Decision support experiments and evaluations using seasonal to ...

including ecological restoration, riparian flow
management, urban water supply, agricultural
water availability, coastal zone issues, and
fire management at diverse spatial scales:
from cities and their surrounding urban
concentrations (New York, Seattle), to regions
(Northern California, South Florida, Intermountain
West); a comprehensively-managed
river basin (CALFED); and a resource (forest
lands) scattered over parts of the U.S. West and
Southwest. These cases also illustrate efforts
to rely on temporally diverse information (i.e.,
predictions of future variability in precipitation,
sea-level rise, and drought as well as past
variation) in order to validate trends.
Most importantly, these experiments represent
the use of different ways of integrating information
into water management to enable better decisions
to be made, including neural networks 1
in combination with El Niño-Southern Oscillation
(ENSO) forecasting; temperature, precipitation
and sea-level rise prediction; probabilistic
risk assessment; integrated weather, climate
and hydrological models producing short- and
longer-term forecasts; weather and streamflow
station outputs; paleoclimate records of streamflow
and hydroclimatic variability; and the use
of climate change information on precipitation
and sea-level rise to address shorter-term
weather variability.
Experiment 1:
How the South Florida Water Management
District Uses Climate Information
The Experiment
In an attempt to restore the Everglades ecosystem
of South Florida, a team of state and
federal agencies is engaged in the world’s largest
restoration program (Florida Department
of Environmental Protection and South Florida
Water Management District, 2007). A cornerstone
of this effort is the understanding that SI
climate variability (as well as climate change)
could have significant impacts on the region’s
hydrology over the program’s 50-year lifetime.
1 A neural network or “artificial neural network”
is an approach to information processing paradigm
that functions like a brain in processing information.
The network is composed of a large number of interconnected
processing elements (neurons) that work
together to solve specific problems and, like the brain,
the entire network learns by example.
Decision-Support Experiments and Evaluations using Seasonal to
Interannual Forecasts and Observational Data: A Focus on Water Resources
The South Florida Water Management District
(SFWMD) is actively involved in conducting
and supporting climate research to improve the
prediction and management of South Florida’s
complex water system (Obeysekera et al., 2007).
The SFWMD is significant because it is one of
the few cases in which decade-scale climate
variability information is being used in water
resource modeling, planning, and operation
programs.
Background/Context
Research relating climatic indices to South
Florida climate started at SFWMD more than
a decade ago (South Florida Water Management
District, 1996). Zhang and Trimble
(1996), Trimble et al. (1997), and Trimble and
Trimble (1998) used neural network models to
develop a better understanding of how ENSO
and other climate factors influence net inflow
to Lake Okeechobee. From that knowledge,
Trimble (1998) demonstrated the potential for
using ENSO and other indices to predict net
inflow to Lake Okeechobee for operational
planning. Subsequently, SFWMD was able to
apply climate forecasts to its understanding of
climate-water resources relationships in order to
assess risks associated with seasonal and multiseasonal
operations of the water management
system and to communicate the projected outlook
to agency partners, decision makers, and
other stakeholders (Cadavid et al., 1999).
Implementation/Application
The SFWMD later established the Water Supply
and Environment (WSE), a regulation schedule
for Lake Okeechobee that formally uses
seasonal and multi-seasonal climate outlooks
as guidance for regulatory release decisions
There are many
different ways
of integrating
information into
water management
to enable better
decisions.
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