Water

31 pages, 2399 KB

Open AccessArticle

CSPP-RNN: A Precipitation Nowcasting Approach That Couples Similar Precipitation Processes with Sequence-to-Sequence RNNs

by Jiachang Tian, Chunxiao Zhang, Yuxuan Wang and Zuhao Zhang

Water 2026, 18(11), 1261; https://doi.org/10.3390/w18111261 (registering DOI) - 22 May 2026

Abstract

Accurate precipitation nowcasting is critical for many aspects of human life. A recurrent neural network (RNN) has demonstrated strong and relatively mature performance in machine learning approaches for precipitation nowcasting. However, their inherent recursive prediction structure leads to error accumulation, causing progressively blurred [...] Read more.

Accurate precipitation nowcasting is critical for many aspects of human life. A recurrent neural network (RNN) has demonstrated strong and relatively mature performance in machine learning approaches for precipitation nowcasting. However, their inherent recursive prediction structure leads to error accumulation, causing progressively blurred outputs and limiting practical applicability. To address this issue, we propose CSPP-RNN (Coupling-Similar-Precipitation-Processes RNN), a net that couples similar precipitation processes with a sequence-to-sequence RNN. For each prediction timestep, similar precipitation processes are retrieved, and their segments are then input into the encoder to obtain the corresponding hidden states. These hidden states replace the ones influenced by earlier predicted results in the recursive structure. Based on radar data from Beijing Daxing station, the comparison experiments of CSPP-RNN and ConvLSTM indicate that: (1) Over the 36–60 min lead time across the 0.1, 5.0, and 20.0 mm/h thresholds, the POD and CSI improved by 0.0334, 0.0170 on average, respectively, whereas the FAR degraded by 0.0586; (2) error accumulation was mitigated, retaining richer fine-scale structures in the predicted images; (3) the extra computational cost of coupling was controlled within an acceptable range. In conclusion, CSPP-RNN mitigates the error accumulation problem in RNN by coupling similar precipitation processes as part of the modification of the recursive prediction structure. This provides a potential new direction for optimizing the application of RNN in precipitation nowcasting. Full article

(This article belongs to the Special Issue Applications of GIS and Remote Sensing in Hydrology and Hydrogeology)

25 pages, 32731 KB

Open AccessArticle

Hydroclimatological Change in a Karst Cryptodepression Lake on a Small Adriatic Island: Lake Vrana (Cres)

by Ognjen Bonacci, Ana Žaknić-Ćatović, Maja Oštrić, Tanja Roje-Bonacci and Tamara Brleković

Water 2026, 18(11), 1260; https://doi.org/10.3390/w18111260 (registering DOI) - 22 May 2026

Abstract

Lake Vrana on Cres Island (northern Adriatic Sea) is a rare hydrogeological system consisting of a large freshwater body located within a karst cryptodepression with its bottom below sea level and surface above it. This study investigates long-term hydroclimatological changes using daily records [...] Read more.

Lake Vrana on Cres Island (northern Adriatic Sea) is a rare hydrogeological system consisting of a large freshwater body located within a karst cryptodepression with its bottom below sea level and surface above it. This study investigates long-term hydroclimatological changes using daily records of lake water level (1978–2024), water temperature (1979–2024), precipitation, and air temperature (1981–2024). Linear regression, the Mann–Kendall trend test, Sen’s slope estimator, and day-to-day variability metrics were applied to quantify long-term trends and system responses. A multi-index approach was used to enable a robust assessment of drought dynamics in this unique karst system: the Standardized Precipitation Index (SPI), representing meteorological conditions based on precipitation; the Standardized Hydrological Index (SHI), reflecting hydrological response derived from lake levels; and the New Drought Index (NDI), integrating precipitation and temperature to account for evapotranspiration effects. Results indicate a statistically significant decline in lake water levels (−4.5 to −5.2 cm yr⁻¹), while precipitation shows no significant trend. In contrast, both air and water temperatures exhibit a significant increase (~0.5 °C per decade) and are strongly correlated (R² = 0.767). The lake demonstrates pronounced thermal inertia and delayed response to atmospheric forcing. Day-to-day analysis reveals increasing variability in water temperature and decreasing variability in air temperature, suggesting changes in system energy dynamics. Drought indices (SHI and NDI) show significant negative trends, whereas SPI does not, indicating that drought intensification is primarily driven by rising temperatures and enhanced evapotranspiration rather than precipitation deficits. These findings demonstrate that Lake Vrana acts as a sensitive integrator of climatic forcing. Full article

