Runoff Response To Climate Variability. An Analysis Of Thika River Basin In Kenya

Master's Thesis, 2016

91 Pages, Grade: 0.89


Table of Contents







1.1. Background information
1.2. Statement of the problem
1.3. Study justification
1.4. Objectives
1.5. Research Questions
1.6. Scope and limitations of the study

2.1 General introduction
2.2 Climate Variability
2.3 Classification of Climate Variability
2.4 Temperatures variability
2.5 Precipitation variability
2.6 Climate variability impacts
2.7 Climate variability and change scenarios
2.8 Runoff under climate variability
2.9 Trends in observed stream flow
2.10 Hydrological models
2.11 Rainfall - Runoff models
2.12 The HYSIM conceptual rainfallrunoff model
2.13 Trend Analysis
2.14 Modeling
2.15 Sensitivity analysis

3.1 Study area
3.2 Runoff Simulation
3.3 Model Calibration and validation
3.4 Sensitivity analysis of the model parameters
3.5 Trend Analysis

4.1 Study Area
4.2 Sensitivity analysis results
4.3 HYSIM model calibration and validation
4.4 Surface Runoff - Comparative period
4.5 Trend analysis

5.1 Conclusions
5.2 Recommendations




Changes in climatic conditions have greatly affected surface runoff and stream flows both at local and global scale. This has led to adverse effects on surface run off and climatic system as a whole. Research on these hydrological changes at basin scale is of great importance to the water managers for the future planning and management of water resources. The Thika River catchment is of great importance to Kenya and plays host to Ndakaini Dam which provides about 84% of Nairobi’s water supply to a population of over 3 million residents, whose contribution to Kenya’s Gross Product is 60%. Observed climatic variability and trends for Thika catchment were assessed for significance with Mann Kendall’s trend test and discussed in light of future climate variability scenarios. The results indicate that the catchment has become relatively warmer over the last four decades. The annual precipitation and means of daily mean temperatures over the past 30 years has increased by about 7.8 mm (although not statistically significant), and 2.14°C respectively. The trend for the annual mean of daily temperatures was statistically significant. Hydrological simulation model was used to simulate runoff and quantify the effects of climate variability on runoff within the area of study. The model was calibrated and validated giving a coefficient of determination (R2) of 0.923, an RMSE of 0.56 and a BIAS of 1.697. The future climate of the catchment is projected to be warmer and, with less confidence, wetter. However, stream flow could increase by between 1.2% on the lower case to 4.5% on the higher case under these projections. There is therefore need to prepare for the increased runoff as it would affect the agricultural sector, industry, urban communities, as well as the environment.


Les changements dans les conditions climatiques ont grandement affecte le ruissellement de surface et le flux circule a la fois a l’echelle locale et mondiale. Cela a conduit a des effets negatifs sur l’execution de la surface hors tension et systeme climatique dans son ensemble. La recherche sur ces changements hydrologiques a l’echelle du bassin est d’une grande importance pour les gestionnaires de l’eau pour la planification et la gestion future des ressources en eau. Le bassin versant de la riviere Thika est d’une grande importance au Kenya et accueille Ndakaini Dam qui fournit environ 84% de l’approvisionnement en eau de Nairobi a une population de plus de 3 millions d’habitants, dont la contribution au produit brut du Kenya est de 60%. la variabilite et les tendances climatiques observees pour Thika bassin versant ont ete evalues pour la signification avec le test de tendance de Mann Kendall et discutees a la lumiere des scenarios futurs de la variabilite du climat. Les resultats indiquent que le bassin versant est devenu relativement plus chaud au cours des quatre dernieres decennies. Les precipitations et les moyens de temperatures moyennes quotidiennes au cours des 30 dernieres annees annuelle a augmente d’environ 7,8 mm (bien que non statistiquement significative), et 2.14 ° C respectivement. La tendance pour la moyenne annuelle des temperatures quotidiennes etait statistiquement significative. modele de simulation hydrologique a ete utilise pour simuler le ruissellement et de quantifier les effets de la variabilite climatique sur les eaux de ruissellement dans la zone d’etude. Le modele a ete calibre et valide en donnant un coefficient de determination (R2) de 0,923, un RMSE de 0,56 et un BIAS de 1.697. Le climat futur du bassin versant devrait etre plus chaud et, avec moins de confiance, plus humide. Cependant, le debit pourrait augmenter de 1,2% sur le cas inferieure a 4,5% sur le cas plus eleve dans ces projections. Il est donc necessaire de se preparer a [’augmentation du ruissellement car il aurait une incidence sur le secteur, l’industrie, les communautes urbaines agricoles, ainsi que l’environnement.


