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5 PRECIPITATION

5.1 Introduction Precipitation represents the falling of water in various forms from the clouds to the surface of the earth. The usual forms are rain and snow, although it may also occur in the form of fog, dew, sheet, hail and frost. Most precipitation in Africa occurs in the form of rain. In a few high altitude places like at the peaks of mountains like Mt. Rwenzori and Mt. Kenya, it occurs as snow. Precipitation occurs as a result of evaporation. Evaporation is caused by the energy from the sun that warms the surface of the earth, which in turn warms the surrounding air making it lighter and causing it to rise. The rate of evaporation is dependent upon i) the temperature at the evaporating surface and that of the surrounding air, ii) the vapour pressure of the existing water vapour in the air, iii) the saturation deficit. (The saturation deficit is the difference between saturated vapour pressure and the air pressure at room temperature) and iv) the wind speed. If the evaporation continues, a state of equilibrium is reached when the air is fully saturated with water vapour and cannot absorb any more water vapour. This point is called the saturated vapour pressure. The oceans constitute 94% of the earth's water and constitute a vast reservoir relatively undisturbed. From the surface of the seas and oceans, water is evaporated and transferred to temporary storage in the atmosphere. This is the first stage in the Hydrological Cycle. The process of evaporation is better appreciated with a few important definitions and reference to Fig 5.1 (Shaw, 1992). a) Saturation - air is saturated when it contains the maximum amount of water vapour at the prevailing

temperature. At any temperature T = Ta the corresponding vapour pressure e = ea. b) Dew Point - is the temperature Td at which a mass of unsaturated air becomes saturated when cooled

while the pressure remains constant. If air at Ta is cooled to Td the saturation vapour pressure is ed. c) Saturation deficit - is the difference between the saturation vapour pressure at air temperature, Ta and

the actual vapour pressure at Td the dew point and is denoted as (ea - ed). It represents the additional amount of water air can hold at temperature Ta.

d) Relative humidity - is the relative measure of the amount of moisture in the air ea to the amount needed to saturate the air at the same temperature ed

RH = ea/ed x 100% (5.1)

e) Super Saturation - is when saturated air takes up more water vapour as a result of being in contact

with water in a sufficiently finely divided state. For instance, very small water droplets in clouds. At temperatures below zero, there are two saturation vapour curves. One with respect to water (ew) and another with respect to ice (ei) as shown in the inset Fig 5.1. In the zone between the curves, air is unsaturated with respect to the atmosphere. As evaporation continues, the air above the water eventually becomes saturated and cannot take up any more moisture, thus evaporation ceases.

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Fig 5.1 The Saturation vapour pressure and temperature curve.

Inset is the curve for temperatures below zero Source: Shaw, 1994

5.2 Precipitation Formation Moisture is always present in the atmosphere and precipitation only occurs when some mechanism cools the atmospheric air and brings it to saturation along with the following conditions (Arora, 2007). I. Accumulation of moisture

II. Cooling of air masses III. Formation of clouds IV. Growth of water droplets i) Accumulation of Moisture The air must contain sufficient amount of moisture, so that appreciable precipitation can occur after meeting the losses between the clouds and the ground. The accumulation of moisture occurs as a result of evaporation from land, vegetation and water surfaces. ii) Cooling of air masses Cooling occurs when air ascends from the earth's surface to the upper levels of the atmosphere. The rate of cooling is governed by the lapse rate in the Troposphere. Depending on the process causing lifting and cooling, precipitation may be classified as orographic, convective or cyclonic. iii) Formation of clouds Clouds are formed due to condensation, when water vapour is converted into liquid droplets or ice crystals, usually at low temperatures. Water droplets in a cloud can be compared to solid particles in a colloidal suspension. The saturation of water vapour in the atmosphere does not necessarily result in the formation of clouds. Condensation nuclei or hygroscopic nuclei are essential for the conversion of water vapour into water droplets. Condensation nuclei are present in the atmosphere due to combustion of solids and particles from the sea. They vary in size from 0.001 microns to 10 microns. The number of nuclei per cm3 varies from a few, to several million in different regions of the atmosphere.

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The rate of condensation increases with the number of nuclei present. Usually there are sufficient numbers of nuclei in the air, to cause condensation as soon as saturation occurs. iv) Growth of water droplets The size of water droplets in a cloud is very small (about 0.02mm in diameter) and it is necessary for them to increase in size before precipitation can occur. The coalescence of droplets occurs to form larger raindrops which can overcome air resistance when falling. Coalescence takes place due to the difference in velocity of the larger droplets and small droplets and coexistence of ice crystal and water droplets. The limit of the water droplets and raindrops is usually 0.2mm. However the diameter of raindrops reaching the ground is much more than 0.2mm.

5.3 Types of Precipitation Precipitation is classified according to the factors responsible for lifting and cooling the air. There are five types: a) Convectional b) Orographic c) Cyclonic d) Frontal and e) Turbulent ascent a) Convectional Convectional rainfall mainly occurs in the equatorial and tropical climatic regions, where the conditions are hot during the day. In these regions, the rate of evaporation of moisture from the water bodies and respiration from the dense vegetation is very high. The evaporated moisture along with its hot surrounding air begins to ascend. With gain in altitude, the air expands dynamically due to a decrease in air pressure.

Fig 5.2 The formation of convectional rainfall Source:www.nature.com/nature/journal

Due to this, the wind experiences a decrease in temperature, which results in the increase of the relative humidity. This causes condensation of water vapour into water droplets to form cumulonimbus clouds. When the cloud droplets become too heavy to be suspended, rain falls. Convectional precipitation is normally of short duration, covers a small area (less than 50km2) and is sometimes of high intensity. It occurs in the form of local whirling thunderstorms and when accompanied by high velocity destructive winds it may become a tornado. The formation is illustrated in Fig 5.2.

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b) Orographic

Orographic rain (relief rain) is a result of warm moisture-laden wind blowing in to the land from the sea encountering a natural barrier such as mountains. This forces the air to rise. With a gain in altitude, the air expands due to a decrease in air pressure. Due to this, the wind experiences a decrease in temperature, which results in the increase of the relative humidity. This causes condensation of water vapor into water droplets to form clouds. The relative humidity continues to increase until the dew point reaches the level of condensation, causing air to be saturated. When the cloud droplets become too heavy to be suspended, then rain falls. The side of the mountain with rain is called the windward side.

Fig 5.3 The formation of orographic (relief) rainfall Source:www.nature.com/nature/journal

As the wind descends on the leeward side of the mountain range, it becomes compressed and warm. This results in the decrease of the relative humidity of the wind on the windward side of the mountain. Hence the leeward side of the mountains does not receive any rain from these winds. The leeward side is called the rain shadow region of the mountains. This is illustrated in Fig 5.3.

This type occurs due to lifting of moist air over mountains by wind. It results in cooling, condensation and precipitation. Heavy precipitation occurs on the windward side of the mountain, whereas the leeward side has very little precipitation. c) Cyclonic A cyclone is a large zone of low pressure, which is surrounded by circular wind motion. Air tends to move into the low-pressure zone from surrounding areas and displaces low pressure air upwards. The winds blow spirally inward counter clockwise in the northern hemisphere and clockwise in the southern hemisphere. Cyclonic precipitation occurs due to displacement of air in the upward direction. The normal extent of a cyclone is 100 - 200 km in diameter while the centre called an eye may extend up to about 10 - 50 km. The eye is relatively quiet while outside very strong winds blow with speed as high as 200 km/hr. The rainfall can be quite high in the cyclonic areas.

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a) Frontal Frontal rain is caused by cyclonic activity and it occurs along the fronts of the cyclone. It is formed when two masses of air of different temperature, humidity and density meet. The layer separating them is called the front. This front has two parts i.e. the warm front and the cold front. At the warm front, the warm lighter air rises gently over the heavier cold air. As the warm air rises, it cools, and the moisture present in it condenses to form clouds, which become heavy and results in rain. This is as shown in Fig 5.4.

Fig 5.4 The formation of frontal rainfall Source:www.nature.com/nature/journal

At the cold front, the cold air forces the warm air to rise rapidly causing its moisture to condense quickly, which results in the formation of cumulonimbus clouds. The rainfall from these clouds is usually heavy and lasts for a short while.It can be divided into two types.

i) Warm frontal precipitation. - In this case, warm air replaces the cold air mass by moving up a relatively stationary wedge of cold air. It is normally spread over a large area 300 - 500 km ahead of the warm front and is usually light and moderate.

ii) Cold frontal precipitation - In this case, the cold air replaces a warm mass forcing the warm air

upwards by an advancing wedge of cold air. It usually occurs over a small area 100 to 150 km ahead of the front. The precipitation is usually intense.

e) Turbulent ascent This type occurs when an air mass is forced to rise up due to friction of the earth surface this being greater than that of the water surface. The air mass after its travel over the ocean rises up because of increased turbulence and friction.

5.4 Forms of Precipitation Precipitation occurs in various forms as are mentioned below: Drizzle: The precipitation occurs in the form of fine sprinkle of very small drops. The diameter of the drops is uniform and it varies from 0.1 to 0.5 mm. The water drops are in very large number and seem to float in air. The intensity is usually less than 0.1 cm/hour.

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Rain: Most of the precipitation in Uganda is in the form of rain. In rain, the precipitation occurs in the form of large water drops, with diameter larger than 0.5mm, but less than 6mm. Snow: Snow is the precipitation in the form of solid ice crystals. These crystals usually carry a thin coating of liquid water and form large flakes when they collide with one another. However, at very low temperature, these crystals are dry and do not form large flakes. Hail: The precipitation in the form of balls or irregular lumps of ice of diameter 5mm or more is called hail. The grains of soft hail are round and opaque. Soft hail disintegrates when it strikes the hard ground surface. Hail usually occurs in violent thunderstorms. Glaze: Glaze is a form of precipitation which falls as rain and freezes when striking the ground. It occurs when there is a cold layer of air with a temperature below zero degrees Celsius. When the objects such as trees and power lines on which precipitation occurs are very cold, glaze occurs on them. Glaze is also known as freezing rain. Select: Select is the precipitation in the form of melting snow. It is a mixture of snow and rain. It consists of transparent, solid grains of ice formed by freezing of rain drops. These pellets are generally between 1 mm and 4 mm in diameter. Select is also known as small hail. Sometimes, precipitation begins as snow in the upper layers of atmosphere, turns into select in the middle layer and reaches the earth as rain. Frost: Frost is a form of precipitation, which occurs in the form of scales, needles, feathers or fans. It is a type of dew in which the water vapour in the air is transformed directly into the ice crystals, which fall on the earth. Dew: Dew is a form of precipitation, which does not occur because of condensation in higher layer of atmosphere, but it is formed by condensation directly on the ground. Dew occurs in the night when the ground surface is cooled by outgoing radiation. Although the quantity of water in dew is quite small, it is extremely useful for the growth of plants and crops in arid regions (Arora, 2007).

