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Journal articles on the topic 'Soil loss estimation'

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Published: 7 June 2025

Last updated: 2 August 2025

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1

Nortcliff, Stephen. "Soil loss estimation." Progress in Physical Geography: Earth and Environment 10, no. 2 (1986): 249–55. http://dx.doi.org/10.1177/030913338601000207.

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Kalita, Nilotpal, and Niranjan Bhattacharjee. "Estimation of Soil Loss in the Nanoi River Basin using Geospatial Techniques." Indian Journal Of Science And Technology 17, no. 18 (2024): 1845–53. http://dx.doi.org/10.17485/ijst/v17i18.1382.

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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

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Nouwakpo, Sayjro K., Mark A. Weltz, Awadis Arslan, Colleen H. Green, and Osama Z. Al-Hamdan. "Process-Based Modeling of Infiltration, Soil Loss, and Dissolved Solids on Saline and Sodic Soils." Transactions of the ASABE 61, no. 3 (2018): 1033–48. http://dx.doi.org/10.13031/trans.12705.

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Abstract. The Colorado River is a central socio-economic resource of the western U.S. but is vulnerable to excessive salt load. To improve knowledge of the surface processes controlling salt loading, a series of rainfall simulation experiments were conducted in saline rangelands of the upper Colorado River basin (UCRB). In this study, data from these rainfall simulation experiments were used to develop predictive equations for the process-based Rangeland Hydrology and Erosion Model (RHEM). Runoff and soil loss prediction performances were assessed with the Nash-Sutcliffe efficiency (NSE), the

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4

Nilotpal, Kalita, and Bhattacharjee Niranjan. "Estimation of Soil Loss in the Nanoi River Basin using Geospatial Techniques." Indian Journal of Science and Technology 17, no. 18 (2024): 1845–53. https://doi.org/10.17485/IJST/v17i18.1382.

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Abstract <strong>Objectives:</strong> 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. <strong>Methods:</strong> 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. <strong>Findings:

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5

Chandramohan, T., and Dilip G. Durbude. "Estimation of soil erosion potential using Universal Soil Loss Equation." Journal of the Indian Society of Remote Sensing 30, no. 4 (2002): 181–90. http://dx.doi.org/10.1007/bf03000361.

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6

KuroTamuno Peace, Jackson,, and Okwere, Chiziandu Enyinda. "Modelling and Estimation of Average Annual Soil Loss in Ika South Local Government Area of Delta State, Nigeria." Journal of Geography, Environment and Earth Science International 29, no. 1 (2025): 45–59. https://doi.org/10.9734/jgeesi/2025/v29i1854.

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Soil-related phenomenon and the Sustainable Development Goals (SDGs) particularly SDG-15 are strictly tied. A healthy soil ecosystem encourages the existence of life on Earth and for agricultural purposes. One of the key factors that is contributing to unhealthy soil ecosystems is soil erosion. The research involves the assessment and modelling of soil erosion using the Revised Universal Soil Loss Equation (RUSLE II) with the sole aim of ascertaining the annual Soil loss within the study area, this will be a veritable tool in estimating soil loss in farming systems and in supporting the achiev

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Noor, Hamze, Seyed Khalagh Mirnia, Somaye Fazli, Mohamad bagher Raisi, and Mahdi Vafakhah. "Application of MUSLE for the prediction of phosphorus losses." Water Science and Technology 62, no. 4 (2010): 809–15. http://dx.doi.org/10.2166/wst.2010.092.

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Soil erosion in forestlands affects not only land productivity but also the water body down stream. The Universal Soil Loss Equation (USLE) has been applied broadly for the prediction of soil loss from upland fields. However, there are few reports concerning the prediction of nutrient (P) losses based on the USLE and its versions. The present study was conducted to evaluate the applicability of the deterministic model Modified Universal Soil Loss Equation (MUSLE) to estimation of phosphorus losses in the Kojor forest watershed, northern Iran. The model was tested and calibrated using accurate

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M, MUTHAMIL SELVAN, SUBRAMANIAN K, RAJENDRAN V, and RANGASAMY K. "Estimation of runoff and soil loss for a hilly watershed." Madras Agricultural Journal 88, june (2001): 284–86. http://dx.doi.org/10.29321/maj.10.a00339.

