Soil Loss Rates Research Articles

Prioritizing sub-watersheds based on soil-erosion potential by integrating RUSLE and game-theory algorithms

Determining areas that are experiencing severe soil erosion and prioritizing these areas for management and conservation planning is a critical contemporary need. The purpose of this research is to use two different approaches - the experimental model of the global soil loss equation (RUSLE) and three game theory algorithms (Fallback, Borda and Condorcet) - to prioritize areas with high erosion in Talar watershed, Iran. Five factors were employed to operationalize the RUSLE model – rainfall erosivity (R), soil erodibility (K), slope length (LS), cover (C), and support practice factor (P). The average of each factor in each sub-watershed provided inputs for the game-theory algorithms. The results indicated that the average soil loss in the entire watershed is 29 t ha−1 yr−1. The RUSLE model revealed that sub-watersheds 9, 11, 12, and 13 have the highest erosion rates in the study area. The three game-theory algorithms agreed that sub-watersheds 1, 2, 3, 4 and 5 possessed the highest erosion potential in the region. Anthropogenic activities like deforestation, farming, and tillage relative to certain slope directions and the development of rainfed agriculture are the main reasons for high soil loss rates in the study area. The conclusions drawn in this study can be helpful for prioritizing areas prone to critical levels of erosion and can help to determine appropriate erosion prevention and loss mitigation methods in other watersheds.

A new method for calculating C factor when projecting future soil loss using the Revised Universal soil loss equation (RUSLE) in semi-arid environments

Understanding how reservoir sedimentation rates may evolve due to climate change is essential for projecting future changes in reservoir water storage capacity. The Revised Universal Soil Loss Equation (RUSLE) is commonly used to assess regional soil loss rates because of its suitability for working with the coarse temporal and spatial scale of climate model outputs. Application of the RUSLE for projecting future erosion rates is constrained by the relatively limited number of classes used in projected changes of native vegetation: this typically reduces a wide range of variation in RUSLE cover (C) factors to a single value per class, and these classes may be insensitive to changes in species composition and canopy density with time and space. This paper develops a low-cost, efficient, and objective approach to projecting future C values directly based on widely available Landsat data, downscaled climate model data, and a limited number of terrain variables. Observed C is estimated from Landsat-derived Normalized Difference Vegetation Index (NDVI) and Modified Soil-Adjusted Vegetation Index (MSAVI) values using standard methods. A linear relationship is established between the observed C values and average annual antecedent temperature, average annual antecedent precipitation, latitude, and percent sand (adjusted R2 = 0.75604, 0.7543, and RMSE = 0.09448, 0.07043, respectively). A proof of principal study demonstrates that, unlike when land cover classes are used to estimates C, a RUSLE model driven by the regression-based C provides a correct order-of-magnitude estimate of observed long-term erosion and sediment yield rates (e.g., an estimate of 2.9–3.2 t ha−1 y-1 is obtained where observed rates range from 1.2 to 3.9 t ha−1 y-1 for the same time period, and up to 8 t ha−1 y-1 historically, while standard formulations of RUSLE yield 16.1 and 0.3 t ha−1 y-1). Using climate model data to estimate both precipitation intensity (R factor) and C in the RUSLE model results in basin wide projected end 21st century soil erosion rates for relative concentration pathway 8.5 of 5.5–7.5 t ha−1 y-1, consistent with expectations for accelerated soil loss in the study area as climate change reduces soil moisture while increasing precipitation intensity. The same model driven by land cover class estimates of C produces soil loss rates an order of magnitude smaller (0.3–0.4 t ha−1 y-1), consistent with a much more heavily vegetated landscape than could be supported under the increasingly arid projected climate.

