Average Annual Erosion Research Articles

Agricultural activities lead to sediment infilling of wetlandscapes in the Canadian Prairies: Assessment of soil erosion and sedimentation fluxes

Wetlandscapes are vulnerable to land conversion and sediment infilling from upland agriculture, causing them to act as sinks for sediment deposition and putting at risk their ecosystem services. Wetlandscapes in the Canadian Prairies agroecosystems are particularly susceptible to sediment infilling because of the intensification of human activities and agricultural practices. The rising risk of soil erosion in cultivated landscapes has generated a need to estimate soil redistribution rates and soil loss monitoring tools and techniques. This research examines the effects of agricultural activities on soil loss and sedimentation rates within agricultural landscapes in the Canadian Prairies. Land and atmospheric fluxes of sediment into wetlands are quantified over the past 60 years using catchment-scale tracing (137Cs) and budgeting techniques. Findings indicate that the pattern of 137Cs erosion/deposition varies along catchment toposequences, with erosion near the top of the toposequences (the average annual soil erosion rates were found to be 1.1 kg m−2 yr−1 for Manitoba and 0.3 kg m−2 yr−1 for Alberta) and deposition within the wetland ecosystem (total deposition rates were estimated at about −3.6 kg m−2 yr-1for Manitoba and −0.9 kg m−2 yr−1 for Alberta). The sediment delivery ratios were approximately 57% and 35% in Manitoba and Alberta, respectively, indicating that a noticeable amount of the mobilized sediment exits the field. These transfers from cultivated fields into wetlands reveal that wetlandscapes in Canadian Prairies are vulnerable to sediment infilling, and soil erosion control practices are needed to achieve sustainable management of agricultural landscapes.

Quantification of water erosion using empirical models RUSLE and EPM in the Rheraya basin in the High Atlas of Marrakech

Soil loss caused by water erosion constitutes a major issue that generate environmental and other several problems. The objective of this work is to quantify and assess the risk of erosion in the region of the Rheraya basin3 and to estimate the rate of soil loss and to characterize the main criteria involved in the evolution of water erosion in the basin. The study revealed that the combined presence of streams, crossing easily eroded surfaces, slopes, as well as anthropogenic action, aggravated the phenomenon. The empirical RUSLE4 and EPM5 models combined with GIS and remote sensing6 were adopted to characterize this rate in the region. The RUSLE model evaluated the rate of soil loss produced by sheet erosion, based on the aggressiveness of the rain, soil erodibility, the vegetation cover, the length and the inclination of the slope as well as the anti-erosion practices in the basin. The "EPM" model estimated the rate of erosion and soil losses caused by water erosion in general. It is based on the mapping and the combination of six parameters which are: average temperature, average annual precipitation, slope, erosion state, land use and soil resistance to erosion. Results showed that the erosion rate using Rusle model varies between 0 and 188 t/ha/year with an average annual soil loss of 46, 85 t/ha/year. The results of the application of the EPM model showed that the average annual of soil loss is 103 t/ha/y with values extending between 0 and 200 t/ha/y. These results exposed that the areas upstream represent a very high risk of erosion, so they can surely aid to manage and reduce soil erosion in the High Atlas mountains.

An Erosion-Based Approach Using Multi-Source Remote Sensing Imagery for Grassland Restoration Patterns in a Plateau Mountainous Region, SW China

Satellite remote sensing of grassland ecosystem restoration requires considering both the above-ground biomass and soil information, and the latter is even more crucial due to the value and restoration difficulty of soil productivity. In this study, we proposed an approach to support the restoration pattern for mountainous grasslands at regional scale. The approach integrates different aspects and key processes, including degradation status, restoration potential and recovery capability, compared to a reference state. Specifically, we illustrated the method with the case of grasslands in southwestern China from a conservation perspective. Soil erosion conditions, net primary productivity and regrowth rate of grasslands were selected as indicators to reveal restoration possibilities. The results showed that the method proposed for remote sensing identification of grassland distribution has an overall accuracy of 88.21% at the regional scale. 59.54% of grasslands in Zhaotong are being eroded with an unsustainable erosion rate greater than the tolerant soil loss, and the average annual soil erosion rate is 952.17 t/(km2·a). Meanwhile, there is obvious spatial heterogeneity in soil erosion factors, vegetation restoration potential and regrowth rate, and the dry–hot valley of Jinsha River in the southwest is much more sensitive to climate change and vulnerable than other regions. The grassland vegetation cover revealed a fluctuating trend and protection of grassland vegetation on soil from erosion has an obvious lag, restoration efforts should be focused on the months before the arrival of the rainy season. In light of various grassland types, the overlay zoning results suggest various restoration patterns of natural repair and manual intervention should be employed for different grasslands. Urgent action is needed to face the challenge and process of grassland degradation and restore its sustainability with shared understanding by taking the stakeholders, collaborations and mutual relationships among different roles into account (e.g., scientist, government and herdsman).

