1

6 DROUGHT

6.1 Introduction Droughts perhaps even more than floods are a matter of definition. The term drought is much more difficult to define than the other extreme event, flood. In one climate, several months without rain may constitute a severe drought, in another it can be the norm as in winter seasons of the savannah lands. In general sense, a drought may be defined as an abnormal moisture deficiency in relation to some need, like the empty reservoir. Some texts regard drought as the period in which rainfall consistently falls short of the climatically expected amount, such that the natural vegetation does not flourish and agricultural crops fail. But in simple terms, drought is lack of water for some purpose. The engineer relates drought to a set of variables which describe rainfall, runoff and water storage. The economist relates drought to factors that affect human activities. The agricultural drought is related to shallow or deep-rooted plants etc. This shows that it is not a question of only trying to draw a line in a continuum of different degrees of water shortage, but also a wider basis of definitions, because droughts tend to cover a larger area than floods and have a greater range of impacts. Droughts are therefore defined differently for different situations. Drought may be in form of shortage of rainfall, according to meteorologists, but hydrologists and agriculturalists are also concerned with net water balance, botanists may be concerned with the quality and therefore the physiological value of the water available. Hydrologists and water resources engineers are also more concerned with longer histories of meteorological shortfalls than meteorologists are because they are dealing with the responses of often complex catchment areas and reservoir systems to droughts.

6.2 Meteorological Drought Perhaps this is the most common basis for definition of drought. This occurs when rainfall received is below expected amount in a given period of time. It also refers to a period of no rain or with rainfall less than some particular value, for example 1mm per month. In defining meteorological drought, it is particularly important to take into account differences between climate. 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. For example, some definitions of meteorological drought identify periods of drought on the basis of the number of days with precipitation less than some specific threshold. Drought must be distinguished from the general aridity of a climate, which can be defined as a long-term ratio between annual precipitation and evapo-transpiration. In case of other regions with more than one rainy season, distinction should be made between seasonal droughts and annual droughts since agricultural and hydrological systems are very sensitive to the seasonal rainfall characteristics. Seasonal droughts occur when the rainfall received within a given season is significantly below the seasonal expectation while annual droughts refer to annual water deficits.

6.3 Hydrological Drought For hydrological drought, the actual flow in the river is of most concern. So, hydrological drought may be defined as a period during which stream flows are inadequate to supply established uses under a given water-management system. Engineers concerned with maintaining water supply or with the dilution of waste effluents must consider the length of the period of low flows as well as the extremity of flows

2

below a certain level. Flows may be low because of lack of precipitation in the preceeding wet period(s) or winter(s) as well as low rainfall during the current summer or dry period. Hence a hydrological drought may be defined as that period when flow in the river is below the minimum required for sustaining demand downstream. The frequency and severity of hydrological drought is often defined on a watershed or river basin scale. Although all droughts originate with a deficiency of precipitation, hydrologists are more concerned with how this deficiency plays out through the hydrologic system. Hydrological droughts are usually out of phase with or lag the occurrence of meteorological and agricultural droughts. It takes longer for the precipitation deficiencies to show up in components of the hydrological system such as soil moisture, stream flow, and ground water and reservoir levels. As a result, impacts are out of phase with those in other economic sectors because different water use sectors depend on these sources for their water supply. For example, a precipitation deficiency may result in a rapid depletion of soil moisture that is almost immediately discernible to agriculturalists, but the impact of this deficiency on reservoir level may not affect hydroelectric power production or recreational uses for many months. Also, water in hydrologic storage systems (e.g., reservoirs, rivers) is often used for multiple and competing purposes (e.g., water supply systems, flood control, irrigation, recreation, navigation, hydropower, wildlife habitat), further complicating the sequence and quantification of impacts. Competition for water in these storage systems escalates during drought and conflicts between water users can increase significantly.

6.4 Agricultural Drought In simple terms, an agricultural drought may be that period when moisture in the soil is insufficient to meet evapo-transpiration needs and also support plant growth or crop production. Agricultural drought links various characteristics of meteorological (or hydrological) drought to agricultural impacts, focusing on precipitation shortages, differences between actual and potential evapo-transpiration, soil water deficits, reduced ground water or reservoir levels, and so forth. Plant water demand depends on the prevailing weather conditions, biological characteristics of the specific plant, its stage of growth, and the physical and biological properties of the soil. A good definition of agricultural drought should be able to account for the variable susceptibility of crops during different stages of crop development, from emergence to maturity. Deficient topsoil moisture at planting may hinder germination, leading to low plant populations per hectare and a reduction of final yield. However, if topsoil moisture is sufficient for early growth requirements, deficiencies in subsoil moisture at this early stage may not affect final yield if subsoil moisture is replenished as the growing season progresses or if rainfall meets plant water needs.

6.5 Other Forms Of Drought a) Physiological Drought This refers to the condition of plants that suffer from excess of saline water, often on poorly drained irrigated land. In this case, the problem is lack of physiologically usable quality rather than quantity of water.

b) Climatological and Atmospheric Drought A climatological drought refers to long periods, such as sequences of years, with precipitation less than some base value, for example, less than 25% of the mean annual precipitation. An atmospheric drought refers to conditions of air temperature and humidity, etc.

c) Socio-economic Drought

3

Socio-economic definitions of drought associate the supply and demand of some economic good with elements of meteorological, hydrological drought. It differs from the aforementioned types of drought because its occurrence depends on the time and space processes of supply and demand to identify or classify droughts. The supply of many economic goods, such as water, forage, food grains, fish, and hydroelectric power, depends on weather. Because of the natural variability of climate, water supply is ample in some years but unable to meet human and environmental needs in other years. Socio-economic drought occurs when the demand for an economic good exceeds supply as a result of weather – related shortfall in water supply. For example, in Uruguay in 1988-89, drought resulted in significantly reduced hydroelectric power production because power plants were dependent on stream flow rather than storage for power generation.

6.6 Drought Impacts Drought produces a complex web of impacts that span many sectors of the economy and well beyond the area experiencing physical drought. This complexity exists because water is integral to our ability to produce goods and provide services. Impacts are commonly referred to as direct or indirect. Reduced crop, rangeland, and forest productivity; increased fire hazard, reduced water levels, increased livestock and wildlife motality rates and damage to wildlife and fish habitat are a few examples of direct impacts. The consequences of these impacts illustrate indirect impacts. For example, a reduction in crop, rangeland, and forest productivity may result in reduced income for farmers and agrobusiness, increased prices for food and timber, unemployment, reduced tax revenues because of reduced expenditures, increased crime, foreclosures on bank loans to farmers and business men, migration, and disaster relief programs. Direct or primary impacts are usually biophysical. The impacts of drought can be categorized as economic, environmental or social.

6.6.1 Economic Impacts Many economic impacts occur in agriculture and related sectors, including forestry and fisheries, because of the reliance of these sectors on surface and subsurface water supplies. In addition to obvious losses in yields in both crop and livestock production, drought is associated with increases in insect infestations, plant disease, and wind erosion. Droughts also bring increased problems with insects and diseases to forests and reduce growth. The incidence of forest and range fires increases substantially during extended droughts, which in turn places both human and wildlife populations at higher levels of risk. Income loss is another indicator used in assessing the impacts of drought because so many sectors are affected. Reduced income for farmers has a ripple effect.

Retailers and others who provide goods and services to farmers face reduced business. This leads to unemployment, increased credit risk for financial institutions, capital shortfalls and loss of tax revenue for local, state, and federal government. Less discretionary income affects the recreation and tourism industries. Prices for food, energy, and other products increase as supplies are reduced. In some cases, local shortages of certain goods result in the need to import these goods from outside the stricken region. Reduced water supply impairs the navigability of rivers and results in increased transportation costs because products must be transported by rail or truck. Hydropower production may also be curtailed significantly.

6.6.2 Environmental Impacts Environmental losses are the result of damages to plant and animal species, wildlife habitat, and air and water quality; forest and range fires, degradation of landscape quality, loss of biodiversity and soil erosion. Some of the effects are short-term and conditions quickly return to normal following the end of

4

the drought. Other environmental effects linger for some time or may even become permanent. Wildlife habitat, for example, may be degraded through the loss of wetlands, lakes, and vegetation. However, many species will eventually recover from this temporary aberration. The degradation of landscape quality, including increased soil erosion, may lead to a more permanent loss of biological productivity of the landscape.

