Search Results (1,588)

Quantifying the net hydrological impact of large dams amidst climatic and anthropogenic influences remains a major challenge. This study isolates the effect of Three Gorges Dam (TGD) regulation on discharge at Jiujiang Station in the middle Yangtze River (2009–2016) using a novel scenario-based framework. A Long Short-Term Memory (LSTM) network, optimized by the Sparrow Search Algorithm (SSA), simulated daily discharge with high accuracy (Nash–Sutcliffe Efficiency coefficient > 0.97). By comparing a “with-TGD” simulation against a “without-TGD” scenario—generated by replacing the dam’s regulated outflow with its reconstructed natural inflow—we quantified the net impact ($\Delta Q$). Results show that $\Delta Q$ is substantially modulated by river–lake interactions. For example, in December, the backwater effect from Poyang Lake amplified the direct flow reduction by an additional −82.5 m³/s. The “peak-shaving” effect was context dependent: TGD regulation increased high flows (>30,870 m³/s) by an average of +372 m³/s while slightly decreasing low flows (<12,711 m³/s) by −31 m³/s. The impact exhibits strong seasonality alongside considerable intra-seasonal variability, reflecting multi-objective operations (flood control, power generation, water supply). This framework provides a transferable approach for attributing hydrological change in large regulated rivers and supports integrated water resources management. Full article

Recurrent and increasingly severe flooding along the Wabi Shabelle River—displacing approximately 184,000 people from Beledweyne, central Somalia, in the 2020 Gu season alone—has made in situ reconstruction untenable and planned resettlement a central adaptation option. Site selection in this agropastoral context must simultaneously avoid the riparian flood corridor and preserve access to the river as the dominant livelihood resource. We develop a transparent, reproducible GIS-based Analytic Hierarchy Process–Weighted Linear Combination (AHP–WLC) framework over a 30 × 30 km region of interest at 30 m resolution. A hard safety mask (Height Above Nearest Drainage > 5 m and slope < 5°) is combined with six normalised criteria, including a non-monotonic, piecewise river-livelihood score, using literature-anchored AHP weights (consistency ratio CR = 0.004). Seven initial criteria were pre-screened with Pearson, Spearman and Variance Inflation Factor diagnostics (maximum |r| = 0.52, maximum VIF = 1.44), removing a perfectly collinear services-distance layer before weighting. Robustness was confirmed by a ±10% one-at-a-time sensitivity analysis with targeted river-weight and HAND-threshold tests, all criteria remaining very robust (|Δ| < 5%). The composite identifies 12,723 ha (14.3%) as highly suitable, resolving into 113 operationally meaningful candidate sites (≥5 ha; 95.6% of the highly suitable area). Candidate areas sufficient to absorb the 2020 displacement land demand (552–1104 ha) lie within 6 km of the city centre. The framework offers an operational, defensible foundation for resettlement planning in flood-exposed agropastoral cities of the Horn of Africa. Full article

The increasing frequency and magnitude of glacial lake outburst floods pose a severe threat to the safety of downstream communities. However, Interference from glacier shadows and mountain shading reduces the accuracy of remote sensing-based glacial lake detection. We propose a two-level nested framework that integrates global spatiotemporal aggregation and local adaptive enhancement. At the global level, the 80th temporal percentile (P80) of multi-temporal AWEI imagery is used to construct a stable water-background composite and suppress short-term seasonal noise. Multi-source physical constraints, including the Normalized Difference Snow Index (NDSI), a DEM-derived slope constraint (slope < 10°), and red-band reflectance thresholds (0.3 < BandRed < 1.6), are applied to suppress interference from land, terrain shadows, snow, and glaciers. At the local scale, an adaptive dynamic segmentation strategy is proposed by establishing an equal-area buffer for each individual lake, where the temporal occurrence frequency of MNDWI is computed to build a stable water probability composite, and the Otsu algorithm is applied to independently derive lake-specific optimal thresholds. Using Landsat imagery and meteorological data from 1990 to 2025, we quantified the spatiotemporal dynamics of typical glacial lakes in the central Himalayas, and explored the driving mechanisms of climate factors on lake area changes. Over the past 35 years, the number and area of lakes have exhibited a pronounced expansion trend under a climatic regime characterized by rising temperatures, increasing precipitation, and decreasing relative humidity. During 1990–2020, lake area variations were primarily governed by strong interactions between temperature and wind speed. Summer variability exerted a more pronounced impact than winter variability. The proposed framework provides an effective approach for glacial lake extraction in the study area and may provide useful technical support for long-term monitoring of alpine lakes. Full article

