Towards the provision of site specific flood warnings using wireless sensor networks

Smith, Paul J.; Hughes, Danny; Beven, Keith; Cross, Philip; Tych, Włodek; Coulson, Geoff; Blair, Gordon S.

doi:10.1002/met.130

Cited by 30 publications

(24 citation statements)

References 19 publications

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“…This approach relies on the rationale that the merging of model results and remote sensing observations, both subject to considerable uncertainty, yields the best possible model analyses and eventually better predictions. Schumann et al (2009) argue that the lack of maturity of processing chains needed to extract hydraulically relevant information from remote sensing data explains why, to date, only point measurements of river stage and discharge are routinely assimilated in hydraulic models (Madsen and Skotner, 2005;Neal et al, 2007;Smith et al, 2009). Data assimilation studies investigating the usefulness of remote sensing derived water level measurements are rare.…”

Section: Introductionmentioning

confidence: 99%

Towards the sequential assimilation of SAR-derived water stages into hydraulic models using the Particle Filter: proof of concept

Matgen

Montanari

Hostache

et al. 2010

Hydrol. Earth Syst. Sci.

141

108

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Abstract. With the onset of new satellite radar constellations (e.g. Sentinel-1) and advances in computational science (e.g. grid computing) enabling the supply and processing of multimission satellite data at a temporal frequency that is compatible with real-time flood forecasting requirements, this study presents a new concept for the sequential assimilation of Synthetic Aperture Radar (SAR)-derived water stages into coupled hydrologic-hydraulic models. The proposed methodology consists of adjusting storages and fluxes simulated by a coupled hydrologic-hydraulic model using a Particle Filterbased data assimilation scheme. Synthetic observations of water levels, representing satellite measurements, are assimilated into the coupled model in order to investigate the performance of the proposed assimilation scheme as a function of both accuracy and frequency of water level observations. The use of the Particle Filter provides flexibility regarding the form of the probability densities of both model simulations and remote sensing observations. We illustrate the potential of the proposed methodology using a twin experiment over a widely studied river reach located in the Grand-Duchy of Luxembourg. The study demonstrates that the Particle Filter algorithm leads to significant uncertainty reduction of water level and discharge at the time step of assimilation. However, updating the storages of the model only improves the model forecast over a very short time horizon. A more effective way of updating thus consists in adjusting both states and inputs. The proposed methodology, which consists in updatCorrespondence to: P. Matgen (matgen@lippmann.lu) ing the biased forcing of the hydraulic model using information on model errors that is inferred from satellite observations, enables persistent model improvement. The present schedule of satellite radar missions is such that it is likely that there will be continuity for SAR-based operational water management services. This research contributes to evolve reactive flood management into systematic or quasi-systematic SAR-based flood monitoring services.

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Section: Introductionmentioning

confidence: 99%

Towards the sequential assimilation of SAR-derived water stages into hydraulic models using the Particle Filter: proof of concept

Matgen

Montanari

Hostache

et al. 2010

Hydrol. Earth Syst. Sci.

141

108

View full text Add to dashboard Cite

show abstract

“…Data-based Mechanistic (DBM) modelling is a methodological approach to identifying and estimating parsimonious representations of time-series data (Smith et al, 2009;Ratto et al, 2007;Young, 2006). DBM models are defined inductively.…”

Section: Dbm -Data Based Mechanistic Flash Flood Forecasting Modelmentioning

confidence: 99%

A staggered approach to flash flood forecasting – case study in the Cévennes region

et al. 2011

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Abstract.A staggered approach to flash flood forecasting is developed within the IMPRINTS project (FP7-ENV-2008-1-226555). Instead of a single solution system, a chain of different models and input data is being proposed that act in sequence and provide decision makers with information of increasing accuracy in localization and magnitude as the events approach. The first system in the chain is developed by adapting methodologies of the European Flood Alert System (EFAS) to forecast flash floods and has the potential to provide early indication for probability of flash floods at the European scale. The last system in the chain is an adaptation of the data based mechanistic model (DBM) to probabilistic numerical weather predictions (NWP) and observed rainfall, with the capability to forecast river levels up to 12 h ahead. The potential of both systems to provide complementary information is illustrated for a flash flood event occurred on 2 November 2008 in the Cévennes region in France. Results show that the uncertainty in meteorological forecasts largely affects the outcomes. However, at an early stage, uncertain results are still valuable to decision makers, as they raise preparedness towards prompt actions to be taken.

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“…There are a large number of sensors and sensor network technologies that can measure the environment at specific points in time, over periods of observation, across a range of physical locations. The environment can be measured as part of day-to-day operation (for example, air conditioning units) or to observe disasters such as flooding of rivers [16] and oil spills [17].…”

Section: Introductionmentioning

confidence: 99%

Enabling commodity environmental sensor networks using multi-attribute combinatorial marketplaces

Lee

Alrawahi

Toohey

2013

2013 19th Asia-Pacific Conference on Communications (APCC)

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Abstract-Large scale distributed e-infrastructures are emerging as commodity resource platforms. The next generation of commodity e-infrastructures will encapsulate the physical or tangible world by integrating ubiquitous sensors. Cheap environmental and physiological sensors are being increasingly deployed by many commercial organisations. The process of discovering and accessing commercially available resources requires a market for providers and consumers to trade these resources. This paper argues that developing a market will encourage the commoditisation of environmental sensor networks. It presents an overall architecture and adopts algorithms to support the trading of commodity environmental sensor networks.

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Towards the provision of site specific flood warnings using wireless sensor networks

Cited by 30 publications

References 19 publications

Towards the sequential assimilation of SAR-derived water stages into hydraulic models using the Particle Filter: proof of concept

Towards the sequential assimilation of SAR-derived water stages into hydraulic models using the Particle Filter: proof of concept

A staggered approach to flash flood forecasting – case study in the Cévennes region

Enabling commodity environmental sensor networks using multi-attribute combinatorial marketplaces

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