Full thesis. - Soil and Water Lab
MODELING RAINFALL-RUNOFF RELATIONSHIP AND ASSESSING
IMPACTS OF SOIL CONSERVATION RESEARCH PROGRAM INTERVENTION
ON SOIL PHYSICAL AND CHEMICAL PROPERTIES AT MAYBAR RESEARCH
UNIT, WOLLO, ETHIOPIA
A Thesis
Presented to the Faculty of the Graduate School of Cornell University in Partial Fulfillment of the Requirements for the Degree of
Masters of Professional Studies
By
Haimanote Kebede Bayabil
August 2009
© 2009 Haimanote Kebede Bayabil
ABSTRACT
This study focuses on characterizing subsurface water flow and ground water table fluctuations in response to rainfall that leads to saturation excess runoff, the basic principle of variable source area hydrology. In particular, this study concentrates to develop a model that efficiently simulates the location of saturated runoff areas and predict river discharge, which finally could help in realistic planning of watershed interventions. Furthermore, the study assesses the impact of soil conservation research program intervention on selected physical and chemical soil properties of the study area. Long-term discharge and rainfall data was available at the watershed outlet and for four test plots. In addition, 29 piezometers were installed in 2008 and water table measurements were taken during the main rainy season. Based on major runoff mechanisms identified at the catchment-level, a conceptual rainfall-runoff model was developed to compute runoff. The model incorporates saturated excess overland flow from both bottomlands and subsoil exposed areas and baseflow and interflow from the hillsides. The model was tested on a daily, weekly, and monthly basis and fitted well the discharge data at the bottom of the watershed. In addition, the distributed model output agreed well with the ground water table measurements. The watershed was saturated (and produced runoff) in the flat areas near the river while the hillsides were unsaturated with a perched water table that responded rapidly to rainfall. Data from test plots showed that flatter areas produced more runoff than test plots at steeper slope areas. The model has potential to predict runoff in ungauged basins but should be further tested to do so. On the other hand, soil samples were tested for selected physical and chemical properties. The result indicated that AP and % OC contents of the soil were found in lower amount than before/early project intervention period, while the Db value has shown an increase.
BIOGRAPHICAL SKETCH
Haimanote Kebede was born and raised in East Gojjam, in 1981. After successfully completing his secondary school study at Debre Markos C.S.S. School, he joined Alemaya Universiy in 1999. During his four-year stay at university, he studied plant science and received his Bachelor degree in June 2003.
In September 2003, he was employed by Elfora Agro Industries P.L.C. where he served as Junior Agricultural Expert for eight months. Then in May 2004, he moved to Finchaa Sugar Factory as a Plantation Section Manager and was assigned to manage a plantation section, including the 1300-hectare farm cultivated under irrigation and all the working staff under this section.
In April 2005, he had an opportunity to participate in a training entitled
‘Sugarcane Micro-Propagation Techniques’ conducted in Havana, Cuba for six months.
After his return to Ethiopia, he continued to work at the Finchaa Sugar Factory until he left the organization in November 2007 for his Masters degree. iii
iv
ACKNOWLEDGMENTS
First and for most I am ever grateful to the Almighty God, without his support and blessings, this piece of work would never have been accomplished.
My sincere gratitude goes to my major advisor Prof. Tammo. S. Steenhuis, from Cornell University, USA, for his sympathy, encouragement, endless patience, and strong belief in me. He has been sensitive and softhearted; he always tried to take care of every problem I had during my entire study and research period. He was more like a friend than just a professor.
Dr. Amy S. Collick was so wonderful. She helped me organize things, gave me valuable ideas, edited my manuscript, and was always in the front line to help me during hard times. Thank you so much.
Very special thanks go to my co-advisor Dr. Ingr. Sileshi Bekele, East Africa
Director of International Water Management Institute (IWMI), for his constructive ideas, encouragement, and worthy comments.
I am thankful to ARARI, for allowing me to work at the Maybar research site and providing long term hydrological and sediment data.
The support and help I got from Mr. Derese G / Wold, former SCRP staff, was more than I could express; he was always generous, cooperative, and friendly. He is an amazing person with never changing smile.
International Water Management Institute and Bahir Dar University are acknowledged for their financial support and Dr Ayalew Wondie helped me in facilitating the financial issues with Bahir Dar University finance division.
I am also greatly indebted to the technicians at Maybar Research Station: Gash
Ali Ahmed, Seid Hussien, and Seid Belay. They were welcoming and allowed me to share everything they have. The life experience I got from them was invaluable. v
I would also like to thank my family for the unconditional love and support they provided me throughout my life and in particular, I must acknowledge my younger sister Nitsuh Kebede, who has always believed in my potentials, and she was the reason and my strength to join this program.
Finally, I would like to express my gratitude for all my friends, who have been helping and encouraging me by telephone and e-mail during the study and thesis writing periods. vi
TABLE OF CONTENTS
MODEL EFFICIENCY EVALUATION ................................................................. 20
Calibration and Validation of Rainfall – Runoff Model .......................................... 20
vii
Soil Sampling Techniques, Site Selection, and Sample Preparation ........................ 52
viii
ix
LIST OF FIGURES
Figure 1-2: Soil map of Maybar watershed (Source: Weigel, 1986) ............................. 5
Figure 1-3: Mean annual rainfall, river discharge, and suspended sediment yield. ....... 7
Figure 1-5: Land use map of Maybar watershed (2008 cropping calendar 2 nd crop) ..... 8
Figure 2-1: Structure of the conceptual water balance model by Steenhuis et al. (2008)
Figure 2-2: Location of piezometer transects at different slope range in the watershed.
Figure 2-10: Comparison of simulated runoff from saturated area and plot runoff
x
Figure 2-14: Comparison of daily model calibration simulated and measured discharge
Figure 2-15: Scatter plot of daily model calibration simulated and measured discharge
Figure 2-18: Comparison of weekly model calibration output .................................... 38
Figure 2-21: Scatter plot of weekly model validation simulated and measured .......... 40
xi
LIST OF TABLES
Table 1-1: Watershed characterization based on slope (Source: Weigel, 1986) ............ 4
Table 1-2: Soil labels and their descriptions (Source: Weigel, 1986) ............................ 6
Table 2-1: Slope range, runoff coefficient, and land use type of test plots .................. 27
Table 3-2: Average AP, %OC, and Db values for different land use areas. ................ 57
xii
LIST OF ABBREVIATIONS
ARARI: Amhara Regional Agricultural Research Institute
EIAR: Ethiopian Institute of Agricultural Research
SCRP: Soil Conservation Research Program
SDC: Swiss Agency for Development and Cooperation
TP: Test Plot
UNDP: United Nations Development Program
WDR: World Development Report xiii
1.
CHAPTER ONE
RESEARCH BACKGROUND AND STUDY AREA
RESEARCH BACKGROUND
In the 21st century, agriculture continues to be fundamental to the overall economy, food security, and poverty reduction in Sub-Saharan Africa countries
(WDR, 2007). In Ethiopia, agriculture is mainly rain-fed, traditional and small scale with low inputs, which often leads to low crop productivity and yield. Furthermore,
Ethiopia’s low crop productivity is further aggravated by water shortage due to scarce rainfall and land degradation caused by excessive soil erosion.
Worldwide awareness of water scarcity has put an emphasis on finding better approaches to meet water demand (Anonymous, 2000b quoted by Bastiaanssen et al.,
2003) and reduce erosion (Nyssen et al., 2008). Soil erosion and water scarcity are the major problems in the Ethiopian highlands, affecting the livelihoods of millions as the associated sedimentation and flooding or drought cause additional problems for downstream populations.
The majority of the Ethiopian human and livestock population reside in the
Ethiopian highlands where soils are degraded due to exacerbated soil erosion reaching up to 400 tons/hectare/year (UNDP, 2002). Increasing population pressure coupled with declining land productivity has led to a demand for additional food production.
To meet the demand, all land types, irrespective of their suitability, are intensively cultivated using poor management practices. In the period between 1950 and 2000, the population in the Ethiopian highlands was estimated to have increased nearly four times from about 16 million to about 65 million (Hurni et al., 2005). In addition to excessive population pressure, the rain-fed, low-input subsistence agriculture of the highlands is further worsened by erratic and unpredictable rainfall resulting in drought
1
or flood conditions. The rainfall in the highlands ranges from very little rain creating extreme drought conditions to excessive rainfall producing floods. Both extremities result in severe crop damage and sometimes complete crop failure. As a result, the
Ethiopian highlands have become very fragile, sensitive to slight environmental changes, and food insecure.
In Maybar, located in the northeastern escarpment of the central highlands of
Ethiopia with attributes similar to the other highland areas in the country, farming practices are suffering from severe land degradation and acute water scarcity problems. Taking these problems into consideration, the Soil Conservation Research
Program (SCRP) was implemented in 1981 by the Ethiopian Ministry of Agriculture
(MoA) in collaboration with the University of Berne, Switzerland and with the support of the Swiss Agency for Development and Cooperation (SDC). Under this program which lasted from 1981-1987, a total of seven research sites were established with the
Maybar research station being SCRP’s first research site (SCRP, 2000). The underlying objective was to provide measures that could be implemented to alleviate the aggravated land degradation and water scarcity problems. During the implementation, soil and water conservation measures, such as physical structures, area closures and biological structures, were put in place through a “food for work” campaign.
Since the establishment of the site, fine resolution data on climate, hydrology and suspended sediment, from both river and test plots, has been collected and an expansive database was established that serves as a data source to carry out hydrological, soil erosion, and conservation research activities at regional, national, and international levels.
The data collected in this watershed has been analyzed by the Amhara
Regional Agricultural Research Institute (ARARI) and the Ethiopian Institute of
2
Agricultural Research (EIAR) researchers, national and international students at
Masters and PhD levels, and other researchers. The research activities, under taken in the watershed, differ both spatially and temporally depending on the objectives and intended outcomes.
This study analyzes the data of the Maybar watershed, but it bases the analysis on specific hydrological processes, specifically from the perspective of variable source area hydrology that relies on saturation excess runoff mechanism. To aid with the analysis, 29 piezometers were installed and ground water levels of the area were measured during the main rainy season of 2008. Furthermore, this study includes the impact assessment of the soil conservation research program intervention on selected physical and chemical soil properties of the study area.
This thesis has three chapters. This chapter gives insight into the complete research project and provides detailed information about the research site. Chapter
Two focuses on the hydrological modeling that incorporates the explanation of the major hydrological processes, identification of the major runoff mechanisms, and determination of the runoff sources and recharge areas in the watershed. This information was further used to model the rainfall-runoff relationships in the area.
Finally, Chapter Three addresses the impact assessment of SCRP interventions on selected physical and chemical properties of soils of the Maybar Watershed.
STUDY AREA
Location and Topography
The study area consists of the Kori Sheleko catchment, which is found in the
Maybar Watershed. It is the first of the SCRP research sites established and is located in the northern eastern part of the central Ethiopian highlands situated in the Southern
Wollo administrative region, approximately 20 km south-southeast of Dessie town.
3
The gauging station lies at 39 o 39’E and 10 o 51’N. The area is characterized by highly rugged topography with steep slopes ranging between 2530 and 2860 meters above sea level (masl), a 330 meter altitude difference within a 112.8 ha catchment area
percent share of the watershed.