Although environmental losses are difficult to quantify, growing public awareness and concern for environmental quality has forced public officials to focus greater attention and resources on these effects. In East Africa (Mubiru, 2006), the low levels of water in the Great Lakes Region, during the 2004-2006 were primarily caused by drought. This affected the output of hydropower plants like the Nalubaale and Kiira dams at Jinja in Uganda, which are fed by Lake Victoria. The combined power output dropped from 270 MW to 120MW. It also affected the rivers that drain into Lake Victoria, like Nzoia in Kenya, Simiyu in Tanzania and Kagera in Rwanda resulting in a decline in hydropower of about 30- 60%. The levels of Lake Tanganyika dropped by more than a meter and this affected the Port of Bujumbura. Lake Nyasa was also affected.

It can be noted that this drought period coincides with the low activity of the Wolf Gliessberg Cycles (Yousef et al, 2000), which range from 80 – 120 years. This was based on available data for over 100 years. The low activity cycle lasts 12 years. The Wolf Gliessburg Cycles is seen in sunspot measured amplitudes, as measured by the annual mean sunspot number. He also notes a very good correlation between sunspot number and the outflows of Lake Victoria (0.86) and Lake Kyoga and forecasts Equatorial droughts for the years 2009 +- 2-3 years, 2021+- 2-3, 2033+- 2-3 and perhaps 2044+- 2-3 years.

6.6.3 Social Impacts Social impacts mainly involve public safety, health, conflicts between water users, reduced quality of life and inequities in the distribution of impacts and disaster relief. Many of the impacts specified as economic and environmental have social components as well.

Population out-migration is a significant problem in many countries, often stimulated by greater availability of food and water elsewhere. Migration is usually to urban areas within the stressed area or to regions outside the drought area; migration may even be to adjacent countries, creating refugee problems. However when drought has abated, these persons seldom return home, depriving rural areas valuable human resources necessary for economic development. For the urban area to which they have immigrated, they place ever-increasing pressure on the social infrastructure, possibly leading to greater poverty and social unrest.

It is important to mention that not all impacts of drought are negative. Some agricultural producers outside the drought area or with surpluses benefit from higher prices, as do businesses that provide water-related services or alternatives to water-dependent services.

6.6.4 Sequences of Drought Impacts The sequence of impacts associated with meteorological, agricultural, and hydrological drought further emphasizes their differences. When drought begins, the agricultural sector is usually the first to be

5

affected because of its heavy dependence on stored soil water. Soil water can be rapidly depleted during extended dry periods. If precipitation deficiencies continue, then people dependent on other sources of water will begin to feel the effects of the shortage. Those who rely on surface water (i.e., reservoirs and lakes) and subsurface water (i.e., ground water) for example, are usually the last to be affected. A short-term drought that persists for 3 to 6 months may have little impact on these sectors, depending on the characteristics of the hydrologic system and water use requirements.

When precipitation returns to normal and meteorological drought conditions have abated, the sequence is repeated for the recovery of surface and subsurface water supplies. Soil water reserves are replenished first, followed by stream flow, reservoirs and lakes, and ground water. Drought impacts may diminish rapidly in the agricultural sector because of its reliance on soil water, but linger for months or even years in other sectors dependent on stored surface or subsurface supplies. Ground water users, often the last to be affected by drought during its onset, may be last to experience a return to normal water levels. The length of the recovery period is a function of the intensity of the drought, its duration, and the quantity of precipitation received as the episode terminates.

6.7 Causes of Droughts

6.7.1 Primary Causes Droughts are primarily generated by aberration in climatic conditions. There is evidence that the climate of Uganda is linked to the major global modes of variability including the El Nino Southern Oscillation (ENSO) signals as well as signals in the Tropical Atlantic and Indian Ocean Sea Surface Temperatures (SSTs).

Inter annual and Intra annual rainfall variability plays the greatest role in the existence of droughts yet this variability is caused by the El Nino Southern Oscillation and Quasi Biennial Oscillation phenomena.

6.7.2 El-Nino Southern Oscillation (ENSO) Phenomenon Every two to seven years off the western coast of South America, ocean currents/ winds shift, bringing warm water westward, displacing the nutrient-rich cold water that normally wells up from deep in the ocean. The invasion of warm water disrupts both the marine food chain and the economies of coastal communities that are based on fishing and related industries.

Because the phenomenon peaks around the Christmas season, the fishermen who first observed it named it El Nino (“the Christ Child”).

El Nino and La Nina are Spanish words that were coned to signify periods (of a year or occasionally longer) when there is pronounced development of sea-surface temperature (SST) anomalies over the Central and Eastern areas of the Equatorial Pacific Ocean.

When this period is significantly anomalously warm (positive SST anomalies) this period is referred to as El Nino and when it is significantly cold (negative SST anomalies) it is referred La Nina period. El Nino often begins early in the year and peaks between the following November and January.

The Southern Oscillation, a “seesaw of atmospheric pressure between the Eastern equatorial Pacific and lndo- Australian areas” (Glantz et al., 1991), is closely linked with El Nino. During an El Nino–Southern Oscillation (ENSO) event, the Southern Oscillation is reversed. Generally, when pressure is high over the Pacific Ocean, it tends to be low in the Eastern Indian Ocean, and vice versa (Maunder, 1992). El Nino and Southern Oscillation often occur together, but also happen separately.

6

ENSO occurrences are global climate events, that are linked to various climatic anomalies, even in ENSO years,. In fact, statistical evidence shows that ENSO can account at most for about 50% of the inter annual rainfall variance in Eastern and Southern Africa (OgaIlo, 1994), but many of the more extreme anomalies, such as severe droughts, flooding, and hurricanes, have strong teleconnections to ENSO events. Teleconnections are defined as atmospheric interactions between widely separated regions (Glantz, 1994).

From his analysis (Kayondo, 2001) of 22 El Nino and 13 La Nina and 6 month SPI data, ENSO has been found to exert an influence on the moisture regime of East Africa. The degree and temporal patterns vary according to the drought zones he developed. North Eastern Tanzania has the strongest response to ENSO. Furthermore, El Nino seems to exert a stronger influence on East Africa than La Nina.

During an ENSO event, drought can occur virtually anywhere in the world (Ropelewski, 1987) looked at the link between ENSO events and regional precipitation patterns around the globe. Eastern and Southern Africa showed a strong correlation between ENSO events and a lack of rainfall that brings on drought in the Horn region and areas South of there. Variations in the SSTs modify the strength, orientation and persistence of the Monsoon winds that are responsible for moisture transportation thus displacing the rainfall patterns within the tropics bringing droughts and floods to vast areas.

In Uganda, El Nino is often equated to floods while La Nina is often equated to droughts. In the analysis of the Net Basin Supply to Lake Victoria demonstrates that ENSO signals are clearly identifiable in the October, November, December, January (ONDJ) wet season and to a lesser extent in the February, March, April, May and June, July, August, September (JJAS) seasons. For Lake Victoria, a La Nina event results in reduced precipitation in the ONDJ and slightly increased precipitation in JJAS season (Wardlaw et al, 2007).

From his analysis (Camberlin, 1997) observes that there exits a close association between summer rainfall variations in India and in the western parts of East Africa. An even closer relationship exists between the latter and Bombay. This relationship has been virtually stable throughout the twentieth century. Although there is also a statistical connection between East African Rainfall and the Southern Oscillation Index (SOI), partial correlation coefficients show that the India–East Africa teleconnection is to a large extent independent of SOI. He suggests that monsoon activity over India is a major trigger for July- September rainfall in the East African Highlands.

6.7.3 Global Warming and the Greenhouse Effect In a greenhouse, solar radiation passes through a mostly transparent piece of glass or plastic and warms the inside air, surface, and plants. As the temperature increases inside the greenhouse, the interior of the greenhouse radiates energy back to the outside and eventually a balance is reached.

The earth and its atmosphere simulate these greenhouse conditions. Short-wave radiation from the sun passes through the earth’s atmosphere. Some of this radiation is reflected back into space, some of it is absorbed by the atmosphere, and some of it makes it to the earth’s surface, where it is either reflected or absorbed. The earth, meanwhile, emits long-wave radiation toward space. Gases within the atmosphere absorb some of this long-wave radiation and re-radiate it back to the surface. These gases are called greenhouse gases and include carbondioxide (CO2), water vapour (H2O), methane (CH4), nitrous oxide (N2O), chlorofluorocarbons (CFCs), and ozone (O3). It is because of this greenhouse-like function of the atmosphere called Global warming that the average global temperature of the earth is 15°C (59°F).

