This study focuses on the evolution of soil erosion rates on artificial covers for low level radioactive waste in the context of climate change. The objective is to test the impacts on erosion of (i) increasing rainfall intensities during storms and (ii) decreasing soil moisture content before storms. The “Centre de stockage de la Manche” (CSM) in Normandy, France, where Low-Level Nuclear waste are stored and monitored for the next centuries, is used as a reference case. There, climatic models anticipate an increase of temperature and seasonality (i.e., dryer Summers and wetter conditions from Fall to Spring) in the next centuries.

First, the soils of the CSM are sampled to be characterized. The densities, moisture, grainsize distribution and organic content of the soil are measured. We find that these values are rather homogeneous at the scale of the CSM. Second, a series of experimental rainfall simulations is performed on the CSM soils, focusing of rates and distribution of erosion processes. We simulate rainfall events of decennial, centennial, millennial and decamillennial intensities on 18° slopes, corresponding to the steeper banks of the CSM. Using the capacities of the climatic chambers at the Ecotron Lab in Nemours, France, we further test several soil moistures, i.e., very wet, moderately wet and dry, before simulating rainfall events. Finally, each experiment is repeated several times to assess the “memory” effect of topography on erosion. We quantify erosion by measuring sediment concentrations in run-off water collected at the outlet of the model and using topographic acquisitions performed using photogrammetry.

The experimental results are compared with estimations based on the Revised Universal Soil Loss Equation. Some propositions for upscaling, which could be used for assessing hypothetical future increase in soil loss in the CSM, are discussed.

How to cite: Portzer, G., Grimaud, J.-L., Marchiol, A., Stab, O., Fleurisson, J.-A., Abiven, S., Chollet, S., Maalouf, Y., Khoueiry, N., and Yadari, N.: Experimental simulation of soil erosion in the context of climate change in NW France., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11138, https://doi.org/10.5194/egusphere-egu24-11138, 2024.

Soil erosion, a global challenge with profound consequences, impacts soil nutrient depletion, land degradation, agricultural productivity, runoff, and geological hazards. Our study assesses soil erosion and land use changes in the Beas Valley, Kullu, Himachal Pradesh, situated in the Western Himalayas. Employing diverse datasets and a comprehensive methodology, we scrutinize the intricate interactions of climate, soil, topography, and land use to comprehend and mitigate soil erosion risks. Data sources include rainfall data from the Climate Research Unit at the University of East Anglia, soil data from the Food and Agriculture Organization, Digital Elevation Model (DEM) data from the Shuttle Radar Topography Mission, and Landsat satellite imagery. We utilize the Revised Universal Soil Loss Equation (RUSLE) for soil erosion assessment, which includes factors like erosivity (R-factor), erodibility (K-factor), slope and flow accumulation (LS-factor), vegetation cover (C-factor), and conservation practices (P-factor).To bolster the credibility of our findings, we complement our methodology with field observations and interviews. These on-ground assessments and stakeholder insights provide practical context and verification for our research. This interdisciplinary approach yields crucial insights into soil erosion and land use changes in the Beas Valley, enriching our understanding of soil erosion in this fragile Himalayan ecosystem. Our findings offer vital support for informed land management decisions and conservation efforts.

Keywords: Soil erosion assessment, Himalayan, RUSLE, GIS and Remote Sensing

How to cite: Maurya, S., Singh, V., Chand, K., and Mishra, P.: Assessment of the spatial distribution of soil erosion using the RUSLE model and field survey study - A case study of Beas Valley, Kullu, India, Western Himalaya , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-135, https://doi.org/10.5194/egusphere-egu24-135, 2024.

