Geospatial Analysis of Soil Erosion Patterns in a river basin of Western Ghats

Soil erosion modelling at European scale by using high resolution input layers

Abstract. Soil erosion is a major problem around the world because of its effects on soil productivity, nutrient loss, siltation in water bodies, and degradation of water quality. By understanding the driving forces behind soil erosion, we can more easily identify erosion-prone areas within a landscape to address the problem strategically. Soil erosion models have been used to assist in this task. One of the most commonly used soil erosion models is the Universal Soil Loss Equation (USLE) and its family of models: the Revised Universal Soil Loss Equation (RUSLE), the Revised Universal Soil Loss Equation version 2 (RUSLE2), and the Modified Universal Soil Loss Equation (MUSLE). This paper reviews the different sub-factors of USLE and RUSLE, and analyses how different studies around the world have adapted the equations to local conditions. We compiled these studies and equations to serve as a reference for other researchers working with (R)USLE and related approaches. Within each sub-factor section, the strengths and limitations of the different equations are discussed, and guidance is given as to which equations may be most appropriate for particular climate types, spatial resolution, and temporal scale. We investigate some of the limitations of existing (R)USLE formulations, such as uncertainty issues given the simple empirical nature of the model and many of its sub-components; uncertainty issues around data availability; and its inability to account for soil loss from gully erosion, mass wasting events, or predicting potential sediment yields to streams. Recommendations on how to overcome some of the uncertainties associated with the model are given. Several key future directions to refine it are outlined: e.g. incorporating soil loss from other types of soil erosion, estimating soil loss at sub-annual temporal scales, and compiling consistent units for the future literature to reduce confusion and errors caused by mismatching units. The potential of combining (R)USLE with the Compound Topographic Index (CTI) and sediment delivery ratio (SDR) to account for gully erosion and sediment yield to streams respectively is discussed. Overall, the aim of this paper is to review the (R)USLE and its sub-factors, and to elucidate the caveats, limitations, and recommendations for future applications of these soil erosion models. We hope these recommendations will help researchers more robustly apply (R)USLE in a range of geoclimatic regions with varying data availability, and modelling different land cover scenarios at finer spatial and temporal scales (e.g. at the field scale with different cropping options).

This research used the Revised Universal Soil Loss Equation (RUSLE) with Sediment Delivery Ratio (SDR) model. The analytic hierarchy process (AHP) method, while also incorporating the use of a geographic information system (GIS) and remote sensing (RS) to predict the annual soil loss rate and spatialise the processes of water erosion at the scale of the Loukkos Watershed, Morocco. The RUSLE model and AHP parameters were estimated using RS data, and the erosion vulnerability zones were determined using GIS. We used five parameters, including precipitation erosivity, soil erodibility, slope length and steepness, vegetation cover, and soil erosion control practices in the RUSLE. For the AHP technique, we used seven geo‐environmental factors, including annual average precipitation, drainage density, lineament density, slope, soil texture, land use/land cover and landform maps. The results of RUSLE indicated that the average annual soil loss varied from 0 to 2388.27 . The total estimated annual potential soil loss was approximately 40 790 220.11 , and a sediment yield estimated by RUSLE‐SDR was 8 647 526.66 , equivalent to 6.65 Mm3. This value is very close to the measured value of 6.81 Mm3, for a difference of 0.16 Mm3. Furthermore, the results of the AHP indicate that the soil erosion potential index varies from 0 to 0.205315 . Overall, nearly 13.7% of the area suffered from severe soil erosion exceeding 50 . Approximately 80% of the Loukkos Watershed area experienced only slight erosion, while the remaining 6% incurred moderate erosion. Integrating GIS and RS into the RUSLE model and AHP helped us robustly estimate the extent and degree of erosion risk. Territorial decision‐makers should adopt our results to develop soil conservation strategies, water management plans and other necessary soil and water conservation measures for this region.

