The Sensitivity of Simulated Streamflow to Individual Hydrologic Processes Across North America

Streamflow sensitivity to different hydrologic processes varies in both space and time. In numerical modeling of streamflow, this sensitivity manifests as parameter sensitivity, which is typically model-specific.

In this study, we apply a novel analysis over more than 3000 basins across North America enabling the estimation of the process sensitivities on streamflow based on basin characteristics that can be derived from physiographic and climatologic data without needing to perform the expensive sensitivity analysis itself. This continental-scale analysis allows for high-level conclusions as to the importance of water cycle components on streamflow predictions, as the analysis considers a flexible model structure rather than an individual model. This work derives the sensitivity of streamflow simulation to entire hydrologic processes rather than only specific parameters. Process sensitivities are computed and provided for each day of the year over a wide range of physiographic and climatologic regimes, enabling future hydrologic model improvement at the continental scale.

A few highlight results are: 1) Baseflow and other sub-surface processes are of low importance across North America- especially when time points of high flows are of interest. 2) Percolation, evaporation, and infiltration show very similar patterns with increased importance in South-eastern US and west of the Rocky Mountains. 3) Up to 30% of the overall model variability can be attributed to snow melt in regions that are snow dominated (Northern Canada and Rocky mountains). Potential melt shows a similar gradient as snow melt with sensitivities of above 60% in the Province of Quebec and the Rocky Mountains. 4) Direct runoff (quickflow) is the most sensitive of all hydrologic processes- especially in South-Eastern US it is responsible for more than 80% of the model variability. 5) The derived functional relationship to estimate the process sensitivities based on basin characteristics has predictive power of at least 0.8 in Pearson correlation coefficients based on more than 1000 basins used for validation.