Increased concentration of these gases along with the depletion of the Ozone layer due to processes (photosynthesis, decomposition) and human activities (pollution, industrialisation, nuclear tests) means increased absorption and re-emission of the infrared radiation that keeps the earth surface warmer than it should be leading to extensive evapotranspiration, a parameter that is very significant to the drought phenomenon.

7

6.7.4 Other Phenomena Other factors that may lead to the occurrence of droughts include:-

• The space-time characteristics of the Inter Tropical Convergence Zone (ITCZ), which influences the seasonal variability of rainfall.

• The Congo air mass

• The position and strength of Mascarene anticyclone over the Southern Indian Ocean, the St. Helena High, the Azores High in the Atlantic Ocean and the Arabian High.

• The three low activity 12 years of the Wolf-Gleissburg solar cycles are associated with droughts, while the long range cycles (80-120 years) are associated with rising water levels.

6.7.5 Inadvertent Causes It is increasingly important to associate role of humanity in altering the hydrological cycle with the existence of droughts. Man has an indirect effect on the hydrological processes simply by altering the vegetation. Vegetation plays a great role in the hydrological cycle as concerns interception, evapotranspiration, rain formation, soil retention and filtration.

In Uganda today like many other developing countries, vast areas including forests, grasslands and wetlands have been cleared for municipal reasons, industrialization, hunting, lumbering and agriculture. The process is being accelerated by a phenomenal increase in population, industrialization and agriculture.

Destruction of natural vegetation slows down the hydrological cycle by reducing evaporative losses. As trees and shrubs have been replaced by grasslands and agricultural crops, the rate of interception and evapotranspiration has been reduced. Potential evaporation is likely to have been reduced further by the increase in surface albedo and the resultant reduction in the net radiation balances as woodlands are cleared. If the clearance covers a broad enough area, then the lower evapotranspiration and net balances might eventually reduce precipitation downward by reducing atmospheric moisture levels and convective cloud formation.

More to the above, we know with certainty that the concentrations of carbondioxide (CO2), water vapor (H2O), methane (CH4), nitrous oxide (N2O) and chlorofluorocarbons (CFCs) in the atmosphere have increased as a result of recent human activity including modernization and technology in form of heavy industrialization leading to air pollution.

By 1896, a Swedish scientist Svante Arrhenius was already calculating that the earth’s surface temperature would increase by 5-6°C (41—42 8°F) with a doubling or tripling of the atmospheric CO2 content. CFCs destroy stratospheric ozone. The resultant effect of increase in CO2 and depletion of the stratospheric ozone is temperature increase, which is an important parameter in the drought phenomenon.

There is probably no simple explanation for the occurrence of droughts. In general, the factors that combine to produce droughts are related to atmospheric and oceanic circulation, and to the influence of continental areas. The atmospheric circulation may fail to follow their normal course, for example the depression tracks are diverted or monsoons fail, or else it may result from cooler temperatures, particularly sea surface temperature (SSTs) which generate less evaporation and less convection activity.

8

Drought in Sahel has been linked to lower SSTs in the tropical Atlantic, which may be due to the strength of oceanic up-swelling, Sahel droughts are also linked with the Pacific El Nino Current and failure of the ITCZ to penetrate as far North as normal so that the West Africa monsoon does not reach the Northern Interior.

If for example, climatic conditions are such that the annual rainfall is derived from a few intense rainstorms, the failure of such storms to occur over an extended period produces the drought. A temporal decrease in the number of rainfalls may arise from variations in the pattern of atmospheric circulation. Variation in the distribution of warm and cold water masses in the oceans can produce slower acting and longer lasting atmospheric effects.

The occurrence of particularly cold or warm periods over continental areas may precede or produce droughts over adjacent continental areas. Attempts at the explanation of droughts are based on the physical relationships and interactions of the drought affecting factors, while the descriptions of drought are based on statistical and analytical methods.

6.8 Analysis of Drought Yevjevich (1991) divides the statistical techniques of drought analysis into four groups:

1) Empirical methods. These are based on the observed data from which deductions are made about the nature of the variables. For example. Joseph (1970) fitted distributions to river flow data and found that Gamma distribution gave the best fit.

2) Generation methods. Techniques, such as the Monte Carlo method and others are used to produce

long sequences of data with the same statistical properties (i.e. mean, variance, etc) as the observed data.

3) Analytical models. These are based mainly on the theory of probability. 4) Analytical runs. The period when the flow is less than the base value Q0 are considered as a statistical

variate, τ , and are associated with the duration of the drought (Fig. 6.1) 6.8.1 Drought in Terms of Streamflow The severity of the drought is measured by the total deficit in the volume of water with respect to Q0

during the period τ . The deficit in the volume of water is given by

∫+

−=τ1

1

)(t

t

t dtQQV O (6.1)

where Vt will have a certain probability distribution. To give a deficit duration curve, this analysis is applied by engineers concerned with dilution of waste effluents who must consider length of period of low flows as well as extremity of flows below a certain level. It is very useful because degree of treatment is dependant on water available for dilution.

9

Fig 0.1 Definition Sketch for Application of Runs in Drought Analysis based on River Flow The above method defines drought in terms of deviations from the river flows. The letter p represents the time between two drought periods. 6.8.2 Drought Volumes Method Deviations from average rainfall (e.g. monthly averages) can also be calculated and the total deficit in terms of volume of water in mm calculated as

∫+

−=τt

t

tto dtRMV )( (6.2)

Where Mt and Rt are the mean monthly rainfall and monthly rainfall respectively t and τ are periods, which may be in months in Fig. 6.2.

Rai

nfal

l

tM

tR

t

Q

QO

p

τ

0 τ+1t1t t

10

Fig 0.2 Application of Runs in Drought Analysis based on Monthly Rainfall However, drought can also be defined in terms of effective precipitation and the mean monthly rainfall. In this case drought volume will be defined by Equation 6.3.

tt

D

tt MEV )(

1−= ∑

=

(6.3)

Where Et is the effective precipitation defined by

ttttt RWMRE +−= −−*

11 )( (6.4) Rt is the monthly rainfall, Mt the monthly mean rainfall and Wt the weighting factor which allows for carry-over from one month to the next.

⎟⎠⎞

⎜⎝⎛ +=

MARM

W tt

1211.0 (6.5)

And MAR is the mean annual rainfall. D is the duration of drought. Basing on the deviation of effective precipitation from the average rainfall, Herbst et al. (1966) defined drought intensity of rainfall as:

( ) ( )[ ]

( )∑

∑

−

−

−−= d

tt

D

tttt

MMD

MMDMEY

1

1 (6.6)

Where (MMD) t is the monthly mean deficit (t = 1, 2, …,12) evaluated from the N years of record and is given by

( ) ( )∑=

−=

N

t

tttt N

RMMMD

1 (6.7)

Where D and E t are defined as above. The carry-over is zero for the first month. The sum Σ(MMD) t gives the mean annual deficit MAD. A severity of drought index is then given by YD. i) Example 6.1 Table 6.6 shows the effective rainfall in Kasese for years 1964 – 1968 and the monthly mean rainfall over 50 years, Table 6.6 shows the mean monthly deficits over 50 years in last row.

11

Table 0.1 Monthly Effective Rainfall (mm) Et Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec1964 45.4 46.3 82.0 156.2 119.9 24.5 31.4 61.6 126.7 194.2 154.8 192.91965 4.5 7.2 111.7 255.7 59.0 0.0 0.0 0.0 0.0 10.5 145.9 38.41966 0.0 54.2 224.5 332.6 136.1 20.8 19.5 108.1 182.1 36.3 0.0 0.01967 4.6 0.0 26.9 145.7 265.0 113.0 105.0 78.8 0.0 0.0 153.7 79.41968 27.4 85.5 237.0 165.4 0.0 54.3 73.9 57.5 12.6 136.2 300.2 127.9Mean 50Years

29.6 39.2 99.0 132.0 106.1 34.8 36.9 73.0 91.1 104.5 103.8 70.6

Effective rainfall was calculated using the equation;

Where Rt is the monthly rainfall, Mt the monthly mean rainfall and Wt the weighing factor which allows for carry-over from one month to next month. The data above was used to study drought occurrences in Kasese. Drought is said to occur if the difference between the effective rainfall and Mt mean monthly rainfall is negative, indicating a deficit. a) Using the information above, copy and complete Table 6.7 The Mean Monthly Deficits (MMD) are

also given. Note that the positive values (surplus) are set to zero since they are not required here.

