metadata: authors: 0: author: Skoufias,Emmanuel
1: author: Strobl,Eric
2: author: Tveit,Thomas Breivik
count: Indonesia
docna: 0: docna: Natural disaster damage indices based on
            remotely sensed data: an application to Indonesia
docty: Policy Research Working Paper
subtopic: Energy Demand,Energy and Environment,Energy and Mining,Natural Disasters
teratopic: Science and Technology Development,Environment,Health, Nutrition and Population,Poverty Reduction,Communities and Human Settlements,Urban Development
abstracts: cdata!: Combining nightlight data as a proxy for
            economic activity with remote sensing data typically used
            for natural hazard modeling, this paper constructs novel
            damage indices at the district level for Indonesia, for
            different disaster events such as floods, earthquakes,
            volcanic eruptions and the 2004 Christmas Tsunami. Ex ante,
            prior to the incidence of a disaster, district-level damage
            indices could be used to determine the size of the annual
            fiscal transfers from the central government to the
            subnational governments. Ex post, or after the incidence of
            a natural disaster, damage indices are useful for quickly
            assessing and estimating the damages caused and are
            especially useful for central and local governments,
            emergency services, and aid workers so that they can respond
            efficiently and deploy resources where they are most needed.
geo_reg_key: 517198,517191
repnme: repnme: Natural disaster damage indices based on
            remotely sensed data: an application to Indonesia
display_title: Natural disaster damage indices
            based on remotely sensed data: an application to Indonesia
listing_relative_url: /research/2017/09/27966148/natural-disaster-damage-indices-based-remotely-sensed-data-application-indonesia
topicv3: Energy and Extractives m1326595 507,Earthquake m1503308 3154,Flood m1503315 3216,Natural Disaster m1503325 3138,Tsunami m1503625 3160
publishtoextweb_dt: 2017-09-08T00:00:00Z
historic_topic: Science and Technology Development,Environment,Health, Nutrition and Population,Poverty Reduction,Communities and Human Settlements,Urban Development
no_of_pages: 36
author_keys: 0: author_key: 177660
1: author_key: 1071872
2: author_key: 1435045
model_extractions: WPS8188 Policy Research Working Paper 8188 Natural Disaster Damage Indices Based on Remotely Sensed Data An Application to Indonesia _Emmanuel Skoufias Eric Strobl Thomas Tveit_ Poverty and Equity Global Practice Group September 2017
Policy Research Working Paper 8188

# **Abstract**

Combining nightlight data as a proxy for economic activity with remote sensing data typically used for natural hazard modeling, this paper constructs novel damage indices at the district level for Indonesia, for different disaster events such as floods, earthquakes, volcanic eruptions and the 2004 Christmas Tsunami. Ex ante, prior to the incidence of a disaster, district-level damage indices could be used to determine the size of the annual fiscal transfers from the central government to the subnational governments. Ex post, or after the incidence of a natural disaster, damage indices are useful for quickly assessing and estimating the damages caused and are especially useful for central and local governments, emergency services, and aid workers so that they can respond efficiently and deploy resources where they are most needed.

This paper is a product of the Poverty and Equity Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at eskoufias@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Natural Disaster Damage Indices Based on Remotely Sensed Data: An Application to Indonesia<sup>_∗_</sup>

## Emmanuel Skoufias (World Bank) Eric Strobl (University of Bern) Thomas Tveit (University of Cergy-Pontoise)

**JEL Classification:** Q54, C63, R11, R5,O18 **Keywords:** Remotely Sensed Data, Natural Disasters, Natural Hazard model, Damage Index, Floods, Earthquakes, Volcanic Eruptions

> _∗_ This paper was financed in part by the Disaster Risk Finance Impact Analytics project of the Disaster Risk Financing and Insurance Program of The World Bank Group
# **1 Introduction**

Quickly assessing and estimating the damage caused after the incidence of a natural disaster is important for both central and local governments, emergency services and aid workers, so that they can respond efficiently and deploy resources where they are most needed. Recently, remote sensing technologies have been used to analyze the impact of disasters, such as hurricanes (Myint _et al._ , 2008; Klemas, 2009), floods (Haq _et al._ , 2012; Wu _et al._ , 2012, 2014; Chung _et al._ , 2015), landslides (Nichol _et al._ , 2006), earthquakes (Fu _et al._ , 2005; Yamazaki & Matsuoka, 2007), wildfires (Holden _et al._ , 2005; Roy _et al._ , 2006), volcanoes (Carn _et al._ , 2009; Ferguson _et al._ , 2010) and tsunamis (R ̈omer _et al._ , 2012). These remote sensing techniques are useful for providing quick damage estimates shortly after the disasters giving emergency services a chance to respond quickly and local governments an overview of estimated costs and necessary repairs.

In addition to their usefulness in the aftermath of a disaster, estimates of the potential damage associated with a natural disaster are also useful for policy making prior to the realization of the natural hazard event. In many cases the incidence of a natural hazard event can turn into a natural disaster simply because of inadequate preparation ex-ante. Indonesia, for example, is highly exposed to natural disasters by being situated in one of the worlds most active disaster hot spots, where several types of disasters such as earthquakes, tsunamis, volcanic eruptions, floods, landslides, droughts and forest fires frequently occur. The average annual cost of natural disasters, over the last 10 years, is estimated at 0.3 percent of Indonesian GDP, although the economic impact of such disasters is generally much higher at local or subnational levels (The Global Facility for Disaster Reduction and Recovery, 2011). The high frequency of disasters experienced has important impacts on expenditures by local governments that could be anticipated, at least in part, through upward adjustments in the annual fiscal transfers from the central government to the subnational governments.<sup>1</sup> Such ex-ante adjustments in the level of fiscal transfers would be more useful if they could be based on estimates of the potential damages associated with the incidence of a natural disaster as opposed to estimates of the intensity of the potential natural hazard that might occur. However, although in recent years there has been much progress towards the modeling of the main natural hazards, there continues to be a scarcity of estimates of the damages associated with the incidence of these disasters. The value of damage caused by a natural disaster is typically a complicated function of the size of population living in that area, the level and type of economic activity carried out, the value of the physical infrastructure in place, and the resilience of infrastructure and people’s livelihoods to the natural hazards.

