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Estimating the Importance of Floating Surface Material to the Total Phosphorus Transport in Silver Creek, Wisconsin Using Particle Image Velocimetry
How to Analyze Censored Concentration Data Using Modern Statistical Methods of Survival Analysis: Background and Nonparametric Methods
From Artificial Structures to Biogenic Habitats: Two-Year Ecological Responses to Eco-Engineered Reefs in a Tourism-Dominated Adriatic Sandy Coast
The Reliability of SBR System During COVID-19 and Its Impact on Water Quality of a Small Flysch River in Protected Areas

Journal Description

Water

Water is a peer-reviewed, open access journal on water science and technology, including the ecology and management of water resources, published semimonthly online by MDPI. Water collaborates with the Stockholm International Water Institute (SIWI). In addition, the American Institute of Hydrology (AIH), Polish Limnological Society (PLS) and Japanese Society of Physical Hydrology (JSPH) are affiliated with Water and their members receive a discount on the article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, GEOBASE, GeoRef, PubAg, AGRIS, CAPlus / SciFinder, Inspec, and other databases.
Journal Rank: JCR - Q2 (Water Resources) / CiteScore - Q1 (Aquatic Science)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.7 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the first half of 2026).
Recognition of Reviewers: Reviewers whose reports are timely and of high quality receive an APC discount voucher for a future publication in an MDPI journal. Become a reviewer.
Companion journals for Water include: Hydropower and Freshwater.
Journal Clusters of Water Resources: Water, Journal of Marine Science and Engineering, Hydrology, Resources, Oceans, Limnological Review, Coasts and Hydropower.

Impact Factor: 3.5 (2025); 5-Year Impact Factor: 3.6 (2025)

Imprint Information Journal Flyer Open Access ISSN: 2073-4441

Latest Articles

23 pages, 2158 KB

Open AccessArticle

Multi-Evidence Identification of Distinct Recharge–Discharge Systems Across the F2 Fault: A Case Study of the Chunjingwa Closed Coal Mine, China

by Yiming Wang, Fenggang Dai, Hongchao Yao, Aihua Wei, Rui Wang, Chaoyue Wang and Wei Zhang

Water 2026, 18(18), 2228; https://doi.org/10.3390/w18182228 - 8 Sep 2026

Determining the recharge sources of adjacent old-working water outlets and their hydraulic connections is essential for zoned pollution control in closed coal mines. This study examined two outlets, S1 and S2, 174 m apart in the Chunjingwa closed coal mine area, Shanxi Province, [...] Read more.

Determining the recharge sources of adjacent old-working water outlets and their hydraulic connections is essential for zoned pollution control in closed coal mines. This study examined two outlets, S1 and S2, 174 m apart in the Chunjingwa closed coal mine area, Shanxi Province, China. Discharge dynamics, hydrochemistry, hydrogen–oxygen stable isotopes, and a goaf pumping test were combined. During natural monitoring, discharges at S1 and S2 ranged from 1.172–1.958 and 1.012–8.630 m³/h, respectively, with the variation at S2 (7.618 m³/h) being 9.7 times that at S1 (0.786 m³/h). Most contamination indicators had higher median exceedance levels at S2; for SO₄²⁻, the median was 14.6 at S2 versus 8.4 at S1. Isotopically, S1 overlapped with ZK2 and ZK3, whereas S2 was distinct. During the 27-day pumping test, S1 discharge fell by about 91%, whereas S2 showed no clear response. The evidence identifies two distinct recharge–discharge systems on opposite sides of F2 at the tested scale. F2 is interpreted as a structural divide, not a uniformly impermeable fault. S1 is regulated mainly by goaf-water storage to the south, whereas S2 is dominated by shallow-catchment, rapid-infiltration recharge to the north. The framework provides a practical alternative to artificial tracer tests for outlet-scale source identification and zoned remediation. Full article

(This article belongs to the Special Issue Research on Water Resources Affected by Acid Mine Drainage)

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33 pages, 5680 KB

Open AccessArticle

Econometric Assessment of Territorial Sustainability in Community Irrigation Systems in Azuay Province, Ecuador

by Javier Ávila-Larrea and Francesc Hernández-Sancho

Water 2026, 18(18), 2227; https://doi.org/10.3390/w18182227 - 8 Sep 2026

Community irrigation systems integrate water, infrastructure, governance, environmental, and market conditions, yet comparative evidence on their multidimensional sustainability remains limited in the Andes. This cross-sectional secondary analysis developed a bounded 10-indicator formative territorial sustainability index for 241 community irrigation systems in Azuay Province, [...] Read more.

Community irrigation systems integrate water, infrastructure, governance, environmental, and market conditions, yet comparative evidence on their multidimensional sustainability remains limited in the Andes. This cross-sectional secondary analysis developed a bounded 10-indicator formative territorial sustainability index for 241 community irrigation systems in Azuay Province, Ecuador, and used multivariable models to examine conditional associations with system characteristics. Physical infrastructure condition and organizational activity planning showed the clearest positive coefficient patterns in the global model under nominal inference, but neither remained below 0.05 after Benjamini–Hochberg adjustment; the global model explained 12.1% of between-system variation. In the original water-domain model, reported source-area contamination was associated with a lower score (B = −14.23, 95% CI: −22.00 to −6.47; BH-adjusted p = 0.016). When perceived water quality was removed from the domain outcome, the contamination coefficient remained negative, although multiplicity-adjusted support was attenuated. The aggregate–domain contrast illustrates how a multidimensional composite score can obscure a domain-specific environmental constraint. The framework extends descriptive inventory information into a comparative, sensitivity-tested territorial assessment, but the non-probability, questionnaire-based design does not support causal or population-wide inference. Direct hydrological and water-quality monitoring should complement future applications. Full article

(This article belongs to the Section Water Resources Management, Policy and Governance)

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19 pages, 1760 KB

Open AccessArticle

Baseline Assessment of Microplastic Occurrence in Groundwater from Alluvial and Karst Aquifers

by Elvira Colmenarejo Calero, Manca Kovač Viršek and Nina Mali

Water 2026, 18(18), 2226; https://doi.org/10.3390/w18182226 - 8 Sep 2026

The occurrence of microplastics in groundwater remains insufficiently documented, particularly across contrasting hydrogeological settings. This study provides the first baseline assessment of microplastic contamination in Slovenian groundwater by comparing its occurrence in urban alluvial and karst aquifers, which represent important drinking-water resources in [...] Read more.

