Comparison of uncertainty in multi-parameter and multi-model ensemble hydrologic analysis of climate change
- 1Department of Agricultural and Biological Engineering & Tropical Research and Education Center, University of Florida, Homestead, FL 33031, United States
- 2Department of Rural and Bio-Systems Engineering, Chonnam National University, Gwangju 500-757, Republic of Korea
- 3Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA 24601, United States
- 4Texas A & M AgriLife Research, Texas A & M University, Temple, TX 76502, United States
- 5Research Department, APEC Climate Center, Busan 612-020, Republic of Korea
- 6Department of Agricultural Engineering, Gyeongsang National University, Jinju 660-701, Republic of Korea
Abstract. Quantification of uncertainty in ensemble based predictions of climate change and the corresponding hydrologic impact is necessary for the development of robust climate change adaptation plans. Although the equifinality of hydrological modeling has been discussed for a long time, its impact on the hydrologic analysis of climate change has not been studied enough to provide clear ideas that represent the relative contributions of uncertainty contained in both multi-GCM (general circulation model) and multi-parameter ensembles toward the projections of hydrologic components. This study demonstrated that the uncertainty in multi-GCM (or multi-model) ensembles could be an order of magnitude larger than that of multi-parameter ensembles for predictions of direct runoff, suggesting that the selection of appropriate GCMs should be much more emphasized than the selection of a parameter set among behavioral ones when projecting direct runoff. When simulating soil moisture and groundwater, on the other hand, equifinality in hydrologic modeling was more influential than uncertainty in the multi-GCM ensemble. Also, uncertainty in a hydrologic simulation of climate change impact was much more closely associated with uncertainty in ensemble projections of precipitation than that in projected temperature, indicating a need to pay closer attention to the precipitation data for improvement of the reliability of hydrologic predictions. From among 35 GCMs incorporated, this study identified GCMs that contributed the most and least to uncertainty in an assessment of climate change impacts on the hydrology of 61 Ohio River watersheds, thereby exhibiting a framework to quantify contributions of individual GCMs to the overall uncertainty in climate change modeling.
Younggu Her et al.
Younggu Her et al.
Younggu Her et al.
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