Estimating Aquifer Transmissivity based on Streamflow ...

Master’s thesisPhysical Geography and Quaternary Geology, 45 Credits

Department of Physical Geography

Estimating Aquifer Transmissivity based on Streamflow Records

using an Analytical Approach in Kilombero Valley, Tanzania

Jamila Tuwa

NKA 1472016

Preface

This Master’s thesis is Jamila Tuwa’s degree project in Physical Geography and Quaternary

Geology at the Department of Physical Geography, Stockholm University. The Master’s

thesis comprises 45 credits (one and a half term of full-time studies).

Supervisor has been Steve Lyon at the Department of Physical Geography, Stockholm

University. Examiner has been Jerker Jarsjö at the Department of Physical Geography,

Stockholm University.

The author is responsible for the contents of this thesis.

Stockholm, 7 June 2016

Steffen Holzkämper

Director of studies

ii

Kilombero Valley, Tanzania

Abstract

Streamflow recession techniques have been useful in estimating aquifer hydraulic properties

that are not easily measured, but are important for the hydrologic responses of catchments.

Compared to other hydraulic properties, aquifer transmissivity (T) is a key parameter

providing insight on groundwater development at a local and regional scale. However, field-

based measurements of T, are often scarce and uncertain. This study aims to use an analytical

approach adopted by Huang et al., (2011) to quantify aquifer T based on streamflow records

in Tanzania‘s Kilombero Valley. The study also considers T derived from groundwater

pumping test data from selected wells located within the Kilombero Valley for comparison.

The programs RECESS and RORA were applied in this study to simplify and minimize

subjectivity inherent in using manual methods associated with recession analysis. Based on

the analysis presented, seasonal variability of T indicated dry season recession events

generated higher T values than wet season events. This variability could be accounted for

when considering the distribution of soil properties relative to water table positions during

wet and dry seasons. Furthermore, the average basin slope contributed to the spatial

variability of T estimates in the valley, as the 1KB14 catchment with steep slopes showed

higher T values compared to the gentle slopes 1KB4 catchment. Interestingly, no significant

relation was found between the elevations of individual well locations and T derived from

pump test data.

By comparison, the T values derived from large-scale streamflow records underestimate

those derived from small-scale field-based measurements. It is however necessary to note

that, thicker aquifers (greater or equal to 30m) gave reasonable T estimates that moderately

agree with streamflow derived T. The differences observed in T estimates suggests

difficulties when comparing the analytical approach with field-based data in the Kilombero

Valley. From a hydrological modeling perspective, the reasons for disparities between two

methods could come from: high heterogeneity of aquifer within the valley, difficulty and

limited number of small-scale measurements, limitation inherent in the modeling approach in

the valley, improper quality of data, seasonality and scale of measurements. In general, as the

analytical approach applied was typically inconsistent with field-based measurements of

aquifer T, this study can be considered as a basis for future work(s). Further, the study

recommends multiple approaches should be considered to provide reliable theoretical T

values in comparison with field-based measurements data.

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Table of Contents

Abstract ........................................................................................................................................... ii

Table of Contents .......................................................................................................................... iii

List of Figures ............................................................................................................................... iv

List of Tables .................................................................................................................................. v

Acknowledgments ....................................................................................................................... vii

1. INTRODUCTION ......................................................................................................................1

1.1. Background information ............................................................................................................ 1

1.2. Main Objective ............................................................................................................................ 3

2. THEORY OF THE ANALYTICAL APPROACH ......................................................................3

2.1. Rorabaugh’s instantaneous recharge theory ......................................................................... 3

2.2. Master Recession Curve (MRC) and Recession index (K) ................................................. 4

2.3. Recession curve-displacement method .................................................................................. 6

3. METHODS ................................................................................................................................7

3.1. Study area description ............................................................................................................... 7

3.1.1. Location ............................................................................................................................... 7

3.1.2. Climate and Hydrology ...................................................................................................... 8

3.1.3. Geology and Hydrogeology .............................................................................................. 9

3.2. Dataset ......................................................................................................................................... 9

3.2.1. Hydrology data .................................................................................................................... 9

3.2.2. Groundwater data ............................................................................................................... 9

3.2.3. Spatial dataset .................................................................................................................... 9

3.3. Procedures for streamflow derived T .................................................................................... 10

3.3.1. Determination of K and MRC .......................................................................................... 10

3.3.2. Estimation of mean aquifer T value ............................................................................... 10

3.4. Pump test Analysis ................................................................................................................... 11

4. RESULTS ............................................................................................................................... 12

4.1. Recession index (K) estimates ............................................................................................... 12

4.2. Master Recession Curve (MRC) ............................................................................................ 14

4.3. T based on streamflow records .............................................................................................. 15

4.4. T based on pump test data analysis ...................................................................................... 17

4.5. Comparison of streamflow derived T and pump test derived T ........................................ 21

5. DISCUSSION ......................................................................................................................... 24

5.1. Seasonal and annual variabilities of T within the Kilombero Valley ................................. 24

5.2. Spatial variability of aquifer T within the Kilombero Valley ................................................ 25

5.3. Comparison of streamflow derived T and pump test derived T ........................................ 26

5.4. Comparison with other study findings ................................................................................... 26

5.5. Uncertainties associated with T estimation .......................................................................... 27

6. CONCLUSION ........................................................................................................................ 28

References .................................................................................................................................... 30

Appendix ....................................................................................................................................... 34

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List of Figures

Figure 1: Definition sketch for Rorabaugh’s equation. A modified from Rorabaugh, 1964........................ 3

Figure 2: Schematic representation of the procedure applied to derive the MRC (A modified sketch

from Rutledge, 2007) ............................................................................................................................ 5

Figure 3: Procedure for use the recession-curve displacement method. A modified sketch from

Bevans, 1986 ......................................................................................................................................... 6

Figure 4: Study Map showing Kilombero Valley located in southern central of Tanzania ......................... 8

Figure 5: A sketch showing a summary of the procedures for streamflow derived T ................................ 11

Figure 6: K values in 1KB14 catchment estimated using wet events (w), dry events (s) and all events

(n) streamflow records from 1960 to 1982 in RECESS program. The best fit linear equation for K

versus LOGQ in each plot is also indicated........................................................................................ 13



versus LOGQ in each plot is also indicated........................................................................................ 14

Figure 8: The MRC of streamflow hydrograph computed from RECESS program whereby (a) and (b)

represents 1KB14 and 1KB4 catchments respectively. The y-axis is presented in logarithmic

scale ........................................................................................................ Error! Bookmark not defined.

Figure 9: T (m2/min) estimates based on median K value analyzed from streamflow recession in 1KB4

and 1KB14 catchments: K(n) means recession index of all events, K(w) - wet events and K(s) -

dry events. The y-axis is presented on linear scale ................................. Error! Bookmark not defined.

Figure 10: T (m2/min) estimates based on median K value analyzed from streamflow recession in

1KB4 and 1KB14 catchments: K(n) means recession index of all events, K(w) - wet events and

K(s) - dry events. The y-axis is presented on logarithmic scale .............. Error! Bookmark not defined.

Figure 11: Pump test derived T across Kilombero valley particularly located in1KB17 Catchment. (b)

A plot shows relationship between pump test derived T and Elevation in linear scale. A blue

rectangular block indicates 35 selected wells considered in this study. . Error! Bookmark not defined.

Figure 12: T values related to elevation (a), hydraulic conductivity (b), aquifer thickness (c) and

borehole depth (d) within Kilombero valley by considering 35 selected wells located at 1KB17

Catchment. The y-axis is presented on linear scale. ............................... Error! Bookmark not defined.

Figure 13: T analysis based on elevation, aquifer thickness, depth and hydraulic conductivity

intervals within Kilombero Valley. The y-axis is presented in logarithmic scale. .............................. 20

Figure 14: Maps show spatial distribution of selected wells in Kilombero valley based on aquifer

transmissivity (m2/min), aquifer thickness (m), hydraulic conductivity (cm/sec), borehole depth

(m) and elevation (m) .............................................................................. Error! Bookmark not defined.

Figure 15: Pump test derived T shows a gap of T values based on elevation (a), hydraulic

conductivity (b), aquifer thickness (c) and borehole depth (d) within Kilombero Valley. A gap is

illustrated by a red line while a trend line is indicated by a dot line. ..... Error! Bookmark not defined.

Figure 16: Comparison of T estimates with aquifer thickness greater than or equal (≥) to 30m and

less than (<) 30m using pump test data derived T in 1KB17 catchment. Error! Bookmark not defined.