Table 0.2 Monthly Rainfall Deficits (mm) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1964 0.0 0.0 17.0 0.0 0.0 10.3 5.6 11.4 0.0 0.0 0.0 0.0 1965 1966 1967 1968 MMD (50yrs)

16.9 18.2 33.4 31.8 30.7 19.2 16.7 28.8 30.8 29.1 25.8 31.8

(b) From the Table 6.8, March is a drought month as well as June, July, August etc. March is a drought period of one month while June-August is a drought period of three months. The summation of rainfall deficits across the drought period gives a drought volume V, the drought intensity Y and Severity index given by the following formulae;

( )( )∑ −=

=D

IiME ttV

1

( ) ( )[ ]( )∑

∑

−

=

−−= d

it

D

ittt

MMD

MMDMEY

1

1

Severity index = YD

Where D is the drought period in months, (MMD)t is the monthly mean deficit (t = 1, 2,…., 12) evaluated from the N years of record and is given in the last row of the table in (a) above. The results should be

12

given as in Table 6.8 similar to the one below in which sample calculations of drought volume, drought intensity and severity index have been made. Note that the expected total deficit is the sum of MMD across the same drought period and a negative value of drought intensity is set to zero because it is of little significance.

Table 0.3 Drought Calculations Year(s) Month(s) Drought

period D Months

Drought volume V (mm)

Expected total deficit ∑(MMD)t

Drought intensity Y

Drought Severity YD

1964 Mar 1 17 33.4 0.00 0.00 1964 Jun-Aug 3 23.7 64.7 0.00 0.00 1965 Jan-Feb 2 57.1 35 0.63 1.26 Solution a) Calculation of rainfall deficit; Jan 1964 Mean rainfall – Effective rainfall = 29.6-45.4 = -15.8mm This value is a surplus and it should be set to zero. Jan 1965, Mean rainfall – Effective rainfall = 29.6-4.5 = 25.1 mm

Table 0.4 Completed Monthly Rainfall Deficit (mm) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1964 0.0 0.0 17.0 0.0 0.0 10.3 5.6 11.4 0.0 0.0 0.0 0.0 1965 25.1 32.0 0.0 0.0 47.1 34.8 36.9 73.1 91.1 94.1 0.0 32.3 1966 29.6 0.0 0.0 0.0 0.0 13.9 17.4 0.0 0.0 68.2 103.8 70.6 1967 25.0 39.2 72.1 0.0 0.0 0.0 0.0 0.0 91.1 104.5 0.0 0.0 1968 2.2 0.0 0.0 0.0 106.1 0.0 0.0 15.6 78.5 0.0 0.0 0.0 MMD 16.9 18.1 33.3 31.8 30.7 19.2 16.7 28.8 30.8 29.1 25.8 31.8 b) For Jan-Feb 1965 drought, Drought period, D= 2 months Drought volume = 25.1+32.0 = 57.1 mm Expected total deficit = 16.9+18.1=35.0mm Drought intensity = (Drought volume – Expected total deficit) / Expected total deficit = (57.1-35.0)/35.0 = 0.63 Since a negative intensity is of little significance, it should be set to zero. Drought severity = Drought period x Drought intensity = 2 x 0.63 = 1.26

13

Table 0.5 Completed Drought Calculations Years Month(s) Drought

period D Months

Drought volume V (mm)

Expected deficit ∑(MMD)t

Drought intensity

Drought severity

1964 Mar 1 17 33.4 0.00 0.00 1964 Jun-Aug 3 23.7 64.7 0.00 0.00 1965 Jan-Feb 2 57.1 35 0.63 1.26 1965 May-Oct 6 377 155.3 1.43 8.57 1965-1966 Dec-Jan 2 61.9 48.7 0.27 0.57 1966 Jun-Jul 2 31.3 35.9 0.00 0.00 1966-1967 Oct-Mar 6 379 155 1.45 8.67 1967 Sep-Oct 2 195.6 59.9 2.27 4.53 1968 May 1 106.1 30.7 2.46 2.46 1968 Aug-Sep 2 94.1 59.5 0.58 1.16 ii) Drought Frequency Relationships There are two fundamental rudiments to the development of a reliable drought frequency relationship, which are;

• Availability of long-term quality precipitation record.

• Assumption that the statistical properties of precipitation are stationary. This implies that the statistics do not change over time, which occurs when land use changes in the area are not expected to be reflected in the precipitation. If a substantial change in precipitation characteristics is expected, then the drought frequency relationship built from historical records might not be valid.

After drought volumes are calculated using the procedures stated above,the drought volumes can be subdivided into equal class intervals each with an increment ΔQ mm. Starting with the first class interval i=1 we count the number of drought volumes n1 in it. The relative frequency of occurrence for the first class interval is f1 = n1/N where N is the total number of drought volumes in the record. This is repeated for each class interval and a plot of i verses fi results in the frequency histogram for the series. The variable drought volumes, V can be created with the same procedures as discussed under Section 4.2(ii) Chapter 4-Statistical Methods.

A study (Rugumayo and Mwebaze, 2002) was carried out for two climatic zones in Uganda (M (Kasese) and C(Mbarara)), with 35 and 37 years of data respectively using the drought volumes method. The drought volumes were considered as a series of random variables and statistical distributions were tested and fitted using the Kolmogorov Smirnov Test. In both cases it was observed that the log normal distribution gave the best fit. Drought volumes were also used to calculate drought magnitudes, return periods, intensities and severities. Curves for drought duration and drought volumes against return period (both linear and logarithmic) were plotted and are useful methods of representing drought intensities. It was noted that Kasese region experiences more drought than Mbarara region. Fig 6.3 shows a drought duration frequency curve for a similar study in Masindi and Rakai. Fig 6.4 shows a curve of drought volumes against return period and Fig 6.5 shows a plot of drought severity against drought volumes for the same areas. A method that uses statistical methods to relate historic groundwater levels and rainfall time series provides a robust approach to predicting minimum groundwater levels and drought. In this case

14

groundwater droughts are defined in terms of the return period of a given groundwater level. A multiple linear regression model (regression of monthly rainfall totals for a given period aginst values of minimum annual groundwater levels for the same period) when used with synthetic rainfall data based on climate-change scenarios, enables changes in future annual groundwater levels to be modeled (Bloomfield et al, 2003). iii) Drought Intensity and Severity Maps The Drought Intensity map of Uganda (based on drought volumes) shown in Fig 6.6 shows higher intensities in the climatic regions (cf Fig 5.6) of MW,CE, part of ME ( these constitute the relatively dry cattle corridor, and traverses in a southwest- northeast direction across the country( NEMA, 2005)), the northern part of L, parts of H, E and I, the northern part of G, which are also part of the cattle corridor . The Drought Severity map of Uganda (based on drought volumes) in Fig 6.7 relates to the duration of the drought as well, shows higher severities in regions MW, ME, CE and a portion of CW in the south west (as part of the cattle corridor); regions B, D, E and a portion of F in the east, as well as the northern parts of H, G , I and J. iv) Drought Prediction With our present knowledge, we are unable to predict future hydrological events in an exact deterministic manner. Hydrologists ‘predict’ the occurrence of extreme events in a broad statistical sense. Based on statistical analyses of the best possible, the longest historical precipitation record in the watershed, it is possible to predict the drought rate of a specific volume associated with a given frequency. In other words instead of predicting the time of occurrence of a drought of a certain magnitude, the hydrologist ascertains how often a drought of such a magnitude occurs.

DURATION CURVE

0

100

200

300

400

500

600

700

800

900

1000

0 10 20 30 40 50 60 70 80 90 100

PERCENTAGE EXCEEDANCE

DR

OU

GH

T V

OL

UM

E(m

m

MASINDI RAKAI

Fig 0.3 Drought Duration Curves for Masindi and Rakai

15

DROUGHT VOLUMES VS RETURN PERIODS

y = 199.21Ln(x) + 5.2083

y = 239Ln(x) - 17.819

1

10

100

1000

10000

1 10 100 1000RETURN PERIOD(years)

DR

OU

GH

T V

OLU

ME(

mm

)

RAKAI MASINDI

DROUGHT VOLUME VS SEVERITY

y = 0.0195x - 0.7334

y = 0.0152x - 0.1273

02468

1012141618

0 200 400 600 800 1000DROUGHT VOLUME (mm)

SEV

ER

ITY

RAKAI MASINDILinear (MASINDI) Linear (RAKAI)

Fig 0.4 Drought Volumes against Return Period Fig 0.5 Severity against Drought Volumes

6.8.3 Drought Severity Drought severity is measured by use of an index. The index shows climatological characteristics of precipitation as a water source for a station area. By using these indices, the onset and ending of a water deficit period is categorized and how long this deficit has lasted is also found. Some of the indices used by hydrologists in drought analysis include:

• Standardized Precipitation Index (SPI), ( Mckee, 1993)

• Surface Water Supply Index (SWSI), ( Shafer and Dezman, 1982)

• Bhalme-Mooley Drought Index, BMDI (Bhalme and Mooley, 1980)

• Palmer Drought Severity Index (PDSI), (Palmer, 1965)

• Deciles (Gibbs, 1965)

• Crop Moisture Index (CMI), (Palmer, 1968)

The main input parameter for calculation of these indices is precipitation and others include; evapotranspiration, temperature, soil moisture, and runoff. The time scale for the parameters is the month and this has various problems associated with it (compared with daily scale) viz;

• Most of these indices assess the deficiency of water from the climatological mean on some predefined duration but leave out the concept of defining the period of water deficit.

