While drought impacts are widespread across the globe, climate change projections indicate more frequent and severe droughts. This underscores the pressing need to increase resistance and resilience to drought. The strategic application of Preventive Drought Management Measures (PDMMs) is a suitable avenue to reduce the likelihood of drought and ameliorate associated damages. In this study, we use an optimisation approach with a multicriteria decision-making method to allocate PDMMs for reducing the severity of agricultural and hydrological droughts. The results indicate that implementing PDMMs can reduce the severity of agricultural and hydrological droughts, and the obtained management scenarios (solutions) highlight the utility of multi-objective optimisation for PDMMs planning. However, examined management scenarios also illustrate the trade-off between managing agricultural and hydrological droughts. PDMMs can alleviate the severity of agricultural droughts while producing opposite effects for hydrological droughts (or vice versa). Furthermore, the impact of PDMMs displays temporal and spatial variabilities. For instance, PDMMs implementation within a specific subbasin may mitigate the severity of one type of drought in a given month yet exacerbate drought conditions in preceding or subsequent months. In the case of hydrological droughts, the PDMMs may intensify streamflow deficits in the intervened subbasins while alleviating the hydrological drought severity downstream (or vice versa). These complexities emphasise a customised implementation of PDMMs, considering the basin characteristics (e.g., rainfall distribution over the year, soil properties, land use, and topography) and the quantification of PDMMs' effect on the severity of each type of drought.

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Water distribution network (WDN) models are a common decision support tool for understanding the behavior and performance of WDNs, aiding in the planning and management of WDN systems. The increasing availability of real-time data has recently promoted the exploration of Data Assimilation (DA) techniques to improve these models. However, flow, pressure and demand data are uncertain, particularly due to sensor characteristics such as precision and noise. An open question is to what extent DA can still improve hydraulic models when the data used to this end is uncertain. This paper proposes a three-step Ensemble Kalman Filter based DA approach for WDNs (3-EnKF-WDN), building on previous approaches, and advancing in two main fronts: the use of extended period simulation, and the use of pressure-dependent demand (PDD) analysis. Different scenarios considering uncertain sensor data, with varied precision and noise, are applied to two networks of different sizes, representative of real-world WDNs. The computational demand of the 3-EnKF-WDN method is also assessed. Results show that increasing sensor’s precision and decreasing the noise in state measurements reduce model error, as expected. However, we also found that model errors: 1) are reduced more effectively by using 3-EnKF-WDN than by increasing sensors’ precision; 2) are not reduced if certain noise thresholds are surpassed; 3) can be reduced without assimilating demand data if the WDNs are fully monitored with head sensors in all the nodes and flow sensors in all the links.

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Water utilities are urged to decrease their real water losses, not only to reduce costs but also to assure long-term sustainability. Hardware- and software-based techniques have been broadly used to locate leaks; within the latter, previous works that have used data-driven models mostly focused on single leaks. This paper presents a methodology to locate multiple leaks in water distribution networks employing pressure residuals. It consists of two phases: one is to produce training data for the data-driven model and cluster the nodes based on their leak-flow-rate-independent signatures using an adapted hierarchical agglomerative algorithm; the second is to locate the leaks using a top-down approach. To identify the leaking clusters and nodes, we employed a custom-built k-nearest neighbor (k-NN) algorithm that compares the test instances with the generated training data. This instance-to-instance comparison requires substantial computational resources for classification, which was overcome by the use of high-performance computing. The methodology was applied to a real network located in a European town, comprising 144 nodes and a total length of pipes of 24 km. Although its multiple inlets add redundancy to the network increasing the challenge of leak location, the method proved to obtain acceptable results to guide the field pinpointing activities. Nearly 70% of the areas determined by the clusters were identified with an accuracy of over 90% for leak flows above 3.0 L/s, and the leaking nodes were accurately detected over 50% of the time for leak flows above 4.0 L/s.

