Project Work Plan

Study Title: Hydrology Monitoring Network: Data Mining and Modeling to Separate Human and Natural Hydrologic Dynamics
Study Start Date: 10/01/2004 Study End Date: 9/30/2007
Web Sites:
Location (Subregions, Counties, Park or Refuge): Total System
Funding Source: USGS Greater Everglades Priority Ecosystems Science (GE PES)
Other Complementary Funding Source(s): none
Funding History: FY05 was the first year of funding for this project
Principal Investigator(s): Paul Conrads
Study Personnel: Paul Conrads, Ed Roehl, Ruby Daamen, Mark Lowery
Supporting Organizations: USGS-South Carolina Water Science Center
Associated / Linked Studies: South Florida Surface Water Hydrologic Network for Support of MAP Projects (Telis, Higer, Pearlstine, Jones PIs); Water Quality Monitoring and Modeling for the A.R.M. Loxahatchee National Wildlife Refuge (Harwell, Waldon, Surratt, and Brandt PIs); Estimation of Critical Parameters in Conjunction with Monitoring of the Florida Snail Kite Population (Wiley Kitchens, PI).

Overview & Objective(s): 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 these data are under utilized and/or under interpreted. 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 data sets to address critical issues such as water depths at ungaged locations, water-depths and water-quality responses to controlled flow releases, and optimization of existing hydrologic data-collection networks. There also is a need to maximize data resources by integrating disparate hydrologic and ecologic data bases.

The objectives of the study for FY07 include: (1) continued development of water-depth prediction models for ungaged locations in the Everglades Depth Estimation Network (EDEN); (2) develop a data viewer and hydrologic process model(s) for the A.R.M. Loxahatchee National Wildlife Refuge; (3) provide technical support and enhancements to the Snail Kite hydrology decision support system (DSS); and (4) analyze the existing coastal water-level monitoring networks to determine priority gaging stations.

Specific Relevance to Major Unanswered Questions and Information Needs Identified: (Page numbers below refer to DOI Science Plan.)

An important part of the USGS mission is to provide scientific information for the effective water-resources management of the Nation. To assess the quantity and quality of the Nation's surface-water, many agencies and universities collect hydrologic and water-quality data from rivers, lakes, and estuaries. The techniques used for this study in FY05 and FY06 has demonstrated how valuable information can be extracted from existing databases to assist local, state and Federal agencies. The application of data-mining techniques, including ANN models, to the CERP supported databases demonstrates how empirical models of complex hydrologic systems can be developed, disparate databases and models can be integrated to support multidisciplinary research, and study results can be easily disseminated to meet the needs of a broad range of end users.

An important part of the USGS mission is to provide scientific information to manage the water resources of the Nation, including the other Agencies of the Department of Interior (DOI). The objectives for this study addresses science needs to support DOI managers in fulfilling their stewardship responsibility as identified in The Science Plan in Support of Ecosystem Restoration, Preservation, and Protection in South Florida (U.S. Department of Interior, 2004). This is consistent with primary USGS activities that include providing knowledge and expertise to assist various levels of government in understanding and solving critical water-resources problems.

The study objective to develop prediction models for water depths at ungaged locations is part of the overall objective of the EDEN project to support the South Florida Hydrology Monitoring Network and the Monitoring and Assessment Plan (MAP). The MAP was developed as the primary tool to assess the system-wide performance of the CERP by the REstoration, COordination and VERification (RECOVER) program (p. 17, DOI Science Plan). The MAP describes and outlines the monitoring and supporting enhancement of scientific information and technology needed to measure the responses of the South Florida ecosystem to CERP projects.

The study objective to develop hydrologic and water-quality process model(s) for the Arthur R. Marshall Loxahatchee NWR, including Internal Canal Structures and STAs (stormwater treatment area) meets a stated need in the Science Plan for the “synthesis and integration of data about historic hydrologic and ecological conditions on the refuge” and “research to understand the ecological effects of hydrology and water quality on refuge resources…”(p. 37 and 40, DOI Science Plan). The study objective will benefit the DOI and other Federal and State Agencies in South Florida by providing data analysis needed by water-resource managers to make decisions concerning the quantity and quality of inflows to the Refuge.