The occurrence of microplastics in groundwater remains insufficiently documented, particularly across contrasting hydrogeological settings. This study provides the first baseline assessment of microplastic contamination in Slovenian groundwater by comparing its occurrence in urban alluvial and karst aquifers, which represent important drinking-water resources in Slovenia. Groundwater was sampled at 19 sites (8 alluvial, 11 karst) using a large-volume in situ filtration system, with three 1 m³ replicates collected at each site. Polymer composition was confirmed using attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR). Microplastics were detected at nearly all sampling sites, with concentrations ranging from 0 to 13.33 particles/m³ (mean: 4.65 particles/m³). Although no statistically significant differences in microplastic occurrence were observed between aquifer types at the available sample size, average concentrations were slightly higher in alluvial aquifers, whereas karst systems showed greater site-level variability. Differences in particle characteristics were observed between aquifer types, with fragments predominating in alluvial aquifers, and fibres and larger particles proportionally more abundant in karst springs. These patterns are consistent with differences in groundwater flow and filtration processes between karst and alluvial aquifers. Polyethylene and polyethylene terephthalate were the most frequently identified polymers across all samples. Correlations between microplastic concentrations and selected water-quality parameters, particularly temperature and nitrate, were identified. Overall, the results emphasise the relevance of hydrogeological context for interpreting microplastic occurrence in groundwater and provide baseline information to support future monitoring efforts. Full article

(This article belongs to the Section Hydrogeology)

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15 pages, 10596 KB

Open AccessArticle

Attractiveness of Different Artificial Light Sources to Aquatic and Semiaquatic Beetles (Coleoptera) and Its Implications for Sampling and Conservation

by Máté Bernát, Tibor Magura, Ádám Egri, Antal Nagy, Szabolcs Szanyi and Kálmán Szanyi

Water 2026, 18(18), 2225; https://doi.org/10.3390/w18182225 - 8 Sep 2026

Aquatic and semiaquatic beetles are important components of wetland ecosystems serving as bioindicators, predators, and vital prey resources. Numerous studies examined how their dispersal flights are influenced by biotic and abiotic factors, but the wavelength-specific attraction of different artificial light sources remains understudied. [...] Read more.

Aquatic and semiaquatic beetles are important components of wetland ecosystems serving as bioindicators, predators, and vital prey resources. Numerous studies examined how their dispersal flights are influenced by biotic and abiotic factors, but the wavelength-specific attraction of different artificial light sources remains understudied. This study evaluated the attractiveness of three portable light traps equipped with different light sources emitting light with different wavelength ranges: a compact light tube, an LED light source, and a UV lamp. A total of 6568 individuals representing 54 species were captured. Total nightly abundance correlated positively with mean nocturnal temperature. Light source type significantly affected both species richness and abundance, with the broad-spectrum compact light tube outperforming the other tested light sources both in abundance and species richness. For Dytiscidae and Hydrophilidae, the compact light tube was significantly more attractive only than the blue LED source, while its attractiveness did not differ significantly from that of the UV lamp, demonstrating family-level differences in attraction. The light source-dependent attraction remained stable across phenological periods. Our findings suggest that while broad-spectrum lights improve the efficiency of faunistic surveys and support conservation-oriented monitoring of aquatic beetle assemblages, their integration into public lighting networks near vulnerable wetlands must be strictly avoided to prevent severe ecological trapping. Full article

(This article belongs to the Special Issue Biodiversity of Freshwater Ecosystems: Monitoring and Conservation, 2nd Edition)

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16 pages, 5458 KB

Open AccessArticle

Response Characteristics of Karst Water Level to Precipitation and Hydrological Circulation Patterns in the Jinan Spring Basin, Northern China

by Dalu Yu, Huan Qi, Qingyu Xu, Caiping Hu, Guomeng Guan, Yan Li and Liting Xing

Water 2026, 18(18), 2224; https://doi.org/10.3390/w18182224 - 8 Sep 2026

Karst groundwater systems are characterized by highly heterogeneous flow networks, resulting in complex responses of groundwater levels to precipitation variability. The Jinan Spring Basin, one of the most representative karst spring systems in northern China, has experienced substantial changes in spring discharge due [...] Read more.

