Assessment of climate change impacts on flooding vulnerability for lowland management in southwestern Taiwan

Taiwan suffers from losses of economic property and human lives caused by flooding almost every year. Flooding is an inevitable, reoccurring, and the most damaging disaster in Taiwan since Taiwan is located in the most active tropic cyclone formation region of the Western Pacific. Flooding problem is further worse in land subsidence areas along southwestern coast of Taiwan due to groundwater overdraft. Increasing number of people is threatened with floods owing to climate change since it would induce sea level rise and intensify extreme rainfall. Assessments of flooding vulnerability depend not only on flooding severity, possible damage of assets exposed to floods should also be simultaneously considered. This paper aims at exploring how climate change might impact the flooding vulnerability of lowland areas in Taiwan. A flooding vulnerability evaluation scheme is proposed in this study which incorporates flooding severity (the maximum inundation depth determined by a two-dimensional model) and potential economic losses for various land uses. Effects of climate change on flooding vulnerability focus on alterations of rainfall depth for various recurrence intervals. The flood-prone Yunlin coastal area, located in southwestern Taiwan, is chosen to illustrate the proposed methodology. The results reveal that reducing flooding vulnerability can be achieved by either reducing flooding severity (implementation of flood-mitigation measures) or decreasing assets exposed to floods (suspension of land uses for flood-detention purpose). Performance of currently implemented flood-mitigation measures is insufficient to reduce flooding vulnerability when facing with climate change. However, the scenario suggested in this study to sustain room for floods efficiently reduces flooding vulnerability in both without- and with climate change situations. The suggestions provided in this study could support decision processes and help easing flooding problems of lowland management in Taiwan under climate change.     

Application of L‐moments and Bayesian inference for low‐flow regionalization in Sefidroud basin,Iran

Reliable estimation of low flows at ungauged catchments is one of the major challenges in water‐resources planning and management. This study aims at providing at‐site and ungauged sites low‐flow frequency analysis using regionalization approach. A two‐stage delineating homogeneous region is proposed in this study. Clustering sites with similar low‐flow L‐moment ratios is initially conducted, and L‐moment‐based discordancy and heterogeneity measures are then used to detect unusual sites. Based on the goodness‐of‐fit test statistic, the best‐fit regional model is identified in each hydrologically homogeneous region. The relationship between mean annual 7‐day minimum flow and hydro‐geomorphic characteristics is also constructed in each homogeneous region associated with the derived regional model for estimating various low‐flow quantiles at ungauged sites. Uncertainty analysis of model parameters and low‐flow estimations is carried out using the Bayesian inference. Applied in Sefidroud basin located in northwestern Iran, two hydrologically homogeneous regions are identified, i.e. the east and west regions. The best‐fit regional model for the east and west regions are generalized logistic and Pearson type III distributions, respectively. The results show that the proposed approach provides reasonably good accuracy for at‐site as well as ungauged‐site frequency analysis. Besides, interval estimations for model parameters and low flows provide uncertainty information, and the results indicate that Bayesian confidence intervals are significantly reduced when comparing with the outcomes of conventional t‐distribution method. Copyright © 2013 John Wiley & Sons, Ltd.     

Return period of bivariate distributed extreme hydrological events

 Extreme hydrological events are inevitable and stochastic in nature. Characterized by multiple properties, the multivariate distribution is a better approach to represent this complex phenomenon than the univariate frequency analysis. However, it requires considerably more data and more sophisticated mathematical analysis. Therefore, a bivariate distribution is the most common method for modeling these extreme events. The return periods for a bivariate distribution can be defined using either separate single random variables or two joint random variables. In the latter case, the return periods can be defined using one random variable equaling or exceeding a certain magnitude and/or another random variable equaling or exceeding another magnitude or the conditional return periods of one random variable given another random variable equaling or exceeding a certain magnitude. In this study, the bivariate extreme value distribution with the Gumbel marginal distributions is used to model extreme flood events characterized by flood volume and flood peak. The proposed methodology is applied to the recorded daily streamflow from Ichu of the Pachang River located in Southern Taiwan. The results show a good agreement between the theoretical models and observed flood data. The author wishes to thank the two anonymous reviewers for their constructive comments that improving the quality of this work.