Current and future assessments of soil erosion by water on the Tibetan Plateau based on RUSLE and CMIP5 climate models

Understanding the mechanism of gully erosion in the alpine region through an interpretable machine learning approach

In the alpine region, climate warming has led to the retreat of glaciers, snow cover, and permafrost. This has intensified water cycling, soil erosion, and increased the occurrence of natural disasters in the alpine region. This study investigated the Lhasa River Basin in the southern Tibetan Plateau, serving as a representative case study of a typical alpine basin, with a specific focus on gully erosion. Based on field investigations and interpretation using high-resolution satellite remote sensing images, the Random Forest (RF) algorithm was applied to evaluate gully erosion susceptibility on watershed level. The Shapley Additive Interpretation method was then used to interpret the RF model and gain deeper insights into the influencing variables of gully erosion. The results showed that the RF model achieved an area under the receiver operating characteristic (AUC) accuracy of 0.99 and 0.98 for the training and testing datasets, respectively, indicating an outstanding performance of the model. The resulting susceptibility map based on the RF model shows that areas with moderate and higher levels of gully erosion susceptibility are covering 50 % of the basin. The model interpretation results indicated that elevation, slope, permafrost, rainstorm, silt loam topsoil, human activity, stream power, and vegetation were the explaining variables with the highest importance for gully erosion occurrence. Different variables are characterized by specific thresholds promoting gully erosion such as: i) elevations higher than 4950 m, ii) slopes steeper than 13.5°, iii) extreme rainstorms longer than 11 days per year, iv) silt loam topsoil, v) presence of permafrost, vi) stream power index higher than 1.2, and vii) normalized difference vegetation index (NDVI) lower than 0.25. Our findings provide the scientific basis to improve soil erosion control in such highly vulnerable alpine area.

Evaluating rainfall erosivity on the Tibetan Plateau by integrating high spatiotemporal resolution gridded precipitation and gauge data

High-precision rainfall erosivity mapping is crucial for accurately evaluating regional soil erosion on the Tibetan Plateau (TP) under the backdrop of climate warming and humidification. Although high spatiotemporal resolution gridded precipitation data provides the foundation for rainfall erosivity mapping, the increasing spatial heterogeneity of rainfall with decreasing temporal granularity can lead to greater errors when directly computing rainfall erosivity from gridded precipitation data. In this study, a site-scale conversion coefficient was established so that rainfall erosivity calculated using hourly data can be converted to rainfall erosivity calculated using per-minute data. A revised model was established for calculating the rainfall erosivity based on high-resolution hourly precipitation data from the Third Pole gridded precipitation dataset (TPHiPr). The results revealed a notable underestimation in the original calculation results obtained using the TPHiPr, but strong correlation was observed between the two sets of results. There was a significant improvement in the Nash-Sutcliffe coefficient of efficiency (from -0.39 to 0.80) and the Percent Bias (from -63.95 % to 0.37 %) after model revision. The TPHiPr effectively depict the spatial characteristics of rainfall erosivity on the TP. It accurately reflected the rain shadow area on the northern flank of the Himalayas and the dry-hot valley in the Hengduan Mountains. It also showed high rainfall erosivity values in the tropical rainforest area on the southern flank of the eastern Himalayas. The overall trend of rainfall erosivity has increased on the TP during the period 1981 to 2020, with 65.91 % of the regions exhibiting an increasing trend and 22.25 % showing significant increases, indicating an intensified risk of water erosion. These findings suggest that the 40-year-high spatial resolution rainfall erosivity dataset can provide accurate data support for a quantitative understanding of soil erosion on the TP.

Impact of forest landscape restoration in combating soil erosion in the Lake Abaya catchment, Southern Ethiopia

As an effect of forest degradation, soil erosion is among Ethiopia's most pressing environmental challenges and a major threat to food security where it could potentially compromise the ecosystem functions and services. As the effects of soil erosion intensify, the landscape's capacity to support ecosystem functions and services is compromised. Exploring the ecological implications of soil erosion is crucial. This study investigated the soil loss and land degradation in the Lake Abaya catchment to explore forest landscape restoration (FLR) implementation as a possible countermeasure to the effects. The study used a geographic information system (GIS)-based approach of the Revised Universal Soil Loss Equation (RUSLE) to determine the potential annual soil loss and develop an erosion risk map. Results show that 13% of the catchment, which accounts for approximately 110,000 ha, is under high erosion risk of exceeding the average annual tolerable soil loss of 10 t/ha/year. Allocation of land on steep slopes to crop production is the major reason for the calculated high erosion risk in the catchment. A scenario-based analysis was implemented following the slope-based land-use allocation proposal indicated in the Rural Land Use Proclamation 456/2005 of Ethiopia. The scenario analysis resulted in a reversal erosion effect whereby an estimated 3000 t/ha/year of soil loss in the catchment. Thus, FLR activities hold great potential for minimizing soil loss and contributing to supporting functioning and providing ecosystem services. Tree-based agroforestry systems are among the key FLR measures championed in highly degraded landscapes in Ethiopia. This study helps policymakers and FLR implementors identify erosion risk areas for future FLR activities. Thereby, it contributes to achieving the country's restoration commitment.

Modeling of land use change under the recent climate projections of CMIP6: a case study of Indian river basin

The aim of the study was to investigate the land use change dynamics under CMIP6 projections using Land Change Modeler (LCM). The Global Sensitivity Analysis (GSA) techniques was applied to quantify the sensitivity of single parameter and combination of parameters. Land use and land cover (LULC) transitions of the baseline period (2006-2016) was assessed with a model performance accuracy of 80%. Receiver operating characteristic (ROC) analysis shows that the model has performed well for all the LULC classes except builtup land. Prediction under the SSP245 scenario depicts that areal extent of agricultural, forest, and snow, and glacier will decrease by the mid-century (2045). However, the grassland and barren land area will increase from the baseline period. A similar change pattern with a higher magnitude has also been predicted under SSP585 scenario. The CMIP6 forcing index considers socio-economic effects and LCM predicted an expansion in barren land which may be attributed to changes in cryosphere in the studied area.

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.

Study on soil erosion and its driving factors from the perspective of landscape in Xiushui watershed, China

Soil erosion (SE) is one of the most serious disasters in the world, which directly damage the productivity of the land and affect human well-being. How to effectively mitigate soil erosion is a challenge faced by all countries in the world. In this study, soil erosion was quantitatively assessed base on the RULSE model in an ecologically fragile area [Xiushui watershed (XSW)], and the effects of three major categories of factors (land use/cover change, landscape fragmentation and climate) on soil erosion were investigated using correlation analysis and structural equation model. The results indicated that there was no continuous increase or decrease trend on the SE of XSW with impact of rainfall, the mean values of SE were 2205.27 t/ha, 3414.25 t/ha and 3319.44 t/ha from 2000 to 2020 and the hot areas of SE were mainly distributed around the Xiushui river channel, respectively. The expansion of urbanization (the area of impervious increased from 113.12 to 252.57 km²) aggravated landscape fragmentation, and the landscape fragmented area had some overlap with the hot zone of SE. Additionally, the LUCC factor dominated by NDVI, landscape fragmentation factor and climate factor dominated by rainfall had a directly driving effect on SE, where the path coefficient of landscape fragmentation was 0.61 (P < 0.01), respectively. We also found that except increasing forest area, improving forest quality (NDVI, canopy closure, structure) deserved emphasized in SE management, and the effect of landscape fragmentation on SE also should not be ignored. Moreover, soil erosion assessment at large scales over long time periods tends to underestimate the driving force of rainfall on SE, and it is a great challenge to evaluate the effect of extreme rainfall on soil erosion at short time scales in a downscale manner. This research provides insights for ecological sustainable management and soil erosion protection policies.

