Lessons learnt on the impact of an unprecedented soil decontamination program in Fukushima on contaminant fluxes.

In the context of elevated concerns related to nuclear accidents and warfare, the lessons learnt from the Fukushima Daiichi Nuclear Power Plant accident in 2011 are important. In particular, Japanese authorities implemented an ambitious decontamination program to reduce the air dose rate in order to facilitate the return of the local inhabitants to previously evacuated areas. This approach contrasts the strategy adopted in Chernobyl, where the most contaminated areas remain off limits. Nonetheless, the effectiveness of the Japanese decontamination strategy on the dispersion of radioactive contaminant fluxes across mountainous landscapes exposed to typhoons has not been quantified. Based on the unique combination of river monitoring and modeling in a catchment representative of the most impacted area in Japan, we demonstrate that decontamination of 16% of the catchment area resulted in a decrease of 17% of sediment-bound radioactive fluxes in rivers. Decontamination operations were therefore relatively effective, although they could only be conducted in a small part of the area due to the dominance of steep forested slopes. In fact, 67% of the initial radiocesium contamination was calculated to remain stored in forested landscapes, which may contribute to future downstream radiocesium dispersion during erosive events. Given that only a limited proportion of the initial population had returned in 2019 (~30%), it raises the question as to whether decontaminating a small percentage of the contaminated area was worth the effort, the price, and the amount of waste generated?

Including land management in a European carbon model with lateral transfer to the oceans.

The use of cover crops (CCs) is a promising cropland management practice with multiple benefits, notably in reducing soil erosion and increasing soil organic carbon (SOC) storage. However, the current ability to represent these factors in land surface models remains limited to small scales or simplified and lumped approaches due to the lack of a sediment-carbon erosion displacement scheme. This precludes a thorough understanding of the consequences of introducing a CC into agricultural systems. In this work, this problem was addressed in two steps with the spatially distributed CE-DYNAM model. First, the historical effect of soil erosion, transport, and deposition on the soil carbon budget at a continental scale in Europe was characterized since the early industrial era, using reconstructed climate and land use forcings. Then, the impact of two distinct policy-oriented scenarios for the introduction of CCs were evaluated, covering the European cropping systems where surface erosion rates or nitrate susceptibility are critical. The evaluation focused on the increase in SOC storage and the export of particulate organic carbon (POC) to the oceans, compiling a continental-scale carbon budget. The results indicated that Europe exported 1.95 TgC/year of POC to the oceans in the last decade, and that CCs can contribute to reducing this amount while increasing SOC storage. Compared to the simulation without CCs, the additional rate of SOC storage induced by CCs peaked after 10 years of their adoption, followed by a decrease, and the cumulative POC export reduction stabilized after around 13 years. The findings indicate that the impacts of CCs on SOC and reduced POC export are persistent regardless of their spatial allocation adopted in the scenarios. Together, the results highlight the importance of taking the temporal aspect of CC adoption into account and indicate that CCs alone are not sufficient to meet the targets of the 4‰ initiative. Despite some known model limitations, which include the lack of feedback of erosion on the net primary productivity and the representation of carbon fluxes with an emulator, the current work constitutes the first approach to successfully couple a distributed routing scheme of eroded carbon to a land carbon model emulator at a reasonably high resolution and continental scale. SHORT ABSTRACT: A spatially distributed model coupling erosion, transport, and deposition to the carbon cycle was developed. Then, it was used to simulate the impact of cover crops on both erosion and carbon, to show that cover crops can simultaneously increase organic carbon storage and reduce particulate organic carbon export to the oceans. The results seemed persistent regardless of the spatial distribution of cover crops.

The extent of soil loss across the US Corn Belt.

