Mitigating the Impacts of Climate Nonstationarity on Seasonal Streamflow Predictability in the U.S. Southwest
Abstract
Seasonal streamflow predictions provide a critical management tool for water managers in the American Southwest. In recent decades, persistent prediction errors for spring and summer runoff volumes have been observed in a number of watersheds in the American Southwest. While mostly driven by decadal precipitation trends, these errors also relate to the influence of increasing temperature on streamflow in these basins. Here we show that incorporating seasonal temperature forecasts from operational global climate prediction models into streamflow forecasting models adds prediction skill for watersheds in the headwaters of the Colorado and Rio Grande River basins. Current dynamical seasonal temperature forecasts now show sufficient skill to reduce streamflow forecast errors in snowmelt‐driven regions. Such predictions can increase the resilience of streamflow forecasting and water management systems in the face of continuing warming as well as decadal‐scale temperature variability and thus help to mitigate the impacts of climate nonstationarity on streamflow predictability.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 23, 2017
- Source ID
- 10.1002/2017gl076043
Entities
People
- Andrew W Wood
- Angus G. Goodbody
- Dagmar Llewellyn
- Douglas Blatchford
- Flavio Lehner
- Florian Pappenberger
Organizations
- Climate Program Office
- National Center for Atmospheric Research
- National Science Foundation
- Natural Resources Conservation Service
- United States Army Corps of Engineers
- United States Bureau of Reclamation