Landscape pattern and ecosystem services are critical for protected areas' contributions to sustainable development goals at regional scale

Protected areas are essential for the conservation of biodiversity, natural and cultural resources, and contribute to regional and global sustainable development. However, since authorities and stakeholders concern more on the conservation targets of protected areas, how to better evaluate the protected areas' contributions to sustainable development goals (SDGs) remains generally understudied. To fill this knowledge gap, we chose the Qinghai-Tibet Plateau (QTP) as the study area, mapped the SDGs in 2010, 2015 and 2020, detecting the interactive relationships among SDGs. Then we used the landscape pattern indices and ecosystem service (ES) proxies to describe the characteristics of national nature reserves (NNRs), and explore the contributions of protected areas to SDGs using panel data models. The results showed that from 2010 to 2020, most cities of QTP improved their SDG scores to >60. The three cities with the best SDG performance improved their average scores by nearly 20 %. Among the 69 pairs correlations of SDG indicators, 13 synergies and 6 trade-offs were observed. About 65 % of the SDG indicators were significantly correlated with landscape pattern or ESs of NNRs. Carbon sequestration had a significant positive effect on 30 % of the SDG indicators, while habitat quality had a negative effect on 18 % of the SDG indicators. For the landscape pattern indices, the largest patch index had a significant positive effect on 18 % of the SDG indicators. This study highlighted that the ESs and landscape pattern could well quantify the contribution of protected areas to SDGs, which can provide essential implications for protected area management and regional sustainable development.

Role of PGPR and silver nanoparticles on the physiology of Momordica charantia L. irrigated with polluted water comprising high Fe and Mn

The current investigation designed to estimate the bioremediation potential of plant growth-promoting rhizobacteria (PGPR) and Ag-nanoparticles. Tube well and HIT water comprising Mn and Fe above recommended values were used as treatments while tap water irrigation was treated as control. The HIT water showed 24, 200, and 64.11% higher content of Na, K Ca over control. Seeds were sterilized in 95% ethanol and soaked for 3 h before sowing in 73 h old culture of Pseudomonas stutzeri (Kx574858) @ 108 cells/ml. Phytotoxic effect of Fe and Mn reduce plant biomass and suppress photosynthetic activity indicates. The carotenoids, proline, and proline activity were 366, 450, and 678% higher in tube well water with combined PGPR and Ag-nanoparticles treatments. Pseudomonas stutzeri was more effective than Ag-nanoparticles to reduce oxidative stress with higher production of carotenoids, flavonoids, proline content, and enzyme SOD and CAT activities in HIT water. It is contingent that the high Mn and Fe bearing waste water enhance PGPR bioremediation potential to reduce metal stress in plants with synergistic action of PGPR and organic matter to alleviate oxidative stresses under metal stress. The residual effect of P. stutzeri on organic matter content of the rhizosphere soil and germination rate was higher for Momordica charantia L.

Predicting soil erosion potential under CMIP6 climate change scenarios in the Chini Lake Basin, Malaysia

Climate change and soil erosion are very associated with environmental defiance which affects the life sustainability of humans. However, the potency effects of both events in tropical regions are arduous to be estimated due to atmospheric conditions and unsustainable land use management. Therefore, several models can be used to predict the impacts of distinct climate scenarios on human and environmental relationships. In this study, we aimed to predict current and future soil erosion potential in the Chini Lake Basin, Malaysia under different Climate Model Intercomparison Project-6 (CMIP6) scenarios (e.g., SSP2.6, SSP4.5, and SSP8.5). Our results found the predicted mean soil erosion values for the baseline scenario (2019-2021) was around 50.42 t/ha year. The mining areas recorded the highest soil erosion values located in the southeastern part. The high future soil erosion values (36.15 t/ha year) were obtained for SSP4.5 during 2060-2080. Whilst, the lowest values (33.30 t/ha year) were obtained for SSP2.6 during 2040-2060. According to CMIP6, the future soil erosion potential in the study area would reduce by approximately 33.9% compared to the baseline year (2019-2021). The rainfall erosivity factor majorly affected soil erosion potential in the study area. The output of the study will contribute to achieving the United Nations' 2030 Agenda for Sustainable Development.

Trends in total nitrogen concentrations in the Three Rivers Headwater Region

It is important to protect the quality of the water in the Three Rivers Headwater Region (TRHR), known as the water tower of China, to guarantee the water security in downstream areas. However, because of a lack of long-term studies that span wide geographical areas, it is difficult to understand how the water resource in the TRHR should be protected. In this paper, we report the findings from our analysis of total nitrogen (TN) concentration data from 39 river monitoring stations for the period from 2012 to 2018. The water quality status was evaluated by comparing the concentrations with the national standards and calculating exceedance ratios for surface water. Trends were calculated with ordinary linear least-squares regression and a weighted least-squares (WLS) meta-analysis method. The results showed that the annual average TN concentrations in the TRHR rivers from 2012 to 2018 ranged from 0.68 to 1.06 mg/L, and were lower than those in the downstream reaches but higher than the global average in natural river waters. For the period from 2012 to 2018, the TN concentrations showed a highly significant increase (0.03 mg/L/year) across the entire TRHR and were increasing and decreasing at 71.8 % and 28.2 % of the stations, respectively. From the trend results, we divided the study area into two zones, one with increasing TN concentrations and one with decreasing TN concentrations. It is found that environmental factors had little influence on TN concentrations in the increasing and decreasing areas, but artificial factors such as population and restoration project areas contributed to the increases in TN concentrations in the increasing area. The TRHR remains a source of clean water in China; however, the water quality should be monitored closely, and measures should be implemented to protect the resource and mitigate the disturbances caused by human activities.

