Integrated multi-hazard assessment for climate-resilient watershed management: A transferable prioritization framework from Nepal's Mid-Hills.

The Guizhou Plateau, with the most extensive and representative karst landforms worldwide, is characterized by severe soil erosion and a highly fragile ecological environment. However, large-scale assessments of soil conservation services in this region remain limited. A key challenge lies in identifying appropriate datasets and methodologies for regional-scale soil conservation service evaluations, particularly under conditions of data scarcity or limited data accuracy. In this study, Unmanned Aerial Vehicle imagery, runoff plot observations, ground survey data, and multi-source satellite remote sensing data were integrated to refine LS and C in the Revised Universal Soil Loss Equation (RUSLE), thereby establishing a parameterized and localized soil erosion model. This improvement provided a methodological foundation for soil conservation service research in the region. Subsequently, the spatiotemporal variations in soil conservation services in the Guizhou Plateau over the past two decades were assessed. Furthermore, the relationships between soil conservation services and other key ecosystem services, including water conservation and carbon sequestration, were quantitatively examined, and the driving factors were analyzed. Soil conservation on the Guizhou Plateau exhibited an improving trend from 2000 to 2020. In karst areas, the relationship between soil conservation and water conservation was primarily influenced by temperature, altitude, and vegetation coverage, whereas in non-karst areas, it was regulated by rainfall and slope. Ecological restoration projects have enhanced the synergy between soil conservation and carbon sequestration by promoting vegetation cover. These findings could contribute to the next stage of ecological engineering initiatives and ecological policy implementation in Guizhou.

Projected sediment supply deficits threaten intertidal wetland resilience to sea-level rise in southeast Australia.

Estimating soil erosion utilizing geospatial method and revised universal soil loss equation (RUSLE) of Abu Ghraibat Watershed, Eastern Misan Governorate, Iraq.

The watersheds of the Senegal River, particularly the Gorgol River, are increasingly affected by hydrological extremes such as floods and soil erosion, pressures that are intensified by ongoing climate change and human activities. This study investigates the hydrological functioning and erosion susceptibility of the Gorgol tributaries to support sustainable watershed management. A multidisciplinary approach was applied, combining spatial analysis of watershed characteristics with hydrological modeling and erosion risk mapping. Key datasets included satellite-derived climate variables, which were validated with ground measurements and integrated with topographic, geological, soil, and land-use data. Climate analysis revealed a pronounced north–south rainfall gradient, with most precipitation occurring between July and September, alongside a +1 °C temperature increase over the past 42 years. Erosion susceptibility was assessed using the Revised Universal Soil Loss Equation, incorporating factors such as rainfall erosivity, soil erodibility, slope parameters, land-cover, and conservation practices. Results indicate that areas in the southern basin and those with fragile soils are most vulnerable, with rainfall erosivity being the primary driver of soil loss. Hydrological study identified flood-prone zones and characterized the regimes. These findings offer a scientific basis for targeted interventions in erosion control and flood risk reduction within the Gorgol basin.

The Mandalika Special Economic Zone (SEZ) is located in Pujut District, Central Lombok Regency, West Nusa Tenggara Province, which has been designated as a tourism area. Land use changes due to tourism development have caused increased erosion, sedimentation, and flood discharge. This study aims to estimate the extent of erosion and sedimentation in the watersheds located in the Mandalika SEZ, namely the Tebelo, Ngolang, and Balak watersheds. Erosion calculations were performed using the Universal Soil Loss Equation (USLE) method, and sedimentation was calculated using the Sediment Delivery Ratio (SDR) following the Menhut (2005) method. The results show erosion rates of 101,189.01 tons/year (Tebelo watershed), 79,158.05 tons/year (Ngolang I watershed), 16,387.94 ton/year (Ngolang II watershed), 123,557.66 tons/year (Balak I watershed), and 2,701.15 ton/year (Balak II watershed). The level of erosion hazard in all watersheds varies from very light to very severe. The sedimentation values produced reach 8,666.24 m³/year (Tebelo watershed), 7,019.14 m³/year (Ngolang I watershed), 1,986.82 m³/year (Ngolang II watershed), 9,990.7 m³/year (Balak I watershed), and 447.25 m³/year (Balak II watershed).