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Estimation and analysis of rainfall-runoff-discharge data for a watershed would be of great use. for optimum planning and managing the watershed. Universal Soil Loss Equation (USLE) was used to estimate the annual soil loss from a hilly watershed in the Nilgiris. A set of linear regression models were developed by relating (1) rainfall and runoff, (2) rainfall and soil loss and they can be used for prediction of the runoff and soil loss for the watershed. Packages of soil and water conservation measures were recommendd for the watershed as the present rinoff and soil loss wre found to be enorm

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Ratul, Das, Gogoi Barnali, and Kumar Jaiswal Monoj. "Soil loss assessment in Sadiya Region, Assam, India using remote sensing and GIS." Indian Journal of Science and Technology 13, no. 23 (2020): 2319–27. https://doi.org/10.17485/IJST/v13i23.588.

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Abstract <strong>Objectives:</strong> This study focuses on identifying areas of intense soil erosion in Sadiya, a subdivision of Tinsukia district of Assam in India to facilitate appropriate implementation of soil management and conservation schemes in an administrative unit. <strong>Methods:</strong> A comprehensive methodology of Remote Sensing (RS) and Geographical Information System (GIS) is implemented with an empirical model called the Revised Universal Soil Loss Equation (RUSLE). RUSLE is the best model to assess soil loss creating different raster layers in GIS. <s

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10

Schnitzer, S., F. Seitz, A. Eicker, A. Güntner, M. Wattenbach, and A. Menzel. "Estimation of soil loss by water erosion in the Chinese Loess Plateau using Universal Soil Loss Equation and GRACE." Geophysical Journal International 193, no. 3 (2013): 1283–90. http://dx.doi.org/10.1093/gji/ggt023.

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11

Siddiqui, Saima, Mirza Wajid Ali Safi, Aqil Tariq, Naveed Ur Rehman, and Syed Waseem Haider. "GIS Based Universal Soil Erosion Estimation in District Chakwal Punjab, Pakistan." International Journal of Economic and Environmental Geology 11, no. 2 (2020): 30–36. http://dx.doi.org/10.46660/ijeeg.vol11.iss2.2020.443.

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Soil erosion is a serious environmental problem faced by district Chakwal. Unpredictable short term and high intensity rainfall, improper cultivation and deforestation have accelerated the soil erosion in the district. The agricultural productivity of the study area can be enhanced by understanding, estimating and controlling the root causes of soil erosion. This study was undertaken to estimate and spatially represent the rate of average annual soil erosion in Chakwal using GIS/RS techniques. The soil erosion was estimated using Universal Soil Loss Equation (USLE) model. To find out parameter

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Suhara K K, Shaheemath, Anu Varughese, Anjaly C. Sunny, and Anjitha Krishna P R. "Erosivity Factor of the Revised Universal Soil Loss Equation (RUSLE) - A Systematized Review." Current World Environment 18, no. 2 (2023): 433–45. http://dx.doi.org/10.12944/cwe.18.2.02.

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The Revised Universal Soil Loss Equation (RUSLE) is a globally accepted erosion model which has gained good acceptability. Among the five influences of the RUSLE method of soil erosion estimation, the erosivity factor (R) represents rainfall event’s ability to produce erosion. It is mainly affected by rainfall intensity and kinetic energy of the rain. The erosion index represented by EI30 is the most common R-factor estimation method. Due to the non-availability of rainfall intensity data in many watersheds, researchers have developed methods for erosivity estimation using rainfall depth. The

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Lee, G. S., and K. H. Lee. "Scaling effect for estimating soil loss in the RUSLE model using remotely sensed geospatial data in Korea." Hydrology and Earth System Sciences Discussions 3, no. 1 (2006): 135–57. http://dx.doi.org/10.5194/hessd-3-135-2006.

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Abstract. Accurate estimation of soil loss/deposition forced by rainfall events plays a major role in water resources management and it directly affects the quality of agricultural land and water storage capacity in reservoirs. In this paper, the soil loss model, Revised Universal Soil Loss Equation (RUSLE) was used to quantify soil loss in a small basin located in southern part of Korea. The surface characteristics, such as soil texture, elevation, and vegetation type, are needed to run the RUSLE model. Remotely sensed geospatial data has been successfully used to derive suitable model factor

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SHIONO, Takahiro, Ken-ichiro KAMIMURA, Shuji OKUSHIMA, and Masato FUKUMOTO. "Soil Loss Estimation on a Local Scale for Soil Conservation Planning." Japan Agricultural Research Quarterly: JARQ 36, no. 3 (2002): 157–61. http://dx.doi.org/10.6090/jarq.36.157.

