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Understanding How Different Versions of Distributed Historical Weather Data Affect Hydrologic Model Calibration and Climate Projections Downscaling - BOR Project, FY2011


Project involves analyzing datasets using two measures: Spatial similarity of the distributed precipitation and temperature fields of the study datasets Implications on hydrologic modeling We will then provide guidance on the choice of datasets for statistical downscaling of GCM outputs used in different types of scale-dependent planning assessments. We will evaluate these differences from a hydrological standpoint at specific Reclamation basins: Animas at Durango, Colorado; Snake at Heise, Idaho; Sacramento at Redding, California; Salt at Chrysotile, Arizona; Yellowstone River at Billings, Montana; and Colorado River at Lees Ferry Utah and Arizona. The analysis will indicate whether the choice of forcing a dataset makes a difference [...]


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Data describing spatially distributed historical weather play a crucial role in climate change impacts assessment. For example, they are used in the process of bias-correcting and spatially downscaling outputs from global climate models (GCMs). In addition, they are used to guide the historical calibration of natural systems impacts models (e.g., watershed hydrology, ecosystems, etc). Multiple versions of distributed historical weather data have been generated by various groups. While each version is similar in that it involves interpolating weather station information over the landscape, differences arise among these versions with respect to choices made on which stations should be polled and how this interpolation should be conducted. Given this situation, this project addresses several research questions: How different are the precipitation, temperature, and spatial patterns among available versions of distributed historical weather data? How sensitive is the hydrology model response when calibration is carried out with one of the presently available distributed weather datasets and forced with the other datasets? What are the implications of the climatology differences from the distributed weather datasets for statistical downscaling of Global Climate Model (GCM) outputs?

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