Spatiotemporal variation characteristics of sediment nutrient load from the soil erosion of the Yangtze River Basin of China from 1901 to 2010

Over the past 70 years, the Yangtze River Basin-the third longest river basin in the world-has been under the influence of augmented human activities. Regarding environmental protection and policy design, the total nitrogen and phosphorus (TN and TP, respectively) loads from soil erosion are objectively and quantitatively critical parameters while assessing the spatio-temporal soil erosion changes over the 1901–2010 period. First, soil erosion in our study area was calculated using the Revised Universal Soil Loss Equation (RUSLE) model. Second, the TN and TP loads from soil erosion were assessed using the constructed Nutrient Loss Empirical Model (NLEM). Third, we conducted spatial autocorrelation analysis, Mann-Kendall (M–K) trend tests, and wavelet analysis of the soil erosion, and related TN and TP loads. At Datong Station over the 1901–2010 period, the average annual TN and TP loads from soil erosion were 1.77 and 0.56 million tonnes, respectively. Moran’s I values of the average annual soil erosion, and related TN and TP loads from soil erosion indicates the existence of positive correlations, while clustering was the prevailing spatial distribution pattern. High-high (H-H) cluster areas were mainly evident in high-altitude region of the western and southern Yangtze River Basin, conversely, low-low (L-L) cluster areas were scattered primarily in regions with high population density and intense human activities. Soil erosion increased rapidly around 2001; hence, 2001 was a changing point. According to the M–K test, the time intersection of soil erosion, and related TN and TP loads at Datong Station was around 1990; hence, 1990 may have been a changing point, likely due to the operation of Gezhouba Dam during 1981-1986. This Dam trapped large amounts of sediments. If the sediment load data of control hydrological station of 12 sub-basins is available, SDR, sediment load, AN and AP calculation could be more accurate and interpretable.

Evaluation of monthly-scale soil erosion spatio-temporal dynamics and identification of their driving factors in Northeast China

Soil water erosion has caused an enormous damage to agricultural production and ecosystems worldwide. Soil erosion exhibits certain seasonal variation patterns within year under the influence of multiple factors. In this study, we calculated the multi-year average monthly-scale soil erosion modulus from 2001 to 2019 to evaluate the soil erosion risk using the revised universal soil loss equation (RUSLE) in the black soil region of Northeast China. The results showed that the average annual soil erosion modulus in this region was 8.53 t•ha−1•year−1, and the average monthly soil erosion modulus was 0.78 t•ha−1•month−1. The months of April-July and October were identified as critical periods for erosion, with erosion modulus values of 1.17, 1.89, 1.61, 1.13, and 0.93 t•ha−1•month−1 during these five months, respectively. High soil erosion was mainly distributed in southern Liaoning province and western Inner Mongolia, and these high erosion areas were mostly distributed in the regions with complex landforms, in grasslands, and in the southern temperate climatic zone. Among multiple driving factors, the topographic factor LS (L = slopelength,S = slopesteepness) was one of the dominant factors determining the spatial distribution pattern of soil erosion with 30 % of the contribution. The rainfall erosivity (R) and cover management (C) were the major factors driving significantly the intra-annual variation of soil erosion in Northeast China with 27.2 % and 28.9 % of the contribution, respectively. The intra-annual erosion varied with the rainfall and the vegetation growth, and the period of high erosion occurs in the early part of the wet season when the rainfall intensity increases and the vegetation growth is insufficient to protect the soil. Our findings highlight the need to take dynamic factors into consideration in the RUSLE model and the importance for identifying the areas at high erosion risk so as to take soil conservation actions.

