Abstract
Climate change is leading to alterations of the hydrologic cycle and sediment movement within watersheds, but the details and impacts of these changes are indeterminate. To reduce this uncertainty, many researchers create ensembles by averaging the projected temperature and precipitation from multiple global climate model (GCM) ensemble members before running these as forcing inputs through hydrologic models. There is little research quantifying if these ensembled climate scenarios produce similar hydrologic model results to those based on individual ensemble members. We created multiple sets of ensembled climate inputs for a pair of hydrologic and sediment yield models of adjacent watersheds that drain to the Great Lakes. We then compared the hydrologic and sediment results of the models forced by these ensembled climate scenarios with hydrologic ensembles created by running the individual climate ensemble members through the same hydrologic models. We found that, in all cases, the streamflow and sediment yield results are significantly different at the 5% confidence level and the ensembled climate scenarios can lead to systematic negative biases. We also looked at three subset hydrologic ensembles: all 10 CMIP5 ensemble members from the CSIRO mk3.6 model; a Representative ensemble with high, moderate, and low precipitation predictions; and a Best Fit ensemble based on GCM performance relative to historic climate. We found that the subset ensembles covered a large portion of the range of outputs for the whole set, while producing mean annual streamflows within 5.5% of the full hydrologic ensemble results and sediment yield and sediment discharge results within 12.2%.
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Model files and results are archived at the U.S. Army Engineer Research & Development Center (DOI: 10.21079/11681/39760).
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Acknowledgments
We thank Sherry Martin for valuable insight and feedback. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP; we thank the climate modeling groups for producing and making their model output available. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.
Funding
Portions of this work were funded by a U.S. Army Corps of Engineers (USACE) Institute of Water Resources (IWR) Responses to Climate Change Pilot Project, USDA NIFA Grants 2015-68007-23133 and 2018-67003-27406, and a Food Energy and Water supplement to the KBS LTER project, NSF grant #1637653. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the USACE, the National Science Foundation, or the USDA NIFA.
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Conceptualization, T.A.D., A.D.K., and D.W.H.; methodology, T.A.D.; investigation, T.A.D.; formal analysis, T.A.D.; supervision, A.D.K. and D.W.H.; visualization, T.A.D.; writing—original draft, T.A.D.; writing—reviewing and editing, T.A.D., A.D.K., and D.W.H..
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Code used to ensemble and downscale inputs are digitally archived at the U.S. Army Engineer Research and Development Center (DOI: 10.21079/11681/39760).
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Dahl, T.A., Kendall, A.D. & Hyndman, D.W. Climate and hydrologic ensembling lead to differing streamflow and sediment yield predictions. Climatic Change 165, 8 (2021). https://doi.org/10.1007/s10584-021-03011-5
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DOI: https://doi.org/10.1007/s10584-021-03011-5