This paper fills some of the gaps in the literature by using different remote sensing sources and data on the physical characteristics of the events to construct four damage indices for natural disasters in Indonesia. The indices cover floods, earthquakes, volcanic eruptions and a tsunami, and are all weighted by local economic activity in an area, and then aggregated up to a district level.<sup>2</sup> All data used in the construction of the indices are free and publicly available, making the methods used a potentially very useful alternative for both central and local governments to quickly get a rough estimate of the damages caused by a disaster (either ex-ante or ex-post).<sup>3</sup> Importantly, all of the indices constructed take into account local exposure. Given limited access to highly disaggregated local economic activity data, nightlight intensity derived from satellite imagery has proved to be a good proxy; see, for instance, Henderson _et al._ (2012), Hodler & Raschky (2014) and Michalopoulos & Papaioannou (2014). By utilizing the grid cells of approximately 1 square kilometer we can break down areas in cities and districts into where they are busiest, and thus take into account not only the local physical characteristics of a natural disaster but also the local economic activity exposed to it.

The paper is structured as follows. Section 2 of the paper discusses in more detail the incidence and types of natural disasters. Section 3 discusses the nightlights data. Sections 4-7 discuss in detail the construction of the four damage indices, while section 8 concludes.

> 1For example, Indonesia experienced 4,000 disasters between 2001 and 2007 alone, including floods (37%), droughts (24%), landslides (11%) and windstorms (9%) (The Global Facility for Disaster Reduction and Recovery, 2011).

> 2A tropical cyclone index was also constructed, but no hurricanes had strong enough winds to cause any damage on land.

> 3In a separate paper, Skoufias _et al._ (2017), we correlate the damage indices of these disasters at the district level with the ex-post allocation of district expenditures in different sectors and by economic classification.

2
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# **4 Flood Damage Index**

The modeling of floods can be done by remote sensing (Brakenridge & Anderson, 2006; Wu _et al._ , 2012; Haq _et al._ , 2012) or through a combination of weather data and GIS systems as for example in Knebl _et al._ (2005); Asante _et al._ (2007); Dessu _et al._ (2016). We utilize the latter, as remote sensing is useful for assessing whether an area is flooded or not, but it is weaker on modeling the intensity of the flood. Moreover, cloud cover generally limits the accurate detection of floods from remote sensing sources.

To model floods we have decided to use the Geospatial Stream Flow Model (GeoSFM) which is a software that is “designed to use remotely sensed meteorological data in data sparse parts of the world”(Artan _et al._ , 2008). GeoSFM was developed by USGS and USAID and is a hydrological modeling tool used to model stream flows across large areas, in particular areas where highly localized data are lacking. It has been used in regions such as the Great Horn of Africa (Asante _et al._ , 2007; Mati _et al._ , 2008; Dessu _et al._ , 2016) and Nepal (Shrestha _et al._ , 2011), with Dessu _et al._ (2016) finding that the model captures 76% of the monthly average variability, making it useful for flood simulation.

The inputs needed to model stream flow for basins are soil- and terrain-based - such as digital elevations models (DEM) and land cover and soil data - and weather-based, such as precipitation and potential evapotranspiration (PET) data. The HYDRO1K data set from USGS, which is a DEM made for hydrological modeling based on the USGS’ 30 arc-second DEM of the world, is used as elevation input. The land cover data are the Global Land Cover Characterization (GLCC) data set also from USGS, while the soil data are from the FAO Digital Soil Map of the World.

The daily precipitation data are from the 3-hourly data set from the Tropical Rainfall Measurement Mission Project (TRMM) and the PET data are 6-hourly data from the Global Data Assimilation System (GDAS), both data sets are aggregated up to daily data. The PET data are available from February 2001 and onwards, while the precipitation data are available for the period 1998-2014. Given that we only have nightlight data through 2013, we will focus on floods for the period 2001-2014.

GeoSFM uses the inputs to construct basins based on the terrain and then uses a linear soil moisture accounting routine to model surface runoff and soil moisture based on precipitation and PET. It is worth noting that although a more complex and better non-linear routine is also supported, it does not work well for our more generalized macro-modeling with fairly low resolution data. Finally, GeoSFM models the stream flow for each basin for each day of our time period.

Note that GeoSFM does not model coastal floods, nor does it model flash floods in areas where there are no rivers or streams of a certain length. Figure 5 shows that there are parts of Indonesia and even one province - Riau Islands - which have no basins. Another weakness is that it does not take into consideration the specific terrain within each basin. Floods are generally very localized events and the low resolution of our data makes it impossible to model the intensity of the stream flow within a basin and also causes some river outlets to be slightly inland instead of running all the way to the ocean.

## **4.1 Creation of Index and Results**

The first part of constructing the index involves defining when a flood event is happening. In Wu _et al._ (2012) they propose four runoff based methods to define a flood threshold, and in addition Wu _et al._ (2014) propose a slightly modified flood threshold definition with a point being flooded when: where _R_ is the routed runoff in millimeters, _P_ 95 is the 95th percentile value and _σ_ is the standard deviation of the routed runoff. _Q_ is the discharge in cubic meters.

We found that with the GeoSFM modeled data, runoff was not a good proxy for flooding, due to it only capturing a limited number of floods. Discharge, _Q_ , was a better proxy, leading to a new - but very similar - equation: 7
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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.