Research and action agencies in the US work collaboratively to develop and use soil erosion technology to support the development of field-specific conservation plans. These tools and accompanying databases are applied in all counties throughout the country covering a wide range of natural and anthropogenic physical conditions. Climate, particularly precipitation, constitutes one of the key drivers directly related to soil detachment and transport. Observations spanning over 30 years have demonstrated that estimated long-term average annual soil loss in agricultural fields is the result of the cumulative effect of many small and moderate-sized storms along with the impact of occasional severe ones. In the Revised Universal Soil Loss Equation version 2 (RUSLE2) technology, the effect of rainfall is represented by the rainfall runoff erosivity index R. This index is designed to serve as an estimation of the potential storm energy specific to each location. In this study, we propose and evaluate a methodology to generate continuous surfaces of monthly R for the continental US from discrete 15-min precipitation data. Over 2000 stations covering more than 50 years of 15-min precipitation data were used. Storm identification algorithms were implemented and evaluated through comparison with existing tools. Outlier events were identified and removed using a 50-year recurrent interval calculated for each station. Using 30-years of recorded data, a custom universal kriging algorithm was employed to generate a smooth continuous surface as a raster grid. This step included a boxcox transformation of the station data, directional variogram fitting, and the removing of external trends using elevation, long-term annual precipitation totals, and distance to the coast. Predicted surfaces were compared with existing RUSLE2 surfaces for the same time period with great level of agreement. The proposed methodology is intended to be comprehensive and reproductible such that it can serve as a template for future updates of erosivity maps for the entire continent at a county-scale. This methodology provides the means for future systematic updates to the RUSLE2 climate database to account for climatic changes and to support continued national efforts in reducing soil erosion and conserving natural resources. 

How to cite: Momm, H., Wells, R., Seever, T., ElKadiri, R., and Bingner, R.: Methodology for Spatially Distributed Rainfall Erosivity Calculations at the Conterminous United States to Support Soil Erosion Studies, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11387, https://doi.org/10.5194/egusphere-egu24-11387, 2024.

Soil erosion causes worldwide land degradation. Long term monitoring of soil erosion is costly and labor intensive. Multiple models using Cs-137 fallout from atomic bomb tests are developed to retrospectively estimate average soil erosion since 1954. However, those models have not been rigorously validated due to the lack of compatible long-term measured soil loss data and thus their usefulness has been seriously challenged. Using 70 years of rare soil loss data measured in two small watersheds of <0.78 ha during 1954 and 2015, the author found that all theoretical models overestimate mean net soil erosion rates by at least 400%, and further confirmed that a key assumption of the homogeneous Cs-137 transfer from rainwater to soil during fallout is invalid and a critical process of the enhanced Cs-137 loss and redistribution during transfer is overlooked. The enhanced Cs-137 uptake by suspended sediment during transfer was responsible for about 8 times more enriched Cs-137 loss in sediment, to which Cs-137 inventory and erosion estimation are extremely sensitive. A new mass balance model is developed to include the dynamic uptake of Cs-137 by suspended sediment in surface runoff and losses of Cs-137 in both runoff solution and uptake by plants. The new model reduced overestimation of soil erosion to about 30%.  The finding of the enhanced radionuclide loss with suspended sediment during transfer is also valid to other fallout radionuclides such as Pb-210 and Be-7, which have been widely used in soil erosion estimation. Taking into account the enhanced radionuclides loss by suspended sediment during fallout will substantially lower soil loss estimation by all fallout radionuclides. 

How to cite: Zhang, X. C. J.: Evaluating and improving cesium-137 technology for estimating soil redistribution using soil loss data measured during 1954-2015, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4180, https://doi.org/10.5194/egusphere-egu24-4180, 2024.

Soil erosion modelling has a large sensitivity to soil water content as it greatly affects soil erodibility. Knowing soil moisture and water content along a soil profile can help to understand the soil ability to absorb water before runoff occurs, then, to predict runoff and potential erosion.

This study presents a combined approach of direct and indirect methods to monitor soil moisture content on a slope, with the goal of using this data in the future for modelling water erosion processes in soils.

Generally, soil moisture data used for erosion models can be acquired through direct methods (e.g., gravimetric method, time or frequency domain reflectometry, moisture sensors) and/or indirect methods (meteorological data, remote sensing, electrical conductivity).

