Antonio Jodar-Abellan

Soil erosion and sediment redistribution in vineyards often exceed tolerable thresholds, but comparative field measurements across regions remain limited because studies use different methods and sampling designs. This study assesses soil redistribution in vineyards using a common field-based approach. Thirteen vineyard sites across Spain, covering contrasting climates, parent materials, soil types, management conditions, and vineyard ages (2–40 years), were surveyed using the Improved Stock Unearthing Method (ISUM). Soil surface changes were measured along systematic transects and combined with site-specific bulk density and vineyard age to estimate soil mobilization rates in t ha⁻¹ yr⁻¹. Measured surface changes ranged from −28 to +19 cm, while soil mobilization rates varied considerably among sites. The highest net soil losses were recorded in a young vineyard developed on limestone (−103.3 t ha⁻¹ yr⁻¹) and in a vineyard on marls (−87.7 t ha⁻¹ yr⁻¹). Other sites showed moderate net erosion (−3.9 to −15.4 t ha⁻¹ yr⁻¹), whereas positive balances (+7.2 to +17.9 t ha⁻¹ yr⁻¹) were observed in some older or more stable vineyards. Differences among sites suggest that vineyard age and parent material are relevant factors when interpreting erosion magnitude, while management conditions may contribute to the spatial variability of soil redistribution. Spatial maps based directly on ISUM measurements showed marked differences between erosion and deposition areas within individual vineyards, without the use of spatial interpolation. Applying the same ISUM protocol across contrasting vineyard conditions allowed soil redistribution patterns to be compared using a consistent field procedure. The results also showed the large variability that can occur among vineyard systems.

Hydrological and soil erosion models are often used to assess the impacts of global change and potential adaptation strategies on flood risks and sediment transport. These hydrology and sediment transport models require channel dimensions as input to quantify flood frequency, runoff, flow velocity, sediment detachment and deposition processes. Especially for large-scale applications, channel dimensions (width and depth) are difficult to obtain. Therefore, simple empirical relations have been developed, relating channel dimensions with catchment area or bankfull discharge, disregarding other important factors affecting these dimensions. Here we present an advanced combined methodology to obtain reliable estimates of channel dimensions for the large Mediterranean Segura catchment (16,000 km2), based on linear statistical regression and machine learning techniques. First, a training dataset of channel dimensions (width and depth) was prepared using a LiDAR high resolution digital elevation model (2 m resolution) and aerial photos (50 cm resolution) for 151 channel segments across four representative large sub-catchments. For each channel segment, 30 variables characterising the upstream catchment were obtained from available spatial data sources (e.g. soil type, slope, annual precipitation). The obtained training dataset was used in a combination of Stepwise Multiple Linear Regression and Random Forest to predict channel width and depth. Best results were obtained with the RF model using the variables selected through the stepwise MLR process, as RF models composed only by these MLR predictor variables showed nodes with more purity rather than RF formed by the complete set of independent variables. Most important variables for prediction of channel width were Calcareous lithology, mean annual temperature, extreme precipitation, and alluvial soils. For channel depth, the most important variables were extreme precipitation, channel slope, and mean annual temperature. Model validation indicated good results for prediction of channel width (R2 0.75) and depth (R2 0.66). These results provide further insights into the factors affecting channel dimensions, and seems to be a promising approach to obtain channel dimensions for hydrological and sediment transport modelling in large catchments.We acknowledge funding for the XTREME project from the Spanish Ministry of Science and Innovation and ‘Agencia Estatal de Investigación’ (PID2019-109381RB-I00/AEI/10.13039/501100011033), and for the LandEX project (PCI2024-153454) financed by the European Commission, Ministry of Science, Innovation and Universities and the Spanish Research Agency (AEI 10.13039/501100011033/EU) in the framework of the European Water4All Partnership 101060874. A. Jodar-Abellan (JDC2022-049314-I) and J.P.C. Eekhout (IJC2020-044636-I) acknowledge financial support from the Ministry of Science, Innovation and Universities for the Juan de la Cierva postdoctoral grants.

