4

Source: Petja, B.M., Moeletsi, M.E, van Zyl, D, Mpandeli, N.S. and Sibandze, P. (2008)

Drought Mapping in South Africa using Coarse Resolution Satellite Imagery. Report No.

GW/A/2008/44. ARC-Institute for Soil, Climate and Water. Pretoria.

Chapter 2: Literature Review on Drought

2.1. Introduction

Drought is a deficiency in precipitation over an extended period, usually a season or

more, resulting in a water shortage causing adverse impacts on vegetation, animals, and

people (NOAA, 2006). Precipitation is the primary factor controlling the incidence,

formation and persistence of drought conditions, but evapotranspiration is also an

important variable (Lloyd-Hughes and Saunders, 2002). Drought in South Africa is a

very important phenomenon that affects not only agricultural production but also society.

Some describe drought as a sustained and extensive occurrence of below average natural

water availability, and can thus be characterized as a deviation from normal conditions of

variables such as precipitation, soil moisture, groundwater and streamflow (Runtunuwu,

2005). It is a recurring and worldwide phenomenon, with spatial and temporal

characteristics that vary significantly from one region to another (NOAA, 2006;

Runtunuwu, 2005; Loukas and Vasiliades, 2004; Wilhelmi and Wilhite, 2002). Drought

is a disastrous natural phenomenon that has significant impact on socio-economic,

agricultural, and environmental spheres (Bhuiyan, 2004; Finan and Nelson, 2001; Loukas

and Vasiliades, 2004). Its effects are recorded even in following periods when

precipitation occurs normally. Damages due to drought depend on its intensity, duration,

frequency and the affected area (Scripcariu et al., undated). This chapter provides a

review of literature on drought. It starts by identifying different types of drought together

with a focus on operational drought indices. It further focuses on the use of remote

sensing in mapping and monitoring drought and the related satellite-derived drought

indices. The chapter concludes by focusing on policy issues related to drought.

5

2.2. Types of Drought

There are four main types of drought, namely: a) meteorological drought, b) hydrological

drought, c) agricultural drought and d) socio-economic drought. The first three categories

are referred to as environmental droughts whilst the socio-economic drought is

considered a water resources systems drought (Loukas and Vasiliades, 2004).

a) Meteorological drought occurs when there is a lack of precipitation over a large

area and for an extensive period of time and is usually defined in comparison to “normal”

or average rainfall at that particular place (NOAA, 2006). Rainfall can be coupled with

either evaporation or temperature to fully identify drought (Runtunuwu, 2005).

Definitions of meteorological drought must be considered as region specific since the

atmospheric conditions that result in deficiencies of precipitation are highly variable from

region to region. Some definitions of meteorological drought identify periods of drought

on the basis of the number of days with precipitation less than some specified threshold,

while other definitions may relate actual precipitation departures to average amounts on

monthly, seasonal, or annual time scales.

b) Hydrological drought is applied to less than normal amounts of water in the

different types of water bodies, represented by low water levels in streams, reservoirs and

lakes as well as a low groundwater level. Usually, hydrological droughts are further

divided into streamflow droughts and groundwater droughts depending on which type of

water body is observed (Fleig, 2004). Hydrological drought follows periods of extended

precipitation shortfalls that impact water supply and potentially resulting in significant

societal impacts. Due to the fact that regions are interconnected by hydrologic systems,

the impact of meteorological drought may extend well beyond the borders of the

precipitation-deficient area (NOAA, 2006). Whether a meteorological drought leads to

deficits in soil water, surface water and groundwater, depends not only on the lack of a

sufficient water input into the hydrological system of the area (no or too little

precipitation) but also on the rate of water losses, naturally, through evapotranspiration or

6

discharge from the area, or artificially, through various kinds of human activities (Fleig,

2004).

c) Agricultural drought occurs when there isn’t enough moisture to support

average crop production on farms or average grass production on rangeland (Nain et al.,

2005). When soil moisture is lacking, this may hinder proper plant development, leading

to low plant numbers and eventually lower final yield. Agricultural drought is a difficult

concept to define as it involves not only the range of water deficiency but its shortage in

relation to the plant’s need. The water demand of the crop, in turn, depends on its variety,

state and stage of its growth (Chowdhury and Gore, 1989). For instance, 6-7 days without

rainfall may characterize a severe drought period for shallow-rooted crops, whereas for

crops with deep rooting systems this may not be considered drought (Brunini et al.,

2000). The concept of agricultural drought also varies depending on the soil

characteristics. Soils with a deep profile and good water retention capability provide a

good water reservoir and also facilitate root expansion. Shallow soils enhance drought

because of the smaller volume of stored water in the soil layer (Brunini et al., 2000).

d) Socio-economic drought is associated with the failure of water resources systems

to meet the societal water demands (Loukas and Vasiliades, 2004). A socio-economic

drought takes place when the supply of an economic good (water) cannot meet the

demand for that product, and the cause of this shortfall is weather related. Socio-

economic droughts occur when there is widespread and significant deficiency of rainfall

(Nain et al., 2005).

2.3. Drought Indices

It is not possible to avoid meteorological droughts, but they can be predicted and

monitored, and their adverse impacts can be alleviated (Smakhtin and Hughes, 2007).

Drought monitoring is an essential component of drought risk management. It is normally

performed using various drought indices that are effectively continuous functions of

7

rainfall and other hydrometeorological variables (Saeid et al., 2006). It is necessary, for

the analysis of droughts, to detect several drought features such as the onset and end time

of drought, drought duration, drought areal extent, drought severity, drought frequency,

and to link the drought variability to climate (Loukas and Vasiliades, 2004). The

quantification of drought and monitoring is of critical importance politically,

economically and environmentally in most countries. Policy makers at the national level,

the provincial governments, researchers, farmers and water managers and

national/international relief agencies are all interested in reliable and accurate drought

information (Runtunuwu, 2005).

There are different indices which have been developed in the past to quantify

environmental droughts. Drought indices assimilate data on rainfall, temperature,

streamflow, and other water supply indicators into a comprehensible big picture that is far

more useful than raw data for decision making (NOAA, undated). The indices used

include deciles index (DI), percent of normal (PN), standard precipitation index (SPI),

Palmer Drought Severity Index (PDSI), Standardized Water-level Index (SWI), Effective

Drought Index (EDI), Normalized Difference Vegetation Index (NDVI), Crop Moisture

Index (CMI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI),

and Vegetation Health Index (VHI) (Bhuiyan, 2004; Saeid et al., 2006). The DI, PN and

SPI have been used to monitor meteorological drought. The SWI has been developed for

efficient analysis of hydrological drought, while, the NDVI, VCI, TCI, PDSI and VHI

have been developed to assess vegetative drought in the terrain (Bhuiyan, 2004).

