Drought Indices Assessment for Sustainable Water Resources Management-a Case Review for upper Tana River basin, Kenya

DOI : 10.17577/IJERTV3IS100155

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Drought Indices Assessment for Sustainable Water Resources Management-a Case Review for upper Tana River basin, Kenya

Raphael M. Wambua1*, Benedict M. Mutua2 and James M. Raude3

1Lecturer and PhD Candidate, 2Associate Professor, Egerton University Department of Agricultural Engineering, Kenya

3Senior lecturer, BEED, Jomo Kenyatta University of agriculture and technology, Kenya

Abstract: Drought indices as means of assessing drought which is a stochastic natural disaster influencing socio-economic development in the upper Tana River basin are presented. Drought is a condition on land or river basin with intense and or persistence below average precipitation and water scarcity. Drought phenomenon may be characterized in terms of its severity, intensity, duration and magnitude. For the purpose of prospective planning and management of drought, its characterization, analysis of drought severity, impacts and risk assessment is vital. Development and application of drought indices have been used to characterize drought at varying spatial and temporal scales for regional, national and river basin levels. This article review drought indices and explores their applicability in different time scales of drought for 1, 3, 6, 12, 24 and 48 months lead times. A reliable index must be able to quantify drought severity, detect drought beginning and end times for early warning systems, monitoring and prospective water resources planning. Since numerous drought indices have been developed over the years, it is critical to provide their detailed analysis review, indicate their differences and applications. A research direction for drought assessment using indices in upper Tana River basin for water resources management is proposed.

Key words: Drought severity, upper Tana River basin, drought characterization, water resources, early warning system


On global scale, drought has become more frequent and severe due to climate variability with different regions experiencing droughts at varying scales and times [14]. Consequently, global impacts of drought on environmental, agricultural and socio-economic aspects need to be studied. As such, four distinct types of droughts namely; meteorological, agricultural, hydrological and socio- economic are recognised. These droughts have either direct or indirect impacts on river basins. The former include degradation of water resources in terms of quantity and quality, reduced crop productivity, increased livestock and wildlife mortality rates, increased soil erosion and land degradation, and increased plant diseases and insect attacks [60: 52]. Severe drought impacts have been experienced in other regions of the world leading to food insecurity and general increase in world food prices. For instance, very notable recent droughts of 2009 and 2011 in Kenya adversely affected the agricultural sector where crop yields were drastically reduced. Due to the problems mentioned above, river basin managers often have a challenge of addressing water risks, conflicts and balancing economic

development while at the same time maintaining reliable water resources [45].

African countries are among the most vulnerable to impacts of climate variability and drought. The impacts adversely affect the well being of the population. These impacts are compounded by numerous factors such as vast arid and semi-arid lands (ASAL) in the region, poverty, high population density, and human diseases. This is expected to multiply the demand for water, food and forage for livestock within the area in the next decades [45]. In East Africa, it has been projected that water availability will decline due to drought. In addition, there is a likelihood of increased desertification due to decline in seasonal amount of precipitation and alteration of its patterns at different magnitudes for different locations [67; 24].

Droughts in Kenya have impacted adversely on rain fed agriculture, water resources, hydropower generation and ecosystems. The agricultural sector alone which contributes to more than 51% of the gross domestic product (GDP) in Kenya [67] has been critically affected by frequent droughts. Over the past 50 years, Kenya has experienced at least one main drought per decade [11] .In addition; there has been a notable increase of drought in terms of frequency, duration and intensity. Any damage caused by drought on agriculture and water resources leads to famine, humanitarian crisis, rationing of water supply and decline in hydropower generation. Effective drought forecast allow water resource decision makers to develop drought preparedness plans. Such plans are critical for advance formulation of programmes to mitigate drought-related environmental, social and economic impacts. Therefore, accurate drought assessment and forecasting with an adequate lead time is paramount for formulation of mitigation measures in river basins [55].

Drought forecasting has received a new approach especially with the development of the Drought Indices and Artificial Neural Networks (ANNs). An ANN is a computing system made up of a number of simple and

highly interconnected information processing elements. Such a system has performance characteristics that resemble biological neural networks of human brain. ANN has numerous merits when used for data processing. The system processes information based on their dynamic state response to external input [39]. ANNs have the capacity to model relationships that are quite dynamic and can capture many kinds of relationships including non-linear functions which are difficult or impossible to determine using other approaches [41]. The ANNs have recently been used in water resources engineering (WRE). WRE comprises the study of hydraulics, hydrology, environment and geological related variables. Such variables are dynamic and exhibit non-linear and stochastic characteristics. These properties make WRE variables complex and difficult to determine due to spatial and temporal variations. Thus due to their advantages, ANNs provides effective analytical techniques in modelling and forecasting non-linear and dynamic time series variables in WRE such as drought [41].

At present, most basins in Kenya have limited or lack of adequate quantifiable information of drought occurrence, frequency and severity. In addition, there is lack of sufficient and appropriate drought assessment and forecasting methods. Drought models can be used to estimate and forecast drought conditions on a spatial and temporal domain. To prepare for effective mitigation of drought risks in Kenya, evaluation and forecasting of drought conditions is vital. Thus, this research presents a review of drought indices for possible drought assessment in upper Tana River basin with a focus to informed decision making on matters relating to water resources management.

