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• 1. [Article] Effects of Climate Change on Water Quality in the Yaquina Estuary, Oregon

  Presented at The Oregon Water Conference, May 24-25, 2011, Corvallis, OR.

  Citation × Citation Citation Abstract Copy Title: Effects of Climate Change on Water Quality in the Yaquina Estuary, Oregon Author: Chang, Heejun, Steele, Madeline, Brown, Cheryl A., Sharp, Darrin Year: 2011 Presented at The Oregon Water Conference, May 24-25, 2011, Corvallis, OR. Title: Effects of Climate Change on Water Quality in the Yaquina Estuary, Oregon Author: Chang, Heejun, Steele, Madeline, Brown, Cheryl A., Sharp, Darrin Year: 2011 Presented at The Oregon Water Conference, May 24-25, 2011, Corvallis, OR. Close

• 2. [Article] Potential effects of climate change on Plethodon shermani

  Climate change is a global factor that has been implicated in many behavioral alterations across various species. We speculate and observe the potential responses to the Plethodon shermani mating system ...

  Citation × Citation Citation Abstract Copy Title: Potential effects of climate change on Plethodon shermani Author: Ennis, Kelli N. Climate change is a global factor that has been implicated in many behavioral alterations across various species. We speculate and observe the potential responses to the Plethodon shermani mating system induced from temperature changes, which may affect the ability of P. shermani to rely on external cues for mating season. The courtship rituals of P. shermani have been well-documented; deviations from the expected patterning of behavior was measured via both initiation of and length of time spent in the “tail straddle walk” in addition to insemination success rate. It has been determined that an increase in temperature has implications in more inseminations, while the reverse is apparent for decreased temperature; indicating that climatic changes may lead to an earlier breeding season with warmer temperatures. The effects on our sampling may translate either directly or indirectly to the population, community, and other amphibian-containing ecosystems. We suggest further research that may aid our ability to predict the capacity for amphibians to adjust to climatic changes. Title: Potential effects of climate change on Plethodon shermani Author: Ennis, Kelli N. Climate change is a global factor that has been implicated in many behavioral alterations across various species. We speculate and observe the potential responses to the Plethodon shermani mating system induced from temperature changes, which may affect the ability of P. shermani to rely on external cues for mating season. The courtship rituals of P. shermani have been well-documented; deviations from the expected patterning of behavior was measured via both initiation of and length of time spent in the “tail straddle walk” in addition to insemination success rate. It has been determined that an increase in temperature has implications in more inseminations, while the reverse is apparent for decreased temperature; indicating that climatic changes may lead to an earlier breeding season with warmer temperatures. The effects on our sampling may translate either directly or indirectly to the population, community, and other amphibian-containing ecosystems. We suggest further research that may aid our ability to predict the capacity for amphibians to adjust to climatic changes. Close

• 3. [Article] Modeling Elevation-Dependent Snow Sensitivity to Climate Warming in the Data Sparse Eastern Oregon Cascades

  In the mountains of the Western US, shifts in the timing and magnitude of snow water equivalent (SWE) over the past century are well documented and attributed to climate warming, but the magnitude of sensitivity ...