Near‐continuous monitoring of a coastal salt marsh margin: Implications for predicting marsh edge erosion

AbstractMechanisms that control marsh edge erosion include wind‐generated waves, vegetation productivity, land use and land change, and geotechnical properties of sediments. However, existing models for predicting marsh edge evolution focus primarily on edge retreat rates as a function of wave energy while accounting for other controlling factors as empirical constants. This simplification arises from a lack of high‐frequency monitoring of marsh evolutions. In particular, marsh erosion is timescale dependent, and conducting field observations on short temporal and spatial scales could elucidate the progression of erosion, which may improve marsh erosion predictive models. This study developed and validated a near‐continuous camera and erosion pin monitoring system to document marsh edge erosion at a high frequency (i.e., daily) in Terrebonne Bay, Louisiana. This was supplemented with daily wave power to explore the relationships between daily erosion and wave power. Long‐term average erosion rates derived from satellite and aerial imagery from 1989 through 2019 compare similarly to rates derived from longer‐term site visits (i.e., monthly) at approximately 2.2 m/yr. High‐magnitude erosion events (>20 cm/day) are driven by a buildup in wave energy over a 7‐day time period coupled with a strong 1‐day wave event, indicating a gradual reduction in marsh edge resistance with continued wave attack. Long‐term erosion monitoring methods, including monthly field visits, provide results that align well with previously reported relationships between wave power and erosion. High‐frequency measurements, however, illustrate that the previously published trends smooth over the large‐magnitude short‐term erosion events, potentially obscuring the physical processes of marsh edge erosion. For example, satellite and aerial imagery provide a long period of record, but they may underestimate the average annual erosion rate in the region, the effect of which may become exasperated over the varying temporal scales considered in coastal planning efforts across the USA and worldwide.

Landsat-Based Assessment of Morphological Changes along the Sinai Mediterranean Coast between 1990 and 2020

The objective of this study was to assess coastal dynamics of the Sinai Mediterranean coastline (SMC) between 1990 and 2020. In this study, remote sensing imagery and geographic information systems-based spatial computing methods were used for coastline change analysis. The study found that the SMC experienced major morphological changes during the period of study. The results show a spatial heterogeneity of coastline change rate over the study period with some periods of rapid change and other periods of stability. The overall analysis during the period of study indicated that the coastline has retreated landward approximately 555 m; the average annual erosion rate was 2.5 m/year. The percentage of the eroded section in the coastline is 60% with the remaining 40% either advancing or remaining stable. Sections of the coastline accreted as much as 542 m. The GIS-based DSAS model application used in this study is unique because it measured spatial variations of coastline dynamics over time using a multiple statistical approach.

GIS-based revised universal soil loss equation for estimating annual soil erosion: a case of lower Kulsi basin, India

Assessment and estimation of soil loss is a fundamental aspect of land and water resource conservation and management practices as it provides necessary information in the course of watershed-level development of a region. The soil loss model of Wischmeier and Smith, popularly known as the Revised Universal Soil Loss Equation, was selected to estimate soil loss in the lower Kulsi river basin due to its simplicity, versatility, and flexibility nature method in the Geographic information system platform. Most original governmental datasets, mainly daily gauge rainfall from 2009 to 2018, satellite images for land use land cover, digital elevation model of Shuttle Radar Topographic Mission for topographic factor, and National Bureau of Soil Survey and Land-use Planning, India soil map were utilized to estimate the average annual soil erosion. The estimated average annual soil erosion ranges from 0.0 to 6.45 thousand t ha−1y−1, grouped into low, moderate, high, and very high risk of soil erosion. A basin area of 36.235 km2 (1.85%) basin area was identified as high to very high zones of soil erosion risk and needed immediate conservation measures to reduce the erosion risk.

