Coweeta Terrain and Station Locations Spatial patterns of precipitation in complex terrain: Using high-density gage data to estimate interpolation uncertainty Joshua A Roberti1, Jeffrey R Taylor1, 2, Melissa Slater1 , Ruth D Yanai3, Adam Skibbe4, Chris Daly5 , Chelcy Ford Miniat6, Stephanie Laseter6, and Lloyd Swift6 1National Ecological Observatory Network (NEON), 1685 38th St. Suite 100, Boulder, CO 80301 2Institute of Arctic and Alpine Research (INSTAAR), University of Colorado-Boulder, 1560 30th St., Boulder, CO 80303 3College of Environmental Science and Forestry, State University of New York, 1 Forest Dr., Syracuse, NY 13210 4 Department of Geographical and Sustainability Sciences, University of Iowa, 328 Jessup Hall, Iowa City, IA,52242 5 PRISM Climate Group, Oregon State University, Corvallis, OR 97331 6 US Forest Service, Coweeta Hydrologic Laboratory, 3160 Coweeta Lab Rd., Otto, NC 28763 Introduction Gridded, national-scale precipitation products provide estimates of precipitation for a wide variety of research, planning, and decision-making activities. These estimates are not perfect, and associated uncertainties can significantly affect the outcomes of analyses using these grids as input. Uncertainties stem from relatively coarse grid resolutions, sparse station data, and interpolation methodology, among others. However, these errors involved are difficult to quantify, because high-resolution ground truth data are typically lacking. A high-quality, high-density rain gauge network maintained at the Coweeta Hydrologic Laboratory Watershed in North Carolina, USA, provides an opportunity to better understand the actual spatial patterns of precipitation in a mountainous region, and help quantify the sources and magnitudes of uncertainties in national-scale precipitation datasets. Approach In the first phase of this study, eight years of precipitation data from sixty-nine gages were used to create maps of the spatial distribution of average seasonal (dormant vs growing season) precipitation within the Coweeta Hydrologic Laboratory Watershed. In the second phase, these “ground truth” maps will be compared to national-scale PRISM maps to quantify their uncertainties, and identify the largest sources of uncertainty, e.g., grid resolution, station data, etc. Coweeta Terrain and Station Locations Precipitation vs. elevation R2 values increase with increasing DEM scale (10m -> 1000m -> 5000m). The optimal DEM is found where the R2 values plateau or peak . The 5000m DEM shows highest R2 values for annual, dormant and growing season precipitation. Discussion Final precipitation maps Coweeta Aerial View 10-m digital elevation model (DEM), smoothed to larger effective resolutions Methods The best predictor of precipitation patterns is often a digital elevation model (DEM), smoothed to a relatively large effective resolution, because precipitation patterns tend to respond to large-scale elevation features. Linear regression functions of precipitation vs. elevation for the dormant (Nov-Apr) and growing (May-Oct) seasons and annual time periods were constructed using various effective DEM smoothing scales to find the optimal scale. “First-guess” precipitation maps were constructed using the regression functions at the optimal DEM smoothing scale. The residuals from these functions were interpolated using two methods: Inverse distance weighting (IDW) interpolation and kriging. The interpolated residual grids were then added to the first-guess grids to produce the final maps. Next Steps In the second phase of this study, these precipitation maps will serve as ground truth and compared to national-scale PRISM maps to quantify their uncertainties, and identify the sources of uncertainty, e.g., grid resolution, station data, etc. Section of a national-scale PRISM precipitation grid encompassing the Coweeta Watershed area. IDW and Kriging model workflow Interpolation of regression model residuals. Largest positive residuals are in leeside “dump zones“ over the southern portion of the watershed. This research was funded through the US Forest Service, Southern Research Station, National Science Foundation Long-Term Ecological Research (LTER) Network Office and the QUEST (Quantifying Uncertainty in Ecosystem Studies) Research Coordination Network (http://www.quantifyinguncertainty.org/).   “The National Ecological Observatory Network is a project sponsored by the National Science Foundation and managed under cooperative agreement by NEON, Inc. This material is based upon work supported by the National Science Foundation under the following grants: EF-1029808, EF-1138160, EF-1150319 and DBI-0752017. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.” We would like to thank Charles Marshall and Mark Crawford.