A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction

Rainfall variation causes frequent unexpected disasters all over the world. Increasing rainfall intensity significantly escalates soil erosion and soil erosion related hazards. Forecasting accurate rainfall helps early detection of soil erosion vulnerability and can minimise the damages by taking appropriate measures caused by severe storms, droughts and floods. This study aims to predict soil erosion probability using the deep learning approach: long short-term memory neural network model (LSTM) and revised universal soil loss equation (RUSLE) model. Daily rainfall data were gathered from five agro-meteorological stations in the Central Highlands of Sri Lanka from 1990 to 2021 and fed into the LSTM model simulation. The LSTM model was forecasted with the time-series monthly rainfall data for a long lead time period, rainfall values for next 36 months in each station. Geo-informatics tools were used to create the rainfall erosivity map layer for the year 2024. The RUSLE model prediction indicates the average annual soil erosion over the Highlands will be 11.92 t/ha/yr. Soil erosion susceptibility map suggests around 30 % of the land area will be categorised as moderate to very-high soil erosion susceptible classes. The resulted map layer was validated using past soil erosion map layers developed for 2000, 2010 and 2019. The soil erosion susceptibility map indicates an accuracy of 0.93 with the area under the receiver operator characteristic curve (AUC-ROC), showing a satisfactory prediction performance. These findings will be helpful in policy-level decision making and researchers can further tested different deep learning models with the RUSLE model to enhance the prediction capability of soil erosion probability.

Temporal and Spatial Variation and Driving Forces of Soil Erosion on the Loess Plateau before and after the Implementation of the Grain-for-Green Project: A Case Study in the Yanhe River Basin, China

To curb soil erosion, the Grain-for-Green Project has been implemented in the Loess Plateau region, and there have been few quantitative evaluations of the impact of ecological engineering on the spatial distribution of soil erosion on the Loess Plateau. In this paper, we used ArcGIS software, the Revised Universal Soil Loss Equation (RUSLE) model and the Geographic Detector (GeoDetector) model to investigate the changes in the spatial distribution of soil erosion and driving forces before and after the implementation of the Grain-for-Green Project in Yanhe River Basin, a typical area on the Loess Plateau. After the implementation of the Grain-for-Green Project, the soil erosion showed a decreasing trend over time and from local improvement to global optimization in space. The implementation of the Grain-for-Green Project led to changes in the dominant driving force of the spatial distribution of soil erosion, with the dominant driving force changing from the slope factor to the vegetation coverage factor. The main driving force of the two-factor interaction on soil erosion spatial differentiation changed from the slope factor and other factors to the vegetation coverage and other factors. The Grain-for-Green Project mainly influenced soil erosion by increasing the vegetation cover. The effect of the Grain-for-Green Project on the spatial distribution of soil erosion had hysteresis and spatial differences, and the direct and indirect driving forces generated by ecological engineering reached more than 50% on average.

Determining the drivers and rates of soil erosion on the Loess Plateau since 1901

Attributing soil erosion to land management and climatic drivers is important for global policy development to protect soils. The Chinese Loess Plateau is one of the most eroded areas in the world. However, there has been limited assessment of historic spatial changes in erosion rates on the Loess Plateau and the major contributors driving these spatial changes. In this study, the Revised Universal Soil Loss Equation was empirically validated and employed to assess spatially distributed historical erosion rates on the Loess Plateau from 1901 to 2016. A double mass curve attribution technique was then used to investigate the impact of land management and climatic drivers on the Loess Plateau. Decadal average erosion rates and the total area with intensive erosion (>5000 t km^-2 yr^-1) experienced a sharp increase from the 1930s to 1970s, followed by a decline to an historic low between the 1980s and 2000s. Mean erosion rates for the 2000s were 54.3% less than those of the 1970s. However, a recent increase in erosion rates was observed between 2010 and 2016. Land management change was the dominant driver of historical erosion rate changes before 2010. Extensive deforestation and farming, driven by population increase, were responsible for intensifying erosion between the 1930s and 1970s, while policy-driven conservation schemes and revegetation led to reduction thereafter. However, the recent increase in erosion between 2010 and 2016 was mainly driven by extreme rainfall events, a major concern given climate change projections. Advanced erosion control strategies are therefore required as part of integrated catchment management that both maintain water supplies for human use during dry periods while reducing erosion during storm events.

Predicting soil erosion susceptibility associated with climate change scenarios in the Central Highlands of Sri Lanka

Soil erosion hazard is one of the prominent climate hazards that negatively impact countries' economies and livelihood. According to the global climate index, Sri Lanka is ranked among the first ten countries most threatened by climate change during the last three years (2018-2020). However, limited studies were conducted to simulate the impact of the soil erosion vulnerability based on climate scenarios. This study aims to assess and predict soil erosion susceptibility using climate change projected scenarios: Representative Concentration Pathways (RCP) in the Central Highlands of Sri Lanka. The potential of soil erosion susceptibility was predicted to 2040, depending on climate change scenarios, RCP 2.6 and RCP 8.5. Five models: revised universal soil loss (RUSLE), frequency ratio (FR), artificial neural networks (ANN), support vector machine (SVM) and adaptive network-based fuzzy inference system (ANFIS) were selected as widely applied for hazards assessments. Eight geo-environmental factors were selected as inputs to model the soil erosion susceptibility. Results of the five models demonstrate that soil erosion vulnerability (soil erosion rates) will increase 4%-22% compared to the current soil erosion rate (2020). The predictions indicate average soil erosion will increase to 10.50 t/ha/yr and 12.4 t/ha/yr under the RCP 2.6 and RCP 8.5 climate scenario in 2040, respectively. The ANFIS and SVM model predictions showed the highest accuracy (89%) on soil erosion susceptibility for this study area. The soil erosion susceptibility maps provide a good understanding of future soil erosion vulnerability (spatial distribution) and can be utilized to develop climate resilience.

Do Ecological Restoration Projects Improve Water-Related Ecosystem Services? Evidence from a Study in the Hengduan Mountain Region

Land use/land cover (LULC) and climate change are major driving forces that impact ecosystem services and affect human well-being directly and indirectly. Under the future interaction between LULC and climate change, the impact of different land management and climate change scenarios on water-related services is uncertain. Based on this, the CLUMondo model, which focuses on land use intensity, was used to simulate the land system under different land management scenarios in the future. By coupling the downscaled climate scenario data, this study used the InVEST and RUSLE models to estimate the annual water yield and soil erosion in 2050 in the Hengduan Mountain region and analyzed the variation differences in different sub-watersheds. The results indicated that, under the influence of LULC and climate change, when compared with the amount for 2020, the soil erosion in the Hengduan Mountain region in 2050 was reduced by 1.83, 3.40, and 2.91% under the TREND scenario, FOREST scenario, and CONSERVATION scenario, respectively, while the water yield decreased by 5.05, 5.37, and 5.21%, respectively. Moreover, the change in soil erosion in the study area was affected by precipitation and closely related to the precipitation intensity, and the impact of climate change on the water yield was significantly greater than that of LULC change. The spatial heterogeneity of soil erosion and water yield was obvious at the sub-watershed scale. In the future, soil erosion control should be strengthened in the northern regions, while water resource monitoring and early warning should be emphasized in the central-eastern regions. Our results provide scientific guidance for policy makers to formulate better LULC policies to achieve regional water and soil balance and sustainable management.