(This article belongs to the Section Hydrology)

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23 pages, 9743 KB

Open AccessArticle

Water–Land–Carbon Coupled Ecosystem Services Assessment and Driving Analysis Based on Composite Ecosystem Service Index

by Ruifeng Jiao, Hao Wei, Yongkang Zhang, Qiting Zuo and Qingsong Wu

Water 2026, 18(11), 1259; https://doi.org/10.3390/w18111259 (registering DOI) - 22 May 2026

Abstract

Ecosystem service assessment provides a critical basis for optimizing regional ecological management and promoting sustainable development. From the water–land–carbon coupling perspective, this study established a technical framework for quantifying individual services, coupling a composite index, and analyzing multidimensional driving mechanisms. The InVEST model [...] Read more.

Ecosystem service assessment provides a critical basis for optimizing regional ecological management and promoting sustainable development. From the water–land–carbon coupling perspective, this study established a technical framework for quantifying individual services, coupling a composite index, and analyzing multidimensional driving mechanisms. The InVEST model was applied to quantify three core ecosystem services: water yield, habitat quality, and carbon storage. A Composite Ecosystem Service Index (CESI) was constructed through normalization and weighted summation. Multidimensional driving factors were identified using the Optimal Parameter-Based Geographical Detector. Taking Ningxia during 2004–2024 as the study area, the results showed that the CESI exhibited a fluctuating upward trend with significant spatial heterogeneity, characterized by a south–high and north–low pattern. Land use transitions were dominated by bidirectional conversions between cropland and grassland, while impervious area expanded rapidly and barren land decreased overall. The spatial differentiation of CESI was jointly controlled by natural and anthropogenic factors, with land use type, precipitation, and digital elevation model showing the strongest explanatory power, and all two-factor interactions displaying pronounced enhancement effects. This study provides a reproducible framework for ecosystem service assessment in arid and semi-arid regions, supporting ecological restoration, land use optimization, and the coordinated development of ecology and economy under water–land–carbon synergy. Full article

(This article belongs to the Special Issue China Water Forum, 4th Edition)

46 pages, 3315 KB

Open AccessArticle

Groundwater Quality, Contamination, and Resource Potential for Pasture Livestock Watering in Arid Western Kazakhstan

by Timur Rakhimov, Sultan Tazhiyev, Valentina Rakhimova, Vladimir Smolyar, Aliya Toktar, Aigerim Akylbayeva, Makhabbat Abdizhalel and Darkhan Yerezhep

Water 2026, 18(11), 1258; https://doi.org/10.3390/w18111258 (registering DOI) - 22 May 2026

Abstract

Groundwater is the primary source of livestock watering across the arid pasturelands of western Kazakhstan, yet no systematic field hydrochemical assessment has been published for this region in over 40 years. This study presents the first systematic field-based hydrochemical characterisation of groundwater sources [...] Read more.

Groundwater is the primary source of livestock watering across the arid pasturelands of western Kazakhstan, yet no systematic field hydrochemical assessment has been published for this region in over 40 years. This study presents the first systematic field-based hydrochemical characterisation of groundwater sources used for pasture livestock watering in the West Kazakhstan Region and Aktobe Region, filling a critical data gap that has persisted since the Soviet era. Specifically, it characterises the hydrochemistry, water quality, and infrastructure condition of groundwater sources, and evaluates the groundwater resource potential against current and projected livestock water demand. A total of 139 groundwater samples were collected along 11,182 km of field routes during May–July 2025, and analysed for 25 physicochemical parameters; hydrochemical classification was performed using AquaChem 11, and spatial analysis was conducted in ArcGIS 10.8. The groundwater chemistry distribution is bimodal: fresh bicarbonate-calcium-magnesium waters (TDS < 3.0 g/L) constitute approximately 80% of samples, while highly mineralised chloride-sulphate-sodium waters (TDS up to 9.91 g/L) occur in salt-dome-influenced discharge zones. Nitrate concentrations exceeded 50 mg/L in 23–36% of samples, with maxima of 635 mg/L, reflecting intensive anthropogenic contamination near livestock facilities. Predictive exploitable fresh groundwater resources exceed current livestock demand by a factor of 162. The principal constraint on pasture water supply is not resource scarcity but the non-operational status of 51–75% of inspected watering infrastructure, a legacy of post-Soviet institutional collapse that requires urgent rehabilitation. Full article