The technical support and enhancements to the Snail Kite Hydrology DSS 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, DOI Science Plan) and supports ecological studies of impacts of hydrologic change on Everglade snail kite habitat. The study also supports the Combined Structural and Operational Plan project (CSOP) by addressing the needed science for “…refinement of hydrologic targets and operating protocols (p. 63, DOI Science Plan).”

Status: There were four objectives for the second year (FY06) of the Data Mining Study:

1. Development of water-depth prediction models for ungaged locations in the Everglades Depth Estimation Network (EDEN),
2. Compilation of data and develop preliminary hydrologic response models for the A.R.M. Loxahatchee National Wildlife Refuge,
3. Completion the development and documentation of the Snail Kite hydrology decision support system (DSS),
4. Documentation of the analysis of the salinity response for five tributaries to Florida Bay.

The first objective is currently being completed for WCA-3a with the 7-year data set (October 1999 to September 2006). Cluster analysis, critical for scaling up the technique developed in FY05 to the larger EDEN domain, proved more time consuming than anticipated. Evaluation of cluster analysis with various combination of gages (marsh and canal) and time-series (7-day moving window average, 1-day median values, and hourly data) was essential to discriminating subtle differences in water-level behavior. With the analysis and modeling approach refined, the other conservation areas will be completed in January 2007. The second objective has been partially met. The data has been compiled and databases created for correlation analysis and the development of a data viewer and ANN process models. The third objective has been met with the development of the Snail Kite Hydrology Decision Support System (DSS) as a spreadsheet application that integrates historical database, ANN models, model controls, and model output. The fourth objective has been completed and the results were presented at the 2005 Florida Bay Conference.

Recent Products: Products from Year Two of the study included:

1. Refereed conference proceedings paper on the use of ANN models to estimate water depths at ungaged sites was presented at the Hydroinformatics 2006 conference.

   Conrads, P.A. and Roehl, E.A., 2006, Estimating water depths using artificial neural networks, Hydroinformatics 2006, edited by Philippe Gourbesville, Jean Cunge, Vincent Guinot, Shie-Yui Liong, Vol. 3, p.1643-1650

2. Two posters were presented at the GEER 2006 conference on estimating water depth at ungaged sites and the use of cluster analysis.

   Estimating Water Depths at Ungaged Locations in the Florida Everglades Using Artificial Neural Networks, by Paul Conrads and Edwin Roehl, Jr.

   Application of a Dynamic Clustering Algorithm to the Water-Level Hydrographs of the EDEN Hydrologic Network, by Paul Conrads and Edwin Roehl, Jr.

3. Referred conference proceedings paper on the integration of hydrologic and ecological data sets used in the Snail Kite Decision Support System was presented at the Hydroinformatics 2006 conference.

   Conrads, P.A. and Roehl, E.A., Daamen, R.C., and Kitchens, W.M., 2006, Using artificial neural network models to integrate hydrologic and ecological studies of the snail kite in the Everglades, USA, Hydroinformatics 2006, edited by Philippe Gourbesville, Jean Cunge, Vincent Guinot, Shie-Yui Liong, Vol. 3, p.1651-1658

4. Oral presentation was given at the GEER 2006 conference on the development of the Snail Kite Decision Support System.

   Using Artificial Neural Network Models to Integrate Hydrologic and Ecological Studies of the Snail Kite in the Everglades by Paul A. Conrads, Ruby Daamen, Edwin A. Roehl, Wiley M. Kitchens, and Christa Zweig

5. Poster was presented at the Florida Bay Science Conference in December 2005 on the salinity response of five tributaries to Florida Bay.

   Analysis of the Process Physics of Tributaries to Florida Bay Using Artificial Neural Networks and Three-Dimensional Response Surfaces by Paul Conrads and Edwin Roehl

Planned Products (FY07): Major products include (1) ANN models for predicting water depths in the EDEN network; (2) Documentation of the water-depth prediction, (3) development of a data viewer and hydrologic process models for the A.R.M. Loxahatchee NWR, (4) updating the Snail Kite DSS and retraining the ANN hindcast models, and (5) optimization of coastal water-level collection data network to identify priority gages.

