Search Results (1,190)

To address compacted soils with high power consumption and waterlogging risks in rice–rapeseed rotation areas of the Yangtze River, this study designed a ditching machine combining a stepped cutter head and trapezoidal cleaning blade, where the mechanical synergy between components minimizes energy loss during soil-cutting and -throwing processes. We mathematically modeled soil cutting–throwing dynamics and blade traction forces, integrating soil rheological properties to refine parameter interactions. Discrete Element Method (DEM) simulations and single-factor experiments analyzed impacts of the inner/outer blade widths, blade group distance, and blade opening on power consumption. Results indicated that increasing the inner/outer blade widths (200–300 mm) by expanding the direct cutting area significantly reduced the cutter torque by 32% and traction resistance by 48.6% from reduced soil-blockage drag; larger blade group distance (0–300 mm) initially decreased but later increased power consumption due to soil backflow interference, with peak efficiency at 200 mm spacing; the optimal blade opening (586 mm) minimized the soil accumulation-induced power loss, validated by DEM trajectory analysis showing continuous soil flow. Box–Behnken experiments and genetic algorithm optimization determined the optimal parameters: inner blade width: 200 mm; outer blade width: 300 mm; blade group distance: 200 mm; and blade opening: 586 mm, yielding a simulated power consumption of 27.07 kW. Field tests under typical 18.7% soil moisture conditions confirmed a <10% error between simulated and actual power consumption (28.73 kW), with a 17.3 ± 0.5% reduction versus controls. Stability coefficients for the ditch depth, top/bottom widths exceeded 90%, and the backfill rate was 4.5 ± 0.3%, ensuring effective drainage for rapeseed cultivation. This provides practical theoretical and technical support for efficient ditching equipment in rice–rapeseed rotations, enabling resource-saving design for clay loam soils. Full article

Due to increased precipitation intensity and sea-level rise, low-lying coastal roads are increasingly vulnerable to subbase saturation. Widely applied lumped hydrological approaches cannot accurately represent time and space-varying groundwater levels in some highly conductive coastal soils, calling for more sophisticated tools. This study assesses the suitability of the Gridded Surface Subsurface Hydrologic Analysis model (GSSHA) for representing hydrological processes and groundwater dynamics in a unique coastal roadway setting in Alabama. A high-resolution model was developed to assess a 2 km road segment and was calibrated for hydraulic conductivity and aquifer bottom levels using observed groundwater level (GWL) data. The model configuration included a fixed groundwater tidal boundary representing Mobile Bay, a refined land cover classification, and an extreme precipitation event simulation representing Hurricane Sally. Results indicated good agreement between modeled and observed groundwater levels, particularly during short-duration high-intensity events, with NSE values reaching up to 0.83. However, the absence of dynamic tidal forcing limited its ability to replicate certain fine-scale groundwater fluctuations. During the Hurricane Sally simulation, over two-thirds of the segment remained saturated for over 6 h, and some locations exceeded 48 h of pavement saturation. The findings underscore the importance of incorporating shallow groundwater processes in hydrologic modeling for coastal roads. This replicable modeling framework may assist DOTs in identifying critical roadway segments to improve drainage infrastructure in order to increase resiliency. Full article

Rainwater harvesting for sustainable agriculture (RWHSA) offers a viable and eco-friendly strategy to alleviate water scarcity in semi-arid regions, particularly for agricultural use. This study aims to identify optimal sites for implementing RWH systems in northern Iraq to enhance water availability and promote sustainable farming practices. An integrated geospatial approach was adopted, combining Remote Sensing (RS), Geographic Information Systems (GIS), and Multi-Criteria Decision Analysis (MCDA). Key thematic layers, including soil type, land use/land cover, slope, and drainage density were processed in a GIS environment to model runoff potential. The Soil Conservation Service Curve Number (SCS-CN) method was used to estimate surface runoff. Criteria were weighted using the Analytical Hierarchy Process (AHP), enabling a structured and consistent evaluation of site suitability. The resulting suitability map classifies the region into four categories: very high suitability (10.2%), high (26.6%), moderate (40.4%), and low (22.8%). The integration of RS, GIS, AHP, and MCDA proved effective for strategic RWH site selection, supporting cost-efficient, sustainable, and data-driven agricultural planning in water-stressed environments. Full article