2.3.1. Percent of Normal (PN)

PN is calculated by dividing actual precipitation by the long-term mean and multiplying

by 100%. This can be calculated for a variety of time scales (weekly, monthly, annual

etc). The percent of normal precipitation is one of the simplest measurements of rainfall

for a location. Analyses using the PN are very effective when used for a single region or a

single season (Hayes, undated).

8

2.3.2. Decile Index (DI)

In this method, monthly precipitation totals from a long-term record are first ranked from

highest to lowest to construct a cumulative frequency distribution. The distribution is

then split into 10 parts (deciles) (Hayes, undated). The first decile is the precipitation

value not exceeded by the lowest 10% of all precipitation values in a record, the second is

between the lowest 10 and 20%, etc. Any precipitation value can be compared with and

interpreted in terms of these deciles. Decile Indices are often grouped into five classes,

two deciles per class. If precipitation falls into the lowest 20% (deciles 1 and 2), it is

classified as “much below normal”. Deciles 3 and 4 (20–40%) indicate “below normal”

precipitation, deciles 5 and 6 (40–60%) give “near normal” precipitation, deciles 7 and 8

(60–80%) “above normal” and deciles 9 and 10 (80–100%) are “much above normal”

(Smakhtin and Hughes, 2007; Hayes, undated).

2.3.3. Standard Precipitation Index (SPI)

The SPI was developed to quantify the precipitation deficit for multiple time scales, such

as for 3-, 6-, 9-, and 12- month prior periods relative to the long-term mean (Loukas and

Vasiliades, 2004; Steinemann et al., 2005). The observed rainfall is represented as a

standardized departure with respect to a rainfall probability distribution function

(Giddings et al., 2005). The SPI calculation for any location is based on the long-term

precipitation record for a desired period (Hayes, undated). This long-term record is fitted

to a probability distribution, such as the Gamma distribution or Pearson III, so that a

percentile on the fitted distribution corresponds to the same percentile on a Gaussian

distribution (Steinemann et al., 2005; Hayes, undated). That percentile is then associated

with a Z-score for the standard Gaussian distribution and the Z-score is the value of the

SPI (Steinemann et al., 2005), where Z-Score = (X – Average)/Standard deviation.

The categories of the SPI are shown in Table 2.1 (Lloyd-Hughes and Saunders, 2002;

Loukas and Vasiliades, 2004).

9

Table 2.1. SPI values and categories

SPI Values Drought Category

2.0 and more extremely wet

1.5 to 1.99 very wet

1.0 to 1.49 moderately wet

-.99 to .99 near normal

-1.0 to -1.49 moderately dry

-1.5 to -1.99 severely dry

-2 and less extremely dry

2.3.4. Palmer Drought Severity Index (PDSI)

The PDSI was created by Wayne Palmer in the 1960s. Palmer developed the index using

monthly data, but it could be computed on a weekly or even daily basis instead of

monthly (Lloyd-Hughes and Saunders, 2002). The PDSI is derived from a moisture

balance model, using historic records of precipitation, temperature, and the local

available water capacity of the soil (Heim, 2005; Steinemann et al., 2005). The PDSI is

computed using the following steps (Bhalme and Mooley, 1979; Lloyd-Hughes and

Saunders, 2002):

1) Month-by-month water balance accounting for a long series of years, using two-

layer soil model and taking into account rainfall, evapotranspiration, soil

moisture, and runoff.

2) Estimation of potential evapotranspiration (PE) by Thornthwaite's method, and of

potential recharge (PR), potential run off (PRO) and potential loss (PL).

3) Computation of the climatological coefficients of evapotranspiration (α), recharge

( ), run off ( ) and soil moisture loss ( ) for each of the months on the basis of

long-period climatological data, where α= ORPORRPREPTE /,/,/ ,

and LPL /

10

4) Computation of CAFEC (Climatically Appropriate For Existing Conditions)

precipitation, P̂ , by using the climatological coefficients, and potential

evapotranspiration, potential recharge, potential run off and potential loss in the

water balance equation, P̂ = (αPE + βPR + yPRO + δPL)

5) Computation of moisture anomaly, P- P̂ , where P is precipitation during the

month is an indicator of water deficiency for each month.

6) Estimation of weighting factor for weighting moisture anomaly. The moisture

anomaly, d = (P- P̂ ) multiplied by a weighting factor, K, gives the moisture

anomaly index, Z = Kd. This moisture anomaly index permits comparison in

space and time of moisture anomaly.

7) The Weighting factor

'

12

1

'

17

KD

KK where

^

PPD and

8) 50.0/80.2log5.1'

D

LP

RORPEK

9) Derivation of drought severity equation by considering accumulated moisture

anomaly index values during the driest periods of various lengths. Palmer's

Drought Index equation is:

10) iii ZPDSIPDSI3

1897.0 1 where the PDSI of the initial month in a dry or wet

spell is equal to iz3

1.

11

2.3.5. Standardized Water-Level Index (SWI)

SWI has been developed to scale the groundwater recharge deficit. The equation for

calculating SWI is (Bhuiyan, 2004):

/imij WWSWI where Wij is the seasonal water level for the ith well and jth

observation, Wim its seasonal mean, and σ is its standard deviation.

Since groundwater level is measured down from the surface, positive anomalies

correspond to drought and negative anomalies correspond to ‘no-drought’ or normal

condition (Bhuiyan, 2004).

2.3.6. Effective Drought Index (EDI)

The EDI is calculated with a daily time step; however, its principles can be used similarly

with monthly precipitation data. The EDI is a function of precipitation needed for a return

to normal conditions (PRN). PRN is precipitation, which is necessary for the recovery

from the accumulated deficit since the beginning of a drought (Smakhtin and Hughes,

2007). PRN, in turn, effectively stems from monthly effective precipitation (EP) and its

deviation from the mean for each month. EDI values are standardized, which allows

drought severity at two or more locations to be compared with each other regardless of

climatic differences between them (Smakhtin and Hughes, 2007). EDI varies in the range

from -2 to 2 and it has thresholds indicating the range of wetness from extremely dry to

extremely wet conditions (Table 2.2).

12

Table 2.2. EDI values and categories

EDI Values Drought Category

2.0 and more extremely wet

1.5 to 1.99 very wet

1.0 to 1.49 moderate wetness

-.99 to .99 near normal

-1.0 to -1.49 moderate drought

-1.5 to -1.99 severe drought

-2 and less extremely dry

2.3.7. Crop Moisture Index (CMI)

The CMI, also developed by Palmer (1968), is a complement to the PDSI. It measures the

degree to which crop moisture requirements are met, is more responsive to short-term

changes in moisture conditions and is not intended to assess long-term droughts. CMI is

normally calculated with a weekly time step and is based on the mean temperature, total

precipitation for each week and the CMI value from the previous week (Hayes, undated).

2.4. Drought Mapping and Monitoring using Remote Sensing

Drought is a recurrent climate process occurs with uneven temporal and spatial

characteristics over a broad area and over an extended period of time. Therefore,

detecting drought onsets and ends and assessing its severity using satellite-derived

information are becoming popular in disaster, desertification, and climate change studies.

