where _Ib,t_ is the intensity of the flood in basin _b_ at date _t_ , _Qb,t_ is the stream flow in the same basin at the same time and _Q_<sup> ̄</sup> _b_ and _σb_ are mean and standard deviation of stream flow in _b_ . The intensity is set to zero if the flood threshold - 95th percentile plus 1 standard deviation above the average - has not been exceeded. By normalizing, we obtain a measure that is comparable across all regions and that is independent of the absolute river flows. The assumption is that people living close to rivers will be prepared for variations in water levels, and that people living close to rivers with highly variable stream flows are more prepared for these events than people living close to more stable rivers.

To aggregate the flood impact each basin is weighted based on the nightlights in it. The weights per basin, _Wb,t−_ 1, used are: where _Lb,t−_ 1 is the sum of lights in basin _b_ one year, _t −_ 1, before the flood year and _Lp,t−_ 1 is the same at a province level.

Finally, the weights from (4) are multiplied with the intensity from (3) to get the overall flood impact, _FI b,t_ in that basin on the province: One thing to note here is that for basins that span several provinces or districts, we have assumed the same intensity, but the weight is based on nightlights within each individual province.

# **4.1.2 Results**

The stream flow was simulated for 5,082 consecutive days, from 1 February 2001 to 31 December 2014.<sup>9</sup> Table 2 shows that the top 10 basins with most flood days had close to 200 days of flooding over the 14-year period. As expected, these basins do overlap with some of the busiest flood areas according to the DFO, as shown in Figure 6. The lower part of Table 2 reveals that the driest basin had a mere 12 days of flooding. All 33 provinces with a basin had days that went above our flood threshold set in Equation (2).

All months in our model have flood events, but there are big differences. The range goes from 527 events every March and down to 154 events every August, with the traditional rainy season (NovemberMarch) producing the highest number of flood events, whereas the dry season months (June-October) are the driest. Aggregating the numbers for the rainy season, there are 2,215 events every year across the basins, while there are only 995 events every year during the dry season.

Total number of basins that are partly or fully inside Indonesia is 495, and these basins have a total of 55,605 flood events or slightly more than 112 per basin. In other words, the average basin has been flooded for a total of 8 days a year over the 14-year period in question. This is not entirely unexpected given the climate in Indonesia and the way our threshold is made. Also, if we compare with the DFO data where they have 3,808 floods of magnitude 4 or higher through their period from 1985-2016, which converts to almost 123 fairly large scale flood events per year, our model provides a reasonable proxy for events.

Even though the results seem logical on a per basin basis, the time steps in the model are 1 day at a time, which is too slow for the unfolding of a flood event, implying that downstream basins that would normally fill up very quickly will now only be filled up the day after, and then the next basin will be filled two days later and so forth. This means that the amount of days with floods are inflated. We believe that this does not affect our results much, though, as the number of events per province will not affect the end results, since we weigh by affected nightlight and not by number of days of floods.

Despite the numerous floods in Indonesia, they generally do not affect a large percentage of the population, as per Table 3. The mean of nightlights when excluding areas with 0 nightlight is 3.39 percent.

> 9For Bali we did it for 5,080 days due to problems with 30 and 31 December 2014.

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