9
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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.

11
|Model Results<br>Total Flood Days<br>Sum on Island<br>Days Flooded on Island<br>During Period|8<br>7|180<br>41|105<br>30|13<br>9|79<br>25|149<br>25|38<br>17|27<br>8|31<br>13|
|---|---|---|---|---|---|---|---|---|---|
|Total Flood Days<br>Sum All Provinces|17|45|30|20|25|25|17|8|31|
|Flooded days<br>in Province|5|18|16|6|23|25|14|6|9|
|Displaced|380,000|350,000|0|10,000|3,000|60,000|0|1,200|20,000|
|Dead|147|148|9|19|0|0|0|0|23|
|DFO Data<br>Centroid Province|Jawa Timur|Jambi|Sumatera Selatan|Jawa Barat|Kalimantan Tengah|Riau|Kalimantan Tengah|Sumatera Selatan|Jawa Barat|
|Magnitude DFO|6.1|6.9|6.4|6.2|6.0|6.3|6.2|6.2|6.2|
|Duration|17|45|31|20|25|25|17|8|31|
|e|2002|2003|2005|2006|2007|2008|2010|2012|2014|
|Dat|Jan|Dec|Jan|Jan|May|Mar|Apr|Feb|Jan|

12
|2014<br>10.571<br>0.691<br>4.890<br>7.279<br>15.921<br>1.307<br>9.362<br>30.362<br>11.156<br>14.672<br>11.372<br>17.092<br>21.549<br>23.723<br>10.496<br>10.416<br>47.995<br>0.705<br>0.796<br>0.009<br>5.676<br>28.788<br>41.465<br>14.663<br>12.707<br>5.688<br>1.015<br>6.018<br>18.169<br>27.107<br>20.517<br>30.332|
|---|
|2013<br>14.864<br>8.580<br>0.623<br>7.630<br>21.045<br>20.092<br>1.710<br>21.573<br>32.401<br>29.462<br>25.344<br>28.322<br>13.122<br>32.648<br>31.707<br>16.020<br>7.374<br>43.979<br>0.278<br>0.913<br>0.048<br>12.688<br>35.261<br>20.114<br>3.596<br>11.202<br>7.999<br>4.780<br>4.719<br>17.472<br>51.858<br>21.185<br>41.429|
|2012<br>10.116<br>7.609<br>0.606<br>15.288<br>6.351<br>3.680<br>1.837<br>25.294<br>21.463<br>27.714<br>20.117<br>16.611<br>6.273<br>29.563<br>18.792<br>11.198<br>5.597<br>22.493<br>0.000<br>2.629<br>0.068<br>8.517<br>24.328<br>15.855<br>3.625<br>7.805<br>4.415<br>2.474<br>5.710<br>10.559<br>23.462<br>15.037<br>41.184|
|2011<br>17.665<br>7.882<br>1.165<br>12.783<br>17.417<br>10.702<br>0.343<br>24.347<br>15.023<br>25.600<br>20.560<br>12.384<br>4.663<br>10.598<br>17.965<br>15.408<br>4.615<br>25.333<br>0.358<br>1.749<br>0.064<br>9.506<br>25.779<br>25.391<br>5.727<br>10.157<br>9.320<br>4.428<br>4.976<br>9.770<br>24.159<br>15.272<br>35.296|
|2009<br>2010<br>7.918<br>20.076<br>9.042<br>7.851<br>0.456<br>0.745<br>4.921<br>21.456<br>3.145<br>17.983<br>1.719<br>5.607<br>1.107<br>0.356<br>3.394<br>22.670<br>1.627<br>32.430<br>7.030<br>35.907<br>1.001<br>22.795<br>4.997<br>28.016<br>8.574<br>6.321<br>1.391<br>16.995<br>6.581<br>8.915<br>4.098<br>15.236<br>6.620<br>6.085<br>9.414<br>49.285<br>0.000<br>0.000<br>0.353<br>1.230<br>0.036<br>0.038<br>6.032<br>9.166<br>4.332<br>31.378<br>3.667<br>29.925<br>4.637<br>5.044<br>2.996<br>9.001<br>8.280<br>0.427<br>3.927<br>4.990<br>3.813<br>8.950<br>4.595<br>18.317<br>4.210<br>34.032<br>6.001<br>18.513<br>4.909<br>29.360|
|2004<br>2005<br>2006<br>2007<br>2008<br>1.213<br>18.127<br>15.643<br>23.155<br>20.799<br>1<br>1.676<br>10.359<br>10.783<br>8.080<br>5.786<br>1.525<br>1.973<br>0.803<br>0.794<br>0.306<br>4.360<br>11.899<br>9.668<br>27.980<br>19.316<br>1<br>3.606<br>14.273<br>10.266<br>11.683<br>16.207<br>1<br>5.281<br>35.026<br>37.234<br>27.620<br>21.607<br>3<br>0.565<br>1.167<br>0.921<br>0.985<br>0.508<br>3.931<br>18.294<br>9.950<br>22.154<br>40.751<br>2<br>1.677<br>19.131<br>13.241<br>16.989<br>34.894<br>3<br>8.629<br>30.536<br>23.842<br>36.484<br>28.958<br>2<br>5.024<br>26.358<br>24.512<br>28.825<br>27.113<br>3<br>1.652<br>22.093<br>19.205<br>28.864<br>19.975<br>2<br>6.320<br>13.792<br>5.801<br>8.921<br>9.740<br>1.410<br>15.397<br>12.302<br