Karst groundwater systems are characterized by highly heterogeneous flow networks, resulting in complex responses of groundwater levels to precipitation variability. The Jinan Spring Basin, one of the most representative karst spring systems in northern China, has experienced substantial changes in spring discharge due to variations in precipitation, groundwater exploitation, and hydrogeological conditions. However, the temporal scales at which precipitation signals control groundwater-level fluctuations and the mechanisms governing their transmission within the karst aquifer remain poorly understood. In this study, daily precipitation data from 30 meteorological stations and groundwater-level records at Baotu Spring during 2016–2018 were analyzed using global wavelet spectrum (GWS) and wavelet transform coherence (WTC) approaches. The dominant precipitation cycles, scale-dependent precipitation–groundwater relationships, and phase-derived groundwater response lags were quantified to reveal the hydrological response characteristics of the Jinan karst system. Three prominent precipitation periods were identified at 17.37, 29.22, and 330.57 days. Groundwater responses presented clear temporal-scale dependence, with short-period signals showing rapid but localized responses, while intermediate and long-period signals demonstrated stronger and more persistent coherence. The percentage of significant coherence area (PASC) increased from 23.50% at the 0–17.37 day scale to 64.28% at the 29.22–330.57 day scale, indicating that accumulated precipitation rather than individual rainfall events exerts the dominant control on groundwater-level variations. The spatial distribution of response lags revealed that rapid responses (17.37 days) mainly occurred in the southern recharge areas, reflecting preferential recharge through well-developed karst conduits. Intermediate responses (29.22 days) showed a progressive increase in lag time from south to north, indicating the influence of regional groundwater flow and aquifer storage. Long-period responses (330.57 days) were locally enhanced near major faults, suggesting structural controls on deeper groundwater circulation. This study reveals that precipitation signals in the Jinan Spring Basin are transmitted through multiple groundwater circulation pathways with distinct temporal characteristics. The identified multi-scale response patterns provide new insights into the internal structure and hydrological functioning of karst aquifers and offer scientific support for sustainable management of spring water resources. Full article

(This article belongs to the Special Issue Advances in Hydrochemistry and Hydrogeology)

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24 pages, 54323 KB

Open AccessArticle

Optimal Joint Operation of Multiple Gates and Pumping Stations in a Tidal River Network

by Jijiang Chen, Qianshun Xu, Yi Hu, Jian Zhang, Yiqing Gong, Peipei Zhang, Jun Shi and Jingqiao Mao

Water 2026, 18(17), 2223; https://doi.org/10.3390/w18172223 - 7 Sep 2026

Coastal plain river networks require coordinated gate-pump operation when typhoon rainfall coincides with tidal backwater. This study implemented a SWMM–NSGA-II simulation–optimization framework for the northern drainage river network of Yuyao City, China, using Typhoon In-Fa as a representative event. The framework links distributed [...] Read more.

Coastal plain river networks require coordinated gate-pump operation when typhoon rainfall coincides with tidal backwater. This study implemented a SWMM–NSGA-II simulation–optimization framework for the northern drainage river network of Yuyao City, China, using Typhoon In-Fa as a representative event. The framework links distributed rainfall-runoff simulation, river-network hydrodynamics, tide-affected outfall pumping, and coordinated operation of coastal and internal hydraulic structures. Water-level bounds at Linshan, Dongpu, and Mazhu were imposed as full-process hard constraints, and three objectives were minimized within the feasible domain: water-level penalty, pumping energy consumption, and gate-pump switching count. The final Pareto set contained 36 feasible non-dominated schedules, all with zero constraint violation (

C V = 0

). The discrete quadratic water-level exceedance penalty index at the three control stations, the total energy consumption of the three controlled pumping stations over the 72 h horizon, and the cumulative number of binary state changes of the controlled sluice stations and individual pump units ranged from 39.29 to 51.59 m², 914.15 to 1364.00 GJ, and 184 to 256, respectively. The equal-weight compromise schedule yielded 42.65 m², 967.82 GJ, and 215 state changes. Compared with the historical baseline operation without the newly commissioned Taojialu and Leanhu pumping stations, this schedule reduced the 72 h accumulated exceedance above the target levels at the three control stations from 25.43 to 8.11 m·h, corresponding to a reduction of 68.1%. The results indicate distinct hydraulic roles for coastal and internal structures: coastal outfalls mainly control tide-window drainage and forced-discharge capacity, whereas internal gates and pumping stations regulate water redistribution and the transmission of water-level reductions. Full article

(This article belongs to the Section Water Resources Management, Policy and Governance)

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20 pages, 2768 KB

Open AccessArticle

Na₂S Enhancement of Pyrrhotite/Sulfur Fixed Bed Reactors: Synergistic Denitrification Mechanism and Microbial Community Restructuring for Low-Alkalinity Advanced Nitrogen Removal

by Yiran Wang, Xiaoqiang Zhu, Yongyou Hu, Donghui Liang, Guobin Wang and Jieyun Xie

Water 2026, 18(17), 2222; https://doi.org/10.3390/w18172222 - 7 Sep 2026

Advanced nitrogen removal from secondary effluent of municipal wastewater treatment plants (WWTPs) faces substantial technical challenges. This study investigated the denitrification performance and underlying mechanisms of a pyrrhotite/sulfur-coupled autotrophic denitrifying biological filter (PS-CFBR) enhanced by Na₂S addition. This study investigated the [...] Read more.

Advanced nitrogen removal from secondary effluent of municipal wastewater treatment plants (WWTPs) faces substantial technical challenges. This study investigated the denitrification performance and underlying mechanisms of a pyrrhotite/sulfur-coupled autotrophic denitrifying biological filter (PS-CFBR) enhanced by Na₂S addition. This study investigated the denitrification performance of aPS-CFBR enhanced by Na₂S addition, operated at a controlled hydraulic retention time (HRT) of 6 h with varying alkalinity dosages (149–446 mg/L as CaCO₃) and influent sulfide-to-nitrogen (S/N) ratios (0.54–1.62). The results indicated that Na₂S addition shortened the PS-CFBR start-up period by 10 days. At an HRT of 6 h and an alkalinity dosage of 149 mg/L as CaCO₃, the TN removal efficiency in the Na₂S-supplemented reactor (R1) was 22.87% higher than that in the control (CK). The corresponding first-order rate constant (k) in R1 was 2.65-fold greater than in CK. Under low-alkalinity conditions (149 mg/L as CaCO₃), the effective influent S/N ratio was determined to be 0.54–1.08. The TN removal efficiency and rate constant (k) in R1 were 22.87% and 2.65-fold higher than those in CK at an S/N ratio of 0.54, respectively. The effluent pH remained stable at 7.30, SO₄²⁻ production was only 5.16 mg/L higher than that in CK, and alkalinity consumption per mg of N removed was 2.15 mg/L lower than that in CK. X-ray photoelectron spectroscopy (XPS) and microbial community analyses revealed that Na₂S promoted Sₙ²⁻ formation on the pyrrhotite surface and enriched denitrifying (Herbaspirillum, Flavobacterium, Sulfurimicrobium), iron-oxidizing (Pseudoxanthomonas), and iron-reducing (Clostridium) bacteria. RT-qPCR further indicated that Na₂S addition increased the abundances of denitrification functional genes (narG, nirS, nirK, norB, and nosZ). These findings provide valuable insights into the development of advanced denitrification technologies for secondary effluent from municipal wastewater treatment plants. Full article

