ABSTRACT Frequent and severe droughts driven by climate change highlight the need for reliable reservoir operation strategies. While most studies evaluate firm yield at the scale of individual reservoirs, few explicitly consider reservoir system operation of multiple reservoirs, despite its potential to enhance downstream supply reliability. The main objectives are to develop a method to estimate the firm yield of the Seomjingang and Juam Reservoirs in South Korea under reservoir system operation and to define and estimate the minimum inflow time series for each reservoir as a basis for assessing water supply reliability during extreme droughts. Results show that reservoir system operation increases firm yield by about seven percent (+1.24 cubic meters per second [CMS]), from 17.92 CMS under independent operation to 19.16 CMS under reservoir system operation, and delays reservoir depletion, thereby enhancing water supply stability during droughts. The novelty of this study lies in developing a firm-yield estimation method that incorporates reservoir system operation and establishing the concept of a minimum inflow time series. The proposed methodology establishes a quantitative framework for minimum inflow time-series estimation and highlights how reservoir system operation can enhance the resilience and reliability of long-term water supply.
ABSTRACT Detecting leaks in underground water networks is essential for reducing water loss and improving the management of this critical infrastructure. Extensive previous studies have explored the use of vibration sensors in detecting such leaks. Effective deployment of such sensors requires characterising their ability to detect leaks of different sizes, through pipes of different materials, and at various distances from the leaks. We present a statistical model that predicts whether a sensor can detect a leak as a function of pipe materials, sensor-leak distances and leak sizes. Parameter estimates are obtained using hierarchical bootstrap sampling. The proposed statistical model is based on logistic regression. The model is trained and evaluated using ground-truth leak and sensor response data from a large urban water distribution network. The sensor sensitivity model shows a predictive accuracy of around 70% for our test cases. Additionally, the model coefficients are consistent with the expected attenuation based on the physical properties of the different pipe material types. These results provide a quantitative basis for planning sensor deployment in water distribution networks.
ABSTRACT Small earth dams play a critical role in sustaining rural livelihoods and enhancing water and food security in semi-arid regions such as southern Zambia. However, their effective management is constrained by limited hydrological monitoring infrastructure and the absence of cost effective, user-friendly tools for tracking water availability. This study developed and evaluated a low-cost hydroinformatics framework that integrates bathymetric surveying, empirical modelling, and an Android-based mobile application to monitor inter-seasonal water availability in small earth dams. Using Makoye Earth Dam in Southern Zambia as a case study, bathymetric data were collected across rainy, cool-dry, and hot-dry seasons to establish depth–volume relationships. An empirical prediction model was trained and validated using standard performance metrics including R2, Nash–Sutcliffe efficiency (NSE), percent bias, root mean square error (RMSE), and RMSE–standard deviation ratio. The model was subsequently embedded into a mobile application (Madwapp 0.1) to enable real-time estimation of reservoir storage from field-measured water depths. Results show that the empirical model performed exceptionally well during the rainy season (R2 = 0.99; NSE = 0.99), demonstrating high predictive accuracy and minimal error. However, performance declined in the cool and dry seasons due to systematic underestimation linked to bathymetric complexity and seasonal water variability. Validation across multiple dams indicated that the volume–volume empirical model significantly outperforms conventional depth–volume based estimation methods, achieving near-perfect agreement with observed volumes (R2 = 0.99). The study highlights the strong seasonality and rapid depletion dynamics of small dams and provides a practical, scalable, and low-cost solution for improving water resource monitoring and decision-making in small to medium data-scarce rural catchments. The empirical regression model performance was strong during recharge conditions but deteriorated substantially during cool and hot-dry seasons, limiting direct transfer without seasonal recalibration. The study recommends multi-basin–based Android app development for enhanced water monitoring efficiency.
ABSTRACT A WDN scheduling agent requires four fundamental capabilities: perception, decision making, action, and learning. A data-centric AI that learns from human operational experience can serve as a decision core for the agent. Urban water distribution networks (WDNs) require real-time scheduling to balance demand, safety, and energy efficiency, requiring intelligent decision-making. Deep learning (DL) efficiently processes large-scale WDN data, but a model-centric perspective limits the optimality of its decisions, thereby restricting its application in intelligent scheduling. This study proposes a data-centric artificial intelligence (DCAI) decision core for WDN scheduling agents. The decision core is constructed through: (1) phase-dependent Gaussian kernel based multi-criteria evaluation of historical strategies; and (2) hybrid objective-subjective data sorting and filtering via heuristic optimization and Pareto-optimal selection. By learning from high-quality operational data, the decision core acquires the ability to make informed scheduling decisions. Evaluated on a large-scale WDN, the method reduced mean absolute error (MAE) by 73.75% compared with unfiltered data under high-demand conditions. Within these scenarios, hydraulic simulations indicated that scheduling decisions maintained >99% nodal pressure satisfaction, while reducing energy consumption by 3.06% and pressure fluctuation by 2.52%. SHAP-based interpretability analysis demonstrated alignment with operational principles, helping to mitigate the ‘black-box’ concern critical for deploying reliable scheduling agents. Evaluated through nearly three years of field operation, this DCAI decision core supports real-time scheduling that balances safety and efficiency in large-scale WDNs.
Flowchart of streamflow gap filling using four methods across six gap scenarios, with statistical and hydrological e valuation. The graphical abstract summarizes a multivariate framework for reconstructing missing daily streamflow at Thumpa mon Station, Kerala, India. Observed streamflow is combined with rainfall, temperature, soil moisture, and other hydrological v ariables. Artificial missing data are generated under six scenarios: long, short, continuous, discontinuous, high flow, and low flo w gaps. Four methods are evaluated: Predictive Mean Matching, k-Nearest Neighbour, Random Forest, and Facebook Prophet. Reconstructions are assessed using RMSE, NRMSE, Kling-Gupta Efficiency, and Pearson's correlation, together with hydrolog ical diagnostics. Random Forest shows strong overall performance across several gap structures, while Predictive Mean Match ing performs comparatively well for high-and low-flow conditions.
