Water quality monitoring strategies - A review and future perspectives.

In this article, two criteria are suggested to inspect whether a model of geospatial information diffusion is valid to fill in the missing observation in a gap unit. The first criterion serves for inspecting a gap unit in the study area, from which a sample is taken to train the model. The second criterion serves for a gap unit outside the area but nearby. The standard deviation of the trained model plays a key role in the former. The valid radiance determined by the maximum distance between the units of the sample point passed the test and the center of the study area plays an important role in the latter.

Risk Analysis Based on Data and Crisis Response Beyond Knowledge : Proceedings of the 7th International Conference on Risk Analysis and Crisis Response (RACR 2019), October 15-19, 2019, Athens, Greece

The surface water quality monitoring network (WQMN) is crucial for effective water environment management How to design an optimal monitoring network is an important scientific and engineering problem that presents a special challenge in the smart city era This comprehensive review provides a timely and systematic overview and analysis on quantitative design approaches Bibliometric analysis shows the chronological pattern, journal distribution, authorship, citation and country pattern Administration types of water bodies and design methods are classified The flexibility characteristics of four types of direct design methods and optimization objectives are systematically summarized, and conclusions are drawn from experiences with WQMN parameters, station locations, and sampling frequency and water quality indicators This paper concludes by identifying four main future directions that should be pursued by the research community This review sheds light on how to better design and construct WQMNs

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A comprehensive review on the design and optimization of surface water quality monitoring networks

A cost-effective and efficient framework to determine water quality monitoring network locations.

Having reliable water monitoring networks is an essential component of water resources and environmental management. A standardized process for the design of water monitoring networks does not exist with the exception of the World Meteorological Organization (WMO) general guidelines about the minimum network density. While one of the major challenges in the design of optimal hydrometric networks has been establishing design objectives, information theory has been successfully adopted to network design problems by providing measures of the information content that can be deliverable from a station or a network. This review firstly summarizes the common entropy terms that have been used in water monitoring network designs. Then, this paper deals with the recent applications of the entropy concept for water monitoring network designs, which are categorized into (1) precipitation; (2) streamflow and water level; (3) water quality; and (4) soil moisture and groundwater networks. The integrated design method for multivariate monitoring networks is also covered. Despite several issues, entropy theory has been well suited to water monitoring network design. However, further work is still required to provide design standards and guidelines for operational use.

Entropy Applications to Water Monitoring Network Design: A Review

Adequate and accurate hydrologic information from optimal hydrometric networks is an essential part of effective water resources management. Although the key hydrologic processes in the water cycle are interconnected, hydrometric networks (e.g., streamflow, precipitation, groundwater level) have been routinely designed individually. A decision support framework is proposed for integrated design of multivariable hydrometric networks. The proposed method is applied to design optimal precipitation and streamflow networks simultaneously. The epsilon-dominance hierarchical Bayesian optimization algorithm was combined with Shannon entropy of information theory to design and evaluate hydrometric networks. Specifically, the joint entropy from the combined networks was maximized to provide the most information, and the total correlation was minimized to reduce redundant information. To further optimize the efficiency between the networks, they were designed by maximizing the conditional entropy of the streamflow network given the information of the precipitation network. Compared to the traditional individual variable design approach, the integrated multivariable design method was able to determine more efficient optimal networks by avoiding the redundant stations. Additionally, four quantization cases were compared to evaluate their effects on the entropy calculations and the determination of the optimal networks. The evaluation results indicate that the quantization methods should be selected after careful consideration for each design problem since the station rankings and the optimal networks can change accordingly.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2016WR019981

Information theory-based decision support system for integrated design of multivariable hydrometric networks

The design of optimal hydrometric networks is an important starting point in water resources planning and management. Redundant or inappropriate networks may require unnecessary monitoring ...

Sensitivity of Entropy Method to Time Series Length in Hydrometric Network Design

Application of SNODAS and hydrologic models to enhance entropy-based snow monitoring network design

The number of water quality monitoring stations in the USA has decreased over the past few decades. Scarcity of observations can easily produce prediction uncertainty due to unreliable model calibration. An effective water quality monitoring network is important not only for model calibration and water quality prediction but also for resources management. Redundant or improperly located monitoring stations may cause increased monitoring costs without improvement to the understanding of water quality in watersheds. In this work, a decision-making methodology is proposed to design a water quality monitoring network by providing an adequate number of monitoring stations and their approximate locations at the eight-digit hydrologic unit codes (HUC8) scale. The proposed methodology is demonstrated for an example at the Upper Colorado River Basin (UCRB), where salinity is a serious concern. The level of monitoring redundancy or scarcity is defined by an index, station ratio (SR), which represents a monitoring density based on water quality load originated within a subbasin. By comparing the number of stations from a selected target SR with the available number of stations including the actual and the potential stations, the suggested number of stations in each subbasin was decided. If monitoring stations are primarily located in the low salinity loading subbasins, the average actual SR tends to increase, and vice versa. Results indicate that the spatial distribution of monitoring locations in 2011 is concentrated on low salinity loading subbasins, and therefore, additional monitoring is required for the high salinity loading subbasins. The proposed methodology shows that the SR is a simple and a practical indicator for monitoring density.

J. Keum

Papers

Entropy Applications to Water Monitoring Network Design: A Review

Information theory-based decision support system for integrated design of multivariable hydrometric networks

Sensitivity of Entropy Method to Time Series Length in Hydrometric Network Design

Application of SNODAS and hydrologic models to enhance entropy-based snow monitoring network design

Development of a decision-making methodology to design a water quality monitoring network