[1] The use of uncertainty analysis is gaining considerable attention in catchment hydrological modeling. In particular, the choice of appropriate model structure, identifiability of parameter values, and the reduction of model predictive uncertainty are deemed as essential elements of hydrological modeling. The chosen model structure must be parsimonious, and the parameters used must either be derivable from field-measured data or inferred unambiguously from analysis of catchment response data. In this paper, a long-term water balance model for the Susannah Brook catchment in Western Australia has been pursued using the ''downward approach,'' which is a systematic approach to determine the model with the minimum level of complexity, with parameter values that, in theory, are derivable from existing physiographic data relating to the catchment. Through analysis of rainfall-runoff response at three different timescales and exploring the climate, soil, and vegetation controls on the water balance response at these timescales, an initial model structure was formulated, and a priori model parameter values were estimated. Further investigation with the use of auxiliary data such as deuterium concentration in the streamflow exposed inadequacies in the chosen model structure. Two more model structures were then proposed and investigated through formulating alternative hypotheses regarding the underlying causes of observed variability, including those suggested by observed deuterium composition in the streamflows and observed groundwater level dynamics. Along the way, the resulting models were systematically evaluated in three dimensions: ability to reproduce observations, predictive uncertainty, and physical realism. The final model, which included an efficient but detailed representation of unsaturated zone time delay, was found to be superior on all three counts, i.e., improved performance, reduced predictive uncertainty, and improved physical realism. These improvements can be directly attributed to the use of the auxiliary data (deuterium composition and groundwater level dynamics), which identified weaknesses in previous model structures, and the multiple wetting front model of water movement in the unsaturated zone, which captured more effectively the time delay in the unsaturated zone.Citation: Son, K., and M. Sivapalan (2007), Improving model structure and reducing parameter uncertainty in conceptual water balance models through the use of auxiliary data, Water Resour. Res., 43, W01415,

Winter is an understudied but key period for the socioecological systems of northeastern North American forests. A growing awareness of the importance of the winter season to forest ecosystems and surrounding communities has inspired several decades of research, both across the northern forest and at other mid‐ and high‐latitude ecosystems around the globe. Despite these efforts, we lack a synthetic understanding of how winter climate change may impact hydrological and biogeochemical processes and the social and economic activities they support. Here, we take advantage of 100 years of meteorological observations across the northern forest region of the northeastern United States and eastern Canada to develop a suite of indicators that enable a cross‐cutting understanding of (1) how winter temperatures and snow cover have been changing and (2) how these shifts may impact both ecosystems and surrounding human communities. We show that cold and snow covered conditions have generally decreased over the past 100 years. These trends suggest positive outcomes for tree health as related to reduced fine root mortality and nutrient loss associated with winter frost but negative outcomes as related to the northward advancement and proliferation of forest insect pests. In addition to effects on vegetation, reductions in cold temperatures and snow cover are likely to have negative impacts on the ecology of the northern forest through impacts on water, soils, and wildlife. The overall loss of coldness and snow cover may also have negative consequences for logging and forest products, vector‐borne diseases, and human health, recreation, and tourism, and cultural practices, which together represent important social and economic dimensions for the northern forest region. These findings advance our understanding of how our changing winters may transform the socioecological system of a region that has been defined by the contrasting rhythm of the seasons. Our research also identifies a trajectory of change that informs our expectations for the future as the climate continues to warm.

Climate warming will have substantial impacts on hydrological fluxes in the California Sierra. A commonly used approach for assessing these impacts, particularly in mountain watersheds, is to substitute space for time. This conceptual model assumes that with warming, the hydrologic behaviour of higher elevation snow dominated watersheds (SDWs) will converge to the hydrologic behaviour of lower elevation transient snow watersheds (TSWs). To investigate the efficacy of this conceptual model, a process‐based model (RHESSys) was applied to a TSW and a SDW with a mean annual temperature 2 °C lower than the TSW in the Sierra National forest, California. This study investigated the effect of climate warming (2 and 4 °C) on the model estimates of snow water equivalent (SWE), streamflow, evapotranspiration (ET), and moisture deficit in the two watersheds. Modelling results show that SDW under 2 °C warming scenarios generates monthly SWE similar in magnitude and timing as TSW under historic conditions. However, SDW under 2 °C warming scenarios generates higher annual and summer streamflow due to shallower groundwater storage and experiences less water limitation due to lower ET, compared with TSW under historical climate conditions. In both watersheds, leaf area index and wetness index are primary factors controlling spatial patterns of seasonal ET under both historical climate conditions and warming scenarios. Climate warming increases the spatial variability in monthly ET, especially in the summer period. These modelling results suggest that vegetation structure and subsurface properties may be as important as climate in explaining hydrologic response to climate warming in small Sierra Nevada watersheds.