Soil erosion and sediment transport under climate change for Mera River, in Italian Alps of Valchiavenna

Erosion is a main form of soil degradation, with severe consequences on slope stability and productivity, and erosion studies are required to predict possible variations of such phenomena, also under climate change scenarios. Here we estimated distributed soil erosion within Valchiavenna valley in the Rhaetian Alps, drained by Mera river, and covering Italy, and Switzerland. We used a Dynamic-RUSLE (D-RUSLE) model, which provides spatially distributed estimates of soil erosion explicitly considering snow dynamic (accumulation/melting) and snow cover, and vegetation seasonality. The model was tuned here during 2010-2019, and validation was pursued using river turbidity data, used to assess riverine sediment transport. The model parameter R-factor for rainfall erosivity was estimated using a hydrological model Poli-Hydro, properly set up in the study area. C-factor for land cover was assessed against land cover maps, with seasonally variable Normalized Difference Vegetation Index from satellite images, to account for variable vegetation stage, and large leaf cover in summer. The K-factor related to erosion susceptibility was evaluated through soil texture and organic content. LS-factor depending on slope was assessed using a DTM. Poli-Hydro and D-RUSLE models were then used to project forward potential soil erosion under climate change scenarios until 2100. Climate series (temperature, precipitation) were generated using 4 shared socio-economic pathways (SSPs) of the Sixth Assessment Report of the IPCC, with 3 global circulation models, properly downscaled locally. We analysed expected soil erosion during 2051-2060, and 2091-2100. We found increase of potential soil erosion, with exception of the EC-Earth model for the SSP2.6. Erosion would especially increase in winter, in response to smaller snow accumulation, and larger liquid rainfall share thereby, and decrease in summer, as due to decreased precipitation. Our results suggest the need for adaptation strategies to counteract increasing soil loss in the future, and may highlight most critical areas of intervention.

Effects of Content of Soil Rock Fragments on Soil Erodibility in China

Soil erosion is serious in China—the soil in plateau and mountain areas contain a large of rock fragments, and their content and distribution have an important influence on soil erosion. However, there are still no complete results for calculating soil erodibility factor (K) that have corrected rock fragments in China. In this paper, the data available on rock fragments in the soil profile (RFP); rock fragments on the surface of the soil (RFS); and environmental factors such as elevation, terrain relief, slope, vegetation coverage (characterised by normalised difference vegetation index, NDVI), land use, precipitation, temperature, and soil type were used to explore the effects of content of soil rock fragments on calculating of K in China. The correlation analysis, typical sampling area analysis, and redundancy analysis were applied to analyse the effects of content of soil rock fragments on calculating of K and its relationship with environment factors. The results showed that (1) The rock fragments in the soil profile (RFP) increased K. The rock fragments on the surface (RFS) of the soil reduced K. The effect of both RFP and RFS reduced K. (2) The effect of rock fragments on K was most affected by elevation, followed by terrain relief, NDVI, slope, soil type, temperature, and precipitation, but had little correlation with land use. (3) The result of redundancy analysis showed elevation to be the main predominant factor of the effect of rock fragments on K. This study fully considered the effect of rock fragments on calculating of K and carried out a quantitative analysis of the factors affecting the effect of rock fragments on K, so as to provide necessary scientific basis for estimating K and evaluating soil erosion status in China more accurately.

Soil Erosion Assessment of Alpine Grassland in the Source Park of the Yellow River on the Qinghai-Tibetan Plateau, China

The source park of the Yellow River (SPYR), as a vital ecological shelter on the Qinghai-Tibetan Plateau, is suffering different degrees of degradation and desertification, resulting in soil erosion in recent decades. Therefore, studying the mechanism, influencing factors and current situation of soil erosion in the alpine grassland ecosystems of the SPYR are significant for protecting the ecological and productive functions. Based on the 137Cs element tracing technique and machine learning algorithms, five strategic variable selection algorithms based on machine learning algorithms are used to identify the minimal optimal set and analyze the main factors that influence soil erosion in the SPYR. The optimal model for estimating soil erosion in the SPYR is obtained by comparisons model outputs between the RUSLE and machine learning algorithms combined with variable selection models. We identify the spatial distribution pattern of soil erosion in the study area by the optimal model. The results indicated that: (1) A comprehensive set of variables is more objective than the RUSLE model. In terms of verification accuracy, the simulated annealing -Cubist model (R = 0.67, RMSD = 1,368 t km–2⋅a–1) simulation results represents the best while the RUSLE model (R = 0.49, RMSD = 1,769 t⋅km–2⋅a–1) goes on the worst. (2) The soil erosion is more severe in the north than the southeast of the SPYR. The average erosion modulus is 6,460.95 t⋅km–2⋅a–1 and roughly 99% of the survey region has an intensive erosion modulus (5,000–8,000 t⋅km–2⋅a–1). (3) Total erosion loss is relatively 8.45⋅108 t⋅a–1 in the SPYR, which is commonly 12.64 times greater than the allowable soil erosion loss. The economic monetization of SOC loss caused by soil erosion in the entire research area was almost $47.90 billion in 2014. These results will help provide scientific evidences not only for farmers and herdsmen but also for environmental science managers and administrators. In addition, a new ecological policy recommendation was proposed to balance grassland protection and animal husbandry economic production based on the value of soil erosion reclassification.

Proposing an ecologically viable and economically sound farming system using a matrix-based geo-informatics approach

Healthy farming systems play a vital role in improving agricultural productivity and sustainable food production. The present study aimed to propose an efficient framework to evaluate ecologically viable and economically sound farming systems using a matrix-based analytic hierarchy process (AHP) and weighted linear combination method with geo-informatics tools. The proposed framework has been developed and tested in the Central Highlands of Sri Lanka. Results reveal that more than 50% of farming systems demonstrated moderate status in terms of ecological and economic aspects. However, two vulnerable farming systems on the western slopes of the Central Highlands, named WL1a and WM1a, were identified as very poor status. These farming systems should be a top priority for restoration planning and soil conservation to prevent further deterioration. Findings indicate that a combination of ecologically viable (nine indicators) and economical sound (four indicators) criteria are a practical method to scrutinize farming systems and decision making on soil conservation and sustainable land management. In addition, this research introduces a novel approach to delineate the farming systems based on agro-ecological regions and cropping areas using geo-informatics technology. This framework and methodology can be employed to evaluate the farming systems of other parts of the country and elsewhere to identify ecologically viable and economically sound farming systems concerning soil erosion hazards. The proposed approach addresses a new dimension of the decision-making process by evaluating the farming systems relating to soil erosion hazards and suggests introducing policies on priority-based planning for conservation with low-cost strategies for sustainable land management.