Figure 17: Comparison of T estimates using aquifer thickness ≥ 30m with streamflow derived T in dry

season, based on K(n) in 1KB4 and 1KB14 catchments. .................................................................... 24

Figure A- 1: The MRC of streamflow hydrograph computed from RECESS program whereby (a) and

(b) represents 1KB14 and 1KB4 catchments respectively. Both axes are presented in linear scale .. 35

v Kilombero Valley, Tanzania

List of Tables

Table 1: Catchments characteristics within Kilombero Valley .................................................................. 10

Table 2: Wells within Kilombero Valley .................................................................................................... 11

Table 3: Summary of K (days) values estimated in this study. Wet season represents December

through April, dry season means May to November and all events stands for a whole year,

January to December. ......................................................................................................................... 12

Table 4: T (m2/min) based on median K values analyzed from streamflow recession. T values are

computed by considering storm events, base flow and recharge events automated from RORA

program............................................................................................................................................... 15

Table 5: Comparison of streamflow derived T and pump test derived T ................................................... 22

Table A- 1: Details of wells within Kilombero Valley ............................................................................... 34

Table A- 2: Summary of Aquifer transmissivity of 35 wells based on borehole characteristics ................ 35

vi Kilombero Valley, Tanzania

vii


Acknowledgments

First and foremost, I would like to thank the Swedish Institute Study Scholarships (SI) for

funding my studies during the whole period of my study programme at Stockholm

University.

Also, I sincerely thank Steve Lyon, my main supervisor, and the head of Master‘s programme

in Hydrology, Hydrogeology, and Water Resources at the Department of Physical Geography

and Quaternary Geology. He provided very useful guidance and overall fruitful supervision

of my work. His general outline of this work, recommendations and feedback were great.

I extend my appreciation to Alexander Koutsouris, assistant supervisor, for data provision

and tremendous contributions to the introduction of this exciting topic.

Above all, I must express my very profound gratitude to my dearest father ―Mzee‖ Tuwa

Bakari, my brothers and sisters at home (Tanzania), and to my dear friend Dr. Mohammed for

providing me with unfailing support and continuous encouragement throughout my years of

study and through the process of writing this thesis. This accomplishment would not have

been possible without them.

Finally, I thank Allah, may He be glorified and exalted for the good health and patience

throughout my study and great favours and blessings that He has bestowed upon me

throughout my life. Alhamdulillah!

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1. INTRODUCTION

1.1. Background information

Besides the difficulties created by long-term climate change reducing the global amount of

natural water resources (Xu and Singh, 2004), increases in human population and correspond

water demand will bring about high demand of water resources in the various sectors

worldwide (Vörösmarty et al., 2000). This places extra pressure on groundwater resources

which are often considered relative climate resilient. In fact, as more than 1.5 billion people

globally rely on groundwater for various purposes (Alley et al., 2008), there is increased

global awareness and desire for assessing accurate ground water information in the last

decade (Soupios et al., 2007).

In developing countries like Tanzania, for example, insufficient provision of water supplies

raises the demand of groundwater utilization. Specifically, 30% of the total population and

42% of the population living in major cities depend on groundwater resource (Mato, 2002).

Access to water supplies in the villages and districts of Tanzania remains a big challenge, as

it requires great investments in initiating new water sources, modification and expansion of

water supply networks (URT, 2010). With respect to the Kilombero Valley in Tanzania,

where the projections of population growth and domestic water demand by 2035 are expected

to be the highest in the Rufiji Basin (WREM, 2013a), groundwater resources are used as an

additional source of water supply, and seen as a potential climate-stable future commodity.

Despite its potential usefulness, extensive studies on groundwater resources in the basin have

not been conducted, and therefore, it is difficult to obtain precise information on the basin‘s

water resources (Kashaigili, 2010; RBWO, 2010).

It is well known that the scarcity and inconvenient quality of data are the key sources

hampering modeling and management of water resources in developing countries (Brunner et

al., 2007; MacDonald et al., 2013; Van Camp et al., 2013; Singh, 2014). In such a way,

inadequate information, particularly hydraulic data often limit development of water

resources structures (Mendoza et al., 2003). Likewise, lack of extensive hydraulic data

reduces the capacity of the modelers to check for the precision of either semi or full

distributed hydrologic parameter models which need other data excluding streamflow.

Consequently, transmissivity and hydraulic conductivity are derived through model

calibrations (Brooks et al., 2004). Kashaigili (2012) showed that the quantity and quality of

groundwater data are the main challenges facing groundwater management in Tanzania. A

recent study by Lyon et al., (2014) also pointed out issues of data scarcity and quality in the

Kilombero Valley. Similarly, a Rufiji Basin report indicated problems of groundwater data

availability and insufficient aquifer information in the basin (RBWO, 2010). Moreover, as

requirement of drilling one borehole in Tanzania costs nearly 6000 US dollars (Baumann et

al., 2005), such observational techniques are expensive, and could be one of the reasons that

led to scanty of groundwater and aquifer information in the Kilombero Valley.

Traditionally, in hydrology, groundwater and surface water flows are considered to be related

(Alley et al., 2008; Dingman, 2008). According to Tallaksen (1995), streamflow has been

widely utilized in early studies due to its inherent connection with subsurface flow. This in

turn has led to advances in techniques using observations in surface water flows to estimate

groundwater factors. Under such conditions, streamflow recession techniques have been

useful in estimating aquifer hydraulic properties that are not easily measured, but are

important in the hydrologic responses of the catchments (Vannier et al., 2014; Oyarzún et al.,

2


2014). Owing to that usefulness, numerous studies have applied recession techniques to

acquire groundwater and aquifer information. For example, Szilagyi et al., (1998) examined

recession flow to determine catchment-scale saturated hydraulic conductivity and average

aquifer depth. Mendoza et al., (2003) estimated basin-hydraulic parameters (transmissivity

and specific yield) of a semi-arid mountainous watershed based on recession flow analysis.

Huang et al., (2011) measured aquifer transmissivity in the Kaoping River basin based on

streamflow records. More recently, Lyon et al., (2014) determined characteristic drainage

timescale variability (storage coefficient) across the Kilombero Valley based on streamflow

recession. Vannier et al., (2014) estimated catchment-scale storage capacity and hydraulic

conductivity by means of streamflow recession analysis. Similarly, the arid basins study

conducted by Oyarzún et al., (2014) focused on recession flow analysis as a tool for

determining hydrogeological parameter such as aquifer thickness, hydraulic conductivity and

porosity.

As described by Tallaksen (1995), recession flow or low flow responses mean the part of

surface flow originating from groundwater or other delayed sources. Various techniques have

been employed in analyzing streamflow recession, and notable among them is the recession

curve displacement method developed by Rorabaugh (1964), commonly known as the

Rorabaugh Method (Rutledge 1998, 2007; Huang et al., 2011). The approach has been

manually used in several early studies (Rorabaugh, 1964; Daniel, 1976; Bevans, 1986).

However, the development of computers has enabled the researchers to automate the

procedures in objective way (Tallaksen, 1995). A recent approach proposed by Rutledge

(1993) based on the computer programs RECESS and RORA helped to simplify and

minimize subjectivity inherent in using manual methods (Rutledge, 2000; Yeh et al., 2015).

The program RECESS provides the master recession curve of streamflow recession data

while the program RORA automates the procedures of recession curve displacement to

estimate recharge for each storm event (Rutledge 1998, 2000). Among few examples of

researchers who applied the RECESS and RORA were Halford and Mayer (2000), Delin et

al., (2007), Huang et al., (2011), and Abo et al., (2015).

In general, multiple approaches such as stream-discharge recessions, groundwater

hydrograph recessions and aquifer test can be used to estimate hydraulic properties of the

aquifer (Halford and Mayer, 2000). What is typically required is full understanding of the

distributions of hydraulic parameters such as aquifer transmissivity to solve problems

concerning hydrogeology and related fields (Leven and Dietrich, 2006). By definition,

transmissivity (T) of an aquifer is described as the ability of an aquifer to transmit water with

the prevailing kinematic viscosity. It is mathematically equivalent to hydraulic conductivity

of the aquifer times saturated thickness of the aquifer (Heath, 1983). Compared to other

hydraulic properties, T is a key parameter in obtaining significant information in sub-surface

flow and contaminant transport modeling (Soupios et al., 2007). However, field-based

measurements of T (made using the pumping test) are often scarce and uncertain, with

variability of observed values characterized by numerous orders of magnitude compared to

other parameters (Jankovic et al., 2006; Mendoza et al., 2003).

In this regard, analytical approaches based on streamflow records to estimate the hydraulic

parameters offer a cost effective alternative approach due to high expenses of drilling

activities, particularly in developing countries (Zecharias and Brutseart 1988; Mendoza et al.,

2003). This is especially true in understanding sub-surface flow in the Kilombero Valley

given its large scale, and limited number of direct observations. In addition, it is applicable to

the Kilombero sub basin because, as stated by Huang et al., (2011), it is a useful approach

for basins where wells data are limited.

