Water

32 pages, 34948 KB

Open AccessFeature PaperArticle

Hydrogeological Response of a Karst Aquifer to Extreme Recharge: Insights from the 2026 Grazalema–Líbar Hydro-Seismic Crisis (Southern Spain)

by Eugenio Sanz-Pérez, María Belén Benito Oterino, Juan Carlos Mosquera-Feijóo, Javier Fernández-Fidalgo, Joaquín Sanz de Ojeda and Felix Escolano

Water 2026, 18(18), 2276; https://doi.org/10.3390/w18182276 (registering DOI) - 12 Sep 2026

Abstract

Extreme precipitation can profoundly modify groundwater dynamics in karst aquifers, although the processes governing their response to exceptional recharge remain poorly understood. In particular, the evolution of hydraulic connectivity and confinement during aquifer filling may strongly influence groundwater pressure propagation and associated geological [...] Read more.

Extreme precipitation can profoundly modify groundwater dynamics in karst aquifers, although the processes governing their response to exceptional recharge remain poorly understood. In particular, the evolution of hydraulic connectivity and confinement during aquifer filling may strongly influence groundwater pressure propagation and associated geological processes. This study investigates the hydrogeological response of the partially confined Grazalema–Líbar karst aquifer (southern Spain) during an exceptional recharge event in January–February 2026, when cumulative rainfall exceeded 2800 mm. Meteorological observations, spring discharge records, hydrogeological information, and seismic data were integrated to analyze groundwater recharge, hydraulic behavior, and seismic activity. The aquifer responded rapidly, with large groundwater level rises, major increases in spring discharge, progressive hydraulic connection between previously disconnected sectors, and expansion of confined conditions. These changes increased hydraulic diffusivity and promoted rapid pressure transmission through the karst conduit network. The strongest seismic response occurred only after this hydraulic reorganization developed, with earthquakes concentrating in confined sectors and progressively migrating towards shallow depths. The results demonstrate that evolving hydraulic connectivity, rather than recharge alone, controls pressure transmission and seismic activation. They provide a conceptual framework for understanding hydro-seismic responses in partially confined karst aquifers during increasingly frequent extreme rainfall events. Full article

(This article belongs to the Special Issue Hydrogeophysical Methods and Hydrogeological Models)

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27 pages, 3987 KB

Open AccessArticle

Data Quality and Indicator Sensitivity in Water-Loss Benchmarking: An Exploratory Study of a Purposive Sample of Eleven Greek Water Service Providers

by Angelos Chasiotis, Dimitrios Piromalis and Panagiotis T. Nastos

Water 2026, 18(18), 2275; https://doi.org/10.3390/w18182275 (registering DOI) - 12 Sep 2026

Abstract

Water-loss indicators answer different management questions, yet they are often compared as interchangeable rankings. This exploratory study asks what regulatory water-loss reporting can support when the inputs behind each indicator are unevenly documented. For a purposive sample of eleven Greek providers reporting under [...] Read more.

Water-loss indicators answer different management questions, yet they are often compared as interchangeable rankings. This exploratory study asks what regulatory water-loss reporting can support when the inputs behind each indicator are unevenly documented. For a purposive sample of eleven Greek providers reporting under a harmonised framework, the 2024 and 2025 IWA water balances were reproduced, volumetric indicators were normalised to the hours under pressure, and the twelve Infrastructure Leakage Index (ILI) values reported by seven providers, or reproducible from their WB-EasyCalc files, were examined. Sensitivity to metering, pressure, and denominator inputs was quantified, and each ILI case was screened for data adequacy. In 2025, non-revenue water ranged from 26.7% to 71.1% of system input volume, with a mean of 46.2%. Six providers reported an identical percentage in the reference table for both years; in four, the leakage level in that table differed from the real losses in the same provider’s balance by 3.8–17.5 percentage points. Average pressure was stated for all seven ILI cases but measured in none, with three adopting the same round value. The study documents failure modes in indicator reporting rather than estimating their prevalence, and proposes a tiered indicator set published with the documentation that makes each indicator readable. Full article

(This article belongs to the Special Issue Optimization and Decision Support for Water Systems: From Network Models to Operational Strategies)

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27 pages, 41173 KB

Open AccessArticle

Morphometric and Geospatial Analysis for Flash Flood Susceptibility Assessment in Wadi Al-Disha, Northern Saudi Arabia: Insights from a Dual Model

by Maan Okayli, Abdullah M. Alanazi and Bashar Bashir

Water 2026, 18(18), 2274; https://doi.org/10.3390/w18182274 (registering DOI) - 12 Sep 2026

Abstract

Flash floods represent an extreme hydro-geomorphological hazard in the arid and semi-arid regions in northwestern Saudi Arabia, presenting major risks to significantly expanding urban regions, infrastructure, and sustainable development projects of Saudi Vision 2030. The present study assesses the flood susceptibility of the [...] Read more.