Drought has a disturbing effect not only on agricultural productivity and hydrological

resources but also on the natural vegetation, and hence it may accelerate desertification

processes when associated with destructive human activities (i.e., overgrazing) in semi-

arid areas (Bayarjargal et al., 2006).

13

2.4.1. Historical Background of Vegetation Indices and Applications

The reason that plants look so green is not because they are reflecting a lot of green light,

but because they are absorbing much of the rest of the visible light spectrum. The cells in

plant leaves are very effective scatterers of light because of the high contrast in the index

of refraction between the water-rich cell contents and the intercellular air spaces.

Vegetation is very dark in the visible spectrum (400 nm-700 nm) because of the high

absorption of pigments that occur in leaves, i.e. chlorophyll and xanthophyll. There is a

slight increase in reflectivity around 550 nm (visible green) because the pigments are

least absorptive there. There is no strong absorption in the spectral range 700 nm-

1300 nm; hence plants appear very bright (Figure 2.1). A vegetation index is a number

that is generated by a combination of remote sensing bands that has some algebraic

relationship to the amount or vigour of vegetation in a given image pixel (Arizona State

University NASA Space Grant Program, 2002).

Figure 2.1 The graph of spectral reflectance for Acacia karoo canopy cover (obtained

using the ARC - ISCW’s in-house ASD field spectrometer).

Filter justification for NDVI

The vegetation indices assume that all bare soil in an image will form a hypothetical line

in spectral space that describes the variation in the spectrum of bare soil in the image.

14

The line can be found by locating two or more patches of bare soil in the image having

different reflectivity and finding the best fit line in spectral space. Nearly all the

commonly used vegetation indices are concerned with red and near-infrared space, so a

red-near-infrared line for bare soil is assumed. This line is considered to be the line of

zero vegetation. Isovegetation lines, or lines of equal vegetation, converge at a single

point for the "ratio based" indices. These indices measure the slope of the line between

the point of convergence and the red-NIR point of the pixel. One of the first vegetation

indices to be developed was the Ratio Vegetation Index (RVI). Essentially, the ratio of

NIR to red is used as the vegetation component of the scene expressed as RVI: RVI

=NIR/red. This is a ratio-based index and the isovegetation lines converge at the origin.

The soil line has a slope of unity and passes through the origin with range zero to infinity

(Arizona State University NASA Space Grant Program, 2002).

For the data to best show vegetation, it is necessary to ratio two different band lengths in

order to minimize albedo effects and atmospheric noise. Essentially a band where

vegetation is bright on top of the ratio and a band where vegetation is dark on the bottom

are needed. For example a camera contains a red pass filter that picks up the visible red

and NIR bands (approximately 650 nm - 1000 nm) where vegetation appears bright and a

second camera has a 550 nm short wave pass filter, which makes vegetation, seem dark.

Also, the spectral sensitivity of the second camera (420 nm – 600 nm) allows for further

applications such as coastal mapping, water body penetration, forest mapping, and

deciduous/coniferous differentiation (Arizona State University NASA Space Grant

Program, 2002).

15

2.4.2. Satellite - Derived Vegetation Indices

When, working with remotely sensed vegetation indices, two important assumptions are

made. Firstly it is accepted that certain algebraic functions utilizing certain remotely

sensed bands provide information about vegetation. Secondly, it is accepted that bare soil

in an image will form a line in spectral space. The soil line is a theoretical line in spectral

space that describes the variations found in bare soil. Kauth and Thomas (1976)

developed a method to obtain the soil line, but the easiest method is to obtain the soil line

through a scatter plot of the RED and NIR pixels in an image. The determination of the

soil line does, however, require the use of judgement. The user can therefore have an

influence on the results.

NDVI

The concept of the Normalized Difference Vegetation Index (NDVI) was first illustrated

by Kriegler et al. in 1969, but the index itself can be accredited to Rouse et al. (1973).

NDVI has values varying between -1 and 1, while the RVI ranges from 0 to infinity. RVI

and NDVI are functionally equivalent and related to each other as follows:

NDVI = (RVI-1)/ (RVI+1)

This is also a ratio-based index with isovegetation lines converging at origin. The soil

line has slope of 1 and passes through origin. The range is between -1 to +1.

NDVI = (NIR-red)/ (NIR+red)

NDVI for NOAA-AVHRR

The NDVI in AVHRR is the difference of near-infrared (channel 2) and visible (channel

1) reflectance values normalized over the sum of channels 1 and 2 (NIR-VIS)/

(NIR+VIS). The NDVI equation produces values in the range of -1 to 1, where increasing

positive values indicate increasing green vegetation and negative values indicate non -

vegetated surface features such as water, barren, ice, snow, or clouds. The NDVI can be

derived at several points in the processing flow. To retain the most precision, the NDVI is

derived after calibration of channels 1 and 2, prior to scaling to byte range. Computation

of the NDVI must precede geometric registration and resampling to maintain precision in

16

this calculation. To scale the computed NDVI results to byte data range, the NDVI

computed value, which ranges from -1 to 1, is scaled to the range of 0 to 255, where

computed -1 equals 0, computed 0 equals 100, and computed 1 equals 255. As a result,

NDVI values less than 100 now represent clouds, snow, water and other non-vegetative

surfaces and values equal to or greater than 100 represent vegetative surfaces. NDVI is

calculated from the visible and near-infrared light reflected by vegetation. Healthy

vegetation absorbs most of the visible light that hits it, and reflects a large portion of the

near-infrared light. Unhealthy or sparse vegetation reflects more visible light and less

near-infrared light (Upper Midwest Aerospace Consortium, 2002).

The first AVHRR channel is in a part of the spectrum where chlorophyll causes

considerable absorption of incoming radiation, and the second channel is in a spectral

region where spongy mesophyll leaf structure leads to considerable reflectance (Tucker,

1979). The NDVI is a ratio, which has been shown to be highly correlated with

vegetation parameters such as green-leaf biomass and green-leaf area (Justice et al.,

1985). A ratio between bands is of considerable use in reducing variations caused by

surface topography (Holben and Justice, 1981). It compensates for variations in radiance

as a function of sun elevation for different parts of an image. The ratios do not eliminate

additive effects caused by atmospheric attenuation, but the basis for the NDVI and

vegetation relationship generally holds. The soil background contributes a reflected

signal apart from the vegetation, and interacts with the overlying vegetation through

multiple scattering of radiant energy. Huete (1988) found the NDVI to be as sensitive to

soil darkening (moisture and soil type) as to plant density over partially vegetated

areas (Climatology Interdisciplinary Data Collection, 2002).

NDVI as an indicator of drought

Satellite remote sensors can quantify what fraction of the photosynthetically active

radiation is absorbed by vegetation. In the late 1970s it was found that net photosynthesis

is directly related to the amount of photosynthetically active radiation that plants absorb.