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1.2. Main Objective

Since the worth of an aquifer as a source of ground water depends mainly on its ability to

store and transmit water (Heath, 1983), this study aims to use an analytical approach adopted

by Huang et al., (2011) to determine aquifer transmissivity based on streamflow records in

Tanzania‘s Kilombero Valley. The study also considers pumping test data from selected

wells located within Kilombero for comparison. The T estimates derived from pumping test

and streamflow records are key parameters to provide insight about groundwater

development at a local and regional scale, respectively (Huang et al., 2011). Towards this

aim, the main study question is: What is aquifer transmissivity T in the Kilombero Valley?

Therefore, in order to answer a basic question, this study will investigate the following:

Seasonal variability of aquifer T within the Kilombero Valley

Spatial variability of aquifer T within the Kilombero

Comparison of large-scale streamflow derived T and small-scale pump tests derived T

Uncertainties associated with T estimation

2. THEORY OF THE ANALYTICAL APPROACH

Huang et al., (2011) applied an analytical approach derived from Rorabaugh theory (1964) to

estimate aquifer transmissivity based on streamflow hydrograph records in southern Taiwan.

The analytical approach was developed by combining instantaneous recharge theory, master

recession curve (MRC) and the recession-curve-displacement method. Since data scarcity is a

major problem facing Kilombero Valley (Lyon et al. 2014), Rorabaugh method was adopted

in this study to derive the aquifer parameter T from streamflow records and compared with T

derived from pumping test data.

2.1. Rorabaugh’s instantaneous recharge theory

Figure 1: Definition sketch for Rorabaugh’s equation. A modified from Rorabaugh, 1964

The basic assumption of Rorabaugh (1964) as an instantaneous recharge theory is based on a

uniform aquifer thickness, hydraulic conductivity, storage coefficient, and aquifer fully

penetrated by a stream (Figure 1). The initial condition of the theory is that the hydraulic

head is homogeneous with equal condition of hydraulic properties as is the stage of the

stream. The theory considers no gains or losses of water to or from the system. The recharge

is defined as a rapid increase in hydraulic head applied evenly throughout the aquifer while

4


stream stage remains constant. In addition, increase in hydraulic head due to recharge events

result in subsequent ground water discharge to the stream. The resulting ground water

discharge to the stream (q) which is commonly known as instantaneous recharge theoretical

model (Huang et al., 2011) is expressed in Eq. 1.

( )(

)

Eq. 1

Where q (m2) is the groundwater discharge (m

3) per unit stream length of one side (m), T

(m2/min) is aquifer transmissivity, (m) means instantaneous rise in water table, t (min) is

the time taken after h0, a (m) is the distance from stream to the groundwater divide and S is

aquifer storativity.

Daniel (1976); Johnston (1976) and Stricker (1983) expressed a quantity L as shown in Eq. 2

Eq. 2

Where L (m) is the length of the stream and A (m2) is drainage area of the basin. Further, a

constant C1 is computed using Eq. 3 where Q (m3) is total groundwater discharge.

Eq. 3

The relationship between T (m2/min) and S of the aquifer shown by exponential function

equation (Eq. 4), is the basis of the current study.

Eq. 4

By substituting Eq. 4 in the Rorabaugh and Simons (1966) baseflow model (Eq. 5), the T

value of the aquifer is estimated (Eq. 6).

( )

Eq. 5

Eq. 6

Where (m3) is a streamflow at the peak estimated during a time when the surface runoff

recession has started and (m3) is the baseflow recession (Huang et al., 2011).

2.2. Master Recession Curve (MRC) and Recession index (K)

Rutledge (1998) highlighted steps to develop a streamflow MRC and for the determination of

K using the program RECESS. The RECESS as a computerized program is based on

interactive procedures to calculate a best fit linear equation in order to obtain the MRC and

value of K. Despite the fact that, the program selects periods of continuous streamflow

5


recession, the nearly linear segment selection is subjective. In the sense that, the program user

specifies which segments are sufficiently linear on the semi-log plot, for example, to be

analyzed. Once finished the selection of a segment, RECESS provides a mathematical

expression in the form as shown below:

Where t represents time (days), LogQ means the logarithm of the flow (cfs), K1 and K2 are the

coefficients derived by linear regression (Rutledge, 2007).

From that expression, the actual value of K (days/log cycle) is determined, and is clearly

represented by K1. This step is a repetitive for each recession segment selected in a period of

interest until adequate numbers of segments are attained, followed by MRC mathematical

derivations as indicated by a polynomial expression below.

( ) ( )

Where A, B and C represent the coefficients of a polynomial equation that are applied to

derive the MRC (Rutledge, 2007). A summary of the procedure is presented (Figure 2).

Therefore, K is simply defined as the period needed for groundwater discharge to decline

through one log cycle just after critical time is reached (Rutledge, 1998).

Figure 2: Schematic representation of the procedure applied to derive the MRC (A modified sketch

from Rutledge, 2007)

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2.3. Recession curve-displacement method

Figure 3: Procedure for use the recession-curve displacement method. A modified sketch from

Bevans, 1986

According to Rutledge (1998), the procedures for recession curve-displacement method

(Figure 3) were developed by Bevans (1986) to estimate the total volume of ground water

recharge in a basin Q (cubic feet) during a single recharge event (Eq. 7). The method requires

K (day), critical time after a streamflow peak (day) (Eq. 8), and groundwater discharge at a

critical time extrapolated from pre-event (cfs) and post-event streamflow recessions

(cfs).

( )

Eq. 7

Eq. 8

Linsley et al., (1982) estimated the time of surface runoff (N) as a function of the drainage

area (A) using Eq. 9. The time of surface runoff is described as the time from a peak in

streamflow at the time when surface runoff ceases (Rutledge, 1997).

7


Eq. 9

Where represents number of days after the peak in streamflow (rounded to the next larger

integer), and A means drainage area of the basin in square miles. These procedures are

automated using the RORA program (Rutledge, 2007), and calculated before the parameter T

is estimated (Huang et al., 2011). Eq. 9 which represents the requirement of antecedent

recession is considered as the basis for the applicability of RORA to a specific hydrologic

system (Rutledge, 2000). The program RORA assumes recharge events and streamflow peaks

are concurrent (Rutledge, 1998). On top of that, the RORA neglects explicitly the effects of

groundwater evapotranspiration (Rutledge, 2000).

3. METHODS

3.1. Study area description

3.1.1. Location

The Kilombero Valley (Figure 4) is geographically located between latitude 34°33′E and

37°20′E, and between longitude 7°39′S and 10°01′S (Lyon et al., 2014), south-central of

Tanzania. The valley lies on the lowland areas of the Rufiji Basin (RBWO, 2010). It is the

one among four sub-basin of the Rufiji River Basin, which encompasses 20% drainage area

and contributes 62% of total Rufiji Basin runoff (Mwalyosi, 1990). Its topographical

depression feature is estimated to range from the south-west to north-east of the valley. In the

north-west, Udzungwa Mountains rise abruptly to an elevation of 2000 m while in the south-

east, the ridge drops after several miles to form the steep Mahenge Mountain block which has

an average elevation between 900 m and 1500 m (Bonarius, 1975). These features create the

main catchment area, and thus, they are important to the hydrology of the valley (RBWO,

2010; WREM, 2013b). The stream monitoring point at 1KB17 thus defines the catchment

that covers nearly all of the Kilombero Valley area including the Kibasila wetland (WREM,

2013b).

8


Figure 4: Study Map showing Kilombero Valley located in southern central of Tanzania

3.1.2. Climate and Hydrology

The climate in the Kilombero Valley varies spatially between the highlands and lowlands,

with hot and humid conditions of average annual temperature 24 °C in the lowlands, and

cooler and wetter conditions of average annual temperature 17 °C in the highlands of the

valley. The warmest months are observed in December and January, when the daily

temperature exceeds 27 °C in the lowlands and 19 °C in the highlands. July is solely

considered as the coolest month with temperature estimated to be 21 °C in the lowlands and

14 °C in the highlands. The annual precipitation varies between 1200 mm and 1400 mm in

the valley bottom lowlands, and between 1500 mm and 2100 mm in the highlands (RBWO,

2010). In addition, the Kilombero Valley is highly exposed to moisture‐laden winds (RBWO,

2010). This brings about a period of wet season (high flows) considered to occur between

December and April, followed by a dry season (low flows) which runs from May to

November (Lyon et al., 2014). Actually, the valley is characterized by a unimodal type of

rainfall (RIS, 2002). Streamflow peaks typically occur between March and April (Bonarius,

1975). Most Kilombero rivers flow are perennial with few depending solely on rainy season

(RBWO, 2010). Apart from that, the presence of the largest freshwater floodplain in East

9


Africa contributes highly to the hydrology of the valley as it generates 2/3 of the annual

Rufiji river flow. The floodplain is considered as part of the Great Selous Ecosystem, an

important World Heritage Site (RIS, 2002).