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15

Madhukar, A., N. Hari, Ch Radha Srivalli, and T. L. Neelima. "Spatio-temporal Soil Erosion Estimation in Sangareddy District, Telangana Using Rusle Model." International Journal of Environment and Climate Change 13, no. 10 (2023): 287–95. http://dx.doi.org/10.9734/ijecc/2023/v13i102638.

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Soil erosion poses a significant environmental challenge worldwide, causing the depletion of fertile topsoil, reduced crop productivity, and heightened sedimentation in water bodies. The extent of erosion depends on factors such as rainfall intensity, land slope, soil type, land use, and management practices. Accurate assessment of soil loss, considering spatial, temporal, and climate change factors, is vital for effective soil and water conservation planning. In this study, a spatial model for estimating soil loss was developed by integrating GIS with the Revised Universal Soil Loss Equation

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16

M. H. Al-Kadhimi, Ahmed, Zainb A. A. Al-Saad, and Fatima A. A. Al-Badran. "ESTIMATING SOIL LOSS BY WATER EROSION." University of Thi-Qar Journal for Engineering Sciences 4, no. 3 (2013): 29–45. http://dx.doi.org/10.31663/utjes.v4i3.183.

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The study is focused on the estimation of rate of soil erosion, using the Universal Soil Loss Equation(USLE), for the embankment of Al-Garma Bridge in Basrah, Iraq. The soil erosion is estimated for the zone of average slope in the study area. The factors considered are rainfall, intensity of rainfall, type of soil, slope length, slope steepness, land use classification, and the existing of soil conservation practices. Detailed analysis of soil samples were done to asses the texture, structure, permeability and organic matter content of the soil samples of the embankment of the Al-Garma Bridge

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17

Baral, Sachit, and Umesh Bhurtyal. "Soil erosion estimation using USLE/RUSLE in Kaski district." OODBODHAN 8 (July 8, 2025): 82–92. https://doi.org/10.3126/oodbodhan.v8i1.81252.

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Soil erosion is a major environmental concern in Nepal’s mid-hill regions, particularly in areas like Kaski District where steep slopes, intense rainfall, and changing land use contribute to land degradation. This study aims to assess soil erosion loss in Kaski District by applying the Universal Soil Loss Equation (USLE) and the Revised Universal Soil Loss Equation (RUSLE) models. Utilizing available datasets such as SRTM DEM for slope and aspects data, CHIRPS rainfall data, NARC soil data and ICIMOD land use landcover map, soil erosion loss maps were generated to identify soil erosion pattern

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18

Gallo, Bruna Cristina, Paulo Sérgio Graziano Magalhães, José A. M. Demattê, et al. "Soil Erosion Satellite-Based Estimation in Cropland for Soil Conservation." Remote Sensing 15, no. 1 (2022): 20. http://dx.doi.org/10.3390/rs15010020.

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Intensive cropland expansion for an increasing population has driven soil degradation worldwide. Modeling how agroecosystems respond to variations in soil attributes, relief and crop management dynamics can guide soil conservation. This research presents a new approach to evaluate soil loss by water erosion in cropland using the RUSLE model and Synthetic Soil Image (spectroscopy technique), which uses time series remotely sensed environmental, agricultural and anthropic variables, in the southeast region of São Paulo State, Brazil. The availability of the open-access satellite images of Tropic

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Hatti, Basavaraj, and Laishram Ibomcha Singh. "Spatial Analysis of Soil Loss Estimation in the Raichur District is part of Krishna River, Peninsular India using Geospatial Technology." YMER Digital 21, no. 05 (2022): 1536–49. http://dx.doi.org/10.37896/ymer21.05/g8.

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A spatial technique is essential for proper utilization and soil preservation management often requires estimates of soil loss at a provincial scale. With the advent of powerful and high-speed personal computers, efficient techniques for soil loss estimation have evolved, of which geoinformatics technology are of great significance. A geospatial analysis was carried out to know the soil loss calculations in the Krishna River in part of Raichur district, Karnataka. An analytical representation such as Universal Soil Loss Equation (USLE) and its following Revised Universal Soil Loss Equation (RU

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20

Vijay, Suryawanshi. "ENVIRONMENTAL ASSESSMENT OF SOIL EROSION ON LAKNAVARAM LAKE BASIN." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 4, no. 9 (2017): 153–66. https://doi.org/10.5281/zenodo.999295.

