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publications > poster > automation of the estimation of missing waterlevel data for the everglades depth estimation network (eden)
Automation of the Estimation of Missing WaterLevel Data for the Everglades Depth Estimation Network (EDEN)Poster presented July 2010, at the Greater Everglades Ecosystem Restoration ConferenceMatthew D. Petkewich^{1}, Paul A. Conrads^{1}, and Brian D. Reece^{2} ^{1}USGS South Carolina Water Science Center, Columbia, SC, mdpetkew@usgs.gov Background
EDEN Data Gap Estimation ProgramTo increase the accuracy of the daily watersurface elevation model, linear regression equations to estimate missing data for each gaging station in EDEN were developed (Conrads and Petkewich, 2009). To minimize the inability to estimate data due to missing data from a single input site, three or four regression equations were developed for each site using different input sites. For each site, an order was established for the regression equation to be used to fill a data gap. The order that the equations will be used was based on performance statistics, visual inspection of equation predictions and measured data, and proximity of input and output stations. Over 740 equations were incorporated into a database application that automatically estimates missing waterlevel records (EDEN GAP). A Microsoft Access database^{®} of the EDEN waterlevel data and estimation equations was developed to automate the filling of missing data. The performance statistics computed for each equation provides documentation of the "goodnessoffit" of the equations (table 1). In addition, although the majority of the equations provide satisfactory estimations of water levels, the performance statistics provide a prioritization for identifying stations where improved equations are needed to provide more satisfactory waterlevel estimates. Once data gaps are estimated, hydrographs of each station are evaluated to delineate potential errant data and to evaluate the fit of any estimates. For some stations, estimated data are a good representation of the missing data and can be used without any adjustment (fig. 3A). For other stations, however, to improve the quality of the waterlevel estimates, shifting techniques are applied to the estimates similar to gageheight correction techniques used for computing continuous waterlevel data records (Rantz and others, 1982). The most straightforward type of data correction is one where a uniform (+/) correction value is applied to the estimates (fig. 3B). Occasionally prorated corrections are applied over time to improve the quality of the waterlevel estimates. Planned improvements to the EDEN GAP application include moving the processing to SQL Server to improve performance and evaluate the possibility of estimating missing data on a daily basis in addition to the current quarterly or annual reviews. A second improvement to EDEN GAP is autofilling missing data gaps of less than 8 hours by using simplelinear interpolation. Another item is to allow the user of the application to choose which estimates to use on a gapbygap basis. For some stations, one site is a better estimator for missing data during the wet season while another station is a better estimator during the dry season.
SummaryConrads, P.A., and Petkewich, M.D., 2009, Estimation of missing waterlevel data for the Everglades Depth Estimation Network (EDEN): U.S. Geological Survey OpenFile Report 20091120, 53 p. Pearlstine, L., Higer, A., Palaseanu, M., Fujisaki, I., and Mazzotti, F., 2007, Spatially continuous interpolation of water stage and water depths using the Everglades Depth Estimation Network (EDEN): Gainesville, Fl, Institute of Food and Agricultural, University of Florida, CIR1521, 18 p., 2 apps. Rantz, S.E., and others, 1982, Measurement and Computation of Streamflow: U.S. Geological Survey WaterSupply Paper 2175, 631 p. Telis, Pamela A., 2006, The Everglades Depth Estimation Network (EDEN) for Support of Ecological and Biological Assessments: U.S. Geological Survey Fact Sheet 20063087, 4 p.

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