With high-resolution topography and imagery in fluvial environments, the potential to quantify physical fish habitat at the reach scale has never been better. Increased availability of hydraulic, temperature and food availability data and models have given rise to a host of species and life stage specific ecohydraulic fish habitat models ranging from simple, empirical habitat suitability curve driven models, to fuzzy inference systems to fully mechanistic bioenergetic models. However, few examples exist where such information has been upscaled appropriately to evaluate entire fish populations. We present a framework for applying such ecohydraulic models from over 905 sites in 12 sub-watersheds of the Columbia River Basin (USA), to assess status and trends in anadromous salmon populations. We automated the simulation of computational engines to drive the hydraulics, and subsequent ecohydraulic models using cloud computing for over 2075 visits from 2011 to 2015 at 905 sites. We also characterize each site's geomorphic reach type, habitat condition, geomorphic unit assemblage, primary production potential and thermal regime. We then independently produce drainage network-scale models to estimate these same parameters from coarser, remotely sensed data available across entire populations within the Columbia River Basin. These variables give us a basis for imputation of reach-scale capacity estimates across drainage networks. Combining capacity estimates with survival estimates from mark-recapture monitoring allows a more robust quantification of capacity for freshwater life stages (i.e. adult spawning, juvenile rearing) of the anadromous life cycle. We use these data to drive life cycle models of populations, which not only include the freshwater life stages but also the marine and migration life stages through the hydropower system. More fundamentally, we can begin to look at more realistic, spatially explicit, tributary habitat restoration scenarios to examine whether the enormous financial investment on such restoration actions can help recover these populations or prevent their extinction.
Research Impact Statement: Hydraulic models are error-prone where rivers interact with large wood jams. Our method for representing wood jams improves hydraulic model accuracy and ecohydraulic analysis.ABSTRACT: Large wood (LW) jams are key riverine habitat features that affect hydraulic processes and aquatic habitat. The hydraulic influence of LW jams is poorly understood due to the complexity of fluid dynamics around irregular, porous structures. Here we validated a method for two-dimensional hydraulic modeling of porous LW jams using the open-source modeling software Delft3D-FLOW. We sampled 19 LW jams at three reaches across the Columbia River Basin in the United States. We used computer-generated porous plates to represent LW jams in the modeling software and calibrated our modeling method by comparing model outputs to measured depths and velocities at validation points. We found that modeling outputs are error-prone when LW jams are not represented. By representing LW jams as porous plates we reduced average velocity root mean square error (RMSE) values (i.e., improved model accuracy) by 42.8% and reduced average depth RMSE values by 5.2%. These differences impacted habitat suitability index modeling. We found a 15.1% increase in weighted useable area for juvenile steelhead at one test site when LW jams were simulated vs. when they were ignored. We investigated patterns in average RMSE improvements with varying jam size, bankfull obstruction, porosity, and structure type, and river complexity. We also identified research gaps related to field estimation of LW jam porosity and porous structure modeling methods.(KEYWORDS: rivers/streams; fluvial processes; 2D simulations; field measurements; large wood jam; hydraulic modeling; fish habitat suitability; river restoration.) Paper No. JAWRA-18-0166-P of the Journal of the American Water Resources Association (JAWRA).
Aspen woodland is an important ecosystem in the western United States. Aspen is currently declining in western mountains; stressors include conifer expansion due to fire suppression, drought, disease, heavy wildlife and livestock use, and human development. Forecasting of tree species distributions under future climate scenarios predicts severe losses of western aspen within the next 50 years. As a result, aspen has been selected as one of 14 vital signs for long-term monitoring by the National Park Service Upper Columbia Basin Network. This article describes the development of a monitoring protocol for aspen including inventory mapping, selection of sampling locations, statistical considerations, a method for accounting for spatial dependence, field sampling strategies, and data management. We emphasize the importance of collecting pilot data for use in statistical power analysis and semi-variogram analysis prior to protocol implementation. Given the spatial and temporal variability within aspen stem size classes, we recommend implementing permanent plots that are distributed spatially within and among stands. Because of our careful statistical design, we were able to detect change between sampling periods with desired confidence and power. Engaging a protocol development and implementation team with necessary and complementary knowledge and skills is critical for success. Besides the project leader, we engaged field sampling personnel, GIS specialists, statisticians, and a data management specialist. We underline the importance of frequent communication with park personnel and network coordinators.
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