Soil erosion in agricultural landscapes reduces crop yields, leads to loss of ecosystem services, and influences the global carbon cycle. Despite decades of soil erosion research, the magnitude of historical soil loss remains poorly quantified across large agricultural regions because preagricultural soil data are rare, and it is challenging to extrapolate local-scale erosion observations across time and space. Here we focus on the Corn Belt of the midwestern United States and use a remote-sensing method to map areas in agricultural fields that have no remaining organic carbon-rich A-horizon. We use satellite and LiDAR data to develop a relationship between A-horizon loss and topographic curvature and then use topographic data to scale-up soil loss predictions across 3.9 × 105 km2 of the Corn Belt. Our results indicate that 35 ± 11% of the cultivated area has lost A-horizon soil and that prior estimates of soil degradation from soil survey-based methods have significantly underestimated A-horizon soil loss. Convex hilltops throughout the region are often completely denuded of A-horizon soil. The association between soil loss and convex topography indicates that tillage-induced erosion is an important driver of soil loss, yet tillage erosion is not simulated in models used to assess nationwide soil loss trends in the United States. We estimate that A-horizon loss decreases crop yields by 6 ± 2%, causing $2.8 ± $0.9 billion in annual economic losses. Regionally, we estimate 1.4 ± 0.5 Pg of carbon have been removed from hillslopes by erosion of the A-horizon, much of which likely remains buried in depositional areas within the fields.

Decoupling the effects of air temperature change on soil erosion in Northeast China.

Changes in the air temperature tend to indirectly affect soil erosion by influencing rainfall, vegetation growth, economic development, and agricultural activities. In this study, the partial least squares-structural equation model (PLS-SEM) was used to decouple the impacts of temperature change on soil erosion in Northeast China from 2001 to 2019, and the indirect effect of temperature change on the pathways of natural and socioeconomic factors was analyzed. The results showed that temperature increase in Northeast China caused an increase in soil erosion by increasing rainfall and promoting economic development. Under the pathway of natural factors, in spring, the promoting effect on soil erosion under the influence of temperature change on rainfall was greater than the inhibiting effect on soil erosion under by the influence of temperature change on vegetation. In summer, the opposite effect was observed. Under the pathway of natural factors, over time, the promoting effect of temperature increase on soil erosion increased by 22.7%. Under the pathway of socioeconomic factors, temperature change not only aggravated soil erosion by promoting economic development, but also indirectly increased investments in agriculture and water conservation by improving the economy, thus inhibiting soil erosion to a certain extent. Over time, the contribution of temperature change to soil erosion through socioeconomic pathway was reduced by 44.4%. When the pathway of natural factors is compared with that of socioeconomics factors, temperature change imposed a more notable effect on the change in soil erosion through the socioeconomic pathway, indicating that human activities are the driving factors with a greater effect on soil erosion. Based on this, reasonable human intervention is an important means to alleviate soil erosion aggravation caused by rising temperatures.

Cost-effectiveness of erosion mitigation to meet water clarity targets in the Manawatu-Whanganui Region of New Zealand.

Soil erosion is a significant environmental issue worldwide. It affects water quality, biodiversity, and land productivity. New Zealand government agencies and regional councils work to mitigate soil erosion through policies, management programmes, and funding for soil conservation projects. Information about cost-effectiveness is crucial for planning, targeting, and implementing erosion mitigation to achieve improvements in sediment-related water quality. While there is a good understanding of the costs of erosion mitigation measures, there is a dearth of literature on their cost-effectiveness in reducing sediment loads and improving water quality at the catchment level. In this study, we estimate the cost-effectiveness of erosion mitigation measures in meeting visual water clarity targets. The analysis utilizes the spatially explicit SedNetNZ erosion process and sediment budget modelling in the Manawatu-Whanganui Region and region-specific mitigation costs. The erosion mitigation measures considered in the analysis include afforestation, bush retirement, riparian retirement, space-planted trees, and gully tree planting. We modelled two scenarios with on-farm erosion mitigation implemented across the region from 2021 to 2100, resulting in a 48% and 60% reduction of total sediment load. We estimate the marginal costs to achieve the visual national bottom line for water clarity, as assessed by the length of waterways that meet the clarity targets. We also estimate the marginal costs of improving average water clarity, which can be linked with non-market valuation studies when conducting a cost-benefit analysis. We find that gully tree planting and space-planted trees are the most cost-effective mitigation measures and that riparian retirement is the least cost-effective. Moreover, cost-effectiveness is highly dependent on current land use and the biophysical features of the landscape. Our estimates can be used in cost-benefit analysis to plan and prioritize soil erosion mitigation at the catchment and regional levels.