Figure 4.1: Simulated against Recorded Flows - Thika catchment

Figure 4.2: Simulated against Recorded Flows - Thika river catchment

Figure 4.3: Base case graphical representation

Figure 4.4: Average annual precipitation against Time

Figure 4.5: Annual mean temperatures against time


Template 3.1: River Thika Catchment


Table 2.1: Continental runoff values

Table 2.2: Model Parameters

Table 3.1: Parameterization variables

Table 4.4: Recorded and Simulated flows from Calibration Process (Year 1981)


illustration not visible in this excerpt



1.1. Background information

Changes in climatic conditions have greatly affected surface run off and stream flows both at local and global scale. This variability also impacts on the the functioning of water facilities in existence including flood control facilities, hydropower, irrigation and drainage facilities as well as water regulation practices (Michael & Cayan, 1995). The variations are a natural component of the climate which is caused by changes in the system(s) which influence the climate such as the General Circulation system. According to UNEP (2013) it has been observed periodically that Nile basin’s weak system has given rise to extreme climate events due to variations in the various climate variability drivers. Climate variability drivers are elements that bring about a climatic variability, they include: precipitation distribution, temperature, soil moisture and evaporation.

Research on hydrological changes at basin scale is of great importance to the water managers for future policies formulation, planning and management of the resource. According to Box and Jenkins (1970), the traditional way of forecasting forthcoming climate conditions based on the assumption of past hydrological occurrences is no longer applicable. Adverse effects of meteorological conditions on drinking water systems aggravate the implications of other stresses, such as human population growth, changing economic activity, landuse change and urbanization (KMD, 2010). While quantitative projections of changes in rain, stream flows and water levels at the riverbasin scale are uncertain, it is very likely that hydrological characteristics will change in the coming years (IPCC, 2014). Adaptation procedures and risk management policies that incorporate projected hydrological changes with related uncertainties are being developed in several countries and regions (IPCC, 2014). The consequences of climate variability may alter the reliability of current water management systems and waterrelated facilities. These changes call for runoff modeling which takes into account the changing conditions. The runoff should be simulated under the different climate variability scenarios. Hydrological Simulation Model (HYSIM) was used in this study by incorporating the various scenarios as suggested by the IPCC to study the impacts of the climate variability in the basin.

According to Turral (2011), despite the great importance of climate variability and its impacts across many sectors of the economy, society, and the environment, there is little understanding of its extent in Kenya. This study was aimed at spearheading initiatives towards such understanding. The Thika River catchment is of great importance to Nairobi County in Kenya since the catchment provides 84% of water supply to a population of over three million residents whose contribution to Kenya’s Gross Domestic Product from Nairobi alone is over 60% (Mogaka, 2006). It is a tributary to the Tana River which provides the bulk of the country’s hydroelectricity power needs, irrigation needs as well as providing drinking water to millions of other individuals. Ndakaini Dam which provides about 84% of Nairobi’s water supply is also located in this catchment (Athi, 2006). According to NEMA (2006), Kenya was mostly characterized by dry conditions in 1950s and early 1970s whereas wet conditions occurred in early 1960s and late 1980s. Recent extreme events include: droughts of 1984, 1990, 1994, and 1999 and El Nino floods in 1997/1998 (NEMA, 2006).

Temperatures in Kenya do not show large variation in its mean throughout the year but shows variation geographically, seasonally and diurnally due to altitude. The mean annual rainfall depicts a wide spatial variation, which ranges from 200mm in the driest areas in northwestern and eastern parts of Kenya to the wetter areas with rainfall of 1200-2000 mm in areas which boarders Lake Victoria and central highlands east of the rift valley (Ndirangu, Kabubi, & Dulo, 2009). The Kenyan climate has evidenced natural disasters, with floods and droughts occurring periodically as result of rainfall anomalies. There has been in the past 50 years at least thirteen serious droughts and six major flooding which have affected Winam gulf of the Lake Victoria and the lower Tana area (NEMA, 2006).