Soil loss due to erosion is a widespread problem in agricultural landscapes. However, a scarcity of long-term datasets and lack of reliable models challenge our understanding of the mechanisms that cause erosion. We developed a novel method for merging sediment core analysis with event-based sediment yield modeling using the Modified Universal Soil Loss Equation. The method was applied to a geographically isolated wetland in southwestern Georgia for a period from 1940 to 2022 using the Soil and Water Assessment Tool. This period included transitions from small-scale farm operations to plantation forestry to industrial, irrigated agriculture. Our results show a distinct spike in sedimentation rate in the 1970s with a peak of 872mg per cm2 of wetland area per year, or 3.6 metric tons of erosion per ha of catchment area. This spike occurred during a transitional period in land use that was also characterized by several large winter rain events. Both sediment core and modeled data indicate that these episodic sedimentation events dominate the long-term sediment record. Disagreement between the temporal pattern of sedimentation predicted by the model and those calculated from the sediment core was evident in certain parts of the sediment record. Our comparison suggested that sediment yield models require a unique calibration to accurately represent historical agricultural practices. The analysis also provides evidence that autochthonous sedimentation rates may be increasing in the most recent decade of the record due to anthropogenic changes.

Water erosion causes soil fertility loss and land degradation, posing serious threats to agricultural production, soil and water conservation, and environmental protection. Accurately assessing the spatiotemporal dynamics and driving mechanisms of water erosion is essential for mitigating regional erosion risks. In this study, the Revised Universal Soil Loss Equation (RUSLE) was coupled with the Transport Limited Sediment Delivery (TLSD) to simulate water erosion in the Shiyang River Basin from 2001 to 2020. Additionally, the Random Forest (RF) algorithm was employed to quantify the contributions of different driving factors to net soil erosion. The results showed that the RUSLE-TLSD model reliably simulated soil erosion processes in the basin (NSE = 0.70). The annual mean net soil erosion rate varied between 2.28 and 9.68 t·ha−1·a−1, with an overall decline, and the most intense water erosion occurred during the summer (June-August). Areas of intense water erosion were primarily concentrated in the southern Qilian Mountains, characterized by steep relief, abundant rainfall, and strong sediment transport capacity. The RF model explained approximately 85% of the variance, indicating that LUCC, NDVI and slope had the most significant impact on the spatial pattern of water erosion in the basin, and the area with sparse vegetation and large topographic relief had a high risk of water erosion. It is hoped that the findings of this study will provide a reference for water erosion risk assessment and management planning in arid zone basins.

The devastating effects of soil erosion on ecosystems, water quality, and agriculture make it one of the world's most pressing environmental crises. Soil erosion likelihood for a specific watershed will be determined using GIS and the Revised Universal Soil Loss Equation (RUSLE). The variables of C: cover management, P: support practices, LS: slope length and steepness, K: soil erodibility, and R: rainfall erosivity were derived using satellite imagery, digital elevation models (DEMs), land cover maps, and field data. The components were analysed spatially in a GIS environment, which enabled the calculation of average soil erosion over a year inside the area's boundaries by adding or developing risk layers individually. Erosion is primarily influenced by geology, vegetation, and land management techniques. The most likely places to find critical conditions are those with sparse vegetation and exposed rocks. The purpose of this research was to create a soil erosion risk assessment map that managers and planners could use to zero in on the most efficient ways to prevent soil erosion. When data is lacking, as is often the case in vast areas, combining GIS with RUSLE provides an accurate and cost-effective way to assess soil erosion and control tactics.

This study examines recent advancements in soil erosion modeling using the Revised Universal Soil Loss Equation (RUSLE), integrated with remote sensing and artificial intelligence techniques. Adopting a Systematic Literature Review (SLR) and bibliometric analysis via Bibliometrix in R, 63 articles were analyzed from an initial 359 based on strict selection criteria. Findings reveal a sharp rise in publications since 2017, especially involving machine learning and Google Earth Engine (GEE) platforms. Co-authorship analysis highlights significant international collaboration, particularly between Asia and Europe. Concept maps and co-word analyses show a shift from traditional RUSLE applications toward AI and big data approaches. Thematic evolution further indicates a growing focus on climate change and the Sustainable Development Goals (SDGs). The review's primary contribution lies in its explicit identification of critical research priorities by pinpointing key gaps: the limited use of field validation, weak SDG integration, and fragmented international research networks. By highlighting these deficiencies, this study provides a clear roadmap for future investigations, steering the field toward more inclusive, data-driven, and validated approaches to address global land degradation and climate resilience. Overall, the study contributes to the development of more effective erosion mitigation models through technological integration and international collaboration.

