2.2.2. Topography

The topography data was derived from the Digital Elevation Model (DEM) obtained from the Shuttle Radar Topography Mission (SRTM). The data was open-source data provided by the United States Geological Survey (USGS). For the SRTM, the vertical accuracy was 16 m for a 90% confidence level [46].

Figure 4 presents the topographical conditions of the Ciliwung–Cisadane Watershed. The resolution of the DEM that was used in this calculation was based on 1 arc second, or about 30 m. Nevertheless, the resolution was scaled up to 100 m due to computational issues. The numbers of rows and columns of pixels are 885 and 891. The upscaling of the DEM resolution increased the topography index (TI) of the DEM because of the nesting process of several grids with different TIs into one grid with one TI [47]. The consequences were areas or grids that should have been submerged becoming dry areas, and vice versa.

**Figure 4.** Digital Elevation Model (DEM) of the Ciliwung–Cisadane Watershed.

### 2.2.3. Land Cover

Land cover data was obtained from the Global Land Cover Characterization Version 2 (GLCC-V2). This database was developed by The U.S. Geological Survey (USGS), the University of Nebraska–Lincoln (UNL) and the European Commission's Joint Research Centre (JRC) in 1992. The land-cover projection had 1 km nominal spatial resolution and unique geographic elements. The land classification for this model has been simplified from the GLCC-V2 for calculation purposes (Figure 5).

Each land-cover classification has different characteristics in the model, based on soil conditions, as presented in Table 2. In this model, the river was distinguished from other water bodies. The river location was autogenerated by the RRI model from the Digital Elevation Model.

**Figure 5.** Land cover of the Ciliwung–Cisadane Watershed.



### *2.3. Rainfall–Runoff–Inundation (RRI) Model*

The rainfall–runoff–inundation (RRI) model is a two-dimensional model with a simplified equation. This model is capable of simulating rainfall–runoff and flood inundation at the same time; it was also designed to be used immediately after a disaster and it can be useful as a tool to analyze large-scale flooding as well [48]. In addition, this model assumes that the river channel location is in the same grid cells as the slope. A river channel is considered a centerline in a grid cell. It indicates an extra flow path between the grid cells and the actual river course. On the other hand, the slope cells function as the two-dimensional simulation area of the lateral flow. Hence, there are two water depths for slope grid cells in water channels, i.e., of the channel and of the slope (floodplain) itself.

The inflow–outflow interaction between the river and the slope is based on different overflowing formulae. The calculation depends on water-level and levee-height conditions. This model was generated based on mass-balance Equation (1) for governing the equation of flow rate. The momentum equation was derived from the governing equation of the model in the x direction (Equation (2)) and the y direction (Equation (3)):

$$\frac{\partial \mathbf{h}}{\partial \mathbf{t}} + \frac{\partial \mathbf{q}\_{\mathbf{x}}}{\partial \mathbf{x}} + \frac{\partial \mathbf{q}\_{\mathbf{y}}}{\partial \mathbf{y}} = \mathbf{r} - \mathbf{f} \tag{1}$$

$$\frac{\partial \mathbf{q\_x}}{\partial \mathbf{t}} + \frac{\partial \mathbf{u\_x q\_x}}{\partial \mathbf{x}} + \frac{\partial \mathbf{v\_x q\_x}}{\partial \mathbf{y}} = -\mathbf{g} \text{ h } \frac{\partial \mathbf{H}}{\partial \mathbf{x}} - \frac{\mathbf{r\_x}}{\rho\_\mathbf{w}} \tag{2}$$

$$\frac{\partial \mathbf{q}\_{\mathbf{y}}}{\partial \mathbf{t}} + \frac{\partial \mathbf{u} \mathbf{q}\_{\mathbf{y}}}{\partial \mathbf{x}} + \frac{\partial \mathbf{v} \mathbf{q}\_{\mathbf{y}}}{\partial \mathbf{y}} = -\mathbf{g} \text{ h } \frac{\partial \mathbf{H}}{\partial \mathbf{y}} - \frac{\mathbf{r}\_{\mathbf{y}}}{\rho\_{\mathbf{w}}} \tag{3}$$

where h is the height of the water from the local surface; qx and qy are the unit width discharges in the x and y directions, respectively; u and v are the flow velocities in the x and y directions, respectively; r is rainfall intensity; H is the height of water from the datum; ρw is the density of the water, g is gravitational acceleration; and τx and <sup>τ</sup>y are the shear stresses in the x and y directions, respectively.