B. WORK PLAN

Title of Task 1: Estimating Water Depths and Water Levels at Ungaged Location in the EDEN Network

Task Funding: USGS Priority Ecosystems Science
Task Leaders: Paul Conrads
Phone: (803) 750-6140
FAX: (803) 750-6181
Task Status: Active (first year)
Task priority: high
Time Frame for Task 1: 2006-2007
Task Personnel: Paul Conrads, Ed Roehl, Mark Lowery, and Ruby Daamen

Task Summary and Objectives: The Everglades Depth Estimation Network (EDEN) was established to support the South Florida Hydrology Monitoring Network module of the Comprehensive Everglades Restoration Plan (CERP) and the Monitoring and Assessment Plan (MAP) and Restoration Coordination and Verification Team (RECOVER). The goals of EDEN are to help guide large-scale field operations, integrate hydrologic and biologic responses, and to support the MAP assessments by scientists and principal investigators across disciplines. One objective of EDEN is to relate water-level data at real-time stage gages to ungaged areas using ground elevation data, so that water depths throughout the greater Everglades can be estimated (Telis, 2005: http://sofia.usgs.gov/projects/eden/ ).

Accurately predicting the hydrologic responses at ungaged locations can be challenging due to the limited number of reference gaging stations and a limited understanding of complex topology and vegetation interactions. Techniques that are often used to estimate hydrologic responses at ungaged locations include combinations of linear regression and interpolation, but often the dynamics between hydrology, topography, and vegetation are nonlinear. The preliminary results from FY05 pilot study and FY06 cluster analysis for applying ANN models to estimate water depths at ungaged locations are very encouraging. 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 at validation gages are approximately a tenth of a foot or 4 percent. The cluster analysis showed that subtle differences in water level time series can be discriminated. The results appear to be similar to error analysis for water-level surfacing techniques using radial basis functions.

Work to be undertaken during the proposal year and a description of the methods and procedures:

The approach taken in FY05 and FY06 will be expanded from a small sub-domain of WCA3a to the domain of the Everglades. For many spatial modeling problems, it is necessary to subdivide a larger study area and create separate models for regions rather than create a single model for an entire study area. The domain of EDEN varies broadly with respect to climate, topography, hydrology, and ecology. To subdivide the water-depth data EDEN dynamic clustering analysis will done to group water-depth time series into homogeneous groups based on similarity of dynamic response. In addition to determining which specific sites fall into which groups, clustering analysis will be used to determine an optimal number of groups. A higher number of groups will create more distinct homogeneous groups. However, these groups will contain a smaller number of sites, which may be insufficient for creating robust ANN models. The clustering of sites into the groups will be evaluated by analyzing the distribution of the groups and their sites across the Everglades. The physical properties of each group will be identified and sites that do not appear to share similar properties will be re-evaluated. If necessary, the number of groups will be recomputed to ensure the robust, homogeneous groups are determined. A quality-assured 7-year data set of hourly data for the EDEN network for the period 1999 to 2006 will be used for the cluster analysis and model development.

A three-step modeling approach will be used to predict water-depths. The first step will be to develop a group assignment model. The model will use static variables of an ungaged site as input variables to determine which group (from the clustering analysis) the site should be assigned. The second model will predict the water-depth using only the static variables of location and vegetation types. Obviously, this model (also called the “static” model) is not able to predict the dynamic variability of the water depth, but it is able to discriminate general differences in the water-depth variable based on differences in location and vegetation. The static model is used to calculate the residual error (difference between the predicted and measured water depth), which is then modeled by the third model. The third model (also call the “dynamic” model) will use time series of water-depths and static variables to predict the variability in water-depth at each site as characterized by the residual in the static model. The final prediction of water depth at each site is the summation of the water-depth prediction from the static model and the prediction of the water-depth residual from the dynamic model.

The models developed for WCA-3a will be incorporated into a prototype Excel/VBA spreadsheet application. User will enter static data from the EDEN grid (x, y, vegetation percentages) and hourly water-level hydrographs for the site will be generated. The prototype may be incorporated into the Snail Kite Decision Support System (see Task 2) to enhance the coverage of water-level information in that application.

