Erosion Hotspots Research Articles

Soil erosion assessment for prioritizing soil and water conservation interventions in Gotu watershed, Northeastern Ethiopia.

Soil erosion by water is a serious problem in Ethiopia, contributing to diminishing crop yields and food shortages. Apart from understanding the magnitude, risk, and spatial distribution of the problem, identifying erosion hotspot areas is essential for effectively reversing the problem. This study aims to identify erosion hotspots in the Gotu watershed, in northeastern Ethiopia, using the revised universal soil loss equation (RUSLE) and incorporating local farmers' perspectives to prioritize conservation efforts. The RUSLE model reveals that 29,744.3 metric tons of soil is lost annually from the Gotu watershed, with an average loss of 65.3 to t ha⁻1year⁻1. The main contributing factors to soil erosion in the watershed include undulating topography, loss of plant cover, and continuous cultivation. The highest soil loss rates (> 80 t ha⁻1year⁻1) were found in the western, northern, and southern parts of the watershed, where cultivation occurs on moderate to steep slopes with sparse vegetation cover. These areas should be prioritized for conservation interventions. Farmers identified poor crop yields and damaged conservation structures as key indicators of soil erosion prevalence in the watershed. Increasing farmer's understanding of soil erosion and the importance of soil and water conservation is essential for effectively controlling soil erosion and improving food security in the area.

Spatio-temporal soil loss modelling using RUSLE and sediment delivery into a reservoir in a semi-arid region of northern Nigeria

Soil loss is aggravated by uncontrolled deforestation, indiscriminate land clearing for agricultural activities, overgrazing, and urban development that leads to severe soil erosion over the land surface. The main objective of this research is to apply the Revised Universal Soil Loss Equation (RUSLE), in conjunction with remote sensing and GIS, to determine the temporal variation of soil loss from the Gubi watershed in the years 2000 and 2017 and to estimate the sediment delivery into the Gubi reservoir in Northern Nigeria. Datasets of rainfall, soil type, topography, cover management, and support practice were utilized to determine the five RUSLE factors. The research identified five levels of erosion ranging from severe to very low-risk areas. The spatial distribution of erosion hotspots in the catchment area was highlighted. Results revealed increasing levels of erosion from 14,244.31 tons/year in 2000 to 16,792.33 tons/year in 2017. Consequently, the catchment's sediment yield was 6903.65 tons/year and 9172.71 tons/year corresponding to sediment delivery ratios of 48.46 % and 54.62 % in 2000 and 2017 respectively. The sediment inflow into Gubi reservoir was 747.38 tons/year and 950.76 tons/year corresponding to 10.83 % and 10.37 % of the total sediment yield in 2000 and 2017 respectively. Soil erosion vulnerability index maps were produced and the erosion is expected to increase with time as agricultural activities and deforestation continue to occur. The sediment deposits have the potential to reduce the designed life of the reservoir. Best management practices, such as tree planting, mulching, and contour ridging are recommended for soil conservation. There is a need to assess the reservoir sediment deposits so that measures can be taken to maintain the reservoir operations properly.

Sediment source tracing in a Brazilian subtropical catchment using diffuse reflectance: Effect of spectral ranges, pre-processing techniques, and multivariate model

ABSTRACT Agriculture intensification in Southern Brazil’s subtropical regions combined with the frequent occurrence of erosive rainfall has rendered the area a global water erosion hotspot. In this scenario, understanding and regulating erosion processes at the river catchment scale is critical for mitigating soil and water resource degradation. Traditional methods for tracing sediment sources are expensive and time-consuming and justify the development of alternative approaches. Therefore, in this study, we employed diffuse reflectance spectroscopy analyses in the ultraviolet-visible (UV-VIS), near-infrared (NIR), and mid-infrared (MIR) ranges, combined with multivariate models and spectral pre-processing techniques to estimate sediment source contributions in a homogeneous subtropical catchment (Conceição River, 804 km²). Soil samples (n = 181) were collected to characterize the four potential sediment sources, including: cropland (n = 78), stream bank (n = 36), unpaved road (n = 40) and pasture (n = 27). Moreover, 44 sediment samples were collected, including suspended sediment (n = 8), fine sediment deposited on the riverbed (n = 15), and suspended sediment samples collected in the water column during storm events (n = 21). Vector machine (SVM) model outperformed the others, with better accuracy and reliability. While UV-VIS spectra proved less effective due to soil homogeneity across the catchment, NIR and MIR spectra provided valuable information for discriminating sediment sources. Furthermore, reducing the number of potential sources (from four to three or two) improved model predictions, especially when distinguishing between surface sources (cropland and pasture) and subsurface sources (unpaved roads and stream banks). The study’s findings shed light on the power of efficient and cost-effective alternative methods for assessing sediment sources, which are vital for promoting effective erosion control and sustainable land management in similar regions.