Flash floods represent an extreme hydro-geomorphological hazard in the arid and semi-arid regions in northwestern Saudi Arabia, presenting major risks to significantly expanding urban regions, infrastructure, and sustainable development projects of Saudi Vision 2030. The present study assesses the flood susceptibility of the Wadi Al-Disha catchment (4212.81 km²) using a high-resolution 12.5 m spatial resolution ALOS-PALSAR digital elevation model (DEM). A dual-model approach, combining the scale-dependent Morphometric Ranking Method and the scale-independent El-Shamy approach, was applied over district 18 sub-catchments to investigate, analyze, and calculate 15 hydro-morphometric effective parameters. The Ranking Method model assigned sub-catchment SC-18 to the high-flood susceptibility zone, SC-4 as a low-flood susceptibility unit, and the rest of the 15 sub-catchments as moderate risk, with SC-9, the largest sub-catchment, scoring highest among the moderate rank, just below the high-susceptibility rank threshold. On the other hand, the El-Shamy Approach model defined SC-3 and SC-8 as high-flood susceptibility classes, while the remaining 16 sub-catchments reflect moderate-flood susceptibility classes. This proposed assessment is a physically-based susceptibility evaluation extracted from terrain morphometry; it does not consider socio-economic exposure or hydrological (rainfall–runoff) information, which we identify explicitly as scope boundaries below. Comparative validation with a regional study states that a bifurcation ratio indicates that tectonic rifting impacts bifurcation parameter values (

R b > 4.0

), forcing the El-Shamy Approach model to underestimate susceptibility in structurally complex, high-relief landscapes. To underestimate the impact of these flood susceptibilities and secure sustainable infrastructure in the very close Prince Mohammed bin Salman Royal Reserve, the study suggests designing targeted dams in rapid-velocity headwaters and creating an early warning system on the plateaus upstream. Full article

(This article belongs to the Special Issue Innovative Approaches in Flood Forecasting and Modeling for Risk Mitigation, 2nd Edition)

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38 pages, 9222 KB

Open AccessArticle

From Compliant to Critical: Forecasting Emerging E. coli Risk in New Zealand’s South Island Rivers

by Parul Tiwari, Tanishqa Goyal and Don Kulasiri

Water 2026, 18(18), 2273; https://doi.org/10.3390/w18182273 (registering DOI) - 12 Sep 2026

Abstract

Freshwater quality is degrading globally, and regulatory monitoring remains largely retrospective, identifying non-compliance only after it occurs. This study identifies whether multi-year, regulatorily relevant compliance breaches can be forecast from sparse monthly monitoring records alone and whether such forecasts improve on the assumption [...] Read more.

Freshwater quality is degrading globally, and regulatory monitoring remains largely retrospective, identifying non-compliance only after it occurs. This study identifies whether multi-year, regulatorily relevant compliance breaches can be forecast from sparse monthly monitoring records alone and whether such forecasts improve on the assumption that next year resembles the current one. Using approximately two decades (2004–2024) of Land, Air, Water Aotearoa (LAWA) data from 497 South Island, New Zealand river sites, a single pooled gradient-boosted (LightGBM) classifier was trained to forecast Escherichia coli worst-band (Band E) non-compliance under the National Policy Statement for Freshwater Management at one-, two-, and three-year horizons, benchmarked against persistence, trend projection, and majority-class baselines under strictly temporal validation. The model discriminated breaches reliably (AUC 0.84–0.85) and exceeded persistence in balanced accuracy at all three horizons, significantly at two and three years. Its principal value was early warning: among currently compliant sites, it recovered roughly half of subsequent breaches, transitions that persistence cannot detect by construction, yielding a forward watchlist of 97 sites at risk of entering the worst band (Band E) by 2027, concentrated in pastoral catchments. Forecasting from monitoring data alone imposes an honest ceiling; scores are reported as risk rankings. The findings support a shift from reactive to anticipatory freshwater management. Full article

(This article belongs to the Special Issue Pollution Process and Microbial Responses in Aquatic Environment)

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21 pages, 6276 KB

Open AccessArticle

Polymer–Shape Coupling and Machine-Learning-Derived Microplastic Assemblages in Surface Seawater of the Southern South China Sea

by Yutao Fu, Bo Hu, Hua Deng, Jiawei Kang and Yugen Ni

Water 2026, 18(18), 2272; https://doi.org/10.3390/w18182272 (registering DOI) - 12 Sep 2026

Abstract

Microplastics (MPs) in offshore surface waters are often characterized using individual particle attributes, whereas the joint organization of size, shape, color, and polymer composition remains poorly resolved. Here, surface seawater samples were collected from 34 stations in the South China Sea. A total [...] Read more.

Microplastics (MPs) in offshore surface waters are often characterized using individual particle attributes, whereas the joint organization of size, shape, color, and polymer composition remains poorly resolved. Here, surface seawater samples were collected from 34 stations in the South China Sea. A total of 1007 recovered particles were individually analyzed using micro-Fourier transform infrared spectroscopy (μ-FTIR). Of these, 874 were confirmed as MPs, whereas 133 were identified as non-plastic materials and treated as excluded particles. MP abundance varied substantially among stations, with an overall mean of 1.70 ± 0.65 items/L. Small particles; fibers; transparent, blue, white, and black particles; polyethylene terephthalate (PET); polypropylene (PP); and polyethylene (PE) predominated. Contingency analysis revealed a significant association between polymer composition and shape, whereas associations with size class and color were weaker. A mixed-type machine-learning workflow identified five interpretable assemblages: small PET fibers, medium-to-large PET fibers, PP-rich fibers, PE-rich fragments, and large mixed-polymer fibers. Random Forest interpretation and Gower distance-based partitioning around medoids sensitivity analysis indicated that these assemblages represent recurrent combinations of particle features rather than discrete natural or source-specific classes. Overall, polymer–shape coupling was a major organizing feature of MPs in the investigated offshore waters, highlighting the value of integrated particle identification and interpretable multivariate analysis for resolving MP heterogeneity. Full article