(This article belongs to the Section Wastewater Treatment and Reuse)

22 pages, 8094 KB

Open AccessArticle

The Water-Sediment Regulation Scheme Drives Phytoplankton Dynamics in the Yellow River Estuary

by Sihan Zhang, Xiaomin Zhang, Ruiting Shen, Shihao Chen, Wenqi Qiao, Fan Li, Yanjie Gao and Jingjing Zhang

Water 2026, 18(17), 2221; https://doi.org/10.3390/w18172221 - 7 Sep 2026

The Water-Sediment Regulation Scheme (WSRS) of the Yellow River is a large-scale anthropogenic intervention designed to alleviate downstream channel siltation, yet its long-term ecological effects on estuarine phytoplankton communities remain contentious. Based on a 12-year (2011–2022) continuous monitoring dataset from the Yellow River [...] Read more.

The Water-Sediment Regulation Scheme (WSRS) of the Yellow River is a large-scale anthropogenic intervention designed to alleviate downstream channel siltation, yet its long-term ecological effects on estuarine phytoplankton communities remain contentious. Based on a 12-year (2011–2022) continuous monitoring dataset from the Yellow River Estuary (YRE), including two consecutive non-WSRS years (2016–2017) as a baseline, this study systematically analyzed the impacts of the WSRS on phytoplankton abundance, community structure, and dominant species succession, as well as the underlying regulatory mechanisms. Our results demonstrate that the WSRS significantly reduced salinity (p < 0.001) and delivered pulsed loads of dissolved inorganic nitrogen (DIN) and silicate (DSi), accounting for 11.64–40.63% of annual fluxes, while intensifying phosphorus-limited stoichiometric imbalance (DIN/DIP mean: 361.67 ± 124.57). Phytoplankton abundance exhibited extreme interannual fluctuations during WSRS years, with an annual mean abundance (957.08 × 10⁴ cells/m³) nearly six times higher than that of non-WSRS years (165.88 × 10⁴ cells/m³), and displayed a distinct nearshore inhibition and offshore stimulation spatial pattern, with the transition zone corresponding to the salinity front zone. At the community level, the WSRS did not alter the absolute dominance of diatoms but drove a directional succession from larger centric diatoms toward small, needle-shaped, low-salinity-tolerant species (Synedra sp., Nitzschia delicatissima); this shift is co-regulated by bottom-up environmental filtering (low salinity and high nutrients) and top-down size-selective grazing. This study reveals the net enhancing effect of the WSRS as a pulsed anthropogenic disturbance on estuarine phytoplankton communities and its dual regulatory mechanisms, providing long-term empirical evidence and a theoretical basis for ecological impact assessment and the adaptive management of large-scale river regulation projects. Full article

(This article belongs to the Section Biodiversity and Functionality of Aquatic Ecosystems)

28 pages, 3719 KB

Open AccessArticle

An Interpretable Ensemble Learning Framework for Leakage Localization in Urban Water Distribution Networks

by Qingfu Li and Ao Chen

Water 2026, 18(17), 2220; https://doi.org/10.3390/w18172220 - 7 Sep 2026

Rapid and accurate leakage localization in urban water distribution networks (WDNs) is vital for reducing non-revenue water and supporting sustainable water resource management. This study develops a spatial–hydraulic coupled and interpretable machine learning framework for zonal leakage localization under operational uncertainties. The WDN [...] Read more.

Rapid and accurate leakage localization in urban water distribution networks (WDNs) is vital for reducing non-revenue water and supporting sustainable water resource management. This study develops a spatial–hydraulic coupled and interpretable machine learning framework for zonal leakage localization under operational uncertainties. The WDN is first partitioned into zones, followed by leakage-region prediction using a Stacking ensemble integrating Random Forest, XGBoost, and MLP. SHAP analysis is employed to characterize the contribution of individual monitoring nodes to regional classification. The proposed Stacking framework achieves an overall localization accuracy of 0.9667 and a Macro-F1 score of 0.9666, with relatively stable diagnostic performance under pressure noise. Global SHAP analysis identifies several monitoring nodes with substantial contributions to the overall classification, while the observed misclassifications are mainly concentrated between a small number of neighboring regions with similar feature representations. A further test on the Net3 network achieves an overall localization accuracy of 0.9812, providing additional evidence of the applicability of the framework across different network configurations. Overall, the results indicate that the proposed framework provides an accurate and interpretable approach for leakage localization in WDNs. Full article

(This article belongs to the Section Urban Water Management)

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31 pages, 14146 KB

Open AccessArticle

Spatiotemporal Prediction Algorithm for Groundwater Quality Under Multi-Indicator Coupling Constraints

by Baojie Fan, Kaoxian Zhou, Chuangming Yang, Tianjiao Yao, Zheng Peng and Xiaonan He

Water 2026, 18(17), 2219; https://doi.org/10.3390/w18172219 - 7 Sep 2026

Spatiotemporal prediction of groundwater quality is of great significance for regional water environmental safety assessment, pollution risk identification, and urban groundwater resource management. To address the difficulty of existing methods in simultaneously characterizing multi-indicator coupling relationships, temporal evolution processes, and spatial heterogeneity, this [...] Read more.

