Fiscal Year: 2013
1234 Texas A&M University-Kingsville (20712)
Principal Investigator: Jones, K.
Total Amount of Contract, Award, or Gift (Annual before 2011): $ 501,559
Exceeds $250,000 (Is it flagged?): Yes
Start and End Dates: 8/24/12 <> 8/31/15
Restricted Research: YES
Academic Discipline: Environmental Engineering
Department, Center, School, or Institute: College of Engineering
Title of Contract, Award, or Gift: Lower Rio Grande Valley Low-Impact Development Implementation and Education - Phase III
Name of Granting or Contracting Agency/Entity:
CFDA Link: EPA
CFDA Linked: Nonpoint Source Implementation Grants
(1.3.2) There is a limited amount of research, implementation projects, and guidance documents that focus on the arid and semi-arid regions like the Lower Rio Grande Valley (LRGV) region. Green roofs and rain harvesting. Green roofs are effective BMPs for mitigation of the environmental impacts to receiving waters associated with urban runoff. Greening of rooftops, by incorporating plants into the design of roofing systems, has been suggested as a method to reduce the impacts of NPS pollution runoff by reducing the impervious surface within a developed zone (Scholz-Barth, 2001). The objective of this task is to develop and validate model parameters for urban BMPs in order to implement them into watershed scale modeling systems such as the SWAT and EFDC models for the Arroyo Colorado region. A foundational strength of these models is the combination of upland and channel processes that are integrated and amenable to calibration within each simulation package. While the incorporation of agricultural and rural BMPs into these models has been a focus for reducing non-point source pollution for many years, modern Low Impact Development urban BMPs are poorly evaluated and their scale up to watershed scale programs even less well understood. Within SWAT, more comprehensive descriptions of hydraulic response units (HRUs) will be needed. The lack of monitoring data, inadequate data needed to characterize input parameters, and insufficient scientific understanding of BMP fundamental processes, are all factors which must be addressed for improved watershed models. This task will help develop the appropriate monitoring and model parameters such as engineered soil and pervious pavement average infiltration rates for the LRGV, parameters for the Green and Ampt method and other alternatives to empirical NRCS curve number approximations. Current SWAT HRU routines lack the information to provide explicit representation of buffer zones, constructed wetlands, residential LID developments, large pervious paved areas and other large urban geospatial features. The subproject will assimilate the collected water quality improvement performance data, along with measured relevant LID practice critical design parameters such as soil type, drainage layer descriptions, surface roughness, nutrient removal efficiencies, suspended sediment particle size distributions, modified runoff coefficients, time of concentration, geotextile parameters, field capacities, irreducible water saturations, quantification of disconnected surface areas and others for each project. The results of this data collection effort will be integrated into a model such as WinSLAMM or equivalent, capable of calibration, validation and prediction of LID BMP impacts which can be aggregated and integrated with watershed models such as SWAT or EFDC suitable for predictive modeling and watershed planning efforts for long range NPS water quality improvement.
Discussion: No discussion notes