Magnitude and hotspots of soil erosion types during heavy rainstorm events on the Loess Plateau: Implications for watershed management

In the context of climate change, rainstorm events are becoming increasingly frequent. In particular, on the Loess Plateau, heavy rainstorms are the primary cause of soil erosion. This study investigated and analysed different types of soil erosion hotspots and influencing factors in small watersheds under different rainstorm events in different areas of the Loess Plateau. The results indicate that the erosion intensities of rills, gullies, landslides and collapses ranged from 13600-46244, 1982-772201, 1163-172153 t km-2 and 1867-94985 t km-2, respectively. Newly constructed terraces exhibited an erosion intensity 1.6 times greater than that of old terraces, while terraces constructed before the rainy season in the current year exhibited an erosion damage intensity 2.6 times greater than that of terraces constructed after the rainy season in the previous year. In addition, under rainstorm conditions, landslides represented the most severe type of erosion in the watersheds, with the maximum amount of erosion accounting for more than 90 % of the total erosion amount, followed by gully or collapse erosion, with the collapse of terrace risers as the main contributor. Slope cultivation land, unpaved roads, terrace risers, and valley slopes below the gully shoulder line were identified as erosion hotspot areas. Rainstorm erosion was significantly influenced by the land use type and slope, which explained 14.2 %-41.5 % and 9.7 %-15.1 %, respectively, of the total variance in erosion intensity. We suggest that soil erosion prevention and control efforts on the Loess Plateau should focus on landslides on valley slopes below gully shoulder lines, followed by gullies on unpaved roads and the collapse of terraced fields. Drainage ditches and water cellars should be constructed above the gully shoulder line and on the inside of roads and terraces, thereby reducing erosion. Our research is crucial for optimizing and adjusting watershed management measures and preventing rainstorm erosion disasters.

Application of fallout radionuclide—137Cs for estimating soil erosion in steep hillslopes with diverse land use of North-western Indian Himalayas

Hilly and mountainous regions are significantly impacted by soil erosion, primarily due to rainfall-runoff processes occurring on the hillslope scale. Assessing soil erosion is crucial for quantifying the loss of soil carbon and nutrients, which diminishes the potential of soil ecosystem services and is critical for mitigating the impacts of climate change on food security. However, the Himalayan landscape poses serious challenges for assessing soil erosion due to its steep and rugged terrain, which hinders the use of conventional and modelling methods. The fallout radionuclide—137Cs has been extensively utilized as an environmental marker for investigating soil redistribution processes. Despite its potential, there is a notable lack of 137Cs-based soil erosion studies in the Himalayan region. In this context, we assessed the applicability of the fallout radionuclide—137Cs method in quantifying soil erosion rates and identifying erosion hotspots on two hillslopes of the Higher Himalayas. On the hillslope scale, we observed that soil erosion rates vary based on slope gradient and land use/land cover. Forested areas exhibited the lowest soil erosion rates compared to cultivated areas, while flat hillslope positions experienced lower erosion rates than steeper positions. The average net erosion rate for the Harsil hillslope varied among different hillslope positions, ranging from – 2.9 to – 15.6 t ha−1 yr−1. Similarly, in the Gangnani hillslope, the net erosion rates varied across different positions, ranging from – 5.6 to – 39 t ha−1 yr−1. Our findings confirm that the middle and lower hillslope positions are the most critical source areas with higher soil erosion rates, while hillslope positions with forest cover demonstrate the lowest erosion rates, identified as helpful in controlling soil erosion. The study has demonstrated the applicability of FRN as a soil erosion measurement method in the complex, rugged, and steep terrain of the Himalayas, highlighting the need for targeted conservation efforts to mitigate soil erosion and preserve ecosystem integrity.

