MODELING SURFACE INUNDATION AND FLOOD RISK IN A FLOOD-PULSED SAVANNAH: CHOBE RIVER, BOTSWANA AND NAMIBIA

Jeri Jacqueline Burke

A Thesis Submitted to the University of North Carolina Wilmington in Partial Fulfillment

of the Requirements for the Degree of Master of Science

Department of Geography and Geology

University of North Carolina Wilmington

2015

Approved by

Advisory Committee

Narcisa G. Pricope Michael Benedetti ______________________________ ______________________________ Chair

James Blum Eman Ghoneim ______________________________ ______________________________

Accepted By

_______________________________ Dean, Graduate School

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JOURNAL PAGE This thesis was formatted according to the requirements of the International Journal of Applied Earth Information and Geoinformation.

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TABLE OF CONTENTS

ABSTRACT .....................................................................................................................................v LIST OF FIGURES ....................................................................................................................... vi 1. INTRODUCTION .......................................................................................................................1 1.1 Study area .......................................................................................................................5 2. DATA. .......................................................................................................................................10 2.1 Thermal Imagery (Land Surface Temperature) ...........................................................10 2.2  Discharge and stage data ..............................................................................................11 2.3 Precipitation .................................................................................................................12 2.4 Population and Ancillary Data .....................................................................................12 3. METHODOLOGY ....................................................................................................................12 3.1 MODIS pre-processing and image differencing ..........................................................13 3.2 Image threshold segmentation .....................................................................................14 3.3 Inundation mapping validation ....................................................................................15 3.4 Regression Analysis of flood, discharge and precipitation patterns ............................16 3.5 At-risk Population analysis ..........................................................................................18 4. RESULTS ..................................................................................................................................19 4.1 Flood frequency map ...................................................................................................19 4.2 Flood frequency 2000 - 2014 .......................................................................................21 4.3 Typical seasonal flood dynamics .................................................................................23 4.4 Years of Extreme Inundation .......................................................................................27 4.5 Regression analysis of flood and precipitation patterns ..............................................33 4.6 Population-flood risk mapping ....................................................................................36

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5. DISCUSSION ............................................................................................................................39 6. CONCLUSIONS .......................................................................................................................45 7. REFERENCES ..........................................................................................................................46 APPENDICIES A. List of evaluated MODIS LST images ..................................................................50 B. IDL Codes for Imagery Processing ........................................................................52 C. SAS Codes for GLMSELECT Model ...................................................................53 D. Results of the GLMSELECT Procedure ................................................................54

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ABSTRACT

The Chobe River Basin, a sub-basin of the Upper Zambezi Basin, is a complex

hydrologic system whose watershed is shared by Namibia and Botswana. Its hydrology is poorly

understood and controlled by the timing and relative contributions of water from the Zambezi,

Kwando. According to key informant interviews with locals and water managers, there has been

an increase in the magnitude of flooding in this region since 2009 resulting in the displacement

of rural communities. This research models seasonal flooding patterns in the Chobe River Basin

for the period 2000-2014, identifies the driving forces that control the magnitude of flooding and

identifies high population density areas that are most at risk of high magnitude floods.  Spatio-

temporal changes in surface inundation determined using NASA Moderate-resolution Imaging

Spectroradiometer (MODIS) thermal imagery revealed that flooding extent in the Chobe River

Basin is extremely variable, ranging from 401km2 to 5779 km2 over the last 15 years. A multiple

regression of lagged discharge of the Zambezi and Kwando and flooding extent in the CRB

indicated that the best predictor of flooding in this region is the discharge of the Zambezi River

64 days prior to flooding. The seasonal floods have increased drastically in magnitude since

2008 causing large populations to be displaced. Over 46,000 people (53% of Zambezi region

population) are living in high magnitude flood risk areas, making the need for resettlement

planning and mitigation strategies increasingly important.

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LIST OF FIGURES

Figure Page

1. Study area showing the Chobe River Basin in Namibia and Botswana ..............................6 2. Mean monthly precipitation in the Upper Zambezi Sub basin ............................................9 3. Daily discharge of the Zambezi River ..............................................................................11 4. Sample differenced MODIS Land Surface Temperature ...................................................15 5. Histogram of land surface temperatures ............................................................................15 6. Sub-regions of the Chobe River Basin used in regression analysis. ..................................17 7. Spatial extent of inundation duration cycles in the Chobe River Basin ............................20 8. A. Derived annual inundation maximums from 2000-2014 B. How inundation maximum for each year varied from the long term average. .............21 9. Inundation duration in the Chobe River Basin for the period 2000-2014. ......................22 10. Ground control points taken in Botswana and Namibia ....................................................23 11. Time series of classified MODIS LST images ..................................................................25 12. Stage of Chobe River at Marina Lodge, Kasane, Botswana ..............................................26 13. A. Mean monthly precipitation in the Upper Zambezi sub basin       B. Derived inundated area in the Mamili wetlands for the period 2000 to 2014 .............28 14. A. Arrival of the Zambezi flood pulse in 2008 and 2010 B., C. Resulting flooding in the Mamili wetlands .............................................................30 15. Connection between Chobe Floodplain and Lake Liambezi .............................................31 16. Flooded area surrounding Lake Liambezi .........................................................................33 17. Correlation of derived flooded area in the CRB and the discharge of the Zambezi River ...................................................................34 18. Comparison of the observed inundation derived from the MODIS LST imagery and with that modeled using Eq. (3) over the period 2001-2003 ........................36 19. Distribution of population in the Zambezi Region of eastern Namibia .............................37

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20. Population centers located within flood zones in years of low magnitude (A.) and high magnitude flooding (B.) ..................................38 21. Daily discharge of the Zambezi and derived area of flooding in the Chobe River Basin from 2000-2014. ...................................................41 22. Lag between peak discharge of the Zambezi and peak flooded area in the CRB ..............42 23. Example of differenced MODIS image from May 25th, 2001 ..........................................44

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1. Introduction

  For centuries, populations worldwide have relied on seasonal flooding for their

livelihoods. This dependence has resulted in settlements being built on riverbanks and

floodplains in order to utilize the water sources and fertile soils. However, with these resources

come risks. It is estimated that one billion people are living in the potential path of a 100-year

flood and this number is expected to double by 2050 due to climate change, sea level rise and

deforestation (Bogardi, 2004). The majority of these communities are some of the poorest people

in the world, making them even more susceptible to natural disasters. Floods affect the lives of

over 500,000,000 people per year and result in death, disease, homelessness and other

devastating losses (Bogardi, 2004). Increasing frequency and intensity of seasonal floods will

continue to drive communities out of their homes and lead to environmental refugees and the

need for resettlements (Field, 2012).

In order for successful resettlement plans to be made, areas at the highest risk from

inundation must be properly identified and predicted. Remote sensing offers the ability to map

and analyze both seasonal and inter-annual flood dynamics at a regional scale, making it an

indispensable tool for flood monitoring. Temporal changes in flooding extent can be monitored

based on remotely sensed imagery using a variety of methods. Methods that are commonly used

for classifying flooding and wetlands include thresholding and ratioing between channels,

supervised and unsupervised classifications, NDWI, RADAR, Land Surface Water Index

(LSWI) and Vegetation Index (EVI) (McMarthy et al, 2003; Sakamoto et al., 2007; Khan et al.,

2011; Handisyde et al., 2014). Studies such as Brivio et al. (2010) and Long et al. (2014) used

RADAR technology to describe flooding extent with RADAR technology. The main advantage

of this method is its ability to penetrate cloud cover while a disadvantage is the difficulty of

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classifying the signal (Handisyde et al., 2014). Khan et al. (2011) and Sakamoto et al. (2007)

demonstrated that MODIS can successfully distinguish between flooded and non-flooded pixels

However, water can be confounded with the spectral signatures of fire scars or wet soil during

the rainy season, causing the spatial extent of flooding to be underestimated (Pricope, 2013).

Thermal data is more sensitive than optical imagery and can be used to overcome this problem

and accurately delineate inundation extent by differentiating between surfaces with otherwise

similar spectral signatures.

It has been determined that the Chobe River receives pulses of floodwater from various

sources throughout the year, but the quantity and impact of each of these sources is not fully

understood and results have been somewhat conflicting. The goals of this research are to reveal

the surficial hydrologic connections of the Chobe with the Kwando and Zambezi Rivers,

quantify the influxes to the Chobe throughout the year and identify high population density areas

that are most at risk. This research will refine work by Pricope (2013) that identified spatial

trends in flooding patterns of the Chobe River between 1985 and 2010. Major changes in

flooding patterns throughout the CRB will be analyzed by mapping inundation for 2000-2014.

The driving forces of flooding in this region will be investigated using discharge and

precipitation data to create a regression model that will have the potential to predict the extent of

future floods.

Specifically, this research will address the following questions:

1.) What are the seasonal and inter-annual patterns of inundation in the Chobe River Basin

and how have they changed over the past 15 years?

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2.) What are the driving forces that control the magnitude of flooding of the Chobe River, do

different variables have more of an impact on the flooding magnitude than others and

how do these variables differ between years that experience average flooding and years

with unusual (high or low) flooding magnitude?

3.) Where in the Chobe River basin are large populations of people most susceptible to high

magnitude floods?

The Zambezi region has been exposed to rapid development in recent years with the current

population estimated at 0.5 million people (Wolski et al, 2014). However, estimates of the

number of people that utilize the river’s ecosystem services, such as irrigation, hydropower, and

fishing, may be up to 2.8 million (Arnall, 2014; Long, 2014). Increasing populations and plans to

extract water for major agricultural developments make understanding the interconnections

between these rivers especially important. Continued declines in flow and discharge, combined

with climate change, could result in inadequate amounts of water for the maintenance of

ecosystem services for economic and domestic uses (Pricope, 2013).