>21.088<br>29.202<br>2<br>7.649<br>27.149<br>20.731<br>32.001<br>29.097<br>2<br>7.066<br>9.124<br>9.208<br>15.114<br>14.168<br>2<br>1.245<br>5.471<br>6.455<br>12.065<br>24.721<br>1<br>2.566<br>31.452<br>21.910<br>23.642<br>37.172<br>3<br>0.000<br>0.000<br>0.014<br>0.000<br>0.000<br>0.510<br>1.006<br>2.406<br>1.574<br>1.873<br>0.021<br>0.080<br>0.034<br>0.059<br>0.093<br>5.209<br>15.937<br>7.311<br>10.590<br>10.489<br>5.338<br>18.461<br>11.763<br>30.377<br>31.351<br>2<br>1.778<br>20.699<br>17.433<br>35.765<br>26.883<br>2<br>3.918<br>4.512<br>4.888<br>5.244<br>14.403<br>2.397<br>8.112<br>15.667<br>11.431<br>14.619<br>1<br>4.693<br>10.258<br>14.202<br>2.255<br>12.771<br>2.396<br>13.335<br>5.188<br>14.381<br>11.368<br>6.575<br>5.250<br>6.360<br>6.551<br>7.062<br>1.787<br>28.464<br>18.737<br>35.011<br>24.624<br>2<br>3.865<br>34.345<br>23.193<br>39.282<br>55.345<br>3<br>1.155<br>19.981<br>14.664<br>15.477<br>19.144<br>1<br>4.707<br>25.250<br>37.895<br>20.736<br>49.592<br>4|
|2003<br>4.712<br>1<br>3.083<br>1<br>1.194<br><br>3.076<br>1<br>5.205<br>1<br>7.260<br>2<br>1.242<br><br>2.681<br><br>0.589<br>1<br>9.439<br>1<br>2.206<br>3<br>5.731<br>2<br>7.145<br><br>9.599<br>2<br>8.512<br>3<br>1.437<br><br>8.894<br><br>0.012<br>3<br>0.216<br><br>0.241<br><br>0.035<br><br>5.109<br>1<br>8.616<br>1<br>7.103<br>1<br>2.650<br><br>4.866<br>1<br>0.433<br>2<br>6.197<br><br>8.590<br><br>6.946<br>2<br>7.852<br>3<br>7.135<br>1<br>2.810<br>2|
|2002<br>7.400<br>1<br>1.780<br>1<br>1.756<br><br>7.632<br>1<br>6.756<br>2<br>6.760<br>3<br>0.647<br><br>7.370<br>1<br>1.141<br>1<br>7.697<br>2<br>1.395<br>4<br>7.287<br>2<br>6.570<br><br>0.917<br>1<br>6.090<br>3<br>3.046<br>1<br>3.949<br><br>0.499<br>3<br>0.000<br><br>0.959<br><br>0.083<br><br>9.770<br>1<br>5.837<br>1<br>0.727<br>1<br>9.094<br><br>9.733<br>1<br>2.603<br>1<br>2.092<br><br>2.709<br><br>6.785<br>2<br>7.191<br>4<br>1.627<br>1<br>7.613<br>4|
|2001<br>12.674<br><br>3.614<br>1<br><br>1.380<br><br>7.118<br><br>6.394<br><br>54.770<br>1<br><br>1.368<br><br>15.099<br><br>44.338<br>1<br>16.616<br>1<br>29.271<br>4<br>13.567<br>1<br>at<br>3.833<br><br>tan<br>26.550<br>1<br>gah<br>11.208<br>1<br>ur<br>7.603<br><br>ra<br>1.098<br><br>25.228<br>2<br>0.000<br><br>0.904<br><br>Barat<br>0.014<br><br>Timur<br>6.168<br><br>17.567<br><br>7.063<br>2<br>6.880<br><br><br>14.729<br><br><br>2.798<br><br>ra<br>2.861<br><br>4.253<br><br>8.944<br>1<br>n<br>27.938<br>1<br>13.788<br>1<br>37.598<br>2|
|e<br>Belitung<br>u<br>lo<br>ya Barat<br> Raya<br>rat<br>ngah<br>mur<br>tan Bar<br>tan Sela<br>tan Ten<br>tan Tim<br>tan Uta<br>g<br> Utara<br>nggara <br>nggara <br>i Barat<br>i Selatan<br>i Tengah<br>i Tengga<br>i Utara<br>a Barat<br>a Selata<br>a Utara<br>rta|
|Provinc<br>Aceh<br>Bali<br>Bangka-<br>Banten<br>Bengkul<br>Goronta<br>Irian Ja<br>Jakarta <br>Jambi<br>Jawa Ba<br>Jawa Te<br>Jawa Ti<br>Kaliman<br>Kaliman<br>Kaliman<br>Kaliman<br>Kaliman<br>Lampun<br>Maluku<br>Maluku <br>Nusa Te<br>Nusa Te<br>Papua<br>Riau<br>Sulawes<br>Sulawes<br>Sulawes<br>Sulawes<br>Sulawes<br>Sumater<br>Sumater<br>Sumater<br>Yogyaka|

13
Table 6: 10 Most Impacted Districts

|District|Province|Year|Flood Impact|
|---|---|---|---|
|Seruyan|Kalimantan Tengah|2010|175.080|
|Aceh Tengah|Aceh|2010|165.381|
|Bener Meriah|Aceh|2010|139.742|
|Pasaman|Sumatera Barat|2010|125.854|
|Sarolangun|Jambi|2010|118.244|
|Lubuk Linggau|Sumatera Selatan|2003|114.790|
|Keerom|Papua|2009|114.289|
|Tana Toraja|Sulawesi Selatan|2013|106.886|
|Klaten|Jawa Tengah|2002|106.488|
|Sukoharjo|Jawa Tengah|2002|106.488|

