Search Results (1,378)

Soil erosion in vineyards is a major environmental problem, particularly in hilly Mediterranean environments. Our study evaluated the effectiveness of permanent grass cover (PG), continuous tillage (CT), and green manure (GM) in reducing soil erosion. Furthermore, a new software tool (ISUMmate_1.1.xlsm), based on the improved stock unearthing method (ISUM), was developed and tested to quantify soil mobilization between successive transects along vineyard inter-row. The field trial was carried out over a three-year period in a Tuscany (Italy) vineyard. The results showed that PG significantly improved aggregate stability and soil organic carbon (SOC) content, while exhibiting the lowest erosion rates. In contrast, GM showed the highest erosion rates as a result of soil disturbance associated with cultivation operations and the occurrence of unexpected intense rainfalls. ISUMmate_1.1 has proven to be a reliable tool for monitoring both water- and tillage-induced erosion, providing valuable information for sustainable vineyard management. Full article

The sustainable use and management of groundwater resources is a challenging issue due to population growth and climate change. Accurate quantification of groundwater recharge is a basic requirement for effective groundwater resource management, yet it is still lacking in many areas around the world. The study was designed to estimate recharge to groundwater from natural rainfall in the Gilgel Gibe and Dhidhessa catchments in southwestern Ethiopia, employing the water table fluctuation (WTF) and chloride mass balance (CMB) techniques. These methods are being applied for the first time in the study area and have not previously been used in these catchments. Given the region’s data scarcity, a community-based data collection program was implemented and supplemented with additional field measurements and secondary data sources. Groundwater level, spring discharge, and rainfall were monitored over the 2022/2023 hydrological year. Groundwater level fluctuations were found to be influenced by topography and rainfall patterns, reaching 8.2 m in amplitude in the upstream part of the catchments. Chloride concentrations were determined in groundwater samples collected from hand-dug wells and springs, and rainwater was also collected. Rainwater exhibited a mean chloride concentration of 2.46 mg/L, while groundwater chloride concentrations ranged from 3 mg/L to 36.99 mg/L. The estimated recharge rates varied spatially, ranging from 170 to 850 mm/year using the CMB method (11% to 55% of annual rainfall, mean recharge rate of 454 mm/year) and from 76 to 796 mm/year using the WTF method (4% to 43% of annual rainfall, mean recharge rate of 439 mm/year). Notably, recharge estimates were lowest downstream in the lowland areas and highest upstream in the highland regions. Rainfall amount, local lithology, and topography were identified as major influences on groundwater recharge across the study area. Both CMB and WTF methods were deemed applicable in the volcanic aquifers, provided that all the respective assumptions are followed. This study significantly contributes to the groundwater dataset for the region, in addition to recharge estimation and the research conclusions, emphasizing the importance of long-term monitoring and time series analysis of chloride data to reduce uncertainties. The work serves as a valuable reference for researchers, policymakers, and regional water resource managers. Full article

Plastic agrotextiles are increasingly used in modern agriculture to protect crops from adverse climatic events, such as excessive rainfall, wind, and solar radiation. Among these, anti-rain nets represent a promising solution to mitigate rain-induced disorders, such as fruit cracking, especially in crops sensitive to water excess. This study investigates the structural and functional properties of eight agrotextiles, including both anti-rain and anti-insect nets. The analysis focuses on geometric characteristics (porosity, thread diameter, mesh density) and on functional performance through experimental evaluation of air and rainwater permeability under different slope conditions. Air permeability was assessed using a wind tunnel, while rainwater permeability was tested via a rainfall simulation bench. The results demonstrate a stronger correlation between the air permeability index (K_a) and the rainwater permeability index Φ_rw (R² = 0.95–0.99), across different net slopes (10° and 30°), than between the net porosity and Φ_rw (R² = 0.86–0.92). These findings emphasize the greater explanatory power of the dynamic performance indicator K_a as a predictor of rainwater permeability, over purely geometric descriptors like porosity, since it inherently accounts for the dynamic performance of the air flow through the net. This contributes to the development of more effective and sustainable net-based crop protection systems tailored to specific environmental and agronomic needs. Full article

