Abstract
Trend, detection, and attribution analyses were performed using naturalized streamflow observations and routed land surface model runoff for 10 subbasins in the Columbia River Basin during water years 1951–2008. The Energy Exascale Earth System land-surface model (ELM) version 1.0 and the Routing Application for Parallel computatIon of Discharge (RAPID) routing model were used to conduct semi-factorial simulations driven by multiple sets of bias-corrected forcing data sets. Four main potential drivers, including climate change (CLMT), CO2 concentration (CO2), nitrogen deposition (NDEP), and land use and land cover change (LULCC), were analyzed during the assessment. All subbasins showed significant (α = 0.10) declines in the observed amount of annual total streamflow, except for the Middle and Upper Snake and Upper Columbia Subbasins. These declines were led by significant decreases in June–October streamflow, which also directly led to significant decreases in peak and summer streamflow. Except for the Snake River Subbasins, LULCC had the same pattern of declines in monthly streamflow, but the period was shifted to May–September. NDEP also had significant trends in June–October; however, rather than decreases, the trends showed significant increases in streamflow. While there were significant trends in CO2, NDEP, and LULCC, their signals of change were weak in comparison to the signal in CLMT and the natural internal variability found in streamflow. Overall, the detection and attribution analysis showed that the historical changes found in annual total, center of timing of, and summer mean streamflow could be attributed to changing climate and variability.
Original language | English |
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Pages (from-to) | 6640-6652 |
Number of pages | 13 |
Journal | Water Resources Research |
Volume | 55 |
Issue number | 8 |
DOIs | |
State | Published - Aug 1 2019 |
Funding
This work is supported by the Terrestrial Ecosystem Science Scientific Focus Area (TES SFA) project funded through the Terrestrial Ecosystem Science Program, partially supported by the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computing Scientific Focus Area (RUBISCO SFA) project funded through the Regional and Global Climate Modeling Program, and partially supported by the Energy Exascale Earth System Model (E3SM) project funded through the Earth System Modeling Program, in the Climate and Environmental Sciences Division (CESD) of the Biological and Environmental Research (BER) Program in the U.S. Department of Energy Office of Science. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under contract DE-AC05-00OR22725. Relevant ELM simulations will be shared at https://daac.ornl.gov when this paper is published.
Funders | Funder number |
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Climate and Environmental Sciences Division | |
Energy Exascale Earth System Model | E3SM |
Oak | |
Terrestrial Ecosystem Science program | |
Terrestrial Ecosystem Science Scientific Focus | |
U.S. Department of Energy | DE-AC05-00OR22725 |
Office of Science | |
Biological and Environmental Research | |
Oak Ridge National Laboratory | |
Stephen F. Austin State University | |
Savannah River Operations Office, U.S. Department of Energy |
Keywords
- detection and attribution
- land surface model
- stream flow