Operational Prediction of Groundwater Fluctuation in South Florida using Sequence Based Markovian Stochastic Model

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 2280 Y. Chebud, A. Melesse 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.

Karst Hydrological Processes and Grey System Model1

“…In the “classic” approaches, differential equation‐based deterministic models, including difference equations and integral equations, and probabilistic indeterminate models, including various ARIMA models that are more conventionally employed in modeling hydrological time series, are the major analytical tools (Dryden et al. , 2005; Gattinoni and Francani, 2010; Chebud and Melesse, 2011; Gárfias‐Soliz et al. , 2010).…”

Identification of Preferential Recharge Zones in Karst Systems Based on the Correlation between the Spring Level and Precipitation: A Case Study from Jinan Spring Basin

“…While precipitation infiltration is the most important recharge source of karst aquifers, and spring level fluctuation is highly dependent on the spatial-temporal distribution of precipitation, the hydrologic process by which precipitation transforms into karst water and then emerges as springs is nonstationary and nonlinear [27]. To investigate the correlation between spring level and precipitation, statistical analysis techniques must be used, taking into account various time scales and the lag time between them [28].…”

Compound flood modelling framework for rainfall-groundwater interactions

“…Previous efforts to model groundwater levels in South Florida have been developed in the form of hydrogeologic maps (Fish & Stewart, 1991), estimation of aquifer parameters to calculate groundwater flow (Cunningham et al, 2004), and statistical analysis of hydrological measurements (Chebud & Melesse, 2011Prinos & Dixon, 2016). Similarly, Hughes and White (2016) investigated the effect of pump practices and sea level rise on surface water routing and groundwater interactions in MDC using MODFLOW.…”