Roehl, Edwin A., Jr.
Roehl, E. A., Jr.
Roehl, E. A. Jr.; Daamen, R.; Kitchens, W. M.
To simulate the water level response at the 20 short-term snail kite monitoring sites, artificial neural network (ANN) models will be developed using long-term water-level data in the study area. The ANNs will then be used to extend the period of record of the short-term monitoring sites. The steps to be taken are described below.
Step 1. Data Compilation and Merging: Hydrologic, meteorological, and operational data from the USGS, the National Weather Service, and other databases will be merged and time synchronized. Variables of interest include river flows, freshwater releases, water levels, specific conductance, wind direction and speed, and rainfall.
Step 2. Data Preparation: Methods will be used to maximize the information content in the raw data, while diminishing the influence of poor or missing measurements. Signal (time series) processing methods include clustering, filtering, spectral decomposition, estimation of data characteristics and time delays, and synthesizing missing data. Signal processing transforms the "raw" data into "pre-processed" data for analysis and modeling. The data collected from the agencies have different sampling frequencies, ranging from every 15 minutes to once per month. The variables must be "time-merged" by either interpolating between less frequent measurements, or by averaging frequent samples to obtain fewer values.
Another signal processing task is "signal decomposition". The complex behaviors of the variables of a natural system result from interactions between multiple physical forces. Signal decomposition involves digital filtering to split a signal into sub-signals, called "components", that are independently attributable to different physical forces. Some components are periodic and some are chaotic. The filtering method of choice is frequency-domain filtering. It is applied to a signal after it has been converted into a frequency distribution by Fast Fourier Transform. This allows a signal component that lies within a window of frequencies, for example, the 12.4-hour tidal cycle lies between periods of 12.0 to 13.0 hours, to be excised, analyzed, and modeled independently of other components. Digital filtering also can diminish the effect of noise in a signal to improve the amount of useful information that it contains. Working from filtered signals makes the modeling process more efficient, precise, and accurate.
Step 3. Correlation Analysis and Sensitivity: Estimation 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, to combinations of controlled and uncontrolled variables, such as river flows, controlled releases, and meteorological conditions. Correlation methods based on statistics and machine learning are applied in combination. Comparing them to known patterns of behavior validates results found by the computer.
Step 4. Predictive Modeling: Using machine learning, a predictive model is developed directly from the data and correlations determined in Steps 2 and 3. 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, represented by input variables, which affect the output variables.
Step 5. Develop Long-Term Water Level Data: For the connection between hydrologic response and the snail kite, many of the water level gages were only operated for a limited number of years. Long-term hind casts of water level for the period of record for the long-term monitors will be produced using the data and correlations of Steps 2 and 3 and predictive modeling of Step 4.
Preliminary ANN models of salinity response for Trout Creek have been built 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.
The approach taken in FY05 will be expanded from a small sub-domain of WCA3a to the domain of the Everglades. 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 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.
Steps 1-3 described for FY 2005 will be followed in this study. However, the predictive modeling (step 4) will be limited to three selected water level sites (1-7, 1-8c, 1-9) and three water quality sites (LOX4, LOX5, LOX13). The water-level sites are critical sites for the operation of the regulation schedule and the water quality sites are critical sites for the water quality compliance consent degree. The anticipated results for FY 2006 are the compiled, time synchronized database and the predictive ANN models of the selected water level and water quality stations. 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.
One of the objectives for FY 2005 was to integrate short- and long-term hydrologic and ecological data for the study of Snail Kites in Water Conservation Area 3a (WCA3a). 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 Decision Support System (DSS) would allow users to interrogate the historical database and model simulations to better understand the water-depth dynamics of the system. The DSS will read and write files for the various run-time options that can be selected by the user through the system's graphical user interface (GUI). The historical database contains thirteen years of hydrodynamic data that will be used to generate water-level simulations using the 17 ANN models. Using GUI controls, the user can evaluate alternative flow and water-level scenarios. The outputs generated by the ANN models will be written to files for post processing in MS Excel. The DSS will provide streaming graphics during model simulations, visually representing historical and predicted behaviors side-by-side.
The following steps will be taken for the development of the Snail Kite Hydrology DSS .
Build DSS shell to include: 1. GUI for setting all simulation run parameters a. Start and stop time of simulation run b. Source input for simulation run (historical data, percentage of actual, user-defined, external file) 2. Streaming graphics to display model outputs 3. Hydrologic parameter/stastistic a. Calculate statistic b. Provide graphical display c. Tabular output values 4. Output model predictions and simulation parameters Integrate 17 ANN models into DSS Shell 1. Define models and inputs within the DSS 2. Calculate model inputs using user selected data source 3. Run models using iQuest Runtime module 4. Write and display output within DSS application
The salinity dynamics of the five tributary creeks are currently being analyzed. Response surface for the five tributary creeks and for various combinations of explanatory variables are used to evaluate system behavior at the five sites. Comparisons and differences in the process physics, as manifest by the response surface for each tributary, between tributaries will be documented.
U.S. Department of the Interior, U.S. Geological Survey
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