MEDRC Series of R & D Reports MEDRC Project: 17-JD-05
Sediment Yield at Mujib Dam Reservoir in Jordan
M.Sc. Thesis By
Aseel Nayef Al-Nawiseh
Supervisors
Prof. Abbas Zaki Ijam Dr. Khaldon Ktishat
Civil Engineering / Water and Environment Mutah University
A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master in Master of Civil Engineering / Water and Environment
MEDRC Water Research Muscat Sultanate of Oman
2018
Mutah University College of the Graduate Studies
Sediment Yield at Mujib Dam Reservoir in Jordan
Presented by Aseel Nayef Al-Nawiseh
Supervised by Prof. Abbas Zaki Ijam and Co-Supervisor Dr. Khaldon Ktishat
A Thesis Submitted to the College of the Graduate Studies in Partial Fulfillments of the Requirements for the Degree of Master of Civil Engineering / Water and Environment
Faculty of Engineering
Mutah University, 2018
اآلراء الواردة في الرسالة الجامعية ال تعبر بالضرورة عن وجهة نظر جامعة مؤتة
Dedication
This thesis is dedicated to: The sake of Allah, my Creator and my Master, My great parents, who never stop giving of themselves in countlessways, My dearest husband, who leads me through the valley of darkness with light of hope and support, My beloved brothers and my sister To all my family, the symbol of love and giving, My friends who encourage and support me, All the people in my life who touch my heart, I dedicate this research.
Aseel Nayef Al-Nawiseh
I I Acknowledgments
In the Name of Allah, the Most Merciful, the Most Compassionate all praise be to Allah, the Lord of the worlds; and prayers and peace be upon Mohamed His servant and messenger. First and foremost, I must acknowledge my limitless thanks to Allah, the Ever-Magnificent; the Ever-Thankful, for His help and bless. I am grateful to some people, who worked hard with me from the beginning till the completion of the present research particularly my supervisor Prof. AbbasZaki Ijam, who has been always generous during all phases of the research, and I highly appreciate the efforts expended by Dr. Khaldon Ktishat,Dr. Suliman Tarawneh and Dr. Hussam Hamideh. And I would like to take this opportunity to say warm thanks to all my beloved friends, who have been so supportive along the way of doing my thesis. I also would like to express my wholehearted thanks to my family for their generous support they provided me throughout my entire life and particularly through the process of pursuing the master degree. Because of their unconditional love and prayers, I have the chance to complete this thesis. I owe profound gratitude to my husband, Mounther, whose constant encouragement, limitless giving and great sacrifice, helped me accomplish my degree. I am also grateful to Directorate of dams at Jordan valley Authority who present great information that I need in my research. I would like to express my gratitude to MEDRC for their support.
II Table of Content
Page Dedication I Acknowledgments II Table of Content III List of Tables V List of Figures VI List of Appendices VIII List of Symbolsand Abbreviations IX Abstract (in English) XII Abstract (in Arabic) XIII Chapter One: Introduction 1 1.1 Problem Identification 1 1.2 Description of the Study Area 2 1.2.1 Climates 6 1.2.2 Topography 6 1.3 Purpose of the study 7 1.4 Methodology 8 1.5 Layout of Thesis 8 Chapter Two: Literature Review 10 2.1 Preface 10 2.2Previous Studies in Soil Erosion and Sedimentation 10 2.3 Previous Studies in Soil Erosion Assessment by GIS 14 2.4technique Previous Studies in Jordan 17 2.5 Importance of the Present Study 20 Chapter Three : Basic Concepts of Dam Reservoirs 22 3.1 Preface 22 3.2 Dam Reservoirs Sedimentation 22 3.3 Sources of Sediments 23 3.4 Sedimentation Mechanism 23 3.5 Problems Associated with Erosion and Sedimentation 24
III Page 3.6 Factors Affecting Sediment Yield 24 3.7 Estimating Sediment Yield 26 3.7.1 Hydrological Factors 27 3.7.1.1 Surface Runoff 27 3.7.1.2 The Peak Runoff Rate 28 3.7.2 The physical Characteristic of the Catchment 30 Chapter Four:Area Results, Conclusions and Recommendations 35 4.1 Preface 35
4.2 The Time of Concentration (Tc) 35
4.3 Soil Erodibility Factor (KUSLE) 37 4.4 The Topographic Factor ( LS ) 40 4.5 Coarse Fragment Factor (CFRG) 47 4.6 Surface Runoff and Peak Runoff Rate 48 4.7 Model Calibration 51 4.8 Sediment Yield Calibration 52 4.9 Model Verification 53 4.10 Prediction Scenario (2018-2030) 59 4.11 Conclusions 62 4.12 Recommendations 63 4.13 Future Work 64 4.14 Limitations 65 References 66 Appendix I : Statistical Measures 71 Appendix II : Tables 73
IV List of Tables
Table Description Page 3.1 P factor values and slope-length limits for contouring 33 4.1 Gravel, Sand, Silt and Clay percent for the soil samples 37
4.2 Org C %, f csand,f cl-si, f org, f hisand and KUSLE for soil samples 38 4.3 Soil Classification for the soil samples in the catchment 39 area 4.4 Coarse Fragment Factor (CFRG) for soil samples 48
4.5 The income and outcome volumes, maximum, 49 minimum and average reservoir water level 4.6 Accumulated observed data by Eco-sounder device 52 4.7 Sediment yields distribution according to the inflow 54 percents for each year 4.8 The annual sediment yield after rearrange Table 4.7 54 4.9 Simulated and Observed data for the years 2003 – 55 4.10 Simulated2015 sediment yield by using MUSLE for the 57 water year 2004 4.11 The annual sediment yields for the years 2003-2017 58 4.12 Cumulative of simulated sediment 59 4.13 The predicted cumulative sediment yields for the years 62 from 2018 until 2030
V V List of Figures
Figure Description Page 1.1 Existing Dams in Jordan valley 3 1.2 Location map of Al-Mujeb basin 4 1.3 Mujib Basin and existing Dams 5 1.4 Al-Mujeb dam reservoir catchment area 5 1.5 Streams of Al-Mujeb catchment area 6 1.6 Contour Map for Mujib Dam Catchment Area 7 4.1 The Slope in percent map for Al-Mujeb catchment area 36 4.2 The hydraulic length of Al-Mujeb catchment area 36 4.3 Soil Classification triangle based on silt%, sand%, and 39 clay % 4.4 Digital Elevation Map (DEM) 41 4.5 The Fill Tool 42 4.6 The Flow Direction Tool 42 4.7 The Flow Direction Raster (Output) 43 4.8 The Flow Accumulation Tool 44 4.9 The Flow Accumulation Raster (Output) 44 4.10 The Slope Tool 45 4.11 The Slope Map (Output Raster ) 45 4.12 The Raster Calculator Tool 46 4.13 The Topographic Factor Map for Al-Mujeb Catchment 47 Area 4.14 The Income and Outcome volumes for Al-Mujeb dam 50 reservoir 4.15 Al-Mujeb dam reservoir average water level for years 50 (2003-2018) 4.16 The Minimum and Maximum Water Level for Al-Mujeb 51 Dam Reservoir from 2003 until 2018 4.17 The Comparison Between the Observed and the 53 Simulated Data for the Calibrated Period 4.18 The Comparison between the Annual Simulated 55 Sediment Yield and the Observed Sediment Yield