This study applies the Weighted Overlay Analysis (WOA) method integrated with GIS to assess landslide susceptibility along Al Figrah Road in Al-Madinah Al-Munawarah, western Saudi Arabia. Seven key conditioning factors, elevation, slope, aspect, drainage density, lithology, soil type, and precipitation were integrated using high-resolution remote sensing data and expert-assigned weights. The output susceptibility map categorized the region into three zones: low (93.5 million m²), moderate (271.2 million m²), and high risk (33.1 million m²). Approximately 29% of the road corridor lies within the low-risk zone, 48% in the moderate zone, and 23% in the high-risk zone. Ten critical sites with potential landslide activity were detected along the road, correlating well with the high-risk zones on the map. Structural weaknesses in the area, such as faults, joints, foliation planes, and shear zones in both igneous and metamorphic rock units, were key contributors to slope instability. The findings offer practical guidance for infrastructure planning and geohazard mitigation in arid, mountainous environments and demonstrate the applicability of WOA in data-scarce regions. Full article

(1) Background: Wetlands are ecologically significant ecosystems that support biodiversity and contribute to essential environmental functions such as water purification, carbon storage and flood regulation. However, these ecosystems face increasing pressures from land-use change and degradation, prompting the need for scalable and accurate classification methods to support conservation and policy efforts. In this research, our motivation was to test whether high-spatial-resolution PlanetScope imagery can be used with pixel-based machine learning to support the mapping and monitoring of wetlands at a national scale. (2) Methods: This study compared four machine learning classification models—Random Forest (RF), XGBoost (XGB), Histogram-Based Gradient Boosting (HGB) and a Multi-Layer Perceptron Classifier (MLPC)—to detect and map wetland areas across New Zealand. All models were trained using eight-band SuperDove satellite imagery from PlanetScope, with a spatial resolution of ~3 m, and ancillary geospatial datasets representing topography and soil drainage characteristics, each of which is available globally. (3) Results: All four machine learning models performed well in detecting wetlands from SuperDove imagery and environmental covariates, with varying strengths. The highest accuracy was achieved using all eight image bands alongside features created from supporting geospatial data. For binary wetland classification, the highest F1 scores were recorded by XGB (0.73) and RF/HGB (both 0.72) when including all covariates. MLPC also showed competitive performance (wetland F1 score of 0.71), despite its relatively lower spatial consistency. However, each model over-predicts total wetland area at a national level, an issue which was able to be reduced by increasing the classification probability threshold and spatial filtering. (4) Conclusions: The comparative analysis highlights the strengths and trade-offs of RF, XGB, HGB and MLPC models for wetland classification. While all four methods are viable, RF offers some key advantages, including ease of deployment and transferability, positioning it as a promising candidate for scalable, high-resolution wetland monitoring across diverse ecological settings. Further work is required for verification of small-scale wetlands (<~0.5 ha) and the addition of fine-spatial-scale covariates. Full article

Flood susceptibility mapping is essential for urban planning and disaster risk management, especially in rapidly urbanizing areas exposed to extreme rainfall events. This study applies an integrated approach combining Geographic Information Systems (GIS), map algebra, and the Analytic Hierarchy Process (AHP) to assess flood-prone zones in Ananindeua, Pará, Brazil. Five geoenvironmental criteria—rainfall, land use and land cover (LULC), slope, soil type, and drainage density—were selected and weighted using AHP to generate a composite flood susceptibility index. The results identified rainfall and slope as the most influential criteria, with both contributing to over 184 km² of high-susceptibility area. Spatial patterns showed that flood-prone zones are concentrated in flat urban areas with high drainage density and extensive impermeable surfaces. CHIRPS rainfall data were validated using Pearson’s correlation (r = 0.83) and the Nash–Sutcliffe efficiency (NS = 0.97), confirming the reliability of the precipitation input. The final susceptibility map, categorized into low, medium, and high classes, was validated using flood events derived from Sentinel-1 SAR data (2019–2025), of which 97.2% occurred in medium- or high-susceptibility zones. These findings demonstrate the model’s strong predictive performance and highlight the role of unplanned urban expansion, land cover changes, and inadequate drainage in increasing flood risk. Although specific to Ananindeua, the proposed methodology can be adapted to other urban areas in Brazil, provided local conditions and data availability are considered. Full article