Spatiotemporal prediction of groundwater quality is of great significance for regional water environmental safety assessment, pollution risk identification, and urban groundwater resource management. To address the difficulty of existing methods in simultaneously characterizing multi-indicator coupling relationships, temporal evolution processes, and spatial heterogeneity, this study proposes a spatiotemporal groundwater quality prediction model under multi-indicator coupling constraints. First, indicators including dissolved oxygen, total nitrogen, electrical conductivity, dissolved organic carbon, pH, permanganate index, and total phosphorus are uniformly mapped into a risk space to construct an integrated groundwater quality risk index. Then, based on monthly groundwater monitoring data from Yiyang City during 2000–2023, continuous regional grid sequences are generated. In terms of model design, the Temporal Difference Interaction Module (TDIM) is introduced to enhance multi-scale temporal variation modeling, Region-Guided Feature Modulation (RGFM) is used to strengthen regional heterogeneity representation, and Spatiotemporal Boundary-Aware Loss (STB Loss) is adopted to maintain spatiotemporal boundary consistency. The experimental results show that the proposed method achieves a Structural Similarity Index Measure (SSIM) of

0.9814 \pm 0.0085

, a Peak Signal-to-Noise Ratio (PSNR) of

40.47 \pm 2.19

, a Mean Absolute Error (MAE) of

2.80 \times 10^{- 3} \pm 1.50 \times 10^{- 3}

, and a Root Mean Square Error (RMSE) of

9.70 \times 10^{- 3} \pm 2.69 \times 10^{- 3}

, outperforming comparison models overall and providing effective support for dynamic groundwater quality prediction and water environmental safety assessment. Full article

(This article belongs to the Special Issue Machine Learning Applications in the Water Domain, 2nd Edition)

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14 pages, 8147 KB

Open AccessArticle

Exogenous Denitrifying Bioaugmentation for Carbon-Efficient Nitrogen Removal in Low C/N Municipal Wastewater

by Xikun Zhu, Chunsheng Liu, Zehua Li and Xufa Ma

Water 2026, 18(17), 2218; https://doi.org/10.3390/w18172218 - 7 Sep 2026

Low carbon-to-nitrogen (C/N) municipal wastewater often limits heterotrophic denitrification and increases external carbon addition. This study evaluated an exogenous denitrifying bioaugmentation (DEN) strategy for improving nitrogen removal per unit of external carbon added. The screened inoculant reached approximately 2.5 × 10⁹ CFU/mL, [...] Read more.

Low carbon-to-nitrogen (C/N) municipal wastewater often limits heterotrophic denitrification and increases external carbon addition. This study evaluated an exogenous denitrifying bioaugmentation (DEN) strategy for improving nitrogen removal per unit of external carbon added. The screened inoculant reached approximately 2.5 × 10⁹ CFU/mL, and maximum nitrate and nitrite reduction rates of 307.42 and 215.42 g N/(m³ h), respectively, were achieved by the selected isolates. Among the tested carbon sources, the highest denitrification rate was obtained with sodium acetate. Across wastewater samples from six municipal wastewater treatment plants (WWTPs), the apparent C/N requirement was reduced from 2.79–9.41 to 1.92–5.20 by DEN addition, corresponding to a 21–55% decrease in chemical oxygen demand (COD) required per unit total nitrogen (TN) removed. During full-scale validation at a 24,000 m³/d WWTP, effluent TN was maintained at approximately 10 mg/L through startup and adaptive maintenance dosing, while specific denitrification rate (SDNR) was increased by approximately 2.3-fold. Activated-sludge community restructuring was indicated by 16S rRNA sequencing, with an increase in the relative abundance of Pseudomonadota from 18.4% to 27.6% after dosing. Based on external carbon-source expenditure, the specific external carbon-source cost per unit TN removed decreased from 0.036 to 0.011 Chinese Yuan (CNY)/(t water·mg TN), corresponding to a 69.4% reduction in this cost indicator. Overall, DEN bioaugmentation was shown to be a practical engineering tool for carbon-efficient nitrogen removal from low-C/N municipal wastewater. Full article

(This article belongs to the Special Issue Advanced Wastewater Treatment for Sustainable Pollution Control)

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15 pages, 1771 KB

Open AccessArticle

Auditing Construction-Dewatering Pumping Logs for Monitoring Prioritization: A Data-Limited Case Study from Loja, Ecuador

by Ulbio Fernando Mendoza-Hidalgo and Jose Luis Chavez-Torres

Water 2026, 18(17), 2217; https://doi.org/10.3390/w18172217 - 7 Sep 2026

Routine construction-dewatering logs can support monitoring decisions when their processing rules and inferential scope are explicitly defined. This study audited 594 well-period records from 11 wells at a single urban construction site in Loja, Ecuador. Reading dates span 618 days (15 July 2024–25 [...] Read more.