VI Page
4.19 The Relationship between the Simulated Data and the 56 Annual Inflow that Inter the Dam Reservoir 4.20 The Annual Sediment Yield for the Water Years 2003- 58 2017 4.21 Regression Line for the Cumulative of Simulated 60 Sediment for the Years 2003- 2017 4.22 Extrapolation Techniques for the Years 2018-2030 61
VII List of Appendices
No. Appendix Page Appendix I Statistical Measures Appendix II Tables
VIII List of Symbols and Abbreviations
Symbol Description Unit
Q Water discharge m3/s S The bed slope m/m 3 Qs The sediment discharge m /s D50 The median sediment size mm Sed. The Sediment yield metric ton Q surf Surface Runoff volume mm/ha 3 qpeak The peak runoff rate m /s A The area of the region ha K The USLE soil erodibility factor 0.013 metric ton m2 hr/ (m3- metric ton cm) C TheUSLE cover and management - factor P The USLE support practice factor - LS The USLE topographic factor - CFRG The USLE coarse fragment factor - Rday The daily rainfall depth mm Ia The initial abstractions which mm includes surface storage, interception and infiltration prior to runoff S The maximum potential retention mm parameter CN The Curve Number - C The runoff coefficient - I The rainfall intensity mm/hr R The total depth of runoff m P The total depth of precipitation m Rtc The amount of rain falling during mm the time of concentration Tc The time of concentration hrs M The particle-size parameter OM The percent organic matter % csoilstr The soil structure code used in soil - classification cperm The profile permeability class - msilt The percent silt content (0.002- % 0.05 mm diameter particles)
IX mvfs The percent very fine sand content % (0.05 – 0.10 mm diameter particles) mc The percent clay content (˂0.002 % mm diameter particles) orgC The percent organic carbon content % of the layer (%) fsand The factor that gives low soil - erodibility factors for soils with high coarse-sand contents and high values for soil with little sand fcl-si The factor that gives low soil - erodibility factors for soils with high clay to silt ratios forg The factor that reduces soil - erodibility for soils with high organic carbon content fhisand The factor that reduces soil - erodibility for soils with extremely high sand ms The percent sand content (0.05- % 2.00 mm diameter particles) l The field slope length ft S The slope gradient in percent % m Exponent dependent on the slope - gradient T lag Thelag time hrs L Thehydraulic length of the Km catchment CNaw The average curve number within - the catchment area Y The average catchment slope in % percents M The mean of residual errors - r The linear correlation coefficient - Emodel The model efficiency in percents %
X X Abbreviation Description
USLE The Universal Soil Loss Equation MUSLE The Modified Universal Soil Loss Equation RUSLE The Revised Universal Soil Loss Equation AGNPS AGricultural Non-Point Source Pollution Model ANSWERS Areal Non point Source Watershed Environment Response Simulation CREAMS Chemicals, Runoff, and Erosion from Agricultural Management Systems WEPP The Water Erosion Prediction Project Model VAR The variance of residual errors RMS The root mean of squared residual errors SDEV The standard deviation of residual errors MA The mean of absolute residual errors Obs. The observed data Sim. The simulated data
XI Abstract Sediment Yield at Mujib Dam Reservoir in Jordan
Aseel Nayef Al-Nawiseh Mu'tah University, 2018
The flow of water from the watershed upstream of a reservoir is capable of eroding the drainage area and of depositing material either upstream of the reservoir, or in the still water of the reservoir causing reservoir sedimentation; which is one of the most common problems facing dams. The impact of reservoir sedimentation can be summarized in: reducing the storage capacity of the reservoir, decreasing ability to produce hydroelectric power and shorting of the life of the reservoir. Jordan is one of the countries which suffer from water shortage. Dams in Jordan represent a strategic source of water demand, so sedimentation at these dams should be studied to minimize and evaluate the quantity ofsediments reaching these reservoirs. In the present study Al-Mujeb dam, with a present reservoir storage capacity of 31.5 MCM, located between Al-Karak and Ma'adaba governorates and constructed in 2003 has been selected to estimate the quantity of sediment that reached its reservoir during the period 6-Nov-2003 until 13-March-2018. The Modified Universal Soil Loss Equation (MUSLE) has been chosen as a model to simulate the sediment yields for Al-Mujeb dam reservoir and depending on the properties of the watershed and measured volumes of runoff into the reservoir. Model calibration and verification were carried out using cumulative sediment yields reading obtained from Eco-sounder device for the years (2003-2005),(2005-2008),(2008-2009)and (2009-2015), which were acquired from Directorate of dams that affiliate to Jordan Valley Authority. According to the model verification the results were satisfactory, indicating that this model can represent well the climatic and physical conditions of the area. The quantities of accumulated annual sediment yield have been predicted for the years 2018 until 2030, the volume of accumulated sediment in the reservoir will reach 7.11 MCM in 2030 which represents 22.8 % of the reservoir capacity. According to the results the study suggested strategies to reduce the soil erosion and minimize the quantities of sediments that reach the dam reservoir.