How does cultivated land fragmentation affect soil erosion: Evidence from the Yangtze River Basin in China.

Reducing soil erosion (SE) is crucial for achieving harmony between human society and the ecological environment. The cultivated land fragmentation (CLF), directly or indirectly, alters soil structure, diminishes its water-holding capacity, and escalates the risk of SE. Scientific assessment of the effect of CLF on SE can provide new insights into controlling of SE across watersheds in China. However, few studies have quantified the effect of CLF on SE. Therefore, we utilized land use change data in the Yangtze River basin from 2000 to 2020, measuring the levels of CLF and SE using Fragstats and InVEST models. The bivariate spatial autocorrelation model was employed to reveal the spatial relationship between CLF and SE. Additionally, we constructed a spatial Durbin model and introduced the geographically and temporally weighted regression model to analyze the role of CLF on SE. The south bank of the upper and middle reaches of the Yangtze River basin exhibited high CLF and SE. The bivariate spatial autocorrelation results showed a significant positive spatial correlation between CLF and SE. The spatial Durbin model results showed that CLF had a spatial spillover effect and time lag on SE, and the effect of CLF on SE had an inverted "N" curve. The study also confirmed that last SE and neighboring SE areas influenced local SE. Currently, CLF had a negative effect on SE in the Sichuan Basin, Yunnan-Guizhou Plateau, and the middle and lower Yangtze River Plain, and positively in Qinghai, Hunan, and Jiangxi provinces. These findings suggest that the government should enhance cross-regional and cross-sectoral cooperation and monitoring of cultivated land changes to prevent and control SE effectively.

Modelling of soil erosion susceptibility incorporating sediment connectivity and export at landscape scale using integrated machine learning, InVEST-SDR and Fragstats.

Evaluating the linkage between soil erosion and sediment connectivity for export assessment in different landscape patterns at catchment scale is valuable for optimization of soil and water conservation (SWC) practices. Present research attempts to identify the soil erosion susceptible (SES) sites in Kangsabati River Basin (KRB) using machine learning algorithm (decision trees, decision trees cross validation, CV, Extreme Gradient Boosting, XGB CV and bagging CV) taken thirty five variables, for investigating the linkage between erosion rates and sediment connectivity to assess the sediment export at sub-basin level employing connectivity index (IC) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) sediment delivery ratio (SDR) model. Based on AUC of receiving operating curve in validation test, excellent capacity of extreme Gradient Boosting, XGB CV and bagging CV (0.95, 0.90) than decision tree and decision tree CV (0.78, 0.82), exhibits about 18.58 % of basin areas facing susceptible to very high erosion. Conversely, considering universal soil loss equation (RUSLE) parameters, InVEST-SDR model estimated about 64.24 % of soil loss rate occurred from high SES in where sediment export rate become very high (136.995 t/ha-1/y-1). The IC result show that high sediment connectivity (<-4.4) measured in high SES of laterite and bare land in upper catchment, and double crop agricultural areas in lower catchment, while least connectivity (>-7.1) observed in low SES of dense forest, vegetation cover and settlement built-up areas. Pearson correlation matrix revealed that four landscape indices category i.e. edge metrics (p < 0.01), aggregation metrics (p < 0.001), shape metrics (p < 0.01-0.001) and diversity metrics (p < 0.01) signified the influence of landscape patterns on IC and SES. Accordingly, RUSLE, SDR and landscape matrices reveals that maximum sediment export rate associated with high connective delivery outlet and high SES in laterite, double crop and bare land due to simple landscape and greater homogeneity, whilst minimum export rate related with low connectivity and low SES in dense forest, vegetation cover and settlement built up area causes of fragmented landscape and spatial heterogeneity. Finally, findings could immense useful for formulating the optimizing measures of SWC in the watershed.

Seasonal and Ecological Determinants of Wild Boar Rooting on Priority Protected Grasslands.