Figure 1-1: Digital terrain map of the Maybar Watershed. Low elevation at the southern end of the watershed, near the outlet, is indicated by blue while high elevation is indicated by red.
Table 1-1: Watershed characterization based on slope (Source: Weigel, 1986)
Slope class
[%] [o]
6.1-13.0 3.5 – 7.4
Description
Sloping
13.1 – 25.0 7.5 – 14.0 Moderately steep
25.1 – 55.0 14.1– 28.8
>55.0 > 28.8
Steep
Very steep
Area (ha)
6.8
22.5
42.9
40.6
Coverage (%)
6
20
38
36
4
Soils
The soil types in Maybar research unit have developed from the alkali-olive basalts and tuffs of the Ashangi group, which are part of the tertiary volcanic trap series
(Weigel, 1986). Figure 1-2 includes the soils map of the Maybar Watershed, and
Table 1-2 defines the soil labels found in the map’s legend. Table 1-3 clearly
indicates that the watershed area is dominated by shallow depth soils classified as phaeozems and phaeozems associated with lithosols and covering more than 93% of the total area in the watershed.
Figure 1-2: Soil map of Maybar watershed (Source: Weigel, 1986)
Agro Climate, Land Use, and Cropping Pattern
The Maybar research watershed receives an average annual rainfall of
1370mm, of which only 1148 mm is effective rainfall (rainfall contributing directly to runoff and recharge), and has an average annual river discharge of 407 mm. The mean
5
annual suspended sediment rate was estimated to be 951 tons/year, which is
approximately 8.4tons/ha/year. Figure 1-3 provides a graphical representation of the
annual rates of rainfall, discharge, and sediment yield from 1989 to 2004.
Table 1-2: Soil labels and their descriptions (Source: Weigel, 1986)
Soil type Label
Soil mapping units
Descriptions
Phaeozems associated with
Lithosols a b c
Hh1ls
Hh2ls
Hh2s
Hapllic phaeozems very shallow (10-25 cm), very stony, (sandy) clay loams.
Hapllic phaeozems shallow to very sahllow (10-50 cm) stony phase, (sandy) clay loams.
Hapllic phaeozems shallow (25-50 cm) stony phase, clay loams.
Phaeozems
Regosols d e g h
Hh3s
Hh4s
Re2s
Gm1w
Hapllic phaeozems moderately deep (50-
100 cm) stony phase, (sandy) clay loams.
Hapllic phaeozems deep to very deep (>
100 cm) stony phase, (sandy) clay loams.
Eutric regosols ver shallow to deep (10-
100 cm) stony phase, clay loams.
Mollic Gleysols water table during growing periods with in < 20 cm of the surface, clay loams.
Gleysols i Gm2v
Mollic Gleysols water table during growing periods with in 20-50 cm of the surface, clay loams.
Table 1-3: Soil types and their area share
Soil type
Phaeozems associated with Lithosols
Phaeozems
Gleysols
Fluvisols
Area (ha)
63.2
42.4
2.8
2.5
Share (%)
56.0
37.6
2.5
2.2
The Kori River, the main river in Kori Sheleko catchment in Maybar, is the main inlet to Lake Maybar, which is approximately 0.5 km below the gauging station.
6
The whole of the Maybar Watershed drains to the Borkena River, ultimately flowing to the Awash River basin, a subcatchment of the central Ethiopian Rift Valley.
Figure 1-3: Mean annual rainfall, river discharge, and suspended sediment yield.
The area is typical for the “Dega” thermal zone with an average daily temperature of 16
O
C. The rainfall pattern commonly follows a bi-modal distribution
(Figure 1-4): the first rainy season, the shorter of the seasons, around mid-March to
April and the second often begins around June/July and ends usually in September.
The Maybar area is known to be a low agricultural potential, intensively cultivated, oxen-ploughed cereal belt of the north-eastern escarpment region of the central
Ethiopian highlands (Boshart, 1997).
According to Hurni et al. (2005), approximately 60% of the total catchment area is cultivated whereas 20% is woodland and the remaining 20% is grassland
(Figure 1-5). There exists two cropping seasons and the predominant crops are cereals
and maize, hence there exists two cropping seasons: the first, “Belg”, is the small rainy season in spring and the second, “Kremt”, the main rainy season during the summer and autumn. During the “Belg” season cereals are predominantly planted while in the
“kremt” season pulses are dominant (SCRP, 2000).
7
Figure 1-4: Long term daily climate record
Figure 1-5: Land use map of Maybar watershed (2008 cropping calendar 2 nd
crop)
8
REFERENCES
Bastiaanssen, W.G.M. and L. Chandrapala, 2003. Water balance variability across
Sri Lanka for assessing agricultural and environmental water use,
Agricultural Water Management 58(2)171-192
Bosshart, U. 1997. Measurement of River Discharge for the SCRP Research
Catchments: Gauging Station Profiles. Soil Conservation Research
Programme, Research Report 31, University of Berne, Switzerland.
SCRP. 2000. Area of Maybar, Wello, Ethiopia: Long-term Monitoring of the
Agricultural Environment.1981-1994. Soil Conservation Research
Programme, University of Berne, Switzerland.
Hurni H., Tato K., Zeleke G.2005. The Implications of Changes in Population, Land
Use, and Land Management for Surface Runoff in the Upper Nile Basin Area of Ethiopia. Mountain Research and Development. 25(2)147-154.
Nyssen J., Poesen J., Deckers J. 2008. Land degradation and soil and water conservation in tropical highlands. Soil & Tillage Research,article in press.
Journal homepage: www.elsevier.com/locate/still.
United Nations Development Program. 2002. Human Development Report. Oxford
University Press, Inc. 198 Madison Avenue, New York, New York, 10016.
Weigel,G. 1986. The Soils of Maybar Area. Soil Conservation Research Programme
(SCRP), Report no. 7. Berne, Switzerland: University of Berne.
World Development Report. 2007. Agriculture for development. 1818 H Street. NW.
Washington D.C 20433.
9
2.
CHAPTER TWO
MODELING RAINFALL - RUNOFF RELATIONSHIPS ATMAYBAR
RESEARCH UNIT: WOLLO, ETHIOPIA
INTRODUCTION
The increasing acuteness of water scarcity problems worldwide requires efforts towards revising and mitigating the approaches of water supply and demand
(Bastiaanssen et al., 2003). In areas where water is scarce, efficient use of all water resources, surface and ground water, is important. Groundwater and surface water are not isolated components of a watershed hydrologic system, but instead interact in a variety of physiographic and climatic landscapes (Sophocleous, 2002). The interactions between groundwater and surface water and the resulting exchange fluxes are often characterized by high temporal and spatial variability. Commonly the type of interaction is classified by the direction of the exchange fluxes: influent (flowing in) fluxes and effluent (flowing out) fluxes (Kalbus et al., 2006; and Zehe, 2007).
There is a real need for improved concepts to determine the source and timing of flow by studying the drainage morphology: from such knowledge watersheds can be evaluated as intermediaries of water flow, and future behavior under specific conditions may be predicted with greater precision (Hewllet and Hibbert, 1963).
Efficient prediction of quantitative runoff and river flow occupies a central place in the technology of applied hydrology (Nash and Sutcliff, 1970; Calvo, 1986; Hosking and
Clarke, 1990; and Cabus, 2008) since these values are useful to avoid risk for water resource planning, flood forecasting, pollution control and many other applications.
The modeling of rainfall-runoff relationships is not a simple task. It requires sufficient knowledge and good understanding of the hydrological processes, rainfall characteristics, runoff mechanism, and the identification of runoff source areas within
10
the watershed, which in turn are determined by the physical properties of the basin
(Shakya and Chander, 1998; and Gomi et al., 2008). If the relationships between these properties and the hydrological behavior could be defined, the hydrological responses of basins could be easily predicted (Acreman and Sinclair, 1986).
Total rainfall falling in a given area will not be directly converted to runoff because before runoff is generated rainfall has to pass different steps (Hewllet and
Hibbert, 1966; Wang et al., 1992; and Huang et al., 2008). As a result, the rainfall – runoff relationship in watersheds is non-linear (Szilagyi, 2007; and Leh et al., 2008).
Generally, there are two types of runoff mechanisms: saturation excess runoff and infiltration excess (Hortonian) runoff (Kubota and Sivaplan, 1995; Sen et al.,
2008; and Wickel et al., 2008). Saturation excess runoff volume is dependent on the aerial extent of saturation within a watershed and the rainfall depth, but it is independent of rainfall intensity. In contrast, infiltration excess runoff volume is directly dependent on rainfall intensity and will not occur at low intensities (Walter et al., 2000). Identification of runoff generation processes within the watershed requires close observations and detailed investigations, but characterization of dominant runoff processes is not an easy task, especially when such processes occur below the soil surface (Beven, 1989 quoted in Latron and Gallart, 2008).
Computer-based rainfall-runoff models at different resolutions have been developed for several decades (Jayakrishnan et al., 2005) with the objective of elucidating the complex and dynamic hydrologic processes and simulating runoff and river discharge from watersheds throughout the world. Most of the models attempt to simulate the complex hydrological processes that lead to the transformation of rainfall into runoff, with varying degree of abstraction from different physical processes
(Jacquin and Shamseldin, 2006). The models differ not only in their level of complexity, but also in their level of applicability, efficiency, and specific data
11
requirements. The efficiency of all the models that simulate the amount of runoff from a given rainfall depends on the ability of the model to simulate, all factors that affect the rainfall-runoff process in a given area (Jacquin and Shamseldin, 2006).
Although hill slopes are responsible for generating 95% of the water in the streams (Shakya and Chander, 1998), hill slope hydrologic response to rainfall is not well studied (Meerveld and Weiler, 2008). Most of early models describing rainfall runoff processes relied on Horton’s (1933) infiltration excess principle (Shakya and
Chander, 1998), but the Horton concept failed to predict runoff on vegetated hill slopes (Meerveld and Weiler, 2008). To better simulate runoff from hills slopes,
Hewlett (1961) introduced the variable source area concept, which is based on saturation excess runoff mechanism.
In Ethiopia, saturation excess overland flow has been identified as one of the mechanisms for generating storm flow (Lui et al., 2008). This study in the Ethiopian highlands focuses on characterizing subsurface water flow and ground water table fluctuations in response to rainfall that leads to saturation excess runoff. In particular, based on these processes, the goal is to develop a model that efficiently simulates the location of saturated runoff areas and predict river discharge. The results of this study will help in realistically planning watershed interventions.
MODEL DEVELOPMENT
Conceptual Watershed model: Watersheds in the Ethiopian highlands are characterized by relatively flat bottomlands and gentle to steep sloping uplands. In our conceptual watershed model, the watershed is divided into two areas, based on slope steepness, soil depth, and infiltration capacity of the soil: runoff source areas near the river and recharge source areas on the hills. The runoff source area was further divided
12
in to two sub-groups based on relative difference in soil depth and amount of moisture required to initiate runoff.
Figure 2-1 illustrates the structure of the watershed model developed in this
study. The basic assumption made was that hill slope areas have very high infiltration capacities and all the rainfall above field capacity percolates downward due to gravity.
