Soil erosion is one of the most challenging environmental issues in the world, causing unsustainable soil loss every year. In South Africa, several episodes of gully erosion have been documented and clearly linked to the presence of Quaternary colluvial deposits on the Drakensberg Mountain footslopes. The aim of this study was to identify and assess the triggering factors of gully erosion in the upper Mkhomazi River basin in KwaZulu-Natal, South Africa. We compiled a gully inventory map and applied remote sensing techniques as well as field surveys to validate the gully inventory. The gullies were subdivided into slope gullies and fluvial gullies. We derived susceptibility maps based on the spatial distribution of gully types to assess the most important driving factors. A stochastic modeling approach (MaxEnt) was applied, and the results showed two susceptibility maps within the spatial distribution of the gully erosion probability. To validate the MaxEnt model results, a subset of the existing inventory map was used. Additionally, by using areas with high susceptibilities, we were able to delineate previously unmapped colluvial deposits in the region. This predictive mapping tool can be applied to provide a theoretical basis for highlighting erosion-sensitive substrates to reduce the risk of expanding gully erosion.

Sustainable agricultural landscape management needs reliable and accurate soil maps and updated geospatial soil information. Recently, machine learning (ML) models have commonly been used in digital soil mapping, together with limited data, for various types of landscapes. In this study, we tested linear and nonlinear ML models in predicting and mapping soil properties in an agricultural lowland landscape of Lombardy region, Italy. We further evaluated the ability of an ensemble learning model, based on a stacking approach, to predict the spatial variation of soil properties, such as sand, silt, and clay contents, soil organic carbon content, pH, and topsoil depth. Therefore, we combined the predictions of the base learners (ML models) with two meta-learners. Prediction accuracies were assessed using a nested cross-validation procedure. Nonetheless, the nonlinear single models generally performed well, with RF having the best results; the stacking models did not outperform all the individual base learners. The most important topographic predictors of the soil properties were vertical distance to channel network and channel network base level. The results yield valuable information for sustainable land use in an area with a particular soil water cycle, as well as for future climate and socioeconomic changes influencing water content, soil pollution dynamics, and food security.

We present a 1:50 000 scale geomorphological map of the upper Mkhomazi River basin, located in the foothills of the Drakensberg mountains in KwaZulu-Natal Province, South Africa. The subhorizontal strata of the Permo-Triassic Beaufort Group forms plateau interfluves with a concave valley slope morphology. Locally, thick sequences of late Pleistocene colluvial deposits and associated buried paleosols (Masotcheni Formation) infill first-order tributary stream valleys and extend across the adjacent lower slopes. Surface runoff processes preferentially incise into the poorly consolidated, highly erodible sediments causing severe gully erosion that is responsible for widespread land degradation and desertification phenomena. The main purpose of this work is to derive a geomorphological map of the study area focussing on the erosional landforms to understand their spatial distribution and their relation to the colluvial deposits. Finally, a local and regional stratigraphic correlation of colluvial deposits and associated buried palaeosol profiles is proposed.

<p>Global changes are impacting water availability, which touch a wide range of human activities, especially agriculture. For this reason, hydrological models have been developed in recent years, which are an important support in the management of water resources.</p> <p>The aim of this study is to setup and calibrate a hydrological model using remote sensing-based evapotranspiration (ET) data, in an area free of natural streams where irrigation channels are the only watercourses, expect for Ticino River. From a hydrological point of view, the study area is quite complex. Rainwater infiltrates into the permeable soils characterizing the area, while the rest of the precipitation leave the soil system through evapotranspiration. In fact, we noticed after periods of rain or irrigation a variation of the discharges of local springs located at the base of the fluvial terrace escarpments of the Ticino River. Moreover, being in a flat area the surface runoff component is almost nil, except for ponding that occur after precipitation or during the period in which the rice fields are flooded. During the spring-summer period, actually, large quantities of water are distributed through a complex network of channels to irrigate the rice and maize fields. So, water distributed for irrigation use is not only important for the agriculture, but also contributes to the recharge of the water table, which then feeds springs, forming a unique cascade system of water reuse that was already created in the15<sup>th</sup> century. However, calibrating a spatially distributed hydrological model of an intensively irrigated and flat agricultural area is a difficult challenge. In this study the Soil Water Assessment Tool (SWAT) was applied, a physically based model used worldwide for soil and water management studies. The SUFI-2 program for model calibration and uncertainty analysis was utilized and Kling-Gupta Efficiency (KGE) was applied as objective function. In the calibration process we used ET data derived from MODIS sensor with a spatial resolution of 1 km².</p> <p>The results show that despite the complexity of the area a calibration of the model with ET’s MODIS data yield a KGE of 0.59. The results indeed highlight that the model simulates well the hydrological dynamics of the area. Although there are some differences between observed and simulated data, due to a strong control of the hydrological dynamics by human activities, as well as the difference in model input data and satellite data used for calibration. Model validation through on-site measured soil water content, with 12 TEROS sensors installed on three different land uses, confirm the feasibility of using satellite data for SWAT model calibration in a complex area. Moreover, with these sensors we assessed the differences between the different crops and get information about the irrigation activities that modify the hydrological cycle of the area.</p> <p>Finally, the calibrated and validated SWAT model allows for a further hydrological analysis of a system altered by human activities in terms of future scenarios. Particularly, we evaluate vertical soil water dynamics and assess the impact of land use change and land management (e.g., irrigation).</p>

<p>The use of hydrological models can be a suitable basis for the development of sustainable land use and respective water management policies, according to the sustainable development goals (SDGs) of EU. In this study, a process-based numerical model was developed, to determine the hydrological dynamics of a micro-scale basin in a flat and intensely used agricultural area that is partly irrigated, in the Lombardy Region, Italy. In this area, agriculture has a fundamental role in the local hydrological cycle, indeed, landuse and land management practices date back to medieval times with the construction of irrigation channels and reuse of water along the fluvial terrace cascade of the Ticino River. From a hydrological point of view the study area is very complex: there is almost no natural surface runoff, but prevailing vertical soil water dynamics. The water infiltrates on the highest and oldest fluvial terrace level and reemerges in form of springs (risorgive) at the base of the terrace escarpments and is further used for irrigation on the next terrace level.</p><p>The objective of this study is to assess the hydrological dynamics of this complex area that is getting under increasing pressure related to climate changes and socioeconomic transformations.  In order to achieve the study goals, we applied the Soil Water Assessment Tool (SWAT), a complex hydrological model that works at the basin scale and generates variable spatio-temporal outputs and is being applied successfully worldwide for soil and water management studies. We present the methodological approach for deriving the model input and boundary conditions. Moreover, we show the effects of selecting different model entity configurations as well as calibration and validation procedures. First preliminary results show that SWAT is able to simulate the general hydrological dynamics of the area according to the use of satellite soil moisture and evapotranspiration data. In addition, through local soil moisture measurements carried out in the field, qualitative evaluation of infiltration capacities have been made and with these measurements it will be possible to validate the model. Hence, the model results obtained, provide information on the soil water dynamics that can be used as a basis for studying future scenarios (i.e., impacts of climate change or different management such as different irrigation schemes).</p>