The importance of efficient water resource supply has been acknowledged, and it is essential to predict short-term water consumption in the future. Recently, it has become possible to obtain data on water consumption at the household level through smart water meters. The pattern of these data is nonlinear due to various factors related to human activities, such as holidays and weather. However, it is difficult to accurately predict household water consumption with a nonlinear pattern with the autoregressive integrated moving average (ARIMA) model, a traditional time series prediction model. Thus, this study used a deep learning-based long short-term memory (LSTM) approach to develop a water consumption prediction model for each customer. The proposed model considers several variables to learn nonlinear water consumption patterns. We developed an ARIMA model and an LSTM model in the training dataset for customers with four different water-use types (detached houses, apartment, restaurant, and elementary school). The performances of the two models were evaluated using a test dataset that was not used for model learning. The LSTM model outperformed the ARIMA model in all households (correlation coefficient: mean 89% and root mean square error: mean 5.60 m³). Therefore, it is expected that the proposed model can predict customer-specific water consumption at the household level depending on the type of use.

Accurate prediction of soil moisture is important yet challenging in various disciplines, such as agricultural systems, hydrology studies, and ecosystems studies. However, many data-driven models are being used to simulate and predict soil moisture at only a single depth. To predict soil moisture at various soil depths with depths of 100, 200, 500, and 1000 mm from the surface, based on the weather and soil characteristic data, this study designed two data-driven models: artificial neural networks and long short-term memory models. The developed models are applied to predict daily soil moisture up to 6 days ahead at four depths in the Eagle Lake Observatory in California, USA. The overall results showed that the long short-term memory model provides better predictive performance than the artificial neural network model for all depths. The root mean square error of the predicted soil moisture from both models is lower than 2.0, and the correlation coefficient is 0.80–0.97 for the artificial neural network model and 0.90–0.98 for the long short-term memory model. In addition, monthly based evaluation results showed that soil moisture predicted from the data-driven models is highly useful for analyzing the effects on the water cycle during the wet season as well as dry seasons. The prediction results can be used as basic data for numerous fields such as hydrological study, agricultural study, and environment, respectively.

Global climate models (GCMs) are used to analyze future climate change. However, the observed data of a specified region may differ significantly from the model since the GCM data are simulated on a global scale. To solve this problem, previous studies have used downscaling methods such as quantile mapping (QM) to correct bias in GCM precipitation. However, this method cannot be considered when certain variables affect the observation data. Therefore, the aim of this study is to propose a novel method that uses a convolution neural network (CNN) considering teleconnection. This new method considers how the global climate phenomena affect the precipitation data of a target area. In addition, various meteorological variables related to precipitation were used as explanatory variables for the CNN model. In this study, QM and the CNN models were applied to calibrate the spatial bias of GCM data for three precipitation stations in Korea (Incheon, Seoul, and Suwon), and the results were compared. According to the results, the QM method effectively corrected the range of precipitation, but the pattern of precipitation was the same at the three stations. Meanwhile, for the CNN model, the range and pattern of precipitation were corrected better than the QM method. The quantitative evaluation selected the optimal downscaling model, and the CNN model had the best performance (correlation coefficient (CC): 69% on average, root mean squared error (RMSE): 117 mm on average). Therefore, the new method suggested in this study is expected to have high utility in forecasting climate change. Finally, as a result of forecasting for future precipitation in 2100 via the CNN model, the average annual rainfall increased by 17% on average compared to the reference data.