Routine construction-dewatering logs can support monitoring decisions when their processing rules and inferential scope are explicitly defined. This study audited 594 well-period records from 11 wells at a single urban construction site in Loja, Ecuador. Reading dates span 618 days (15 July 2024–25 March 2026), with the first monitoring interval beginning on 10 July 2024. Construction drawings indicate well lengths of 15.5–19.0 m. Reconstructed quality-control rules classified 406 records as Valid, 113 as Valid with caution, and 75 as Not recommended. Among 519 usable observation pairs, 509 (98.1%) had non-five-day intervals, resulting in a median bias of +60.0% when a default five-day divisor was applied. The inclusive 13-date panel comprised 111 Valid and 32 caution observations and showed pronounced inter-well ordering (Kendall’s W = 0.946). Sensitivity analyses based on quality thresholds and reduced-well configurations quantified the dependence of results on record-retention rules. Ordinary Theil–Sen screening was complemented by an irregular-time, dependence-aware log-GLS analysis and conditional detectability estimates; therefore, temporal signals are interpreted as exploratory rather than diagnostic. Recalculated Hydro-Operational Monitoring Priority Scores (HOMPS) placed Wells 10, 2, 8, and 5 in the highest sample-defined priority tier. Well 10 remained the highest-priority well under every component-omission scenario, providing partial support for H3. The study provides a transparent and reproducible framework for auditing routine dewatering records and prioritizing monitoring attention while explicitly avoiding unsupported inference about aquifer stress. Full article

(This article belongs to the Special Issue Hydrogeological and Hydrochemical Investigations of Aquifer Systems)

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27 pages, 5050 KB

Open AccessArticle

Physics-Informed Neural Network for Reconstructing Free-Surface Transient Flow Fields in Long-Distance Water-Conveyance Tunnels from Sparse Observations

by Xiulian Li, Zhiyuan Chen, Donghui Qi, Yize Zhang, Zhaoyang Deng and Ling Zhou

Water 2026, 18(17), 2216; https://doi.org/10.3390/w18172216 - 7 Sep 2026

Long-distance free-surface water-conveyance tunnels require a spatially continuous representation of transient water depth, yet in practice flow is monitored at only a few sections. This study develops a physics-informed neural network (PINN) that reconstructs the transient water-depth field of unsteady free-surface flow in [...] Read more.

Long-distance free-surface water-conveyance tunnels require a spatially continuous representation of transient water depth, yet in practice flow is monitored at only a few sections. This study develops a physics-informed neural network (PINN) that reconstructs the transient water-depth field of unsteady free-surface flow in such tunnels from two- or three-point sensors. The one-dimensional Saint-Venant equations, closed with a Darcy–Weisbach steady friction term in hydraulic-radius form, are embedded as a soft constraint in the training loss, so that sparse depth observations are combined with the governing conservation laws to recover the field at unobserved interior and downstream locations. High-resolution finite-volume (FVM) solutions of a 500 m circular tunnel under a flood-rise scenario provide the reference data. The PINN reduces the relative L₂ error at unobserved sections to approximately one-quarter of that of an otherwise identical, physics-free ANN (2.50% versus 10.05% at an interior section; 4.77% versus 13.69% at an extrapolation section). Runs repeated with different random seeds confirm statistical stability at zero noise, while revealing that a minority of trainings at 10% noise converge to spurious solutions. Sensor-placement experiments, including layouts anchored at the true domain boundaries (x = 0 and 500 m), show that boundary anchoring—particularly of the upstream boundary—governs both accuracy and noise robustness: boundary-anchored two-sensor layouts remain accurate in most runs under 10–20% observation noise, whereas interior-only layouts degrade sharply. The method is presented as an offline reconstruction tool; its extension to streaming data assimilation is discussed as future work. Full article

(This article belongs to the Section Hydraulics and Hydrodynamics)

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32 pages, 8384 KB

Open AccessArticle

Explainable ML for Irrigation Water Quality Prediction in the Sedrata Aquifer, Plain (Algeria)

by Fethi Nadour, Mohammed Adjili, Abdallah Chabbi, Mohamed Nadour, Noureddine Zenati, Nabiha Belahcene, Abdelaziz Rabehi and Mustapha Habib

Water 2026, 18(17), 2215; https://doi.org/10.3390/w18172215 - 7 Sep 2026

Accurate assessment of irrigation water quality is essential for sustainable groundwater management in semi-arid regions. Conventional Irrigation Water Quality Index (IWQI) assessment requires multiple physicochemical measurements and manual computation of a composite index, which can be time-consuming, costly, and difficult to scale across [...] Read more.

Accurate assessment of irrigation water quality is essential for sustainable groundwater management in semi-arid regions. Conventional Irrigation Water Quality Index (IWQI) assessment requires multiple physicochemical measurements and manual computation of a composite index, which can be time-consuming, costly, and difficult to scale across repeated monitoring campaigns. Machine learning (ML) approaches may provide a practical surrogate for reconstructing IWQI from a reduced set of routinely measured variables. This study developed an interpretable ML framework for IWQI reconstruction in the shallow phreatic aquifer of the Sedrata Plain, northeastern Algeria. One hundred groundwater samples were collected from 25 open wells across four seasonal campaigns (February 2023–May 2024). IWQI was calculated from EC, Na⁺, Cl⁻, HCO₃⁻, and SAR using the weighted aggregation approach of Meireles et al. Six ML algorithms were assessed using nested Recursive Feature Elimination with Cross-Validation (RFECV) within a Leave-One-Campaign-Out (LOCO) validation framework, with predictive performance evaluated using R², RMSE, and MAE. IWQI values ranged from 28.72 to 76.28, with 8%, 56%, 28%, and 8% classified as low, moderate, high, and severe restriction, respectively. Extreme Gradient Boosting (XGBoost) achieved the highest LOCO performance (R² = 0.732 ± 0.109; RMSE = 5.075 ± 1.403 IWQI units), compared with Multiple Linear Regression (MLR) (R² = 0.563 ± 0.304). SHapley Additive exPlanations (SHAP) identified Cl⁻, EC, and SAR as the dominant predictors. The findings demonstrate the potential of an interpretable ML framework for grouped-validation IWQI reconstruction from routinely measured variables, supporting efficient irrigation-water quality screening. External validation is required before wider application. Full article

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

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32 pages, 14134 KB

Open AccessArticle

New Interpretable Framework for Clustering Spatial Hydrogeochemical Data and Assessing Groundwater Quality and Chemical Evolution Factors: A Topological Synthesis Approach

by Dzhema Melkonyan and Vegard Berg Kvernelv

Water 2026, 18(17), 2214; https://doi.org/10.3390/w18172214 - 7 Sep 2026

This study proposes a new method for the topological synthesis of principal component projections and hydrogeochemical stoichiometric equality lines on self-organizing map (SOM) component planes to assess groundwater chemistry forming factors and quality in the Pleistocene unconfined aquifer of the Southern Bug and [...] Read more.

