Water

17 pages, 2794 KB

Open AccessArticle

Quantitative Analysis of Hydraulic Fracture Geometry and Its Relationship with Key Water Hammer Pressure Features

by Yanchao Li, Hu Sun, Wei Liu, Longqing Zou, Liang Yang, Kai Wu, Lijun Liu and Shuangshuang Sun

Water 2025, 17(18), 2741; https://doi.org/10.3390/w17182741 - 16 Sep 2025

Abstract

Hydraulic fracturing technology is crucial for promoting oil and gas resource development. In recent years, water hammer fracture diagnostic techniques, derived from the water hammer effect in hydraulic fracturing, have garnered significant attention due to their low cost and ease of operation. The [...] Read more.

Hydraulic fracturing technology is crucial for promoting oil and gas resource development. In recent years, water hammer fracture diagnostic techniques, derived from the water hammer effect in hydraulic fracturing, have garnered significant attention due to their low cost and ease of operation. The characteristic parameters of water hammer pressure are closely related to fracture geometry parameters. Monitoring the characteristics of water hammer pressure at the wellhead allows for rapid assessment of fracturing effectiveness. This study comprehensively considers wellbore friction, perforation friction, and the fluid loss effect within hydraulic fractures, establishing a mathematical model for the evolution of water hammer pressure during multi-cluster staged fracturing in horizontal wells. Based on field-monitored water hammer data from multiple stages, this study employed water hammer fracture diagnostics to inversely determine the geometric parameters of fractures in different fracturing stages. Characteristic parameters of the water hammer pressure, including the initial amplitude, number of oscillations, oscillation duration, and attenuation rate, were calculated for different well sections. Furthermore, the correlations between these water hammer characteristics and the fracture geometric parameters were analyzed. The correlation analysis between characteristic parameters of water hammer pressure and geometric parameters of hydraulic fractures indicates that under conditions of longer fracture half-length and smaller fracture height, the generated water hammer pressure exhibits a higher initial amplitude, fewer oscillations, a shorter oscillation duration, and a larger attenuation rate. The research findings can facilitate rapid estimation of fracture geometry using water hammer pressure, thereby optimizing fracturing design and enhancing fracturing effectiveness. Full article

(This article belongs to the Special Issue Groundwater Environmental Impacts and Control Strategies of Coal Mining and Energy Development/Storage Activities)

18 pages, 1220 KB

Open AccessArticle

Study of the Correlation Between Water Resource Changes and Drought Indices in the Yinchuan Plain Based on Multi-Source Remote Sensing and Deep Learning

by Hong Guan, Zhiguo Jiang, Jing Lu and Yukuai Wan

Water 2025, 17(18), 2740; https://doi.org/10.3390/w17182740 - 16 Sep 2025

Abstract

This study examines the intricate relationship between water resource dynamics and drought indices in the Yinchuan Plain, China, by integrating multi-source remote sensing data with advanced deep learning techniques. Using data from 2002 to 2022, we applied Long Short-Term Memory (LSTM) networks to [...] Read more.

This study examines the intricate relationship between water resource dynamics and drought indices in the Yinchuan Plain, China, by integrating multi-source remote sensing data with advanced deep learning techniques. Using data from 2002 to 2022, we applied Long Short-Term Memory (LSTM) networks to model the spatiotemporal dynamics of water resources and their relationships with the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Palmer Drought Severity Index (PDSI). Our findings reveal a strong correlation between total water resources and the SPEI (r = 0.81, p < 0.001), underscoring the pivotal role of evapotranspiration in this region’s water balance. The LSTM model outperformed traditional statistical methods, achieving a Root Mean Square Error of 0.142 for water resource predictions and 0.118 for drought index forecasts. Spatial analysis indicated stronger correlations in the northern Yinchuan Plain, likely influenced by its proximity to the Yellow River and regional water management practices. Wavelet coherence analysis identified significant coherence at the 6–12-month scale, highlighting the importance of seasonal to inter-annual strategies for water resource management. These results provide a robust foundation for developing effective water management policies and drought mitigation strategies in arid and semi-arid regions. The methodologies presented are broadly applicable to similar water-scarce regions, contributing to global efforts in sustainable water resource management under changing climatic conditions. Full article

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

18 pages, 5055 KB

Open AccessArticle

A Novel Multimodal Large Language Model-Based Approach for Urban Flood Detection Using Open-Access Closed Circuit Television in Bandung, Indonesia

by Tsun-Hua Yang, Obaja Triputera Wijaya, Sandy Ardianto and Albert Budi Christian

Water 2025, 17(18), 2739; https://doi.org/10.3390/w17182739 - 16 Sep 2025

Abstract

Monitoring urban pluvial floods remains a challenge, particularly in dense city environments where drainage overflows are localized, and sensor-based systems are often impractical. Physical sensors can be costly, prone to theft, and difficult to maintain in areas with high human activity. To address [...] Read more.