5.4.1 Cloud Seeding Precipitation normally results from freezing of super-cooled water onto small atmospheric particles (ice nuclei). It was discovered that certain salts, notably Silver iodide or common salt, can induce precipitation by acting as an additional nuclei. Cloud seeding is therefore the introduction of salt crystals, usually from an aircraft, into existing clouds so that immediate freezing occurs. This presses the cloud particles to precipitate and result in rain (Mansell, 2002). Cloud seeding is seen to be as one of the ways to mitigate droughts. However, it should be known that cloud seeding can only accelerate and increase the amount of rainfall and not create rainfall when conditions are not favourable. Seeding is unlikely to be effective in clear skies or where the cloud temperature is above -5C because of absence of super-cooled water droplets. The relative humidity must also be high and wind velocity <15-20 km/hr. Cloud seeding is particularly effective with convective cumulus clouds and is less effective with the stratus type of clouds (Anon, 2008).

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5.5 Recording Precipitation 5.5.1 Rainfall measurement i) Non Recording Standard rain gauges are made from copper and consist of a 5-in. diameter

copper cylinder, with a chamfered upper edge, which collects the rain and allows it to drain through a funnel into a removable container of metal or glass from which the rain may be poured into a graduated glass measuring cylinder each day. There are prescribed patterns for the standard gauge and for its installation and operation.

ii) Recording Gauges (or autographic rain recorders) usually work by having a clockwork-driven drum carrying a graph on which a pen records either the total weight of container plus water collected, or a series of blips made each time a small container of known capacity spills its contents. Such gauges are for the more rarely visited sites. They have the great advantage that they indicate intensity of rainfall, which is a factor of importance in many problems. For this reason some stations are equipped with both standard and recording gauges. Examples of this type include Tipping Bucket Type, the weighing Bucket type and the Natural Syphon type. A typical rain gauge is shown in Fig 5.5.

iii) Telemetering rain gauge

These are the recording type and contain electronic units to transmit the data on rainfall to a base station both at regular intervals and on interrogation. The tipping bucket type is ideally suited. The Telemetering rain gauges are particularly useful for gathering rainfall data from generally inaccessible places.

iv) Radar measurement

Radio Detecting and Ranging (RADAR) is a powerful tool for determining the areal extent, orientation and movement of rainstorms. Furthermore, the amounts of rainfall over large areas can be measured to a good degree of accuracy. When rain drops intercept a radar beam, it has been shown that

2rCZPr (5.2)

where:

Pr = average echo power

Z = radar - echo factor

r = distance to target volume and C a constant

In general, the factor Z is related to the intensity of rainfall as

Z = aIb (5.3)

where a and b are constant and I is intensity of rain in mm/h.

The values a and b are determined by calibration. Present day developments in the field include, on line processing of radar data on a computer and Doppler type radars for measuring the velocity and distribution of raindrops (Subramanya, 1994).

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v) Remote Sensing and Rainfall Estimation

Remote sensing is the science and art of obtaining useful information about an object on or near the earth’s surface, without being in direct or physical contact, or the techniques of using sensing devices that are located at a distance from the object under investigation and analysing the captured data to provide meaningful information. Remote sensing can provide direct inputs to forecasting procedures in the areas of area rainfall; areal extent of flood plain inundation; cloud images in different spectral bands (visible, infrared and water vapour); typical cyclone or hurricane movements; areal extent and water equivalent of snow pack and water quality.

Rainfall is one of the noisiest meteorological parameters, whether considered through space or through time, yet one of the most important in agrometeorology. At any instant, the fraction of the earth actually receiving rain cannot be much more than one half of 1%. There is a wide range of intensities of instantaneous rain rates from practically zero to above 100mm/h. The variation of rainfall intensity with duration can be large from storm to storm as well as from region to region (Barret and Martin, 1981).

Many satellite based techniques have been developed since the late 1960’s when meteorological satellite imagery became available. Many of these techniques have been developed for particular needs in specific areas. These methods are still being developed and require calibration with field data (Sayed, 2002). Satellite data for rainfall estimation originate from two types of satellite. The first are geostationary satellites, which remain stationary with respect to the earth, use infrared channels to infer rainfall rates from cloud top temperatures and provide high resolution data with continuous temporal coverage for the observed region. The second are polar orbiting satellites, which use microwave channels and can provide better estimates of rainfall by monitoring the scattering of naturally emitted (passive) microwaves within the clouds. However, as these satellites pass over a given location only once or twice a day, there are gaps in the time series of data for any studied region.

A study was carried out in Uganda, to evaluate the ability of satellite products to: i) replicate the gauged monthly variability of rainfall amounts and occurrence within each region, ii) replicate the spatial variation of rainfall amounts and occurrence between regions. The recent satellite estimates 2003-2007 were compared with the historical (1960-1990) gauged statistics. The results showed that TRMM 3B42 (from NASA Goddard Space Flight Centre, USA) and TAMSAT indicated the greatest similarity to gauged data across most aspects of rainfall estimation. CMORPH (from NOAA, Climate Prediction Centre, USA) showed greater similarity to historical data in the seasonal progression than spatial patterns of rainfall. Furthermore, the African Rainfall Estimation Algorithm Version 2 (RFE 2.0) showed greater spatial ability than temporal, while the PERSIANN system (from the University of California, Irvine USA) compares favourably in terms of seasonal patterns of rainfall in regions with lower elevation. This showed that satellite based rainfall estimation products can be used to represent the main seasonal and spatial feature of monthly rainfall in Uganda, especially if the patterns of occurrence are scaled to amounts using calibration to the ground gauged values (Asadullah et al, 2008).

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Fig 5.5 A Standard Raingauge Errors in measurement A number of errors can occur in the measurement of rainfall with a rain gauge. In general a rain gauge underestimates the actual rainfall. The sources of errors are as follows:

i) Some rainfall is always lost in wetting the dry surface of the receiver. The error can be reduced by keeping the surface smooth.

ii) Some spillage always occurs when transferring water from the bottle to the measuring jar in a non recording gauge and it is not always possible to completely empty the bottle.

iii) There can be change in the exposure area of the receiver due to bends and dents in the rim or improper readings by the observers. These errors can be reduced by proper training of the observer and taking necessary precaution (Arora, 2007).

Site for a Rain Gauge Station

When selecting a site for installation of a rain gauge station, the following points should be borne in mind.

1. The site should be in an open area of at least 5.5m square. 2. The distance of the instrument from the nearest abstraction should not be less than 30m. 3. It should not be placed at the top of a hill but rather on the side of a hill as protection against

high winds is required. 4. A fence should be erected around to protect the gauge against animals. 5. It should be firmly mounted to prevent disturbance by strong winds and near the ground

surface to prevent wind effects but also high enough to prevent splashing of water.

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6. It should have a horizontal level of catch surface (Arora, 2007). Rain Gauge Network

Since the catchment area of a rain gauge is very small compared to the areal extent of a storm, it is necessary that in order to get a representative picture of a storm over a catchment, the number of rain gauges should be as large as possible. The catchment area per gauge should be small. Economic considerations to a larger extent, while topography, accessibility to a lesser extent, restrict the number of gauges to be installed and maintained. World Meteorological Organization (WMO, 1994) recommends the following minimum densities per station (area in km2 per station) as shown in Table 5.1.

Table 5.1: Recommended minimum densities of precipitation stations Physiographic Unit Non Recording km2 per station Recording km2 per station

Coastal

Mountainous

Interior Plains

Hilly/ undulating

Small Islands

Urban Areas

Polar/ arid

900

250

575

575

25

-

10,000

9000

2500

5750

5750

250

10-20

100,000

Source: (WMO, 1994) Ten percent of rain gauge stations should be equipped with self - recording gauges to know the intensities of rainfall. If there are already some rain gauge stations in a catchment, the optimal number of stations that should exist to have an assigned percentage of error in the estimation of mean rainfall is obtained by statistical analysis as: N = Cv2 (5.4) where: N = optimal number of stations

= allowable degree of error in the estimate of the mean rainfall and Cv = coefficient of variation of the rainfall values at the existing m stations (in percent).

If there are m stations in the catchment each recording rainfall values P1, P2 ……, Pi …. Pm in a known time, the coefficient of variation Cv is calculated as:

_1100

P

xC mv

(5.5)

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Where

11

2_

1 m

PPm

i

m

is the standard deviation Pi is the precipitation magnitude in the ith station

m

iPm

P1

_ 1 is the mean precipitation

In calculating N from the equation 5.4 above, it is usual to take = 10%. It is seen that if the value of is small, the number of rainguage stations will be more.

In Uganda, rainfall records are received and recorded by the Meteorological Office from some 360 rain gauges scattered over Uganda, the majority giving daily values of rainfall. In addition there are a further 260 stations equipped also with recording rain gauges that record continuously.

Considerable effort has been devoted to this question of hydrological network design and the reader is referred to the further reading at the end of the chapter. A study by Basalirwa, (1995) based on the spatial and temporal rainfall characteristics, using the Principal Component Analysis showed that Uganda can be divided into 14 homogeneous climatic subregions. These climatological zones are useful in the planning and management of rainfall dependant activities such as determining crop varieties for a specific area, delineation of risk zones for drought forecasting and flood warning, or areas where there is rainfall surplus. Fig 5.6 shows the 16 updated climatological zones by the Directorate of Water Resources Management, Entebbe, Uganda.

