Search Results (1,472)

Pesticide residues from agriculture pose persistent threats to ecosystems and human health. Precipitation and surface runoff facilitate the transport of pesticide residues, leading to their subsequent accumulation in lakes and rivers. Microalgae-based bioremediation offers a promising and environmentally friendly approach for degrading and detoxifying these residues. This study employed liquid chromatography–mass spectrometry (LC-MS) to determine pesticide residues in various microalgal solutions. Using three-dimensional excitation-emission matrix (3D-EEM) spectroscopy and fluorescence regional integration (FRI), we quantified the dynamics of dissolved organic matter (DOM) and its relationship with pesticide degradation in the microalgal system. Over time, Tolypothrix tenuis exhibited the highest degradation rate for THX (95.7%), while Anabaena showed the most effective degradation for ATZ (53.8%). Based on structural analysis of degradation products, three potential degradation pathways for THX and ATZ under microalgae action were proposed. Moreover, the degradation process may also involve reactive oxygen species and intracellular enzymes. Hydroxylation and carboxylation were the primary reactions involved in THX degradation, leading to ring opening and subsequent mineralization. In ATZ, the initially removed groups included methyl and carbonyl groups, with the final products undergoing hydroxylation and subsequent mineralization to water and carbon dioxide. This study, conducted within the context of aquatic environmental protection, investigates the threat of pesticide residues to aquatic ecosystems. It further elucidates the associated environmental impacts and degradation mechanisms from a microalgal perspective. Full article

The subject of this research is the exploration of the potential of remote sensing and Geographic Information Systems (GIS) for basin-scale spatio-temporal monitoring of drought and its impacts in the Ukrina River Basin, Bosnia and Herzegovina (BH), during the last decade (2015–2024). The aim is to integrate meteorological, hydrological, agricultural, and socio-economic drought signals and to delineate areas of long-term drought exposure. Meteorological drought was evaluated using CHIRPS precipitation and the Standardized Precipitation Index (SPI) calculated at 1-, 3-, 6-, and 12- month accumulation scales using Gamma fitting and a fixed long term reference period; hydrological drought was examined using available water-level records complemented by the Standardized Water Level Index (SWLI) and supported by correspondence with standardized ERA5-Land runoff anomalies; agricultural drought was mapped using remote sensing indices—the Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI)—calculated from MODIS satellite data; and socio-economic effects were assessed using municipal crop-production statistics (2015–2019). The results indicate that drought conditions were most pronounced in 2015, 2017, 2021, and especially 2022, showing consistent agreement between precipitation deficits, hydrological responses, and vegetation stress, while 2016, 2018–2020, 2023, and 2024 were generally more favorable. As a key novelty, a persistent drought-prone zone was delineated by intersecting drought-affected areas across major episodes, providing a basin-scale identification of chronic drought hotspots for a river basin in BH. The persistent zone covers 40.02% of the basin and spans nine cities and municipalities, with >93% located in Prnjavor, Derventa, Stanari, and Teslić. Hotspots are concentrated mainly in lowlands below 400 m a.s.l., with a statistically significant concentration across lower elevation classes, indicating higher long-term exposure in the central and northern valley sectors, and land use overlay further highlights high relative exposure of productive land. Overall, the integrated remote sensing and GIS framework strengthens drought monitoring by providing spatially explicit and repeatable evidence to support targeted adaptation planning and drought-risk management. Full article

Mediterranean peri-urban forests play a crucial role in urban sustainability, yet their ecosystem services remain underexplored. This study quantifies and maps six regulating ecosystem services—carbon sequestration, air pollutant removal, surface runoff retention, precipitation interception, soil water regulation, and wildlife refuge—in a representative Mediterranean peri-urban forest, Monte de El Pardo (Spain). The analysis integrates cartographic and environmental data, biophysical modelling (i-Tree), and field surveys to provide a spatially explicit assessment. The results reveal that riparian formations and mixed stone pine–broadleaved woodlands provide the highest values across most services, while holm oak forests and dehesas contribute substantially due to their extensive coverage. Total annual carbon sequestration was estimated at 27,917,803 kg C yr⁻¹, equivalent to 102,329,511 kg CO₂e yr⁻¹. Hydrological regulation was also significant, with 94.5% of the area showing medium soil permeability and over half the territory presenting complex, multi-layered vegetation structure. Overall, Mediterranean peri-urban forests function as major carbon sinks, hydrological regulators, and biodiversity cores, reinforcing their importance as ecological and climatic stabilisers in metropolitan regions. Full article

