Sarah Giles Holly Kuestner Steven Orr Qi Zhang. 1.Impervious Surfaces’ Effects on Flow Accumulation (Holly) 2.Variable Source Area (Holly) 3.Catchment.

Carolina North Catchments

Sarah Giles

Holly Kuestner

Steven Orr

Qi Zhang

Contents

1. Impervious Surfaces’ Effects on Flow

Accumulation (Holly)

2. Variable Source Area (Holly)

3. Catchment Delineation (Qi)

4. Erosion Potential (Qi)

5. Maximum Likelihood Classification (Steven)

6. Assessment of Future Development Scenarios

(Sarah)

Identification of Sensitive Areas KUESTNER

1. Fill DEM2. Calculate Flow Direction3. Reclassify Steven’s land-cover raster as a

binary raster of impervious/non-impervious surfaces

4. Weight the flow direction raster with this binary raster to calculate impervious-weighted flow accumulation (stream network accounting for paved areas)

1. Isolate stream reaches with high accumulation (to aid in storm water planning)

1 2 3

4

Isolate catchments with high impervious sensitivity (to aid in storm water planning)

Identification of Sensitive Areas KUESTNER

Weighted accumulation map shows how impervious surfaces impact flow accumulation.

WeightedUnweighted

Isolate catchments with high impervious sensitivity (to aid in storm water planning)

Identification of Sensitive Areas KUESTNER

Weighted accumulation map shows how impervious surfaces impact flow accumulation.

Isolate catchments with high impervious sensitivity (to aid in storm water planning)

Catchments with areas accumulating

>50 upslope impervious pixels

Weighted Flow Accumulation

Areas accumulating >50 upslope

impervious pixels

Identification of Sensitive Areas KUESTNER

1. Load DEM into TAS, breach all pits2. Calculate Wetness Index using

d_infinity3. Calculate mean Wetness Index

using Statistical Analysis 4. Use raster calculator to calculate

saturation deficit :

5. Graph variable source area versus mean saturation deficit

Identify areas likely to become saturated (to aid in storm water planning)

Breach Pits in TAS

Calculate WI Mean WI Saturation Deficit

Use m, s_ parameters from class:• m = 0.4517• s_ = 2.5, 2.9, 3.19, 3.60,

4.0

Identification of Sensitive Areas KUESTNER

Further extension: Repeat the prior procedure for all 127 sub-catchments of the Carolina North forest.

Identify areas likely to become saturated (to aid in storm water planning)

A comparison of the results for all catchments could reveal which are most prone to saturation during storms.

2.5 2.9 3.19 3.6 40

0.005

0.01

0.015

0.02

0.025

Variable Source Area with Different Saturation Deficit Scenarios for Car-

olina North

Average Saturation Deficit

Vari

able

Sourc

e A

rea

M- and S_bar values should be specific to Carolina North.

Catchment delineationProcess in creating sub-catchment polygons

Zhang

CAROLINA NORTH

Steps: 1. Pre-processing Fill depressions Flow direction Flow accumulations2. Stream definition (fig. a) Number of cells – 150 Areas – 0.015118 Km2

2. Stream segmentation3. Catchment delineation (Fig. b)4. Catchment polygons (Fig. b)

Fig. a) Stream

Fig. b) Polygons

Soil ErosionRelative Stream Power & Downslope Change

Zhang

Fig. c) Relative Stream Power (RSP)

Fig. d) Downslope change

To measure the change of the slope, defined as the derivative of relative stream power. The higher the value is, the larger extent the stream power changes and thus the easier the soil erosion happens.

RSP = As ^ 1.0 * tan(S) As: specific catchment area S: local slopeA measure of the erosive power of flowing stream network.

Relative Stream Power

Downslope change

Soil ErosionAnalysis of Downslope Change

Zhang

Fig. e) Selected catchments

High values of dRSPdx: 33 catchments soil erosion are potentially to happen conifer

Low downslope change: 94 catchments sediment are potentially to deposit impervious surface other land cover

Catchment selection

Soil ErosionStatistical Analysis of Downslope Change

Zhang

Data Source: Steven’s Classification Results

Image Analysis CHARACTERIZING THE FOREST AND DETERMINING CANOPY COVER.

ORR

RGB:432

CAROLINA NORTH

Image Analysis CLASSIFIED IMAGE LAND TYPE OR CLASS FINDINGS/STATISTICS.