Traditional monitoring frameworks heavily rely on static, total heavy metal concentrations and deterministic indices, failing to capture how climate-driven stressors and micro-interface interactions alter the stability, speciation, and bioavailability of toxic metals. This review synthesizes the state-of-the-art literature at the intersection of hydrology, geochemistry, microbial ecology, and toxicology to address this gap. We investigate how shifting redox (Eh-pH) gradients and climate-forced hydrological extremes—ranging from drought-induced sediment acidification to flood-driven shear stress—accelerate the reductive dissolution of iron/manganese oxyhydroxides. This process consequently triggers seasonal pulses of bioavailable metals. Furthermore, we evaluate how aged microplastics act as dynamic vector interfaces, altering competitive adsorption kinetics and biological uptake. Crucially, we highlight the heavy metal-microbiome-antibiotic resistance axis, demonstrating how sublethal metal exposure drives the co-selection and proliferation of antibiotic resistance genes (ARGs) via mobile genetic elements, revealing an indirect public health hazard. Finally, we critique deterministic assessments and advocate for probabilistic modeling via Monte Carlo simulations to capture exposure heterogeneity. By bridging macro-scale forcing with microscopic chemical and biological transformations, this review provides a comprehensive synthesis for shifting regulatory frameworks toward dynamic, bioavailability-based ecological governance. Full article

This study presents DEDICA (Database of Hydrogeological Instability Events in Calabria, southern Italy), developed by the District Basin Authority of the Southern Apennines (ABDAM) in collaboration with the CNR-IRPI. The database integrates digitized historical sources, chronicle-based records, and previously unpublished archival data that were systematically analyzed, validated, and georeferenced within a GIS environment. After two years of development, DEDICA includes 5329 landslides, 2097 flood events, and 1711 urban flooding occurrences spanning the period 1900–2025. The system supports continuous data updating, enabling both the integration of recent events and the refinement of historical records. The database provides a comprehensive tool for identifying areas prone to geo-hydrological hazards based on historical recurrence, supporting hazard assessment, land-use planning, and risk management strategies. The methodological framework, database structure, and data processing workflow are described in detail. Spatio-temporal analyses highlight the distribution of instability processes, identifying the most affected sectors and revealing seasonal patterns and long-term trends. DEDICA represents a pilot initiative within a broader program aimed at extending the inventory to all regions under ABDAM jurisdiction, ultimately contributing to the development of a unified geo-hydrological hazard database for southern Italy. Full article

This study addresses the dual challenges of seasonal water scarcity and urban flooding in the Garzan River basin, a region with a semi-arid climate. We propose and analyze an integrated water management system designed to mitigate these risks and promote both ecological and economic sustainability. Our methodology began with a comprehensive analysis of meteorological data from 2000 to 2024, which quantified the significant seasonal irregularity in the annual rainfall regime. The findings revealed that the bulk of the average 800 mm of rainfall occurs between January and May, while the summer months experience near-drought conditions. Based on this, we calculated the potential of various water conservation strategies. The system combines rainwater harvesting from a 1000 m² roof and a 500 m² parking lot, projected to collect 1020 m³ annually, with greywater reclamation and low-flow fixtures, which add a combined 400 m³ of annual savings. The total annual water savings of 1420 m³ were found to provide a gross annual economic benefit of $3550. Considering the installation and maintenance costs, the project’s payback period is estimated to be around 32 years. We also developed an annual precipitation prediction model providing a locally applicable early warning mechanism that forecasts total rainfall based on spring data. The use of proactive hydrometeorological data can improve the feasibility of long-term infrastructure projects to a certain extent. Finally, the proposed system’s design was confirmed to be eligible for multiple LEED certification credits, demonstrating its alignment with international sustainability standards. In conclusion, this research provides a comprehensive and viable solution that addresses local water issues and offers a valuable model for other regions facing similar challenges. Full article

