ESRI Eastern Africa Education GIS User Conference 10th- 11th September, 2015 – JKUAT- Nairobi, Kenya

GIS- Based Flood Risk Mapping for

Kilifi county, Kenya

A Paper Presented by

Maina P. M., Ochola S and Onywere S.

paulosteve013@gmail.com

Kenyatta University

Introduction

Flood- Natural phenomenon that leads to temporary submerging of

a piece of land with water

Floods in Kilifi have been increasing in frequency and magnitude

Kilifi has experienced changes in land cover over the years which

exposes the area to flood risk

Land Cover Changes; affect the hydrological cycles, impact on speed and volume of runoff, alters a soil’s degree of infiltration,

impacts on groundwater recharge

The study endeavored to establish the role of land use and land

cover changes on flood risk in ArcGIS 10.1 and generate a flood risk

map

GIS offers a platform for combining different factors to identify flood

prone areas and generate risk maps

Flood risk was assessed based on land cover, soil distribution, rainfall

distribution, drainage density and nature of slope

Introduction Cont’d

Problem Statement

Some parts in Kilifi get flooded after rainfall events

Increased population has led land dynamics

leading to loss of natural vegetation

Alteration of natural drainage systems, impacts on surface runoff and infiltration capacities

Soil erosion and sedimentation downstream

Information on Land dynamics is often theorized

There lacks concrete scientific basis to link land dynamics to floods

Many areas in the county prone to floods are not zoned and there’s no flood risk map

Materials and Methods Cont’d

MAP OF STUDY AREA

Spatial extent -12,245.90 km2

Population- 1,109,735 (2009 census)2˚ 20’ 4” South, and 39˚05’ 14” East

Materials and Methods Cont’d

Data- All the data used in the study was obtained from different sources through RCMRD and were in GeoTIFF format

Landsat 5 TM, 7 ETM and 8 ETM+ for 1990, 2000 and 2014

Digital Elevation Model (Obtained from CIAT-CSI SRTM

Soil Data (Distribution, type, lithology and geology)- from the Survey of Kenya

Rainfall Distribution Data (from the Tropical Rainfall Measuring Mission- TRMM)

Ancillary data- Field Ground Truth Mapping, aerial photographs downloaded from Google Earth (http://www.googleearth.com) and topographic maps (1:50000)

Software

Data processing was conducted using various tools in ArcGIS 10.1

Materials and Methods Cont’d

Landsat 5, 7 and 8

Images

Mosaicking and

Subsetting

Image Classification

Accuracy analysis

Reclassification of 2014

LULC Map

Land Use Land Cover Maps

and Matrices

Stream Network and

Drainage Density

Drainage Basins

30M DEM Data

Flow Direction

Flow Accumulation

DATA ACQUISITION

Auxiliary Data: Aerial

Photos, Topographic

Maps

Ranking, Weighted Layer

Overlay and Flood Risk Map

Production

Calculation of Slope

and Reclassification

Soil Data/

Soil Map

Reclassification of

Soil Map

Rainfall

Distribution Map

Reclassified

Rainfall Map

Research Design

Results and Discussions

Image Classification- Years1990, 2000 and 2014

Land cover classes: croplands, forestlands, grasslands, settlements,

shrublands and wetlands.

There were significant gains and losses in land cover in the area

between 1990 and 2014 as indicated in the table below

Overall classification accuracy was 86.7% for 1990, 90.2% in 2000 and 92.4% in 2014

0

1000

2000

3000

4000

5000

6000

7000

Croplands Forestlands Grasslands Shrublands Settlements Wetlands

Are

a (

Km

²)

Land Cover Classes

Land Cover Extent in 1990, 2000 and 2014

1990

2000

2014

Results and Discussions

a) Land use land cover map for 1990 b) Land use land cover map for 2000 c) Land Use Land Cover Map for 2014

Results and Discussions Cont’d

Risk based on Land Cover DistributionLand Cover Map for 2014 was used

Six classes were generated; croplands,

forestlands, grasslands, shrublands,

settlements and wetlands

Classes were then reclassified into 5.

Highest rank (1)- assigned to settlements (have lowest soil infiltration

capacities, are highly devegetated,

highly impermeable)

Lowest rank (5)- assigned to wetlands

(are often flooded, not inhabited by humans unless they are reclaimed)

Risk areas based on land cover

Results and Discussions Cont’d

Risk zones based on slope angles

ii) Risk Based on Slope angles and Elevation

Slope angles were generated from the

DEM of the area

Highest rank (1) was assigned to slope

angles between 0 – 1.238˚ as this area is

relatively flat and highly vulnerable to risk

Lowest rank (5) was assigned to areas ,

≥10.244 as they are steep and less likely to

accumulate water

Slope Angle (Degrees) Area (Km²) % Area

0 – 1.238 6520.5 53.3

1.238 – 2.814 3012.5 24.6

2.814 – 5.403 1487.9 12.2

5.403 – 10.244 446.8 3.7

10.244 – 28.705 777.6 6.4

Results and Discussions Cont’d

Risk map based on soil type

Risk Based on Soil Type

Soils were categorized into five classes; very

clayey, clayey, loamy, sandy loam and sandy

Reclassification into 3 classes: Sandy, loamy

and Clayey

Highest rank (1)- Clay soil- has low permeability

Lowest rank (3)- Sandy soil- high infiltration

capacity

Soil Type Area (Km²) % Area

Sandy 2602.3 21.3

Loamy 4800.4 39.1

Clayey 4004.4 32.7

Very Clayey 842.8 6.9

Results and Discussions Cont’d

Risk map based on rainfall

distribution

Risk Based on Rainfall DistributionA mean annual rainfall for the years

between 1990 and 2013 used

5 classes were generated

542mm recorded as the least annual

mean rainfall.

1471mm recorded as the highest annual mean rainfall

High rainfall areas assigned rank (1)

Areas with the least rainfall were

assigned rank (5)

Results and Discussions Cont’d

Risk zones based on drainage

density

Risk Based on Drainage Density

High drainage density areas are

highly susceptible to erosion and

result to massive sedimentation on

the lower grounds

Flow direction oriented towards

the Indian Ocean

Highest ranks assigned to areas

with low drainage density

Lowest ranks assigned to areas

with high drainage density

Results and Discussions Cont’d

Risk Category Area (Km²) % Area

Low 122.5 1.1

Moderate 4163.6 34.0

High 6735.3 54.9

Very High 1224.6 10.0

Flood Risk Map

4 flood risk zones were generated;

very high, high, moderate and low

risk areas respectively

Conclusion

Flood risk maps are necessary and are the first step towards

flood risk management

Land use and land cover changes contribute to flooding

Will the necessary data, software and expertise, flood risk maps

are easy to make

Flood risk maps can be made for local, national or even

regional scales

The flood risk map produced in this study integrates the effect

of different parameters.

Just a short period of precipitation data may not be enough to

give a good visualization on flood risk. Its not only the

hydrological features that contribute to flooding, but an

integrated response of the ground.

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