The Loess Plateau is one of the most severely eroded regions in the world, facing challenges from soil and water loss. Since the implementation of the "Grain for Green Program," its ecological environment has improved, but the dynamic impact of vegetation restoration on erosion over the long-term and on a large scale still requires study. This study develops an integrated framework that leverages Google Earth Engine (GEE) for cloud-based computation alongside GIS spatial analysis. Hua Chi County in Gansu Province serves as a representative case to examine the influence of afforestation on soil and water loss. Multi-temporal Landsat and Sentinel-2 imagery (20082020), together with DEM, precipitation, and soil datasets, were systematically combined to assess these effects. The Revised Universal Soil Loss Equation (RUSLE) model was applied to assess erosion risk. Results show that, based on the afforestation_mask used in this analysis, the suspected afforestation area contains 1,975,341 pixels at 30 m resolution, corresponding to approximately 17,777.7 ha (percentage of the study area should be recalculated against the study area total). Within these masked pixels, mean NDVI increased from ~0.08328 in the pre-period to ~0.26032 in the post-period, with the mean NDVI increment in improved subareas 0.25299. Under the implemented RUSLE setup, the E index averaged 0.021931 in the pre-period and 0.016785 in the post-period (mean relative change 23.59%). The image-mean Risk_Index over the mask is 0.008548917485758648, indicating that vegetation recovery contributed to reduced potential erosion risk.

Soil loss, also known as erosion, is an irreversible natural phenomenon that affects the topsoil of the Earth’s surface. It reduces soil fertility and water availability, and initiates geohazards, leading to negative environmental consequences. A research study was conducted in part of the Musi River sub-basin, a tributary of the Krishna River basin in India, which is undergoing a lot of changes due to anthropogenic factors. The novelty of this study lies in the integration of the RUSLE (Revised Universal Soil Loss Equation) model with advanced Geographical Information System (GIS) techniques to evaluate soil erosion and sediment yield in the basin. Leveraging the capabilities of the Google Earth Engine platform, the study employs the CART (Classification and Regression Trees) machine learning algorithm to generate the LULC (Land Use Land Cover) map, crucial for accurate C factor estimation. This innovative approach improves the precision of erosion modeling by seamlessly integrating GIS, machine learning, and remote sensing technologies. The analysis reveals that the LULC map has a total accuracy of 89.6% and a kappa coefficient of 0.86. The analysis also shows that the agriculture class dominates the research area with 51.4%. The results reveal that 95.6% of the research area has very low soil erosion of 0-1 ton/ha/ year, and 60.8% of the area has low sediment yield of 0-1 ton.ha-1.y-1. As the study area consists of major towns and cities, and the agricultural area is being converted to open plots (barren lands for developmental activities), erosion may increase in the future. The findings of this study may be used by managers and legislators to suggest soil conservation laws to expedite development projects.

Abstract Land-use change in the Northern Part of Bandung Regency presents a significant risk of soil erosion and contributes to flooding in the city of Bandung. The purpose of this study is to identify the soil erosion risk associated with land-use change using geographic information systems by employing the Universal Soil Loss Equation (USLE) model in Cimenyan and Cilengkrang Districts. The model integrates datasets from various sources, including Landsat imagery, Digital Elevation Models (DEM), rainfall data, and soil data, which are subsequently utilized as contributing factors to erosion. Each of these factors has been represented on a thematic map. Landsat satellite images from 2013, 2018, and 2023 were used to assess the land-use change. The image analysis indicates a significant decrease in forest areas (from 33.72% to 12.65%) and a substantial increase in settlement areas (from 13.46% to 59.16%). The erosion rate is categorized into four classes: very slight, slight, moderate, and severe. Overall, soil erosion in the Northern part of Bandung Regency has increased significantly between 2013 and 2023. This is evidenced by the substantial rise in the proportion of areas experiencing erosion rates greater than 180 tons/ha/year, which increased from 30.07% to 67.19% of the total area. The increase in erosion rates is significantly affected by changes in land use, particularly deforestation and the expansion of settlement areas.

