The decision making process in flood mitigation typically involves a number of factors reflecting flood severity, flood vulnerability and the cost of the mitigation measures, which implies that the decision framework needs to combine both social-economic parameters and flood extent prediction analysis. A socio-economic vulnerability index (SEVI) is developed here to represent social-economic factors and its use demonstrated within a multi-criteria decision analysis (MCDA) for assessing flood levee options for a central basin of Jakarta, Indonesia. The variables defining the SEVI are selected based on available national social-economic data reported for Indonesia with overlapping information removed using Pearson's correlation analysis. Two different methods are used to further scale the SEVI which is developed along administrative boundaries into a Net SEVI which is dependent on the predicted flood hazard as resulting from the levee plan options while capturing uncertainty in the rainfall forecasting. The MCDA technique adopted uses criteria of Net SEVI, annual expected loss, graduality and levee construction cost for analyzing six different levee plans and with uncertainty in the rainfall incorporated. The Net SEVI thus specifically reflects the social-economic impact on the flood-affected population, and this approach thereby provides a higher degree of granularity in the flood mitigation decision process. The MCDA decision framework developed is general in that the Net SEVI can be applied for consideration of other flood mitigation strategies. Here, it is shown that the inclusion of the Net SEVI criteria changes the best choice levee plan decision to a higher protection level for the basin considered.

The changing climate and the rapid urbanisation may alter flood severity and influence the decision‐making process for flood management. In this study, a Multi‐Criteria Decision Analysis (MCDA) framework for optimal decision‐making in flood protection is developed and applied to a central flood‐prone basin of Jakarta, Indonesia. Specifically, the decisions are on levees corresponding to protection under different rainfall return periods (RP), considering climate change and associated uncertainties, urbanisation, and evolving socio‐economic features of the flood plain. Three cases were studied to analyse future (year 2050) conditions (i) future rainfall/current urban, (ii) current rainfall/future urban and (iii) future rainfall/future urban. Future climate change projections from the NASA Earth Exchange are used to obtain information about changes in rainfall, whereas Landsat derived imperviousness maps along with the population projections are used for future urban conditions. Annual Expected Loss, Graduality, upgrade Construction cost and Net‐Socio‐Economic Vulnerability Index are the criteria used in the MCDA. It is found that climate change has a higher impact compared to urbanisation on the flood protection decisions. For the basin studied, the extreme future case of increased rainfall and urbanised conditions have the optimal decision in levee protection level corresponding to 250 years RP under current rainfall which corresponds to ~60 years RP under future rainfall.

The impact of changing climate on the frequency of daily rainfall extremes in Jakarta, Indonesia, is analysed and quantified. The study used three different models to assess the changes in rainfall characteristics. The first method involves the use of the weather generator LARS-WG to quantify changes between historical and future daily rainfall maxima. The second approach consists of statistically downscaling general circulation model (GCM) output based on historical empirical relationships between GCM output and station rainfall. Lastly, the study employed recent statistically downscaled global gridded rainfall projections to characterize climate change impact rainfall structure. Both annual and seasonal rainfall extremes are studied. The results show significant changes in annual maximum daily rainfall, with an average increase as high as 20% in the 100-year return period daily rainfall. The uncertainty arising from the use of different GCMs was found to be much larger than the uncertainty from the emission scenarios. Furthermore, the annual and wet seasonal analyses exhibit similar behaviors with increased future rainfall, but the dry season is not consistent across the models. The GCM uncertainty is larger in the dry season compared to annual and wet season.

Accurate building inventories are essential for city planning and disaster risk management. Traditionally generated via census or selected small surveys, these suffer from data quality and/or resolution. High-resolution satellite imagery with object segmentation provides an effective alternative, readily capturing large extents. This study develops a highly automated building extraction methodology for location-based building exposure data from high (0.5 m) resolution satellite stereo imagery. The development relied on Taipei test areas covering 13.5 km2 before application to the megacity of Jakarta. Of the captured Taipei buildings, 48.8% are at one-to-one extraction, improving to 71.9% for larger buildings with total floor area >8000 m2, and to 99% when tightly-spaced building clusters are further included. Mean absolute error in extracted footprint area is 16% for these larger buildings. The extraction parameters are tuned for Jakarta buildings using small test areas before covering Jakarta's 643 km2 with over 1.247 million buildings extracted.

No abstract