• Using monthly units is problematic because an affected drought region can return to normal condition after a day’s rainfall thus justifying the use of daily values.

• These indices do not effectively take into account the aggravating effects of runoff and evapotranspiration that build up with time.

• They have limited usefulness in monitoring ongoing drought because they are based on monthly time step. Furthermore, most of them fail to differentiate the effects of drought on surface and sub-surface water supplies.

16

Fig 0.6 A Drought intensity map for Uganda

17

Fig 0.7 A Drought Severity map for Uganda

18

6.8.4 The Standardized Precipitation Index Method The Standardized Precipitation Index (SPI) was designed to quantify the precipitation deficits for multiple time scales. These time scales reflect the impact of drought on the availability of the different water sources. Soil moisture conditions respond to precipitation anomalies on a relatively short scale while ground water stream flow and reservoir storage reflect the long-term precipitation anomalies. For these reasons, (Mckee et al, 1993) originally calculated the SPI for 3-, 6-, 12-. 24- and 48-month time scales.

The SPI for any location is based on the long-term precipitation record for a desired period and is calculated by taking the difference of the precipitation for a particular time scale and the long-term mean and then dividing by the standard deviation.

SPI = t

tt MRσ−

(6.8)

Where tσ is the standard deviation for the month Rt and Rt is the monthly rainfall and Mt is the mean monthly rainfall.

Positive SPI values indicate greater than mean precipitation while negative values indicate less than mean precipitation. Because the SPI in normalized wetter and drier climates can be represented in the same way, (Mckee et al, 1993) used the classification system shown in the SPI values in Table 6.1 to define drought intensities resulting from the SPI climatic analysis. This technique is being used at the National Drought Mitigation Centre, USA to monitor moisture supply conditions. The SPI can be used to monitor hydrological conditions and flood risk, because it can also determine, wetter than normal conditions (Seiler et al, 2002).

From 6 month and 12 month SPI data (Kayondo, 2001), East Africa was delineated into 7 homogenous drought zones, which resembled the six homogenous zones identified by harmonic analysis.

A study (Rugumayo, Maiteki, 2006) using both drought volumes and SPI methods was carried out on two Ugandan climatic regions, CE (Rakai) and K (Masindi). Fig 6.8 shows the logarithmic plot of SPI drought volumes against return period for Rakai, which gives a straight line and Fig 6.9 gives the plot of SPI against drought volumes for Moroto (Rugumayo et al, 2007). A comparison of the results shows a linear relationship between the two indices, with a good correlation.

Table 0.6: SPI ranges and Drought Severities SPI range Intensity of Climate

2.0 and above Extremely wet

1.5 to 1.99 Very wet

1.0 to 1.49 Moderately wet

-0.99 to 0.99 Near Normal

-1.0 to – 1.49 Moderately dry

-1.5 to – 1.99 Severely dry

- 2.0 and below Extremely dry

19

SPI Drought Plot for Rakai

y = 1.6211Ln(x) + 0.0779

0123456789

1 10 100 1000Return Period(years)

Dro

ught

Vol

ume

A drought event occurs any time the SPI is continuously negative and reaches intensity where the SPI is -1.0 or less. The event ends when the SPI becomes positive. Each drought event therefore has a duration defined by its beginning and end and intensity for each month that the event continues.

The cumulated magnitude of the drought or the drought magnitude is the positive sum of the SPI for all the months within the drought event. This standardization allows the SPI to determine the rarity of the current drought as well as the probability of the precipitation necessary to end the current drought. Example 6.2

The data given below are monthly rainfall values (Rt) taken from a rain-gauge station at Kisoro

Police Post.Using the SPI method estimate the Annual Drought Magnitudes of the area.

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1943 84 162 131 160 153 32 2 104 65 143 212 143 1944 53 41 99 50 58 37 0 151 260 248 139 133 1945 59 69 125 68 89 138 33 80 154 274 288 100 1946 145 120 189 321 166 13 26 54 110 252 305 190 1947 239 142 215 171 70 11 149 38 170 141 228 128 1948 164 226 200 381 107 143 46 74 466 335 250 100 1949 67 23 89 188 233 0 19 117 163 109 134 45 1950 97 60 247 180 42 63 38 31 217 125 174 62

y = 0.007x + 1.1533

0

2

4

6

8

10

0 100 200 300 400 500 600 700 800 900Drought volumes

SPI

Fig 0.8 A Plot of SPI Drought against Return Period for Rakai

Fig 0.9 SPI and Drought Volumes for Moroto compared

20

Solution

a) Calculate the monthly means (mmt) and monthly standard deviations (mstdev ) for the

given data

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1943 84 162 131 160 153 32 2 104 65 143 212 143 1944 53 41 99 50 58 37 0 151 260 248 139 133 1945 59 69 125 68 89 138 33 80 154 274 288 100 1946 145 120 189 321 166 13 26 54 110 252 305 190 1947 239 142 215 171 70 11 149 38 170 141 228 128 1948 164 226 200 381 107 143 46 74 466 335 250 100 1949 67 23 89 188 233 0 19 117 163 109 134 45 1950 97 60 247 180 42 63 38 31 217 125 174 62

mmt 114 105 162 190 115 55 39 81 201 203 216 113 mstdev 64.6 69 58 113 65 56 47 41 123 84 64 46

b) Calculate the SPI values for the different months using Equation 6.8

SPI = (Rt- mmt)/mstdev

Where

SPI = standard precipitation index, Rt = monthly rainfall, mmt = mean monthly rainfall

and mstdev= monthly standard deviation

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1943 -0.5 0.8 -0.5 -0.3 0.6 -0.4 -0.8 0.6 -1.1 -0.7 -0.1 0.7 1944 -0.9 -0.9 -1.1 -1.2 -0.9 -0.3 -0.8 1.7 0.5 0.5 -1.2 0.4 1945 -0.8 -0.5 -0.6 -1.1 -0.4 1.5 -0.1 0.0 -0.4 0.8 1.1 -0.3 1946 0.49 0.2 0.5 1.2 0.8 -0.7 -0.3 -0.7 -0.7 0.6 1.4 1.7 1947 1.94 0.5 0.9 -0.2 -0.7 -0.8 2.3 -1.0 -0.2 -0.7 0.2 0.3 1948 0.78 1.7 0.7 1.7 -0.1 1.6 0.1 -0.2 2.2 1.6 0.5 -0.3 1952 -0.7 -1.2 -1.2 0.0 1.8 -1.0 -0.4 0.9 -0.3 -1.1 -1.3 -1.5 1953 -0.3 -0.7 1.5 -0.1 -1.1 0.1 0.0 -1.2 0.1 -0.9 -0.7 -1.1

c) From the above table drought exists, IF SPI<0. This implies that there exists a drought

only if the monthly rainfall is less than expected monthly mean, hence the negative

21

values in the table. The positive values imply that the monthly rainfall is more than the

expected monthly mean hence no drought. Record the magnitude of negative SPI values

as drought and obtain the annual drought magnitudes as below,

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec magnitude 1943 0.5 0.0 0.5 0.3 0.0 0.0 0.8 0.0 1.1 0.7 0.1 0.0 3.9 1944 0.9 0.9 1.1 1.2 0.9 0.3 0.8 0.0 0.0 0.0 1.2 0.0 7.4 1945 0.8 0.5 0.6 1.1 0.4 0.0 0.1 0.0 0.4 0.0 0.0 0.3 4.3 1946 0.0 0.0 0.0 0.0 0.0 0.7 0.3 0.7 0.7 0.0 0.0 0.0 2.4 1947 0.0 0.0 0.0 0.2 0.7 0.8 0.0 1.0 0.2 0.7 0.0 0.0 3.7 1948 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.2 0.0 0.0 0.0 0.3 0.6 1952 0.7 1.2 1.2 0.0 0.0 1.0 0.4 0.0 0.3 1.1 1.3 1.5 8.7 1953 0.3 0.7 0.0 0.1 1.1 0.0 0.0 1.2 0.0 0.9 0.7 1.1 6.0

6.8.5 Palmer Drought Severity Index (PDSI) The PDSI index results from calculations of a soil moisture algorithm, a model developed by Palmer in 1965, which uses precipitation, temperature data and local available water content (AWC) of the soil. The index indicates standardised moisture conditions and allows comparisons to be made between locations and between months. PDSI varies roughly between -6.0 and +6.0. The Palmer classifications are shown in the Table 6.2.