XII
الملخص محصول الرسوبيات في بحيرة سد الموجب في األردن أسيل نايف النوايسة جامعة مؤتة / 2018 تعتبرررر الترسررربا دا ررر ال زانرررا الما يرررة مرررن اكلرررر الماررراك التررري تواجررر السررردود حيررررك تتكررررون بسرررربه ترررردف الميرررراو الناتجررررة عررررن ا م ررررار وال ادمررررة مررررن اع رررر مجررررر المستجمع الما ي ل ررزان مسرربب انجررراب التربررة فرري من ررة المسررتجمع المررا ي وترسرريبها فرررري اع رررر المجررررر المررررا ي مررررن ال ررررزان ممررررا يررررؤلر ع رررر السررررعة الت زينيررررة والعمررررر االفتراضرري ل ررزان وي رر مررن ال رردرة ع رر نترراو ال ا.ررة الكهروما يررة تملرر السرردود فرري ا ردن امصدر استراتيجيا مهما لسد الن ص الما ي ؛ كون ا ردن من الرردو الترري تعرراني . ة توافر المياو لذلك يجه دراسة الرواسه في السدود لت ي وت ييم الترسرربا فيهررا و.ررد ا تررار هررذو الدراسررة سررد الموجرره الررذ انارر عررام 2003م فرري واد الموجرره الوا.ع بين محافظتي الكرك ومأدبررا والررذ تب رر سررعت الت زينيررة مررا ي رراره 31,2 م يررون متررر مكعرره وتهرردب هررذو الدراسررة لرر ت يرريم كميررا الرواسرره الواصرر ة لرر ررزان سررد الموجررررره فررررري العتررررررة الوا.عرررررة مرررررا برررررين /6 11/2003 -م 13 /3/ 2018م( واسرررررت دم الدراسة نموذو اداة ت ييم التربة ( MUSLE) لحساه كميا الرواسه في زان السررد معتمرردةع ع رر صا صرر ال بو رافيررة والجيولوجيررة وحجررم جريرران المرراء فيرر و.ررد تمرر معررايرة ا نمرروذو وا تبررارو باالعتمرراد ع رر .ررراءا تراكميررة لجهرراز الموجررا الصرروتية (Eco-sounder)الذ تم اسررت دام مررن .برر مديريررة السرردود فرري سرر ة واد ا ردن ررررررررررررررررررر السرررررررررررررررررررنوا 2003 -م 2005م( 2005 -م 2008م( 2008 -م 2009م( و 2009 -م 2015م( و ااررررار النترررررا ة لررررر .رررردرة هرررررذا ا نمررررروذو ع رررر تمليررررر هرررررذو المن ة و است دام ل تنبؤ بكمية الرواسه المتو.عة في ررزان سررد الموجرره رر العترررة 2018 -م 2030م( وتتو.ع هذو الدراسة ان تص كميررة الرواسرره فرري سررد الموجرره عررام 2030م لرر 952 6 م يررون متررر مكعرره ا مررا نسرربت 3 22% مررن السررعة الت زينيررة ل سد وبناءا ع هذو النتا ة تم ا.تراح بعض االستراتيجيا لت ي انجررراب التربررة ومررن كميرررررررررررررررررررررررررررررررررا الرواسررررررررررررررررررررررررررررررررره الواصررررررررررررررررررررررررررررررررر ة لررررررررررررررررررررررررررررررررر بحيررررررررررررررررررررررررررررررررررة السرررررررررررررررررررررررررررررررررد
XIII Chapter One Introduction
1.1 Problem Identification With 66% of the world's surface secured by water and the human body comprising of 75 percent of it, it is obviously evident that water is one of the prime components in charge of life on earth. Jordan has one of the lowest levels of water resource availability, per capita, in the world. Water scarcity will become an even greater problem over the next two decades, with sensational populace increment and the populace anticipated that would twofold by 2047; as well the climate change potentially makes precipitation more uncertain and variable, particularly in this region. Management of water resources is therefore a key issue facing national government authorities. In order to give a well planned scheme for the future, Jordan has adopted a National Water Strategy. The strategy is a comprehensive set of guidelines employing a dual approach of water demand management and water supply management. It places particular emphasis on the needs for improved water resource management, stressing the sustainability of present and future uses. There are 10 main dams with a total storage capacity of 340.1 MCM, these dams include:Wadi Arab, Ziglab, Al Wehdah , King Talal, Karameh, Shueib, Kafrein, Wala, Al Mujeb and Tannur as presented in Figure (1.1). (Ministry of Water and Irrigation, Jordan Valley Authority, 2006). Dams have a common problem which is sediment accumulating in the reservoir. This situation is commonly addressed by allocating "dead storage" capacity at the bottom of the reservoir where alluvium is allowed to settle. But if these silt deposits are not cleared out, the reservoir will become blocked in as soon as a few decades. To meet this problem many methods were developed to measure the amount of sediment and to reduce the quantities of it. A good method to estimate the sedimentation of a reservoir is by performing a bathymetric survey. But, these surveys can be expensive and time consuming. Therefore it will not always be possible to perform them on a regular basis. A supplementary method is demanded to predict sedimentation in an easy and inexpensive way and to help interpreting the bathymetric survey’s results. This may be possible by modeling the catchment area of a reservoir. By using the Modified Universal Soil Loss Equation (MUSLE) model which will be used in this study. Then use it to make prediction about the accumulated sediment in the reservoir.
1 1.2 Description of the Study Area Among the major hydrologic bowls of Jordan, Al-Mujib Basin involves an impressive unpredictable formed territory speaking to around 7% of the zone of Jordan, Located in the area between Karak and Madaba, between 210-300 ͦ E and 30 – 150 ͦ N according to the Palestine Grid. As presented in Figure (1.2). Al-Mujeb basin length is 80 Km and the area reaches 6596 km2.It consists mainly of two main valleys (Wadi Al-Mujeb and Wadi Al-Walaa) with an area of 4800 and 2200 km2 respectively. Wadi Al-Mujeb contains several dams such as: Mujib dam, Siwaqa dam, Qatraneh dam and Sultani dam as presented in Figure (1.3), noting that Sultani dam no longer exists due to sedimentation. Al-Mujeb dam was constructed for municipal and industrial supply and irrigation with storage capacity of 31.2 MCM. It is located in Wadi Al-Mujeb, south of Madaba and north of Al-Karak. Al-Mujeb dam is a composite dam; consisting of roller-compacted concrete (RCC) middle section and clay core rockfill (CCR) sections at both abutments. Al-Mujeb dam catchment area will be the study area for this research with area of 1311 Km2, lying between the desert highway which link the south cities to Amman and the king highway where the site of dam as shown in Figure (1.4). While Figure (1.5) shows the streams that feed Al-Mujeb dam reservoir.
2
Figure (1.1) Existing Dams in Jordan Valley (Ministry of water and irrigation, annual report, 2012)
3
Figure (1.2) Location Map of Al-Mujeb Basin
4
Figure (1.3) Mujib Basin and Existing Dams (Howared and Humphreys, 1992)
Figure (1.4) Al-Mujeb Dam Reservoir Catchment Area
5
Figure (1.5) Streams of Al-Mujeb Catchment Area
1.2.1 Climates Al-Mujib basin is semi-arid to arid, with low rainfall in most parts of the basin in winter and high temperatures in summer. More than 80 % of the basin is covered by Badia, and the remainders 20 % are agricultural lands and residential areas. Wadi Al-Mujeb river basin has very cold winters with temperatures that might go down up to −10 ◦C in the mountainous area, and very hot, dry summers with temperatures that may exceed 40 ◦C at the Dead Sea shore area. The average amount of rainfall varies from 400 mm/year in the mountainous area to 100 mm/year at the Dead Sea shore line area.
1.2.2 Topography The majority of the catchment to the east lies at elevation of 700-900 m above sea level while in the west the wadis have cut deep gorges through the escarpment to where they join approximately 2.5 km upstream of the Dead Sea. The south western region elevation ranged between (900 – 1200) and these values decreasing in the south eastern to reach from (600-900), while the north part of the catchment ranged from (600-1000), and also decreasing to (200-300) nearing the dam site according to the contour map as shown in Figure (1.6).
6
Figure.1.6 Contour Map for Mujib Dam Catchment Area
1.3 Purpose of the study The following objectives are related to the present study: 1. Estimating the sediment yield for Al-Mujeb dam reservoir by using the Modified Universal Soil Loss Equation(MUSLE) depending on the properties of the watershed and measured volumes of runoff into the reservoir. 2. Comparing the results obtained by the Modified Universal Soil Loss Equation (MUSLE) with the results that measured accumulatively for the years (2003-2005), (2005-2008),(2008-2009) and (2009- 2015), which were gained from Directorate of dams that affiliate to Jordan Valley Authority. 3. Predicting the quantities of sediment yield for the duration from 2018 to 2030. 4. Proposing suitable preventation methods to reduce the soil erosion and the sediment yield reach the reservoir.