This study provides a quantitative assessment of the combined effects of progressive urbanization and changes in precipitation patterns (PPs) on the urban water cycle. The primary objective was to evaluate historical (1940–2024) and projected (to 2060) changes in total annual surface runoff (TSR) and retention capacity (RC) in the highly urbanized catchment of the Dłubnia River in Cracow, Poland. Simulations were performed using the EPA SWMM hydrodynamic model, supported by digitized historical land-use maps and long-term meteorological records. The results demonstrate that the dominant driver of the observed 6.4-fold increase in TSR and 6.8-fold loss of retention capacity (LRC) over the study period was the progressive increase in impervious surfaces. Although inter-annual variability in the amount and structure of annual precipitation (AP) strongly correlates with annual TSR (r = 0.97), its contribution to the long-term upward trend in TSR is marginal (r = 0.19). Land use and land cover change (LULC) exhibits an extremely strong correlation with the long-term TSR trend (r = 0.998). The study also highlights the high effectiveness of nature-based solutions (NbSs), particularly bioretention cells (BCs)/rain gardens, in mitigating the adverse hydrological effects of excessive surface sealing. Implementation of BCs covering just 3–4% of the total drained roof and road area is sufficient to fully offset the projected combined negative impacts of further urbanization and climate change (CC) in scope Representative Concentration Pathways (RCP4.5 and RCP8.5) projections on catchment retention capacity by 2060. These findings position strategically targeted, relatively small-scale bioretention as one of the most effective and feasible urban adaptation measures in mature, densely developed cities. Full article

In recent years, human activities have impacted surface water and groundwater and their interactions with natural water bodies. Lake Cuitzeo is one of Mexico’s most important water bodies but has significantly reduced its flooded area in recent years. Previous studies did not explicitly evaluate the combined effects of hydrological variables on lake dynamics, limiting the understanding of how basin-scale processes influence lake-level. The objective of this study is to evaluate the change in spatio-temporal patterns of hydrological variables under climatic and anthropogenic stress in the Lake Cuitzeo endorheic basin. The proposed methodology uses the SWAT model to analyze at the basin scale, land use and land cover changes, and trends in precipitation and their effect on hydrological processes. Consequently, groundwater flow interactions were assessed for the first time for the Cuitzeo Lake Basin using an automatically coupled SWAT-MODFLOW (v3, 2019), despite limited observational data. A statistically significant change in mean precipitation was detected beginning in 2015, with a decrease of 10.22% compared to the 1973–2014 mean. Land use and land cover changes between 1997 and 2013 resulted in a 26.20% increase in surface runoff. In contrast, estimated evapotranspiration decreased by 1.77%, potentially associated with the reduction in forest cover. As a combined effect of decreased precipitation and land use and land cover change, groundwater percolation declined by 6.34%. Overall, the combined effects of climatic variables and anthropogenic activities have altered lake–aquifer interaction. Full article