Acid mine drainage, a consequence of exposure of sulfide mining waste to weathering processes, results in significant water, sediment, and soil contamination. This contamination results in acidophilic ecosystems, with low pH values and elevated concentrations of sulfate and potentially toxic elements. The São Domingos mine, an abandoned site in the Iberian Pyrite Belt, lacks remediation measures and has numerous waste dumps, which are a major source of contamination to local water systems. Therefore, this study examines sediment accumulation in five mine dams along the São Domingos stream that traverses the entire mine complex. Decades of sediment and waste transport since mine closure have resulted in dam-clogging processes. The geochemical, mineralogical, and magnetic properties of the sediments were analyzed to evaluate the mineralogical controls on the mobilization of potentially toxic elements. The sediments are dominated by iron oxides, oxyhydroxides, and hydroxysulfates, with jarosite playing a key role in binding high concentrations of iron and toxic elements. However, no considerable correlation was found between potentially toxic elements and magnetic parameters, highlighting the complex behavior of these contaminants in acid mine drainage-affected systems. Full article

According to Victor Ernest Shelford’s ‘Law of Tolerance,’ organisms within ecosystems thrive optimally when environmental conditions are favorable. Applying this principle to ecosystems and agro-ecosystems facing water scarcity or environmental challenges can significantly enhance their productivity. In these ecosystems, phytocenosis adjusts its conditions by utilizing water with varying salinity levels. Moreover, establishing optimal drinking water conditions for human populations within an ecosystem can help mitigate future negative succession processes. The purpose of this study is to evaluate the quality of two distinct water sources in the Amudarya district of the Republic of Karakalpakstan, Uzbekistan: collector-drainage water and groundwater at depths of 10 to 25 m. This research is highly relevant in the context of climate change, as improper management of water salinity, particularly in collector-drainage water, may exacerbate soil salinization and degrade drinking water quality. The primary methodology of this study is as follows: The Food and Agriculture Organization of the United Nations (FAO) standard for collector-drainage water is applied, and the water quality index is assessed using the CCME WQI model. The Canadian Council of Ministers of the Environment (CCME) model is adapted to assess groundwater quality using Uzbekistan’s national drinking water quality standards. The results of two years of collected data, i.e., 2021 and 2023, show that the water quality index of collector-drainage water indicates that it has limited potential for use as secondary water for the irrigation of sensitive crops and has been classified as ‘Poor’. As a result, salinity increased by 8.33% by 2023. In contrast, groundwater quality was rated as ‘Fair’ in 2021, showing a slight deterioration by 2023. Moreover, a comparative analysis of CCME WQI values for collector-drainage and groundwater in the region, in conjunction with findings from Ethiopia, India, Iraq, and Turkey, indicates a consistent decline in water quality, primarily due to agriculture and various other anthropogenic pollution sources, underscoring the critical need for sustainable water resource management. This study highlights the need to use organic fertilizers in agriculture to protect drinking water quality, improve crop yields, and promote soil health, while reducing reliance on chemical inputs. Furthermore, adopting WQI models under changing climatic conditions can improve agricultural productivity, enhance groundwater quality, and provide better environmental monitoring systems. Full article

The city of Uvira, located in the eastern Democratic Republic of Congo (DRC), is increasingly experiencing flood events with devastating impacts on human life, infrastructure, and livelihoods. This study evaluates flood susceptibility in Uvira using Geographic Information Systems (GISs), and an Analytical Hierarchy Process (AHP)-based Multi-Criteria Decision Making approach. It integrates eight factors contributing to flood occurrence: distance from water bodies, elevation, slope, rainfall intensity, drainage density, soil type, topographic wetness index, and land use/land cover. The results indicate that proximity to water bodies, drainage density and slope are the most influential factors driving flood susceptibility in Uvira. Approximately 87.3% of the city’s land area is classified as having high to very high flood susceptibility, with the most affected zones concentrated along major rivers and the shoreline of Lake Tanganyika. The reliability of the AHP-derived weights is validated by a consistency ratio of 0.008, which falls below the acceptable threshold of 0.1. This research provides valuable insights to support urban planning and inform flood management strategies. Full article