Groundwater serves a range of essential functions such as supplying drinking water, facilitating agricultural practices, and supporting industrial processes. This study examines with multiple methods the quality of groundwater in the agricultural region of Dzira, Algeria. By collecting 38 groundwater samples of different wells and boreholes, valuable awareness of the aptness of groundwater for irrigation in this arid landscape was gained. Most wells met Food and Agriculture Organization (FAO) criteria for the total dissolved solids (TDS) and the potential of hydrogen pH, but some areas had higher mineral content and electrical conductivity. Results show significant TDS variations, with 10.81% of wells exceeding limits and acceptable pH levels. Elevated EC values in 67.57% of wells show high salinity, affecting soil and plant growth. Major ions such as Mg²⁺ and SO₄⁻ exceeded FAO standards in 43.24% and 64.86% of wells, respectively, highlighting substantial mineral content in the groundwater. Suitability indices reveal that most wells pose low sodium hazards and are generally suitable for irrigation, though some areas face moderate to high restrictions. The irrigation water quality index (IWQI) ranged from 45.36 to 96.30, averaging 80.77, with 54.04% classified as “low restriction,” suitable for sandy soils with good permeability but requiring caution on salt-sensitive soils. Hydrogeochemical analysis using principal component analysis (PCA) and hierarchical cluster analysis (HCA) identifies rapid evaporite dissolution from Triassic saline formations, with a correlation matrix showing associations between TDS and Ca²⁺, Mg²⁺, Na⁺, Cl⁻, and SO₄²⁻. This mineralization is likely from gypsum and halite. Zoning maps based on IWQI and other parameters depicted spatial variations in groundwater quality, guiding effective irrigation management strategies. Overall, the study underscores the importance of comprehensive water quality assessment for sustainable agriculture and emphasizes the need for targeted interventions to mitigate potential challenges associated with soil salinity and sodicity. Therefore, these findings can be useful to decision-makers and stakeholders in order to optimize water use and protect this vital resource.

Hydrological simulation in karstic areas is a hard task due to the intrinsic intricacy of these environments and the common lack of data related to their geometry. Hydrological dynamics of karstic sites in Mediterranean semiarid regions are difficult to be modelled mathematically owing to the existence of short wet episodes and long dry periods. In this paper, the suitability of an open-source SWAT method was checked to estimate the comportment of a karstic catchment in a Mediterranean semiarid domain (southeast of Spain), which wet and dry periods were evaluated using box-whisker plots and self-developed wavelet test. A novel expression of the Nash–Sutcliffe index for arid areas (ANSE) was considered through the calibration and validation of SWAT. Both steps were completed with 20- and 10-year discharge records of stream (1996–2015 to calibrate the model as this period depicts minimum gaps and 1985–1995 to validate it). Further, SWAT assessments were made with records of groundwater discharge and relating SWAT outputs with the SIMPA method, the Spain’s national hydrological tool. These methods, along with recurrent neural network algorithms, were utilised to examine current and predicted water resources available to supply urban demands considering also groundwater abstractions from aquifers and the related exploitation index. According to the results, SWAT achieved a “very good” statistical performance (with ANSE of 0.96 and 0.78 in calibration and validation). Spatial distributions of the main hydrological processes, as surface runoff, evapotranspiration and aquifer recharge, were studied with SWAT and SIMPA obtaining similar results over the period with registers (1980–2016). During this period, the decreasing trend of rainfalls, characterised by short wet periods and long dry periods, has generated a progressive reduction of groundwater recharge. According to algorithms prediction (until 2050), this declining trend will continue reducing groundwater available to meet urban demands and increasing the exploitation index of aquifers. These results offer valuable information to authorities for assessing water accessibility and to provide water demands in karstic areas.