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This chapter aims to describe the latest innovative approaches for integrating heterogeneous observations from static social sensors within hydrological and hydrodynamic modelling to improve flood prediction. The distinctive characteristic of such sensors, with respect to the traditional ones, is their varying lifespan and space-time coverage as well as their spatial distribution. The main part of the chapter is dedicated to the optimal assimilation of heterogeneous intermittent data within hydrological and hydraulic models. These approaches are designed to account for the intrinsic uncertainty contained into hydrological observations and model structure, states and parameters. Two case studies, the Brue and Bacchiglione catchments, are considered. Finally, the evaluation of the developed methods is provided. This study demonstrates that networks of low-cost static and dynamic social sensors can complement traditional networks of static physical sensors, for the purpose of improving flood forecasting accuracy. This can be a potential application of recent efforts to build citizen observatories of water, in which citizens not only can play an active role in information capturing, evaluation and communication but also can help improve models and increase flood resilience.

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Floods are the natural hazards that are causing the most deaths worldwide. Flood early warning systems are one of the most cost-efficient methods to reduce death rates, triggering decisions about the evacuation of exposed population. Although previous studies have investigated the effect of human behaviours on evacuation processes, studies analysing a combination of behaviours, flood onset and warning timing are limited. Our objective is to explore how changes on the aforementioned factors can affect casualties. This is done within a modelling framework that includes an agent-based model, a hydraulic model, and a traffic model, which is implemented for the case study of Orvieto (Italy). The results show that the number of casualties is most impacted by people’s behaviour. Besides, we found that a delay of 30 min in releasing the warning can boost the number of casualties up to six times. These results may help managers to propose effective emergency plans.

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Water distribution networks (WDNs) need to guarantee that water is delivered with adequate quality. This paper compares the performance of 12 multiobjective procedures to limit water quality deterioration in a WDN through the optimal operation of valves. The first objective (ObF1) is to minimize the water age, chosen as a surrogate parameter of quality deterioration, and the second objective (ObF2) is to minimize the number of valve closures. The 12 procedures are derived from the combination of 4 different optimization algorithms and 3 formulations of ObF1, namely, to minimize the maximum, the arithmetic mean, and the demand-weighted mean water age. The optimization algorithms considered are random search (RS), Loop for Optimal Valve Status Configuration (LOC), and a combination of each of these two with the Archive-based Micro Genetic Algorithm. The procedures are tested on two networks of different complexity. Results show how LOC is able to find near-optimal solutions using a fraction of the computational time required by a brute force search. Furthermore, among the ObF1 formulations, the use of the averages (either arithmetic or demand-weighted) gives better results in terms of impact on the population served by a WDN.

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To improve hydrological predictions, real-time measurements derived from traditional physical sensors are integrated within mathematic models. Recently, traditional sensors are being complemented with crowdsourced data (social sensors). Although measurements from social sensors can be low cost and more spatially distributed, other factors like spatial variability of citizen involvement, decreasing involvement over time, variable observations accuracy and feasibility for model assimilation play an important role in accurate flood predictions. Only a few studies have investigated the benefit of assimilating uncertain crowdsourced data in hydrological and hydraulic models. In this study, we investigate the usefulness of assimilating crowdsourced observations from a heterogeneous network of static physical, static social and dynamic social sensors. We assess improvements in the model prediction performance for different spatial-temporal scenarios of citizen involvement levels. To that end, we simulate an extreme flood event that occurred in the Bacchiglione catchment (Italy) in May 2013 using a semi-distributed hydrological model with the station at Ponte degli Angeli (Vicenza) as the prediction-validation point. A conceptual hydrological model is implemented by the Alto Adriatico Water Authority and it is used to estimate runoff from the different sub-catchments, while a hydraulic model is implemented to propagate the flow along the river reach. In both models, a Kalman filter is implemented to assimilate the crowdsourced observations. Synthetic crowdsourced observations are generated for either static social or dynamic social sensors because these measures were not available at the time of the study. We consider two sets of experiments: (i) assuming random probability of receiving crowdsourced observations and (ii) using theoretical scenarios of citizen motivations, and consequent involvement levels, based on population distribution. The results demonstrate the usefulness of integrating crowdsourced observations. First, the assimilation of crowdsourced observations located at upstream points of the Bacchiglione catchment ensure high model performance for high lead-time values, whereas observations at the outlet of the catchments provide good results for short lead times. Second, biased and inaccurate crowdsourced observations can significantly affect model results. Third, the theoretical scenario of citizens motivated by their feeling of belonging to a "community of friends" has the best effect in the model performance. However, flood prediction only improved when such small communities are located in the upstream portion of the Bacchiglione catchment. Finally, decreasing involvement over time leads to a reduction in model performance and consequently inaccurate flood forecasts.@en