(This article belongs to the Section Hydrogeology)

14 pages, 2270 KB

Open AccessArticle

Acute Effect of Acetaminophen and Chloramphenicol on Hydrogenotrophic Denitrification Driven by Anaerobic Granular Sludge

by Emanuele Marino, Armando Oliva, Stefano Papirio, Giovanni Esposito and Francesco Pirozzi

Water 2026, 18(11), 1257; https://doi.org/10.3390/w18111257 (registering DOI) - 22 May 2026

Abstract

Hydrogenotrophic denitrification (H₂Den) is a promising strategy for NO₃⁻ removal from a supply water with low or negligible organic carbon content. However, its performance may be affected by emerging contaminants (ECs), which pose increasing risks to the environment and [...] Read more.

Hydrogenotrophic denitrification (H₂Den) is a promising strategy for NO₃⁻ removal from a supply water with low or negligible organic carbon content. However, its performance may be affected by emerging contaminants (ECs), which pose increasing risks to the environment and human health. This study investigates the acute effect of two widely detected ECs, acetaminophen (ACN) and chloramphenicol (CHP), at a 200 mg/L concentration, on H₂Den using anaerobic granular sludge (AnGS) as inoculum. Acute exposure to ACN enhanced NO₃⁻ removal, likely due to the formation of oxidizable metabolites serving as electron donors through the heterotrophic pathway. On day 3, the residual NO₃⁻ concentration had already dropped below the regulatory limit of 50 mg/L, reaching 4.3 mg NO₃⁻/L. In contrast, CHP initially inhibited the denitrification process, resulting in limited NO₃⁻ removal, i.e., a residual concentration of 145.4 mg NO₃⁻/L on day 3. Nevertheless, short-term microbial adaptation likely enabled performance recovery under CHP exposure. On day 6, both EC exposure tests allowed a NO₃⁻ removal above 97%, although CHP resulted in residual NO₂⁻, i.e., 37 mg NO₂⁻/L. In the presence of ACN, the accumulation of gaseous denitrification intermediates was observed, with NO concentration in the headspace peaking at 9.5% (i.e., 16.2 × 10⁻² µg NO/min/g VS) on day 6. Thus, in terms of either the production of gaseous intermediates or the presence of residual nitrogen in the liquid phase, ACN and CHP significantly influenced the denitrification performance, highlighting the importance of considering their presence in the operation of the denitrification process. Full article

(This article belongs to the Section Water Quality and Contamination)

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22 pages, 6344 KB

Open AccessArticle

Simulated Annealing-Optimized LSTM for Large-Scale Temperature Forecasting Across Türkiye

by Vahdettin Demir

Water 2026, 18(11), 1256; https://doi.org/10.3390/w18111256 - 22 May 2026

Abstract

Accurate temperature prediction is essential for understanding climate variability and hydrological extremes. In this context, Long Short-Term Memory (LSTM) networks have become a widely adopted tool for temperature forecasting; however, their performance strongly depends on hyperparameter selection. This study proposes a combinatorial optimization [...] Read more.

