15 research outputs found

    Automated Upscaling of River Networks for Macroscale Hydrological Modeling

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    We developed a hierarchical dominant river tracing (DRT) algorithm for automated extraction and spatial upscaling of basin flow directions and river networks using fine-scale hydrography inputs (e. g., flow direction, river networks, and flow accumulation). In contrast with previous upscaling methods, the DRT algorithm utilizes information on global and local drainage patterns from baseline fine-scale hydrography to determine upscaled flow directions and other critical variables including upscaled basin area, basin shape, and river lengths. The DRT algorithm preserves the original baseline hierarchical drainage structure by tracing each entire flow path from headwater to river mouth at fine scale while prioritizing successively higher order basins and rivers for tracing. We applied the algorithm to produce a series of global hydrography data sets from 1/16 degrees to 2 degrees spatial scales in two geographic projections (WGS84 and Lambert azimuthal equal area). The DRT results were evaluated against other alternative upscaling methods and hydrography data sets for continental U. S. and global domains. These results show favorable DRT upscaling performance in preserving baseline fine-scale river network information including: (1) improved, automated extraction of flow directions and river networks at any spatial scale without the need for manual correction; (2) consistency of river network, basin shape, basin area, river length, and basin internal drainage structure between upscaled and baseline fine-scale hydrography; and (3) performance largely independent of spatial scale, geographic region, and projection. The results of this study include an initial set of DRT upscaled global hydrography maps derived from HYDRO1K baseline fine-scale hydrography inputs; these digital data are available online for public access at ftp://ftp.ntsg.umt.edu/pub/data/DRT/

    Projected Climate Change Impacts on the Hydrology and Temperature of Pacific Northwest Rivers

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    A dominant river-tracing-based streamflow and temperature (DRTT) model was developed by coupling stream thermal dynamics with a source-sink routing model. The DRTT model was applied using 1/16 degree (similar to 6 km) resolution gridded daily surface meteorology inputs over a similar to 988,000 km(2) Pacific Northwest (PNW) domain to produce regional daily streamflow and temperature simulations from 1996 to 2005. The DRTT results showed favorable performance for simulation of daily stream temperature (mean R-2 = 0.72 and root-mean-square error = 2.35 degrees C) and discharge (mean R-2 = 0.52 and annual relative error 14%) against observations from 12 PNW streams. The DRTT was then applied with a macroscale hydrologic model to predict streamflow and temperature changes under historical (1980s) and future (2020s, 2040s, and 2080s) climate change scenarios (IPCC AR4) as they may affect current and future patterns of freshwater salmon habitat and associated productivity of PNW streams. The model projected a 3.5% decrease in mean annual streamflow for the 2020s and 0.6% and 5.5% increases for the 2040s and 2080s, respectively, with projected increase in mean annual stream temperatures from 0.55 degrees C (2020s) to 1.68 degrees C (2080s). However, summer streamflow decreased from 19.3% (2020s) to 30.3% (2080s), while mean summer stream temperatures warmed from 0.92 degrees C to 2.10 degrees C. The simulations indicate that projected climate change will have greater impacts on snow dominant streams, with lower summer streamflows and warmer summer stream temperature changes relative to transient and rain dominant regimes. Lower summer flows combined with warmer stream temperatures suggest a future with widespread increased summertime thermal stress for coldwater fish in the PNW region

    Projecting marine mammal distribution in a changing climate

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    Climate-related shifts in marine mammal range and distribution have been observed in some populations; however, the nature and magnitude of future responses are uncertain in novel environments projected under climate change. This poses a challenge for agencies charged with management and conservation of these species. Specialized diets, restricted ranges, or reliance on specific substrates or sites (e.g., for pupping) make many marine mammal populations particularly vulnerable to climate change. High-latitude, predominantly ice-obligate, species have experienced some of the largest changes in habitat and distribution and these are expected to continue. Efforts to predict and project marine mammal distributions to date have emphasized data-driven statistical habitat models. These have proven successful for short time-scale (e.g., seasonal) management activities, but confidence that such relationships will hold for multi-decade projections and novel environments is limited. Recent advances in mechanistic modeling of marine mammals (i.e., models that rely on robust physiological and ecological principles expected to hold under climate change) may address this limitation. The success of such approaches rests on continued advances in marine mammal ecology, behavior, and physiology together with improved regional climate projections. The broad scope of this challenge suggests initial priorities be placed on vulnerable species or populations (those already experiencing declines or projected to undergo ecological shifts resulting from climate changes that are consistent across climate projections) and species or populations for which ample data already exist (with the hope that these may inform climate change sensitivities in less well observed species or populations elsewhere). The sustained monitoring networks, novel observations, and modeling advances required to more confidently project marine mammal distributions in a changing climate will ultimately benefit management decisions across time-scales, further promoting the resilience of marine mammal populations

    Christian Perspectives in Global Warming

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    2001/05/10. Presents evidence for global warming and discusses its potential impact on the world and the Pacific Northwest. Researcher with Climate Impact group, University of Washington

    How survival curves affect populations' vulnerability to climate change.

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    Human activities are exposing organisms not only to direct threats (e.g. habitat loss) but also to indirect environmental pressures such as climate change, which involves not just directional global warming but also increasing climatic variability. Such changes will impact whole communities of organisms and the possible effects on population dynamics have raised concerns about increased extinction rates. Conservation-minded approaches to extinction risk vary from range shifts predicted by climate envelope models with no population dynamics to population viability analyses that ignore environmental variability altogether. Our modelling study shows that these extremes are modelling responses to a spectrum of environmental sensitivity that organisms may exhibit. We show how the survival curve plays a major role in how environmental variability leads to population fluctuations. While it is often supposed that low-fecundity organisms (those with high parental investment) will be the most vulnerable to climate change, it is those with high fecundity (low parental investment) that are likely to be more sensitive to such changes. We also find that abundance variations in high fecundity populations is driven primarily by fluctuations in the survival of early life stages, the more so if those environmental changes are autocorrelated in time. We show which types of conservation actions are most appropriate for a number of real populations. While the most effective conservation actions for organisms of low fecundity is to avoid killing them, for populations with high fecundity (and low parental investment), our study suggests conservation should focus more on protecting early life stages from hostile environments
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