Year-end Sale % OFF 
Abstract
Streamflow sensitivity to different hydrologic processes varies in both space and time. This sensitivity is traditionally evaluated for the parameters specific to a given hydrologic model simulating streamflow. In this study, we apply a novel analysis over more than 3000 basins across North America considering a blended hydrologic model structure, which includes not only parametric, but also structural uncertainties. This enables seamless quantification of model process sensitivities and parameter sensitivities across a continuous set of models. It also leads to high-level conclusions about the importance of water cycle components on streamflow predictions, such as quickflow being the most sensitive process for streamflow simulations across the North American continent. The results of the 3000 basins are used to derive an approximation of sensitivities based on physiographic and climatologic data without the need to perform expensive sensitivity analyses. Detailed spatio-temporal inputs and results are shared through an interactive website.
Highlights
Streamflow sensitivity to different hydrologic processes varies in both space and time
Models with adequate performance in the calibration period are subjected to xSSA analyses, enabling the deduction of functional relationships between basin attributes and the sensitivity of hydrologic processes at any location
The median daily streamflow Nash-Sutcliffe efficiency (NSE) is 0.73 in calibration and 0.64 in validation. This is comparable to the performance of other models applied across the continental US (CONUS)
Summary
Streamflow sensitivity to different hydrologic processes varies in both space and time. Notwithstanding the repute of SA as a tool, there are several challenges limiting the transferability and insights of individual analyses Four such challenges, here highlighted for hydrologic applications, are: Model parameters only: SAs traditionally only estimate the sensitivities of model parameters on streamflow[35,38] or sensitivity indices of parameters on components or processes of the water cycle[34], rather than quantifying the sensitivity of streamflow to hydrologic processes, which limits insights in process understanding. Dependence on location: SAs are based on thousands of model runs, which makes them computationally very expensive They are usually only carried out for individual locations[39,40,41,42], and this limits the transferability of the obtained results to other locations. Dependence on model structure: SAs are generally performed for individual models, which further limits the generality of conclusions[30,33,43]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