  Citation × Citation Citation Abstract Copy Title: Modeling Elevation-Dependent Snow Sensitivity to Climate Warming in the Data Sparse Eastern Oregon Cascades Author: Cooper, Matthew Guy In the mountains of the Western US, shifts in the timing and magnitude of snow water equivalent (SWE) over the past century are well documented and attributed to climate warming, but the magnitude of sensitivity depends on elevation. We modeled the spatial distribution of SWE and its sensitivity to climate warming in the 1500 km² Upper Deschutes River Basin, Oregon, with a spatially distributed snowpack energy balance model forced by a gridded meteorological dataset. The forcing data, gridded at a spatial scale of 1/16°, were downscaled to a 100 m spatial-scale digital elevation model using two sets of temperature lapse rates, with and without bias-correction applied prior to downscaling. The bias-correction method adjusted the spatial patterns of temperature and precipitation in the 1/16° gridded data to match 30 arcsecond Parameter Regressions on Independent Slopes Model (PRISM) climate data. During production, the 1/16° temperature data was adjusted for the effect of elevation using a spatially uniform and temporally constant 6.5°C km⁻¹ lapse rate, whereas PRISM adjusts temperature for the effect of elevation using spatially and temporally variable lapse rates that are computed directly from regional weather station data. Thus, bias-correction implicitly adjusted the lapse rates in the 1/16° gridded data to match measured lapse rates. To test the effect of this implicit adjustment of the input data lapse rates vs. adjusting the lapse rates during downscaling, the 30 arcsecond bias-corrected data and 1/16o original data were each downscaled with 1) a spatially uniform and temporally constant 6.5°C km⁻¹ lapse rate, and 2) with monthly varying lapse rates computed from PRISM. Precipitation was downscaled with the same method for each case. This procedure produced four sets of 100 m spatial scale data used as input to the snow model. Model parameters that control empirical estimates of incoming irradiance and the partitioning of precipitation into rain and snow were estimated independently with each dataset to optimize the agreement between modeled and observed SWE. We then modeled the sensitivity (percent change) of basin SWE in response to +2°C and +4°C warming with each of the four downscaled datasets and their respective optimized parameters. Pre-calibration, modeled SWE for the historical climate period differed depending on bias correction and choice of downscaling lapse rates. Post-calibration, modeled SWE for the historical climate period did not differ depending on choice of lapse rates but substantial differences emerged between modeled SWE with the original and bias-corrected forcing data. Inter-forcing dataset differences in modeled SWE during the historical period were largely controlled by differences in estimates of longwave irradiance and temperature between datasets. For the warming scenarios, the SWE sensitivity differed significantly at all elevations between the bias-corrected and original data, but (as in the post-calibration historical period) did not depend on choice of lapse rates. At low to mid elevations, climate change impacts on snow were largely controlled by temperature-driven shifts from snowfall to rainfall, while at high elevations, precipitation variability controlled SWE sensitivity. With just a 2°C increase in temperature, peak snow accumulation occurred 20-30 days earlier and was 20-60% smaller, the length of the snow covered season decreased up to 50 days, and winter rainfall increased by 20-60%. With a 4°C increase, the shifts in timing were roughly doubled and the declines in snow and snowfall increased up to 80%. A 10% increase in precipitation had a negligible impact on basin-integrated declines, indicating that future precipitation variability has little chance of offsetting regional climate warming impacts on snow in the Oregon Cascades. These results highlight the challenges of modeling SWE in data sparse regions, the importance of bias correcting gridded meteorological forcing datasets for hydrologic modeling applications in regions of complex topography, and the strong temperature dependence of snow in