On the other hand, the excess rainfall when the soil is saturated from runoff source areas (flatter areas) becomes overland flow. In addition the flatter areas remain wet even during the extreme dry months of the year, only the top most soil layer will dry due to small amounts of water percolating downward from the hills. And hence these areas need only a small amount of rainfall, to start generating surface runoff.
Figure 2-1: Structure of the conceptual water balance model by Steenhuis et al. (2008)
13
Model Description : A water balance model was modified from the model in Collick et al. (2008) for small watersheds in the upper Blue Nile basin and in Steenhuis et al.
(2008) for the whole Blue Nile basin. The basic inputs to the model are daily precipitation and potential evapotranspiration. Model outputs include daily runoff, interflow, and base flow according to the type and proportion of area under consideration within the watershed.
The amount of water stored in the topmost layer (root zone) of the soil, S
(mm), for hill slopes and the runoff source areas were estimated separately with a water balance equation of the form:
S
S t
t
P
AET
R
Perc
t ..........
..........
..........
..........
..........
..........
.....[ 2
1 ]
Where P is precipitation, (mm d -1 ); AET is the actual evapotranspiration, (mm d -1 ), S t-
Δt
, previous time step storage, (mm), R saturation excess runoff (mm d -1 ), Perc is percolation to the subsoil (mm d -1 ) and Δt is the time step.
During wet periods when the rainfall exceeds potential evapotranspiration,
PET (i.e., P>PET), the actual evaporation, AET, is equal to the potential evaporation,
PET. Conversely, when evaporation exceeds rainfall (i.e., P<PET), the Thornthwaite and Mather (1955) procedure is used to calculate actual evapotranspiration, AET
(Steenhuis and van der Molen, 1986). In this method, AET decreases linearly with moisture content, e.g.:
AET
PET
S t
S max
..........
..........
..........
..........
..........
..........
..........
..........
[ 2
2 ]
Where S t
(mm) is the available water stored in the root zone per unit area and S max
(mm) is the maximum available soil storage capacity defined as the difference between the amount of water stored in the top soil layer at wilting point and the maximum moisture content, equal to either the field capacity for the hill slope soils or saturation (e.g., soil porosity) in runoff contributing areas. S max
varies according to soil
14
characteristics (e.g., porosity, bulk density) and soil layer depth. Based on Eq. 2-2 the surface soil layer moisture storage can be written as:
S t
S t
t
exp
( P
PET
S max
)
t
..........
..........
..
when P
PET ..........
..........
..........
.[ 2
3 ]
In this simplified model, direct runoff occurs only from the runoff contributing area, when the soil moisture balance indicates that the soil is saturated. Recharge and interflow originate from the remaining hill slopes. It is assumed that the surface runoff from these areas is minimal. This will underestimate the runoff during major rainfall events and, to test its significance, the model was run on a daily, weekly, and monthly basis.
In the overland flow contributing areas when rainfall exceeds evapotranspiration and fully saturates the soil, any moisture above saturation becomes runoff, and the runoff, R, can be determined by adding the change in soil moisture from the previous time step to the difference between precipitation and actual evapotranspiration, e.g.:
R
S t
S t
t
S max
P
AET
t ..........
..........
..........
..........
..........
..........
..........
..........
..[ 2
..........
..........
..........
..........
..........
..........
..........
..........
..........
..........
.......[ 2
4 a ]
4 b ]
For high infiltration areas on hill slopes the water flows either as interflow or baseflow to the stream. Rainfall in excess of field capacity becomes recharge and is routed to two reservoirs that produce baseflow or interflow. We assumed that the baseflow reservoir is filled first and when full, the interflow reservoir starts filling.
The baseflow reservoir acts as a linear reservoir and its outflow, BF, and storage, BS t, are calculated when the storage is less than the maximum storage, BS max
as:
15
BS t
BF t
BS t-Δ-
BS
1 t
Perc exp(
BF t
t )
t
2
..........
..........
..........
..........
..........
..........
..........
..........
......[ 2
5 a ]
5 b ]
t
t ..........
..........
..........
..........
..........
..........
..........
......[
Where α is the half-life of the aquifer, or the time it takes for half of the volume of the aquifer to flow out without the aquifer being recharged.
When the maximum storage, BS max
, is reached then:
BS t
BF t
IF t
BS max
BS max
*
0 , 1 , 2
..........
..........
..........
..........
..........
..........
..........
..........
..........
..........
....[ 2
1
exp(
2
t
Perc t
*
t )
1
*
,
..........
..........
..........
..........
..........
*
..........
..........
.[ 2
*
2
..........
..........
..........
..........
..........
....[ 2
6 a ]
6 b ]
7 ]
Interflow originates from the hill slopes with the slope of the landscape as the major driving force of the water. Under these circumstances, the flow decreases linearly (i.e., a zero order reservoir) after a recharge event. The total interflow, IF t
at time t can be obtained by superimposing the fluxes for the individual events.
Where τ* is the duration of the period after the rainstorm until the interflow ceases, IF t is the interflow at a time t, Perc* t-τ
is the percolation on t-τ days.
MATERIALS AND METHODS
The study was carried out in the Maybar watershed, fully described in Chapter
One. Discharge was collected at the outlet of the watershed for the periods 1988 –
1989, 1992-2000, 2002, 2004 and 2008 and from the test plots from 1988-1994. In addition, 29 piezometers were installed during 2008 and the (perched) groundwater table was measured during the rainy season. Infiltration measurements were carried out in a previous study by Derib et al., (2005).
Additional information on major runoff mechanism was based on the information and informal discussions with farmers and technicians.
16
Discharge at watershed outlet
At Maybar research station watershed discharge was measured with a flume installed in the Kori River. The water level height is measured in two ways: floatactuated recorder and manual recording. The maximum stage height at the gauging station was set to be 1.8 meter. Using the discharge rating equation all water level records (stage height) including during the year 2008 was converted to discharge volumes (m 3 ).
The final discharge rating curve was set to be as follows:
Q
Q
Q
(
(
(
21
49
21
Q
)
H
H
2 .
016 *
49 )
180 )
Discharge
H
0 .
1 .
311
003 *
1 .
023 *
(l / s)
H
H
3 .
356
1 .
862
..........
..........
..........
..........
..........
..........
..........
[ 2
8 ]
( Source : Bosshart , 1997 )
Discharge from Runoff Plots
In the research station, there are four test plots from which runoff and sediment data is being monitored. Each test plot covers 2 m x 15 m area and represents different land use areas and different slope gradients. Surface runoff water is collected in a tank.
Water is removed if the rainfall is in excess of 12.5 mm in less than six hours or if the water depth in the collection tank is more than 25.5 cm.
Long term runoff data (1988-1994) from test plots was analyzed and compared with the rainfall data. The runoff amount and runoff coefficient of each test plot was calculated and the result was compared taking land use/land cover and slope gradient differences into consideration.
17
Climatic Data
Daily maximum and minimum air and soil temperatures, wind direction, wind strength, and evaporation (using piche tube evaporimeter) were collected twice per day at 8:00 A.M and 18:00 P.M. Rainfall data was monitored via two procedures: (1) using automatic rain gauge which uses chart role (one chart role for one month) and the data obtained was to be used for further determination of rainfall characteristics such as intensity, duration, frequency, and erosivity values of individual storm events and (2) using two manual rain gauges at two different locations, one in the upper part of the catchment and the other near the office (climatic station).
Ground Water Table Measurement
Ground water table levels were measured with 29 piezometers during the
(2008) main rainy season. These were installed at two different locations in eight transects, 16 pierzometers in four transects in the upper watershed (Atarimesk ) and 13
piezometers in four transects in the lower watershed (near the gauging station) (Figure
The piezometers used were prepared from 5 cm diameter PVC pipes of varying lengths. The bottom 30 cm of the piezometers was perforated at four places (at 5 cm interval) in four columns. The perforated part was covered with cloth that allows water inflow towards the tubes but prevents inflow of sediment. The bottom end (opening) of the pipe was closed with a plastic cap and sealed with plastic bandage to block inflow and outflow of water, while the above ground opening of the piezometers was capped to protect against the entrance of rainfall.
18
T-1
T-6
Figure 2-2: Location of piezometer transects at different slope range in the watershed.
Since the topography of the watershed is highly undulated and very steep, piezometers were installed at two different locations in the watershed that are relatively suitable for piezometer installation. The two locations were selected based on the presence of better subsurface water flow from the top of the hill slope down to the saturated area near the river. During the installation, an “ Idle Man Auger
” was used to drill the boreholes and drilling was done until the impermeable layer, bedrock, or the ground water was reached. Piezometer installation depth from the earth surface ranged form 0.64 to 2.02 meters. Detailed information on piezometer installation sites can be found in Appendix 5.
.
19
Saturated Area Delineation
The saturated area in the watershed was delineated and mapped by combining information collected using geographic positioning system (GPS) instrument, field observation, and ground water level data (piezometer head readings). The result (size and extent of saturated area) was cross validated with the results obtained from the topographic index (TI) map of the area. Topographic Index maps are grids derived from digital elevation models (DEMs). It computes topographic indices for each grid cell based on upslope contributing area per unit length of contour and topographic slope of the cell. As a result, bottomlands, with large upslope contributing areas, have higher topographic index values and are prone to saturation
Data Checking and Analysis
Data checking was done mainly for dates with missing values, time sequence discontinuities, and negative values. Finally, the long-term data set from SCRP databases and primary data collected during the major rainy season (2008) were incorporated and analyzed using Microsoft Excel spreadsheets.
MODEL EFFICIENCY EVALUATION
Calibration and Validation of Rainfall – Runoff Model
Evaluation of the hydrologic model behavior and performance is commonly made and reported through comparisons of simulated and observed values. For model testing, the thirteen years of hydrological data was to be used was divided into two sets. The first set was used for calibration and was comprised of the years 1992 to
2000, 2002, 2004 and 2008. The second set, used for model verification, consisted of data from 1988 and 1989, which was collected under supervision of Bern University and generally believed to be of the best quality.
20
Model calibration was done manually through randomly varying input parameters in order that the best “closeness” or “goodness-of-fit” was achieved between simulated and observed river discharge. The calibrated input parameters consisted of maximum storage S max of the three regions and the reservoir parameters t*, α, and SB max
.
Model efficiency was evaluated based on Nash and Sutcliffe (1970) efficiency index (E) and coefficient of determination (R 2 ) values. Nash-Sutcliffe (E) Index can be expressed as:
E
1
i n
1
( P i
O i
2 i n
1
( O i
__
O
2
………………………….………………………… [2-10]
Where P i
is the simulated discharge for each time step, O i
is the observed discharge value,
__
O the average measured discharge, N is the total number of values within the period of analysis. The value of E ranges from -∞ to 1, where a value of 1 indicates perfect fit between simulated and measured values, while 0 implies the model efficiency in predicting discharge is equal to the mean of the observed data, but if E is less than zero the observed mean is better than the model in predicting.
RESULTS AND DISCUSSION
Rainfall Amount, Intensity and Infiltration Capacity
Temporal and spatial rainfall characteristics are very important factors that affect runoff generation. The two rain gauges installed in the 112.8 ha watershed give temporal effects of rainfall i.e., intensity and amount, that were obtained from pluviograph readings of the automatic rain gauge. The annual precipitation based on
21
13 years of observation is 1374 mm and the coefficient of variation for annual
precipitation is 0.15 (Figure 2-3).