7 1.4 Methodology The methodology of this study was in the following order: - Meaning of the investigation territory, which is Al-Mujeb Dam catchment zone. - Collecting data from Directorate of dams; inflow, outflow and accumulated sediment yield obtained by Eco-sounder device for the years (2003-2005), (2005-2008), (2008-2009) and (2009-2015) - Collecting soil samples randomly from different points in the catchment area and analyze them by using the tests below to estimate the erodibilty factor (KUSLE) and the coarse fragment factor(CFRG)for the catchment area: - Water content test (ASTM test D-2216). - Specific gravity test (ASTM test D-854). - Sieve analysis test (ASTM test D-421) - Hydrometer analysis test (ASTM test D-422). - Estimating the topographic factor (LS) for the catchment area by using ArcGIS 10.3 software. - Using ArcGIS 10.3 software for estimating Al-Mujeb catchment area time of concentration (tc) - Using the calibration technique to obtain the cover and management factor (CUSLE) by using the observed data which was obtained from the Eco-sounder device and the simulated data obtained from applying the Modified Universal Soil Loss Equation (MUSLE) for the years from 2003 to 2015. - Verifying the results by using the annual sediment yield from 2003 to 2015 by using the percent of inflow that inter the dam reservoir. - Predicting the cumulative amount of sediment yield for the years 2018 until 2030. - Recommendation of some land protection techniques and dregs diminishment measures relying on the outcomes.
1.5 Layout of Thesis The chapters of this thesis can by summarized as follow:
Chapter one: Handles the problems associated with water in the world generally and in Jordan particularly, the study area description, the methodology and the purpose of this study. Chapter two: Consists of the literatures review in soil erosion and sedimentation, soil erosion assessment by GIS technique, and previous studies in Jordan. Chapter three: Contain the methodology that was used to obtain the parameters in the Modified Universal Soil Loss Equation (MUSLE).
8 Chapter four: Presents the detailed results for the time of concentration (tc), the soil erodibility factor (KUSLE), Coarse fragment factor (CFRG), the topographic factor (LS), calibration the Modified Universal Soil Loss Equation (MUSLE) using the cover and management factor (CUSLE), verification and prediction the sediment yields for the years (2018-2030), the conclusions, recommendations, the future work and the limitations
In addition to the following appendences: Appendix І: Statistical Measures
Appendix II: Tables
9 Chapter Two Literature Review
2.1 Preface Studies and simulation models have been developed all-around the world during recent decades in order to estimate, analyze or predict runoff, soil erosion and sediment yield. Some of recent studies have been selected and chosen as a good reference in this chapter.
2.2 Previous Studies in soil erosion and sedimentation M.A. Romero Diaz et al (1992) presented a study of hydraulic erosion and fluvial sedimentation of the River Segura basin in Spain. The aims of this study are: To quantify the soil losses in the drainage area of the most important reservoirs in the Segura basin; to determine the volume of sediment yields; to determine the amount of sediment that has been retained in the reservoirs; and to evaluate the effects that sediment accumulation has on the hydraulic systems. The Universal Soil Loss Equation (USLE) was applied to the drainage basin and the depth of water in the reservoir was measured, with the aim of establishing significant statistical relationships between the sediment delivery ratios and the geo- morphological parameters of the basins. The results that was obtained showed that gross erosion in the study area, according to the USLE has an average value of about 30 ton/ ha/ year. Annual decrease of available storage volume in the observed reservoirs is 3.6 Mm3, which is equivalent to 0.5% of the total volume every year. A total of 4 million tons of sediment were retained in the reservoirs every year. E.Wohl et al (2000) studied the deposition and transport of sediment patterns following a reservoir sediment release; they picked the north Fork Poudre river drains ≈ 1470 Km² of north central Colorado in USA. Then established three study reaches within the 12 Km of channel affected by the sediment release. Reach 1 is located 500 m downstream from the dam. Reach 2 is located 2.8 - 3.2 Km below the dam and includes a set of two pools and two riffles. Reach 3 is located 4.9 Km below the dam and includes a 40 m long pool.They inferred that as fine bed load moves down stream it will be at least temporary stored a long pool margins and in the lee of clasts protruding from the bed of riffles discharge. Magnitude will govern the volume and grain size distribution of bed load as long as the channel remains transport limited. Flow duration will also govern bed load volume under these conditions. They concluded that the finer grain sized fractions are removed first as discharge rises, and the portions of the channel closest to the sediment source become supply-limited first. E. Amorea et al (2004) published a paper to present the results of the application of two soil erosion models to three large Sicilian basins in Italy upstream of reservoirs. Each basin was subdivided into hillslopes, using
10 three different classes of average area, in order to estimate the scale effect on the sediment yield evaluation. The first model was the empirical Universal Soil Loss Equation (USLE), and the other one was the physically based model of the Water Erosion Prediction Project (WEPP). A Geographical Information System was used as a tool to handle and manage data for application of the models. Computed sediment yields were compared with each other and with measurements of deposited sediment in the reservoir, and for these cases the WEPP estimates better approximated of the measured volumes than did the USLE.The model appeared to be particularly sensitive tothe size area of the hillslopes, at least within the range of values considered. F. Onori et al (2006) measured the amount of sediment yield in Sicilywhich is one of the Italian administrative regions prone to desertification. This study was conducted in the Comunelli catchment in south-central Sicily, to predict potential annual soil loss using the Revised Universal Soil Loss Equation (RUSLE) and to test the reliability of this methodology to predict reservoirs siltation. The RUSLE factors were calculated for the catchment using survey data and rain gauge measurement data. The Runoff factor (R) was calculated from daily, monthly and annual precipitation data. The Erodibility factor (K) was calculated from soil samples collected in May and November 2004. The topographic factor (LS) was calculated from a 20 m digital elevation model. The cover and monument (C) andpractice (P) factors, in absence of detailed data, were set to 1. The results were compared with those obtained from another soil loss estimation method and with the soil loss estimated from the sediment volume stored in the Comunelli reservoir between 1968 and 2004. M.H.Nichols (2006) published a paper to describe long-term sediment yield rates on semiarid rangeland watersheds. He had taken Walnut Gulch ExperimentalWatershed (WGEW) in southeastern Arizona (150 Km2) as a study area which was located in the transition zone between the Sonoran and Chihuhuan deserts in the southeastern Arizona.Nichols measured the sediment accumulation through periodic topographic surveys of the surface of each stock pond when ponds were dry. He noticed that the methods for measuring the volume of sediment in small reservoirs were established in 1935 by USDA Soil Conservation Service (SCS) personnel (Eakin 1936; Brakensieket al.1979); consist of measuring the location and elevation of a sufficient number of points within the pond to map the surface shape. Pond surfaces are surveyed up to spillway elevation or a point inclusive of the highest water level achieved during the period between surveys.Nichols found that the sediment accumulation records ranging from 30 to 47 years were elevated for sub-watersheds ranging in size from 35.2 to 159.5 ha. Sediment yield range from 0.5 to 0.3 m3/ha/year. He suggested that more detailed measurements are needed to characterize channel networks to relate internal watershed sediment transport and deposition processes to
