Articles | Volume 29, issue 22
https://doi.org/10.5194/hess-29-6333-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-6333-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Nov 2025
Research article |
| 17 Nov 2025
| 17 Nov 2025
Improving model calibrations in a changing world: controlling for nonstationarity after mega disturbance reduces hydrological uncertainty
Elijah N. Boardman
CORRESPONDING AUTHOR
Graduate Program of Hydrologic Sciences, University of Nevada, Reno, Reno, Nevada, 89557, USA
Mountain Hydrology LLC, Reno, Nevada, 89503, USA
Gabrielle F. S. Boisramé
Hydrologic Sciences Division, Desert Research Institute, Reno, Nevada, 89512, USA
Mark S. Wigmosta
Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington, 99354, USA
Robert K. Shriver
Department of Natural Resources and Environmental Science, University of Nevada, Reno, Reno, Nevada, 89557, USA
Adrian A. Harpold
Graduate Program of Hydrologic Sciences, University of Nevada, Reno, Reno, Nevada, 89557, USA
Department of Natural Resources and Environmental Science, University of Nevada, Reno, Reno, Nevada, 89557, USA
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Short summary
Environmental changes can cause hydrological model biases that vary over time (nonstationarity). We demonstrate a new calibration framework to detect and correct nonstationary streamflow biases after a large wildfire, which reduces predictive uncertainty and constrains parameter equifinality.
Environmental changes can cause hydrological model biases that vary over time (nonstationarity)....
