Recent studies predict that projected climate change will lead to significant reductions in summer streamflow in the mountainous regions of the Western US. Hydrologic modeling directed at quantifying these potential changes has focused on the magnitude and timing of spring snowmelt as the key control on the spatial-temporal pattern of summer streamflow. We illustrate how spatial differences in groundwater dynamics can also play a significant role in determining streamflow responses to warming. We examine two contrasting watersheds, one located in the Western Cascades and the other in the High Cascades mountains of Oregon. We use both empirical analysis of streamflow data and physically based, spatially distributed modeling to disentangle the relative importance of multiple and interacting controls. In particular, we explore the extent to which differences in snow accumulation and melt and drainage characteristics (deep ground water vs. shallow subsurface) mediate the effect of climate change. Results show that within the Cascade Range, local variations in bedrock geology and concomitant differences in volume and seasonal fluxes of subsurface water will likely result in significant spatial variability in responses to climate forcing. Specifically, watersheds dominated by High Cascade geology will show greater absolute reductions in summer streamflow with predicted temperature increases.

Distributed hydrologic models which link seasonal streamflow and soil moisture patterns with spatial patterns of vegetation are important tools for understanding the sensitivity of Mediterranean type ecosystems to future climate and land use change. RHESSys (Regional Hydro-Ecologic Simulation System) is a coupled spatially distributed hydroecological model that is designed to be able to represent these feedbacks between hydrologic and vegetation carbon and nutrient cycling processes. However, RHESSys has not previously been applied to semiarid shrubland watersheds. In this study, the hydrologic submodel of RHESSys is evaluated by comparing model predictions of monthly and annual streamflow to stream gage data and by comparing RHESSys behavior to that of another hydrologic model of similar complexity, MIKE-SHE, for a 34 km 2 watershed near Santa Barbara, California. In model intercomparison, the differences in predictions of temporal patterns in streamflow, sensitivity of model predictions to calibration parameters and landscape representation, and differences in model estimates of soil moisture patterns are explored. Results from this study show that both models adequately predict seasonal patterns of streamflow response relative to observed data, but differ significantly in terms of estimates of soil moisture patterns and sensitivity of those patterns to the scale of landscape tessellation used to derive spatially distributed elements. This sensitivity has implications for implementing RHESSys as a tool to investigate interactions between hydrology and ecosystem processes. (KEY TERMS: modeling; watershed management; surface water hydrology; Mediterranean type ecosystems; soil moisture; seasonal streamflow; semiarid.)

Increasingly sophisticated process-based ecosystem models account for the ability of plants to vary the proportion of net photosynthate that is allocated to individual organssuch as leaves, stems and roots. Because the governing mechanisms are still not well understood, models differ in the strategies used to represent carbon allocation processes. Allocation schemes may have important implications for ecosystem structure and biogeochemical cycling, thus there is a need to better understand how different carbon allocation strategies influence estimates of variables that are of interest to model users. At the same time, uncertainty in other ecophysiological parameters that are commonly used in carbon cycling models may influence these estimates and interact with different carbon allocation strategies. We use a coupled ecohydrologic model to understand how uncertainty in three relatively simple allocation strategies affects carbon (C) and streamflow estimates in two case study forested mountain watersheds in the western United States: a relatively wet site located in the western Oregon Cascades, and a drier site in California's Sierra Nevada. Ecophysiological parameters controlling productivity rates, morphology, and nutrient requirements for growth are varied as well. The influence of specific ecophysiological parameters and allocation strategies on C sequestration and streamflow estimates differed between sites. At the wetter site, uncertainty in C cycling processes resulted in a threefold difference in potential sequestered carbon, but had a negligible effect on annual and low monthly streamflow estimates. Conversely, at the drier site, C pool estimates showed limited sensitivity to ecophysiological parameter uncertainty, but considerable difference in annual and low monthly streamflow estimates across ecophysiological assumptions. At both sites, stemwood C pool estimates exceeded literature-derived field values when branch mortality-a surrogate for density thinning-was not included in addition to background mortality. Despite using site-and species-specific information, we are unable to invalidate any of the allocation strategies considered. Our results suggest that uncertainty in parameterization of ecophysiological parameters and assumptions about carbon allocation can strongly influence model estimates of both streamflow and forest carbon sequestration potential, but that influence is likely to vary with site bioclimatic characteristics.

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 endmember 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.

As managers of agricultural and natural resources are confronted with uncertainties in global change impacts, the complexities associated with the interconnected cycling of nitrogen, carbon, and water present daunting management challenges. Existing models provide detailed information on specific sub-systems (e.g., land, air, water, and economics). An increasing awareness of the unintended consequences of management decisions resulting from interconnectedness of these sub-systems, however, necessitates coupled regional earth system models (EaSMs). Decision makers' needs and priorities can be integrated into the model design and development processes to enhance decision-making relevance and "usability" of EaSMs. BioEarth is a research initiative currently under development with a focus on the U.S. Pacific Northwest region that explores the coupling of multiple stand-alone EaSMs to generate usable information for resource decision-making. Direct engagement between model developers and non-academic stakeholders involved in resource and environmental management decisions throughout the model development process is a critical component of this effort. BioEarth utilizes a bottom-up approach for its land surface model that preserves fine spatialscale sensitivities and lateral hydrologic connectivity, which makes it unique among many regional EaSMs. This paper describes the BioEarth initiative and highlights opportunities and challenges associated with coupling multiple stand-alone models to generate usable information for agricultural and natural resource decision-making.