1.2. Statement of the problem

Determining climate variability effects on surface runoff both on a local and regional scale helps in effective planning of the future water resources. According to IPCC (2000) temperature is expected to increase by between 0.2oC and 0.5oC consequently triggering a rainfall increase of about 5 to 20% during the wet months and 5 to 10% during the dry months. Determining these changes in runoff in both climate variability and climate change is not enough without taking into account the climate variability scenarios. Unfortunately, there is a major challenge in taking account of climate change scenarios making it hard to significantly determine climate variability effects. This leads to use of assumptions on the climate change effects resulting to wrong data when predicting surface runoff (Arnell, 2003). The outcome of wrong assumptions compromises future planning of water resources.

According to Hulme et al. (2001), there are two main reasons why there is little confidence about the magnitude, and even direction, of regional rainfall changes in Africa. The first reason relates to ambiguous representation of climate variability in the tropics. This is in most GCMs via mechanisms such as ENSO, for example, which is a key determinant of African rainfall variability. The second reason is the omission in all current global climate models of any representation of dynamic land coveratmosphere interactions. These interactions have been suggested to be vital in determining climate variability in Africa during Holocene and may also have played a role to the more recently observed desiccation of the Sahel (Hulme et al. 2001). Work is now underway however, to incorporate such links in regional climate models (Moore et al. 2009).

Unless credible models are used, the problem cannot be effectively solved. Hydrological models have been used in exploring the implication of making certain assumptions about the real world system and predicting the behavior of the real system under a set of naturally occurring circumstances. This research used the HYSIM model to overcome the challenge of taking into account climate variability scenarios while accessing runoff within the Thika river catchment.

1.3. Study justification

Over recent years there has been increasing evidence that the earth's climate will become warmer in 21st century, which raises the essential question: What impacts will global warming have on the environment and human activities (IPCC et al., 2000). Global warming will lead to hydrologic changes that will affect freshwater resources including surface runoff. These are among the most significant potential impacts of climate change and variability. As the climate warms, changes in the nature of global precipitation, evaporation, snowpack, stream flow and other factors that will affect freshwater supply and quality will be evidenced. According to Athi (2006), Thika River not only provides water to power the energy needs of the country but also 84% of its water needs to drive Nairobi, the country’s capital city. Nairobi is periodically faced with serious water shortages as water levels in Ndakaini Dam hit unprecedented low levels during dry spells. The energy situation is made worse by frequent low water levels at Masinga dam along the Tana River which leads to shut downs. All this has come about due to the serious changes and variability in climate. Kenya was faced with the worst dry spell in early 2015 followed by a season of extreme rainfall events. Climate variability and change will present challenges to water utilities, and developing mitigation now could prevent freshwater crises in upcoming years (KMD, 2010). Determination of the effect of climate variability on surface runoff in Thika catchment helps the country to be in a position to determine future floods or drought and predict possible future trends to enable formulation of mitigation and preparedness measures. Exploring vulnerability means extreme events providing most important message needed for impact prediction, analysis and development of mitigation measures.

1.4. Objectives

1.4.1 General objective

The main objective of this study was to evaluate the response of surface runoff due to climate variability in Thika river basin in Kenya using hydrological simulation model.

1.4.2 Specific objectives:

The specific objectives are to:

i. Calibrate and validate HYSIM model’s ability to simulate surface runoff through use of rainfall, stream flow, and PET data for the period between 1960 and 1995.
ii. Simulate changes in runoff from different climate variability scenarios developed by IPCC with weather data, stream flow data and topological maps.
iii. Analysis of the trends in temperature and rainfall generated in the catchment based on the climatic data for the period starting 1976 to 2006.

1.5. Research Questions

Research questions were;

a) How does surface runoff in the catchment respond due to climate variability?
b) How can HYSIM be used in runoff simulation in a watershed?
c) Are there any trends in temperature and rainfall generated in the catchment?

1.6. Scope and limitations of the study

The area that was under this study is Thika river catchment. The main river in this catchment is Thika River which is part of the larger Tana catchment. HYSIM model was used to simulate surface runoff from the available weather data and hence thereafter the results evaluated. The model used a 15 years’ dataset in batches of 5 years each three stream flow gauging stations within the catchment.