Figure 2-3: Long-term rainfall amount and distribution
Rainfall is distributed annually into a major and a minor rainy season. The minor rainy season ( Belg ) extends from March to April while the main rainy season
( keremt ) starts from June/July and ends in September. The average precipitation is
295 mm during the belg and 780 mm during the keremt . For determining the hydrologic response of a watershed, the effective rainfall (defined as the precipitation
minus the potential evaporation) is an important parameter (Figure 2-4). During both
rainfall seasons, precipitation exceeds evaporation. The excess leaves the watershed over time. Long-term rainfall and river discharge data is summarized on a monthly and annual basis in Appendix 1 and Appendix 2, respectively.
22
River Discharge (mm) Rainfall (mm) Runoff Coefficicent
450
400
350
300
250
0.7
0.6
0.5
0.4
200
150
100
50
0.3
0.2
0.1
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Months
Figure 2-4: Long-term average annual hydrograph
0
Rainfall Intensity and Soil Infiltration Rate
Rainfall intensity is an important parameter to model rainfall-runoff relationships, especially in areas where infiltration excess runoff is expected (Beven,
2004; Amore et al., 2004). Rainfall intensity was calculated from continuous pluviograph recordings by dividing the amount of storm rainfall by its duration. The average daily rainfall intensity for six years (1988-1995 except data from 1990) was
7 mm/hr, Figure 2-5 depicts six years event based rainfall amount and corresponding
intensity values. The highest intensity rainfall, recorded in the watershed, was equivalent to 162 mm/hr on 29 July 1992 from a rainfall amount of 2.7 mm lasted only for one minute.
23
Figure 2-5: Long-term rainfall amount and intensity values
Soil infiltration tests were conducted in the Maybar watershed by Derib
(2005). He performed 16 infiltration tests, inside and outside of the watershed down to Lake Maybar, varying from just over one day to ten days. The final infiltration
rates at the end of the experiment ranged from 19 mm/hr to 600 mm/hr (Figure 2-6
and Appendix 3). In this watershed, all infiltration rates were greater than the observed average rainfall intensity rate of 7 mm/hr, and most of the 16 locations had a final infiltration rate that was less than the maximum observed rainfall intensity in six years. Since the final infiltration rates in almost all cases were greater than the rainfall intensity, using the infiltration excess concept common to most models developed in temperate climates would result in the majority of the rainfall events not producing runoff. However, there was significant runoff reported. Hence, the saturation excess concept, used in our simple water balance model and discussed in the Model
Description section in Materials and Methods, is a valid concept to simulate runoff in
24
the Maybar and other watersheds in the Ethiopian highlands with a monsoonal climate
(Engida et al., 2009; and Legesse et.al.,2009 ).
Figure 2-6: Infiltration test results (Source: Derib, 2005)
Runoff from Test Plots
The rainfall-runoff data collected from runoff plots for the years (1988, 1989,
1992 and 1994) allow us to clarify the runoff processes in the watershed further. The
average annual runoff from the four runoff plots is depicted in Figure 2-7.
25
Figure 2-7: Average annual plot runoff and rainfall values
The runoff from runoff plot 1 is the greatest followed by plot 4 and 3. Plot 2 has the least amount of runoff. Dividing the total runoff over the total precipitation, the runoff coefficients are obtained. The runoff coefficients range from 0.69 to 0.33
(Table 2-1). A distinct relationship exists between slope and runoff coefficient, but not as one would expect; the steeper the slope the smaller the runoff. The results from
Figure 2-8 clearly indicate that as slope increases the runoff coefficient decreases, and this implies that areas with the steepest slope in the watershed have the least runoff coefficient compared with mid-slope and gentle-slope areas. The results further strengthen the concept of saturation excess runoff from gentle slope areas being the major runoff mechanism in the watershed.
26
Table 2-1: Slope range, runoff coefficient, and land use type of test plots
Test Plot No. Slope (%) Runoff Coefficient Land Use Type
1
2
3
4
16
64
43
37
0.69
0.54
0.47
0.33
Cultivated land
Grass land + Bush land
Grass land + Bush land
Cultivated land
Figure 2-8: Plot runoff coefficients at different slope gradients
To understand the relationship between slope and runoff coefficient, we considered the infiltration data in the previous section indicating that all rainfall infiltrates the soil and flows as interflow down slope. On the hillside itself, most flow is interflow. However, at the downstream end of the plot, the runoff trog and the related structure with the large containers hampers down-slope movement, and the water table will rise creating a saturated area. Rainfall on this saturated area will become overland flow. The plot with the smallest slope (Plot-1) is located in the saturated area and any rainfall on the whole plot after the ground is fully saturated
27
becomes runoff. After accounting for evaporation the runoff coefficient would be
100% when effective rainfall is considered. Simulated runoff results from the saturated area in the watershed and runoff data from the lowest slope plot (Plot-1) was compared on daily basis for two years (1988 and 1989). Thus the data from the runoff plot is consistent with our conceptual model, in which most of the runoff is produced
in the bottom and flattest area of the watershed (Figure 2-9).
Figure 2-9: Comparison of simulated runoff from saturated area and plot runoff (Plot-
1) on daily basis.
Groundwater in the watershed
The model assumes that during the rainy season, rain falling on the steep hillsides infiltrates and flows as saturated subsurface flow over the impermeable layer down the hill. Thus, the water table is below the surface and since the flow is driven by the hydraulic gradient that approximates the slope of the land, the saturated layer above the restricting layer should be smaller for steep slopes than gentle slopes. In
28
addition, the contributing area is greater for the gentle slopes than for the steep upper parts, which also increases the depth of the saturated layer. In the bottom concave areas of the watershed with the smallest slope and the largest contributing areas, we expect the water table to be at the surface. To test the conceptual model further, 29 piezometers were installed in eight transects. The location of these piezometers can be
In general, piezometeric head data from the two installation sites indicated that the water level at comparable levels in the upper watershed shows a larger response to
the rainfall than those in the lower watershed (Figure 2-10). The difference in response
is likely caused by the larger contributing area in the upper watershed than in the lower watershed. Simply said, more water needs to be transported through the soil in the upper watershed, resulting in a greater saturated water depth than in the lower watershed. In addition, the water depth above the impermeable layers measured in the piezometers in the upper part of the watershed (hillsides) declines faster than in piezometers at down slope (gentle slope). In general, the ground water table response was more closely related to landscape position than to crop type. All daily piezometeric head data is summarized for each transect in Appendices 6 and 7.
29
Figure 2-10: Comparison of average response of piezometers to rainfall from upper and lower watersheds.
Ground water level at different slope range
To examine further the effect of slope on ground water level, the whole watershed was divided in to three slope ranges; steep slope [25.1° – 53.0°], mid slope
[14.0°– 25.0°], and relatively low-lying areas[0°- 14.0°]. For each slope class the daily perched ground water depths were averaged (i.e., the height of the saturated layer
above the restricting layer) (Figure 2-11). The observed perched water depths are in
accordance with the model assumptions presented at the beginning of the section. The depth of the perched ground water above the restricting layer in the steep and upper parts of the watershed is very small and disappears if does not rain for few days. The depth of the perched water table on the mid slopes is greater than upslope areas. The perched ground water depths are as expected the greatest in relatively low-lying areas.
30
The springs occur at the locations where the depth from the surface to the impermeable layer is the same as the depth of the perched water table.
Figure 2-11: Water level at different slope ranges calculated above the impermeable layer.
Ground water level at different land use areas
Finally, we determined the average daily depth of the perched water table
under the different crop types (Figure 2-12). Unexpectedly, there was a strong
correlation of perched water depth with crop type as well. The grassland had the greatest perched water table depth, followed by cropland and brush land with the lowest ground water level. The distribution of the land use in the landscape is given in
Figure 1-5. The grasslands are mainly located in the lower lying areas, while the cropland and bush land are in the mid-slope and upper steep slope areas, respectively.
Since crop type is related to slope class, we expect the same relationship between crop type and soil water table height as slope class and water table height.
31
Figure 2-12: Water levels at different land use types
The question arises if slope or crop type is responsible for the observed perched water table depths. It is our belief that farmers select the crop based on the available water. The upper slope does not hold sufficient water for withering drought periods during the monsoon and hence this land is left uncultivated. The mid slopes and the lower portion of the watershed have sufficient water for growing agricultural crops, but the lowest portion is too wet for maize and sorghum, while grass does well under saturated conditions.
Simulating Watershed Discharge
Discharge was simulated for the Maybar research watershed that consists of the Kori Sheleko catchment. The watershed is the first of the SCRP’s research sites established in the northeastern part of the central highlands of Ethiopia. The 112.8 ha watershed mainly consists of two sub-watersheds separated by rock formations (one upslope and another down slope). Although the watershed represents small geographic
32
area coverage, distinct hydrological processes are observed at different parts of the watershed, which might have arisen from differences in soil type, land use, relief, and vegetation cover.
Using long-term hydrological data collected in the study area, we were able to predict river discharge at the outlet for daily, weekly, and monthly time setups, and both model calibration and validation were performed.
For model testing, thirteen years of hydrologic data was divided in two sets to be used for calibration and validation. The first set, used for calibration, was comprised of the years 1992 to 2000, 2002, 2004 and 2008. The second set, used for model verification, consisted of 1988 and 1989. Detailed information on the modeling
process and results from the three time setups is provided in Table 2-2 below.
Table 2-2: Optimized values of model parameters
Time setup
Daily
&
Weekly
Monthly
Area
1
2
3
1
2
3
Area share (%)
15
00
50
30
00
50
AWi
(mm)
80
-
150
80
-
150
AWC
(mm)
90
-
200
90
-
200
Calibration and simulation
SBmax
(mm)
τ*
(days)
Half life,
α (days)
70 3 16
Parameters needed to simulate discharge include potential evaporation (PET), which varies little between years and was obtained from the thirteen years data (1988-
2002 and 2004, and 2008, but did not include data from 1990, 1991, 1999, and 2001).
The average PET was 5.2 mm d -1 during the dry season and 2.9 mm d -1 during the rainy season. The precipitation values were measured with an automatic pluviograph
33
located near the gauging station. The maximum storage values, S max
, for the contributing areas and hill slopes were based initially on the values from Steenhuis et al. (2008) for the whole Blue Nile basin. It should be noted that, for the relatively flat contributing areas and bedrock areas, this maximum storage term represents the amount of water that is required to fill up dry soils before saturation and overland flow occurs. For the hill slopes, S max
is the moisture required to wet a dry soil up to field capacity, after which any extra water will percolate downward.
The values from Steenhuis et al. (2008) did not give an optimal fit because the watershed had few if any really degraded soils or rock outcrops that would produce runoff even during the smallest rainstorm. Moreover, some of the water passed by the gage as demonstrated by a large wet area in proximity to the gauging site. This will be discussed more in the next section. For the Blue Nile basin, Steenhuis et al. (2008), used a value of 10% to represent the runoff source areas. Here, for the Maybar
Watershed, we increased it to 15% for the daily and weekly simulation periods, and, after the dry season, 80 mm of effective precipitation was necessary before runoff was generated (i.e., S max
= 80 mm). The hillsides constituted the remaining 85% of the watershed. Fifty percent of the area contributed water to the gauge as interflow and baseflow, while the remaining 35% of the hillsides was not hydrologically connected to the gauge and is likely to be the saturated area on the left side of the lower watershed (in front of the river gauge, which is assumed part of the watershed but
practically flows outside the gauge). Note that in Table 2-2 for the monthly time step
a larger saturated area was needed to get the best fit.