A Circum-Arctic Monitoring Framework for Quantifying Annual Erosion Rates of Permafrost Coasts

This study demonstrates a circum-Arctic monitoring framework for quantifying annual change of permafrost-affected coasts at a spatial resolution of 10 m. Frequent cloud coverage and challenging lighting conditions, including polar night, limit the usability of optical data in Arctic regions. For this reason, Synthetic Aperture RADAR (SAR) data in the form of annual median and standard deviation (sd) Sentinel-1 (S1) backscatter images covering the months June–September for the years 2017–2021 were computed. Annual composites for the year 2020 were hereby utilized as input for the generation of a high-quality coastline product via a Deep Learning (DL) workflow, covering 161,600 km of the Arctic coastline. The previously computed annual S1 composites for the years 2017 and 2021 were employed as input data for the Change Vector Analysis (CVA)-based coastal change investigation. The generated DL coastline product served hereby as a reference. Maximum erosion rates of up to 67 m per year could be observed based on 400 m coastline segments. Overall highest average annual erosion can be reported for the United States (Alaska) with 0.75 m per year, followed by Russia with 0.62 m per year. Out of all seas covered in this study, the Beaufort Sea featured the overall strongest average annual coastal erosion of 1.12 m. Several quality layers are provided for both the DL coastline product and the CVA-based coastal change analysis to assess the applicability and accuracy of the output products. The predicted coastal change rates show good agreement with findings published in previous literature. The proposed methods and data may act as a valuable tool for future analysis of permafrost loss and carbon emissions in Arctic coastal environments.

The spatial and temporal variability and influence factor analysis of soil erosion in a grass farming area: a case study in central China

Context Analysing soil erosion has important research significance for the protection of the ecological environment and the prevention and control measures of soil erosion. Methods This paper aims to discuss the soil erosion degree in the warm temperate grass farming subregion of the southern Shanxi and Guanzhong Basin, China, based on Universal Soil Loss Model, RUSLE. Aims The soil erosion modulus from 1998 to 2020 of the study area was calculated and divided into five periods: 2000, 2005, 2010, 2015, and 2020. Key results We highlight two key findings: (1) the average soil erosion modulus changed from 498.86 t km−2 a−1 in 2000 to 316.94 t km−2 a−1 in 2020, and the proportion of soil area with an unchanged erosion degree is above 85%; (2) the average annual erosion area is the largest when rainfall is greater than 550 mm and less than 620 mm. From 2000 to 2020, the area of cultivated land decreased by 3497.47 km2, and the area of grassland increased by 1364.96 km2. The degree of erosion of grassland is the most severe, with soil erosion is most intense when the Normalised Vegetation Index (NDVI) is greater than 0.55 and less than 0.75. Conclusions The results show that the soil erosion in this area is slight on the whole and its degree has been decreasing. Implications The analysis in this paper can elucidate the seriousness of the soil erosion problem so that the government can strengthen the key management of soil and water conservation and achieve the purpose of reducing soil erosion.

Monitoring and modelling of debris-flow erosion in the Rebaixader catchment (Pyrenees, Spain)

Debris flow and other torrential processes can entrain large volumes of sediments along their runout path. Since the debris-flow hazard strongly depends on the volume, the research on the entrainment is relevant and has been analysed by multiple approaches in the past. In this study, the erosion volume due to torrential processes has been monitored in the Rebaixader catchment (Spain) by digital elevation models obtained from Uncrewed Aerial Vehicle (UAV) surveys and by the instrumental monitoring system installed in the channel reach. In addition, the entrainment of debris flows has been modelled by the numerical code FLATModel. The results of both approaches show that debris flows in the studied catchment are characterised by a large entrainment and the assumption of landslide-triggered debris flows has been refuted. Important erosion in the gullies of the initiation area has been detected by the UAV data and the numerical modelling. An average annual erosion volume of about 6100 m3/y has been determined.

Soil erosion prediction and risk assessment using RUSLE model and GIS techniques in the Nangka watershed

Soil erosion in the Nangka watershed has always been a matter of concern. Over the last decade, soil erosion has led to continuous environmental problems. A thorough examination of the extent of the problem was required to identify an appropriate soil conservation strategy within the watershed. This study was conducted to observe erosion rates and map out the erosion hazard level. Erosion predictions were analysed by using the Revised Universal Soil Loss Equation (RUSLE) model with the help of ArcGIS software. RUSLE was selected because of its quantitative ability to estimate average annual soil erosion and its compatibility with the GIS interface. The potential hazard of soil erosion was classified and ranked into five class categories as set by the national authority. The results reveal that the Nangka watershed is prone to soil erosion with the annual average values ranging from 1.33 Mg·ha –1·y –1 to 2472.29 Mg·ha –1·y –1. High soil erosion rates of 9.8% are in severe (class IV) and very severe (class V) conditions, primarily in the upper course of the watershed. The low annual average of soil erosion (class I and class II), which accounted for 75.95% of the total erosion, mostly took place in the steepness below 35%. The remaining area of 14.25% within the watershed is in moderate condition (class III). It is expected that the results of this study will help the authority in the implementation of soil conservation measures.

