DEM has been widely used for hydrological analysis. With ASTER DEM available, the higher resolution of 30m by 30m elevation data make the hydrological and flood risk analysis applicable at regional level. For example, Wyatt 2003, Brakebill and Preston 2003, Arthur et al., 2005, Thanapakpawin et al. 2006 used a 30-m DEM to obtain topography and flow network. In this study, two steps analysis is taken: (1) A hydrologic analysis of Malawi is conducted based on ASTERDEM data, and Flood risk map is generated at a TA municipal area using an integrated flooding risk index. Enumerator areas (EA), which are based 2008 census map, are the smallest geographic unit of the census. TA is a regional level of municipality area. Each TA is consisted of a number of EA areas. There are 368 TAs in the whole country. Precipitation data are obtained from “WorldClim” Global Climate data. It is interpolations of observed data, representative of 1950-2000 (http://www.worldclim.org/), with a resolution of 30 arc-seconds (~900m). The heavy rainfall happens in the middle east of the country during January, but highest in March in northeast of the country. We calculate the average rainfall of the five months in the rain seasons, January to April and December, and use it as an indicator for flood risk evaluation. (2) Following the analysis of flooding risk at TA level, we evaluate the risk of five selected cities – Lilongwe, Blantyre, Zomba, Mzuzu and Karonga. Using Karonga city as an example, we tentatively map the potential floodplains by applying a method of various buffer area based on elevation.
To map flooding vulnerability area, we integrated several indices at TA municipal level: mean value of elevation (López L 2009); the maximum value of elevation in the TA as a shelter, the average rainfall level in the rain season; the distance from the TA center to the nearest stream; and the density of stream in the TA area. These indicators are widely used for predicting flood risk. Rainfall is an important indictor when mapping flood vulnerability. Then all the values are changed to relative value which is from 0-5. We applied a weight as: (0.2*Average DEM+0.2*Highest DEM+0.4 Average Preceipitation+0.1*Distance to Stream+0.1*Stream density). The weight is based on knowledge. Stream density is the percentage of stream cells in the total number of cells in the TA area. Each indicator was classified to five categories from 1-5, indicating low to high risk, based on percentile (Table 1).
Dist. to Stream
Table 1 Rank of the values of each indicator
The five categories for each indicator are classifies evenly using percentile classification. The overall indicator was the integration of all the five indicators with a weight. Then we use standard deviation to classify the integrated rank to five categories, and mapped the risk from very low to very high. Because we are unable to find the absolute risk, the risk is relative risk to differentiate various locations in the country. Standard Deviation is used to map vulnerability and extreme environmental conditions such as urban heat island (Chow et al., 2012). The results are shown in Figure 1. At regional level, the preliminary results from flooding risk analysis shows that the high risk areas are located in the east region. In the 368 TAs, 79TAs are in the list of high risk with 0.50-1.50 standard deviation, and 34 TAs are list as very high risk TAs with 1.50-2.50 standard deviations. In the selected five cities and adjacent TA areas, partial of the TAs in Lilongwe and Mzuzu region are listed as medium risk. The urban areas are exposed in “high” and “very high” risk categories include Karonga, Blantyre and Zomba. Karonga and Zomba are the cities are facing the highest risk of flooding among the five cities.
Karonga is a city where climate and natural hazards frequently happen, including flooding, drought, cyclone, and earthquake. Zomba and southeast Malawi are also indicated as the region where flooding frequently happens. These historical flooding records are roughly consistent with the mapping results, and it reflects the feasibility of the proposed method of ranking flood risk of municipal TAs.
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