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A Bayesian maximum entropy-based methodology for optimal spatiotemporal design of groundwater monitoring networks

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Abstract

This paper presents a new methodology for analyzing the spatiotemporal variability of water table levels and redesigning a groundwater level monitoring network (GLMN) using the Bayesian Maximum Entropy (BME) technique and a multi-criteria decision-making approach based on ordered weighted averaging (OWA). The spatial sampling is determined using a hexagonal gridding pattern and a new method, which is proposed to assign a removal priority number to each pre-existing station. To design temporal sampling, a new approach is also applied to consider uncertainty caused by lack of information. In this approach, different time lag values are tested by regarding another source of information, which is simulation result of a numerical groundwater flow model. Furthermore, to incorporate the existing uncertainties in available monitoring data, the flexibility of the BME interpolation technique is taken into account in applying soft data and improving the accuracy of the calculations. To examine the methodology, it is applied to the Dehgolan plain in northwestern Iran. Based on the results, a configuration of 33 monitoring stations for a regular hexagonal grid of side length 3600 m is proposed, in which the time lag between samples is equal to 5 weeks. Since the variance estimation errors of the BME method are almost identical for redesigned and existing networks, the redesigned monitoring network is more cost-effective and efficient than the existing monitoring network with 52 stations and monthly sampling frequency.

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Acknowledgments

This research has been financially supported by Iran National Science Foundation (INSF) under grant number no. 95849284. The authors would like to thank Dr. Mark Serre from the University of North Carolina for his invaluable clarification of some aspects related to the BME. Also, we would like to acknowledge the Mahab Ghods Consulting Engineering Company for sharing the MODFLOW-based groundwater flow simulation model of the Dehgolan plain.

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Authors and Affiliations

  1. School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Marjan Hosseini

  2. School of Civil Engineering and Center of Excellence for Engineering and Management of Civil Infrastructures, College of Engineering, University of Tehran, Tehran, Iran

    Reza Kerachian

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  1. Marjan Hosseini
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  2. Reza Kerachian
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Correspondence to Reza Kerachian.

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Hosseini, M., Kerachian, R. A Bayesian maximum entropy-based methodology for optimal spatiotemporal design of groundwater monitoring networks. Environ Monit Assess 189, 433 (2017). https://doi.org/10.1007/s10661-017-6129-6

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