Rapid assessment of soil erosion in the Rio Lempa Basin, Central America, using the universal soil loss equation and geographic information systems

Spatial analysis and assessment of soil erosion in the southern Western Ghats region in India

Soil erosion is expected to worsen in the future as a result of climate change, growing population demands, improper land use, and excessive exploitation of natural resources in India. Due to the growing population and changes in land use, it has become increasingly crucial to map and quantitatively assess soil for the purpose of sustainable agricultural usage and planning conservation efforts. The problem of soil erosion is mainly on steeper slopes with intense rainfall in parts of Western Ghats. The 20.17% of geographical area have been converted into wasteland due to soil erosion. The Revised Universal Soil Loss Equation (RUSLE) is a highly prevalent and effective technique utilized for estimating soil loss in order to facilitate the planning of erosion control measures. Despite the fact that RUSLE is accurately estimate sediment yields from gully erosion, it is an effective tool in estimating sheet and rill erosions losses from diverse land uses like agricultural to construction sites. The current study is mainly about combining the RUSLE model with GIS (Geographic Information System) to find out how much soil is being lost, particularly in Noyyal and Sanganur watersheds which is located in Coimbatore district of Tamil Nadu, India. This analysis is based on the soil order, with a significant proportion of alfisols and inceptisols being considered. The obtained outcome is contrasted with the established soil loss tolerance threshold, leading to the identification of the areas with the highest susceptibility to erosion. Within the narrower and more inclined section of the watershed, yearly soil loss scales from 0 to 5455 tonnes/ha/year, with an average annual loss of soil of 2.44 tonnes/ha. The severe soil erosion of 100 to 5455 tonnes/ha/year is found along the steep and greater slope length. The generated soil map was classified into six categories: very slight, slight, moderate, high, severe, and very severe. These classifications, respectively, occupied 6.23%, 14.88%, 10.56%, 15.70%, 7.73%, and 6.63% of the basin area. Based on the results of cross-validation, the estimated result of the present study was found to be very high compared to past studies conducted 0 to 368.12 tonnes/ha/year especially in very severe erosion zones. But very slight to severe erosion zones nearly matched with same level of soil loss. To protect the soil in the study area from erosion, more specific actions should be taken. These include micro-catchment, broad bed furrows, up-and-down farming, soil amendment with coconut coir pith composition, streambank stabilization with vegetation, and micro-water harvesting with abandoned well recharge. These actions should be carried out over time to make sure to work.

The Assessment of Climate Change on Rainfall-Runoff Erosivity in the Chirchik–Akhangaran Basin, Uzbekistan

Changes in the frequency or intensity of rainfall due to climate always affect the conservation of soil resources, which leads to land degradation. The importance of assessing past and future climate differences plays an important role in future planning in relation to climate change. The spatiotemporal variability of erosivity depending on precipitation using the rainfall erosivity (R) of Universal Soil Loss Equation under the global circulation model (GCM) scenarios in the Chirchik–Akhangaran Basin (CHAB), which is in the northeastern part of the Republic of Uzbekistan, was statistically downscaled by using the delta method in Representative Concentration Pathways (RCPs) 4.5 and 8.5 during the periods of the 2030s, 2050s and 2070s. The (R) was used to determine the erosivity of precipitation, and the Revised Universal Soil Loss Equation (RUSLE) itself determined the effects of changes in erosivity. Ten weather station observational data points for the period from 1990 to 2016 were used to validate the global circulation models (GCMs) and erosion model. The assessment results showed an increase in precipitation from the baseline by an average of 11.8%, 14.1% and 16.3% for all models by 2030, 2050 and 2070, respectively, while at the same time, soil loss increased in parallel with precipitation by 17.1%, 20.5 % and 23.3%, respectively, in certain scenarios. The highest rainfall was observed for the models ACCESS1–3 and CanESM2 on both RCPs and periods, while more intense rainfall was the main reason for the increase in the spatial and temporal erosion activity of the rainfall-runoff. This study is a useful reference for improving soil conservation, preventing water erosion and ensuring the future sustainability of agricultural products, as well as improving the operational management and planning of agriculture.

Geospatial assessment of bioenergy land use and its impacts on soil erosion in the U.S. Midwest

Agricultural land use change, especially corn expansion since 2000s, has been accelerating to meet the growing bioenergy demand of the United States. This study identifies the environmentally sensitive lands (ESLs) in the U.S. Midwest using the distance-weighted Revised Universal Soil Loss Equation (RUSLE) associated with bioenergy land uses extracted from USDA Cropland Data Layers. The impacts of soil erosion to downstream wetlands and waterbodies in the river basin are counted in the RUSLE with an inverse distance weighting approach. In a GIS-ranking model, the ESLs in 2008 and 2011 (two representative years of corn expansion) are ranked based on their soil erosion severity in crop fields. Under scenarios of bioenergy land use change (corn to grass and grass to corn) on two land types (ESLs and non-ESLs) at three magnitudes (5%, 10% and 15% change), this study assesses the potential environmental impacts of bioenergy land use at a basin level. The ESL distributions and projected trends vary geographically responding to different agricultural conversions. Results support the idea of re-planting native prairie grasses in the identified High and Severe rank ESLs for sustainable bioenergy management in this important agricultural region.