(This article belongs to the Special Issue Aquatic Microplastic Pollution: Occurrence and Removal)

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22 pages, 676 KB

Open AccessArticle

A Quantum Electrodynamical Model of Magnetic Nanobubble Stabilization in Water

by Elmar C. Fuchs, Zahra Taghavi Zinjenab and Thomas Warmann

Water 2026, 18(18), 2271; https://doi.org/10.3390/w18182271 (registering DOI) - 12 Sep 2026

Abstract

This work describes the formation of electrically charged nanobubbles and collective electrodynamical ordering in liquid water based upon the framework of the quantum electrodynamical theories of Del Giudice, Preparata, Vitiello and their co-workers. Nanobubbles with experimentally observed negative zeta potentials are predicted to [...] Read more.

This work describes the formation of electrically charged nanobubbles and collective electrodynamical ordering in liquid water based upon the framework of the quantum electrodynamical theories of Del Giudice, Preparata, Vitiello and their co-workers. Nanobubbles with experimentally observed negative zeta potentials are predicted to generate interfacial electric fields on the order of 10⁵–10⁶ V m⁻¹, comparable to field strengths previously associated with collective vibrational coupling in electrically stressed water. The model addresses magnetic stabilization of the electrically induced vibronically coupled interfacial state, while the observed changes in nanobubble size and number are discussed within the broader framework, including a hypothesized preconditioning effect of the dynamically varying magnetic field on nanobubble formation. Under these conditions, regions of enhanced collective coupling of vibronic modes around a nanobubble with characteristic thicknesses of approximately 9.6–52.5 nm become physically plausible. Furthermore, a phenomenological Landau-type free-energy model is used to investigate the influence of external magnetic fields on the process. We suggest that magnetic fields primarily couple to the low-energy protonic and vibronic modes within this shell. These theoretical predictions are qualitatively consistent with recent experimental observations showing stronger negative zeta potentials, and higher nanobubble concentrations under the influence of magnetic fields, together with smaller characteristic nanobubble radii under an alternating field configuration. Our results support the interpretation that magnetic fields stabilize electrically induced mesoscopic coupling of vibronic modes that emerge transiently during cavitation-driven nanobubble formation. Full article

(This article belongs to the Special Issue Nanobubbles in Aqueous Systems: Generation, Magnetic-Field Interactions, and Electrokinetic Stabilisation)

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25 pages, 16039 KB

Open AccessArticle

Investigation of the Characteristics and Evolution of Seepage Field Distortion in Dams Induced by Core Wall Leakage

by Qibing Zhan, Lei Tang and Shenghang Zhang

Water 2026, 18(18), 2270; https://doi.org/10.3390/w18182270 (registering DOI) - 12 Sep 2026

Abstract

Core wall leakage is a common seepage-related defect occurring during the operation of core wall dams. Clarifying the characteristics and evolution of seepage field distortion under leakage conditions is an important basis for assessing dam safety status and evaluating leakage risks. In this [...] Read more.

Core wall leakage is a common seepage-related defect occurring during the operation of core wall dams. Clarifying the characteristics and evolution of seepage field distortion under leakage conditions is an important basis for assessing dam safety status and evaluating leakage risks. In this study, short-duration, small-scale physical model tests of an asphalt concrete core wall dam were conducted under leakage heads of 14 cm (S1) and 20 cm (S2) to characterize the spatial response of the seepage field. Finite element simulations were subsequently validated against the experimental results and extended through parametric analyses of leakage channel widths of 1–10 mm and burial depths of 5–29 m. The experiments showed that the distortion profiles on horizontal sections were approximately semi-elliptical, with the maximum extent occurring at the elevation of the leakage center and progressively decreasing with vertical distance. The maximum dam-axis diffusion width increased from 9.00 cm under S1 to 11.05 cm under S2, while the maximum streamwise diffusion distance increased from 4.70 to 6.00 cm, corresponding to increases of 22.8% and 27.7%, respectively. Three-dimensional reconstruction further showed that the distorted region exhibited a quasi-semi-ellipsoidal morphology centered on the leakage location. Quantitative comparison between the experimental and numerical results yielded MAEs of 1.325 and 0.550 cm, RMSEs of 1.506 and 0.628 cm, and MAPEs of 18.01% and 13.90% for the dam-axis diffusion width and maximum streamwise diffusion distance, respectively, indicating that the numerical model captured the observed spatial response with moderate quantitative discrepancies. Engineering-scale simulations further showed that increasing leakage channel width generally enlarged the distorted region, whereas the effect of burial depth was non-monotonic and controlled by the downstream phreatic surface. Above the phreatic surface, seepage field distortion increased with leakage channel burial depth; once the leakage channel reached or extended below the phreatic surface, the distortion weakened with further increases in depth. These findings quantitatively characterize the spatial response of the internal seepage field to local core wall leakage and provide a physical basis for interpreting leakage-induced anomalies in seepage monitoring of core wall dams. Full article