Urban underpasses are critical flood-prone hotspots during extreme rainfall, posing significant threats to urban resilience and infrastructure safety. However, a scale gap persists between catchment-scale hydrological models, which often oversimplify local geometry, and high-fidelity hydrodynamic models, which typically lack realistic boundary conditions. To bridge this gap, this study develops a multi-scale framework that integrates the Storm Water Management Model (SWMM) with 3D Computational Fluid Dynamics (CFD). The framework employs a unidirectional integration (one-way forcing), utilizing SWMM-simulated runoff hydrographs as dynamic inlet boundaries for a detailed CFD model of a 2.8 km underpass in Shanghai. Simulations across six design rainfall events (2- to 50-year return periods) revealed two distinct flooding mechanisms: a systemic response at the hydraulic low point, governed by cumulative inflow; and a localized response at entrance concavities, where water depth is rapidly capped by micro-topography. Informed by these mechanisms, an intensity-graded drainage strategy was developed. Simulation results show significant differences between different drainage strategies. Through this framework and optimized drainage system design, significant water accumulation within the underpass can be prevented, enhancing its flood resistance and reducing the severity of disasters. This integrated framework provides a robust tool for enhancing the flood resilience of urban underpasses and offers a basis for the design of proactive disaster mitigation systems. Full article

This study examines the hydrological response of the Klambu Dam Catchment in Central Java, Indonesia, to climatic and land cover changes from 2000–2023, with simulations extending to 2040. Utilizing CHIRPS satellite data calibrated with six ground stations, monthly precipitation and temperature datasets were analyzed and projected via linear regression aligned with IPCC scenarios, revealing a marginal temperature decline of 0.21 °C (from 28.25 °C in 2005 to 28.04 °C in 2023) and a 17% increase in rainfall variability. Land cover assessments from Landsat imagery highlighted drastic changes: a 73.8% reduction in forest area and a 467.8% increase in mixed farming areas, alongside moderate fluctuations in paddy fields and settlements. The Thornthwaite-Mather water balance method simulated monthly discharge, validated against observed data with Pearson correlations ranging from 0.5729 (2020) to 0.9439 (2015). Future projections using Cellular Automata-Markov modeling indicated stable volumetric flow but a temporal shift, including a 28.1% decrease in April rainfall from 2000 to 2040, contracting the wet season and extending dry spells. These shifts pose significant threats to agricultural and aquaculture activities, potentially exacerbating water scarcity and economic losses. The findings emphasize integrating dynamic land cover data, climate projections, and empirical runoff corrections for climate-resilient watershed management. Full article

The water yield (WY) service is a critical ecosystem service in arid regions, and understanding its spatiotemporal heterogeneity and controls is important for sustainable watershed management. Annual water yield (WY) in the Aksu River Basin (ARB), China, from 2000 to 2020 was simulated using the InVEST model, with validation against observed runoff (NSE = 0.840, R² = 0.846, RMSE = 1.787). The results revealed a decline in WY from 66.49 mm in 2000 to 43.15 mm in 2015, while retaining a clear north–south gradient, with higher values in the north. Areas showing decreasing and increasing trends accounted for 45.34% and 3.14% of the basin, respectively. WY exhibited strong spatial autocorrelation (global Moran’s I = 0.912–0.941), with high-value clusters in the north and low-value clusters in the south. GeoDetector identified precipitation, temperature, and potential evapotranspiration as key drivers (q = 0.889, 0.880, and 0.832, respectively), with precipitation-related interactions generally exceeding 0.9, indicating enhanced explanatory power through multi-factor coupling. After variable screening and collinearity control, MGWR revealed spatially varying effects of drivers and significant spatial non-stationarity. Overall, despite the declining trend, WY in the ARB maintained a relatively stable spatial structure, with its heterogeneity primarily driven by the coupling of climatic forcing and topographic constraints, providing a scientific basis for zonal water resource management in arid river basins. Full article