2.1 General introduction

In 1997, the UN Comprehensive Assessment of the Freshwater Resources of the World (WMO, 1997) gave estimations that a third of the world’s population was living in countries suffering from water stress. This meant that the populations in these countries were drawing more than 20% of the water resources available. The report also came up with estimations that by 2025, two thirds of the world population would be living in water stressed countries. With the increasing levels of greenhouse gases (GHG), the volume and timing of the runoff and ground recharge will be affected. This will have a great impact on the number of people who are affected by water scarcity. The estimates on the impact of climate change are based on the assumed emissions scenarios, climate models and the assumed changes in population (Luijten, 1999). This means that climatic conditions will vary along with time (IPCC et al., 2000).

In a study by Lukeman (2003), it is reported that the main drivers of Climate variability include precipitation distribution, soil moisture, evaporation and temperature. Other drivers of weather and climate variability include solar energy from the sun, earth’s pressure systems, moisture sources, sea/ocean land interface, the surface albedo, the topography and relief (Ndirangu, Kabubi, & Dulo, 2009). Climate variability implies variations in the mean state and other climate statistics such as standard deviations, the occurrence of extremes amongst others on all temporal and spatial scales beyond those of individual weather events. Universal climatic variability may be resulting from internal natural processes or perhaps even external processes. Global climate warming is the increase of earth's nearsurface air and ocean temperatures which is usually as a result of the accumulation of more and more greenhouse gases resulting from anthropogenic pursuits like burning of fossil fuels (NEMA, 2014). It is essential to note from the above definition that global climatic change, climatic variability and global climate warming are not quite the same though closely related.

2.2 Climate Variability

Based on the Millennium Ecosystem Assessment (2005), natural hazards and disasters are products of both natural variability and humanenvironment interactions. It is important to note that extremes in variability are defined as hazards when they represent threats to people and what they value and defined as disasters when an event overwhelms local capacity to cope. There is little information on changes in African climate variability (Sivakumar et al. 2005; Christensen et al. 2007). The increase in African rainfall is associated partly with an increase in atmospheric water vapor. The increase in number of wet seasons is estimated to be at 20% (Christensen et al. 2007).

2.2.1 Reasons for climate variability and its factors

Climate variability changes as a consequence of a several variables. These variables are; interface between ocean and the atmosphere, changes in the world's orbit and changes in energy from the sun. There is proof that the late global warming is not only attributable to natural variables but rather human causes which appear to be the significant cause (IPCC, 2013). The progressions seen over late years and those anticipated for the following century are taken to be mostly the consequence of human conduct through interaction with the environment. Human activity is the primary driver of the changes found in atmosphere in the recent decades.

The earth has warmed by 0.75 degrees Celsius in the most recent 100 years globally and ocean levels have gone up (Stocker et al., 2014). Extreme weather, such as floods and dry spells, are likely to happen regularly resulting in immediate direct and indirect impacts. These can lead to flareup of ailments such as Malaria (Hay et al., 2002). The increase in carbon dioxide concentration worldwide are significant because of fossil fuel usage and land use change, while those of methane and nitrous oxide are principally because of agribusiness that generally comes about from the production process utilizing animals (IPCC, 2007).

Unlike weather which varies day to day, climate varies seasonally. Some summers are colder than others while some years have high overall precipitation. Climate variability is not as noticeable as weather variability since it happens over seasons and years. Common drivers for climate variability are El Nino and La Nina events, volcanic eruptions and sunspots. El Nino and La Nina events are shifts of warm tropical Pacific Ocean currents (Hay et al., 2002).

2.3 Classification of Climate Variability

Climate variability can be classified into two major subgroups depending on their causes;

a) Natural variability: This is the disparity in the various components of climate resulting from the effects of natural processes. For example, the natural greenhouse effect where the natural greenhouse gases and clouds cause changes in temperatures and also in the solar radiation (Fu et al., 2009).

b) Anthropogenic variability: This is the variation resulting from impact of Human activities for instance the burning of fossil fuel resulting into the alteration of the composition of the atmosphere. A good example is through land use changes such as urbanization, deforestation, construction of land water reservoirs (Lukeman, 2003).