Fertile alluvial soils, fishery resources and grazing lands in the Upper Zambezi support

one of the most densely populated areas in southern Africa (Scudder, 1991). Although seasonal

floods are essential to the ecology of this region, floods of extreme magnitude cause the rapid

migration of people and animals alike, increasing human-wildlife conflict and leading to

temporary or permanent displacements. A series of large floods have occurred in the Zambezi

region since 2009, and have drawn the attention of relief agencies and placed additional burdens

on local communities as well as land and water managers (Long, 2014). Areas that had been dry

for decades have recently been experiencing floods of extreme magnitudes exposing un-

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expecting villages to a variety of threats (Inambao, 2009). Similarly, perennial lakes and

wetlands that have been dry since the 1980s have been returning in recent years (Long, 2014).

Prior research has attempted to identify the causes of the increase in high magnitude

floods in recent years in the Upper Zambezi region. McCarthy and Gumbricht (2003) used

NOAA Advanced Very High Resolution Radiometer (AVHRR) to estimate inundation and

concluded that changes in channel distribution and the resulting spatial extent of the flooded area

can been attributed to external climate changes and El Nino/ Southern Oscillation (ENSO)

effects. This is further supported by Wolski et al. (2014) who found the annual flood patterns in

the Okavango basin have been changing over past decades and examined the extent to which

extreme floods, such as those between 2009 and 2011, were caused by greenhouse-gas-driven

climate change. Wolski determined that the probability of such extreme floods would be lower in

a climate without anthropogenic greenhouse gas forcings. The increased flooding from 2009-

2011 coincided with extreme and destructive flooding in Pakistan and other places around the

world, indicating a global driver (Wolski et al, 2014).

Floods of this magnitude had not been seen in the region for the last 20-30 years, and the

unexpectedness of the events negatively impacted both local populations and the tourism

industry, a crucial component of the regional economy (Wolski et al., 2014). Rural communities

in the Zambezi Region are dominated by agricultural livelihoods and therefore are almost

completely reliant on the land itself for their survival. Flooding can destroy agricultural fields

and make roads impassable. A better understanding of the flooding patterns and the driving

forces of these floods can reveal which regions are most susceptible to high magnitude floods

and can help determine whether relocation actions are required from the government. Though

there are risks, a flood can also present short-term economic opportunities, which may make it

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difficult to convince communities to move from their homes. Fishing, for instance, becomes

possible in areas that are dry under normal conditions (Frederick Kachingo, personal

communication, 2014).

Although seasonal floods can impose risk on populations living in their path, they are

essential to wetland ecology. Seasonal floods allow the flow of a river to become connected to

the landscape by promoting the exchange of nutrients and cycling water between air and land

(Postel and Brian, 2003). Seasonally inundated wetlands are some of the most biologically rich

places on earth and support a plethora of habitats and fauna. Floodplains and wetlands also often

serve as breeding areas for fish, break down pollutants, store water and influence sediment

mitigation (Jia and Luo, 2009; Postel and Brian, 2003).

1.1 Study Area

This study focuses on the flooding patterns of the Chobe River basin (CRB), a sub-basin

of the Upper Zambezi in Botswana and Namibia (Fig. 1). The basin boundaries and drainage

area of the Chobe River basin that contain the Mamili and Zambezi wetlands, the Chobe and

Linyanti channels and floodplains, and Lake Liambezi were delineated previously using a 10-m

spatial resolution DEM, 1-m spatial resolution orthophotograph, AVHRR NDVI and MODIS

images and permanent water training samples (Pricope, 2013). The basin contains extensive

seasonal wetlands that provide essential resources for people and wildlife alike. This region is a

unique confluence of two major river systems of southern Africa, the Zambezi and Kwando

Rivers. The Chobe River system, along with the Okavango Delta, is one of the major perennial

surface water sources for Botswana and Namibia and is one of the world’s most important

wetland systems because of its relatively undisturbed state (Cronberg et al, 1995).

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Together with the Okavango Delta, the Chobe River lies at the center of the Kavango–

Zambezi Transfrontier Conservation Area (KAZA), the largest transfrontier conservation area in

the world spanning more than 170,000 km2 (Fraser, 2012). KAZA is the home to the largest

elephant population on earth, as well as many other migratory wildlife populations, providing

habitat to over 250,000 elephants with around 128,000 in northern Botswana alone (Fraser,

2012). This conservation area provides a wildlife corridor and contains vital water sources for

agriculture and grazing in a water-limited ecosystem (Gaughan and Waylen, 2012). Increased

fencing, human use and grazing of cattle have caused steep declines in wildlife outside of Chobe

National Park (Fraser, 2012).  Increased flooding decreases the amount of land for human use,

and increases human-wildlife conflict (Elvis Simba, KAZA, May 23rd, 2014, personal

communication).

Figure 1 Study area showing the Chobe River Basin in Namibia and Botswana. Also shown are the rivers that provide water to the region: the Zambezi River, the Linyanti and Chobe Rivers in the center of the basin, and the Kwando river.

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The preservation of the natural hydrologic system and connectivity in the Upper Zambezi

is crucial to ensuring the sustainability of the ecosystem. The development of hydropower dams

in the Middle and Lower Zambezi regions has drastically modified the hydrological conditions

impacting biodiversity and livelihoods downstream. The duration, frequency and magnitude of

seasonal flood pulses have been especially impacted. Although the Upper Zambezi has not been

modified, it has been recently identified as a region with significant hydropower potential

(Beilfuss, 2012). As the ecology of a floodplain is greatly dependent on the spatial extent and

duration of flooding, modeling potential flood inundation and mapping the extent, timing, and

intensity of actual inundation is crucial in understanding the dynamics of vegetation on the

floodplain (Townsend and Walsh, 1998). More specifically, a firm understanding of seasonal and

inter-annual inundation patterns is invaluable to water and land resource managers.

The annual flood pulses of the Chobe River are influenced by multiple factors, including

the timing and relative contributions of runoff from the Zambezi, Kwando and Okavango Rivers,

which in turn are determined by precipitation and evapotranspiration rates of each of these

basins. Peak flow in the Okavango, Kwando and Zambezi Rivers occurs at slightly different

times of the year, controlling the flow rate and flooding extent of the Chobe River. The multiple

influxes of water to the river can cause an unusual back-flowing regime in the Chobe River

channel during portions of the year (Pricope, 2013).

The discharge patterns of the Zambezi and Kwando Rivers are ultimately controlled by

regional precipitation, which are determined by the migration of the inter-tropical convergence

zone (ITCZ) and sea surface temperatures in the Indian and Atlantic oceans (Gaughan and

Waylen, 2012). Seasonal rainfall in Africa occurs in a confined rain-belt, which annually moves

to Southern Africa in January (Giannini, 2008). The amount of rainfall this region receives each

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summer is controlled by the location of the boundary between the South-west Monsoon and the

South-east trades, increasing when these boundaries of the ITCZ move farther south (Moore et

al, 2007). Warming and cooling events of equatorial sea-surface temperatures, known as El

Niño and La Niña events impact precipitation and temperature patterns worldwide (Mason,

2001). Warm phase El Niño Southern Oscillation (ENSO) events commonly reduced rainfall in

southern Africa during the main December to March growing season because of weakened

tropical convection (Mason, 2001). However, not all warm ENSO events result in reduced

rainfall. The El Niño event of 2009/10 brought above average rainfall to parts of southern Africa

(Mason, 2001). The intensity of the El Niño and La Niña events have increased and the

occurrence of these ENSO events have become less predictable (Mason, 2001). Mean annual

precipitation is declining and there has been an increase in variability and the number of warm

phase ENSO events (Gaughan and Waylen, 2012).

The entire Zambezi catchment receives an average of 990 mm of rainfall per year (Fig.

2). Although this is a relatively large amount of rainfall, the majority of the precipitation arrives

in less than six months. The rest of the year has very little rainfall and drought-like conditions

(Long, 2014). The Kwando/Chobe sub-catchment, which provides water to the Chobe

floodplain, receives about 800mm/year of rainfall with most of it falling between October and

March (Beilfuss, 2012). The variable precipitation patterns in this region influence many aspects

of this ecosystem including agricultural land-use decisions, wildlife movement and most

importantly for this study, the flooding patterns in the Chobe River Basin (Gaughan and Waylan,

2012). Precipitation modeling and observational studies have suggested a drying trend over

recent decades throughout Africa (Giannini, 2008).

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At over 2575 km in length, the Zambezi River is the fourth longest river in the world and

has a catchment area of 1,320,000 km2 in eight countries (Moore et al, 2007). The Zambezi

River basin contains three major regions (Upper, Middle and Lower Zambezi) containing 13 sub-

basins. The Kwando/Chobe sub-basin is within the Upper Zambezi region along with the

Kabompo, Lungwebungo, Luanginga and Barotse. The Zambezi River originates in northwest

Zambia before flowing through east-central Angola capturing runoff from the Angolan highlands

and the Kabompo and Lungwebungu Rivers. After flowing through Angola, the Zambezi River

slowly passes through the grasslands and wetlands of the Barotse Plain in Zambia, delaying peak

discharge downstream by 4-6 weeks (Beilfuss, 2012). After this delay, the Zambezi meanders

through the Upper Zambezi wetlands. If water levels are high enough, water is forced southeast

to the Chobe floodplain. The average annual discharge of the Zambezi is 3,400 m3/s, but this

figure is variable as a result of atmospheric circulation patterns (Beilfuss, 2012).