Effect of multiple climate change scenarios and predicted land-cover on soil erosion: a way forward for the better land management

The ecosystem, biodiversity, and anthropological existence in the Chitral district are in danger due to the sediments and soil erosion stemming from the changes in the land-cover and climate. This research aims to practice the RUSLE model with the changes in the land-cover and climate in upcoming situations for 2030 and 2040 to evaluate soil erosion annually as per the spatial dissemination and the tendency of sediment yield. The multilayer perceptron (MLP), an artificial neural network (ANN), besides the Markov chain analysis was used to model upcoming land-cover. The Max Planck Institute model, which demonstrated a revised bias as well as downscaled grid size under the Representative Concentration Pathways (RCPs), was used for examining the future changes in the climate. The modeled land-cover showed that the areas that are primarily comprised of natural trees and shrubs were transformed largely to agriculture and build-up areas. The average rainfall in the future under different RCP situations was elevated compared to the rainfall through historical time. The continuous variability in the R and C factors affects the probable soil erosion rate and sediment yield. Under RCP8.5 for both future years of 2030 and 2040, the extreme erosion rate was assessed at around 500 and 550 t/ha/year. Additionally, under the different RCP scenarios in 2030 and 2040, the outcomes of sediment yield were more significant than the sediment yield through historical time. The results showed that lower regions of the Chitral district are at risk of amplified soil erosion and sediment yield presently, as shown by the historical data and in the future. The produced soil erosion maps using ArcGIS 10.2 can play a valuable role in managing sustainable development, conservation of the watershed of the Chitral River, and reducing soil loss. Effective measures to overcome these concerns and mitigate the possible effects need to be planned and practiced, particularly the decrease in the storage volume of the reservoirs situated on the river.

Spatial-Temporal Variability of Future Rainfall Erosivity and Its Impact on Soil Loss Risk in Kenya

Ongoing climate change poses a major threat to the soil resources of many African countries that mainly rely on an agricultural economy. While arid and semi-arid lands (ASALs) take up most of Kenya’s land mass, approximately 64% of its total croplands lie within mountainous areas with high rainfall, hence, areas highly vulnerable to water erosion. Flooding of the Great Lakes and increasing desertification of the ASALs are illustrative cases of the implications of recent precipitation dynamics in Kenya. This study applied the Revised Universal Soil Loss Equation (RUSLE) to estimate future soil erosion rates at the national level based on four Coupled Model Intercomparison Project v5 (CMIP5) models under two Representative Concentration Pathway (RCP) scenarios. Results showed the current soil loss rate to be at 4.76 t ha−1 yr−1 and projected an increase in average rainfall erosivity under the two scenarios, except for RCP-2.6 (2030s) and (2080s) for the MIROC-5 model. Future projections revealed an incremental change in rainfall erosivity from the baseline climate by a cumulative average of 39.9% and 61.1% for all scenarios by the 2030s and 2080s, respectively, while soil loss is likely to increase concomitantly by 29% and 60%, respectively. The CCCMA_CANESM2 model under the RCP 8.5 (2080s) scenario projected the highest erosion rate of 15 t ha−1 yr−1 over Kenya, which is a maximum increase of above 200%, with the Rift Valley region recording an increase of up to 100% from 7.05 to 14.66 t ha−1 yr−1. As a first countrywide future soil erosion study, this assessment provides a useful reference for preventing water erosion and improving ecosystem service security.

Grazing Exclusion Changed the Complexity and Keystone Species of Alpine Meadows on the Qinghai-Tibetan Plateau

Grazing exclusion is an effective approach to restore degraded grasslands. However, the effects of grazing exclusion on keystone species and the complexity of plant community were poorly investigated. Here, we conducted a field survey among different grazing exclusion durations, i.e., Grazing, grazing exclusion below 5 years, grazing exclusion with 5 years, grazing exclusion with 7 years, and grazing exclusion over 7 years, in alpine meadows on the central Qinghai-Tibetan Plateau (QTP). The complexity and keystone species of alpine meadows were analyzed by a network analysis. The results showed the following: (1) The species richness did not change, but aboveground biomass and the coverage of the plant community tended to increase with the extension of the grazing exclusion duration. (2) The soil nutrients, i.e., total nitrogen, total organic carbon, available nitrogen, and available potassium, remained stable, while the soil bulk density decreased under grazing exclusion conditions. (3) There was a hump-shaped change of the complexity (i.e., average connectivity and average clustering coefficient) of the plant community along with the extension of the grazing exclusion duration. Moreover, the keystone species were different among the grazing exclusion treatments. Based on the complexity of the plant community and the changes of keystone species, the optimum duration of grazing exclusion for alpine meadows should be between 5 and 7 years. Our results suggest that besides the productivity, the change of the complexity and keystone species of plant community should be considered when grazing exclusion is adopted to restore the degraded alpine meadows.

Effects of vegetation and climate on the changes of soil erosion in the Loess Plateau of China

Soil erosion is simultaneously driven by multiple factors. Identifying the dominant controlling factors and quantifying the contribution of each factor would be helpful to sustain water and soil resources. China's Loess Plateau was taken as an example area to investigate the above issues since it is the most eroded region in the world, and its soil loss is being controlled by a large-scale revegetation program. We extended the Revised Universal Soil Loss Equation (RUSLE) to large-scale erosion estimation with the aid of GIS for the period of 1986-2015, analyzed the relationship between erosion and controlling factors by correlation and wavelet coherence analysis, and quantified the contribution of each factor to erosion change by the elasticity coefficient method. Results showed that the soil erosion decreased from 1013 t·km^-2·a^-1 in 1991-1995 to 595 t·km^-2·a^-1 in 2011-2015, with a downward trend in the whole period. Spatially, most areas had soil erosion of slight intensity, and the areas with high-intensity erosion concentrated in a northeast-southwest strip with hilly-gully landscapes or densely distributed rivers. The changes in surface conditions including vegetation cover and soil conservation measures had dominant effects on the spatial heterogeneity of erosion, their contribution to erosion reduction was 119%. But rainfall erosivity increased soil erosion, and it had a contribution to erosion reduction of -28%. These results are helpful in understanding the mechanism behind the changes in soil erosion and providing information for sustainable soil and water management and vegetation restoration.

Modelling Sediment Retention Services and Soil Erosion Changes in Portugal: A Spatio-Temporal Approach

Soils provide important regulating ecosystem services and have crucial implications for human well-being and environmental conservation. However, soil degradation and particularly soil erosion jeopardize the maintenance and existence of these services. This study explores the spatio–temporal relationships of soil erosion to understand the distribution patterns of sediment retention services in mainland Portugal. Based on Corine Land Cover maps from 1990 to 2018, the InVEST Sediment Delivery Ratio (SDR) model was used to evaluate the influence of sediment dynamics for soil and water conservation. Spatial differences in the sediment retention levels were observed within the NUTS III boundaries, showing which areas are more vulnerable to soil erosion processes. Results indicated that the Region of Leiria, Douro and the coastal regions have decreased importantly in sediment retention capacity over the years. However, in most of the territory (77.52%), changes in sediment retention were little or were not important (i.e., less than 5%). The statistical validation of the model proved the consistency of the results, demonstrating that the InVEST SDR model is an appropriate tool for estimating soil loss potential by water at regional/national levels, although having its limitations. These findings can be relevant to support strategies for more efficient land-use planning regarding soil erosion mitigation practices and to stimulate further investigation at a national level on this important ecosystem service.