The shift from traditional irrigation methods to pressurized irrigation has become essential, particularly considering the water scarcity in the US Southwest. In this context, this study evaluated the effects of different irrigation systems and rates on crop yield (Y) and water productivity (WP) within a multi-cropping system consisting of cantaloupe (Cucumis melo L.), broccoli (Brassica oleracea var. italica), and silage corn (Zea mays L.) under arid conditions in Arizona. Field experiments compared flood (F) and subsurface drip irrigation (SDI) systems at two crop evapotranspiration (ET_c) replacement levels (100% and 80%), resulting in four treatments: F100, F80, SDI100, and SDI80. Seasonal total water applied (TWA), crop yield, water productivity, and silage corn forage-quality parameters were measured. The effects of irrigation systems varied among crops, whereas cantaloupe achieved the highest yield under flood irrigation, with maximum production observed in F100 (63.8 t ha⁻¹). Meanwhile, cantaloupe yields declined under deficit irrigation and SDI treatments (57.2, 39.8, and 27.4 t ha⁻¹ for F80, SDI100, and SDI80, respectively). In contrast, broccoli and silage corn generally performed better under SDI, where more frequent water applications might have improved root-zone moisture conditions and enhanced water productivity. Deficit irrigation substantially increased WP relative to full irrigation without significantly affecting yield for broccoli and silage corn. Broccoli WP ranged from 3.9 kg m⁻³ (F100) to 6.3 kg m⁻³ (SDI80), while silage corn WP increased from 8.7 to 8.8 kg m⁻³ under flood irrigation to 11.6–12.8 kg m⁻³ under SDI. Silage corn forage-quality parameters were not significantly affected by irrigation system or irrigation rate, indicating that moderate water deficits improved seasonal water use without compromising nutritive value. Overall, deficit irrigation reduced seasonal water use and improved WP, with the greatest benefits observed under subsurface drip irrigation. The results demonstrate distinct crop-specific responses to irrigation management, highlighting the necessity of customized optimization strategies. Moreover, our findings highlight that the SDI with moderate deficit irrigation (80% ET_c) can provide an effective balance between water conservation and productivity, enhancing WP without compromising yield or quality under arid conditions. Full article

Earthquake-induced accidents involving buried oil and gas pipelines in water-network regions are governed by coupled seismic, hydrological, geotechnical, and emergency-response factors, while complete accident records are scarce. To support scenario-based consequence analysis under sparse-data conditions, this study develops an accident scenario analysis framework that integrates numerical simulation with Bayesian probabilistic inference. Scenario elements are organized according to four categories: disaster-causing factors, elements at risk, hazard-inducing environment, and emergency management. Finite element analysis and computational fluid dynamics are used to quantify pipeline mechanical response and hydraulic-scour effects, and the resulting physical responses are embedded in a dynamic Bayesian network as state evidence and transition constraints. Triangular fuzzy numbers are used to process expert evaluations and determine node probabilities. The resulting multi-mechanism simulation-Bayesian inference framework quantifies the accident chain from earthquake loading to pipeline deformation, leakage, fire or explosion, and emergency control. Forward reasoning estimates the probability of each scenario state, sensitivity analysis identifies key drivers, including strong earthquakes triggering landslides and rainfall during flood seasons, and disaster-chain analysis clarifies the dominant causative pathways. The framework provides a reproducible basis for scenario analysis, consequence assessment, monitoring and early warning, and emergency response planning for buried oil and gas pipelines exposed to seismic hazards in water-network regions. Full article

Recurrent flooding along the Wabi Shabelle River has repeatedly displaced communities in Beledweyne, Somalia, prompting a 2020 government-led relocation policy intended to reduce long-term flood risk exposure. Whether such resettlement constitutes a durable change-detection method disaster risk reduction strategy in semi-arid East Africa remains empirically untested. We integrate ten years of Sentinel-1 SAR (259 scenes, 2015–2025), three global DEMs (Copernicus GLO-30, FABDEM, SRTM), CHIRPS precipitation, and BFAST changepoint analysis to map flood frequency at 10 m resolution. The Z-score showed the strongest coupling with 12-day cumulative precipitation (Pearson r = +0.338; block-bootstrap 95% CI [+0.13, +0.49], excluding zero) and strong agreement with the log-ratio method (r = +0.676), whereas the conventional fixed −17 dB threshold produced a physically implausible negative correlation (r = −0.248). These conclusions were stable across alternative thresholds. HAND from all three DEMs was positively associated with flood frequency (Spearman ρ ≈ +0.30); GLO-30 and FABDEM were near-equivalent in this low-relief setting (median pairwise difference, 0.13 m). BFAST detected 476,955 changepoints (49.9% post-2020 vs. 35.6% pre-2020), concentrated in high-flood-frequency pixels (Kolmogorov–Smirnov D = 0.854, p < 0.001). The mean flooded area fraction rose from 4.68% to 5.61%, a relative increase of +19.8% (95% CI 9.1–32.0); this remained significant after controlling for precipitation (+0.96 pp, p < 0.001) and excluding the extreme 2023 events (+0.81 pp). Because standard optical and multi-year surface water products are unsuitable for pixel-level validation in this turbid seasonal river, we demonstrate that SAR flood frequency is significantly higher within independently mapped JRC water corridors (median, 0.070 vs. 0.042; p < 0.001). These convergent lines of evidence are consistent with re-encroachment into hazardous floodplains, suggesting that structural relocation alone is unlikely to deliver durable flood risk reduction without parallel investment in tenure security, livelihoods, and inclusive governance (SDGs 11.5, 13.1). The reproducible, open-source SAR framework provides a transferable monitoring template for data-sparse Horn of Africa floodplains. Full article