Drought Definition

Drought is a condition on land characterised by scarcity of water that falls below normal average or defined truncation level. The term drought has been defined differently in numerous applications [61]. However, it is a challenge to quantitatively define the term. Drought is perceived as a stochastic natural disaster that adversely affects water resources within river basins [26]. It may be described as a hydro-meteorological epoch on land characterized by temporary and recurring water scarcity. The magnitude of the drought is measured by the extent with which it falls below a threshold level over an extended period of time [39]. Drought has been identified as the most complex natural hazard since it is difficult to detect, develops slowly and impact on numerous aspects within a region [39].

Success of drought preparedness and mitigation depends upon timely information on its onset, and propagation in terms of temporal and spatial domain. Such information is usually obtained via effective and continuous drought monitoring using drought indices. The study of spatial and temporal drought conditions is fundamental in offering a wide range of solutions for control and management water resource systems. Assessment of drought conditions is critical for planning water supplies, irrigation systems, crop and food security programmes, hydropower generation, water quality management and waste disposal systems [1]. Drought may also be described by two distinct dimensions such as conceptual and operational terms [69]. The former is developed to offer a general understanding and develop a policy for drought management [44]. The latter objectively defines criteria for drought beginning, end and severity of drought for different applications.

Drought Characteristics

Droughts are characterized into numerous aspects. These include drought severity, duration, frequency, magnitude and spatial distribution (Figure 1) [3; 63]. These dimensions of drought are described below.

Drought severity: This is the extent of precipitation deficit in terms of magnitude or degree of impacts resulting from precipitation deficit [68]. In addition, Drought severity can be mathematically defined as a product of its magnitude and duration (Figure 1).

Duration: Drought duration refers to any continuous period of sequence with deficit of water below a defined truncation level.

Intensity: It the ratio of the drought magnitude to its duration.

Drought frequency: the frequency also called return period of a drought is the mean time period between two consecutive drought events that have the same severity either equal or greater than a defined threshold.

Magnitude: It is the accumulated water deficit in terms of precipitation, soil moisture, runoff, stream flow, water reservoir levels and ground water below a certain truncation or threshold level for a given duration


Drought index

Spatial distribution: It is the geographical extend in terms of areal coverage of drought which is variable during a drought event.




S=Drought severity M=Drought magnitude D=Drought duration


Truncation level

Median magnitude line

Fig.-1. the characteristics of a drought event

Impacts of Droughts

Different droughts have direct or indirect impacts on river basins (Table 1; Figure 2). The former include degradation of water resources in terms of quantity and quality, reduced crop productivity, increased livestock and wildlife mortality rates, increased soil erosion and land degradation, and increased plant diseases and insect attacks [60; 52]. On the other hand, the indirect impacts comprise reduced

Table 1. Historical drought effects in Kenya (E-Eastern,RV-Rift valley, NE- North eastern, C-central,, W-Western, Co-Coast , CW-Countrywide

Period (years)


Remarks on the drought effects




Caused Cattle deaths



Water shortage livestock deaths




Human and livestock deaths



E, N, and C

Heavy livestock losses, food and water shortages


C, E, W

and Co

Low crop production,

water shortages



Famine and water




Water shortages, human

and livestock migration


C, RV,


Huge food shortages


E and C

Severe food shortages




and E

Moderate food shortages




Deficit food supply,

interuption of electricity supply, water scarcity



E, C,


Food and water deficit

Impacts of drought

Indirect Impacts


Food prices


Tax revenues


Direct Impacts

Famine: 1900-

2013, 11 million

died, 2 critical

(FAO, 2013

Land degradation

Food insecurity

Water Resources

Fig-2. Flow chart showing the impacts of drought in a river basin

income, tax revenues increased prices for food, unemployment, and migration of people and animals (Figure 2). Worldwide, more than eleven million persons have died since 1900 as a result of drought related impacts. In addition, two billion persons have been critically affected by the impacts [10]. The main challenges associated with drought are that it causes ill health through water scarcity, malnutrition and famine [60].

Main Drought types

There are four main types of droughts as described by [73]. The four types of drought are: the Hydrological, Meteorological, Agricultural and Socio-economic droughts. The hydrological drought characteristics are defined by magnitude, severity, duration and frequency, and can be studied at a basin scale. Hydrological drought may be categorized into surface and ground water droughts. Agricultural drought links meteorological or hydrological drought to agricultural impact. Agricultural droughts impact negatively on farming systems whenever they occur. Their impacts are normally two-fold; environmental and economic impacts. The agricultural drought is a type associated with low agricultural production, decline in output from agro-processing industries and unemployment incidents in the agricultural sector.

Fundamental dimensions of drought indices

Numerous approaches for drought characterization have been used. However, drought indices are the most preferred methods. A drought index is single numeric value calculated by integration of drought indicators. A drought index gives a distinct representation of drought and guide decision-making in a more effective way than the drought indicators that are presented by the raw data sets of drought [19]. The following are the basic dimensions of the drought indices that make it more effective in drought characterization than the drought indicators.

