Melissa Slater, Josh Roberti, Chris Daly, Stephanie Laseter, Lloyd Swift, Adam Skibbe

Overview

• Data

• Methods

• Statistical Analysis

• Results

Purpose

This study examines the spatial distribution of mean annual and seasonal (winter/summer) precipitation readings collected from sixty-nine gauges within the forest from 1951 to 1958.

The intent was to evaluate the relationship between elevation and precipitation, identify an optimal scale and interpolation model, and quantify spatial uncertainty by cross-validating the interpolated results against observed data.

Data• 69 gauges recorded

continuous precipitation measurements from 1951-1958. This is the longest continuous record with the largest number of fully functioning gauges.

• A 1/3 arc-second Digital Elevation Model from the USGS National Elevation Dataset provided the base elevations for the 69 gauges.

• The Coweeta Creek – Little Tennessee River watershed of the Coweeta basin provided the extent for the interpolation models.

• The 1983 North American Datum was utilized.

• A local Universal Transverse Mercator projection for Zone 17 North was applied.

Methods

• n-1 shapefiles were generated by eliminating gauges one-by-one for each of the 69 gauges. The result was a total of 70 shapefiles.

• The 10m DEM was smoothed using a low-pass filter. This reduces local variation by removing artifacts and noise and averaging high and low values to diminish extremes within neighboring cells.

• Mean annual and seasonal (winter/summer) totals were calculated then imported into ArcMap using the xy coordinates for gauge locations.

Methods Continued

• A Focal Statistics tool was then applied to the filtered DEM to smooth the grid and generate additional scales from 100-10,000 meter resolution.

• Extract Values to Points was used to derive elevation values (z) at each gauge location for each DEM scale.

• Ordinary Least Squares (OLS) linear regression script was run using derived elevation values and actual precipitation values on the X/Y axes.

Methods Continued

• The coefficient of determination was examined along with the scatterplots produced from the OLS regression to determine optimal scale for modeling precipitation in the Coweeta Experimental Forest.

• At 7,000 m the R² for the Annual and Seasonal values showed that approximately81-95% of prediction errors could be reduced.

Methods Continued• A Pivot Table model was run to extract the coefficient values from

the optimal 7,000 meter DEM.

• A Raster Calculation was performed with the regression function to create an “initial” precipitation grid.

• IDW and Kriged interpolation methods were run on the residuals produced from the regression analysis with various neighborhood and power settings adjusted to obtain the optimal modeling parameters.

Methods Concluded

• A batch Raster Calculation was used to add the interpolated residual grids to the “initial” precipitation grids to generate a set of “final” precipitation grids.

• Extract Values to Points was run again to extract the predicted precipitation values from each of the final grids.

Statistical Analysis

• Signed error (residual bias = observed – predicted) was calculated along with the unsigned Mean Absolute Error (%MAE = ((abs([bias])/observed)*100)) for the annual and seasonal values.

• Mean predicted and observed values, mean bias, %MAE, root mean square error and the standard deviation for the mean absolute error, predicted, observed and biased results were calculated.

Results

• Elevation explains a majority of the spatial patterns of precipitation with in the Coweeta Experimental Forest and varies with season.

• A 7000-m effective resolution DEM produced statistically improved results, showing that the dominant scale of orographic processes is relatively large.

• The Kriged residual interpolation performed better than the IDW model.

Next Steps

• These high-quality precipitation maps can serve as “ground truth” for quantifying uncertainty in US-wide spatial precipitation products used in a large number of hydrologic and ecological applications.

• The uncertainty in PRISM US-wide precipitation grids will be evaluated and sources of error identified, including the effects of coarse grid resolution and

Questions?

Acknowledgements

• Josh Roberti – The National Ecological Observatory Network

• Chris Daly – The PRISM Climate Group, Oregon State University

• Jeff Taylor – Aspen Global Change Institute• Lloyd Swift – US Forest Service, Coweeta Hydrological

Laboratory• Stephanie Laseter – US Forest Service, Coweeta

Hydrological Laboratory• Ruth D Yanai – College of Environmental Science and

Forestry, State University of New York• Chelcy Ford Miniat – US Forest Service, Coweeta

Hydrological Laboratory• Adam Skibbe – Department of Geographical and

Sustainability Sciences, University of Iowa