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Fig 5.6 Map showing the Climatological Regions in Uganda Source: Directorate of Water Resources Management, Entebbe, Uganda, 1998

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Table 5.2 Description of Climatological zones

Zone Annual Rainfall and its zonal

variability Main rainy seasons Main dry seasons

G Average of 745 mm, STD 145 mm. High variability, from ~ 600 over the north and northeastern parts to ~ 1000 mm over the southern and western parts.

One rainy season of about 5½ months, from April to early September with the main peak in July/August and a secondary peak in May.

One long dry season of about 6 months, October to March. Driest months December to February.

H Average of 1197 mm, STD 169 mm. Moderate variability, from ~ 1000 over the north and northeastern parts to ~ 1300 mm over western and southern parts

One rainy season of about 7 months, April to late October with the main peak in July/August and a secondary peak in May.

One long dry season of about 4 months, mid-November to late March. Driest months December to February.

I Average of 1340 mm, STD 155 mm. Moderate variability, from ~ 1200 over northwestern and western parts to ~ 1500 mm over the southern parts.

One rainy season, about 7½ months, April to about mid November with the main peak in August to mid October and a secondary peak in April/May.

One long dry season of about 4 months, mid-November to late March. Driest months December to February.

J Average of 1371 mm, STD 185 mm. Moderate variability, from ~ 1200 over the eastern parts and highest ~ 1500 mm over the western parts.

One rainy season of about 7½ months, April to about mid November with the main peak August to October and a secondary peak in April/May.

One long dry season of about 4 months, late November to late March. Driest months December to February.

K Average of 1259 mm, STD 195 mm. High variability, from ~ 800 within the Lake Albert basin to ~ 1500 mm over the western parts

Mainly one rainy season of about 8 months, late March to late November with the main peak August to October and a secondary peak in April/May.

One long dry season of about 3½ months, December to about mid March. Driest months December to February.

L Average of 1270 mm, STD 135 mm. High variability, from ~ 800 over eastern L. Albert parts to ~ 1400mm over the western parts.

Two rainy seasons, main season August to November with peak in October and secondary season March to May with peak in April.

Main dry season December to about mid March, secondary dry season is June to July.

MW Average of 1223 mm. High variability, lowest ~ 800 mm Kasese Rift Valley, highest over slopes of Rwenzori mountains, over 1500mm.

Two rainy seasons, main season August to November with peak in September to November and secondary season March to May with peak in April.

Main dry season December to late March, secondary dry season is June to July.

ME Average of 1021 mm. Two rainy seasons, main season March to May with peak in April and secondary season September to December with a modest peak in November.

Main dry season June to August, secondary dry season is January to February.

B Average of 1250 mm. Two rainy seasons, main season March to May with peak in April and secondary season August to November with a modest peak in October/November.

Main dry season December to February, secondary dry season is June to July.

Source: (ACE, 2005)

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Table 5.2 Description of Climatological zones

Zone Annual Rainfall and its zonal variability Main rainy

seasons

Main dry seasons

CW Average of 1120 mm. Two rainy seasons, main season September to December with peak in October/November and secondary season March to May with a peak in April.

Main dry season June to August, secondary dry season is January and February.

CE Average of 915 mm. Two rainy seasons, main season March to May with peak in April and secondary season September to December with a peak in October/November.

Main dry season June to August, secondary dry season is January and February.

A1_W Average of 1057 mm. Two rainy seasons, main season March to May with peak in April and secondary season October to December with a peak in November.

Main dry season June to September, secondary dry season is January and February.

A1_E Average of 1414 mm. Two rainy seasons, main season March to May with peak in April and secondary season October to December with a peak in November.

Main dry season June to August, secondary dry season is January and February.

A2 Average of 1443 mm. Two rainy seasons, main season March to May with peak in April and secondary season October to December with a peak in November.

Main dry season June to August, secondary dry season is January and February.

D Average of 1316 mm. Two rainy seasons, main season March to May with peak in April and secondary season August to November with a peak in October/November.

Main dry season December to February, secondary dry season is June and July.

F Average of 1328 mm. Virtually one rainy season, March to October, with the main peak in April and a secondary peak in August.

One dry season December to about mid March.

E Average of 1215 mm. Virtually one rainy season, March to November, with the main peak in April/May and a secondary peak in August/September.

One dry season December to about mid March.

Source: (ACE, 2005)

Table 5.2 and Table 5.3 show the different zones with the duration of the dry and wet

seasons

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5.5.2 Measurement of other forms of Precipitation Snow and ice. Snow has the capacity to retain water and so acts as a form of storage. Its density and, therefore the quantity of water contained, varies from as little as 0.005 microns for newly fallen snow to as much as 0.6. in old, highly compressed snow. Since density varies with depth, samples must be taken at various horizons in a snow pack before the water equivalent can be computed. This is usually done with a sampling tube.

Snowfall may be measured directly by an ordinary rain gauge fitted with a heating system or by a simple snow stake if there is no drifting and density is determined simultaneously.

Snow traverses are made as field surveys along lines across catchments, usually perpendicular to the direction of flow, to determine snow thickness and densities at depth so that water equivalents can be calculated for flood forecasts.

Fog. Estimates of amounts of moisture reaching the ground from fog formation have been made by installing fog collectors over standard rain gauges. Collectors consist of wire gauge cylinders on which moisture droplets form and run down into the rain gauge. Comparisons with standard rain-gauge records at the same locality show differences that are a measure of fog precipitation. The interpretation of such data requires experience and the use of conversion factors, but can make substantial differences (of the order of 50 to 100 per cent) to precipitation in forest areas.

Dew. Dew collectors have been used in Sweden and Israel to measure dew fall. They are made as conical steel funnels, plastic coated and with a projected plan area of about one square metre. Dew ponds are used as a source of water in some countries. They are simply shallow depressions in the earth lined with ceramic tiles.

Condensation. Although fog and dew are condensation effects, condensation also produces precipitation from humid air flows over ice sheets and in temperate climates by condensation in the upper layers of soil. Such precipitation does not occur in large amounts but may be sufficient to sustain plant life (Wilson, 1990).

5.6 Climate in Uganda Some of the main synoptic and mesoscale factors (Majugu, 2003) that influence weather and climate in East Africa include in particular the following: - a) Monsoons b) The Inter Tropical Convergence Zone (ITCZ) c) The meso-scale circulations d) Teleconnections related to the El Nino/Southern Oscillation (ENSO) phenomena. Consequently, the rainfall patterns of the East African region and Uganda in particular are complex, with rainfall amounts changing markedly over short distances. Details of the main influencing atmospheric meteorological systems are as follows:

5.6.1 The Main Influencing Factors a) The Monsoons The most fully developed phases of monsoons that affect East Africa are the North East (December to February) and the South East monsoons (June to August). These phases correspond to the maximum positions of the ITCZ to the South (Southern summer) and to the North (Northern summer). Unlike the West African and the Asian, monsoons, the fully developed East African monsoons are associated with

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relatively little rainfall and coincide with the main dry periods within the bimodal areas. Both monsoon currents are generally divergent in the low levels and flow parallel to the coast and thus they do not advect much moisture inland. They are also relatively shallow, extending up to about 600 hectapascals and are capped aloft by an easterly flow. The South East monsoon is cool and moist and the persistent inversion near 600 hectapascals inhibits cloud development leading to extensive low-level cloud cover especially over the east facing slopes of the rift valley mountain ranges. b) The ITCZ The Inter Tropical Convergence Zone is the main synoptic scale system that controls the intensity and migration of the seasonal rainfall over the East African region. The ITCZ is a narrow zone into which the low-level equator-ward-moving air masses from both hemispheres converge. It is closely linked to the position of the overhead sun, due to the heating of the overhead sun a wide belt of low level pressure develops and the subsequent tendency of the air zone of convergence forming behind the overhead sun. The characteristics of the ITCZ over East Africa are rather complex; it consists of a North – South dynamic arm, which is locally referred to as the meridional arm and the East – West arm called the zonal arm. The meridional arm is a zone of convergence between the westerlies from the Atlantic Ocean and the easterlies from the Indian Ocean, while the zonal arm is the convergence between the North East and south East monsoons/trades. c) Meso – Scale Circulations Due to the proximity of East Africa to the Indian Ocean, the highly variable topography and the existence of the large Lake Victoria basin the region experiences vigorous Meso-scale circulations. In fact the spatial distribution of weather in East Africa is to a large extent determined by the interaction between the quasi-stationary Meso-scale circulations and the seasonally varying large-scale monsoon/trade flows. Further the region experiences marked diurnal variation of precipitation due to the vigorous Meso-scale circulations as they contribute substantially to the distribution and intensity of rainfall over the region. The Lake Victoria influence is due to its large body of water, the temperature contrasts between the Lake and land during the day and night result in a Lake breeze being advected towards the land during the day and a land breeze towards the Lake during the night. Overall, this land - lake breeze phenomenon results in the lake basin region getting some rains almost throughout the year. This rainfall is however significantly enhanced during the main rainy seasons. d) Teleconnections (El Nino / Southern Oscillation – ENSO) The El Nino / southern oscillation (ENSO) is the principal mode of inter annual variability in the global tropics. To a first approximation, the ENSO can be viewed as a modulation of the global monsoon / trade wind system. This modulation is manifested in the modification and displacement of large-scale precipitation patterns and includes episodes of both floods and drought. The modification may occur at various phases of the ENSO evolution but is normally most pronounced and most extensive during the opposite extremes of the ENSO cycle, that is, during the El Nino and La Nina phases. Some of the countries / regions of the world where significant impacts of ENSO are felt include India, Northern Australia, Equatorial Central Pacific, Eastern Equatorial Africa (including Uganda in particular), South Eastern and South America and Gulf coast of the United States. The 16 climatological zones can be regrouped (MWE, 2005) into five major zones described below;

5.6.2 Zone I: Lake Victoria Basin This is a strip of 30 to 50 miles wide (48-80km) wide extending around the shores of Lake Victoria and consists of Zones A1 and A2. The climate of this zone is dominated by the wide diurnal variation of temperature and the resulting convective activities between the lake and the surrounding in land areas. The inland extent is determined by the extent of the inland penetration of the on-shore breeze, during the

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day. Thus the rainfall over the surrounding inland areas is mainly during the afternoon to late evening while over the lake and surrounding shores it is mainly during late night to early morning due to the night time breezes. The climate of the Lake Victoria basin displays comparatively small seasonal variations of temperature, humidity and wind throughout the year. However, the rainfall seasons are quite marked with the main season, March to May, contributing about 40% of the annual rainfall amount while the secondary season October to December contributes about 30%. That means that the dry season of January to February and the main one of June to September contribute about 30%. However the dry seasons are frequently broken by thunderstorms and there are often wide season to inter-annual variability especially as related to the El-Nino/Lanina events. Furthermore rainfall amounts vary quite significantly across, from East to West and from South to the North.

