Impact/Purpose:

The primary objectives of this research are to:

- Provide information on the variability in water supply that can be expected under varying climatic conditions. Early efforts will be focused on assembling regional databases for at least two counties (Mecklenberg County and York County) within SEQL region that can be used for water supply generation and model development.

- Develop tools that will help improve our ability to evaluate, study, and model linkages between different types of environmental systems: hydrologic, geomorphic, ecological, and climatic.

- Explore the use of annual and seasonal measurements of large lake surface temperatures as a new ecological indicator of the overall thermal content of those lakes, and construct an estimator of seasonal large lake heat budgets.

Description:

Water supply from ground water (i.e. well) or surface water (i.e reservoirs) is of great importance to the planning for future needs for local and regional use. However, despite scientific evidence that climatic shifts are likely to affect every part of the region and availability of techniques to supply this data, little work has being done that can help in the development of local, long-term water supply plans. In 2002, drought left the counties surrounding Charlotte, NC, and Rockhill, SC, near to running out of water. These events have fueled discussion on how to update water supply plans to meet city and county needs in the future. The EPA program Sustainable Environment for Quality of Life (SEQL) has set as one of its water planning goals the development and implementation of a plan of action that will result in an adequate supply of quality water and maximizes the use of available water resources.

Another important goal in environmental modeling is to link hydrologic, geomorphic, and ecological models for the purpose of evaluating potential outcomes of environmental change. To do this it is beneficial to focus on the region of greatest overlap between hydrologic, geomorphic, and ecological systems. That region is the watershed channel network, which concentrates flows of energy and materials and evolves in response to their passage. Characterization of the channel network for model parameterization, however, is tedious and expensive because survey crews must visit every reach in a watershed to do a thorough job. The advent of high resolution Light Detection and Ranging (LIDAR) data offers the promise of permitting remote channel characterization with substantially reduced cost. This research will develop an automated GIS-based tool to extract information about channel morphology and riparian vegetation from LIDAR data.

Another part of this project explores the use of annual and seasonal measurements of large lake surface temperatures as a new ecological indicator of the overall thermal content of those lakes. Surface temperature measurements will be compared with in situ vertical temperature profiles to determine if seasonal and/or annual changes in remotely sensed surface temperatures are correlated with estimated whole lake thermal content as extrapolated from the in situ vertical temperature profiles.

This task is currently aligned with the Global Change Research Program and Ecological Research Multi-Year Plans, and is closely associated with landscape indicator and model development (Task 5447), Environmental Montoring and Assessment Program (EMAP) activities in the western US and planned activities in the Great Rivers of the Mid-western US (Task 6821), and Regional Vulnerability Assessment Program (ReVA) activities in Mid-Atlantic States and the Southeastern US (Task 8877).

Record Details:

Record Type:PROJECT

Start Date:12/10/2009

Completion Date:12/10/2009

OMB Category:Other

Record ID: 16003

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Project Information:

Progress :

Research done from FY99-FY02 found significant increases in both the surface temperature of Lake Superior (over the 20 years ending in 2000) and lake-effect snowfall in the lee of the lake (over the 50 years ending in 2000). A significant correlation between the two was established in conjunction with seasonal measurements of winter air temperature. These results were presented at IAGLR, June 2001. Subsequent data indicates that the surface temperature increases seen in the 1990s may not persist in the 2000s but that the remotely sensed and in situ measurement of surface temperature remain highly correlated.

Landcover and soils data has been collected which will be utilized in for running SWAT, LTHIA and N-SPECT models. Several meetings with local stakeholders who are interested in helping to develop assessment questions related to water supply have taken place.

Development of the stream-reach characterization tool began in the fall of 2004, with a Beta version of the geomorphic characterization component due in August 2005. LIDAR data to be used during the development and evaluation of the tool was collected during the summers of 2003 and 2004.



Relevance :

This research could yield a landscape-scale indicator of ecological condition, integrity, and sustainability of large lake ecosystems that could be incorporated into long-term monitoring programs. The methods being developed could have wide applicability for using remote sensing to estimate whole-lake surface temperatures of large lakes.

The Southeast has many of the fastest growing cities in the U.S. This growth results in change to the landscape around them. Previous drought conditions in the region have made local decision makers aware that more information is needed in order to better prepare for the impacts that shifts in climate can have on local water supply.

Automated tools will be designed to facilitate the integration of different types of environmental models, and improve the ability to represent changing precipitation regimes that may accompany climate change.

Clients :CENR, Heinz Center, OWOW, EMAP (NHEERL), OPPE, EPA Regional Offices, County and State Planners; basin- or regional- level organizations (e.g., Great Lakes Program Office, Chesapeake Bay Program Office),
U.S. EPA ReVA/SEQL project, Regions, and Off

Project IDs:

ID Code :5442

Project type :OMIS