Accurate temperature prediction is essential for understanding climate variability and hydrological extremes. In this context, Long Short-Term Memory (LSTM) networks have become a widely adopted tool for temperature forecasting; however, their performance strongly depends on hyperparameter selection. This study proposes a combinatorial optimization framework that integrates the Simulated Annealing (SA) algorithm with LSTM networks to enhance long-term temperature forecasting performance. To evaluate the proposed approach, monthly temperature data (1927–2024) from the Turkish State Meteorological Service (MGM) were used. A spatial hold-out strategy (57 training and 24 testing provinces) was employed to assess generalization performance. Model performance was evaluated using MAE, RMSE, R², and NSE. Results indicate that the SA-LSTM model significantly improves prediction accuracy compared with the conventional LSTM configuration. The optimized model achieved lower prediction errors (MAE = 2.56; RMSE = 3.42) and higher agreement metrics (R² = 0.856; NSE = 0.848) on the independent testing dataset. These findings demonstrate that combinatorial hyperparameter optimization enhances the robustness and predictive capability of deep learning models for large-scale temperature forecasting and provides a robust and reliable tool for climate and hydrological modeling. Full article

(This article belongs to the Special Issue Advances in Extreme Hydrological Events Modeling)

12 pages, 2961 KB

Open AccessArticle

Predicting Wastewater Influent Characteristics Using Data-Driven Modeling Approaches

by Omar El-Dakhakhni, Zhong Li, Pengxiao Zhou and Spencer Snowling

Water 2026, 18(11), 1255; https://doi.org/10.3390/w18111255 - 22 May 2026

Abstract

Accurate prediction of wastewater influent quality is critical for optimizing treatment plant operations, minimizing environmental impact, and enabling proactive management under dynamic conditions. However, the complex, nonlinear, and temporally dependent nature of influent processes poses significant challenges to traditional modeling approaches. This study [...] Read more.

Accurate prediction of wastewater influent quality is critical for optimizing treatment plant operations, minimizing environmental impact, and enabling proactive management under dynamic conditions. However, the complex, nonlinear, and temporally dependent nature of influent processes poses significant challenges to traditional modeling approaches. This study introduces a robust stacked ensemble learning framework that integrates Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) to forecast three key influent quality parameters: biochemical oxygen demand (BOD₅), total phosphorus (TP), and total solids (TS) at a municipal wastewater treatment plant (WWTP) in Canada. Through sequential backward feature selection and SHapley Additive exPlanations (SHAP), the model achieves both high predictive accuracy and interpretability, providing insights into temporal, environmental, and process-based drivers of influent variability. The ensemble consistently outperforms individual models, delivering high generalization performance across all three influent quality targets. This work demonstrates that stacked ensemble models, when coupled with explainable AI techniques, can bridge the gap between black-box performance and operational transparency in wastewater forecasting. The proposed framework lays the groundwork for more resilient, data-driven decision-making in municipal WWTPs. Full article

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

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35 pages, 775 KB

Open AccessSystematic Review

Smart Water and Sanitation 4.0: A Systematic Review of Industry 4.0 Technologies in Urban Water Systems

by Anna Paula Marchezan, Luciana Rosa Leite and Vanessa Nappi

Water 2026, 18(11), 1254; https://doi.org/10.3390/w18111254 - 22 May 2026

Abstract

Water is fundamental to urban sustainability, structuring the urban water cycle from supply to wastewater treatment and discharge. Basic sanitation services are a core component of this system, directly influencing sustainable water use and environmental quality. Sanitation 4.0 applies Industry 4.0 technologies to [...] Read more.

Water is fundamental to urban sustainability, structuring the urban water cycle from supply to wastewater treatment and discharge. Basic sanitation services are a core component of this system, directly influencing sustainable water use and environmental quality. Sanitation 4.0 applies Industry 4.0 technologies to enable real-time monitoring, data-driven management, and process optimization. This study investigates how the implementation of Industry 4.0 technologies transforms the management of basic sanitation services. A systematic literature review (SLR) was conducted to provide a theoretical foundation and identify research gaps. Articles were selected using a structured and reproducible method, and qualitative data were coded and analyzed with NVivo software. The results indicate that Sanitation 4.0 encompasses diverse applications, with artificial intelligence (AI), big data and data analytics, and internet of things (IoT) emerging as the most frequently implemented technologies in water distribution, wastewater treatment, and service management. IoT demonstrated broad versatility, while robots and augmented reality remain underexplored. Data security emerged as the area most in need of attention. This research concludes that Industry 4.0 technologies are reshaping the management and delivery of sanitation services, supporting innovation and progress toward universal access. Full article