Wetlands sustained some of the most exceptionally dynamic human–environment relationships in past societies. Tracing their presence and ecological characteristics in antiquity requires integrated recovery strategies that link excavation, systematic sampling, and laboratory analysis. This paper presents new zooarchaeological evidence from Middle and Late Bronze Age Kaymakçı in the Marmara Lake Basin of western Türkiye to present evidence of an ancient wetland. Situated in the middle Gediz Valley within a pulse-lake landscape shaped by seasonal flooding, spring discharge, and ecological verticality extending from the basin floor to approximately 2150 m at the peak of Bozdağ, Kaymakçı is currently the largest-known second-millennium BCE settlement not only in this niche zone, but also in wider western Anatolia. The Kaymakçı Archeological Project (KAP) results show that recovery methods, especially heavy fraction, may significantly affect the resulting data and, therefore, the interpretations. The identified fish remains from KAP, dominated by carp and catfish, confirm a large, shallow, vegetated wetland with fluctuating littoral and flood-zone habitats. Bird remains also evidence a taxonomically diverse bird community typical of large wetland zones with nearby mountain ranges, including waterfowl, marsh-edge, and terrestrial taxa. Compared with contemporaneous Anatolian assemblages from central and southeastern regions of Anatolia, the KAP data extend our understanding of seasonally dynamic wetlands in western Anatolia and further confirm the value of integrated faunal analysis. Full article

Reservoir operation under hydrological uncertainty has become increasingly challenging under changing climate conditions. This study proposes a Probabilistic Dynamic Reservoir Operation Framework (PDROF) that integrates stochastic inflow modeling, Monte Carlo simulation, and dynamic rule extraction for adaptive reservoir management. Historical inflow records were transformed into stochastic inflow ensembles and propagated through reservoir operation simulations to generate reservoir storage trajectories under varying hydrological conditions. From these trajectories, a representative operational rule, referred to as the Most Likely Line (MLL), was extracted to characterize the dominant storage behavior of the system. The results demonstrate that conventional deterministic rule curves are constrained by predefined hydrological classifications and limited flexibility under variable inflow conditions. In contrast, the proposed framework effectively captures seasonal variability and propagates hydrological uncertainty throughout the operational cycle. Long-term simulation over a 23-year period resulted in a total spill volume of 17.74 million cubic meters (MCM), with spill events occurring in only 7 months, indicating improved operational robustness and storage stability. A real flood event in 2024 further demonstrated reductions of 47.42 MCM in spill volume and 3.46 MCM in reservoir storage compared with conventional operation. These improvements are attributed to the anticipatory storage behavior of the MLL-based operational rule, which preserves flood-buffer capacity prior to peak inflow periods and reduces the likelihood of uncontrolled spill events. The proposed framework provides a practical transition from deterministic reservoir operation toward uncertainty-aware and adaptive water resources management. The methodology is scalable to data-scarce and climate-sensitive regions and can be further extended through real-time forecasting and multi-objective optimization in future studies. Full article

Sustainable management of hydrogeological systems that supply water and exhibit high hydrologic complexity can be studied through pragmatic numerical modeling supported by field-constrained conceptualization. This study develops a local-scale three-dimensional groundwater flow numerical model using FEFLOW for the Barranca Lebrija settlement in Aguachica town, where the Lebrija River, the Musanda floodplain lake, and groundwater system converge. The numerical model incorporates: (i) the three-dimensional distribution of geological units and lithology; (ii) water level observations from the Musanda floodplain lake; (iii) stage records from the Lebrija River; (iv) boundary conditions and flux estimates inherited from a previous regional groundwater model; and (v) hydraulic heads from two monitoring wells and five community wells. Steady-state and transient conditions were calibrated, and a sensitivity analysis was performed to identify the parameters that most strongly control surface water–groundwater exchange. The simulations reproduce seasonal groundwater level trends and demonstrate the exchange pathways among the river, floodplain lake, and groundwater system. Results indicate dual behavior: during wet periods, flooding of the Musanda floodplain lake driven by high river levels seeps into the underlying aquifer, whereas in dry periods the floodplain lake reverses its role and becomes a principal discharge boundary. This local-scale, boundary-driven approach provides a computationally tractable framework to quantify SW–GW exchange in data-scarce tropical floodplains and supports monitoring design and water-supply management. Full article