In this research project, an experiment was carried out with the aim of combining direct and indirect methods to maximize the information on the rate of change of soil moisture in a 9*9 m plot by exploring depths from 0 to 50 cm. We used the water content sensor, SoilVUE10 by Campbell, recently released on the market, and based on Time Domain Reflectometry (TDR) technology conjointly with the Electrical Resistivity Tomography (ERT). Moisture sensors are known to create a disturbance in the ground, while geophysical techniques such as ERT are indirect, non-destructive measurements. Furthermore, they have the great advantage of being able to investigate a significantly larger area than classic humidity sensors.

The conductivity varied in average between 0.02 and 0.08 S/m with a little more evident relationship between the values measured with the two methods in deeper layers than at soil surface (i.e., r=0.31 at -30cm).

Overall, further investigations will be conducted, the ERT system needs data acquisition integration, i.e., remote data acquisition so that much more data can be acquired (at least one data set per day). The moisture values acquired by the SoilVUE10 probe require further analysis and comparison, possibly with other TDR probes. Furthermore, it may be necessary to install a surface moisture sensor capable to improve data acquisition even for the first 10cm soil layer.

How to cite: Innocenti, A., Pazzi, V., and Fanti, R.: Correlating different evaluation methods for SWC as support for soil processes modelling , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10794, https://doi.org/10.5194/egusphere-egu24-10794, 2024.

AnnAGNPS (ANNualized AGricultural NonPoint Source model) is a watershed-scale hydrologic model designed to analyze the impact of non-point pollutants in predominantly agricultural watersheds. It has capabilities that make it unique and indispensable on the world scene, such as an integrated simulation of all types of erosion and all major sources of non-point agricultural pollution. However, AnnAGNPS does not currently have a graphical user interface that allows the user to perform the simulation in a simple way. It is in this context that QAnnAGNPS has been created. QAnnAGNPS is a model developed in QGIS and written in Python 3 that fulfills two general functions. The first is to provide a simple to use graphical user interface to run AnnAGNPS. The second is to incorporate extra functionalities to the model, which are already included in similar hydrological models. The plugin has been used in the simulation of the Latxaga basin, a 207-hectare cereal basin located in Navarra (northern Spain). Its use has allowed to verify that QAnnAGNPS is able to perform the AnnAGNPS simulation and to visualize the results in a simpler way than the original one.

How to cite: Barberena, I., Campo-Bescós, M. A., and Casalí, J.: QAnnAGNPS: a new plugin in QGIS to facilitate the use of AnnAGNPS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17727, https://doi.org/10.5194/egusphere-egu24-17727, 2024.