In short, the more a plant is absorbing visible sunlight (during the growing season), the

more it is photosynthesizing and the more productive it is. Conversely, the less sunlight

17

the plant absorbs, the less it is photosynthesizing, and the less productive it is. Either

scenario results in an NDVI value that, over time, can be averaged to establish the

"normal" growing conditions for the vegetation in a given region for a given time of the

year. In short, a region’s absorption and reflection of photosynthetically active radiation

over a given period of time can be used to characterize the health of the vegetation there,

relative to the norm.

IPVI

IPVI is the Infrared Percentage Vegetation Index. The Infrared Percentage Vegetation

Index (IPVI) was developed by Crippen (1990). Crippen found the use of the RED in the

numerator of the formula to be irrelevant, and suggested that the IPVI was easier to

calculate. It is restricted to values between 0 and 1, which eliminates the need for storing

a sign for the vegetation index values, hence improving calculation speed. IPVI and

NDVI are functionally equivalent and related to each other.

IPVI = (NDVI+1) / 2

This is a ratio-based index. The soil line has slope of 1 and passes through origin with the

range 0 to +1.

IPVI = NIR / (NIR+red)

DVI

The Difference Vegetation Index (DVI) was first described by Lillesand and Kiefer in

1987. However, Richardson and Everitt (1992) further experimented its applications.

DVI = NIR – RED

PVI

The Perpendicular Vegetation Index (PVI) can be seen as a generalized version of the

DVI (Richardson and Wiegand, 1977). The PVI is very sensitive to atmospheric

variations, and good atmospheric correction is needed (Qi et al., 1994).

PVI = sin(a) NIR – cos(a) RED

18

where

a = angle between soil line and NIR

WDVI

The Weighted Difference Vegetation Index (WDVI) was introduced by Clevers (1988)

and is a simpler version of the PVI.

WDVI = NIR – g × RED

where

g = slope of soil line

Various vegetation indices were developed to eliminate soil noise. Different soils have

different spectral signatures. The above indices assume a single soil line, but there are,

however, soils with different soil lines present in most imagery. The indices that correct

for soil noise are generally less sensitive than NDVI when monitoring change in

vegetation cover, and are more sensitive to atmospheric variations (Qi et al., 1994).

SAVI

SAVI (Soil Adjusted Vegetation Index) is a hybrid between the ratio - based indices and

perpendicular indices. The reasoning behind this index is that it acknowledges that the

isovegetation lines are not parallel and that they do not all converge at a single

point (Figure 2.2). The initial construction of this index was based on measurements of

cotton and range grass canopies with dark and light soil backgrounds, and the adjustment

factor L which gave equal vegetation index results for the dark and light soils. The result

is a ratio-based index where the point of convergence is not the origin. The point ends up

being in the quadrant of negative NIR and red values, which causes the isovegetation

lines to be more parallel in the region of positive NIR and red values than is the case for

RVI, NDVI, and IPVI (Huette, 1988).

19

Figure 2.2 Soil Adjusted Vegetation Index (SAVI) (Adapted from Huete, 1988)

SAVI = (NIR-red) / (NIR+red+L) / (1+L)

where L is a correction factor which ranges from 0 for very high vegetation cover to 1 for

very low vegetation cover. The most typically used factor is 0.5, which is for

intermediate vegetation cover. This multiplicative term is present in SAVI to cause the

range of the vegetation index to be from -1 to 1. This is done so that the index reduces to

NDVI when the adjustment factor L tends to zero (Arizona State University NASA Space

Grant Program, 2002).

TSAVI

The Transformed Soil Adjusted Vegetation Index (TSAVI) was developed by Baret et al.

(1989, 1991). This index assumes that the soil line has random slope and intercept, and it

makes use of these values to correct the vegetation index. The X parameter was

20

introduced to reduce soil background effect. Literature suggests the use of 0.8 for the X

parameter.

TSAVI = s(NIR – s × RED – a) ÷ (a × NIR + RED – a × s + X × (1 + s × s))

where

a = soil line intercept

s = soil line slope

X = adjustment factor

MSAVI

The Modified Soil Adjusted Vegetation Index (MSAVI) was developed by Qi et al.

(1994). The MSAVI was developed to provide a variable correction factor for vegetation

cover. The correction factor (L) is based on the product of the NDVI and WDVI, and this

is the only difference between the SAVI and MSAVI.

MSAVI = (NIR – RED) ÷ (NIR + RED + L) × (1+L)

where

L = 1 – 2 × s × NDVI × WDVI

Atmospheric noise is another problem faced when doing remote sensing. Attenuation and

scattering of solar radiation can fluctuate over a single image. This predicament becomes

more of a problem when comparing images over time or doing time-series analysis.

Indices developed to overcome this problem are once again less sensitive to changes in

vegetation cover over time and are very sensitive to the soil background.

GEMI

The Global Environmental Monitoring Index (GEMI) was developed by Pinty and

Verstraete (1991). The GEMI uses a generalised method to do atmospheric corrections.

21

Observations of the atmosphere were used to determine a method to characterize it.

GEMI is extremely sensitive to soil noise (Qi et al., 1994). Although literature suggests

the index to be superior (Leprieur et al., 1994) to other indices, it is not used as often as

the NDVI.

GEMI = eta × (1 – 0.25 × eta) – (RED – 0.125) ÷ (1 – RED)

where

eta = (2×(NIR2-RED2) + 1.5 × NIR + 0.5 × RED) ÷ (NIR + RED +0.5)

ARVI

The Atmospherically Resistant Vegetation Index was introduced by Kaufman and Tanre

(1992). The RED reflectance in the NDVI formula is replaced by rb, which refers to the

red an blue bands that is used.

rb = RED – gamma (BLUE – RED)

where

gamma = 1

By substituting the RED with rb, indices such as the SARVI (Soil Adjusted

Atmospherically Resistant Vegetation Index) can be calculated. The Atmosphere Soil

Vegetation Index (ASVI) also developed out of the MSAVI (Qi et al., 1994). Qi

indicated that the indices using rb were slightly more sensitive to change in vegetation

cover than the GEMI. The index, however, only works well in moderate to highly

vegetated areas.

Although there are many different vegetation indices, the NDVI remains the index of

choice. It has the best dynamic range of values, and is far more sensitive to changes in

22

vegetation cover. In low vegetated areas, the SAVI will be the better index to use.

Atmospheric corrections become more critical when using indices such as the SAVI.

The NDVI data alone cannot provide sufficient information for vegetation monitoring.

Various indices were developed from NDVI time-series data to provide information on

vegetation activity and drought. The Vegetation Condition Index (VCI) and the

Standardized Difference Vegetation Index (SDVI) are examples of these indices. In this

study these indices will be used and tested on a newly developed long-term dataset that

incorporates data from the NOAA - AVHRR and SPOT VEGETATION sensor. Indices

such as the Percentage of Average Seasonal Greenness (PASG) will also be used in this

analysis.