Soil and stone terraces offset the negative impacts of sloping cultivation on soil microbial diversity and functioning by protecting soil carbon

Cultivation of sloping land is a main cause for soil erosion. Conservation practices, such as soil and stone terraces, may reduce the impacts of erosion but their impacts on soil microbial diversity and functioning related to carbon (C) and nutrient metabolisms remain unclear. This study was conducted to evaluate the effects of slope gradients (5°, 8°, 15°, 25°) and conservation practices (cultivated, uncultivated, soil terrace, and stone terrace) on bacterial and fungal diversities, metagenomic and metabolomic functioning associated with basic soil properties. Our results showed that steep slopes at 25° significantly decreased soil pH, silt percentage, and bacterial and fungal abundances, but that soil and stone terraces increased soil organic C (SOC), silt and clay contents, and fungal abundance compared to sloping cultivated lands. In addition, soil and stone terraces increased both bacterial and fungal alpha diversities, and relative abundances of Crenarchaeota, Nitrospirota, and Latescibacterota, but reduced the proportions of Actinobacteriota and Patescibacteria, thus shifting microbial beta diversities, which were significantly associated with increased SOC and silt content. For metagenomics, soil and stone terraces greatly increased the relative abundance of functional genes related to Respiration, Virulence, disease and defense, Stress response, and nitrogen and potassium metabolisms, such as Denitrification and Potassium homeostasis. For soil metabolomics, a total of 22 soil metabolites was enriched by soil and stone terraces, such as Lipids and lipid-like molecules (Arachidonic acid, Gamma-Linolenic acid, and Pentadecanoic acid), and Organoheterocyclic compounds (Adenine, Laudanosine, Methylpyrazine, and Nicotinic acid). To sum up, soil and stone terraces could reduce some of the negative impacts of steep slope cultivation on soil microbial diversity as well as their metagenomic and metabolomic functioning related to C and nutrient metabolism useful for soil health improvement, potentially bolstering the impact of sustainable practices in erosion hotspots around the world.

GIS-DRASTIC integrated approach for groundwater vulnerability assessment under soil erosion hot spot areas in Northern Pakistan

GIS-DRASTIC integrated approach for groundwater vulnerability assessment under soil erosion hot spot areas in Northern Pakistan

Estimating erosion vulnerability within agricultural fields by downscaling the Daily Erosion Project (DEP): the OFEtool

AbstractAgriculture continues to be one of the most important sources of nonpoint source pollution to surface water bodies. Consequently, it is critical to identify and prioritize high‐contributing agricultural fields and sub‐field areas for reducing soil erosion and sediment delivery by implementing best management practices (BMPs). Current erosion risk assessment tools are either complex modelling approaches or rely on a simplified reality and generalized assumption. The Daily Erosion Project (DEP) is a daily estimator of precipitation, hillslope runoff, detachment and soil loss covering ~630 000 km2 across the Midwest United States. These estimations are reported daily and publicly at the hydrologic unit code 12 watershed resolution (approximately 100 km2). The main objective of this study was to develop a new tool (named Overland Flow Element tool [OFEtool]) that downscales the watershed scale of DEP to estimate average runoff and soil displacement within a field, helping to locate erosive hotspots at multiple scales. We also demonstrated the applicability of OFEtool in Bennet Creek‐Sugar Creek in East Central Iowa (the United States) and compared its results with other erosion vulnerability tools such as the Soil Vulnerability Index for Cultivated Cropland (SVI‐cc) and a GIS‐based Revised Universal Soil Loss Equation (RUSLE). The same erosion risk classes and ranges (low, moderate, moderately high and high) were implemented for all indexes. The advantages of the OFEtool compared to the SVI‐cc and RUSLE models are related to the use of an event‐based modelling approach, such as DEP, with updated soil loss estimates based on temporal changes in climate inputs and land use and management. The OFEtool uses a 6‐year time frame and a more up‐to‐date field inputs, while RUSLE provides a long‐term average and SVI‐cc only considers soil and topographical factors for risk assessment. Results indicated that the spatial distribution of vulnerable fields (and parts of the fields) followed a similar trend as other tested indices. However, the risk level associated with each tool differed (SVI‐cc > RUSLE > OFEtool). These differences could arise from intrinsic disparities within the tools (inputs, timing, processes considered, assumptions). While currently limited to the DEP domain and relying on the DEP random sampling scheme, further research is warranted to validate the tool at other Midwest locations and ensure it captures the watershed's landscape variability (combination of terrain, soil, land use and management) required to identifying critical erosion hotspots.

