Shien-Tsung Chen

Groundwater is an essential water resource in the Choushui River alluvial fan, Taiwan, where intensive groundwater abstraction has caused severe land subsidence and groundwater management challenges. Accurate groundwater level forecasting and regional groundwater assessment are therefore important for sustainable groundwater management. This study integrates support vector regression (SVR) and Space-Time Analytics and Rendering tool–Bayesian Maximum Entropy (STAR-BME) to investigate groundwater level dynamics in the Choushui River alluvial fan. Monthly groundwater level data from 11 monitoring stations, rainfall data, and groundwater pumping data were used to develop groundwater level forecasting models. Four SVR models with different combinations of groundwater level, rainfall, pumping, and seasonal variables were developed and independently validated. Results showed that the model incorporating groundwater level, rainfall, pumping, and seasonal information achieved the best forecasting performance. Forecasting performance gradually decreased as lead time increased from one to three months; however, satisfactory predictive skill was still maintained for short-term groundwater level forecasting. The results also revealed substantial spatial variability in groundwater predictability among monitoring stations. In addition, STAR-BME was applied to reconstruct regional groundwater level distributions and characterize the spatiotemporal variability of groundwater conditions across the alluvial fan. The reconstructed groundwater level maps successfully captured regional groundwater patterns and seasonal variations. The proposed framework provides a practical approach for groundwater level forecasting and regional groundwater assessment and can support groundwater resource management in heavily exploited alluvial aquifer systems.

This study combines future rainfall and temperature projections with the Budyko framework to analyze future water resource changes in four key watersheds in southern Taiwan under climate change scenarios. Since only temperature and rainfall data are available in the future climate change projections, the future evapotranspiration projections are estimated by this study. This study establishes Budyko equations of each watershed, using historical evapotranspiration and temperature data along with potential evapotranspiration estimates, to project future evapotranspiration based on future temperature and rainfall data. The future rainfall is expected to gradually increase, with the most significant increase under the SSP5-8.5 scenario. However, the rainfall increase varies with the geographical location (latitude) of the watershed, with higher rainfall increases in northern watersheds, such as Zengwen, Jiaxian, and Nanhua. Moreover, the projected evapotranspiration differs in watersheds in southern Taiwan indicating the uncertainty and challenge of evapotranspiration projection under climate change. The future runoff projections indicate an increasing trend with a significant increase (13.4–20.3%) by the end of the century (2081–2100), due to projected increases in rainfall and minor changes in evapotranspiration. The projected increase in runoff implies an optimistic outlook in water resources planning and management, but may pose a threat in flood disasters due to increased rainfall in the wet season.

This study aims to detect changes in rainfall variability caused by climate change for various scenarios in the CMIP6 (Coupled Model Intercomparison Project Phase 6) multi-model ensemble. Projected changes in rainfall unevenness in terms of different timescale indices using three categories, namely WD50 (number of wettest days for half annual rainfall), SI (seasonality index), and DWR (ratio of dry-season to wet-season rainfall) are analyzed in Zengwen Reservoir watershed, southern Taiwan over near future (2021–2040) and midterm future (2041–2060) relative to the baseline period (1995–2014) under SSP2-4.5 and SSP5-8.5 scenarios. The projected rainfall for both baseline and future periods is derived from 25 GCMs (global climate models). The results indicate that noticeably deteriorated rainfall unevenness is projected in the Zengwen Reservoir watershed over future periods, which include decreased WD50, increased SI, and decreased DWR. Though there were noticeable differences in the rainfall projections by the different GCMs, the overall consensus reveals that uncertainties in future rainfall should not be ignored. In addition, WD50 has the greatest deviated relative change in mean, which implies that the short-timescale rainfall unevenness index is easily affected by climate change in the study area. Distributional changes in rainfall unevenness determined by simultaneously considering alterations in relative changes in mean and standard deviation indicated that there was no single dominant category. However, the top two categories, with summed frequencies exceeding 0.5, characterize different properties of rainfall unevenness indices. The top two categories of WD50 and SI commonly have decreased mean and increased mean, respectively, but nearly equal frequencies of the top two categories in DWR exhibit opposite variations. The proposed rainfall unevenness change detection approach provides a better understanding of the impacts of climate change on rainfall unevenness, which is useful for preparing adaptive mitigation measures for coping with disasters induced by climate change.

