Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/1/2008
Publication Date: 12/1/2008
Publication URL: http://earth.boisestate.edu/home/jmcnamar/publications/Nayak_wrr.pdf
Citation: Nayak, A., Chandler, D., Marks, D.G., Mcnamara, J., Seyfried, M.S. 2008. Correction of electronic record for weighing bucket precipitation gauge measurements. Water Resources Research, 44, W00D11, doi:10.1029/2008WR006875. Interpretive Summary: A computer utility is presented that can be used to process and correct long-term precipitation data. These data are typically very noisy and can require hundreds of hours of effort for manual processing. The computer utility can do the same thing to decades of data in a few minutes. The paper presents the computer utility, and tests it on 58 station-years of data from the Reynolds Creek Experimental Watershed (RCEW), showing that the automated utility is just as accurate, and far more consistent than manual methods. The method is also used to process time domain reflectometry (TDR) soil moisture content data, which is also frequently subject to substantial noise.
Technical Abstract: Electronic sensors generate valuable streams of forcing and validation data for hydrologic models, but are often subject to noise, which must be removed as part of model input and testing database development. We developed Automated Precipitation Correction Program (APCP) for weighting bucket precipitation gauge records, which are subject to several types of mechanical and electronic noise and discontinuities including gauge maintenance, missing data, wind vibration and sensor drift. Corrected cumulative water year precipitation from APCP did not exhibit an error bias and matched measured water year total precipitation within 2.1 % for 58 station-years tested. Removal of low amplitude periodic noise was especially important for developing accurate instantaneous precipitation records at sub-daily time steps. Model flexibility for use with other data types is demonstrated through application to time domain reflectometry soil moisture content data, which is also frequently subject to substantial noise.