Monitoring urban pluvial floods remains a challenge, particularly in dense city environments where drainage overflows are localized, and sensor-based systems are often impractical. Physical sensors can be costly, prone to theft, and difficult to maintain in areas with high human activity. To address this, we developed an innovative flood detection framework that utilizes publicly accessible CCTV imagery and large language models (LLMs) to classify flooding conditions directly from images using natural language prompts. The system was tested in Bandung, Indonesia, across 340 CCTV locations over a one-year period. Four multimodal LLMs, ChatGPT-4.1, Gemini 2.5 Pro, Mistral Pixtral, and DeepSeek-VL Janus, were evaluated based on classification accuracy and operational cost. ChatGPT-4.1 achieved the highest overall accuracy at 85%, with higher performance during the daytime (89%) and lower accuracy at night (78%). A cost analysis showed that deploying GPT-4.1 every 15 min across all locations would require approximately USD 59,568 per year. However, using compact models like GPT-4 nano could reduce costs by up to seven times, with minimal loss of accuracy. These results highlight the trade-off between performance and affordability, especially in developing regions. This approach offers a scalable, passive flood monitoring solution that can be integrated into early warning systems. Future improvements may include multi-frame image analysis, automated confidence filtering, and multi-level flood classification for enhanced situational awareness. Full article

(This article belongs to the Special Issue Machine Learning Models for Hydrological Inference: A Case Study for Flood Events)

19 pages, 17160 KB

Open AccessArticle

Spatiotemporal Monitoring of the Effects of Climate Change on the Water Surface Area of Sidi Salem Dam, Northern Tunisia

by Yosra Ayadi, Malika Abbes, Matteo Gentilucci and Younes Hamed

Water 2025, 17(18), 2738; https://doi.org/10.3390/w17182738 - 16 Sep 2025

Abstract

This research presents a comprehensive spatiotemporal assessment of the effects of climate change and anthropogenic pressures on the water surface area and quality of the Sidi Salem Dam, the largest reservoir in Northern Tunisia. Located within a sub-humid to Mediterranean humid bioclimatic zone, [...] Read more.

This research presents a comprehensive spatiotemporal assessment of the effects of climate change and anthropogenic pressures on the water surface area and quality of the Sidi Salem Dam, the largest reservoir in Northern Tunisia. Located within a sub-humid to Mediterranean humid bioclimatic zone, the dam plays a vital role in regional water supply, irrigation, and flood control. Utilizing a 40-year dataset (1985–2025), this study integrates multi-temporal satellite imagery and geospatial analysis using Geographic Information System (GIS) and remote sensing (RS) techniques. The temporal variability of the dam’s surface water extent was monitored through indices such as the Normalized Difference Water Index (NDWI). The analysis was further supported by climate data, including records of precipitation, temperature, and evapotranspiration, to assess correlations with observed hydrological changes. The findings revealed a significant reduction in the dam’s surface area, from approximately 37.8 km² in 1985 to 19.8 km² in 2025, indicating a net loss of 18 km² (47.6%). The Mann–Kendall trend test confirmed a significant long-term increase in annual precipitation, while annual temperature showed no significant trend. Nevertheless, recent observations indicate a decline in precipitation during the most recent period. Furthermore, Pearson correlation analysis revealed a significant negative relationship between precipitation and temperature, suggesting that wet years are generally associated with cooler conditions, whereas dry years coincide with warmer conditions. This hydroclimatic interplay underscores the complex dynamics driving reservoir fluctuations. Simultaneously, land use changes in the catchment area, particularly the expansion of agriculture, urban development, and deforestation have led to increased surface runoff and soil erosion, intensifying sediment deposition in the reservoir. This has progressively reduced the dam’s storage capacity, further diminishing its water storage efficiency. This study also investigates the degradation of water quality associated with declining water levels and climatic stress. Indicators such as turbidity and salinity were evaluated, showing clear signs of deterioration resulting from both natural and human-induced processes. Increased salinity and pollutant concentrations are primarily linked to reduced dilution capacity, intensified evaporation, and agrochemical runoff containing fertilizers and other contaminants. Full article

(This article belongs to the Section Water and Climate Change)

18 pages, 2324 KB

Open AccessArticle

Numerical Study on the Hydrodynamic Force on Submarine Pipeline Considering the Influence of Local Scour Under Unidirectional Flow

by Yadong Wang, Songsong Yu, Siyu Wang, Bolin Zhan, Yubin Jin and Guoqiang Tang

Water 2025, 17(18), 2737; https://doi.org/10.3390/w17182737 - 16 Sep 2025

Abstract

The effect of local scour on the hydrodynamic force upon a submarine pipeline under unidirectional flow has been numerically investigated. The flow field around the pipeline is obtained using the Navier–Stokes equations with the SST k-ω turbulence model, and the sediment transport model, [...] Read more.

