco's western Rif region, the Raouz watershed faces soil losses annually that disrupt the basin's ecosystems and impede socio-economic progress. These erosional losses manifest in the Moulay El Hassan Ben Mehdi (MHBM) Dam's sedimentation, causing substantial financial repercussions of several million dirhams/year. To comprehensively assess and quantify this soil degradation phenomenon, this study evaluates soil losses employing the revised universal soil loss equations (RUSLE), strategically integrating geographic information system (GIS) layers that encapsulate pertinent environmental variables such as climate, topography, soil composition, and land use. By utilizing these empirical equations, the identification of predominant factors responsible for soil water erosion within the Raouz watershed is achieved. The outcomes of this study methodology demonstrate an average soil loss estimate of 16.53 t/ha/yr through RUSLE application. Furthermore, comparing soil loss values from this study using RUSLE to those derived from prior research using the Caesium-137 method reveals relatively minor disparities.

Soil erosion and degradation assessment integrating multi-parametric methods of RUSLE model, RS, and GIS in the Shaqlawa agricultural area, Kurdistan Region, Iraq.

This study evaluated soil erosion rates in the Shaqlawa district using the Geographical Information System (GIS)-based Revised Universal Soil Loss Equation (RUSLE) model. The primary objective was to identify areas within the district that are prone to significant erosion and develop appropriate soil conservation schemes accordingly. A combination of primary and secondary data from diverse sources was utilized to achieve this objective. The GIS-based RUSLE model used variables like soil erodibility (K), soil coverage (C), topographic effect (LS), rainfall runoff (R), and erosion control practices (P) to estimate the amount of soil that had been washed away in the study area. The study provided valuable information that can be used to plan and administer soil protection in the Shaqlawa district. The average yearly soil loss in the study region is estimated to be 65.66 t ha-1 year-1. The district is experiencing significant soil erosion rates, which may have detrimental effects on agricultural productivity, water quality, and environmental health. The analysis revealed that Balisan, Hiran, Shaqlawa center, and part of the Salahaddin subdistrict are the most affected areas, with high values of LS and R factors contributing to significant soil erosion rates. These results underscore the importance of soil protection and management efforts in the Shaqlawa district. The combination of the RUSLE with GIS and remote sensing techniques has been recognized as an essential, cost-effective, and highly accurate approach for estimating soil erosion.

NITIAL RESULTS OF COMPARATIVE ASSESSMENT OF SOIL EROSION INTENSITY USING THE WIntErO MODEL: A CASE STUDY OF POLIMLJE AND SHIRINDAREH DRAINAGE BASINS

This work aims to determine the current state of sediment production and propose land use measures that will affect the reducing the intensity of soil erosion for the areas of the Polimlje drainage basin on the territories of Montenegro and Serbia, and the small Shirindareh sub-basin of Iran. The approach is based on field and laboratory methods, which are processed by Web-based Intensity of Erosion and Outflow (WIntErO)model used to calculate erosion intensity. By using the computer-graphical method of the "WIntErO" software, in the study of erosion intensity, surface values (watershed surface, surface between isohypsies, etc.) and length, i.e. deviations from the map (length of the main watercourse, length of the watershed line, etc.) is processed very precisely, which was not the case before when using mechanical instruments, planimeters and curvimeters. The new WIntErO model is an integrated computer-graphic program package of the third-generation method based on the earlier generations of the modelling tools "River basins", and IntErO and calculates the amount of sediment, the intensity of soil erosion, as well as the maximum runoff from the basin, according to the EPM model of Gavrilović.During the procedure, an accuracy assessment is conducted with measurements of reservoir sediment deposition. These measurements were performed in April 2017 using professional hydrographic recording equipment, following the same methodology as in 2012. The measurement of point locations was conducted using a GPS receiver and a Trimble R6 base station. The reservoir's depth was measured using a single-frequency portable echo sounder, specifically the Odom Hydro Track. The initial assessment shows fairly acceptable results of the implemented WIntErO modelling for both study areas. Z coefficient values ranging from 0.01 to 1.00 for the observed period indicate that the river basins delineate areas with varying levels of susceptibility to water erosion processes—ranging from very low to moderate and high risk—within the studied drainage basins. Based on the analysis, we found that the average erosion intensity in Polimlje is 331.78 m³ km⁻² year⁻¹ per square kilometer. For the Shirindareh sub-basins, the average actual soil losses per square kilometer are 201 m³ year⁻¹ km⁻². Given these findings, it is evident that these basins require prompt implementation of soil conservation measures.