5.6.3 Zone II: Buganda – Busoga – Ankole (Central and Western) This zone includes the western parts of Busoga, Buganda and all except the western most parts of Ankole and includes ME, B, CE and CW. It is a hilly region with tops in the order of 5000ft, which are mostly flat-topped in the Northern parts giving way to the rounded hills of Ankole in the South. There are considerable extensions of swamps at an altitude of about 3,800ft but away from the swamps Savanna vegetation predominates. The rainfall, which is mainly convectional and mainly an afternoon to evening occurrence averages about 40 inches. Two peaks associated with the Equatorial Trough are evident, one during April to May and the other one September to November. Two dry seasons occur, a pronounced one in June to July and the other one between December and February. This zone also exhibits high a degree of seasonal to inter-annual variability especially as related to the El-Nino/Southern Oscillation phenomenon. During a typical El-Nino event rainfall tends to be highly amplified during the second rain season of September/October to December/January. On the other hand during a typical La Nina event, the opposite of El-Nino, rainfall tends to be highly suppressed during the same period of September/October to December/January. Furthermore the El-Nino heavy rains are usually preceded by highly suppressed rains between July to September, while the poor La Nina rains are preceded by highly amplified rains from July to September.

5.6.4 Zone III: Western Uganda This relatively narrow zone covers Zones L,K,J and MW and traverses the Western boundary of the country embracing the high grounds of West Nile, the escarpments on the Eastern side of Lake Albert, Tooro, the high grounds of the Southwest and the rift valley lakes, Albert, George and Edward. It also includes the chain of large forests, Zoka, Budongo, Bugoma, Itwara, Kibale, Kalinzu and Maramagambo. Furthermore, Western Uganda is marked by high variability in altitude, from the rift valley Lake Albert at 2,030ft to the West Nile open grasslands at 4,000 to 5000ft to the Tooro dominating Rwenzori mountains reaching up 16,000ft. Lake Albert at 2,030ft is virtually the lowest and hottest region of Uganda. In addition, Lakes Edward and George at just under 3000ft are very little different from Albert climatically. All the three are hot, with intense dry seasons and rainfall of the order of 35 to 40 inches. The Western zone in general might fairly be described as a transition zone between the Congo Basin forest climate and the Uganda Savanna climates. The incursion of the Congo basin westerlies into the

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zone produces masses of cumulonimbus and thunderstorms except over the lake areas. These conditions can persist for 7 – 10 days at a time. Rainfall is by no means an entirely afternoon phenomenon, but might occur at any time of the day. However, the rainfall season over the North western region is from July to September while over the South western it is from September to December. Furthermore the main dry season over the North western is December to February while over the South western it is June to August. The Eastern borders of the Western zone are not nearly so readily definable as the inland borders of the Lake Victoria Basin zone. There is more of a gradual transition from the Western zone into the Acholi-Kyoga and Ankole-Buganda zones. This is particularly the case in the North where the rainfall from August to November is virtually the same in the West Nile, the Albert Nile and the Acholi region.

5.6.5 Zone IV: Acholi – Kyoga This zone embraces the greater part of Northern and Eastern Uganda and consists of Zones I, E, F and D. It is largely flat, at an altitude of 3,000 to 4,000ft with few pronounced hills. A large proportion of its surface is represented by the Lake Kyoga Catchment, which is characterized by considerable areas of papyrus swamps. Northwards of the large swamps the vegetation is typically savanna. The rainy season to the North is unimodal, April to October and to the South bimodal with the main season being March to May and a secondary rainy season during September/October to December. The Lake Kyoga area is a transition between the strongly unimodal pattern to the North and a strongly bimodal pattern to the South. Accordingly the dry season to the North is December to February/March while to the South there are two dry seasons, December to February and July to August. The variations in the rainfall patterns over this region are as a result of latitudinal variations and are particularly amplified by the impacts of the El-Nino/La Nina events. The Unimodal pattern to the North can be modified to virtually bimodal patterns during a typical El-Nino event while the bimodal patterns to the South can be modified to virtually unimodal patterns during a typical La Nina event. Thus the zone as a whole quite often undergoes wide season to inter-annual variations.

5.6.6 Zone V: Karamoja The Karamoja zone is a flat plain at an altitude of between 3500 to 4000ft with a number of isolated peaks rising to between 8000 to 9000ft and covers Zones H and G. This zone experiences an intense dry and hot season from November to March when most of the streams dry up. December and January are the driest months. The region experiences mainly one rainy season April to August/September with the main peak during July/August and a secondary peak during May. The annual rainfall is generally between 20 to 40 inches being generally wetter over the Southern areas and over the Western areas. The Hills of Karamoja do have a fairly important influence on the rainfall patterns in the area. During the South-Eastern monsoon, at times when it is blowing strongly, it is noticeable that large Cumulus and Cumulonimbus clouds build up on the hills early in the day. The lee sides of the hills experiences relatively heavy rainfall as the clouds move towards the North-West under the influence of the wind drift.

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Fig 5.7 The main climatic zones of Uganda

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5.6.7 Rainfall Trends The average rainfall (NEMA, 1999) in Uganda varies from 700mm in the semi arid areas of North East (Kotido) to 1500 mm on the Islands of Kalangala District in Lake Victoria and over 2000mm on the slopes of Mt Rwenzori. The average of 28 stations countrywide is 1217mm. In general it exhibits bimodal characteristics and this is linked to the double passage of the Inter Tropical Convergence Zone (ITCZ), which gives rise to two rainy seasons in about March-May and October-December. The spatial distribution is influenced by Lake Victoria as well as local topography. The atmospheric circulation over Lake Victoria is approximately from east to west, but is strongly influenced by onshore and offshore breezes generated by the lake itself. This local circulation frequently results in the formation of cumulonimbus clouds over the southwestern portion of the lake and in a narrow strip of land some 30 km wide around the shore (Sutcliffe and Parks, 1999). Rainfall analysis (NEMA, 2002) over the years 1943-99 indicate all regions experience wide seasonal to inter annual rainfall variations. Regionally incidences of drought conditions are more frequent in the Northern and Eastern regions than Central and Western regions. Alternating deficits and rainfall have led to droughts (and sometimes crop failure) and floods respectively. The recent El Nino rainfall is the heaviest on record, with annual rainfall between 600 - 2500mm. Further analysis of rainfall from 33 sites (Phillips and McIntyre, 2000) show that when the data is separated between unimodal and bimodal zones reveals the importance of ENSO is different in the two zones. El Nino events are associated with a depression of the August peak in rainfall, but a lengthening season, potentially providing an opportunity for growing later – maturing crops. At bimodal sites there is very little impact in August, but November rainfall is enhanced in El Nino years and depressed in La Nina years. Given a forecast of ENSO, the primary strategies that will be useful in farm management will differ by rainfall zone and will evolve around the choice of crop or cultivar and the planting in order to make optimal use of the growing period. This is good potential for rainwater harvesting. The impact of Lake Victoria has been illustrated by measurements of rainfall near its centre, which indicated rainfall some 30% higher than observed at any lakeshore station (Sutcliffe and Parks, 1999). A recent estimate (Mangeni and Katashaya, 2006) of the Lake Victoria rainfall using data from the islands and lake shore data and applying the Simple Exponential Kriging technique gave a value of 1815mm. The mean annual rainfall map for Uganda shown in Fig 5.7 displays regions of relatively low rainfall (400 to1000 mm) and high rainfall (1400mm and above). The relatively low rainfall areas are dominated by the so-called cattle corridor axis running the Karamoja region to the northeast to the Ankole region to the Southwest. The other rather elongated area of low rainfall is along the Western Rift Valley running through Lake Albert. On the other hand the main area of relatively high rainfall is over the central and western parts of the Lake Victoria Basin and over Mt Elgon in the east.

5.6.8 Seasonal Rainfall Percentage Patterns The 3-monthly seasonal rainfall maps (Fig 5.8) display the spatial and temporal seasonal migration of the dry and wet seasons across the country and within the year. The season December to February is the driest period over most parts of the country and especially over the northern (5 - 10%) and parts of central region (10 – 15%). It is only the south-western region that receives moderate rains (20 – 25%). It should be noted that during the season December to February the Inter-tropical Convergence Zone (ITCZ) is over southern Africa and most of Uganda is dominated by the hot and dry north-to-northern easterly flow. On the other hand the season March to May is the main wet season over the most parts of Uganda with the percentage levels highest over the south-eastern areas (40 –45%) and lowest over the north-western areas (20 – 25%). During this period the zonal arm of the ITCZ is within the equatorial areas and the country is dominated by the moist south-easterly flow. During the season June to August the main wet season is now centred over the northern parts of the country, while the southern parts experience their secondary dry season. Over the northern region the highest percentages (40 – 45%) are recorded over the north-eastern parts of Karamoja, while over the

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southern regions the lowest percentages (5 – 15%) are reported over the south-western parts of the country. During the season June to August the main zonal arm of the ITCZ is within the vicinity of the northern region. Finally, the season September to November is the secondary rainy season over most parts of the Western and Central parts of the country with the main wet season centred over the western parts of the country and extending into the central areas (30 – 40%) while the areas with lowest percentages (15 – 20%) are recorded over the north-eastern parts of Karamoja. During the season September to November the main dominant synoptic feature is the meridional arm of the ITCZ, which is normally within the vicinity of the western parts of the country super-imposed the zonal, arm which is within the vicinity of the equatorial areas (Majugu, 2003). Based on the analysis for different periods using annual and seasonal datasets of 20 stations in the Lake Victoria Basin, it was noted that positive trends were dominant with an average increase of 2-4 mm per year in the twentieth century. It was further observed that there were more positive trends in the short rainy season (October – December) than the long rainy season (March – May). The similarities between trend and step changes results suggest that when changes in the basin occur, they are not entirely monotonic but stepwise with sequences of dry years separated by wet years (Kizza, 2008)

