Determinant factors
Elevation and slope
The movement and direction of the flood is controlled by elevation of the terrain (Gigovi´c et al., 2017). The elevation of the study area varying from 362.5–2,989.64 m has been reclassified into five classes. Topographic lows (362.5–960.04 m) were found to cover 34.41% of the total area of the Awash basin. The topographic highs (2,309.68–2,989.64 m) were found to cover only 7.25% of the study area. Each class has been given weightage depending on its vulnerability towards flood. Low laying areas (for example 362.5–960.04 m has been given the highest weight of 5 as these areas are more vulnerable to flood; whereas areas at the highest elevation of 2,309.68–2,989.64 m has been given the lowest weightage of 1 as such areas are the least vulnerable towards flooding. The weighted map of the elevation is shown in Fig. 3. Slope is another determinant factor for flood occurrence. The slope of the study area ranging from 0- 46.380 have been reclassified into five classes. Flat to gentle slopes are more vulnerable to overland flow accumulation compared to steep slopes. Most of the study area (49.32%) is underlain by flat to gentle slopes (0–4.060). Flat to gentle slopes (0 -4.060) has been assigned the highest weightage of 5, as such slopes are more vulnerable towards flooding. Whereas steep slopes (20.28–46.380) has been given the flood potential value of 1 because such slopes are the least vulnerable towards flooding. The weighted map of the slope is shown in Fig. 3.
Rainfall and drainage density
The rainfall (178.67–1452.86 m) is another important factor determining the flood vulnerability. The highest rainfall (1170.41–1452.86 mm) was found to cover 13.58% of the study area and the lowest rainfall (178.67–461.13 mm) was found to cover 26.65% of the area. More rainfall in any area means more proneness to floods (Guo et al., 2014). Accordingly suitable weightages have been given to different rainfall classes depending on their potential towards flood. For example, rainfall ranging between 178.67–461.13 mm was assigned the least weight of 1, whereas, rainfall ranging between 1170.41–1452.86 mm was assigned the highest weight of 5. The weighted rainfall map is shown in Fig. 4. The drainage density of the area ranges from 0.08 to 9.56 km/km2. About 22.08% of the area is underlain by the highest Dd values (5–9.56 km/km2). Drainage densities of the study area have been reclassified according to their flood hazard potential. Drainage density of the range 5-9.56 km/km2 is the highest contributor towards flood and hence the highest weightage of 5 has been given. Whereas, drainage density of 0.08–1.16 km/km2 have been given the lowest weightage of 1 because low drainage densities are generally located at the high elevations. The weighted drainage density map is shown in the Fig. 4.
Landuse and soils
Water bodies, settlements, and wetlands were found to cover 1.29% of the total study area. The computed areal extent of forest, woodland, and afro alpine is 3.67%. The landuse map of the study area was analyzed with respect to the flood vulnerability. Accordingly suitable weightages have been given to the different landuse classes. For example, forest, woodland, and afro alpine landuse classes were given the least potential of 1. Whereas, landuse classes like water bodies, wetlands, and settlements has been assigned the highest weightage of 1. Landuse classes like bare land and urban land were given the weightage of 4 and landuse classes like cultivation land; and grassland were assigned a weightage of 3. The weighted landuse map is depicted in the Fig. 5. Different soils have different infiltration rates, permeability, and porosity. The soil characteristics define their potential with regard to flood potential (Rimba et al., 2017). Therefore, suitable weightages have been given to different soil types of the study area. Pellic and chromic vertisols have been given the highest weightages of 5 indicating their highest contribution towards flood due to their low infiltration rates. Conversely, chromic cambisols, eutric nitisols have been assigned the least weight of 1 due to their least susceptibility towards flooding. The weighted soil map of the study area is shown in the Fig. 5.
Topography index and Lineament density
Compound topography index (CTI) of the study area has been computed in the GIS domain. CTI values of the study area varies from − 0.02 to 8. The areas underlain by the highest CTI values (1.46–8) were computed to be 24.03% of the total area. Areas with the higher value of CTI have higher wetland potential compared to the areas with lower CTI values. Therefore, the highest weightage of 5 has been given to the areas with CTI values of 1.46- 8. Whereas, areas with the lowest CTI values of -0.02 - -0.16 were assigned the lowest weightage of 1 due to their lowest wetland potential. The weighted CTI map is shown in the Fig. 6.
Lineament density (Ld) of the study area has been calculated using GIS. Ld of the study area varies from 6.81- 0.43km/km2. Areas with higher Ld values have more channels to infiltrate water downwards. Therefore areas with the highest Ld values (6.81–12.91 km/km2) were assigned the lowest weightage of 1. Whereas, areas underlain by lowest Ld values (0.16–0.43) were assigned the highest weightage of 5. The weighted map of the Ld is shown in the Fig. 6.
Flood hazard and flood risk
Each factor was reclassified into five classes and weightages have been given to each class in GIS domain (Table 1). All the weighted factors were allowed to overlay in GIS domain to get the flood hazard map. Flood vulnerability raster map have been reclassified into 5 classes viz., Very low (1), Low (2), Moderate (3), High (4) and Very high (5) hazards. The areal extent of very low, low, moderate, high and very high flood zones area 6.96, 10.21, 33.09, 38.28 and 11.46 km2 respectively.