The Kwando River, also commonly spelled Cuando, originates in the central plateau of

Angola, draining approximately 22% of the Upper Zambezi region (Beilfuss, 2012). The river

flows south paralleling the Okavango River along the Namibia-Botswana border where it flows

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)Mean Monthly Precipitation in Upper Zambezi Sub-basin

Figure 2 Mean monthly precipitation in the Upper Zambezi Sub basin calculated from TRMM 3B43 data for the period 1998-2014.

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through the Mamili wetlands. After pushing water through the Mamili wetlands, the river makes

a series of sharp turns following fault lines before going northeast to join the Linyanti system

(Shaw, 1985). It discharges into the upper end of the Chobe River floodplain once it reaches the

Eastern Zambezi Region (Beilfuss, 2012).

2. Data sources

2.1 Thermal Imagery (Land Surface Temperature)

A series of level-3 MODIS Land Surface Temperature (LST) and Emissivity products

from 2000 to 2014 were used to create a time series of annual floods.  LST is a product of the

MODIS instrument that is onboard the EOS (Earth Observing System) AM (Terra) and PM

(Aqua) platforms (Wan, 1999). Land Surface Temperature & Emissivity 8-Day L3 Global 1km

(MOD11A2) images were acquired in 2014 from the USGS EarthExplorer (EE) tool from 2000

to 2014. Upon downloading, MOD11A2 8-day data are composed of daily 1-km LST product

(MOD11A1) and stored on a 1-km sinusoidal grid as the average values of clear-sky LST during

an 8-day period. These images are validated to stage 2 indicating that their accuracy has been

assessed via ground truth over numerous locations and time periods. Land surface temperature is

one of nine scientific datasets contained within the MODIS L2 LST product (Wan, 2006).

LST is estimated from space with an algorithm created by Wan and Li, 1997 that uses

day/night pairs of TIR data in seven MODIS bands (Wan, 1999). The accuracy specification for

MODIS LST is 1°K at 1km resolution under clear-sky conditions (Wan, 1999). Atmospheric and

emissivity effects for several land cover types are corrected using a view-angle dependent split-

window LST algorithm.

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2.2 Discharge and stage data

A key dataset for the execution of this research is the discharge and river stage data that

were collected for multiple stations throughout the Chobe River Basin. The Department of Water

Affairs office in Kasane, Botswana provided daily discharge data for the Chobe, Okavango, and

Boteti Rivers, monthly channel depths of the Chobe River at Kasane from 1971 to present and

weekly water situation reports for major dams and rivers in the Chobe/Zambezi, Limpopo and

Okavango Basins (Fig. 3). Daily water level and daily flow data for the Okavango, Zambezi and

Kwando Rivers from 2007-present were received from the Department of Water Affairs in

Windhoek, Namibia.

The daily and monthly flow data allow for the quantification of the driving forces of

flooding in the Chobe River Basin. Coupling this analysis with the flooding extent derived from

the MODIS LST data aids in understanding how the streamflow at various locations in the basin

impacts flooding patterns through time.

Figure 3 Time series of daily discharge of the Zambezi River collected at the Katima Station from 2000-2014.

 

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Zambezi Daily Discharge at Katima

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2.3 Precipitation

To account for precipitation patterns in the Chobe River Basin, as well as the surrounding

watersheds, data from the Tropical Rainfall Measuring Mission (TRMM) 3B43 was used. The

dataset from 1998-2014 was downloaded using the Mirador Data Access on the NASA website.

Stacks of monthly and annual rainfall totals were derived and converted to millimeters. The

TRMM data, with a spatial resolution of 0.25 x 0.25 degrees, was subset to both the CRB and the

larger, surrounding watersheds to gain insight into the influence of local precipitation patterns on

flooding area. TRMM precipitation data has proven successful in previous studies

2.4 Population and Ancillary Data

Gridded population datasets created by the WorldPop Project were used to identify areas

with high population density that have the highest risk of being exposed to a high magnitude

flooding event. The population grids for 2000, 2011 and 2015 with a spatial resolution of

0.000833333 decimal degrees, or approximately 100 meters, were created using a spatial model

to disaggregate census counts and estimate the number of people within each grid cell of the area

(Linard, 2012). The findings of this research may aid in future resettlement planning in the

Upper Zambezi region of Botswana and Namibia.

3. Methods

A combination of remote sensing and GIS analysis was used to determine inundation

patterns from 2000 to 2014. Firstly, as optical imagery alone is not effective at capturing

inundation in savannahs, MODIS LST data from 2000-2014 was retrieved and processed to

derive inundation area throughout the basin. Secondly, daily discharge data from the Zambezi

and Kwando Rivers and precipitation data were aggregated to correspond temporally with the

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flooding data. Thirdly, a statistical analysis was conducted to determine the most influential

driving forces of flooding in the CRB and identify temporal lags among the parameters. To

determine what variables control the magnitude of flooding of the Chobe River, daily discharge

data and the area of inundation in the floodplains throughout the year were analyzed. Finally,

highly populated areas in the CRB that are at risk of being exposed to high magnitude flooding

were identified.

3.1 MODIS pre-processing and image differencing

The MODIS Conversion Toolkit (MCTK) was used to extract the daytime and nighttime

temperature bands from the original LST Hierarchical Data Format (HDF) stack. During the

conversion process, the MCTK automatically applies the scaling factor of 0.02 to accurately

adjust the temperature values to degrees Kelvin. Bands 1 (LST_Day_1km) and 5

(LST_night_1km) were selected to be stacked in the output image.  The output file is a standard

grid which maintains the native map information and sinusoidal projection (White, 2007). All

pixels with bad data were filled with NaN (Not a Number). These values were later masked out

when located within an obvious body of water.

Similarly to the approach used in Ordoyne and Friedl (2008), surface water was extracted

from the MODIS images based on diurnal temperature differences of land and water (Fig. 4a).

Bodies of water retain heat longer than land at night because of the high specific heat capacity of

water. The differencing methods utilized the different diurnal temperature fluctuations of land

and water to identify areas of the landscape that are covered with a significant amount of

flooding. Once the MODIS images were processed in the MCTK, Interactive Data Language

(IDL) was used to difference the 8-day composite daytime and 8-day composite nighttime bands

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Equation 1

using band math (Eq 1). The following difference expression was applied to the processed

MODIS images to determine the change in temperature (∆T) between the daytime (D) and

nighttime (N) land surface temperatures:

∆𝑇 = 𝑓𝑙𝑜𝑎𝑡 𝐷 − [𝑓𝑙𝑜𝑎𝑡 𝑁 ]

3.2 Image threshold segmentation

A fixed threshold for every image of the time series was not an appropriate approach

because of the variation in diurnal temperatures during different seasons. Many previous studies

applied an unsupervised classification to their data in order to map inundation. This previously

utilized method proved to be unsuccessful on the data for the CRB and incorrectly classified

much of the landscape as flooding. Image specific thresholds proved to be a much more effective

method because temperature thresholds change throughout the year along with local air

temperature and differential land/water specific heat. Otsu thresholding is an unsupervised and

nonparametric method of threshold selection from a gray level histogram (Otsu, 1979).

Advantages of this method include its simplicity, range of applications and the automatic and

stable threshold selection (Otsu, 1979). The Otsu method “maximizes the ratio of the between-

class variance to the within-class variance” (Shu et al 2010), thus in our case, pixels with values

below the determined threshold are regarded as water while pixels above the threshold are

background, or land. A total of 386 images were segmented in IDL using OTSU thresholding

(Fig. 4). Each image was visually inspected and compared to the original differenced images. In

some cases, the OTSU thresholding method overestimated the area of flooding. The OTSU

thresholding method was especially unsuccessful during the wet season because the images

appeared too dark and resulted in many pixels being inaccurately classified as water. For images

that could not be segmented using the OTSU thresholding method, a threshold was chosen based

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upon visual inspection of each corresponding histogram at the inflexion point of said histogram

(Fig. 5). Subsequently, a rule-based classification, using the determined threshold, was

performed to identify the flooding extent on case-by-case basis.

3.3 Inundation mapping validation

The successful application of remote sensing techniques over large areas requires

accurate ground truthing (Dobriyal et al, 2012). Field work was conducted in May and June of

2014 in Namibia and Botswana to obtain training samples (ground control points) to validate the

water signal obtained from MODIS LST images. The ground control points were collected using

Figure 4 A. Sample differenced MODIS Land Surface Temperature image for May 16th, 2000 using the day and night LST bands B. Sample inundation extent extracted from differenced MODIS LST image using OTSU thresholding

Figure 5 Histogram of land surface temperatures on August 5th, 2009. Red arrow at the inflection point indicates the temperature value is used as a maximum threshold to extract water from the thermal image.

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a Trimble Juno SD high-accuracy GPS and opportunistic sampling across the floodplain at all

major access points along different types of roads and even boats. Standing water, sites with

homogeneous vegetation and relative soil moisture data were collected to be used to train the

signal obtained from the thermal imagery at the scale of this study area. Data collected at each

site included topography, inundation, soil type, soil color, soil moisture and vegetation type. A

total of 69 training samples were collected throughout the basin to validate the thresholding

technique allowing us to create classes of inundation frequency. The derived inundation extents

are compared with these training samples in the results section.