Defining an exposure index along the Schleswig-Holstein Baltic Sea coast

A wind exposure index (EI), which indicates the main physical driver of a coastal system, was developed along the Schleswig-Holstein Baltic Sea (SH) coast – Germany, to demonstrate the highly dynamic coastal stretches (i.e., potential erosion hotspots). The approach used three steps to define more accurate EIs. Initially, a representative wind year (RWY), which has similar physical characteristics as in the long-term data, was defined by analysing measured wind data from 2000 to 2019 at four stations distributed in the entire area of interest. The RWY was identified by a statistical comparison of wind speeds in 5 classes and 36 directional sectors between summer to summer yearly wind and the overall data. The selected RWY spanned from 01.09.2016 to 31.08.2017 and showed a reasonable agreement with the overall data (Skill = 0.77 and rmsd = 0.56 m/s). Next, high spatiotemporal nearshore hydrodynamics over the RWY were predicted using a model nesting approach of two domains in Delft3D. The predicted nearshore hydrodynamics indicated fair agreements with the measured data (R2: 0.87–0.90 for water levels and 0.75–0.86 for wave heights). Finally, the predicted water level and wave height time series in the nearshore area (∼ 5 m MSL depth) were used for the analysis of the EI adopting a 2-step procedure capturing short- and long-term correlations as well as seasonal long-range dependencies of the time series. This approach allows to model the clustering behaviour of extreme values of both parameters and provides reasonable EIs along the SH coast. The exposed areas display high EIs (e.g., 1 at the east of Fehmarn), while sheltered areas and bays have low values (e.g., 0 at Eckernförde Bay). The higher the EI the stronger the coastal dynamics and thus strong erosion can be expected. Interestingly, the EI varies considerably even along the exposed coastal stretches with long fetches, which indicates the sensitivity of the EI to the local morphology, which determines the nearshore hydrodynamics. Therefore, a definition of the EI based on nearshore hydrodynamics provides an accurate index of local physical drivers of a coastal system. The developed approach can be adopted to any coast, and provides useful information on the potential erosion areas for the coastal managers.

Spatial assessment of soil resources on different land uses and slope gradient as a planning tool, in Ferenjua watershed, Ethiopia

Soil erosion, land degradation and climate change are the major environmental threats attributed to low agricultural productivity in Ethiopia. To reduce soil erosion and restore degraded lands, integrated soil and water conservation interventions have been implemented. However, the impacts of the intervention measures were not sufficiently studied due to the lack of baseline information. Therefore, this study was carried out to evaluate and understand the current conditions of biophysical resources in Ferenjua watershed, which is located in Gondar zuria district. In this study, more emphasis was given to the levels of total nitrogen (TN), available phosphorous (P), organic matter (OM), bulk density (BD), pH, moisture, texture and soil erosion. To collect the biophysical data, the watershed was divided into 144 grid-based sampling points with a resolution of 250 m by 250 m. In addition, the data were collected in 8 representative profile pits. Three primary land uses were identified from supervised land use and land cover classification and described as cultivated land (55.08 %), forestland (30.80 %), and grassland (14.12 %). Erosion hotspot map shows that >40 % of the watershed is moderately and highly susceptible to soil erosion. The highest BD (1.36 gm cm−3) was observed at Vertisols-dominated cultivated lands located in the flatlands, whereas, the lowest BD (1.28 gm cm−3) was observed under forestland. Regarding the results of TN, P, and OM, >60 % of the watershed was classified as having low to very low soil nutrient levels. Consequently, development interventions should be considered to restore degraded lands for the sustained use of resources in the watershed.