3.1.3. Geology and Hydrogeology

The Kilombero Valley itself is a geologic feature resulted from tectonic movements which

cause folding, faulting and uplift of the earth‘s surface. According to Tanzania geology

settings, the valley is located within the Usagaran system of the Precambrian basement

complex rocks, an asymmetrical rift valley depression caused by Pliocene faultings. The

rocks composed in this system mostly are dominated with magmatic gneiss and acidic

granulites. The gneiss, rich in biotite is very easily weathered to fine grits and coarse sands;

the granulites are characterized by disintegration to stony material and fine grits. The parent

material consists of loamy gravelly in the transitional stage followed by loamy sand and

sandy loam soils (Bonarius, 1975; RBWO, 2010).

Groundwater recharge takes place at high altitude, mainly from rainwater infiltration

(Kashaigili, 2010) with smaller amount from rivers and lakes. The recharge is basically

controlled by climate, geology and geomorphology (RBWO, 2010). The infiltration

capacities in the Kilombero Valley are typically attributed to the presence of alluvial fans

(coarsely grained sands) and colluvial sand-flats or miombo plains soil types. Rapid to very

rapid infiltration rates with sufficient soil moisture availability occur in alluvial fans on the

valley bottom. The miombo plains allows very high infiltration rates with minimum soil

moisture availability (Bonarius, 1975).

3.2. Dataset

3.2.1. Hydrology data

The long-term daily streamflow and gage height data from 1KB4 and 1KB14 catchments

were considered in this study (Figure 4). These time series data that span 23 consecutive

years from 1960 to 1982 were derived from Rufiji Basin Water Board (RBWO). The data

quality was not good in general, as there were mismatch of peaks between gauge heights and

streamflow records in some periods. Also intermittents gaps in gage height records were

observed in 1KB4 catchment from 1973 to 1982.

3.2.2. Groundwater data

Since this study aims at comparison of T value estimates, pumping well data available in

1KB17 catchment were included (Figure 4). These data were already evaluated using the

Cooper–Jacob technique in which hydraulic parameters such as hydraulic conductivity and

transmissivity are estimated. Other information such as aquifer type, aquifer thickness and

borehole depth were also provided. The pump test activities were conducted in the dry season

between June and September, 2014.

3.2.3. Spatial dataset

A spatial dataset corresponding to basin drainage areas and wells locations derived from

RBWO was also considered. The general characteristics of catchments included in this study

are summarized in Table 1.

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Table 1: Catchments characteristics within Kilombero Valley

Catchment ID 1KB17 1KB4 1KB14

River Kilombero Kilombero Lumemo

Location Swero Ifwema Kiburubutu

Topographic Area (km2) 34230 18048 580

Stream length (km) 5916 2859 63

Average slope (%) 7 8 14

Landform Valley and alluvial plain (%) 22 8 0

Hill and mountain (%) 78 92 100

Source:Lyon et.al (2014)

3.3. Procedures for streamflow derived T

3.3.1. Determination of K and MRC

K was estimated from the program RECESS using all streamflow data (n) and the data were

divided into, wet (w) and dry (s) seasons. Streamflow (cfs) from 1960 to 1982 was

considered as the model input data required to generate the recession index and a master

recession curve in 1KB4 and 1B14 catchments. Initially, recession segments of streamflow

records were selected based on recession length and initial discharge. The segment selections

depended solely on days of antecedent recession (Eq. 9) which also represents groundwater

discharge (Rutledge, 2007). High variability among recession segments were observed, since

segment selection is subjective (Tallaksen, 1994). Therefore, a specific range regarding

segment lengths was defined in order to be consistent. Similarly, the RECESS program

produced a curve data as an output file whereby the MRC was created based on wet, dry and

all streamflow events for each catchment.

3.3.2. Estimation of mean aquifer T value

In order to automate recession curve displacement procedures (Figure 3) using the

computerized RORA program, the median K estimated from the program RECESS together

with streamflow records (cfs) and catchment area (sq.mile) were applied as model input

variables. The program RORA determined values of Qt, Q0, Q1 and Q2. Recharge events Q in

each storm, days of antecedent recession N, and critical time tc were also automated in the

program RORA using Eq. 6, 8 and 9, respectively. Nevertheless, not all events were used for

analysis. The most representative events were selected, and those events which did not meet

the specified conditions (predefined N, tc) were ignored. After having all necessary variables

from the program RORA, the next step was to quantify T value (Eq. 6). A manual procedure

to determine constant C1 was used by considering an aquifer half-width a calculated using

Eq. 2, h0 calculated from daily mean stream water table and recharge events Q. Finally, T

values were estimated as the product of C1 and Q0 divide by Qt. A summary of these

procedures are shown on the sketch below (Figure 5).

11


Figure 5: A sketch showing a summary of the procedures for streamflow derived T

3.4. Pump test Analysis

According to Jankovic et al., (2006), it is important to have knowledge of the spatial

distribution of aquifer parameter T. As observed in Figure 4, the spatial distributions of wells

considered in Kilombero Valley were not uniform. Therefore, in order to understand the

behavior of these wells, 35 among 41 wells that spatially concentrated together (Figure 4,

Table 2) were selected for detailed analysis. The analysis was done based on T in relation to

aquifer thickness, hydraulic conductivity, borehole depth and elevation. In addition, values of

T, hydraulic conductivity, aquifer thickness, borehole depth and elevation were interpolated

in arcGIS using inverse distance weighted method and presented as maps to observe local

variations in spatial scale. Furthermore, the study investigates T derived from aquifer

thickness greater or equal to 30m and compared with streamflow derived T as there is

potential for thicker aquifer to be more ‗‗stable‘‘ for estimating T. In addition, thicker

aquifers are characterized by T values that potentially integrate more soil layers. The wells‘

details are attached in Table A- 1 (see Appendix).

Table 2: Wells within Kilombero Valley

Well ID Selected wells 5, 12, 23 21, 22 40

Total 35 wells 3 wells 2 wells 1 well

Location Kilombero Ulanga Ulanga A Ulanga B

12


4. RESULTS

4.1. Recession index (K) estimates

Table 3: Summary of K (days) values estimated in this study. Wet season represents December

through April, dry season means May to November and all events stands for a whole year, January to

December.

Recession Index K(days)

Wet season (w) Dry season (s) All events (n)

Catchment ID Year K(w) K(s) K(n)

1KB14 1960 -1982 46 110 97

1KB4 1960 -1982 144 233 189

Figure 6 and Figure 7 present estimates of median K values for catchments 1KB14 and 1KB4

based on recession segments of streamflow hydrographs computed from the RECESS

program during prolonged periods of negligible recharge, respectively. Statistical summary of

the estimates is indicated in Table 3. In all catchments, dry season events gave higher median

K values compared to wet season events. However, when K values were compared between

the two catchments, 1KB14 showed lower K than 1KB4 in all seasons. According to this

analysis, the median K values during wet and dry seasons for 1KB14 were 46 and 110 days

(Figure 6) while for 1KB4 values of K were 144 and 233 days (Figure 7), respectively. In

addition, based on all events, the median K values were 97 days in 1KB14 and 187 days in

1KB4 catchments.

1

1.5

2

2.5

3

0 100 200 300 400

Log

Q (

cfs)

K (days per log cycle)

1KB14: Wet season

K= (2.76 * LOG Q) + -54.57 0

0.01

0.02

0.03

0.04

1 1.5 2 2.5 3

-1/K

(1

/day

)

Log Q (cfs)

K(w) = 46 days

1

1.5

2

2.5

3

0 100 200 300 400

Log

Q (

cfs)


1KB14: Dry season

K = (-34.27 * LOGQ ) + -52.51 0

0.01

0.02

0.03

0.04

1 1.5 2 2.5 3

-1/K

(1

/day

)

Log Q (cfs)

K(s) = 110 days

13




versus LOGQ in each plot is also indicated.

1

1.5

2

2.5

3

0 100 200 300 400

Log

Q (

cfs)


1KB14: All events

K = (-28.1 * LOGQ ) + -53.61 0

0.01

0.02

0.03

0.04

1 1.5 2 2.5 3

-1/K

(1

/day

)

Log Q (cfs)

K(n) = 97 Days

3

3.5

4

4.5

5

50 200 350 500 650

Log

Q (

cfs)


1KB4: Wet season

K = (43.48*LOGQ) + -345.81

0

0.004

0.008

0.012

0.016

3 3.5 4 4.5 5

-1/K

(1

/day

)

Log Q (cfs)

K(w) = 144 Days

3

3.5

4

4.5

5

50 200 350 500 650

Log

Q (

cfs)


1KB4: Dry season

K = (4.95*LOGQ) + -258.38 0

0.004

0.008

0.012

0.016

3 3.5 4 4.5 5

-1/K

(1

/day

)

Log Q (cfs)

K(s) = 233 Days

14




versus LOGQ in each plot is also indicated.