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

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33 pages, 86671 KB

Open AccessArticle

Using Sodium Humate and Desulfurization Gypsum to Improve Saline Water Irrigation for Better Soil Water Movement and Salt Balance in Saline-Alkali Soils

by Ying Deng, Qiuping Fu, Shudong Lin, Zhenghu Ma, Chuhan Wang, Hailiang Xu and Quanjiu Wang

Water 2026, 18(11), 1253; https://doi.org/10.3390/w18111253 - 22 May 2026

Abstract

Saline water irrigation has emerged as a promising approach to mitigate agricultural water shortages; however, its improper use may induce secondary soil salinization. In this study, saline-alkali soil collected from Hami, Xinjiang, was used to conduct a series of indoor one-dimensional vertical soil [...] Read more.

Saline water irrigation has emerged as a promising approach to mitigate agricultural water shortages; however, its improper use may induce secondary soil salinization. In this study, saline-alkali soil collected from Hami, Xinjiang, was used to conduct a series of indoor one-dimensional vertical soil column experiments. The aim was to systematically investigate the effects of sodium humate and desulfurization gypsum on soil infiltration behavior and the distribution patterns of key cations and anions under different levels of irrigation water salinity. The results showed that sodium humate application markedly improved soil infiltration capacity, while the duration of infiltration decreased with increasing salinity. Under salinity levels of 12 and 16 g/L, the 4 g/kg sodium humate treatment exhibited the most rapid advancement of the wetting front. In contrast, desulfurization gypsum reduced infiltration rates, with the lowest infiltration observed under the 12.5 g/kg treatment at 16 g/L salinity. Under different treatments, the adjusted coefficients of determination (adjusted R²) for the Philip, Kostiakov, and Horton models ranged from 0.8450 to 0.9841, 0.9901 to 0.9989, and 0.9748 to 0.9942, respectively, while the global performance indicator (GPI) ranged from 1.619 × 10⁻³ to 5.103 × 10⁻¹, 4.998 × 10⁻⁹ to 2.166 × 10⁻⁵, and 1.505 × 10⁻⁶ to 2.438 × 10⁻⁴, respectively. These results indicate that the Kostiakov model outperformed the other models in terms of fitting accuracy and overall performance for describing the soil infiltration process. In addition, sodium humate generally increased the sorptivity parameter S in the Philip model and the empirical coefficient K in the Kostiakov model, whereas desulfurization gypsum showed the opposite trend. In terms of salt regulation, sodium humate demonstrated optimal desalination performance at application rates of 6–8 g/kg under low salinity and 4–6 g/kg under high salinity conditions. Conversely, excessive gypsum application tended to exacerbate salt accumulation, although a moderate dosage (5 g/kg) effectively limited the downward migration and accumulation of Na⁺ and Cl⁻. These two ions were identified as the dominant contributors to soil salinization, showing strong positive correlations with soil salt content (SSC), sodium adsorption ratio (SAR), and exchangeable sodium percentage (ESP). In contrast, Ca²⁺, Mg²⁺, and HCO₃⁻ played beneficial roles in alleviating sodicity through ion exchange and buffering mechanisms. Overall, sodium humate enhanced infiltration and facilitated salt leaching in the upper soil layers under saline irrigation conditions. Although desulfurization gypsum reduced infiltration and increased overall salt content, it contributed to mitigating Na⁺ accumulation in deeper soil profiles. These findings highlight the critical importance of selecting appropriate soil amendments and optimizing their application rates to improve saline water use efficiency and promote sustainable management of saline-alkali soils. Full article

(This article belongs to the Section Soil and Water)

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28 pages, 8218 KB

Open AccessArticle

Projected Changes in Dry and Wet Conditions in the Henan Section of the Yellow River Based on the CMIP6 Multi-Model Ensemble

by Changwei Yan, Wenzhao Qiao, Ruyi Huang, Jie Tao, Qiting Zuo and Zhiqiang Zhang

Water 2026, 18(11), 1252; https://doi.org/10.3390/w18111252 - 22 May 2026

Abstract

Under the continuous impact of global warming, the water cycle has undergone significant changes, causing a series of problems such as water shortage, frequent climate disasters and ecological environment deterioration. Therefore, understanding the evolution of regional historical and future drought and wet conditions [...] Read more.