(This article belongs to the Special Issue Risk Assessment and Mitigation for Water Conservancy Projects)

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28 pages, 3231 KB

Open AccessArticle

A Simulation-Based Climate-Adaptive Framework for Deficit Irrigation of Melon in Karapınar, Konya Closed Basin, Türkiye

by Almujtaba H. M. Abdallh, Katsuyuki Shimizu, Yuri Yamazaki, Mohamed Farig, Takashi Kume and Erhan Akça

Water 2026, 18(18), 2269; https://doi.org/10.3390/w18182269 - 11 Sep 2026

Abstract

Groundwater-dependent irrigated agriculture in semi-arid basins is increasingly threatened by climate variability and aquifer depletion. Karapınar district, located within Türkiye’s Konya Closed Basin, represents a water-stressed melon-producing area where deep-well drip irrigation and rigid calendar-based scheduling remain common. This study developed a prototype [...] Read more.

Groundwater-dependent irrigated agriculture in semi-arid basins is increasingly threatened by climate variability and aquifer depletion. Karapınar district, located within Türkiye’s Konya Closed Basin, represents a water-stressed melon-producing area where deep-well drip irrigation and rigid calendar-based scheduling remain common. This study developed a prototype AquaCrop-based decision-support framework for adapting mature and pickled melon irrigation to climatic conditions classified by the crop-season weighted three-month Standardized Precipitation Index (SPI-3). SPI-3 was selected to represent short-term seasonal precipitation anomalies relevant to agricultural water availability, and three historical years were used as representative scenarios: Dry (2020), Normal (2018), and Wet (2017). Fourteen irrigation treatments, including farmer practice and phenologically targeted Late-Season Event (L.S.E.) deficit strategies, were simulated using FAO AquaCrop v7.1. Irrigation timing was more important than seasonal irrigation volume alone. Under Normal conditions, an L.S.E. strategy that redistributed irrigation toward four late fruit-development events (T4.2) maintained yield stability comparable to farmer practice (CV < 2%), increased mature melon yield by 12.7% relative to farmer practice, and reduced applied irrigation by 19–21%. Under Dry conditions, a lower-volume L.S.E. strategy with the same late-season targeting (T4.3) maintained baseline-level yield while reducing applied irrigation by 28–31%. The resulting SPI-based decision matrix is forecast-compatible but not operationally validated; field validation, long-term climate testing, and forecast-skill evaluation remain necessary before deployment. Full article

(This article belongs to the Section Water Use and Scarcity)

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43 pages, 1036 KB

Open AccessReview

Sustainable Fouling Management in Renewable-Energy-Driven Reverse Osmosis for Wastewater Reuse: Mechanisms, Mitigation Strategies, and Future Perspectives

by M. A. Uddin, M. G. Rasul, Abul Kalam Azad, M. M. Hasan and A. S. M. Sayem

Water 2026, 18(18), 2268; https://doi.org/10.3390/w18182268 - 11 Sep 2026

Abstract

Freshwater scarcity and rising wastewater generation have intensified global reliance on desalination and reuse, with reverse osmosis (RO) providing 65–70% of installed desalination capacity and achieving energy reductions from 15 kWhm⁻³ in the 1970s to 1.8–2.5 kWhm⁻³ today. However, fouling caused [...] Read more.

Freshwater scarcity and rising wastewater generation have intensified global reliance on desalination and reuse, with reverse osmosis (RO) providing 65–70% of installed desalination capacity and achieving energy reductions from 15 kWhm⁻³ in the 1970s to 1.8–2.5 kWhm⁻³ today. However, fouling caused by organics, inorganics, microorganisms, and colloids remains the major operational challenge, accounting for ≈25% of RO costs and over USD 15 billion annually. This review synthesises fouling mechanisms and mitigation strategies in renewable energy (RE)-driven RO wastewater-treatment systems, where intermittency exacerbates fouling through start–stop cycles and low-shear conditions. Analysis of recent literature highlights that mixed fouling reduces flux by 10–30%, increases transmembrane pressure, and deteriorates permeate quality. Advances in pretreatment (coagulation, MF/UF), antifouling membranes (hydrophilic coatings, zwitterionic surfaces), and cleaning protocols (osmotic backwashing, nanobubbles) have improved performance, yet complete prevention remains elusive. Persistent gaps include predictive fouling models, standardised performance metrics, and scalable green chemistries for silica and combined fouling control. Future directions emphasise integrated solutions combining advanced materials, AI-driven monitoring, and renewable-aware operational strategies, alongside circular economy approaches for brine valorisation. These innovations are critical for achieving sustainable, low-carbon RO systems for global water security. Full article

(This article belongs to the Special Issue Advances in Sustainable Water Resources Management and Water–Energy Nexus)

30 pages, 6942 KB

Open AccessSystematic Review

Standardization and Benchmarking of Datasets and Evaluation Protocols for Machine Learning-Based Water Leak Detection: A Systematic Review

by Elias Farah and Isam Shahrour

Water 2026, 18(18), 2267; https://doi.org/10.3390/w18182267 - 11 Sep 2026

Abstract

Despite the increasing use of machine learning (ML) and deep learning (DL) for leak detection and localization in water distribution networks (WDNs), standardized datasets, common evaluation protocols, and consistent reporting practices remain limited, thereby restricting meaningful comparison among studies. In this systematic review, [...] Read more.