3.4 Regression analysis of flood, discharge and precipitation patterns

Regression models are often used to relate hydrological response to different catchment

descriptors which are the drivers of flooding in a given region (Mediero and Kjeldsen, 2014). To

understand the lag between discharge in the Zambezi and Kwando rivers and the resulting

flooding extent, the daily discharge data (m3/s) was lagged back by 9 (8-day) time steps to

correspond to the flooded area derived from the MODIS imagery. To determine which variables

were the most influential on the flooding extent in the CRB, a series of stepwise general linear

models with flooded area as the dependent variable and the lagged discharge of the Zambezi and

Kwando Rivers (as the predictor variables were performed. The region was subdivided into six

sub-regions (Fig. 6) to enable the independent analysis of the different hydrologic features within

the basin, with the flooded area for each of the sub-regions the dependent variable and the lagged

discharge of the Zambezi and Kwando rivers as the independent variables To determine what the

best predictor of the discharge of the Zambezi River, monthly precipitation data for the

surrounding watershed was lagged back in three (one month) time steps. This type of modeling

also defines relationships between the different independent variables.

17  

Equation 2

A statistical model selection of general linear models was conducted using the

GLMSELECT procedure from Statistical Analysis System (SAS). This method of predictive

modeling provides effect selection based on a variety of selection criteria. The stepwise selection

method was used which switches between adding and deleting parameters one at a time (Cohen,

2006). The model first fits an intercept value to the data. Next, the variable with lowest p-value is

selected as the first entry to the model. SAS then performs a backward elimination to determine

if any remaining explanatory variables should be included or removed from the model. The

GLMSELECT procedure is based on the following general multiple linear regression equation:  

𝛾! = 𝛽! +  𝛽!𝜒!! +⋯+ 𝛽!𝜒!" 𝑖 = 1,… ,𝑛,

Figure 6 Sub-regions of the Chobe River Basin used in regression analysis.

18  

The null hypothesis (Ho) was that there was no relationship between flooding area and the

discharge of the Kwando and Zambezi Rivers, the alternate hypothesis being that the flooding

area of various regions of interest throughout the basin were positively correlated with the flows

of the Kwando and Zambezi rivers at lagged time steps. The p-value calculated for each

regression represents the probability associated with the null hypothesis.

3.5 At-risk Population analysis

By considering the relationship between the timing, quantity and influence of the

discharge of the rivers that feed the Chobe and the resulting inundation extent in the Chobe River

Basin, areas in the region that are at the highest risk of being exposed to high magnitude flooding

can be identified. Once identified, these high risk flooding areas are compared to population data

to identify densely populated regions in the CRB most at risk of long term flooding. Two time-

steps of the gridded population datasets for Namibia only, one from 2000 and one from 2011

based on the Namibia 2010 census, were used to identify population epicenters that are located

within high frequency flood areas. The population dataset for Botswana was not included

because of the scarcity of data surrounding the CRB. The two population time steps were used to

locate regions that experienced the greatest change in population to determine if these areas

correlate to regions of high frequency flooding. Once the regions of high population density

were identified, they were combined with inundation duration maps representing years of high

magnitude flooding, low magnitude flooding and the cumulative inundation map containing all

years of this study to identify areas in this region most susceptible to high magnitude flooding

and observe how the number of people impacted changes depending on the scale of flooding.

The inundation duration indexes were resampled using ArcGIS to have the same spatial

resolution as the population data.

19  

4. Results

The results of the work outlined above are presented in this section which is composed of

four sub-sections. First, the inter-annual spatial extent of duration of inundation in the CRB for

the period 2000-2013 is shown with derived flood frequency maps. Then, the results of the

MODIS LST and hydrological analyses are discussed, including major floods and dry years as

well as controls of the major subsystems. Third, the results of the predictive regression models

are discussed. Lastly, at-risk population centers are identified using models of low and high

magnitude flooding.

4.1 Flood frequency map

Figure 7 shows the inter-annual spatial extent and duration of inundation in the CRB for

the period 2000-2014 from March to November of each year. Even in years of low flooding,

surface water remains in topographic depressions along the main channel of the Chobe and

regions of the Zambezi and Mamili wetlands for as long as four months. Communities of the

region tend to cluster around these semi-permanent water bodies, as will be discussed in Section

6.6. Temporal changes in flood frequency are reflective of the changes in annual discharge and

precipitation. Long-term water bodies were identified in Lake Liambezi, portions of the

northeastern Zambezi wetlands before reaching the bottleneck in Zambezi East as well as along

the ridge of the Chobe River. The number of images included in each year’s flood frequency

map varied depending on the amount of viable images. Appendix 1 contains the dates that were

included in the flood frequency map and statistical analysis.

20  

 

Figure 8a shows the annual inundation maxima for the period 2000-2014 which shows

an increasing trend through time. As shown in figure 7, the years of low flooding include 2002,

2005, and 2006, all of which are relatively early in the study period. In more recent years, the

duration and extent of flooding have increased dramatically, especially between 2009 to 2012.

These interannual changes are discussed in more detail in Part 2. The average annual maximum

Figure 7 Spatial extent of inundation duration cycles in the Chobe River Basin calculated from MODIS LST data for the period 2000-2014

 

21  

flood for the period 2000-2014 covered 4097 km2 of land in the CRB. Figure 8b shows how the

consecutive years varied from this average.

4.2 Flood frequency 2000 – 2014

The flood frequency maps for each individual year were combined to identify regions in

CRB that have experienced the most inundation over the past 15 years (Fig. 9). Depressions in

the Zambezi east and along the Zambezi and Chobe channels consistently retain surface water.

The portion of the Zambezi wetlands adjacent to Kasane tends to maintain floodwater for much

of the year. Other areas in the landscape that are often inundated for extended periods of time

Figure 8 A. Derived annual inundation maximums from 2000-2014. B. Maximum for each year varied from the long-term average of 4097 km2.

A.

B.

22  

include Lake Liambezi and regions of the Mamili wetlands.  

For verification of derived flooding extent using the LST methodology, ground control

points collected in the field were visually compared with the derived flooding extent on June

10th, 2014 (Julian Date 2014161) (Fig. 10). The furthest flooding extents marked in the field

corresponded well with the extents derived with the MODIS imagery. The boundaries of Lake

Liambezi and the Chobe Floodplain were especially well correlated, probably due to the large

amount surface water present while in the field.

Figure 9 Flood frequency in the Chobe River Basin for the period 2000-2014.

23  

Figure 10 Examples of ground control points taken in the field with their corresponding flooding extent derived from MODIS imagery on the 8-day aggregated June 10th image. All shown GCPs were marked on the furthest extent of flooding in that area. Images shown were taken on location with a Trimble Juno SB device.

4.3 Typical seasonal flood dynamics

The MODIS LST imagery provided a way to observe the mechanism in which flood

water moves through the interrelated sub-regions in this unique landscape each year. Figure 11

shows thirty time-steps from 2012, chosen due to the higher duration of inundation relative to

years at the beginning of the time series, the large area that was flooded and the number of viable

images from this year. The annual flood pulse from the Zambezi can first be seen entering the

24  

Zambezi wetlands at the end of February after passing Katima Mulilo (Day 57). The water

moves south-eastward, spreading through the Zambezi wetlands until it reaches a bottleneck at

Impalila Island where it is forced back. The Zambezi wetlands reach peak inundation within a

week of maximum discharge of the Zambezi at the Katima Station. After the Zambezi wetlands

are fully inundated, floodwater is pushed from the Zambezi east, first through the Bukalo

channel, and then down the Chobe, usually around the beginning of March. Floodwater then

continues to spill over the Chobe floodplain throughout March and April (Day 60-120),

depending on the continued discharge of the Zambezi. A smaller flood pulse arrives later in the

wet season (~Day 161) from the Kwando, which enters the CRB in the Mamili wetlands. Water

can be seen moving down the channel of the Chobe River by the end of March (~Day 88). Years

that are subsequent to wet years, such as 2012, often retain water from the previous year on the

landscape in places like Lake Liambezi. The Mamili wetlands remained slightly flooded in the

beginning of 2012 because of the large flood events in 2011. The second flood pulse from the

Kwando can be seen arriving in mid-June (Day 161) of 2012 pushing flood water northeast up

the Linyanti channel where it connects with Lake Liambezi. If the pulse of water in a given year

is of a large enough quantity, it begins to be pushed northeast through the Linyanti channel,

eventually meeting Lake Liambezi. When flows from both rivers are high enough, the Chobe

floodplain and Lake Liambezi become connected via the Savute channel. The area of inundation

abruptly falls in the Zambezi wetlands between July and August, around 3 or 4 months after the

second peak flow event of the Zambezi.

25  

The direction of water flow in the Chobe River is controlled by the depth of water and

flow rates in the channel.  The bankfull level of the Chobe River is 5.35 meters. According to the

field hydrologist from the Department of Water Affairs in Kasane, when the stage is above

Figure 11 Time series of classified MODIS LST images showing the movement of surface flooding through the CRB in 2012.

26  

around 5.3 meters, water from the Chobe begins flowing west towards Lake Liambezi via the

Savute channel (Motanyane, May 29th, 2014, personal communication). As the flood water

recedes and the stage level of the Chobe River drops below 4.5 meters, water begins to once

more flow forward towards the Zambezi River confluence just east of Kasane. Between 2000

and 2014, there is a clear contrast in the timing of peak stage of the Chobe that ranges from

March 1st – May 17th. In some years, such as 2001 and 2008, the stage of the Chobe peaked

twice (Fig.12). Although the timing of peak stage was different for all of the years, all began to

decrease at the same time at a very similar rate suggesting a reduction in the amount of water

moving through the system by that time since we’re entering the dry season and the supply from

the north is stalled. Stage peaked relatively early in 2007 and 2013 in the beginning of March

corresponding to an early arrival of peak discharge of the Zambezi for these years. The early

arrival of Zambezi discharge in 2007 caused the Zambezi wetlands and Chobe Floodplain to be

significantly inundated by the beginning of March (Day 65).