This study integrates 90 years of hydrometeorological observations (1930/31–2019/20) with end-century projections (2080–2099) to evaluate climate-driven changes in the water balance of the Nitra River basin (2094 km²), Slovakia. Despite a modest 2–3% increase in annual precipitation from 1930/31–1959/60 to 1990/91–2019/20, mean annual runoff declined from 229 mm to 201 mm (≈−12%), primarily due to enhanced evapotranspiration stemming from a +1.08 °C basin-wide temperature increase. An empirical regression from 90 hydrological years shows that +100 mm in precipitation boosts runoff by ≈41 mm, while +1 °C in temperature reduces it by ≈13 mm. The BILAN monthly water balance model was calibrated for 1930/31–2019/20 to decompose runoff components. Over the 90-year period, the modeled annual runoff averaged 222 mm, comprising a 112 mm baseflow (50.4%), a 91 mm interflow (41.0%), and a 19 mm direct runoff (8.6%), underscoring the key role of groundwater and subsurface flows in sustaining streamflow. In the second part of our study, the monthly water balance model BILAN was recalibrated for 1995–2014 to simulate future runoff under three CMIP6 Shared Socioeconomic Pathways. Under the sustainability pathway SSP1-1.9 (+0.88 °C; +1.1% precipitation), annual runoff decreases by 8.9%. The middle-of-the-road scenario SSP2-4.5 (+2.6 °C; +3.1% precipitation) projects a 17.5% decline in annual runoff, with particularly severe reductions in autumn months (September −32.3%, October −35.8%, December −40.4%). The high-emission pathway SSP5-8.5 (+5.1 °C; +0.4% precipitation) yields the most dramatic impact with a 35.2% decrease in annual runoff and summer deficits exceeding 45%. These results underline the extreme sensitivity of a mid-sized Central European basin to temperature-driven evapotranspiration and the critical importance of emission mitigation, emphasizing the urgent need for adaptive water management strategies, including new storage infrastructure to address both winter floods and intensifying summer droughts. Full article

The emergence and dissemination of antimicrobial resistance (AMR) are driven by complex, interconnected mechanisms involving microbial communities, environmental factors, and human activities, with climate change playing a pivotal and accelerating role. Rising temperatures, altered precipitation patterns, and other environmental disruptions caused by climate change create favorable conditions for bacterial growth and enhance the horizontal gene transfer (HGT) of antibiotic resistance genes (ARGs). Thermal stress and environmental pressures induce genetic mutations that promote resistance, while ecosystem disturbances facilitate the stabilization and spread of resistant pathogens. Moreover, climate change exacerbates public and animal health risks by expanding the range of infectious disease vectors and driving population displacement due to extreme weather events, further amplifying the transmission and evolution of resistant microbes. Livestock agriculture represents a critical nexus where excessive antibiotic use, environmental stressors, and climate-related challenges converge, fueling AMR escalation with profound public health and economic consequences. Environmental reservoirs, including soil and water sources, accumulate ARGs from agricultural runoff, wastewater, and pollution, enabling resistance spread. This review aims to demonstrate how the Mediterranean’s strategic position makes it an ideal living laboratory for the development of integrated “One Health” frameworks that address the mechanistic links between climate change and AMR. By highlighting these interconnections, the review underscores the need for a unified approach that incorporates sustainable agricultural practices, climate mitigation and adaptation within healthcare systems, and enhanced surveillance of zoonotic and resistant pathogens—ultimately offering a roadmap for tackling this multifaceted global health crisis. Full article

In Long Short-Term Memory (LSTM)-based runoff forecasting models, the selection of input schemes is critically important. This study, using daily rainfall and runoff data from the Jingle Basin (2006–2014), investigated three input schemes to evaluate their forecasting efficacy and employed the Shapley Additive Explanation (SHAP) method to quantitatively analyze variable contributions. The results demonstrate that LSTM model performance deteriorates with increasing lead time, achieving optimal accuracy at a 1-day lead (MAE: 0.90 m³/s, RMSE: 3.09 m³/s, NSE: 0.84). The results, validated by significance testing, are reasonable; incorporating precipitation characteristics significantly enhances model performance compared to baseline schemes, reducing RMSE by 6–34% and improving NSE by 9–14%. SHAP analysis reveals antecedent runoff as the dominant influencing factor, accounting for 65.9–84.7% of total importance. Furthermore, the contributions of trend, seasonal, and residual components progressively increase with extended lead times, demonstrating non-negligible roles in forecast outcomes. These findings, confirmed by significance testing, provide quantitative insights into input variable contributions to target uncertainty and enhance the mechanistic understanding of precipitation-runoff relationships, offering valuable references for optimizing hydrological forecasting systems. Full article

