# **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