CMIP5 climate projections and RUSLE-based soil erosion assessment in the central part of Iran

Soil erosion (SE) and climate change are closely related to environmental challenges that influence human wellbeing. However, the potential impacts of both processes in semi-arid areas are difficult to be predicted because of atmospheric variations and non-sustainable land use management. Thus, models can be employed to estimate the potential effects of different climatic scenarios on environmental and human interactions. In this research, we present a novel study where changes in soil erosion by water in the central part of Iran under current and future climate scenarios are analyzed using the Climate Model Intercomparison Project-5 (CMIP5) under three Representative Concentration Pathway-RCP 2.6, 4.5 and 8.5 scenarios. Results showed that the estimated annual rate of SE in the study area in 2005, 2010, 2015 and 2019 averaged approximately 12.8 t ha^-1 y^-1. The rangeland areas registered the highest soil erosion values, especially in RCP2.6 and RCP8.5 for 2070 with overall values of 4.25 t ha^-1 y^-1 and 4.1 t ha^-1 y^-1, respectively. They were followed by agriculture fields with 1.31 t ha^-1 y^-1 and 1.33 t ha^-1 y^-1. The lowest results were located in the residential areas with 0.61 t ha^-1 y^-1 and 0.63 t ha^-1 y^-1 in RCP2.6 and RCP8.5 for 2070, respectively. In contrast, RCP4.5 showed that the total soil erosion could experience a decrease in rangelands by - 0.24 t ha^-1 y^-1 (2050), and - 0.18 t ha^-1 y^-1 (2070) or a slight increase in the other land uses. We conclude that this study provides new insights for policymakers and stakeholders to develop appropriate strategies to achieve sustainable land resources planning in semi-arid areas that could be affected by future and unforeseen climate change scenarios.

Grid-Scale Impact of Climate Change and Human Influence on Soil Erosion within East African Highlands (Kagera Basin)

Under global climate change and pressure from human activities, soil erosion is becoming a major concern in the quest for regional sustainable development in the Kagera basin (KB). However, few studies in this region have comprehensively considered the impact of climate change and human influence on soil erosion, and the associated processes are unclear. Based on the premise of quantifying climate change, human influence, and soil erosion, this study undertook a neighborhood analysis as the theoretical support, for a grey relation analysis which was conducted to realize the qualitative assessment of the influence of climate change and human activities on soil erosion. The results show that 90.32% of the KB saw climate change as having a greater influence on soil erosion than human influence, with the remaining area 9.68% seeing human influence having a greater impact than climate change, mainly as a result of the effect of rangeland and farmland. The average soil erosion rate of the KB shows a very low level (10.54 t ha-1 yr-1), with rangeland and farmland being the main land use/land cover (LULC) types that see soil loss, followed by forest, wetland, and built-up areas. The climate change trends of the KB show the most dramatic changes in the northeast and southwest, gradually decreasing towards the line crossing from the Birunga National Park (Rwanda) to the Keza district (Tanzania). The human influence intensity (HII) shows a high level in the KB (21.93), where it is higher in the west and lower in the east of the basin.

Examining Soil Erosion Responses to Grassland Conversation Policy in Three-River Headwaters, China

Soil erosion in the Three-River Headwaters (TRH) region has continued to intensify in recent decades due to human activities and climate change. To reverse this situation, the Chinese central government has launched the Subsidy and Incentive System for Grassland Conservation (SISGC). As a sign of the effectiveness of SISGC implementation, the dynamic changes of soil erosion can provide timely feedback for decision makers and managers. The Revised Universal Soil Loss Equation (RUSLE) model was used to simulate the spatial distribution of soil erosion before and after SISGC implementation, and Mann–Kendall (MK) test to reveal the effect of policy implementation. The results showed that: (1) the soil erosion in the TRH was mainly mild (83.83% of the total eroded area), and the average soil erosion rate and the total erosion were 13.63 t ha−1 y−1 and 323.58 × 106 t y−1 respectively before SISGC implementation; (2) SISGC implementation has curbed soil erosion. After SISGC implementation, the total soil erosion decreased by 3.80%, which showed obvious differences between grassland types; (3) The influences of SISGC were mainly because it has increased vegetation cover, further decreasing soil erosion. However, soil erosion in Alpine grassland has deteriorated, indicating direct targeted policymaking should be on the agenda. Furthermore, SISGC should be continued and grassland-type-oriented to restore the grassland ecosystem.

Effect of Freeze-Thaw Cycles on Soil Detachment Capacities of Three Loamy Soils on the Loess Plateau of China

Soil detachment is the initial phase of soil erosion and is of great significance to study in seasonal freeze-thaw regions. In order to elucidate the effects mechanism of freeze-thaw cycles on soil detachment capacity of different soils, a sandy loam, a silt loam, and a clay loam were subjected to 0, 1, 5, 10, 15, and 20 freeze-thaw cycles before they were scoured. The results revealed that with increased freeze-thaw cycles, soil bulk density and water-stable aggregates content decreased after the first few times and then kept nearly stable after about 10 cycles, especially for sandy loam. The shear strength of all soils gradually decreased as freeze-thaw cycles increased, except the values of clay loam increased subsequent to the 5th and 15th cycles. After the 20th cycle, the degree of decline of silt loam was the greatest (77.72%), followed by sandy loam (63.18%) and clay loam (39.77%). The soil organic matter of clay loam was much greater than silt loam and sandy loam and all significantly increased after freeze-thaw. Soil detachment capacity of silt loam and sandy loam was positively correlated with freeze-thaw cycle, which was contrary to findings for clay loam. The values of clay loam increased at first and then decreased during the cycles, reaching minimum values at about the 15–20th cycle. After the 20th cycle, the values of sandy loam and silt loam significantly increased 1.62 and 4.74 times over unfrozen, respectively, which was greater than clay loam (0.53 times). A nonlinear regression analysis indicated that the soil detachment capacity of silt loam could be estimated well by soil properties (R2 = 0.87, p < 0.05). This study can provide references for the study of the soil erosion mechanism in seasonal freeze-thaw regions.

Assessment of Soil Erosion Using the RUSLE Model for the Epworth District of the Harare Metropolitan Province, Zimbabwe

Urban development without adequate soil erosion control measures is becoming a major environmental concern in developing urban areas across Africa. These environmental disturbances encompass rampart Land Use and Land Cover changes (LULC) due to a high population growth rate and increased economic activities. To understand the influence of accelerated LULC changes and urban expansion as major drivers in landscape degradation in the Epworth district of the Harare Metropolitan Province, the RUSLE model was employed. This considers land use, soil, climate and topography as input parameters in the assessment of the extent and impact of these drivers on soil erosion. The Revised Universal Soil Loss Equation (RUSLE) was used to predict the potential erosion between 1984 and 2018 and soil erosion risk for the years 2000 and 2018. The mean rate of the predicted potential soil erosion was 13.2 t ha−1 yr−1 (1984–2018); areas especially vulnerable to erosion were predicted for foot slope areas with direct tributaries to the major streams and steep sloping zones. The average soil erosion risk was estimated at 1.31 t ha−1 yr−1 for the year 2000 and 1.12 t ha−1 yr−1 for 2018. While the overall potential soil loss decreased between 2000 and 2018, the potential soil loss was observed to increase tremendously in residential areas, which doubled in extent between 2000 and 2018. The findings reveal that about 40% of the Epworth district was threatened by unsustainable soil loss resulting from increased soil erosion risk within the built-up areas.