Assessment of the effects of land use/cover changes on soil loss and sediment export in the Tul Watershed, Northwest Ethiopia using the RUSLE and InVEST models

ABSTRACT Numerous studies found that the main factor contributing to the export of sediment from the Ethiopian highlands is soil erosion, which is related to the expansion of cultivated land at the expense of steep slopes of natural vegetation covers. Plantation practices on steep slopes of bare surfaces and utilizing effective water and soil conservation measures on cultivated lands, may, however, have an impact on the rate of soil loss and the sediment yield ratio. Thus, the main objective of the research was to determine how changes in land use and land cover introduced by soil conservation affected the spatiotemporal variability in soil loss and sediment delivery ratio. RUSLE, InVEST, and integrated GIS technology were used to quantify the study's results. The expansion of farmed land at the expense of natural forests, woodlands, shrubs, and grasslands between 1990 and 2000 resulted in a significant increase in soil loss and sediment yield. This was mostly seen where natural vegetation cover areas on steep slopes were turned into cultivable land. Watershed development interventions significantly decreased both the mean annual soil loss and sediment yields between 2003 and 2021. For instance, from 2010 to 2021, the mean annual soil loss in the watershed dropped by 23.5 t ha−1 and the sediment export declined by 6.13 t ha−1yr−1. To prevent soil loss and sediment export at the extensive level, it is therefore concluded that sustainable soil management on cultivated land and plantation techniques on community land should be expanded up to the adjacent watersheds.

Estimation of Soil Erosion Using RUSLE Model and GIS Tools: A Study of Chilika Lake, Odisha

Abstract Chilika Lake is the largest coastal lagoon on the eastern coast of India. The aerial extension of the lake fluctuates between 1165 km2 to 906 km2. The opening of new inlet into the lake has enriched its biodiversity but also has increased the rate of siltation, which inturn affected the lake’s water holding capacity, causing ecological repercussions and impacting its flora and fauna. The RUSLE model integrated with remote sensing and GIS techniques was used to assess the soil loss in watershed of Chilika Lake. The model takes into account various factor viz. Crop /cover management factor (C), Soil erodibility factor (K), slope length (L), Conservation support practice factor (P), Rainfall erosivity factor (R), and steepness factor (S) to estimate potential soil loss. The results of the study reveal that 486.92 km2 i.e., 73.16% area of the Chilika watershed falls under least risk category of soil erosion, 44.17 km2 (6.6%) under moderate category, while, about 11% is at high, 8.8% at very high, and 0.043 at severe risk of soil erosion. The analysis estimates the annual average soil loss rate in the catchment of Chilika Lake to be 32.41 ton/ha/yr. The raster layers developed to study spatial soil loss indicate that the catchment of the lake is under the grip of soil erosion and siltation problems. Therefore, the study recommends immediate action of conservation and management planning processes to save the lake from further degradation.

Spatial modeling of soil loss as a response to land use-land cover change in Didessa sub-basin, the agricultural watershed of Ethiopia

Soil erosion is a vector of disturbances to agricultural productivity and economic development in the western highlands of Ethiopia. Yet, tough vegetation cover loss swapped to other land uses could have amplified the soil loss rate at which land cover change preceded, but little is known about their effects on soil loss in the Limu-Seqa watershed. This study was designed to evaluate the historical trends of the effects of land use-land cover change on soil erosion dynamics as a threshold for potential monitoring of soil loss. Satellite image data of 1987, 2002, 2021, and DEM-20 m resolution were used. The RUSLE model was applied with primary parameters to generate soil loss. Findings show that average annual soil loss increased from 4.5 in 1987 to 13.5 t ha−1 yr−1 in 2002 and surpassed to 45.35 t ha−1 yr−1 in 2021 as a result of LULC changes, particularly the transition of forest and overgrazed land to cropland (43.83%) and dense-forest to poor-open-up forest (6.92%) between 1987 and 2021. Soil loss during the recent study period was substantially affected by a substantial LULC change, from forest to cropland. The severe and very severe erosion risk categories jointly cover more than half of the entire catchment, which contributes to two-thirds of the total mean annual soil loss in the watershed, which is found to be over and above soil loss tolerance (SLT) in Ethiopia and tropical regions. Therefore, given the robust economic and political status of priority conservation measures, red hues areas are significant.