VCI

The Vegetation Condition Index was developed by Kogan (1990). Vegetation condition

is represented as a percentage value, and provides a measure to determine drought

conditions. A VCI of 50% reflect normal conditions, while higher values will show the

optimal condition of the vegetation. Lower than 50% may reflect drought conditions.

VCI = 100 * (NDVI - NDVIMIN) ÷ (NDVIMAX - NDVIMIN)

where

NDVIMAX = Maximum pixel value for a given period

NDVIMIN = Minimum pixel value for a given period

SDVI

The Standardized Difference Vegetation Index (SDVI) utilizes a simple statistical method

to calculate a drought index. The long-term mean and standard deviation is used in the

formula. The use of the standard deviation in the formula makes this index ideal to

monitor climate extremes such as drought severity.

23

SDVI = (NDVI – NDVIMEAN) ÷ NDVIS

where

NDVIMEAN = Mean pixel value for a given period

NDVIS = Standard deviation for a pixel for a given period

VPI

The Vegetation Productivity Indicator (VPI) is used to evaluate the overall vegetation

condition and is a categorical type of difference vegetation index. The NDVI is

referenced against the NDVI percentiles of the historical years. The VPI was developed

by Sannier et al. (1998) using NOAA AVHRR data for a study in Zambia. The VPI can

be used to determine agricultural productivity, but is useful to identify drought prone

areas as well.

LST

The use of Land Surface Temperature (LST) data for drought monitoring was introduced

by Kogan (1995), and it was recently used in a study by Wan et al. (2004). Surface

temperature response is determined by incoming solar radiation but it is also determined

by variables associated with the atmosphere conditions, thermal inertia and albedo. Over

vegetated surfaces, surface temperature is indirectly controlled by available water and

more directly by evapotranspiration (Carlson 1986). Thermal infrared measurements

made by satellites reveal temperature patterns of surface temperatures over large spatial

and temporal scales. Land surface temperatures can be estimated from the split window

algorithms that use the information conveyed in the thermal infrared channels of several

satellites (Pozo Vázquez et al 1997).

An example of a Land Surface Temperature Algorithm:

NOAALST = Ch4 + 3.3 × (Ch4 – Ch5) - 2730

24

The effect of orbital drift and the solar zenith angle on LST values will be investigated in

this study. Orbital drift is the term that describes the changes in the satellite crossing time

of polar orbiting satellites. Over time, most polar orbiting satellites are affected, and this

should have an effect on LST values. Drift rates between morning and afternoon satellites

are also different, which causes further sampling errors. MSG and MODIS LST data will

be used to determine the usefulness of LST for drought monitoring.

TVDI

The Temperature Vegetation Dryness Index (TVDI) was introduced by Sandholt et al.

(2002). Sandholt showed that the TVDI was closely related to soil moisture, and should

therefore provide useful information for drought monitoring. The TVDI will be

calculated, depending on the result for the LST study.

TVDI = (LST – LSTmin) ÷ (a + b × NDVI - LSTmin)

The parameters a, b and LSTmin are estimated empirically from the scatter plot of LST

and NDVI.

2.4.3. The Vegetation Productivity Indicator (VPI)

Premise for the establishment of the VPI

NDVI has been widely used for assessment of biomass productivity and net primary

productivity (NPP). Empirical studies demonstrated that vegetation production and

biomass have been successfully estimated with the NDVI derived from satellite data

(Deering et al., 1975; Prince and Tucker, 1986; Tucker and Sellers, 1986; Prince, 1991;

Jury et al., 1997; Myneni et al., 1997). Sannier et al., (1998) argues that NDVI is difficult

to interpret for non-technical users. They suggest that the difficulty in interpretation is

posed by the fact that an explicit relationship between NDVI and vegetation condition is

not available, and that there are different relations for each vegetation type. The

interpretation of NDVI is influenced by geographical variations. Most currently

25

operational early warning systems compare the current NDVI images with the previous

dekad or the mean image of the dekad (Sannier et al., 1998; Petja et al., 2002; Mudau,

2002; Masamvu and Siwela, 2004). Comparison of the actual dekadal NDVI with the

mean dekad is very simple but relies on the temporal variation of the NDVI for a location

and a given dekad being normally distributed. Sannier et al. (1998) regard the assumption

for this simple approach as unreasonable because the lower limit of NDVI is bounded by

the response for the bare soil.

Kogan (1990) took a different approach and defined it a Vegetation Condition Index

(VCI):

VCI =

where NDVImax and NDVImin are maximum and minimum NDVI values in the time

series, for the dekad. This assumes that the current range represents the maximum

possible variation and that all values of the NDVI within the range occur with the same

frequency and therefore have the same possibility. However, Sannier et al. (1998) regard

this as an unrealistic assumption.

VPI as an improvement to the classic approaches

Sannier et al., (1998) proposed a VPI, regarded as an alternative method to compare the

current NDVI with historical NDVI to assess the vegetation conditions. The method

estimates the statistical distribution of the NDVI empirically from available data without

limiting assumptions and is sensitive to background vegetation types.

VPI methodology as applied to Etosha National Park

Sannier et al.,(1998) experimented with the use of VPI at Etosha National Park. They

used a Landsat TM derived vegetation map (supervised classification) to delineate

vegetation types together with field surveys. The vegetation types were mapped in

randomly aligned systematic samples of 1 km square area equivalent to 1 of the park

area, excluding the pan. The supervised classification was performed using 33 classes and

26

into four categories in the post classification. The groups are bare ground (including

saltpan), grassland (and steppe), shrub savanna and tree savanna. The accuracy

assessment of Landsat TM derived vegetation map and ground observations was 89%

using confusion matrix. The stratification of each study area allowed representative time

series NDVI profiles for each stratum to be extracted. Homogeneous areas of each main

cover types, equivalent to the size of AVHRR pixels, were identified using the TM

vegetation classification map and field survey data. A number of sites were chosen to

represent each of the main cover classes except for the grassland and steppe class, which

covers a small area of the park (10%).

An NDVI time series was averaged for each dekad resulting in 10 - year averaged

seasonal profiles. The profile for each site in shrub and tree savanna showed class

variation, which is greater for tree savanna, but the general pattern is always distinct from

that of shrub savanna (Figure 2.3). The start of the season is always three months earlier

for tree savanna and the maximum NDVI is high (Sannier et al.,1998).

.

Figure 2.3. NDVI response of Etosha main vegetation types (Sannier, 2002).

