以不同大气环流模型评估气候变迁对高屏溪流域河川流量

由于气候变迁对于世界各地所造成之影响不一，且不同大气模型模拟所得结果不尽相同，故此类研究存在一定的不确定性。本研究将台湾大学全球变迁中心针对国际政府间气候变迁观察小组(IPCC)公告之大气环流模型(GCM)模拟结果的统计降尺资料，经水文模拟，分析气候变迁对此区域水资源可能造成的冲击及不同模型所得结果之变异。比较不同大气环流模型(包括CGCM2、CCCSR/NIES、ECHAM4、GFDL-R30、HadCM3等模型)所得的情景模拟降雨径流结果，虽然以不同大气环流模型资料模拟后，所得到之流量变化不尽相同，但仍可明显看出在气候变迁影响下，高屏溪流域流量变化趋势大致为，未来丰水期流量上升，而枯水期流量呈现小幅减少趋势。且各模型之变异范围随未来时间愈长其可能变异愈大。枯水期各模型之变异约在-26%~+15%，而丰水期之变异约在-10%~+82%。其中A2情景下枯水期各模型之变异约在-26%~+13%，而丰水期之变异约在-10%~+66%。而B2情景下枯水期各模型之变异约在-18%~+15%，而丰水期之变异约在-3%~+82%。此结果可供相关研究或决策单位参考，以期使气候变迁对水资源之冲击的评估能更佳周详。     

Application of genetic programming to project climate change impacts on the population of Formosan Landlocked Salmon

This work presents a novel methodology, genetic programming (GP), for developing environmental response functions for Formosan Landlocked Salmon (Oncorhynchus masou formosanus); these functions are then applied to evaluate the impacts of climate changes. Average daily temperature and maximal flows between two sampling periods were adopted as principal factors for categorizing environmental conditions. The GP successfully identified the response functions for various environmental categories. The response functions were further applied to assess the impact of climate change. Fourteen future possible climate scenarios were derived based on the equilibrium and transition experiments by GCMs. Impact assessment results indicated that climate change may significantly influence populations of Formosan Landlocked Salmon due to more frequent higher temperatures. Adaptation strategies are required to mitigate the impact of global climate change as current conservation measures for Formosan Landlocked Salmon habitat only reduce local human-induced effects. In the situation of complicated relationships between fish population and environmental conditions, GP provides a useful tool to obtain some information from the limited data.     

Toward a clean energy economy: With discussion on role of hydrogen sectors

This study analyzes how advances in clean energy production technology have facilitated related economic development and intensified competition between clean energy sources and fossil fuels. A forecasting model, the Taiwan general equilibrium model–Clean Energy (TAIGEM-CE), is adopted to elucidate the role of wind, solar, tidal current, geothermal, algal biodiesel, biohydrogen, hydrogen fuel cell, biodiesel and bioethanol. Baseline results indicate that biohydrogen, hydrogen fuel cell, biofuels and wind perform satisfactorily when external support is unavailable. Additionally, strong governmental investment in clean energies significantly contributes to development of biodiesel, wind, biohydrogen and hydrogen fuel cell sectors. Results of this study provide feasible means of allocating resources to achieve a smooth transition to a clean energy economy, in which biohydrogen and fuel cell will play a prominent role.     

Spatial Assessment of Climate Risk for Investigating Climate Adaptation Strategies by Evaluating Spatial-Temporal Variability of Extreme Precipitation

In response to the impacts of extreme precipitation on human or natural systems under climate change, the development of climate risk assessment approach is a crucial task. In this paper, a novel risk assessing approach based on a climate risk assessment framework with copula-based approaches is proposed. Firstly, extreme precipitation indices (EPIs) and their marginal distributions are estimated for historical and future periods. Next, the joint probability distributions of extreme precipitation are constructed by copula methods and tested by goodness-of-fit indices. The future joint probabilities and joint return periods (JRPs) of the EPIs are then evaluated. Finally, change rates of JRPs for future periods are estimated to assess climate risk with the quantitative data of exposure and vulnerability of a protected target. An actual application in Taiwan Island is successfully conducted for climate risk assessment with the impacts of extreme precipitation. The results indicate that most of regions in Taiwan Island might have higher potential climate risk under different scenarios in the future. The future joint probabilities of precipitation extremes might cause the high risk of landslide and flood disasters in the mountainous area, and of inundation in the plain area. In sum, the proposed climate risk assessing approach is expected to be useful for assisting decision makers to draft adaptation strategies and face high risk of the possible occurrence of natural disasters.

     

Optimal balance between land development and groundwater conservation in an uncertain coastal environment

An optimization problem for balancing land carrying capacity and groundwater consumption in Central Taiwan was solved using a problem-oriented grey linear programming (POGLP) algorithm. Satisfactory grey solutions were obtained through formulating and solving the proposed POGLP models. These models provide useful information to decision makers regarding the possible ranges of final alternatives when system parameters are highly uncertain. Results indicate that all transferable lands currently used for agriculture and fish cultivation should be converted into other usage. This would conserve the groundwater resources, thereby preventing further land subsidence in that area. Grey sensitivity analyses performed well in the decision-making process when a set of interval regions instead of trend curves were systematically generated without increasing the complexity of the mathematical models.     

Evaluating Future Joint Probability of Precipitation Extremes with a Copula-Based Assessing Approach in Climate Change

Changes in climate extremes may cause the variation of occurrence and intensity of floods and droughts. To investigate the future changes in joint probability behaviors of precipitation extremes for water resources management, an approach including three stages for analyzing the spatial variation of joint return periods of precipitation extremes is proposed in this paper. In the first stage, a weather generator model (WGM) was conducted with general circulation models (GCMs) under representative concentration pathway (RCP) scenarios to generate daily rainfall time series during 2021–2040 (S) and 2081–2100 (L) based on the statistics of the observed rainfall data. Four extreme precipitation indices are defined to represent extreme precipitation events. In the second stage, copula methods are adopted to establish the joint distribution of the precipitation extreme indices. The watershed-scale assessment of flood and drought applied in Shih-Men reservoir in northern Taiwan is conducted to demonstrate the possible change of joint return period. In the third stage, the change rates of joint return periods for bivariate extreme indices are demonstrated to present the occurrence possibility of floods or droughts in the future. The results indicate that floods and droughts might occur more frequently in the upstream region of the reservoir during the twenty-first century. The reservoir operations would be more important for water supply and flood mitigation. In conclusion, the possible changes of future joint probability of the precipitation extremes should be paid attention to for water resources management and draft plans to confront potential challenges in the future.     

A Novel Similarity-Based Fuzzy Clustering Algorithm by Integrating PCM and Mountain Method

The fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms have been utilized in a wide variety of fields and applications. Although many methods are derived from the FCM and PCM for clustering various types of spatial data, relational clustering has received much less attention. Most fuzzy clustering methods can only process the spatial data (e.g., in Euclidean space) instead of the nonspatial data (e.g., where the Pearson's correlation coefficient is used as similarity measure). In this paper, we propose a novel clustering method, similarity-based PCM (SPCM), which is fitted for clustering nonspatial data without requesting users to specify the cluster number. The main idea behind the SPCM is to extend the PCM for similarity-based clustering applications by integration with the mountain method. The SPCM has the merit that it can automatically generate clustering results without requesting users to specify the cluster number. Through performance evaluation on real and synthetic data sets, the SPCM method is shown to perform excellently for similarity-based clustering in clustering quality, even in a noisy environment with outliers. This complements the deficiency of other fuzzy clustering methods when applied to similarity-based clustering applications.