National hydrologic connectivity classification links wetlands with stream water quality.

Wetland hydrologic connections to downstream waters influence stream water quality. However, no systematic approach for characterizing this connectivity exists. Here using physical principles, we categorized conterminous US freshwater wetlands into four hydrologic connectivity classes based on stream contact and flowpath depth to the nearest stream: riparian, non-riparian shallow, non-riparian mid-depth and non-riparian deep. These classes were heterogeneously distributed over the conterminous United States; for example, riparian dominated the south-eastern and Gulf coasts, while non-riparian deep dominated the Upper Midwest and High Plains. Analysis of a national stream dataset indicated acidification and organic matter brownification increased with connectivity. Eutrophication and sedimentation decreased with wetland area but did not respond to connectivity. This classification advances our mechanistic understanding of wetland influences on water quality nationally and could be applied globally.

Using hydrologic landscape classification and climatic time series to assess hydrologic vulnerability of the western U.S. to climate.

We apply the hydrologic landscape (HL) concept to assess the hydrologic vulnerability of the western United States (U.S.) to projected climate conditions. Our goal is to understand the potential impacts of hydrologic vulnerability for stakeholder-defined interests across large geographic areas. The basic assumption of the HL approach is that catchments that share similar physical and climatic characteristics are expected to have similar hydrologic characteristics. We use the hydrologic landscape vulnerability approach (HLVA) to map the HLVA index (an assessment of climate vulnerability) by integrating hydrologic landscapes into a retrospective analysis of historical data to assess variability in future climate projections and hydrology, which includes temperature, precipitation, potential evapotranspiration, snow accumulation, climatic moisture, surplus water, and seasonality of water surplus. Projections that are beyond 2 standard deviations of the historical decadal average contribute to the HLVA index for each metric. Separating vulnerability into these seven separate metrics allows stakeholders and/or water resource managers to have a more specific understanding of the potential impacts of future conditions. We also apply this approach to examine case studies. The case studies (Mt. Hood, Willamette Valley, and Napa-Sonoma Valley) are important to the ski and wine industries and illustrate how our approach might be used by specific stakeholders. The resulting vulnerability maps show that temperature and potential evapotranspiration are consistently projected to have high vulnerability indices for the western U.S. Precipitation vulnerability is not as spatially uniform as temperature. The highest-elevation areas with snow are projected to experience significant changes in snow accumulation. The seasonality vulnerability map shows that specific mountainous areas in the west are most prone to changes in seasonality, whereas many transitional terrains are moderately susceptible. This paper illustrates how HL and the HLVA can help assess climatic and hydrologic vulnerability across large spatial scales. By combining the HL concept and HLVA, resource managers could consider future climate conditions in their decisions about managing important economic and conservation resources.

Embedding co-production and addressing uncertainty in watershed modeling decision-support tools: successes and challenges.

Decision-support tools (DSTs) are often produced from collaborations between technical experts and stakeholders to address environmental problems and inform decision making. Studies in the past two decades have provided key insights on the use of DSTs and the importance of bidirectional information flows among technical experts and stakeholders - a process that is variously referred to as co-production, participatory modeling, structured decision making, or simply stakeholder participation. Many of these studies have elicited foundational insights for the broad field of water resources management; however, questions remain on approaches for balancing co-production with uncertainty specifically for watershed modeling decision support tools. In this paper, we outline a simple conceptual model that focuses on the DST development process. Then, using watershed modeling case studies found in the literature, we discuss successful outcomes and challenges associated with embedding various forms of co-production into each stage of the conceptual model. We also emphasize the "3 Cs" (i.e., characterization, calculation, communication) of uncertainty and provide evidence-based suggestions for their incorporation in the watershed modeling DST development process. We conclude by presenting a list of best practices derived from current literature for achieving effective and robust watershed modeling decision-support tools.