The effect of local scour on the hydrodynamic force upon a submarine pipeline under unidirectional flow has been numerically investigated. The flow field around the pipeline is obtained using the Navier–Stokes equations with the SST k-ω turbulence model, and the sediment transport model, considering suspended load and bed load, is accounted for. Firstly, the influences of the Reynolds number (1 × 10⁴ ≤ Re ≤ 1 × 10⁵) and Shields number (1.2 ≤ θ/θ_cr ≤ 2.5) on the scour below the pipeline are analyzed; then, the effect of local scour on the hydrodynamic force upon the pipeline is examined by comparing with the condition that the pipeline is put on the flat seabed. It is found that the presence of local scour leads to a significant effect on the hydrodynamic force acting on the pipeline. Additionally, the Reynolds number affects the hydrodynamic force significantly, while the Shields number has a relatively low effect. The reduction coefficient (λ) is adopted to quantify the influence of the local scour around the pipeline on the hydrodynamic force. According to the reduction coefficient, the presence of local scour increases the drag coefficient by about 10% when the Reynolds number is 1 × 10⁴, while it decreases the drag coefficient significantly when the Reynolds number is larger than 2 × 10⁴, and the reduction coefficient trends towards a constant value with the increase in the Reynolds number. Full article

(This article belongs to the Special Issue Hydrodynamics and Sediment Transport in Ocean Engineering—Second Edition)

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29 pages, 17104 KB

Open AccessArticle

Projection of Hydrological Drought in Chinese River Basins Under Climate Change Scenarios and Analysis of the Contribution of Internal Climate Variability

by Haochuan Li, Xue Wang, Xinyi Liu, Han Wu, Yi Liu, Hai Hu, Cong Cheng, Xu Peng and Jun Guo

Water 2025, 17(18), 2736; https://doi.org/10.3390/w17182736 - 16 Sep 2025

Abstract

This study focuses on 120 representative river basins across China, utilizing CMIP6 multi-model climate data and CESM2-LE large ensemble climate data to develop a bias-correction framework for climate models that integrates statistical methods, with the aim of enhancing the spatiotemporal accuracy of climate [...] Read more.

This study focuses on 120 representative river basins across China, utilizing CMIP6 multi-model climate data and CESM2-LE large ensemble climate data to develop a bias-correction framework for climate models that integrates statistical methods, with the aim of enhancing the spatiotemporal accuracy of climate model outputs. Building on this framework, the study simulates the evolution of hydrological drought characteristics in Chinese river basins during 2071–2100 under the SSP370 scenario and quantifies the relative contributions of internal climate variability (ICV), anthropogenic climate change (ACC), and inter-model uncertainty (IMU) to hydrological drought projections. Results reveal a pronounced south–north divergence in future drought risk. Southern China—especially the middle–lower Yangtze and Pearl River basins—exhibits a >10% increase in drought frequency, with event totals exceeding 30 per 30 years, yet individual droughts remain short and moderate in intensity. Conversely, northern basins—particularly the Songliao and Liao River systems—display pronounced lengthening and intensification of droughts, with mean duration surpassing 12 months and severity indices rising above 38, translating to 20~40% increases relative to the 1985~2014 baseline. Nationwide, ICV emerges as the dominant driver of projected changes: signal-to-noise ratios for frequency, intensity, and duration fall below unity across more than 70% of basins, indicating that unforced variability overshadows the anthropogenic trend. ACC signals only exceed ICV in southeastern coastal regions and parts of the Pearl River basin for intensity and duration. Inter-model spread rivals or exceeds ICV uncertainty in these same humid subtropical basins, underscoring the sensitivity of projections to model structure. Full article

(This article belongs to the Section Hydrology)

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25 pages, 12457 KB

Open AccessArticle

Comparative Study of Shallow and Deep Learning-Based Data-Driven Models for Monthly Runoff Simulation in the Yalong River Basin

by Zhaoxin Yue, Li Wang, Jun Zhu and Hui Zhou

Water 2025, 17(18), 2735; https://doi.org/10.3390/w17182735 - 16 Sep 2025

Abstract

Accurate runoff simulation is essential for water resources planning and development projects. At present, the commonly employed runoff simulation approaches are categorized into two types: process- and data-driven models. Process-driven models pertain to the enhancement of the structural framework in conceptual rainfall–runoff models [...] Read more.