Table 0.7 Palmer Classifications Range Classification 4 or more extremely wet 3 to 4 very wet 2 to 3 moderately wet 1 to 2 Slightly wet - 1 to +1 Normal conditions -1 to -2 Mild drought -2 to -3 Moderate drought -3 to -4 Severe drought < -4 Extreme drought The PDSI was the first comprehensive drought index developed in the United States and is widely used in U.S. They are normally calculated on a monthly basis. Some limitations of PDSI are as follows; Palmer values may lag emerging droughts by several months which limits their application in areas of frequent climatic extremes, like south west Asia. Another limitation is that its computation is complex and requires substantial input of meteorological data. The use of Palmer’s arbitrary threshold is also not very appealing compared to other engineering terms such as return period.

22

6.8.6 Bhalme-Mooley Drought Index (BMDI) BMDI is defined for a month period as;

∑=

=k

kki

KBMDI

1

1

(6.9)

where 011 =+= − okokk iPcici is the monthly index, and Pk is the standardardized precipitation amount for month k.

( ) kkkk dmpP /−= (6.10)

Here, pk is the monthly precipitation with the mean of mk and standard deviation of dk. The two coefficients – c1 and c0 – can be estimated by assigning a value BMDI = -4 to severe historical droughts and proportionally higher values to normal conditions: BMDI=0. BMDI may be considered as a simplified version of the PSDI. Monthly moisture conditions can be defined as in the Table 6.3:

Table 0.8 BMDI Classifications Value Condition

BMDI>4 Extremely wet

4>BMDI>3 Very wet

3>BMDI>2 Moderately wet

2>BMDI>1 Slightly wet

1>BMDI>-1 Near normal

-1>BMDI>-2 Mild drought

-2>BMDI>-3 Moderate drought

-3>BMDI>-4 Severe drought

-4>BMDI Extreme drought

6.8.7 Surface Water Supply Index (SWSI) Here, snow forms a large component of the water balance. SWSI integrates reservoir storage, streamflow and two precipitation types (snow and rain) at high elevations into a single index number, and is expressed as;

(6.11) Where

- a, b, c and d are weights for snow, rain, streamflow and reservoir storage respectively (a + b + c + d =1). - P = the probability (%) of non-exceedance for each of these four water balance components.

23

Calculations are performed on a monthly time step. In winter, snow pack, precipitation and reservoir storage data are used while in summer, stream flow, precipitation and reservoir storage data are used. The SWSI index is easy to calculate and gives a representative measure of water availability across a river basin or region. The limitations of the method are that they may not apply for large regions with significant spatial hydrological variability. Also the weights may differ from one region to another. Similarly, hydraulic structures like dams, diversions will require modification of weights for each water-balance component. A modification of SWSI is called the Reclamation Drought Index (RDI), similar to SWSI, but includes the evaporation component. RDI classifications: 0 to -1.5 = Normal to mild droughts, -1.5 to -4.0 = Moderate droughts, less than -4.0 = Severe drought.

6.8.8 Crop Moisture Index (CMI) The Crop Moisture Index, also developed by Palmer (1968) is a compliment of the PDSI. It measures the degree to which crop moisture requirements are met. It 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, based on the mean temperature, total precipitation for each week and the CMI value from the previous week. Each growing season, CMI typically begins and ends near zero.

6.8.9 Effective Drought Index (EDI) EDI is a function of precipitation need for a return to normal conditions (PRN). PRN is the precipitation which is necessary for the recovery from the accumulated deficit since the beginning of the drought. PRN, in turn, effectively stems from daily effective precipitation and its deviations from the mean for each day. Unlike other drought indices, the EDI is calculated with a daily time step. It can at the same time be calculated using monthly data. The range of EDI variation is from -2 to 2. Table 6.4shows the EDI classification as was for the PDSI.

Table 0.9 EDI Classifications EDI Range Classification Less than -2 Extreme dry conditions -1.5 < EDI < 1.99 Severe drought - 1 < EDI < 1.49 Moderate drought - 0.99 < EDI < 0.99 Near normal conditions The limitation of the method is that it is based on precipitation data, which may not be readily available, from government agencies in many developing countries in Africa and Asia.

6.8.10 Deciles In this approach, monthly precipitation totals from a long term record (preferably 30-50 years) are ranked from highest to lowest to construct a cumulative frequency distribution. The distribution is then split into 10 parts (deciles). 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 the deciles. Decile Indices (DI) are classified into 5 classes, 2 deciles per class as shown in Table 6.5.

24

Table 0.10 Decile Classifications Decile Classification Deciles 1 and 2 (less than 20%) Much below normal Deciles 3 to 4 (20 to 40%) Below normal precipitation Deciles 5 to 6 (40 to 60%) Near normal precipitation Deciles 7 to 8 (60 to 80%) Above normal Deciles 9 to 10 (80 to 100%) Much above normal DI is relatively simple to calculate, requires only precipitation data and fewer assumptions. Hence it is appropriate for conditions in South West Asia and Africa where there is limited data. 6.8.11 Percent of Normal Normal usually refers to a long term mean or median precipitation value. It may be calculated for a day, a month, a season or a year and is considered 100%. There are many definitions of drought based on percentage of normal. What is normal may be perceived differently in different regions. For instance, in India, meteorological drought is defined when rainfall in a month or a season is less that 75% of its long-term mean. If rainfall is 50-74% of the mean, a moderate drought event is assumed to occur, and when rainfall is less than 50% of its mean, a severe drought occurs. Droughts in South Africa are defined as periods with less than 70% of the normal precipitation.

6.8.12 Indices from Remote Sensing There are several other drought related indices which are derived from remote sensing. These include Normalized Difference Vegetation Index (NDVI), Enhanced vegetation Index (EVI), Vegetation Condition Index (VCI) and Temperature Condition Index (TCI).

6.8.13 Incorporating Drought Indices into Software Packages Until recently, most hydrological models had no specific routines to address issues of drought assessment and management. Current models are now being incorporated with that component. One of these models is called the SPATSIM (Spatial and Time series Information Modelling) software package which was developed by the Institute of Water Research (IWR) of Rhodes University in South Africa during 1999-2002. It is a relatively new software product and is quickly gaining recognition in South Africa and other countries. It contains an integrated database management system that uses GIS shape files as the main form of data access and also includes a number of input models such as rainfall-runoff simulations, flow assessment and design flood models. Its flexible environment incorporates such routines that address requirements of IWMI’s drought assessment and mitigation in south East Asia. These requirements include; ensuring that existing data-import facilities are satisfactory for the south west Asia region, facility for generating time-series of drought indices from rain data interpolated from station rainfall data, facility for regional analysis of drought indices and documentation of all new facilities in the SPATSIM help system. Below are the main steps to set up a typical project:

i) Organise spatial coverage (SPATSIM features) representing rain gauge locations for monthly rainfall data and administrative areas or catchments.

ii) Add and give names to new attributes of different types that will store and manage raw and processed data. E.g. for rain gauge point coverage, a time series attribute will be used to store monthly rainfall data.

iii) Import raw data.