  1. Drought indices can define the drought beginning and end times

  2. The indices provide detection and real time drought monitoring

  3. The indices provide reliable means for drought evaluation

  4. Its possible to represent drought conditions for a given region using drought indices

  5. Drought indices provide correlation of drought with drought impacts over geographical time scales

    Increased evapo- transpiration

    Reduced infiltration, runoff, deep percolation, ground water recharge

    Meteorological drought

  6. It is possible to express drought conditions in different levels and communicate the implications to various stakeholders

Drought indices

Drought indices or models are used for assessment of occurrence and severity of droughts. The Drought Indices (DIs) were developed for specific regions using specific structures and forms of data input. Drought indices may be categorized into two broad categories; remote-sensing based and the data driven drought indices [2]. The remote- sensing based indices are presented from data obtained from remote sensors to map te conditions of land. Data driven indices are those calculated using ground based data recorded over time [40; 51].

Different perceptions pertaining drought may be presented using drought indices. The indices are used to identify the three main physical droughts such as meteorological, agricultural and hydrological droughts. All the three types of droughts are triggered by precipitation deficiency on a river basin. A decrease in precipitation below a truncation level may cause low moisture content leading to fundamental agricultural drought. This in turn may cause reduced water flows in surface, sub-surface and ground water resources contributing to the hydrological drought. Thus the propagation of three main droughts originate from discrepancies in the hydrologic cycle caused by climate variability (Figure 3).

Natural climate variability

Deficiency in precipitation; quantity, intensity, timing

High temperature, wind, low RH, cloud cover

Soil-Water deficiency

Reduced stream flow

Reduced inflow to reservoirs

Reduced inflow into lakes, ponds and wetlands

Reduced wildlife habitat

Environmental impacts

Economic impacts

Social impacts

Plant water stress, reduced biomass and yield

Duration of time

Hydrological Drought

Agricultural drought

Fig- 1. Propagation of drought via hydrological cycle

The drought indices are used to compute drought conditions for different time scales. Typical time scales for drought forecasting include 1, 3, 6, 12, 24 and 48 months

lead time. Each time scale has its corresponding effects [44]. as described using the standardized precipitation index (SPI) in table 1.

Table 1. The Structure and Scope of Drought Forecasting

SPI Input

Phenomena reflected

Application of the output


One-month lead time drought conditions

For detection of short-term agricultural drought during growing period


Three-month lead time drought conditions

For short-term and seasonal metorological drought esmimation


Six-month lead time drought conditions

Medium-term trends in precipitation over seasons detected


Nine-month lead time drought conditions

Detects any significant medium effects of drought on agriculture


Twelve-month lead time drought conditions

For detecting drought effects on stream flows, researvoir and

ground water levels


Twenty four- month lead time drought conditions

For detecting long-term drought effects on stream flows,

researvoir and ground water levels

Table 2. Summary of different drought indices for (Hydrological (H), Meteorological (M), Agricultural (A) and Remote Sensing (RS))

Drought index/Developed by

Type of drought

Key notes


Surface Water Supply Iindex (SWSI)



Calculates the weighted average of the standardized anomarlies for

precipitation,reservoir storage, runoff and snowpack

Drought Severity Index (PDSI)



Analysies precipitation and temperature in a water balance model. It compares meteorological and hydrological drought on spatial and temporal domain

Aggregated Drought Index (ADI)

/ [28]


It is a multivariate, aggregate drought index with five to six hydrological inputs such as precipiattion, streamflow, reservoir storage, evapotranspiration, soil moisture and snow water content. Its fist priciple component(PC1) is normalized by

the standard deviation

Standardized Precipitation Index (SPI)

/ [36] , Duosken, N. J. and Kleist, J. 1993


It is a simple index,has reliable spatial consistency, its probablistic in nature and useful in risk and decision analysis. It is adjustable to user defined time periods and can be used to determine three main dimensions of drought; intensity, duration and

spatial extend

Crop Moisture Index (CMI)

/ [48]


It analyses precipitation and temperature in a water balance model

Normalized Difference Drought Index (NDVI)


It uses visible red and near infrared bands for calculation of vegetation conditions.

An adnanced radiometer is used to capture ad reflect the bands.

Regional Stream flow Defiiciency Index (RSDI)/ [55]


It is used to characterize drought within a homogeneous region. It uses flow duration curve for for discharges that exceed 90% of the time defined as Q90. A

RSDI is computed for each homogeneous region using time series stream flow

Soil moisture deficit index (SMDI)/ [43]


It considers spatial variability of hydrological parameters of soil type, land cover and meteorological variables.

Palmer Modified Drought Index

(PMDI)/ [47]


It is used to calculate the beginning and end time of drought and wet periods which

may otherwise be impossible to detect using the PHDI.

Z-Index /[47]


a monthly standrdized anormally of the available moisture; its an intermediate term

within the oroginal PDSI and used for short term drought forecasting

Effective Drought Index (EDI)/ [3]

Used to detect drought beginning and ending and accumulated stress while ignoring

the effect of runoff and ET

Reclamation Drought Index (RDI)/ [66]


Similar to SWSI and incorporates temperaure-variable and duration into basin-wise

index computation

Crop Water Stress Index (CWSI)/ [21; 24]



The CWSI is calculated from the expression: CWSI 1 where aet is


the actual ET, PET is the potential ET. The index is mainly used for irrigation scheduling

Vegetation Condition Index (VCI)/ [32]


It is used to determine the departure of amount of NDVI from long term NDVI as a

measure of the health of the vegetation

Crop Specific Drought Index (CSDI)/Meyer et al.,



It uses the crop and soil phenology and climatological data to estimate soil water

availability for different zones and soil types

Keetch-Byram Drought Index (KBDI) /[31]


M for analysinsg precipitation and soil moisture in a water budget model. It is used

to monitor forest fires and is for fire control and management

Temperature Condition Index (TCI) /[33]


It uses te brightness temperature to determine the deviation of month from recorded maximum temperature.in general, the highre the temperature the higher the drought.