Accurate runoff simulation is essential for water resources planning and development projects. At present, the commonly employed runoff simulation approaches are categorized into two types: process- and data-driven models. Process-driven models pertain to the enhancement of the structural framework in conceptual rainfall–runoff models using hydrological principles to estimate runoff but have low accuracy at the monthly scale. Unlike the process-driven models, data-driven models (DDMs) can simulate the relationship between input factors and output runoff data without regard to complex and unknown runoff production and acquire satisfactory simulation results. Here, we comparatively investigate the applicability of DDMs, including traditional shallow DDMs, deep learning-based (DL) models for monthly runoff simulation, and select the Autoregressive (AR) model as the baseline model for comparison. Moreover, four evaluation indicators, including mean absolute percentage error (MAPE), root mean squared error (RMSE), Nash–Sutcliffe efficiency (NSE), and coefficient of determination (R²), are employed to evaluate the runoff simulation effects of the above methods. We systematically compare the AR model, and shallow and deep learning-based DDMs for runoff simulation at four hydrological stations in the Yalong River basin (YRB), respectively. The finding results reveal that the DDMs demand limited data and can offer satisfactory prediction effects. Also, the DL models outperform other shallow DDMs and the AR model in terms of the above evaluation criteria. Full article

(This article belongs to the Special Issue Application of Various Hydrological Modeling Techniques and Methods in River Basin Management, 2nd Edition)

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15 pages, 3013 KB

Open AccessArticle

Air Entrainment of Chute Aerators Under Different Atmospheric Pressures

by Yameng Wang, Wangru Wei, Tantao Song and Jun Deng

Water 2025, 17(18), 2734; https://doi.org/10.3390/w17182734 - 16 Sep 2025

Abstract

In high-altitude regions, the influence of atmospheric pressure on the air entrainment capacity of chute aerators remains inadequately quantified. This study characterizes aerator efficiency through the air entrainment coefficient under variable atmospheric pressures. Systematic physical modeling was employed to investigate air transport downstream [...] Read more.

In high-altitude regions, the influence of atmospheric pressure on the air entrainment capacity of chute aerators remains inadequately quantified. This study characterizes aerator efficiency through the air entrainment coefficient under variable atmospheric pressures. Systematic physical modeling was employed to investigate air transport downstream of chute aerators. The results demonstrate that the air entrainment coefficient decreases with reduced atmospheric pressure, manifested by declining air concentrations, particularly in the bottom flow layer. Downstream of the aerator, both the average and bottom air concentrations exhibit diminished streamwise development under lower pressures. Specifically, a 100 hPa reduction in atmospheric pressure corresponds to a 0.69–0.93% decrease in the air entrainment coefficient, accompanied by a 3.16% reduction in the regionally averaged air concentration downstream of the impact zone. For the first time, a comprehensive formula of the air entrainment coefficient expressed by basic parameters and an empirical formula of the bottom air concentration under different atmospheric pressures was established based on extensive tests, with results in good agreement. Based on the present experimental results, the atmospheric pressure can significantly affect the air intake of the aerator. Consequently, the atmospheric pressure should be considered as an important parameter in hydraulic aeration design. Full article

(This article belongs to the Special Issue Advanced Numerical Approaches for Multiphase and Cavitating Flows)

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5 pages, 143 KB

Open AccessEditorial

A Review of China’s Water Security Issues: Insights from the Special Issue of China Water Forum 2024

by Qiting Zuo, Fuqiang Wang, Jiaqi Zhai, Xiuyu Zhang, Dunxian She, Lei Zou, Rong Gan and Zengliang Luo

Water 2025, 17(18), 2733; https://doi.org/10.3390/w17182733 - 16 Sep 2025

Abstract

Affected by global climate change and rapid socio-economic development, China, along with many other countries worldwide, faces a series of water security issues, such as water shortages, flood disasters, and water-related ecological and environmental problems [...] Full article

(This article belongs to the Special Issue China Water Forum 2024)

23 pages, 6536 KB

Open AccessArticle

Developing a Composite Hydrological Drought Index Using the VIC Model: Case Study in Northern Thailand

by Duangnapha Lapyai, Chakrit Chotamonsak, Somporn Chantara and Atsamon Limsakul

Water 2025, 17(18), 2732; https://doi.org/10.3390/w17182732 - 16 Sep 2025

Abstract

Hydrological drought indices, while critical for monitoring, are often limited by their reliance on single variables, failing to capture the multidimensional complexity of water scarcity, particularly in data-scarce and climate-sensitive regions. This study addresses this critical gap by introducing a Composite Hydrological Drought [...] Read more.

Hydrological drought indices, while critical for monitoring, are often limited by their reliance on single variables, failing to capture the multidimensional complexity of water scarcity, particularly in data-scarce and climate-sensitive regions. This study addresses this critical gap by introducing a Composite Hydrological Drought Index (CHDI) for a northern watershed in Thailand, a region where drought risk is intensified by climatic shifts and intensive land use. The proposed methodology integrates multiple outputs from the Variable Infiltration Capacity (VIC) hydrological model, including precipitation, runoff, evapotranspiration, baseflow, and soil moisture layers, and employs Principal Component Analysis (PCA) to synthesize the dominant drivers of water-level variability. The first principal component (PC1), which accounted for over 50% of the total variance, served as the basis for the CHDI, and was strongly correlated with precipitation, surface runoff, and surface soil moisture. The performance of CHDI was rigorously evaluated against observed data from eight hydrological stations. The index demonstrated significant predictive skill, with Pearson’s correlation coefficients (R) ranging from 0.49 to 0.79 (p < 0.05), a maximum Nash–Sutcliffe Efficiency (NSE) of 0.63, and F1-scores for drought detection as high as 0.92. It effectively captured seasonal and interannual variability, including the accurate identification of low-flow events reported by the National Hydro Informatics Data Center (NHC). While the CHDI showed robust performance, particularly under high-flow conditions and in drought classification, some limitations were observed in complex or anthropogenically influenced sub-catchments. These findings highlight the potential of CHDI as a reliable and integrative tool for hydrological drought monitoring and for supporting water resource management in data-scarce and climate-sensitive regions. Full article