Modeling Soil Loss and Its Association to Site Physical Characteristics in Majang Watershed, Baro Abobo River Basin

One of Ethiopia’s threatening environmental problems is soil erosion. Minimizing soil erosion to the tolerable level needs evidence-based sustainable land management. This study aimed to investigate the soil erosion rate and its relation with site physical characteristics (slope, land use/cover, and soil properties) using the GIS-based RUSLE model in the Majang watershed. Climate data, DEM, Landsat image, and soil map were used to model soil erosion by applying the RUSLE model. The results showed that cultivated land is the most vulnerable type of land use to soil loss (35.1·t ha−1 year−1) followed by grasslands (19.6·t ha−1 year−1) in the watershed. Conversely, forest land is the least vulnerable land use and generates a very low amount of soil loss (12·t ha−1 year−1). Similarly, the average soil loss of the watershed is strongly related to the slope gradient. The model result indicated that a high amount of soil loss was observed in very steep slope land (62.8·t ha−1 year−1) but lower in the gentle slope (13.6·t ha−1 year−1). Soil types and their characteristics have greater roles in generating a high amount of soil loss. Acrisols, which lack organic matter content, have experienced a high soil loss rate (20·t ha−1 year−1). This implies soil loss is highly associated with site-specific characteristics such as slope gradient, land cover/use, and soil condition. The greatest share of the soil loss was estimated from steep slopes, bare and cultivated land, and less fertile soils. Therefore, building an integrated participatory approach needs immediate attention, and all farmers and stakeholders need to focus on on-site prioritization and invest more in conserving vulnerable areas.

Assessment of soil loss susceptibility in geodetic landmarks for the past and future climate change scenarios

Geodetic landmarks (GLs) are essential for obtaining precise height, horizontal coordinates, and the Earth's gravity field. This study aims to assess the soil susceptibility of GLs for past, present, and future scenarios, considering the projected anthropogenic effect from Coupled Model Intercomparison Project, Version 6 (CMIP6). Therefore, the soil loss estimations were made for all the GLs in the southern region of Santa Catarina state in Brazil using the Revised Soil Loss Equation (RUSLE). Our results showed average soil loss decreasing from 1985 to 2020. There was an increase in GLs in the null class (soil loss = 0 t/ha/year), mainly caused by urban growth. A decrease occurred in the low (0 t/ha/year < soil loss ≤10 t/ha/year), very severe (soil loss >200 t/ha/year), severe (50 t/ha/year < soil loss ≤200 t/ha/year), and moderate classes (10 t/ha/year < soil loss ≤50 t/ha/year). In addition, most future scenarios projected an increase in soil loss susceptibility, which also increased the GLs' susceptibility to soil loss from 2020 to 2100, albeit with lower values than the historical series. The soil loss remained stable in ssp126, slightly increased in ssp245 and ssp370, and increased in ssp585. The future scenarios only take into account changes in rainfall. Thus, the land cover change forecast would also be necessary for better analysis for future studies. Therefore, climate simulations can be used to understand the effects of climate change on soil erosion to support decision-making regarding GLs maintenance and the construction of new ones.

Assessment of carbon loss related to Soil loss in the tropical watershed of Maharashtra, India

Soil carbon pools have a significant impact on the global carbon cycle and soil erosion caused by natural or human activities is one of the main drivers of changes in soil carbon sequestration. The present study aimed to estimate the carbon loss associated with soil loss in the watershed using remote sensing and GIS techniques. The study was carried out at the Central MPKV Campus Watershed, Rahuri, located in the rain shadow region of the Maharashtra state, India. The soil loss from the watershed was estimated using USLE model. The soil loss and carbon loss from the watershed were estimated before the implementation of conservation measures and after the implementation of conservation measures. It was found that the average annual soil loss from the watershed before and after conservation measures was 18.68 t/ha/yr and 9.41 t/ha/yr, respectively. Carbon loss was determined by soil loss rate, organic carbon content and the carbon enrichment ratio. The carbon loss from the watershed before and after conservation measures was 348.71 kgC/ha/yr and 205.52 kgC/ha/yr. The findings revealed that soil and carbon erosion was very severe on steep slopes without conservation measures and with limited vegetation cover. It was found that by reducing the carbon loss associated with soil loss, soil conservation measures not only aid in the conservation of natural resources but also serve as a climate change mitigation measure.

