Understanding, management and modelling of urban hydrology and its consequences for receiving waters: A state of the art

In various disciplines, information about the same phenomenon can be acquired from different types of detectors, at different conditions, in multiple experiments or subjects, among others. We use the term “modality” for each such acquisition framework. Due to the rich characteristics of natural phenomena, it is rare that a single modality provides complete knowledge of the phenomenon of interest. The increasing availability of several modalities reporting on the same system introduces new degrees of freedom, which raise questions beyond those related to exploiting each modality separately. As we argue, many of these questions, or “challenges,” are common to multiple domains. This paper deals with two key issues: “why we need data fusion” and “how we perform it.” The first issue is motivated by numerous examples in science and technology, followed by a mathematical framework that showcases some of the benefits that data fusion provides. In order to address the second issue, “diversity” is introduced as a key concept, and a number of data-driven solutions based on matrix and tensor decompositions are discussed, emphasizing how they account for diversity across the data sets. The aim of this paper is to provide the reader, regardless of his or her community of origin, with a taste of the vastness of the field, the prospects, and the opportunities that it holds.

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Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects

In various disciplines, information about the same phenomenon can be acquired from different types of detectors, at different conditions, in multiple experiments or subjects, among others. We use the term ''modality'' for each such acquisition framework.Duetothe richcharacteristics of natural phenomena, it is rare that a single modality provides complete knowledge of the phenomenon of interest. The increasing availability of several modalities reporting on the same system introduces new degrees of freedom, which raise questions beyond those related to exploiting each modality separately. As we argue, many of these questions, or ''challenges,'' are common to multiple domains. This paper deals with two key issues: ''why we need data fusion'' and ''how we perform it.'' The first issue is motivated by numerous examples in science and technology, followed by a mathematical framework that showcases some of the benefits that data fusion provides. In order to address the second issue, ''diversity'' is introduced as a key concept, and a number of data-driven solutions based on matrix and tensor decompositions are discussed, emphasizing how they account for diversity across the data sets. The aim of this paper is to provide the reader, regardless of his or her community of origin, with a taste of the vastness of the field, the prospects, and the opportunities that it holds.

Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects This paper provides an overview of the main challenges in multimodal data fusion acrossvariousdisciplinesandaddressestwokeyissues:''whyweneeddatafusion''and ''how we perform it.''

It is well acknowledged that there are large uncertainties associated with radar-based estimates of rainfall. Numerous sources of these errors are due to parameter estimation, the observational system and measurement principles, and not fully understood physical processes. Propagation of these uncertainties through all models for which radar-rainfall are used as input (e.g., hydrologic models) or as initial conditions (e.g., weather forecasting models) is necessary to enhance the understanding and interpretation of the obtained results. The aim of this paper is to provide an extensive literature review of the principal sources of error affecting single polarization radar-based rainfall estimates. These include radar miscalibration, attenuation, ground clutter and anomalous propagation, beam blockage, variability of the Z–R relation, range degradation, vertical variability of the precipitation system, vertical air motion and precipitation drift, and temporal sampling errors. Finally, the authors report some recent results from empirically-based modeling of the total radar-rainfall uncertainties. The bibliography comprises over 200 peer reviewed journal articles.

Review of the Different Sources of Uncertainty in Single Polarization Radar-Based Estimates of Rainfall

In just the past five years, the field of Earth observation has progressed beyond the offerings of conventional space agency based platforms to include a plethora of sensing opportunities afforded by CubeSats, Unmanned Aerial Vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically on the order of one billion dollars per satellite and with concept-to-launch timelines on the order of two decades (for new missions). More recently, the proliferation of smartphones has helped to miniaturise sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist five years ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of the cost of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-meter resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen-scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the Internet of Things as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilise and exploit these new observing systems to enhance our understanding of the Earth and its linked processes.

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The Future of Earth Observation in Hydrology.

The global spread of wireless networks brings a great opportunity for their use in environmental studies. Weather, atmospheric conditions, and constituents cause propagation impairments on radio links. As such, while providing communication facilities, existing wireless communication systems can be used as a widely distributed, high-resolution atmospheric observation network, operating in real time with minimum supervision and without additional cost. Here we demonstrate how measurements of the received signal level, which are made in a cellular network, provide reliable measurements for surface rainfall. We compare the estimated rainfall intensity with radar and rain gauge measurements.