(This article belongs to the Section Hydrology)

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29 pages, 5375 KB

Open AccessArticle

Application of PINNs to Define Roughness Coefficients for Channel Flow Problems

by Sergei Strijhak, Konstantin Koshelev and Andrei Bolotov

Water 2025, 17(18), 2731; https://doi.org/10.3390/w17182731 - 16 Sep 2025

Abstract

This paper considers the possibility of using Physics-Informed Neural Networks (PINNs) to study the hydrological processes of model river sections. A fully connected neural network is used for the approximation of the Saint-Venant equations in both 1D and 2D formulations. This study addresses [...] Read more.

This paper considers the possibility of using Physics-Informed Neural Networks (PINNs) to study the hydrological processes of model river sections. A fully connected neural network is used for the approximation of the Saint-Venant equations in both 1D and 2D formulations. This study addresses the problem of determining the velocities, water level, discharge, and area of water sections in 1D cases, as well as the inverse problem of calculating the roughness coefficient. To evaluate the applicability of PINNs for modeling flows in channels, it seems reasonable to start with cases where exact reference solutions are available. For the 1D case, we examined a rectangular channel with a given length, width, and constant roughness coefficient. An analytical solution is obtained to calculate the discharge and area of the water section. Two-dimensional model examples were also examined. The synthetic data were generated in Delft3D code, which included velocity field and water level, for the purpose of PINN training. The calculation in Delft3D code took about 2 min. The influence of PINN hyperparameters on the prediction quality was studied. Finally, the absolute error value was assessed. The prediction error of the roughness coefficient n value in the 2D case for the inverse problem did not exceed 10%. A typical training process took from 2.5 to 3.5 h and the prediction process took 5–10 s using developed PINN models on a server with Nvidia A100 40GB GPU. Full article

(This article belongs to the Special Issue Application of Machine Learning in Hydrologic Sciences)

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32 pages, 2529 KB

Open AccessArticle

Radionuclide Distribution and Hydrochemical Controls in Groundwater of the Nile Valley, Upper Egypt: Health and Environmental Implications

by Khaled Ali, Zinab S. Matar, Clemens Walther, Khaled Salah El-Din, Shaban Harb, Mahmoud Kilany and Karem Moubark

Water 2025, 17(18), 2730; https://doi.org/10.3390/w17182730 - 15 Sep 2025

Abstract

This study provides a comprehensive evaluation of naturally occurring radionuclides—radium-226 (²²⁶Ra), thorium-232 (²³²Th), and potassium-40 (⁴⁰K)—in groundwater systems across the Nile Valley regions of Upper Egypt, based on the analysis of 85 groundwater wells. Measured mean activity [...] Read more.

This study provides a comprehensive evaluation of naturally occurring radionuclides—radium-226 (²²⁶Ra), thorium-232 (²³²Th), and potassium-40 (⁴⁰K)—in groundwater systems across the Nile Valley regions of Upper Egypt, based on the analysis of 85 groundwater wells. Measured mean activity concentrations were 0.74 ± 0.3 Bq/L for ²²⁶Ra, 0.24 ± 0.1 Bq/L for ²³²Th, and 13 ± 4 Bq/L for ⁴⁰K, with ²²⁶Ra displaying low correlations with salinity indicators including chloride (Cl⁻), sodium (Na⁺), electrical conductivity (EC), and total dissolved solids (TDS). Notably, approximately 30% of sampled wells exceeded the World Health Organization (WHO) guidance level of 1 Bq/L for ²²⁶Ra, primarily in central and eastern zones influenced by elevated salinity and evaporite dissolution processes. Geospatial mapping combined with multivariate statistical analysis identified four principal components accounting for over 85% of total data variability, demonstrating that depth-dependent processes, including prolonged water–rock interaction and redox evolution, are the primary controls on ²²⁶Ra mobilization, with salinity-driven ion exchange as a secondary factor. Minor anthropogenic influences, potentially linked to agricultural activities in shallow aquifers, were also detected. Radiological risk assessment confirmed that calculated annual effective doses remain well within international safety limits (<1 mSv/year), although infants and children demonstrated relatively higher exposure levels due to increased water intake per unit body weight. Lifetime cancer risk estimates via ingestion pathways yielded values below 1 × 10⁻⁴, aligning with global health organization benchmarks and reinforcing the general safety of groundwater use in the region. The study highlights potential risks posed by saline groundwater to ancient monuments and archaeological sites, as the cycles of salt forming and breaking down might speed up damage to buildings made of limestone and sandstone. These findings establish a robust scientific foundation for future groundwater quality management and cultural heritage conservation efforts in the Nile Valley region of southern Egypt. Full article

