The accuracy and sufficiency of precipitation data play a key role in environmental research and hydrological models. They have a significant effect on the simulation results of hydrological models; therefore, reliable hydrological simulation in data-scarce areas is a challenging task. Advanced techniques can be utilized to improve the accuracy of satellite-derived rainfall data, which can be used to overcome the problem of data scarcity. Our study aims to (1) assess the accuracy of different satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM 3B42 V7), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), PERSIANN-Climate Data Record (PERSIANN-CDR), and China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) by comparing them with gauged rainfall data; and (2) apply them for runoff simulations for the Han River Basin in South Korea using the SWAT model. Based on the statistical measures, that is, the proportion correct (PC), the probability of detection (POD), the frequency bias index (FBI), the index of agreement (IOA), the root-mean-square-error (RMSE), the mean absolute error (MAE), the coefficient of determination (R²), and the bias, the rainfall data of the TRMM and CMADS show a better accuracy than those of PERSIANN and PERSIANN-CDR when compared to rain gauge measurements. The TRMM and CMADS data capture the spatial rainfall patterns in mountainous areas as well. The streamflow simulated by the SWAT model using ground-based rainfall data agrees well with the observed streamflow with an average Nash-Sutcliffe efficiency (NSE) of 0.68. The four satellite rainfall products were used as inputs in the SWAT model for streamflow simulation and the results were compared. The average R², NSE, and percent bias (PBIAS) show that hydrological models using TRMM (R² = 0.54, NSE = 0.49, PBIAS = [−52.70–28.30%]) and CMADS (R² = 0.44, NSE = 0.42, PBIAS = [−29.30–41.80%]) data perform better than those utilizing PERSIANN (R² = 0.29, NSE = 0.13, PBIAS = [38.10–83.20%]) and PERSIANN-CDR (R² = 0.25, NSE = 0.16, PBIAS = [12.70–71.20%]) data. Overall, the results of this study are satisfactory, given that rainfall data obtained from TRMM and CMADS can be used to simulate the streamflow of the Han River Basin with acceptable accuracy. Based on these results, TRMM and CMADS rainfall data play important roles in hydrological simulations and water resource management in the Han River Basin and in other regions with similar climate and topographical characteristics.

River flood routing computes changes in the shape of a flood wave over time as it travels downstream along a river. Conventional flood routing models, especially hydrodynamic models, require a high quality and quantity of input data, such as measured hydrologic time series, geometric data, hydraulic structures, and hydrological parameters. Unlike physically based models, machine learning algorithms, which are data-driven models, do not require much knowledge about underlying physical processes and can identify complex nonlinearity between inputs and outputs. Due to their higher performance, lower complexity, and low computation cost, researchers introduced novel machine learning methods as a single application or hybrid application to achieve more accurate and efficient flood routing. This paper reviews the recent application of machine learning methods in river flood routing.

Substantial greenhouse gas (GHG) emissions from wastewater treatment plants (WWTPs) increase the global warming potential, underscoring the importance of addressing their role in GHG mitigation. This study proposes a strategy development approach that analyzes unit-process-based energy consumption, direct and indirect GHG emissions, and scenario impacts to create integrated water–energy–GHG solutions. The analysis of four WWTPs in Seoul Metropolitan City (SMC) identified aeration as the most energy-intensive process, consuming over 40% of the total energy. In addition, substantial GHG emissions were observed, with total indirect emissions surpassing direct emissions. To address these challenges, five future scenarios targeting 2050 were developed and analyzed: (1) replacing aeration diffusers, (2) reducing wastewater production, (3) adjusting treatment levels, (4) increasing renewable energy production, and (5) integrating all measures. Scenario 1 proved most effective in reducing energy and GHG emission intensity, Scenario 4 achieved high energy self-sufficiency, and Scenario 5 enabled some plants to achieve net-zero energy and carbon conditions. The approach proposed in this study provides actionable insights to support carbon neutrality through targeted water–energy–GHG strategies.

A model that computes the per-unit process energy consumption, energy intensity, CO₂ emission, and CO₂ intensity of water treatment plants is developed. This model is used to estimate the total energy consumption of six water treatment plants in Seoul Metropolitan City (SMC), which is comprised 80–85% for finished water pumping, 6–10% for ozone disinfection, 2–4% for rapid mixing, and 1–3% for non-process loads. The model results are validated against actual data for 2020 and 2021. The net energy consumption considering renewable energy production and use is then calculated, and the corresponding level of CO₂ emissions is predicted. Four scenarios based on the projected water requirements for the year 2045 were evaluated as follows: increased energy efficiency in finished water pumping (Scenario 1), increased renewable energy production in water treatment plants (Scenario 2), increased energy efficiency in raw water pumping (Scenario 3), and reduced water supply per capita (Scenario 4). Compared to a baseline do-nothing scenario (Scenario 0), the net energy consumption is reduced by 3.57%, 2.61%, 3.42%, and 4.67% for Scenarios 1–4, respectively. Scenario 4, which is a water-driven approach, is best for reducing CO₂ emissions, while Scenario 1 and 3, which are energy-driven approaches, are more effective at reducing CO₂ intensity.

River flood routing computes changes in shape of a flood wave over time as it travels downstream along a river. Conventional flood routing models, especially hydrodynamic models require high quality and quantity of input data such as measured hydrologic time series, geometric data, hydraulic structures and hydrological parameters. Unlike physically based models, machine learning algorithms, which are data driven models, do not require much knowledge about underlying physical processes and can identify complex nonlinearity between inputs and outputs. Due to the higher performance, less complexity, and low computation cost, novel machine learning methods as a single application or hybrid application were introduced by researchers to achieve more accurate and efficient flood routing. This paper reviews the recent application of machine learning methods in river flood routing.

Sustainable urban water system (UWS) management is vital for climate-resilient, resource-efficient cities. This study presents the first comprehensive life cycle assessment (LCA) of Seoul Metropolitan City (SMC)’s UWS, encompassing water abstraction, treatment, distribution, wastewater collection and treatment, and sludge management. Nine midpoint impact categories from ReCiPe 2016 (H) were analyzed to identify environmental hotspots and mitigation pathways. Results show that wastewater treatment dominates impacts, contributing 57.3% of global warming potential (GWP; 0.947 kg CO₂-eq per functional unit of 1 m³ of potable water supplied) and 71.1% of freshwater eutrophication (FE; 0.00066 kg P-eq/m³), driven by electricity use, sludge disposal, and direct CH₄/N₂O emissions. Electricity consumption is the leading driver across GWP, terrestrial acidification (TA), and fossil resource scarcity (FRS). Infrastructure construction notably influenced terrestrial ecotoxicity (TET) and human toxicity. Sensitivity analysis showed that SMC’s projected 2030 electricity mix could reduce GWP and FRS by up to 18%. Scenario evaluations revealed that sludge ash utilization in concrete and expanded wastewater reuse improve resource circularity, whereas biogas upgrading, solar generation, and heat recovery significantly lower GWP and FRS. The findings underscore the importance of energy decarbonization, resource recovery, and infrastructure longevity in achieving low-carbon and resource-efficient UWSs. This study offers a transferable framework for guiding sustainability transitions in rapidly urbanizing, energy-transitioning regions.