Effects of Stream Order and Data Resolution on Sinuosity Using GIS

This research focuses on estimation and analysis of stream sinuosity using GIS. Fifty-five streams including 13 streams of order 0, 17 streams of order 1, 15 streams of order 2 and 10 streams of order 3 in Virginia were considered. Several GIS datasets from various sources, including the Virginia Base Mapping Program (VBMP) and United States Geological Survey (USGS), were used to generate stream networks using GIS.

Sinuosity was computed using GIS based on a technique comparable to the approach used in an Environmental Monitoring and Assessment Program's (EMAP's) field survey report. Field sinuosity data from EMAP report were used as reference data for analyzing the accuracy of sinuosity values from different GIS data sources and resolutions. The GIS technique was implemented for computing sinuosity for 55 streams in Virginia using vector data including the VBMP Hydro44 and National Hydrography Data (NHD). Insufficient statistical evidence was found to support the hypothesis that the computed sinuosity values using Hydro44 and NHD data are different from EMAP field data for all 55 streams. Sinuosity values computed using Hydro44 and NHD were found to increase with the increase in EMAP sinuosity (positive correlation) for all 55 streams. EMAP data on sinuosity, however, did not predict sinuosity values computed using Hydro44 (R² = 27%) and NHD (R² = 10%) sources well. It was found that the GIS technique of computing sinuosity using digital data such as Hydro44 (VBMP source) and NHD (USGS source 1:24,000) is better suited for stream orders 2 and 3. Insufficient statistical evidence was found that computed sinuosity values for streams derived using various resolutions (i.e., DTM 3m, DTM 10m, DTM 30m, DEM 10m and DEM 30m) are different from EMAP field data. Positive correlation was observed between sinuosity values for streams derived in all resolutions with EMAP field data. DTM 10m resolution data yielded best correlation value (75%) with EMAP field data.