the Oregon Cascades. Title: Modeling Elevation-Dependent Snow Sensitivity to Climate Warming in the Data Sparse Eastern Oregon Cascades Author: Cooper, Matthew Guy In the mountains of the Western US, shifts in the timing and magnitude of snow water equivalent (SWE) over the past century are well documented and attributed to climate warming, but the magnitude of sensitivity depends on elevation. We modeled the spatial distribution of SWE and its sensitivity to climate warming in the 1500 km² Upper Deschutes River Basin, Oregon, with a spatially distributed snowpack energy balance model forced by a gridded meteorological dataset. The forcing data, gridded at a spatial scale of 1/16°, were downscaled to a 100 m spatial-scale digital elevation model using two sets of temperature lapse rates, with and without bias-correction applied prior to downscaling. The bias-correction method adjusted the spatial patterns of temperature and precipitation in the 1/16° gridded data to match 30 arcsecond Parameter Regressions on Independent Slopes Model (PRISM) climate data. During production, the 1/16° temperature data was adjusted for the effect of elevation using a spatially uniform and temporally constant 6.5°C km⁻¹ lapse rate, whereas PRISM adjusts temperature for the effect of elevation using spatially and temporally variable lapse rates that are computed directly from regional weather station data. Thus, bias-correction implicitly adjusted the lapse rates in the 1/16° gridded data to match measured lapse rates. To test the effect of this implicit adjustment of the input data lapse rates vs. adjusting the lapse rates during downscaling, the 30 arcsecond bias-corrected data and 1/16o original data were each downscaled with 1) a spatially uniform and temporally constant 6.5°C km⁻¹ lapse rate, and 2) with monthly varying lapse rates computed from PRISM. Precipitation was downscaled with the same method for each case. This procedure produced four sets of 100 m spatial scale data used as input to the snow model. Model parameters that control empirical estimates of incoming irradiance and the partitioning of precipitation into rain and snow were estimated independently with each dataset to optimize the agreement between modeled and observed SWE. We then modeled the sensitivity (percent change) of basin SWE in response to +2°C and +4°C warming with each of the four downscaled datasets and their respective optimized parameters. Pre-calibration, modeled SWE for the historical climate period differed depending on bias correction and choice of downscaling lapse rates. Post-calibration, modeled SWE for the historical climate period did not differ depending on choice of lapse rates but substantial differences emerged between modeled SWE with the original and bias-corrected forcing data. Inter-forcing dataset differences in modeled SWE during the historical period were largely controlled by differences in estimates of longwave irradiance and temperature between datasets. For the warming scenarios, the SWE sensitivity differed significantly at all elevations between the bias-corrected and original data, but (as in the post-calibration historical period) did not depend on choice of lapse rates. At low to mid elevations, climate change impacts on snow were largely controlled by temperature-driven shifts from snowfall to rainfall, while at high elevations, precipitation variability controlled SWE sensitivity. With just a 2°C increase in temperature, peak snow accumulation occurred 20-30 days earlier and was 20-60% smaller, the length of the snow covered season decreased up to 50 days, and winter rainfall increased by 20-60%. With a 4°C increase, the shifts in timing were roughly doubled and the declines in snow and snowfall increased up to 80%. A 10% increase in precipitation had a negligible impact on basin-integrated declines, indicating that future precipitation variability has little chance of offsetting regional climate warming impacts on snow in the Oregon Cascades. These results highlight the challenges of modeling SWE in data sparse regions, the importance of bias correcting gridded meteorological forcing datasets for hydrologic modeling applications in regions of complex topography, and the strong temperature dependence of snow in the Oregon Cascades. Close