Water quality deterioration in water distribution networks can be associated with high water residence time in the network. To this end, some previous studies have proposed optimization procedures for valve management. However, these studies generally come up with operational configurations assuming deterministic user demand patterns that may never occur in reality. In consequence, the proposed solutions may not be effective for improving water quality or do not comply with pressure constraints if different demand patterns are observed. This study proposes a methodology to determine robust configurations of the valves to keep water residence time at acceptable levels regardless of the variability in demand patterns. The methodology is tested on four different distribution systems of varying topology and size. Results show the importance of executing robust – instead of deterministic, optimization to find valve configurations that guarantee the performance of the networks in terms of hydraulics and water quality. @en

The introduction of new information and communication technologies enables communities to share information and self-organize in the response to disasters. Crowd-sourcing approaches enable professional authorities to capture information from the ground in real-time. However, there is a gap between the professional and community-driven response: locally emergent initiatives may lack the overview needed for efficient coordination, while decisions taken by professionals may not consider the actual situation on the ground. We study this information sharing and coordination gap through the lens of urban flood early warning and response systems. Based on a literature review combining academic articles as well as guidelines and reports from practice, we derive design principles for these systems. Considering the case study of Accra, specific requirements are individuated. The design principles are then used to address the requirements, resulting in a set of functionalities for a collaborative flood warning and response system. These functionalities provide the basis for further development and evaluation.@en

Floods are among the most threatening natural hazards that exist and the leading cause of deaths due to natural disasters worldwide. The quantification of hazards related to the increment of water levels and consequent flood extent requires an adequate estimation of extreme discharges. Traditionally, extreme river discharges are obtained by univariate frequency analysis of peak discharges that are obtained either from discharge observations or from hydrological models. However, either of these two options requires an important amount of historical data. The Magdalena-Cauca river basin in Colombia, as in many basins in the developing world, has insufficient amount of available records of discharge observations in several locations. Therefore, the quantification of hazards becomes a challenging problem, particularly in completely ungagged areas. To overcome this problem, we used and proposed different methods that are computationally inexpensive to indirectly estimate extreme discharges from different and easy to obtain data sources. One of them, a stochastic model based on Bayesian Networks (BNs), developed for data-rich basins and previously applied in Europe and the contiguous USA is used in this study. A Bayesian Network is a direct acyclic graph that consists of nodes that represent random variables and arcs that represent the dependence structure between these variables. To validate the performance of the Bayesian Network model in the Magdalena- Cauca basin, two different deterministic data driven models that weren’t explored earlier for this purpose, based on lazy learning (k-nearest neighbours) and eager learning (M5 Model Trees) are proposed and used. In addition, the BN model performance is evaluated with regional frequency analysis. The inputs of all the models utilize the same data, related to topography, land-use characteristics and local climate. In particular, these inputs, seven in total, are: 1) the steepness of the catchment; 2) the catchment area; 3) the annual maximum of daily precipitation and snowmelt; 4) the maximum runoff coefficient of the catchment; 5) the percentage of the catchment area covered by marshes, 6) lakes and 7) built-up areas. All the models establish relations among them and the annual maximum of daily river discharges, in such a way that it can be predicted for different inputs. Results show that all the models perform generally well in estimating annual peak discharges. All the models perform well when used in big sub-catchments and tend to underperform in small ones. Univariate frequency analysis applied on the peak discharges obtained by all the models show that the output of the BN model outperforms other models in estimating extreme discharges with certain exceedance probabilities. In this context, the BN model is used to evaluate the effect of climate change projection scenarios (RCP4.5 and RCP8.5) on extreme river discharges. Interestingly, the results contradict a recent study based on a distributed hydrological model built with free global products. @en

This paper comparatively assesses the performance of five data assimilation techniques for three-parameter Muskingum routing with a spatially lumped or distributed model structure. The assimilation techniques used include direct insertion (DI), nudging scheme (NS), Kalman filter (KF), ensemble Kalman filter (EnKF) and asynchronous ensemble Kalman filter (AEnKF), which are applied to river reaches in Texas and Louisiana, USA. For both lumped and distributed routing, results from KF, EnKF and AEnKF are sensitive to the error specification. As expected, DI outperformed the other models in the case of lumped modelling, while in distributed routing, KF approaches, particularly AEnKF and EnKF, performed better than DI or nudging, reflecting the benefit of updating distributed states through error covariance modelling in KF approaches. The results of this work would be useful in setting up data assimilation systems that employ increasingly abundant real-time observations using distributed hydrological routing models.