Figure 12 Stage of Chobe River at Marina Lodge, Kasane, Botswana (Data courtesy of the Department of Water Affairs office in Kasane, Botswana)

27  

The majority of the landscape that becomes inundated annually retains surface water for

less than four months of the year. The furthest extent of the flood water Zambezi wetlands is

sometimes only present for two to three weeks before quickly receding. The same topographic

depressions called milapos where water remains for 3-5 months per year, discussed by Pricope,

2013 were noted throughout the Zambezi, Chobe and Mamili wetlands. Milapos are highly

fertile depressions that are commonly used for flood- recession agriculture.

4.4 Years of Extreme Inundation

The driest year in terms of discharge for the Zambezi for the time period used in this

study was 2005, followed by 2002. As shown in figure 6, the annual maximum floods of 2002

and 2005 were significantly below the average flood area for the period of this study. The lowest

spatial extent of flooding was observed in 2005, corresponding to the lowest observed discharge

of the Zambezi River on Day 305 as well as one of the lowest years of precipitation for this time

series. The Zambezi wetlands, which are typically fully flooded by the end of April, experienced

a very low amount of inundation through April and May in 2005. The majority of the flooding

this year occurred in the Zambezi East where a small portion of the wetlands were inundated for

three months or less. Portions of the Mamili wetlands also remained inundated for 2-3 months,

but long term surface water was limited to the Zambezi channel. As noted in figure 6, even in

years of low flooding extent, the upper region of the Zambezi wetlands and the main channel of

the Chobe River along the ridge retain water for at least three to four months.

Maximum flooding in the Chobe floodplain generally occurs in response to the peak

discharge of the Zambezi River in March-April. Precipitation generally peaks between January

and February in the Upper Zambezi sub-basin, resulting in maximum discharge in the Zambezi

28  

in March-April. The highest spatial extent of flooding was observed in 2009, which was

followed by a series of high magnitude flooding through 2014.

In 2008, an unusually large pulse from the Kwando River caused the Mamili wetlands

and the southwestern portion of the CRB to become flooded (Fig. 7). Mean precipitation

drastically peaked in January 2008, corresponding to the increase in discharge of the Kwando

River and the resulting inundation area in the Mamili wetlands (Fig. 13). This high discharge of

the Kwando continued in 2009.

Figure 13 A. Mean monthly precipitation in the Upper Zambezi sub basin for June 2006 to June 2009 showing the dramatic increase in precipitation in January 2008. B. Derived inundated area in the Mamili wetlands for the period 2000 to 2014 demonstrating the extreme increase in flooding in the Mamili wetlands in 2008.

0 50

100 150 200 250 300 350 400

06/2

006

08/2

006

10/2

006

12/2

006

02/2

007

04/2

007

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007

08/2

007

10/2

007

12/2

007

02/2

008

04/2

008

06/2

008

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008

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008

12/2

008

02/2

009

04/2

009

06/2

009 M

ean

mon

thyl

pre

cipi

tatio

n (m

m)

0 200 400 600 800

1000 1200 1400

Apr

-00

Sept

-00

Apr

-01

Sept

-01

Apr

-02

Sept

-02

Apr

-03

Sept

-03

Apr

-04

Sept

-04

Apr

-05

Sept

-05

Apr

-06

Sept

-06

Apr

-07

Sept

-07

Apr

-08

Sept

-08

Apr

-09

Sept

-09

Apr

-10

Sept

-10

Apr

-11

Sept

-11

Apr

-12

Sept

-12

Apr

-13

Sept

-13

Apr

-14

Sept

-14

 Inun

date

d A

rea

(km

2 )

A.

B.

29  

Although the flow of the Zambezi was high after a year of moderate flow in both 2007

and 2009, the resulting flooding in the CRB was different. In 2007, the Zambezi flow was large

enough to fill Lake Liambezi and the Chobe floodplain for much of the wet season. However, in

2009 extreme flooding in the Zambezi, coupled with increased flow in the Kwando resulted in

magnitudes of flooding throughout the CRB that had not been seen in many years. Locations

throughout the Chobe Enclave that had been dry for decades became flooded. Roads became

impassable and crops and buildings were destroyed (Burke, personal communication, 2014).

Flooding extent in the CRB generally peaks twice throughout the year, corresponding

with the two peak discharge events of the Zambezi (Fig. 14). Inundation reaches its maximum in

CRB 6-9 weeks after the discharge of the Zambezi peaks at the Katima Station. Discharge of the

Zambezi often peaks twice a year, generally once in mid-March and again at the end of April. As

shown in figure 14, the timing of these peak discharges sometimes occurs at different times in

different years. Both pulses of peak discharge in the Zambezi arrived early in 2008, the first at

the end of February and the second in mid-April. As a result of these early pulses, the maximum

area of inundation occurred earlier than usual in 2008. In 2008, the Zambezi peaked at the end of

February and mid-April, causing maximum flooding to arrive earlier than usual in mid-April

(Day 104) and June 17. In 2010, Zambezi discharge peaked at the end of March and April,

causing maximum flooding in the CRB to arrive in mid-May (Day 137) and the beginning of

July (Day 185).

30  

Although the discharge of the Zambezi is the main control on flooding in the CRB, the

discharge of the Kwando River is also very influential. The importance of the flood pulse from

the Kwando River can be seen in the high magnitude flooding event of 2009. The high discharge

0

2000

4000

6000

8000

1 16

31

46

61

76

91

106

121

136

151

166

181

196

211

226

241

256

271

286

301

316

331

346

361

Zam

bezi

Riv

er D

aily

D

isch

arge

(m3)

DOY

2008   2010  

0 500

1000 1500 2000 2500 3000

2008

049

2008

065

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081

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089

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097

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105

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113

2008

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Inun

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d (k

m2 )

2008

0

1000

2000

3000

2010

065

2010

105

2010

121

2010

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2010

137

2010

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2010

185

2010

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2010

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2010

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241

2010

249

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273

2010

281

2010

289

2010

297

2010

305

Are

a In

unda

ted(

km2 )

2010

Figure 14 Example of the impact of the arrival of the Zambezi flood pulse on the timing of maximum inundation in the Zambezi wetlands A. Daily discharge of the Zambezi River in 2008 and 2010 B., C. Resulting flooding in the Zambezi wetlands in 2008 and 2010

A.

B.

C.

31  

of the Zambezi, coupled with a high discharge event from the Kwando resulted in floodwaters

reaching areas that had been dry for four decades (Bosch, 2011). In years of high discharge from

the Kwando River, CRB receives water through connections from the Linyanti channel.

Discharge of the Kwando River peaks in June-August, and results in a secondary flood pulse to

the region. When this peak discharge is of great enough volume, floodwater pushes up through

the Linyanti channel, after passing through the Mamili wetlands to Lake Liambezi. For the first

time since at least 2000, the Chobe River became connected to Lake Liambezi via the Savute

channel in 2009 (Fig. 15). This event observed in the imagery analysis was confirmed by

personal communication with hydrologists from The Department of Water Affairs office in

Kasane, Botswana and field observations by Dr. Pricope during both 2009 and 2010. Water

began to flow in the channel in June of 2009 and continued to carry water until July. In 2010, the

Savute channel began to flow earlier, with the first surface water detected in May.

Figure 15 Connection between Chobe Floodplain and Lake Liambezi through the Savute channel on June 18th, 2009 (the connection is highlighted in red).

32  

In 2009, all of the sub-regions of the CRB remained inundated and connected between

June 12th and July 18th (Day 163-169). Typically at this time of the year the Zambezi wetlands

have begun to recede and there is no connection between the flood pulse of the Kwando and the

rest of CRB.  The high magnitude floods of 2009 and 2010 greatly impacted drylands and areas

toward the Chobe Enclave, Botswana with even remote roads being completely covered by

floodwater (Burke, personal communication). This was also the first time that water began

flowing through the Savute channel after being dry for many years.

The sub-regions of the Chobe River Basin have become increasingly interconnected since

the arrival of high peak discharge from both the Kwando and Zambezi Rivers in 2009. When the

Zambezi and Kwando rivers both have relatively high discharge, the sub-regions of the CRB

become connected through intermittent channels. The Chobe floodplain is linked to Lake

Liambezi through the Savute channel in years of high flooding. Water from the Kwando River

moves through the Mamili wetlands before being pushed into the Linyanti channel. When

discharge is high enough, the water is then pushed back to the Chobe Floodplain. The water level

of Lake Liambezi experienced major rise between 2000 and 2014 (Fig. 16). In dry years (i.e.

2002, 2003 and 2005), Lake Liambezi remained dry throughout the year. The lake became filled

in 2004 and 2007, but at the end of 2008, the area of inundation surrounding the lake peaked

significantly.

33  

Equation 3

4.5 Regression analysis of flood and precipitation patterns:

Chobe River Basin Statistics:

A stepwise regression was performed with the flooding extent of the Chobe River Basin

as the dependent variable. The best predictor of flooding was chosen based on the standardized

estimate, which standardizes the independent variables to have a variance of one. The

GLMSELECT procedure yielded the following equation where Zi  and Ki are the observed

discharge of the Zambezi and Kwando Rivers respectively at the ith time step:

𝐹𝑙𝑜𝑜𝑑𝑒𝑑  𝑎𝑟𝑒𝑎  𝑖𝑛  𝐶𝑅𝐵   = 518.575   +  0.318𝑍! +  0.369𝑍! +  15.744𝐾!