Landslides pose significant threats to sustainable development by causing infrastructure damage and ecosystem degradation, particularly in densely vegetated mountainous regions. To support sustainable land-use planning and disaster-resilient development, this study integrates three advanced vegetation metrics—Vegetation Formation Group (VFG), aboveground biomass (AGB), and forest canopy height (FCH)—into landslide susceptibility modeling. Using Yuanling County, a subtropical vegetated region in China, as a case study, we developed a novel ensemble model, AdaBoost-CB (AdaBoost-CatBoost), and compared its performance with mainstream machine learning models including RF, XGBoost, and LGB. The results show that AdaBoost-CB achieved the highest Area Under the Curve (AUC) value of 0.915. Furthermore, it yielded the highest landslide frequency ratio of 6.51 in the very-high-susceptibility zones. The dominant landslide-controlling factors—NDVI, elevation, slope gradient, slope aspect, and rainfall—were consistently identified across six models. These findings provide a scientific basis for sustainable land-use planning and disaster risk reduction strategies, contributing directly to the goals of sustainable development in vulnerable mountainous regions. Full article

In semi-arid regions of Morocco, where the majority of water withdrawals are devoted to irrigation, optimizing irrigation practices in agriculture is a national priority in the face of recurring droughts and growing pressure on groundwater resources. However, the hydrological impacts of different drip-irrigation systems in the soil–plant–atmosphere continuum remain insufficiently understood. We monitored the stable isotope composition (δ²H, δ¹⁸O) across the two agricultural plots in Marrakech (Morocco) with surface drip and subsurface drip irrigation treatments for a complete hydrologic year (June 2022 to June 2023). Weekly to daily samples of rainfall, irrigation water, groundwater, and soil at various depths (5–50 cm) were sampled, and water from branch xylem was extracted using the cryogenic vacuum distillation method. We found that the subsurface irrigation treatment, which delivered water directly to the root zone, maintained narrow isotopic ranges in water of soils beyond 30 cm, as well as in branch xylem and leaf water. By contrast, surface irrigation treatment plots showed pronounced evaporative isotopic enrichment: summer topsoil water δ¹⁸O peaked at −1.1‰ (vs. −8.7‰ in subsurface irrigation treatment), and leaf water reached +13‰ (vs. +8‰ in subsurface). Despite this larger isotopic heterogeneity in surface irrigation site, branch xylem water δ¹⁸O remained within −6 to 2.5‰ across all soil depth, similar to subsurface irrigation treatment, which ranged between −5 and 0‰. This suggests that olive roots accessed soil water uniformly from the upper 50 cm under both irrigation treatments. Seasonal xylem isotopic enrichment in spring and midsummer mirrored shifts towards shallow, evaporatively altered soil water under surface irrigation, but not under the subsurface. The results suggest that subsurface drip irrigation can significantly improve drought resilience and water-use efficiency in the expanding olive sector of the Maghreb, while continuous isotope monitoring serves as a practical approach to enhance sustainable and adaptive water management in water-limited regions. Full article

Soil erosion is a major threat to land productivity and environmental sustainability in Mediterranean regions, where sloping terrain, intense seasonal rainfall, and traditional agricultural practices accelerate soil loss. Olive orchards, which dominate much of the Mediterranean landscape, are particularly vulnerable. As climate change increases the frequency of extreme weather events, understanding and controlling erosion becomes even more critical. This study investigates soil erosion dynamics in a representative olive-growing watershed in Messenia, Greece, by combining field monitoring with erosion modeling using the Revised Universal Soil Loss Equation (RUSLE). A field experiment was carried out during the 2024–2025 wet season, using runoff plots installed on a 16% slope to directly measure sediment loss from natural rainfall events. The observed erosion data served as a basis for calibrating a GIS-based RUSLE model applied across the 60 km² watershed. Model predictions showed strong agreement with field measurements, with estimated soil loss closely matching the observed seasonal total (~0.6 t/ha). This consistency demonstrates the reliability of the RUSLE model when supported by localized data. The spatial analysis further revealed that erosion risk varies widely across the landscape, with steep, poorly vegetated areas being most at risk. The results highlight the importance of local field measurements for improving model accuracy and guiding sustainable land management. Continuous monitoring and targeted erosion control strategies are essential to protect soil resources, maintain agricultural productivity, and reduce downstream environmental impacts under increasing climate pressures. Full article