4.2. Master Recession Curve (MRC)

The MRC (Figure 8) indicates the behaviour of streamflow recession for 1KB14 and 1KB4.

The curves showed that the recession of the streamflow (Q) during wet season was rapid as

the time of the occasion was short compared to dry season events. When considering site

specific conditions, the 1KB14 which covers a small area (Table 1) was characterized by low

streamflow with 689 cfs (20 m3/s) as a maximum discharge (Figure 8a). The 1KB4 with a

larger surface area (Table 1) had higher streamflow discharge compared to 1KB14, with a

maximum discharge of 17824 cfs which was equivalent to 505 m3/s (Figure 8b). The same

graph on a linear scale is shown as Figure A- 1 (see Appendix).

3

3.5

4

4.5

5

50 200 350 500 650

LogQ

(cf

s)


1KB4: All events

K = (66.86*LOGQ) + -451.74 0

0.004

0.008

0.012

0.016

3 3.5 4 4.5 5

-1/K

(1

/day

)

Log Q (cfs)

K(n) = 189 Days

0.1

1

10

100

1000

10000

100000

0 30 60 90 120 150 180

Q (

cfs)

Time (days)

MRC at 1KB14

a

15


Figure 8: The MRC of streamflow hydrograph computed from RECESS program whereby (a) and (b)

represents 1KB14 and 1KB4 catchments respectively. The y-axis is presented in logarithmic scaleT

based on streamflow records

Temporal variability of T values based on recession indexes of streamflow hydrographs for

the 1KB14 and 1KB4 catchments is numerically summarized (Table 4) and graphically

presented (Figure 9). Since there was high variability between wet and dry events, the same

results are presented on a logarithmic scale for better visualization and comparison (Figure

10). T values during wet season events were relatively low with a median below 0.1 m2/min.

In contrast, the median values in dry season events were above 0.1 m2/min with the

maximum value being higher in 1KB14 than 1KB4 catchments. When looking at the standard

deviation, the 1KB4 provided minimum variability in T estimates compared to 1KB14. Also,

it was observed that T during dry events were extremely variable compared to T based on wet

events. In addition, as shown in all catchments, T estimates based on all data events were

slightly similar to T values estimated during dry events than wet season events (Table 4).

Table 4: T (m2/min) based on median K values analyzed from streamflow recession. T values are

computed by considering storm events, base flow and recharge events automated from RORA

T (m

2/min)

Catchment ID 1KB4 1KB14

K (days) K(n) K(w) K(s) K(n) K(w) K(s)

Wet

season

events

Min 0.015 0.014 0.016 0.007 0.003 0.01

Med 0.044 0.079 0.045 0.065 0.039 0.067

Max 0.125 0.541 0.124 0.459 1.034 0.503

Mean 0.057 0.987 0.058 0.107 0.117 0.131

Stdev 0.041 0.185 0.041 0.127 0.243 0.154

Dry

season

events

Min 0.043 0.107

0.022 0.061 0.022

Med 0.318 0.38 0.344 0.201 1.927 0.082

Max 0.485 0.652

8.746 4.819 6.588

Mean 0.282 0.38

1.048 2.082 0.846

Stdev 0.223 0.385

2.708 1.566 2.156

All data

events

Min 0.015 0.014 0.016 0.007 0.003 0.01

Med 0.051 0.096 0.054 0.123 0.106 0.074

Max 0.485 0.652 0.344 8.746 4.819 6.588

Mean 0.132 0.194 0.099 0.535 0.93 0.489

Stdev 0.162 0.235 0.114 1.838 1.402 1.528

0.1

1

10

100

1000

10000

100000

0 30 60 90 120 150 180

Q (

cfc)

Time (days)

MRC at 1KB4

Wet season

Dry seaon

All events

b

16


Again, when considering T values in relation to recession index K in a specific catchment

(Figure 9 and 10), it is observed that, the T values estimated based on recession index of all

data K(n) were more closely related with T values estimated based on dry events recession

index K(s) than wet events recession index K(w). In this regard, this similarities between K(s)

and K(n) might suggest that, in the absence of either of the two, one could represent the other.


1KB4 and 1KB14 catchments: K(n) means recession index of all events, K(w) - wet events and K(s) -

dry events. The y-axis is presented on linear scale

0

2

4

6

8

10

K(n) K(w) K(s) K(n) K(w) K(s)

1KB4 catchment 1KB14 catchment

T (m

2/m

in)

T based on wet events

Min Med Max Mean Stdev

0

2

4

6

8

10



T (m

2 /m

in)

T based on dry events


0

2

4

6

8

10



T (m

2 /m

in)

T based on all events


17



1KB4 and 1KB14 catchments: K(n) means recession index of all events, K(w) - wet events and K(s) -

dry events. The y-axis is presented on logarithmic scale

4.3. T based on pump test data analysis

Figure 11(a) presents spatial distribution of aquifer transmissivity across the Kilombero

Valley, derived from all pump test data on a logarithmic scale (y-axis). The corresponding

0.001

0.01

0.1

1

10



T (m

2/m

in)

T estimates based on wet season events


0.001

0.01

0.1

1

10



T (m

2 /m

in)

T estimates based on dry season events


0.001

0.01

0.1

1

10



T (m

2/m

in)

T estimates based on all events


18


plot (Figure 11b) illustrates the relationship between pump test derived T and elevation data

on a linear scale (y-axis). There was a high local variation across the valley. As shown by 35

wells located at Kilombero, the median T value was 4.83 m2/min with minimum and

maximum ranges from 0.01 to 85.55 m2/min respectively. Compared to other areas within

the valley, there were a few wells in the Ulanga area with uneven distribution and hence these

wells were not considered in this analysis. A trend line (Figure 11b) shows that with

increased elevation, ability of the aquifer to transmit water also increased. This behavior is

largely triggered by the presence of one well in the Ulanga B area, 1012 m high, with T value

445.40 m2/min. However, by considering merely the selected 35 wells situated in Kilombero

area, and ignored other points, including the Ulanga B point, the trend line changed (Figure

12a).

Figure 11: Pump test derived T across Kilombero valley particularly located in1KB17 Catchment. (b)

A plot shows relationship between pump test derived T and Elevation in linear scale. A blue

rectangular block indicates 35 selected wells considered in this study.

Figure 12 illustrates how T values were related to elevation, hydraulic conductivity, aquifer

thickness and borehole depth by considering 35 wells within the Kilombero Valley. Aquifer

transmissivity typically depends on hydraulic conductivity K. High K means rapid water

flow, which increases the ability of an aquifer to transmit water and vice versa (Figure 12b).

In contrast, aquifer thickness and borehole depth varied inversely with T (Figure 12c & 12d).

Kilombero Ulanga Ulanga A Ulanga B

35 wells 3 wells 2 wells 1 well

Min 0.01 0.18 0.02

Med 4.83 8.69 0.12

Max 85.55 371.6 0.22 445.40

Mean 17.09 126.83 0.12

Stdev 25.85 212.02

0.010.1

110

1001000

T (m

2 /m

in)

0

100

200

300

400

500

200 400 600 800 1000 1200

T (m

2/m

in)

Elevation (m)

b

a

19


As shown by the trend line, this means that an increase in borehole depth and a decrease in

aquifer thickness decrease the flow rate, and as a result, T also decreased.

Figure 12: T values related to elevation (a), hydraulic conductivity (b), aquifer thickness (c) and

borehole depth (d) within Kilombero valley by considering 35 selected wells located at 1KB17

Catchment. The y-axis is presented on linear scale.

Unlike Figure 12 plots, the bar charts (Figure 13) classify elevation, aquifer thickness,

borehole depth and hydraulic conductivity into various intervals in order to show variation of

aquifer T within the valley. This reveals that T was high when borehole depth was shallow

(22-41 m) and aquifer thickness was thin (10-24 m), but this condition was only applicable to

a certain limit. As indicated when borehole depth was between 62 to 81 m and aquifer

thickness was between 25 to 39 m, T was lowered. Furthermore, as observed in the previous

plot (Figure 12b), low rate of water flow contributes to low ability of the aquifer to transmit

water. For example (Figure 13), hydraulic conductivity with a range from 0.001 to 0.20

cm/sec gave a median T value of 0.059 m2/min with minimum and maximum values being

0.01 and 20.17 m2/min respectively. While hydraulic conductivity with a range from 0.21 to

1.00 cm/sec gave 6.948 m2/min as the median T value, and therefore trends of the T value

increased as the hydraulic conductivity range increased too. By considering the relation

between T and elevation, elevation trends were difficult to clearly define. The minimum

value of T increased with a rise in elevation while the maximum value decreased too. A

summary of these characteristics are found in Table A- 2 (see Appendix).

