[1] A method for siting water level monitors based on information theory measurements is presented. The first measurement is joint entropy, which evaluates the amount of information content that a monitoring set is able to collect, and the second measurement is total correlation, which evaluates the level of dependency or redundancy among monitors in the set. In order to find the most convenient set of places to put monitors from a large number of potential sites, a multiobjective optimization problem is posed under two different considerations: (1) taking into account the costs of placing new monitors and (2) considering the cost of placing monitors too close to hydraulic structures. In both cases, the joint entropy of the set is maximized and its total correlation is minimized. The costs are considered in terms of information theory units, for which additional terms affecting the objective functions are introduced. The proposed method is applied in a case study of the Delfland region, Netherlands. Results show that total correlation is an effective way to measure multivariate independency and that it must be combined with joint entropy to get results that cover a significant proportion of the total information content of the system. The maximization of joint entropy gives results that cover between 82% and 85% of the total information content.Citation: Alfonso, L., A. Lobbrecht, and R. Price (2010), Optimization of water level monitoring network in polder systems using information theory, Water Resour. Res., 46, W12553,
[1] Data collection is a critical activity in the management of water systems because it supports informed decision making. Data are collected by means of monitoring networks in which water level gauges are of particular interest because of their implications for flood management. This paper introduces a number of modifications to previously published methods that use information theory to design hydrological monitoring networks in order to make the methods applicable to the design of water level monitors for highly controlled polder systems. The new contributions include the use of a hydrodynamic model for entropy analysis, the introduction of the quantization concept to filter out noisy time series, and the use of total correlation to evaluate the performance of three different pairwise dependence criteria. The resulting approach, water level monitoring design in polders (WMP), is applied to a polder in the Pijnacker region, Netherlands. Results show that relatively few monitors are adequate to collect the information of a polder area in spite of its large number of target water levels. It is found, in addition, that the directional information transfer DIT YX is more effective in finding independent monitors, whereas DIT XY is better for locating sets of monitors with high joint information content. WMP proves to be a suitable and simple method as part of the design of monitoring networks for polder systems.Citation: Alfonso, L., A. Lobbrecht, and R. Price (2010), Information theory-based approach for location of monitoring water level gauges in polders, Water Resour. Res., 46, W03528,
Abstract. Monitoring stations have been used for decades to properly measure hydrological variables and better predict floods. To this end, methods to incorporate these observations into mathematical water models have also been developed. Besides, in recent years, the continued technological advances, in combination with the growing inclusion of citizens in participatory processes related to water resources management, have encouraged the increase of citizen science projects around the globe. In turn, this has stimulated the spread of low-cost sensors to allow citizens to participate in the collection of hydrological data in a more distributed way than the classic static physical sensors do. However, two main disadvantages of such crowdsourced data are the irregular availability and variable accuracy from sensor to sensor, which makes them challenging to use in hydrological modelling. This study aims to demonstrate that streamflow data, derived from crowdsourced water level observations, can improve flood prediction if integrated in hydrological models. Two different hydrological models, applied to four case studies, are considered. Realistic (albeit synthetic) time series are used to represent crowdsourced data in all case studies. In this study, it is found that the data accuracies have much more influence on the model results than the irregular frequencies of data availability at which the streamflow data are assimilated. This study demonstrates that data collected by citizens, characterized by being asynchronous and inaccurate, can still complement traditional networks formed by few accurate, static sensors and improve the accuracy of flood forecasts.
Floods are one of the most frequent and disruptive natural hazards that affect man. Annually, significant flood damage is documented worldwide. Flood mapping is a common preimpact flood hazard mitigation measure, for which advanced methods and tools (such as flood inundation models) are used to estimate potential flood extent maps that are used in spatial planning. However, these tools are affected, largely to an unknown degree, by both epistemic and aleatory uncertainty. Over the past few years, advances in uncertainty analysis with respect to flood inundation modeling show that it is appropriate to adopt Probabilistic Flood Maps (PFM) to account for uncertainty. However, the following question arises; how can probabilistic flood hazard information be incorporated into spatial planning? Thus, a consistent framework to incorporate PFMs into the decision-making is required. In this paper, a novel methodology based on Decision-Making under Uncertainty theories, in particular Value of Information (VOI) is proposed. Specifically, the methodology entails the use of a PFM to generate a VOI map, which highlights floodplain locations where additional information is valuable with respect to available floodplain management actions and their potential consequences. The methodology is illustrated with a simplified example and also applied to a real case study in the South of France, where a VOI map is analyzed on the basis of historical land use change decisions over a period of 26 years. Results show that uncertain flood hazard information encapsulated in PFMs can aid decision-making in floodplain planning.
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