Abandoned smelting sites in karst terrain pose a serious environmental problem due to the complex relationship between specific hydrogeological elements and heavy metal contamination. This review combines work from across the globe to consider how karst-specific features (i.e., rapid underground drainage, high permeability, and carbonate mineralogy) influence the mobility, speciation, and bioavailability of “metallic” pollutants, such as Pb, Cd, Zn, and As. In some areas, such as Guizhou (China), the Cd content in the surface soil is as high as 23.36 mg/kg, indicating a regional risk. Molecular-scale analysis, such as synchrotron-based XAS, can elucidate the speciation forms that underlie toxicity and remediation potential. Additionally, we emphasize discrepancies between karst in Asia, Europe, and North America and synthesize cross-regional contamination events. The risk evaluation is complicated, particularly when dynamic flow systems and spatial heterogeneity are permanent, and deep models like DI-NCPI are required as a matter of course. The remediation is still dependent on the site; however, some technologies, such as phytoremediation, biosorption, and bioremediation, are promising if suitable geochemical and microbial conditions are present. This review presents a framework for integrating molecular data and hydrogeological concepts to inform the management of risk and sustainable remediation of legacy metal pollution in karst. Full article

Forest harvesting can alter sedimentary processes in catchments by reducing vegetation cover and exposing the soil surface. To mitigate these effects, post-harvest residue management is commonly used, though its effectiveness needs individual evaluation. This study assessed how windrowed harvest residues influence structural sediment connectivity in two forest catchments in south-central Chile with a Mediterranean climate. Using digital terrain models and the Index of Connectivity, scenarios with and without windrows were compared. Despite similar windrow characteristics, effectiveness varied between catchments. In catchment N01 (12.6 ha, average slope 0.28 m m⁻¹), with 13.6% windrow coverage, connectivity remained unchanged, but in contrast, catchment N02 (14 ha, average slope 0.27 m m⁻¹), with 21.9% coverage, showed a significant connectivity reduction. A key factor was windrows’ orientation: 83.9% aligned with contour lines in N02 versus 58.6% in N01. Distance to drainage channels also played a role, with the decreasing effect of connectivity at 50–60 m in N02. Bootstrap analysis confirmed significant differences between catchments. These results suggest that windrow configuration, particularly contour alignment, may be more critical than coverage percentage. For effective connectivity reduction, especially on moderate to steep slopes, forest managers should prioritize contour-aligned windrows. This study enhances our understanding of structural sediment connectivity and offers practical insights for sustainable post-harvest forest management. Full article

Soilborne diseases cause losses of 45–70% in faba bean in Ethiopia. Studies were undertaken to define soilborne pathogens and their complexes in Ethiopia. First, the severity of root rot was assessed in 150 field sites across seven Ethiopian regions. Soil samples were collected, and the DNA of 29 pests and pathogens was quantified using a commercial quantitative PCR (qPCR) soil testing service. There was a very high incidence rate of Macrophomina phaseolina, as well as Pythium clades F and I. The other detected species in order of incidence included Fusarium redolens, Rhizoctonia solani, Aphanomyces euteiches, Phytophthora megasperma, Sclerotinia sclerotiorum and S. minor, and Verticillium dahliae, as well as low levels of Thielaviopsis basicola. Five anastomosis groups (AG) of R. solani, namely AG2.1, AG2.2, AG3, AG4, and AG5, were detected, of which AG2.2 and AG4 were most prevalent. We believe this is the first report of occurrence for Ethiopia of A. euteiches, Ph. megasperma, T. basicola, and the five AGs for R. solani. There were very high incidence rates of the foliar pathogens Botrytis cinerea, B. fabae, Didymella pinodes, and Phoma pinodella and of the nematode Pratylenchus thornei, followed by P. neglectus and P. penetrans. The root rot severity and distribution varied significantly across regions, as well as with soil types, soil pH, and soil drainage. Subsequently, metabarcoding of the soil DNA was undertaken using three primer pairs targeting fungi (ITS2), Fusarium species (TEF1 α), and Oomycetes (ITS1Oo). The ITS2 and TEF1α primers emphasized F. oxysporum as the most abundant soilborne fungal pathogen and highlighted F. ananatum, F. brachygibbosum, F. brevicaudatum, F. clavum, F. flagelliforme, F. keratoplasticum, F. napiforme, F. nelsonii, F. neocosmosporiellum, F. torulosum, and F. vanettenii as first reports of occurrence for Ethiopia. The ITS1Oo primer confirmed Pythium spp. as the most prevalent of all Oomycetes. Full article