Thermal pollution reduces water quality through any process that leads to a change in the water’s ambient temperature. Karun is one of the most relevant sources of water supply in Iran, and its pollution, created by industrial, urban, and agricultural issues, has been one of the most critical challenges throughout the last few years. As the water temperature rises, the amount of dissolved oxygen in it decreases, thereby affecting the entire ecosystem associated with it. Drainage of urban and industrial runoff into surface water sources can increase the water temperature. Dams also constitute a significant part, modifying spatial patterns of temperature along river routes and causing thermal contamination. In this paper, the thermal pollution of the Karun River was assessed, and regions along this river with unusually raised water temperatures were identified and compared over 20 years. By analyzing the results, it can be found that the thermal pollution from dams has a significant impact on the downstream river environment and ecology that is considerably relevant during summer periods, showing average decreases of 3 degrees Celsius immediately beyond the dams’ locations (from 41 degrees Celsius upstream dams to 38 degrees Celsius beyond them) or even bigger (reductions of 13 degrees Celsius in one of the studied dams). Hence, our results showed that water temperature is colder downstream in the hot seasons of the year than upstream of the dams. The results suggest that the usage of remote sensing data effectively could complement collected data from ground-based sensors to estimate water temperature and to identify pollution areas. It provides experts with spatially extensive and highly synchronized data.

Floods cause severe natural disasters over the world generating property and infrastructures damages, poverty and loss of human life, among others. Mediterranean watersheds are especially sensible to floods due to their typical drainage basin features (steep slopes, short concentration times, complex orography, etc.) and the high rainfall intensity typical of convective systems. Knowing channel dimensions and other fluvial morphological features is key to (i) understanding the morphological evolution of the fluvial system in response to changes in land use and climate, and (ii) as an input for hydrological and erosion modelling. The objective of this study is to develop a simple method to obtain reliable estimates of channel dimensions and granulometry of bed material, based on statistical relations with catchment characteristics (e.g. topography, land use, soil properties, lithology, precipitation, connectivity indicators).First, channel dimensions were estimated based on GIS analysis using a high resolution digital elevation model (2x2m) and ortophotos (50 cm resolution) for the Upper Segura River catchment, a Mediterranean catchment of 2,592 km2 located in the southeast of Spain. These estimates were validated with field measurements of depth and width of bankfull channels in the catchment headwaters. This comparison revealed that there was generally good agreement between channel dimensions obtained with the GIS method and those observed in the field for all evaluated channels (depth: RMSE=0.06; R2=0.93; width: RMSE=0.59, R2=0.45), although with better results for dry channels than for channels with continuous water flow. At each field observation, we also took sediment samples and characterised the granulometry of the channel bed material in the laboratory through dry sieving. Preliminary results show that bed material is composed mainly by gravels (67.8%), followed by sands (31.1%) and clays and silts (1.1%). Next, channel dimensions, obtained using GIS analysis across the entire catchment, and granulometry of bed material were used as dependent variables in advanced statistical analyses (such as machine learning algorithms) at sub-basin scale, with catchment characteristics as independent variables. The outcome of this analysis is now being used to make spatially continuous predictions of channel dimensions and granulometry of bed material at the catchment scale. This information will then ultimately serve as input for a coupled model that simulates channel hydraulics and morphodynamics at the catchment level.Keywords: geomorphology; sediment yield; depth and width of bankfull channels; Mediterranean environment; GIS based tools; southeast of Spain.We acknowledge funding from the Spanish Ministry of Science and Innovation and ‘Agencia Estatal de Investigación’ (PID2019-109381RB-I00/AEI/10.13039/501100011033).

Ain Sefra is part of the Ksour Mountains and it's situated in southwestern Algeria, where the climate is arid. The study area is progressively facing regression and degradation exacerbated by climate change. These trends point to a significant acceleration of desertification and drought and the loss of production systems that play a critical social, ecological, and economic role in the region. To better understand the natural hazard of dryness in Ain Sefra and the impact of climate change, we used various drought indices and remote sensing data. Hence, analyzing precipitation records from 1965 to 2021, through several drought indices, droughts were identified as a recurring phenomenon. Moreover, the frequency of successive dry years is relatively high. There were three most extended continuous dry periods. The first phase lasted seven years from 1980 to 1987, the second twelve years from 1994 to 2006, and the third nine years from 2012 to 2021. Calculation of the Modified Soil-Adjusted Vegetation Index (MSAVI) for five multidate satellite images allowed us to follow the evolution of land use elements in this region from 1977 to 2017. Indeed, the study of these multi-temporal images reveals a considerable growth of sands, moving towards the north and northeast of the zone during the last decades. The combination of drought indices and remote sensing seems to be most promising; whose results are valuable tools for guidance and decision support to local and regional authorities.