14
0144/1134) 3487 |] 31-16 16-37 37-60 60-116
<!-- Start of picture text --> 4.00<br>0.75<br>faa8& 050<br>0.25<br>0.00 025 050Peak Ground Acceleration075 1.00 125<br>Vulnerability Curve for Building- Quality —o—2| 4—6<br><!-- End of picture text -->

<!-- Start of picture text --> 4.00<br>0.75<br>a&8e& 050<br>0.25<br>0.00 025 050Peak Ground Acceleration075 1.00 125<br>Vulnerability7 Curve for Building. Quality. —0—2 4—6<br><!-- End of picture text -->
To model estimated damage due to a particular earthquake event the data from the ShakeMaps and GESI are used. Then, one identifies the value of peak ground acceleration that each nightlight cell in Indonesia experiences by matching each earthquake point with its nearest nightlight cell. If the cell is further away than 1.5 kilometers or if it experiences shaking (PGA) of less than 0.05 the value is set to 0. In order to derive at a region _j_ specific earthquake damage index, _ED_ , the following equation is applied: where _DR_ is the damage ratio according to the peak ground acceleration, _pga_ , and the urban-rural qualification of cell _i_ , defined for a set of 8 different building quality _q_ categories. The weight _wi_ is the same as before; the sum of the nightlights of the affected cells over the sum of the total provincial nightlights.

# **5.1.2 Results**

With the above method, we find that 27 of the 34 provinces were damaged by earthquakes at some point in time.<sup>11</sup> Table 7 shows that the big islands Java and Sumatra have the most affected nightlight cells, given how densely populated they are and how much seismic activity is experienced there this is expected.

Table 7: Times a Lit Nightlight Cell is Damaged by Earthquake by Province

|Province<br>Times|Nightlight Cell|Damaged<br>Percentage of Total|
|---|---|---|
|Aceh|1,170|22.28|
|Sumatera Utara|722|13.75|
|Sumatera Barat|511|9.73|
|Sulawesi Tengah|354|6.74|
|Jawa Barat|353|6.72|
|Sumatera Selatan|283|5.39|
|Jawa Tengah|259|4.93|
|Jawa Timur|242|4.61|
|Bengkulu|165|3.14|

Finally, there were 5,251 cases where the instance hit a nightlight cell that was lit. Table 8 shows that the individual nightlight cell weights are small, as expected, but the impact of having buildings of quality 4 is that within a cell that is hit, on average a bit more than 6 percent of the buildings are destroyed.<sup>12</sup> Table 8: Descriptives of Weights and Intensity for Building Quality 4 (excluding weights of zero)

|Statistic|N|Mean|St. Dev.|Min|Max|
|---|---|---|---|---|---|
|Weights|5,251|0.226|0.407|0.009|9.328|
|Damage|5,251|0.062|0.043|0.046|0.547|
|Intensity|5,251|0.016|0.035|0.0004|0.859|

Weights and Intensity multiplied by 1,000

> 11The seven not affected were Bangka Belitung, Kalimantan Barat, Kalimantan Selatan, Kalimantan Tengah, Kalimantan Timur, Kalimantan Utara and Kepualauan Riau.

> 12We did the same for buildings of quality 0 (the best) and 7 (the worst), something which led to maximum damage values of 33% for the best buildings and 84% for the worst versus 55% for our base case, showing how the overall damage is highly dependent on building quality information.

17
# **5.1.3 Aggregated data**

When aggregating, a similar method as in section 4 is used, but now the aggregation is done directly by nightlight cells instead of by basin. The equation is: where _t_ is year, _l_ is all nightlight cells in the province or district _j_ and _ED_ is the damage from equation 7.

Table 9 provides the full overview of damage by province, showing the large differences between the provinces and how they vary from year to year, as one can expect with highly randomized events such as earthquakes. Using Yogyakarta - which was only impacted by earthquakes in 2006 - as an example, there was a loss of 0.4 percent of the total building mass, causing damages estimated to be approximately 3.1 billion US dollars in addition to more than 5,000 deaths and tens of thousands of injured and displaced people. Apart from that, provinces on Sumatra make up 6 of the top 10 most damaging years. Even though Indonesia is often hit by severe earthquakes, even in the worst of years they only destroy about 1 percent of the buildings in a province. That being said, 1 percent of total building mass and infrastructure being damaged does constitute a significant portion of local budgets. As another example, the September 2009 earthquake in West Sumatra inflicted damages for an estimated 2.3 billion US dollars, with repair costs and losses of 64 million US dollars on government buildings (Raschky, 2013).

The numbers per district are shown in Table 10, and they suffer much more damage than the provinces, with the most impacted district losing 5 percent of building mass.