Estimation of average annual soil loss rates and its prioritization at sub-watershed level using RUSLE: A case of Finca’aa, Oromiya, Western Ethiopia

Background: Because of natural and anthropogenic phenomena, most mountain areas experience significant soil loss. It is critical for watershed management to identify high soil loss rates and prone areas. Therefore, the present research aimed to estimate spatial annual soil loss rates and prioritize soil erosion prone areas of the Finca’aa watershed at sub-watershed level. Methods: The revised universal soil loss equation (RUSLE) model, the extension of geographic information system based on five parameters: rainfall erosivity (R), soil erodibility (K), slope length and slope steepness (LS), vegetation cover (C), and conservation techniques (P), was applied. This study also used weather data, a soil type map, a digital elevation model (DEM), and land use land cover, which were all analyzed using ArcGIS 10.4. Results: Annual soil loss rates ranged from negligible to 234 t ha-1 yr-1. The average rates of soil loss was 33.3 t ha-1 yr-1. Approximately 63.36% of the catchment was within and 36.64% of the catchment was above the maximum permissible level, respectively. Approximately 1.96% were in critical condition. Agricultural practices were the primary cause in the watershed’s mountain and hilly areas. Conclusion: The outcome is critical for planners and resource managers interested in long-term watershed management. Also, it is very important for sustainable growth development of 2030 agendas.

Measuring Annual Sedimentation through High Accuracy UAV-Photogrammetry Data and Comparison with RUSLE and PESERA Erosion Models

Model-based soil erosion studies have increased in number, given the availability of geodata and the recent technological advances. However, their accuracy remains rather questionable since the scarcity of field records hinders the validation of simulated values. In this context, this study aims to present a method for measuring sediment deposition at a typical Mediterranean catchment (870 ha) in Greece through high spatial resolution field measurements acquired by an Unmanned Aerial Vehicle (UAV) survey. Three-dimensional modeling is considered to be an emerging technique for surface change detection. The UAV-derived point cloud comparison, applying the Structure-from-Motion (SfM) technique at the Platana sediment retention dam test site, quantified annual topsoil change in cm-scale accuracy (0.02–0.03 m), delivering mean sediment yield of 1620 m3 ± 180 m3 or 6.05 t ha−1yr−1 and 3500 m3 ± 194 m3 or 13 t ha−1yr−1 for the 2020–2021 and 2021–2022 estimation. Moreover, the widely applied PESERA and RUSLE models estimated the 2020–2021 mean sediment yield at 1.12 t ha−1yr−1 and 3.51 t ha−1yr−1, respectively, while an increase was evident during the 2021–2022 simulation (2.49 t ha−1yr−1 and 3.56 t ha−1yr−1, respectively). Both applications appear to underestimate the net soil loss rate, with RUSLE being closer to the measured results. The difference is mostly attributed to the model’s limitation to simulate gully erosion or to a C-factor misinterpretation. To the authors’ better knowledge, this study is among the few UAV applications employed to acquire high-accuracy soil loss measurements. The results proved extremely useful in our attempt to measure sediment yield at the cm scale through UAV-SfM and decipher the regional soil erosion and sediment transport pattern, also offering a direct assessment of the retention dams’ life expectancy.

The Interaction of Aeolian Sand and Slope on Runoff and Soil Loss on a Loess Slope via Simulated Rainfall under Laboratory Conditions

The wind–water erosion crisscross region, where the topography is complicated, is the most severe area of soil erosion on the Loess Plateau. The wind and terrain both have an impact on the soil water erosion process. In order to evaluate the effects of sand cover on runoff and soil loss characteristics, a series of experiments was conducted in two contrasting treatments. One treatment was a bare loess soil slope serving as the control, and the others were sand-covered loess slopes with five different slopes. The results showed that the runoff time, total runoff yield, and total soil loss were different between the sand-covered slope and the loess slope on the slope of 15°. The sediment concentration of the sand-covered slope was significantly higher than that of the loess slope during the entire rainfall process (p < 0.05). The increase in the slope gradient shortened the surface runoff initiation times and enhanced the total runoff volume and soil loss. The total runoff volume and the total soil loss were 39.7 L and 44.3 kg, respectively, on the sand-covered slope of 10°. When the slope gradient increased from 10° to 30°, the total runoff volume and the total soil loss increased by 22.8 L and 42.8 kg, respectively, while the surface runoff initiation times shortened by 300 s. For the sand-covered slopes, the erosion processes appeared to be mainly dominated by sediment transport. The correlation between soil loss rates and slope gradients demonstrated the secondary polynomial function. In addition, the critical slope of sand-covered slopes was from approximately 23° to 28°. The proportion of sand cover and slope responsible for soil erosion was 3:1, which means the wind effect was more important than the terrace factor in terms of soil water erosion in the wind–water erosion crisscross region. The results provide a theoretical basis for soil erosion control in this area.