0

20

40

60

80

100

250 260 270 280 290 300 310

T (m

2 /m

in)

Elevation (m) a

0

20

40

60

80

100

0 2 4 6 8 10

T (m

2 /m

in)

Hydraulic Conductivity K (cm/sec) b

0

20

40

60

80

100

10 20 30 40 50 60 70

T (m

2 /m

in)

Aquifer thickness (m) c

0

20

40

60

80

100

20 40 60 80 100 120

T (m

2 /m

in)

Borehole depth (m) d

20


Figure 13: T analysis based on elevation, aquifer thickness, depth and hydraulic conductivity

intervals within Kilombero Valley. The y-axis is presented in logarithmic scale.

Similarly, the same data plotted in Figure 12 are presented as maps (Figure 14) to depict the

spatial distribution of selected wells. These maps can be interpreted as aquifer transmissivity,

aquifer thickness, hydraulic conductivity, borehole depth and elevation within the Kilombero

Valley. To a large extent, Figure 14 illustrated how transmissivity and hydraulic conductivity

patterns were alike and how borehole depth and aquifer thickness corresponded to each other.

When considering elevation patterns, they behaved slightly similar with borehole depth and

aquifer thickness, but differently with hydraulic conductivity and aquifer transmissivity.

0.001

0.01

0.1

1

10

100

10 - 24 25 - 39 40 - 54 55 - 69

T (m

2/m

in)

Aquifer thickness (m) Min

Med

Max

Mean

stdev

0.001

0.01

0.1

1

10

100

0.001-0.20 0.21-1.00 1.10-5.00 5.10-9.30

T (m

2/m

in)

Hydraulic Conductivity K (cm/sec) Min

Med

Max

Mean

stdev

0.001

0.01

0.1

1

10

100

22 - 41 42 - 61 62 - 81 82 -101

T (m

2/m

in)

Borehole depth (m) Min

Med

Max

Mean

stdev

0.001

0.01

0.1

1

10

100

252 - 265 266 - 279 280 - 293 294 - 305

T (m

2/m

in)

Elevation (m) Min

Med

Max

Mean

stdev

21


4.4. Comparison of streamflow derived T and pump test derived T

In general, when comparing streamflow derived T and pump test derived T estimates (Table

5), it can be observed that there were no clear correlations of T values based on two methods.

Nevertheless, when considering streamflow derived T, the 1KB14 catchment in which the

whole area is covered by hills and mountains (Table 1) indicated higher T values compared to

1KB4 catchment (Table 5a). The 1KB4 which drains through lower elevation and is

surrounded by hills and mountains showed lower values of T. When looking at the pump test

derived T particularly a well located at Ulanga B area (Figure 11) it is indicated that the well

situated on a higher elevation (1012 m) gave higher T value of 445.400 m2/min compared to

wells located on low laying areas within the valley. In this regard, the presence of Ulanga B

Figure 14: Maps show spatial distribution of

selected wells in Kilombero valley based on

aquifer transmissivity (m2/min), aquifer

thickness (m), hydraulic conductivity (cm/sec),

borehole depth (m) and elevation (m)

22


well could be considered as supporting general agreement between the two methods.

However, it is only one well and could have much uncertainty associated with its measured T

value.

Table 5: Comparison of large scale streamflow derived T and small scale pump test derived T

T (m2/min) based on (a) Streamflow (b) Pump test

Catchments 1KB4 1KB14 1KB17

K (days) K(n) K(w) K(s) K(n) K(w) K(s) 35 Selected wells

Dry season Min 0.043 0.107 0.022 0.061 0.022 Min 0.010

Med 0.318 0.380 0.344 0.201 1.927 0.082 Med 4.826

Max 0.485 0.652 8.746 4.819 6.588 Max 85.550

Mean 0.282 0.380 1.048 2.082 0.846 Mean 17.091

Stdev 0.223 0.385 2.708 1.566 2.156 Stdev 25.849

Wet season Min 0.015 0.014 0.016 0.007 0.003 0.010

Med 0.044 0.079 0.045 0.065 0.039 0.067

Max 0.125 0.541 0.124 0.459 1.034 0.503

Mean 0.057 0.987 0.058 0.107 0.117 0.131

Stdev 0.041 0.185 0.041 0.127 0.243 0.154 All wells

All data Min 0.015 0.014 0.016 0.007 0.003 0.010 Min 0.010

Med 0.051 0.096 0.054 0.123 0.106 0.074 Med 4.826

Max 0.485 0.652 0.344 8.746 4.819 6.588 Max 445.400

Mean 0.132 0.194 0.099 0.535 0.930 0.489 Mean 34.739

Stdev 0.162 0.235 0.114 1.838 1.402 1.528 Stdev 89.443

On the other hand, the magnitudes of T values estimated by the two methods were apparently

different (Table 5). Regardless the two methods were applied in different catchments; it could

be understandable for 1KB4 and 1KB14 catchments (Table 5a) to show higher T values

compared to 1KB17 which is spatially located within the valley (low elevation). Instead,

1KB17 is characterized by high T values with a median value of 4.826 m2/min, maximum

value of 85.550 m2/min and average T value of 17.091 m

2/min for 35 selected wells (Table

5b). As indicated in Table 5, a wide gap existed between the median, maximum, mean, and

the standard deviation of streamflow derived T and pump test derived T.

Contrary to that, when looking thoroughly at individual pump test data (Figure 12 and Figure

15), as separated by a red line (Figure 15), there was a broad gap of T values between the

lowest and the highest. The lowest T values included a narrow range from 0.01 m2/min to

0.22 m2/min, and the highest T values consisted of a wide range from 3.15 m

2/min to 85.55

m2/min. Under such scenario, it is clear that, if points below the red line are solely compared

with the large-scale streamflow derived T, the average T estimates of the small-scale pump

test data (1KB17) would be slightly lower than the large-scale T estimates in 1KB4 and

1KB14 catchments (Table 5a). And hence, in one way, there would be a partial agreement

between the two methods.

23


Figure 15: Pump test derived T shows a gap of T values based on elevation (a), hydraulic

conductivity (b), aquifer thickness (c) and borehole depth (d) within Kilombero Valley. A gap is

illustrated by a red line while a trend line is indicated by a dot line.

Moreover, the small-scale and large-scale T estimates can be compared with regard to the

distributions of T values when an aquifer thickness ≥ 30m is considered. As observed in

Figure 12c and 15c, most of the points showed smaller T when the aquifer was thicker than

shallow (< 30m). With exception of the points situated above the trendline, the aquifer

thickness ≥ 30m provided 0.02 m2/min as a minimum, 11.65 m

2/min as a maximum, and 3.82

as a standard deviation (Figure 16). Based on the same assumption, shallow aquifer provided

0.01 m2/min as a minimum value of T with higher maximum of 48.94 m

2/min, and higher

standard deviation of 16.91. Therefore, looking at the two cases, it is absolutely thicker

aquifer gave reasonable T estimates that could moderately agree with streamflow derived T

particularly, during dry season using K(n) (Figure 17).

0.01

0.10

1.00

10.00

100.00

250 260 270 280 290 300 310

T (m

2 /m

in)

Elevation (m)

a

0.01

0.10

1.00

10.00

100.00

0 2 4 6 8 10

T (m

2 /m

in)

Hydraulic Conductivity K (cm/sec)

b

0.01

0.10

1.00

10.00

100.00

10 20 30 40 50 60 70

T (m

2 /m

in)

Aquifer thickness (m)

c

0.01

0.10

1.00

10.00

100.00

20 40 60 80 100 120

T (m

2 /m

in)

Borehole depth (m)

d

24


Figure 16: Comparison of T estimates with aquifer thickness greater than or equal (≥) to 30m and

less than (<) 30m using pump test data derived T in 1KB17 catchment.

Figure 17: Comparison of T estimates using aquifer thickness ≥ 30m with streamflow derived T in dry

season, based on K(n) in 1KB4 and 1KB14 catchments.

5. DISCUSSION

5.1. Seasonal and annual variabilities of T within the Kilombero Valley

As explained earlier, the Kilombero Valley is characterized by wide variations of climate

between highlands and lowlands (RBWO, 2010) that might influence the temporal

variabilities of T within the valley. The large-scale T estimates during dry season events

showed remarkable agreement with T estimates when all streamflow events were applied.