Assessment of Soil Erosion at Multiple Spatial Scales Following Land Use Changes in 1980-2017 in the Black Soil Region, (NE) China

Impact of land use and land cover change on soil erosion is still imperfectly understood, especially in northeastern China where severe soil erosion has occurred since the 1950s. It is important to identify temporal changes of soil erosion for the black soil region at different spatial scales. In the present study, potential soil erosion in northeastern China was estimated based on the Revised Universal Loss Equation by integrating satellite images, and the variability of soil erosion at different spatial scales following land use changes in 1980, 1990, 2000, 2010, and 2017 was analyzed. The regionally spatial patterns of soil loss coincided with the topography, rainfall erosivity, soil erodibility, and use patterns, and around 45% of soil loss came from arable land. Regionally, soil erosion rates increased from 1980 to 2010 and decreased from 2010 to 2017, ranging from 3.91 to 4.45 Mg ha-1 yr-1 with an average of 4.22 Mg ha-1 yr-1 in 1980-2017. Areas with a rate of soil erosion less than 1.41 Mg ha-1 yr-1 decreased from 1980 to 2010 and increased from 2010 to 2017, and the opposite changing patterns occurred in higher erosion classes. Arable land continuously increased at the expense of forest in the high-elevation and steep-slope areas from 1980 to 2010, and decreased from 2010 to 2017, resulting in increased areas with erosion rates higher than 7.05 Mg ha-1 yr-1. At a provincial scale, Liaoning Province experienced the highest soil erosion rate of 9.43 Mg ha-1 yr-1, followed by Jilin Province, the eastern Inner Mongolia Autonomous Region, and Heilongjiang Province. At a county scale, around 75% of the counties had a soil erosion rate higher than the tolerance level. The county numbers with higher erosion rate increased in 1980-2010 and decreased in 2010-2017, resulting from the sprawl and withdrawal of arable land.

Method of Estimating Degraded Forest Area: Cases from Dominant Tree Species from Guangdong and Tibet in China

Forest degradation has been considered as one of the main causes of climate change in recent years. The knowledge of estimating degraded forest areas without the application of remote sensing tools can be useful in finding solutions to resolve degradation problems through appropriate restoration methods. Using the existing knowledge through literature review and field-based primary information, we generated new knowledge by combining the information obtained from multi-criteria decision analyses with an analytic hierarchy process, and this was then used to estimate degraded forest area. Estimation involves determining forest degradation index (FDI) and degradation threshold. Continuous inventory data of permanent sample plots collected from degraded forests, consisting of various forest types divided by dominant tree species in the Guangdong province and Tibet autonomous region of China, were used for the purposes. We identified four different forest degradation levels through the determination and comprehensive evaluation of FDI. The degraded forest area with broad-leaved species as dominant tree species in the Guangdong province was estimated to be 83.3% of a total forest area of 24,037 km2. In the same province, the degraded forest area with eucalyptus as a dominant tree species was 59.5% of a total forest area of 18,665 km2. In the Tibet autonomous region, the degraded forest area with spruce as a dominant tree species was 99.1% of a total forest area of 17,614 km2, and with fir as a dominant tree species, the degraded area was 98.4% of a forest area of 12,103 km2. A sampling accuracy of forest areas with national forest inventory was about 95% in both provinces. Our study concludes that the FDI method used has a certain scientific rationality in estimating degraded forest area. The forest provides a variety of tangible and intangible goods and services for humans. Therefore, forest management should focus on the improvement of its overall productivity, which is only possible with improving forest site quality. One of the important steps to improve the quality of a forest site is to resolve its degradation issues. The presented method in this article will be useful in finding the solutions to forest degradation problems. This method, which does not need any remote sensing tool, is simple and can be easily applied for estimating any degraded forest area and developing effective forest restoration plans.

GIS Based Assessment and Design for Areas Vulnerable to Soil Disasters: Case Study of Namhyeun-dong, South Korea

Due to climate change, heavy rainfall events that trigger landslips are becoming common. This study investigates patterns in the soil’s response to major rainfall events in mountainous areas and proposes a new approach for resilient disaster prevention technology and recovery based on the effect of soil runoff. Namhyeon-dong within Seoul was selected for the case study because of its vulnerable location between two mountains. A master plan was developed to cope with the predicted soil runoff based on the annual rainfall, local land use and a series of 10-year forecasts covering the period from 2021 to 2100. A total of 22 catchments in the study area were analyzed with Arc Hydro, an ArcGIS plug-in and appropriate technologies proposed to deal with the soil runoff likely to be experienced in each catchment in an extreme disaster. The resulting model was deemed adequate to deal with disasters during the period predicted to represent the highest risk, 2051–2060. The study’s findings will help to forecast disasters from flood that could impact residential areas in mountainous regions, to predict the magnitude of potential soil disasters in individual regions and develop design guidelines for disaster prevention technology based on the predicted amount of soil runoff.

Assessment of soil erosion risk and its response to climate change in the mid-Yarlung Tsangpo River region

Soil erosion is sensitive to climate change, especially in high mountain areas. The Tibetan Plateau has experienced dramatic land surface environment changes under the impact of climate change during the last decades. In this study, we focused on the mid-Yarlung Tsangpo River (MYZ River) located in the southern part of the Tibetan Plateau. The revised universal soil loss equation (RUSLE) was applied to assess soil erosion risk. To increase its applicability to high mountain areas with longer periods of snowfall, snowmelt runoff erosivity was considered in addition to rainfall erosivity. Results revealed that soil erosion of the MYZ River region was of a moderate grade with an average soil erosion rate of 29.1 t ha^-1 year^-1 and most serious erosion in wet and cold years. Soil erosion rate in the MYZ River region showed a decreasing trend of - 1.14% year^-1 due to the precipitation, temperature, and vegetation changes from 2001 to 2015, with decreasing precipitation being the most important factor. Increasing precipitation and temperature would lead to increasing soil erosion risk in ~ 2050 based on the Coupled Model Intercomparison Project (CMIP5) and RUSLE models. It is clear that soil erosion in high mountain areas greatly depends on climate state and attentions should be paid to address soil erosion problem in the future.