Hydrological behavior plays a critical role in seasonally dry forest ecosystems, as it underpins water availability for multiple productive activities, including forestry, agriculture, grazing, and urban supply. This study evaluated the hydrological effects of thinning treatments in a semi-arid oak–pine forest of Chihuahua, Mexico, using a Before–After–Control–Impact (BACI) design. Three Micro-catchments (MC) with initially comparable tree density and canopy cover were monitored during the rainy seasons of 2018 (pre-thinning) and 2019 (post-thinning). Thinning treatments were applied at 20% and 60% canopy cover in two MC, while a third remained unthinned as a 100% control. Precipitation and surface runoff were recorded at the event scale, and data were analyzed using Weibull probability models with a log link to capture the frequency and magnitude of runoff events. Precipitation patterns were broadly comparable across years, although 2018 included an extreme storm event (59 mm). In contrast, runoff volumes in 2019 were lower despite marginally higher seasonal rainfall, reflecting the absence of large storms. Statistical modeling indicated that for each additional millimeter of precipitation, mean runoff increased by approximately 12%, although thinning significantly altered baseline conditions. Relative to 2018, mean runoff ratios were 0.087 in the 100% canopy catchment, 0.296 in the 60% treatment, and 0.348 in the 20% treatment, suggesting that reduced canopy cover retained proportionally more runoff than the control. BACI contrasts confirmed that thinned catchments maintained higher proportions of runoff than the unthinned control, although statistical significance was marginal for the 20% canopy treatment. Overall, the study provides ecohydrological insights relevant to the management of semi-arid forest ecosystems. Full article

High-resolution satellite precipitation products are key inputs for basin-scale rainfall estimation, but they still exhibit substantial biases in complex terrain and during heavy rainfall. Recent multi-source fusion studies have shown that simply stacking multiple same-type microwave satellite products yields only limited additional gains for high-quality precipitation estimates and may even introduce local degradation, suggesting that targeted correction of a single, widely validated high-quality microwave product (such as IMERG) is a more rational strategy. Focusing on the mountainous, gauge-sparse Lüshui River basin with pronounced relief and frequent heavy rainfall, we use GPM IMERG V07 as the primary microwave product and incorporate CHIRPS, ERA5 evaporation, and a digital elevation model as auxiliary inputs to build a daily attention-enhanced CNN–LSTM (A-CNN–LSTM) bias-correction framework. Under a unified IMERG-based setting, we compare three network architectures—LSTM, CNN–LSTM, and A-CNN–LSTM—and test three input configurations (single-source IMERG, single-source CHIRPS, and combined IMERG + CHIRPS) to jointly evaluate impacts on corrected precipitation and SWAT runoff simulations. The IMERG-driven A-CNN–LSTM markedly reduces daily root-mean-square error and improves the intensity and timing of 10–50 mm·d⁻¹ rainfall events; the single-source IMERG configuration also outperforms CHIRPS-including multi-source setups in terms of correlation, RMSE, and performance across rainfall-intensity classes. When the corrected IMERG product is used to force SWAT, daily Nash-Sutcliffe Efficiency increases from about 0.71/0.70 to 0.85/0.79 in the calibration/validation periods, and RMSE decreases from 87.92 to 60.98 m³ s⁻¹, while flood peaks and timing closely match simulations driven by gauge-interpolated precipitation. Overall, the results demonstrate that, in gauge-sparse mountainous basins, correcting a single high-quality, widely validated microwave product with a small set of heterogeneous covariates is more effective for improving precipitation inputs and their hydrological utility than simply aggregating multiple same-type satellite products. Full article

