Development and Implementation of a Comprehensive Lake and Reservoir Strategy for Nebraska as a Model for Agriculturally Dominated EcosystemsEPA Grant Number: R828635
Title: Development and Implementation of a Comprehensive Lake and Reservoir Strategy for Nebraska as a Model for Agriculturally Dominated Ecosystems
Investigators: Holz, John C. , Bogardi, Istvan , Fritz, Sherilyn C. , Gitelson, Anatoly A. , Hoagland, Kyle D. , Merchant, James W. , Rundquist, Donald C.
Institution: University of Nebraska at Lincoln
EPA Project Officer: Hiscock, Michael
Project Period: January 1, 2001 through December 31, 2003 (Extended to December 31, 2004)
Project Amount: $1,224,706
RFA: Development of National Aquatic Ecosystem Classifications and Reference Conditions (2000) RFA Text | Recipients Lists
Research Category: Aquatic Ecosystems , Water , Ecosystems
In agriculturally dominated regions, land use practices have an unusually large impact on water bodies and, therefore, land use may reduce the utility of current ecoregion-based approaches to lake classification by dampening the signals which underlie the ecoregion framework. An exceptional team of water quality researchers has been assembled to develop a comprehensive classification scheme for agriculturally dominated ecosystems, using Nebraska as a highly representative model. Three objectives critical to achieving this goal are to (1) establish a protocol for aggregating lakes and reservoirs in agricultural ecosystems into appropriate classification strata and identifying reference conditions for these lake classes, (2) establish the role of remote sensing and GIS in a classification strategy, and (3) establish a dynamic technology transfer link between the proposed classification system and the end-users.
We will build on our current water quality database for 159 Nebraska lakes and reservoirs by sampling an additional 45 water bodies, strategically located to provide exceptional spatial coverage of the State. From this database, lakes and reservoirs will be classified hierarchically using a combination of rule-based and data-based approaches. Potential representative reference water bodies with relatively little human impact will be selected for each lake class. The absolute selection of a reference lake from the pool of potential lakes will be based on the temporal limnological stability of a lake, determined by paleolimnological reconstructions of historical water quality. Biological indicators will then be developed, based on plankton assemblages, that integrate lake conditions of each stratum. Improved methods will be developed for integrating field data, data collected via airborne and close range remote sensing, data collected via operational and near future satellite remote sensing systems, and ancillary geospatial data in a multistage approach to lake classification. Special emphasis will be placed on developing methods to remotely sense biological indicators of water quality based on the optical phytoplankton pigment structures of lakes. Geospatial information technologies will also be used to identify the mechanisms that are known to have impacts on water quality and generate distinct classes of lakes (e.g., land use, soil). Analyses of shallow sediment core and intra-annual variation of field-derived and remotely sensed limnological parameters will be used to establish a strategy for monitoring temporal variation of water quality and for lake reclassification. Included in this robust framework is a unique system that maximizes the transfer of classification technology and protocols to resource agencies in an efficient and uncomplicated manner.
State-of-the-art methodologies and protocols, utilizing the powerful approaches and tools of GIS, remote sensing and Multiple Criterion Decision Making, will be developed for classifying lakes, determining reference conditions, identifying biological indicators, and technology transfer in agricultural systems. These results will address EPA's need for improved ecosystem classification schemes and reference condition identification.