Estimation of Soil Erosion in the Chaohu Lake Basin through Modified Soil Erodibility Combined with Gravel Content in the RUSLE Model

It is generally acknowledged that soil erosion has become one of the greatest global threats to the human–environment system. Although the Revised Universal Soil Loss Equation (RUSLE) has been widely used for soil erosion estimation, the algorithm for calculating soil erodibility factor (K) in this equation remains limited, particularly in the context of China, which features highly diverse soil types. In order to address the problem, a modified algorithm describing the piecewise function of gravel content and relative soil erosion was used for the first time to modify the soil erodibility factor, because it has been proven that gravel content has an important effect on soil erosion. The Chaohu Lake Basin (CLB) in East China was used as an example to assess whether our proposal can improve the accuracy of soil erodibility calculation and soil erosion estimation compared with measured data. Results show that (1) taking gravel content into account helps to improve the calculation of soil erodibility and soil erosion estimation due to its protection to topsoil; (2) the overall soil erosion in the CLB was low (1.78 Mg·ha−1·year−1) the majority of which was slight erosion (accounting for 85.6%) and no extremely severe erosion; and (3) inappropriate land use such as steep slope reclamation and excessive vegetation destruction are the main reasons for soil erosion of the CLB. Our study will contribute to decision-makers to develop soil and water conservation policies.

Modeling the Impact of Climate Change and Land Use Change Scenarios on Soil Erosion at the Minab Dam Watershed

Climate and land use change can influence susceptibility to erosion and consequently land degradation. The aim of this study was to investigate in the baseline and a future period, the land use and climate change effects on soil erosion at an important dam watershed occupying a strategic position on the narrow Strait of Hormuz. The future climate change at the study area was inferred using statistical downscaling and validated by the Canadian earth system model (CanESM2). The future land use change was also simulated using the Markov chain and artificial neural network, and the Revised Universal Soil Loss Equation was adopted to estimate soil loss under climate and land use change scenarios. Results show that rainfall erosivity (R factor) will increase under all Representative Concentration Pathway (RCP) scenarios. The highest amount of R was 40.6 MJ mm ha−1 h−1y−1 in 2030 under RPC 2.6. Future land use/land cover showed rangelands turning into agricultural lands, vegetation cover degradation and an increased soil cover among others. The change of C and R factors represented most of the increase of soil erosion and sediment production in the study area during the future period. The highest erosion during the future period was predicted to reach 14.5 t ha−1 y−1, which will generate 5.52 t ha−1 y−1 sediment. The difference between estimated and observed sediment was 1.42 t ha−1 year−1 at the baseline period. Among the soil erosion factors, soil cover (C factor) is the one that watershed managers could influence most in order to reduce soil loss and alleviate the negative effects of climate change.

The Implication of Land-Use/Land-Cover Change for the Declining Soil Erosion Risk in the Three Gorges Reservoir Region, China

The Three Gorges Reservoir Region (TGRR) in China is an ecologically and politically important region experiencing rapid land use/cover changes and prone to many environment hazards related to soil erosion. In the present study, we: (1) estimated recent changes in the risk pattern of soil erosion in the TGRR, (2) analysed how the changes in soil erosion risks could be associated with land use and land cover change, and (3) examined whether the interactions between urbanisation and natural resource management practices may exert impacts on the risks. Our results indicated a declining trend of soil erosion risk from 14.7 × 10⁶ t in 2000 to 1.10 × 10⁶ t in 2015, with the most risky areas being in the central and north TGRR. Increase in the water surface of the Yangtze River (by 61.8%, as a consequence of water level rise following the construction of the Three Gorges Dam), was found to be negatively associated with soil erosion risk. Afforestation (with measured increase in forest extent by 690 km² and improvement of NDVI by 8.2%) in the TGRR was associated with positive soil erosion risk mitigation. An interaction between urbanisation (urban extant increased by 300 km²) and vegetation diversification (decreased by 0.01) was identified, through which the effect of vegetation diversification on soil erosion risk was negative in areas having lower urbanisation rates only. Our results highlight the importance of prioritising cross-sectoral policies on soil conservation to balance the trade-offs between urbanisation and natural resource management.

Projected Rainfall Erosivity Over Central Asia Based on CMIP5 Climate Models

Climate change-induced precipitation variability is the leading cause of rainfall erosivity that leads to excessive soil losses in most countries of the world. In this paper, four global climate models (GCMs) were used to characterize the spatiotemporal prediction of rainfall erosivity and assess the effect of variations of rainfall erosivity in Central Asia. The GCMs (BCCCSM1-1, IPSLCM5BLR, MIROC5, and MPIESMLR) were statistically downscaled using the delta method under Representative Concentration Pathways (RCPs) 2.6 and 8.5 for two time periods: “Near” and “Far” future (2030s and 2070s). These GCMs data were used to estimate rainfall erosivity and its projected changes over Central Asia. WorldClim data was used as the present baseline precipitation scenario for the study area. The rainfall erosivity (R) factor of the Revised Universal Soil Loss Equation (RUSLE) was used to determine rainfall erosivity. The results show an increase in the future periods of the annual rainfall erosivity compared to the baseline. For all GCMs, with an average change in rainfall erosivity of about 5.6% (424.49 MJ mm ha−1 h−1 year−1) in 2030s and 9.6% (440.57 MJ mm ha−1 h−1 year−1) in 2070s as compared to the baseline of 402 MJ mm ha−1 h−1 year−1. The magnitude of the change varies with the GCMs, with the largest change being 26.6% (508.85 MJ mm ha−1 h−1 year−1), occurring in the MIROC-5 RCP8.5 scenario in the 2070s. Although annual rainfall erosivity shows a steady increase, IPSLCM5ALR (both RCPs and periods) shows a decrease in the average erosivity. Higher rainfall amounts were the prime causes of increasing spatial-temporal rainfall erosivity.

Overtopping Failure of a Reinforced Tailings Dam: Laboratory Investigation and Forecasting Model of Dam Failure

Overtopping failure of reinforced tailings dam may cause significant damage to theenvironment and even loss of life. In order to investigate the feature of overtopping of the reinforcedtailings dam, which has rarely appeared in the literature, the displacement, the phreatic level and theinternal stress of dam during overtopping were measured by a series of physical model tests. Thisstudy conclusively showed that, as the number of reinforcement layers increased, the anti-erosioncapacity of tailings dam was notably improved. It could be supported by the change of the dimensionof dam breach, the reduction of stress loss rate, and the rise of phreatic level from the tests. Based onthe erosion principle, a mathematical model was proposed to predict the width of the tailings dambreach, considering the number of reinforcement layers. This research provided a framework for theexploration of the overtopping erosion of reinforced tailings dam, and all presented expressions couldbe applied to predict the development of breach during overtopping.

Effect of Climate Change on Soil Erosion in a Mountainous Mediterranean Catchment (Central Pindus, Greece)

The aim of this study was to assess soil erosion changes in the mountainous catchment of the Portaikos torrent (Central Greece) under climate change. To this end, precipitation and temperature data were derived from a high-resolution (25 × 25 km) RegCM3 regional climate model for the baseline period 1974–2000 and future period 2074–2100. Additionally, three GIS layers were generated regarding land cover, geology, and slopes in the study area, whereas erosion state was recognized after field observations. Subsequently, the erosion potential model (EPM) was applied to quantify the effects of precipitation and temperature changes on soil erosion. The results showed a decrease (−21.2%) in annual precipitation (mm) and increase (+3.6 °C) in mean annual temperature until the end of the 21st century, and the above changes are likely to lead to a small decrease (−4.9%) in soil erosion potential.