Reinaldo Garcia-Martinez and Fernando Miralles-Wilhem Southeast Climate Consortium

1. Influence of climate anomalies on water inflow and water quality indicators in Lake Okeechobee Watershed Reinaldo Garcia-Martinez and Fernando Miralles-Wilhem Southeast Climate Consortium Department of Civil, Architectural and Environmental Engineering University of Miami

2. Objective Explore the relationships between climate anomalies and water runoff, stream flow and water quality indicators in the Lake Okeechobee watershed using an Artificial Neural Network (ANN) model.

3. Background Lake Okeechobee is the largest freshwater lake in Florida Stores large volumes of water during wet periods that are used for meeting environmental, urban and agricultural needs during subsequent dry periods.

4. Managing LO water levels • Providing adequate flood protection for the regions surrounding the Lake, • Meeting the water use requirements of the agricultural and urban areas dependent on Lake Okeechobee for water supply (water quality), • Preserving the biological integrity of the estuaries downstream of the Lake, • Supplying water to the remnant Everglades as part of the effort to restore more natural hydroperiods within this region, • Preserving and enhancing the lake's littoral zone which provides a natural habitat for fish and wildlife, • Serving the recreational needs of south Florida, • Providing navigational waterways.

5. How Climate Predictions could help LO management? • When water levels in the lake reach certain elevations designated by the operational schedule, discharges are made through the major outlets. • The releases made in the springs of 1980, 1984 and 1988 were followed by extended dry periods of mandatory regional water supply cutbacks. • If these extended dry periods had been predicted at that time, along with recognition that south Florida was in a climate period of less wet season rainfall, the excess water could have been retained and used during the very dry subsequent conditions to supply irrigation needs and/or enhancing hydro-patterns in the Everglades.

6. Indices for South Florida Regional Water Management • El Niño - Southern Oscillation Index ENSO • Solar activity Indices • Atlantic Ocean Conveyor Current Index • Atlantic Multidecadal Oscillation

7. El Niño - Southern Oscillation Index ENSO • The Florida climate has the greatest statistical association with the ENSO process during the winter months. • The warm phase of the ocean temperature anomalies (El Niño) have been identified with greater than normal winter rainfalls (50%). • Cold phase of ocean temperature anomalies (La Niña) have been identified with drier than normal winter rainfalls in Florida • Hurricanes are about half as likely to make landfall on the United States during tropical seasons associated with the El Niño warm sea surface temperature anomaly (O'Brien et al, 1995).

8. El Niño – SOI vs. LO climate SFWMD (1997)

9. Solar Indices • Increasing statistical evidence of a relationship between solar sunspot cycles (9-14 years) and the earth's climate fluctuations in certain parts of the world

10. Trimble et al. (1998)

11. Atlantic Ocean Conveyor Thermohaline Current Index • When the North Atlantic Ocean is experiencing warm anomalies and the South Atlantic Ocean cold anomalies the current is described as being in a stronger phase. • Strong phases of the current are associated with increased, more intense tropical activity and weaker, less numerous El Niño's events (Gray et al. 1997) • Florida experienced much wetter conditions and more intense tropical storms prior to 1970, the last period the Atlantic Ocean Conveyor was recognized as being in the strong phase.

12. Trimble et al. (1998)

13. Atlantic Multidecadal Oscillation (AMO) • North Atlantic sea surface temperatures for 1856-1999 contain a 65-80 year cycle with a 0.4 C range (AMO) • AMO warm phases occurred during 1860-1880 and 1940-1960, and cool phases during 1905-1925 and 1970-1990. • Lake Okeechobee inflow varies by 40% between AMO warm and cool phases.

14. Enfield et al. (2001)

15. Atlantic Multidecadal Oscillation (AMO) • During the positive phase of the oscillation (1930-1964), net average annual inflow to Lake Okeechobee was about double that during the ensuing negative phase (1965-1994). This would imply a near complete reversal in water management priorities for multi-decadal periods. • During the negative AMO phase, inflow to the Lake is barely enough to meet the significant water needs of south Florida and management policy must be biased in favor of water conservation. • The AMO-related rainfall variability has immediate practical implications for water management policies in the affected regions of the United States.

16. Summary of Main Indicators of Florida's Climate • Florida rainfall has been significantly tied to El Niño-Southern Oscillation Events • Florida rainfall fluctuations are tied to solar activity. • The strong phase of the Atlantic Ocean Conveyor Current is associated with more frequent and intense tropical activity and much wetter conditions • AMO has a significant impact in South Florida rainfall

17. Impact of combined indicators • In Florida, strong El Niño events that occur during a periods of high solar activity appear to produce consistently wet conditions in Florida. • Strong La Niña conditions together with low solar activity appear to consistently produce the most significant droughts. • A strong Atlantic Ocean Conveyor current is believed to be associated with weaker and less frequency of El Niñoevents. • When considered jointly, the Atlantic Ocean Conveyor Current and solar activity best explain multi-decadal variability of Florida's climate.

18. Artificial Neural Networks (ANN) • We are developing an Artificial Neural Network (ANN) model to study the relationships between climate anomalies and water runoff, stream flow and water quality indicators in the Lake Okeechobee watershed.

19. ANN ANN models are particularly attractive tools to detect complex non-linear relationships when : • The data is not precise or is subject to possibly large errors. • The patterns important to the problem are subtle or hidden. • The data exhibits significant unpredictable nonlinearity.

20. Feed forward • Back Propagation

21. ANN • Building a neural network model for a particular problem is a nontrivial task (number of layers, the number of nodes in each layer, etc.) • Selection of activation functions, • Training algorithm, • Input data transformation or normalization methods, • Training and test sets, • All these aspects imply extensive trial and error experiments and a thorough understanding of the limitation and quality of the available data.

22. Previous works SFWMD, 1998 INPUTS (7 units) • the Southern Oscillation Index, • the sunspot number, • trend in sunspot number, • maximum sunspot number of each cycle, • geomagnetic index, • Atlantic Ocean thermohaline index and • the month of the year. (previous six months) OUTPUTS (next six months) • LO Inflow discharge (1 unit)

23. ANN SFWMD, 1998

24. ANN Inputs/Output INPUTS (previous six months) • Southern Oscillation Index, • Sunspot number, • Geomagnetic index, • Atlantic Ocean thermohaline index • AMO OUTPUTS (next month) • LO Inflow discharge • LO Phosphorous loading

25. ANN (preliminary tests) • 30 units in input layer (only data) • One hidden layer with 40 units • One output layer (next month LO monthly inflow) • 1933-1983 TRAINING • 1984-2004 TESTING

26. ANN (preliminary results)

27. Ongoing work • Other network architectures (recursive, etc.) • Monthly precipitation as output parameter instead of discharge, • Watershed Assessment Model (WAM) results as input parameters, • Total Phosphorus data and WAM results as new output parameter.

28. THANKS