Based on the standardized estimates of the variables, the stepwise regression determined

that the most influential variable on flooding in the Chobe River Basin is the discharge of the

Zambezi River 64 days prior to observed flooding followed by the discharge of the Zambezi 8

days prior (Fig. 17).The immediate flow of the Kwando also influences the extent of flooding in

the CRB. All of the yielded predictors are significant at the 99 % confidence interval. The

correlation between the selected model of inundation in the CRB and lagged discharge values

yielded an R2 of 0.658 with 178 degrees of freedom for a two tailed t-test. When only the lagged

Figure 16 Flooded area surrounding Lake Liambezi for the period 2000-2014.

0 100 200 300 400 500 600

2000

097

2000

177

2001

105

2001

209

2002

057

2002

169

2002

289

2003

145

2003

233

2004

121

2004

201

2004

305

2005

161

2005

249

2006

169

2006

281

2007

137

2007

217

2008

065

2008

153

2008

233

2009

081

2009

169

2009

249

2010

121

2010

209

2010

289

2011

121

2011

201

2011

281

2012

105

2012

185

2012

265

2013

089

2013

177

2013

257

2014

129

2014

209

2014

289 In

unda

ted

Are

a (k

m2 )

Julian Date

34  

Equation 4

Equation 5

discharge of the Zambezi is used in the model, the Zambezi discharge at the ninth time-step is

identified as the best predictor again, but has a lower standardized estimate than when the lagged

discharges of both rivers are used as variables.

The same statistical methodology was conducted to identify the best predictor of flooding

in the sub-basins of the CRB. Regression analysis using lagged Zambezi and Kwando discharge

with flooding area in the Chobe Floodplain Sub-basin yielded the following equation:

𝐹𝑙𝑜𝑜𝑑𝑒𝑑  𝑎𝑟𝑒𝑎  𝑖𝑛  𝐶ℎ𝑜𝑏𝑒  𝐹𝑙𝑜𝑜𝑑𝑝𝑙𝑎𝑖𝑛 =  −103.246 +  0.045𝑍! +  0.064𝑍! +  3.11𝐾!

The selected model yielded an R2 value of 0.598 with 175 degrees of freedom. All of the

variables chosen to be in the model have p-values of <.0001 and are significant at the 99th

percentile. The best predictor of flooding extent in the Chobe Floodplain is the discharge of the

Zambezi River at the ninth time-step, or 64 days prior to flooding based on this variables

standardized estimate (0.46).

 𝑀𝑎𝑚𝑖𝑙𝑖  𝑤𝑒𝑡𝑙𝑎𝑛𝑑𝑠  𝑓𝑙𝑜𝑜𝑑𝑒𝑑  𝑎𝑟𝑒𝑎 =  −179.467   −  0.086𝑍!  +  4.214𝐾!  +  9.165𝐾!

Figure 17 Correlation of derived flooded area in the CRB and the discharge of the Zambezi River at the Katima station lagged by 64 days.

35  

Equation 6

Equation 7

The selected model for inundation area in the Mamili wetlands identified the flow of the

Kwando at the 9th time-step, or 64-days prior, as the best predictor based on its standardized

estimate (0.57). The selected model yielded an R2 value of 0.862 with 175 degrees of freedom.

All three variables in the model have p-values of <.0001 and are significant at the 99 percentile.

Because of the known nature of this part of the system, the model for flooding in the Mamili

wetlands was also run with only the flow of the Kwando as an independent variable and

identified the same lag as the best predictor.

𝐿𝑖𝑛𝑦𝑎𝑛𝑡𝑖  𝑓𝑙𝑜𝑜𝑑𝑒𝑑  𝑎𝑟𝑒𝑎 =  −82.766− 0.013𝑍!  +  3.316𝐾!

The selected model yielded an R2 value of 0.699 with 176 degrees of freedom. The

predictor of flooding area in the Linyanti based on the standardized estimate (0.787) is the

Kwando discharge at the 9th time-step, or 64 days prior. The K9 and Z1 variables have p-values of

<.0001.

To determine what the best predictor of the discharge of the Zambezi River, monthly

precipitation data for the surrounding watershed was lagged back in three (one month) time

steps. The GLMSELECT procedure yielded the following equation:

𝑍𝑎𝑚𝑏𝑒𝑧𝑖  𝐷𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒 = 34,571− 87.606(𝑊𝑝𝑝!)

where Wpp2 is equal to the precipitation of the watershed at the second time-step, or 1 month

before the resulting discharge of the Zambezi River at the Katima station. This model has an R2

value of 0.0781 and a p-value of 0.0002. Therefore, the best predictor of discharge of the

Zambezi River is the precipitation that occurs in the Upper Zambezi sub-basin a month prior.

36  

The predictive equation for flooding extent in the CRB was modeled and plotted against

the observed flooding extent derived from the MODIS imagery (Fig. 18). The model performed

well with exception of the extreme peaks in observed inundation that occur during the wet

season.

4.6 Population-flood risk mapping

The population centers of this region are small and clustered around major town centers,

such as Kasane, roadways and villages throughout the floodplains. The communities that reside

in the floodplain usually live on termite mounds and small islands during the wet season. Based

on the WorldPop dataset, there were approximately 90,000 people living in the CRB in 2011

Figure 18 Comparison of the observed inundation derived from the MODIS LST imagery and with that modeled using Eq. (3) over the period 2001-2003.

0 1000 2000 3000 4000 5000 6000 7000

2007

217

2007

233

2007

249

2007

265

2007

313

2008

065

2008

089

2008

105

2008

121

2008

137

2008

153

2008

169

2008

185

2008

201

2008

217

2008

233

2008

249

2008

265

2008

281

2008

297

2009

081

2009

097

2009

113

2009

129

2009

145

2009

161

2009

177

2009

193

2009

209

2009

225

2009

241

2009

257

2009

273

2009

289

2009

313

2010

105

2010

129

INU

ND

ATED

AR

EA IN

CR

B

(KM

2 )

JULIAN DATE

Observed Inunadtion Modeled Inundation

0 1000 2000 3000 4000 5000 6000

Dat

e 20

0010

5 20

0012

1 20

0013

7 20

0015

3 20

0016

9 20

0018

5 20

0020

1 20

0023

3 20

0026

5 20

0108

9 20

0111

3 20

0112

9 20

0115

3 20

0118

5 20

0120

1 20

0121

7 20

0123

3 20

0124

9 20

0126

5 20

0128

1 20

0208

9 20

0211

3 20

0212

9 20

0214

5 20

0216

1 20

0217

7 20

0220

1 20

0221

7 20

0224

1 20

0228

1 20

0232

1 20

0308

1 20

0310

5 20

0312

1 20

0313

7 20

0315

3 20

0316

9 20

0318

5 20

0320

1 20

0322

5 20

0324

1 20

0326

5

INU

ND

ATED

AR

EA IN

CR

B

(KM

2 )

JULIAN DATE

Observed Inundation Modeled Inundation

37  

(Fig. 19). Many of these population epicenters are located within communal conservancies

surrounding the Chobe River in Namibia such as the Lusese, Salambala, Kasika and Impalila

Conservancies in the North East, centrally located Bamunu Conservancy and Shikhakhu, Dzoti,

Wuparo and Balyerwa in the Mamili region. The communities are strategically located along the

floodplains to utilize the natural resources, but the high magnitude and unpredictable nature of

the floods in recent years has put many people in the path of high magnitude flooding.

The inundation duration index for 2002 was used as an example  to examine how

population centers are impacted during a year with low flooding spatial extent (Fig. 20a). Even

in years that experience low flooding events, over 22,000 people are exposed to flooding for over

a month in the Namibian floodplain of the CRB. Many communities in the Zambezi east are

located in locations that receive significant amounts of annual flooding almost every year. To

quantify the how the number of people in the flood zones changes during years of high flooding,

the inundation duration index of 2009 was used (Fig. 20b). In a year of high magnitude flooding,

Figure 19 Distribution of population in the Zambezi Region of eastern Namibia.

38  

such as 2009, approximately 53%, or 46,801 people were living in an area that was flooded for

over a month a year. 22%, or 19,652 people are living in an area that will be inundated for more

than three months of the year. Over 3,000 of these people live in an area that was flooded for

over six months in 2009.  The high flood event of 2009 caused many communities in the Zambezi

East, especially within the Impalila, Kabulabula and Kasika conservancies to be flooded for 3-6

months.

Figure 20 Population centers located within flood zones in years of low magnitude (A.) and high magnitude flooding (B.).

39  

5. Discussion

This study analyzed 15 years of flooding in the Chobe River Basin. The area of

inundation throughout the Chobe River Basin exhibits extreme spatial and temporal variability

and responds to external variables such as local precipitation and the relative discharge of the

Kwando and Zambezi Rivers. The area of maximum flood varies greatly between years and there

is a complex link between the different sub-basins of the CRB.  The driest year in terms of both

rainfall and inundation area was 2002. The highest observed discharge of the Zambezi River

occurred on March 22nd, 2010 (Julian Date 2010081), followed by March 25th, 2009 (Julian Date

2009084), corresponding to the highest observed maximum inundation areas in the CRB for the

study period.

