Daeryong Park

Prioritizing alternatives in strategic environmental assessment (SEA) using VIKOR method with random sampling for data gaps

Developed a framework to prioritize the plans in SEA with incomplete information.The proposed framework was applied to dam construction planning in Korea.Applied the Monte Carlo method for the data gaps to the VIKOR method.Provided quantitative information for decision-making in SEA. This study suggests a framework to prioritize the plans in strategic environmental assessment (SEA) with incomplete information. The Monte Carlo method for the data gaps in SEA and the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, which is a multicriteria decision analysis (MCDA) method, are used. The VIKOR method is used to prioritize the plans based on a number of decision criteria because its decision philosophies, considering both the utility and regret of performance measures in MCDA, support the main objective of SEA, which is to minimize potential negative impacts and maximize potential positive impacts of plans. In this study, the proposed framework is applied to the SEA that is part of the long-term plan for dam construction in Korea. This study quantifies the environmental feasibility scores of ten alternative dam construction sites based on multiple criteria, including landscape and geology, ecological value, water quality, and environmental toxicity, and generates sets of random numbers to fill the gaps resulting from the incomplete data. By varying the importance between the regret and utility of performance measures, the rankings of feasible sites are quantified with the uncertainty bounds from the randomly generated numbers. We find that the resulting ranks among the sites can vary significantly according to the decision philosophy of stakeholders. Our results imply that the proposed framework can be utilized to provide quantitative information for decision making in SEA, considering various decision criteria pertaining to environmental aspects, uncertainty of incomplete data, and decision flexibility according to decision-makers tendency.

Robust Decision-Making Technique for Strategic Environment Assessment with Deficient Information

This study developed a framework for an evaluative decision–making system that compensates for information deficiencies by considering the sensitivity of weight factor criteria. The developed decision–making system was applied to the strategic environmental assessment (SEA) for dam planning in South Korea. The SEA investigation included ten potential dam sites (four sites in the Han River, four in the Nakdong River, and two in the Geum River), performing an evaluative comparison of dam construction effects using environmental adequacy criteria and ranking the ten potential sites appropriately. Six different deficient information filling methods were applied: the normal and uniform distribution generations, Maximax, Maximin, Hurwicz, and the equal likelihood criterion. Results indicated sites GM2, HN3, ND4, and GM2 demonstrated the highest environmental adequacies across the combined effected rivers, the Han River, the Nakdong River, and the Geum Rivers, respectively. The probabilistic distribution generations, Hurwicz and the equal likelihood criterion methods produced similar priority scores and rankings based on different river conditions; however, the Maximax and Maximin methods calculated different priority scores and rankings, indicating users should be careful when applying the latter two methods for use in a decision support system (DSS) with deficient information. In future works, it will be necessary to apply other deficient data conditions or SEA examples to perform a more robust verification of the proposed framework.

A new method for establishing hydrologic fidelity of snow depth measurements based on snowmelt–runoff hydrographs

ABSTRACT For snowmelt-driven flood studies, snow water equivalent (SWE) is frequently estimated using snow depth data. Accurate measurements of snow depth are important in providing data for continuous hydrologic simulations of such watersheds. A new hydrologic fidelity metric is proposed in this study to evaluate the potential contribution of particular snow depth datasets to flow characteristics using observed data and hydrologic modeling using the Variable Infiltration Capacity (VIC) model. Data-based hydrologic fidelity of snow depth measurements is defined as a categorical skill score between the snow depth in the watershed and the hydrograph peak or volume at the watershed outlet. Similarly, model-based hydrologic fidelity is defined as a categorical skill score between the model-simulated snow depth and the model-simulated hydrograph peak or volume. The proposed framework is illustrated using the Pecatonica River watershed in the USA, indicating which sites have a higher hydrologic fidelity, which is preferred in hydrologic studies.

Development of an ecological impact assessment model for dam construction

The purpose of this study was to develop a model for assessing the ecological value of a dam reservoir. Various evaluable characteristics (“elements”) that influence the ecology of the target area around the dam were grouped into three classes: (1) physical elements, such as altitude, slope, and aspect; (2) vegetation elements, such as forest physiognomy, vegetation type, age class, diameter at breast height (DBH) class, and density; and (3) habitat elements, such as ecological conditions, vegetation conservation classification, and frequency of wildlife appearances. The evaluation standard was quantified considering the ecological function of each element. The developed assessment model was applied to the Yeongju Dam in the Nakdong River basin in Korea. This study assumed that the ecological condition before the dam construction was 100xa0%. The results of this study showed that the physical, vegetation, and habitat elements were downgraded to 82.8, 95.5, and 90.7xa0%, respectively, after the construction of the dam. The overall ecological value was estimated to be 90.0xa0% and thus decreased by 10.0xa0% due to dam construction. Additionally, by combining the results for the evaluation elements, an ecologically healthy area was selected. The results of this study should prove useful for quantifying ecological impact and for establishing an ecological restoration plan for dam reservoirs.