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Environmental Monitoring by Wireless Communication Networks

In this paper, we propose a novel method for estimating the rain rate at any given point within a two-dimensional plain using measurements of the received signal level extracted from power control records of an existing deployed fixed wireless communication network. The path-average rainfall intensity along each microwave radio link is estimated from the rainfall-induced attenuation using an empirical relationship. The proposed algorithm consists of appropriate preprocessing of the links data, followed by a modified weighted least squares algorithm to infer on the rain level at any given point in space. The algorithm can be used to interpolate measurements onto a regular grid to construct a two-dimensional rainfall intensity field. The novelty of the proposed estimation method comes from its ability to be applied on an arbitrary geometry network comprising different microwave links lengths and frequencies and allowing easy integration of rain gauge observations into the model to improve estimation accuracy. The technique has been applied to an existing fixed wireless communication network comprising 22 microwave links covering an area of about 15times15 km2 and operating at carrier frequencies of about 20 GHz. The resulting rainfall field estimates have been compared to rain gauge stations in the vicinity and to weather radar data, showing good agreement.

Rain Rate Estimation Using Measurements From Commercial Telecommunications Links

https://www.tau.ac.il/~pinhas/papers/2008/Zinevitz_et_al_AWR_2008.pdf

Estimation of rainfall fields using commercial microwave communication networks of variable density

A novel approach for reconstruction of rainfall spatial–temporal dynamics from a wireless microwave network is presented. It employs a stochastic space–time model based on a rainfall advection model, assimilated using a Kalman filter. The technique aggregates the data in time and space along the direction of motion of the rainfall field, which is recovered from the simultaneous observation of a multitude of microwave links. The technique is applied on a standard microwave communication network used by a cellular communication system, comprising 23 microwave links, and it allows for observation of near-surface rainfall at the temporal resolutions of 1 min. The accuracy of the method is demonstrated by comparing instantaneous rainfall estimates with measurements from five rain gauges, reaching correlations of up to 0.85 at the 1-min time interval with a bias and RMSE of −0.2 and 4.2 mm h−1, respectively, and up to 0.96 with RMSE of 1.6 mm h−1 at the 10-min time interval for a 22-h intensive rainsto...

Frontal Rainfall Observation by a Commercial Microwave Communication Network

. Commercial microwave radio links forming cellular communication networks are known to be a valuable instrument for measuring near-surface rainfall. However, operational communication links are more uncertain relatively to the dedicated installations since their geometry and frequencies are optimized for high communication performance rather than observing rainfall. Quantification of the uncertainties for measurements that are non-optimal in the first place is essential to assure usability of the data. In this work we address modeling of instrumental impairments, i.e. signal variability due to antenna wetting, baseline attenuation uncertainty and digital quantization, as well as environmental ones, i.e. variability of drop size distribution along a link affecting accuracy of path-averaged rainfall measurement and spatial variability of rainfall in the link's neighborhood affecting the accuracy of rainfall estimation out of the link path. Expressions for root mean squared error (RMSE) for estimates of path-averaged and point rainfall have been derived. To verify the RMSE expressions quantitatively, path-averaged measurements from 21 operational communication links in 12 different locations have been compared to records of five nearby rain gauges over three rainstorm events. The experiments show that the prediction accuracy is above 90% for temporal accumulation less than 30 min and lowers for longer accumulation intervals. Spatial variability in the vicinity of the link, baseline attenuation uncertainty and, possibly, suboptimality of wet antenna attenuation model are the major sources of link-gauge discrepancies. In addition, the dependence of the optimal coefficients of a conventional wet antenna attenuation model on spatial rainfall variability and, accordingly, link length has been shown. The expressions for RMSE of the path-averaged rainfall estimates can be useful for integration of measurements from multiple heterogeneous links into data assimilation algorithms.

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Artem Zinevich

Papers

Environmental Monitoring by Wireless Communication Networks

Rain Rate Estimation Using Measurements From Commercial Telecommunications Links

Estimation of rainfall fields using commercial microwave communication networks of variable density

Frontal Rainfall Observation by a Commercial Microwave Communication Network

Prediction of rainfall intensity measurement errors using commercial microwave communication links