Despite the increasing use of machine learning (ML) and deep learning (DL) for leak detection and localization in water distribution networks (WDNs), standardized datasets, common evaluation protocols, and consistent reporting practices remain limited, thereby restricting meaningful comparison among studies. In this systematic review, PRISMA 2020 was followed to examine the benchmarking practices, data sources, sensing modalities, and evaluation strategies adopted in recent ML/DL-based leak-detection studies. Scopus was searched in April 2026, and 90 studies were included after screening against predefined eligibility criteria. Substantial variation was identified in dataset generation, model validation, and performance assessment. Thirty studies were assigned to the public-benchmark category, but only 23 used one of the three named water-leak resources tracked in this review; the remainder included topology-only reuse or other heterogeneous public resources. Pressure measurements were the dominant sensing modality, whereas flow, transient, and smart-meter data remained comparatively underexplored. Considerable inconsistency was also observed in performance metrics, validation procedures, and leak-scenario definitions, preventing fair comparison of detection and localization performance. Based on these findings, a practical reporting framework is proposed to improve transparency, reproducibility, and comparability. The principal recommendations include standardized dataset documentation, consistent evaluation protocols, expanded community benchmarks, uncertainty quantification, and model interpretability to support operational deployment. Full article

(This article belongs to the Special Issue Review Papers of Urban Water Management 2026)

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29 pages, 25758 KB

Open AccessArticle

Generation and Propagation Mechanisms of Leak-Induced Acoustic Waves in Water Pipelines Based on Coupled Fluid–Acoustic–Structural Responses

by Jiaonv Gan, Tianwen Pan, Yun-Jie Li, Zhiguo Tao, Zhizhong Zhou, Yaodong Zhang and Ling Zhou

Water 2026, 18(18), 2266; https://doi.org/10.3390/w18182266 - 11 Sep 2026

Abstract

Pipeline leakage acoustic signals are governed by the coupled effects of leakage excitation, acoustic–structural interaction, and propagation filtering, while the physical relationship between leakage conditions and measurable spectral characteristics remains insufficiently understood. In this study, a three-dimensional numerical framework combining leakage flow simulation, [...] Read more.

Pipeline leakage acoustic signals are governed by the coupled effects of leakage excitation, acoustic–structural interaction, and propagation filtering, while the physical relationship between leakage conditions and measurable spectral characteristics remains insufficiently understood. In this study, a three-dimensional numerical framework combining leakage flow simulation, acoustic–structural coupling analysis, and dual-hydrophone experiments was developed to investigate the formation and propagation mechanisms of leak-induced acoustic signals in water pipelines. The investigated steel pipeline had an inner diameter of 50 mm, with leak hole diameters of 1–4 mm (d/D = 0.02–0.08). The leakage flow remained turbulent, with a Reynolds number of approximately 7.0 × 10³, and the dominant acoustic response corresponded to Strouhal numbers of 1.7 × 10⁻³–2.1 × 10⁻². The results show that supply pressure mainly controls leakage excitation intensity through hydraulic power, whereas the leak hole diameter primarily modifies the frequency-band distribution. A narrow-band pipe wall vibration enhancement was identified near 280 Hz, which was associated with the coupled pipe–water mode at 278.97 Hz. Experimental measurements further demonstrated strong propagation-induced frequency filtering, with more than 97% of far-field signal energy retained within the 20–250 Hz band. These findings establish a continuous relationship between hydraulic leakage input, local acoustic–structural response, and measurable leakage spectra, providing physical insights into acoustic-based pipeline leak detection. Full article

(This article belongs to the Section Hydraulics and Hydrodynamics)

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18 pages, 10162 KB

Open AccessArticle

A Machine Learning Framework for Regional Identification of Landslide and Debris Flow Hazard Chains

by Jingren Ma, Shunrong Duan, Ruihua Xiao, Song Li, Fuyun Guo, Fenghua Ma and Yan Zhao

Water 2026, 18(18), 2265; https://doi.org/10.3390/w18182265 - 11 Sep 2026

Abstract

Regional identification and risk mapping of landslide and debris flow hazard chains (LDHCs) are essential for disaster prevention and land-use planning in mountainous regions. This study proposes a machine learning framework for the regional identification and risk mapping of LDHCs in southern Gansu [...] Read more.

Regional identification and risk mapping of landslide and debris flow hazard chains (LDHCs) are essential for disaster prevention and land-use planning in mountainous regions. This study proposes a machine learning framework for the regional identification and risk mapping of LDHCs in southern Gansu Province, China. A spatial database was established using an inventory of 160 landslide hazard chains (LHCs) and 121 debris-flow hazard chains (DHCs), together with eight environmental conditioning factors. Considering the distinct geomorphological characteristics of the two hazard types, grid-based and watershed-based mapping units were adopted for landslide and debris-flow susceptibility mapping, respectively. Five ensemble learning algorithms were evaluated to identify the optimal susceptibility models. Hazard maps were subsequently generated by integrating susceptibility with earthquake- and extreme rainfall-triggering factors, and regional risk maps were produced by incorporating population and Gross Domestic Product (GDP) exposure data. The Random Forest model achieved the best performance for LHC susceptibility mapping, with an accuracy of 84.9% and an AUC of 0.914, whereas the Extra Trees model performed best for DHC susceptibility mapping, with an accuracy of 92.6% and an AUC of 0.978. The resulting risk maps indicate that the middle and lower reaches of the Bailong River, including Wudu, Zhouqu, Qin’an, and Tianshui, represent the highest-risk areas for LDHCs. The proposed framework provides an effective and transferable approach for regional identification and risk mapping of LDHCs, offering valuable support for disaster prevention, emergency planning, and land-use management in mountainous regions. Full article

