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Sci Rep. 2019 Mar 21;9(1):4974. doi: 10.1038/s41598-019-41334-7.

Uncertainty in hydrological analysis of climate change: multi-parameter vs. multi-GCM ensemble predictions.

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Department of Agricultural and Biological Engineering/Tropical Research and Education Center, Institute of Food and Agricultural Sciences, University of Florida, Homestead, Florida, USA.
Department of Rural and Bio-Systems Engineering, Chonnam National University, Gwangju, Republic of Korea.
Climate Services and Research Department, APEC Climate Center, Busan, Republic of Korea.
Department of Agricultural Engineering, Institute of Agriculture and Life Science, Gyeongsang National University, Jinju, Republic of Korea.
Texas A&M AgriLife Research, Texas A&M University, Temple, Texas, United States.
Bureau of Watershed Management & Modeling, St. Johns River Water Management District, Palatka, Florida, USA.


The quantification of uncertainty in the ensemble-based predictions of climate change and the corresponding hydrological impact is necessary for the development of robust climate adaptation plans. Although the equifinality of hydrological modeling has been discussed for a long time, its influence on the hydrological analysis of climate change has not been studied enough to provide a definite idea about the relative contributions of uncertainty contained in both multiple general circulation models (GCMs) and multi-parameter ensembles to hydrological projections. This study demonstrated that the impact of multi-GCM ensemble uncertainty on direct runoff projections for headwater watersheds could be an order of magnitude larger than that of multi-parameter ensemble uncertainty. The finding suggests that the selection of appropriate GCMs should be much more emphasized than that of a parameter set among behavioral ones. When projecting soil moisture and groundwater, on the other hand, the hydrological modeling equifinality was more influential than the multi-GCM ensemble uncertainty. Overall, the uncertainty of GCM projections was dominant for relatively rapid hydrological components while the uncertainty of hydrological model parameterization was more significant for slow components. In addition, uncertainty in hydrological projections was much more closely associated with uncertainty in the ensemble projections of precipitation than temperature, indicating a need to pay closer attention to precipitation data for improved modeling reliability. Uncertainty in hydrological component ensemble projections showed unique responses to uncertainty in the precipitation and temperature ensembles.

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