This study investigates flood risk trends using rainfall data collected from 13 districts of Diyarbakır Province, Turkey, with a focus on supporting sustainability-oriented climate adaptation. Both annual and seasonal precipitation variations were examined, with particular emphasis on the role of maximum daily rainfall in driving flood potential. In addition, the analysis integrates extreme precipitation patterns with regional hazard characteristics to provide a more comprehensive flood risk assessment framework. Non-parametric statistical methods, including the Mann–Kendall trend test and Spearman’s Rho correlation, were applied to detect trends in annual and seasonal datasets. Flood magnitudes were estimated using the Generalized Extreme Value (GEV) and Peaks Over Threshold (POT) approaches. The dataset covers varying periods between 2009 and 2023, depending on station availability. The results show a statistically significant increase in both annual and winter precipitation at Bismil, and a significant winter increase at Çermik. Other stations displayed upward trends that were not statistically significant. Çüngüş, Lice, and Kulp were identified as particularly susceptible to extreme rainfall. Although the relatively short observation period poses a limitation, consistent patterns of intensified precipitation were detected. Previous studies in Turkey have demonstrated that such events often cause severe infrastructure damage and displacement of vulnerable communities. The findings of this study provide practical insights for national and regional authorities, including the Disaster and Emergency Management Authority (AFAD), the General Directorate of State Hydraulic Works (DSİ), and the Ministry of Environment, Urbanization, and Climate Change, to strengthen sustainable climate adaptation planning and disaster risk reduction strategies. Overall, this research highlights the importance of integrating extreme precipitation analysis into sustainable flood management, resilient infrastructure development, and long-term sustainability policies, thereby reinforcing the connection between hydrological risk assessment and sustainability science. Full article

Reliable spatio-temporal rainfall prediction is a key element in disaster mitigation and water resource management in dynamic tropical regions such as the Bengawan Solo River Watershed. However, high climate variability and data limitations often pose significant challenges to the accuracy of conventional prediction models. This study introduces an innovative approach by applying ensemble stacking, which combines machine learning models such as Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Light Gradient-Boosting Machine (LGBM) and deep learning models like Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Temporal Convolutional Networks (TCN), Convolutional Neural Network (CNN), and Transformer architecture based on monthly Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data (1981–2024). The novelty of this research lies in the systematic exploration of various model combination scenarios—both classical and deep learning and the evaluation of their performance in projecting rainfall for 2025–2030. All base models were trained on the 1981–2019 period and validated with data from the 2020–2024 period, while ensemble stacking was developed using a linear regression meta-learner. The results show that the optimal ensemble scenario reduces the MAE to 53.735 mm, the RMSE to 69.242 mm, and increases the R² to 0.795826—better than all individual models. Spatial and temporal analyses also indicate consistent model performance at most locations and times. Annual rainfall projections for 2025–2030 were then interpolated using IDW to generate a spatio-temporal rainfall distribution map. The improved accuracy provides a strong scientific basis for disaster preparedness, flood and drought management, and sustainable water planning in the Bengawan Solo River Watershed. Beyond this case, the approach demonstrates significant transferability to other climate-sensitive and data-scarce regions. Full article

Climate change threatens the agricultural field by affecting production yields and crop quality. Yield protection is an increasingly pressing priority to preserve the ability of agriculture to meet food demand with more sustainable production of appropriate quality and quantity and with less demand for plant protection products. For this reason, nowadays the use of agricultural nets is becoming increasingly widespread to counteract possible risks from abiotic stresses. Among all agricultural nets, the anti-rain ones have the predominant purpose of protecting crops from damage caused by severe weather events. The present study aims to verify whether anti-rain nets could be used as greenhouse covering material, starting from the evaluation of the rainwater permeability index Φ_rw. For this purpose, a laboratory rain simulator was designed and several tests were performed on the chosen anti-rain net, varying its inclination and the duration and intensity of the simulated rainfall, returning different normalized permeability indices NPI, of which the standard deviation (SD) was calculated. The optimal rainfall duration of the artificial rain test was determined at the minimum value of the sum of the SDs, identified as about 25%, at a duration of 10 min. Subsequently, tests were carried out to define the Φ_rw index for a rainfall lasting 10 min, by varying the other parameters, returning the lowest Φ_rw index of approximately 45% at a 20° net inclination and with the weave perpendicular to the slope. The results highlight the possibility to use anti-rain nets for greenhouse covering, replacing or supplementing commonly used nets, facilitating oxygen exchange and maximizing light capture capacity, essential for vegetative–productive balance. A proposal for standardizing the procedures to test nets, based on experimental tests, has never been proposed in the scientific literature. Regarding fruit and vegetable crops, there are several issues to be evaluated; this study only considers rain protection, through nets can be used for different fruit and vegetable varieties. Full article