Soil Water Erosion Modeling in Tunisia Using RUSLE and GIS Integrated Approaches and Geospatial Data

Soil erosion is an important environmental problem that can have various negative consequences, such as land degradation, which affects sustainable development and agricultural production, especially in developing countries like Tunisia. Moreover, soil erosion is a major problem around the world because of its effects on soil fertility by nutriment loss and siltation in water bodies. Apart from this, soil erosion by water is the most serious type of land loss in several regions both locally and globally. This study evaluated regional soil erosion risk through the derivation of appropriate factors, using the Revised Universal Soil Loss Equation (RUSLE), which was applied to establish a soil erosion risk map of the whole Tunisian territory and to identify the vulnerable areas of the country. The RUSLE model considers all the factors playing a major role in erosion processes, namely the erodibility of soils, topography, land use, rainfall erosivity, and anti-erosion farming practices. The equation is, thus, implemented under the Geographic Information System (GIS) “Arc GIS Desktop”. The results indicated that Tunisia has a serious risk of soil water erosion, showing that 6.43% of the total area of the country is affected by a very high soil loss rate, estimated at more than 30 t/ha/year, and 4.20% is affected by high mean annual soil losses, ranging from 20 to 30 t/ha/year. The most eroded areas were identified in the southwestern, central, and western parts of the country. The spatial erosion map can be used as a decision support document to guide decision-makers towards better land management and provide the opportunity to develop management strategies for soil erosion prevention and control on the global scale of Tunisia.

GIS-Based Soil Erosion Risk Assessment in the Watersheds of Bukidnon, Philippines Using the RUSLE Model

The sustainability of watersheds for supplying water and for carbon sequestration and other environmental services depends to a large extent on their susceptibility to soil erosion, particularly under changing climate. This study aimed to assess the risk of soil erosion in the watersheds in Bukidnon, Philippines, determine the spatial distribution of soil loss based on recent land cover maps, and predict soil loss under various rainfall scenarios based on recently reported climate change projections. The soil erosion risk assessment and soil loss prediction made use of GIS and the RUSLE model, while the rainfall scenarios were formulated based on PAGASA’s prediction of drier years for Bukidnon in the early-future to late-future. Results showed that a general increase in soil loss was observed in 2015, over the period from 2010 to 2020, although some watershed clusters also showed a declining trend of soil erosion, particularly the Agusan-Cugman and Maridugao watershed clusters. Nearly 60% of Bukidnon has high to very severe soil loss rates. Under extreme rainfall change scenario with 12.61% less annual rainfall, the soil loss changes were only +1.37% and −2.87% in the category of none-to-slight and very severe, respectively. Results showed that a decrease in rainfall would have little effect on resolving the excessive soil erosion problem in Bukidnon. Results of this study suggest that having more vegetative land cover and employing soil conservation measures may prove to be effective in minimizing the risk of soil erosion in the watersheds. This study provides valuable information to enhance the sustainability of the watersheds. The erosion-prone areas identified will help decision-makers identify priority areas for soil conservation and environmental protection.

Influence of precipitation change and topography characteristics on the development of farmland gully in the black soil region of northeast China