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The aim of this work is to estimate soil erosion on Laknavaram lake basin, JayashankarBhupalpally district, Telangana. Soils play a major role in production of food, storage of water, nutrients and organic matter. Loss of soils effects its ability to perform these functions. Soil loss reduces its potential in crop production, damages drainage network, decreases the capacity of hydraulic structures. Estimation of soil erosion in this study has been done using USLE model. The USLE was developed from soil erosion plot and rainfall simulator experiments. The USLE is composed of six factors to esti

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Zonunsanga, R. "Estimation of Soil Loss in Teirei Watershed of Mizoram by using Universal Soil Loss Equation Model." Science & Technology Journal 4, no. 1 (2016): 43–47. http://dx.doi.org/10.22232/stj.2016.04.01.06.

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22

Saikia, Ananya, Monjit Borthakur, and Bikash Jyoti Gautam. "GIS-Based Assessment of Soil Erosion Using the Revised Universal Soil Loss Equation (RUSLE) Model in Morigaon District, Assam, India." Nature Environment and Pollution Technology 24, no. 1 (2025): B4226. https://doi.org/10.46488/nept.2025.v24i01.b4226.

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Soil erosion in the agricultural landscape of Assam has been impacting the livelihoods of millions. In administrative regions like districts, which are vulnerable to natural disasters like floods and bank erosion, GIS-based soil erosion estimating studies can help planners and policymakers identify areas of soil erosion to implement scientific conservation measures. The main purpose of this study is to estimate soil loss and to determine soil loss zones in the Morigaon district of Assam. The Revised Universal Soil Loss Equation (RUSLE) combined with GIS has been incorporated into the present s

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23

Hrabalíková, M., and M. Janeček. "Comparison of different approaches to LS factor calculations based on a measured soil loss under simulated rainfall." Soil and Water Research 12, No. 2 (2017): 69–77. http://dx.doi.org/10.17221/222/2015-swr.

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Geographic Information Systems (GIS) in combination with soil loss models can enhance evaluation of soil erosion estimation. SAGA and ARC/INFO geographic information systems were used to estimate the topographic (LS) factor of the Universal Soil Loss Equation (USLE) that in turn was used to calculate the soil erosion on a long-term experimental plot near Prague in the Czech Republic. To determine the influence of a chosen algorithm on the soil erosion estimates a digital elevation model with high accuracy (1 × 1 m) and a measured soil loss under simulated rainfall were used. These then provide

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Abdiaziz, Hassan Nur, Ali Ibrahim Abdullahi, Abdi Warsame Abas, and Hurre Farah Abdikarim. "Estimation of soil erosion and risk assessment in Somalia." International Journal of Agricultural Research, Innovation and Technology 14, no. 2 (2024): 111–21. https://doi.org/10.3329/ijarit.v14i2.79422.

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Soil erosion is a pressing issue that threatens environmental stability and agricultural productivity across the globe. In Somalia, where the population already contends with severe socioeconomic and environmental challenges, the effects of erosion compound problems like food insecurity and land degradation. This study investigates erosion patterns and identifies areas prone to soil loss, aiming to provide actionable insights for improving land use practices and resource management. The research employs advanced analytical tools to study how erosion factors are distributed geographically. Data

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Lee, Sanghyun, Maria L. Chu, Jorge A. Guzman, and Dennis C. Flanagan. "Modeling soil erodibility and critical shear stress parameters for soil loss estimation." Soil and Tillage Research 218 (April 2022): 105292. http://dx.doi.org/10.1016/j.still.2021.105292.

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Marques, Valter, Marcos Ceddia, Mauro Antunes, et al. "USLE K-Factor Method Selection for a Tropical Catchment." Sustainability 11, no. 7 (2019): 1840. http://dx.doi.org/10.3390/su11071840.

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The use of the Universal Soil Loss Equation (USLE) and the Sediment Delivery Ratio (SDR) facilitates sediment yield (SY) estimates in watersheds. However, the soil loss predictions are frequently unrealistic because of the methods used to estimate the USLE’s factors. Here, we evaluated the performance of methods to estimate the soil erodibility (K-factor) and the influence of its estimation in the SY predictions. K-factor values were obtained from three widely used equations and using a portable rainfall simulator. These values were used to compute annual average soil loss and SY in a tropical

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Guedes, Nayme Soares, Camila de Sousa Queiroz Almeida, Caio Herman Teixeira de Oliveira, et al. "Soil and Water Loss Estimation Mathematical Models: A Review." International Journal of Geoscience, Engineering and Technology 11, no. 1 (2024): 1–10. https://doi.org/10.70597/ijget.v11i1.554.

