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• 1. [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

• 2. [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