Assessment of the Annual Erosion Rate along Three Hiking Trails in the Făgăraș Mountains, Romanian Carpathians, Using Dendrogeomorphological Approaches of Exposed Roots

Mountain hiking trails are vital components of tourist infrastructure and provide recreational opportunities for a large number of tourists. Exposed roots along the tourist trails in the forested mountains are impacted by tourist trampling and various natural processes, thus becoming even more exposed and eroded. The aim of our study was to estimate the annual erosion rates along three hiking trails in the Făgăraș Mountains using dendrogeomorphological approaches. The three used routes were: Bâlea Hotel—Bâlea Waterfall (BWFHT), Bâlea Hotel—Bâlea Glacial Lake (BLHT), and Bâlea Hotel—Doamnei Glacial Valley (DVHT). The average annual erosion rates in BWFHT, BLHT, and DVHT were 10.6 ± 4.4, 6.8 ± 3.9, and 6.1 ± 3.3 mm·y−1, respectively. Over a 56-year interval (1965–2021), 610 scars were recorded among the annual growth rings of the sampled tree roots; 172, 213, and 225 scars were recorded in BWFHT, BLHT, and DVHT, respectively. Moreover, we identified 1022 rows of traumatic resin ducts (TRDs) associated with scars: 237, 343, and 442 in BWFHT, BLHT, and DVHT, respectively. Additionally, the climate of the Făgăraș Mountains is humid with a multiannual average precipitation of 1366.2 mm; the precipitation in 24 h, between 1979 and 2021 in seven and three cases exceeded 70 mm/24 h and 100 mm/24 h, respectively. Thus, there were synchronous situations of root exposure with 24 h rainfall. However, it is unclear whether precipitation plays a decisive role in root exposure or in triggering erosion processes on tourist trails. We considered that tourist traffic plays a decisive role in root exposure and erosion, however locally and complementarily, 24 h precipitation must also be considered.

Soil Erosion mapping using Remote Sensing and GIS - A case study of Rewa River Watershed, Naitasiri province, Fiji

Rewa river watershed covers approximately up around 33% of the Viti Levu’s total Land area in Fiji’s central division. Due to anthropogenic development activities, this region has undergone soil erosion and flooding. Soil erosion leads to a variety of environmental issues, with a direct impact on agriculture and infrastructure development. An evaluation of soil erosion in the Rewa river watershed can be used to plan conservation activities. Modeling provides a quantitative and consistent technique to estimating the quantity of soil loss under various situations. The goal of this research is to estimate average annual soil erosion in the Rewa river watershed in Naitasiri province, Fiji, using a GIS (Geographic Information System) combined with the RUSLE (Revised Universal Soil Loss Equation) model. According to the findings, a maximum annual soil loss occurs near to developing regions and river tributaries in areas with a high risk of erosion. To avoid potential concerns in places prone to moderate to high erosion risk, erosion control and watershed protection measures must be implemented immediately. In this context, the RUSLE model can be used to analyse soil erosion in small watersheds in a quick, cost-effective, and less labor-intensive manner, hence assisting in sustainable development and supporting healthy land use practices.

Assessment of Net Erosion and Suspended Sediments Yield within River Basins of the Agricultural Belt of Russia

The SEA/Balance (soil erosion–accumulation balance) model and the WATEM/SEDEM model both mapping the erosion–accumulation budget of sediment within river basins were tested for 11 river basins of the eastern Russian Plain. The dynamics of river sediment yield were evaluated within one of the river basins. The analysis is based on observations of suspended sediment yield in test river basins with an area ranging from 100 to 1500 km2. The maps of the average annual erosion–accumulative budget of sediment were constructed using two methods, making it possible to quantitatively assess the amount of sediment yield from the catchment area for the river basins under study. The WATEM/SEDEM model and the author’s SEA/Balance model were used for sediment yield estimation. The results of calculation using the WATEM/SEDEM model have an average bias of +11% compared to observed suspended sediment yield. The corresponding value in the case of the SEA/Balance model application is −29%. SEA/Balance model assessment sediment yield dynamics for the Sterlya river basin show an 11 percent reduction. It is possible to conclude that the proposed method of SEA/Balance can be applied to river basins of the agricultural zone of the east Russian Plain.