There are several forms of climate variation as can be seen in Figure (2.1) after Fu et al. (2009).

illustration not visible in this excerpt

Figure 2.1: Types of climate variations

a) Periodic change: This is as shown in Figure 2.1 it occurs in form of periodic cycles. The length of the cycles corresponds to the time scale adopted for the variability study that can either be daily, annual, decadal or longer time scales.
b) The climate may undergo a sudden shift from its current state to another state, maybe one characterized by significantly colder or warmer conditions as shown in Figure 2.1.
c) The climate may also undergo a steady change until a stable state is reached, for instant, steady warming or cooling as shown in Figure 2.1.
d) Another common pattern is where the climate maintains what appears to be a steady state when characterized by a specific variable e.g. Mean annual temperature, but variations in other measures e.g. seasonal temperature, diurnal range of temperature indicates that significant change has taken place as shown by Fu et al., (2009) on Figure 2.1.

2.4 Temperatures variability

2.4.1 Global variability

It is estimated that global temperature has increased by 0.6oC since the 19th century (KNMI. 2006). In the recent decades (1950 - 1993), the increase in temperatures has involved a faster rise in daily minimum (0.20C / decade) than in daily maximum (0.10C / decade) in many continental regions. This has led to a decrease in diurnal temperature range (-0.1 0C / decade trend) in many parts of the world (Parry, 2007). This is a rate higher than for the mean temperatures for the entire 20th century, indicating very strong warming in recent decades. The challenge with these ranges is that they were done for only 57% of the global surface and hence may not accurately reflect the global trend. However, with more GCM models in the market this is likely to change (Westmacott & Burn, 1997).

2.4.2 Regional variability

According to Lins and Slack (1999), temperatures have increased almost everywhere with the exception of eastern Canada, small areas of Eastern Europe and the middle east. The diurnal temperature range has decreased in most areas except over middle Canada, and parts of South Africa, southwest Asia, Europe and western tropical pacific islands. In New Zealand and central Europe, maximum and minimum temperatures have increased at similar rates. Other regions like India have experienced increased diurnal temperature range due to decrease in minimum temperatures (Lins and Slack, 1999). These different occurrences of variability across regions make the assumption of a global value insufficient for evaluation of impacts and planning of mitigation at local scale. This leads to the need of assessing variability even at local scale i.e. sub regions of the continental regions are generally adopted in such studies (Fu et al., 2009).

2.5 Precipitation variability

Over the 20th century, the annual precipitation increased by between 7% and 12% for the zones 300N to 850N and by about 2% between 0o to 55oS (IPCC et al., 2000). In the year 1998 the high latitudes (55°N and higher) of the Northern hemisphere had their wettest year on record and the midlatitudes recorded precipitation totals exceeding the 1961 to 1990 mean every year since 1995 (Beven, 1989). Studies have shown that precipitation in Canada has increased by about 10% during the 20th century. In china there has been a declining trend in total annual precipitation for the period 1950 to 2000 (Ndirangu et al., 2009). Studies have shown multidecadal variation in the Indian monsoonal rainfall, from 1906 to 1960 the rainfall increased and then decreased through 1974 and has continued to increase since then, western Mexico has experienced an increasingly erratic monsoonal rainfall since 1940s (Fu et al., 2009). The driest period was in the 1980s. Southern Africa region has experienced significant decreases in precipitation since 1970s. Early 2000 have seen floodproducing rains in the eastern part of South Africa (Lukeman, 2003). From all this consideration, it is seen generally that there is an increasing trend in precipitation.

2.6 Climate variability impacts

Climate variability has several impacts (Hay et al., 2002). Bases on climate change projections made by IPCC, climate variablity increases the occurrence of droughts, floods and extreme rainfall events (IPCC, 2007). Recent studies show that cyclones intensity will increase with up to 10-20% due to climate variability. More El Nino like weather conditions are expected as a result of climate variability. All these will have an impact on crop agriculture, forestry, livestock and human life in general (van de Steeg et al., 2009).

2.6.1 Assessment of climate variability effects

As presented by IPCC (2007), a set of four future climate scenarios to project emission gases and temperatures are paramount to addressing these unforeseen challenges. These scenarios are used by researchers and policy makers to assess potential future conditions and compare them to baseline conditions in the absence of climate change. As an example rainfall variability in Kenya affects agricultural production and the livelihoods of people, especially in the ASAL areas, like Makindu (John Walker Recha et al., 2016). These scenarios can also be used to analyze adaptation scenarios to mitigate the negative effects of climate change.