Wild ungulates can influence various trophic levels, regulating carnivore abundance and affecting habitat structure. Conservation problems can arise when high ungulate densities threaten species or habitats with conservation concern. Assessing factors influencing the intensity of their impact is important to identify appropriate measures enhancing habitat conservation. We assessed factors influencing wild boar Sus scrofa pressure on EU protected grasslands in three protected areas of central Italy, by modelling the effects of environmental variables and wild boar density on rooting activity. We seasonally estimated rooting in 126 sampling plots from spring 2019 to spring 2021, and we used faeces counts to estimate summer wild boar densities. Estimates of density and rooting varied from 3.5 to 22.2 individuals/km2 and from 1.1 to 19.2%, respectively. We detected a clear seasonal trend in rooting activity, that peaked in autumn and winter. We also found a strongly positive correlation between spring-summer rooting and summer density, across sites. Rooting intensity was negatively related to the local extent of rock cover and increased with the 1 month-cumulative rainfall, the perimeter of the grassland patch, and the forest cover around plots. These results emphasise the tendency of wild boar to exploit feeding sites in ecotonal areas, i.e., at the interface between forest and meadows, which maximises security and ease of finding food resources. Actions aiming at the protection of focal plants in grassland habitats, as well as reducing wild boar presence, are supported (e.g. fencing and/or targeting population control at vulnerable patches).

Plutonium isotopes can be used to model soil erosion in Kenya.

Climate change poses an immediate threat to tropical soils with changes in rainfall patterns resulting in accelerated land degradation processes. To ensure the future sustainability of arable land, it is essential to improve our understanding of the factors that influence soil erosion processes. This work aimed to evaluate patterns of soil erosion using the activity of plutonium isotopes (Pu) at sites with different land use and clearance scale in the Winam Gulf catchment of Lake Victoria in Kenya. Erosion rates were modelled at potential erosive sites using the MODERN model to understand small-scale erosion processes and the effect of different management practices. The lowest soil redistribution rates for arable land were 0.10 Mg ha-1 yr-1 showing overall deposition, resulting from community-led bottom-up mitigation practices. In contrast erosion rates of 8.93 Mg ha-1 yr-1 were found in areas where steep terraces have been formed. This demonstrates the significance of community-led participation in effectively managing land degradation processes. Another key factor identified in the acceleration of soil erosion rates was the clearance of land with an increased rate of erosion over three years reported (0.45 to 0.82 Mg ha-1 yr-1) underlining the importance vegetation cover plays in limiting soil erosion processes. This novel application of fallout plutonium as a tracer, highlights its potential to inform the understanding of how soil erosion processes respond to land management, which will better support implementation of effective mitigation strategies.

Assessment of soil erosion in the Upper Citarum watershed for sustainability of the Saguling reservoir: unmixing model approach.

The Citarum watershed and the Saguling reservoir are vital natural resources in Indonesia, affecting the livelihood of West Java and the DKI Jakarta population. This study aimed to assess the soil erosion in the Upper Citarum watershed and identify its source. The study used the fallout radionuclide technique, geochemical tracers, and an unmixing model to measure soil erosion and the contribution of suspended sediment sources due to erosion. Soil bulk transects and surface soil were sampled using a coring tool on the Ciwidey and Cisangkuy sub-watersheds. Riverbank and suspended sediment samples were collected from tributaries and rivers. With 137Cs, 40% of the samples had values below the minimum detectable activity, and vice versa for 210Pbex, all samples are detectable. For mitigation, bare land needs to be recovered due to its erosion (25.6 t ha-1 year-1) exceeding the tolerance erosion value (17 t ha-1 year-1). Statistically, Mg and Na were the most appropriate composite tracers for suspended sediment contribution. The unmixing model predicted the sediment contributors from bare land (58%), the riverbank (32.7%), and plantation land (9.3%). Proper land conservation could reduce sediment supply by almost 14.7% and extend the reservoir's life. This is the first study to report the feasibility of the unmixing model in Indonesia.

Spatial analysis and assessment of soil erosion in the southern Western Ghats region in India.

