An overview of the applications for early warning and mapping of the flood events in New Brunswick

Assessment of Flood Early Warning Systems with Social Media

10.5194/egusphere-egu21-8816 ◽

2021 ◽

Author(s):

Thierry Hohmann ◽

Judit Lienert ◽

Jafet Andersson ◽

Darcy Molnar ◽

Peter Molnar ◽

...

Keyword(s):

Social Media ◽

Performance Assessment ◽

Early Warning ◽

Early Warning Systems ◽

Probability Of Detection ◽

Economic Losses ◽

Warning Systems ◽

Flood Events ◽

Spatial Coverage ◽

De Bruijn

IntroductionFlood early warning systems (FEWS) can reduce casualties and economic losses (UNEP, 2012). The EC Horizon 2020 project FANFAR (www.fanfar.eu) aims to co-develop a FEWS in West Africa together with stakeholders, predicting streamflow and return period threshold exceedance (Andersson et al., 2020). A Multi-Criteria Decision Analysis (MCDA) indicated, that stakeholders find information accuracy especially important, among a broad set of fundamental objectives (Lienert et al., 2020). Social media have the potential to support accuracy assessment by detecting flood events (Lorini et al., 2019; de Bruijn et al., 2019) due to their large spatial coverage (Restrepo-Estrada et al., 2018). We investigated the potential of social media to assess FANFAR forecast accuracy.&#160;Research ApproachFANFAR forecasts are based on HYPE, which is a semi-distributed land-cover and sub-catchment based hydrological model (Arheimer et al., 2020). We lumped the forecasted flood risk (FFR) on a country scale and compared it to flood events detected on Twitter, using an algorithm (FEDA) developed by de Bruijn et al. (2019). FEDA detects flood-related tweet bursts based on regionally and temporally adjusted thresholds (de Bruijn et al., 2019). We compared FEDA detected events with floods from the disaster database EM-DAT (https://www.emdat.be/), to find if tweets indicate flooding. We also compared FEDA to the lumped FFR to identify false positives (FP), false negatives (FN), and true positives (TP), from which we deduced the probability of detection (POD) and false alarm rate (FAR). We further calculated the correlation of single flood-related tweets with the lumped FFR and investigated seasonality, lag, and the influence of rainfall.&#160;FindingsThe detailed findings are described in Hohmann (2021). FEDA (i.e., tweets) and EM-DAT events (i.e., floods) mostly occurred in the same period. However, FEDA detected shorter and more frequent events than EM-DAT. In the Upper Niger, PODFEDA and FARFEDA (deduced from FEDA) were of similar order of magnitude as the PODS and FARS (deduced from streamflow) but were different in the Lower Niger region. This suggests that tweets can be employed additionally to e.g. streamflow timeseries as a complementary way to evaluate accuracy. Correlation analysis between single flood-related tweets and the lumped FFR showed no relationship. We also did not find a systematic influence of seasonality or a lagged response between tweets and FFR. The correlation coefficients between tweets and rainfall ranged from 0.1-0.9, but were mostly non-significant. This suggests that a performance assessment based on single tweets is not (yet) adequate. Also, since FEDA does not differentiate between pluvial and fluvial floods, it is less suited to assess the accuracy of FANFAR. Our findings suggest the need for inclusion of other factors into the performance assessment of FEWSs, such as regional thresholds to identify TP, FP, and FN. Also, rainfall causing pluvial flooding must be considered. Finally, our approach is limited to Twitter. Further research should assess the potential of e.g. Facebook to be included in FEWS performance assessment. The question whether social media, FEWSs, or EM-DAT are correct remains, and is in our opinion best addressed by employing multiple data sources.

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Early Warning And On-Line Mapping For Flood Events

Geoscience and Remote Sensing New Achievements ◽

10.5772/9097 ◽

2010 ◽

Cited By ~ 1

Author(s):

D. Mioc ◽

B. Nickerson ◽

F. Anton ◽

E. MacGillivray ◽

A. Morton ◽

...

Keyword(s):

Early Warning ◽

Flood Events ◽

On Line

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An Early Warning System for Flood Detection Using Critical Slowing Down

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17176131 ◽

2020 ◽

Vol 17 (17) ◽

pp. 6131

Author(s):

Syed Mohamad Sadiq Syed Musa ◽

Mohd Salmi Md Noorani ◽

Fatimah Abdul Razak ◽

Munira Ismail ◽

Mohd Almie Alias ◽

...

Keyword(s):

Time Series ◽

Early Warning ◽

Early Warning System ◽

Warning System ◽

Warning Signal ◽

Quantile Estimation ◽

Flood Events ◽

Slowing Down ◽

Level Data ◽

Water Level Data

The theory of critical slowing down (CSD) suggests an increasing pattern in the time series of CSD indicators near catastrophic events. This theory has been successfully used as a generic indicator of early warning signals in various fields, including climate research. In this paper, we present an application of CSD on water level data with the aim of producing an early warning signal for floods. To achieve this, we inspect the trend of CSD indicators using quantile estimation instead of using the standard method of Kendall’s tau rank correlation, which we found is inconsistent for our data set. For our flood early warning system (FLEWS), quantile estimation is used to provide thresholds to extract the dates associated with significant increases on the time series of the CSD indicators. We apply CSD theory on water level data of Kelantan River and found that it is a reliable technique to produce a FLEWS as it demonstrates an increasing pattern near the flood events. We then apply quantile estimation on the time series of CSD indicators and we manage to establish an early warning signal for ten of the twelve flood events. The other two events are detected on the first day of the flood.

