Understanding the changes on future water resources resulting from climate variations will assist in developing effective management strategies for a river basin. Our area of interest is the Osan watershed in South Korea, where the summer monsoon contributes approximately 60–70% of the annual runoff and precipitation for the country. We determined the effects that future climatic changes have on this area. To accomplish this, we made use of global climate models (GCMs). A total of 10 GCMs were downscaled with the help of climate information production tools. Coupled with the GCMs and the Soil and Water Assessment (SWAT) model, three periods were used to assess these climate impacts. The baseline, mid-century (MC), and end-century (EC) periods include 1993–2018, 2046–2065, and 2081–2099, respectively. The entire process was performed using two scenarios (4.5 and 8.5) from the representative concentration pathways (RCPs). Some of the statistical metrics used for model calibration and validation were p-factor, r-factor, percent bias, root-mean-square error (RMSE), and Nash–Sutcliffe model efficiency. Their respective values were 0.88, 0.88, 8.3, 0.91, and 0.91 for calibration, and 1.16, 0.85, 7.9, 0.88, and 0.87 for validation. For the MC and EC periods under both scenarios, we projected an increase in temperature and precipitation of approximately 2–5 °C and 15–30%, respectively. A predicted rise in precipitation, surface flow, lateral flow, and water yield were noted for the month of June. Subsequently, a decline in July followed during the summer monsoon season. Summer monsoon rains will fluctuate more sharply, with heavy rainfall in June, lower rainfall in July, and more rain in the late summer, leading to the possibility of both flooding and drought within a given period. Annual precipitation, surface flow, lateral flow, and water yield will increase whereas evapotranspiration would decrease in both periods under both scenarios during the summer monsoon period, which will lead to wetter conditions in the future.

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Water demand in Korea has triggered the need for fresh water to be used for agriculture. Agricultural drainage water (ADW) is a way of coping with the growing demand for fresh water for agriculture. In this study, a water quality model (WQM), and an algorithm were used in order to determine the water quality and optimize the water reuse quantity in the Osan stream drain, South Korea. The water quality associated with the drain was stimulated using the QUAL2Kw model and the uncertainty analysis and sensitivity analysis with the use of Monte Carlos Simulation was performed to determine the performance of the WQM. Jaya algorithm technology was used as an optimization tool to find optimal ADW reuse quantities at particular withdrawal points. For calibration and validation, the model was applied twice for both summer and winter seasons. The results show that the reuse quantities represent 77.2% and 49.8% of the available ADW in the study area for summer and winter, respectively, representing 49.1% and 54.5% of seasonal canal delivery. The utilization of the simulation-optimization model is usually well suited for decision support leading to near-optimum reuse assortment of ADW for irrigation.

The conjunctive management of surface water and groundwater resources is essential to sustainably manage water resources. The target study is the Osan watershed, in which approximately 60–70% of rainfall occurs during the summer monsoon in Central South Korea. Surface water resources are overexploited six times as much as groundwater resources in this region, leading to increasing pressure to satisfy the region’s growing agricultural water demand. Therefore, a simulation-optimization (S-O) model at the sub-basin scale is required to optimize water resource allocation in the Osan watershed. An S-O model based on an artificial neural network (ANN) model coupled with Jaya algorithm optimization (JA) was used to determine the yearly conjunctive supply of agricultural water. The objective was to minimize the water deficit in the watershed subject to constraints on the cumulative drawdown in each subarea. The ANN model could predict the behaviour of the groundwater level and facilitate decision making. The S-O model could minimize the water deficit by approximately 80% in response to the gross water demand, thereby proving to be suitable for a conjunctive management model for water resource management and planning.

Understanding hydrological processes using hydrological model parameters can improve the management of water resources in a watershed. This research uses the Soil and Water Assessment Tool (SWAT) model in examining the water balance in the Yeongsan River Basin, South Korea. Summer monsoon dominates the region, accounting for about 60–70% of the rain between June to September. The basin is facing significant challenges in water management due to the limited availability of water and the high demand for agricultural water due to the construction of two weirs on the river. To this end, a new multi-site calibration approach-based SWAT hydrological model that can accurately reproduce the hydrological trend and average discharges of the Yeongsan basin for 42 years (1980–2021) was developed. Some statistical matrices (such as Nash–Sutcliffe model efficiency) were utilized in calibrating and validating the model. Results show that the performance indicators for the four investigated stream flow stations were satisfactory. In addition, the water balance study revealed that the highest precipitation and evapotranspiration occurred in August, whereas the highest water yield, lateral flow, and surface flow occurred in July. Further, the model revealed that the Yeongsan river basin receives the majority of its water from the rains during the monsoon season. The model developed in this study can aid planners in managing water resources in the Yeongsan river basin.