(This article belongs to the Special Issue AI-Empowered Landslide Susceptibility Assessment with Soil–Water Interactions)

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23 pages, 4291 KB

Open AccessArticle

Integrated Assessment of Coastal Water Quality and Trophic Status Along the Aegean Coast of Türkiye

by Orkide Minareci, Ersin Minareci, Furkan Bilgiç and Ergün Taşkın

Water 2026, 18(18), 2264; https://doi.org/10.3390/w18182264 - 11 Sep 2026

Abstract

Increasing anthropogenic pressures and nutrient inputs threaten the ecological status of coastal ecosystems along the Aegean coast of Türkiye. This study evaluated coastal water quality and trophic conditions using physicochemical parameters, nutrient concentrations, chlorophyll-a, national eutrophication criteria, the Trophic Index (TRIX), [...] Read more.

Increasing anthropogenic pressures and nutrient inputs threaten the ecological status of coastal ecosystems along the Aegean coast of Türkiye. This study evaluated coastal water quality and trophic conditions using physicochemical parameters, nutrient concentrations, chlorophyll-a, national eutrophication criteria, the Trophic Index (TRIX), and multivariate statistical analyses across 25 coastal stations monitored during 2022–2023. While basic physicochemical variables showed limited spatial variability, nutrient concentrations, chlorophyll-a, and TRIX values exhibited pronounced spatial differences among stations (p < 0.05), identifying Bostanlı (Inner İzmir Bay) as the principal eutrophication hotspot. Principal Component Analysis (PCA) revealed two dominant environmental gradients: a Nutrient Enrichment Gradient associated with total phosphorus (TP), ammonium, nitrite, and nitrate nitrogen (32.6% of total variance), and a Hydrographic–Thermal Gradient associated with salinity, conductivity, total dissolved solids, temperature, and dissolved oxygen (30.4%). Hierarchical and k-means clustering analyses further distinguished environmentally coherent coastal sectors and confirmed the clear separation of Bostanlı from the remaining sampling stations. The close agreement among nutrient distributions, chlorophyll-a concentrations, TRIX values, and multivariate analyses indicates that localized anthropogenic nutrient enrichment, rather than basin-scale hydrographic variability, drives trophic differentiation along the coast. These findings demonstrate the value of integrated monitoring approaches combining physicochemical, trophic, and multivariate indicators for identifying eutrophication hotspots and supporting ecosystem-based management in Mediterranean coastal ecosystems. Full article

(This article belongs to the Section Oceans and Coastal Zones)

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20 pages, 36281 KB

Open AccessArticle

by Yinzhe Yang, Dongdong Yan, Guan Chen, Ranwei Ding and Yan Wang

Water 2026, 18(18), 2263; https://doi.org/10.3390/w18182263 - 11 Sep 2026

Abstract

This study investigates the deformation evolution and stability degradation of a rainfall-reactivated loess–carbonaceous slate landslide in Luoda Town, Gansu Province, China. Field investigation, borehole logging, water-content-controlled direct shear tests, GNSS monitoring, rainfall analysis, and FLAC3D modeling were integrated to examine the weak shear [...] Read more.

This study investigates the deformation evolution and stability degradation of a rainfall-reactivated loess–carbonaceous slate landslide in Luoda Town, Gansu Province, China. Field investigation, borehole logging, water-content-controlled direct shear tests, GNSS monitoring, rainfall analysis, and FLAC3D modeling were integrated to examine the weak shear zone developed near the lithological contact. The landslide comprises loessial–colluvial deposits overlying weathered carbonaceous slate and exhibits a progressive rotational-slide pattern characterized by rear tensile cracking, middle translational movement, and frontal compressional bulging. As specimen water content increased from 17% to 24%, the cohesion and internal friction angle of the shear-zone soil decreased from 22.6 to 14.5 kPa and from 17.0° to 9.7°, respectively. GNSS monitoring identified steady creep, accelerating creep, and rapid failure, with G1 accelerating earlier than G2. The strongest observed rainfall–displacement correlations occurred at antecedent windows of 48 h for G1 and 120 h for G2, indicating spatially variable rainfall responses. Across the corresponding laboratory-derived strength states, the calculated factor of safety decreased from 1.512 to 0.898. These results indicate that the 2021 reactivation was controlled by a weak shear-zone layer near the lithological contact, with rainfall-related wetting likely contributing to strength degradation and progressive deformation. Full article

(This article belongs to the Special Issue Water-Triggered Geo-Hazards in Underground and Geotechnical Engineering: Mechanisms, Early Warning and Sustainable Mitigation)

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27 pages, 30861 KB

Open AccessArticle

Assessing Whitewater Difficulty Using Specific Stream Power Based on Site-Scale UAV Measurements on the Deschutes River

by Dan J. Shelby

Water 2026, 18(18), 2262; https://doi.org/10.3390/w18182262 - 11 Sep 2026

Abstract

Boaters use class ratings to describe whitewater difficulty on a scale from I to VI, with ratings assigned and refined through the judgment of experienced boaters. These practices are effective, but the addition of physical measurements could improve their reliability and facilitate whitewater [...] Read more.