• 4. [Article] Introducing a sensor to measure budburst and its environmental drivers

  This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by the Frontiers Research Foundation. The published article can be found at: http://www.frontiersi...

  Citation × Citation Citation Abstract Copy Title: Introducing a sensor to measure budburst and its environmental drivers Author: Lintz, Heather E., Thomas, Christoph K., Kim, Youngil, Kleinknecht, George J., Still, Christopher J., Niemeier, James J., Salino-Hugg, Michael J., Kruger, Anton This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by the Frontiers Research Foundation. The published article can be found at: http://www.frontiersin.org/Plant_Science. Title: Introducing a sensor to measure budburst and its environmental drivers Author: Lintz, Heather E., Thomas, Christoph K., Kim, Youngil, Kleinknecht, George J., Still, Christopher J., Niemeier, James J., Salino-Hugg, Michael J., Kruger, Anton This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by the Frontiers Research Foundation. The published article can be found at: http://www.frontiersin.org/Plant_Science. Close

• 5. [Article] Introducing and validating a high-throughput bud break sensor

  Bud break is a key adaptive trait that can help us understand how plants respond to a changing climate from the molecular to landscape scale. Despite this, acquisition of bud break data is currently constrained ...

  Citation × Citation Citation Abstract Copy Title: Introducing and validating a high-throughput bud break sensor Author: Kleinknecht, George J. Bud break is a key adaptive trait that can help us understand how plants respond to a changing climate from the molecular to landscape scale. Despite this, acquisition of bud break data is currently constrained by cost, scale, and a lack of information at the plant scale on the environmental stimuli that precede and cause bud dormancy to release. Additionally, to date, little effort has been devoted to phenotyping plants in nature owing to the challenge of controlling for environmental variation to isolate genomic effects on plant phenotype. Notwithstanding, natural selection operates on natural populations, and investigation of adaptive phenotypes in situ is warranted and can validate results from controlled laboratory experiments. In order to identify genomic effects on individual plant phenotypes in nature, environmental drivers must be concurrently measured and characterized. Here, we innovated and evaluated a sensor to meet these requirements for temperate woody plants. It was designed for use on a tree branch to measure the timing of bud break together with its key environmental drivers, temperature and day length. Specifically, we evaluated the sensor through independent corroboration with time-lapse photography and a suite of environmental sampling instruments. We also tested whether the presence of the device itself on a branch influenced the timing of bud break. Our results indicated the following: the bud break sensor's digital thermometer closely approximated a thermocouple touching plant tissue; the photoperiod detector measured ambient light with the same accuracy as time lapse photography; the phenology sensor accurately detected the timing of bud break; and the sensor itself did not influence bud phenology. Future use of the sensor can help galvanize a new field of study, landscape phenomics, or high-throughput phenotyping of organisms at the landscape level for integration with landscape genomics among other potential applications and disciplines. Title: Introducing and validating a high-throughput bud break sensor Author: Kleinknecht, George J. Bud break is a key adaptive trait that can help us understand how plants respond to a changing climate from the molecular to landscape scale. Despite this, acquisition of bud break data is currently constrained by cost, scale, and a lack of information at the plant scale on the environmental stimuli that precede and cause bud dormancy to release. Additionally, to date, little effort has been devoted to phenotyping plants in nature owing to the challenge of controlling for environmental variation to isolate genomic effects on plant phenotype. Notwithstanding, natural selection operates on natural populations, and investigation of adaptive phenotypes in situ is warranted and can validate results from controlled laboratory experiments. In order to identify genomic effects on individual plant phenotypes in nature, environmental drivers must be concurrently measured and characterized. Here, we innovated and evaluated a sensor to meet these requirements for temperate woody plants. It was designed for use on a tree branch to measure the timing of bud break together with its key environmental drivers, temperature and day length. Specifically, we evaluated the sensor through independent corroboration with time-lapse photography and a suite of environmental sampling instruments. We also tested whether the presence of the device itself on a branch influenced the timing of bud break. Our results indicated the following: the bud break sensor's digital thermometer closely approximated a thermocouple touching plant tissue; the photoperiod detector measured ambient light with the same accuracy as time lapse photography; the phenology sensor accurately detected the timing of bud break; and the sensor itself did not influence bud phenology. Future use of the sensor can help galvanize a new field of study, landscape phenomics, or high-throughput phenotyping of organisms at the landscape level for integration with landscape genomics among other potential applications and disciplines. Close

• 6. [Article] Characterizing Linkages Between the Climate, Cryosphere, and Impacts on Run-of-River Hydropower in Data-Sparse Mountain Environments

  In many regions of the world, a significant portion of the surface water originates in mountain headwaters where the timing and magnitude of streamflow is largely dictated by the seasonal storage of precipitation ...