Likewise, based on recession index K, T estimates calculated from K(s) and K(n) have nearly

the same values compared to K(w), which apparently indicated lower T values. In general, T

results derived from streamflow records indicated that T was higher during dry than wet

seasons. The reasons for this could be explained based on soil properties and climate

conditions of the area. As described by Bonarius (1975) that, the Kilombero Valley is

composed of sandy and clay soil. The smallest value of T during the wet season might

indicate movement of water in the upper horizons of the soil profile which are dominated

0.01

0.10

1.00

10.00

100.00

Min Max Mean Stdev

T (m

2 /m

in)

Aquifer thickness (m)

< 30m ≥ 30m

0.01

0.10

1.00

10.00

100.00

Min Max Mean Stdev

T (m

2 /m

in)

Aquifer thickness 1KB14 1KB4

25


with clay soil. On the other hand, during the dry season as the water movement reaches

deeper profile layers and the sand soil becomes more dominant, the T became higher due to

high porous nature of the sandy soil. However, this is in the case when the soil layers are

homogeneous throughout. In non-homogeneous soil, the case could be different. This later

issue could indicate the differences between the well derived T and the streamflow derived T,

but needs more consideration.

Besides that, when relating the average small-scale T values estimated from thicker aquifers

(≥ 30m) with soil properties, it is sufficient to assume that, the thicker aquifer that showed

lower T values are possibly dominated by clay soil, and the shallow aquifer that indicated

higher T values are possibly dominated by unconsolidated sandy soil. However, it should be

noted that, if the aquifer thickness is interpreted as equal to soil profile depths, this case could

probably contradict the above discussion.

5.2. Spatial variability of aquifer T within the Kilombero Valley

Considering the spatial variability of T within Kilombero Valley, an agreement was found

between streamflow recessions and catchment topography. The 1KB14 catchment with an

average slope of 14%, which is higher value compared to other catchments, has smaller K

values than 1KB4 with an average slope of only 8% (Table 1 and Table 3). Consequently,

these topographic characteristics of catchment scale gave higher estimates of T values in

1KB14 than 1KB4 based on streamflow records (Figure 9 and Figure 10). This means that,

the aquifer with larger slope drains rapidly (Brutsaert, 1994) and hence, influences the high

capacity of the aquifer to transmit water. In addition, as presented by Brutsaert (1994), the

subsurface outflow from a hill slope is influenced by pressure gradients due to the inclination

of the water table with reference to the underlying restrictive layer, and magnitude of the

slope due to gravity. This proves how the 1KB14 with high slope could have higher T values

compared to 1KB4 and 1KB17 catchments. The lower variability of T in large-scale

estimates could probably accounted by drainage area. The 1KB4 with larger drainage area

indicated minimum T variability compared to 1KB14 which has a smaller drainage area

(Figure 9 and Figure 10).

By looking at the spatial distribution of wells (Figure 14), one would anticipate that there is

uniformity between aquifer T and elevation trends. However, for the Kilombero Valley

particularly 1KB17 wells, the case was obviously not true. Interestingly, the distributions of

individual wells based on aquifer T in relation to the elevation data provided no clear

description about spatial variability of T in the valley. Both wells located on either high or

low elevation showed mixed trends (Figure 15a), except for the one well located in Ulanga B

(Figure 11). This means that, there are multiple factors affecting the spatial variability of T in

the valley, and therefore more research should be conducted. Nevertheless, the higher

variability of T (25.85 standard deviation) exhibited by the small-scale pump test data, could

certainly a potential indicator describing aquifer inhomogeneity within the Kilombero Valley

or, might be due to a limited number of measurements on a local scale. A point of

inhomogeneity was also emphasized by Foster and Chilton (2003) that a broad variability of

aquifer saturated thickness leads to higher variability of aquifer T. This is a fact, and it is

typically correlated with pump test data presented in this study.

26


5.3. Comparison of streamflow derived T and pump test derived T

In general, comparison between the T derived from streamflow (large-scale T estimate) and

pump test (small-scale T estimate) can be accounted based on either, time-space variations

within the Kilombero Valley, or other factors. Spatially, the measurements of T obtained

from pump test data at 1KB17 catchment showed imprecise values with a high degree of

variation ranging from 0.01 m2/min as a minimum, to 85.55 m

2/min as maximum (Table 5b).

The T derived from the analytical approach at 1KB4 and 1KB14 catchments showed lower

consistent values compared to the direct field measurements of T, with minimum values

greater than 0.01 m2/min and maximum values less than 10 m

2/min (Table 5a). This defines

how the T derived from streamflow records and that from pump test data are inconsistent

with each other. In spite of inconsistency between them, a large-scale T estimate can solely

be compared with a small-scale T estimate derived from thicker aquifer, as both of them gave

reasonable lower T estimates that partly agree with each other (Figure 17). It would be better

to compare T derived from pump test at 1KB17 with T derived from the analytical approach

on the same catchment. But, as important information required using the analytical approach

in 1KB17 was missing, the analysis was excluded. Further, given the 1KB17, like 1KB4 and

1KB14, integrate large regions of land and drains both the upland and valley regions, it is not

necessarily a given that the analytical method applied at this scale would match exactly with

the well derived T values.

Based on geomorphology, the 1KB17 which lies on the valley bottom should provide low

estimates followed by 1KB4 and 1KB14. Notwithstanding, the small-scale T estimates

generated in 1KB17, were characterized by higher values with greater deviation than

streamflow derived T in 1KB4 and 1KB14 catchments. This characteristic is supported by

findings of Jankovic et al., (2006) and Mendoza et al., (2003) studies about uncertainty and

high variability of parameter T regarding to field-based measurements. And, it reveals how

difficulties it is in comparing T derived from the analytical approach with the field

measurement method in the Kilombero Valley. In addition, the difference in the period of

records between streamflow (1960-1982) and pump test (2014) dataset might contribute to

the variability of T estimates. This is typically true, as described by Halford and Mayer

(2000), more frequently the recession-curve-displacement method tends to be untrustworthy,

due to failure of underlying analytical groundwater flow model to interpret the contributing

aquifer. Such an interpretation would be consistent with the general findings of Lyon et al.,

(2014) where seasonality and scale greatly impacted the linearity of the hydrologic response

across the Kilombero Valley.

5.4. Comparison with other study findings

Since, both hydraulic conductivity (Ks) and T, are important hydraulic parameters in the

hydrological cycle and modeling perspective in a catchment (Davis et al., 1999; Sobieraj et

al., 2001; Brooks et al., 2004; Soupios et al., 2007), the disparity between the large-scale

derived T and small-scale derived T can also be discussed on a basis of Ks findings from

other study. With reference to Brooks et al., (2004) views on hillslope-scale experiment about

lateral Ks, they found that a reason for the small-scale Ks to have lower values than the

calibrated lateral Ks were due to stumbling blocks implicit in the small-scale sampling

27


technique. Hopmans et al., (2002) investigation of soil hydraulic properties using small-scale

and large-scale estimates demonstrated that dealing with the scale disparity by looking at

spatial or temporal scale variability, was something to consider. This is because, space and

time differences in the soil develop uncertainties when large and small scales are subjected to

changes. Also, a huge nonlinearity characterized by flow and transport processes in

geophysics and large-scale vadose zone hydrology was another issue to take into account

(Hopmans et al., 2002). However, they argued that a proper knowledge of large-scale flow

processes in non-homogeneous soils, could likely be achieved when there is suitable

technologies for scale appropriate measurement. Davis et al., (1999) employed different

measurement techniques to determine the sensitivity of a catchment model to soil hydraulic

properties. They found that different methods may have little or higher estimate relevant for a

specific soil layer. Therefore, it is unreasonable to have one conclusion on the performance of

all measurement techniques. They suggested that, in order to acquire accurate model results,

it is better to check for the accuracy of a particular method on a basis of a site-specific soil

characteristic.

Moreover, the concept of sample size and macropores distribution at catchment scale

(Mohanty et al., 1994; Davis et al., 1999; Sobieraj et al., 2001; Brooks et al., 2004), could

perhaps reflect the limitation of why the large-scale and small-scale T estimates were

typically not comparable in this study. A main reason speculated by Brooks et al., (2004) was

an inadequate number of sample size inhibited a real average of Ks in small-scale

measurements. This case is obviously valid for T estimates based on pump test data applied

in this study. Fewer observation points were not sufficient to represent the actual average

estimates, and hence affect the interpretation of hydrological model in the catchment scale.

Even though, as clearly pointed out by Brooks et al., (2004), despite the appropriate number

of samples are considered, the small-scale may not depict the same values with the large-

scale measurements. In addition, as explained by Davis et al., (1999), macropores as

structural features of soil are potential of the soil hydraulic properties and their determined

values have a potential effect on the scale of measurement. This is true for small-scale

sampling techniques whereby the presence of large macropores or weak structures in the soil

hinder quantification (Brooks et al., 2004). Simultaneously, lack of information on how

macropores are connected in a large-scale measurement (Beven and Germann, 1982), adds

more difficulties for the two methods to be comparable (Brooks et al., 2004). Therefore, in

connection with this study, these findings could reasonably prove that, it is impossible for T

values derived from small-scale and those from large-scale measurements to be statistically

the same.