(This article belongs to the Section Hydrogeology)

20 pages, 3476 KB

Open AccessArticle

Water Demand Prediction Model of University Park Based on BP-LSTM Neural Network

by Hanzhi Yu, Hao Lv, Yuhang Yang and Ruijie Zhao

Water 2025, 17(18), 2729; https://doi.org/10.3390/w17182729 - 15 Sep 2025

Abstract

Accurate water demand prediction is essential for optimizing the daily operations of water treatment plants and pumping stations. To achieve accurate prediction of water demand for university campuses, this study utilizes real hourly water consumption data collected over 380 observation days from a [...] Read more.

Accurate water demand prediction is essential for optimizing the daily operations of water treatment plants and pumping stations. To achieve accurate prediction of water demand for university campuses, this study utilizes real hourly water consumption data collected over 380 observation days from a water treatment plant located on a university campus in Zhenjiang, Jiangsu Province. Based on periodicity analysis of the original data through Fast Fourier Transform (FFT) and autocorrelation coefficients, the data were preprocessed and aggregated into two-hour intervals. The processed water consumption data, along with temporal information (month, day of the week, date, and hour) and weather conditions (daily average wind speed, maximum and minimum temperature), were used as model inputs. The first 352 days of data were utilized to train the model, followed by 14 days serving as the validation set and the final two weeks as the test set. A hybrid forecasting model for campus water demand was developed by integrating a Back Propagation (BP) neural network with a Long Short-Term Memory (LSTM) neural network. The model’s performance was compared with standalone BP, LSTM, and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Simulation results demonstrate that, compared to other models, the proposed BP–LSTM hybrid model achieves a reduction in Mean Absolute Percentage Error (MAPE) ranging from 4.4% to 15.8%, and a decrease in Root Mean Squared Error (RMSE) between 2.5% and 16.8%. These findings indicate that the BP–LSTM model offers higher prediction accuracy and greater reliability compared to traditional single-model approaches. Full article

(This article belongs to the Special Issue Advances in Hydrodynamics for Pumping Systems: Modeling, Optimization, and Applications)

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12 pages, 1209 KB

Open AccessArticle

Characteristics of Recharge in Response to Rainfall in the Mu Us Sandy Land, China

by Wanyu Zhang, Zaiyong Zhang, Xueke Wang, Hengrui Zhang and Yue Hu

Water 2025, 17(18), 2728; https://doi.org/10.3390/w17182728 - 15 Sep 2025

Abstract

Water scarcity is a significant issue in arid and semi-arid regions and improving our understanding of infiltration and recharge processes is crucial for water resource management. In this study, weighing lysimeters were employed in Mu Us Sandy Land, China to continuously monitor soil [...] Read more.

Water scarcity is a significant issue in arid and semi-arid regions and improving our understanding of infiltration and recharge processes is crucial for water resource management. In this study, weighing lysimeters were employed in Mu Us Sandy Land, China to continuously monitor soil water content and recharge rates during the non-freezing seasons from April 2021 to October 2023. The performance of three empirical weight functions, including Poisson, Rayleigh, and Gamma distributions in simulating the recharge process was evaluated. The results indicate that: (1) The process of groundwater recharge displays significant interannual and seasonal differences. Due to low initial soil moisture and scarce rainfall, recharge accounts for only 10.9% of the averaged annual rainfall in 2021. Due to extreme rainfall events (126.8 mm), groundwater recharge increased by 649.4% compared to 2021, accounting for 51.2% of the annual rainfall in 2022. Because of relatively high soil moisture, groundwater recharge accounted for 18.3% of the annual rainfall in 2023. (2) The Poisson empirical weight function is more suitable for simulating a gradual increase in recharge rate, while it fails to accurately capture the rapid rise in recharge rates associated with extreme rainfall events. (3) Compared to the Poisson empirical weight function, the Gamma distribution performs better under extreme rainfall conditions. This study provides a detailed analysis of groundwater recharge dynamics in semi-arid regions and offers technical support for a deeper understanding of groundwater recharge processes. Full article

(This article belongs to the Special Issue The Impact of Human Activities on Groundwater Resources in Arid and Semi-arid Regions)

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25 pages, 8212 KB

Open AccessArticle

Spatiotemporal Variations of Inorganic Carbon Species Along the Langtang–Narayani River System, Central Himalaya

by Maya P. Bhatt and Ganesh B. Malla

Water 2025, 17(18), 2727; https://doi.org/10.3390/w17182727 - 15 Sep 2025

Abstract

The production and transport of dissolved inorganic carbon (DIC) is central to weathering reactions and the global carbon cycle. We investigated the spatiotemporal variability and export of inorganic carbon species along the rapidly weathering Langtang–Narayani river system in the central Nepal Himalaya. Over [...] Read more.