The Bonsa river is an important tributary of the Ankobra river in the Western Region of Ghana. The catchment of the Bonsa river has been undergoing rapid land cover changes due to human activities such as farming, illegal mining, population growth, among others which are likely to promote soil erosion and sediment yield in the river basin. To estimate the amount of soil eroded over a period and subsequent sediment yielded along the Bonsa river basin, the Revised Universal Soil Loss Equation (RUSLE) was integrated with Geographic Information System (GIS) to model the spatial distribution patterns in soil erosion and sediment yield within the catchment. Data used included annual rainfall records, soil map, Digital Elevation Model (DEM) and land-use map of the study area. Parameters of the model were determined and converted into raster layers using the raster calculator tool in ArcMap to produce a soil erosion map. The concept of Sediment Delivery Ratio (SDR) was applied to determine the annual sediment yield by combining a raster SDR layer with soil erosion map. The predicted soil loss and sediment yield values were found to be low. This may be due to high soil protective cover provided by vegetation as well as low topographic relief in the river basin. Though, the elements and processes responsible for soil erosion and sediment yield prevailing in the basin was found to be low, adverse situations could be developed with time if the prevailing conditions are not checked, as soil erosion is a natural gradual slow process. The gains made could be sustained by putting measures in place to control human activities, particularly, illegal mining (galamsey) in the basin, indiscriminate cutting down of trees and farmining activities along the Bansa river basin. This study will support monitoring, planning of water resources and help to improve sustainable water quality.

Soil erosion risk map for Swiss grasslands : a dynamic approach to model the spatio-temporal patterns of soil loss

Quantifying soil erosion and soil organic carbon conservation services in indian forests: A RUSLE-SDR and GIS-based assessment

ABSTRACTThe long-term average annual soil loss (A) and sediment yield (SY) in a tropical monsoon-dominated river basin in the southern Western Ghats, India (Muthirapuzha River Basin, MRB; area: 271.75 km2), were predicted by coupling the Revised Universal Soil Loss Equation (RUSLE) and sediment delivery ratio (SDR) models. Moreover, the study also delineated soil erosion risk zones based on the soil erosion potential index (SEPI) using the analytic hierarchy process (AHP) technique. Mean A of the basin is 14.36 t ha−1 year−1, while mean SY is only 3.65 t ha−1 year−1. Although the land use/land cover types with human interference show relatively lower A compared to natural vegetation, their higher SDR values reflect the significance of anthropogenic activities in accelerated soil erosion. The soil erosion risk in the MRB is strongly controlled by slope, land use/land cover and relative relief, compared to geomorphology, drainage density, stream frequency and lineament frequency.

Assessment of soil erosion, sediment yield and basin specific controlling factors using RUSLE-SDR and PLSR approach in Konar river basin, India

The present work integrates analytical hierarchy process (AHP) with Revised Universal Soil Loss Equation (RUSLE) model to determine the critical soil erosion prone areas along with the spatial pattern of annual average soil erosion rates of an upland agricultural sub-watershed in the Western Ghats of Kerala, India. The critical soil erosion prone areas were identified by integrating geo-environmental variables such as land use/land cover, geomorphology, drainage density, drainage frequency, lineament frequency, slope, and relative relief after determining its relative contribution in conditioning the terrain susceptible to soil erosion by AHP technique, in a raster-based Geographic Information Systems environment. The spatial pattern of average annual soil erosion rates was obtained by RUSLE model that consider five factors, viz., rainfall erosivity (R), soil erodability (K), slope length and steepness (LS), cover management (C) and conservation practice (P) factors. The soil erosion probability map prepared by the AHP method was reclassified into soil erosion severity map showing regions of different erosion probability. Among this, the critical erosion zone occupies 4.23 % of the total area followed by high erosion severity zone occupies 18.39 % of the study area. Nil and low zones together constitute 44.15 % of the total area. The assessed annual average soil loss from the watershed shows an increased value of 4,227 t−1 h−1 year−1 as the maximum loss. The cross-comparison of potential soil erosion severity map with annual average soil loss in the area validates the finding of the study by a high spatial correlation. More erosion proneness and annual loss were observed in areas where the side slope plateau, denudational slope, and valley fills comes with high slope and relative relief. The intense terrain modification in this area with improper soil conservation measures makes the watershed more vulnerable to soil erosion.