Under the continuous impact of global warming, the water cycle has undergone significant changes, causing a series of problems such as water shortage, frequent climate disasters and ecological environment deterioration. Therefore, understanding the evolution of regional historical and future drought and wet conditions is crucial for adapting and mitigating disasters. This paper discusses the evolution of drought and pluvial events in the Henan section of the Yellow River from 1970 to 2014, projects the future evolution of drought and wet conditions, and assesses the performance of various climate models from Coupled Model Intercomparison Project Phase 6 in simulating precipitation and temperature. Subsequently, future drought and wet conditions in the Henan section were projected for the 2015–2100 period across four SSP-RCP scenarios using Standardized Precipitation and Evapotranspiration Index (SPEI) and run theory. The results indicate that the Henan section of the Yellow River exhibited a significant drying trend during the historical period, with a rate of 0.15 per decade. Looking ahead, a wetting tendency is projected under the SSP1-2.6 scenario, with an increasing rate of 0.02 per decade, whereas the other three scenarios consistently show drying trends, with rates of −0.11, −0.15, and −0.23 per decade, respectively. Across all scenarios, drought and wetness variations exhibit pronounced periodicity, particularly at timescales of approximately 20–30 years, suggesting the persistence of multi-decadal hydroclimatic oscillations. Furthermore, drought and wetness events are projected to become more persistent and severe during the mid-to-late 21st century. Compared with the historical baseline, increasing radiative forcing is associated with an expansion in drought-affected areas, accompanied by reduced event frequency but longer duration and greater severity. In terms of risk, the SSP3-7.0 scenario presents the highest overall drought and wetness risk with the widest spatial extent, whereas the SSP2-4.5 scenario shows relatively lower risk levels and a more balanced spatial distribution. Full article

(This article belongs to the Special Issue Yellow River Basin Management Under Pressure: Present State, Restoration and Protection, 4th Edition)

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19 pages, 15550 KB

Open AccessArticle

Characterization of the Hyporheic Zone in the Lower Yellow River by Integrating Time-Lapse Electrical Resistivity Tomography and Hydrological Monitoring

by Yajing Yan, Yuxiang Chen, Ying Li, Jiangfeng Wang, Yongshuai Yan and Guizhang Zhao

Water 2026, 18(11), 1251; https://doi.org/10.3390/w18111251 - 22 May 2026

Abstract

The hyporheic zone (HZ) mediates biogeochemical exchanges between rivers and aquifers, yet its spatial and temporal dynamics in large, regulated rivers remain poorly characterized due to limitations of point-based measurements. Here, we combined three time-lapse electrical resistivity tomography (T-ERT) surveys with continuous hydrological [...] Read more.

The hyporheic zone (HZ) mediates biogeochemical exchanges between rivers and aquifers, yet its spatial and temporal dynamics in large, regulated rivers remain poorly characterized due to limitations of point-based measurements. Here, we combined three time-lapse electrical resistivity tomography (T-ERT) surveys with continuous hydrological and hydrochemical monitoring along a meandering reach of the lower Yellow River, generating a two-dimensional, profile-integrated view of HZ geometry under three hydrodynamic states: low flow (1 December 2020), natural rising stage (1 March 2021), and peak stage during the Xiaolangdi (XLD) water-and-sediment regulation (1 July 2021). Absolute tomograms identified two hydrostratigraphic units: an upper sandy-silt cap (35–170 Ω·m) and an underlying sand aquifer (12–35 Ω·m). Percent-difference tomograms, relative to the low-flow baseline, revealed lateral HZ expansion from ~15 m and vertical growth of 2.5 m at the rising stage to ~36 m and 4.5 m at peak stage, with local resistivity decreases exceeding 38%. In contrast, the deeper mixing zone varied by <10% across surveys. Temperature, rainfall infiltration, and groundwater freshening could not explain the observed patterns. These results were corroborated by three independent lines of evidence: lateral conductivity excursions and in-well temperature records at floodplain well W2, and analytical Darcy–Archie calculations, all consistent with the predicted lateral extent and mixing fraction. River stage, amplified by the XLD release, emerged as the dominant control on two-dimensional HZ geometry. This study provides direct empirical evidence of hyporheic dynamics in a large regulated river and demonstrates that T-ERT, supported by sparse hydrological data, offers a minimally invasive and effective tool for characterizing hyporheic zones. Full article

