Statistical filtering of river survey and streamflow data for improving At-A-Station hydraulic geometry relations
| Autor: | Naresh Devineni, S. Lawrence Dingman, Reza Khanbilvardi, David M. Bjerklie, Shahab Afshari, Balázs M. Fekete |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Hydrology
010504 meteorology & atmospheric sciences Hydraulics 0208 environmental biotechnology Context (language use) Geometry 02 engineering and technology 01 natural sciences Synthetic data 020801 environmental engineering law.invention law Data quality Streamflow Geological survey Environmental science Regression diagnostic Uncertainty analysis 0105 earth and related environmental sciences Water Science and Technology |
| Zdroj: | Journal of Hydrology. 547:443-454 |
| ISSN: | 0022-1694 |
| Popis: | Natural streams are characterized by variation in cross-section geometry, bed-slope, bed roughness, hydraulic slope, etc., along their channels resulting from several interacting features of the riverine system including the effects of discharge changes, geologic context, sediment load, etc. Quantitative and qualitative assessment of river flow dynamics requires sufficient knowledge of hydraulics and these geophysical variables. Average flow condition theory expressed as “At-A-Station” hydraulic geometry (AHG) relations are site-specific power-functions, relating the mean stream channel forms (i.e. water depth, top-width, flow velocity, and flow area) to discharge, have been studied since 50s. Establishing robust AHG relations requires pre-assessment of data quality by means of uncertainty analysis. Our paper introduces a recursive data filtering procedure to find both random and systematic errors in streamflow and river-survey data that can be used to produce robust and informative AHG relations. The method is first verified on synthetic data and then by experiments over: (1) real discharge-stage ratings provided by daily streamflow records of U.S. Geological Survey/National Water Information System dataset (USGS/NWIS), and (2) field river survey measurement data from USGS/NWIS. This produces robust AHG relations at 4472 monitoring stations across the U.S. |
| Databáze: | OpenAIRE |
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