Table 3: Descriptives of Weights and Intensity (excluding zero damage observations)

|Statistic|N|Mean|St. Dev.|Min|Max|
|---|---|---|---|---|---|
|Weights|45,005|0.034|0.060|0.00003|0.557|
|Intensity|45,005|4.516|2.608|0.989|50.944|
|Damage Index|45,005|0.155|0.351|0.0001|12.780|

# **4.1.3 Comparison of Model versus DFO Floods**

The DFO flood database is mostly based on news sources, providing an overview of the big floods in Indonesia. To check the database against the GeoSFM model, the focus will be on the largest events of magnitude 6 and above. Given how the DFO data do not give any intensity estimates and focus primarily on displacement numbers and area, while our model is driven by intensity the comparison will only focus on whether GeoSFM results do overlap in time and/or province with the centroid of the DFO floods.

Table 4 shows the DFO data on the left side, first column being the start month of the flood, followed by duration, magnitude, the province where the centroid of the flood is, dead and displaced. The right side shows the modeling results where the focus is on duration. The first column under model results shows the number of days for the centroid province, then overall number of days with floods anywhere in Indonesia during the period, then looking at the same island - using that as a proxy for neighboring regions - where one examines total days the island provinces were flooded during the flood and finally how many of the days of the flood duration that a province on the same island was flooded.

Generally, the model performs well, in particular on Sumatra and Kalimantan (Borneo), with the example where the 2008 flood was captured for all 25 days in the centroid province. Overall, it shows at least one flooded basin on Kalimantan and Sumatra for 85% of the days the major floods happened. The results are somewhat worse on Java, where only 37% of the days have a flood. A primary reason for this might be that Java is very narrow and hence the streams are short and might not be captured in our model. Java also has larger percentage of land not covered by a basin, ref Figure 5, also due to its narrowness which makes the low resolution landcover data underestimate the size of the basins.

# **4.1.4 Aggregated numbers**

Finally, to aggregate up to a district or province level, we have used a simple method for the total damage experienced per year: where _j_ is the province or district, _T_ is the year, sum of _t_ is all the days for year _T_ , sum of _b_ are all the basins in the province or district and _FI b,t_ is the flood impact from Equation 5. Normally one might use an average flood impact across the year, but by doing this, we capture repeated flood events and areas that experience generally high flooding.

Using the above method, Table 5 provides the aggregated data for all provinces across all years. The impact is fairly even for the most impacted ones, with the impact numbers for the top 10 ranging from 44 to 55. Furthermore, Sumatera Selatan, Lampung and Yogyakarta make up 8 of the top 10 impacted provinces. The overall picture fits with the DFO floods in Figure 1, with the populous provinces in Java, Sumatra and Sulawesi being impacted, whereas the smaller island provinces and parts of Kalimantan are not affected much. For some of the island provinces the numbers are probably underestimated, no basins will have been constructed and modeled there due to the many small islands.

Finally, Table 6 shows the most impacted districts over the years 2001 through 2014. The impact is much larger than for the provinces as one would expect due to the more localized data and impact. The districts are also more geographically spread out than the provinces.

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