The subsurface flow parameters indicate that all the water would drain in 3 days (i.e., t*=3 days) from the hill. This is reasonable given both the extremely high infiltration rates in these watersheds and the steep slopes. The baseflow parameter indicated that, on average, there is 7 cm of water that can be stored in the watershed,
34
and it is a fitting parameter that is difficult to check. Finally, the half-life for this aquifer is 16 days.
Daily Model Calibration & Validation: Acceptable model efficiency results were obtained for both the calibration and validation periods. For the calibration period 72% Nash Sutcliffe coefficient and 72 % “R
2”
values, and for the validation period 65% Nash coefficient and 65% “R 2” values
were obtained. Daily model calibration outputs are presented under Figure
2-13 and Figure 2-14, and daily model validation outputs are presented in
Figure 2-15 and Figure 2-16. Though good fits were obtained between
simulated and observed values for both the daily calibration and validation periods, the model under predicted most peak flow periods and over predicted low flow periods. Sometimes it did not catch the recession curves well.
Figure 2-13: Comparison of daily model calibration simulated and measured discharge
35
Figure 2-14: Scatter plot of daily model calibration simulated and measured discharge
Figure 2-15: Comparison of daily model validation simulated and measured discharge against rainfall amount
36
Figure 2-16: Scatter plot of daily model validation simulated and measured discharge result
Weekly model calibration and validation outputs: Higher model efficiency results were obtained from both calibration and validation periods: 80% R
2
and 80% Nash
Sutcliff values for the calibration period and 86% R
2 and 85% Nash Sutcliff values for
the validation period (Table 2-3). Even though better model efficiency results were
obtained during periods of the weekly time step simulations, as compared with daily time step results, the limitations of the model (over prediction of low-flow periods and insufficiency to effectively catch the recession curves) was still observed. Low-flow periods were slightly over predicted and the falling limb of the weekly hydrograph
(Figure 2-17) was also over predicted, likely due to the hillslope processes taking
place on a three day scale that do not fit well within the weekly time scale. Graphical comparisons of predicted and observed discharge both for the calibration and
validation periods are depicted under Figure 2-17, Figure 2-18, Figure 2-19, and
37
Table 2-3: Summary of data used during modeling and model efficiency results for three time setups
Data used Purpose Time setup
Nash Coeff.
(E)
(R
2
)
[1992 – 1998] + [2000,
2002, 2004 and 2008]
[1992 – 1998] + [2000,
2002, 2004 and 2008]
[1992 – 1998] + [2000,
2002, 2004 and 2008]
1988 – 1989
Calibration
Calibration
Calibration
Validation
Daily
Weekly
Monthly
Daily
72
80
86
65
72
80
88
65
1988 – 1989
1988 – 1989
Validation Weekly
Validation Monthly
85
94
86
96
Figure 2-17: Comparison of weekly model calibration output
38
Figure 2-18: Scatter plot of weekly model calibration simulated and measured discharge results
Figure 2-19: Comparison of weekly model validation simulated and measured discharge against rainfall amount
39
Figure 2-20: Scatter plot of weekly model validation simulated and measured
Monthly model calibration and validation output: During the monthly model calibration period, unlike the above cases, the model under predicted low-flow periods to some extent resulting in an R 2 value of 88% and a Nash-Sutcliff coefficient of 86%
(Table 2-3). During the validation period, the model predicted all flow periods (peak
and low-flow periods) relatively well, and higher efficiency results were obtained: an
R 2
of 96% and a Nash Sutcliff coefficient of 94% (Table 2-3). Monthly model
calibration outputs are presented under Figure 2-21 and Figure 2-22, and monthly
model validation outputs are shown under Figure 2-23 and Figure 2-24 below.
40
Figure 2-21: Comparison of monthly model calibration simulated and measured discharge against rainfall amount
Figure 2-22: Scatter plot of monthly model calibration simulated and measured discharge values
41
Figure 2-23: Comparison of monthly model validation simulated and measured discharge results
Figure 2-24: Scatter plot of monthly model validation simulated and measured discharge results
42
Summary of Overall Model Efficiency
The overall model performance for the three time steps was very good and highly acceptable. Despite the fact that simulations with a daily time step is usually much more sensitive if the correct processes are represented in the model, in our case, good efficiency results were obtained even at a daily time setup. Although the overall model performance was very good, some of the model problems observed during model prediction processes were: the model over predicted discharge during some dry seasons (base flow was over predicted); it did not catch the recession curve well after peak runoff events; the model under predicted peak runoff periods during wet seasons
(peak flow). The last is likely caused by an expansion of the saturated area, which this model with its fixed areas, cannot simulate.
Runoff Source Area in the watershed
Identification and mapping of major runoff source areas in a watershed facilitate the selection and design of better soil and water management practices and enhance efficient utilization of available water resources.
Saturation excess runoff from gently sloping saturated areas was verified as the major runoff mechanism in the watershed. In addition, results from plot runoff, rainfall intensity, and soil infiltration capacity reinforced our findings that saturation excess runoff was the dominant runoff mechanism in the study area.
Gentle slope saturated areas in the watershed were identified and manually delineated using GPS instruments and the result was validated using the topographic
index map result and a very good fit was obtained in the upper watershed (Figure
43
Figure 2-25: Map of runoff source area
The pink color on the map represents the highest topographic index value (19) and red with least values (2). Topographic index values for individual cells represent the total number of cells flowing towards it. In other words, the flatter the area, the more cells will flow towards it.
Although the flatter area in the lower part of the watershed (indicated by pink
in Figure 2-25) is geographically part of the watershed, water from this area flows out
of the watershed and joins the Kori River below the gauging station.
44
CONCLUSION
Long-term climatic and hydrologic data both at watershed and test plot levels were analyzed and used to develop a modified water balance model and the model was tested for three time setups (daily, weekly, and monthly basis). Model efficiency results from both calibration and validation periods were very high and more than acceptable. However, despite all the achievements, it was understood that there is still opportunity to increase the model efficiency further through the use of better quality/better predicted evaporation data and by better estimation of the proportion of rainfall lost as deep percolation from hillside areas. Apart from this, the modified water balance type model was found to be user friendly and applicable with higher predicting efficiency.
Hence, the modified water balance type model could be used to predict runoff and characterize the runoff mechanism in hill-slope areas with some calibration parameters, specific to the area under consideration. However, it is necessary to test the model more widely so as to improve the model efficiency with the best set of parameter combinations in order to use it for larger areas and data scarce watersheds.
45
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Base Flow and Recession Annalyses. Water Resources Research 26(7) 1465-
1473.
Sen S., Srivastava P., Yoo K.H., Dane J. H., Shaw J. N., and Kang M.S. 2008. Runoff generation mechanisms in pastures of the Sand Mountain region of Alabama— a field investigation. Hydrol. Process. 22:4222–4232.
Shakya N. M. and Chander S. 1998. Modeling of hill slope runoff Processes.
Environmental Geology 35 (2–3) 115-123.
Sophocleous, M.A., 2002. Interactions between groundwater and surface water: The state of the science. Journal of Hydrogeology, 10(1):52-67.
Steenhuis T.S. and van der Molen W.H. 1986. The Thornthwaite-Mather Procedure as a Simple Engineering Method to Predict Recharge. J. Hydrol . 84: 221-229.
Steenhuis T.S., Collick A.S., Easton Z.M., Leggesse E.S., Bayabil H.K., White E.D.,
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Erosion for the Abay Blue Nile with a Simple Model. Hydrol. Proc. (In Press).
Szilagyi J.2007. Analysis of the nonlinearity in the hill slope runoff response to precipitation through numerical modeling. Journal of Hydrology 337:391–
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Thornthwaite C. W. and Mather J. R. (1955), The Water Balance , Publications in
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Walter T.M., Walter M.F., Brooks E.S., Steenhuis T.S., Bol J., and Weiler K.2000.
Hydrologically Sensitive Areas: Variable Source Area Hydrology Implications
49
for Water Quality Risk Assessment. Journal of Soil and Water Conservation
3:277-284.
Wang G.T., Singh V.P., and Yu F.X. 1992. A rainfall-runoff model for small watersheds. Journal of Hydrology 138:97-117.
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Processes 22: 3285–3293.
50
3.
CHAPTER THREE
IMPACT ASSESSMENT OF SOIL CONSERVATION RESEARCH PROJECT
INTERVENTION ON SOIL PHYSICAL AND CHEMICAL PROSPERITIES OF
MAYBAR RESEARCH UNIT; WOLLO, ETHIOPIA
INTRODUCTION
Rapid population growth and a long history of sedentary agriculture has changed the land use/land cover system and has been a major cause of environmental degradation on most parts of the world, including Ethiopia (Feoli et al., 2002; and
Hurni et al., 2005). Increasing population requires additional farmlands to increase the food production, and one way to expand the croplands is by deforestation of the natural forests. On the other hand, agricultural activities change the soil chemical, physical, and biological properties, and play a major role in soil degradation, mainly soil fertility decline because of a lack of nutrient inputs (Lal, 1986, quoted in Alfred et al., 2008). In Ethiopia, where agriculture is the main stay of the economy
(approximately 50% of GDP, 90% of foreign exchange earnings (EEA, 2002) an estimated half of the Ethiopian highlands’ arable lands are moderately to severely degraded and nutritionally depleted due to over cultivation, over grazing, primitive production techniques, and over dependence on rainfall (Hugo et al., 2002).
Hence, over four percent of the country’s arable land has completely lost its ability to produce food (Teketay et al., 2003), and much more area is in a state of active degradation that makes restoration difficult and/or requires a considerable investment for mitigation (Lu et al., 2007).
51
Maybar is similar to the other highland areas in the country. The farming practice is suffering from severe land degradation and recurrent drought conditions resulting from erratic and unpredictable rainfall.
Taking the above problems into consideration, the soil conservation research program (SCRP) was implemented in 1981 with the objective to alleviate the aggravated land degradation problem. As a result, during the project implementation period, different soil and water conservation measures, such as physical structures, area closure and biological structures were intensively introduced to the area through a “food for work” campaign.
Despite the introduction of these soil and water conservation structures, there was no inclusive and sufficient work done towards generating quantitative information on their agro-ecological effects. In addition, the most efficient soil and water conservation methods and associated problems of economic viability, social acceptance, and technical feasibility are not clearly understood (SCRP, 2000).
Thus, the main objective of this study is to investigate the impact of soil and water research project interventions on selected physical and chemical soil properties of the area, which are the basic determining factors for the natural resource base and environmental sustainability.
This research builds on previous study done by Weigel (1986), who conducted a detailed soil survey and produced a detailed soil map of the watershed.
RESEARCH METHODS
Soil Sampling Techniques, Site Selection, and Sample Preparation
Since the underlying objective was to assess the spatial and temporal variation of chemical and physical soil properties after the implementation of soil conservation structures in the watershed (1981), soil survey results from Weigel (1986) were the
52
basis for this study. During the study by Weigel (1986), a total of seventeen profile pits were opened inside the watershed and infield characterization and laboratory analyses were carried out separately for each horizon in each profile.