USLE-Based Assessment of Soil Erosion by Water in the Nyabarongo River Catchment, Rwanda

Soil erosion has become a serious problem in recent decades due to unhalted trends of unsustainable land use practices. Assessment of soil erosion is a prominent tool in planning and conservation of soil and water resource ecosystems. The Universal Soil Loss Equation (USLE) was applied to Nyabarongo River Catchment that drains about 8413.75 km² (33%) of the total Rwanda coverage and a small part of the Southern Uganda (about 64.50 km²) using Geographic Information Systems (GIS) and Remote Sensing technologies. The estimated total annual actual soil loss was approximately estimated at 409 million tons with a mean erosion rate of 490 t·ha⁻¹·y⁻¹ (i.e., 32.67 mm·y⁻¹). The cropland that occupied 74.85% of the total catchment presented a mean erosion rate of 618 t·ha⁻¹·y⁻¹ (i.e., 41.20 mm·y⁻¹) and was responsible for 95.8% of total annual soil loss. Emergency soil erosion control is required with a priority accorded to cropland area of 173,244 ha, which is extremely exposed to actual soil erosion rate of 2222 t·ha⁻¹·y⁻¹ (i.e., 148.13 mm·y⁻¹) and contributed to 96.2% of the total extreme soil loss in the catchment. According to this study, terracing cultivation method could reduce the current erosion rate in cropland areas by about 78%. Therefore, the present study suggests the catchment management by constructing check dams, terracing, agroforestry and reforestation of highly exposed areas as suitable measures for erosion and water pollution control within the Nyabarongo River Catchment and in other regions facing the same problems.

Soil erosion evaluation in a rapidly urbanizing city (Shenzhen, China) and implementation of spatial land-use optimization

Soil erosion has become a pressing environmental concern worldwide. In addition to such natural factors as slope, rainfall, vegetation cover, and soil characteristics, land-use changes-a direct reflection of human activities-also exert a huge influence on soil erosion. In recent years, such dramatic changes, in conjunction with the increasing trend toward urbanization worldwide, have led to severe soil erosion. Against this backdrop, geographic information system-assisted research on the effects of land-use changes on soil erosion has become increasingly common, producing a number of meaningful results. In most of these studies, however, even when the spatial and temporal effects of land-use changes are evaluated, knowledge of how the resulting data can be used to formulate sound land-use plans is generally lacking. At the same time, land-use decisions are driven by social, environmental, and economic factors and thus cannot be made solely with the goal of controlling soil erosion. To address these issues, a genetic algorithm (GA)-based multi-objective optimization (MOO) approach has been proposed to find a balance among various land-use objectives, including soil erosion control, to achieve sound land-use plans. GA-based MOO offers decision-makers and land-use planners a set of Pareto-optimal solutions from which to choose. Shenzhen, a fast-developing Chinese city that has long suffered from severe soil erosion, is selected as a case study area to validate the efficacy of the GA-based MOO approach for controlling soil erosion. Based on the MOO results, three multiple land-use objectives are proposed for Shenzhen: (1) to minimize soil erosion, (2) to minimize the incompatibility of neighboring land-use types, and (3) to minimize the cost of changes to the status quo. In addition to these land-use objectives, several constraints are also defined: (1) the provision of sufficient built-up land to accommodate a growing population, (2) restrictions on the development of land with a steep slope, and (3) the protection of agricultural land. Three Pareto-optimal solutions are presented and analyzed for comparison. GA-based MOO is found able to solve the multi-objective land-use problem in Shenzhen by making a tradeoff among competing objectives. The outcome is alternative choices for decision-makers and planners.

Assessment of hazards and risks for landscape protection planning in Sicily

Landscape protection planning is a complex task that requires an integrated assessment and involves heterogeneous issues. These issues include not only the management of a considerable amount of data to describe landscape features but also the choice of appropriate tools to evaluate the hazards and risks. The landscape assessment phase can provide fundamental information for the definition of a Landscape Protection Plan, in which the selection of norms for protection or rehabilitation is strictly related to hazards, values and risks that are found. This paper describes a landscape assessment methodology conducted by using GIS, concerning landscape hazards, values and risk. Four hazard categories are introduced and assessed concerning urban sprawl and erosion: landscape transformations by new planned developments, intensification of urban sprawl patterns, loss of agriculture land and erosion. Landscape value is evaluated by using different thematic layers overlaid with GIS geoprocessing. The risk of loss of landscape value is evaluated, with reference to the potential occurrence of the previously assessed hazards. The case study is the Province of Enna (Sicily), where landscape protection is a relevant issue because of the importance of cultural and natural heritage. Results show that high value landscape features have a low risk of loss of landscape value. For this reason, landscape protection policies assume a relevant role in landscapes with low-medium values and they should be addressed to control the urban sprawl processes that are beginning in the area.

Empirical models based on the universal soil loss equation fail to predict sediment discharges from Chesapeake Bay catchments

The Universal Soil Loss Equation (USLE) and its derivatives are widely used for identifying watersheds with a high potential for degrading stream water quality. We compared sediment yields estimated from regional application of the USLE, the automated revised RUSLE2, and five sediment delivery ratio algorithms to measured annual average sediment delivery in 78 catchments of the Chesapeake Bay watershed. We did the same comparisons for another 23 catchments monitored by the USGS. Predictions exceeded observed sediment yields by more than 100% and were highly correlated with USLE erosion predictions (Pearson r range, 0.73-0.92; p < 0.001). RUSLE2-erosion estimates were highly correlated with USLE estimates (r = 0.87; p < 001), so the method of implementing the USLE model did not change the results. In ranked comparisons between observed and predicted sediment yields, the models failed to identify catchments with higher yields (r range, -0.28-0.00; p > 0.14). In a multiple regression analysis, soil erodibility, log (stream flow), basin shape (topographic relief ratio), the square-root transformed proportion of forest, and occurrence in the Appalachian Plateau province explained 55% of the observed variance in measured suspended sediment loads, but the model performed poorly (r(2) = 0.06) at predicting loads in the 23 USGS watersheds not used in fitting the model. The use of USLE or multiple regression models to predict sediment yields is not advisable despite their present widespread application. Integrated watershed models based on the USLE may also be unsuitable for making management decisions.