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300 310 320 330 340 350

2000 - 22001800 - 20001600 - 18001400- 16001200- 14001000 - 1200800- 1000600 - 800400- 600

Prepared by the GIS UNIT Water Resources Management Department Entebbe, DWD and Department of Meteorology Kampala (c)2002

-1 0

0 0

1 0

2 0

3 0

4 0

100 0 100 200 KilometersScale 1:4,500,000

Legend

N

Fig. 3.1 Uganda's Mean Annual Rainfall (mm)

Fig 5.8 Uganda Mean Annual Rainfall

Source: Water Resources Management and Department of Meteorology Uganda, 2002

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Fig. 3.3 December - February Season as % of Annual Rainfall

5 - 10 10 - 15 15 - 20 20 - 25

Legend

Prepared by the GIS UNIT Water Resources Management Department Entebbe, DWD and Department of Meteorology Kampala

100 0 100 200 Kilometers

Scale :4,500,000

300 310 320 330 340 350

-1 0

0 0

1 0

2 0

3 0

4 0

N

20 - 25 25 - 30 30 - 35 35 - 40 40 - 45

Legend

Fig.3.4 March - May Season as % of Annual Rainfall

Prepared by the GIS UNIT Water Resources Management Department Entebbe, DWD and Department of Meteorology Kampala

N

100 0 100 200 KilometersScale 1:4,500,000

300 310 320 330 340 350

-1 0

0 0

1 0

2 0

3 0

4 0

Fig. 3.5 June- August Season as % of Annual Rainfall

5 - 10 10 - 15 15 - 20 20 - 25 25 - 30 30 - 35 35 - 40 40 - 45

Legend

Prepared by the GIS UNIT Water Resources Management Department Entebbe, DWD and Department of Meteorology Kampala

100 0 100 200 KilometersScale 1:4,500,000

300 310 320 330 340 350

-1 0

0 0

1 0

2 0

3 0

4 0

N

Fig. 3.6 September-November Season as % of Annual Rainfall

15 - 20 20 - 25 25 - 30 30 - 35 35 - 40

Scale 1:4,500,000

Legend

Prepared by the GIS UNIT Water Resources Management Department Entebbe, DWD and Department of Meteorology Kampala

N

100 0 100 200 Kilometers

300 310 320 330 340 350

-1 0

0 0

1 0

2 0

3 0

4 0

Fig 5.9 Three (3) monthly seasonal rainfall maps

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5.7 Precipitation Analysis

5.7.1 Estimation of Missing Rainfall Data Before analyzing the rainfall data of a region, it is necessary to check its continuity. The data would be discontinuous when a particular rain gauge is not operative for some period of the record. In such a case, it becomes necessary to supplement the missing data by an intelligent estimation. The missing data is usually estimated from the available data of the neighbouring rain gauge stations called index stations. For estimation of the missing data, the normal annual rainfalls of all the rain gauge station, including the station with the missing data, are required. The normal annual rainfall of a station is the average value of the annual rainfall over a specified period of 30 years or so. The normal rainfall is updated every ten years. There are two main methods (Subramanya, 2001):

i) The Comparison method and ii) The Normal ratio method

i) Comparison method: If the rainfall record of a rain gauge station (say, X) is missing for a relatively long period, such as a month or a year, it can be estimated by comparing the mean annual rainfall of the station X with that of an adjoining station A. Thus

A

x

A

x

NN

PP

(5.6)

Where:

Px and PA are the precipitations of the stations X and A for the missing period and Nx and NA are the mean annual rainfalls of the stations X and A.

ii) Normal ratio Method: When there is a short break in the precipitation data of a rainguage station, it can be estimated from the observed data of three adjoining index stations A, B and C which are evenly distributed around the stations X.

The following two cases are dealt with separately.

a) When the mean annual rainfall at each of the index stations A, B, and C, is within 10% of the mean rainfall of station X, a simple average of the values of the index station is taken. Thus:

CBAx PPPP 31

(5.7)

b) When the mean annual rainfall at each of the index stations differs from the station X by more than 10% the normal ratio method is used. Thus:

C

C

B

B

A

Axx N

PNP

NPN

P3

(5.8)

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Where the symbol N is used for the mean annual rainfall (also called average annual precipitation) and the symbol P is used for the precipitation. When there are M Index stations,

M

M

B

B

A

Axx N

PNP

NP

MN

P ... (5.9)

Another method used is the Isohyetal map method, which is useful for estimating missing data of a station X due to a particular storm. Other methods used for infilling and extending data series are Markov and random generation time series techniques.

5.7.2 Tests for Consistency From several years’ records it may seem that annual rainfall is, say, declining. It is important to know that this trend is independent of the gauging, and is due to meteorological conditions only. This may be checked by plotting a double mass curve as shown in the figure.

This technique is based on the principle that when each recorded data comes from the same parent population, they are consistent. A group of 5 to 10 base stations in the neighborhood of the problem station X is selected. The data of the annual rainfall of the station X and also the average rainfall of the group of base stations covering a long period is arranged in the reverse chronological order. The accumulated precipitation of the station X (i.e Px) and the accumulated values of the average of the group of base stations (i.e. Pav) are calculated starting from the latest record. Values of Px are plotted against Pav for carious consecutive time periods in the figure). A decided break in the slope of the resulting plot indicates a change in the precipitation regime of station X. The precipitation values at station X beyond the period of change of regime the year 1958 in Fig 5.13 is corrected by using the relation.

a

cxcx M

MPP (5.10)

where:

Pcx = corrected precipitation at any time period t1 at station X

Px = original recorded precipitation at time period t1 at station X

Mc = corrected slope of the double-mass curve

Ma = original slope of the mass curve

In this way the older records are brought up to the new regime of the station. It is apparent that the more homogeneous the base station records are, the more accurate will be the corrected values at station X. A change in slope is normally taken as significant only where it persists for more than five years. The double-mass curve is also helpful in checking arithmetical errors in transferring rainfall data from one record to another.

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A sudden divergence from the straight-line correlation, shown by the dashed line in the figures, indicates that a change has occurred in gauging and that the meteorology of the region is probably not the cause of the decline. Such a change might be due to the erection of a building or fence near the gauge, which changes the wind pattern round the gauge, the planting of trees, the replacement of one measuring vessel by another, even the changing of an observer who uses different procedures, the change in the ecosystem due to calamities such as forest, fires or landslides or an observational error from a particular date.

A considerable amount of work is being focused towards eliminating homogeneities within the national data set. This is particularly necessary for modeling meteorological trends associated with climate change. Under the Hydroclimatic Study (MWLE, 2003), the detection of errors and inhomogeneities was undertaken using metadata and quality control measures through an interdisciplinary study between the Uganda Meteorological Department and the Directorate of Water Resources Management. A total of 597 rainfall and 30 temperature stations were digitized using CLICOM software, which generated 16,363 and 1500 records of rainfall and temperature respectively that spanned the years 1902 to 2003. Different types of errors and their relationships were identified. Checking logics was applied by both manual methods and computer programmes, which varied according to the type of data, whether daily, monthly, annual or suspicious zeros. Suspicious results were compared to their neighbouring stations and available metadata was checked to prove worthiness of record. As a result, inconsistent records and extreme rainfall events were identified and inhomogeneities were discovered in Mbarara, Buvuma and within farming estates stations where management changed abruptly (Lubega, 2008).

5.7.3 Presentation of Rainfall Data Definitions. The total annual amount of rain falling at a point is the usual basic precipitation figure available. For many purposes, however, this is not adequate and information may be required under any or all of the following headings.

i) Intensity. This is a measure of the quantity of rain falling in a given time; for example, mm per hour.

ii) Duration. This is the period of time during which rain falls. iii) Frequency. This refers to the expectation that a given depth of rainfall will fall in a given time.

Such an amount may be equaled or exceeded in a given number of days or years. iv) Areal extent. This concerns the area over which a point’s rainfall can be held to apply.

The world’s highest recorded intensities are of the order of 40 mm (or 1 ½ in) in a minute, 200 mm (or 8 in) in 20 minutes and 26 m (or 1000 in.) in a year.

A few commonly used methods of presentation of rainfall data which have been found to be useful in interpretation and analysis of such data are given below: i) Mass Curve of Rainfall The mass curve of rainfall is a plot of the accumulated precipitation against time, plotted in chronological order. Records of float type and weighing bucket type gauges are of this form. A typical mass curve of rainfall at a station during a storm is shown in Fig.5.10a. Mass curves of rainfall are very useful in extracting the information on the duration and magnitude of a storm. Also, intensities at various time intervals in a storm can be obtained by the slope of the curve. For non-recording rain gauges, mass curves are prepared from arknowledge of the approximate beginning and end of a storm and by using the mass curves of adjacent recording gauge stations as a guide.

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ii) Hyetograph A hyetograph is a plot of the intensity of rainfall against the time interval. The hyetograph is derived from the mass curve and is usually represented as a bar chart as shown in Fig 5.10b. It is a very convenient way of representing the characteristics of a storm and is particularly important in the development of design storms to predict extreme floods. The area under a hyetograph represents the total precipitation received in the period. The time interval used depends on the purpose; in urban-drainage problems small durations are used while in flood-flow computations in larger catchments the intervals are of about 6h. iii) Areal Extent Three main ways are considered:

a) Arithmetical Mean b) Thiessen Polygons c) Isohyetal

a) Arithmetical Mean: In the assessment of total quantities of rainfall over large areas, the incidence of particular storms and their contribution to particular gauges is unknown, and it is necessary to convert many point values to give an average rainfall depth over a certain area. The simplest way of doing this is to take the arithmetical mean of the amounts known for all points in the area. If the distribution of such points over the area is uniform and the variations in the individual gauge’s amounts are not large, then this method gives reasonably good results.