Slope length gradient (LS) is one of the crucial factors in the Universal Soil Loss Equations (USLE, RUSLE). This study aimed at estimating the slope-length and slope-steepness (LS) factor for the entire watersheds of Afghanistan by using three different methods, namely; (1) LS-TOOLMFD (Method 1); (2) The Method of Equations (Method 2); and (3) The approach of Moore and Burch (Method 3). The first method uses the digital elevation model (DEM) in the ASCII format, and the other two methods use the DEM in the spatial domain. The results show that the LS-factor of the study area ranges from 0.01 to 44.31, with a mean of 5.24 and standard deviation of 6.95, according to Method 1; 0.03 to 163.49, with a mean of 9.6 and standard deviation of 13.58, according to Method 2; and 0 to 3985, with a mean of 7.16 and standard deviation of 29.7, according to Method 3. The study reveals that Methods 1 and 2 are more appropriate than Method 3 because Method 3 yields high LS-factor values close to or at streamlines located near mountainous regions. The highest LS values are found to be in the northeast, north, and central regions of Afghanistan, which is consistent with the high mountains and deep valley geomorphology, indicating that these regions are particularly vulnerable to soil erosion by rainfall-runoff processes. The sediment delivery ratio (SDR) for the Upper-Helmand River Basin (Upper-HRB) is also estimated by the RUSLE, employing the LS factors produced by the three methods. The results revealed that the average annual soil loss is found to be, respectively, 9.3, 18.2, and 11.1 (ton/ha/year) by using the three methods, corresponding to SDR of 23.5%, 12.1%, and 19.9%. Abbreviations: ABD: Asian Development Bank; ASCII: American Standard Code for Information Interchange; Bsh: cold semiarid steppes; Bsk: cold semiarid steppes; Bwh: warm and cold deserts; Bwk: warm and cold deserts; CMS: Convention on Migratory Species; Csa: humid subtropical; Csb: Mediterranean; D: humid continental; DEM: Digital Elevation Model; ET: extreme tundra; GCS: Geographical Coordinate System; GIS: Geographic Information System; GUI: Graphically User Interface; Ha: Hectare; HMRB: Harirod-Murghab River Basin; HRB: Helmand River Basin; KRB: Kabul River Basin; LS: Slope–Length and Slope–Steepness; Masl: meters above sea level; MFD: Multiple-Flow Direction; NRB: Northern River Basin; PARB: Panj-Amu Darya River Basin; RUSLE: Revised Universal Soil Loss Equations; SDR: Sediment Delivery Ratio; SFD: Single–Flow Direction; SRTM: Shuttle Radar Topography Mission; SY: Sediment Yield; UCA: Unit Contributing Area; USGS: United States Geological-Survey; USLE: Universal Soil Loss Equations

Soil erosion is a severe environmental problem worldwide, especially in tropical regions. The Revised Universal Soil Loss Equation (RUSLE), one of the universally accepted empirical soil erosion models, is quite commonly used in tropical climatic conditions to estimate the magnitude and severity of soil erosion. This study, apart from identifying the role of individual parameters in influencing the results of the RUSLE, also aims at refining the RUSLE results by incorporating the state-of-the-art technique Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) in a GIS environment by utilizing its ability to measure minute surface changes in millimetre levels. Apart from this novel approach of prioritising soil erosion classes using PSInSAR, the eroding surface conditions were also studied using low coherence value (<0.75 in this study). The spatially and temporally averaged annual soil loss and net soil erosion (2015–2019), derived through RUSLE and transport limited sediment delivery (TLSD) approach, respectively, was improved by spatially integrating the PSInSAR velocity map. The integrated methodological framework is demonstrated for a tropical river basin in South India (Muvattupuzha River Basin [MRB]), which shows a mean rate of net soil loss of 6.8 ton/ha/yr, and nearly 8% of the area experiences deposition. Our approach to improve the accuracy of RUSLE-based soil erosion classes using PSInSAR techniques clearly demarcated the areas that call for utmost priority in implementing management practices. The corollary results show that the very severe soil erosion class is characterized by PSI velocity with higher negative values, followed by the successively lower classes. Results strongly suggest that RUSLE output can be improved as well as validated using a velocity map derived from radar data.