Soil erosion is expected to worsen in the future as a result of climate change, growing population demands, improper land use, and excessive exploitation of natural resources in India. Due to the growing population and changes in land use, it has become increasingly crucial to map and quantitatively assess soil for the purpose of sustainable agricultural usage and planning conservation efforts. The problem of soil erosion is mainly on steeper slopes with intense rainfall in parts of Western Ghats. The 20.17% of geographical area have been converted into wasteland due to soil erosion. The Revised Universal Soil Loss Equation (RUSLE) is a highly prevalent and effective technique utilized for estimating soil loss in order to facilitate the planning of erosion control measures. Despite the fact that RUSLE is accurately estimate sediment yields from gully erosion, it is an effective tool in estimating sheet and rill erosions losses from diverse land uses like agricultural to construction sites. The current study is mainly about combining the RUSLE model with GIS (Geographic Information System) to find out how much soil is being lost, particularly in Noyyal and Sanganur watersheds which is located in Coimbatore district of Tamil Nadu, India. This analysis is based on the soil order, with a significant proportion of alfisols and inceptisols being considered. The obtained outcome is contrasted with the established soil loss tolerance threshold, leading to the identification of the areas with the highest susceptibility to erosion. Within the narrower and more inclined section of the watershed, yearly soil loss scales from 0 to 5455 tonnes/ha/year, with an average annual loss of soil of 2.44 tonnes/ha. The severe soil erosion of 100 to 5455 tonnes/ha/year is found along the steep and greater slope length. The generated soil map was classified into six categories: very slight, slight, moderate, high, severe, and very severe. These classifications, respectively, occupied 6.23%, 14.88%, 10.56%, 15.70%, 7.73%, and 6.63% of the basin area. Based on the results of cross-validation, the estimated result of the present study was found to be very high compared to past studies conducted 0 to 368.12 tonnes/ha/year especially in very severe erosion zones. But very slight to severe erosion zones nearly matched with same level of soil loss. To protect the soil in the study area from erosion, more specific actions should be taken. These include micro-catchment, broad bed furrows, up-and-down farming, soil amendment with coconut coir pith composition, streambank stabilization with vegetation, and micro-water harvesting with abandoned well recharge. These actions should be carried out over time to make sure to work.

Improved soil carbon stock spatial prediction in a Mediterranean soil erosion site through robust machine learning techniques.

Soil serves as a reservoir for organic carbon stock, which indicates soil quality and fertility within the terrestrial ecosystem. Therefore, it is crucial to comprehend the spatial distribution of soil organic carbon stock (SOCS) and the factors influencing it to achieve sustainable practices and ensure soil health. Thus, the present study aimed to apply four machine learning (ML) models, namely, random forest (RF), k-nearest neighbors (kNN), support vector machine (SVM), and Cubist model tree (Cubist), to improve the prediction of SOCS in the Srou catchment located in the Upper Oum Er-Rbia watershed, Morocco. From an inventory of 120 sample points, 80% were used for training the model, with the remaining 20% set aside for model testing. Boruta's algorithm and the multicollinearity test identified only nine (9) factors as the controlling factors selected as input data for predicting SOCS. As a result, spatial distribution maps for SOCS were generated for all models, then compared, and further validated using statistical metrics. Among the models tested, the RF model exhibited the best performance (R2 = 0.76, RMSE = 0.52 Mg C/ha, NRMSE = 0.13, and MAE = 0.34 Mg C/ha), followed closely by the SVM model (R2 = 0.68, RMSE = 0.59 Mg C/ha, NRMSE = 0.15, and MAE = 0.34 Mg C/ha) and Cubist model (R2 = 0.64, RMSE = 0.63 Mg C/ha, NRMSE = 0.16, and MAE = 0.43 Mg C/ha), while the kNN model had the lowest performance (R2 = 0.31, RMSE = 0.94 Mg C/ha, NRMSE = 0.24, and MAE = 0.63 Mg C/ha). However, bulk density, pH, electrical conductivity, and calcium carbonate were the most important factors for spatially predicting SOCS in this semi-arid region. Hence, the methodology used in this study, which relies on ML algorithms, holds the potential for modeling and mapping SOCS and soil properties in comparable contexts elsewhere.

Does spatial resolution matter in the estimation of average annual soil loss by using RUSLE?-a study of the Urmodi River Watershed (Maharashtra), India.

The study investigates the influence of multispectral satellite data's spatial resolution on land degradation in the Urmodi River Watershed in which Kaas Plateau, a UNESCO World Heritage site, is located. Specifically, the research focuses on soil erosion and its risk zonation. The study employs Landsat 8 (30-m resolution) and Sentinel-2 (10-m resolution) data to assess soil erosion risk. The Revised Universal Soil Loss Equation (RUSLE) is used to quantify the average annual soil erosion output denoted by (A), by using its factors such as rainfall (R), soil erodibility (K), slope-length (LS), cover management (C), and support practices (P). R-factor was computed from MERRA-2 rainfall data, K-factor was derived from field soil sample-based analysis, LS factor was from Cartosat Digital Elevation Model-based data. The C factor was derived from NDVI of Landsat 8 and Sentinel-2, and the P factor was prepared from LULC derived from Landsat 8, and Sentinel-2 was incorporated in the final integration. The soil erosion hazard map ranged from slight to extremely severe. Remote sensing (RS)-based parameters like Land Use Land Cover (LULC) are derived from the Landsat 8 and Sentine-2 satellite data and used to compute the difference in the final outcome of the integration. The study found similarities in average annual soil loss (A) in plain areas, but differences in final soil erosion risk zone (A) were influenced by LULC map variations due to different cell sizes, P factor, and slope gradient. Notable differences were observed in soil erosion risk categories, particularly in high to very severe zones, with a cumulative difference of 73.85 km2. In addition to this, a scatterplot between the final outputs was computed and found the moderate (R2 = 42.08%) correlation between Landsat 8 and Sentinel-2 imagery-based final average annual soil erosion (A) of RUSLE. The study area encompasses various landforms ranging from the plateau to pediplain, and in such situation, the water-led soil erosion categories vary depending on terrain condition along with its biophysical factors and, hence, need to analyze the need of such factors on the average annual soil erosion quantification. Different spatial resolution has an effect on the final output, and hence, there is a need to track this change at various spatial resolutions. This analysis highlights the significant impact of spatial resolution on land degradation assessment, providing precise identification of surface features and enhancing soil erosion risk zoning accuracy.

Wildfires in Europe: Burned soils require attention.

Annually, millions of hectares of land are affected by wildfires worldwide, disrupting ecosystems functioning by affecting on-site vegetation, soil, and above- and belowground biodiversity, but also triggering erosive off-site impacts such as water-bodies contamination or mudflows. Here, we present a soil erosion assessment following the 2017's wildfires at the European scale, including an analysis of vegetation recovery and soil erosion mitigation potential. Results indicate a sharp increase in soil losses with 19.4 million Mg additional erosion in the first post-fire year when compared to unburned conditions. Over five years, 44 million Mg additional soil losses were estimated, and 46% of the burned area presented no signs of full recovery. Post-fire mitigation could attenuate these impacts by 63-77%, reducing soil erosion to background levels by the 4th post-fire year. Our insights may help identifying target policies to reduce land degradation, as identified in the European Union Soil, Forest, and Biodiversity strategies.

Assessment of badland susceptibility and its governing factors using a random forest approach. Application to the Upper Llobregat River Basin and Catalonia (Spain).

Badlands are considered hotspots of sediment production, contributing to large fractions of the sediment budget of catchments and river basins. The erosion rates of these areas can exceed 100 t ha-1 y-1, leading to significant environmental and economic impacts. This research aims to assess badland susceptibility and the relevance of its governing factors at different spatial scales using the well-known machine learning approach random forest (RF). The Upper Llobregat River Basin (ULRB, approx. 500 km2) and Catalonia (approx. 32,000 km2) have been selected as study areas. Previous studies stated that the RF approach is successful at making predictions for the same area where it has been trained, but the results of testing it in a different area remains unexplored. This work aims to evaluate the feasibility of upscaling to the large region of Catalonia a RF model trained in the small ULRB area. Two badland datasets of both small and large regions and a total of eleven governing factors have been used to determine the areas susceptible to badlands. Models performance has been analyzed through three different evaluation metrics: overall accuracy, kappa coefficient and area under receiver operating characteristic curve (AUC). The outcomes of this work confirmed that RF is a powerful tool for badland susceptibility analysis, specially when predictions are made in the same scale and spatial context where the model has been trained. Upscaling a RF model defined in the ULRB to the large area of Catalonia has been possible, but improved results have been obtained when the training of the models has directly been performed in the large region. Our final RF modelling results have facilitated the development of a large scale (32,000 km2) Badland Susceptibility Map for the full extension of Catalonia with a predictive overall accuracy of 97%, which strongly emphasizes lithology and Normalized Difference Vegetation Index (NDVI) as the main conditioning factors of badland distribution.