Gully erosion in cultivated land has seriously damaged land resources and reduced crop production. The long-term changes of gully erosion have not been well understood because of limited continuous observed data on the gully development process, which further causes difficulty in establishing universal models of gully erosion rates. Hillslope gullies and valley floor gullies were continuously monitored by RTK GPS and Unmanned Aerial Vehicle (UAV) from 2002 to 2021 in the Heshan farm of Songnen black soil region of northern China. UAV-generated DEMs, historical remote sensing images, and daily precipitation data were combined to analyze the influence of precipitation changes and topographic differences on the development process of gully erosion. The results showed that gully erosion had strong variability between years due to the difference in annual precipitation. The volumetric increment in high precipitation years was 6.5 times and 16.5 times that in normal precipitation years and low precipitation years, respectively, and the variability of valley floor gullies was greater than that of hillslope gullies. The annual average headcut retreat of valley floor gullies and hillslope gullies was 13.1 m y-1 and 4.2 m y-1, respectively, but the volumetric soil loss was mainly contributed by gully bottom deepening and gully sidewall collapsing. The ratio of volumetric soil loss from the gully head to total volumetric soil loss was the smallest in high precipitation years, followed by normal precipitation years and low precipitation years because more runoff in high precipitation years enhanced erosion kinetic energy, which accelerated gully bottom deepening and gully sidewall collapsing. Topography and rainfall were the important influencing factors for the continuous gully development. The maximum value of 3-day accumulative rainfall best explained valley floor gully erosion, and the accumulative value of erosive rainfall best accounted for hillslope gully erosion. A statistical model for estimating volumetric soil loss rate (△V) was developed that included as independent variables drainage area (A) and rainfall characteristic index (RI): △V = 1.571*10-7*（A*RI）1.983 (R2 = 0.98).

Land use/land cover change and its implication on soil erosion in an ecologically sensitive Himachal Himalayan watershed, Northern India

Soil erosion is a major environmental problem that affects land and w ater resources. It has many negative implications that lead to deforestation, poor agricultural practices, loss of soil fertility, and siltation that hinder socio-economic development. In view of this, the present study was conducted with the aim of estimating soil loss in relation to long-term land use/land cover change (LULC) in the Dehar watershed, Himachal Himalaya, North India. The study was carried out using Landsat and Sentinel imageries for the years 1999, 2010, and 2020. A GIS-based Revised Universal Soil Loss Equation (RUSLE) model was applied to assess the potential soil risk. The parameters used as input for computing the spatiotemporal changes of soil loss were rainfall erosivity, soil erodibility, topographic, crop management, and conservation support practice factors. The results showed a mean soil loss of 63.71, 60.99, and 66.71 t/ha/yr for the years 1999, 2010, and 2020, respectively. In the LULC class defined as Built-up Land, the mean soil loss decreased from 32.19 t/ha/yr in 1999 to 18.77 t/ha/yr in 2010, and in the year 2020 the mean soil loss slightly increased to 20.15 t/ha/yr. Moreover, the LULC class Barren Land registered a decrease in mean soil loss for the years 1999, 2010, and 2020 of 86.43, 74.60, and 73.19 t/ha/yr, respectively. Regarding the Agriculture Land class, the rate of mean soil loss slightly increased from 32.55 t/ha/yr in 1999 to 33.35 t/ha/yr in 2010, and the mean soil loss decreased to 25.43 t/ha/yr in the year 2020. Areas covered under Forest Land experienced an increase in mean soil loss from 65.30 t/ha/yr in 1999 to 65.87 in 2010 and 74.72 t/ha/yr in 2020. The study demonstrated that LULC changes apparently influenced the soil loss in the Dehar watershed. Therefore, urgent interventions are required with the involvement of scientists, policymakers, and the general public for conservation and management of soil resources.

Soil erosion in the savanna biome national parks of South Africa

Soil erosion has become a common feature in certain national parks across the global biomes, and national parks in the Savanna biome of South Africa have not been spared. Given their prominence in hosting large land mammals that contribute significantly to the tourism sector, the vulnerability of the parks to soil erosion merits attention. This study examines soil erosion status in the Savanna biome national parks of South Africa to recommend management practices aligned with the degree of erosion susceptibility. The results show that erosion rates across the national parks range between 0 and 25 t/ha/yr, and only a few have soil loss rates above 25 t/ha/yr. The Kgalagadi National Park has the lowest erosion rates, with a water erosion severity index (ESI) of 0. The Kruger and Mokala National Parks have low erosion rates, with an ESI of 0.017 and 0.01, respectively. The Mapungubwe National Park has moderate rates of soil loss, with an ESI of 0.023, while Marakele has the highest rates of soil erosion, with an ESI of 0.39. Soil erosion by water action is generally a minor environmental threat for the Kgalagadi, Kruger and Mokala National Parks. However, for Mapungubwe, soil erosion has a moderate threat, while in Marakele, it imposes a very high threat level. The different rates of soil erosion are attributed to the heterogeneous biophysical conditions of the Savanna biome, as well as different historical land use patterns across the parks. Climate change could be exacerbating the susceptibility of national parks to soil erosion. Park-specific management strategies that consider historical land use patterns and biophysical conditions within a climate change context are recommended.