25

iv) Generate area rainfall data. v) Generate drought indices. The package allows the user to select from different index types. vi) Generate Summary drought index information. vii) Visualise drought-Summary information using graphs and maps. The maps show the spatial

distribution of the drought-index selected for a particular month. 6.8.14 Applications of Drought Indices in East Africa A study( Kayondo and Gan, 2003) was carried to analyse the properties of three popular drought indices (as non basin specific) in East Africa,namely the Palmer Drought Severity Index(PSDI), the Bhalme Mooley Index (BMI) and the Standard Precipitation Index (SPI) and modify them where necessary, to increase their general effectiveness and dependability in detecting droughts. A second objective was by using East Africa as a case example: identify assessment criteria for determining the most appropriate drought index on a regional basis. The results show that the PSDI was modified, since it did not give reasonable results in some drier parts of East Africa, the SPI was also modified to produce more representative results of East Africa Drought conditions. Furthermore, although BMI only uses precipitation data, its index values still strongly correlate to the modified PSDI in East Africa, which suggest that precipitation data alone can be used to explain the variability of East African drought. They concluded that SPI is more suitable for monitoring droughts in East Africa than PSDI or BMI, because, among other reasons, i) it is easily adapted to local climate, ii) it has modest data requirements, iii) it can be computed at any time scale and yet can extract more or less the same information contained by the temporally fixed PDSI and iv) easy to interprete. They propose a criteria for determining a drought index most appropriate for monitoring drought as follows: i) characteristics, statistical properties and variability of drought indices, ii) detailed analysis of a major historical drought, iii) adaptation of drought indices to local climate, iv) unbounded index values, v) spatially invariable ( representing the same information independant on the site being investigated, vi) should have a flexible time scale, vii) modest data requirements and viii) ease of interpretation.

6.9 Drought Mitigation

6.9.1 Constraints A study (Rugumayo et al, 2006) was done on drought management in Karamoja region, being a representative of a semi arid and war torn region and in the northern part of the cattle corridor, whose main livelihood is pastoral nomadism. It was observed that most of the community was not aware of the use and value of drought monitoring facilities such as weather stations. As such, they end up destroying these facilities by vandalizing and looting them. The end result is that, very few weather stations are left operational hence making the availability of up-to-date data to analyze difficult. The difficulty in accessing accurate up-to-date data to analyze may also have been brought about by a lack of a culture of record-keeping and whenever warriors vandalize a weather station, sometimes they burn or destroy weather station records. There is need to back up these records. The insecurity in the region is another hinderance to proper drought management. The weather station premises, staff and their property are at a risk because of this insecurity. For people to get water during a drought, many times water supply facilities are installed in the region. Unfortunately, many a time these facilities are damaged, vandalized or abandoned by the people.

26

6.9.2 Methods In order to improve the capacity to monitor drought in a region like Karamoja and implement drought mitigation measures nationally, the following need to be carried out; i) Sensitisation of the community on the importance of water conservation and significance of the

weather stations in data collection. ii) Establishing a national or regional task force that can work with the community on a drought

mitigation plan. iii) Rehabilitation of non functional weather stations and water schemes. iv) Monitoring of groundwater resources, to determine their potential as alternative water sources.

Available data suggests that recharge occurs through indirect and localised mechanisms. If so, effects caused by higher temperatures due to global warming may be more than offset by the predicted increase in future precipitation resulting in an overall increase in groundwater resources. This availability may have a significant role in peace building (Gavigan et al, 2008)

v) The use of remote sensing, global information systems and global positioning systems in data analysis. Different types of drought require indicators. Some indicators are more suited to monitor agrcultural drought and others to assess hydrological and meteorological drought. This should be guided by the goal of the assessment, which can be the intensity, exceptionality or impact of drought. For agricultural drought, water balance indicators are preferred (De Pauw, 2000).

vi) The incorporation of traditional methods in the development of new technologies for water conservation.

vii) Development of early warning and decision support systems.This requires the availability of historical data. Recent developments in early warning and climate prediction services make it possible to predict ENSO-related extreme climate events with a lead time of a few months. On this basis target groups can be alerted (Ambeje, 2000). At the international level there is FAO-coordinated Global Information Early Warning Systems (GIEWS), which is an information network for early warning of food shortages. There is also need for coordination at the national level between the water supply, irrigation, agricultural extension services, meteorological departments and NGOs about the extent and impact of drought.

viii) The expansion of the existing water network to include more facilities like valley tanks earth dams, protected wells, springs and boreholes. Valley tanks, earth dams are particularly important especially, for livestock. Individual households could construct smaller water ponds and leave valley tanks for extreme conditions.

ix) The use of rainwater harvesting techniques should be encouraged from the household to the communal and institutional levels.

x) Herd management is an important strategy. Factors to be considered include the drought duration, the current water and feed supplies, the composition and body condition of the herd. The herd management practices will include the following techniques i) reduction of herd numbers through sale or agistment,ii) strategic weaning of calves, which gives a cow better chances of survival and providing calves with a supplemental diet,iii) herd segregation which gives the herd a better chance of survival through preferential treatment of vulnerable classes, iv) parasite control, since cattle under nutritional and other stresses are more vulnerable than those under normal conditions , v) Optimal use of drought affected paddocks and vi) avoidance of contaminated water sources by the cattle, by fencing for instance.

6.9.3 An Integrated Framework An Expert Group Meeting on Early Warning Systems for Drought Preparedness and Drought Management, organized by World Meteorological Organization and the Secretariat of the United Nations Convention to Combat Desertification, held in Lisbon, Portugal, in September 2000, made the following recommendations:

27

i) A Drought Mitigation Plan should be integrated, proactive and incorporate the following elements: a) Drought Monitoring and early warning system b) Drought risk and impact assessment and c) Institutional arrangements including mitigation and response actions and programs

All the above elements need to be supported by research. ii) A vulnerability profile should also be developed in order to capture the socioeconomic conditions of diverse population and regions. iii) There is a need to improve existing observation networks and establish new networks for hydrological, meterological and hydrological observations and the associated analytical predictive tools. This would include:

a) Identifying weaknesses in the current observation systems, including critical needs of marginal areas and most drought prone areas

b) Using drought monitoring products that are prepared, in collaboration with decision makers and are user friendly.

c) Perodic user evaluation, of drought monitoring products. iv) The social, economic and environmental assessment of drought impacts should be assessed by:

a) Identifying appropriate and relevant physical and social indicators b) Developing triggers that link indicators of drought severity to impacts duribg the onset and

termination of drought conditions c) Appropriate interpretation of information and clearly informing decision makers in a timely

manner. v) The oobjectives of a national drought policy should be broadly stated and:

a) Establish a clear set of principles or operating guidelines to govern drought management b) Be consistent and equitable for all regions, population groups and socio economic sectors c) Be consistent with the goals of sustainable development d) Reflect regional differencesin drought characteristics, vulnerability and impacts e) Promote principles of risk management by encouraging the development of:

o Reliable forecast o comprehensive early warning systems o preparedness plans at all levels o Mitigation policies and programsthat reduce drought impacts o A coordinated emergency response thatensures timely and targeted relief during

drought emergencies

Summary Drought affects our meteorology, hydrology, agriculture, plant life, animals, and our socio- economic livelihood in different ways. It is necessary to be able to understand these effects, both qualitatively and quantitatively. This chapter discusses the various types of drought the causes and impacts and several methods of analysing drought and subsequently predicting it.. The effects of drought are very significant though varying depending on the purpose for which water is required. An understanding of the impacts that a lack of water give rise to is imperative if one has to mitigate them. This extreme event in hydrology needs to be examined when considering the hydrology of any area, for proper water resources management. Early warning systems and a drought management plans at the national level need to be developed to reduce the adverse impacts of drought.

28

References 1. Ambenje,P.G., Regional Drought Monitoring Centeres – The Case of Eastern and Southern Africa

Proceedings of an Expert Group Meeting on Early Warning Systems for Drought Preparedness and Drought Management, September 5-7, 2000, World Meteorological Organisation, Lisbon, Portugal.

2. Bloomfield,J.P., Gaus,I., Wade, S.D., A Method for Investigating the Potential Impacts of Climate Change Scenario on Minimum Groundwater Levels, Journal of Chartered Institution of Water and Environmental Management, Vol 17, pp.86-91, 2003 London, UK.

3. Camberlin, P., Rainfall Anomalies in the Source Region of the Nile and their Connection with the Indian Summer Monsoon, Journal of Climate, American Meteorological Society 1997, Vol 10, pp1380-1392, Washington, USA.

4. DePauw,E., Drought Early Warning Systems in West Asia and North Africa, Proceedings of an Expert Group Meeting on Early Warning Systems for Drought Preparedness and Drought Management, September 5-7, 2000, World Meteorological Organisation, Lisbon, Portugal.

5. Gavigan, J., Cuthbert, M., Mackay, R., Climate Change on Groundwater Recharge in Northeastern Uganda and potential Role of Groundwater Development in Livelihood Adaptation and Peace Building, Proceedings Groundwater and Climate in Africa, 2008, Kampala, Uganda, Ministry of Water and Environment, University College, London.