Subsurface drainage and biochar application are conventional measures for improving saline–alkali soils. However, their combined effects on the fate of nitrogen (N) fertilizers remain unclear. This study investigated the combined effects of subsurface drainage and biochar amendment on the fate of nitrogen (N) in coastal saline–alkali soils, where these conventional remediation measures’ combined impacts on fertilizer N dynamics remain seldom studied. Using ¹⁵N-labeled urea tracing in an alfalfa–soil system, we examined how different drainage spacings (0, 6, 12, and 18 m) and biochar application rates (5, 10, and 15 t/ha) influenced N distribution patterns. Results demonstrated decreasing in drainage spacing and increasing in biochar application; these treatments enhanced ¹⁵N use efficiency on three harvested crops. Drainage showed more sustained effects than biochar. Notably, the combination of 6 m drainage spacing with 15 t/ha biochar application achieved optimal performance of ¹⁵N use, showing N utilization efficiency of 46.0% that significantly compared with most other treatments (p < 0.05). ¹⁵N mass balance analysis revealed that the plant absorption, the soil residual and the loss of applied N accounted for 21.6–46.0%, 38.6–67.5% and 8.5–18.1%, respectively. These findings provide important insights for optimizing nitrogen management in coastal saline–alkali agriculture, demonstrating that strategic integration of subsurface drainage (6 m spacing) with biochar amendment (15 t/ha) can maximize N use efficiency, although potential N losses warrant consideration in field applications. Full article

Mechanically stabilized earth (MSE) retaining walls have become a favored substitute for traditional poured concrete walls due to their affordability, minimal site preparation needs, and practical construction advantages. However, using backfill material with too many small particles and poor drainage qualities may cause the wall to rotate and shift a lot or collapse completely, especially when water pressure is present. This study examines an MSE wall considering different variables, such as water pressure, the type of soil materials in the backfill materials, external load, and the type of analysis. To this aim, both PLAXIS V20 and SLOPE/W (GeoStudio 2019 Suite) software were employed, and after the verification, further investigations were carried out. These numerical analyses aligned with the real-world failure reported by previous researchers, departments, and companies. The findings suggest that the elevated presence of fine particles likely contributed to the wall’s excessive shift. Also, hydrostatic pressure behind a wall, especially in the rainy season, plays a crucial role in the factor of safety reduction by 45% and wall failure, which leads us to consider it an appropriate factor of safety for the MSE wall. Full article

Urban pluvial flooding (UPF) is an increasingly critical issue due to rapid urbanization and intensified precipitation driven by climate change that yet remains understudied in the Caribbean. This study analyzes the effects of UPF resulting from the transformation of a natural karstic landscape into an urbanized area considering a sub-watershed in Chetumal, Southern Mexican Caribbean, as a case study. Hydrographic numerical modeling was conducted using the IBER 2.5.1 software and the SCS-CN method to estimate surface runoff for a critical UPF event across three stages: (i) 1928—natural condition; (ii) 1998—semi-urbanized (78% coverage); and (iii) 2015—urbanized (88% coverage). Urbanization led to the orthogonalization of the drainage network, an increase in the sub-watershed area (20%) and mainstream length (33%), flow velocities rising 10–100 times, a 52% reduction in surface roughness, and a 32% decrease in the potential maximum soil retention before runoff occurs. In urbanized scenarios, 53.5% of flooded areas exceeded 0.5 m in depth, compared to 16.8% in non-urbanized conditions. Community-based knowledge supported flood extent estimates with 44.5% of respondents reporting floodwater levels exceeding 0.50 m, primarily in streets. Only 43.1% recalled past flood levels, indicating a loss of societal memory, although risk perception remained high among directly affected residents. The reported UPF effects perceived in the area mainly related to housing damage (30.2%), mobility disruption (25.5%), or health issues (12.9%). Although UPF events are frequent, insufficient drainage infrastructure, altered runoff patterns, and limited access to public shelters and communication increased vulnerability. Full article