2.6.2 Water resources under climate variability

Runoff is a key area in water resources and can be affected by environmental changes. It is also influenced by the changes in temperature and precipitation. Runoff being an important in water supplies dynamics has led to more and more research on the impacts of environmental changes on overflow. It is important to look at runoff as a little representation of precipitation. Its magnitude depends on temperature, dampness, solar intensity, vegetation, wind speed, and soil moisture. In this case, changes in runoff are not similar to changes in precipitation. Fredrick and Gleick (1999) were able to model effects of temperature and changes in precipitation on different stream bowls hence effectively investigating the effects of environmental change on water supplies. They found that by expanding temperature by 2oC and reducing precipitation by 10%, the measure of runoff inside the Great Basin Rivers, Upper Colorado, Lower Colorado, and Colorado River will diminish by - 17% to - 28%, - 35%, - 56%, and - 40% (Miller, 1997) respectively.

In Kenya dry extremes are projected to be less severe than they used to be during September to December (Wambua et al. 2014). Despite this, the GCM does not give a good agreement on the projected changes in dry extremes during the months of March to May (KNMI, 2006; Thornton et al., 2006). The wet extremes also known as short and long rains are experienced from September to December and March to May rainy season respectively. In the northern regions, the dry extremes are projected to be less severe during September to December (KNMI, 2006). Despite this, the GCM model fails to show a good agreement in the projected changes of the dry extremes which are expected from March to May (Thornton et al. 2006).

According to KNMI (2006), on average, the projected variations in wettest events occur once in every 10 years. It is important to take note of the fact that the existing climate models underestimate the strength of long rains in current climate. This limits the confidence of the projections (KNMI, 2006; Thornton et al. 2006). There are 112 models which were used by KNMI (2006). Through use of these models, KNMI investigated on the precipitation changes using the runs forced through Special Report Emission Scenario (SRES) A1B scenario (KNMI, 2006). According to Osbahr and Viner (2006), increases in temperatures have a significant impact on water availability. The increases are expected to exacerbate drought conditions which are experienced regularly. Rainfall in Kenya is unpredictable and has a tendency to fall heavily in short periods. This is likely to cause problems through increased occurrences of heavy rainfall periods as well as flooding (Osbahr and Viner, 2006).

2.7 Climate variability and change scenarios

Based on state of the Special Report on Emissions Scenarios (SRES) (IPCC, 2000), climate change scenarios are not the prediction or forecast of the future but rather they are potential future scenarios and each of scenario represents a way in which the future might unfold. There is little information on climate change available for East Africa at both country and local scale. In Kenya, rainfall projections are inconsistent as evidenced by a range of models and scenarios which shows increases and decreases in total precipitation (Osbahr and Viner, 2006).

According to IPCC (2007), a climate variability scenario is the estimation of future resource availability factoring in the estimation of the implications of climate change or variability for water stress. The scenarios describe future demographic conditions, environmental conditions, social conditions, economic conditions, technologies, and policies. The four scenarios described by the IPCC (2000) are Al, A2, B1 and B2 Scenarios.

2.7.1 A1 Scenario:

"This scenario describes a world with a very rapid economic growth and a global population that attains its peaks in midcentury. The population declines thereafter with new and more efficient technologies being rapidly introduced. Major underlying themes are convergence among regions, capacity building and increased cultural and social interactions, with a substantial reduction in regional differences in per capita income. The three A1 groups are distinguished by their technological emphasis: fossilintensive (A1FI), nonfossil energy sources (A1T) or a balance across all sources (A1B)."

2.7.2 A2 Scenario:

"The A2 scenario and scenario is based on describes a very heterogeneous world. The main theme is selfreliance and preservation of local identities. Fertility patterns across regions converge very slowly, which results in continuously increasing population. Economic development is regionally oriented and per capita economic growth and technological change more fragmented and slower than other storylines."

2.7.3 B1 Scenario:

"This scenario shows a convergent world with the same low population growth as in the A1 scenario, but with a fast change in economic structures toward a service and information economy, with reductions in material intensity and the introduction of clean and resourceefficient technologies. The emphasis is on global to economic, social and environmental sustainability which includes improved equity, but without additional climate initiatives."