6. Gibbs, W.J., Maher, J.V., Rainfall Deciles as Drought Indicators, Meteorology Bulletin, No 48, Commonwealth of Australia, Melbourne.

7. Glantz, M.H., Teleconnections Linking Worldwide Climate Anomalies: M.H. Glantz, R. Wikatz., N. Nicholls (Ed), pp 401-430, Cambridge University Press,1991, Cambridge,UK.

8. Haan.,C.T., Statistical Methods in Hydrology, Iowa State University, 1983, Iowa USA. 9. Hayes, M.J., What is Drought? National Drought Mitigation Centre Nebraska, Lincoln, USA

http://www.drought.unl.edu/whatis?indices.htm accessed on February 9 2008. 10. Herbst, P.H., Bredenkamp, D.B. and Barker, H.M.G. (1966), “A Technique for the Evaluation of

Drought from Rainfall Data’, Journal of Hydrology, Vol.4, pp.264-272. 11. Jones, J.A.A., Global Hydrology, Longman, 1996, London, UK. 12. Kayondo, H.N., Gan, T.Y.,Drought Indices and their Application to East Africa, International

Journal of Climatology, 23, pp1335-1357, 2003,Royal Meterological Society, London UK. 13. Kayondo, H.N., The Analysis and Prediction of Droughts in East Africa, PhD Thesis, 2001,

University of Alberta, Edmonton, Canada. 14. Maunder, W.J., A Dictionary of Climate Change, The University College, London Press, 1992,

London, UK . 15. McKee,T.B., Doesken, N.J., Kliest, J., Standardized Precipitation Index, Use in Drought Analysis

Colorado University,1993, Colorado, USA. 16. McKee,T.B., Doesken, N.J., Kliest, J., The relationship to Drought Duration to Time Scales,

Preprints, 8th Conference on Applied Climatology,1993, Annaheim, California, USA. 17. Mubiru, P., Lake Victoria’s Water Levels and Power Generation in Uganda, A Guest Lecture,

Uganda Institution of Professional Engineers,2006 Kampala, Uganda. 18. Ogallo, L.A., Validity of the ENSO –Related Impacts in Eastern and Southern Africa in

M.Glantz(ed). Usable Science: Food Security and El Nino, pp. 179-184. Proceedings of the workshop on ENSO/FEWS, Budapest Hungary, UNEP 1994, Nairobi, Kenya and NCAR Boulder, Colorado, UK.

19. Palmer, W.C., Keeping track of Crop moisture conditions nationwide, A Crop Moisture Index, Weatherwise 21, pp 156-161,1968.

20. Palmer, W.C., Meteorological Drought, Research Paper No 45, U.S, Department of Commerce, 1965, Weather Bureau, Washington, DC, USA.

21. Ropelewski, C.F., Halpert, M.S., Global and Regional Scale Precipitation Patterns Associated with El Nino Southern Oscillation, Monthly Weather Review, 1987, 115, pp1606-1626.

29

22. Rugumayo, A.I., Eyagu, E., Kizza, M.K., Drought Analysis and Mitigation in Karamoja Region, Proceedings of the 3rd Asia Pacific Hydrology and Water Resources Conference, 2006, Bangkok, Thailand.

23. Rugumayo, A.I., Maiteki, J.M., Drought Analysis and a Comparison of Methods, A Case Study of Western Uganda. Advances in Geosciences, Vol 4: Hydrological Science. World Scientific Publishing Company, 2006, Singapore.

24. Rugumayo, A.I., Mwebaze, D.B., Drought Intensity and Frequency Analysis: A Case Study of Western Uganda. Journal of Chartered Institution of Water and Environmental Management, Vol 16, pp.111-115, 2002 London, UK.

25. Seiler, R.A., Hayes, M., Bressan, L., Using the Standardized Precipitation Index for Flood Index Monitoring, , International Journal of Climatology, 22, pp1365-1376, 2002,Royal Meterological Society, London UK .

26. Shafer, B.A., Dezman, L.E., Development of a Surface Water Supply Index (SWSI) to Assess the Severity of Drought Conditions in Snowpack Runoff Areas, Proceedings of the Western Snow Conference, pp164-175, Colorado State University, 1982, Fort Collins, Colorado, USA.

27. Smakhtin,V.U., Hughes, D.A., 2004. Review, Automated Estimation and Analyses of Drought indices, in South Asia. Working Paper 83. International Water Management Institute. Colombo, Sri Lanka.

28. Subramanya, K., Engineering Hydrology, 1995, McGraw Hill-Tata, Delhi, India. 29. Wardlaw, R., Jaigopaul, D., Rahaman, Z., Influence of El Nino on Rainfall in Guyana and Uganda,

Proceedings of the Institution of Civil Engineers, Water Management Journal 2007,Vol 160 pp135-144, London, UK.

30. Wilhite, D.A.,Sivakumar, M.V.K., Wood, D.A., Ed., Proceedings of an Expert Group Meeting on Early Warning Systems for Drought Preparedness and Drought Management, September 5-7, 2000, World Meteorological Organisation, Lisbon, Portugal.

31. Yeyjevich, V., Tendencies in Hydrology Research and its Applications in the 21st Century, Water Resources Management, Vol. 5, pp. 1-23, 1991, Springer, Netherlands.

32. Yousef, S.M., Amer, M., The Sharp Rise of Lake Victoria, A Positive Indicator to Solar Wolf Gleissberg Cycles Turning Points., ICEHM ,2000, Cairo Universiy, Egypt.

33. http://www.iwmi.cgiar.org/droughtassessment/index.asp?nc=2&id=843&msid=138 accessed on February 19 2008.

34. http://www.ru.ac.za/institutes/iwr/software/spatsim.html accessed on February 19 2008. 35. http://www.ponce.sdsu.edu/three_issues_droughtfact04.html accessed on 14th April 2009.

Questions 1. What do you understand by the term drought?

2. How can drought be mitigated?

3. Determine the effective rainfall for Moroto in the month of February 1947 given the monthly rainfall

as 0.0mm, the mean monthly rainfall as 19.00mm, the monthly rainfall in January 1947 as 2.00mm and the mean monthly rainfall in January as 9.8mm. The mean annual rainfall is 83.33mm.

4. The table below gives the monthly effective rainfall, the mean monthly rainfall and the mean monthly

deficit for 50 years for Kasese Meteorological station. Determine the monthly rainfall deficits, the drought volumes, intensity and severity.

30

Monthly Effective Rainfall in Kasese YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1931 32.00 83.19 262.66 333.00 89.44 0.00 3.33 86.65 145.25 88.91 92.20 129.94 1932 82.86 22.13 47.08 85.77 22.56 0.00 0.00 0.00 109.36 280.09 33.04 56.79 1933 109.89 116.29 0.00 0.00 31.72 1.27 0.00 109.07 120.22 0.00 100.64 26.09 1934 0.00 0.00 0.00 43.76 122.93 0.00 9.29 36.48 27.86 85.39 141.46 95.97 Mean 50yrs

53.06 64.27 100.27 125.07 81.65 28.93 21.27 61.21 94.75 107.58 124.10 74.55

MMD 50yrs

31.16 30.22 28.79 33.33 32.13 19.28 15.84 29.01 38.35 32.31 39.20 32.42

5. Discuss the causes of drought. 6. Discuss the sequences of the impacts of drought. 7. Explain four methods of analyzing drought. 8. The data given below are monthly rainfall values taken from a rain-gauge station at Kapkwata in

Kapchorwa District, using the SPI method calculate the Annual Drought Magnitudes of the area.

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1956 65 31 28 301 203 104 186 244 113 135 46 25 1957 53 8 63 324 316 128 240 188 33 53 44 30 1958 85 56 40 158 255 201 225 230 127 63 47 102 1959 88 97 54 222 236 70 188 166 141 173 111 10 1960 35 63 143 156 154 78 130 214 192 153 104 62 1961 0 27 77 160 215 166 213 322 159 209 442 115 1962 52 7 76 165 232 124 220 142 123 92 88 63 1963 74 70 75 342 178 115 267 153 17 58 190 228 1964 6 53 113 178 149 105 201 195 326 129 14 142 1965 0 0 109 121 121 82 103 145 49 249 204 62

9. Briefly explain what you understand by:

i. Physiological drought ii. Climatological and atmospheric drought

iii. Socio- economic drought

10. Distinguish between meteorological drought and agricultural drought. 11. Discuss the constraints to mitigating drought and how they can be overcome. 12. Why is integrated drought management important?