Boaters use class ratings to describe whitewater difficulty on a scale from I to VI, with ratings assigned and refined through the judgment of experienced boaters. These practices are effective, but the addition of physical measurements could improve their reliability and facilitate whitewater comparisons across different rivers and flows. This exploratory study considers specific stream power (SSP), a physics-based metric describing energy transfer in rivers, as an indicator of whitewater difficulty. Data were collected at eight study sites on the Upper Deschutes River in Oregon using a camera-mounted DJI Phantom 4 RTK quadcopter. Sites were assessed at one or two flows and whitewater difficulty ranged from Class I flatwater to Class V cascading rapids. Stream slope and width data were derived from a combination of unmanned aerial vehicle (UAV) photogrammetry and aerial light detection and ranging (LiDAR), and SSP was calculated for each site. Whitewater class ratings were strongly associated (df = 6, p < 0.05) with site average SSP, r_s= 0.95, 95% CI [0.72, 1.00], site average slope, r_s = 0.95, 95% CI [0.72, 1.00], and within-site maximum SSP, r_s = 0.93, 95% CI [0.58, 1.00]. The within-site maximum slope, minimum width, and constriction ratio had significant but smaller relationships. Physical assessments of whitewater conditions may help support management decisions in dam removal, hydropower relicensing, or other instream flow negotiations. Full article

(This article belongs to the Special Issue River Channel Hydraulics, Fluvial Dynamics and Re-Opening Floodplains)

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28 pages, 6419 KB

Open AccessArticle

Purpose-Specific Conditioning of Continuous Radar Surface Velocity Records for Real-Time Monitoring and Retrospective Analysis

by Chanwoo Kim, Sanguk Cho, Hyeokjin Lim, Youngyong Ryu, Dongheon Oh, Yeongil Lee and Jaehyun Song

Water 2026, 18(18), 2261; https://doi.org/10.3390/w18182261 - 11 Sep 2026

Abstract

Continuous radar surface velocity records require purpose-specific conditioning because the temporal information available for processing differs between real-time monitoring and retrospective analysis. Short-period fluctuations and spikes can obscure stage-related flow responses under both settings. We evaluated a stepwise quality control framework that separates [...] Read more.

Continuous radar surface velocity records require purpose-specific conditioning because the temporal information available for processing differs between real-time monitoring and retrospective analysis. Short-period fluctuations and spikes can obscure stage-related flow responses under both settings. We evaluated a stepwise quality control framework that separates these two processing roles using 10 min records from six monitoring sites in South Korea. Causal preprocessing combined Huber-weighted recursive least squares, fuzzy correction, and a trailing Hampel filter to generate a provisional series. Retrospective processing applied a centered Hampel filter followed by criterion-based zero-phase moving average smoothing. Causal preprocessing reduced the standard deviation of successive velocity increments by 14.1–53.4%, with a further 1.4–7.8% reduction observed after centered filtering. A three-point moving-average window was selected at all sites, retaining 98.4–99.9% of the peak velocity and a velocity sum ratio of 1.000. Three sites satisfied all the selection criteria, two satisfied the shape retention criteria, and one was retained under a flagged fallback because the increment variance and slope criteria were not met. Postprocessed index-velocity-method-derived hydrographs showed a lower RMSE and higher R² when compared to the operational stage–discharge benchmark at all sites, while signed biases varied by site. The proposed framework provides a traceable pathway from observation availability and data status to shape assessment and downstream discharge evaluation. Full article

(This article belongs to the Section Hydrology)

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22 pages, 5635 KB

Open AccessArticle

A Landscape Limnology Framework for Lake Typology: Refining Geochemical Baselines and Heavy Metal Assessment in the Middle and Lower Yangtze River Plain

by Shengjia Feng, Yan Li, Zhiwei Xia, Mengjia Luo, Yingqi Yao, Jing Chen, Aiying Liu, Xiuyun Chen and Giri Raj Kattel

Water 2026, 18(18), 2260; https://doi.org/10.3390/w18182260 - 11 Sep 2026

Abstract

Sedimentary heavy metal background levels vary among lake types in heterogeneous floodplain lake systems worldwide. Uniform regional values may therefore bias contamination assessment. We developed a source–process–sink framework linking lake typology to type-specific geochemical baselines for 91 lakes in the Middle and Lower [...] Read more.