2.7.4 B2 Scenario:

"The B2 storyline and scenario family is based on a world in which the emphasis is on local solutions to economic, social and environmental sustainability. It is a world with an increasing population, at a rate lower than A2. The level of economic development is intermediate, and there is less rapid and more diverse technological change compared to B1 and A1 storylines. It is important to note that while the scenario is also oriented towards environmental protection and social equity, it focuses on local and regional levels."

These scenarios are as summarized in Table 2.1.

Table 2.1 Scenarios to be adopted

illustration not visible in this excerpt

2.7.5 Generation Global Climate Model (GCM)

GCM is widely applied for weather forecasting, understanding the climate and projecting climate change. Mathematical models are used to simulate the present climate and predict future climate while considering impacts of greenhouse gases and aerosols. As they are based on physical laws and physically based empirical relationships, GCMs are, therefore, the only tools that estimate changes in climate due to increased greenhouse gases for a large number of climate variables in a physically consistent manner (Watson et al., 2001). Praveen Kumar et al. (2012) demonstrated that GCMs are very important in climate variability studies were they found that they were very reliable in predicting India’s rainfall. This was achieved through simulation of monsoon rainfall at a 95% confidence level.

2.8 Runoff under climate variability

By far the highest frequency of hydrological studies into the effects of climate variability has focused on potential changes on river flow (Maidment, 1992). The distinction between “stream flow” and “runoff” may well be vague, however in general terms stream flow is water within a river channel, usually expressed to be the rate of flow past a point typically in m3 s-1, whereas runoff is truly the quantity of precipitation that does not evaporate, usually expressed as an equivalent depth of water along the area of the catchment. A good link between them is that runoff can easily be considered stream flow divided by catchment area, although in dry areas this doesn't necessarily hold because runoff generated in a single area of the catchment may infiltrate before reaching a channel and becoming stream flow. Over short durations, the amount of water leaving a catchment outlet usually is expressed as stream flow; over durations of a month or more, it usually is expressed as runoff (Maidment, 1992).

Lvovitch (1972) suggests that the distribution of runoff per continent shows some interesting patterns as stipulated in Table 2.1. Areas having the most runoff are the ones with high rates of precipitation and low rates of evaporation.

Table 2.1: Continental runoff values

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2.9 Trends in observed stream flow

Since the second assessment report, there have been many notable hydrological eventsincluding floods and droughtsand therefore many studies into possible trends in hydrological data (Wambua et al. 2014). In general, the patterns found are consistent with those identified for precipitation: Runoff tends to increase where precipitation has increased and decrease where it has fallen over the past few years (Lins and Slack, 1999). The variability of runoff and water resources is particularly higher for drier climates, e.g., a higher percent change in runoff resulting from a small change in precipitation and temperature in arid or semiarid regions (Fu et al., 2009). It is very important for water resources managers to figure out and prepare to tackle the effects of global climatic variability on the changes of hydrological cycles and stream flow regimes. The better understanding toward the relationship between global climatic change/variability, anthropogenic activities and the water resources availability in addition to its withdrawal and exploit, will allow water resources managers to make more rational decisions on water allocation and regulation (Pacini and Harper, 1998).

2.10 Hydrological models

The rainfall runoff models classifications are based on the input parameters together with the physical principles applied (Moradkhani and Sorooshian, 2008). This leads to classification of models as lumped and distributed based on the parameters which are function of both time and space. They are also classified as deterministic and stochastic models according to other criteria. The deterministic models can only give same output based on a given set of input (Refsgaard, 1996). This is contrary to stochastic models which give varying values of outputs for a given single set of inputs (Moradkhani and Sorooshian, 2008).

Moradkhani and Sorooshian (2008) assert that the entire river based output is considered as a single unit. In this case, spatial variability is disregarded and outputs are generated without considering the spatial processes. In this case, a distributed model can be used in making predictions that are distributed in space. This is realized by dividing the entire catchment into small units which in most cases are square cells or triangulated irregular network. In this case, the parameters, inputs and outputs vary in a spatial manner. Based on time factor, it is possible to classify the models as static or dynamic (Osbahr and Viner 2006). Static model excludes time unlike the dynamic model. According to Sorooshian et al. (2008), the models can be classified as event based or continuous. It is important to note that the former can only produce output in specific time intervals unlike the latter which has a continuous output. The most vital classifications are empirical model, conceptual models and physically based models (Osbahr and Viner 2006).


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Runoff Response To Climate Variability. An Analysis Of Thika River Basin In Kenya
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