Since current study results were to be statistically compared with previous work data the location of fifteen profile pits out of the seventeen were identified with maximum care so as to avoid errors due to sampling location. The objective was to take soil samples from similar locations where the previous samples were taken.
During the survey, a total of fifteen composite soil samples were collected from the top (20 cm) soil depth. All the necessary sample preparation work was done before the laboratory analyses. The pre-laboratory analyses sample preparation process included air-drying, crushing of the clods by hand (reduction of the aggregates to the right size, mostly less than 2 mm), and sieving so as to make the particle size uniform.
Laboratory Analyses
Soil Chemical Property Analyses
Laboratory analyses for selected soil chemical parameters were done at the
Dessie Soil Laboratory Center. Analyses were done for soil organic carbon (%OC) and available phosphorous content (AP) (See Appendix 4). The selection of soil chemical parameters was based on the general fact that among many soil chemical properties, soil organic carbon content, which is an indication of organic matter status of the soil, is the most important and most limiting factor in most cultivated lands of the Ethiopian highlands, and the same is true for phosphorous. Soil organic matter significantly affects soil water holding capacity, acts as nutrient storehouse, acts as the main site of cation exchange, and provides energy for soil organisms.
53
Organic Matter Content (%OC)
Soil organic content is one of the most important soil characteristics. The depletion of the soil organic matter, because of deforestation and other factors, reduces the soil structural stability and favors crusting, runoff production and gully erosion
(Valentine et al., 2005).
The “Weekly Black method” was followed during laboratory analysis to determine soil organic carbon content. During the process, soil organic matter was oxidized under standard conditions with potassium dichromate in sulfuric acid solution. A measured amount of K
2
Cr
2
O
7
was used in excess of that needed to destroy the organic matter, and the excess was determined by titration with ferrous sulfate solution, using a diphenylamine indicator to detect the first appearance of un-oxidized ferrous iron.
2 Cr
2
O
7
2
3 C
16 H
4 Cr
2
3 CO
2
8 H
2
O ..........
..........
..........
....[ 3
1 ]
Excess Cr
2
O
7
-2 was then back titrated with standard Fe ++ solution to determine the amount that had reacted.
It is believed that soil organic matter contains 58% carbon. Conversion of % carbon to % organic matter, therefore, was done with the empirical factor of 1.724, which is obtained by dividing 100/58.
% OrganicMat ter
1 .
724 * % Carbon ..........
..........
..........
..........
.........[ 3
2 ]
Available Phosphorous (AP)
Phosphorous is one of the most essential elements required by crops and plays a key role in plants’ growth, especially for energy synthesis and transfer. P exists in the soil in different forms including available phosphorous (AP), which is readily available for plants use. Optimum levels of AP are good both for agricultural
54
productivity and a healthy environment. Low amounts of AP in the soil will lead to reduced crop productivity, while excess P is a potential source of contamination both for surface and groundwater.
The “Olsen method” was followed during the determination of available phosphorous in the soil. Samples were extracted with sodium bicarbonate solution at pH 8.5, and the level of phosphate in the extract was determined colorimetrically after treating it with an ammonium molybdate-sulfuric acid reagent with ascorbic acid as a reducing agent.
Calculation:
Exch .
K / Na ( Cmol
) / Kg Soil
( a
b ) * 250 * mcf
10 * 39 .
1 ( 23 .
0 ) * S
..........
..........
..........
..[ 3
3 ]
Where a is the mg/l of P in sample extract, b is the mg/l of P in blank, S is the sample weight in grams, mcf is the moisture correction factor, and 100 indicates ml of extracting solution.
Soil Physical Property Analysis
Bulk Density Determination (Db)
Bulk density represents the overall density of a soil mass (the weight of the dry mineral divided by the overall volume occupied by the soil particles and pore spaces). Bulk density values are highly indicative of the nature and status of the soil quality. In particular, it greatly affects the infiltration capacity of the soil, nutrient availability and other physical and chemical processes that take place in the soil system. Higher bulk density values indicate higher degree of soil compaction, which further affects the infiltration capacity of the soil. The infiltration capacity of the soil greatly affects the hydrological and ecological regimes of the area.
55
Twelve soil samples were collected, using a core sampler, from different land use areas and slope gradients. Dry soil mass was determined after the soil sample was oven-dried, at 105°C for 24 hours.
Calculation:
Db ( g cm
3
)
Ovendry
Volume of soil core weight sampler
( gm )
( cm
3
)
.......
..........
..........
..........
....
3
4
Statistical Analysis
Paired t-test statistical analysis was done using Microsoft Excel spreadsheets.
The core objective of the statistical analysis was to evaluate whether the two mean values of the previous work results by Weigel (1986) and current results (2008) were statistically different or not.
RESULTS AND DISCUSSION
Available Phosphorous (AP)
Mean AP content in the catchment was 20.3 ppm in 1986 and 10.4 ppm in
2008. Statistical analyses results indicated that the AP content of the soils in the study
area were significantly different (at P < 0.05 level) (Table 3-1), which implies there
has been a significant amount of change in the AP status of soils over time (from
1986 to 2008). The direction of change was decreasing from an average amount of
20.3 ppm during 1986 down to 10.4 ppm in 2008. Thus, it can be seen that the AP content of soils has been reduced (depleted) by nearly 49% within 22 years time.
The available phosphorous (AP) content of the soil samples ranged between a minimum value of 6.5 ppm for a sample collected from the bush land area to a maximum value of 40.7 ppm for a sample taken from cultivated land. The above results indicate the existence of significant differences in the AP level of soils from different land use areas.
56
The average AP values for three land-use areas were calculated and the results
are presented in Table 3-2. The results indicate that cultivated lands had the highest
AP content (12.1 ppm); while grass lands and bush lands had almost similar AP content, 8.6 ppm and 8.5 ppm respectively. The finding that the highest AP content comes from cultivated land areas could be due to the addition of chemical fertilizers and manure.
Table 3-1: Statistical analysis result for AP
Available Phosphorous (ppm) t-Test: Paired Two Sample for Means
Mean
Variance
Observations
Pearson Correlation
Hypothesized Mean Difference df t Stat
P(T<=t) one-tail t Critical one-tail
P(T<=t) two-tail t Critical two-tail
Variable 1 Variable 2
20.3 10.4
255.5
15
0.16
0
72.1
15
14
2.3
0.019
1.76
0.039
2.14
Table 3-2: Average AP, %OC, and Db values for different land use areas.
Land Use Type Available P (ppm) % OM Bulk Density (g/cc)
Cultivated land
Grass land
Bush land
12.1
8.6
8.5
2.9
3.2
3.0
1.0
1.2
0.8
Percentage Organic Carbon (% OC)
From the current study, % OC was found to be less changed within the two time periods (1986-2008), though mean % OC has decreased from 3.68% in 1986 to
3.02% in 2008.
57
Statistical analysis results for the two mean values from the two time periods
indicated that % OC did not significantly change through time (Table 3-3). This could
be due to higher fertilizer addition in recent years as compared with previous periods that could offset the loss. On the other hand, % OC content was different at different land use areas. Grasslands had the highest % OC content (3.2 %) while cultivated lands were found to have the least amount of %OC (2.9 %).
Table 3-3: Statistical analysis result for (%OC)
Organic Carbon (%) t-Test: Paired Two Sample for Means
Mean
Variance
Observations
Pearson Correlation
Hypothesized Mean Difference df t Stat
P(T<=t) one-tail t Critical one-tail
P(T<=t) two-tail t Critical two-tail
Variable 1
3.68
1.59
15
0.21
0
14
1.75
0.05
1.76
0.10
2.14
Variable 2
3.03
1.04
15
Bulk Density (Db)
Mean soil bulk density (Db) values for samples taken at the two different
periods (1986 and 2008) were found to be not significantly different at p< 0.05 (Table
3-4). Mean bulk density value in 2008 was to some degree higher than the previous
result, though the difference was statistically insignificant.
Specifically, grassland areas had the greatest Db value (1.2 g/cc). The lowest
was from bush land areas with mean value of 0.8 g/cc (See Table 3-1). The reason for
the highest Db value coming from the grassland areas could be attributed to the compaction and trampling of the soil by grazing animals. The least Db value
58
corresponding to the bush-land areas was in line with the scientific expectation that forested and bush-land areas are expected to have low Db values.
Table 3-4: Statistical analysis result for (Db)
Bulk Density (g /cc) t-Test: Paired Two Sample for Means
Variable 1
Mean
Variance
Observations
Pearson Correlation
Hypothesized Mean Difference df t Stat
P(T<=t) one-tail t Critical one-tail
P(T<=t) two-tail t Critical two-tail
1.00
0.02
11
0.12
0
10
-0.95
0.18
1.81
0.37
2.23
Variable 2
1.06
0.03
11
CONCLUSION
The result from laboratory analyses of soil samples on selected physical and chemical properties indicated that only AP content was found to be significantly changed (decreased) from previous years’ work results. The AP and % OC contents of the soils were found in lower amounts than before (in 1986), while the Db value has shown an increase. The nature of the change of soil properties considered during this study indicated that the soil is under the same or even worse conditions than before the project implementation period.
In recent years, the application of chemical fertilizers became a viable option throughout the watershed. Therefore, the present nutrient status of the soil might have been offset by the application of these fertilizers and the actual depletion of nutrients may not be fully realized.
59
Decreased nutrient availability and increased bulk density values could be due to many factors, but under all the existing conditions the ultimate objectives of SCRP was to decrease soil erosion and associated nutrient losses, and in turn, to increase the soil moisture conditions of the area. However, current results are not in-line with the above mentioned objectives. Although there could exist other factors affecting soil properties, the overall objectives of SCRP in conserving the soil and nutrients were not fully met.
As a result, further detailed studies should be undertaken towards identifying major factors that affect soil characteristics and assessing the impact of the SCRP program on the physical and chemical soil properties.
60
REFERENCES
Alfred E. H., Tom, V.Z.2008. Land Cover Change and Soil Fertility Decline in
Tropical Regions . Turk J Agric For 32: pp195-213.
Ethiopian Economic Association (EEA).2002. Second annual report on the Ethiopian
Economy, Vol II 2000/2001. Addis Ababa: EEA.
Feoli1, E., Gallizia, L.V., and Woldu, Z.2002. Processes of Environmental
Degradation And Opportunities for Rehabilitation in Adwa, Northern Ethiopia.
Landscape Ecology 17: 315–325.
Hugo, L.P., Johann, B., Juergen, G., Hiremagalur, G., Mohammad, J., Victor, M.,
John, M., Martin, O., and Mohamed, S. 2002. Linking Natural Resources,
Agriculture and Human Health: Case Studies from East Africa. LEISA
Magazine supplement, pp 17-20.
Hurni H., Tato K., Zeleke G.2005. The Implications of Changes in Population, Land
Use, and Land Management for Surface Runoff in the Upper Nile Basin Area of Ethiopia. Mountain Research and Development. 25(2)147-154.
Lu, D. Batistella, M., Mausel, P., Moran, E.2007. Mapping and Monitoring Land
Degradation Risks in the Western Brazilian Amazon Using Multitemporal
Land sat Tm/Etmþ Images. Land Degrad. Develop. 18: 41–54.