This study proposes a new method for the topological synthesis of principal component projections and hydrogeochemical stoichiometric equality lines on self-organizing map (SOM) component planes to assess groundwater chemistry forming factors and quality in the Pleistocene unconfined aquifer of the Southern Bug and Sinyukha interfluve area, Ukraine. The hydrogeochemical characteristics clustered by the SOM were further examined using the graphical cross-validation method. The groundwater dataset used in the analysis consisted of 10 parameters (i.e., pH, total dissolved solids,

C a^{2 +}

,

M g^{2 +}

,

N a^{+}

,

K^{+}

,

H C O_{3}^{-}

,

C l^{-}

,

S O_{4}^{2 -}

, and

N O_{3}^{-}

) from 91 samples collected during the dry season. Subsequently, for SOM construction, we utilized six log-ratio relationships of milliequivalent ion concentrations. Based on the results, the hydrogeochemical groundwater data were classified into three clusters, which revealed three water types and processes controlling their chemistry: salinity driven by sulfate inputs (Cluster 1), highly salinity driven by nitrate-chloride and sulfate pollution (Cluster 2), and relatively fresh water governed by natural carbonate dissolution and silicate weathering (Cluster 3). The salinity types were identifiable in the northern part of the study area, characterized as the primary zone of initial intense pollution. High salinity types were identified in the eastern and southeastern parts of the territory (with delayed water exchange), whereas relatively fresh types were identified in the central part (with active water exchange) as well as in the western and southwestern parts. Modeling confirmed that extensive sulfate, nitrate, and chloride contamination led to anthropogenic degradation of the aquifer system. Full article

(This article belongs to the Section Hydrogeology)

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55 pages, 75886 KB

Open AccessArticle

Two Decades of Optical and Thermal Variability in Lake Nasser: A Multi-Variable MODIS Assessment on a 5 km Analysis Grid (2005–2025)

by Youssef M. Youssef, Bojan Đurin, Afnan Abdullah Alturki, Marko Šrajbek and Islam M. Hamdi

Water 2026, 18(17), 2213; https://doi.org/10.3390/w18172213 - 7 Sep 2026

Multi-decadal trajectories of large arid-zone reservoirs are seldom described by integrated satellite observation. This study characterises the optical and thermal variability of Lake Nasser, Egypt, over 2005–2025. Eight monthly MODIS indicators—NDVI, EVI, NDWI, NDTI, a near-infrared reflectance index, white-sky albedo, and day- and [...] Read more.

Multi-decadal trajectories of large arid-zone reservoirs are seldom described by integrated satellite observation. This study characterises the optical and thermal variability of Lake Nasser, Egypt, over 2005–2025. Eight monthly MODIS indicators—NDVI, EVI, NDWI, NDTI, a near-infrared reflectance index, white-sky albedo, and day- and night-time land surface temperature (LST)—together with the derived diurnal temperature range (DTR), were compiled in Google Earth Engine over 186 cells of a 5 km grid and analysed by non-parametric trend, change-point and correlation procedures under false-discovery-rate control, correction for serial correlation, and effective-sample-size significance testing. Because a fixed polygon cannot separate environmental change from shoreline migration, every trend was recomputed on four domains of decreasing shoreline exposure. Three signals survive on all four: night-time LST rises (+0.51 to +0.76 °C decade⁻¹), DTR contracts (−0.83 to −1.95 °C decade⁻¹) and albedo declines (−0.0034 to −0.0193 decade⁻¹). The NDVI, EVI, near-infrared and day-time LST declines that the fixed polygon reports are not reproduced under the control, whereas NDWI declines (−0.048 to −0.077 decade⁻¹) only once it is applied. Change-point tests date a shift in level to 2016–2017, three years before the first filling of the Grand Ethiopian Renaissance Dam. Full article

(This article belongs to the Special Issue Sustainable Urban Water Management: The Role of Nature-Based Solutions, 2nd Edition)

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7 pages, 174 KB

Open AccessEditorial

Editorial: Regional Geomorphological Characteristics and Sedimentary Processes

by Aqsa Anees

Water 2026, 18(17), 2212; https://doi.org/10.3390/w18172212 - 7 Sep 2026

Understanding regional geomorphological characteristics and sedimentary processes is essential for interpreting how landscapes evolve, how environmental dynamics shift, and how water systems interact across different spatial and temporal scales [...] Full article

(This article belongs to the Special Issue Regional Geomorphological Characteristics and Sedimentary Processes)

22 pages, 3758 KB

Open AccessArticle

Well-Scale Groundwater-Level Forecasting and Gap Reconstruction in Qatar’s Arid Aquifer Using Interpretable XGBoost

by Fatima Kastali, Mohamed Meddi, Ala Gouissem, Ravi Rangarajan, Amin Esmaeili Khalil Saraei and Rachid Benlamri

Water 2026, 18(17), 2211; https://doi.org/10.3390/w18172211 - 7 Sep 2026

Reliable use of machine learning in groundwater management requires a clear distinction between forecasting, reconstruction, and spatial extrapolation. This study evaluates eXtreme Gradient Boosting (XGBoost) as a data-driven methodological benchmark, not as a process-based groundwater-flow model or monitoring-network design tool. Using 25,133 observations [...] Read more.

