2001 Progress Report: 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: Packard, Benjamin H
Project Period: January 1, 2001 through December 31, 2003 (Extended to December 31, 2004)
Project Period Covered by this Report: January 1, 2001 through December 31, 2002
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
The overall objective of this research project is to develop a dynamic lake classification system for agricultural ecosystems, utilizing the innovative and state-of-the-art approaches of a unique combination of disciplines. Three major critical objectives are to: (1) establish a protocol for aggregating lakes and reservoirs in agriculturally dominated ecosystems into appropriate classification strata and identifying reference conditions for these lake classes; (2) establish the role of remote sensing and Geographic Information Systems (GIS) in a lake and reservoir classification strategy; and (3) establish a dynamic technology transfer link between the proposed classification system and the end users.
The first tier of classification was established and separates water bodies on the basis of physical characteristics known to influence water quality in this region. During Project Year 1, 98 water bodies (32 natural Sand Hill lakes, 21 sandpit lakes, and 45 reservoirs) were selected to fill data gaps in our current database and to provide representative spatial coverage of the state. All water bodies were sampled at one deep water site monthly, from May thru September, and analyzed for numerous limnological parameters, including phytoplankton and zooplankton. Five Sand Hill lake classes were defined by a factor analysis, with significant factors plotted to identify five groups of lakes with similar water quality characteristics. The factor analysis revealed three significant factors explaining over 73 percent of the variability of the data; with alkalinity, conductivity, chlorophyll a, nitrate + nitrite, Secchi disk depth, total suspended solids, orthophosphate, and total phosphorus loading significantly into the three factors. These lake groups were compared to rule-based Level IV Ecoregions, and we found that the Level IV Ecoregions do not accurately represent water quality in the Sand Hills.
During the reporting period, a GIS database was developed that contains data on lake water chemistry, lake morphology, and landscape data. In addition to the current use of hydrologic accounting units (HUCs), the GIS group has implemented a modified ArcView script to delineate watersheds for all lakes included in the GIS database. The GIS database is comprised of both actual lake water quality data and landscape data. Lake coordinates obtained from digital orthophotographs were used to develop a map of lake locations, and the current GIS database contains data for about 200 lakes sampled between 1989 and 2001. The water quality data has been summarized into annual and seasonal means to ensure that all analysis are consistent with the national framework. The landscape data was obtained from different sources and have different cartographic representations, scales, intended use of the data, and error sources. The data have been screened in order to use most current versions of the required data that also are readily available nationally. The different coordinate systems of all the data sets have been transformed into the Universal Transverse Mercator (UTM) coordinate system to facilitate data integration in the GIS. The landscape data is however being maintained in different raster and vector formats due to the different tasks involved at each stage of research. A procedure for using U.S. Geographical Survey (USGS) 30 m digital elevation models to delineate watersheds also has been implemented with ArcView GIS. This procedure facilitates quick delineation of the watershed of any sampled lake.
Special emphasis also was placed on developing methods to sense remotely biological indicators of water quality based on the optical phytoplankton pigment structures of lakes. Project Year 1 was primarily dedicated to: (1) establishing relationships between remotely sensed reflectances and biophysical variables such as chlorophyll concentration, secchi depth, turbidity, total suspended matter for Nebraska lakes and reservoirs; (2) assessing temporal variability of biophysical variables estimated remotely in selected reservoirs by comparing them among sample dates within a year to ascertain the relative sensitivity of remote sensing approaches to detecting the changes; and (3) investigating the sensitivity and efficiency of collecting hyperspectral data with a radiometer installation on aircraft.
An integrated decision support system (DSS) was developed and is capable of: (1) using a Multiple Criterion Decision Making approach, namely Composite Programming to measure and visualize the sets of lake characteristics, each contributing to lake quality to a different degree, in an integrated way, and (2) using fuzzy logic to account for spatial and temporal variability and measurement imprecision in the lake classification. Neglecting uncertainty may result in incorrect lake classification. As an alternative to statistical representation, the classification program will use fuzzy logic to account for uncertainty in lake classification.
The research activities for Project Year 2 are to: (1) conduct water quality sampling and analyses in 56 reservoirs, 18 Sand Hill lakes, and 9 sandpit lakes to complete the water quality database; (2) classify reservoirs and sandpits using a combination of rule and databased approaches; (3) compare other landscape stratification approaches (e.g., hydrologic landscapes) to ecoregions as a-priori lake classes; (4) develop a method to test the performance of different lake classification approaches; (5) select and collect sediment cores at potential reference lakes and reservoirs; (6) develop phytoplankton bioindicators; (7) evaluate algorithms for using GIS data to enhance classification; (8) evaluate possible impacts of lake environs on lake classes using GIS-based analysis; (9) continue aircraft data acquisition; (10) use algorithms to interpret data obtained using hyperspectral airborne imaging spectrometer AISA from aircraft; (11) investigate accuracy of optically active constituent concentrations from aircraft; (12) develop algorithms for quantitative estimation of blue green algae; (13) develop prototype for GIS-enhanced decision support system (DSS); (14) evaluate methodology for integrating results of remote sensing and GIS analyses in overall lake classification strategy; (15) incorporate data variability into DSS; (16) apply DSS to combinations of traditional, remote sensing, and GIS data.