GIS based soil loss assessment using RUSLE model: A case of Horo district, western Ethiopia

Land degradation in the form of soil erosion is a worldwide challenge and make environmental problem that affects crop yields, makes livelihoods difficult, and creates crises. The main objective of this study was to measure soil loss using the Revised Universal Soil Loss Equation (RUSLE) Model in Horo district, Western Ethiopia. RUSLE with a Geographical Information System (GIS) was used to quantify soil loss using rainfall, soil, a digital elevation model (DEM), and satellite image datasets as factor value inputs. Those factors are erosivity (R), erodibility (K), topography (LS), cover management (C), and conservation support practice (P) layer values that can be interactively used using weighted overlay in ArcGIS 10.8. The result shows that the maximum and minimum potential annual soil loss of the study area ranged from nil (0.01 t/ha/yr) on plain surfaces to 216.01 t/ha/yr. The average annual soil loss rate in the study area was 13.27 t ha/yr. The highest mean annual soil loss of 216.01 t/ha/yr were observed from farmland and it was the largest portion of the study area, which covered about 64243.02 ha and represented about 73.75% of the total. As a result, forest land (16383.23 ha) was the second-largest, accounting for 18.81% of the total area. Consequently, the study revealed that the farmland was more vulnerable to erosion than other land uses and land cover types. Hence, information on average annual soil loss is important for selecting appropriate conservation measures to reduce on-site soil loss and its off-site effects. Therefore, farmers and other expected bodies should have focused on soil conservation and management practices at the highest soil loss severity classes, which must get priority for conservation by stakeholders, agents, and the government.

Hydrological and erosion responses of steep spoil heaps to taproot and fibrous root grasses under simulated rainfalls

Spoil heaps, resulting from excavation and backfilling activities at construction sites, are one of the most important risk sources of soil erosion, threatening local ecosystems and even leading to geological disasters. However, after revegetation, spoil heaps can be transformed into available land resources such as cultivated land and forest land. Previous studies have reported the runoff and sediment reduction benefits of different vegetation measures on spoil heaps, while little information has been documented about what differences in the taproot and fibrous root grasses were transferred into the differences in hydrological and erosion processes. This study compared the effects of taproot (Artemisia gmelinii (A. gmelinii)) and fibrous root (Cynodon dactylon (C. dactylon)) grasses on soil erosion and runoff production processes and the reduction benefits of spoil heaps with silt loam soil. A series of rainfall events at three intensities (0.8, 1.2, and 1.8 mm min−1) were simulated on runoff plots (length of 3.0 m and width of 1.0 m) under treatments of A. gmelinii, C. dactylon, and bare soil (BS, as control) (each treatment was replicated), and the initial runoff time (IRT), saturated hydraulic conductivity, runoff rate (RR), flow velocity, and soil loss rate (SLR) were measured. The results showed that grasses delayed the IRTs on spoil heaps by 0.69–12.96 min compared BS, and the delay effects were more significant for fibrous root grass than for taproot grass. The soil loss reduction benefits (91.1 %) of the two kinds of grass were approximately 2.2 times greater than those of runoff volume reduction (42.1 %), and shear stress could be used to predict the soil loss rate following power functions (R2 = 0.72–0.85) for the grass-covered plots. The taproot grass exhibited a higher erosion reduction than fibrous root grass and is thus preferred to control the soil erosion of spoil heaps. Taproot grass has a greater stem diameter and can insert deeper into spoil heaps to consolidate the soil particles, which can improve roughness and hydraulic resistance, thereby producing a lower effective hydraulic shear stress that is directly exerted on soil particles. This study enhances the understanding of different grasses on the water erosion process and mechanism of spoil heaps and provides scientific guidance for selecting erosion control practices.