A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction

Rainfall variation causes frequent unexpected disasters all over the world. Increasing rainfall intensity significantly escalates soil erosion and soil erosion related hazards. Forecasting accurate rainfall helps early detection of soil erosion vulnerability and can minimise the damages by taking appropriate measures caused by severe storms, droughts and floods. This study aims to predict soil erosion probability using the deep learning approach: long short-term memory neural network model (LSTM) and revised universal soil loss equation (RUSLE) model. Daily rainfall data were gathered from five agro-meteorological stations in the Central Highlands of Sri Lanka from 1990 to 2021 and fed into the LSTM model simulation. The LSTM model was forecasted with the time-series monthly rainfall data for a long lead time period, rainfall values for next 36 months in each station. Geo-informatics tools were used to create the rainfall erosivity map layer for the year 2024. The RUSLE model prediction indicates the average annual soil erosion over the Highlands will be 11.92 t/ha/yr. Soil erosion susceptibility map suggests around 30 % of the land area will be categorised as moderate to very-high soil erosion susceptible classes. The resulted map layer was validated using past soil erosion map layers developed for 2000, 2010 and 2019. The soil erosion susceptibility map indicates an accuracy of 0.93 with the area under the receiver operator characteristic curve (AUC-ROC), showing a satisfactory prediction performance. These findings will be helpful in policy-level decision making and researchers can further tested different deep learning models with the RUSLE model to enhance the prediction capability of soil erosion probability.

A spatial-temporal assessment of groundwater development in response to soil erosion

The sustainable management of water resources plays a significant role in boosting the country's economy, mainly in the agriculture sector. In many regions globally, the availability of surface water resources is limited and thus depends on the groundwater for irrigating crops. Furthermore, soil erosion is a severe problem such as worsened soil fertility, land-use disturbances, and sedimentation, especially in large-scaled river basins having complex topography. Therefore, assessment of the spatial-temporal variation of groundwater development, such as groundwater recharge (GWR) and groundwater safe yield (GWSY) and their response to topsoil erosion in large-scaled river basins, are crucial for sustainable groundwater development. This paper aims to assess the spatial-temporal dynamics of groundwater development in response to runoff–induced topsoil loss. A large-scale East African river basin such as the Omo River basin was chosen to demonstrate the proposed modeling approaches. The WetSpass-M physical-based hydrological model was used to estimate the longtime average annual GWR and runoff. The estimated spatial variation of GWR was used for the analysis of GWSY. Further, combined methods such as the RUSLE model, ArcGIS tool, and remote sensing (RS) data were used to estimate the spatial variation of annual topsoil erosion. The result showed that groundwater resources in cultivation land use in the Omo River basin have moderate to high productive aquifers (2–28 l/s), with maximum GWSY capacity ranging from 0.4 to 4 m3/day/ha. The estimated long-term average annual topsoil erosion is 12.35 t ha−1yr−1 and is comparable with the observed sediment yield of 13.24 t ha−1yr−1. The cultivation regions of the river basin receive maximum GWR and experience moderate to severe topsoil erosion, indicating that GWR has a direct response to topsoil erosion in the disturbed soil regions.

Understanding the impact of changes in land-use land-cover and rainfall patterns on soil erosion rates using the RUSLE model and GIS techniques: A study on the Nagavali River basin