Risk analysis of rainfall erosion on upland adjacent to riparian zone of the Three Gorges Reservoir

The Three Gorges Reservoir (TGR) is an artificial riparian ecosystem and influenced on its soil erosion. However, the probability and magnitude of the erosion on upland adjacent to riparian zone and its impacts on the TGR are still unclear. In this research, we determined the relationship between minute-scale rainfall erosivity and soil erosion from three replicated upland bare plots (5 m × 20 m for each) in Zhong County, southwest of China, from 2017 to 2019 (short-term), and estimated the rainfall erosion based on the daily-scale rainfall erosivity from 1992 to 2015 (long-term). We performed generalized extreme value (GEV) modeling to quantify three risk elements (scenario, probability-magnitude relation, consequences) and predicted the erosion risks. The results indicated that, over the short-term, wet season increased the average runoff depth and soil loss by a factor of 1.6 (p < 0.01) and 1.4 (p < 0.01), respectively. Low and rare rainfall erosivity (5 years < return period ≤ 20 years) caused the erosion event with relative low occurrence probability (return period > 5 years) to dominantly contribute the total erosion (>65%) in dry season. Common and normal rainfall erosivity (return period ≤ 5 years) caused the common erosion events (return period ≤ 2 years) to contribute nearly 50% of the total erosion in wet season. Over the long-term, extreme rainfall erosivity (1621 MJ·mm·ha−1·h−1) in dry season was predicted to be more effective on upland erosion, but a uniform pattern of erosion contribution of incremental rainfall return periods was estimated in wet season. Our findings improve the understanding of the uncertainty of upland-surface hydrologic processes in the TGR, and could provide useful risk management or ecological restoration strategies for the soil and water conservationists.

Calibration and validation of hillslope runoff and soil loss outputs from the Water Erosion Prediction Project model in Minnesota agricultural watersheds

AbstractThere is growing interest in studying the impact of alternative agricultural management practices on runoff and soil loss under future climate change scenarios. In order to address this interest, it is important to demonstrate that runoff and soil loss can be accurately simulated under existing climates based on comparisons between modeled and experimental results. This study calibrates and validates the Water Erosion Prediction Project (WEPP) model to quantify the accuracy of predicting growing season runoff and soil erosion in agricultural hillslopes based on comparisons with experimental data from five Minnesota hydrologic unit code 12 watersheds. In order to accurately predict runoff and soil erosion in each watershed, the baseline effective hydraulic conductivity (Kbe), interrill and rill erodibility (EIR and ER), and monthly precipitation standard deviations (Pstdev) were calibrated in WEPP using observed runoff and total suspended solids data from five Minnesota Discovery Farms field sites. Before calibration, Nash–Sutcliffe model efficiency (NSE) and percent bias (PBIAS) values for predicted versus measured monthly average total runoff (Ravg‐T), runoff ratios (RRT), and total soil loss were generally not in acceptable ranges. After calibration, the NSE values showed very good fits between measured and predicted monthly Ravg‐T (0.64–0.98), RRT (0.66–0.93), and soil loss (0.58–0.80). PBIAS values were also within acceptable ranges for Ravg‐T and RRT (±25%) and soil loss (±55%), except for RRT at site BE1. NSE and PBIAS values during validation were within acceptable ranges, except for RRT at site BE1. These findings suggest that the WEPP hillslopes calibrated in this study are sufficiently robust to accurately predict monthly runoff and soil erosion in Minnesota agricultural fields during the growing season.

Assessing the Impact of Engineering Measures and Vegetation Restoration on Soil Erosion: A Case Study in Osmancık, Türkiye

The prioritization of preventing soil loss in Türkiye’s watersheds has become a pressing concern for planners. Numerous mathematical models are presently utilized on a global scale for soil erosion prediction. One such model is the Revised Universal Soil Loss Equation (RUSLE), commonly used to estimate average soil loss. Recently, there has been an increased emphasis on utilizing USLE/RUSLE in conjunction with Geographic Information System (GIS) technology, enabling grid-based analysis for predicting soil erosion and facilitating control measures. This study evaluates the effectiveness of erosion and flood control initiatives started in the 1970s within the Emine Creek watershed and its tributary rivers in Osmancık, Türkiye, utilizing RUSLE/GIS technologies. Two distinct maps illustrating the potential erosion risks were produced for two distinct temporal intervals, and a comparative analysis was conducted to evaluate the alterations that transpired. The implementation of various measures such as terracing, afforestation, and rehabilitation in the watershed led to a notable prediction of decreasing soil loss in the watershed. From 1970 to 2020, the rate of estimated soil loss was reduced from 417 to 256 metric tons per hectare per year, demonstrating the effectiveness of soil conservation measures in a semi-arid and weakly vegetated area at reducing potential soil loss.