There are a large number of coastal water-level gages that could be incorporated into the EDEN network. Using cluster analysis techniques similar to the ones used to develop the water-depth estimation models, gages with similar behaviors will be grouped and used to determine a set of priority coast water-level gages.

Specific Task Product(s):

1. Cluster analysis by conservation areas using 7-year data base (January 2007)
2. ANN models for ungaged site for sub-domain of EDEN (January 2007)
3. Hourly hindcasts of water-level data for the 23 “new” EDEN sites (January)
4. Conduct network analysis of coastal water-level gage network to select potential coastal gages to incorporate into the EDEN network (April 2007)
5. Manuscript summarizing development and application of ANN models for predicting water depths at ungaged sites (September 2007).
6. Prototype Excel/VBA application to compute hourly hydrographs (July 2007).

Title of Task 2: A Synthesis of Hydrology and Water-Quality Data of A.R.M. Loxahatchee NWR

Task Funding: USGS Priority Ecosystems Science
Task Leaders: Paul Conrads
Phone: (803) 750-6140
FAX: (803) 750-6181
Task Status: Active (second year)
Task priority: High
Time Frame for Task 2: 2006-2007
Task Personnel: Paul Conrads, Ed Roehl, and Ruby Daamen

Task Summary and Objectives: The Arthur R. Marshal Loxahatchee National Wildlife Refuge is the last of the soft-water ecological systems in the Everglades. Historically, the ecosystem was driven by precipitation inputs to the system that were low in conductance and nutrients. With controlled releases into the canal that surround the Refuge, the transport of water with higher conductance and nutrient concentration could potentially alter critical ecosystem functions. With potential alteration of flow patterns to accommodate the restoration of the Everglades, the Refuge could be affected not only by changes in the timing and frequency of hydroperiods but by the quality of the water that inundate the Refuge.

There is a long history of collecting hydrologic and water quality data in the Refuge. Data characterizing the hydrology of the system - inflows, outflows, precipitation and water levels have been collected since the 1950's. Data characterizing the water quality of the system, including conductance and phosphorus, has been collected since the late 1970's. To enhance the understanding of the hydrology and water quality of the Refuge, there is an immediate need to apply new methodologies to systematically synthesize and analyze the data set to answer critical questions such as relative impacts of controlled releases, precipitation, groundwater interaction, and meteorological forcing on water level, conductance, and phosphorous. There also is a need to integrate longer-term hydrologic data with shorter-term hydrologic data collected for biological and ecological resource studies.

Work to be undertaken during the proposal year and a description of the methods and procedures:

ANN model hydrologic and water-quality process model development will continue. The following steps will be undertaken during the proposal year.

Development of a Temporal and Spatial Data Viewer

A three-dimensional hydrologic and water-quality data viewer will be developed to visualize the historical data over time and by location. The general approach involved overlaying a rectangular grid onto Refuge; aggregating data collection sites into the grid cells; and aggregating measurements from sites within each cell into time steps. The viewer will provide an integrated, interactive environment for exploring and analyzing the data.

Correlation Analysis and Sensitivity Estimation

Continue correlation analysis quantifies the relationships between many variables and provides deeper understanding of the data. The computer systematically correlates factors that influence parameters of interest, such as water level, conductance, and phosphorous to combinations of controlled and uncontrolled variables, such as inflows, outflows and rainfall. Correlation methods based on statistics and machine learning are applied in combination. Comparing them to known patterns of behavior validates promising results found by the computer. Correlation analysis identifies:

1. Relative impact - For example, “What variables impact the increased conductance and phosphorous? And to what degree?”
2. Relationships between controlled (inflows and outflows) and uncontrolled variables (meteorology forcing).
3. Quantifiable answers to complex questions - For example, “What are the critical temporal and spatial relationships between the controlled releases and the water level, conductance, and phosphorous response in the interior of the Refuge? Which has more effect on these responses - large releases over a short period of time or weekly flow volumes? What are the relative impacts of the inflows/outflow locations on these responses?”

