# Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images
## 2 Materials and Methods
### Methodology
#### 2.3.2 Satellite-Based Flood Damage Assessment and Its Validation  
Flood damage was assessed using a change detection technique, which consisted of calculating the surface area of the different LULC types of the watershed changing category because of the flood. For instance, an area shifting from \"built-up area\" or \"agricultural/cropland\" to \"deposited material\" in a satellite image obtained after the flood was considered a damaged area. Figure 2: Schematic representation of the methodological approach used in this study. Google Earth images and a total of 212 samples with a minimum of 30 samples for each LULC type were collected by a team of WAPDA together with their GPS location. These were used to validate the LULC type classification after the flood. The verification was done at random points using the random sample points method in the ArcGIS Spatial Analyst extension. These points were then converted into KML format so that they could be displayed in Google Earth. The accuracy of the LULC type classification after the flood was then measured using 600 ArcGIS random points and the 212 GPS points. Furthermore, 25 GPS points of the selected groups were used for the post-flood verification process. The accuracy of maps that depicted flooded areas was assessed using 440 ArcGIS random points on the Google Earth images and 160 GPS points, with the uncertainty matrix established using the results obtained from this comparison. The assessment was then quantified using the Kappa coefficient (K). 3.3 Mapping the Spatial Extent of Floods of Different Return Periods and Comparing the Extent of the 2014 Flood with MODIS Imagery  
The floodplain maps were produced using the following steps: (1) a flood frequency analysis was performed to relate the discharge data at Taunsa Barrage to specific return periods; (2) development of the SRTM-based DTM model; (3) extract the boundary of the watershed and its drainage network; (4) prepare the geometric data using the HEC-GeoRAS extension; (5) run the HEC-RAS model to simulate river discharge; (6) produce floodplain maps for floods of various return periods using the peak discharge identified from the flood frequency analysis. In the flood frequency analysis, the log-Pearson form III [38], the Gumbel [39; 40], and the log-normal [41; 42] distributions were fitted to the discharge data depicted in Table 1. The Kolmogorov-Smirnov (KS) test identified the log-Pearson III as the best distribution using the approach described in [PERSON] et al. [32]. The distribution was then used to estimate the peak discharge value for a flood of a given return period. This peak discharge value was input into the HEC-RAS model to map the extent of the flood. The watershed characteristics, such as the river centerline, were extracted from the DTM using the Geospatial Hydrologic Modeling Extension of the HEC model (HEC  
Figure 3: LULC classification of Muzzaffargarh District before the flood (**left**), during (**middle**), and after (**right**) the 2014 flood as identified using Landsat images. GeoHMS). Geometric data for the river, such as cross-sections and bank stations and flow direction lines, were prepared using HEC-GeoRAS. In addition to the DTM, geo-referenced natural-color images from Google Earth were obtained to estimate Manning's roughness coefficient, a model parameter, for each of the relevant LULC categories (Table 2). For this, separate polygons were drawn for each LULC category and [PERSON]'s roughness coefficient was calculated for each polygon using the method proposed by [PERSON] [43] and [PERSON] et al. [20], while assigning a value of 0.32 for single-story and double-story houses [44]. After implementing all geometric data specifications and assigning [PERSON]'s roughness coefficient values to each LULC category, the GIS file was imported into the HEC-RAS. The next step consisted of providing the steady flow and boundary conditions.