Informal and poor neighbourhoods in cities in developing countries, are often located in flood prone areas. This is particularly the case for Dar Es Salaam – Tanzania. The Tanzanian Red Cross Society has a volunteering network for relief actions, but there is no timely information about flood hazards and no means of communication to act upon them. Forecasting systems and monitoring are to expensive to maintain and use locally. FloodTags develops software and methods to use social media as a means to monitor floods as they appear, and to actively and passively involve the local community in the forecasting process. The result is being served to the audience through the web. In past projects (in particular TKI project DEL018), Deltares made a connection between early warning systems using Delft-FEWS, en this social media harvested information, and to convert flood locations into probabilistic flood maps. The result is a method to prepare real-time flood maps from social media. This method is succesfully being applied in the Phillippines (see TKI project DEL018). In this way, provision of information during floods becomes feasible, community-based and cost effective, which may lead to rapid and tailored relief actions after a flood.

These innovations make it feasible to strongly involve local communities in the forecast process, and activate them for response. The question is: can the developed technology also be used to activate them before an event? A prerequisite is that good quality models must be available to predict and event's impact, before the event strikes. Good quality models, predicting flood genesis and impacts are usually not available, because these require long historical records of meteorological conditions (rainfall and other parameters), hydrological data (water levels and rated flows) and impact information (amount of affected houses/households, damage, casualties). Furthermore, a cascade of hydrological, hydraulic, flood iniundation and impact models is likely to result in a cascade of uncertainties as well. Hence, the accurate simulation of impacts is very much dependent on local quality of flood models, quality of hydrometeorological forcing, as well as exposure data on presence of infrastructure, houses, and population in potentially affected areas. 


In this project, we will establish and apply an impact-based forecast model for Tanzania, and specifically Dar Es Salaam. Other than traditional approaches, we will circumvent the requirement of a hydrology-hydraulic-flood-impact (4 models in total) model cascade, but instead use data-mined relationships between monitored events and impacts in public and social media, and large amounts of globally available hydro-meteorological Earth Observation data, established in the Deltares-led Earth2Observe project. Hence, the innovation in this project is not operational monitoring and flood mapping, but derivation of impact models using historical media records and data on hydro-meteorology, and applying these in an operational setting for predictions and early warning. During the project we will assess at what scale (provincial/country/neighbourhood) we can establish and apply such impact models.

By using social and public media (instead of a physical impact model cascade, requiring enormous amounts of local and accurate data) and globally covering hydro-meteorological data, we can in principle establish a first-order impact-forecasting system anywhere in the world, providing insight in how many people and assets may be affected by floods in the forthcoming time (hours/days). Hence when this project succeeds, the result will be a scalable and innovative method to establish impact models in data poor areas. This is in stark contrast with traditional, more physically based models, requiring very accurate data on the drainage system, terrain and exposed people, houses, roads and other objects. 

We will develop and apply this for Tanzania. The found relationships will form the basis for a first impact-based forecasting system and dashboard, aimed to improve the forecast capabilities of the Tanzania Meteorological Agency, and to further support the Disaster Management Department of the Tanzania Red Cross Society (TRCS). The relationships found can also be used to establish and validate more physically based models at a later stage. This is part of an ongoing application for Partners for Water. With this project, we connect to the challenge in the Water and ICT KIC (translating large amounts of data of different quality to useful information aimed for decision-making for complex questions with many stakeholders). Besides this we contribute to the challenge that earth observation data should be used more appropriately. 



The inception phase of the project included a design workshop, to identify the needs and product requirements for two products, the media monitoring dashboard (this part is co-funded by GFDRR-DFID Challenge Fund) and the impact-forecasting product, envisaged in this TKI project. The design workshop was used. The consortium visited the Tanzania Meteorological Agency (contact persoon Hellen Msemo), the Tanzania Red Cross Society (contact persons Nyambiri Kimacha and Renatus Mkaruka) and the local World Bank office (contact person Edward Anderson). We established a flow of information and then prepared mockups to support these.

Flow of events

The most important consideration in the flow of information is that we have to be aware where the mandate for forecasting and early warning lies, and where the mandate for response. Early warning (whether related to meteorology, severe weather, floods or impacts, expected from floods) is done by TMA, and early warnings issued by any other agency or actor is an illegal act. Therefore, any early warning made should either be issued by TMA, or it should be kept completely internal with the Red Cross and only used for internal considerations.

