S One of the primary roles of modeling in critical zone research studies is to provide a framework for integrating field measurements and theory and for generalizing results across space and time. In the Southern Sierra Critical Zone Observatory (SCZO), significant spatial heterogeneity associated with mountainous terrain combined with high inter-annual and seasonal variation in climate, necessitates the use of spatial-temporal models for generating landscape scale understanding and predictions. Science questions related to coupled hydrologic and biogeochemical fluxes within the critical zone require a framework that can account for multiple and interacting processes. One of the core tools for the SCZO will be RHESSYs (Regional hydro-ecologic simulation system). RHESSys is an existing GIS-based model of hydrology and biogeochemical cycling. For the SCZO, we use RHESSys as an open-source, objected oriented model that can be extended to incorporate findings from field-based monitoring and analysis. We use the model as a framework for data assimilation, spatial-temporal interpolation, prediction, and scenario and hypothesis generation. Here we demonstrate the use of RHESSys as a hypothesis generation tool. We show how initial RHESSys predictions can be used to estimate when and where connectivity within the critical zone will lead to significant spatial or temporal gradients in vegetation carbon and moisture fluxes. We use the model to explore the potential implications of heterogeneity in critical zone controls on hydrologic processes at two scales: micro and macro. At the micro scale, we examine the role of preferential flowpaths. At the macro scale we consider the importance of upland-riparian zone connectivity. We show how the model can be used to design efficient field experiments by, a-priori providing quantitative estimate of uncertainty and highlighting when and where measurements might most effectively reduce that uncertainty. Spatial modeling of coupled hydrologic-biogeochemical processes for the Southern Sierra Critical Zone Observatory Christina Tague, Bren School of Environmental Science and Management, University of California at Santa Barbara; RHESSys is a GIS based, terrestrial eco- hydrologic modeling framework designed to simulate carbon, water, and nutrient fluxes at the watershed scale. RHESSys models the temporal and spatial variability of ecosystem processes and interactions at a daily (and in some cases hourly) time step over multiple years by combining a set of physically based process models and a methodology for partitioning and parameterizing the landscape. Model predictions of spatial patterns of soil moisture under historic climate variability show distinct zones of response - with transpiration in upper elevations showing high sensitivity to temperature and lower elevations show moisture limitations - Elevational gradients in transpiration response could be used to identify strategic elevations for sap-flow and other transpiration measurements Streamflow comparison and snow cannot constrain critical zone soil parameters (due to equifinality) - and we still don’t know how well the model accounts for spatial patterns of lateral and vertical movement of water within the watershed. One way to better constrain this is to look at spatial response variables like carbon storage and transpiration. As an example of how RHESSys can be used to guide field data collection, we show results from a prior modeling study in the Upper Merced River basin - another snow dominated Sierra catchment. Hydrograph comparison below shows the classic approach to model validation. We extend this by comparing RHESSys predictions of percent basin snow cover with those derived from remote sensing. At the Sierra CZO, we will take advantage of the coupling between carbon and water in RHESSys, and use comparison between modeled and observed measures of carbon stores such as LAI to further constrain model uncertainty. RHESSys has been utilized in a number of locations to examine climate and land use change impacts on hydrology, carbon and nitrogen cycling. Testing modeling approaches and theories for a range of sites and research questions allows us to continually refine the model and use the model as a evolving ‘knowledge’ base. Watershed Regional Model to scale up in space and time (apply to climate change and fire scenarios) Science Questions Five immediate research questions will define and focus the core measurement and research program, though many additional, more specific questions will also build on the CZO resources. 1.How do coupled hydrologic and biogeochemical fluxes into and out of the critical zone vary along a gradient from rainfall- to snowfall dominated mountain catchments, and how do different parts of this system respond to seasonal transitions, e.g. from cold and wet in the winter/spring to hot and dry in the summer/fall? 2.What is the role of extreme hydrologic events in hydrologic and biogeochemical balances, including erosion, sedimentation and movement of both solutes and sediments through the system, I.e. to what extent are they perturbations or are they responsible for much of the material movement? 3.To what extent does vegetation modulate or actively control the primary subsurface fluxes of water and nutrients, versus act as passive agents? 4.Over what time and space scales, and during what seasons, are macropores and other short-circuit pathways dominant in the critical zone, and what is the role of disturbance in these pathways? 5.How does the presence of a seasonal snowpack affect critical zone processes in catchments and on hillslopes, and how will the relevant processes and reservoirs respond as the climate warms and mountain snowpacks recede? PI’s: Roger C. Bales, University of California Merced;Elizabeth W. Boyer, University of California, Berkeley; Martha H. Conklin, University of California Merced; James W. Kirchner, University of California Berkeley; Michael L. Goulden, University of California Irvine; Jan W. Hopmans, University of California Davis; Carolyn T. Hunsaker, University of California Los Angeles; Dale W. Johnson, University of Nevada, Reno; Christina Tague, University of California Santa Barbara Sierra CZO Improving the integration of models and field data: a teaser RHESSys Carbon & Nitrogen processing in RHESSysVertical hydrological processing in RHESSys How we will use modeling in the Sierra CZO Lateral hydrological processing in RHESSys MODEL FIELD EXPERIMENTS Hillslope Scale As with all hydrologic models, calibration is needed to define flow rates through critical zone soil and permeable bedrock layers. In RHESSys, we typically calibration two shallow subsurface flow parameters: m - decay of saturated hydraulic conductivity with depth In systems where there is evidence of significant deeper groundwater flow beneath the soil, we include additional parameters to define amount of bypass flow and drainage rates of these deeper aquifers. K - saturated hydraulic conductivity at the surface Fall 2007 Christensen, L, Tague, C, and Baron, J (in press) Hydrological Prcoesses Spatiotemporal response of transpiration to climate variation in a snow dominated mountain ecosystem wide average NPP tends to increase with warming. Without this connection NPP decreases. In other words, conclusion about whether the ecosystem will be more or less productive under a warm climate are likely to depend on lateral redistribution of water. We also look at model estimates of NPP at a function of peak annual SWE and how this changes with a 1.5C warming. Again we consider the two different connectivity scenarios, calibrated above. Results show that the more connection between upland and riparian areas, basin (Note that poor performance for both approaches in years such as 1983 reflect years with significant misclassification of incoming precipitation as rain vs snow - linkage with remote sensing snow data as part of the CZO will help to reduce these types of errors). In cases where we cannot constrain critical zone soil parameters and hydrologic function, we can use the model to explore sensitivity to different assumptions. Here, we calibrate the model using two scenarios: one in which there is significant lateral redistribution between upland and riparian area and a second where vertical hydrologic processes dominate. Hydrologic performance is not substantially degraded by limiting lateral connectivity. Baseline 119 With Warming 125 Baseline 106 With Warming 90