Does soil thinning change soil erodibility? An exploration of long-term erosion feedback systems

Abstract. Soil erosion rates on arable land frequently exceed the pace at which new soil is formed. This imbalance leads to soil thinning (i.e. truncation), whereby subsoil horizons and their underlying parent material become progressively closer to the land surface. As soil erosion is a selective process and subsurface horizons often have contrasting properties to the original topsoil, truncation-induced changes to soil properties might affect erosion rates and runoff formation through a soil erosion feedback system. However, the potential interactions between soil erosion and soil truncation are poorly understood due to a lack of empirical data and the neglection of long-term erodibility dynamics in erosion simulation models. Here, we present a novel model-based exploration of the soil erosion feedback system over a period of 500 years using measured soil properties from a diversified database of 265 agricultural soil profiles in the UK. For this, we adapted the Modified Morgan–Morgan–Finney model (MMMF) to perform a modelling experiment in which topography, climate, land cover, and crop management parameters were held constant throughout the simulation period. As selective soil erosion processes removed topsoil layers, the model gradually mixed subsurface soil horizons into a 0.2 m plough layer and updated soil properties using mass-balance mixing models. Further, we estimated the uncertainty in model simulations with a forward error assessment. We found that modelled erosion rates in 99 % of the soil profiles were sensitive to truncation-induced changes in soil properties. The soil losses in all except one of the truncation-sensitive profiles displayed a decelerating trend, which depicted an exponential decay in erosion rates over the simulation period. This was largely explained by decreasing silt contents in the soil surface due to selective removal of this more erodible particle size fraction and the presence of clayey or sandy substrata. Moreover, the soil profiles displayed an increased residual stone cover, which armoured the land surface and reduced soil detachment. Contrastingly, the soils with siltier subsurface horizons continuously replenished the plough layer with readily erodible material, which prevented the decline of soil loss rates over time. Although our results are limited by the edaphoclimatic conditions represented in our data, as by our modelling assumptions, we have demonstrated how modelled soil losses can be sensitive to erosion-induced changes in soil properties. These findings are likely to affect how we calculate soil lifespans and make long-term projections of land degradation.

Sustainability Analysis of Soil Erosion Control in Rwanda: Case Study of the Sebeya Watershed

Soil erosion is a complex process that results in soil and fertility losses from agricultural land and, ultimately, leads to river sedimentation. This study aimed to assess various influential factors and processes affecting soil erosion and to recommend suitable site-based Soil Erosion Control Measures (SECM) for sustainable agriculture while minimizing the downstream rivers and reservoir sedimentation in the Sebeya watershed of Rwanda. The present research used a literature review, site visits, and focus groups to assess various SECM within the Sebeya watershed. As a result, various site-based SECM were evaluated, recommended, and simulated to alleviate high soil loss rates in the Sebeya watershed using the Universal Soil Erosion Equation (USLE) model. Simulating existing and proposed SECM, soil loss was reduced significantly from 73 t/ha/y to 29 t/ha/y. To highlight the implication of the site-based recommended SECM in improving agricultural productivity, this study suggests field investigations in soil erosion plots and prediction of crop yields from an established linear correlation model between soil loss and crop yields in the Sebeya watershed. For effective action in reducing high soil erosion rates to tolerable rates in the Sebeya watershed, the present research recommends implementing the site-based recommended SECM with mulching and drainage channels on the same farmland. However, lack of money and knowledge are the main limitations in implementing SECM in the Sebeya watershed. Therefore, governmental and non-governmental organizations should technically and financially help farmers in the Sebeya watershed.

