2018-issue 2-item 06 - Delft-FEWS
2018 issue 2-item 06
FEWS news: issue 2, 2018
Collaboratively modelling a FEWS system
Jakarta has many water issues, floods being one of the most obvious. This excellent article explains these water-related challenges of Jakarta very clearly. The population of flood prone areas has extensive experience with flood forecasts. Multiple times per year the inhabitants of Jakarta are being warned about approaching floods. In general, these forecasts are quite accurate. The reason is that the early warning is based on observed water levels at a weir upstream the river. When water level rise, the local operator informs other stations by a radio transmitter, who will warn nearby neigbourhoods. Also citizens with a radio reciever pick up the message and spread the word. They have then about 8 to 12 hours to move their belongings and seek a shelter. To get an idea how it is to live in such an area, read the interesting experiences of Roanne van der Voorst (in Dutch).
8 to 12 hours is maybe sufficient for local citizens to move their stuff, but not for the metropolitan administration to take appropriate measures. Therefore in 2012 we implemented an advanced flood forecasting system based on a high-resolution 1D2D SOBEK model and using available real-time datafeeds: Jakarta-FEWS (J-FEWS). Since then, we've learnt a lot about implementing advanced technology in a country that is rapidly developing: Apart from good functionalities and advanced technology, it is also very crucial that the technology is embedded within the organizations and stakeholders.
I often see that organisations, wanting to develop a flood forecasting systems (or any other operational system) approach it from a technical perspective: connect some datafeeds from organisations that monitor and/or provide useful data, integrate these in a hydrological model, set-up pre- and postprocessing, and maybe disseminate the results. In reality the technical integration is just a relatively (but of course essential) small aspect of development of a FEWS. Actually development and operation of a FEWS should be considered a form of collaborative modelling. In most cases, several organisations are involved that provide part of the puzzle: Weather institutes provide data about past and expected meteorological conditions, but often also have a role in flood warning. Central and decentral water management agencies monitor water systems, and want to anticipate high inflows. Disaster management agencies are the core users of flood early warnings, but interestingly also can often provide interesting data.
When these organisations collaborate, big opportunities arise:
Here in Jakarta I've learned the importance of the collaborative approach. Since 2012 gradually many data feeds stopped, there were no system updates, and due to network changes eventually the system became inaccessible. Due to the technology push approach, no one really felt responsible, or "owner" of the system. Also data providing organisations did not feel involved, and there were no arrangements for monitoring and restoring datafeeds. So currently, the Jakarta administration and its inhabitants have to rely on an accurate and reliable, but short-term forecast.
Fortunately we have a chance to set things right! March 1 we started the Joint Cooperation Program Phase 3, in which Deltares, along with our Dutch and Indonesian counterparts, cooperate on high-impact water-related activities. One of these activities is Urban Flood Forecasting, focussing on Jakarta and Upper-Citarum (Bandung). In a collaborative approach we are going to revive the system, step-by-step, and focussing on the institutional consolidation. In a collaborative modelling trajectory with our Indonesian counterparts and all involved organisations, we are going to shape a Jakarta-FEWS that is beneficial for all involved parties, and that is based on a solid cooperation. We are going to define essential datafeeds, and make sure that these are being monitored and agreements are in place on maintenance and stability. We are going to define essential outputs that align with the requirements of the end-users, where possible taking into account gender sensitivity, and have these outputs accessible all the time. Furthermore, we are going to integrate the J-FEWS in the operational organisations so that they are able to interpret the data and make full use of the forecasts. But technically, there will probably not be a lot of changes…
Those who enjoy working on the technical parts of a FEWS might not get excited about this approach. But I can tell you: there is a lot of joy to be found when a FEWS, depending on the support of multiple organisations, remains operational for a long time. This is a forecast I dare to make…