If P1, then P2,………. Pi ……… PN are rainfall values in a given catchment with N stations then:

N

ii

Ni PNN

PPPPP

1

21 1...... (5.11)

b) Another method, due to Thiessen, defines the zone of influence of each station by drawing lines between pairs of gauges, bisecting the lines will perpendiculars, and assuming all the area enclosed within the boundary formed by these intersecting perpendiculars has had rainfall of the same amount as the enclosed gauge.

The areas of any six Thiessen polygons are determined either with a planimeter or by using an overlay grid. If P1, P2 ………….. P6 are the rainfall magnitudes recorded by the stations 1, 2 ………… 6, respectively, and A1, A2, ……… A6 are the respective areas of the Thiessen polygons, then the average rainfall over the catchment P is given by:

621

662211

......

AAAAPAPAPP

(5.12)

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Thus in general for M stations,

M

i

ii

M

iii

AAP

A

APP

1

1 (5.13)

The ratio Ai/A is called the weightage factor for each station. The Thiessen - polygon method of calculating the average precipitation over an area is superior to the arithmetic - average method as some weightage is given to the various stations on a rational basis. Further, the raingauge stations outside the catchment are also used effectively. Once the weightage factors are determined, the calculation of P is relatively easy for a fixed network of stations.

A variation of this technique is to draw the perpendiculars to the line joining the gauges at points of median altitude, instead of at mid length. This altitude-corrected analysis is sometimes held to be a more logical approach but as a rule produces little difference in result. Either method is more accurate than that of the simple arithmetic mean but involves much labour. Thiessen polygons are as shown in the Fig 5.11.

d) A third method is to draw isohyets or contours of equal rainfall depth. The areas between successive isohyets are measured and assigned an average value of rainfall. The overall average for the area is thus derived from weighted averages. The average value of the rainfall indicated by two isohyets is assumed to be acting over the inter-isohyet areas. Thus P1, P2, ………., Pn are the values of isohyets and if a1, a2 …….., an-1 are the inter-isohyet areas respectively, the mean precipitation over the catchment of area A is given by:

A

PPa

PPaPPa

P

nnn

2

.....22

11

322

211

(5.14)

The isohyetal method is superior to the other two methods especially when the stations are large in number. This method is possibly the best of the three and has the advantage that the isohyets may be drawn to take account of local effects like prevailing wind and uneven topography. Fig 5.12 shows a map of isohyets over a study catchment area.

Example 5.1 . A rain gauge recorded the following accumulated rainfall during the storm. Draw the mass rainfall curve and the hyetograph. Time (am) 9.00 9.05 9.10 9.15 9.20 9.25 9.30 Accumulated Rainfall (mm) 0 1 2 5 11 15 16

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Solution The mass rainfall curve is directly plotted from the given data [Fig. 5.10a]. For plotting the hyetograph, the rainfall intensity in the successive 5 minute intervals is determined as follows:

Time interval 9:00 to 9:05

9:05 to 9:10

9:10 to 9:15

9:15 to 9:20

9:20 to 9:25

9:25 to 9:30

Increment of rainfall (mm) 1 1 3 6 4 1 Average intensity (mm/hr) 12 12 36 72 48 12 (=1/5 x 60) Fig. 5.9b shows the rainfall hyetograph.

0

2

4

6

8

10

12

14

16

18

0 5 10 15 20 25 30TIME

ACC

UM

ULA

TED

RA

INFA

LL (m

m)

0 5 1 0 15 20 25 30

10

20

3 0

4 0

5 0

6 0

70

8 0

0

1 2 1 2

3 6

72

48

12

T IM E

RA

INFA

LL IN

TEN

SITY

(mm

/hr)

Fig 5.10(a) Rainfall Mass Curve (b) Rainfall Hyetograph Example 5.2 A watershed has ten rain gauge stations with their recorded average annual precipitation in centimetres as shown in the map Fig 5.10.

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Fig 5.11 A Watershed a) Calculate the average precipitation of the total watershed using:

i) the Arithmetic mean method ii) the Thiessen polygon method iii) the Isohyetal method

Solution a) i) Using the Equation 5.11

Mean Precipitation, P =

n

nnP

n 1

1 , where n is number of rain gauge stations

Therefore P = 6

647269758185

P = 74.33 cm ii) Using Equation 5.13

Mean Precipitation,

n

n n

nn

AAPP

1 , where n = number of rain gauge stations, A is the area of the

Thiessen Polygon surrounding each station and P is the average precipitation at each station. These are calculated and shown in Table 5.4.

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90

81

72

70

64

69

75

85

79

71

Fig 5.12 Thiessen Polygons over the Area

Table 5.3 Thiessen Polygon Estimates

Precipitation P Area

A P * A 85 195 16575 75 145 10875 81 206 16686 90 2 180 72 116 8352 70 4 280 64 94 6016 69 181 12489 71 26 1846 79 38 3002

1007 76301 Therefore P =

100776301 = 75.77 cm

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iii) Using Equation 5.14

Mean Precipitation,

n

nn

nnn

A

PPA

PPA

PPA

P

1

11

322

211 2

.....22

,

where n = number of rain gauge stations, A is the area of the Thiessen polygon surrounding each station and P is the average precipitation at each station. The estimates are then calculated according to Table 5.5.

81

71

79

85

75

69

64

70

72

90

65

70

75

80

85

Fig 5.13 A Map showing the Isohyets

Table 5.4 The Isohyet Estimates

Isohyets Average Pa Area A Pa * A

65 65.0 20 1300 65-70 67.5 237 15997.5 70-75 72.5 249 18052.5 75- 80 77.5 212 16430 80-85 82.5 186 15345 85-90 87.5 163 14262.5 1067 81387.5

Therefore P = 1067

5.81387 = 76.28 cm

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Example 5.3 The annual rainfall at station X and the average annual rainfall at 18 surrounding stations are given in Table 5.6. Check the consistency of the record at station X and determine the year in which a change in regime has occurred. State how you are going to adjust the records for the change in regime. Determine the Average Annual Rainfall (AAR) for the period 1952-1970 for the changed regime. Table 5.5: Annual Rainfalls Table 5.6: Cumulative Totals

Annual rainfall (cm)

Cumulative Annual rainfall (cm)

Year Stn X 18-Stn Avg

Year Stn.X 18-Stn.Average

1952 30.5 22.8 1952 30.5 22.8 1953 38.9 35 1953 69.4 57.8 1954 43.7 30.2 1954 113.1 88 1955 32.2 27.4 1955 145.3 115.4 1956 27.4 25.2 1956 172.7 140.6 1957 32 28.2 1957 204.7 168.8 1958 49.3 36.1 1958 254 204.9 1959 28.4 18.4 1959 282.4 233.3 1960 24.6 25.1 1960 307 258.4 1961 21.8 23.6 1961 328.8 282 1962 28.2 33.3 1962 357 315.3 1963 17.3 23.4 1963 374.3 338.7 1964 22.3 36 1964 396.6 374.7 1965 28.4 31.2 1965 425 405.9 1966 24.1 23.1 1966 449.1 429 1967 26.9 23.4 1967 476 452.4 1968 20.6 23.1 1968 496.6 475.5 1969 29.5 33.2 1969 526.1 508.7 1970 28.4 26.4 1970 554.5 525.1

The cumulative rainfalls in Table 5.7 are plotted as shown in the Fig 5.14. It can be seen from the figure that there is a distinct change in slope in the year 1958, which indicates that a change in regime (exposure) has occurred in the year 1958. To make the records prior to 1958 comparable with those after change in regime has occurred, the earlier records have to be adjusted by multiplying by the ratio of slopes m2/m1 i.e., 0.9/1.24.

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Double Mass C urve Analysis

0

100

200

300

400

500

600

0 100 200 300 400 500 600

C umulative annual - 18 stations average (cm)

Cum

ulat

ive

annu

al ra

infa

ll of

stat

ion

X

S lope, m1 = 1.24Slope, m2 = 0.9

C hange in regime in 1958

(cm

)

Fig 5.14 Double Mass Curve

Cumulative rainfall 1958-1970

=554.5-204.7 = 349.8 cm

Cumulative rainfall 1952-1957 Adjusted for changed environment =204.7 x 0.9 = 148.6 cm

1.24 Cumulative rainfall 1952-1970 (for the current environment) = 497.4 cm

AAR adjusted for the current regime

= 497.4cm = 26.2 cm 19 years

iv) Depth Area Duration Curves The depth-duration frequency curves are similar to the intensity duration curves with one basic difference that the ordinate represents the total depth of the rainfall instead of the intensity of rainfall. Depth area duration curves show accumulated average precipitation on the ordinate scale against the area at selected times during a storm period on the abscissa. This is as shown in Fig 5.15. Depth Area Relationship. A storm over a particular catchment does not produce uniform rainfall depth over the entire catchment. Each storm usually has its centre called the eye where the precipitation is

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maximum. As the distance of a point from the eye increases, the precipitation decreases.It can be seen that the average depth of rainfall for the given storm decreases with the increase in area and for a given area, the average depth of rainfall increases, with the increase in duration. In the same manner, maximum depth area duration curves may be prepared. Such curves are needed for estimation of the severest floods. The preparation of Depth Area Duration Curves requires considerable effort and the procedure is as explained below in the following steps (Duggal and Soni 2007):

i. Decide on the duration of storm for which DAD curves are to be plotted. ii. Select the storms representative of the region. In the case of maximum area duration curves select

the maximum storms that have occurred in the region. iii. Prepare the mass curves for the recording stations. Here, hourly amounts of rainfall are

accumulated and plotted against time to get mass curves. The time distribution at non recording gauging stations is developed with the help of a base recording station associated with it.

iv. Prepare the isohyets with the knowledge of point precipitation (total) at the various recording and non recording gauges in the drainage basin.

v. Find out the area enclosed by the isohyets. In case the same isohyets encloses separate areas, total up the two areas.

vi. Find out the difference of areas of adjoining isohyets. vii. Find out the average rainfall depth, which is the mean of the isohyets values of two adjoining

isohyets. viii. Compute the product of values in step vi and vii. This will give volume of rain between one set of

isohyets. Then work the accumulated volumes. ix. Divide each accumulated value obtained in step viii by the corresponding area in step (v) to

obtain the average depth of rainfall. x. Plot average depth of rainfall (values obtained in step ix) against the area enclosed by each set of

isohyets (values obtained in step v). This will yield a depth area curve for the selected duration. Isopluvial maps: Isopluvial maps show the extreme values of total rainfall depth for storms of different duration. World's greatest recorded rainfall: If the total rainfall depth is plotted against duration on a log-log paper, the world's greatest recorded rainfalls lie on or just under a straight line whose equation is Pm = 42.16 (t)0.475 (5.15) Where Pm is the extreme rainfall depth (cm) and t is the duration of rainfall (hours) Example 5.4 The storm of a duration of 12 hours occurred over a catchment as shown inTable 5.8. If the areas enclosed by different isohyets are as follows, plot the depth area-duration-curve for 12 hour duration.