Spatial distribution of soil erosion and its impacts on soil productivity in Songnen typical black soil region

Soil erosion is an important reason for land degradation. The black soil region of Northeast China is the main grain production base in China, however, the black soil productivity in this region is at risk and unsustainable now because there was few soil conservation measures in nearly 100 years of cultivation. To assess the impact of erosion on land productivity, both 210 sampling points for 137Cs measurement and 112 sampling points for soil profile features were laid out on cultivated land of Songnen typical black soil region in combination with the 2010 China land use map. The current soil erosion/deposition rate and land productivity index PI were calculated over the study area, and then the PI values were predicted erosion by 20, 50, 100, and 200 years without soil and water conservation measures based on the current erosion rate and soil profile characteristics. The results showed that the average soil erosion rate of cultivated land in the study area was 705.8 t/(km2⋅a), and 77.0% of the cultivated land had soil erosion rate larger than the soil loss tolerance of 200 t/(km2⋅a). Spatially, the soil erosion rate in the northern region was greater than that in the southern region significantly, which was mainly affected by slope steepness. The relationship between soil erosion rate (SL) and slope steepness (θ) in degree was established according to the results of 137Cs measured at 210 sample points: SL = 556.4∗θ or SL = 0.464∗θ in mm/a (given soil bulk density is 1200 kg/m3). The current mean value of PI for cultivated land was 0.59, decreasing significantly from north to south, which was mainly influenced by organic matter content. The predicted PI values decreased significantly with the increase of erosion years in the northern area. This is related to the topography of rolling and hills in the northern area with a greater soil loss rate, leading to a rapid decrease of organic matter content and the increase of sand content in the topsoil in the future. The spatial distributions of the current soil erosion rates and predicted PI values after years of erosion for cultivated land would be helpful for soil conservation policy-making.

Comparing two weather generator‐based downscaling tools for simulating storm intensification and its impacts on soil erosion under climate change

AbstractStorm intensification is projected to increase under climate change; however, it is quite challenging to statistically downscale storm intensification for climatic impact assessment. The objectives of this study are to compare two weather generator‐based tools in simulating daily precipitation extremes at a point scale, both with and without storm intensification options, and to further evaluate the responses of simulated surface runoff and soil loss to generated precipitation extremes under various cropping and tillage systems at a station in central Oklahoma, U.S.A. Results show that all data sources including raw GCM, raw RCM, and statistically downscaled GCM datasets are capable of providing information on future storm intensification, depending on particular ensemble members or models in each source. Thus, it is recommended that all data sources including as many members as possible should be used in model screening. Compared with the SYNthetic weather generaTOR (SYNTOR), the Generator for Point Climate Change (GPCC) tool tends to generate a stronger storm intensification, partially because GPCC takes into consideration the increased variance of monthly precipitation as projected by GCMs in adjusting daily precipitation variance for future climate generation. However, there are no discernable differences between storm intensification options with each tool because weaker signals of storm intensification are projected for the study site coupled with a projected decline in future annual precipitation. Simulated soil loss and surface runoff rates with GPCC were significantly greater than those with SYNTOR due to GPCC generating more frequent and heavier storms. Given the sizable differences in extreme precipitation generation, both tools could be used to generate a range of the potential impacts of storm intensification under climate change on soil erosion and surface hydrology.

Simulating Dynamic of Suspended Sediment Flow Based on Data of Primorskaya Water Balance Station

Soil loss rate, suspended load, and suspended sediment discharge were simulated for the drainage basin of the Komarovka R., located at the territory of the former Primorskaya Water Balance, Russian Far East. The study was based on an open-code SWAT model. The model was calibrated and verified using archive measurement data on sediment load and suspended sediment discharge at PWBS by data of Tsentral’nyi hydrological gage (157 km2). The simulation efficiency of the suspended load and suspended sediment discharge corresponds to categories satisfactory and higher. On the average for the drainage basin, the rate of soil washout is 11–12 t/ha per year. The washout rate is maximal in the period of active snow melting. The mean monthly value for April–May is 75 thous. t. The mean annual washout is 171 thous. t, and the maximal is 800 thous. t/year. The mean annual runoff of suspended sediments in the outlet section of the drainage basin is 2.6 thous. t. The highest monthly values of sediment runoff correspond to the period of summer floods in August, when with average of 1 thous. and maximum of 7.7 thous. t. The obtained data on the heterogeneity of the spatial distribution of the simulated values of washout rate are in good agreement with the landscape and climatic features of runoff formation in the drainage river basin. The major errors in the estimation of sediment load are due to the simulation quality of the hydrological regime and dynamics of runoff components that form flood hydrographs.