ORR

Land Type/ClassPixel

Count Total Percentage

Dirt 15024 6.05%

Turbid Water 1137 0.46%

Pavement 9548 3.85%

Grass 14077 5.67%

Coniferous 105074 42.33%

Deciduous 83082 33.47%

Urban 20307 8.18%

Total 248249 100.00%

Land Type/Class Acreage Total Percentage

Dirt 53.06 6.05%

Turbid Water 4.0342 0.46%

Pavement 33.765 3.85%

Grass 49.726 5.67%

Coniferous 371.23 42.33%

Deciduous 293.53 33.47%

Urban 71.739 8.18%

Total 877.0842 100.00%Land

Type/Class Acreage Total Percentage

Forest 664.76 75.80%

Water 4.0342 0.46%

Grass 76.256 8.70%

Commercial 132.034 15.06%

Total 877 100.01%

Land Type/Class Pixel Count Total %Non-Vegetation 46016 18.54%

Vegetation 202233 81.46%

    100.00%

Non-Forest 60093 24.21%

Forest 188156 75.79%

    100.00%

Impervious 29855 12.03%

Pervious 218394 87.97%

    100.00%In this table, we have the total number or percentage of pixels that are classified as the specific land type or class.

In these tables, we have the total number or % of catchments that are greater than 50% of Non-Vegetation or Vegetation.

In this table, we have a breakdown of how much acreage each land type or class takes up in the total area of Carolina North.

Image Analysis INDIVIDUAL WATERSHED/CATCHMENT FINDINGS/STATISTICS.

ORR

As you can see, a majority of nearly 83% of all 127 catchments have a greater than 50% vegetation surface type.

Surface Type Total Number Total %Non-Vegetation

(>50%) 22 17.32%

Vegetation (>50%) 105 82.68%

   127 100.00%

Non-Forest (>50%) 30 23.62%

Forest (>50%) 97 76.38%

   127 100.00%

Impervious (>50%) 11 8.66%

Pervious (>50%) 116 91.34%

   127 100.00%

As expected with the other two statistical figures, there is a majority (91%) of catchments that are pervious greater than 50%.

76% of the catchments have mostly forest within their individual areas.

From these numbers, it is easy to see that of the 127 catchments found on the Carolina North Property, it is more likely for a catchment to have the properties of a vegetative, forest with pervious qualities.

Looking at the image, one can verify this by seeing the majority of area covered with forest and only a minority portion of cleared land visible.

WHAT THESE NUMBERS MEAN:

Image Analysis RESULTS OF THE CLASSIFICATION OF CAROLINA NORTH.

ORR

The image to the right makes a big impression on the land cover use with the total acreage of Carolina North. We see the entirety of the land and can clearly know that forest cover is the majority. Another key point to mention is the loss of a carbon sink when this space is cleared for development.

The largest area that is present where human interaction is evident is in the bottom right hand corner where urbanization and the airport are the majority of land cover.

CLASSIFICATION:

Remote sensing image

of Carolina North

Number of acres of each type of land

cover

Estimate changes in recharge,

runoff, and nonpoint source

pollution L-THIA (Long Term Hydrologic Impact Analysis)

Land use classification using ENVI and ArcMap.

Land Type/Class Acreage

Total Percentage

Dirt 53.06 6.05%Turbid Water 4.0342 0.46%

Pavement 33.765 3.85%Grass 49.726 5.67%

Coniferous 371.23 42.33%Deciduous 293.53 33.47%

Urban 71.739 8.18%

Total877.084

2 100.00%

Land Type/Class Acreage

Total Percentage

Forest 664.76 75.80%Water 4.0342 0.46%Grass 76.256 8.70%

Commercial 132.034 15.06%Total 877 100.01%

AssessmentGILES

POSSIBLE DEVELOPMENT SCENARIOS

Scenario 1: + 18 acres of commercial, -9 acres forest, -9

acres grass

Scenario 2: + 18 acres of commercial, -18 acres grass

AssessmentGILES

POSSIBLE DEVELOPMENT SCENARIOS: DATA

So, more of the 311 acres of conserved land should be in the form of forest (scenario 2), preferably mixed in with the development.

AssessmentGILES

POSSIBLE DEVELOPMENT SCENARIOS: RESULTS