18
|2014<br>0.000<br>1.217<br>0.918<br>1.056<br>0.048<br>0.111<br>0.000|
|---|
|2013<br>3.869<br>0.022<br>0.176<br>0.000<br>0.000<br>0.190<br>0.105<br>4.216<br>0.392<br>0.000<br>0.316|
|2012<br>4.597<br>0.142<br>1.224<br>0.404<br>0.000<br>0.047<br>5.202<br>0.238<br>0.804<br>0.687<br>0.000<br>5.346<br>0.000<br>0.048|
|2011<br>0.345<br>0.184<br>0.154<br>0.147<br>0.026<br>0.272<br>1.431<br>0.261<br>1.513<br>0.464<br>0.000<br>2.175|
|2010<br>0.269<br>0.378<br>0.000<br>0.000<br>0.007<br>0.049<br>0.000<br>0.031<br>0.235<br>0.050<br>0.008<br>0.175|
|2009<br>0.010<br>0.014<br>0.032<br>0.270<br>0.000<br>0.410<br>0.060<br>0.070<br>0.171<br>0.000<br>0.000<br>0.000<br>0.187<br>0.274<br>0.021<br>0.360<br>0.123<br>0.659<br>0.263<br>3.072<br>0.022|
|2008<br>0.044<br>0.380<br>0.929<br>0.178<br>0.033<br>0.000<br>0.644<br>0.038<br>0.070<br>0.056<br>0.180<br>0.060<br>0.011<br>0.173<br>0.000<br>0.041|
|2007<br>0.152<br>8.905<br>1.071<br>0.047<br>0.040<br>0.176<br>0.769<br>0.771<br>0.048<br>0.000<br>0.056<br>0.651<br>9.094<br>0.816<br>0.010|
|2006<br>0.160<br>0.268<br>0.021<br>0.018<br>0.404<br>0.012<br>0.019<br>0.378<br>0.095<br>0.248<br>0.000<br>0.185<br>0.052<br>0.050<br>0.000<br>0.072<br>4.318|
|2005<br>0.728<br>0.003<br>0.623<br>0.253<br>0.006<br>0.000<br>0.003<br>0.000<br>0.654<br>0.110<br>0.069<br>0.175<br>0.000<br>0.208|
|2004<br>0.040<br>0.557<br>0.447<br>0.030<br>0.000<br>0.215<br>1.070<br>0.000<br>0.032<br>0.002<br>00|
|Province<br>Aceh<br>Bali<br>Banten<br>Bengkulu<br>Gorontalo<br>Irian Jaya Barat<br>Jakarta Raya<br>Jambi<br>Jawa Barat<br>Jawa Tengah<br>Jawa Timur<br>Lampung<br>Maluku<br>Maluku Utara<br>Nusa Tenggara Barat<br>Nusa Tenggara Timur<br>Papua<br>Riau<br>Sulawesi Barat<br>Sulawesi Selatan<br>Sulawesi Tengah<br>Sulawesi Tenggara<br>Sulawesi Utara<br>Sumatera Barat<br>Sumatera Selatan<br>Sumatera Utara<br>Yogyakarta<br>Note: Multiplied by 1,0|

19
Table 10: 10 Most Impacted Districts

|District|Province|Year|Intensity|
|---|---|---|---|
|Alor|Nusa Tenggara Timur|2014|53.184|
|Waropen|Papua|2010|45.070|
|Mukomuko|Bengkulu|2007|44.215|
|Aceh Tengah|Aceh|2013|41.736|
|Nabire|Papua|2004|39.864|
|Bener Meriah|Aceh|2013|37.750|
|Sangihe Talaud|Sulawesi Utara|2009|32.947|
|Lembata|Nusa Tenggara Timur|2012|31.692|
|Halmahera Selatan|Maluku Utara|2013|31.511|
|Aceh Barat|Aceh|2012|22.141|

Note: Multiplied by 1,000 20
# **6 Volcano Damage Index**

A volcanic eruption consists of ash clouds, pyroclastic flows and lava flows, the latter two which are very difficult to model without extensive local data. Unfortunately, there is little to no academic research that has looked into large scale volcanic modeling for all aspects of eruptions. For modeling ash clouds, Joyce _et al._ (2009) points to remote sensing through satellite images that detect SO2 emissions as a potential method.

To construct a damage index for eruptions, we use a two-fold process. First, volcanic ash advisory data are used from Volcanic Ash Advisory Centers (VAAC) to detect eruptions; second, satellite images containing sulphur dioxide data from the OMI/AURA satellite are used to model the intensity of the eruptions. The OMI/AURA images have been utilized by Carn _et al._ (2009) and Ferguson _et al._ (2010) to model eruption intensity.

## **6.1 Volcano Modeling**

There are at least two types of software that are used to model eruptions. Voris (Felpeto _et al._ , 2007), which models ash clouds, lava flows and energy cones, and HYSPLIT from Air Resources Laboratory, which models air pollution dispersion. Voris relies on highly localized data due to the lava flow and energy cone modeling, which one will not have in most cases and that does not fit well for large scale modeling across time and space. HYSPLIT does have batch inputs, but still requires several inputs per eruption, some which are not easily obtainable.

The third solution, which is related to the ash clouds mentioned by Joyce _et al._ (2009), is based off ash advisory data to determine whether an eruption happened and OMI/AURA satellite data to determine the scale of the eruption. This will not help with modeling lava flows and energy cones, but due to the very localized nature of the flows, there are no good sources or methods to model it for several eruptions from different volcanoes, leaving the ash clouds as a good proxy for all damages.

### **6.1.1 Ash advisory data**

Ash advisories from the Darwin VAAC (DVAAC), which are ash cloud warnings for airplanes, are used to determine whether an eruption is happening. The warnings show relevant data such as volcano name, position, summit height, height of clouds and a color code that reflects the condition of the air/volcano. There are 4 different codes ranking from the normal state, green, to imminent danger of or ongoing volcanic eruption, red. Over the period from 2004 until 2015, the DVAAC issued 12,962 warnings and of these more than 90 percent were either of code red or orange. Data on advisories from code orange or below were not used, due to eruptions of this scale not being large enough to be properly captured by the SO2-data. By limiting the data to code red events, there are 1,785 events spread across 587 dates.<sup>13</sup>

### **6.1.2 OMI/AURA Satellite images**

To measure the intensity of an eruption, data from the Sulphur Dioxide images of the OMI/Aura project (Krotkov & Li, 2006) are used. These consist of satellite images from October 2004 and onwards. The data have been used to model ash cloud intensity and movement in several articles such as Carn _et al._ (2009) and Ferguson _et al._ (2010).

The satellite images have a spatial resolution of 13 _∗_ 24km and are taken from 80km above ground. The spectral imaging shows the SO2 vertical column density in Dobson Units and there are 14 or 15 orbits per day, where one orbit covers an area approximately 2,600km wide. A dobson unit is a measure of density, and at sea level the typical concentration in clean air is less than 0.2DU. The images contain 4 values for column density based on the center of mass altitude (CMA), which is a measure of the altitude one assumes the center of the distribution is at. There are 4 different estimates for the vertical column density, ranging from 0.9km above ground to 18km above ground.