Climate change and coastal erosion hotspots in West Africa: The case of Togo

Global warming is a worldwide phenomenon with disastrous implications ranging from ocean warming and glacier melting to sea level rise and coastal damage. This research assesses climate change trends, investigates coastal erosion hotspots and forecasts shoreline dynamics in West Africa. It also includes information on coastal erosion impacts and current coastal defence measures. The data set includes annual mean sea level anomaly, eddy kinetic energy, wave energy data and monthly climate data, such as precipitation, temperature, wind speed, and sunshine, from 1988 to 2023. Additionally, field survey data and Sentinel-2 satellite images were employed from 2015 to 2023 for coastal erosion monitoring. The Mann-Kendall test and Sen's slope test have been employed for climate trend analysis, while the DSAS tools have been used to identify coastal erosion hotspots and forecast future trends. The results revealed a significant increasing trend in annual sea level rise anomaly, annual wave energy, annual eddy kinetic energy, monthly temperature, and monthly sunshine. According to the shoreline dynamics investigation on Togolese coastal area, the Aflao-Port transect had significant accretion rates (up to 15 m/years), whereas the Port-Aneho transect experienced coastal erosion (up to −12 m/years). Climate change may increase wave energy intensity in certain regions, while coastal development can alter littoral transport and shoreline changes. Although there are soft and hard stabilisation solutions to counteract coastal erosion, such as groynes, sandbags, reforestation, and a series of nozzles in Togolese coast, the phenomenon of coastal erosion has evolved in recent years. Future projections in 2033 show that coastal erosion could move from 50 to 250 m inland. It is recommended that climate trend indicators, shoreline dynamics, and coastal stabilisation solutions be incorporated into all coastal and marine policies for sustainable management, with a view to ensuring minimal disruption to the ecosystem services provided by the sea and coast to people.

Scrutinizing gully erosion hotspots to predict gully erosion susceptibility using ensemble learning framework

Gully erosion poses a threat to the sustainability of cultivated regions worldwide, is environmentally problematic, and causes significant rates of soil erosion. Understanding the formation process and gully development requires mapping gully erosion. An innovative modeling approach was used in this work using a case study in northern Iran. 14 gully-erosion parameter measurements from 1042 gully erosion locations were combined to produce a geographic dataset. For the purposes of modeling and validation, four training data sets (each in the standard 70:30 ratio) that represented 100 %, 75 %, 50 %, and 25 % of the total database were employed. The four machine learning models that were used to assess the utility of the four training situations were Bloom Filter Trees, Random Forest, (RF), Kernel Logistic Regression (KLR), and ensemble of KLR-RF. The most important effective factors, as determined by the RF research, were distance from the stream (72.61), vertical distance of the channel network (49.34), distance from the road (48.81), and elevation (47.68). The findings were validated using the receiver operating characteristic. Based on our findings, the combination of KLR and RF is the most optimum combination for both improved accuracy (0.928) and predictive capability (0.911). Out of them, the KLR-RF 50 % training scenario is the most optimal when compared to the other scenarios.

Functional connectivity related to road linear erosion at rainfall event scale in an agricultural watershed on the Loess Plateau

Intersections of road and channel networks assist in better understanding of runoff and sediment transportation process feedback using the hydrological connectivity concept. However, quantifying functional connectivity and exploring the complex relationship between hydrological connectivity and the processes of runoff and sediment associated with road networks have largely been unexamined by field work. Therefore, our team conducted a 3-year monitoring of hydrological connectivity and road erosion from selected road segments in an agricultural watershed on the Loess Plateau. Rainfall events occurring during 2020–2022 were categorized into three types using K-means clustering analysis: small amounts of low-intensity rainfall (SL-R), large amounts of low-intensity rainfall (LL-R), and large amounts of high-intensity rainfall (LH-R). The monitored roads primarily consist of paved roads and unpaved roads (agricultural roads, abandoned roads, and petroleum roads). In our study, the intersections extracted by the road and channel networks were defined as hydrological connections, and sticks wrapped with clean gauze at the hydrological connections were monitored under variable rainfall conditions for functional connectivity assessment. The runoff heights and sediment traces attached to the sticks are alternative hydrological connectivity indicators. The results revealed an obvious increasing trend in the activation frequency and degree of hydrological connectivity as rainfall types transitioned from SL-R to LL-R and LH-R. Agricultural roads had a more intense response to hydrological connectivity between upslope drainage areas and road surfaces, and petroleum roads performed well in response to hydrological connectivity between road surfaces and down hillslopes. Random forest models indicate that PI30 is the most dominant influencing factor of road erosion and hydrological connectivity between road upslope drainage areas and road surfaces. Road surface erosion rates are the primary determinants of hydrological connectivity between road surfaces and down hillslopes. These findings suggest a close interaction between road erosion and hydrological connectivity driven by these off-site effects. They offer additional evidence concerning runoff and sediment patterns and dynamics in relation to road networks and identify hydrological connectivity and road erosion hotspots. Coordinating measures for hydrological connectivity regulation and road erosion mitigation are essential for minimizing hydrological connectivity and road erosion off-site effects within watersheds.