Small hydropower (SHP) plants are advantageous as they have a short construction period and can be easily maintained. They also have a higher energy density than other alternative energy sources as environmentally-friendly energy sources. In general, hydropower potential is estimated based on the discharge in the river basin, and the discharge can be obtained from the stage station in the gaged basin. However, if there is no station (i.e., ungaged basin) or no sufficient discharge data, the discharge should be estimated based on rainfall data. The flow duration characteristic model is the most widely used method for the estimation of mean annual discharge because of its simplicity and it consists of rainfall, basin area, and runoff coefficient. Due to the characteristics of hydroelectric power depending on the discharge, there is a limit to guaranteeing the accuracy of estimating the generated power with only one method of the flow duration characteristic model. Therefore, this study assumes the gaged basins of the three hydropower plants of Deoksong, Hanseok, and Socheon in Korea exist as ungaged basins and the river discharges were simulated using the Kajiyama formula, modified-TPM(Two-Parameter Monthly) model, and Tank model for a comparison with the flow duration characteristics model. Furthermore, to minimize the uncertainty of the simulated discharge, four blending techniques of simple average method, MMSE(Multi-Model Super Ensemble), SMA(Simple Model Average), and MSE(Mean Square Error) were applied. As for the results, the obtained discharges from the four models were compared with the observed discharge and we noted that the discharges by the Kajiyama formula and modified-TPM model were better fitted with the observations than the discharge by the flow duration characteristics model. However, the result by the Tank model was not well fitted with the observation. Additionally, when we investigated the four blending techniques, we concluded that the MSE technique was the most appropriate for the discharge simulation of the ungaged basin. This study proposed a methodology to estimate power generation potential more accurately by applying discharge simulation models that have not been previously applied to the estimation of SHP potential and blending techniques were also used to minimize the uncertainty of the simulated discharge. The methodology proposed in this study is expected to be applicable for the estimation of SHP potential in ungaged basins.

The impact of irrigation reservoirs requires investigation through hydrological analysis to identify the flood control functions of these reservoirs. However, there is insufficient information concerning important geographical, morphological, and topographic characteristics, such as the reservoir cross-section. Therefore, this study aimed to identify the morphological and topographic characteristics of reservoirs using geographical information instead of measurement data. Ten reservoirs, including the Ga-Gog reservoir located in Miryang City, South Korea, were selected. The topographic information of the reservoirs was obtained using topographic maps and GIS techniques. Based on this information, the volume (V)-area (A)-depth (H) relationship and the hypsometric curve (HC) according to the relative area (a/A) and relative height (h/H) were created. A comparison of the reservoir volume, estimated using topographic information, with the measured volume revealed an error rate between 0.23% and 14.27%. In addition, two collapsed reservoirs located near Miryang City were investigated by creating V-A-H relationships and HCs using topographic information. The morphological characteristics of the reservoirs were identified by analyzing the (1) morphology index, (2) full water storage area-levee height relationship, and (3) full water storage area relationship. The analysis results showed that the collapsed reservoirs had high water depth and a large area relative to other reservoirs. Similar types of reservoirs were grouped by conducting a cluster analysis using basic properties such as the basin area, storage, and levee height. The cluster analysis results, based on HC analysis, grouped the reservoirs into three shapes: convex upward (youthful stage), relatively flat (mature stage), and convex downward (old stage). The HCs of the collapsed reservoirs exhibited a convex downward shape, indicating that they were subjected to considerable erosion due to aging. Moreover, this considerable erosion caused a large quantity of sediment to accumulate in the reservoirs, resulting in an insufficient allowable storage capacity of the reservoir because the flood control capacity was reduced, which may have led to their collapse during heavy rainfalls. Therefore, the identification of potential causes of reservoir collapse through the morphological characteristics and HCs of reservoirs are expected to support the operation and management of reservoirs to reduce flood damage.

For risk assessment, two methods, quantitative risk assessment and qualitative risk assessment, are used. In this study, we identified the regional risk level for a disaster-prevention plan for an overall area at the national level using qualitative risk assessment. To overcome the limitations of previous studies, a heavy rain damage risk index (HDRI) was proposed by clarifying the framework and using the indicator selection principle. Using historical damage data, we also carried out hierarchical cluster analysis to identify the major damage types that were not considered in previous risk-assessment studies. The result of the risk-level analysis revealed that risk levels are relatively high in some cities in South Korea where heavy rain damage occurs frequently or is severe. Five causes of damage were derived from this study—A: landslides, B: river inundation, C: poor drainage in arable areas, D: rapid water velocity, and E: inundation in urban lowlands. Finally, a prevention project was proposed considering regional risk level and damage type in this study. Our results can be used when macroscopically planning mid- to long-term disaster prevention projects.