> 13There were 571 different dates, but some of these dates issued red warnings to 2 or more volcanoes.

21
For volcanic activities one normally uses a CMA of either 8km or 18km (OMI team, 2012), where the former is a middle tropospheric column (TRM) and is for use in medium eruptions, while the latter is an upper tropospheric and stratospheric column (STL) and is for explosive eruptions. Despite this difference, the data are interchangeable in the sense that one can interpolate from one CMA to the others.

OMI is more sensitive above clouds, which both measures mentioned will normally be. The standard deviation for both measures is as low as 0.1DU over Indonesia. The data for both STL and TRM are very similar and this paper uses the STL-data as that are most useful for the biggest events.

# **6.2 Creation of Damage Index and Results**

## **6.2.1 Damage Index**

When constructing a damage index based on SO2 values from ash clouds, one has to set thresholds for distance from the event and from the centroid of the nightlight cell and also a lower sulphur dioxide-value. There are no papers or literature that have estimated any parameter values and there are no usable local data, so the thresholds have been set somewhat arbitrarily.

First off, one wants to set a distance threshold estimating how far away the eruption could cause damage. Note that one wants ground results and not for the aviation industry, since planes can be affected very far away as evidenced by the total stand still of planes across Europe during the 2010 Eyjafjallaj ̈okull eruption. We decided to set a very relaxed condition with any point closer than 10 degrees of latitude and longitude included. Figure 9 portrays the plume approximately 7 hours after one of the biggest Merapi eruptions on 4 November 2010, where the plume moved relatively slowly and after 1000km it dissipated at the lower altitudes, which shows that our 10 degree threshold works well.

Secondly, to match the nightlight data with the OMI/Aura data, a maximum distance between a nightlight point and the nearest SO2 point is set at 50km. The SO2 points are fairly scattered due to cloud covers, hence to get a more consistent grid of nightlight and SO2 values we have chosen a distance that is approximately two times the longest side of an OMI cell.

Finally, a minimum SO2 value in Dobson Units is chosen. According to the Belgian Institute for Space Aeronomy, a typical normal level in air is 0.1DU and a strong eruption is above 10, which is the threshold value chosen.

Once the thresholds have been set, the same nightlight weighting method as for our other indices is applied and then the weights are multiplied with the SO2 value to get an intensity value. The equation is: where _i_ is the nightlight cell on date t, and _wi,T −_ 1 is the previously used weight where _i_ is the nightlight cell, _T −_ 1 is the nightlight strength from the prior year and it is divided by the sum of total nightlights in the province or district.

22
<!-- Start of picture text --> vn<br>amet ee<br>pee A<br>\ ee i ‘1ame<br><!-- End of picture text -->
Table 12: Affected Nightlight Cells by Province and Year

|Province|2004|2005|2007|2008|2010|2014|Total|
|---|---|---|---|---|---|---|---|
|Aceh||||||2,078|2,078|
|Banten|||||940||940|
|Jawa Barat|||||52,034||52,034|
|Jawa Tengah|||||71,356||71,356|
|Jawa Timur|||||2,320|2,311|4,631|
|Nusa Tenggara Timur||||||1,156|1,156|
|Papua||62|||||62|
|Sulawesi Utara|6,164|586|4,672|3,704|||15,126|
|Sumatera Utara||||||1,488|1,488|
|Yogyakarta|||||2,702||2,702|
|Total|6,164|648|4,672|3,704|129,352|7,033|151,573|

Table 13: Top 10 Districts with Most Affected Nightlight Cells

|District|Province|Afected Cells|
|---|---|---|
|Cilacap|Jawa Tengah|16,114|
|Sukabumi|Jawa Barat|10,724|
|Kebumen|Jawa Tengah|10,080|
|Ciamis|Jawa Barat|9,612|
|Banyumas|Jawa Tengah|9,384|
|Cianjur|Jawa Barat|8,260|
|Brebes|Jawa Tengah|6,112|
|Minahasa Selatan|Sulawesi Utara|5,960|
|Garut|Jawa Barat|5,622|
|Bandung|Jawa Barat|5,342|

Table 14: Descriptives of Weights and Intensity (Excluding weights of zero)

|Statistic|N|Mean|St. Dev.|Min|Max|
|---|---|---|---|---|---|
|Weights|114,587|0.046|0.084|0.007|2.354|
|SO2 level|114,587|19.748|10.556|10.083|57.231|
|Intensity|114,587|0.855|1.707|0.075|42.857|

Weights and Intensity multiplied by 1,000

# **6.2.3 Aggregated data**

To aggregate the data, the same method as before is applied, with: where all days _t_ of year _T_ and all nightlight cells _i_ in province or district _j_ are aggregated.

The province overview, shown in Table 16, is caused by the Merapi eruption, with 3 of the top 4 being due to that eruption. Jawa Tengah and Yogyakarta are the two most affected provinces, given their immediate proximity to Merapi. One thing to note is that Jawa Timur, which is east of Merapi, was hardly affected at all.

For districts, the Merapi results are even more pronounced, with all districts in the top 10 being from the 2010 eruption. All the Jawa Tengah districts are west of the volcano. It is somewhat surprising to find that some of the districts in the immediate vicinity of Merapi are not on the list, but this can be due to the timing and quality of the satellite images. Given the time interval between images, the SO2 24
clouds could have traveled past the closest districts by the time an image was taken. Regardless, the results are uniform in that all affected districts are neighbors. Overall, the model seems to give a fair picture of when and where the eruptions were at their most intense, although the ground level intensity can be hard to specify.

