Quantifying reservoir water balance is an essential process for the efficient management of water resources. Water level records are often the only data available for reservoir analysis, which causes an ill‐posed problem in water resource system planning. This study proposes an analytical framework to estimate reservoir inflow and outflow from water level observations using hydrological models and reasoning carefully derived from expert knowledge and soft data. Partial reservoir inflow hydrographs were constructed from water level observations using a continuity equation and knowledge‐based constraints developed for periods of no spillway discharge. A bucket‐type rainfall‐runoff model was then calibrated to the partial inflow hydrographs. Finally, a complete reservoir inflow hydrograph was constructed using the calibrated models, which were then employed to estimate detailed reservoir outflow components under a full water balance relationship between inflow, outflow, water levels, and reservoir operation rules. The proposed solution for the ill‐posed water balance equation outperformed conventional (benchmark) approaches in accuracy and uncertainty in its application to agricultural reservoirs. This study demonstrates how hydrological modeling and reasoning, which are discreetly designed based on expert knowledge, can help to solve the ill‐posed water balance equation by creating supplementary information regarding the problem. The proposed framework is expected to assist in reconstructing reservoir routing processes using water level observations for hydrological analysis and water resource management planning.

The combined effect of limited water resources and ever-growing water demand necessitates the use of more-accurate approaches for reservoir water-balance analyses. Therefore, this study aims to develop a new operational scheme by considering flood control and variations in irrigation efficiency for simulating reservoir water-balance components. This study is conducted on three reservoirs in Korea, which were monitored for 4 years. Based on the findings of the sensitivity analyses, irrigation efficiency variation appears to be most sensitive to the outflow variables and was thus calibrated on a yearly basis. Compared with the two other schemes, i.e., with respective exclusions of flood control and irrigation efficiency variations, the new scheme performs better in simulating reservoir water levels. Additionally, it is found that without the consideration of the newly developed scheme, significant errors could occur in the simulation of water-balance components, which could lead to incorrect decisio...

This research aims to assess the impact of climate change on water balance components in irrigated paddy cultivation. The APEX-Paddy model, which is the modified version of the APEX (Agricultural Policy/Environmental eXtender) model for paddy ecosystems, was used to evaluate the paddy water balance components considering future climate scenarios. The bias-corrected future projections of climate data from 29 GCMs (General Circulation Models) were applied to the APEX-Paddy model simulation. The study area (Jeonju station) forecasts generally show increasing patterns in rainfall, maximum temperature, and minimum temperature with a rate of up to 23%, 27%, and 45%, respectively. The hydrological simulations suggest over-proportional runoff–rainfall and under-proportional percolation and deep-percolation–rainfall relationships for the modeled climate scenarios. Climate change scenarios showed that the evapotranspiration amount was estimated to decrease compared to the baseline period (1976–2005). The evaporation was likely to increase by 0.12%, 2.21%, and 7.81% during the 2010s, 2040s, and 2070s, respectively under Representative Concentration Pathway (RCP)8.5, due to the increase in temperature. The change in evaporation was more pronounced in RCP8.5 than the RCP4.5 scenario. The transpiration is expected to reduce by 2.30% and 12.62% by the end of the century (the 2070s) under RCP4.5 and RCP8.5, respectively, due to increased CO₂ concentration. The irrigation water demand is generally expected to increase over time in the future under both climate scenarios. Compared to the baseline, the most significant change is expected to increase in the 2040s by 3.21% under RCP8.5, while the lowest increase was found by 0.36% in 2010s under RCP4.5. The increment of irrigation does not show a significant difference; the rate of increase in the irrigation was found to be greater RCP8.5 than RCP4.5 except in the 2070s. The findings of this study can play a significant role as the basis for evaluating the vulnerability of rice production concerning water management against climate change.

Regionalized lumped rainfall-runoff (RR) models have been widely employed as a means of predicting the streamflow of an ungauged watershed because of their simple yet effective simulation strategies. Parameter regionalization techniques relate the parameter values of a model calibrated to the observations of gauged watersheds to the geohydrological characteristics of the watersheds. Thus, the accuracy of regionalized models is dependent on the calibration processes, as well as the structure of the model used and the quality of the measurements. In this study, we have discussed the potentials and limitations of hydrological model parameter regionalization to provide practical guidance for hydrological modeling of ungauged watersheds. This study used a Tank model as an example model and calibrated its parameters to streamflow observed at the outlets of 39 gauged watersheds. Multiple regression analysis identified the statistical relationships between calibrated parameter values and nine watershed characteristics. The newly developed regional models provided acceptable accuracy in predicting streamflow, demonstrating the potential of the parameter regionalization method. However, uncertainty associated with parameter calibration processes was found to be large enough to affect the accuracy of regionalization. This study demonstrated the importance of objective function selection of the RR model regionalization.