Table 1
Thematic layers, their areal extent along with their potential values and the associated hazard
Theme | Classes | Area (%) | Potential value | Hazard |
Elevation | 2,309.68–2,989.64 | 7.25 | 1 | Very high |
1,938.79–2,309.67 | 15.90 | 2 | High |
1,506.09–1,938.78 | 18.68 | 3 | Moderate |
960.05–1,506.08 | 23.76 | 4 | Low |
362.5–960.04 | 34.41 | 5 | Very low |
Slope | 0–4.06 | 49.32 | 5 | Very high |
4.07–6.36 | 31.05 | 4 | High |
6.37–10.11 | 13.09 | 3 | Moderate |
10.12–20.27 | 5.21 | 2 | Low |
20.28–46.38 | 1.34 | 1 | Very low |
Rainfall | 178.67–461.13 | 26.65 | 1 | Very low |
461.14–707.94 | 22.89 | 2 | Low |
707.95–923.59 | 17.93 | 3 | Moderate |
923.6–1170.4 | 18.95 | 4 | High |
1170.41–1452.86 | 13.58 | 5 | Very high |
Drainage density | 0.08–1.16 | 2.60 | 1 | Very low |
1.17–1.69 | 11.68 | 2 | Low |
1.7–2.77 | 25.12 | 3 | Moderate |
2.78–4.99 | 38.51 | 4 | High |
5–9.56 | 22.08 | 5 | Very high |
Land-use | Forest, Woodland, Afro alpine | 3.67 | 1 | Very low |
Grassland, Shrub land, Plantation | 40.64 | 2 | Low |
Cultivation land | 21.70 | 3 | Moderate |
Bare land, Urban land | 32.70 | 4 | High |
Water bodies, Settlements, Wetland | 1.29 | 5 | Very high |
Soils | Chromic cambisols, Eutric nitisols | 21.43 | 1 | Very low |
Calcaric regosols | 36.76 | 2 | Low |
Lithosols | 5.37 | 3 | Moderate |
Fluvisols, Yermisols | 3.44 | 4 | High |
Pellic and chromic vertisols | 32.99 | 5 | Very high |
Topography index | −0.02 -−0.16 | 10.54 | 1 | Very low |
−0.17–0.19 | 8.72 | 2 | Low |
0.2–0.37 | 22.33 | 3 | Moderate |
0.38–1.45 | 34.38 | 4 | High |
1.46–8 | 24.03 | 5 | Very high |
Lineament density | 6.81–12.91 | 3.35 | 1 | Very low |
0.44–1.49 | 6.09 | 2 | Low |
1.5–3.39 | 13.49 | 3 | Moderate |
0.44–1.49 | 30.62 | 4 | High |
0.16–0.43 | 46.44 | 5 | Very high |
For computing the flood risk map, flood vulnerability map was overlaid with weighted map of population distribution and landuse map. The output flood risk map was prioritized accordingly to get a clear picture about which areas should be given preferences for flood management processes. The flood vulnerability and flood risk map is shown in Fig. 7. Areas such as Adi Gala, Asbuli, Gewane, Loqiya, Weranso, and Mile have been found in the highest flood vulnerability zone; hence these areas are kept in the first priority for management process. Whereas, towns such as Alem Gena, Sendafa, Gina Ager, Ankober, Assagirt, Aliyu Amba, Debre Sina, Armanaya, Jimate, Ancharo, Karakore, Kombolcha, Boru, Wirgesa, Sirinka, Kobo, Hurso, Gota, and Teferi Ber fall in “very low” flood hazard; hence were giving the least priority for flood management process. The areal extent of each flood hazard zone along with the towns associated and their priority is shown in Table 2.