Table 5.7 Storm Records Isohyets (mm) 21 20 19 18 17 16 15 14 13 12 Enclosed area (km2) 543 1345 2030 2545 2955 3280 3535 3710 3880 3915

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Solution The average maximum rainfall depth is calculated for different areas in Table 5.9:

Table 5.8: Determining average rainfall depths Isohyets Area enclosed Net area Average Volume of Cumulative Average

(mm) (km2) between rainfall rainfall volume rainfall depth isohyets (mm) (mm.km2) (mm.km2) (mm)

= (3) x (4) = 26

(1) (2) (3) (4) (5) (6) (7) 21 543 543 21.5 (say) 11674.5 11674.5 21.5 20 1345 802 20.5 16441.0 28115.5 20.9 19 2030 685 19.5 13357.5 41473.0 20.4 18 2545 515 18.5 9527.5 51000.5 20.0 17 2955 410 17.5 7175.0 58175.5 19.7 16 3280 325 16.5 5362.5 63538.0 19.4 15 3535 255 15.5 3952.5 67490.5 19.1 14 3710 175 14.5 2537.5 70028.0 18.9 13 3880 170 13.5 2295.0 72323.0 18.6 12 3915 35 12.5 437.5 72760.5 18.6

Fig 5.15 Equivalent uniform depths: The Depth Area Duration DAD Curve

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v) Intensity Duration Frequency Curves It has been observed in practice that the greater the intensity of rainfall, the shorter its duration of the rainfall. In other words, very intense storms occur for a short duration, and as the duration of a storm increases, its intensity decreases. The intensity-duration curve can be obtained by plotting the rainfall intensity against duration of the storm. The rainfall intensity is usually expressed in cm/hour and the duration is in minutes. The curve is usually drawn on a natural graph paper. Sometimes, it is drawn on a log-log plot. For plotting an intensity duration curve, the observed maximum rainfall intensities at a place for storms of different duration are obtained from the available rainfall record. While selecting the storms of different maximum intensity, the following points should be kept in mind (Kolsky, 1999). 1. The severest storm of longer duration need not include the severest storm of shorter duration. 2. Even for the same storm, the maximum rainfall intensity for 5 minute duration and that for 10

minute duration may not be successive. As the rainfall intensity (i) varies inversely with the duration (t), the relation between the two can be expressed by Talbot's formula.

btai

(5.16)

where a and b are constants. The Talbot formula is applicable for storms of duration 5 to 120 minutes. For storms of duration longer than 2 hours, the Sherman formula is commonly used. According to this formula

nbt

aI

(5.17) where n is a constant Sometimes the following alternative formula is used.

ntki (5.18)

where k is a constant The value of the constants a, b, k and n for the given catchment are usually determined by plotting the given data on a log-log plot. The plot is usually in the form of a straight line. For more accurate values, the theory of least squares is used. When several points are plotted together for different return periods, they become Intensity Duration Frequency Curves and are useful in sewer and drainage design. a) Recent Studies Intensity Duration and Frequency (IDF) curves have been found to be very helpful in the estimation of design flows for the design of hydraulic structures like culverts and sewers. IDF curves show the intensity of rainfall for a given duration and expected frequency. From the curves, intensities of given duration at required return periods are obtained and used in estimating peak flows (Watkins and Fiddes, 1984). In a

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study on the development of IDF Curves in Uganda, the following procedure was adopted (Rugumayo, et al 2002). b) Watkins and Fiddes (i) Rainfall data for the selected urban centres can be obtained from the Meteorology Department.

The collection of data involved extraction of rainfall depths together with duration of occurrence. The maximum rainfall depth in every month was considered from which the maximum in every year was selected. Rainfall depths, with duration between 15 and 210 minutes were selected.

(ii) For each town, daily maximum rainfall values together with the duration for the available period,

were read from the cards and ranked from 1 to N (the number of years of record) in a decreasing order.

(iii) The corresponding return period was estimated for each data set, using Weibull’s plotting

position formula

M

NT 1 (5.19)

where M is the event rank number (1, 2, …, N).

(iv) The maximum rainfall depths were plotted against return periods on a semi-log paper and a line

of best fit plotted through the points. (v) The correlation coefficient between the maximum daily rainfall and log of return period was

determined. (vi) From the graphs, maximum rainfall depths at desired return periods were read off from the

graphs. (vii) The desired sequence of duration (e.g. 10, 15, 30 minutes) was chosen for each data set and used

to calculate the rainfall intensity using the basic mathematical form of the intensity-duration-frequency curve represented by the rectangular hyperbola as Equation 5.17 below:

nbt

aI

where I is the intensity (mm/hr), t the duration (hours) and a, b, n are coefficients/constants

developed for each IDF curve.

(viii) The intensities were plotted against duration for each desired return period to give the IDF curves.

The following procedure was adopted for the determination of coefficients a, b and n in the formulae used.

a) A value of b from other studies in the region; (for East Africa, b = 1/3) was applied.

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b) The effective duration of storms in the area, teff was estimated. This is the length of time over which 60% of the days’ rainfall occurs. This can be done by either judgement, discussion with local experts or a review of local storm data. The latter option was used in this study.

c) The value of n from the original Watkins and Fiddes equation was calculated:

eff

eff

tbbIn

tIn

n24

4.14

(5.20)

d) From the graphs plotted in (iv) the maximum daily rainfall for a particular return period RT

24 can be determined. This is divided by 24 to determine the daily rainfall intensity for a particular return period iT

24:

2424

24

TT Ri (5.21)

e) The value of aT for each return period was calculated using the formula:

nTT bxia 2424 (5.22) The value of aT and other constants b and n were applied in equation (5.17) to determine the intensity in mm/hr for a particular duration and return period. c) Bell’s Method Bell’s method computes the intensity as a function of duration and the hourly precipitation for any given return period by the relation:

t

PtiT

Tt

6024.0 50.054.0

(5.23)

where iT

t = the rainfall intensity (in mm/hour) of return period T and duration t minutes and PT60 is the 60

minute of return period T. It can be applied to storms of less than two hours duration where hourly totals are available. The following procedure was adopted. a) Based on the data available for each station the hourly intensities were calculated and were

ranked according to Weibull formula.

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b) A semilog plot of return period verses hourly intensities in mm/hr was made. c) The correlation coefficient between the log of return period and the hourly intensities was

determined. d) The 60-minute intensities for given return periods were calculated from the graph. e) The intensities for other durations were applied to Bells formula to obtain a set of IDF Curves. The study on data for different stations in Uganda observed that there is a good correlation between log of return period and the daily maximum rainfall, which is easier to measure accurately, whereas a poor correlation between the log of return period and hourly rainfall intensities on which, Bells method is based. In the absence of data, only estimates of hourly rainfall can be made from daily rainfall. Furthermore, the original Bells equation was based on North American catchments, which have less thunderstorm days than tropical thunderstorms. The approach of Watkins and Fiddes method is based on the determination of constants a, b and n in the basic equation describing IDF curves in which, intensity is a function of duration at any given return period, T. Example 5.5 Thus;

nTt

btai

where Tti is the intensity(in mm/hr) of t hours duration with a return period of T years. Watkins and

Fiddes suggest the following procedure for the determination of constants: (i) Start with a value of b from other studies in the region (b=1/3 for East Africa) (ii) Estimate the effective duration of the area, teff. (iii) Given teff compute the value of n from the original Watkins and Fiddes equation as

effeff tbbIntnIn 244.14

(iv) Finally the value of a can be computed from the equation nTT bxia 2424 where Ti24 is the rainfall intensity (mm/hr) of duration 24 hours with return period T years. It can be estimated as

242424TT Ri where TR 24 is the maximum 24-hour rainfall with a T-year return period.

Using the daily maximum rainfall and corresponding duration of Kabale develop the IDF curves for the area.

Year Maximum Daily Rainfall Duration (hours) 1967 12.3 1.1 1968 12.2 0.4 1969 25.6 1.5 1970 39.1 3.5 1971 233 0.9 1972 55.1 2.3 1973 44.2 1.5 1975 26.7 1.5 1976 11.3 0.7

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Take b to be 1/3 and a review of local data shows that teff (60% of the day rainfall) for Kabale can be taken as 1.5 hours. (Hint: First rank the maximum daily rainfall and determine the return period (or frequency) T from rNT 1 where N is the number of years of record and I is the rank). Solution a) Calculation of the coefficient n: B=1/3(value for East Africa) teff= 1.5 hours Therefore

5.13124315.14.14 InInn

b) Calculation of coefficient a (i) Calculate the return periods for Maximum daily rainfall

Year Maximum daily rainfall (mm) Duration (hours) Rank r

Return period rNT 1

1972 55.1 2.3 1 10.0 1973 44.2 1.5 2 5.0 1970 39.1 3.5 3 3.3 1975 26.7 1.5 4 2.5 1969 25.6 1.5 5 2.0 1971 23.3 0.9 6 1.7 1967 12.3 1.1 7 1.4 1968 12.2 0.4 8 1.3 1976 11.3 0.7 9 1.1

(ii) Plot rainfall depths against return period

Daily Maximum rainfall Vs Return Period

y = 4.9092x + 12.319

020406080

100120140

1 3 5 7 9 11 13 15 17 19

Return Period(hours)

Dai

ly M

axim

um

Rai

nfal

l(mm

)