Table 15: 10 Most Impacted Provinces

|Province|Year|Intensity|
|---|---|---|
|Jawa Tengah|2010|34.641|
|Yogyakarta|2010|18.607|
|Sulawesi Utara|2004|16.224|
|Jawa Barat|2010|10.271|
|Sulawesi Utara|2007|6.387|
|Sulawesi Utara|2008|4.861|
|Nusa Tenggara Timur|2014|2.356|
|Aceh|2014|2.134|
|Sulawesi Utara|2005|0.684|
|Jawa Timur|2010|0.660|
|Note: Multiplied by 1,000|||

Table 16: Aggregated Volcano Intensity Data by Province and Year

|Province|2004|2005|2007|2008|2010|2014|
|---|---|---|---|---|---|---|
|Aceh||||||2.134|
|Banten|||||0.163||
|Jawa Barat|||||10.271||
|Jawa Tengah|||||34.641||
|Jawa Timur|||||0.660|0.432|
|Nusa Tenggara Timur||||||2.356|
|Papua||0.061|||||
|Sulawesi Utara|16.224|0.684|6.387|4.861|||
|Sumatera Utara||||||0.487|
|Yogyakarta|||||18.607||

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Table 17: 10 Most Impacted Districts

|District|Provi|nce|Year|Intensity|
|---|---|---|---|---|
|Purwokerto|Jawa|Tengah|2010|184.673|
|Banyumas|Jawa|Tengah|2010|168.916|
|Cilacap|Jawa|Tengah|2010|149.878|
|Kebumen|Jawa|Tengah|2010|141.635|
|Banjarnegara|Jawa|Tengah|2010|106.472|
|Purbalingga|Jawa|Tengah|2010|98.697|
|Purworejo|Jawa|Tengah|2010|92.593|
|Kulon Progo|Yogy|akarta|2010|72.177|
|Wonosobo|Jawa|Tengah|2010|69.568|
|Banjar|Jawa|Barat|2010|60.208|

Note: Multiplied by 1,000 26
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# **7.1.3 Aggregated data**

Aggregating the data is done using the same method as in all previous sections, where the nightlight cells across the province or district is summed up: where all nightlight cells _i_ in province or district _j_ are aggregated.

Since the tsunami only affected one province, it is easy to see the total damage done by it on Aceh. With our assumptions, the tsunami destroyed about 4 percent of the buildings in Aceh. This is clearer once broken down into district level damage. There were 6 districts affected by the tsunami, with Aceh Jaya, Banda Aceh and Pidie all experiencing damage of more than 20 percent. The other 3 affected districts - Aceh Barat, Aceh Besar and Bireuen - experienced damage between 5 and 10 percent due to the tsunami.

Table 19: Aggregated Tsunami Damage by District and Province

|District|Province|Intensity|
|---|---|---|
|Aceh Barat|Aceh|0.071|
|Aceh Besar|Aceh|0.078|
|Aceh Jaya|Aceh|0.221|
|Banda Aceh|Aceh|0.229|
|Bireuen|Aceh|0.055|
|Pidie|Aceh|0.210|
|Aggregated|Aceh|0.042|

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# **8 Conclusion**

With the continuous increase in remote sourcing data, it has gotten much easier and cheaper to monitor and assess the damages from natural disasters. Joyce _et al._ (2009) and Gillespie _et al._ (2007) have done an extensive review of how satellite images can be used to map natural disasters, while this paper has contributed with providing new techniques that utilize other remote sourced data such as ShakeMaps and ash advisory data.

Throughout, techniques based on freely available data have been used to construct damage indices for different disaster types. Generally the indices can be used in any area of the world, and if calibrated with local data, they could provide an excellent tool for local governments or stakeholders in early disaster assessments.

The indices can be used to get quick damage estimates and inform where to provide relief, as well as in research such as what the authors have done in Skoufias _et al._ (2017), where the indices are used to analyze district budget redistributions following natural disasters.

The main caveat is the indices have not been validated against local level damage data. If one had access to high resolution monetary or intensity data, the estimates would be much more precise.

Table 20 gives an overview of the different data sources and software used. All disasters have used the DMSP global nightlights data to weight the indices based on economic activity. Recently, the VIIRS nightlight data provide an alternative for assessing economic activity or events, as showcased for GDP in China (Li _et al._ , 2013; Shi _et al._ , 2014) and Africa (Chen & Nordhaus, 2015) and for storms and floods in the US (Cao _et al._ , 2013; Sun _et al._ , 2015). If one is interested in events after 2012, the VIIRS data provide higher spatial resolution and track changes by month instead of yearly.

Alternatives do exist for choice of software if one wants everything to be open source and free. Instead of ArcGIS, packages such as QGIS and RGDAL for R are potential alternatives. R and Python are used more and more for spatial data and can often provide the necessary tools to do the modeling. Instead of statistical software such as Matlab and Stata, R and Python once more provide excellent alternatives.

Table 20: Disasters and the Data Sources and Softwares used

|Disaster Type|Data Sources|Software Used|
|---|---|---|
|Flood|Hydro1K<br>GLCC<br>FAO Digital Soil Map<br>TRMM<br>GDAS|GeoSFM (ArcView 3.3)<br>Python<br>Stata<br>ArcGIS|
||USGS ShakeMaps||
||USGS Building Inventory|Python|
|Earthquake|World Housing Encyclopedia<br>Urban Extent Map (CIESIN)<br>GESI|Stata<br>ArcGIS|
|||Python|
|Volcanic Eruption|Ash Advisory Data<br>OMI/AURA Satellite Images|Matlab<br>Stata<br>ArcGIS|
|||Python|
|Tsunami|DFO Inundation Map|ArcGIS<br>Stata|

30