Soil erosion risk for farming futures: Novel model application and validation to an agricultural landscape in southern England.

Increasingly, agricultural land managers are seeking new approaches for understanding the potential challenges posed by sediment connectivity across catchments from source to sink, and implications for delivery of ecosystem services determined by the condition of natural capital assets. Connectivity indices have been frequently applied in the calculation of risk in spatial and temporal assessment frameworks, and tools which facilitate rapid modelling and mapping of soil erosion risk using broad-scale environmental data are therefore of considerable interest. One such indicative tool is SCIMAP (Sensitive Catchment Integrated Mapping and Analysis Platform), which highlights where sediment runoff is likely to occur and be delivered to a watercourse by simulating the generation of saturation-excess overland flow. In this paper, we examine the utility of SCIMAP for exploring the changing nature of soil erosion risk as a function of land use change in the lower Rother catchment in West Sussex, southern England through the formulation of a suite of foresight scenarios informed by knowledge of historical land cover conditions and current management practice. The study area has previously been investigated at the field scale in terms of locating and quantifying sources of erosion and areas where in-stream sedimentation manifests. Output risk values from all simulations were quantified, mapped and compared to highlight areas of greatest/lowest risk. An area was identified immediately north of the main Rother channel that consistently exhibited greatest risk across each land cover scenario. We explore (i) the spatial and temporal variation in modelled risk and (ii) the utility value of SCIMAP for agricultural land-managers and policy-makers in generating robust risk estimates of soil erosion and in-stream sedimentation, and challenges with model verification in a foresight context.

"Estimating soil surface roughness by proximal sensing for soil erosion modeling implementation at field scale".

Soil Surface Roughness (SSR) is a physical feature of soil microtopography, which is strongly influenced by tillage practices and plays a key role in hydrological and soil erosion processes. Therefore, surface roughness indices are required when using models to estimate soil erosion rates, where tabular values or direct measurements can be used. Field measurements often imply out-of-date and time-consuming methods, such as the pin meter and the roller chain, providing inaccurate indices. A novel technique for SSR measurement has been adopted, employing an RGB-Depth camera to produce a small-scale Digital Elevation Model of the soil surface, in order to extrapolate roughness indices. Canopy cover coverage (CC) of the cover crop was also detected from the camera's images. The values obtained for SSR and CC indices were implemented in the MMF (Morgan-Morgan-Finney) model, to validate the reliability of the proposed methodology by comparing the models' results for sediment yields with long-term soil erosion measurements in sloping vineyards in NW Italy. The performance of the model in predicting soil losses was satisfactory to good for a vineyard plot with inter-rows managed with recurrent tillage, and it was improved using spatialized soil roughness input data with respect to a uniform value. Performance for plot with permanent ground cover was not so good, however it was also improved using spatialized data. The measured values were also useful to obtain C-factor for RUSLE application, to be used instead of tabular values.

Challenges and progresses in the detailed estimation of sediment export in agricultural watersheds in Navarra (Spain) after two decades of experience.