SCRP. 2000. Area of Maybar, Wello, Ethiopia: Long-term Monitoring of the
Agricultural Environment.1981-1994. Soil Conservation Research Programme,
University of Berne, Switzerland.
Teketay, D., Masresha, F., and Asferachew, A.2003.The state of the environment in
Ethiopia: Past, present, and future prospects. consultation papers on environment No. 1. Addis Ababa:
61
Valentin, C., Poesen, J., and Yong, L.2005. Gully erosion: Impacts, factors and control. Catena 63: 132–153.
Weigel,G. 1986. The Soils of Maybar Area. Soil Conservation Research Program
(SCRP), Report no 7. Berne, Switzerland: University of Berne.
62
APPENDICES
Appendix 1: Long-term monthly & annual rainfall data from Maybar Research Station
Month 1988 1989 1992 1993 1994 1995 1996 1997 1998 2000 2002 2004 2008 Average
Jan
Feb
Mar
Apr
37
85
17
38
38
71
57 142 0
47 106 13
1
29
54 44 228
2 0 66
85 70 45 60 68 244 147 30
133 85 162 35 334 68 43 49
0
0
64 27 19
28 44 0
54.6
37.8
2 44 51 0 66.4
1 193 156 45 103.2
May
Jun
3
14
23
5
16 144 50
11 0 14
21 164 8
16 47 88
84
0
0
30
18 16 36
14 75 38
44.8
27.1
Jul 323 189 91 174 379 256 251 224 512 364 325 324 290 284.6
Aug 324 264 255 149 362 304 337 176 230 365 308 299 175 272.9
Sep 188 97 141 147 161 62
Oct 39 30 33 94 10 13
Nov 0 1 39 0 36 0
58 13 91 82 138 108 115 107.6
5 202 95 86 22 119 79 63.6
38 210 0 56 0 35 34.6
Dec 0 189 27 1 0 137 2 0 0 106 75 33
Total 1067 1124 873 1163 1120 1242 1268 1154 1383 1089 1229 1288 797
47.4
Appendix 2: Long-term monthly & annual river flow data from Maybar Research Station.
Month 1988 1989 1992 1993 1994 1995 1996 1997 1998 2000 2002 2004 2008 Average
Jan 0 0 8 46 5 5 13 5 31 3 4 5 5 10.1
Feb 3 0 8 62 5 4 10 5 29 2 4 4 4 10.9
Mar 1 0 11 27 5 7 51 11 21 1
Apr 0 17 15 33 9 96 20 5 9 2
4
27
5
16
5
5
11.5
19.4
May 0
Jun 0
5
3
9
5
73
21
8
5
24
11
42
30
5
7
8
5
5
5
11
8
5
5
Jul 60 13 12 27 99 59 69 47 149 114 58 39
5
5
15.4
8.3
13 62.2
Aug 182 93 150 68 251 155 159 64 132 235 120 143 49 146.2
Sep 117 50 90 81 111 30 50 18 48 71 48 20 45 61.3
Oct 12 15 48 46 21 16 12 41 37 39 10 35
Nov 2 4 15 14 13 11 9 80 13 23 5 8
10 27.7
16.3
Dec 0 16 30 11 9 12 5 33 5 31 5 5
Total 378 215 402 511 540 431 471 319 485 531 305 290 145
13.4
Apendix 3: Infiltration Test Results
Set up Fc(mm/hr) f o
(mm/hr) k (hr-1) S (mm) I a
(mm) t (hr)
1 429
2 185
3 48
4 289
5 38
6 113
7 98
8 19
9 370
10 368
11 132
12 600
13 156
14 402
15 466
16 175
753
957
279
1110
185
568
320
275
681
622
1221
1830
533
764
2390
598
0.02
0.14
0.16
0.13
0.15
0.09
0.12
0.15
0.03
0.08
0.08
0.2
0.12
0.04
0.11
0.02
15035 11350 268
5623 2470
1439 598
6597 3059
998 454
5083 2264
1915 1058
1757 463
10894 8036
3082 2370
13599 4472
6157 3525
3199 1734
9684 7030
48
34
51
34
68
47
38
201
67
87
36
50
158
18146 8015 71
26736 14454 382
(Source: Derib, 2005)
65
Appendix 4: Soil laboratory analysis result from two periods (1986) and (2008)
Available P (ppm)
Profile
No.
1986 2008
8
9
11
12
1
3
4
5
6
7
15
19
23
24
25
18.00
9.00
16.00
21.00
20.00
7.00
32.00
55.00
13.00
10.00
17.00
10.00
57.00
4.00
16.00
%OM
1986
10.46 3.30
7.10 3.40
8.34
9.40
5.20
4.30
10.50 4.50
9.70 6.70
40.72 2.20
7.08 2.60
9.30
7.94
3.60
3.50
6.10
7.90
7.20
8.26
6.50
3.20
3.00
2.00
2.70
5.00
2008
3.54
1.82
2.34
3.55
1.44
4.37
4.67
2.85
3.51
3.20
1.64
2.44
2.68
2.78
4.57
Bulk Density (gm/cc) Moisture Content (%)
1986
1.08
0.94
0.89
1.01
0.83
0.83
1.04
0.91
1.13
1.19
NA
NA
NA
1.19
NA
2008
1.20
0.94
1.08
0.91
0.79
1.30
1.00
1.16
1.14
0.87
NA
NA
NA
1.30
0.83
2008
20.17
22.89
23.23
30.09
24.45
19.73
29.43
23.97
25.16
28.55
NA
NA
NA
30.09
32.48
66
Appendix 5: Piezometer installation sites and specific locations in the watershed
23
24
25
26
27
28
29
19
20
21
22
15
16
17
18
11
12
13
14
7
8
9
10
Piezometer
No.
1
2
3
4
5
6
LAT LONG Land use type
11.00430882 39.66016668 Grass land
11.00457519 39.65903060 Cultivated land
11.00517425 39.65896941 Grass land
11.00526620 39.65824630 Bush land
11.00565713 39.66045468 Grass land
11.00588093 39.66019141 Grass land
11.00608025 39.65887109 Cultivated land
11.00637881 39.65840413 Bush land
11.00683244 39.66060815 Grass land
11.00702237 39.66053984 Grass land
11.00728003 39.66011790 Cultivated land
11.00738690 39.65989745 Bush land
11.00743443 39.66135146 Grass land
11.00781932 39.66117033 Grass land
11.00801278 39.66094167 Cultivated land
11.00848082 39.66053984 Bush land
10.99681808 39.65793123 Grass land
10.99718068 39.65741859 Grass land
10.99808634 39.65681157 Grass land
10.99966222 39.65620740 Cultivated land
10.99798065 39.65819961 Grass land
10.99967371 39.65807481 Grass land
10.99969550 39.65805033 Cultivated land
10.99770161 39.65828586 Cultivated land
10.99849060 39.65891425 Cultivated land
10.99906082 39.65944391 Bush land
10.99645321 39.65825602 Grass land
10.99712393 39.65871368 Cultivated land
10.99745804 39.65910268 Cultivated land
67
Appendix 6: Summary of daily piezometeric head data from above watershed on transect basis
Date
TRANSECT - 1 TRANSECT - 2 TRANSECT - 3 TRANSECT - 4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
8/21/08
8/22/08
8/23/08
8/24/08
8/25/08
8/26/08
8/27/08
8/28/08
8/29/08
8/30/08
8/31/08
8/11/08
8/12/08
8/13/08
8/14/08
8/15/08
8/16/08
8/17/08
8/18/08
8/19/08
8/20/08
9/1/08
9/2/08
9/3/08
9/4/08
9/5/08
9/6/08
9/7/08
9/8/08
9/9/08
9/10/08
9/11/08
9/12/08
9/13/08
9/14/08
9/15/08
9/16/08
9/17/08
9/18/08
9/19/08
9/20/08
9/21/08
50 29 15 0
67 93 30 5
135 120 20 6
132 106 17 2
154 114 15 2
154 122 16 5
157 126 20 6
176 122 17 7
185 115 16 5
183 112 15 3
184 132 17 3
177 135 17 2
185 136 17 2
193 138 18 2
190 140 33 4
174 137 59 5
186 150 45 2
182 150 35 6
180 145 20 2
180 160 32 3
180 153 35 5
180 152 35 2
180 150 33 3
180 160 30 2
180 155 35 2
180 160 39 3
180 160 38 4
180 155 36 7
180 153 32 1
180 155 35 2
15
15
37
18
25
25
40
40
25
35
43
45
45
49
55
52
55
55
50
69 153 55 2 26 86 8
60 153 53 4 22 73 5
65
65
65
65
73
75
65
126
122
130
135
140
146
150
155
148
150
150
150
150
145
158
157
155
156
150
153
153
153
153
153
153
153
28
25
39
42
48
44
49
55
35
45
41
45
55
57
53
50
55
57
55
54
56
55
45
44
42
48
0
0
7
14
0
5
3
1
0
0
0
1
3
4
5
2
7
5
0
0
5
6
5
7
2
0
20
25
20
33
22
24
20
22
20
23
24
50
26
28
30
27
24
28
25
26
30
30
30
27
25
30
40
55
80
90
94
97
90
82
80
66
63
70
80
90
103
100
90
98
92
68
76
90
86
95
82
80
5
10
17
32
26
27
33
10
5
10
0
12
25
29
34
27
18
0
21
0
7
30
38
44
35
30
66 153 45 2 26 85 30
70 153 47 5 26 87 42
180 160 50 4
180 160 47 5
180 162 48 6
180 157 45 5
180 155 35 7
175 145 25 8
175 145 23 5
178 145 23 8
80
75
75
73
73
70
70
69
153
153
153
153
153
153
153
153
48
49
50
55
65
60
55
55
6
4
4
1
0
0
0
0
26
25
30
13
13
13
13
13
88
80
82
76
70
60
48
48
50
40
30
22
10
0
0
0
178 145 23 10 69 153 53 0 13 47 0
178 144 22 9 69 153 45 0 13 41 0
178 144 22 6
178 144 22 4
69
69
153
153
38
31
0
0
13
13
38
38
0
0
10 25 0 40 0
0 25 20 50 0
5 30 20 30 0
14
16
14
10
6
3
4
0
0
5
0
2
1
3
5
4
10 56 117 26 10
10 50 120 20 10
5 50 120 26 5
10 48 118 26 0
5 50 120 26 2
2 52 120 26 0
3 57 118 25 4
6 58 120 25 4
5 56 119 24 0