11 sediment delivery at the outlet, and to generalize sediment yield rates across rangeland regions. A. Ozcan et al (2007) predicted the soil erosion risk by the USLE/GIS methodology for planning conservation measures for Indagi Mountain Pass of Cankiri province in the north of Ankara in Turkey. Rainfall-runoff erosivity factor (USLE-R) and topographic factor (USLE-LS) were greatly involved in Geographic Information System (GIS). These were surfaced by correcting USLE-R site-specifically using DEM and climatic data and by evaluating USLE-LS by the flow accumulation tool using DEM and watershed delineation tool to consider the topographical and hydrological effects on the soil loss. . The study assessed the soil erodibility factor (USLE-K) by randomly sampled field properties by geo-statistical analysis. Crop management factor for different land-use/land cover type and land use (USLE-C) was assigned to the numerical values from crop and flora type, canopy and density of five different land uses, which areplantation, recreational land, cropland, forest and grassland, by means of reclassifying digital land use map available for the site. Support practice factor (USLE- P) was taken as a unit assuming no erosion control practices. Resulting soil loss map revealed that spatial average soil loss in terms of the land uses were 1.99, 1.29, 1.21, 1.20, 0.89 ton/ha/year for the cropland, grassland, recreation, plantation and forest, respectively. Ozcan noticed that the results should be very useful to take mitigation measures in the site. K. Boomer et al (2008) compared sediment yields estimated from regional application of the USLE, the automated revised RUSLE2, and five sediment delivery ratio algorithms to measured annual average sediment delivery in 78 catchments of the Chesapeake Bay watershed. They did the same comparisons for another 23 catchments monitored by the USGS. Predictions exceeded observed sediment yields by more than 100% and were highly correlated with USLE erosion predictions (Pearson r range, 0.73–0.92; p < 0.001). RUSLE2- erosion estimates were highly correlated with USLE estimates (r = 0.87; p < 001), so the method of implementing the USLE model did not change the results. The research noticed that the use of USLE or multiple regression models to predict sediment yields is not advisable despite their present widespread application. Integrated watershed models based on the USLE may also be unsuitable for making management decisions. S. Arekhi et al (2011) estimated the sediment yield of the Kengir watershed in Iyvan City, Ilam Province, Iran by using the Modified Universal Soil Loss Equation (MUSLE). The runoff factor of MUSLE was computed using the measured values of runoff and peak rate of runoff at outlet of the watershed. Topographic factor (LS) and crop management factor(C) were determined using theGeographic Information System (GIS) and field-based survey of land use/land cover. The conservation practice factor (P) was obtained from the literature. Sediment yield at the outlet of
12 the study watershed was simulated for six storm events spread over the year 2000 and validated with the measured values. The high coefficient of determination value (0.99) indicates that MUSLE model sediment yield predictions are satisfactory for practical purposes. R.Gharehkhani (2011) took Siazakh dam as a case to study problems of sedimentation in reservoirs; she noticed that under natural conditions sediment is transported from land to water in runoff. The increased rate of delivery of sediment can cause stream sedimentation problem. Gharehkhanimentioned that when particle of sediment enters a riversystem, its transfer is determined by the equations or measured from the river.Comparing the incoming sediments to the reservoir and their sedimentation indicated that the amount of sedimentation in reservoir always is less than their incoming sediments; alsorecalled that the volume of sedimentary deposits depends on two parameters: trapping coefficients which decrease over time and should be considered during the useful life of reservoir. The second parameter is the sedimentary materials density in reservoir which should be converted from weight to volume unites in order to determine the how much sediments occupy the reservoir, thus it is required to have the sediment density. Hidde Kats (2016) took "Nga Moe Yeik" reservoir in Myanmar as a case to study the sedimentation in reservoirs. "Nga Moe Yeik" is situated 100 Km to the north of Yangon city. This reservoir fulfils an important function within the water supply of Yangon city. The catchment area is 414 Km², with a capacity of 222 MCM. Kats used the InVEST model, based on the Universal Soil Loss Equation (USLE) and a method to predict the trap efficiency of the reservoir; therefore it is possible to make predictions about the accumulated sediment in the reservoir. Kats used the spatially explicit InVEST model to make average annual prediction for the watershed sediment yield. She estimated the total sediment yield accumulation for the "Nga Moe Yeik" catchment during the past years to be between 44.5 Mm3 and 64.4 Mm3. Thus the annual erosion rates were estimated to be between 14.5*103ton / km2 and 42.3*103 ton /km2. Kats mentioned that when the eroded material enters the reservoir, some of it will deposit and some of it will flow out. She defined the trap efficiency as the ratio of sediment inflow and outflow and estimated it by using empirical equations, by using data about daily inflow and stored volume and based on the residence time of water within the reservoir. The longer water stays in the reservoir, the more sediment will be deposit and that will increase the trap efficiency. The average trap efficiency for the "Nga Moe Yeik" reservoir was 97.65%. Kats performed a bathymetric survey to assess the capacity loss of the reservoir. She used a bathymetric survey to build a Digital Elevation Model (DEM) of the reservoir bed using ArcGis, she presented an old map of the "Nga Moe Yeik" to model the before dam situation also by using ArcGis. The differences between two DEMs resulted in a capacity loss that
13 represent the real capacity loss during the last 21 years. Therefore she concluded that the sediment accumulation was between 14.74 Mm3 and 27.66 Mm3.