The removal of soil by runoff is a major ecological concern, exacerbated by human-caused land degradation. A detailed procedure that combines the Revised Universal Soil Loss Equation (RUSLE) with Geographic Information System (GIS) methods was used to estimate soil erosion in Arequa watershed. The RUSLE components were applied to evaluate the average annual soil loss due to runoff in the region. The fundamental GIS data layers for RUSLE, encompassing precipitation, soil properties, topography, land use, and agricultural management approaches, were compiled in raster format. The raster calculator was utilized with these layers as input to ascertain the spatial distribution of yearly soil erosion throughout the watershed. A large portion of the drainage area had extremely low (0-6.7 t/ha/yr) to low (6.7-11.2 t/ha/yr) erosion rates, although a considerable segment also indicated moderate (11.2-22.4 t/ha/yr) to elevated (22.4-33.6 t/ha/yr) erosion rates. In certain locations in the study area, the model identified significant erosion rates. Therefore, the combined RUSLE and GIS methodology facilitates a relatively straightforward, rapid, and cost-effective estimation of spatial distribution of sediment output and soil loss, and it aids in determining which watershed areas should be prioritized and receive early treatment, taking into account time and budget limitations. Therefore, to lessen the consequences of erosion on agricultural areas, different cropping patterns and conservation support techniques should be put into place.

ABSTRACT Soil erosion is a significant global threat, with gully erosion forming deep hillside channels that substantially contribute to sediment yield. While erosion is often assessed using models like the Revised Universal Soil Loss Equation (RUSLE), its limitation to only consider surface erosion, avoiding severe erosion processes, led us to develop RUSLEad, an adapted version for gully erosion. The method integrates the Topographic Wetness Index (TWI) and a region-specific sediment delivery ratio in its formulation and includes generalized likelihood uncertainty estimation for model validation. Our pilot study is a basin of 13 km2 located in the Central Peruvian Andes. There, we collected detailed topographic and soil data. Chosen for its cost-effectiveness and data accessibility, RUSLEad produced erosion maps and average sediment yield estimates, and identified erosion hotspots. This method, applicable to similar terrain elsewhere, supports Peru’s Climate Change Strategy 2050 by informing gully control and contributing to prioritizing potential remediation efforts.

Water erosion is a major driver of soil degradation in arid and semi-arid regions, where the lack of vegetative cover and intense rainfall accelerate erosion processes. Field experiments were conducted to evaluate the effectiveness of stone walls (SW) as a soil conservation practice in reducing soil erosion using the universal soil loss equation. Furthermore, the support practice factor (P) was estimated via integrating computational measurements of changes in A horizon thickness with slope profiles. Six sites with varying slope gradients (8%, 10%, 15%, and 25%) implementing SW were compared to neighboring sites lacking this practice in the northeastern parts of Jordan. SW reduced average annual soil loss by 83%, lowering the average annual erosion rate from 58 t.ha−1.yr−1 (severe risk) to 10 t.ha−1.yr−1 (slight risk). The implementation of SW stabilized the thickness of the A horizon and organic matter contents across different slope gradients. In contrast, the absence of SW led to greater soil displacement and accumulation of organic matter at the lower slopes, indicating higher erosion risks. The average estimated P factor was 0.35. These findings underscore the effectiveness of conservation practices in controlling soil erosion, enhancing soil quality, and promoting sustainable land use in arid and semi-arid environments. Wider adoption of such measures can significantly contribute to combating soil degradation and improving agricultural productivity in similar regions worldwide.

Land-cover change driven by agricultural expansion, mining activities, and forest conversion is a major cause of declining hydrological function in watershed ecosystems. The loss of vegetation accelerates soil erosion, sedimentation, and ecological instability. This study aims to model and quantify soil erosion rates across land units in the Poboya Watershed by integrating the Universal Soil Loss Equation (USLE) with Geographic Information Systems (GIS). Analytical units were generated through an overlay of slope, soil type, and land-use maps, and subsequently validated through field surveys. The USLE factors (R, K, LS, C, and P) were derived from climate data, laboratory soil analyses, a Digital Elevation Model (DEM), vegetation indices, and observations of conservation practices. The results indicate that 56% of the Poboya Watershed area falls within the very low to low erosion classes; however, 33% of the area exhibits moderate to very severe erosion, particularly in zones with steep slopes, highly erodible soils, and areas affected by mining and dryland agriculture. These conditions highlight the presence of erosion hotspots that may accelerate downstream sedimentation and intensify land degradation. The findings underscore the need for critical-land rehabilitation, enhanced vegetative and mechanical conservation measures on steep slopes, and forest protection to sustain the watershed’s ecological functions.