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The erosion process is natural but has intensified in recent years due to anthropogenic activities, becoming a socio-environmental issue resulting from soil degradation. This process impacts various areas, and its measurement is a critical tool for adopting management strategies and conservation practices. Mathematical models can estimate soil losses under different environmental conditions. When integrated with GIS environments, these models significantly reduce execution and study costs, while generating valuable information, creating maps, and characterizing the environment. This study aime

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Park, Youn-Shik, Jong-Gun Kim, Sung-Gu Heo, et al. "Comparison of Soil Loss Estimation using SWAT and SATEEC." Journal of The Korean Society of Agricultural Engineers 50, no. 1 (2008): 3–12. http://dx.doi.org/10.5389/ksae.2008.50.1.003.

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Ciesiolka, C. A. A., B. Yu, C. W. Rose, H. Ghadiri, D. Lang, and C. Rosewell. "Improvement in soil loss estimation in USLE type experiments." Journal of Soil and Water Conservation 61, no. 4 (2006): 223–29. https://doi.org/10.1080/00224561.2006.12435887.

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30

Demir, Saniye, Halis Şimşek, and Yağmur Kaya. "Evaluation of WEPP and Its Comparison with USLE and MUSLE in Yozgat-Kadılı Village." Turkish Journal of Agriculture - Food Science and Technology 12, no. 10 (2024): 1684–95. http://dx.doi.org/10.24925/turjaf.v12i10.1684-1695.6895.

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The water erosion is a significant environmental issue in arid and semi-arid regions. It leads to soil degradation, reduced agricultural productivity, and desertification. This article used The WEPP, the USLE, and the MUSLE models to estimate the average soil loss in the Yozgat-Kadılı village. Also, The MUSLE model utilized the WEPP model-estimated runoff for soil loss estimation. The USLE model, which estimates soil erosion using six factors (R, K, L, S, P, and C), can be improved by incorporating the Modified Fournier Index (MFI). Results indicated that the MUSLE model (3.66 t/ha) performed

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Amulya, T. H. M., and Nerlikar Dhanashree. "WATERSHED MANAGEMENT BETTAHALASURU USING GIS AND REMOTE SENSING." International Journal of Advanced Trends in Engineering and Technology 3, no. 1 (2018): 12–19. https://doi.org/10.5281/zenodo.1135259.

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Satellite remote sensing data with geographic information system has a pragmatic approach to map and generate spatial input layers of predicting response behavior and yield of watershed. Hence, in the present study an attempt has been made to understand the hydrological process of the command area at the watershed level by drawing the inferences from soil loss estimation. The study area chosen for the present study is Bettahalasuru command area situated in Bengaluru North  lies geographically  between 13°10'8"N and 13°9'12"N latitude and 77°35'58&quo

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Ghosh, Amlan, Sayandeep Rakshit, Suvarna Tikle, et al. "Integration of GIS and Remote Sensing with RUSLE Model for Estimation of Soil Erosion." Land 12, no. 1 (2022): 116. http://dx.doi.org/10.3390/land12010116.

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Globally, soil erosion is a significant problem contributing to nutrient loss, water quality degradation, and sand accumulation in water bodies. Currently, various climate factors are affecting the natural resources entire worldwide. Agricultural intensification, soil degradation, and some other human impacts all contribute to soil erosion, which is a significant issue. Management and conservation efforts in a watershed can benefit from a soil erosion study. Modeling can establish a scientific and accurate method to calculate sediment output and soil erosion below a variety of circumstances. T

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Akbar, Ruzbeh, Daniel J. Short Gianotti, Kaighin A. McColl, Erfan Haghighi, Guido D. Salvucci, and Dara Entekhabi. "Estimation of Landscape Soil Water Losses from Satellite Observations of Soil Moisture." Journal of Hydrometeorology 19, no. 5 (2018): 871–89. http://dx.doi.org/10.1175/jhm-d-17-0200.1.