The RRI model separates the calculations of the discharge and the hydraulic gradient relationship. Hence, simulations of surface and subsurface flow proceed in the same algorithm. In addition, kinematic-rainfall–runoff-wave and diffusive-wave approximation are also derived in this model. The kinematic wave is calculated with the assumption that the water-surface slope is the hydraulic gradient. On the other hand, diffusion-stream approximation is utilized to form the streamflow equation.

The calibration process was carried out via a simulation of the flood events on 1 January. Then, the inundation area from the simulation was compared with the inundation map obtained from remote sensing by satellites at the same time. The model would have been well-calibrated if the result showed similarities to the inundation obtained from the remote sensing.

### **3. Results and Discussion**

### *3.1. Model Calibration*

The model was calibrated before being used to simulate the return-period flood. It was calibrated with a flood event that occurred on 1 January 2020. The simulation calculated the distribution of flood inundation with the condition of maximum water depth and compared it to the inundated area's satellite data at the same event (Figure 6). Figure 6 shows the comparison of the simulated flood inundation and the flood inundation from Sentinel 1A acquired on 2 January 2020. The flood inundation from Sentinel 1A was generated using an algorithm that was proposed by Chini et al. [49]. The algorithm can detect flood water not only on bare soil but also in urban regions. Even though the Sentinel 1A data was acquired a day after the flood event, some inundation still remained on the land. The results shows similar inundation in the northeast part between the simulation and satellite data, whereas in the middle part, near the ocean, the figure shows that the simulated inundation areas are larger than in the satellite data because the flood waters in the urban area of Jakarta receded on 2 January 2020.

**Figure 6.** Rapid assessment of Jakarta flood inundation: (**a**) simulated 1 January flood inundation and (**b**) flood inundation data from Sentinel 1A acquired on 2 January 2020.

The inundation in this model consisted of local inundation and large-scale inundation. Local inundation was defined as local water depth that was barely moving. Accordingly, this type of inundation has a limited range of area and shallow water depth. On the other hand, large-scale inundation is water depth that was growing around the river with a wide range of areas.

Circle A, B, C, D and E represent large-scale inundation during the simulation period. The locations of the circles are in the Cimanceuri, Cisadane, Angke, Ciliwung and Bekasi River Basins, respectively. Table 3 shows the approximation of the total affected area due to river inundation. The total area of river inundation was approximately 106.54 km2. Hence, the percentage of river inundation area during this event was 56.51% of the total inundation area.

**Table 3.** Large-scale inundation area.


### *3.2. Model Application*

The model that was calibrated was used to perform the return-period analysis of the flood. The flood return periods were simulated in mostly the same conditions as the main simulation. Nevertheless, the precipitation used as input in these simulations was modified for rainfall return periods that were forecast from historical rainfall data. The periods of rainfall that were used in this calculation were 2 years, 5 years, 10 years, 25 years, 50 years and 100 years. The results of these simulations are shown in Figure 7. Most of the large-scale inundation in each return period was found in the same locations, i.e., the Cimanceuri, Cisadane, Angke, Ciliwung and Bekasi River Basins. Therefore, the location of the large-scale inundation was the same as in the main simulation.

The area and volume of the inundation were calculated in the flood return period simulation. Then, the area and volume of the inundation were compared with the inundation characteristics of the flood that occurred on 1 January 2020. Table 4 shows that the closest return-period flood area to the flood on 1 January was the period with 100 yearly floods. The inundated areas of both flood maps were quite similar (Figure 8). This strengthens the evidence that the flood that occurred on 1 January was a flood with a return period of 100 years, whereas the rainfall on that day was greater than the rainfall for a return period of 100 years. The flood may not have been as large as the rainfall due to the spatial distribution of rainfall. The variability of rainfall spatial distribution could have affected the amount of flood discharge, which generates different flooding [50,51].

**Figure 7.** The inundation of flood return periods. (**a**) Flood return period of 2 years. (**b**) Flood return period of 5 years. (**c**) Flood return period of 10 years. (**d**) Flood return period of 25 years. (**e**) Flood return period of 50 years. (**f**) Flood return period of 100 years.


**Table 4.** The characteristics of flood return periods.

The flood discharge was calculated for the main model and the flood return period. It was measured with RRI hydro calculation at Water Gate Manggarai on the Ciliwung River (106◦1227.48 S and 106◦5054.55 E). The maximum discharge of each model is presented in Table 4. The discharge of the main model was compared with floods with 2-, 5-, 10-, 25-, 50- and 100-year return periods (Figure 9). The maximum discharge of the main model exceeded the 100-year return period. Therefore, the main model corresponded better with the rain-gauge data, which was more than the 100-year return-period rain data.

**Figure 8.** The comparison between (**a**) the 100-year flood return period and (**b**) the flood on 1 January 2020.

**Figure 9.** Discharge at Water Gate Manggarai.
