Sarah Giles Holly Kuestner Steven Orr Qi Zhang

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)

1.Fill DEM 2.Calculate Flow Direction 3.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) Isolate catchments with high impervious sensitivity (to aid in storm water planning)

Weighted accumulation map shows how impervious surfaces impact flow accumulation. Weighted Unweighted Isolate catchments with high impervious sensitivity (to aid in storm water planning)

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

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 = s_ = 2.5, 2.9, 3.19, 3.60, 4.0

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

Steps: 1. Pre-processing Fill depressions Flow direction Flow accumulations 2. Stream definition (fig. a) Number of cells – 150 Areas – Km 2 2. Stream segmentation 3. Catchment delineation (Fig. b) 4. Catchment polygons (Fig. b)

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 slope A measure of the erosive power of flowing stream network. Relative Stream Power Downslope change

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

Land Type/ClassPixel CountTotal Percentage Dirt % Turbid Water % Pavement % Grass % Coniferous % Deciduous % Urban % Total % Land Type/ClassAcreageTotal Percentage Dirt % Turbid Water % Pavement % Grass % Coniferous % Deciduous % Urban % Total % Land Type/ClassAcreageTotal Percentage Forest % Water % Grass % Commercial % Total % Land Type/ClassPixel CountTotal % Non-Vegetation % Vegetation % % Non-Forest % Forest % % Impervious % Pervious % % 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.

As you can see, a majority of nearly 83% of all 127 catchments have a greater than 50% vegetation surface type. Surface TypeTotal NumberTotal % Non-Vegetation (>50%) % Vegetation (>50%) % % Non-Forest (>50%) % Forest (>50%) % % Impervious (>50%)118.66% Pervious (>50%) % % 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:

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 AcreageTotal Percentage Dirt % Turbid Water % Pavement % Grass % Coniferous % Deciduous % Urban % Total % Land Type/Class AcreageTotal Percentage Forest % Water % Grass % Commercial % Total %

Scenario 1:  + 18 acres of commercial, -9 acres forest, -9 acres grass Scenario 2:  + 18 acres of commercial, -18 acres grass

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