It has been suggested that the overall trend in flooding extent in the CRB has decreased

between 1997 and 2010 (Pricope, 2013). The results of the analysis of annual inundation

maximum for the time period of this study, which extends four years further, revealed a positive

overall trend, indicating that the area inundated has generally increased since 2000. The positive

trend captured in this time series was likely influenced by the high magnitude floods experienced

in the CRB since 2009. As shown in figure 8b, the maximum inundation area for 2009-2012 was

well above the long-term average of this study. A member of the IRDNC who states that floods

from Zambezi and Chobe have been increasing in recent years corroborates the increase in flood

frequency and extent seen over the study period (Burke, personal communication).

The compiled MODIS images allow the creation of flood frequency maps to identify

regions throughout the landscape that are flooded for extended periods of time. The flood

frequency map identified regions in the Zambezi east, Chobe floodplain and Mamili wetlands

40  

that retain surface water. These results correspond to work done by Pricope, 2013 which

discussed low topographic depressions in this region that hold surface water throughout the year.

The highest annual inundation maximum was observed in 2009. Results from Long

(2014) noted the same increase in inundation between 2008 and 2009. The flood of 2009 caused

an estimated $5,000,000 worth of damage to infrastructure in Zambia, destroyed roads and

schools and destroyed crops (IRIN, 2009). Not only can floodwaters destroy homes and block

main roads, they can also bring dangerous wildlife such as hippopotamuses and crocodiles into

residential regions. The large flood of 2009 brought hippos and crocodiles 20 km further inland

than usual, resulting in injuries and the death of villagers (Inambao, 2009).

Flooding in this region is a building process that is controlled by multiple flood pulses

received from the Kwando and Zambezi Rivers, as well as antecedent floodwater in the system.

The antecedent effect can be observed in 2008 and 2012 when a relatively low discharge of the

Zambezi resulted in high flooding extent in the CRB (Fig. 21).  The common consensus of locals

and in the literature is that the flooding in this region is controlled by the discharge of the

Zambezi. Instead, this research indicates that after a year of moderate flooding, the high

discharge of the Kwando River can amplify the impacts on the current year, resulting in major

flooding that covers vast areas and connects the sub-basins.  The predictive models of inundation

determined by the GLMSelect procedure exhibit how the flooding in the region is not entirely

controlled by one variable. The regression equation determined by Gumbricht et al., 2004 to

predict spatial extent of inundation in the Okavango delta also yielded multiple variables that

stongly influence the resulting flooding extent. As determined by Gumbricht et al. (2004),

because evapotranspiration varies very little annually, including it the predictive model provides

no improvement to the analysis.

41  

Figure 21 Daily discharge of the Zambezi and derived area of flooding in the Chobe River Basin from 2000-2014.

The nature and strength of El Nino and La Nina govern precipitation patterns for the

region, and therefore control the variation of flow in the Zambezi and Kwando, as well as

inundation patterns (Pricope, 2013). For example, the low spatial extent of flooding in the CRB

in 2002 and 2005 can be accredited to low precipitation totals in the Upper Zambezi sub-basin

corresponding to the warm phase ENSO. The El Nino event of 2009/10, however, brought above

average rainfall conditions to the region that resulted in the high magnitude floods seen in this

study, as well as by Long, 2014 (Southern Africa Special Report). Climate forecasts from the

NOAA Climate Prediction Center (CPC) and the International Research Institute for Climate and

Society (IRI) indicate an elevated chance for El Nino to continue through 2015 (Southern Africa

Special Report).

The timing of maximum inundation in the CRB is determined by the arrival of the flood

pulses from the Zambezi and Kwando Rivers. The annual cyclic lag between peak discharge in

the Zambezi and maximum flooding extent can be seen in figure 21. The multiple regression

model identified the discharge of the Zambezi 64 days prior as the best predictor of flooding

0

2000

4000

6000

8000

0

2000

4000

6000

8000 A

pr-0

0

Aug

-00

Apr

-01

Aug

-01

Apr

-02

Aug

-02

Apr

-03

Aug

-03

Apr

-04

Aug

-04

Apr

-05

Aug

-05

Apr

-06

Aug

-06

Apr

-07

Aug

-07

Apr

-08

Aug

-08

Apr

-09

Aug

-09

Apr

-10

Aug

-10

Apr

-11

Aug

-11

Apr

-12

Aug

-12

Apr

-13

Aug

-13

Apr

-14

DIS

CH

AR

GE

(M3)

INU

ND

ATED

AR

EA

( KM

2)

Inundated Area in CRB Zambezi Discharge

42  

throughout the CRB. This lag can be seen in figure 21 where the Zambezi peaks first, followed

by a peak in flooded area in the CRB on average 55 days after (Fig. 22). The magnitude of flow

in Zambezi also seems to impact the lag between peak discharge and peak flooded area in the

following year. For example, the high discharge of the Zambezi in 2001and 2004 both resulted in

a long lag time between peak discharge and flooding area. The years following these high

discharge events (2002 and 2005) however did not experience high discharge, but had much

shorter lag times. Similarly, the lags between peak discharge and flooded area in 2010 and 2011

were relatively rapid following the high discharge event of 2009. This faster flooding response to

peak discharge may result of the landscape still being flooded for the previous year. If the

channels are already flooded, they will surpass bankfull levels and water will enter the

floodplains at a faster rate.

The Kwando/Chobe Sub-basin, which is usually the driest of the Upper Zambezi region,

received a large amount of rainfall in January 2008 (Beilfuss, 2012). This spike in rainfall caused

the discharge of the Kwando River to drastically increase, producing high amounts of flooding in

0

20

40

60

80

100

Num

ber o

f Day

s

Lag Average Lag

Figure 22 Lag between peak discharge of the Zambezi and peak flooded area in the CRB derived with MODIS LST. The average lag for the period 2000-2014 was 55 days.

43  

the Mamili wetlands. The Kwando River, which normally peaks in May-June and quickly

recedes, continued to rise resulting in the area surround the river to become swamps (Bosch,

2011). The large floods of 2008 also caused Lake Liambezi swelling of Lake Liambezi brings

economic opportunities to this region. The drastic increase water level in Lake Liambezi that

began in late 2008 has drawn people from all over the region to its shores to utilize the fishing

opportunities. 2008 also marked the onset of increased levels of Lake Liambezi

The benefits and economic opportunities associated with living on a seasonal floodplain

draw populations closer to riverbanks, making them more susceptible to large flooding events.

The high magnitude floods observed in recent years have caused the displacement of many

communities in throughout the Zambezi basin. For example, the floods of the Zambezi in 2008

displaced 90,000 people in Mozambique (IPCC, 2014). In the CRB, the amount of people

impacted by flooding each year can increase by more than 48% between years of low and high

magnitude flooding. To local farmers and residents, rainfall in this region is unpredictable

making it difficult to accurately determine when to plant their crops and almost every year, more

people are forced to move because of flooding (Elvis, May 23rd, 2014, personal comm). High

magnitude floods force entire communities to leave their homes and relocate to drier ground.

Resettlement has caused tension between conservancies and has forced communities to relocate.

A band of landscape surrounding the Linyanti channel experienced extreme diurnal

temperature differences during years that do not receive large amounts of flooding from the

Kwando (2000, 2001, 2002 and 2005) (Fig. 23). This large temperature flux is most likely due to

exposed sand surface, area that has been burned or deforested for agriculture. The area of

landscape that shows extreme diurnal temperature differences was likely incorrectly classified as

flooding in previous studies because of the similar spectral signatures of water and fire scars. The

44  

flooding extent determined by Pricope (2013) may have been over estimated because of the fire

scars in this region. Using land surface temperature as a proxy for flood detection was a way to

overcome the problem that often results in the overestimation of flooding extent. This thermal

anomaly is concluded to be a burned area. Pricope and Binford (2012) identified this region as a

burned area as well.

Limitations of this study include the missing MODIS LST imagery throughout the rainy

season and the missing years of discharge data for the Kwando River. The relatively coarse

resolution of the MODIS dataset limits the detection of small bodies of water and stream

channels.

Figure 23 Example of differenced MODIS image from May 25th, 2001 showing the area of the landscape that experienced extreme diurnal temperature differences outlined in the rectangle.

45  

6. Conclusions

Seasonal and interannual inundation patterns of the CRB were observed using 386

MODIS LST images for the period 2000 to 2014. Results show that the area of inundation in the

CRB has varied from 401km2 to 5779 km2 and has drastically increased since 2000 with a major

rise in 2008 and 2009. The timing and magnitude of flooding in the CRB is influenced by

multiple variables. To understand the relationship between relative discharge of the Zambezi and

Kwando Rivers and inundation extent in the CRB, as well as sub-regions within the basin, a

multiple regression was performed. The model determined that the flooding extent in the CRB

can be best predicted by the discharge of the Zambezi River 64 days prior to flooding. This type

of modelling has the potential to predict the extent of flooding weeks in advance, which could be

invaluable to land use managers and resettlement planning. There are over 46,000 people in the

Zambezi region living in areas at high risk of long-term flooding. The majority of these people

are living in communities throughout the Zambezi wetlands and depend on the seasonal floods

for flood recession, or molapo farming. This dependence, along with the economic opportunities

that floods can provide, often make it difficult to convince communities to relocate. With large

flood event becoming increasingly common and unpredictable, it is critical to quantify the

drivers of flooding in this region and understand how they are changing. Understanding these

changes and their implications in the Zambezi Region will aid in future adaptation and

resettlement planning.

