assessment of a rapid approach for estimating catchment areas for surface drainage lines lawrence...

Assessment of a Rapid Approach for Assessment of a Rapid Approach for Estimating Catchment Areas for Estimating Catchment Areas for

Surface Drainage LinesSurface Drainage Lines

Lawrence Stanislawski, Science Applications International Lawrence Stanislawski, Science Applications International Corporation (SAIC)Corporation (SAIC)Michael Finn, U.S. Geological SurveyMichael Finn, U.S. Geological SurveyE. Lynn Usery, U.S. Geological SurveyE. Lynn Usery, U.S. Geological SurveyMark Barnes, U.S. Geological SurveyMark Barnes, U.S. Geological Survey

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

Brief overview of the National Hydrography Dataset Brief overview of the National Hydrography Dataset (NHD)(NHD)

Generalization Process for NHDGeneralization Process for NHD• Pruning of Drainage NetworkPruning of Drainage Network• Preprocessing RequirementsPreprocessing Requirements

MethodsMethods• Thiessen-polygon-derived (TPD) catchmentsThiessen-polygon-derived (TPD) catchments• Elevation-derived (ED) catchmentsElevation-derived (ED) catchments• Catchment comparisonsCatchment comparisons

ResultsResults• Between subbasin comparisonsBetween subbasin comparisons• Within subbasin comparisonsWithin subbasin comparisons• Extended resultsExtended results

SummarySummary


Conceptual Generalization Conceptual Generalization FrameworkFrameworkEnrichment (NHD)

Stratification Catchments

Upstream DensityDrainage Area Partitions

LowHigh

Feature Class Generalization

• Pruning (Radical Law)

• Simplification and other generalization operations

• Symbolization

Database

Validation

• Generalization metrics

• Benchmark comparisons

Products

• Level of detail

• Graphic product

Summary report

Users

NHD FeaturesNHD Features

Areal Stream/River

Areal Lake/Pond

Areal Lake/Pond

Linear Streams

Linear Canal/Ditch

Linear Connector

Artificial Paths


NHD FeaturesNHD Features


NHD FeaturesNHD FeaturesExample surface water flow network (NHDFlowline feature class)


National Hydrography Dataset (NHD)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

08010100000413

region-subregion-accounting unit-subbasin-reach number

08010100000696

08010100000413


NHD Generalization StrategyNHD 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.


NHD GeneralizationNHD 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.



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).


Generalization: Network Pruning Generalization: Network Pruning ExampleExample

Gasconade-Osage subregion (1029) falls in the Interior Plains and Interior Highlands physiographic divisions.


Generalization: Network Pruning ExampleGeneralization: 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)Pruning test on Gasconade-Osage subregion (1029)Green, blue, red: 1:100,000Green, blue, red: 1:100,000 Blue, red: 1:500,000 Blue, red: 1:500,000Red: 1:2,000,000Red: 1:2,000,000



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.


MethodsMethodsThiessen-polygon-derived (TPD) catchmentsThiessen-polygon-derived (TPD) catchments


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MethodsMethodsComparison to elevation-derived (ED) catchmentsComparison to elevation-derived (ED) catchments


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.

MethodsMethods


Study subbasins

Subbasin name State

NHD subbasin number Regime

Physiographic division1

Upper Suwannee

FL, GA 03110201 Flat Humid

Atlantic Plain of Coastal Plain

Lower Beaver UT 16030008 Flat Dry

Intermontane Plateaus of Basin and Range

Pomme De Terre MO 10290107 Hilly Humid

Interior Highlands of Ozark Plateaus

Lower Prairie Dog Town Fork Red TX 11120105 Hilly Dry

Interior Plains of Great Plains and Central Lowland

South Branch Potomac WV 02070001

Mountainous Humid

Appalachian Highlands of Valley and Ridge

Piceance-Yellow CO 14050006

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.

MethodsMethods


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 .

MethodsMethods


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 .

ResultsResults


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.

ResultsResults


0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

Flat Humid Flat Dry Hilly Humid Hilly Dry Mountainous Humid Mountainous Dry

Average Total Percent Correct

Average Mean Percent CorrectAverage Mean Percent Commission Error

Average Mean Percent Omission Error

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.

ResultsResults


0

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0 500000 1000000 1500000 2000000 2500000

Catchment Area (sq m)

Per

cen

t C

orre

ct

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).