Reliable use of machine learning in groundwater management requires a clear distinction between forecasting, reconstruction, and spatial extrapolation. This study evaluates eXtreme Gradient Boosting (XGBoost) as a data-driven methodological benchmark, not as a process-based groundwater-flow model or monitoring-network design tool. Using 25,133 observations from 879 wells in Qatar’s carbonate aquifer system (2011–2016), we assessed three operational tasks: sequential one-step-ahead forecasting at monitored wells, retrospective gap reconstruction, and strict spatial transfer without local groundwater-level history. For forecasting, the principal no-climate model achieved RMSE = 0.31 m, approximately 40% lower than persistence (0.51 m). The held-out-well design included 107 of 664 supervised wells (16.1%; 3143 observations); on the common subset of 2856 observations from 92 wells, XGBoost achieved RMSE = 0.17 m versus 0.40 m for persistence. Gap reconstruction yielded RMSE values ranging from 0.37 m to 0.46 m across random- and block-masking scenarios; XGBoost outperformed persistence but was not consistently more accurate than linear or PCHIP interpolation. Strict spatial transfer failed when local history was unavailable (mean R² = −0.29 ± 1.52 across four K-means blocks). SHapley Additive exPlanations (SHAP) indicated that antecedent groundwater levels accounted for approximately 99% of predictive importance. Thus, the framework can complement monitoring through short-horizon forecasting and gap reconstruction where local observations exist but should not replace field monitoring or be used for extrapolation to unmonitored areas. Full article

(This article belongs to the Special Issue Advances in Hydrogeological Investigations: Field Monitoring, GIS, AI, Remote Sensing, Geophysical Techniques, and Hydrochemical Analysis)

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30 pages, 5948 KB

Open AccessSystematic Review

Development Trends and Challenges of Smart Irrigation and Scheduling Optimization in Irrigation Districts

by Chenchen Lou, Wene Wang and Qianxi Li

Water 2026, 18(17), 2210; https://doi.org/10.3390/w18172210 - 6 Sep 2026

Irrigation scheduling plays a pivotal role in bridging water resource allocation and farmland production management. For decades, scheduling in irrigation districts has predominantly relied on operators’ experience and relatively rigid water delivery plans, making it difficult to simultaneously meet the demands for timely [...] Read more.

Irrigation scheduling plays a pivotal role in bridging water resource allocation and farmland production management. For decades, scheduling in irrigation districts has predominantly relied on operators’ experience and relatively rigid water delivery plans, making it difficult to simultaneously meet the demands for timely responsiveness and precise water allocation under the combined influence of meteorological variability, changing crop water requirements, and the dynamic adjustments of water conveyance and distribution systems. The advancement of digital technologies, such as the Internet of Things, machine learning, deep reinforcement learning, and digital twins, has opened new technical pathways for optimizing irrigation scheduling. Focusing on the development of smart irrigation and scheduling optimization in irrigation districts, this paper systematically reviews the relevant literature published from January 2000 to June 2026 and delineates its evolution into three stages. Early-stage research was grounded in physical models, empirical rules, and hydraulic simulations, establishing fundamental methods for evapotranspiration estimation, crop water requirement calculation, and canal water delivery simulation. The middle stage, marked by the introduction of the Internet of Things and machine learning, enabled real-time monitoring of hydrological conditions, soil moisture, and meteorological data and promoted a data-driven transformation of water demand forecasting methods. The recent stage is characterized by the integration of deep reinforcement learning, digital twins, and knowledge graphs, which extends irrigation district scheduling from isolated single-point optimization toward multi-agent coordination and closed-loop management. Existing evidence confirms that digital technologies have yielded water-saving and yield-increasing benefits at the field scale and improved water distribution efficiency in several demonstration irrigation districts; however, their wider deployment at the district scale still faces bottlenecks such as inadequate sensing of physical execution processes, underdeveloped multi-objective trade-off mechanisms, and limited model transferability and long-term operational sustainability. To address these challenges, this paper proposes future research directions oriented toward real-time perception of water delivery and distribution status, multi-objective robust optimization, explainable artificial intelligence, and human–machine collaborative decision-making, thereby providing a reference for the theoretical development, engineering deployment, and operational management of smart irrigation district scheduling systems. Full article

(This article belongs to the Special Issue Application of Water-Saving Irrigation in Agricultural Development)

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38 pages, 6573 KB

Open AccessReview

A Review of Machine Learning-Based Time-Series Anomaly Detection in the Water Domain

by Zhuang Liu, Zheng Wang, Chengcheng Ding, Jun Luo, Xiao Luo, Rujiao Tan, Yang Li, Yonghai Gan and Yibin Cui

Water 2026, 18(17), 2209; https://doi.org/10.3390/w18172209 - 5 Sep 2026

Anomaly detection in water-related time-series data often reveals important environmental problems and serves as a starting point for scientific discoveries. Machine learning has become the mainstream method and a research hotspot for anomaly detection in recent years. This review examines 106 research articles [...] Read more.

Anomaly detection in water-related time-series data often reveals important environmental problems and serves as a starting point for scientific discoveries. Machine learning has become the mainstream method and a research hotspot for anomaly detection in recent years. This review examines 106 research articles from the Web of Science database published over the past 10 years. Unlike other surveys, this review focuses on anomalies arising from the water-related variables themselves rather than from equipment malfunctions. The work assesses the overall trends in the application and development of machine learning models for water-related anomaly detection. It classifies machine learning-based anomaly-detection models from two dimensions: development stage and anomaly-detection paradigm. Our analysis covers the mechanisms, strengths, limitations, and applications of various machine learning-based anomaly-detection models across different paradigms, highlighting current challenges and prospective research directions in water-related anomaly detection. Full article

(This article belongs to the Special Issue Machine Learning Applications in the Water Domain, 2nd Edition)

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