(This article belongs to the Section Hydrogeology)

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12 pages, 2277 KB

Open AccessArticle

Off the Record: Unveiling Volume of Unreported Catch in Marine Fisheries with Data from Labuan Fishing Port, Java, Indonesia

by Ernik Yuliana, Yonvitner, Sissi Athirah Syahira and Jiří Patoka

Water 2026, 18(11), 1250; https://doi.org/10.3390/w18111250 - 22 May 2026

Abstract

Marine fisheries provide a nutrient source for humans, and Indonesian marine fisheries have the second-highest production rate globally. Reliable evidence of the volume of captured fish is crucial for the sustainable management of Indonesian fisheries. The Labuan Fishing Port in Banten Province, Sunda [...] Read more.

Marine fisheries provide a nutrient source for humans, and Indonesian marine fisheries have the second-highest production rate globally. Reliable evidence of the volume of captured fish is crucial for the sustainable management of Indonesian fisheries. The Labuan Fishing Port in Banten Province, Sunda Strait, was surveyed between September 2022 and March 2023. Based on personal inspections and an anonymous questionnaire, fishermen used various methods to catch fish. The captures by fisheries showed that the gear types, including purse seines and a mix of several types of gear, were the largest contributors to officially registered (auctioned) production, with 85.85% and 83.91% of their captures being auctioned, while bottom otter trawls auctioned 7.91% of their capture only. The reported reasons for unrecorded catch varied, with time pressure and lack of supervision being the leading factors. Most unrecorded captured fish were sold directly to buyers or taken home for consumption. Thus, the reports are considered inaccurate. Implementation of real-time data capture techniques and enhancements to marketing and auction systems was recommended. Full article

(This article belongs to the Special Issue Advances in Sustainable Fisheries and Aquaculture: Practices, Ecosystems, and Socio-Economic Perspectives)

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14 pages, 2192 KB

Open AccessArticle

Sediment-Derived Turbidity Reduces Survival of Planktonic Crustaceans: Effects of Substrate Type, Load, and Disturbance Frequency

by Kacper Nowakowski and Łukasz Sługocki

Water 2026, 18(11), 1249; https://doi.org/10.3390/w18111249 - 22 May 2026

Abstract

Sediment-derived turbidity, intensified by anthropogenic activities, is a widespread form of particulate pollution in aquatic ecosystems. Yet, its effects on planktonic crustaceans remain insufficiently quantified across particle types and disturbance regimes. We exposed five species (Daphnia magna, Leptodora kindtii, Eurytemora [...] Read more.

Sediment-derived turbidity, intensified by anthropogenic activities, is a widespread form of particulate pollution in aquatic ecosystems. Yet, its effects on planktonic crustaceans remain insufficiently quantified across particle types and disturbance regimes. We exposed five species (Daphnia magna, Leptodora kindtii, Eurytemora velox, Thermocyclops crassus, and T. oithonoides) to turbidity generated by red clay, diatomaceous earth (amorphous silica), and bentonite at three substrate loads (0.5, 1.5, and 3 g/100 mL) and three resuspension regimes (1, 12, and 24 disturbances per day) for 72 h. Particle size distributions and turbidity reduction under free sedimentation were measured using NTU and FAU. Survival decreased across all species, with substrate load as the most consistent predictor, while disturbance frequency showed taxon-dependent effects, particularly in D. magna and L. kindtii. Sensitivity differed among taxa, with L. kindtii and E. velox being the least tolerant, whereas cyclopoid copepods (Thermocyclops spp.) were comparatively resistant. Substrate identity also affected responses, with D. magna being particularly sensitive to amorphous silica relative to clay and bentonite. These findings indicate that survival under sediment-derived turbidity depends on both particle properties and exposure regime, suggesting that increasing sediment mobilization may act as an ecological filter shaping plankton communities. Full article

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

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Water, Volume 18, Issue 11 (June-1 2026) – 13 articles

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