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Project Summary Sheet

U.S. Geological Survey, Greater Everglades Priority Ecosystems Science (GE PES) Initiative

Fiscal Year 2005 Study Summary Report

Project Title: Hydrology Monitoring Network: Data Mining and Modeling to Separate Human and Natural Hydrologic Dynamics
Project Start Date: 2005 Project End Date: 2007
Web Sites: http://sofia.usgs.gov/, http://sofia.usgs.gov/projects/hydro_monnet/
Location (Subregions, Counties, Park or Refuge): Dade and Monroe Counties, Everglades National Park, Central Everglades, A.R.M. Loxahatchee National Wildlife Refuge
Funding Source: USGS's Greater Everglades Priority Ecosystems Science (GE PES) Initiative
Principal Investigator(s): Paul A. Conrads, Edwin A. Roehl
Project Personnel: Mark Lowery, Doug Nagle
Supporting Organizations: USGS-South Carolina Water Science Center

Associated / Linked Projects: Freshwater Flows into Northeastern Florida Bay, Estimation of Critical Parameters in Conjunction with Monitoring of the Florida Snail Kite Population, Southern Inland and Coastal Systems (SICS) Model Development

Overview & Objective(s): The emerging field of Data Mining addresses the issue of extracting information from large databases. It is comprised of several technologies that include signal processing, advanced statistics, multi-dimensional visualization, machine learning (including artificial neural networks (ANN)), and Chaos Theory. Data Mining can solve complex problems that may be unsolvable by any other means. The data from the CERP monitoring is a tremendous resource for addressing the critical questions for restoring the South Florida ecosystem. Estuarine systems are difficult systems to analyze due to the complexity of environmental factors occurring simultaneously. To enhance the evaluation of the CERP data base, there is an immediate need to apply new methodologies to systematically analyze the data set to answer critical questions such as relative impacts of controlled freshwater releases, tidal dynamics, and meteorological forcing on streamflow, water level, and salinity. This project will directly address the data analysis issues outlined above.

The first year of the Data Mining Analysis Project will address these issues by demonstrating how data mining techniques can be applied to the Everglades data bases and ecological studies. Three studies have been selected for the demonstration work - Freshwater Inflows to Northeastern Florida Bay (Mark Zucker, Clinton Hittle), Estimation of Critical Parameters in Conjunction with Monitoring the Florida Snail Kite Population (Wiley Kitchens), and Southern Inland and Coastal Systems (Eric Swain). In addition, time will be spent during the first year identifying other issues of concern where data mining techniques can be applied during Years 2 and 3 of the project.

Status: We have built preliminary ANN models of salinity response for Trout Creek using the 1996-2000 USGS data for the five gaging stations of creeks entering Florida Bay. The database used to build the preliminary model has been updated to include the recently available data for the period 2001 to 2004. We have been working with developers of the SICS model on how results from the ANN models can be used to assist in the calibration and confirmation of the SICS model. Short-term water level data (12 months) at sites instrumented for the Snail Kite study in WCA-3 have been hindcasted to create a 14 year water-level record for analysis. We are developing potential methodologies for estimating water levels and water depths at ungaged areas using ANN models. The approach utilizes static variables of location and percent vegetation and dynamic variables of water levels at known locations. On a side effort, we have also worked with the Loxahatchee National Wildlife Refuge staff and identified two problems in the Refuge that could be addressed using data mining techniques. These problems are related to the operations schedule and control of high conductivity water intrusion into the Refuge.

Recent & Planned Products: Major products include (1) data bases of the measured and derived hydrologic data that will be used for integration with the Everglades snail kite study and analysis of freshwater inflows; (2) artificial neural network (ANN) models used to hindcast long-term water level response at 17 sites in WCA 3A; (3) ANN models used to analyze freshwater inflows for natural and anthropogenic components; and (4) a summary document describing the assessment of data networks for further integration and analysis using data mining techniques.

Specific Relevance to Information Needs Identified in DOI's Science Plan in Support of Ecosystem Restoration, Preservation, and Protection in South Florida (DOI's Everglades Science Plan) [Page numbers listed below are from the DOI Everglades Science Plan. The Science Plan is posted on SOFIA's Web site: http://sofia.usgs.gov/publications/reports/doi-science-plan/]:

  • The study supports the ecological studies of impacts of hydrologic change on the Everglade snail kite habitat and the Combined Structural and Operational Plan project (CSOP) by addressing the needed science for “…refinement of hydrologic targets and operating protocols (p. 63).”
  • The study supports the science needs related to the Combined Structural and Operational Plan (CSOP), including the C111 Spreader Canal, by separating the impacts of controlled flows and tidal dynamics on salinity on the five tributaries in Northeast Florida Bay (p. 66).
  • The study supports the Water Conservation Area 3 Decompartmentalization and Sheetflow Enhancement Project (DECOMP) by addressing the science needed for “...additional research to understand the effects of different hydrologic regimes and ecological processes on restoring and maintaining ecosystem function…” (p. 64, 66) by hindcasting hydrologic records.
  • The study supports the water project of the Loxahatchee National Wildlife Refuge by addressing links between hydrology, water quality, and ecology in the refuge (p. 40, 43).

Key Findings:

  1. We have been able to model the salinity response of Trout Creek and are able to evaluate the relative contribution of control flows and Florida Bay dynamics using all the historical data. Using visualization techniques, the historical interaction between two explanatory variables (for example, flows and water levels) are shown with the response variable (salinity). The approach is an alternative method to analyzing, understanding, and visualizing long-term data of complex systems.
  2. We have developed high fidelity ANN models to predict and hindcast water levels at short-term gaging stations from long-term index stations located miles from the short-term stations. The approach has demonstrated daily water levels can be estimated in remote areas and the approach may be applicable to other studies, including the EDEN network.
  3. The preliminary results from applying ANN models to estimate water levels at ungaged locations are very encouraging. In the Snail Kite study area, we used 17 continuous water level sites and the static variables from the EDEN network to build a database to evaluate potential methodologies for estimating water levels at ungaged sites. By using a combination of static variables (x, y, percent vegetation type - sawgrass, slough, prairie, upland) from the EDEN grid along with dynamic variables (water levels and differences in water levels), the ANN models are essentially performing a multi-variate kreiging of water levels in the study area. The ANN models are able to interpolate spatially from the static variables and temporally from the dynamic variables. Eleven gages were used to develop the prediction ANN models and 6 gages were used to evaluate the predictions. The spatial domain of the model is 370 square kilometers or about 2300 cells in the EDEN grid network. The average root mean square error for the prediction model is approximately a tenth of a foot.



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