BT max BT j

It is computed from: TCI 100

BTmax BTmin

Btmax=maximum brightness temperature,Btmin=minimum brightness temperaturebtj=the jth month brightness temperature

Normalized Difference Water Index (NDWI)/ [12]


Used to determine volumetric water content based on physical principles

Vetetaion Health Inde (VHI)/ [33]


It combines vci and tci with some weight factor a for the contribution of vci and tci. The factos amay be set to 0.5 where information is lacking . the following relation applies: VHI a VCI TCI 1 a

Evapo-transpiration Drought Index (ETDI) [43].


It combines the hydrological parameters of soil and land use material in a spatial domain. The ETDI considers water stress ratio as expressed in the form:

ETDI PET AET weekly values reflect short-term droughts


Standardized precipitation index (SPI)/ [64]


Used to fatcor in the impact of cliamte change on drought characteristics which

could not be detected by the original spi. It uses both the precipitation and the temperature data sets. It considers water balance and evapotranspiration

Hybrid Drought Index (HDI)/ [27]



It combines the SPI, PDSI, and SWSI to represent various drought impacts

Vegetation Drought Response Index (VegDRI)/ [4]


Used to characterize specific droughts and combines indices SPI,PDSI and NDVI

Modified Perpendicular Drought Index (MPDI)/ [13]


It is best used to accurately differentiate between the surfaces that are bare soils and densely vegetated agricultural or forest areas. A vegetation fraction that considers soil moisture and vegetation growth is used for undulating topography and variable

soil types, the mpdi performs better than PDI.

Recconnaissance Drought Index (RDI)/ [59]


It uses the precipitation and potential evapotranspiration. It is a more comprehensive DI than SPI. It is a physically based and calculates the deficit between the

evaporative demand of atmosphere and precipitation. It is a simple index in computation of drought.

Normalized Multi-band Drought Index (NMDI)/



It is an index used to improve sensitivity od ndwi and ndii by using inrformation

from nir and swi bands. It extracts both vegetation and soil water content.

Relative Soil Moisture (RSM) Index/[58]


It is a water component calculated from water balance. It considers climate, soil physical properties, potentail ET ,precipitation, and crop charcetristics and

managemnet pracices variables

Agricultural Drought Index /[35]

Uses water balance model and crop transpiration to calculate an integrated transpiration deficit

Rainfall Anormally Index (RAI)/ [62]


An average precipitation based on weekly, monthly and annual time periods is used to characterize relative drought. The resulting drought is then ranked with respect to

fisrt ten severe droughts in a long-term record. The drought is then asigned a magnitude based on resulting values

Drought Severity Index (DSI)/ [5]


Accumulated monthly deficit of precipitation, temperature and ground water

conditions data

National Rainfall Index (NRI)/ [15]


The total annual precipitation agianst its long term average are correlated. It reveals patterns and abnormalities of yearly precipitation on continental scale.

Drought frequency index (DFI)/ [16]


The index uses the mean frequency of of reccurrence for assessing the magnitude of

drought in an area.

Ground water resource index (GRI)/ [37]


A simplified distributed water balance model is applied. It considers river basin geo-physical characteristics that affect hyrological response to precipitation

Water balance drought derived index (Vasiliades et al., 2011)


A water balance model is used to simulate runoff and the index is created by

developed by normalizing and standardizing the generarted runoff to the mean runoff

Vegetation outlook (VegOut)/ [56]


It combines the cliamte information and RS data of current vegetation conditions with oceanic index data and environmental information including land cover type, irrigation status, soils and ecological settings to predict future outlook of vegetation


Ration Vegetation Index (RVI)/ [49]



The index is computed from the relation: RVI where the NIR is near


infra red and R is red bands repsectively

Weighted Diffrence Vegetation Index (WDVI)/ [7]


The index is a function of near infra-red (NIR), rainfall (R) and slope of soil line

as expressed in the relation: WDVI NIR R

Perpendicular Vegetation Index (PVI)/ [50]


this index is calculated for the function: PVI SinaNIR CosaR

where a is the angle between the soil line and the NIR

Difference Vegetation Index (DVI)/ [34; ]; Ray 1994


The index is a function of near infra red band and the rainfall computed from the relation: DVI NIR R

Soil Adjusted Vegetation Index (SAVI)/ [20]


It is a function of near infrared band, red band and soil adjustiment fcator as per the


expression: SAVI NIR R L where L is the soil adjustment factor

to account for soil variation

Infrared Percentage Vegetation Index (IPVI)/ [8]


It is calculated from the following function:



Atmospherically Resistant Vegetation Index (ARVI)/ [30]


It based on atmoshperic variables as per the relatiion:

RB R B R NIR RB where is a correlation parameter


which is optimum at =1

Anormally Vegetation Index (AVI)/ [6]


It uses the annual ndvi to assess vegetation dynamics and surface dryness of land

Cubed Ratio Vegetation Index (CRVI)/ [57]



The CRVI MIR is the landsat-5 thematic mapper mid infrared


Simple Ratio Water Index (SRWI)/ [72]


Uses NDVI applied in MODIS with feedback loops to minimize atmospheric and soil bias that is present in ndvi and other Vis

Vegetation Tempertaure Condition Index (VTCI)/ [65].