5.5. Uncertainties associated with T estimation

Although no uncertainty tests were performed to measure the reliability of T values

estimated, still, the results obtained from this study were associated with uncertainties. This is

because no model is error free as mathematical computations are often not similar to the real

catchment characteristics (Vogel and Kroll, 1992). The quality of the data itself was

somewhat imprecise. Presence of intermittent gaps in gage height records and mismatch of

peaks created questions in particular over the measurement techniques used. Moreover, as the

RORA (Rutledge, 2000; Delin et al., 2007) and RECESS (Rutledge, 1998) require the long-

28


term continuous streamflow data with high quality, the inconvenient quality of data adds

more uncertainty of the results. Lack of abundant information such as water table and pump

test data in other catchments limited a full understanding of the study site. The temporal

variability between pump test and streamflow data might contribute to the uncertainty due to

impact of anthropogenic and climate changes that occur over time. Different measurement

techniques could also raise uncertainty, as small-scale pump tests derived T is a point

measurement, while streamflow derived T is a non-point measurement.

Based on scale of measurement, the program RORA needs drainage area exceeding 1 sq. mile

and less than 500 sq. mile (Rutledge, 2000). But, the 1KB4 catchment covers an area of

18048 km2 (6968 sq. mile) which is out of the RORA limitation. As specified by Rutledge

(2000), although that limitation is not universal and is subjective relying on hydrologic

setting, the effect of large areas create non-uniformity of storm events and lead to mix

multiple hydrogeologic settings. Also, the assumption that streamflow recession depends

solely on groundwater discharge, and exclude other sources such as river bank storage and

wetlands (Rutledge, 2000) might bring doubt about the model applicability in the Kilombero

Valley since some parts of 1KB4 catchment is covered by wetland.

6. CONCLUSION

The problem of data scarcity in an aquifer transmissivity (T) can be dealt with by using

different modeling approaches to derive the aquifer T .This can serve various purposes in

water resources management at local and regional scale. In this study, Rorabaugh model was

applied as an analytical approach to derive large-scale aquifer T based on streamflow records

in the Kilombero Valley. To ensure the accuracy, the modeled T estimates were compared

with T derived from pump test records. Based on specific objectives of the study, the

following were observed:

The close correlations between T estimates based on dry events and all data events

suggests that it is appropriate to apply dry events to calculate annual (all data events) T in

the absence of wet events in the Kilombero Valley.

Soil behavior during wet and dry seasons influenced temporal changes of T estimates

derived from large-scale streamflow records. Likewise, for small-scale measurements,

since thicker (≥ 30m) and shallow (< 30m) aquifers are characterized by lower and higher

T values, they are probably dominated by clay and unconsolidated sandy soil,

respectively.

As surface and subsurface flow characteristics vary in time, the temporal changes of

dataset records between streamflow (1960-1982) and pump tests (2014) reduce the

accuracy of the results, and may significantly influence uncertainty when comparing T

estimates.

Average basin slope appears to have contributed to the spatial variability of the large-

scale T estimates in the valley. The 1KB14 catchment with steep slope showed higher T

29


values than the gentle slope 1KB4 catchment. However, no significant relation was found

between the elevations of individual wells location and T derived from pump test of

wells. The higher T estimates derived from the small-scale measurement could either

infer high heterogeneity of aquifer within the valley specifically, 1KB17 catchment,

difficulty of measurement technique, and limited number of measurements.

General results of streamflow derived T and pump test derived T suggest difficulties in

comparing the analytical approach with field-based measurement data in the Kilombero

Valley. The large-scale T estimates derived from the analytical approach indicates

significantly lower values with smaller variation than the small-scale estimates, hence,

typically incomparable.

With reference to other findings, possible reasons for large-scale T estimates to be lower

than small-scale estimates may be due to the limitation inherent in the RORA method

within the valley, seasonality and scale of measurements, and improper quality of data.

However, when considering T derived from field-based measurement is convenient, it is

obvious to conclude that, the analytical approach based on streamflow records

underestimate the T values in the Kilombero Valley.

In this regard, this study can be considered as a basis for future work. Therefore, further

study, including multiple approaches should be subjected to the area to find out the

appropriate approach which will provide reliable theoretical T values in comparison with

field-based measurement data. For a detailed analysis, the study should also consider missing

information such as pump test wells, streamflow, water table, and gauge height data are

included.

30


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Appendix

Table A- 1: Details of wells within Kilombero Valley

WELL_NAME ID ELEVATION(m) T (m2/min) LOCATION

Idete_crossing 1 293 5.33 KILOMBERO

Idete_dispensary 2 279 3.15 KILOMBERO

Idete_magereza 3 287 73.41 KILOMBERO

Ifakara_High_Schl 4 278 0.22 KILOMBERO

Igota_village 5 315 0.18 ULANGA

Ihanga_Pr_Schl 6 274 0.02 KILOMBERO

Ihanga_Dispensary 7 276 0.03 KILOMBERO

Kapolo_Pr_Schl 8 283 0.02 KILOMBERO

Kibaoni_keepleft 9 268 11.65 KILOMBERO

Kibaoni_Dispensary 10 277 0.02 KILOMBERO

Kichangani_lupiro 12 321 8.69 ULANGA

Kilombero_Sec_Schl 13 261 0.06 KILOMBERO

Kining'ina_Pr_Schl 14 266 0.03 KILOMBERO

Kiyongwile_subvillage 15 259 0.22 KILOMBERO

KKT_DUK 16 281 4.66 KILOMBERO

Kwashungu_Sec_Schl 17 262 0.05 KILOMBERO

Lipangalala_Kwangahemela 18 259 20.17 KILOMBERO

Lipangalala_Lihami 19 262 0.12 KILOMBERO

Lipangalala Obs_well 20 252 56.57 KILOMBERO

Lugala_hosp_BH1 21 291 0.02 ULANGA A

Lugala_hosp_BH2 22 291 0.22 ULANGA A

Lupiro_ndoro 23 304 371.60 ULANGA

Machinjioni_kibaoni 24 267 8.23 KILOMBERO

Mahutanga_Mikoroshini 25 261 0.03 KILOMBERO

Mahutanga_Pr_Schl 26 262 0.12 KILOMBERO

Mbasa_kati 27 262 0.09 KILOMBERO

Mbasa_Pr_schl 28 262 4.94 KILOMBERO

Minepa_Alabama 29 266 6.66 ULANGA

Mlabani_2B_Ifakara 30 256 83.59 KILOMBERO

Malabani_B_Kwamkope 31 260 0.01 KILOMBERO

Muhola_1_Ifakara 32 261 48.94 KILOMBERO

Muhola_viwanja_60 33 259 0.03 KILOMBERO

Nduna_Ifakara 34 262 56.27 KILOMBERO

RC_MISSION 35 262 4.83 KILOMBERO

Shungu_2_area 36 265 39.65 KILOMBERO

Signal_Pr_Schl 37 305 19.61 KILOMBERO

Site_Kibaoni 39 271 85.55 KILOMBERO

Soga_Sec_Schl 40 1012 445.40 ULANGA B

Ukumbini_DED_Office 41 274 7.24 KILOMBERO

Viwanja_60A 42 264 21.06 KILOMBERO

Viwanja_60B 43 262 35.64 KILOMBERO

35


Table A- 2: Summary of Aquifer transmissivity of 35 wells based on borehole characteristics

Transmissivity T (m2/min)

Borehole characteristics Range Min Med Max Mean Stdev

Borehole depth (m) 22 - 41 0.020 20.170 83.590 27.499 31.802

42 - 61 0.010 0.219 56.570 13.172 20.545

62 - 81 0.019 0.169 11.650 2.767 4.220

82 -101 0.029 6.033 19.610 6.665 7.230

Aquifer thickness (m) 10 - 24 0.010 10.194 85.550 25.778 31.473

25 - 39 0.019 5.325 11.650 4.676 4.675

40 - 54 0.021 4.798 35.640 10.820 14.131

55 - 69 0.030

Elevation (m) 252 - 265 0.010 0.219 85.550 22.168 30.027

266 - 279 0.019 5.325 73.410 10.125 23.252

280 - 293 0.020 2.494 4.660 2.494 3.454

294 - 305 5.330 12.468 19.610 12.470 10.100

Hydraulic conductivity

K(cm/sec)

0.001- 0.20 0.010 0.059 20.170 1.707 4.931

0.21-1.00 4.83 6.95 19.61 8.56 5.00

1.10-5.00 21.06 48.94 56.57 43.70 15.24

5.10-9.30 39.65 78.50 85.55 70.55 21.28

Figure A- 1: The MRC of streamflow hydrograph computed from RECESS program whereby (a) and

(b) represents 1KB14 and 1KB4 catchments respectively. Both axes are presented in linear scale

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Q (

cfs)

MRC at 1KB14

Dry season Wet season All events

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Q (

cfs)

T (days)

MRC at 1KB4

Estimating Aquifer Transmissivity based on Streamflow ...

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