The probability distribution of the NDVI for each dekad during the growing season and

each stratum was calculated (Figure 2.4) with the method used for assessing the

probability of extreme hydrological events (which Sannier et al., 1998, adapted from

27

Linsley et al., 1975). The NDVI values extracted from historical data were ranked from

the lowest to the highest for each dekad and stratum. This enabled the computation of the

probability (p) of having an NDVI less or equal to a given value by applying a formula

defined by Weibull (1939) as cited by Sannier et al. (1998):

p= m/n+1

where m is the rank and n is the number of years. This can also be expressed as a return

period,

Tr = 1/p

which is the average number of years between the occurrences of event. The probability

was plotted against the corresponding NDVI values as for hydrological events. In

hydrology, the aim is often to extrapolate the distribution in order to predict the size of

events, which have very low return periods, and special statistics are fitted to the data to

do this. In Sannier’s study case, the time series of 10 years was too short to fit any

distribution. Therefore a simple least square fit polynomial was used to interpolate the

estimates of the NDVI for specified probabilities. This enabled calculation for quintile

ranges of the NDVI for each vegetation class and each dekad, and to define five classes

indicated in Table 2.3 below.

28

Table 2.3: Description of VPI classes to probability and return periods

VPI CLASS PROBABILITY LEVEL RETURN PERIODS (YEARS)

VERY LOW P<0.2 Tr>5

LOW 0.2<P<0.4 5<Tr>2.5

AVERAGE 0.4<P<0.6 Tr>2.5

HIGH 0.6<P<0.8 5<Tr>2.5

VERY HIGH P<0.8 Tr>5

Figure 2.4. Probability analysis (Sannier, 2002).

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.00 0.20 0.40 0.60 0.80 1.00

Probability to get a lower NDVI

ND

VI

Shrub Savanna Tree Savanna Grassland & Steppe

29

Production of VPI maps

Figure 2.5 shows both the NDVI and VPI maps for Etosha. The appropriate probability

distribution for the NDVI was determined from the date and by reference to its position

in the stratification map. The probability of having and NDVI equal to or smaller than the

current value was calculated from the polynomial equation, which was also used to assign

the appropriate VPI class assigned in Table 2.3 and Figure 2.6.

Figure 2.5. NDVI image and VPI map of Etosha, 27 March 1995 (Sannier et al., 2002)

-0.25

0.45

0 50Km

VPI removes the influence of vegetation type, and characterises departures from normal conditions

30

Figure 2.6. Determination of VPI classes from Etosha study (Sannier et al., 2002)

Field based biomass estimation for calibration of NOAA-AVHRR observations

The sampling strategy below has been described by Sannier et al., (2002). In this

approach, sampling sites should:

- be of sufficient size and internally homogeneous in order to reduce the effects of

errors in co-location of the ground observations with NDVI values.

- be accessible and reflect the range of biomass levels.

Wessels et al., (2001) share the views above. According to Sannier et al. (2002), sample

sites should be selected to reflect variation in vegetation types (biomes). Sannier adapted

a formula from Justice and Townshend (1981), which gives a guideline for the minimum

size (a) of a sampling unit in relation to geometric accuracy, i.e.

a = p(1+2l)

where p is the pixel dimensions in distance units and l the geometric accuracy of number

of pixels. For example, 1.1 km pixel size of AVHRR and a geometric accuracy of 0.5

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.00 0.20 0.40 0.60 0.80 1.00

Probability to get a lower NDVI

ND

VI

Shrub Savanna Tree Savanna Grassland & Steppe

Very Low Low Average High Very High

NDVI = 0.25

If Tree Savanna

If Shrub Savanna

If Grassland or Steppe

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.00 0.20 0.40 0.60 0.80 1.00

Probability to get a lower NDVI

ND

VI

Shrub Savanna Tree Savanna Grassland & Steppe

Very Low Low Average High Very High

NDVI = 0.25NDVI = 0.25

If Tree SavannaIf Tree Savanna

If Shrub SavannaIf Shrub Savanna

If Grassland or SteppeIf Grassland or Steppe

31

pixel should result in a sampling unit of 2.2 km on each side. Homogeneous locations of

1 km2 in size were selected by interpretation of geometrically corrected false colour

composite Landsat TM imagery. By selecting a 1 km2 site in the middle of a

homogeneous area, it is expected that it will minimize the effects of geometric errors as

variation of biomass in the immediate surrounding (unlikely to be great) and surrounding

pixels would be mixed responses including other vegetation types.

Observations Derived from the VPI

From Sannier’s observations and experimentation, the following conclusions were

reached.

- NDVI images are difficult to interpret for monitoring vegetation conditions.

- The VPI serves as a reliable indicator of the current vegetation conditions.

- The method was developed in Etosha, and applied experimentally in Zambia to

assess maize production.

- The method was applied successfully in Ethiopia, Zambia, Namibia, Jordan and

Afghanistan.

- In Botswana and Namibia it is used operationally to monitor rangeland

productivity.

- VPI products can be produced in near-real time, can be easily interpreted and

disseminated to relevant organizations.

- There is a need for sufficient institutional support to make the technique

sustainable.

2.5 Review of Mitigation/ Adaptation Strategies and Policy Issues

2.5.1 Drought Mitigation Strategy in Morocco

Genetic approach

This is aimed at producing drought tolerant crops. Recently released varieties are

characterized by large adaptation. This characteristic is due to their optimal earliness,

their tolerance to drought and their fair resistance to certain parasites. In the case of

32

cereals, more than 75 varieties have been released by INRA with 80% of them after 1983.

The adoption of the new varieties by the farmers allowed 35% and 50% increases in grain

yield of bread wheat and barley, respectively. For this last 20 years, the yield

improvement of cereals corresponds to an increase of 2 to 4 quintals per hectare at the

national level, although this period was characterized by many dry years. The shift from

the old varieties to the newest ones increased also water use efficiency which jumped

from 8 to 17 kg of grains/mm of water used. Among the cultivars that are proven to be

adapted to the arid and semi-arid zones are Aglou, Taffa, Acsad 60, Annoceur and

Tamellalt, for barley; Sarif, Yasmine, Amjad, Tomouh, Oum Rabia and Marzak for

durum wheat and Arrihane, Aguilal, Achtar, Kenz, Merchouch for bread wheat. For food

legume crops, the most important research achievement is the shift of the period of

sowing chick pea from spring to autumn by developing adapted varieties (Rizki, Douyet,

Farihane). The advantage of this type of crop (called winter chick pea) is that it takes

advantage of autumn and winter rains. For faba bean and lentil, two adapted varieties per

species to drought prone areas were recently released. In the case of forages, in addition

to the development of nine varieties of Medics and three of trifolium subterraneum,

Acacia and more importantly Atriplex and the alley-cropping system (annual forage

grown between strips or bands of Atriplex) were appreciated by farmers in the arid and

semi-arid areas (Karrou, undated).

Conservation Agriculture

In addition to the use of adapted species and varieties, the adoption of dry land

agriculture techniques by farmers in rainfed agriculture areas of Morocco can also

substantially improve and stabilize crop yields and protect the environment. On-station

and on-farm trials have in time prioritized the importance of the use of the minimum

tillage, no-till and mulching technologies. These conservation agriculture techniques

reduce evaporation, increase the interception of rain and its infiltration and ensure the

saving of water, energy and time, guaranteeing a long - term productivity increase and

increase in the sequestration of carbon (reduction of greenhouse gas emission).