Uses NDVI and LST and is computes from:


where LSTNDVIimax and


LSTNDVIimin are the maximum and minimum land surface temperature of the pixels

in the study region and lstndvii is the land surface temperature of the pixel

Effect of Global Warming on Drought

Global warming is caused by two main aspects; first is the climate variability which slows down the global circulation of ocean currents due to moderated differences in temperature between tropical and temperate sea water bodies. Secondly, the ice melting in the Polar Regions implying cold water entering the oceans and drifting into the tropics affect global warming. The ice melting flowing and flowing leads into cooling of tropical oceans whose effect is picking significantly of low moisture by the prevailing winds [23]. The wind takes with it the little moisture piced along its course. Global warming influences the rate and timing of evapo-transpiration. Due to global warming, some regions in the world are likely to get wetter while those that are already under dry conditions likely getting drier. Thus, global warming is likely to increase drought occurrence and expansion of the dry areas [9]. For instance, the regions in southern Africa, the Sahel region of Africa, southern Asia, south west of United States of America have generally been getting drier over the years [10]. In addition, water resources are expected to decline by up to 30% in the affected areas. These notable changes will occur partly because of an expanding atmospheric circulation pattern. This pattern is called Hadley cell in which warm air in the tropics rise, losses moisture to the thunderstorms, and descends in the sub-tropics as dry air. During this process, jet streams shift to the higher latitudes, and storm patterns shift along with them leading to expansion of arid and semi-arid lands [9].

Stochastic drought forecasting models

The term stochastic refers to the spatial and temporal variation of hydrological parameters. Numerous approaches have been developed for analysing stochastic characteristics of time series hydrological variables [18]. Development in forecasting and early warning of the drought phenomena is increasingly being applied in many regions in the world. This is being done to help mitigate consequences of drought on vulnerable river basins. Different drought modeling and forecasting techniques are in use today. Some of the common stochastic drought

forecasting models include; autoregressive integrated moving average model (ARIMA), Adaptive Neuro-fuzzy inference system, Markov chain model, Log-linear model and Artificial Neural Network (ANN) model. Some of the hydrological models may use probabilistic approaches [67].

Autoregressive integrated moving average (ARIMA) model One of the most widely used approaches in stochastic hydrological modelling is the Autoregressive Integrated Moving Average Model (ARIMA). It is used for both the non-seasonal and multiplicative seasonal modelling of hydrological parameters. For a non-seasonal ARIMA modelling, a differencing operator represented in the following equation is used:

1 B

Where is the differencing operator, B is a backward shift operator. For the purpose of seasonal differencing, an operator which is expressed in the following relation is used:

d 1 Bd

Where d is the seasonal differencing operator while d is the differencing parameter which is used to make the series stationary. The complete function of a non-seasonal ARIMA is defined by the relation:

BZt B1 B Z Ba




Where zt is the observed data series, Bis a polynomial of order p, Bis a polynomial of order q, p is a non-

seasonal autoregressive average parameter and q is a moving average parameter.

On the other hand, the multiplicative model that accounts for seasonal time series data with cyclic

characteristics of the order p, d, q P, D,Qis expressed as:




s d D s

p B p B Z Z BQ B a

Where p is a seasonal polynomial of order P, Q a seasonal polynomial of order Q.

Causes of Droughts in Kenyas river basins

Climate variability and global warming that affect atmospheric circulation play a fundamental role in influencing drought occurrences in Kenya. When the Indian Ocean surface water temperature is abnormally low, it leads to the cooling of South-East and North-East trade winds. These two air masses converge near the equator within a region called Inter-Tropical Convergence Zone (ITCZ). When the winds are cool, they do not pick up enough moisture from the ocean water surface and thus lead to erratic rainfall patterns in eastern parts of Kenya [17]. On the western parts of Kenya, the Atlantic and Congo prevailing winds bring the same drought conditions in case they are abnormally too cool to pick sufficient moisture. Other factors leading to increase in drought frequency and severity include poor land use practices and deforestation, destruction of catchment areas. Some of land use patterns combined with certain cultivation methods contributes to the global warming through atmospheric carbon changes. Most farmers in Kenya are not properly controlling on-site soil detachment and therefore most farmland is exposed to soil erosion. The fertile top-soil is

continuously being eroded due to agents of erosion mainly water and wind. Due to the erosion, most of the rain water does not infiltrate into the soil and instead flows as excess runoff. This leads to depletion of soil moisture and plant nutrients.


Destruction of catchment areas and deforestation is another critical contributor of drought in Kenya. Generally the forest cover has decreased by 72% between the years 2000 and 2007 [17] to 6.1% of the total land mass in 2011 [70]. Among the five main water towers in Kenya, the Mau Complex lost the highest with 70% of the forest cover destroyed within the period. The chief causes of deforestation are the need to expand agricultural land, uncontrolled exploitation of forest resources, overgrazing and establishment of new settlements on forest land as accelerated by increasing population pressure.

