In this study, five hydrological models of increasing complexity and 12 Potential Evapotranspiration (PET) estimation methods of different data requirements were applied in order to assess their effect on model performance, optimized parameters, and robustness. The models were applied over a set of 10 catchments that are located in South Korea. The Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm was implemented to calibrate the hydrological models for each PET input while considering similar objective functions. The hydrological models’ performance was satisfactory for each PET input in the calibration and validation periods for all of the tested catchments. The five hydrological models’ performance were found to be insensitive to the 12 PET inputs because of the SCE-UA algorithm’s efficiency in optimizing model parameters. However, the five hydrological models’ parameters in charge of transforming the PET to actual evapotranspiration were sensitive and significantly affected by the PET complexity. The values of the three statistical indicators also agreed with the computed model evaluation index values. Similarly, identical behavioral similarities and Dimensionless Bias were observed in all of the tested catchments. For the five hydrological models, lack of robustness and higher Dimensionless Bias were seen for high and low flow as well as for the Hamon PET input. The results indicated that the complexity of the hydrological models’ structure and the PET estimation methods did not necessarily enhance model performance and robustness. The model performance and robustness were found to be mainly dependent on extreme hydrological conditions, including high and low flow, rather than complexity; the simplest hydrological model and PET estimation method could perform better if reliable hydro-meteorological datasets are applied.

As a significant portion of the available water resources in volcanic terrains such as Jeju Island are dependent on groundwater, reliable groundwater level forecasting is one of the important tasks for efficient water resource management. This study aims to propose deep-learning-based methods for groundwater level forecasting that can be utilized in actual management works and to assess their applicability. The study suggests practical forecasting methodologies through the Gyorae area of Jeju Island, where the groundwater level is highly volatile and unpredictable. To this end, the groundwater level data of the JH Gyorae-1 point and a total of 12 kinds of daily hydro-meteorological data from 2012 to 2021 were collected. Subsequently, five factors (i.e., mean wind speed, sun hours, evaporation, minimum temperature, and daily precipitation) were selected as hydro-meteorological data for groundwater level forecasting through cross-wavelet analysis between the collected hydro-meteorological data and groundwater level data. The study simulated the groundwater level of the JH Gyorae-1 point using the long short-term memory (LSTM) model, a representative deep-learning technique, with the selected data to show that the methodology is adequately applicable. In addition, for its better utilization in actual practice, the study suggests and analyzes (i) a derivatives-based groundwater level learning model which is defined as derivatives-based learning to forecast derivatives (gradients) of the groundwater level, not the target groundwater time series itself, and (ⅱ) an ensemble forecasting methodology in which groundwater level forecasting is performed repetitively with short time intervals.

Groundwater withdrawal results in a significant depletion of groundwater storage due to the frequency and intensity of droughts and increasing irrigation demands. To ensure the sustainable use of groundwater resources, it is necessary to accurately simulate the groundwater behavior of catchments using a surface–groundwater integrated runoff model. Most of the existing catchment runoff models have been applied to surface water management; thus, integrated runoff analysis studies that consider the interaction between surface water and groundwater are required. Due to the intensive agricultural sector in Korea and the position of rice as the staple in the Korean diet, more than 50% of groundwater abstraction is used for irrigation. Therefore, it is very important to understand the hydrological interrelationships between agricultural areas and the entire watershed. This study aimed to compare and analyze the groundwater levels in the mountainous areas and paddy field areas in the Boryeong Dam catchment through a surface–groundwater integrated runoff simulation using the Catchment Hydrologic Cycle Assessment Tool model, and to compare the hydrological responses in wet years (2010–2012) and dry years (2014–2016). The maximum difference in the monthly groundwater level in the dry years compared to the wet years was 1.07 m at the forest catchment and 0.37 m at the paddy catchment. These results indicate that the impact of drought on the groundwater level of paddy catchments is not significant compared to the forest catchments; however, drought slows the recovery of the groundwater level before the rainy season, thereby limiting the agricultural groundwater use in the catchment.