  Citation × Citation Citation Abstract Copy Title: Characterizing Linkages Between the Climate, Cryosphere, and Impacts on Run-of-River Hydropower in Data-Sparse Mountain Environments Author: Mosier, Thomas M. In many regions of the world, a significant portion of the surface water originates in mountain headwaters where the timing and magnitude of streamflow is largely dictated by the seasonal storage of precipitation as snowpack and long-term storage as glaciers. Accumulation, persistence, and melt of snow and ice are functions of the climate in which they exist and therefore respond to changes in that climate. One important use of water in many regions is for hydropower energy production. While reservoir-based hydropower infrastructure has some ability to absorb changes in timing of streamflow, run-of-river hydropower infrastructure does not. Thus, in assessing the economic feasibility of new or existing run-of-river infrastructure, it is important to account for potential impacts climate change may have over the lifetime of the project. Projecting impacts of climate change on surface water resources, and in particular on run-of-river hydropower resource potential, requires robustly characterizing the linkages between the climate, cryosphere, and streamflow. Two obstacles to increasing our understanding of mountain systems are the sparsity of observation data and complexity of weather patterns. The first part of my research addresses the issue of climate data availability in mountain regions through development of statistical models to characterize the high-spatial resolution distribution of historic and projected future precipitation and temperature. I demonstrate these climate products through projecting long-term changes in snowfall for the Alaska Range, Alps, Central Andes, and Himalaya-Karakoram-Hindu Kush ranges. I then present a framework for assessing conceptual cryosphere hydrology models and implement the framework for two long-term glacier study sites in Alaska, USA. Using this framework, I identify novel formulations for modeling the heat transfer and energy balance of snowpacks and glaciers that improve model robustness relative to the current generation of cryosphere hydrology models typically used in data-sparse mountain environments. I then demonstrate a method for understanding the impacts of projected future climate change on run-of-river hydropower resource potential, using Falls Creek in Oregon, USA as a test case. A core value of this work is producing models that can be straightforwardly applied to any region, thus decreasing the impacts of data disparities between regions on our ability to characterize climate change impacts on mountain regions. Title: Characterizing Linkages Between the Climate, Cryosphere, and Impacts on Run-of-River Hydropower in Data-Sparse Mountain Environments Author: Mosier, Thomas M. In many regions of the world, a significant portion of the surface water originates in mountain headwaters where the timing and magnitude of streamflow is largely dictated by the seasonal storage of precipitation as snowpack and long-term storage as glaciers. Accumulation, persistence, and melt of snow and ice are functions of the climate in which they exist and therefore respond to changes in that climate. One important use of water in many regions is for hydropower energy production. While reservoir-based hydropower infrastructure has some ability to absorb changes in timing of streamflow, run-of-river hydropower infrastructure does not. Thus, in assessing the economic feasibility of new or existing run-of-river infrastructure, it is important to account for potential impacts climate change may have over the lifetime of the project. Projecting impacts of climate change on surface water resources, and in particular on run-of-river hydropower resource potential, requires robustly characterizing the linkages between the climate, cryosphere, and streamflow. Two obstacles to increasing our understanding of mountain systems are the sparsity of observation data and complexity of weather patterns. The first part of my research addresses the issue of climate data availability in mountain regions through development of statistical models to characterize the high-spatial resolution distribution of historic and projected future precipitation and temperature. I demonstrate these climate products through projecting long-term changes in snowfall for the Alaska Range, Alps, Central Andes, and Himalaya-Karakoram-Hindu Kush ranges. I then present a framework for assessing conceptual cryosphere hydrology models and implement the framework for two long-term glacier study sites in Alaska, USA. Using this framework, I identify novel formulations for modeling the heat transfer and energy balance of snowpacks and glaciers that improve model robustness relative to the current generation of cryosphere hydrology models typically used in data-sparse mountain environments. I then demonstrate a method for understanding the impacts of projected future climate change on run-of-river hydropower resource potential, using Falls Creek in Oregon, USA as a test case. A core value of this work is producing models that can be straightforwardly applied to any region, thus decreasing the impacts of data disparities between regions on our ability to characterize climate change impacts on mountain regions. Close

• 7. [Article] Regional Scale Modeling of Climate, Cryosphere, and Freshwater Discharge in Changing Coastal Mountain Environments

  The glaciated coastal mountain watersheds that drain into the Gulf of Alaska (GOA) provide a model laboratory to explore the challenges of hydrological modeling and study the impact of climate and glacier ...

