Flood Forecasting and Warning

EXAMPLE: Early warning system in Sogn og Fjordane (NOR)

nst — Wed, 25 Jan 2017 15:39:06 +0000

EXAMPLE: Early warning system in Sogn og Fjordane (NOR) nst Wed, 01/25/2017 - 16:39

Riverine or slow rise floods

Flash floods

Estuarine floods

Coastal floods or storm surges

Urban floods

Flood Forecasting and Warning

Non-structural measure

The county of Sogn og Fjordane frequently experiences avalanches and landslides, storm surges and flooding. A demonstration project explored the potential for an effective, reliable and cost-efficient early warning system that has a multi-hazard approach and makes use of location and population-based communication technologies, such as mobile phones, as well as social media such as Facebook and Twitter. The system was tested with a sample warning followed by a survey and data analysis to judge its efficacy.

Based on information from the Climate-ADAPT website.

General description

Sogn og Fjordane is a coastal, mountainous region of Norway that boasts hundreds of thousands of tourist visits annually. Several communities in Sogn og Fjordane are facing numerous hazards such as flooding, avalanches, rock slides and other extreme weather events, that might be exacerbated by climate change. To respond to the challenge an early warning system was developed and tested within a EU research project. The multi-hazard warning system aimed at optimising rescue and other emergency services provided by the county. Due to tourism, it aims to be a cost-effective method reaching all people in the geographic area and not only residents.

A public warning exercise was carried out in 2010 with 2,500 mobile phones receiving the alert as text message and 322 fixed line phones in Aurland received the alert as voice message. The warning exercise was visible on Facebook for 2 hours and received 201,849 viewings. A post-exercise survey was carried out online and a door-to-door survey was conducted in parts of the area to assess the public’s thoughts on the exercise. The population warning exercise was evaluated to measure the efficiency of the warning system by combining an electronic evaluation form and a door-to-door survey.

Key lessons learnt

The project demonstrated how an existing county-encompassing organization could be used to issue the population warning. While the technical aspects of people-centred warning systems are at large readily available, issues concerning confidentiality legislation and system regulations must be solved before successfully implementing efficient location-based warning systems. In order to use social media during crisis situations, the projected concluded that research is needed.

Relevant case studies and examples

Early warning systems

Measure category

Preparedness

Flood and hazard forecasting

giacomo.cazzola — Thu, 15 Sep 2016 12:13:22 +0000

Flood and hazard forecasting giacomo.cazzola Thu, 09/15/2016 - 14:13

Riverine or slow rise floods

Flash floods

Estuarine floods

Coastal floods or storm surges

Urban floods

Flood Forecasting and Warning

Non-structural measure

Flood forecasting is an essential tool for providing people still exposed to risk with advance notice of flooding, in an effort to save life and property.

Based on: Jha, Abhas K., Robin Bloch, and Jessica Lamond. Cities and Flooding: A Guide to Integrated Urban Flood Risk Management for the 21st Century. World Bank Publications, 2012.

Different flood forecasting service models exist based on the needs of end users: a system may be developed for the public or strictly dedicated to the authorities. There is no single consistent approach worldwide but the basic principles of a good warning system are shared by all. These comprise:

Better detection in times of need well before the actual event occurs
Interpretation of the detected phenomena and forecasting this to the areas likely to be affected
Dissemination of the warning message to the relevant authorities and public via the media and other communication systems.

The fourth and final aspect is to encourage the appropriate response by the recipients by preparing for the upcoming event. This can be improved through flood response planning by people at risk and their support groups.

Uncertainty in flood forecasting

Models, by definition, are approximations of reality. As described earlier, all models suffer from a certain level of approximation or uncertainty in spite of powerful computing systems, data storage and high level technologies. Decision makers have to consider the effects of uncertainties in their decision-making process. Errors in forecasting of an event, for example stage or time of arrival, may lead to under-preparation (at the cost of otherwise avoidable damage) or over-preparation (resulting in unnecessary anxiety). The balance between failure to warn adequately in advance and the corrosive effects of too many false alarms must be carefully managed.

The reliability of flood forecasting models relies on the quantification of uncertainty. All natural hazards are uncertain. The various sources that give rise to uncertainty in forecasting and early warning can be classified (Maskey. 2004) as:

Model Uncertainty
Parameter Uncertainty
Input Uncertainty
Natural and Operational Uncertainty.

It is necessary to gain a better understanding of the options available to deal with the uncertainties within the system arising from these different sources.

In order to produce a forecast, the initial conditions are typically determined by means of observations from rain gauges; these may, however, be unevenly spaced throughout the catchment, leading to uncertainty as to the total volume of rainfall. Where hydrologically important areas (such as steep slopes) are unrepresented, the model may utilize an interpolation method (introducing another element of uncertainty) in order to estimate run-off volume and peak flows. More sophisticated modeling can address these issues, but this in turn may demand high processing speeds and lengthy run-times.

