Parameterizing sub-surface drainage with geology to improve modeling streamflow responses to climate in data limited environments
Abstract. Hydrologic models are one of the core tools used to project how water resources may change under a warming climate. These models are typically applied over a range of scales, from headwater streams to higher order rivers, and for a variety of purposes, such as evaluating changes to aquatic habitat or reservoir operation. Most hydrologic models require streamflow data to calibrate subsurface drainage parameters. In many cases, long-term gage records may not be available for calibration, particularly when assessments are focused on low-order stream reaches. Consequently, hydrologic modeling of climate change impacts is often performed in the absence of sufficient data to fully parameterize these hydrologic models. In this paper, we assess a geologic-based strategy for assigning drainage parameters. We examine the performance of this modeling strategy for the McKenzie River watershed in the US Oregon Cascades, a region where previous work has demonstrated sharp contrasts in hydrology based primarily on geological differences between the High and Western Cascades. Based on calibration and verification using existing streamflow data, we demonstrate that: (1) a set of streams ranging from 1st to 3rd order within the Western Cascade geologic region can share the same drainage parameter set, while (2) streams from the High Cascade geologic region require a different parameter set. Further, we show that a watershed comprised of a mixture of High and Western Cascade geologies can be modeled without additional calibration by transferring parameters from these distinctive High and Western Cascade end-member parameter sets. More generally, we show that by defining a set of end-member parameters that reflect different geologic classes, we can more efficiently apply a hydrologic model over a geologically complex landscape and resolve geo-climatic differences in how different watersheds are likely to respond to simple warming scenarios.