Abstract Soil erosion is the most common type of land degradation, and it has become a global environmental issue that reduces soil productivity and water quality and is accelerated by human-induced activities. The Revised Universal Soil Loss Equation (RUSLE) model, which includes factors like rainfall, soil, land-cover, cultivation practices, and slope, was incorporated into the Geographic Information System (GIS) database to estimate average annual soil erosion rates in the Nagavali River basin (NRB) during 1990 and 2020, and this study also focuses on analyzing the impact of the land-use and land-cover (LULC) change, as well as climate variability on the annual average soil erosion rates in the NRB. Results indicate that the average annual soil erosion rate in the NRB ranged from 0 to 2364.46 t/ha/yr in the year 1990 was increased to 7857.21 t/ha/yr by the year 2020 as a result of an increase in rainfall and LULC changes over the study period. Based on the spatial distribution of soil erosion risk classes, it was identified that the area under the very severe erosion class increased drastically from 4.34 to 13.97%, while the area under the very slight erosion class got decreased between the years 1990 and 2020. Accordingly, a correlation analysis was carried out to determine the spatial correlation between the soil erosion pattern and changes in driver variables such as rainfall and land-use classes. These findings can be used to make well-informed decisions at the local and regional levels for the sake of soil conservation.

Assessment of Riverbank Erosion Hotspots along the Mekong River in Cambodia Using Remote Sensing and Hazard Exposure Mapping

The large-scale monitoring of riverbank erosion is challenging because of human, equipment, and financial limitations, particularly in developing countries. This study aims to detect riverbank erosion and identify riverbank erosion hotspots along the Mekong River in Cambodia. A riverbank erosion rate map was developed using satellite images from Landsat 5, 7, and 8 (1990–2020) using the modified normalized difference water index (MNDWI) at a resolution of 30 m and Sentinel-2 (2016–2021) using the normalized difference water index (NDWI) at a resolution of 10 m. Detecting riverbanks in satellite images using a water index depends greatly on image resolution and water threshold. The riverbank lines were validated using Google Earth images. In the data used in December 2017, the root mean square error (RMSE) of Sentinel-2 was 6.00 m, while the RMSE of Landsat was 6.04 m. In the data used in January 2019, the RMSE of Sentinel-2 was 4.12 m, while the RMSE of Landsat was 5.90 m. The hotspots were identified by overlaying the riverbank erosion rate map and the exposure map of population density and land cover. Field surveys and interviews were conducted to verify riverbank erosion hotspots in the Ruessei Srok and Kaoh Soutin communes. The results showed that within the last 30 years (1990–2020), the riverbank eroded more than 1 km in a direction perpendicular to the river in some segments of the Mekong River in Cambodia. The highest average annual erosion rate was in the Ruessei Srok Commune in Kampong Cham Province, at approximately 43 m/yr. Most eroded areas were farmland and rural residential areas. The riverbank hotspots are situated mainly in the lower part of the Mekong River, where the population is dense, and the erosion rate is high. Riverbank erosion hotspots with a very high impact level and ongoing active erosion include the Peam Kaoh Sna, Kampong Reab, Kaoh Soutin, and Ruessei Srok communes in Kampong Cham Province. This study provides an efficient tool for using satellite images to identify riverbank erosion hotpots in a large river basin. The riverbank erosion hotspot map is essential for decision-makers to prioritize interventions to reduce the risk of riverbank erosion and to improve the livelihood of the people residing along the Mekong River.

Impacts des décisions et actions de protection sur l’évolution morphologique du littoral lacustre de Thonon-les-Bains (France, Haute-Savoie)

The landscape heritage of Thonon-les-Bains’ Lake Geneva shoreline is a key component of the local population's living environment and tourist activity. Since the mid-1990s however, coastal erosion has been observed. With this worrying development, experts were called in to monitor the shoreline’s morphology. The first study (2011-2014) showed that the majority of the shoreline was eroding (60%) at an average rate of 0.40 m.yr and a maximum of 1.5 m. The primary cause is sediment deficit linked to hydroelectric developments in the Dranse catchment area, and sediment extraction at the mouth of the river Dranse on Lake Geneva (Maleval et al., 2016). In light of the study’s findings, the Prefecture refused the extraction company's (Sagradranse) application to continue its activity, and Thonon-les-Bains municipality undertook renaturation work on its shoreline using soft protection methods (plant engineering and removal of hard structures). The latest study (2019), using the same methodology as the first (mainly manual topography and acoustic bathymetry), revealed an encouraging change: between 2014 and 2019, the portion of eroded shoreline decreased to 42%, and the percentage of severely-eroded shorelines decreased from 39% to 5%. On the basis of these latest data, the average annual erosion rate of the Thonon-les-Bains shoreline can be considered as minor (0.13 m), thereby supporting the public authorities’ decisions.