ResultsResults


0

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0 500000 1000000 1500000 2000000 2500000

Catchment Area (sq m)

Per

cent

Cor

rect

Distribution of percent correct values compared to catchment size for the 100-cell stream-formation threshold in the mountainous humid subbasin (WV).

0

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0 1000 2000 3000 4000 5000 6000

Segment length (m)

Per

cent

Cor

rect

Distribution of percent correct values compared to network segment length for the 100-cell stream-formation threshold in the mountainous humid subbasin (WV).

ResultsResults


0.000

0.100

0.200

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0.400

0.500

0.600

Flat Humid Flat Dry Hilly Humid Hilly Dry MountainousHumid

Mountainous Dry

Co

eff

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of

Are

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orr

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ce

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 / (1 + 0.93(0.5)) = 0.34

Hilly and Mountainous subbasins: ~ 0.67 / (1 + 0.93(0.33)) = 0.51

ResultsResultsWithin subbasin comparisonsWithin subbasin comparisons

y = -0.012x + 0.5559

R2 = 0.6398

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.765 0.912 1.228 1.395 1.673 2.233 2.947

Drainage Density (km/sq km)

Co

effi

cien

t o

f A

real

Co

rres

po

nd

ence

y = -0.0075x + 0.5364

R2 = 0.7053

0.430

0.440

0.450

0.460

0.470

0.480

0.490

0.500

0.510

0.520

0.530

0.540

0.712 0.861 1.189 1.364 1.660 2.311 3.232


Coe

ffici

ent o

f Are

al C

orre

spon

denc

e

y = 0.0014x + 0.3341

R2 = 0.5311

0.326

0.328

0.330

0.332

0.334

0.336

0.338

0.340

0.342

0.344

0.346

0.348

0.838 1.042 1.498 1.736 2.153 3.148 4.540


Coe

ffici

ent o

f Are

al C

orre

spon

denc

e


Humid: Mountainous (WV) Hilly (MO) Flat (FL,GA)

Dry: Mountainous (CO) Hilly (TX) Flat (UT)

y = -0.0099x + 0.5053

R2 = 0.8358

0.400

0.410

0.420

0.430

0.440

0.450

0.460

0.470

0.480

0.490

0.500

0.510

0.748 0.901 1.230 1.401 1.683 2.323

Drainage Density (km/sq km)C

oeffi

cien

t of A

real

Cor

resp

onde

nce

y = -0.0042x + 0.5044

R2 = 0.4456

0.440

0.450

0.460

0.470

0.480

0.490

0.500

0.510

0.736 0.899 1.240 1.411 1.689 2.270


Coe

ffici

ent o

f Are

al C

orre

spon

denc

e

y = -0.0078x + 0.3813

R2 = 0.969

0.320

0.330

0.340

0.350

0.360

0.370

0.380

0.669 0.838 1.286 1.549 1.983


Coe

ffici

ent o

f Are

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e

ResultsResults


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.

ResultsResults


0.0

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Flat Humid Flat Dry Hilly Humid Hilly Dry Mountainous Humid Mountainous Dry

CA

C

Headwater Average Mean CAC

Average Mean CAC

Non-Headwater Average Mean CAC

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 TestsResults: Pruning Tests


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 TestsResults: Pruning TestsPrune to 0.925 km/sq km

(1:100,000-scale)

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Mountainous Dry

Pe

rce

nt

Percent CorrectPercent OmissionPercent Commission


Prune to 0.150 km/sq km(1:500,000-scale)

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Mountainous Dry

Perc

en

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Percent CorrectPercent OmissionPercent Commission

Results: Pruning TestsResults: Pruning TestsPrune to 0.925 km/sq km (1:100,000-scale)

0.50

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0.90

1.00


MountainousDry

Co

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Prune to 0.150 km/sq km (1:500,000-scale)

0.50

0.60

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1.00


MountainousDry

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SummarySummaryResults 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.


Questions?Questions?

Assessment of a Rapid Approach for Estimating Assessment of a Rapid Approach for Estimating Catchment Areas for Surface Drainage LinesCatchment Areas for Surface Drainage Lines

Lawrence Stanislawski, Science Applications International Lawrence Stanislawski, Science Applications International Corporation (SAIC)Corporation (SAIC)Michael Finn, U.S. Geological SurveyMichael Finn, U.S. Geological SurveyE. Lynn Usery, U.S. Geological SurveyE. Lynn Usery, U.S. Geological SurveyMark Barnes, U.S. Geological SurveyMark Barnes, U.S. Geological Survey


assessment of a rapid approach for estimating catchment areas for surface drainage lines lawrence...

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