Assessment of a Rapid Approach for Estimating Catchment Areas for Surface Drainage Lines Lawrence Stanislawski, Science Applications International Corporation (SAIC) Michael Finn, U.S. Geological Survey E. Lynn Usery, U.S. Geological Survey Mark Barnes, U.S. Geological Survey ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO

Brief overview of the National Hydrography Dataset (NHD) Generalization Process for NHD Pruning of Drainage NetworkPruning of Drainage Network Preprocessing RequirementsPreprocessing RequirementsMethods Thiessen-polygon-derived (TPD) catchmentsThiessen-polygon-derived (TPD) catchments Elevation-derived (ED) catchmentsElevation-derived (ED) catchments Catchment comparisonsCatchment comparisonsResults Between subbasin comparisonsBetween subbasin comparisons Within subbasin comparisonsWithin subbasin comparisons Extended resultsExtended resultsSummary ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO

Conceptual Generalization Framework Enrichment (NHD) Stratification Catchments Upstream Density Drainage Area Partitions Feature Class Generalization Pruning (Radical Law) Simplification and other generalization operations Symbolization Validation Generalization metrics Benchmark comparisons Products Level of detail Graphic product Summary report Users

NHD Features Areal Stream/River Areal Lake/Pond Areal Lake/Pond Linear Streams Linear Canal/Ditch Linear Connector Artificial Paths ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO

NHD Features ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO

NHD Features Example surface water flow network (NHDFlowline feature class) ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO

National Hydrography Dataset (NHD) Vector data layer of The National Map representing surface waters of the United States. Includes a set of surface water reaches  Reach: significant segment of surface water having similar hydrologic characteristics, such as a stretch of river between two confluences, a lake, or a pond.  A unique address, called a reach code, is assigned to each reach, which enables linking of ancillary data to specific features and locations on the NHD. Reach code from Lower Mississippi subbasin region-subregion-accounting unit-subbasin- reach number ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO

NHD Generalization Strategy Base data: highest resolution NHD that covers desired area. Feature pruning – removal of features that are too small for desired output scale. select a subset of network features select a subset of area features remove point features associated with pruned line or area features Feature simplification removal of vertices aggregation, amalgamation, merging, linearization of area features, etc. ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO

NHD Generalization Catchment: The area associated with a segment of a drainage network is referred to as the segment’s catchment area, or just catchment. Surface runoff in the catchment flows into the associated network segment. Catchments (cyan) associated with each network segment (red) of a hydrographic network. The network pruning strategy of our NHD generalization process is based on upstream drainage area, which requires catchment area estimates for each network segment. ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO

NHD Generalization Upstream drainage area (UDA) for any network segment is the sum of all upstream catchment areas, including the segment of interest. For instance, the UDA for the network segment marked with the green square is the yellow shaded area (~ 11.2 sq km). ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO

Generalization: Network Pruning Example Gasconade-Osage subregion (1029) falls in the Interior Plains and Interior Highlands physiographic divisions. ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO

Generalization: Network Pruning Example Feature pruning – removal of feature that are too small for desired output scale. select a subset of network features Pruning test on Gasconade-Osage subregion (1029) Green, blue, red: 1:100,000 Blue, red: 1:500,000 Red: 1:2,000,000 ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO

NHD Generalization Preprocessing requirements for network pruning: Catchment area estimates Upstream drainage area estimates Values not available for high resolution NHD Layer Therefore, we developed a rapid approach to estimate catchment areas using Thiessen polygons. ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO

Methods Thiessen-polygon-derived (TPD) catchments ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO

Methods Comparison to elevation-derived (ED) catchments ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO  Each ED catchment is precisely geospatially associated to one segment of a surface drainage network that is derived from the same elevation model.  Associating ED catchments to each segment of a hydrographic network, such as that in the NHD, is a complex and imprecise process.  Thiessen-polygon derived catchments can be precisely associated with individual segments of any network regardless of how the network is derived. 1.Generate surface drainage network from an elevation model. 2.Compute ED catchments for ED network. 3.Compute TPD catchments for ED network. 4. Compare ED and TPD catchments through an overlay process.

Methods ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO Study subbasins Subbasin nameState NHD subbasin numberRegime Physiographic division 1 Upper Suwannee FL, GA Flat Humid Atlantic Plain of Coastal Plain Lower BeaverUT Flat Dry Intermontane Plateaus of Basin and Range Pomme De TerreMO Hilly Humid Interior Highlands of Ozark Plateaus Lower Prairie Dog Town Fork RedTX Hilly Dry Interior Plains of Great Plains and Central Lowland South Branch PotomacWV Mountainous Humid Appalachian Highlands of Valley and Ridge Piceance- YellowCO Mountainous Dry Intermontane Plateaus of Colorado Plateaus Six NHD subbasins that fall in one of six regimes based on climate and topography were evaluated. For each subbasin:  A 30-meter resolution DEM was extracted from the National Elevation Dataset.  ED Streams and catchments were derived for several (7) stream formation thresholds.  TPD catchments were generated for all ED network segments  ED catchments and TPD catchments were compared through a spatial union.

Methods ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO Catchment comparison computations: For each TPD catchment in all networks: percent correct, percent omission, and percent commission For all TPD catchments in each stream- formation threshold: Mean percent correct Mean percent omission, and Mean percent commission Total percent correct For all stream-formation thresholds in each subbasin: Average mean percent correct Average mean percent omission, and Average mean percent commission Average total percent correct Thiessen-derived catchment (red outline) overlaying associated elevation-derived catchment (gray outline) with correct area in green, and areas of commission error in purple and omission error in pink.

Methods ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO Computations: Coefficient of areal correspondence (CAC) is computed for any two associated areas as the area of intersection, divided by the area of union. In the figure, CAC is the computed as the green area divided by the sum of all colored (pink, purple, and green) areas. CAC was computed for all catchments of each subbasin and stream-formation threshold, and summarized in the same manner as percent correct values. Thiessen-derived catchment (red outline) overlaying associated elevation-derived catchment (gray outline) with correct area in green, and areas of commission error in purple and omission error in pink.

Results ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO Number of catchments computed ranged from 957 to 24,603, with catchment density increasing with decreasing stream-formation threshold. Processing (Pentium 4 CPU, 3.0 GHz, 1 GB RAM)  Speed ED catchments: ~ 10 minutes to 2 hours. TPD catchments: 2 to 14 minutes (5 to 10 times faster)  Reliability ED process failed for some of the more dense network computations. TPD process never failed. Applicability ED catchments: requires an integrated DEM and ED network. TPD catchments: can be applied to any network.

Results ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO Between subbasin comparisons:  Averages of mean percent correct values range from about 50 to 65, with averages better than 60 on hilly and mountainous subbasins.  Average total percent correct values range from about 58 to 75. greater than average mean percent correct for all subbasins.  Average mean omission errors are about 7 percent larger that average mean commission errors.

Results ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO Distribution of percent correct values for all catchments from the 100-cell stream-formation threshold for the mountainous humid subbasin (WV). Mode of distribution is 71. Distribution of percent correct values compared to catchment size for the 100-cell stream- formation threshold in the mountainous humid subbasin (WV).

Results ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO Distribution of percent correct values compared to catchment size for the 100-cell stream- formation threshold in the mountainous humid subbasin (WV). Distribution of percent correct values compared to network segment length for the 100-cell stream-formation threshold in the mountainous humid subbasin (WV).

Results ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO Between subbasin comparisons: Average mean coefficient of areal correspondence (CAC) ranges from 0.34 to 0.51, with better correspondence in the hilly and mountainous subbasins. CAC = Co / (Co+Om+Cm) and Co + Om = 1  Flat subbasins: ~ 0.5 / ( (0.5)) = 0.34  Hilly and Mountainous subbasins: ~ 0.67 / ( (0.33)) = 0.51

Results Within subbasin comparisons ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO Humid: Mountainous (WV)Hilly (MO)Flat (FL,GA) Dry: Mountainous (CO)Hilly (TX)Flat (UT)

Results ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO Within subbasin comparisons: Catchments were separated into headwater (light green) and non- headwater catchments (light blue) based on whether or not they contained a dangling node (cyan) of a stream line. Mean percentages were recomputed for headwater and non-headwater catchments.

Results ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO Headwater average, average, and non-headwater average of the mean coefficient of areal correspondence (CAC) for each formation threshold is shown for each subbasin.

Results: Pruning Tests ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO 200-cell stream-formation threshold network (green, pink, blue) for hilly dry subbasin (TX). Pruned networks using UDA values based on ED catchments  Pruned to 1:100,000-scale (pink, blue)  Pruned to 1:500,000-scale (blue) 200-cell stream-formation threshold network (green, purple, cyan) for hilly dry subbasin (TX). Pruned networks using UDA values based on TPD catchments  Pruned to 1:100,000-scale (purple, cyan)  Pruned to 1:500,000-scale (cyan)

Results: Pruning Tests ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO

Results: Pruning Tests ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO

Summary Results suggest that the TPD catchment process is:  Less likely to fail because of hardware or software limitations than ED process,  About 5 to 10 times faster than the ED catchment process,  Logistically much simpler to implement than ED process which requires a network integrated to an elevation model. And that the:  Fractional part that TPD catchments overlay ED catchments is about ½ for subbasins in flat terrain and about 2/3 for subbasins in hilly or mountainous terrain.  Headwater TPD catchments exhibit better areal correspondence (up to 17 percent) with ED catchments than do non-headwater catchments.  The lowest areal correspondence of TPD catchments to ED catchments occurs on relatively small catchments or on very short network segments.  Better than 80 percent linear correspondence can be expected between networks pruned to 1:100,000-scale or smaller using UDA based on TPD catchments and UDA based on ED catchments. ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO

Questions? Assessment of a Rapid Approach for Estimating Catchment Areas for Surface Drainage Lines Lawrence Stanislawski, Science Applications International Corporation (SAIC) Michael Finn, U.S. Geological Survey E. Lynn Usery, U.S. Geological Survey Mark Barnes, U.S. Geological Survey ACSM-IPLSA-MSPS 2007, March 9-12, St. Louis, MO