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Diverse hydrologic and hydraulic models of varying complexities have been proposed in the past few decades to accurately predict the water levels and discharges along rivers. Among them, the hydrologic routing models are widely used because of their simplicity, minimal data, and computational requirements. Due to their simplified assumptions, however, they are subject to various sources of uncertainty. To reduce their predictive uncertainty and improve their operational forecast abilities, data assimilation techniques have been proposed to update the states and/or parameters of the mathematic models by integrating real-time river observations with them. However, the characterization of the model errors and the location of the sensors used for data assimilation have an important effect on the model performance. The main objective of this study was to assess the effect of sensor placement and the errors of both the model and the boundary conditions on the assimilation of flow observations in the distributed hydrologic routing models. A Muskingum-Cunge routing model was applied first to a synthetic river reach with a rectangular cross section and then to a more complex natural river, the Bacchiglione River in Italy, with varying geometry of the river cross sections. The Kalman filter was used to assimilate the flow observations. Synthetic and real-world experiments were carried out. The results showed an improved model performance after the assimilation of the flow observations (e.g., a Nash index higher than 0.9 in the synthetic river and 0.85 in the Bacchiglione River); however, the procedure was sensitive to the model error and the locations of the sensors. In particular, when the model error was larger than the boundary condition error, it was suggested to place the sensors in the lower part of the river reach to maximize the model improvement at the river outlet. On average, the model performance was improved by 14% in terms of the Nash index when the sensor was located in the upstream part of the reaches of the Bacchiglione River instead of in the downstream part. Sensors placed in the upper part of the reaches enabled the improved skills to persist for additional lead time of up to 6 h for the forecasting of the water level at the reach outlet. This study presented a method that allowed identifying the optimal locations of the sensors and thus helped to improve the flood forecasts.

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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.

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Waternomics is a European Union-funded research project aspiring to develop and introduce Information and Communication Technology (ICT) as an enabling technology to manage water as a resource, increase end-user conservation awareness, affect behavioural changes and avoid water losses through leak detection. Existing leakage detection methods are generally focused on scrutinising large diameter pipes in water supply distribution networks or transmission pipes. However, it has been estimated that the average household's leaks can be as much as 35m³ of water per year. In order to solve the problem, analysis of different types of data in the household piping system is required, including detection and identification. One conventional approach is to use flow sensors installed at several locations within the household piping system and perform a mass balance approach to detect leakage. However, this method is expensive and difficult to implement. This research proposes a novel approach to household leakage detection by means of sound signal recordings. The approach consists of recording the sound signals that are produced by water fixtures and appliances, and then use these recordings to detect any abnormal situation which may be an indication of a leak. The method comprises three major steps: recording, storing and processing of sound signals. The recording step is done by means of a non-intrusive sound sensor that sends records remotely; the storage step is made in a database of sound signals for different types of uses; finally, the processing step is made through a sound signal identification software tool that is able to search the database libraries for related sounds, in a similar way as the Shazam app for music. Tests of the leak detection method are presented for data collected in laboratory conditions. Results show that this detection method has a potential to help reducing leakages through an easy-to-install and non-intrusive sensor.

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The aim of this study is to assess the influence of sensor locations and varying observation accuracy on the assimilation of distributed streamflow observations, also taking into account different structures of semi-distributed hydrological models. An ensemble Kalman filter is used to update a semi-distributed hydrological model as a response to measured streamflow. Various scenarios of sensor locations and observation accuracy are introduced. The methodology is tested on the Brue basin during five flood events. The results of this work demonstrate that the assimilation of streamflow observations at interior points of the basin can improve the hydrological models according to the particular location of the sensors and hydrological model structure. It is also found that appropriate definition of the observation accuracy can affect model performance and consequent flood forecasting. These findings can be used as criteria to develop methods for streamflow monitoring network design.@en

Sensors and sensor networks play an important role in decision-making related to water quality, operational streamflow forecasting, flood early warning systems, and other areas. In this paper we review a number of existing applications and analyse a variety of evaluation and design procedures for sensor networks with respect to various criteria. Most of the existing approaches focus on maximising the observability and information content of a variable of interest. From the context of hydrological modelling only a few studies use the performance of the hydrological simulation in terms of output discharge as a design criterion. In addition to the review, we propose a framework for classifying the existing design methods, and a generalised procedure for an optimal network design in the context of rainfall-runoff hydrological modelling.