2.3 Previous studies in soil erosion assessment by GIS technique. K. Manoj (2009) chose Nagwa and Karso catchments in Bihar (India) as a case study for identification of sediment source areas and prediction of storm sediment yield from catchments. A Geographic Information System (GIS) methodology was proposed and validated in this reserch. The Integrated Land and Water Information System (ILWIS) GIS was used for discretizing the catchments into grid cells and the ERDAS Imagine image processor used for processing satellite data related to land cover and soil characteristics. Grid cell drainage directions and catchment boundaries were generated by forming the DEM using a pour point model. The DEM was further analyzed to classify grid cells into overland region cells and channel region cells by using the concept of a channel initiation threshold area. Then after assigning values to the various parameters of the Universal Soil Loss Equation(USLE) in individual cells, their gross surface erosion was calculated. Y. Zhang et al (2009) integrated the Modified Universal Soil Loss Equation (MUSLE) in a Geographic Information System (GIS) framework in the form of a tool called ArcMUSLE, an extension of ArcGIS software to assist soil and water conservation agencies in soil erosion risk assessment and define of critical areas of soil erosion control practices. ArcMUSLE tool can be used to determine curve numbers, estimate peak flow and soil loss for a rainfall event within a watershed. Zhang chose a watershed in Black Hawk country, Iowa in USA with an area of 24.2 Km2 as an application for this tool. W. Esther (2009) estimated some of soil erosion factors including rainfall runoff erosivity (R) and slope length steepness factor (LS) by using Geographic Information System (GIS) technique. Esther chose Kapingenzi River catchment as an application for the study. The results of the preliminary soil erosion assessment indicated that the average annual soil loss within the catchment range from 0 to 449 tons/acre with zero erosion occurring along the channel. According to this results Esther recommended that ground survey be under taken on areas showing high risk of soil erosion. Tao Chen et al (2010) mapped the soil erosion risk in Miyun watershed, North China, by applying the Revised Universal Soil Loss Equation (RUSLE), remote sensing technique, and Geographic Information System (GIS). The soil erosion parameters were evaluated: the erodibility factor (K) map was generated from the soil map. The Runoff factor(R) was developed from the rainfall data. The Cover and management factor (C) map was generated based on a Bak Propagation (BP) neural network
14 method of Land sat ETM+data. A Digital Elevation Model (DEM) with a spatial resolution of 30 m was derived from topographic map at the scale of 1:50,000 to develop the topographic factor (LS) map. The research concluded that the annual average soil loss was 9.86 ton/ hec./year in 2005. And the area of 47.5 Km2 (0.3%) expresses extremely severe erosion risk, which needs suitable conservation measures to be adopted on a priority basis. A. Demiric et al (2011) chosethe Buyukcekmece lake watershed, in Turkey, as a case study to estimate the soil erosion using the Revised Universal Soil Loss Equation (RUSLE) in a GIS framework. The factors in the RUSLE were computed by using different data obtained or produced from meteorological station, soil surveys, topographic maps and satellite images. The RUSLE factors were represented by raster layers in a GIS environment and then multiplied together to estimate the soil erosion rate in the study area by using the spatial analyst tool of ArcGIS 9.3 .the soil below 1 ton/ha/year in this study was defined as low erosion, while those > 10 ton/ha/year were defined as severe erosion. The values between low and severe erosion were classified as slight, moderate and high erosion areas. As the study revealed, nearly half of the Buyukcekmece lake watershed required implementation of effective soil conservation measures. Demirci et al mentioned that the topographic factor (LS) depends on slope percentage and length of the slope which is also defined as a ratio of soil loss under given conditions to that at a site with the "standard" slope steepness of 9% and slope length of 22.13 m. The steeper and longer the slope, the higher is the risk for erosion. Where flow accumulation is the grid layer of flow accumulation expressed as the number of grid cells, while cell size is the length of a cell side. Slope length and steepness factors were computed from a DEM of the study area by using ArcGIS software. P. Csafodia et al (2012), the purpose of this study is to implement the analysis of soil erosion with the Universal Soil Loss Equation (USLE) in the Geographic Information System (GIS) environment. A surface erosion scenario for the hydrologic year 2008–2009 was modeled in a small forested catchment,Farkas Ditch in the Sopron Hillsin Hungary, using the developed ArcGIS model.Csafodi et al. noticed that the regenerated areas with dense grass cover have a significant soil protection function, because neither the mean nor the maximum surface soil loss exceeds the limit value. Csafodi et al observed that the predicted soil erosion mostly depends on the slope conditions in the forest sub compartments. The research recommended that to confirm the results of USLE-evaluations, direct field measurements of surface soil erosion are required, to do some improvements for the future in order to achieve more reliable prediction results for smaller catchments and to extend the framework for larger watersheds. Also the research concluded that the detailed DEM involving
15 artificial linear elements, such as roads and ditches ensured more precise automatic delineation of stream networks and catchment boundaries, which modify the slope-length and rainfall runoff, also recommended that the slope-length should be considered and use different calculation technique to obtain an accurate topographic factor (LS). Furthermore the research recalled that manual uploading of cover-management and erosion-control practice factors can be automatized, but this operation may lead to a decrease of the spatial resolution of factor values, because the user has no control over the setting of the C and P factors. Y. Kim (2014), the amount of soil erosion was calculated in this research by using ArcGIS and the Revised Universal Soil Loss Equation (RUSLE). He increased the understanding of the water-born soil erosion directly related to river system and the GIS combined RUSLE model. Kim chose San Marcos just south of the Austin area in central Texas and some of its surrounding area as a case study to predict the sediment yield. He chose to use the Unit Stream Power Erosion and Deposition model (USPED) to calculate the topographic factor by ArcGIS program in combination with a proposed equation depends on Unit Stream Power Theory. K. Adhar (2016) modeled soil erosion for the present and future (2027) with two climate scenarios in Kungsbackaån watershed in the south west of Sweden. The Revised Universal Soil Loss Equation (RUSLE) was used within a GIS environment coupled with high resolution elevation data of 2 meters.Publically available data was gathered on rainfall, soil, elevation and vegetation to be implemented within the ArcGIS 10.3.1 suite.Results show that present and future estimated mean annual soil loss in Kungsbackaån watershed will be within bounds of very low soil loss of 0 – 0.5 ton/ha/year but with isolated locations of higher estimated mean annual soil loss up to >50 ton/ha/year. This method was deemed credible when compared to a study on present estimated mean annual soil loss by the European Soil Data Centre (ESDAC). However there are a problems with the method when coupled with high resolution elevation data which makes the very high values of estimated mean annual soil loss questionable depending on locations. These locations can be streams or on bare bedrock and thus finer processes of removing non-erodible areas need to be implemented. N. Moses (2017) noticed that the topography is one of the fundamental factors affecting soil erosion by water. The objective of the study is to use the Geographic Information System (GIS) technique to determine the topographic factor (LS) for river Nzoia basin in Kenya. The main input data was a 90 m Digital Elevation Model (DEM). The model was manipulated in a GIS environment to generate both slope length (L) and steepness (S) factors. The steepness factor (S) ranged from 3.84 to 45.9 ͦ indicated. While the slope length factor (L) ranged from a minimum of 0 to
16 a maximum of 0.265 with 0.02 as mean and standard deviation. Raster calculator (ArcGIS spatial tool) was applied to compute the topographic factor (LS). The LS factor varied from 0 to 0.3 with mean and standard deviation value of 0.02. Higher values of the LS factor were a characteristic of a high altitude area and the lowest LS factor values were attribute of low altitude areas where the topography is flate-like in nature.