Agricultural water demands are mainly dependent on the supply from groundwater withdrawals and the supply from agricultural reservoirs. To understand the water cycle of the agricultural catchment, it is necessary to consider the actual situation of the water cycle of paddy fields in catchments through accurate hydrological modeling. In this study, streamflow simulations were implemented in consideration of the levee height of paddy fields and the irrigation period for one sub-catchment of the Boryeong Dam catchment using the integrated surface–groundwater model, CAT (Catchment Hydrologic Cycle Assessment Tool). To consider the agricultural reservoirs in modeling, the catchment was divided into the reservoir sub-catchments, upstream sub-catchments, downstream sub-catchments, and irrigated districts of each sub-catchment. This study aims to analyze the hydrological effects of agricultural reservoirs and groundwater pumping on the hydrological cycle of the catchment and on the soil moisture and groundwater level. As a result of the simulations, we found that the direct flow, baseflow, and groundwater recharge of the catchment increased with the agricultural reservoir supply water. In addition, the effect of drought on soil moisture content and groundwater level in the irrigated paddy fields from agricultural reservoirs was evaluated. The soil moisture increased by about 10% according to the water supply of agricultural reservoirs. The groundwater level rapidly decreased due to the groundwater abstraction during the irrigation period; however, it was analyzed that the water supply from agricultural reservoirs is significantly effective in preventing the decrease in the groundwater level in the irrigation season.

This study compares the streamflow simulation performance of a semi-distributed hydrological model, DWAT (Dynamic Water Resources Assessment Tool), and two conceptual models, PRMS and TANK, across three watersheds in the Republic of Korea representing mountainous (Okdong-gyo), mixed-use (Wonbu-gyo), and urbanized (Daegok-gyo) conditions. All models were calibrated and validated using identical hydroclimatic datasets and evaluation periods to ensure a fair comparison. Model performance was evaluated using nine statistical metrics (R², NSE, LogNSE, KGE, RMSE, MAE, RE, VE, and RSR), supplemented by low-flow analysis based on a Q90 threshold and non-parametric statistical tests. DWAT exhibited the most stable and highest overall performance across all watersheds, with particularly strong results in the urbanized Daegok-gyo basin (NSE = 0.85, R² = 0.88). The TANK model performed best in the mixed-use Wonbu-gyo basin (NSE = 0.82, R² = 0.83), whereas PRMS showed a systematic tendency to underestimate streamflow, especially under high-flow and low-flow conditions. Statistical comparisons using Friedman and post hoc Dunn tests confirmed that performance differences among models were statistically significant (p < 0.001). Overall, the results demonstrate that hydrological model performance strongly depends on watershed characteristics and provide a quantitative and statistically supported basis for selecting appropriate runoff simulation models according to basin type.

The increasing frequency and severity of extreme droughts caused by climate change has emerged as a key risk factor exerting complex effects on the overall national economy through a structure of interconnected industries. The Water Input–Output Linear Programming (WIOLP) model was applied to data from 2015 to 2018 to quantitatively assess the effects of drought-induced water use constraints on production and socioeconomic potential losses. By modeling scenarios in which water use decreased by 10% from 100%, changes in the gross output, the value added, the socioeconomic potential loss, and the shadow price by industry were evaluated. Results showed that socioeconomic potential losses increased nonlinearly, with maximum potential losses of 311,118 billion Korean Won (KRW) in 2015 and 355,260 billion KRW in 2018. The shadow price rose from 7311 to 73,186 KRW/m³ in 2015 and from 3291 to 89,586 KRW/m³ in 2018, confirming that the marginal productivity of water increased exponentially under stricter constraints. Industry-level analysis revealed the largest losses in high water use industries (e.g., agriculture, forestry, fisheries, chemicals, and non-metals), whereas electricity, electronics, and machinery sectors maintained relatively stable production. This study demonstrates that the WIOLP model can empirically analyze nonlinear economic ripple effects under resource constraints, overcoming limitations of conventional input–output and computable general equilibrium models.