Description of upper Tana River basin

The upper Tana River basin has an area of 17,420 km2 and is part of the larger Tana River basin, which is the largest river system in Kenya with an area of 100,000 km2 [25; 71] Its forest land resources located along the eastern slopes of Mount Kenya and Aberdares range have a critical role in regulating the hydrology and hydro-power generation within the entire basin [22]. The upper Tana River basin lies between latitudes 000 05' and 010 30' south and

longitudes 360 20' and 370 60' east (Figure 4). The basin is fundamental in influencing the ecosystem downstream.

Fig.- 4: The location and drainage network of the upper Tana River basin

Impacts of Droughts in upper Tana River basin

Drought has impacted in numerous aspects within the past period. It has led to degradation of water resources in terms of quality and quantity. Due to increased evepo- transpiration and reduced runoff as a result of decreased precipitation, the quantity of surface and sub-surface water has declined. The upper Tana River basin has been negatively affected by notable droughts such as the La Niña of 1999 to 2000, and 2008 to 2009 [46]. These led to

severe water scarcity. Concentrations of sediment and chemicals have increased as there is low quantity water that can dilute these substances originating within the basin. Agricultural production and forest resources have reduced since most farming is rain-fed. Due to low stream flow, hydropower generation declined and power rationing especially during specific seasons when drought severity affects water levels in Seven-Folk dams within the basin. The indirect impacts of drought are immense and adversely affect socio-Economic Development within the basin.

Water resources management in upper Tana River basin Water is a critical natural resource which is fundamental for a health ecosystem functioning and socio-economic development. A sound water resources management in the upper Tana River basin is crucial in reduction of poverty, acceleration of socio-economic growth and equitable distribution of resources. Water resources planning in the basin call for planning, development, distribution and optimization in the water resource utilization. Proper water management should focus in optimizing the use of surface and ground water flows for sustainable socio-economic development. All stakeholders should participate in an integrated manner to contribute to the aspects of river basin planning, pollution control, water allocation, monitoring of water flows, financial management and information dissemination.


This article aimed at exploring drought definition, effects of drought, and the available drought indices used in evaluation of drought and gaps in drought studies with a special focus of upper Tana River basin. This gives direction to further studies of drought assessment and with a view to guiding decision making in water resources management. Such information is vital in quantifying water available for different allocations such as hydropower generation, domestic water supply, irrigation and operation of hydraulic structures. It has been noted that comprehensive drought evaluation have been reported for upper Tana River basin and yet it is a key and largest water resource system in Kenya. From the study it is recommended that:

  1. assessment of spatial and temporal drought conditions using available data driven and remote- sensing based drought indices be explored for proper water resources planning and management within the upper Tana River basin

  2. an integrated drought evaluation index with multiple lead times (short, medium and long-term lead times), be formulated based on available hydro-meteorological and remote-sensing data sets to guide in planning mitigation programmes within the basin


  1. Abad, M. B. J., Zade, A. H., Rohina, A., Delbalkish, H. and Mohagher, S. S. (2013).The effect of climate change on flow regime in Basher river using two meteorological and hydrological standards, International Journal of Agriculture and Crop Sciences, 5: 2852- 2857.

  2. Belayneh, A. and Adamowski, J. (2013). Drought forecasting using new machine learning methods, Journal of Water and Land Development,18 (I-IV): 3-12.

  3. Byurn, H. R., Wilhite, D. A. (1999). Objective quantification of drought severity and duration, J. of climatology, 12(9):2747-2756.

  4. Brown, J. F. Wardlow, B. D., Tadesse, T., Hayes, M. J. and Reed, B.

    C. (2008). The vegetation drought response index (VegDRI). A new

    integrated approach for monitoring drought stress in vegetation, geosciences and remote sensing , 45(1): 16-46.

  5. Bryant, S. and Arnell, N. W. and Law, F. M. (1992). The long-term context for the current hydrological drought, institute of water and environmental management conference on management of scarce water resources October 1992 18th-24th

  6. Chen w, xiano q and sheng y 1994. Application of anormally vegetation index to monitoring heavy drought remote sensing . environ. 9(2): 106-112

  7. Clevers, J. G. P. W. (1988). The derivative of simplified reflectance model for the estimation of leaf area index, Remote sens. Environ. 25(1) 53-69

  8. Crippen, R. E. 1990. Calculating the vegetation index remote sensing environ. 34(1): 71-73

  9. Dai, A. (2011). Drought under global warming: A review, WIREs climate change, global circulation, 2: 45-65.

  10. FAO. (2013). Crop evapo-transpiration guidelines for computing crop water requirements; cooperate document repository report.

  11. FAOSTAT. (2000). AQUASTAT Land use database, FAO, Rome.

  12. Gao, B. C. (1996). NDWI, a normalized difference water index for remote sensing of vegetation liquid water from space, Remote sensing environ 58(3): 257-266.

  13. Ghulam, A., Li, Z. L., Qin, Q., and Tong, Q. (2007a). Exploration of the spectral space based on vegetation index and albedo for surface drought estimation, J. Applied remote sensing 1(13529): 1-12.

  14. GoK. (2012). Kenya : adapting to climate variability in Arid and Semi-Arid Lands (KACCAL), project report Wilby, R. L., Orr, H. G., Hedger, M, Forrow D. and Blakmore, M. (2006a). Risks posed by climate variability to delivery of water framework directive objectives. Environ. Int., in press.