Over cultivation, deforestation and free grazing are major factors facilitating soil erosion. Nowadays; in lower parts of Muga watershed soil erosion become as a continuous environmental problem. In this study an attempt has been made to modeling soil loss and identify the most erosion sensitive areas by using Revised Universal Soil Loss Equation integrated with GIS and remote sensing techniques for planning appropriate conservation measures in Muga watershed. The annual soil loss amount was estimated by using the Revised Universal Soil Loss Equation (RUSLE). Digital Elevation Model, digital soil map, thirty years rainfall records of six stations, and land cover data (Landsat images) were used to develop RUSLE soil loss variables. The annual soil loss rate from the catchment were estimated by integrating RUSLE parameters using raster calculator tool. The annual soil loss rate varies between 0.02 ton/ha/yr and 41.789 ton/ha/yr. The total annual soil loss in the watershed was 59751.41 tones, of these, 12806.15 tons were lost from 371.19 km 2 , 26562.44 tons from 214.30 km 2 , 15300.94 tons from 50.52 km 2 , 4059.05 tons from 4.61 km 2 , and 1022.83 tons from 0.37 km 2 of land per year. The rate of soil eroion was high in the lower part of the watershed. Slope gradient and length factor was the main factor for soil erosion increment followed by Support Practice (P) factor. As result of soil erosion cross tabulation; steep slopes, Rendzic leptosols and dominantly cultivated areas were detected as very severe erosivity. Therefore, the lower parts of the study needs to undertake effective soil and water conservation practices. Keywords: Soil loss, RUSLE, GIS, Remote sensing, Moga Watershed, Erosion hot spot areas DOI : 10.7176/JEES/9-10-03 Publication date :October 31 st 2019

Sediment transport from steep slopes and agricultural lands into the Uluabat Lake (a RAMSAR site) by the Mustafakemalpasa (MKP) River is a serious problem within the river basin. Predictive erosion models are useful tools for evaluating soil erosion and establishing soil erosion management plans. The Revised Universal Soil Loss Equation (RUSLE) function is a commonly used erosion model for this purpose in Turkey and the rest of the world. This research integrates the RUSLE within a geographic information system environment to investigate the spatial distribution of annual soil loss potential in the MKP River Basin. The rainfall erosivity factor was developed from local annual precipitation data using a modified Fournier index: The topographic factor was developed from a digital elevation model; the K factor was determined from a combination of the soil map and the geological map; and the land cover factor was generated from Landsat-7 Enhanced Thematic Mapper (ETM) images. According to the model, the total soil loss potential of the MKP River Basin from erosion by water was 11,296,063Mgyear(-1) with an average soil loss of 11.2Mgyear(-1). The RUSLE produces only local erosion values and cannot be used to estimate the sediment yield for a watershed. To estimate the sediment yield, sediment-delivery ratio equations were used and compared with the sediment-monitoring reports of the Dolluk stream gauging station on the MKP River, which collected data for >41years (1964-2005). This station observes the overall efficiency of the sediment yield coming from the Orhaneli and Emet Rivers. The measured sediment in the Emet and Orhaneli sub-basins is 1,082,010Mgyear(-1) and was estimated to be 1,640,947Mgyear(-1) for the same two sub-basins. The measured sediment yield of the gauge station is 127.6Mgkm(-2)year(-1) but was estimated to be 170.2Mgkm(-2) year(-1). The close match between the sediment amounts estimated using the RUSLE-geographic information system (GIS) combination and the measured values from the Dolluk sediment gauge station shows that the potential soil erosion risk of the MKP River Basin can be estimated correctly and reliably using the RUSLE function generated in a GIS environment.

Soil erosion prediction in the Grande River Basin, Brazil using distributed modeling

Objectives: The main purpose of this research is to identify severe soil loss areas in the Nanoi river basin of Assam, India to suggest appropriate soil management planning in the river basin. Methods: The Revised Universal Soil Loss Equation (RUSLE) integrates geospatial technologies to assess overall soil loss in the Nanoi river basin which provides a faster and more accurate estimation. It is possible to understand soil erosion patterns more thoroughly using RUSLE which makes sustainable soil management easier. Findings: According to the RUSLE equation, the Nanoi river basin experiences an estimated total soil loss of 18,562.5 tons per year, with an average annual soil loss of 0.32 t/h/y. This suggests that approximately 0.64 km2 of the area falls into the Moderate, high and Extreme soil erosion sensitivity zones. Furthermore, a final map is generated to display different areas with varying levels of soil loss rates. Novelty: The geospatial approaches used produce precise findings at a reasonable price and demonstrate the severity of soil loss in the river basin. If soil loss continues at the current rate, there is a high likelihood that certain areas on both sides of the river, particularly in the downstream region of the basin, will experience fluvial hazards, such as drainage congestion and floods. Hence, the results generated will be helpful in the management practices of the river basin. Keywords: Soil loss, Soil erosion, Geospatial, Revised universal soil loss equation, River basin