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Abstract This study presents an observation-driven technique to delineate the dominant boundaries and temporal shifts between different hydrologic regimes over the contiguous United States (CONUS). The energy- and water-limited evapotranspiration regimes as well as percolation to the subsurface are hydrologic processes that dominate the loss of stored water in the soil following precipitation events. Surface soil moisture estimates from the NASA Soil Moisture Active Passive (SMAP) mission, over three consecutive summer seasons, are used to estimate the soil water loss function. Based on analys

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S.Poongodi, R.Rajkumar Dr., and Rose Dr.R.S.Suja. "An Estimation of Soil Erosion in Gandamanur Watershed Using Geospatial Technology." RESEARCH REVIEW International Journal of Multidisciplinary 3, no. 12 (2018): 908–12. https://doi.org/10.5281/zenodo.2531836.

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Soil erosion is one of the critical environmental problems and a major threat to many natural resources. Stream bank erosion is a kind of water erosion, in which soil is removed by the runoff flowing over the sides of the stream coming from the areas or by undercutting of soil below the water surface from the stream section. Quantitative analysis of soil loss and description of erosion prone areas are essential for conservation programme. The present study focuses on estimation of soil erosion in Gandamanur watershed of Theni District. The soil loss values estimated for Gandamanur watershed ra

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Ende, J. van den. "Estimating the chemical composition of the soil solution of glasshouse soil. 2. Relationships between the compositions of soil solution and aqueous extracts." Netherlands Journal of Agricultural Science 37, no. 4 (1989): 323–34. http://dx.doi.org/10.18174/njas.v37i4.16617.

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The possibilities of estimating ECs and K, Na, Ca, Mg, NO3, Cl and SO4 contents of press extracts of glasshouse soils from corresponding analytical data of saturation extracts or 1:5 by-weight extracts were examined through construction of regression equations. The analytical data of saturation and 1:5 by-weight extracts were and were not corrected by multiplication with dilution factors, these being the ratios between the water contents of the soils employed to obtain the extracts and the water contents of the soils at field capacity. The dilution factors used were not derived from actual wat

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Parmar, Sushma. "Estimation of Soil Loss and Soil Erodibility for Different Crops, Nutrient Managements and Soil Series." Indian Journal of Pure & Applied Biosciences 8, no. 1 (2020): 204–12. http://dx.doi.org/10.18782/2582-2845.7984.

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37

Deka, Dhanjit, Jyoti Prasad Das, Madine Hazarika, and Debashree Borah. "Prioritization of Sub-Watershed Based on Soil Loss Estimation Using RUSLE Model." International Journal of Applied Geospatial Research 15, no. 1 (2024): 1–25. http://dx.doi.org/10.4018/ijagr.340039.

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Soil erosion is one of the most crucial land degradation problems and is considered the most critical environmental hazard worldwide. The present study uses remote sensing data integrated with the geographical information system (GIS) technique and the revised universal soil loss equation (RUSLE) model for assessing the annual average soil loss of the Digaru watershed of India for 1999 and 2020. The estimated mean gross yearly soil loss from the entire watershed was 102716 t yr-1 in 1999 and 178931.6 t yr-1 in 2020. The overall average soil loss rate increased significantly between 1999 and 20

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Todisco, F., L. Brocca, L. F. Termite, and W. Wagner. "Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale." Hydrology and Earth System Sciences 19, no. 9 (2015): 3845–56. http://dx.doi.org/10.5194/hess-19-3845-2015.

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Abstract. The potential of coupling soil moisture and a Universal Soil Loss Equation-based (USLE-based) model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e., the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observa

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Nanjundi.P, Dr.M.Inayathulla, Jairaj, and Vijaykumar.H. "ESTIMATION OF SOIL EROSION USING REVISED UNIVERSAL SOIL LOSS EQUATION FOR HAROHALLI WATERSHED." International Journal of Advances in Engineering & Scientific Research 1, no. 5 (2014): 01–11. https://doi.org/10.5281/zenodo.10721038.

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<strong><em>Abstract</em></strong><em>-</em>   <em>Soil erosion is the detachment and transportation of soil materials from one place to another, resulting in the removal of the uppermost fertile soil layer, thus decreasing its productivity. Soil erosion was estimated by using Universal Soil Loss Equation in a GIS environment and prioritization of catchments on that basis, the present study Harohalli watershed, Kanakapura Taluk, Ramanagara District area was chosen since it falls in the rocks and granite area.</em><em> soil and slope maps were prepared from Survey of India maps and NB

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Bouhlassa, S., and N. Bouhsane. "ESTIMATION OF SOIL LOSSES BY THE IMPROVED TILLAGE HOMOGENIZATION MODEL AND RUSLE MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 961–67. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-961-2020.