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Efficient management of a sewer system includes the control of the conveyed wastewater quality to adequately operate treatment plants and protect the receiving water bodies. Moreover, these systems are vulnerable to either accidental spills or intentional unauthorized discharges. To properly manage them, a limited number of sensors could be placed at different locations to monitor the water quality. In this paper, multiobjective and single-objective optimization procedures to optimally locate sensors in sewer systems are proposed, tested, and compared. The multiobjective procedures include objective functions related to information theory (IT procedure), detection time and reliability (DR procedure), and a combination of them (IT_DR procedure). The single-objective procedures include a greedy-based objective function (GR procedure) and a merged objective function (DR_IT_GR procedure). The procedures show a similar performance when applied on a small network, whereas in a real system, the results show that (1) the IT-based method can be effectively used as a filtering technique(2) the DR_IT_GR procedure outperforms the other multiobjective onesand (3) the GR procedure is very efficient in finding the Pareto extreme solutions.

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Design of optimal precipitation sensor networks is a common topic in hydrological literature, however this is still an open problem due to lack of understanding of some spatially variable processes, and assumptions that often cannot be verified. Among these assumptions lies the homoscedasticity of precipitation fields, common in hydrological practice. To overcome this, it is proposed a local intensity-variant covariance structure, which in the broad extent, provides a fully updated correlation structure as long as new data are coming into the system. These considerations of intensity-variant correlation structure will be tested in the design of a precipitation sensor network for a case study, improving the estimation of precipitation fields, and thus, reducing the input uncertainty in hydrological models, especially in the scope of rainfall-runoff models.@en

The reliable evaluation of the flood forecasting is a crucial problem for assessing flood risk and consequent damages. Different hydrological models (distributed, semi-distributed or lumped) have been proposed in order to deal with this issue. The choice of the proper model structure has been investigated by many authors and it is one of the main sources of uncertainty for a correct evaluation of the outflow hydrograph. In addition, the recent increasing of data availability makes possible to update hydrological models as response of real-time observations. For these reasons, the aim of this work it is to evaluate the effect of different structure of a semi-distributed hydrological model in the assimilation of distributed uncertain discharge observations. The study is applied in the Brue catchment, located in UK. The first methodological step is to divide the basin in different sub-basins according to topographic characteristics. Secondly, two different structures of the semi-distributed hydrological model are implemented in order to estimate the outflow hydrograph. Then, synthetic observations of uncertain value of discharge are generated as a function of the observed and simulated value of flow at the basin outlet, and assimilated in the semi-distributed models using a Kalman Filter. Finally, different spatial patterns of sensors location are assumed to update the model state as response of the uncertain discharge observations. The results of this work pointed out that different model structures can provide different improvements of model performances, and, different optimal location of the sensors. @en

Accurate real-time forecasting of river water level is an important issue that has to be addressed in order to prevent and mitigate water-related risk. To this end, data assimilation methods have been used to improve the forecasts ability of water model merging observations coming from stations and model simulations. As a consequence of the increasing availability of dynamic and cheap sensors, having variable life-span, space and temporal coverage, the citizens are becoming an active part in information capturing, evaluation and communication. On the other hand, it is difficult to assess the uncertain related to the observation coming from such sensors. The main objective of this work is to evaluate the influence of the observational error in the proposed assimilation methodologies used to update the hydrological model as response of distributed observations of water discharge. We tested the developed approaches on a test study area - the Brue catchment, located in the South West of England, UK. The Ensemble Kalman filter is applied to the semi-distributed hydrological model. Distributed observations of discharge are synthetically generated. Different types of observational error are introduced assuming diverse sets of probability distributions, first and second order moments. The results of this work show how the assimilation of distributed observations, can improve the hydrologic model performance with a better forecast of flood events. It is found that different observational error types can affects the model accuracy. @en