Fig 5.16 Rainfall depth against return period

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(iii) Read off from the line, at desired return periods depths (Max. 24 hour rainfall) and calculate Max.24 –hour intensity and the coefficient a: For T=2 years, Max. 24 hour rainfall TR24 =23.8mm

99.024/8.2824/2424 TT Ri Hence 16.13243/199.024 81.0

24 xbxia nTT

Return Period T

(years) Max.24-hour Rainfall (mm) Max. 24 hour Intensity

(mm/hr) aT

1 9.12 0.38 5.04 2 23.80 0.99 13.16 5 43.20 1.80 23.88 10 57.87 2.41 32.00 25 77.28 3.22 42.72 50 91.95 3.83 50.84 100 106.63 4.44 58.95

c) Calculation of intensity and plotting of IDF curves Calculate the intensity for each desired duration with the given return period T years. For T=2 years, a=13.16 Using t=10 minutes Intensity 81.03/160/106.13 nbtaI =23.06mm/hr Return Period T

(years) 1 2 5 10 25 50 100

Duration t (min) 10 8.84 23.06 41.87 56.10 77.54 89.13 103.35 20 7.00 18.27 33.17 44.44 60.54 7.060 81.87 30 5.84 15.25 27.68 37.09 49.97 58.93 68.33 45 4.72 12.33 22.38 29.99 39.88 47.64 55.25 50 4.45 11.61 21.08 28.24 37.42 44.87 52.03 60 3.99 10.42 18.92 25.35 33.36 40.27 46.70 75 3.47 9.07 16.46 22.05 28.77 35.04 40.63 90 3.08 8.05 14.62 19.58 25.37 31.11 36.08 120 2.54 6.62 12.02 16.11 20.61 25.59 29.68

Intensity Duration Frequency Curves

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0

20

40

60

80

100

120

0 20 40 60 80 100 120 140

Duration (min)

Ave

rage

Inte

nsity

(mm

/min

)

1 RT 2 RT 5 RT 10 RT 25 RT 50 RT 100 RT

Fig 5.17 Intensity Duration Frequency Curves Fig 5.17 shows the Intensity Duration Frequency (IDF) curves of return periods of 1,2,5,10,25,50 and 100 years for Kabale Town.

Summary Precipitation is considered one of the major components of the hydrological cycle. Therefore a correct understanding of its formation, types, measurement and statistical analysis such as IDFs, mass curves, among others, is important to have correct and representative information on whose basis different conclusions can be drawn and design decisions made. This chapter includes aspects of all these issues identified. Furthermore, the Climate of Uganda is discussed in some detail on zone-basis. References 1. Arora S.O., Water Resources Hydropower and Irrigation Engineering, Standard Publishers and

Distributors, 1996, Dehli, India. 2. Asadullah,A., McIntyre, N., Kigobe, M., Evaluation of Five Satellite Products for Estimation of

Rainfall over Uganda, Journal of Hydrological Sciences, 2008, 53(6) pp1137-1150, International Association of Hydrological Sciences, London, UK.

3. Ayoade J.O., Tropical Hydrology and Water Resources, Macmillan, 1998, London, UK. 4. Barret E.C., Martin D.W., The Use of Satellite Data in Rainfall Monitoring, Academy Press,1981,

London, UK. 5. Basalirwa C.P.K., Delineation of Uganda into Climatological Rainfall Zones using the method of

Principal Component Analysis. International Journal of Climatology, 1995, Vol 15 pp1161-1177, London, UK.

6. Das G., Hydrology and Soil Conservation Engineering, Prentice Hall 2006, New Dehli, India.

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7. Duggal K. N., Soni J.P., Elements of Water Resources Engineering, New Age International Publishers 2007, New Dehli India.

8. Garg S.K., Hydrology and Water Resources Engineering, Khanna Publishers, 1998, Dehli, India. 9. Guide to Hydrological Practices, 5th Edition, World Meteorological Organization, 1994, Geneva,

Switzerland. 10. Hydroclimatic Study, Directorate of Water Resources Management, Ministry of Water and

Environment 2008, Kampala, Uganda. 11. Jones J.A.A. Global Hydrology, Longman, 1996, London, UK. 12. Kizza, M., Rainfall Trend Analysis and Uncertainty Related Runoff Modeling within the Lake

Victoria Basin, Licentiate Thesis, Department of Earth Sciences, University of Uppsala, 2008, Uppsala, Sweden.

13. Kolsky P., Storm Drainage, An Engineering Guide to the Low Cost Evaluation of System Performance in the Tropics, Intermediate Technology Publications, 1999, London,UK.

14. Lubega F., Detection of Inhomogenieties in the National Climate Dataset (1902-2003) of Uganda, Proceedings, Groundwater and Climate in Africa, 2008, Kampala, Uganda, University College London/Ministry of Water and Environment, Kampala,2008, Uganda.

15. Majugu A.W., The Generation and Application of Climate Information Products and Services for Disaster Preparedness, WMO Drought Monitoring Centre, 2003, Nairobi, Kenya.

16. Mangeni B. M., Katashaya G. N., Surface Rainfall Estimate from Islands Stations Data’ Proceedings of the First International Conference on Advances in Engineering and Technology,16-19 July,Entebbe, Elsevier, 2006, London, UK.

17. Phillips J., McIntyre B., ENSO and Inter annual Rainfall Variability in Uganda: Implications for Agricultural Management, International Journal of Climatology, 2000,Vol 20 pp171-182, Royal Meteorological Society, London, UK.

18. Raghunath H.M., Hydrology, Principles, Analysis, Design, New Age International Publishers, 2006, India.

19. Rainwater Harvesting Strategy Final Report, 2005 Directorate of Water Development, Ministry of Water Lands and Environment, Kampala, Uganda.

20. Rugumayo A. I., Kiiza B, Muzira S., Developing Hydrological Models using Limited Data Sets. Proceedings of the Institution of Engineers of Tanzania – International Association for Hydraulic Research (IET – IAHR) Conference 2002, Arusha, Tanzania.

21. Sayed M.A.A., Water Resources Management and its Role in Integrated Development, Licentiate Thesis, Royal Institute of Technology, 2002, Stockholm, Sweden.

22. Shaw, E.M., Hydrology in Practice, Chapman, 1992, London, UK. 23. State of Environment Report 1998 National Environment Authority, Ministry of Water, Lands and

Environment 1999, Kampala, Uganda. 24. State of Environment Report 2000/2001, National Environment Management Authority, Ministry of

Water, Lands and Environment 2002, Kampala, Uganda. 25. Subramanya K., Engineering Hydrology, 2nd Edition, Tata, McGraw Hill, 1994, New Dehli. 26. Sutcliffe J.V., Parks Y.P., The Hydrology of the Nile, International Association of Hydrological

Sciences, 1999, Wallingford, UK. 27. Uganda National Environment Management Authority, State of Environment Report, 1996, Kampala,

Uganda. 28. Watkins L.H, Fiddes D., Highway and Urban Hydrology in the Tropics. Pentech Press, 1984,

London, UK. 29. Wilson E.M., Engineering Hydrology, 4th Edition, Macmillan, 1996.London, UK. 30. http://en.wikipedia.org/wiki/cloud_seeding

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Questions 1. Discuss the formation of precipitation. 2. Discuss the various forms of precipitation 3. Describe the different methods of recording rainfall 4. Explain the different methods of obtaining the average rainfall over a catchment due to a storm together with their advantages and disadvantages. 5. Explain a procedure for checking rainfall data for inconsistency. 6. Explain a procedure for supplementing the missing rainfall data. 7. Explain briefly the significance of the following in relation to precipitation. a) Depth Area curves b) Maximum Depth Area Duration Curves c) Intensity Duration Frequency Curves 8. The rainfall data given below was obtained from a single rain gauge for 50 consecutive days of the year. Derive the rainfall hyetograph and the rainfall mass curve. (Hint: Read data down column starting with column 1-5) Columns 1 2 3 4 5

Rows 1 9.4 1.3 18.8 2.5 7.4 2 1.8 42.9 3.0 1.5 1.8 3 7.9 32.0 15.5 1.5 20.8 4 11.4 2.3 6.9 0.8 2.0 5 1.3 4.8 17.0 43.9 9.7 6 15.0 1.5 16.5 4.3 5.6 7 17.0 8.1 16.5 17.5 4.8 8 65.5 3.0 4.1 18.8 27.2 9 34.0 3.6 7.9 2.3 1.8 10 9.9 2.0 1.5 26.9 11.2

9. For the catchment shown in the figure below, the readings for average precipitation at each station are indicated. Find the average precipitation for the entire watershed using:

i. The Average method ii. The Thiessen polygon method

iii. The Isohyetal method

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64

60

626670

68

72

75

80

75

84

908765

76

78

82

7774

72

70

66

68

58

70

69

10. Given the catchment in the figure below showing the rainfall isohyets,

i. Find the average precipitation for the entire watershed.

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ii. Plot a curve of equivalent uniform depths vs area enclosed by isohyets and basin.

90

85

80

75

70

65

60

55

50

140115

93

67

53

41

38

24

9

38

41

3

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1. Given the following maximum daily rainfall for 12 years measured at Masindi Meteorological station. Prepare a set of IDF curves using the Watkins and Fiddes method for Return Periods of 2, 5, 10, 25, 50 (You may assume b = 0.33 and teff = 1.0hrs)

Year Duration (hour) Maximum daily rainfall (mm) 1966 1.3 22.0 1967 1.1 31.3 1968 1 24.5 1969 0.6 8.2 1970 0.5 31.9 1971 0.6 41.5 1972 2.1 4.0 1973 1.2 29.6 1974 1.4 34.2 1975 1.1 28.9 1976 0.9 35.2 1977 0.8 32.7

12.i) What are the main factors that are considered in the siting of a rain gauge? ii) Explain the errors associated with the estimation of rainfall using a raingauge. 13. Discuss the types of precipitation and distinguish between warm frontal rainfall and cold frontal rainfall. 14. Define the following terms

i. Relative humidity ii. Saturation

iii. Saturation deficit iv. Dew point v. Cloud seeding

vi. Areal extent 15. What are the main influencing factors that determine the Climate of Uganda? 16. Discuss the estimation of rainfall using remote sensing.