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Retrospective Analysis of Recent Flood Events With Persistent High Surface Runoff From Hydrological Modelling

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-8-359-2014 ◽

2014 ◽

Vol XL-8 ◽

pp. 359-365 ◽

Cited By ~ 1

Author(s):

S. Joshi ◽

K. Abdul Hakeem ◽

P. V. Raju ◽

V. V. Rao ◽

A. Yadav ◽

...

Keyword(s):

Land Cover ◽

Early Warning ◽

Surface Runoff ◽

Hydrological Model ◽

High Surface ◽

Early Warning Systems ◽

Flood Forecasting ◽

Flood Events ◽

High Runoff ◽

Flood Maps

Floods are one of the most common and widespread disasters in India, with an estimated 40Mha of land prone to this natural disaster (National Flood Commission, India). Significant loss of property, infrastructure, livestock, public utilities resulting in large economic losses due to floods are recurrent every year in many parts of India. Flood forecasting and early warning is widely recognized and adopted as non-structural measure to lower the damages caused by the flood events. Estimating the rainfall excess that results into excessive river flow is preliminary effort in riverine flood estimation. Flood forecasting models are in general, are event based and do not fully account for successive and persistent excessive surface runoff conditions. Successive high rainfall events result in saturated soil moisture conditions, favourable for high surface runoff conditions. The present study is to explore the usefulness of hydrological model derived surface runoff, running on continuous times-step, to relate to the occurrence of flood inundation due to persistent and successive high surface runoff conditions. Variable Infiltration Capacity (VIC), a macro-scale hydrological model, was used to simulate daily runoff at systematic grid level incorporating daily meteorological data and land cover data. VIC is a physically based, semi-distributed macroscale hydrological model that represents surface and subsurface hydrologic process on spatially distributed grid cell. It explicitly represents sub-grid heterogeneity in land cover classes, taking their phenological changes into account. In this study, the model was setup for entire India using geo-spatial data available from multiple sources (NRSC, NBSS&LUP, NOAA, and IMD) and was calibrated with river discharge data from CWC at selected river basins. Using the grid-wise surface runoff estimates from the model, an algorithm was developed through a set of thresholds of successive high runoff values in order to identify grids/locations with probable flooding conditions. These thresholds were refined through iterative process by comparing with satellite data derived flood maps of 2013 and 2014 monsoon season over India. India encountered many cyclonic flood events during Oct–Dec 2013, among which Phailin, Lehar, and Madi were rated to be very severe cyclonic storm. The path and intensity of these cyclonic events was very well captured by the model and areas were marked with persistent coverage of high runoff risk/flooded area. These thresholds were used to monitor floods in Jammu Kashmir during 4–5 Sep and Odisha during 8–9 Aug, 2014. The analysis indicated the need to vary the thresholds across space considering the terrain and geographical conditions. With respect to this a sub-basin wise study was made based on terrain characteristics (slope, elevation) using Aster DEM. It was found that basins with higher elevation represent higher thresholds as compared to basins with lesser elevation. The results show very promising correlation with the satellite derived flood maps. Further refinement and optimization of thresholds, varying them spatially accounting for topographic/terrain conditions, would lead to estimation of high runoff/flood risk areas for both riverine and drainage congested areas. Use of weather forecast data (NCMWRF, (GEFS/R)), etc. would enhance the scope to develop early warning systems.

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An early warning system for flash floods in hyper-arid Egypt

Natural Hazards and Earth System Science ◽

10.5194/nhess-12-443-2012 ◽

2012 ◽

Vol 12 (2) ◽

pp. 443-457 ◽

Cited By ~ 52

Author(s):

J. Cools ◽

P. Vanderkimpen ◽

G. El Afandi ◽

A. Abdelkhalek ◽

S. Fockedey ◽

...

Keyword(s):

Early Warning ◽

Early Warning System ◽

Flash Flood ◽

Temporal Distribution ◽

Warning System ◽

Field Measurements ◽

Flash Floods ◽

Data Availability ◽

Flood Events ◽

Emergency Procedures

Abstract. An early warning system (EWS) for flash floods has been developed for part of the Sinai peninsula of Egypt, an hyper-arid area confronted with limited availability of field data, limited understanding of the response of the wadi to rainfall, and a lack of correspondence between rainfall data and observed flash flood events. This paper shows that an EWS is not a "mission impossible" when confronted with large technical and scientific uncertainties and limited data availability. Firstly, the EWS has been developed and tested based on the best available information, this being quantitative data (field measurements, simulations and remote sensing images) complemented with qualitative "expert opinion" and local stakeholders' knowledge. Secondly, a set of essential parameters has been identified to be estimated or measured under data-poor conditions. These are: (1) an inventory of past significant rainfall and flash flood events, (2) the spatial and temporal distribution of the rainfall events and (3) transmission and infiltration losses and (4) thresholds for issuing warnings. Over a period of 30 yr (1979–2010), only 20 significant rain events have been measured. Nine of these resulted in a flash flood. Five flash floods were caused by regional storms and four by local convective storms. The results for the 2010 flash flood show that 90% of the total rainfall volume was lost to infiltration and transmission losses. Finally, it is discussed that the effectiveness of an EWS is only partially determined by technological performance. A strong institutional capacity is equally important, especially skilled staff to operate and maintain the system and clear communication pathways and emergency procedures in case of an upcoming disaster.

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