Soil erosion is a very serious environmental problem worldwide, with agriculture considered the main source of sediment in inland waters. In order to determine the extent and importance of soil erosion in the Spanish region of Navarra, in 1995 the Government of Navarra established the Network of Experimental Agricultural Watersheds (NEAWGN), which consists of five small watersheds representative of local conditions. In each watershed, key hydrometeorological variables, including turbidity, were recorded every 10 min, and daily samples were taken to determine suspended sediment concentration. In 2006, the frequency of suspended sediment sampling was increased during hydrologically relevant events. The main objective of this study is to explore the possibility of obtaining long and accurate time series of suspended sediment concentration in the NEAWGN. To this end, simple linear regressions between sediment concentration and turbidity are proposed. In addition, supervised learning models incorporating a larger number of predictive variables are used for the same purpose. A series of indicators are proposed to objectively characterize the intensity and timing of sampling. It was not possible to obtain a satisfactory model for estimating the concentration of suspended sediment. This would be mainly due to the large temporal variability found of the physical and mineralogical characteristics of the sediment, which would be affecting the turbidity value, independently of the sediment concentration, per se. This fact would be particularly important in small river watersheds such as those of this study, and especially if their physical conditions are spatially and temporally radically disturbed by agricultural tillage and by a constant modification of the vegetation cover, as is the case in cereal basins. Our findings suggest that better results could be obtained by including in the analysis variables such as soil texture and exported sediment texture, rainfall erosivity, and the state of vegetation cover and riparian vegetation.

Combining cosmic-ray neutron sensor and fallout 137Cs to explore the connection of soil water content with soil redistribution in an agroforestry hillslope.

To ensure sustainable agricultural management, there is a need not only to quantify soil erosion rates but also to obtain information on the status of soil water content and soil loss under different soil types and land uses. A clear understanding of the temporal dynamics and the soil moisture spatial variability (SMSV) will help to control soil degradation by hydrological processes. This study represents the first attempt connecting cosmic-ray neutron sensors (CRNS) with soil erosion research, a novel approach to explore the complex relationships between soil water content (SWC) and soil redistribution processes using two of the most powerful nuclear techniques, CRNS and fallout 137Cs. Our preliminary results indicate that CRNS captured soil moisture dynamics along the study toposequence and demonstrated the sensitivity of neutron sensors to investigate the effect of parent material on soil water content. The Empirical Orthogonal Function (EOF) analysis of the comprehensive data from seven CRNS surveys revealed that one dominant spatial structure (EOF1) explains 89.2% of SMSV. The soil redistribution rates estimated with 137Cs at the nine locations along the hillslope, together with local factors related to soil properties (SOC, soil depth, hydraulic conductivity) and land use showed significant correlations with EOF. This study provides strong field evidence that soil type significantly affect SMSV, highlighting the key impact on soil erosion and sedimentation rates. Nevertheless, more research is needed to investigate the specific contributions of soil properties to the spatial variability of soil moisture and their subsequent effects on soil redistribution dynamics of interest for soil management.

A novel GIS-based multicriteria analysis approach for ascertaining the catchment-scale degradation of a Himalayan wetland.

Wetland degradation through a diverse spectrum of anthropogenic stressors worldwide has taken a heavy toll on the health of wetlands. This study examined the health of a semi-urban wetland Bodsar, located in the Kashmir Himalaya using multicriteria analysis approach assimilating data on land use land cover (LULC), landscape fragmentation, soil loss, and demography. Wetland and catchment-scale land system changes from 1980 to 2022 were assessed using high-resolution imagery. Fragmentation of the natural landscape was assessed using the Landscape Fragmentation Tool (LFT) and soil loss was assessed using the Revised Universal Soil Loss Equation (RUSLE). In addition, the water quality was examined at 12 sites distributed across the wetland using standard methods. Satellite data revealed 12 categories of land use with areas under exposed rock, orchards, built-up and sparse forest having increased by 1005%, 623%, 274%, and 37% respectively. LFT indicated that the core (>500 acres) and core (<250 acres) zones decreased by approximately 16% and 64%, respectively, whereas the areas under the perforated, edge and patch classes increased significantly. RUSLE estimates show a ∼77% increase in soil erosion from 116.26 Mg a-1 in 1980 to 205.68 Mg a-1 in 2022, mostly due to changes in LULC. Total phosphorus (0.195-2.04 mg L -1), nitrate nitrogen (0.306-2.79 mg L -1), and total dissolved solids (543-774 mg L-1) indicated nutrient enrichment of the wetland influenced by anthropogenically-driven land system changes. The wetland degradation index revealed that 21% of the wetland experienced high-to-severe degradation, 62% experienced moderate degradation, and 17% did not face any significant degradation pressure. The novel GIS-based approach adopted in this study can act as a prototype for ascertaining the catchment-scale degradation of wetlands worldwide.