The sustainable use of groundwater has become increasingly challenging due to extreme hydrological events and anthropogenic activity. In this study, the basin-scale groundwater response to precipitation variation was analyzed using an integrated model that comprises lumped models for land and river recharges and a distributed model for groundwater. The integrated model was applied to the Chih-Ben watershed, Taiwan, using 20years (1988Taiwan, using 20years ( -2007 of data. The hydrological data were analyzed for trends using statistical tests. Based on decreasing trends in precipitation and groundwater levels and an increasing trend in stream flow, the oblique-cut method was applied to precipitation and excess infiltration to assess land and streambed recharge. Distributed numerical groundwater modeling was used to simulate the basin-scale groundwater responses to precipitation variation and anthropogenic pumping. The model was calibrated using stable-isotope and groundwater-level data. The safe yields were estimated for the Chih-Ben watershed for dry, wet, and normal precipitation scenarios. The safe yield of groundwater was shown to vary with precipitation, which does not guarantee the sustainable use of groundwater resources. Instead, water resources should be assessed at a basin scale, taking into account the whole ecosystem, rather than only considering water for human consumption in the alluvium.

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Typhoon events occur frequently in Taiwan resulting in flood-related disasters. A well-operated reservoir can reduce the severity of a disaster. This study incorporates a genetic algorithm, a river hydraulic model, an artificial neural network and a simulation model of Tseng-Wen Reservoir to propose a real-time flooding operation model. The model includes two parts: an optimal flooding operation model (OFOM) and a reservoir inflow forecasting. Given an inflow condition, the OFOM is run based on the safety of the dam structure, reservoir flooding operation rule, and minimization of the downstream loss due to flood. A simple and robust model for reservoir inflow forecasting, which automatically chooses the most similar event from a typhoon event database as the future inflow, is developed. This study compares the model results with the real operations during Typhoons Sepat, Krosa, Kalmaegi, Fung-wong, Sinlaku, and Jangmi. This study compares the performances of the proposed model with the practical operation operated by the management center of Tseng-Wen Reservoir. The proposed model indicates shorter flooding duration in the downstream area. For example, the flood durations of the model output are 4 and 3 hours shorter during Typhoon Krosa and Sinlaku, respectively, than the practical operations.

Hydraulic conductivity (K) is crucial for groundwater studies and is conventionally obtained through pumping tests, which are costly due to well installation, resulting in a limited amount of data. Recent studies have addressed this limitation by estimating K through the integration of a pumping-test K and electrical resistivity measurements. While this approach is suitable for local areas, it cannot readily be applied to determine K fields for the composite fan delta. This study proposes and demonstrates an advanced method to estimate the K's spatial distribution of a composite fan delta. The proposed method included data classification, linear regression, and kriging interpolation. Data classification was conducted using a physical-based zonation method. The K and formation factor (F) data pairs were classified into several groups. Linear regression was used to develop K-F mapping for each group. The regression results showed a good correlation between K and F in each group. These K-F mappings were verified by additional pumping tests. These results indicate that estimation errors were between 7 m/day and 58 m/day, and the correlation coefficient of each data group was greater than 0.8. Based on these regression equations and ordinary kriging method, the detailed K spatial distribution of the study area was derived. According to these results, the proposed method is efficient and economical to delineate K's spatial features for regional groundwater systems and can benefit groundwater-related studies.

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