Moreover, chemical fallow (weeds are controlled chemically by herbicides) allows the

conservation of an amount of 75 to 100 mm of water in the soil which is available to the

33

following crop (usually wheat). When limited quantities of irrigation water are available,

the application of 60 to 70 mm at critical growth stages as supplemental irrigation

(tillering, heading and during grain filling in the case of wheat) can increase yields by 70

to more than 100 %. To take advantage of water saved due to conservation techniques

and supplemental irrigation and from the rains received during the growing season, and

hence increase yield and water use efficiency, crops have to be well managed. Early

planting can help the plant to use more water (early autumn rains) and to avoid terminal

drought stress and high temperatures. If this technique is used, cereals can produce 40%

more than when late sowing is practised. Moreover, early weed control (at 3 to 4 leaf-

stage) reduces the competition between the crop and weeds for water and hence this

water is better used to increase crop yield (Karrou, undated).

2.5.2. Policy Analysis and Government Participation (Zimbabwe, 1999 Period)

A drought mitigation projects in Zimbabwe funded by International Institute for

Sustainable Development (IISD) adopted the principles of the sustainable livelihood

approach, a holistic approach to sustainable development (Agobia, 1999). It uses adaptive

strategies of communities as the entry point for development. Linked to adaptive

strategies are enhancing policy environment and science and using appropriate

technologies. Hence, in the first part of the project, which was funded by UNDP in 1994,

IISD identified a number of policies that have an impact (either beneficial or detrimental)

on the adaptive strategies of the communities. Among the policies identified were

extension policies, the drought-recovery programme, credit and marketing policies, the

Mines and Mineral Act, Land-Tenure Policies including communal lands, the Forest Act,

and the Wildlife and National Parks Act. But no analysis of how these policies have an

impact on the adaptive strategies on the communities of Gwanda and Makaha was done.

Before and after independence, the government of Zimbabwe’s agricultural policies were

geared toward supporting commercial farmers rather than subsistence farmers. In the

1980s the government embarked on promoting maize countrywide, including in drought

prone areas, at the expense of the traditional crops such as sorghum and millet. In view of

this the project sought to counteract the move by attempting to lobby the government to

34

put in place appropriate policies for dryland farming systems and use of environmentally

friendly pesticides. Hence, the project developed strategies for handling the issue and

lobbying the government. These strategies included the following:

a) Creating a Technical Advisory Committee, composed of well-placed individuals who

were expected to influence decision makers.

b) Hiring a policy analyst to work with IISD staff to identify key policies that have a

direct impact on the adaptive strategies of the communities.

c) Establishing a policy working group to analyze the key policies and make

recommendations for the reform of those policies.

d) Holding a national workshop to review the findings, analysis and recommendations of

the policy working group.

Among the above-mentioned strategies, only one of them was implemented: the hiring of

the policy analyst, who identified 27 policies that have a direct or indirect impact on the

sustainable livelihoods of the communities in the drought prone areas of Zimbabwe

(Agobia, 1999)..

2.5.3. Adaptive Strategies for Sustainable Livelihoods in Arid and Semi-Arid Lands

Project (Proposed Approach)

Under existing conditions (of externally driven development policies, concentration on

the cash economy and existing trade relations), the typical responses of the poor have

been to appropriate common property resources, intensify agriculture on marginal lands,

increase heads of livestock and shorten fallow periods; migrate seasonally or permanently

to cities, towns, agricultural plantations and to more vulnerable and marginal lands; and

have large families in order to diversify sources of income and labour. These responses

generally have not provided long-term benefits to the poor. However, there is a growing

interest in the poor as agents for their own self - improvement guided by their own

knowledge base and strategies which could lead to sustainable livelihoods. There is a

need for clear and detailed documentation of adaptive strategies that have led to

35

sustainable livelihoods and the policy issues that enhance or constrain the development

and implementation of these strategies. These strategies are likely to have evolved from

an interaction between contemporary and indigenous knowledge. Hence the initiative

sought to capture these synergies and the conditions and processes which produced and

reinforced them. It was recognized that these strategies were diverse and included

adaptations to ecological, social, political and cultural risks and shocks. IISD recognized

that the problems enunciated above occur globally in diverse socio-ecological systems. It

was agreed that initially, the initiative would focus on agro-pastoralists in arid and semi-

arid areas with the view of using the lessons learned from this experience to develop

similar initiatives in other regions and socio-ecological systems. Our entry point was the

identification of adaptive strategies, which are the result of indigenous knowledge and

experiences, contemporary knowledge including scientific and technological innovations

and policy issues, and which have led to sustainable livelihoods in arid and semi-arid

lands (Community Adaptation and Sustainable Livelihoods, 2008).

2.5.6. Adaptive Strategies and Related Policies for Burkina Faso by GREFCO/IISD

Land restoration

Land management policies encourage the rehabilitation of soils and vegetation cover

through such activities as setting aside forest reserves. The major obstacle to

implementation has been lack of adequate supervision by the management committee set

up to coordinate the different activities. In order to resolve this, it is necessary to:

organize pastoralists so as to facilitate participation, discussion and decision making;

and

motivate supervisors by giving them bonuses and vesting power in them.

Transhumance

As a means of adapting to the degraded lands, new forms of transhumant pastoralism

have evolved in Menegou. The main obstacles to this production system were linked to

water and land management. In order to resolve these it will be necessary to:

36

ensure the proper management of pasture grounds, including those outside village

lands;

achieve a better distribution of watering sites, which has implications for land

management;

institute national policies and laws which grant land tenure, and thus authority for

people to enact rules; and

put in place mechanisms which allow for the adequate local enforcement of

regulations governing the management of natural resources at the village level.

The national decentralization exercise currently underway should take the above into

consideration.

Research and Extension

Research in the agricultural sector should be geared towards supporting actual producer

needs for rainy season and dry season crop production, as well as livestock rearing.

Currently, research is grossly under-funded and is plagued with problems of staffing.

Likewise extension should also encourage the participation of producer organizations in

defining national extension policy and also in implementing various programmes

currently being undertaken by extension officials. Participation should also be enhanced

in the evaluation of the current structures which provide extension services

(GREFCO/IISD, undated).

2.5.7. Major Ingredients of Drought Mitigation as put forward by FAO

The following important recommendations were adapted from a Food and Agriculture

Organization document authored by Mohamed Bazza (Bazza, 2001), prepared for the

Near East Region. However, it is important to recognize that most of the indicated

strategies have already been implemented in South Africa.

37

Policy and Legal Framework

The prerequisite to successful drought management planning for reduced societal

vulnerability is the development and adoption of a policy on drought. Such a policy,

which should have the support of all decision-making levels (national, provincial and

local), should also be the pivotal element of national development strategies, particularly

the aspects related to water and other natural resources development and management.