Rainfall erosivity is considered as a vital factor in computing soil loss through erosion prediction models such as original and derived versions of the universal soil loss equation model. The accurate estimates of the rainfall erosivity require high-resolution rainfall measurements, which are still not widely available for many parts of the world. In this study, a set of conversion factors was developed to adjust rainfall erosivity estimates derived from rainfall data recorded at various temporal resolutions to those based on 1 min interval rainfall measurements. For the first time in the western arid region of India, 1 min interval rainfall data for two years (2020 and 2024) were utilized to compute the total kinetic energy (E), maximum 30 minute rainfall intensity (I30), and rainfall erosivity factor (R-factor) for individual rainfall events using the EI30 index method. Results of the study indicated that I30 values were severe for 5% to 10% of the total rainy storms, and high to very high for 75% to 80% storms. It is further revealed that as rainfall measurement interval decreases, the peaks of I30 are easily captured, which ultimately leads to enhanced erosive energy of the rainfall. The conversion factors obtained for the arid region in this study are relatively less as compared to that reported for humid and semi-arid regions in earlier studies. Also, underestimations of the E are evidenced on increasing the time interval from 5 min to 60 min with relative error within -10% whereas, the R-factors showed -4.5, -8.0, -9.6, -5.8 and -96.7% underestimations at 5, 15, 30 and 60 min and 24 h, respectively. The relationships developed for computing the precise and accurate E, I30 and R-factors for high-resolution (1 min) data based on coarser data at different time intervals (5 min, 15 min, 30 min, 60 min and 24 h) can be used adequately as the estimations involves a strong interactions confirmed among the factors.

In recent decades, human activities have intensified soil erosion, making it a global environmental problem. Yet, its quantification remains challenging. Here, we analyze and compare sheet and gully erosion rates using dendrogeomorphological methods (exposed roots) and the Universal Soil Loss Equation (USLE) in a severely eroded area of central Mexico. Sheet erosion rates were calculated from disturbances in the exposed root tree rings of Juniperus deppeana, with rates ranging from 5.1 to 14.4 mm/year. In addition, the origin and evolution of a gully were reconstructed, with the first erosional pulse dated to 2006, followed by other major pulses in 2015, 2016 and 2018. Sheet erosion rates obtained with the USLE were between 4.5 and 11.7 mm/year for “sparse vegetation cover” and between 2.0 and 5.1 mm/year for “reforestation of eroded areas”. These values were similar to those calculated with the dendrogeomorphological method; therefore, they can be used in a complementary way. The results obtained with the dendrogeomorphological method are precise and cover a longer time span (tens of years), but they require validation with other methods. The USLE is a model of easy application, but it requires calculating and adapting the factors to the study site. This research represents a significant contribution to the understanding of erosional dynamics in tropical highlands, providing valuable knowledge for the development of effective environmental management and conservation strategies.

Watershed conservation is crucial for water resources, ecological balance and agricultural productivity. Remote Sensing and Geographic Information System have revolutionized watershed assessment and planning by enabling efficient monitoring of terrain, land use, hydrology and soil properties. The application of Remote Sensing and Geographic Information System technologies is being utilized for watershed conservation using satellite-based datasets such as Landsat, Sentinel and Moderate Resolution Imaging Spectroradiometer to analyses land use and land cover dynamics, soil erosion, vegetation health and soil moisture. Techniques such as morphometric analysis, Revised Universal Soil Loss Equation modeling, Soil and Water Assessment Tool, Light Detection and Ranging, optical sensors and Digital Elevation Models enhance sub-watershed prioritization and erosion risk assessment; while integrating Light Detection and Ranging, optical sensors and Digital Elevation Models aid in delineating watershed boundaries and conducting topographic analysis. Recent advancements in artificial intelligence and machine learning are being used for real-time water quality prediction and conservation planning with Geographic Information System-based multi-criteria decision analysis aiding in identifying suitable zones for check dams and recharge pits. However, challenges remain related to data scalability, cloud interference and integration limitations. Despite these challenges, Remote Sensing and Geographic Information System tools offer immense potential for enhancing climate resilience, groundwater mapping and disaster risk reduction in watershed ecosystems. There is a growing need for integrated, data-driven and adaptive watershed management supported by modern geospatial technologies and approaches that combine science, policy and community involvement for long-term watershed sustainability.