Detention ponds are a key component of urban flood control as they mitigate the additional runoff generated by land-cover changes and the associated reduction in time of concentration. Estimating the required reduction in peak discharge involves hydrological reservoir routing. This study proposes a novel simulation-driven routing methodology based on a modified Rosenbrock numerical scheme to solve the mass continuity equation coupled with the storage-outflow relationship. A key advantage of the proposed approach is its automatic time-step adjustment. Monte Carlo simulations are employed to generate multiple routing scenarios, which are subsequently used to train machine learning surrogate models. Among the evaluated ML algorithms, a Rational Quadratic Gaussian Process Regression model exhibited the best predictive performance. The methodology is demonstrated through a case study of a 17,340 m(2) urban catchment. The proposed framework constitutes a practical decision-support tool for engineers and environmental agencies involved in the design and assessment of detention ponds.
This study evaluates the technical and economic feasibility of a solar-powered water pumping system for Al Makhwah in the Al Baha province of Saudi Arabia. The region has high solar potential, with an average global irradiance of 5.5-6.0 kWh/m2 /day, making photovoltaic pumping suitable for agricultural and domestic water supply. The proposed system consists of a photovoltaic array, a 3 kW submersible pump operating at a total head of 60 m, and a 10 kWh energy storage unit to ensure reliable operation. System performance was assessed using numerical modeling and energy balance analysis. Results show an annual energy production of approximately 13,000 kWh, allowing the system to satisfy about 98% of the daily water demand (50-70 m3). A performance ratio of 78% was achieved despite high ambient temperatures and dust effects. Economic results indicate clear cost advantages over diesel-powered pumping systems, along with significant reductions in CO2 emissions.
The expansion of impermeable surfaces has made urban regions more vulnerable to flooding, while water resources are depleting due to rising urbanization, population growth, and water usage. To mitigate water demand and prevent flood risks, it is imperative to utilize alternative water sources. The sustainable management of urban water resources presents considerable opportunities via rainwater harvesting (RWH). This project aims to implement RWH from road and roof surfaces at seven sites in the central district of Çanakkale Province, Türkiye, by establishing rain gardens and utilizing the collected rainwater for irrigation in urban recreational areas. The Analytic Hierarchy Process (AHP) and Geographic Information Systems (GIS) were used to find the best locations for RWH and to assess how much rainwater could be collected in different scenarios.The findings indicated that the AHP method designated 118 out of the 346 sites evaluated for RWH as suitable. The Barbaros neighborhood exhibited the highest potential for RWH; however, no suitable sites were identified in the Fevzipaşa and Namıkkemal neighborhoods. These findings highlight the effectiveness of RWH as a feasible strategy for urban water management for the city center of Çanakkale province. RWH can mitigate flood risks, reduce pressure on water resources, and improve urban cooling by decreasing surface runoff through landscape irrigation and rain gardens. The implementation of RWH devoid of energy requirements promotes sustainable water management and enhances the resilience of urban water systems.
The study takes 27 core cities in the Yangtze River Delta from 2014 to 2023 as samples, uses a super efficiency data envelopment analysis model considering unexpected output to measure efficiency, combines Gini coefficient and Theil index to analyze regional differences, and uses spatial autocorrelation analysis and spatial Durbin model to reveal spatial effects. The results show that the water resource utilization efficiency in Shanghai increased from 1.23 in 2014 to 1.78 in 2023, and the Gini coefficient of water resource utilization efficiency in the Yangtze River Delta decreased from 0.321 in 2014 to 0.259 in 2023. Research has shown that there are significant regional differences and positive spatial autocorrelation in the water resource utilization efficiency of cities in the Yangtze River Delta, with obvious spatial spillover effects. This provides a scientific basis for optimizing the allocation and collaborative management of water resources, which helps promote regional sustainable development.
The objective of the present study was to investigate water quality status and respondents' perception on current land uses, and water quality at Kaliakair upazila in Gazipur, Bangladesh. The majority of respondents agreed that rapid industrialization and urbanization (94%) was affecting forest coverage (70%), surface water quality (78%), natural biodiversity (56%), polluted water harmful for fisheries/agriculture (82%) and increased health risk (46%). From water analysis, average temperature, pH, total dissolved solids (TDS), dissolved oxygen (DO), and chemical oxygen demand (COD) were 35.20 degrees C, 7.77, 746.5 mgL(-1), 4.65 mgL(-1), and 35.5 mgL(-1), respectively. The ranges of heavy metals and other elements in surface water were 0.015-0.93, 0.024-0.042, 0.83-1.01, 0-0.34, 10.65-12.01, 1.44-8.8, and 0.43-31.47 mgL(-1) for Mn, Cd, Pb, Cu, Na, Mg, and Ca, respectively. The decreasing trends of heavy metals and other elements were Ca > Na > Mg > Pb > Mn > Cd > Cu > Fe > Ni > Cr. Considering their toxicity level the ecological risk can be categorized as Pb > Mn > Cd. The findings can be used by researchers, environmentalists and policy makers for better utilization of land uses and management purposes.
Urban water SCADA (supervisory control and data acquisition) loops must track setpoints (SP) under actuator constraints while remaining robust to disturbances and model uncertainty. This study presents a reproducible benchmarking framework for fair comparison of constrained model predictive control (MPC) and conventional proportional-integral-derivative (PID) under identical sampled-data execution and saturation limits. The framework represents reduced-order SCADA-type single-loop regulation using a discrete-time single-input single-output (SISO) deviation model with a strict fairness protocol (T_s = 0.1 s, u is an element of [-1, 1], fixed tuning). Three scenarios are evaluated: nominal step tracking, constant input disturbance (d = -0.25), and plant model mismatch via increased damping. Performance is assessed using transient metrics, root mean square error (RMSE), integral absolute error (IAE), steady-state error, and saturation activity. Results show MPC achieves faster responses and lower error via brief saturation, while PID remains largely unsaturated but slower. Under disturbance, both exhibit steady-state offset; however, MPC reduces overall and final error, motivating offset-free MPC.