This study investigated density-driven mixing and stratified flow dynamics in Paldang Reservoir, a river-type reservoir formed at the confluence of the South Han River, North Han River, and Gyeongan Stream in South Korea. High-resolution field observations were conducted under varying hydrologic and hydraulic conditions using an Acoustic Doppler Current Profiler (ADCP) and multi-parameter water quality sensors (EXO2). Spatial distributions of flow velocity, water temperature, and electrical conductivity (EC) were analyzed to evaluate tributary interaction and mixing behavior within the reservoir. Distinct spatial mixing structures associated with tributary inflow heterogeneity and hydraulic operation conditions were identified. During flood-season conditions, highly turbid and high-conductivity inflow from the South Han River propagated beneath the North Han River inflow, generating density-driven lower-layer intrusion near the confluence region. Under intermittent discharge conditions at the Cheongpyeong Dam, unstable upper- and lower-layer separation structures and localized reverse-flow behavior developed. In contrast, continuous discharge conditions promoted stable tributary propagation and persistent stratified mixing structures. Case-based Richardson number (Ri) estimates further indicated localized shear-driven mixing at low-Ri inflow sections and relatively stable stratification at high-Ri sections, providing quantitative support for the observed spatial heterogeneity in density-driven mixing. Overall, spatial mixing in Paldang Reservoir was governed by tributary density contrasts and further shaped by hydraulic operation conditions. These findings improve understanding of density-driven mixing processes in river-type reservoirs under varying hydraulic conditions. Full article

Accurate monitoring of inundation in rice paddies is essential for optimizing water use efficiency and mitigating methane emissions; yet, detecting water beneath dense rice canopies remains a major challenge. This study proposed a reliable classification approach applied to the Winter–Spring 2025 season in An Giang province, Vietnam, by integrating multi-temporal ALOS-2 PALSAR-2 (L-band) and Sentinel-1 (C-band) SAR data with in situ field surveys. Time-series Sentinel-1 observations were used to estimate rice phenology (rice age), while multi-polarization backscatter from ALOS-2 PALSAR-2 was analyzed to discriminate inundated from non-inundated conditions across different growth stages. Results demonstrated that L-band signals, particularly in VV polarization, penetrated dense vegetation effectively, enabling classification of inundated vs. non-inundated fields with an overall accuracy of 81% and a Kappa coefficient of 0.77. The resulting multi-date inundation maps revealed distinct flooding regimes consistent with local field survey observations. These findings demonstrated the potential of L-band VV SAR data for characterizing sub-canopy inundation conditions under rice canopies. Crucially, the approach provides essential data for greenhouse gas inventories and supports the verification of low-emission water management practices, such as Alternate Wetting and Drying (AWD). Overall, the study demonstrated the value of multi-frequency SAR integration for advancing agricultural monitoring and climate-smart management in rice-growing regions. Full article

Flood-season lake spatiotemporal dynamics are vital for ecological security and socioeconomic development, requiring consistent high-resolution monitoring. However, precipitation fluctuations and sediment turbidity significantly alter water quality, while blurred boundaries between water and floodplain wetlands challenge precise monitoring. To address these issues, this study proposes a water body extraction method leveraging polarimetric Synthetic Aperture Radar data. utilizes the maximum between-class variance algorithm for initial segmentation, optimizes the threshold via a genetic algorithm, and employs dynamic morphological operations to refine boundary details. The method was validated using 2015–2025 Sentinel-1 flood-season time series of Dongting Lake on Google Earth Engine. The results demonstrate that the proposed method achieves stable and accurate water extraction across various years and seasons, with an overall accuracy surpassing 0.93, confirming its robustness and broad applicability. Furthermore, the spatiotemporal hydrodynamics and driving mechanisms of Dongting Lake were analyzed by integrating the extracted water areas with multi-source data, including water level, precipitation, discharge, temperature, and sunshine duration. Findings indicate that the flood-season water area exhibited a fluctuating trend, initially increasing and subsequently decreasing, peaking at 2202.26 km² in 2020 and dropping to 614.04 km² in 2025, a pattern primarily driven by extreme meteorological events such as heavy rainfall and prolonged droughts. Spatially, inundation patterns were characterized by deeper water in the north and shallower depths in the south, separated by a topographically higher central region. Regression analysis revealed a robust correlation between water area and water level with an R² of 0.931, providing a quantitative reference for water level estimation in ungauged regions. Additionally, discharge and precipitation were positively correlated with water area, whereas temperature and sunshine duration exerted a negligible influence. This study supports flood regulation in the Dongting Lake basin and provides a robust framework for analyzing lake dynamics using long-term SAR data. Full article