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

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  • Yirgalem Chebud
  • Assefa Melesse

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. Copyright Springer Science+Business Media B.V. 2011

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  • Yirgalem Chebud & Assefa Melesse, 2011. "Operational Prediction of Groundwater Fluctuation in South Florida using Sequence Based Markovian Stochastic Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(9), pages 2279-2294, July.
    • Handle: RePEc:spr:waterr:v:25:y:2011:i:9:p:2279-2294
    DOI: 10.1007/s11269-011-9808-z
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    References listed on IDEAS

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    1. Shayne Paynter & Mahmood Nachabe & George Yanev, 2011. "Statistical Changes of Lake Stages in Two Rapidly Urbanizing Watersheds," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(1), pages 21-39, January.
    2. Sajjad Ahmad & Dinesh Prashar, 2010. "Evaluating Municipal Water Conservation Policies Using a Dynamic Simulation Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(13), pages 3371-3395, October.
    3. Shamshad, A. & Bawadi, M.A. & Wan Hussin, W.M.A. & Majid, T.A. & Sanusi, S.A.M., 2005. "First and second order Markov chain models for synthetic generation of wind speed time series," Energy, Elsevier, vol. 30(5), pages 693-708.
    4. Gunderson, Lance H., 2001. "SOUTH FLORIDA: THE REALITY OF CHANGE AND THE PROSPECTS FOR SUSTAINABILITY: Managing surprising ecosystems in southern Florida," Ecological Economics, Elsevier, vol. 37(3), pages 371-378, June.
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    1. Yonghong Hao & Bibo Cao & Xiang Chen & Jian Yin & Ronglin Sun & Tian-Chyi Yeh, 2013. "A Piecewise Grey System Model for Study the Effects of Anthropogenic Activities on Karst Hydrological Processes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1207-1220, March.
    2. Jinjie Miao & Guoliang Liu & Bibo Cao & Yonghong Hao & Jianmimg Chen & Tian−Chyi Yeh, 2014. "Identification of Strong Karst Groundwater Runoff Belt by Cross Wavelet Transform," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 2903-2916, August.
    3. O. Barron & M. Donn & A. Barr, 2013. "Urbanisation and Shallow Groundwater: Predicting Changes in Catchment Hydrological Responses," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(1), pages 95-115, January.
    4. Y. Chebud & A. Melesse, 2012. "Spatiotemporal Surface-Groundwater Interaction Simulation in South Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(15), pages 4449-4466, December.

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