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Abstract. Soil erosion by water is a major environmental problem in the Mediterranean areas. It results in land degradation and soil losses, decreases soil structural stability, and increases soil erodibility. Hence, the need for reliable scientific methods for obtaining soil erosion data becomes crucial. The study aims to estimate soil loss in the Moroccan watershed using two soil erosion estimation models and to discuss the differences between those models. The first model used in this study is the improved tillage homogenization model (T-H) which permits to predict the magnetic susceptibili

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Mandal, U. K., and K. Kumari. "GEOSPATIAL TECHNOLOGY BASED SOIL LOSS ESTIMATION FOR SUSTAINABLE URBAN DEVELOPMENT OF BUTWAL SUBMETROPOLITAN CITY, NEPAL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 137–44. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-137-2020.

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Abstract. Geo-spatial technology was attempted to estimate the potential and actual soil loss and its correlative interpretation with physiographic soil units and land use and cover types in Butwal sub-metropolitan city, Central Region of Nepal. Among several empirical and physically based soil erosion models, widely used RKLS and RKLSCP, Revised Universal Soil Loss Equation (RUSLE) were employed to estimate the potential and actual soil loss in the present investigation, respectively. Five years of rainfall, topographic contour-spot height and soil map were basically used as source of informa

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Todisco, F., L. Brocca, L. F. Termite, and W. Wagner. "Use of satellite and modelled soil moisture data for predicting event soil loss at plot scale." Hydrology and Earth System Sciences Discussions 12, no. 3 (2015): 2945–73. http://dx.doi.org/10.5194/hessd-12-2945-2015.

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Abstract. The potential of coupling soil moisture and a~USLE-based model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in Central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e. the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for

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Joshi, Veena, Nilesh Susware, and Debasree Sinha. "Estimating soil loss from a watershed in Western Deccan, India, using Revised Universal Soil Loss Equation." Landscape & Environment 10, no. 1 (2016): 13–25. http://dx.doi.org/10.21120/le/10/1/2.

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USLE (Universal Soil Loss Equation) is the original and the most widely accepted soil loss estimation technique till date which has evolved from a design tool for conservation planning to a research methodology all across the globe. The equation has been revised and modified over the years and became a foundation for several new soil loss models developed all around the world. The equation has been revised as RUSLE by Renard et al. (1991) and is computed in GIS environment. The Revised equation is landuse independent which makes it a useful technique to apply in a variety of environment. The p

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BYTYQI, Valbon, and Tropike AGAJ. "SPATIAL ESTIMATION OF SOIL EROSION USING GEOSPATIAL TECHNIQUE – A CASE STUDY OF JANJEVA RIVER CATCHMENT (KOSOVO)." Carpathian Journal of Earth and Environmental Sciences 19, no. 2 (2024): 233–43. http://dx.doi.org/10.26471/cjees/2024/019/294.

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The aim of the study is to analyse the spatial estimation of soil erosion in a small size river catchment of Janjeva (Kosovo). Soil erosion is becoming a serious threat and its spatial estimation is crucial for soil resource protection and management. The Revised Universal Soil Loss Equation (RUSLE) methodology integrated with GIS techniques was applied to estimate the annual erosion rate. Different datasets including open-source data were used to find main components of soil erosion. The results show different soil erosion classes depending on lithological settings, landforms, climate conditi

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Amaral, Lucas Kister, Sabrina Baesso Cadorin, Álvaro José Back, Fernanda Dagostin Szymanski, and Claudia Weber Corseuil. "Estimation of soil loss by the USLE model in a mountain basin in the south of Santa Catarina state, Brazil." Revista Eletrônica em Gestão, Educação e Tecnologia Ambiental 24 (December 4, 2020): e20. http://dx.doi.org/10.5902/2236117062695.

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Water erosion is a factor of soil degradation that is triggered by the impact of raindrops originated by intense rainfall disaggregating the soil, followed by the carrying of particles by surface runoff. In the erosion process, in addition to soil loss, nutrients, fertilizers, and pesticides are carried resulting in water courses and water pollution. Erosion can have a major impact on agricultural production, when soil use and management techniques are not used. Therefore, this study aimed to evaluate the soil loss in the Malacara river basin, which is a sub-basin of the Mampituba river basin

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Gwapedza, David, Andrew Slaughter, Denis Hughes, and Sukhmani Mantel. "Regionalising MUSLE factors for application to a data-scarce catchment." Proceedings of the International Association of Hydrological Sciences 377 (April 16, 2018): 19–24. http://dx.doi.org/10.5194/piahs-377-19-2018.