To offset some of this uncertainty, operational flood forecasting systems are moving towards Hydrological Ensemble Prediction Systems (HEPS), which are now the ‘state of the art’ in forecasting science (Schaake et al. 2006; Thielen et al. 2008). This method formed part of initiatives such as HEPEX (Hydrological Ensemble Prediction EXperiment) which investigated how best to produce, communicate and use hydrologic ensemble forecasts for short, medium and long-term predictions. Despite its demonstrated advantages the use of this system is still limited: it has been installed on an experimental basis in France, Germany, Czech Republic and Hungary.

To deal with the uncertainty in spatio-temporal distribution and prediction of rainfall for extreme events, especially through radar derived data, a promising approach has been to combine stochastic simulation and detailed knowledge of radar error structure (Germann et al. 2006a, 2006b, 2009; Rossa et al. 2010). Radar ensembles have the potential benefits of increasing the time for warning especially for flash floods (Zappa et al. 2008). Advanced techniques, such as disdrometer networks (equipment capable of measuring the drop size, distribution and velocity of different kinds of precipitation) and LIDARs are being used to capture small scale rainfall phenomenon, whilst satellite remote sensing is more appropriate for regional and global level applications. A combination of all these methods and blending information is considered to be the most promising way forward.

There are a several useful examples of such systems:

DELFT-FEWS: one of the state of the art hydrological forecasting and warning systems developed by Deltares. This system is an integration of a number of sophisticated modules specialized in their individual capacities and the system is highly configurable and versatile. The system can be used as a standalone environment, or it can be used as a compliant client server application. Through its advanced modular system FEWS has managed to reduce the challenges like handling and integration of large datasets to a considerable extent.
Automated Local Evaluation in Real Time (ALERT) is the method used within the AUG member states to transmit data and information using remote sensors for warning against flash floods.
Central America Flash Flood Guidance is an example of regional flash flood warning. The national Hydrologic Warning Council (NHWC) has member countries across North America and many parts around the world; it is also a major organization in data dissemination for early warning for flood events.
The Mekong River Commission flood forecasting system, discussed above, has been operating since 1970. It is an integrated system which provides timely forecasting to its member countries. It consists of three main systems of data collection and transmission, forecast operation and information dissemination at both national and regional level.
The Southern African regional model for flood forecasting Stream Flow Model (SFM) has been applied after the Mozambique flood in 2000. The USGS along with Earth Resource Observation System (EROS) supports monitoring and modeling capacities of Southern African Countries.
Regional Water Authority of Mozambique (ARA-Sul) is responsible for issuing flood warning and real time forecasting. The system is operational in Southern Africa with a mean area of 3,500 square kilometers. A simplified flood warning system, the Mozambique Flood Warning Project, is specially tailored to the needs of the local population. It also involves the local people and trains them to install, monitor and maintain the structures.
Hydro Met Emergency Flood Recovery Project is used in Poland.
Bhutan’s Glacial Lake Outburst Flood (GLOFs) Iridium Satellite Communications is used as the telemetry back-bone for Bhutan’s GLOF Early Warning Project.
In the Toronto region of Canada, the Toronto and Region Conservation Authority (TRCA) flood forecasting and warning system is used; this is a scalable flood warning system including web-based data and video for nine watersheds.
The Automatic Dam Data acquisition and alarm reporting system, is the Puerto Rican System to obtain, monitor and analyze, in real- time, critical safety parameters such as inflows, outflows, gate openings and lake elevations for 29 principal reservoirs
Central Water Commission (CWC) in India provides the Turnkey Flood forecasting system across 14 states having 168 remote sites in six river basins.

Literature sources

Maskey, S., Guinot, V. and Price, R.K. 2004. “Treatment of precipitation uncertainty in rainfall-runoff modeling: a fuzzy set approach.” Advances in Water Resources 27 (9): 889-98.

Schaake, J., Franz, K., Bradley, A., and Buizza, R. 2006. “The Hydrological Ensemble Prediction Experiment (HEPEX).” Hydrological and Earth System Sciences Discussions 3: 3321–32.

Thielen, J., Schaake, J., Hartman, R. and Buizza, R. 2008. “Aims, challenges and progress of the hydrological ensemble prediction experiment (HEPEX) following the third HEPEX workshop held in Stres 27-29 June 2007.” Atmospheric Science Letters 9: 29-35.

Germann, U., Berenguer, M., Sempere-Torres, D., and Salvadè, G. 2006a. “Ensemble radar precipitation estimation — a new topic on the radar horizon.” Proceedings of the 4th European Conference on Radar in Meteorology and Hydrology (ERAD). Barcelona. September 18–22, 2006. 559–62.

Germann U., Galli, G., Boscacci, M, and Bolliger M. 2006b. “Radar precipitation measurement in a mountainous region.” Quarterly Journal Royal Meteorological Society 132: 1669–92.

Germann, U., Berenguer, M., Sempere-Torres, D., and Zappa, M. 2009. “REAL — Ensemble radar precipitation estimation for hydrology in a mountainous region.” Quarterly Journal Royal Meteorological Society 135: 445–56.