But its initial step is to recognize drought as a normal, recurrent and inevitable feature of

the climate rather than an unusual event. As in almost all countries of the world, those of

the Near East Region have no policy directly related to drought. Countries have often

reacted to drought by providing assistance, essentially on an ad hoc basis, as it is the case

for other natural disasters. However, such assistance is not considered as a right of the

affected population on the one hand, and there exists no clear and explicit regulations on

the issue on the other. The activities are also often fragmented between several

institutions, with no or limited coordination. With time, this way of operating has become

a de facto policy on crisis management, with all its limitations and drawbacks. To

overcome these limitations, a new vision is needed with a policy that focuses on prior

preparedness, with a close linkage between regular development programmes and

drought mitigation. The policy would also address response to drought in a manner that

profits sustainable development and management of natural resources. At the level of

each country, it is recommended to establish an integrated national policy that focuses on

the key sectors, which may differ from one country to another. The same approach would

apply at the level of local governments or communities. Another characteristic of the

policy is that it should link between the different levels (government, local authorities,

communities, etc.) and cater for good coordination of drought management. The

development of national drought policy should be built on discussion among and

consensus of all concerned sectors, institutions and groups of interest that should be

convinced of the necessity for such actions. They should further strongly recommend

their development and support their implementation.

38

Priority objectives for drought mitigation policy should be:

1. To recognize that drought is no longer a natural disaster but rather a natural feature of

the region's highly variable climate and is a risk for social and economic development;

2. To orient all resource management practices towards alleviating the effects of drought;

3. To recognize and achieve sustainability of the agricultural resource base and the

environment.

The development of policies should not be an end but only the beginning of the process.

Additional regulations, mechanisms and structures for implementing and enforcing the

policies are also needed as a follow-up. The policy is followed by clear, comprehensive

and appropriate legislation targeting drought mitigation. For instance, in the United States

where the economy is much less vulnerable to drought than in most other countries of the

world, a “National Drought Policy Act” exists since 1997. The regulations should spell

out in a clear fashion all aspects related to drought, such as the institutional set-up and

their mandates, coordination of the programmes and activities, etc.

Leadership

The leadership for coordinating the preparation of drought mitigation plans and

supervising their implementation should rest within a high authority such as that of a

Head of State, particularly for formulation of policies and elaboration of the plan.

Implementation of activities, however, is the mandate of all institutions dealing with

development in the sectors involved. At the level of local governments or communities,

coordination would also be entrusted with an authority that includes representatives from

the different sectors and groups of interest. Drought monitoring and decision making on

the time and type of response would be the mandate of the coordinating bodies.

Decentralization is essential for rapid monitoring and response implementation.

However, there needs to be coherence between the various levels (local, district, national

institutions, etc.) as well as coordination between sectors and with regional and

international organizations and programmes.

39

Planning

The main objective of a national drought mitigation plan should be the efficient

preparation for and management of drought, as a normal feature of the climate. This

objective is achieved through the elaboration and implementation of programmes on

drought monitoring and early warning as well as on the enhancement of preparedness and

response to reduce the effects of drought and facilitate rapid recovery from it. As such,

the plans are the fundamental bases of both decision-making and intervention for drought

management. The plan should also be conducive to better coordination within levels of

decision-making as well as between the different sectors of development. An adequate

plan would have two alternatives: One for implementation during normal conditions and

a contingency plan for rapid implementation during drought. The former incorporates

drought mitigation measures in regular development activities, whereas the latter replaces

regular programmes and is implemented during drought periods. Substitution of the

regular plan by the contingency plan should normally take place in a gradual manner, at

well-specified drought severity levels. These two plans or alternatives are not

independent, but are closely linked. As an example, conservation of the available water

supplies through good management, during regular periods, results in reduced potential

effects of drought and reduced needs for mitigation efforts during drought.

Drought monitoring and early warning

Because of the confusion associated with the definition of drought and the fact that

different people often have different perceptions of it, its onset is often unnoticed and its

impacts are not detected in time. As a result, reaction to drought is usually late, resulting

in greater losses and hardships, and making the cost of emergency and recovery even

higher. This situation can be avoided only through well planned monitoring of drought,

according to clear and well-defined criteria, applied by trained personnel. Drought

monitoring would also serve as the basis for response, to which it would be closely

linked. Both monitoring and response are to be coordinated by a single agency, under the

supervision of a high authority. Monitoring, also termed drought watch, concerns several

variables and parameters (climatic, hydrologic, agronomic, social, economic, etc.), and

their evolution in time and space. Each monitored factor should be linked to drought and

40

its extent, through a pre-defined relationship obtained on a scientific basis or simply on

facts such the flow of a river, the depth of cumulative precipitation, etc. This relationship

should also indicate pre-defined threshold values of the monitored factors and parameters

that are used as criteria or triggers for drought related actions such as the shift to a

contingency plan or the start of water rationing, etc. The entire process of monitoring-

early warning-action taking should flow automatically through existing mechanisms and

procedures.

Drought Preparedness Plans

From past droughts in the Near East Region and elsewhere in the world, most countries

have gained some experience in drought management. In particular, many countries have

built substantial capacity in the management of water resources during drought periods,

through the elaboration of emergency plans and institutional arrangements established for

the occasion to manage water in a more rational manner. Other measures adopted include

the establishment of an insurance system for drought. However, the actions are generally

not planned and remain fragmented. The sectors to be involved as well as the respective

activities would vary in time and space, both within a country and between countries.

From a general perspective, the two main sectors in the Region would be Water

Resources and Agriculture with all its sub-sectors (Crop production, Animal Production,

Pasture and Range Lands, Forestry, etc.); however, other sectors such as energy,

transportation and tourism would also be involved. The economy of the country would

also have to accommodate for the foreseen programmes and activities. The common

ground for all sectors is that all activities should aim at adopting risk management

practices to promote self-reliance and protect the natural and agricultural resource base.

Need for a Regional Drought Action Plan and a Drought Information Centre

In view of the need to consolidate efforts of countries of the Near East Region to monitor

drought and prepare for its mitigation, the creation of a drought watch and information

centre in the Region is highly recommended. The centre would have the following

mandate and responsibilities:

41

- Assisting member states in planning and developing national drought policy and action

plans and in building their capacity for their implementation,

- Hosting a Regional Council for coordinating between member countries,

- Assembling the principal drought indicators already monitored by different institutions

and agencies in and outside of the region,

- Developing a system for making information about drought and drought management

easily accessible,

- Synthesizing data and releasing ready-to-use information to decision makers and other

parties such as NGOs, so that timely actions are taken on resources management issues,

- Disseminating information about drought management and developing basic

information to help people understand the phenomenon of drought and how to cope with

it,

- Organizing regional activities on drought mitigation to enhance the exchange of

experience among decision makers and technicians from the member States,

- Enhancing and fostering assistance and coordination at the regional level.

The centre is to work closely with policy makers, international and regional

organizations, national institutions in member countries and other interested parties to

ensure that it is meeting their information needs. It would have a focal point in every

country as well as a regulatory body for participation and management by all countries

(Bazza, 2001).