In this study, a stochastic rainfall generator was developed to create continuous rainfall time series with a high temporal resolution of 10 min. The rainfall-generation process involved Monte Carlo simulation for stochastically generating rainfall parameters such as rainfall quantity, duration, inter-event time, and type. A bivariate copula was used to preserve the correlation between rainfall quantity and rainfall duration in the generated rainfall series. A modified Huff curve method was used to overcome the drawbacks of rainfall type classification by using the conventional Huff curve method. The number of discarded rainfall events was lower in the modified Huff curve method than in the conventional Huff curve method. Moreover, the modified method includes a new rainfall type that better represents rainfall events with a relatively uniform temporal pattern. The developed rainfall generator was used to reproduce rainfall series for the Yilan River Basin in Taiwan. The statistical indices of the generated rainfall series were close to those of the observed rainfall series. The results obtained for rainfall type classification indicated the necessity and suitability of the proposed new rainfall type. Overall, the developed stochastic rainfall generator can suitably reproduce continuous rainfall time series with a resolution of 10 min.

Modified fuzzy inference model with revised implication and defuzzification processes Resampling process to generate a predicted probability distribut...

Probabilistic flood forecasting, which provides uncertain information in the forecasting of floods, is practical and informative for implementing flood-mitigation countermeasures. This study adopted various machine learning methods, including support vector regression (SVR), a fuzzy inference model (FIM), and the k-nearest neighbors (k-NN) method, to establish a probabilistic forecasting model. The probabilistic forecasting method is a combination of a deterministic forecast produced using SVR and a probability distribution of forecast errors determined by the FIM and k-NN method. This study proposed an FIM with a modified defuzzification scheme to transform the FIM’s output into a probability distribution, and k-NN was employed to refine the probability distribution. The probabilistic forecasting model was applied to forecast flash floods with lead times of 1–3 hours in Yilan River, Taiwan. Validation results revealed the deterministic forecasting to be accurate, and the probabilistic forecasting was promising in view of a forecasted hydrograph and quantitative assessment concerning the confidence level.

Coastal freak waves (CFWs) are unpredictable large waves that occur suddenly in coastal areas and have been reported to cause casualties worldwide. CFW forecasting is difficult because the complex mechanisms that cause CFWs are not well understood. This study proposes a probabilistic CFW forecasting model that is an advance on the basis of a previously proposed deterministic CFW forecasting model. This study also develops a probabilistic forecasting scheme to make an artificial neural network model achieve the probabilistic CFW forecasting. Eight wave and meteorological variables that are physically related to CFW occurrence were used as the inputs for the artificial neural network model. Two forecasting models were developed for these inputs. Model I adopted buoy observations, whereas Model II used wave model simulation data. CFW accidents in the coastal areas of northeast Taiwan were used to calibrate and validate the model. The probabilistic CFW forecasting model can perform predictions every 6 h with lead times of 12 and 24 h. The validation results demonstrated that Model I outperformed Model II regarding accuracy and recall. In 2018, the developed CFW forecasting models were investigated in operational mode in the Operational Forecast System of the Taiwan Central Weather Bureau. Comparing the probabilistic forecasting results with swell information and actual CFW occurrences demonstrated the effectiveness of the proposed probabilistic CFW forecasting model.

This study is aimed at applying support vector regression to perform real-time typhoon wave height forecasting with lead times of 1 to 3 h. Two wave rider buoys in the coastal ocean northeast of Taiwan provided real-time observation wave and meteorological data for the study. Information from actual typhoon events was collected and used for model calibration and validation. Three model structures were developed with different combinations of input variables, including wave, typhoon, and meteorological data. Analysis of forecasting results indicated that the proposed models have good generalization ability, but forecasts with longer lead times underestimate extreme wave heights. Comparisons of models with different inputs indicated that adding local meteorological data enhanced forecasting accuracy. Backup models were also developed in case local wave and meteorological data were unavailable. Analysis of these models revealed that when local wave heights are unknown, using neighboring wave heights can improve forecasting performance.