Assessing the impact of sea level rise on the Indus delta in Pakistan: A comprehensive analysis of flooded areas and future vulnerabilities

This research investigates the impact of sea level rise (SLR) on the Indus Delta, a vital ecosystem increasingly vulnerable to climate change repercussions. The objective of this study is to comprehensively assess the flooded areas under various shared socioeconomic pathway (SSP) scenarios based on the Intergovernmental Panel on Climate Change's (IPCC) 6th Assessment Report. The study employs a GIS-based bathtub model, utilizing historical (1995–2014) and IPCC-projected (2020–2150) tide gauge data from Karachi, Kandla, and Okha stations to identify potential inundated areas threatened by coastal flooding. Additionally, it analyzes LANDSAT-derived multispectral images to identify coastal erosion hotspots and changes in the landscape. A supervised random forest classifier is used to classify major landforms and understand alterations in land cover. Furthermore, neural network-based cellular automata simulations are applied to predict future land cover for 2050, 2100, and 2150 at risk of inundation. The results indicate that under different SSP scenarios, the estimated inundated land area varies from 307.36 km2 (5 % confidence on SSP1-1.9) to 7150.8 km2 (95 % confidence on SSP5-8.5). By 2150, the region will lose over 550 km2 of agricultural land and 535 km2 of mangroves (mean SLR projection). This work emphasizes identifying sensitive land cover for SLR-induced coastal flooding. It might fuel future policy and modeling endeavors to reduce SLR uncertainty and build effective coastal inundation mitigation methods.

Soil erosion assessment and identification of erosion hotspot areas in the upper Tekeze Basin, Northern Ethiopia

Soil erosion is a major environmental problem in Ethiopia, reducing topsoil and agricultural land productivity. Soil loss estimation is a critical component of sustainable land management practices because it provides important information about soil erosion hotspot areas and prioritizes areas that require immediate management interventions. This study integrates the Revised Universal Soil Loss Equation (RUSLE) with Google Earth Engine (GEE) to estimate soil erosion rates and map soil erosion in the Upper Tekeze Basin, Northern Ethiopia. SoilGrids250 m, CHIRPS-V2, SRTM-V3, MERIT Hydrograph, NDVI from sentinel collections and land use land cover (LULC) data were accessed and processed in the GEE Platform. LULC was classified using Random forest (RF) classification algorithm in the GEE platform. Landsat surface reflectance images from Landsat 8 Operational land imager (OLI) sensors (2021) was used for LULC classification. Besides, different auxiliary data were utilized to improve the classification accuracy. Using the RUSLE-GEE framework, we analyzed the soil loss rate in different agroecologies and LULC types in the upper Tekeze basin in Waghimra zone. The results showed that the average soil loss rate in the Upper Tekeze basin is 25.5 t ha−1 yr−1. About 63 % of the basin is experiencing soil erosion above the maximum tolerable rate, which should be targeted for land management interventions. Specifically, 55 % of the study area, which is covered by unprotected shrubland is experiencing mean annual soil loss of 34.75 t ha−1 yr−1 indicating the need for immediate soil conservation intervention. The study also revealed evidence that this high mean soil loss rate of the basin can be reduced to a tolerable rate by implementing integrative watershed management and exclosures. Furthermore, this study demonstrated that GEE could be a good source of datasets and a computing platform for RUSLE, in particular for data scarce semi-arid and arid environments. The results from this study are reliable for decision-making for rapid soil erosion assessment and intervention prioritization.