  15. Gommes, R. A. and Petrassi, F. (1994). Rainfall variability and drought in sub-saharan Africa since 1960, Rome Italy: 100

  16. Gonzalez, J. and Valdes, J. (2006). New drought frequency index, definitions and evaporative performance analysis, Water resources Res 42(11): w11421

  17. Government of Kenya (GoK) .(2009). Changes in forest cover in Kenyas five water towers report 2000-2007.

  18. Hanson, K. T., Newhouse, M. W and Dettinger, M. D. (2004). A methodology to assess relations between climate variatiability and variation in hydrologic time series in the southwestern united states,

    Journal of hydrology , 287:252-267

  19. Hayes, M. J. (2003). Drought Indices, national drought mitigation centre, university of Nebraska-Lincoln, http://www.drought.unl.edu/whatis/indices.htmspi

  20. Huete a Jackson r and post d 1985. Spatial response of a plant canopy with different soil backgrounds, remote sensing environ 17(1): 37-53

  21. Idso, S, Jackson, R., Pinter, P., Reginato, R., Hatfieldj, J. (1981). Normalizing the stress degree-day parameter for environmental variability, Agricultural meteorology, 24:45-55

  22. IFAD, (2012).Upper Tana Catchment natural resources management projects report east and southern Africa division, project management department.

  23. IPCC (2013). Climate change 2013, fifth assessment report of IPCC, the physical science basis, summary for policy makers.

  24. Jackson, R., Idso, S., Reginato, K. and Pinter, P. (1981). Canopy temperature as a crop water stress indicator. Water Res. Res. 17(4):1133-1138

  25. Jacobs, J. Angerer, J., Vitale, J., Srinivasan, R., Kaitho, J. and Stuth,

    J. (2004). Exploring the Potential Impact of Restoration on Hydrology of the Upper Tana RiverCatchment and Masinga Dam, Kenya, a Draft Report, Texas A & M University.

  26. Jahangir, A. T. M., Sayedur, R. M. and Saadat, A. H. M. (2013). International Journal of Geomatics and Geosciences, 3 (3): 511- 524.

  27. Karamouz, M., Pasaouli, K. and Wzif, S. (2009). Development of a hybrid drought prediction, a case study, J. Hydro Eng. 14(6): 617- 627.

  28. Kayantash, J. A. and Dracup J. A. (2004). An aggregate drought index: assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage. J. Water resources research, 40(9):doi101029/2003wr002610.

  1. Kaufman, Y. J., Tanne, D. (1992). Atmospherically resistant vegetation index for EOS-MODDIS IEEE trans. Geosc, remote sensing, 30(2): 261-270

  2. Keetch, J. J. and Byuram, C. M. (1968). A drought index for forest fire control, Sheville N. C.: 32

  3. Kogan, F. N. (1990). Remote sensing of weather impacts of vegetation in a non-homogeneous areas. Int. Journal of remote sensing, 11(8): 1405-1419

  4. Kogan, F. N. (1995). Application of vegetative index and brightness temperature for drought detection. Adv. Space Res. 15(11): 91-100

  5. Lillesand, T., Kieffer, R. (1987). Remote sensing and image interpretation, John Willey new Conc: 721

  6. Matera, A., Fontana, G. and Marleto, V. (2007). Use of a new agricultural drought index within a regional drought observatory. In methods and tools for drought analysis and management Edited by

    G. Rossi, Springer the Netherlands:103-124

  7. Mckee, T. B., Doesken, N. J. and Kleist, J. (1993). The relationship of drought frequency and duration to time scales in proceedings of 8th conference on applied climatology, Anaheim, California, U.S.A., 179-184.

  8. Mendicino, G., Senatore, A., Versace, P. (2008). Aground water resource index for drought monitoring and forecasting in mediterean climate . J. of hydro. 357 (3-4) 282-302

  9. Modarres, R. (2007). Stream flow drought time series forecasting,

    Journal of Stochastic Environ Res Risk Assess, 21:223-233.

  10. Morid, S. Smakhtin, V. and Bagherzadeh, K. (2007). Drought forecasting using artificial neural networks and networks and time series of drought indices International Journal of climatology, 27 (15): 2103-2111.

  11. Mulla, D. (2013). Twenty five years of remote sensing n precision agriculture: key advances and remaining knowledge gaps, Journal of Biosystems Engineering, 114 (4): 358-371.

  12. Mustafa, M. R. Isa, M. H. and Rezaur, R. B. (2012). Artificial neural networks in Water Resources Engineering; Infrastructure and applications, Journal of World Academy of Science, Engineering and Technology, 6: 2-24.

  13. Mwangi, E., Watterhall, F., Dutra, E., Giuseppe, F. D. and Pappenbenger, F. (2013). Forecasting Droughts in East Africa, Journal of Hydrology and Earth Sciences, 10: 10209-10230.

  14. Narasimham, B. and Srinivasan, R. (2005). Development and evaluation of soil moisture deficit (SMDI) and evapo-traspiration deficit index (ETDI) for agricultural drought monitoring, Journal of agricultural and forest meteorology , 133: 69-88.