2.4 Previous Studies in Jordan Q. Gharaibeh (1987) noticed that the location of deposited sediment is important since different elevations are allocated for different purposes (flood control, irrigation, navigation, etc), thus it is important to know the location and volume of sediment that will be deposited for planning purposes.The distribution of sediment in King Talal reservoir wasdiscussed and described; also the factors affecting volume and distribution of sediment in reservoirs were described. The surveyedsediment volume in King Talal reservoir was distributed by applyingempirical models (Increment Area and Empirical Area Reduction) then calculated the useful life of King Talal reservoir by Gill´s Method, and developed a sediment rating curve for Zarqa river at New Jerash Bridge using Colby`s Method.A sub-routing was developed and added to the Rice Model to distribute the sediment coming from the tributaries S. Shraidehet al. (2000) estimated the sediment yield at Amman- zerqa basin. The estimation was based on Agricultural Non-Point Source (AGNPS) model in order to enhance the reservoir capacity of King Talal Dam. This model developed by Agricultural Researches Services (ARS). Using this model, the actual sediment yield was correlated with simulated sediment yield by AGNPS. Based on this study, terracing was suggested as a conservation measure to curb erosion to the reservoir. A. Malkawiet al. (2000) conducted a study to map landslide hazard zones using remote sensing and Geographic Information System(GIS), The main objective of his study was to serve planners and civil engineers for suitable selection of road alignment and to provide appropriate assessment and mitigation measure to be taken into account during the preliminary stages of future development schemes.Malkawi implemented remote sensing and GIS-assisted modeling of soil induced erosion hazards on Amman- Zerqa basin. An attempt was made to estimate mean annual soil losses using Revised Universal Soil Loss Equation (RUSLE) and Stehlik’s model. Some of models parameters were obtained by remote sensing techniques. Five categories of soil erosion risks were generated based on the estimated annual mean soil losses from each model. It was concluded that areas in the central and western parts of the basin have the highest erosion potential; this is related to high mean annual rainfall amounts and high topographic relief. Furthermore Stehlik’s model gives more importance to the rainfall factor and slope steepness thanThe Revised
17 Universal Soil Loss Equation(RUSLE), also it gives less importance to changes in soil erodibility and vegetation cover type. N. Al Ansari (2003) in his research picked Wadi Al-Arab dam as a case study with its drain area of 267 Km² and construction of a maximum storage capacity of 20 Mm3. Al- Ansari investigated the nature of sediment and their accumulation through the analysis of 36 bottom sediment samples and by constructing a new bathymetric map for the reservoir. The results showed that sand mainly covers the bed of the reservoir (80.5%) with minor amounts of silt and clay (19.5%). Siltation and suspension were the main transport mechanisms of the sediments in this area. The new path metric map suggested that the maximum storage capacity of the dam is now only 13.744 Mm3. N. Hammouri (2007) selected Amman- Zarqa basin as a case study, since it is one of the areas in Jordan that has several hydrological and agricultural activities that may be affected by the soil erosion. The basin consists of several agricultural lands. King Talal Dam is also located in the basin and suffers from the amount of incoming sediments that decreases its design storage. An attempt was made to estimate mean annual soil losses using Revised Universal Soil Loss Equation (RUSLE) and Stehlik´s model. Some of RUSLE and Stehlik`s model parameters where obtained by remote sensing techniques. Also a digital GIS data base was completed to store all data needed to compute RUSLE and Stehlik´s model. Each factor in these two models has been generated as a separated GIS layer. A final soil erosion map has been estimated using spatial arithmetic operation for the six GIS layers that describe these two models. Five categories of soil erosion risks were generated based on the estimated annual mean soil losses from each model. Hammouri noticed that the areas in the central and north western parts of the basin have the highest erosion potential. This was supported by the high mean annual rainfall amounts in these areas and the high topographic relief. He noticed that Kurnub, Na´ur, Shueib and Ghudran formations have high potential erosion. The litho logy of these formations is soft marl or sand. Hammouri proposed a practice management to decrease the erosion potential based on the land cover in the areas with high soil losses. M. Al Mahamid (2007) selected Al Mujeb dam to estimate the quantity of sediment reached to its reservoir during the period between Nov-2003 to Dec-2006. AVSWAT (Aric-Veiw Soil and water Assessment Tool) 2000 model was used to simulate Mujeb dam catchment area. The model uses (Geographic Information System) GIS interface tool and needs GIS layer for soil, land cover, elevation. The results of this study identified the quantity of water and sediment inflow to the reservoir, also identified the regions of high soil erosion, sediment yield and delivery ratio in order to manage these regions by applying techniques which reduce these values in sequence to decrease the sediment yield reaches to the reservoir.
18 Wala Dam was selected by E. Tarawneh (2007) as a case study to recognize the threats of water and soil loss with the consequence sedimentation problems. The study comprised an application of the Soil and Water Assessment Tool (SWAT), associated with the Geographic Information System (GIS) to simulate the hydrology, soil erosion and sedimentation of Wala Dam catchment area. A set of hydrological techniques was utilized to simulate various components such as the Curve Number Method, the Rational Method and the Universal Soil Loss Equation (USLE) and ModifiedUniversal Soil Loss Equation (MUSLE) models. A set of data was prepared and collected earthier as data base files such as the daily rainfall records, or as analytical GIS layers of soil, land use/ cover, drainage pattern, Digital Elevation Model (DEM).Tarawneh dicritized the area into 43 sub basins and 82 hydrologic response units. Two simulation series were performed using annual and monthly printout frequency. Several results were obtained including water and sediment yield at Wala Dam location with the respective delivery ratios, and spatial representation of precipitation, surface runoff, soil erosion, sediment and water yield on sub basin level. Model calibration and verification were carried out using flow rate and sediment yield data observed at Wala flow station and the results were satisfactory, including that this model can represent well the climate and physical conditions of the area. Two prediction scenarios were performed, both indicated that the western and northern sub basins yield more water to the dam and are more susceptible to soil erosion and sediment generation. Tarawneh recommended and suggested soil conservation and sediment reduction measures to control soil loss and maintain storage in Wala Dam reservoir, in addition to protecting its bottom against clogging through which the process of ground water recharge might malfunction. A.Farhan (2012) assessed soil stalinization and erosion in the Yarmouk basin by using (GIS) and remote sensing. The main idea of this study is to implement a GIS-based approach to map and assess lad degradation by incorporating soil, water, land use/cover, climate and remote sensing data. Farhan used multi-type remote sensing images of 1992, 2002, and 2009, with field surveys which avoided by global positioning system (GPS), within Geographic Information System (GIS) to model soil salinity and soil erosion by water. Also he combined soil salinity, organic matter content, soil texture, rainfall data, and the digital elevation model (DEM) with land use/cover map to produce combined maps of soil salinity and erosion. Furthermore he assessed potential soil loss by using Universal Soil Loss Equation (USLE). spatial layers of crop management (C) and support practice (P) from land use/cover was derived, while slope length and steepness (LS) was derived from DEM. Rainfall and runoff erosivity (R) was derived from rainfall data of four weather stations, while soil erodibilty (K) was derived from soil erodibility
19 nomogragh based on soil texture, structure permeability and organic matter. Farhan concluded that a high soil loss rate that reached 5 ton/ha/yr for 60% of the study area, 5-25 ton /hr/yr for 34% of the study area and more than 25 ton / ha/yr for 4% of the study area. Farhan noticed that the study area was suffering from high degradation and facing a high risk of soil erosion, which implied the need for proper land management for the study area. S. Nawaiseh et al (2015) submitted a study intended to estimate the annual soil loss in Wadi Al-Karak watershed, and to examine the spatial patterns of soil loss and intensity. The Revised Universal Soil Loss Equation (RUSLE) model was applied in a geographical information system framework to compute the RUSLE parameters then measure the soil erosion risk, by generate the intensity maps and integrate with physical factors (terrain units, elevation, slope, and land uses/cover) to explore the influence of these factors on the spatial patterns of soil erosion loss. The estimated potential annual average soil loss was 64 ton/ ha/ year, and the potential erosion rates from calculated class ranges was from 0.0 to 790 ton/ ha/ year. Soil erosion risk assessment indicates that 54.5 % of the catchment is prone to high to extreme soil losses higher than 25 ton/ ha/ year/ . The lower and middle parts of the catchment suffer from high, severe, to extreme soil erosion. While 45.5 % of the basin still undergoes slight and moderate levels of soil loss of less than 25 ton /ha/ year, yet 76.91 % of soil erosion occurred on four different terrain units, and 72.29 % of soil erosion occurred in zones less than 600 m in elevation, with 88 % present on areas of 0–6, 5–15, and 15–25 slope categories. 32.6, 30.3and 33.1 % of soil erosion occurred on rainfall mixed farming and irrigated areas, barren, and rangeland, respectively. The results provided a vital database necessary to control soil erosion in order to ensure sustainable agriculture in the highland region of Jordan.