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Abstract. The estimation of soil loss and sediment transport is important for effective management of catchments. A model for semi-arid catchments in southern Africa has been developed; however, simplification of the model parameters and further testing are required. Soil loss is calculated through the Modified Universal Soil Loss Equation (MUSLE). The aims of the current study were to: (1) regionalise the MUSLE erodibility factors and; (2) perform a sensitivity analysis and validate the soil loss outputs against independently-estimated measures. The regionalisation was developed using Geograp

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Wang, Xuezhi, Wenhui Li, Qingliang Li, and Xiaoning Li. "Modeling Soil Temperature for Different Days Using Novel Quadruplet Loss-Guided LSTM." Computational Intelligence and Neuroscience 2022 (February 17, 2022): 1–17. http://dx.doi.org/10.1155/2022/9016823.

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Soil temperature (Ts), a key variable in geosciences study, has generated growing interest among researchers. There are many factors affecting the spatiotemporal variation of Ts, which poses immense challenges for the Ts estimation. To enrich processing information on loss function and achieve better performance in estimation, the paper designed a new long short-term memory model using quadruplet loss function as an intelligence tool for data processing (QL-LSTM). The model in this paper combined the traditional squared-error loss function with distance metric learning between the sample featu

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Petroselli, Andrea, Ciro Apollonio, Davide Luciano De Luca, et al. "Comparative Evaluation of the Rainfall Erosivity in the Rieti Province, Central Italy, Using Empirical Formulas and a Stochastic Rainfall Generator." Hydrology 8, no. 4 (2021): 171. http://dx.doi.org/10.3390/hydrology8040171.

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Soil erosion caused by intense rainfall events is one of the major problems affecting agricultural and forest ecosystems. The Universal Soil Loss Equation (USLE) is probably the most adopted approach for rainfall erosivity estimation, but in order to be properly employed it needs high resolution rainfall data which are often unavailable. In this case, empirical formulas, employing aggregated rainfall data, are commonly used. In this work, we select 12 empirical formulas for the estimation of the USLE rainfall erosivity in order to assess their reliability. Moreover, we used a Stochastic Rainfa

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Assefa, Fikru, Eyasu Elias, Teshome Soromessa, and Abreham Berta Aneseyee. "Evaluating Soil Loss for Identification of Land Risk Area in the Kabe Watershed of Ethiopia." Applied and Environmental Soil Science 2022 (November 9, 2022): 1–13. http://dx.doi.org/10.1155/2022/7679104.

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Soil erosion is the main cause of topsoil loss in farming land, which results in reduction in cropland productivity. Soil loss estimation is crucial for developing soil and water conservation strategies for Ethiopia. The investigation aimed to estimate the soil loss in various intensifications of land use patterns, including slope categories, using the soil estimation model associated with the ArcGIS process. It is analyzed in Ethiopian conditions based on erosivity, soil erodibility, vegetative cover (C) remote sensing data, slop-length factor (LS), and management practices (P). The mean soil

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Salino, Beanne Wil Angelo L., Kryztyll Jydd T. Medrano, Mary Grace L. Mosquito, Viene Xelo Pearl B. Dagaraga, and Jefferson Jr Ramos Vallente. "A GIS-RS Approach for RUSLE-Based Method of Mean Estimation of Mean Annual Soil Loss of the Tagoloan River Basin, Philippines." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-5-2024 (November 12, 2024): 97–103. http://dx.doi.org/10.5194/isprs-archives-xlviii-5-2024-97-2024.

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Abstract. Soil erosion is a serious environmental concern in Tagoloan River Basin (TRB), a major watershed in Northern Mindanao, Philippines. It leads to soil loss causing detrimental impacts such as decreased soil productivity, nutrient loss, siltation, and water quality degradation among others. These impacts are better understood by estimating the degree of soil loss in the watershed and visualized in a GIS-based and factor-based approach using the Revised Universal Soil Loss Equation (RUSLE) model. Thematic maps of soil loss were generated with factors for rainfall erosivity (R), soil erod

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