  15. NDMC, (2006c). Monitoring drought, the Standardized Precipitation Index Interpretation of SPI maps, national climate data centre

  16. Okoro, B. C., Uzoukwu, R. A. and Chiomezie, N. M. (2014). River basins of Imo State for sustainable water resources management, Journal of Civil and Environmental Engineering, 4(1): 1-8.

  17. Oludhe, C. (2012). Impacts of climate variability on power generation within the 7-forks dams in Tana River basin, Journal of meteorological and related sciences, KMS 10th conference issue 13- 20.

  18. Palmer, W. C. (1965). Meteorological drought research paper 45, weather Bureau, Washington D.C, U.S.A.

  19. Palmer, W. C. (1968). Keeping track of crop moisture conditions nationwide, the new crop moisture index weatherwise 21(4):156-161

  20. Pearson, R. C., and Miller, L. D., (1972). Remote mapping of crop biomass for estimation of productivity of short grass prairie, Remote sensing environ. 8(1): 1355

  21. Richardson, A. J., Wiegand, C. L. (1977). Distinguishing vegetation from soil background information, photogram Eng. Remote sensing 43: 1541-1552

  22. Sayanjali, M. and Nabdel, O. (2013). Remote sensing satellite design using model based systems Engineering, Journal of Science and Engineering,1(1): 43-54.

  23. Scheffran, J., Marmer, E., and Show, P. (2012). Migration as a contribution to resilience and innovation in climate adaptation: social networks and co-development in North West Africa, applied geography,33: 119-127.

  24. Shafer, B. A. and Desman, L. E. (1982). Development of a Surface Water Supply Index (SWSI) to assess drought conditions in snowpack Runoff Areas, proceedings of the Western snow conference Reno, Nevada, U.S.A.,164-175.

  25. Sharda, V., Srivasta, P., Kalin L., Ingram K., and Chelliah, M. (2012). Development of community water deficit index (CWDI) – Drought forecasting tool for small to mid-size communities of south eastern united state, Journal of Hydrologic Engineering,18(7): 846- 858.

  26. Stahl, K. (2001). Hydrological drought, a study across Europe, Freiburgo.

  27. Tadesse, T., Wardlow, B. (2007). The vegetation outlook, a new tool for providing outlooks of general vegetation data mining tech, In IEEE Int. Conference on data mining workshops :667-672

  28. Thenkabail, P. S., Ward, A. D. and Lyon, J. G. (1994). Landsat 5 thematic mapper models for soybean and corn characteristics. Int. J. of remote Sens. 15(1): 49-61

  29. Thornwaite, C. W. and Mather, J. R. (1995). The water balance

    climatology 8:1-104

  30. Tsakiris, G. and Vangelis, H. (2005). Establishing a drought index incoproprating evapo-transpiration, European water J. 9(10):3-11

  31. UN. (2008). Trends in sustainable development, agriculture, rural development, land desertification and drought. Department of economic and social affairs, United Nations, New York.

  32. UNDP. (2012). Kenya : adapting to climate variability in Arid and Semi-Arid Lands (KACCAL), project report Wilby, R. L., Orr, H. G., Hedger, M, Forrow D. and Blakmore, M. (2006a). Risks posed by climate variability to delivery of water framework directive objectives. Environ. Int., in press.

  33. Van-rooy, M. P (1965). A Rainfall Anormally Index (RAI), independent of the time and space, Notos 14: 43-48

  34. Vasiliades, L. Loukas, A. and Liberis, N. (2011). A water balance derived drought index for Pincos River basin, Greece, J. Water resources management, 25(4): 1087-1101

  35. Vicente-Serano, S. M., Begneria, S. and Lopez-Moreno, J. I. (2010). A multi-scalar drought index sensitive to global warming, the standardized precipitation evapotranspiration index, J. of clim. 23(7)1696-1711

  36. Wang, L. and Qu, J. J. (2007) NMDI: A normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote Sensing, J. Geophys Res. Lett, 34(20):L20405.

  37. Weghorst, K. (1996). The reclamation drought index, guidelines and practical applications, ASCE, Denver Colorado.

  38. Wilby, R. L. and Harris 2006. A framework for assessing uncertainities in climate change impacts, low flow scenarios for the river thames, uk, water resources research 42: w2419

  39. Wilhite, D. A. (2004). Drought as a natural hazard, in international perspectives on natural disasters ; occurrence, mitigation, and concequencies, edited by J. P. Stollman, J. Lidson and L. M.

    Dechano, Kluwer academic publishers, the Netherlands: 147-162

  40. Wilhite, P. A., Clantz, M. (1955). Understanding the drought phenomena, the role of definitions, Water Int. 10(3)111-120

  41. World Bank (2014). Report on climate variability and water resources degradation in Kenya; improving water resources development and management, Report No. 69, http://wdronline.worldbank.org/worldbank/p/developmentdatabase

  42. WRMA, (2010). Physiological survey in the upper Tana river catchment, a natural resources project report, Nairobi.

  43. Zarco-tejada, P. J. and Ustin, S. L. (2001). Modelling canopy water content for carbon estimates form MODDIS data at land EOS validation sites in geosciences and RS symbosium, 1: 342-344

  44. Zoljoodi, M. and Didevarasl, A. (2013). Evaluation of spatio- temporal variability of droughts in Iran using Palmer Drought Severity Index and its precipitation factors through (1951-2005), Atmosphere and Climate Sciences.

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