Pakistan’s coastline encompasses the Indus Delta, a critical ecosystem that sustains biodiversity, fisheries, and local livelihoods, yet it is increasingly threatened by both natural and anthropogenic pressures. This study quantifies multi-decadal shoreline changes in the Indus Delta and examines how changes in climatic factors (precipitation and wind) affect these changes, using the Digital Shoreline Analysis System (DSAS v5.1) and multi-temporal Landsat imagery (TM, ETM+, OLI) to quantify long-term shoreline dynamics from 1990 to 2020 (30-year period). Key metrics, including End Point Rate (EPR), Net Shoreline Movement (NSM), and Linear Regression Rate (LRR), indicated an overall retreat, with a mean NSM of −1810 m and a mean LRR of −173 m·year across the 30-year period. Shoreline change rates exhibited a significant relationship with climatic variables, particularly wind speed and precipitation, with dynamics shifting from erosion-dominated to localized accretion in areas where mangrove rehabilitation programs were implemented after 2005. Seasonal variability further influenced shoreline behavior: low-rainfall years intensified erosion due to reduced sediment availability, while high-rainfall years enhanced accretion through increased sediment input. These findings underscore the urgent need for integrated coastal management strategies, including mangrove conservation, sustainable sediment management, and climate-adaptive planning, to strengthen the resilience of the Indus Delta. Full article

Water is a vital resource for human survival, economic development, and environmental sustainability. It is essential to agriculture, energy production, public health, and biodiversity preservation. Efficient water management is even more important in areas that are prone to scarcity. This paper presents a systematic review of the management of water resources in South Africa, a country characterized by significant water scarcity challenges compounded by its socio-economic and ecological needs. South Africa’s limited freshwater resources are under extreme stress due to its semi-arid climate, unequal rainfall distribution, expanding population, and industrial needs. The nation’s water security has also been made more difficult by historical injustices, climatic fluctuations, and decaying infrastructure. Through a systematic review of 60 scholarly articles published between 2011 and 2025 in the Web of Science database, this study discusses the historical context of water management in South Africa, including the legacy of apartheid-era policies and their impact on access to water. It also examines current management practices, governance structures involving national and local authorities, the role of key institutions such as the Department of Water and Sanitation (DWS), climate change impact on water availability, population growth and urbanization, inequality and access, and challenges in South Africa’s water resources management (WRM). In particular, this review highlights the integration of scientific water quality and biostability assessment into the Integrated Water Resources Management (IWRM) framework in order to produce actionable insights that enhance resilience, sustainability, and equity in WRM. Furthermore, it explores future strategies for sustainable WRM, emphasizing the importance of IWRM, community participation, technological innovation, and climate change adaptation. Through this comprehensive analysis, the paper aims to contribute to a deeper understanding of the complexities and opportunities in ensuring water security for all South Africans. Full article

The rising urbanization and climate change have increased pluvial flood risks, especially in highly urbanized areas. This study focuses on the Lake Ganzirri area in Messina, Italy, where street-level floods have raised concerns for infrastructure resilience and public safety. This study aims to explore how to effectively represent key urban features, emphasizing buildings and low-impact development/sustainable urban drainage systems (LID/SUDS). For the buildings, a combination of referred approaches to represent buildings is compared against the widely used method to represent buildings as voids in a 2D mesh, ignoring them in the water balance calculations. For the LID/SUDS control elements, a 2D representation is presented and compared against the widely used 1D approach to model such elements. The study uses three well-known software packages—EPA-SWMM 5.2, HEC-RAS 6.2, and InfoWorks ICM 2021.9—applied to the Lake Ganzirri area, to explore the representation of buildings using the building void method (available in InfoWorks ICM 2021.9) against the proposed method (in HEC-RAS 6.2) to replicate runoff flow over a 2D model of a highly urbanized area. From scenario S0, three more scenarios were derived: S1 (S0 with pluvial drainage network), S2 (S1 with LID/SUDS control elements), and S3 (S0 with 2D representation of LID/SUDS), which were then compared against using four comparison schemes. Results show that the proposed method for representing buildings computed the propagation of the runoff comparable to when the building void method is used, with some shortcomings regarding mesh adjustments and computational times. Regarding the 2D representation of LID/SUDS, the effects were unperceivable on water depth maps (reduction in water depths of 1.5 mm on average for all the rainfall events). Still, they were reflected in the increase of 62% of the infiltration volume on average of all the rainfall scenarios and a decrease of 9.1% of water flowing outside the 2D area, therefore replicating the effect of capturing water. Full article

