Water Resources Management/Ecological Engineering Program

Transcription

1 Water Resources Management/Ecological Engineering Program Fouad H. Jaber, PhD, PE Associate Professor and Extension Specialist Biological and Agricultural Engineering Texas A&M AgriLife Extension,

2 Urban Watersheds Increased stormwater peak flow Increased stormwater volume Higher contaminant content Reduced groundwater recharge Flooding Stream degradation Pollution of lakes

3 Program Focus Green infrastructure Stream Restoration Water Conservation Small agricultural sites ponds Residential Tanks; A/C condensate; Graywater

4 Green infrastructure Low impact development (LID) is increasingly being adopted as an alternative to traditional water management systems. LID includes practices such as bioretention, green roofs, rainwater harvesting, and permeable pavements.

5 Economic Impact In addition to obvious environmental benefits, economic benefits can result due to: Less costly water treatment Smaller stormwater systems Reduced flooding

6 Impact Recognition as LID experts Frequently asked to present in LID workshops in and out of state Cooperate with leading LID program in North Carolina State Grants Leader in construction of LID Demonstrations Texas

7 Evaluation Project in Dallas Five LID BMPs were built on the campus of Texas AgriLife Research and Extension, Dallas. The grant is funded by the Clean Water Act Section 319 urban nonpoint source pollution prevention program (TCEQ; EPA) BMPs Permeable pavement Bioretention area Rainwater harvesting Green roof Detention Pond Monitoring for hydrology, N, P, TSS, bacteria, legacy pollutant Chlordane

8 Rain Garden BMP Locations Detention Pond Permeable pavement Green roof RWH

9 Bioretention Area

10 Volume Reduction Average Reduction: 49%

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12 Permeable Pavement Newly constructed parking lot Comparison of 5 types pavement 25 experimental stalls among 52 total functional stalls Perforated underdrain pipes Total thickness = 16 inches Gravel layer Hydrologically separated with concrete curbs

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14 Volume Reduction Rates Reduction Rate PICP Pervious Concrete Grass Pavers Gravel Pavers 71% 74% 78% 93%

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16 Green Roofs Experimental Component 4 roof shelters, represent residential roofs Each divided into 4 parts, with 4 types of growing media Different layers of soil, drainage, insulation, roofing membrane Runoff volume, water quality

17 Monitoring Design

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19 Volume Reduction Event Rainfall C H H reduction S S reduction SD SD Reduction Date inches gals gals % gals % gals % 12/28/ % % % 01/10/ % % % 02/11/ % % % 03/11/ % % % 04/01/ % % % 04/04/ % % % 04/18/ % % % 05/16/ % % % 05/22/ % % % 06/10/ % % % 06/17/ % % % 07/11/ % % % 07/17/ % % % 09/21/ % % % 10/16/ % % % 10/27/ % % % 11/05/ % % % 11/26/ % % % 12/21/ % % %

20 Volume Reduction Event Rainfall C H H reduction S S reduction SD SD Reduction Date inches gals gals % gals % gals % 05/09/ Total Volume Reduction from C 65.39% 76.05% 75.33% 05/12/ /09/ /03/ /17/ /31/ /06/ /17/ /06/ /13/ /13/ /05/ /23/ /23/ /12/ /23/ /02/ /25/ /06/

21 TSS Loads C H S SD % 68.67% 49.59%

22 Rainwater Harvesting

23 Runoff from time based

24 Water Savings from RWH

25 Stream Structure and Hydrology Stream Classification and Evolution Stream Restoration

26 McKinney Project Meander wavelength (L m ) Radius of curvature (R c ) 45.0 Belt width (W blt ) Pattern

27 Greywater multi-grade greywater effluent

28 Greywater treatment system

29 Water treatment evaluations Total coliform removal (cfu/ml) 1.00E E+03 CFU/ml 1.00E E+01 S5 S4 S2 S1 S3 S6 1.00E+00 Raw CR-F M-F C-F UF UV RO Treatment-Train

30 Artificial Neural Network (ANN) Model to predict water quality (Turbidity) PREDICTED TURBIDITY (NTU) -100 OBSERVED AND PREDICTED TURBIDITY FOR TEST DATA ( CAORSE- F) Coarese-F L-R-O-B-L Linear Reg Coarese-F TLRN-1-B-M 0 R² = R² = Reg Coarese-F-PNN O N-N R² = R² = OBSERVED TURBIDITY FOR COARSE -F( NTU) TURBIDITY (NTU) Comparison of observed and best ANN module(fl-r-o-b-l Linear R) for tubidity prediction ( Coarse -F) Coarese-F TEST DATA POINTS Coarese-F L-R-O-B-L Linear Reg

31 ACON model: Operation details 31

32 ILANN-ACON validation for Test Building Simulated condensate data(l) 0.9 ILAN-ACON (PNN) R² = Observed condensate data (l) Q_MLP(L) R² =

33 Fouad H. Jaber, PhD, PE Associate Professor and Extension Specialist Biological and Agricultural Engineering Texas A&M AgriLife Extension Dallas Research and Extension Center