| Nebraska was selected as a representative region to develop a lake and reservoir classification method appropriate for agriculturally impacted ecosystems and to classify lakes and reservoirs. There are over 3,000 constructed reservoirs, sandpit and borrow pit lakes and natural Sand Hills lakes in the state. Due to the large number of lakes, a methodology for easily assessing lake health is critical for managing lakes and monitoring changes. There has been much interest in determining if natural ecological regions (ecoregions) based on terrestrial factors are representative of lake water quality in other parts of the U.S. Within an ecoregion, lakes that remain undisturbed by anthropogenic sources could be used to set water quality benchmarks for other lakes within the region. However, the validity of the ecoregion approach needs to be tested in the mid-west. A novel ecological continuum approach was developed utilizing factor analysis to identify lake and reservoir classes. In general, the following factors were found to be important for most lake types: water clarity, water chemistry, depth and temperature, and chlorophyll a (chl a). For all lake types, conductivity was found to be positively correlated with N:P, and negatively correlated with chl a, and conductivity and N:P were higher in lakes and reservoirs in the east. Elevation in Nebraska decreases from west to east by 840 m, climate changes from sub-arid to temperate, and evapotranspiration increases. There was no clear relationship between chl a and nutrients for reservoirs and Sand Hills lakes; however, TP appeared to limit chl a in most sandpit and borrow pit lakes. The ability of lakes grouped by ecoregions to predict water quality was tested for all lake types. Ecoregions did not adequately predict reservoir groups, often because reservoir drainage areas extended beyond ecoregion boundaries, and local hydrologic effects appeared to impact Sand Hills lakes more than ecoregional effects. Sandpit and borrow pit lakes grouped by ecoregions were reasonably good at predicting water quality. |