The production and transport of dissolved inorganic carbon (DIC) is central to weathering reactions and the global carbon cycle. We investigated the spatiotemporal variability and export of inorganic carbon species along the rapidly weathering Langtang–Narayani river system in the central Nepal Himalaya. Over the course of one year, surface water samples were collected from sixteen stations spanning a wide range of elevations. DIC concentrations generally declined with increasing elevation, except in mid-mountain sites influenced by hot springs. Bicarbonate (HCO₃⁻) was identified as the dominant inorganic carbon species, contributing approximately 85% to the total DIC and with a similar dominant export rate of bicarbonate to total DIC export rate, followed by carbon dioxide (CO₂) and carbonate (CO₃²⁻). The river water exhibited a strong altitudinal gradient in carbonate chemistry, with CO₂ supersaturation in the lowlands and undersaturation at higher elevations. Metamorphic activities in the lower mid-mountain sites significantly influenced CO₂ concentrations and inorganic carbon dynamics. The partial pressure of CO₂ (pCO₂) varied widely (56 to 33,869 μatm), reflecting distinct geochemical and seasonal controls. The estimated DIC export rates were 93.66, 37.81, and 12.59 tons km⁻² yr⁻¹ from the Narayani River in the lowlands, the Trisuli River in the mid-mountains, and the Langtang River in the high Himalaya region, respectively. These findings highlight the critical role of elevation, seasonality, and geological processes in regulating carbon dynamics in Himalayan river systems, providing new insights into their contribution to regional carbon fluxes. A comprehensive array of significant univariate and multivariate predictive models is presented here, offering versatile applications, including the interpretation of full and partial derivatives explaining inorganic carbon dynamics within the Himalayan basin. Full article

(This article belongs to the Section Water and Climate Change)

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24 pages, 1812 KB

Open AccessArticle

Temperature Trends and Seasonality in Neritic and Transitional Waters of the Southern Bay of Biscay from 1998 to 2023

by Ibon Uriarte, Arantza Iriarte, Xabier Larrinaga, Gorka Bidegain and Fernando Villate

Water 2025, 17(18), 2726; https://doi.org/10.3390/w17182726 - 15 Sep 2025

Abstract

Temporal and spatial variations in water temperature were analyzed from 1998 to 2023 across two contrasting southeast Basque coast estuaries: the deeper, stratified estuary of Bilbao and the shallower, mixed estuary of Urdaibai. We assessed long-term trends, seasonality, intra- and inter-estuary differences, and [...] Read more.

Temporal and spatial variations in water temperature were analyzed from 1998 to 2023 across two contrasting southeast Basque coast estuaries: the deeper, stratified estuary of Bilbao and the shallower, mixed estuary of Urdaibai. We assessed long-term trends, seasonality, intra- and inter-estuary differences, and links to hydro-meteorological drivers using time-series decomposition, clustering, cumulative sum, regression, and correlation analyses. The largest differences in interannual and seasonal patterns occurred between outer neritic and shallow transitional waters. Most water masses warmed overall, with increases until 2003–2006, followed by cooling until 2013–2015, and sharp warming in 2020–2023. The strongest trends (0.24–0.25 °C decade⁻¹) occurred in middle-estuary waters, while inner above-halocline waters of the stratified estuary showed no trend or slight cooling. The strongest warming occurred in spring, particularly in the easternmost mixed estuary (0.49 °C decade⁻¹), especially in May (0.88 °C decade⁻¹). Seasonal minima and maxima occurred earlier in surface transitional waters than in neritic and deep transitional waters of the stratified system. Over time, temperature maxima advanced, minima were delayed, shortening the warming phase, and springs became warmer, extending the warm season. Air temperature was the main driver of water temperature trends, while river flow modulated patterns at annual and seasonal scales, with negative correlations with temperature, mainly in spring. Full article

(This article belongs to the Topic Climate Change and Aquatic Ecosystems: Impacts, Mitigation and Adaptation)

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20 pages, 6246 KB

Open AccessArticle

GIS-Based Automated Waterlogging Depth Calculation and Building Loss Assessment in Urban Communities

by Chun-Pin Tseng, Xiaoxian Chen, Yiyou Fan, Yaohui Liu, Min Qiao and Lin Teng

Water 2025, 17(18), 2725; https://doi.org/10.3390/w17182725 - 15 Sep 2025

Abstract

Urban pluvial waterlogging has become a major challenge for densely populated cities due to increasingly extreme rainfall events and the rapid expansion of impervious surfaces. In response to the growing demand for localized waterlogging risk assessments, an automated evaluation framework is proposed that [...] Read more.