Table 2
Areal extent of flood vulnerability in the Awash basin, Ethiopia
Flood hazard | Area (Km2) | Percent | Representative towns | *Priority |
Very low | 7777.28 | 6.96 | Alem Gena, Sendafa, Gina Ager, Ankober, Assagirt, Aliyu Amba, Debre Sina, Armanaya, Jimate, Ancharo, Karakore, Kombolcha, Boru, Wirgesa, Sirinka, Kobo, Hurso, Gota, and Teferi Ber | 5 |
Low | 11414.23 | 10.21 | Bantu, Melka Kunture, Dukem, Debre Zeyt, Enjere, Holota, Iteya, Sire, Angada, Balchi, Koremas, Chelekko, Meteh Bela, Arba Reketi, Kersa, Arabi, Gogli, Mersa, Hayk, and Kemise. | 4 |
Moderate | 36975.46 | 33.09 | Harbu, Bati, Mekoy, Shewa Robit, Debel, Dulecha, Metehara, Lemen, Kondaliti, Arboye, Wolonkomi, Bike, Shinili, Dire Dawa, Dehabo, Wenji, Nazeret, Sodore, Alem Tena, and Ambosa | 3 |
High | 42773.32 | 38.28 | Harawa, Aydora, Dubti, Melka Werer, Awash, Koka, and Tendaho | 2 |
Very high | 12801.18 | 11.46 | Adi Gala, Asbuli, Gewane, Loqiya, Weranso, and Mile | 1 |
*Priority: 1–5; where 1 means the highest priority and 5 means the lowest priority. |
Table 3
Explanatory regression models
Model | AdjR2 | AICc | JB | K-BP | MaxVIF | X1 | X2 | X3 | X4 | X5 | |
1 | 0.3762 | 1913.932 | 0.152 | 0.412 | 1.000 | Rf | | | | | |
2 | 0.035779 | 2216.597 | 0.174 | 0.496 | 1.000 | Dd | | | | | |
3 | 0.41192 | 1872.95 | 0.132 | 0.008 | 1.000 | Lc | | | | | |
4 | 0.104506 | 2165.205 | 0.144 | 0.102 | 1.000 | Ld | | | | | |
5 | 0.062832 | 1873.78 | 0.167 | 0.232 | 1.000 | Cti | | | | | |
6 | 0.314543 | 1979.439 | 0.165 | 0.109 | 1.000 | Elev | | | | | |
7 | 0.05857 | 2199.972 | 0.199 | 0.090 | 1.000 | Slp | | | | | |
8 | 0.379233 | 1911.564 | 0.188 | 0.477 | 1.199 | Rf | Dd | | | | |
9 | 0.621215 | 1568.243 | 0.183 | 0.008 | 1.078 | Rf | Lc | | | | |
10 | 0.383913 | 1906.304 | 0.196 | 0.137 | 1.180 | Rf | Ld | | | | |
11 | 0.439723 | 1840.309 | 0.197 | 0.070 | 1.001 | Dd | Lc | | | | |
12 | 0.128952 | 2146.988 | 0.186 | 0.002 | 1.011 | Dd | Ld | | | | |
13 | 0.318867 | 1976.061 | 0.193 | 0.090 | 1.266 | Dd | Elev | | | | |
14 | 0.076497 | 2187.63 | 0.171 | 0.073 | 1.060 | Dd | Slp | | | | |
15 | 0.47169 | 1799.479 | 0.171 | 0.089 | 1.016 | Lc | Ld | | | | |
16 | 0.614277 | 1580.857 | 0.151 | 0.076 | 1.036 | Lc | Elev | | | | |
17 | 0.424097 | 1859.427 | 0.192 | 0.080 | 1.045 | Lc | Slp | | | | |
18 | 0.341823 | 1952.234 | 0.199 | 0.065 | 1.094 | Ld | Elev | | | | |
19 | 0.238 | 1953.543 | 0.178 | 0.048 | 1.012 | Ld | Cti | | | | |
20 | 0.126371 | 2149.045 | 0.149 | 0.078 | 1.105 | Ld | Slp | | | | |
21 | 0.627226 | 1558.149 | 0.192 | 0.083 | 1.253 | Rf | Lc | Ld | | | |
22 | 0.628398 | 1555.961 | 0.192 | 0.471 | 2.659 | Rf | Lc | Elev | | | |
23 | 0.492162 | 1773.036 | 0.188 | 0.453 | 1.026 | Dd | Lc | Ld | | | |
24 | 0.444778 | 1835.035 | 0.192 | 0.347 | 1.106 | Dd | Lc | Slp | | | |
25 | 0.345323 | 1949.552 | 0.193 | 0.479 | 1.372 | Dd | Ld | Elev | | | |
26 | 0.14216 | 2137.393 | 0.181 | 0.387 | 1.160 | Dd | Ld | Slp | | | |
27 | 0.629715 | 1553.492 | 0.299 | 0.451 | 1.122 | Lc | Ld | Elev | | | |
28 | 0.5432 | 1544.362 | 0.289 | 0.442 | 1.134 | Lc | Ld | Cti | | | |
29 | 0.636866 | 1540.968 | 0.382 | 0.441 | 2.204 | Rf | Lc | Elev | | | |
30 | 0.732728 | 1548.844 | 0.398 | 0.498 | 1.222 | Lc | Ld | Elev | Slp | | |
31 | 0.839539 | 1536.866 | 0.432 | 0.421 | 2.353 | Rf | Lc | Ld | Elev | Slp | Cti |
Where AdjR2, Adjusted R squared1; AICc, Akaike’s Information criterion1; JB; Jarque- Bera statistic2; K-BP, Koenker (BP) statistic3; MaxVIF, Maximum Variance Inflation4; X, variables |
1Measures of model performance |
2When this test is statistically significant (p < 0.01) model predictions are biased (the residuals are not normally distributed) |
3When this test is statistically significant (p < 0.01), the relationships modeled are not consistent (either due to non-stationarity or heteroskedasticity). |
4Large VIF (> 7.5, for example) indicates explanatory variable redundancy. |
Rf, Rainfall; Dd, Drainage density, Lc, Landcover; Ld, Lineament density, Elev, Elevation; Slp, Slope; Cti, Compound topography index. |