An assessment of anticipated future changes in water erosion dynamics under climate and land use change scenarios in South Asia

Water erosion has emerged as a significant global environmental challenge, particularly in South Asia, recognized as one of the regions hardest to hit globally by climate change. This study aims to identify the potential erosion hotspots in South Asia and to assess areas under risk in the future due to climate and land use change scenarios. We utilized the Revised Universal Soil Loss Equation (RUSLE), an empirical erosion model, to estimate erosion for both historical and future periods, considering two distinct Shared Socio-economic Pathways (SSP126 and SSP585). The baseline model predicts a mean water erosion rate of 10.6 t. ha−1. year−1 in South Asia, with more than 30 t. ha−1. year−1 recorded for 6.3 % of the total land area. Notably, Bangladesh and Nepal exhibit significantly higher erosion rates than other South Asian countries. Further, our projection indicates an + 10 % increase in erosion rates during the near future (2021–2040) under SSP585 compared to the historical estimates. A substantial rise of approximately + 31 % and + 39 % in water erosion rates in South Asia is anticipated during the far future periods of 2061–2080 and 2081–2100 under SSP585. At the country level, Bangladesh (+42 %), Bhutan (+108 %), and Nepal (+70 %) are expected to experience substantial increases in erosion rates during the 2081–2100 period, with India projecting a + 38 % increase. Additionally, a significant + 35 % expansion in the spatial extent of high erosion areas (>30 t. ha−1. year−1) is foreseen in 2081–2100 under SSP585. Furthermore, under the low climate change scenario (SSP126), the impact on the erosion rates is minimal, while under SSP585, a substantial increase in erosion rates is anticipated. Regions such as Southern India, the arid and semi-arid areas near the India-Pakistan border, and the Himalayan foothills are identified as hotspots for significant changes in erosion rates. Acknowledging a certain level of uncertainty, our study provides valuable insights into erosion risk areas in South Asia and their plausible future impacts due to climate and land use changes. This information can assist policymakers in South Asian countries in developing national-level strategies for soil conservation and climate resilience.

Quantifying aboveground biomass, soil organic carbon and erosion with a detailed crop map and PESERA model in the Yangtze River Basin

AbstractSoil erosion represents a primary threat to soil systems with adverse implications for ecosystem services, crop production, potable water and carbon storage. While numerous studies have quantified the spatial distribution of aboveground Biomass (AGB), soil erosion and soil organic carbon (SOC) in the Yangtze River Basin (YRB), limited attention has been given to assessing the contributions of different land use types and especially crop types to AGB, soil erosion and SOC. In most studies, cropland is taken as a land use class, while detailed crop types and rotation patterns, and their effect on soil erosion and SOC, vary significantly. In this study, we used the Metronamica model to generate a detailed crop rotation and distribution map across the YRB and subsequently employed the Pan‐European Soil Erosion Risk Assessment (PESERA) model to simulate the spatial distribution of AGB, soil erosion and SOC on a monthly basis. PESERA model simulations indicate an average soil erosion rate across the entire YRB of 7.7 ton/ha/yr, with erosion hotspots concentrated in the Sichuan Basin and the central‐southern regions. The southwestern region and western Sichuan show elevated levels of AGB and SOC, while the eastern plains display lower levels. Erosion rates are lowest in areas designated as artificial land, pasture and grassland, whereas croplands and fruit tree plantations experience the highest erosion rates. In terms of crop types, the highest erosion rates and lowest AGB are observed under fallow and potato cultivation, while the lowest erosion rates and highest AGB are found in rice‐wheat rotation fields. To the best of our knowledge, this is the first study taking detailed crop types and patterns into account while evaluating their effect at a relatively large scale (i.e., YRB). These findings can help to develop sustainable soil management and (cropping) conservation strategies.