Abstract:The effect of fine-scale topographic variability on model estimates of ecohydrologic responses to climate variability in California's Sierra Nevada watersheds has not been adequately quantified and may be important for supporting reliable climate-impact assessments. This study tested the effect of digital elevation model (DEM) resolution on model accuracy and estimates of the sensitivity of ecohydrologic responses to inter-annual climate variability. The Regional Hydro-Ecologic Simulation System (RHESSys) was applied to eight headwater, high-elevation watersheds located in the Kings River drainage basin. Each watershed was calibrated with measured snow depth (or snow water equivalent) and daily streamflow. Modeled streamflow estimates were sensitive to DEM resolution, even with resolution-specific calibration of soil drainage parameters. For model resolutions coarser than 10 m, the accuracy of streamflow estimates largely decreased. Reduced model accuracy was related to the reduction in spatial variance of a topographic wetness index with coarser DEM resolutions. This study also found that among the long-term average ecohydrologic estimates, summer flow estimates were the most sensitive to DEM resolution, and coarser resolution models overestimated the climatic sensitivity for evapotranspiration and net primary productivity. Therefore, accounting for fine-scale topographic variability in ecohydrologic modeling may be necessary for reliably assessing climate change effects on lower-order Sierra Nevada watersheds (ď2.3 km 2 ).

The potential for increased loads of dissolved organic carbon (DOC) in streams and rivers is a concern for regulating the water quality in water supply watersheds. With increasing hydroclimatic variability related to global warming and shifts in forest ecosystem community and structure, understanding and predicting the magnitude and variability of watershed supply and transport of DOC over multiple time scales have become important research and management goals. In this study, we use a distributed process‐based ecohydrological model (Regional Hydro‐Ecological Simulation System [RHESSys]) to explore controls and predict streamflow DOC loads in Biscuit Brook. Biscuit Brook is a forested headwater catchment of the Neversink Reservoir, part of the New York City water supply system in the Catskill Mountains. Three different model structures of RHESSys were proposed to explore and evaluate hypotheses addressing how vegetation phenology and hydrologic connectivity between deep groundwater and riparian zones influence streamflow and DOC loads. Model results showed that incorporating dynamic phenology improved model agreement with measured streamflow in spring, summer, and fall and fall DOC concentration, compared with a static phenology. Additionally, the connectivity of deep groundwater flux through riparian zones with dynamic phenology improved streamflow and DOC flux in low flow conditions. Therefore, this study suggests the importance of inter‐annual vegetation phenology and the connectivity of deep groundwater drainage through riparian zones in the hydrology and stream DOC loading in this forested watershed and the ability of process‐based ecohydrological models to simulate these dynamics. The advantage of a process‐based modelling approach is specifically seen in the sensitivity to forest ecosystem dynamics and the interactions of hydroclimate variability with ecosystem processes controlling the supply and distribution of DOC. These models will be useful to evaluate different forest management approaches toward mitigating water quality concerns.

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While the hyporheic zone (HZ) accounts for a significant portion of whole stream CO2 concentrations, HZ respiration modeling studies are lacking in quantifying their contributions to the total CO2 at large watershed/basin scales. Quantifying the contribution of anaerobic respiration is also underappreciated. This study used a carbon‐nitrogen‐coupled river corridor model to quantify HZ aerobic and anaerobic respiration and determined the key factors controlling their spatial variability within the Columbia River Basin (CRB). The modeled respiration patterns showed high spatial variability. Among the nine sub‐basins composing the CRB, the Lower Columbia and the Willamette, which receive higher precipitation, had higher respiration. Medium‐sized rivers (fourth to sixth orders) produced the highest aerobic and anaerobic respiration among reaches of different sizes. At the basin scale, aerobic respiration is dominant, representing approximately 98.7% of the total respiration across the CRB. While most of the reaches were dominant with aerobic respiration, reaches in agricultural land showed a relatively higher anaerobic respiration (18%) ratio. A variable importance analysis showed that hyporheic exchange flux controlled most of the spatial variability of HZ respiration, dominating over other physical variables such as residence time, stream dissolved organic carbon (DOC), nitrate, and dissolved oxygen (DO). The influence of substrate concentration (DOC and DO) is larger in modeling anaerobic respiration than aerobic respiration. Future efforts will focus on improving the estimation of the HZ exchange flux and the implementation of spatially explicit parameterizations for the reactions of interest to reduce model uncertainty.