Hydrologically Consistent Pruning of the High- Resolution National Hydrography Dataset to 1:24,000-scale
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1 Hydrologically Consistent Pruning of the High- Resolution National Hydrography Dataset to 1:24,000-scale Lawrence V. Stanislawski 1, Ariel Doumbouya 2, Barbara P. Buttenfield 3, 1 Center for Excellence in Geospatial Information Science (CEGIS), United States Geological Survey (USGS), Rolla, Missouri, USA, lstan@usgs.gov 2 National Geospatial Program, Applied Research, USGS Denver CO atdoumbouya@usgs.gov 3 Department of Geography, University of Colorado-Boulder, USA, babs@colorado.edu 1
2 Problem: Drainage density variations in highresolution (HR) NHD are caused by natural conditions and compilation differences Virginia North Carolina Virginia North Carolina 2
3 Objective of hydrologic generalization: Estimate 1:24,000-scale (24K) density patterns from channels derived from a weighted flow accumulation (WFA) model Derive a natural density pattern of surface water channels (at 24K) that varies with terrain, runoff, soil permeability, soil depth, and ground water Derived 24K density patterns are used to generalize highresolution (HR) NHD to 24K and smaller scales (Radical Law) Advantages: Can eliminate compilation inconsistencies in HR NHD while maintaining natural variation Can identify missing or improper content in HR NHD by comparing 24K derived channels to HR NHD 3
4 Estimating Weight for Flow Accumulation RUNOFF SLOPE SOIL Permeability Rock Depth 4
5 Estimating Weights for Flow Accumulation: Base Flow Index Base flow is the component of streamflow that can be attributed to ground-water discharge into streams. The baseflow index (BFI) is the ratio of base flow to total flow, expressed as a percentage. 1-kilometer raster (grid) dataset for the conterminous United States was created by interpolating BFI values estimated at U.S. Geological Survey (USGS) stream gages. Wolock, D.M., 2003a, Flow characteristics at U.S. Geological Survey streamgages in the conterminous United States: U.S. Geological Survey Open-File Report , digital dataset, available on the World 5 Wide Web, accessed June 30, 2003, at URL
6 Weights for Flow Accumulation R a = R * p np * p nss. Runoff adjusted for loss to soil permeability (p np ) and soil saturation (p nss ). (Adjusted to range from 0 to 1, i.e. R a /max. R a ) R = mean annual runoff based on precipitation and evapotranspiration (Wolock and McCabe, 1999) p np = proportion of runoff not lost to permeability of saturated soil, which is estimated as 1 (soil permeability / maximum soil permeability) p nss = proportion of runoff not lost to soil saturation, which is estimated as 1 relative soil saturation potential (SSP), which is SSP/ maximum SSP SSP = relative soil saturation potential estimated from rock (or soil) depth / (1.50 * soil permeability). This rough estimate is based on Horton s (1940) equation for soil infiltration rate, using an initial (unsaturated) infiltration rate of twice the saturated soil infiltration rate, where soil permeability is the saturated soil infiltration rate. 6
7 Weight Refinements for local slope and base flow R s = R a * (slope in degrees / 90.0). Adjustment for local terrain conditions determined from 10-m National Elevation Dataset (NED) data. R s is used to compute weighted flow accumulation (WFA) for a subbasin from the flow direction grid (from 10-m NED). WFA BF = WFA / (1.0 BFI / 100.0). WFA is an estimate of stream flow potential without a ground water contribution. BFI = base flow index, which is an estimate of the percent of ground water contributing to stream flow. Stream channels are extracted from WFA BF. Horton, R.E., 1940, An approach towards a physical interpretation of infiltration capacity: Soils Science Society of America Proceedings, v. 5, p Wolock, D.M., 1993, Simulating the variable-source-area concept of streamflow generation with the watershed model TOPMODEL: U.S. Geological Survey Water-Resources Investigations Report , 33 p. Wolock DM and McCabe GJ, 1999, Estimates of runoff using water-balance and atmospheric general circulation models. Journal of the American Water Resources Association, 35(6):
8 30 Test subbasins distributed over six climate and slope conditions in the coterminous US Methods: Extract 24k drainage density pattern through weighted flow accumulation model. Parameters derived from 24k NHD flowline data (TL, NF, MLFT, Voids,canal/pipe distribution) Result reflect natural density variations without compilation differences. Compare extracted channels to 24K NHD flowlines, which may include compilation inconsistencies. Visual Metric (Coefficient of Line Correspondence or CLC) 8
9 30 Subbasins: Dry, MT Mizpah Average slope (deg): 6.0 St. Dev.: 5.6 Ave. runoff (mm/yr): 13.7 Total channel length (km):
10 30 Subbasins: Dry, MT
11 30 Subbasins: Humid, NC Upper Little Tennessee Ave. slope (deg): 20.8 St. Dev. (deg): 10.0 Ave. runoff (mm/yr): 1059 Tot. channel length (km): 3323 Subbasin CLC:
12 30 Subbasins: CLC Comparing Elevation-derived to 24K NHD: Terrain Slope Average CLC for all subbasins: 0.56 Subbasins with average slope <1 degree, CLC average 0.38; Subbasins with average slope >1 degree, CLC average 0.59 (Quality of elevation data affects extraction) 12
13 30 Subbasins: CLC Comparing Elevation-derived to 24K NHD: Terrain Slope First-order features average 0.40 CLC. Second and higher order average 0.80 CLC. 13
14 30 Subbasins: Comparision of first order features (ED vs 24k NHD) Sum of length of first order tributaries is about 10 percent longer on average in the 24k NHD than the ED channels. On average there are about 20 percent more first-order tributaries in the ED channels than in the 24k NHD. 14
15 Using 24K Density Pattern to Prune HR NHD Flowlines to 24K Derived 24k Density Pattern HR NHD Flowline Density Partitions HR NHD Flowlines Target Densities HR Flowlines Thinned to 24k Target Densities 15
16 Subregion 0710 HR Flowlines Pruned to Smaller Scales 16
17 Subregion 0710 HR Flowlines Pruned to Smaller Scales 17
18 Summary Statements for 30 Subbasin Study 1. Matches between 24K derived and NHD networks Average match about 56 percent (range 30 to 70). Most mismatching in first order channels, 40 percent average match. Second and higher order features match 80 percent on average. 20 percent more first order channels are derived than what exists in the 24K NHD, but first order NHD features are about 10 percent longer than derived first orders. Fewer matches are found in low slope areas. Less than 1 degree slope average match is 38 percent, whereas 59 percent matching found in higher slope subbasins. Higher resolution elevaton data may improve matching in low slope areas. 2. Automated conflation tools identify missing features, which can guide additions to HR NHD K density patterns derived from WFA model are sufficient to reduce the density of HR NHD flowlines in over-compiled areas to 24K and smaller scales. The pruning process maintains natural variations. Extraction from higher resolution (< 10m cells) elevation data may be needed for low slope areas, and consideration is needed for non-natural features (canals, pipes). 18
19 Future Work 1. Develop and test parallel processing workflow. 2. Possible model refinements: Use of NDVI to account vegetative cover on runoff in weight estimates. Use of terrain curvature to identify maximum extraction thresholds. Alternative depression filling algorithms. Alternative flow-direction algorithms (e.g. TauDem D-infinity). Adjust slope effect. 3. Compare to lidar-derived channels particularly in low-slope and braided channel areas. (e.g. GeoNET extraction) 4. Enhance workflow to automatically account for canal/ditch and pipeline features within 24k density patterns. 5. Test the use of NHDPlus flow-direction grid for possible reduction of processing time. 19
20 Thank you! Questions? 20
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