Urban pluvial waterlogging has become a major challenge for densely populated cities due to increasingly extreme rainfall events and the rapid expansion of impervious surfaces. In response to the growing demand for localized waterlogging risk assessments, an automated evaluation framework is proposed that integrates high-resolution digital elevation models (DEMs), rainfall scenarios, and classified building data within a GIS-based modeling system. The methodology consists of four modules: (i) design of rainfall scenarios and runoff estimation, (ii) waterlogging depth simulation based on volume-matching algorithms, (iii) construction of depth–damage curves for residential and commercial buildings, and (iv) building-level economic loss estimation though differentiated depth–damage functions for residential/commercial assets—a core innovation enabling sector-specific risk precision. A case study was conducted in the Lixia District, Jinan City, China, involving 15,317 buildings under a 50-year return period rainfall event. The total economic losses were shown to reach approximately USD 327.88 million, with residential buildings accounting for 88.6% of the total. The model achieved a mean absolute percentage error within 5% for both residential and commercial cases. The proposed framework supports high-precision, building-level urban waterlogging damage assessment and demonstrates scalability for use in other high-density urban areas. Note: all monetary values were converted from Chinese Yuan (CNY) to U.S. Dollars (USD) using an average exchange rate of 1 USD = 7.28 CNY. Full article

(This article belongs to the Section Urban Water Management)

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40 pages, 9182 KB

Open AccessArticle

Optimal Design of Combined Weir–Orifice Tail Escape Structures Using Graphical Methods and the TAILOPT Tool

by Ahmed M. Tawfik and Mohamed H. Elgamal

Water 2025, 17(18), 2724; https://doi.org/10.3390/w17182724 - 14 Sep 2025

Abstract

Dual-inlet tail escapes, combining an orifice and a weir, are key hydraulic structures that evacuate excess water from canal termini during maintenance and protect berms by discharging surplus irrigation flows. Conventional sizing methods typically depend on trial and error, which is time-consuming and [...] Read more.

Dual-inlet tail escapes, combining an orifice and a weir, are key hydraulic structures that evacuate excess water from canal termini during maintenance and protect berms by discharging surplus irrigation flows. Conventional sizing methods typically depend on trial and error, which is time-consuming and may yield suboptimal design. This study introduces a graphical design approach and a MATLAB-based tool, TAILOPT, developed to streamline tail escape design. The tool incorporates both the Fanning and Darcy–Weisbach friction formulations for head loss estimation and can automatically generate an “.inp” file for EPA-SWMM, enabling direct unsteady-state hydraulic assessment. This integration reduces design effort and supports evaluation of alternative hydraulic and drainage scenarios within a single workflow. Two applications illustrate the framework. The first shows that overly steep drainage slopes (Sp > 2%) are impractical, while vertical drops may require larger pipe diameters. The second application applies TAILOPT to a distributary canal, determining the optimal pipe size and verifying its performance in EPA-SWMM under emergency surplus flow and routine dewatering conditions. The results demonstrate that the method yields economical, robust, and practitioner-friendly designs; however, modeling simplifications, such as assuming continuously submerged orifice flow, can introduce minor deviations in the predicted channel emptying times. Full article

(This article belongs to the Section Hydraulics and Hydrodynamics)

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61 pages, 12444 KB

Open AccessArticle

Time Series Analysis of Influence of Water Cycle on Nitrate Contamination in Miyako Island Ryukyu Limestone Aquifer

by Masayuki Imaizumi

Water 2025, 17(18), 2723; https://doi.org/10.3390/w17182723 - 14 Sep 2025

Abstract

This study investigates the complex factors influencing groundwater NO₃-N concentrations on Miyako Island, which has a geological structure of highly permeable Ryukyu Limestone over less permeable mudstone. The groundwater NO₃-N levels peaked at nearly 10 mg/L in 1989 and [...] Read more.

This study investigates the complex factors influencing groundwater NO₃-N concentrations on Miyako Island, which has a geological structure of highly permeable Ryukyu Limestone over less permeable mudstone. The groundwater NO₃-N levels peaked at nearly 10 mg/L in 1989 and have since declined. Our analysis used agricultural statistics, machine learning, and time-series correlation to elucidate the causes of these changes. We found that the decline in concentrations since 1989 was directly linked to a reduction in sugarcane cultivation. However, the mechanism of increase is more complex. A cross-correlation analysis over 60 years revealed two distinct infiltration mechanisms: a rapid one with zero-time lag, responsible for approximately 70% of the NO₃-N concentration, and a slow one with a 15-year lag, accounting for the remaining 30%. The slow infiltration is likely due to temporary nitrogen storage in the clay layer. These findings have significant implications for water quality management. The recent shift from summer planting to ratoon cultivation has increased fertilizer use, and this, combined with the 15-year lag effect, suggests that NO₃-N concentrations may begin to rise again in about a decade, possibly exceeding the environmental standard. Therefore, continuous monitoring is crucial to prevent future pollution. The methodology developed in this study is also applicable to other islands with similar environments. Full article

(This article belongs to the Special Issue Water-Induced Geo-Disaster Reduction in the Context of Climate Change: Hydrology, Management Strategies, and Ecological Geological Engineering)

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