Quantification of soil erosion and sediment yield using the RUSLE model in Boyo watershed, central Rift Valley Basin of Ethiopia

Changes in land use and land cover (LULC) are becoming recognized as critical to sustainability research, particularly in the context of changing landscapes. Soil erosion is one of the most important environmental challenges today, particularly in developing countries like Ethiopia. The objective of this study was evaluating the dynamics of soil loss, quantifying sediment yield, and detecting soil erosion hotspot fields in the Boyo watershed. To quantify the soil erosion risks, the Revised Universal Soil Loss Equation (RUSLE) model was used combined with remote sensing (RS) and geographic information system (GIS) technology, with land use/land cover, rainfall, soil, and management approaches as input variables. The sediment yield was estimated using the sediment delivery ratio (SDR) method. In contrast to a loss in forest land (1.7 %), water bodies (3.0 %), wetlands (1.5 %), and grassland (1.7 %), the analysis of LULC change (1991–2020) showed a yearly increase in the area of cultivated land (1.4 %), built-up land (0.8 %), and bare land (3.5 %). In 1991, 2000, and 2020, respectively, the watershed's mean annual soil loss increases by 15.5, 35.9, and 38.3 t/ha/y. Approximately 36 cm of the watershed's economically productive topsoil was lost throughout the study's twenty-nine-year period (1991–2020). According to the degree of erosion, 16 % of the watershed was deemed seriously damaged, while 70 % was deemed slightly degraded. Additionally, it is estimated for the year 2020 that 74,147.25 t/y of sediment (8.52 % of the total annual soil loss of 870,763.12 t) reach the Boyo watershed outlet. SW4 and SW5 were the two sub-watersheds with the highest erosion rates, requiring immediate conservation intervention to restore the ecology of the Boyo watershed.

Effects of major storms over a spit-tidal flat interaction system (Punta Rasa-Samborombón, Argentina)

Sandy coastal areas are very dynamic systems in which morphological changes occur over different time scales from hours to decades. However, it has been widely reported that major storms are the main responsible of the most significant changes in short to medium time scales. Major storms have been defined using a variety of environmental variables, but they are normally associated with high values of wave height, duration, return period and direction.Here, we aim to characterize types of major storms and to categorize associated morphological impacts over a complex coastal system. The study site, known as Punta Rasa, is located in the Samborombón bay in the outer part of the Río de La Plata estuary (Argentina) and corresponds to a zone of interaction between a large sandy spit and a backwash tidal flat system. Methods combine statistics of wave climate time-series, analysis of wave energy using nearshore numerical modelling (SWAN) and comparison of pre- and post-storm morphological changes by means of shoreline change detection and satellite images derived indexes (CoastSat Toolkit and NDWI index respectively).Results allowed to characterize four types of major storms impacting the study area: High-Energy Storms (HES), defined by an average storm wave below the 1 % exceedance (>2.6 m), Long-Lived Storms (LLS) represented by an exceedance of the 1 % of Du (>60 h), Storm Groups (SG) in which storm return period is <6 days and Northeastern moderate storms (NMS) defined by their eastern, onshore oriented direction. Under HES and NMS storms erosional areas are dominant over depositional, causing shoreline retreat, a growth of the end-spit and the increase on sand deposition on the back-barrier areas. Under LLS and SG storms, the morphological impact varies alongshore, with multiple erosional hotspots found along the shoreline accompanied by a general flattens of the end-spit system.

Differential responses of soil cellulase enzymes and oxidative enzymes to soil erosion

The activities of soil carbon-degrading enzymes play an important role in determining carbon mineralization. Soil erosion is the most common form of soil degradation worldwide, but the responses of cellulase enzymes and oxidative enzymes to erosion remain elusive. Here, we studied the effects of erosion on β-1,4-Glucosidase (BG) and phenol oxidase (PHO) activities as well as their contributions to carbon mineralization at three erosion hotspots in China. We found that erosion did not change SOC-normalized BG activity in both eroded plots and depositional plots, but significantly increased SOC-normalized PHO activity in eroded plots. The SOC-normalized PHO activity explained more variance in carbon mineralization than BG. Further analysis showed that the lignocellulose index (the ratio of PHO to the sum of BG and PHO) was closely related to microbial stoichiometric balance rather than the carbon composition under the impact of erosion. These results indicate that erosion may stimulate the utilization of recalcitrant carbon by microbial oxidase in eroded areas, potentially exacerbating the loss of soil carbon in slopes.