Sedimentary heavy metal background levels vary among lake types in heterogeneous floodplain lake systems worldwide. Uniform regional values may therefore bias contamination assessment. We developed a source–process–sink framework linking lake typology to type-specific geochemical baselines for 91 lakes in the Middle and Lower Yangtze River Plain. Ward hierarchical clustering of 24 indicators spanning freshwater, terrestrial and human landscapes plus two spatial metrics identified four groups: shallow river network (Group I, n = 28), small peri-urban lakes (Group II, n = 20), hill–plain transition lakes (Group III, n = 23) and large river-connected lakes (Group IV, n = 20). Pre-industrial layers of four dated representative sediment cores yielded type-specific Cr, Cu, Pb and Zn baselines. Surface heavy metal enrichment and contamination were assessed using the single-factor pollution index (Pi) and geo-accumulation index (Igeo), respectively. Baselines differed markedly with lake groups, with maximum-to-minimum ratios of 2.18, 3.82, 2.39 and 3.64 for Cr, Cu, Pb and Zn, respectively. Greater catchment development triggered the strongest multi-metal enrichment in Group I: P_i values for all four metals exceeded 1 in 92.9% of lakes. Igeo values for Pb exceeded 0 in 85.0% and 78.3% of Group II and III lakes, respectively. Group IV showed the weakest overall enrichment under lower catchment development. Although P_i values for Pb exceeded 1 in 85.0% of its lakes, no lake reached moderate or higher contamination for any metal. Coupling lake typology with type-specific baselines supports enrichment assessment and differentiated management at the lake group scale in China’s Middle and Lower Yangtze River Plain. Full article

(This article belongs to the Special Issue Geochemistry and Removal of Heavy Metals and Other Pollutants in Water, 2nd Edition)

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21 pages, 3515 KB

Open AccessArticle

Machine-Learning Modelling of Normalized Water-Pipe Failure Rates Using Random Forest and XGBoost: A Clustered Case Study from Malatya, Türkiye

by Tarkan Koca, Mehmet Bilal Er and Nagehan Ilhan

Water 2026, 18(18), 2259; https://doi.org/10.3390/w18182259 - 11 Sep 2026

Abstract

Water-distribution-system failure records often contain nonlinear, heterogeneous patterns that are difficult to summarize with conventional statistics alone. This study evaluates how Random Forest and XGBoost reconstruct normalized water-pipe failure-rate patterns in a field-derived Malatya, Türkiye dataset and uses distributional diagnostics, empirical ranking, and [...] Read more.

Water-distribution-system failure records often contain nonlinear, heterogeneous patterns that are difficult to summarize with conventional statistics alone. This study evaluates how Random Forest and XGBoost reconstruct normalized water-pipe failure-rate patterns in a field-derived Malatya, Türkiye dataset and uses distributional diagnostics, empirical ranking, and multi-method explainability to identify stable model drivers. The analysis includes 1231 records from nine source-defined clusters and four modelling inputs: cluster identifier, normalized pipe length, normalized pipe diameter, and normalized second-failure age. Because the models were fitted to and evaluated on the same records, the reported performance represents apparent/full-data goodness of fit rather than independent predictive validation. XGBoost provided the closer reconstruction (R² = 0.9189 versus 0.8523 for Random Forest; MAE = 0.0230 versus 0.0321). Across permutation importance, SHAP attribution, and variable-removal sensitivity, pipe length and second-failure age emerged as the most robust model-relevant features, whereas native importance was more method-dependent. The framework demonstrates how ensemble models can support transparent retrospective database screening and pattern interpretation while maintaining a clear boundary between historical fit and forecasting. Temporal, spatial, pipe-grouped, or external validation is required before prospective decision use. Full article

(This article belongs to the Section New Sensors, New Technologies and Machine Learning in Water Sciences)

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21 pages, 4387 KB

Open AccessArticle

Nitrate-Dominated Multi-Receptor Environmental Risks and PMF Source Apportionment in Shallow and Deep Groundwater of the Baiyangdian Lake Basin, North China Plain: Implications for Multi-Scale Management

by Ruihui Chen, Bin Hu, Xiaoyu Liu, Yuanyuan Li, Linying Cai, Qiang Hu, Qiaochu Han and Ganghui Zhu

Water 2026, 18(18), 2258; https://doi.org/10.3390/w18182258 - 11 Sep 2026

Abstract

Groundwater is the main source of drinking, irrigation, and ecological water in the Baiyangdian Lake Basin (BLB), but growing human activity has raised concerns about its quality. This study assesses major ion geochemistry and multi-receptor environmental risks across the BLB using 1113 groundwater [...] Read more.

Groundwater is the main source of drinking, irrigation, and ecological water in the Baiyangdian Lake Basin (BLB), but growing human activity has raised concerns about its quality. This study assesses major ion geochemistry and multi-receptor environmental risks across the BLB using 1113 groundwater samples (983 shallow, 130 deep). Risks to drinking water supply, agricultural irrigation, and wetland health are evaluated within a single framework, and Positive Matrix Factorization (PMF) is used for quantitative source apportionment. Shallow groundwater shows markedly higher dissolved solids and nitrate than deep groundwater, with 20.7% of shallow samples exceeding the WHO nitrate guideline. Multi-receptor assessment finds that 41.2% of shallow wells pose risk to at least one endpoint and 12.5% to two or more simultaneously. PMF identifies three sources—geogenic weathering, agricultural nitrate, and wastewater discharge—with nitrate as the largest single contributor to water quality impairment. The findings point to an urgent need for targeted agricultural non-point source control and provide a basis for nested, multi-scale groundwater management in the BLB and similar intensively exploited alluvial aquifers. Full article

(This article belongs to the Special Issue Source Tracing, Safety Early Warning and Risk Control of Emerging Pollutants in Soil-Groundwater System)

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