Operational Prediction of Groundwater Fluctuation in South Florida using Sequence Based Markovian Stochastic Model |
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Authors: | Yirgalem Chebud Assefa Melesse |
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Affiliation: | (1) South Carolina Water Science Center, U.S. Geological Survey, 720 Gracern Road, Columbia, SC 29210, USA |
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Abstract: | The ecosystem of South Florida is characterized by a vast wetland system, karst surficial hydrogeology, and extended coastal
boundary. The ecosystem is poised under risks of: ecological failure due to increased fragmentation by urbanization; groundwater
flow disruption because of sinkhole formation; and intrusion of oceanic water with decreasing water table head because of
drought or over pumping. It was found important to synthesize the spatiotemporal state of the groundwater hydrology and also
develop a forecasting model to support the intensive management and monitoring in place. In this study, an objective was set
to develop a stochastic sequence model capable of forecasting groundwater levels on a monthly span at a daily time scale.
The groundwater level simulation was conceptualized as a sequence of daily fluctuating states of magnitudes and patterns that
has a defined probability of occurrence. The model setup involved representation of daily fluctuation magnitudes in ten states
and pattern changes in three states. The sequential occurrence of states of magnitudes and patterns at each time step was
used for estimation of the transitional probabilities and employed in a hidden Markov model frame work for ensemble generation
and estimation of posterior probabilities. A realization was chosen based on the highest maximum likelihood ratio of 90% and
smallest root mean square error of 0.05–0.12 m against the historical data. A monthly forecasting at daily time step was done
dynamically incorporating observed data at each time step and revising prior and posterior probability estimation in the hidden
Markov model formulation. A case study was conducted at three well sites, which are situated at three different hydrogeologic
settings. The model not only reproduced annual groundwater fluctuation patterns but also forecasted preceding monthly fluctuations
at maximum likelihood ratio above 90% and root mean square error below 0.15 m. A further study was recommended first to analyze
break point parametric estimation for seasonal analysis, and secondly to integrate the approach in other hydrological models
for the purpose of synthetic groundwater fluctuation generation. |
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