Water erosion is a current issue, especially in hilly and areas, where driving force such as surface runoff and subsurface flow can mobilize large amounts of sediment to rivers. In fact, how and at which timescale, seasonality precipitation is turned into runoff or streamflow (Q) it is difficult to be predicted without calibrating site-specific models. The potential soil erosion can be assessed through the study of the relationships between sediment sources and sinks in a watershed (i.e., sediment connectivity assessment) and associated suspended sediment (SS) transport in rivers. On the other hand, sediment connectivity, defined as structural (from a geomorphological point of view) and functional connectivity (considering forcing processes), can be evaluated by the using of specific indexes (e.g., Index of connectivity – IC).  SS transport processes are intermittent processes fluctuating over a large range of temporal and spatial scales, making it challenging to develop predictive models applicable across timescales and rivers. While temporal variability in sediment transport is explained by the concept of “effective timescale of connectivity”; the mechanism behind this variability remains unknown. Here we used a data-driven approach considering two years of monitoring Q and SS to develop and demonstrate a proof of concept for automating the classification of event-based sediment dynamics by using a machine learning approach.  For each storm event we i) calculate the sediment connectivity (extreme rainfall events also are considered) and ii) define the link between sediment transport and deposition by considering SS transport as a fractal system (i.e. fractal storage time distributions in streams). Fractals are here used to describe and predict patterns over different temporal scales of dynamics in SS   The statistic and dynamics of Q, SSCs and associated grain size distribution, at event based, were considered by assessing their probability distribution function, Fourier power spectra, and the machine-learning classification of hysteresis index. Indeed, by approaching SS transport dynamics as a fractal system, it is assumed that patterns of variation in SS transport exist over different timescales, while linkages across those temporal scales are expressed as fractal power-laws. The study site, located near Florence in the Chianti area, is a 1 Km² agricultural watershed with different types of land cover and characterize by a first-order mixed bedrock and alluvial stream channels. The area was mapped at high resolution with a Drone LIDAR scanner and equipped with a submersible laser diffraction particle size analyser (LISST) for long-term measuring suspended particle size and its volume concentration. Preliminary results showed a robust correlation between sediment connectivity, land cover, and sediment connectivity. Q-SS information flows exhibit seasonally varying behaviour consistent with dominant runoff generation mechanisms (catchment connectivity in wet to dry season). However, the timing and the magnitude of runoff also reflect considerable catchment heterogeneity, likely attributable to differences in baseflow contributions from different lithologies, and variation in of preferential flow paths (land use/land cover).  In conclusion, this study allowed to analyse a small catchment area in term of sediment connectivity and related sediment transport to identify potential areas of (dis)connectivity in the basin.

How to cite: Barbadori, F., Pelacani, S., and Raspini, F.: Investigating water erosion dynamics through connectivity based on fractal approaches: A case study in the Chianti area (Florence, Italy), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18276, https://doi.org/10.5194/egusphere-egu24-18276, 2024.

Radar-based rainfalls are currently used for process monitoring from remote in a large panel of domaines including hydrology and soil erosion modelling. Nevertheless, such data may include systemic and natural perturbations that need to be corrected before using these data. To encompass this problem, adjustments based on raingauge observations are frequently adopted. Here, we analysed the performance of different radar-raingauge merging procedures using a regional raingauge-radar network (Tuscany, Italy) focusing on a selected number of rainfalls events.

The computational methods adopted were: 1) Kriging with External Drift (KED) interpolation (Wackernagel 1998), 2) Probability-Matching-Method (PMM, Rosenfeld et al., 1994), and 3) an Adjusted kriging mixed method exploiting the conditional merging (ADj) process (Sinclair-Pegram, 2005). The latter made available by DPCN (Italian National Civil Protection Department), while methods 1) and 2) were applied on recorded raingauge rainfalls over the regional territory at 15’ time-step, and CAPPI (Constant Altitude Plan Position Indicator) reflectivity data from the Italian radar network at 2000/3000/5000 m at 5’ and 10’.

The comparisons between the three rainfall fields were based on the analyses of variance, Cumulative Distribution Function (CDF), and explicative coefficients such as BIAS, RMSE (Root Mean Square Error), MAD (Median Absolute Deviation). In average, rainfalls showed a moderate variability between the methods. Comparing CDFs, slight differences were detected between KED and ADj with bias mostly pronounced in lower quantiles, while more marked differences are observed in higher quantiles for the ADj-PMM methods. The analyses presented different spatial patterns depending on the applied procedure, closer to the radar data when using ADj, and more reflecting the gauge’s data structure when adopting KED. The probabilistic method (PMM) had the advantage to account for gauge data while preserving the spatial radar patterns, thus confirming interesting perspectives. Globally, the KED method provided more accurate coverage in the calculation by better compensating for local topographical shadows in the data, while ADJ confirmed the more detailed product in terms of time resolution (e.g. 5minute res.).

How to cite: Moretti, S., Ciampalini, R., Antonini, A., Mazza, A., Melani, S., Ortolani, A., Rosi, A., and Segoni, S.: Comparing radar-raingauge precipitation-merging-methods for soil erosion modelling support, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21549, https://doi.org/10.5194/egusphere-egu24-21549, 2024.