There are 4 actors involved in the process of using the impact forecast table. These are:

  • TMA: The Tanzania Meteorological Agency, responsible for delivering severe weather warnings

  • TRCS HQ: The Tanzania Red Cross Society Head Quarters, responsible for alerting the TRCS branch offices and coordinating relief actions

  • TRCS BO: the Tanzania Red Cross Society Branch Office of a specific district, responsible for coordination of the volunteering network on the ground in the given district

  • TRCS Volunteers: the district level volunteers that operationally provide and distribute relief on the ground

Below, the basic flow of information, foreseen with the product is described. There is a relationship with OP1, the social media dashboard.

  1. TMA runs their numerical weather prediction model operationally. Rainfall amounts are accumulated per district and per day.

  2. If expected rainfall in the forthcoming 24 hours exceeds 50 mm, a severe warning is sent to DMD, Red Cross and communicated through media. These communications consist of the severe weather warning, as well as a recommendation to individuals living in the region (such as “individuals living near flood zones are recommended to move to high grounds”). If expected rainfall in the forthcoming 24 hours exceeds only 20 mm such severe warnings are sent for those regions that have been dry or are likely already saturated.

  3. Both TMA and TRCS HQ have access to the digital dashboard and are able to insert a given forecast rainfall amount for a given province, resulting in a highlighted expected impact for that province region. TMA can use the expected impacts to improve their severe weather warning. For the time being we will stick to the same thresholds as used by TMA (20 mm and 50mm, as well ass dry, moderate and wet conditions) to ensure that our method connects the most to the currently used practices.

  4. When this occurs, TMA sends a report in PDF form to TRCS HQ, containing the severe weather warning as well as recommended actions for individuals. With the impact warning table, TMA can also add the expected impacts to the PDF.

  5. TRCS HQ takes the impact table for the affected district and looks up what the expected impact will be given the forecast rainfall amount. Either the digital version or the lookup table can be used. At the moment, the information is deterministic, and therefore our impact table also assumes a deterministic impact forecast only. In the future, when TMA considers using probabilistic forecasts, we could adapt this to accommodate probabilistic forecasts as well.

  6. Two options are possible

    1. If the impact table indicates that there is no impact to be expected; do nothing

    2. If the impact table indicates an impact (low, medium or severe) is expected; contact the affected branch office actively by phone.

  7. The affected branch office receives the TMA weather warning from HQ and also reads the impact table and decides upon an action. What the level of impact is per severity level, and which preparatory actions should be connected to the expected impacts should be defined with TRCS during the preparation of the impact tables. The actions are likely to be of a preparatory nature. An example is provided below:

    1. low: send a whatsapp message to the volunteers to be on the alert, ensure their phones are powered enough to ensure communication channels are open upon an event.

    2. medium: do the same as low and start monitoring the dashboard actively (if internet is available)

    3. severe: start to take preventive actions. In particular ensure relief places (schools, churches, hospitals, police stations) are aware of the potential forthcoming situation, and ensure these are protected as much as possible. Alert the neighbourhood ward manager.

These are example actions and will be extended in interaction with TRCS while developing the impact tables.

8.Branch officers stay in touch with HQ about the situation. When impacts are experienced on the ground, HQ starts monitoring the dashboard and where necessary coordinate relief actions using their available materials (inception report: cars, mattresses, blankets, mosquito nets, buckets).


The requirements led to a mockup of the impact table. We are currently pursuing both a "paper" form impact table and a very simple to understand dashboard. The paper table can be read without a computer by any field officer or regional red cross office. 

The dashboard is more advanced and can be read by the Tanzania Meteorological Agency. At the moment we are implementing this using GFS forecasts as a placeholder, but we plan to ensure that the forecasts that TMA produces will be used instead. This would make the dashboard directly connecting to TMA's already ongoing practices. 



  • Design inception mission Dar Es Salaam with the parties involved, these include the Tanzania Meteorological Agency and Tanzania Red Cross Society.
  • Set up of Delft-FEWS environment, standard data feeds and media monitoring dashboard / SMS warnings
  • Review of methods and datasets in Earth2Observe and other reanalysis projects - result: list of historical meteorological/hydrological time series potentially useful for impact forecasting
  • Review of online media data availability, setup of queries and access to repositories. The result is an online media dataset of public media based flood conditions and impacts in Tanzania / Dar Es Salaam (including geolocations at provincial, city or neighbourhood scale)


  • Development of a correlation method to relate hydrometeorological thresholds in Earth2Observe data to events, found in historical media-based database. 
  • Applying method in Dar Es Salaam and 1 other region in Tanzania, selected with visited groups during the inception mission
  • Setup of data feeds for impacts models in Delft-FEWS
  • Monitoring and testing period (monitoring real events)


Questions about this project, possible applications elsewhere and related new applications, can be directed to:

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