Rossa, A. M., Cenzon, G. and Monai, M. 2010. “Quantitative comparison of radar QPE to rain gauges for the 26 September 2007 Venice Mestre fl ood.” Natural Hazards and Earth System Science 10 (2): 371–7.

Zappa, M., Rotach, M.W., Arpagaus, M., Dorninger, M., Hegg, C., Montani, A., Ranzi, R., Ament, F., Germann, U., Grossi, G., Jaun, S., Rossa, A., Vogt, S., Walser, A., Wehrhan, J., and Wunram, C. 2008. “MAP D-PHASE: Real-time demonstration of hydrological ensemble prediction systems.” Atmospheric Science Letters 2: 80–7.

Measure category

Preparedness

Early warning systems

giacomo.cazzola — Thu, 15 Sep 2016 11:06:41 +0000

Early warning systems giacomo.cazzola Thu, 09/15/2016 - 13:06

Riverine or slow rise floods

Flash floods

Estuarine floods

Coastal floods or storm surges

Urban floods

Flood Forecasting and Warning

Non-structural measure

The purpose of early warning systems (EWS) is simple. They exist to give advance notice of an impending flood, allowing emergency plans to be put into action. EWS, when used appropriately, can save lives and reduce other adverse impacts.

Based on: Jha, Abhas K., Robin Bloch, and Jessica Lamond. Cities and Flooding: A Guide to Integrated Urban Flood Risk Management for the 21st Century. World Bank Publications, 2012.

Warning systems can be used to alert relevant authorities or the public or both. The scale of a warning system can be national, based on a river basin, or local and run by volunteers. Most are stand-alone national operations, but warning systems have been developed covering several international rivers, such as the Rhine, Danube, Elbe and Mosel in Europe, the Mekong, Indus and Ganges-Brahmaputra-Meghna basins in Asia and the Zambezi in Southern Africa (United Nations 2006). However, the utility of EWS is crucially dependent on the underlying forecasting system, the quality of emergency plans and the level of preparedness of the community at risk. The quality of forecasting is also dependent on the nature of the hazard. Warning systems related to river flooding have a longer lead time than those for cyclonic events; seismic induced tsunamis may have very short warning periods. Forecasting flash flooding is also very problematic; this has implications for developing nations which are more exposed to such risks, due to the prevalence of monsoon type flooding. Whilst there is general agreement about the desirability of EWS, the implementation of such a system is necessarily subject to local factors.

Essentials for an effective EWS

The four main essentials for any flood warning system are:

Detection of the conditions likely to lead to potential flooding, such as intense rainfall, prolonged rainfall, storms or snowmelt
Forecasting how those conditions will translate into flood hazards using modeling systems, pre-prepared scenarios or historical comparisons
Warning via messages developed to be both locality- and recipient-relevant and broadcasting these warnings as appropriate
Response to the actions of those who receive the warnings based on specific instructions or pre-prepared emergency plans

Failure in any one of the four key elements of an EWS will lead to a lack of effectiveness. Inaccurate forecasts may lead to populations ignoring warnings issued subsequently.

The lack of clear warning and instruction may have resulted, for example, in the deaths of people escaping the Big Thompson Canyon flood in the US in the 1970s. Without clear instructions many people were killed trying to drive out of the canyon rather than taking the safer option of abandoning cars and climbing to higher ground.

Finally, the case of Hurricane Katrina demonstrated the scenario where clear advanced warnings failed to protect the population because the evacuation planning was inadequate.

Organizational aspects of flood warning dissemination

There are multiple communication channels by which a flood warning may be broadcast and the choice of media will vary depending on the intended recipients. It is also essential to consider the use of media that will be robust to the impacts of a flood.

The most successful warning services use a combination of media, ideally with consistent messages and timescales, as well as the response the message hopes to initiate. For example, an individuals whose home is likely to be flooded will probably react best to a personal message either via phone, fax or in person; people who should avoid travelling to or through an affected area may prefer a news bulletin backed up by an internet or press map of the affected area.

Costs and resources

Setting up a warning system may be a low cost option for countries and is often seen as the first line of defense for that reason. The cost will be lowest in nations with existing and adequate forecasting and monitoring services. In this case the setting up of a warning center can be a very low cost process and this can be quickly established during consultation and stakeholder identification.

Setting up adequate forecasting and monitoring serviced can require much larger investments in expertise, software and hardware for modeling and monitoring equipment. The lead time to establish forecasts of the required reliability and timeliness may be a deterrent.

Key lessons learnt

Once established the service will require continuous investment in manpower, data and other resources in order to be functionally useful. Recruitment and retention of qualified personnel, continuity of funds and operations and maintenance of monitoring, modeling and dissemination equipment can be key challenges in the long term sustainability of systems. This can be particularly acute for low frequency events.

Relevant case studies and examples

EXAMPLE: Early warning system in Sogn og Fjordane (NOR)

Literature sources

United Nations. 2006. Global survey of early warning systems. UN.

Measure category

Preparedness