4 58 122 26 3
5 60 122 19 2
0
3
5
3
2
1
0
0
0
0
0
0
33
36
34
37
34
30
37
40
60
63
50
52
47
50
50
45
60
58
55
57
57
57
56
56
55
54
53
53
65
87
93
100
104
100
98
103
110
110
117
114
117
115
117
115
123
125
124
124
124
124
124
124
124
122
118
112
28
22
26
27
26
25
15
21
20
22
26
26
20
26
24
23
16
12
14
17
18
20
28
27
20
26
30
35
0
0
3
0
0
0
0
2
0
0
2
5
10
14
10
5
3
5
7
12
10
8
5
4
4
4
4
4
68
10/4/08
10/5/08
10/6/08
10/7/08
10/8/08
10/9/08
10/10/08
10/11/08
10/12/08
10/13/08
10/14/08
10/15/08
10/16/08
10/17/08
10/18/08
9/22/08
9/23/08
9/24/08
9/25/08
9/26/08
9/27/08
9/28/08
9/29/08
9/30/08
10/1/08
10/2/08
10/3/08
10/19/08
10/20/08
10/21/08
10/22/08
10/23/08
10/24/08
10/25/08
10/26/08
10/27/08
10/28/08
10/29/08
10/30/08
10/31/08
11/1/08
11/2/08
11/3/08
11/4/08
179 146 25 2
179 146 25 2
177 145 25 1
179 146 26 1
179 146 26 1
176 145 25 1
172 144 25 2
170 140 25 2
168 140 25 2
170 140 25 1
178 142 40 4
176 132 37 2
72 153 31 0 14 34 0
72 153 31 0 14 33 0
72 153 30 0 13 30 0
76 153 53 0 14 31 5
76 153 55 0 14 30 3
75 147 45 0 14 28 0
75 138 40 0 13 20 0
75 143 38 0 13 20 0
75 147 38 0 13 19 0
85 147 40 0 14 30 5
99 150 48 1 14 30 5
98 149 46 1 15 23 3
0 54 108 38 5
0 54 108 40 4
0 54 100 42 4
1 55 104 49 6
0 55 104 50 6
0 54 98 40 5
0 54 90 36 5
0 54 85 32 5
0 54 80 30 4
1 55 77 29 5
4 57 75 40 10
3 56 70 40 10
178 134 40 10 103 152 48 3 16 23 9
177 134 38 8 108 153 43 2 16 25 7
177 134 35 8 110 153 40 1 16 29 5
176 134 34 5 108 151 37 1 15 20 4
10
10
5
1
57
57
59
59
72
78
82
78
42
40
48
50
14
11
11
11
175 133 34 5 115 151 25 1 15 21 4 0 59 80 55 11
176 140 33 5 120 200 32 1 15 45 3 0 59 90 75 11
178 140 34 6 125 200 30 1.5 15 40 2.5 0 59 90 65 10
177 139 33 5 125 200 27 1 15 32 2.5 0 60 90 55 10
176 140 35 4.5 130 200 26 1 18 30 3 0 60 90 40 10
170 140 35 4 130 196 23 0.5 17 25 2.5 0 60 88 25 10
160 140 32 3.5 128 194 22 0.5 16 22 2 0 60 88 17 9
155 140 28 3 128 192 19 0.5 15 20 2
150 138 27 3 126 187 17 0 15 20 2
144 134 26 3 126 180 15 0 15 19 2
141 132 26 2.5 125 172 15 0 15 18 2
0
0
60
64
88
86
11
9
0 64 84 8
0 64 77 8
9
9
9
9
135 127 26 2 123 167 14 0 15 13 2
133 100 26 2 123 166 13 0 15 13 2
130 90 25 2 121 164 11 0 15 13 2
126 85 32 1.5 119 164 9 0 20 13 2
0 63 72 7
0 63 65 8
0 63 62 8
0 63 60 8
124 80 31 1.5 118 162 7 0 20 15 2
121 74 29 1.5 116 162 5 0 20 15 2
0 63 56 8 9
0 63 55 8 8.5
118 69 28 1.5 115 160 5 0 20 12 1.5 0 63 51 6
117 65 28 1.5 115 160 5 0 20 11 1.5 0 63 49 7
127 65 26 2 117 162 6 0 21 11 3.5 0 75 53 7
8
8
8
9
9
9
9
125 65 22 1.5 117 166 25 0 21 10 3
120 60 21 1.5 112 166 24 0 21 10 3
0 66 46 7
0 65 44 7
8
8
150 57 35 6 152 172 45 0 21 10 5
153 55 35 6 152 172 40 0 22 8 4
12
11.5
65
65
43
45
7
7
11
10
160 55 36 7 153 178 45 0 25 10 10 15 65 46 9 11
163 41 34 3 165 190 35 0 25 9 3.5 2 67 40 6 10
177 42 33 2.5 162 187 33 0 22 7
178 60 34 2 163 187 45 1 22 8
8
6
5
16
67
67
52
62
4
3
10
13
69
Appendix 7: Summary of daily piezometeric head data from lower watershed
(Gauging station) on a transect basis.
TRANSECT - 5
Date 17 18
8/11/08 52 57
19
35
20
2
21
71
TRANSECT-6
22
115
23
0
8/12/08 47 70
8/13/08 60 86
8/14/08 54 95
8/15/08 50 113
8/16/08 52 120
8/17/08 51 130
8/18/08 65 135
32
54
80
92
97
2
0
4
4
5
83
101
106
109
99
95 4 98
120
130
130
130
130
97 5 106 130
130
6
5
7
0
0
10
5
8/19/08 87 132
8/20/08 97 120
8/21/08 101 108
8/22/08 103 124
90 3 116 130
87 2 106 130
82 1 104 128
85 2 106 128
8/23/08 100 133
8/24/08 106 146
80 2 105 130
96 7 108 130
8/25/08 105 161 104 9 111 132
8/26/08 100 177
8/27/08 102 174
97
97
5
3
109
109
134
130
4
6
35
28
37
7
10
5
6
8/28/08 97 181
8/29/08 92 178
97 4 109 135
95 2 106 134
8/30/08 97 186 105 5 116 130
8/31/08 89 179 87 3 102 130
9/1/08 88 177
9/2/08 82 183
9/3/08 69 180
85 2 96 130
85 1 103 130
85 2 104 130
9/4/08 71 185
9/5/08 80 185
9/6/08 91 188
9/7/08 88 187
9/8/08 89 185
9/9/08 105 187
9/10/08 107 192
9/11/08 107 190
88
90
90
89
93
95
92
88
4
4
3
2
4
5
4
4
103
101
106
106
107
108
102
100
130
130
130
130
130
130
130
130
9/12/08 97 195
9/13/08 80 187
9/14/08 72 187
9/15/08 57 187
9/16/08 51 187
9/17/08 44 185
9/18/08 37 180
9/19/08 28 180
9/20/08 25 180
9/21/08 22 177
87 3 101 130
85 2 100 130
85 2 100 126
85 2 100 122
85 1 100 121
83 2 100 121
80 1 100 116
78 1 100 113
76 1 100 108
74 1 100 104
22
25
20
15
15
33
28
22
35
30
45
43
20
22
20
6
6
6
6
6
6
11
15
10
6
0
3
2
21 11 0
0 11 0
0 11 0
0 11 0
7
0
1
0
0
0
2
3
0
5
3 11 1
2 11 0
0
2
5
8
9
8
7 11 0
2 15 1
0
2
2 13 0
1 11 0
1 10 0
1 10 0
1 10 0
1 10 0
1 10 0
1 10 0
1 10 0
1 10 0
TRANSECT - 7
24 25 26
0 11 0
0 11 0
3 11 0
0 11 0
0 11 0
0 11 0
0 11 0
0 12 0
0 11 0
0 11 0
0 11 0
2 12 0
2 12 0
2 13 0
0 12 0
0 11 0
0 11 0
122 43 9
110 46 5
108 44 5
95 32 4
97 28 7
106 33 7
100 40 9
107 33 5
108 48 9
113 43 7
115 35 6
110 35 7
111 43 8
110 37 7
108 35 6
105 35 7
102 33 4
97 32 4
95 32 4
96 32 4
88 30 3
87 30 3
85 28 3
82 25 3
80 22 3
TRANSECT - 8
27 28 29
78 8 4
80 3
90 7
4
7
91 38 9
100 23 12
107 23 11
110 30 9
104 26 9
120 27 11
100 23 7
93 14 7
94 15 14
93 13 15
70 45 9
120 53 11
120 48 7
123 45 7
70
9/22/08 33 186
9/23/08 29 186
9/24/08 25 186
9/25/08 24 181
9/26/08 23 182
9/27/08 19 182
9/28/08 16 182
9/29/08 15 185
9/30/08 12 185
10/1/08 12 186
10/2/08 32 197
10/3/08 27 197
10/4/08 37 199
10/5/08 27 199
10/6/08 24 199
10/7/08 21 199
10/8/08 27 200
72 1 101 110
70 1 101 110
70 1 100 109
69 1 101 117
66 1 100 116
64 1 99 114
58 1 98
55 1 98
53 1 98
110
107
107
50 1 99
55 1 99
110
127
55 2 98 123
58 1 100 130
70 1 100 130
67 1 100 125
61 1 99
62 1 99
120
116
10/9/08 15 200
10/10/08 13 200
10/11/08 11 200
10/12/08 9 200
10/13/08 8 200
10/14/08 7 200
10/15/08 6 200
10/16/08 5 200
10/17/08 39 200
10/18/08 2.5 200
10/19/08 2 200
10/20/08 2 200
10/21/08 1 200
0
-3
-5
-6
40 1 100 113
32 1 100 115
28 1 101 115
23 1 100 113
17 1 100 111
13 1 100 105
8
4
2
1 100
1 99
1 99
98
95
92
1 99
1 99
1 99
1 99
88
84
82
80
10/22/08 1 200 -6 1 99
10/23/08 0 200 -6.5 1 99
10/24/08 0 200 -6.5 1 99
10/25/08 0 200 -6.5 1 99
10/26/08 0 200 -6.5 1 99
10/27/08 3 200
10/28/08 3 200
-6
-7
1 101 75
1 101 73
10/29/08 2.5 200 -7.5 1 101 72
10/30/08 25 200 -7 1 100 95
77
75
71
70
66
10/31/08 21 200
11/1/08 27 200
11/2/08 39 200
11/3/08 49 200
11/4/08 49 202
-7 1 101 95
0 1 101 95
12 1 101 114
15 1 101 115
18 1 101 132
0
0
0
0
0
0
5
0
0
5
5
5
5
6
3
2.5
2
2
1.5
3
2.5
3
3
1
1
1
1
3
3
3
5
4
5
5
6
6
4
4
3
3
4
4
4
5
5 21 0
5 20 0
5 21 0
5 20 0
5 18 0
5 17 0
4.5 16 0
4.5 15 0
4.5 15 0
4.5 15 0
4 15 0
4 15 0
4 15 0
4 14 0
4 14 0
4 14 0
4 14 0
4 14 0
5 16 0
6 16 0
5.5 15 0
12 16 4
8 16 3
10 17 3
7 16 3
6 16 3
7 16 4
4
3
1 10 0
1 10 0
9
6
0
0
3 -7 0
3 11 0
3 10 0
3 10 0
3 10 0
3 11 0
5 18 0
3 13 0
7 15 0
5 15 0
5 18 0
5 18 0
5 17 0
69 2
68 0
68 0
65 -1
64 -3 2.5
64 -4 2.5
63 -5
63 -5
63 -5
2
2
2
62 -6
61 -6
61 -6
61 -6
3
3
3
3
2
2
2
2
60 -6
59 -6
59 -6
59 -6
59 -6
60 -3 2.5
59 -3 2
59 -4 1.5
65 -3 2
2
2
2
2
2
65 -3
65 3
70 4
78 3
86 4
2
3
2
2
3
77 19 3
77 19 3
74 19 3
75 17 2
75 13 2
72 8 1
65 3
65 3
63 2
1
1
1
65 0
80 5
74 3
83 7
80 5
78 5
74 3
71 3
3
3
3
3
2
2
2
2
71