Urbanization imposes complex environmental gradients that threaten plant diversity and urban ecosystem integrity. Understanding the multifactorial drivers that govern species distribution in urban contexts is essential for biodiversity conservation and sustainable landscape planning. This study addresses this challenge by examining the environmental determinants of urban flora in a rapidly developing city. We integrated data from 397 floristic sampling sites and 13 environmental monitoring locations across Düzce, Türkiye. A multidimensional suite of environmental predictors—including microclimatic variables (soil temperature, moisture, light), edaphic properties (pH, EC (Electrical Conductivity), texture, carbonate content), precipitation chemistry (pH and major ions), macroclimatic parameters (CHELSA bioclimatic variables), and spatial metrics (elevation, proximity to urban and natural features)—was analyzed using nonlinear regression models and machine learning algorithms (RF (Random Forest), XGBoost, and SVR (Support Vector Regression)). Shannon diversity exhibited strong variation across land cover types, with the highest values in broad-leaved forests and pastures (>3.0) and lowest in construction and mining zones (<2.3). Species richness and evenness followed similar spatial trends. Evenness peaked in semi-natural habitats such as agricultural and riparian areas (~0.85). Random Forest outperformed other models in predictive accuracy. Elevation was the most influential predictor of Shannon diversity, while proximity to riparian zones best explained richness and evenness. Chloride concentrations in rainfall were also linked to species composition. When the models were recalibrated using only native species, they exhibited consistent patterns and maintained high predictive performance (Shannon R² ≈ 0.937474; Richness R² ≈ 0.855305; Evenness R² ≈ 0.631796). Full article

The Lake Velence watershed faces increasing challenges driven by local and global factors, including the impacts of climate change, energy resource limitations, and greenhouse gas emissions. These issues, particularly acute in water management, are exacerbated by prolonged droughts, growing population pressures, and shifting land use patterns. Such dynamics strain the region’s scarce water resources, negatively affecting the environment, tourism, recreation, agriculture, and economic prospects. Nadap, a hilly settlement within the watershed, experiences frequent flooding and poor water retention, yet it also boasts the highest solar panel capacity per property in Hungary. This research addresses these interconnected challenges by designing a solar-hydrodynamic network comprising four multi-purpose water reservoirs. By leveraging the settlement’s solar capacity and geographical features, the reservoirs provide numerous benefits to local stakeholders and extend their impact far beyond their borders. These include stormwater management with flash flood mitigation, seasonal green energy storage, water security for agriculture and irrigation, wildlife conservation, recreational opportunities, carbon-smart winery developments, and the creation of sustainable blue-green settlements. Reservoir locations and dimensions were determined by analyzing geographical characteristics, stormwater volume, energy demand, solar panel performance, and rainfall data. The hydrodynamic system, modeled in Matlab, was optimized to ensure efficient water usage for irrigation, animal hydration, and other needs while minimizing evaporation losses and carbon emissions. This research presents a design framework for low-carbon and cost-effective solutions that address water management and energy storage, promoting environmental, social, and economic sustainability. The multi-purpose use of retained rainwater solves various existing problems/challenges, strengthens a community’s self-sustainability, and fosters regional growth. This integrated approach can serve as a model for other municipalities and for developing cost-effective inter-settlement and cross-catchment solutions, with a short payback period, facing similar challenges. Full article

Habitat alteration due to agricultural expansion and heavy livestock grazing is a major threat for open natural ecosystems (ONEs). Within the Indian Thar Desert, such land-use transformations are altering native grassland habitats, with consequential effects on insect communities that perform vital ecological functions and support higher trophic levels. Between 2020 and 2022, we surveyed a 641 km² area, using belt transect and visual detection methods, to quantify insect densities at the order level across different seasons. Linear mixed-effect (LME) models revealed that the orthopteran insect densities, primarily grasshoppers, were significantly higher in grasslands compared to agriculture and barren lands and were lower in the presence of livestock grazing. Orthopteran densities were higher and showed strong seasonal dependencies, likely driven by rainfall-mediated vegetation growth during monsoons. Intense grazing and agricultural expansion reduced vegetation biomass and resource availability, which affected the insect populations negatively. These research findings underscore the urgent need to implement ecologically sensitive land management practices, including sustainable grazing regimes and grassland conservation, to maintain insect biodiversity and the broader ecological network. Given the role of insects in ecosystem functioning and their importance to conservation dependent species of, such as the critically endangered Great Indian Bustard (Ardeotis nigriceps), these findings underscore the ecological significance of preserving native grassland habitats in the Thar Desert landscape. Full article