46  

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Gumbricht, T., McCarthy, T.S., and Merry, C.L., 2001. The topography of the Okavango Delta, Botswana, and its tectonic and sedimentological implications. South African Journal of Geology v.104, p. 243-264. Gumbricht, T., Wolski, P., Frost, P., McCarthy, T. S., 2004. Forecasting the spatial extent of the annual flood in the Okavango Delta, Botswana. Journal of Hydrology, v. 290, p. 178-191. Handisyde, N., Lacalle, D. S., Arranz, S., Ross, L. G., 2014. Modelling the flood cycle, aquaculture development potential and risk using MODIS data: A case study for the floodplain of the Rio Paraná, Argentina. Aquaculture, v. 422, p. 18-24. Inambao, C., 2009, Namibia: Caprivi floods reach historic mark (http://reliefweb.int/report/namibia/namibia-caprivi-floods-reach-historic-mark 3 July 2013). IPCC (Intergovernmental Panel on Climate Change), 2012. Field, C.B., Barros, V.R., Stocker, T.F., Qin, D., Dokken, D.J., Ebi, K.L., Mastrandrea, M.D., Mach, K.J,, Plattner, G.K., Allen, S.K., Tignor, M., Midgley, P.M., (Eds.), Managing the risks of extreme events and disasters to advance climate change adaptation. A Special report of working groups I and II of the intergovernmental panel on climate change. Cambridge University Press, Cambridge. IPCC (Intergovernmental Panel on Climate Change), Field, C.B., Barros, V.R., Dokken, D.J., Mach, K.J., Mastrandrea, M.D., Bilir, T.E., Chatterjee, M., Ebi, K.L., Estrada, Y.O., Genova, R.C., Girma, B., Kissel, E.S., Levy, A.N., MacCracken, S., Mastrandrea, P.R., White, L.L. (Eds.) Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge. IRIN (Integrated Regional Information Networks), 2009. Namibia/Zambia: flood insecurity looms as floods swallow crops (www.irinnews.org/report/83623/namibia-zambia-flood-in-security-looms-as-floods-swallow-crops, 30 March 2014). Jia, Z., & Luo, W., 2009. A modified climate diagram displaying net water requirements of wetlands in arid and semi-arid regions. Agricultural water management v. 96, p. 1339-1343. Kgathi, D. L., Kniveton, D., Ringrose, S., Turton, A. R., Vanderpost, C. H. M., Lundqvist, J., Seely, M., 2006. The Okavango; a river supporting its people, environment and economic development. Journal of Hydrology v. 331, p. 3-17. Khan, S. I., Hong, Y., Wang, J., Yilmaz, K. K., Gourley, J. J., Adler, R. F., Irwin, D., 2011. Satellite remote sensing and hydrologic modeling for flood inundation mapping in Lake Victoria basin: Implications for hydrologic prediction in ungauged basins. Geoscience and Remote Sensing v. 49, p. 85-95. Linard, C., Gilbert, M., Snow, RW, Noor, A.M., Tatem, A.J., 2012. Population distribution, settlement patterns and accessibility across Africa in 2010. PLoS ONE, 7:e31743.

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Appendix A:

Julian Date

2000097 2001265 2003177 2004305 2006217 2008097 2009161 2000105 2001273 2003185 2005057 2006241 2008105 2009169 2000113 2001281 2003193 2005097 2006249 2008113 2009177 2000121 2002057 2003201 2005105 2006265 2008121 2009185 2000129 2002089 2003209 2005113 2006281 2008129 2009193 2000137 2002105 2003225 2005121 2007041 2008137 2009201 2000145 2002113 2003233 2005129 2007049 2008145 2009209 2000153 2002121 2003241 2005137 2007065 2008153 2009217 2000161 2002129 2003249 2005145 2007073 2008161 2009225 2000169 2002137 2003265 2005153 2007081 2008169 2009233 2000177 2002145 2003273 2005161 2007105 2008177 2009241 2000185 2002153 2003281 2005169 2007113 2008185 2009249 2000193 2002161 2004089 2005177 2007121 2008193 2009257 2000201 2002169 2004097 2005185 2007129 2008201 2009265 2000217 2002177 2004105 2005193 2007137 2008209 2009273 2000233 2002193 2004113 2005201 2007145 2008217 2009281 2000241 2002201 2004121 2005209 2007153 2008225 2009289 2000265 2002209 2004129 2005217 2007161 2008233 2009297 2000273 2002217 2004137 2005225 2007169 2008241 2009313 2001089 2002225 2004145 2005233 2007177 2008249 2010065 2001105 2002241 2004153 2005241 2007185 2008257 2010105 2001113 2002257 2004161 2005249 2007193 2008265 2010121 2001121 2002281 2004169 2005257 2007201 2008273 2010129 2001129 2002289 2004177 2005273 2007209 2008281 2010137 2001145 2002321 2004185 2005281 2007217 2008289 2010145 2001153 2003073 2004193 2006097 2007225 2008297 2010153 2001161 2003081 2004201 2006105 2007233 2008305 2010161 2001185 2003097 2004209 2006113 2007241 2009081 2010169 2001193 2003105 2004217 2006121 2007249 2009089 2010177 2001201 2003113 2004225 2006129 2007257 2009097 2010185 2001209 2003121 2004233 2006153 2007265 2009105 2010193 2001217 2003129 2004241 2006169 2007289 2009113 2010209 2001225 2003137 2004249 2006177 2007313 2009121 2010217 2001233 2003145 2004257 2006185 2008049 2009129 2010225 2001241 2003153 2004273 2006193 2008065 2009137 2010233 2001249 2003161 2004289 2006201 2008081 2009145 2010241 2001257 2003169 2004297 2006209 2008089 2009153 2010249

51  

2010257 2011305 2013089 2014185 2010265 2011313 2013097 2014193 2010273 2012001 2013105 2014201 2010281 2012057 2013113 2014209 2010289 2012065 2013121 2014217 2010297 2012089 2013129 2014225 2010305 2012097 2013145 2014233 2011041 2012105 2013153 2014241 2011065 2012113 2013161 2014249 2011073 2012121 2013169 2014257 2011089 2012129 2013177 2014265 2011097 2012137 2013185 2014273 2011105 2012145 2013193 2014281 2011113 2012153 2013201 2014289 2011121 2012161 2013209 2014297 2011129 2012169 2013217

2011137 2012177 2013225 2011145 2012185 2013233 2011153 2012193 2013241 2011161 2012201 2013249 2011169 2012209 2013257 2011177 2012217 2013265 2011185 2012225 2013273 2011193 2012233 2013281 2011201 2012241 2013289 2011209 2012249 2013297 2011217 2012257 2014081 2011225 2012265 2014089 2011233 2012273 2014105 2011241 2012281 2014121 2011249 2012289 2014129 2011257 2012297 2014137 2011265 2012305 2014145 2011273 2013049 2014153 2011281 2013057 2014161 2011289 2013073 2014169 2011297 2013081 2014177

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Appendix B:

pro  band_math      compile_opt  idl2        ;  Launch  the  application      e  =  ENVI()        ;  Open  a  day  file      day_file  =  dialog_pickfile(path='C:\Users\jjb9330\Desktop\Imagery_Outputs\Subset')      day_raster  =  e.OpenRaster(day_file)            ;  Open  a  night  file      night_file  =  dialog_pickfile(path='C:\Users\jjb9330\Desktop\Imagery_Outputs\Subset')      night_raster  =  e.OpenRaster(night_file)            ;  Pull  day  data      day_data  =  day_raster.getData()            ;  Pull  night  data      night_data  =  night_raster.getData()            ;  Perform  day  -­‐  night  calculation      math  =  (float(day_data)-­‐float(night_data))            ;  Draw  an  image      math_img  =  image(math,  /order)            ;  get  the  specific  filename      file  =  day_file        ;  Get  the  name  of  the  original  file      base  =  file_basename(file)      ;  Remove  day  portion      dotpos  =  strpos(file,  '_',  /reverse_search)      ext  =  strmid(file,  dotpos)      ;  Save  basename  without  extension      base_new  =  file_basename(file,  ext)        ;  create  a  filename      fileName  =  'C:\Users\jjb9330\Desktop\Imagery_Outputs\Difference\'  +  base_new  +  '_difference'            math_file  =  fileName            math_raster  =  e.CreateRaster(math_file,  math,  inherits_from=day_raster)      math_raster.save        end  

 

 

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Appendix C:

data jeri.ag_sub_lagged; set jeri.agflow_subset; array zamb(5);/*these two numbers must match...*/ array kwan(5); zamb(1)=zambezi;/*first is original...*/ kwan(1)=kwando; do j=2 to 5;/*this last one here*/ kwan(j)=lag1(kwan(j-1));/*rest are lags*/ zamb(j)=lag1(zamb(j-1)); end; drop j; run; proc glmselect data=jeri.ag_all_lagged3; model crb_water = zamb1-zamb9 kwan1-kwan9/selection=stepwise stb; *where substr(put(date,7.),1,7) between '2007000' and '2014135'; run; quit; proc reg data=jeri.ag_all_lagged3; model liambezi_water = kwan2; output out=results p=predicted; /*makes a data set called results in work library, predicted values are included*/ run; quit;  

proc corr data=jeri.crb_and_flow; var water_pixels--chobe_water; with zambezi kwando; where substr(put(date,7.),5,3) between '110' and '170'; /*substr(thing,start,number) put changes numbers to characters*/ run; quit;

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Appendix D:

Chobe River Basin:

Zambezi Wetlands:

55  

Mamili Wetlands:

Chobe Floodplain:

56  

Linyanti: