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Greater Everglades Hydrology Monitoring Network: Data Mining and Modeling to Separate Human and Natural Hydrologic Dynamics

photo of water level gauge
Project Investigator: Paul Conrads

Project Personnel: Matt Petkewich, Jimmy Clark, Brian Clark, Ruby Daamen, Ed Roehl, Stephen Benedict, Andrew O'Reilly

Project Start Date: 2004 End Date: 2014

Recent Funding: (FY14) USGS GE PES, (FY13) USGS GE PES, (FY12) USGS GE PES, (FY11) USGS GE PES, (FY10) USGS GE PES


The goal of this project is to enhance the evaluation of the CERP database and address hydrologic issues important to DOI's efforts in South Florida.

New technologies in environmental monitoring have made it cost effective to acquire tremendous amounts of hydrologic and water-quality data. Although these data are a valuable resource for understanding environmental systems, often there is seldom a thorough analysis of the data. The monitoring network(s) supported by the Comprehensive Everglades Restoration Plan (CERP) records tremendous amounts of data each day and the data base incorporates millions of data points describing the environmental response of the system to changing conditions. 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. There also is a need to integrate longer-term hydrologic data with shorter-term hydrologic data collected for biological resource studies. This study will be undertaken as a series of pilot studies to demonstrate the efficacy of data mining techniques to evaluate CERP data and address hydrologic issues important to DOI's efforts in South Florida. In addition, preliminary assessment of the complete set of hydrologic data networks for further integration and analysis using data mining techniques will be conducted.

The objectives of the study include: (1) integration of hydrologic analysis and synthesis with biological studies; (2) separation of water level, stream flow, and salinity time series into the natural (tidal, climate) and anthropogenic components; and, (3) identification of additional areas where application of data mining techniques can address the DOI science needs in South Florida.

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Script last updated: 23 October 2018 @ 12:03 PM by THF. Record creator: BJM. Record last updated by: KP.