Predictive Modeling

Using machine learning, predictive models are developed directly from the data and correlations (Step 1 above). To maximize accuracy, the model is constructed from sub-models, which independently correlate periodic and chaotic components. Their outputs are combined to obtain an overall prediction that manifests all of the different forcing functions that are represented by input variables, which affect the output variables. The models of the Refuge will predict water level, conductance, and phosphorous at multiple locations from inputs such as inflow, outflow, rainfall, wind direction and speed. The models will provide powerful analysis tools for understanding the dynamics of the system. In particular, 3-dimensional response surfaces showing the interaction of two explanatory variables (such as canal inflow, outflow, canal water level, and rainfall) on a response variable (interior water level, conductance, and phosphorous) will be generated.

Develop the DSS with Water-Level, Conductance, and Phosphorous Optimizer and Write User's Manual

The models will be integrated in a user-friendly Excel/Visual Basic program called a Decision Support System (DSS). The DSS requires no typing to operate or to obtain output. It will contain a historical database (including hind-casted values) to allow for running long-term simulations. Run-time monitoring and DSS output will be in the form of supporting graphics and Excel worksheets. A constrained optimization routine will be integrated into the DSS that will determine the necessary inflows/outflows to meet specified water level, conductance, and phosphorus levels. A user's manual will describe installation, operation, and features of the Simulator.

Specific Task Product(s):

1. Develop spatial and temporal data viewer (January 2007)
2. Preliminary hydrologic process model (February 2007)
3. Preliminary water-quality process model (March 2007)
4. Develop prototype DSS (September 2006)

Title of Task 3: Integration of Long-term Hydrologic Data with Snail Kite Study

Task Funding: USGS Priority Ecosystems Science
Task Leaders: Paul Conrads
Phone: (803) 750-6140
FAX: (803) 750-6181
Task Status: Active
Task priority: low
Time Frame for Task 3: (2007FY)
Task Personnel: Paul Conrads, Ruby Daamen, and Ed Roehl

Task Summary and Objectives: One of the objectives for FY2006 was to integrate short- and long-term hydrologic and ecological data for the study of Snail Kites in Water Conservation Area 3a (WCA3a). Ecologists from the USGS Florida Coop Unit are studying the Snail Kite and its habitat quality as it relates to vegetative community structure. The vegetative structure of these sites is an expression of both recent past and current hydrological conditions. It is critically important to determine how the species associations within these communities respond differentially to changes in hydrology through time and space. The monitoring network for the snail kite study has established 17 continuous water-depth monitors to understand differences in hydrology in the study area. In addition to the 17 water-depth gages (short-term, < 2 years of record), there are 3 long-term (>13 years of record) in the study area. To maximize the information content ANN models were developed to predict the water depths at the 17 monitoring stations. These models are used to extend the period of record of the short-term monitoring stations to be concurrent with the three long-term stations.

The Snail Kite Decision Support System DSS was developed to disseminate the models in an easily used package. The DSS is a MS Excel^TM/VBA application that integrates the models and database with interactive controls and streaming graphics to run long-term simulations. As part of the Everglades restoration Interim Operating Plan (IOP), a regional hydrologic model is used to generate water levels for alternative flow regulation schedules. The alternative IOP water levels are input to the DSS to predict the hydrology of the snail kite habitat. The application demonstrates how very accurate empirical models can be built directly from data and readily deployed to end-users to support interdisciplinary studies.

Work to be undertaken during the proposal year and a description of the methods and procedures:

During the proposal year, continued technical support for the Snail Kite DSS users, especially ecologist with the Florida Coop Unit. The architecture of the DSS is very flexible for incorporating new features. Enhancements to the DSS, as identified by users, will be incorporated into the DSS as resources are available. The data base will be updated to include the latest data and the models will be retrained to reflect the additional data. Upon completion of the prototype water-level hydrograph spreadsheet application developed for EDEN (see Task 1), it will be decided whether this is an appropriate feature to include in the Snail Kite DSS.

Specific Task Product(s):

1. Update of Snail Kite DSS data base (June 2006)
2. Retraining of hindcast models (August 2006)