In this paper we present a Histogram Matching Approach (HMA) for assessment of the flow regime alteration. The HMA uses the degree of histogram dissimilarity as a metric for impact assessment, which is based on the quadratic-form distance between the frequency vectors of the pre- and post-impact histograms weighted by a specified similarity matrix. The HMA is coupled with an aggregated multiobjective optimization genetic algorithm and applied to a case study on the Kaoping diversion weir (Taiwan) for determining the optimal environmental flow scheme that balances the ecosystem and human needs objectives. Two key issues are addressed in this study. First, we compare the performances of the HMA and existing Range of Variability Approach (RVA). Second, we employ three types of similarity function to investigate their effect on the outcomes of the HMA. The results reveal that the HMA consistently outperforms the RVA in preserving the natural flow variability regardless of what type of similarity function is used. No single type of similarity function can be found that would simultaneously best preserve the natural patterns of 32 Indicators of Hydrologic Alteration (IHA). For the situations where the water-supply reliability is of critical concern, the pulse similarity is recommended because it would assure the smallest water-supply deficit. If, however, minor degradation in the water-supply reliability may be overlooked, the linear similarity is suggested because it would generally result in the post-impact flows that most satisfactorily resemble to the natural flow regime.
All Science Journal Classification (ASJC) codes
- Environmental Chemistry
- Water Science and Technology
- Environmental Science(all)