Shiqian County, located within a key geothermal fluids belt in Guizhou Province, China, has abundant underground hot water resources. Therefore, elucidating the hydrogeochemical characteristics and formation mechanisms of thermal mineral water in this area is essential for evaluating and sustainably utilizing regional geothermal fluids. This study focuses on the Shiqian Hot Spring Group and employs integrated analytical techniques, including rock geochemistry, hydrogeochemistry, isotope hydrology, digital elevation model (DEM) data analysis, remote sensing interpretation, geological surveys, mineral saturation index calculations, and PHREEQC-based inverse hydrogeochemical modeling, to elucidate its hydrogeochemical characteristics and formation mechanisms. The results show that strontium concentrations range from 0.06 to 7.17 mg/L (average 1.65 mg/L) and metasilicic acid concentrations range from 19.46 to 65.51 mg/L (average 33.64 mg/L). Most samples meet the national standards for natural mineral water and are classified as Sr-metasilicic acid type. Isotope analysis indicates that the geothermal water is recharged by meteoric precipitation at elevations between 911 m and 1833 m, mainly from carbonate outcrops and fracture zones on the southwestern slope of Fanjingshan, and discharges south of Shiqian County. The dominant hydrochemical types are HCO₃·SO₄-Ca·Mg and HCO₃-Ca·Mg. Strontium is primarily derived from carbonate rocks and celestite-bearing evaporites, whereas metasilicic acid mainly originates from quartz dissolution along the upstream groundwater flow path. PHREEQC-based inverse modeling indicates that, during localized thermal mineral water runoff in the middle-lower reaches or discharge areas, calcite dissolves while dolomite and quartz tend to precipitate, reflecting calcite dissolution-dominated water–rock interactions and near-saturation conditions for some minerals at late runoff stages. Full article

Agricultural nonpoint source pollution is emerging as one of the increasingly serious environmental concerns all over the world. This study conducted field experiments in Zengcheng District, Guangzhou City, from 2019 to 2023 to explore the mechanisms by which different crop types, fertilization modes, and meteorological conditions affect the loss of nitrogen and phosphorus in agricultural nonpoint source pollution. In rice and corn, the CK and PK treatment groups showed significant fitting advantages, such as the R² of rice-CK reaching 0.309. MAE was 0.395, and the R² of corn-PK was as high as 0.415. For compound fertilization groups such as NPK and OF, the model fitting ability decreased, such as the R² of rice-NPK dropping to 0.193 and the R² of corn-OF being only 0.168. In addition, the overall performance of the model was limited in the modeling of total phosphorus. A relatively good fit was achieved in corn (such as NPK group R² = 0.272) and in vegetables and citrus. R² was mostly below 0.25. The results indicated that fertilization management, crop types, and meteorological conditions affected nitrogen and phosphorus losses in agricultural runoff. Cornfields under conventional nitrogen, phosphorus, and potassium fertilizer (NPK) and conventional nitrogen and potassium fertilizer treatment without phosphorus fertilizer (NK) treatments exhibited the highest nitrogen losses, while citrus fields showed elevated phosphorus concentrations under NPK and PK treatments. Organic fertilizer treatments led to moderate nutrient losses but greater variability. Organic fertilizer treatments resulted in moderate nutrient losses but showed greater interannual variability. Meteorological drivers differed among crop types. Nitrogen enrichment was mainly associated with high temperature and precipitation, whereas phosphorus loss was primarily triggered by short-term extreme weather events. Linear regression models performed well under simple fertilization scenarios but struggled with complex nutrient dynamics. Crop-specific traits such as flooding in rice fields, irrigation in corn, and canopy coverage in citrus significantly influenced nutrient migration. The findings of this study highlight that nutrient losses are jointly regulated by crop systems, fertilization practices, and meteorological variability, particularly under extreme weather conditions. These findings underscore the necessity of crop-specific and climate-adaptive nutrient management strategies to reduce agricultural nonpoint source pollution. By integrating long-term field observations with machine learning–based analysis, this study provides scientific evidence to support sustainable fertilizer management, protection of water resources, and environmentally responsible agricultural development in subtropical regions. The proposed approaches contribute to sustainable land and water resource utilization and climate-resilient agricultural systems, aligning with the goals of sustainable development in rapidly urbanizing river basins. Full article