  Citation × Citation Citation Abstract Copy Title: Regional Scale Modeling of Climate, Cryosphere, and Freshwater Discharge in Changing Coastal Mountain Environments Author: Beamer, Jordan P. The glaciated coastal mountain watersheds that drain into the Gulf of Alaska (GOA) provide a model laboratory to explore the challenges of hydrological modeling and study the impact of climate and glacier cover change on regional hydrology. The region is data-sparse and contains a complex assemblage of topography and land cover, including a system of mountain glaciers that are retreating at some of the highest rates on Earth. The high rates of runoff from precipitation and glacial melt delivered by coastal rivers influence ocean circulation patterns, rates of global sea level rise, and provide spawning habitat for the large salmon populations. Physically-based hydrological modeling of the major water budget components of the GOA, driven using historical reanalysis weather data and land cover, reveals that the modeled water budget components, particularly precipitation input, vary widely between commonly-used weather products. The majority of the large freshwater flux into the GOA is derived from distributed coastal streams rather than the large inland rivers. The modeled seasonal aggregated GOA hydrograph is dominated by the spring and early summer snowmelt, and supplemented by late summer glacial ice melt. Model results demonstrate good agreement with NASA Gravity Recovery and Climate Experiment (GRACE) satellite data in terms of annual amplitudes and long term losses (ice loss), and suggest that existing GRACE solutions, previously reported to represent glacier mass balance alone, are actually measuring the full water budget of land and ice surfaces. An ensemble of climate models and future emissions scenarios were paired with systematically altered land cover to test the sensitivity of the hydrologic system to changes in regional climate patterns and glacier coverage representative of late twenty first century conditions. Compared with the hindcast simulations, the model results forced with increased regional air temperatures and precipitation inputs and reduced glacier cover produce an increase in the annual GOA freshwater discharge volume. The seasonal GOA hydrograph is flattened due to increased winter runoff from more winter rainfall and less snow accumulation, and lower levels of snowmelt and glacier ice contribution. Large uncertainties exist in the direction of change in the glacier runoff component, primarily due to uncertainties that exist in predicting glacier response to climate change. Hydrological modeling with high resolution and inclusion of relevant physical processes can produce significantly improved products that are of high value to and in demand by numerous other scientific communities. However, the value and accuracy of the output from the hydrologic model is highly dependent on the weather forcing quality. Given the considerable importance of quality weather forcing for hydrologic modeling, it is imperative to assess the suitability of multiple products by evaluating local and regional performance and accounting for uncertainty. Additional efforts should be made to improve the spatial resolution of the reanalysis through downscaling and to strategically increase the number of weather stations at high elevations and incorporate that data into weather forcing datasets. Title: Regional Scale Modeling of Climate, Cryosphere, and Freshwater Discharge in Changing Coastal Mountain Environments Author: Beamer, Jordan P. The glaciated coastal mountain watersheds that drain into the Gulf of Alaska (GOA) provide a model laboratory to explore the challenges of hydrological modeling and study the impact of climate and glacier cover change on regional hydrology. The region is data-sparse and contains a complex assemblage of topography and land cover, including a system of mountain glaciers that are retreating at some of the highest rates on Earth. The high rates of runoff from precipitation and glacial melt delivered by coastal rivers influence ocean circulation patterns, rates of global sea level rise, and provide spawning habitat for the large salmon populations. Physically-based hydrological modeling of the major water budget components of the GOA, driven using historical reanalysis weather data and land cover, reveals that the modeled water budget components, particularly precipitation input, vary widely between commonly-used weather products. The majority of the large freshwater flux into the GOA is derived from distributed coastal streams rather than the large inland rivers. The modeled seasonal aggregated GOA hydrograph is dominated by the spring and early summer snowmelt, and supplemented by late summer glacial ice melt. Model results demonstrate good agreement with NASA Gravity Recovery and Climate Experiment (GRACE) satellite data in terms of annual amplitudes and long term losses (ice loss), and suggest that existing GRACE solutions, previously reported to represent glacier mass balance alone, are actually measuring the full water budget of land and ice surfaces. An ensemble of climate models and future emissions scenarios were paired with systematically altered land cover to test the sensitivity of the hydrologic system to changes in regional climate patterns and glacier coverage representative of late twenty first century conditions. Compared with the hindcast simulations, the model results forced with increased regional air temperatures and precipitation inputs and reduced glacier cover produce an increase in the annual GOA freshwater discharge volume. The seasonal GOA hydrograph is flattened due to increased winter runoff from more winter rainfall and less snow accumulation, and lower levels of snowmelt and glacier ice contribution. Large uncertainties exist in the direction of change in the glacier runoff component, primarily due to uncertainties that exist in predicting glacier response to climate change. Hydrological modeling with high resolution and inclusion of relevant physical processes can produce significantly improved products that are of high value to and in demand by numerous other scientific communities. However, the value and accuracy of the output from the hydrologic model is highly dependent on the weather forcing quality. Given the considerable importance of quality weather forcing for hydrologic modeling, it is imperative to assess the suitability of multiple products by evaluating local and regional performance and accounting for uncertainty. Additional efforts should be made to improve the spatial resolution of the reanalysis through downscaling and to strategically increase the number of weather stations at high elevations and incorporate that data into weather forcing datasets. Close