2.5 Importance of the present study This study differs from the previous studies in using the Modified Universal Soil Loss Equation (MUSLE) model to simulate the daily amount of sediment yield from the start running the dam (6/12/2003) until (13/3/ 2018) using measured volumes of runoff into the reservoir, soil erodibility factor (KUSLE) from analysis of soil samples collected randomly from different points in the catchment area, and a topographic factor (LS) analyzed from the Geographic Information System (GIS) by using ArcGIS (10.3) software. The aim of this study is to compare the results that obtained from the MUSLE and the results that observed from the Eco- sounder device that was used by the Directorate of dams. The Eco-sounder device depended on the sound wave to measure the amount of sediment in the bottom of the dam reservoir by producing a several paths and give a scheme for each path then analysis the scheme and measuring the amount of sediment yields for dam reservoir. While the MUSLE depends on the
20 climate and geological characteristics of the study area, in this study all the parameters that used in the MUSLE were obtained as a real reading for the study area (Al-Mujeb catchment area). Since these two methods were measured experimentally, the comparison will be fair enough between the simulated and observed data, and based on this comparison the MUSLE will be evaluated if it is an effective way to measure the sediment yield after simulating reality.
21 Chapter Three Basic Concepts of Sedimentation in Dam Reservoirs
3.1 Preface This chapter provides information on various components and processes of watersheds, such as surface runoff, soil erosion and sedimentation, as well as presenting the theoretical perspectives of the techniques utilized to simulate them, considering the options provided by the Modified Universal Soil Loss Equation (MUSLE) for such techniques.
3.2 Dam Reservoirs Sedimentation Climate and topography erosion is posing a big threat to the life time of the reservoirs. Erosional processes cause reservoirs to silt up, resulting in major capacity losses. To improve the reservoir and irrigation management, it is important to make estimations about the reservoir life expectancy. Erosion and sedimentation are major problems that reduce the productivity of cropland, degrade water quality, carry polluting chemicals, and reduce the capacity of water conveyance structures. The erosion- sedimentation system is composed of upland and channel components. While the uplands act somewhat independently of the channels, the behavior of the channels is directly influenced by the upland inputs. Spatial and temporal variations in erosion are common with time lags in sediment yield as great as several decades. Climate, soil, topography, and land use are major factors influencing erosion and sedimentation. (Ray. K. Linsley, 1949). (Ray K. Linsley, 1949) defined the sediment geologically asany fragmental material transported by, suspended in, or deposited by water or air. While water infiltrating through the soil surface transports minor quantities of very fine soil particles downward through the soil profile, sedimentation is essentially a surface phenomenon. In addition (L. Lane et al, 1984) defined the Sediment yield as the final and net result of detachment, transport, and deposition processes occurring from the watershed divide down to the point of interest where sediment yield information is needed. Erosion rates are measured using small plots, and the distance that a particle must travel before being counted as having been “eroded” may be a few meters or less. Erosion rates from farms and watersheds are computed by empirical models, such as the Universal Soil Loss Equation (USLE) and its variants Modified Universal Soil Loss Equation (MUSLE), Revised Universal Soil Loss Equation (RUSLE), or the more complex physically based detachment and transport models, such asAGricultural Non-Point Source Pollution Model(AGNPS),The Areal Non point Source Watershed Environment Response Simulation(ANSWERS), Field scale model for
22 Chemicals, Runoff and Erosion from Agricultural Management Systems (CREAMS), andThe Water Erosion Prediction Project(WEPP). (G. L. Morris and J.Fan, 1997)
3.3 Sources of Sediments It is well known that only a fraction, and perhaps rather a small fraction, of the sediment eroded within a drainage basin will find its way to the basin outlet and be represented in the sediment yield. Deposition and temporary or permanent storage may occur on the slope, particularly where gradient decline down slope, at the base of the slope, in swales, on the flood plain, Orin the channel itself. The relative magnitude of this loss tends to increase with increasing basin size. (D. E.Walling, 1982). The source of sediment can be as following. (M. Al-Mahamid, 2007): 1. Erosion from agricultural, forest and waste lands. 2. Movement of soil mass due to landslides, slumps and soil creep. 3. From gully by concentrated runoff. 4. Stream bank erosion including cutting of banks and scouring from bed. 5. Erosion caused by occurrence of flood in watershed. 6. Incident to the roads, railroads, cleaning of houses, industries etc, and 7. Mining dumps left as waste materials over the ground surface.
3.4 Sedimentation Mechanism. The falling raindrop breaks down the clods and aggregate at the soil surface into a single-grained soil structure. The fine particles are in turn thrown into suspension by the energy of the falling drops and then carried along with the overland flow toward established channels. The force of the flowing water tends to losses additional particles, and they, too, may be transported toward the stream channel. Upon entering the channel, the flow velocities may decrease and thereby cause some of the heavier suspended particles to fall to the bottom. If the velocity of the water increases after it enters the stream, bottom material in the channel will be picked up and transported downstream of the channel.(Ray K. Linsley, 1949) (Change, 1998) defines the transport capacity of the channel as the maximum amount of sediment (in volume or weight) that can be entrained and transported by a stream channel for a specific discharge, and it varies both spatially and temporally. River beds will be stable when there is a balance between driving forces and the factors (framework) resisting that erosion. Sedimentation or erosion occurs when there is an imbalance between the driving forces and the resisting framework within a stream channel. Lane (1957) proposed a
23 function to describe the balance:
s .D50 (3.1)
Where; Q is the water discharge,Q.S ∝ Q S is the bed slope, Qs is the sediment discharge, and D50 isthe median sediment size. This function, termed Lane’s Law, balances the driving forces on oneside against the resisting framework on the opposite side.
3.5 Problems Associated with Erosion and Sedimentation Some of the most significant problems associated with erosion and sedimentation: 1- Eroded soils in the form of sediment particles become contaminant and pollutants. Eroded soils may contains nitrogen, phosphorus, and other nutrients that when carried into water bodies would trigger algal bloom that reduces water clarity, depletes oxygen, leads to fish kills and creates odor (Goldman et al., 1986). 2- Turbidity from sediment reduces in-stream photosynthesis leading to reduced food supply and habitat loss for aquatic lives. 3- Reduce the design capacity for reservoirs; pave a way for possible structural failures. Uncontrolled sedimentation may require frequent maintenance and dredging operations to restore affected water bodies to their best operating conditions. 4- Eroded soils reduce the effective flow areas for drainage ways, plug culverts and damage adjacent properties. 5- Erosion removes the smaller and less dense constituents of top soil comprising clay, fine silt particles and organic material, which hold nutrients required by plants. The remaining sub soil is often hard, rocky and making the re-establishment of vegetation difficult.
3.6 Factors Affecting Sediment Yield There are many factors affecting the sediment yield in any catchment area, and can be summarized as the following.(PSIAC, 1968): 1- Surface Geology The effect of surface geology on erosion is readily apparent. The weaker and softer rocks are more easily eroded and generally produce more sediment than do the harder more resistant types, sandstones and similar coarse- textured rocks that disintegrate to form permeable soils erode less than shale's and related mudstones and siltstones under the same conditions of precipitation. On the other hand, because of absence of cementing agents in some soils derived from sandstone, large storms may produce some of the highest sediment yields known.
24 2- Soils There are essentially three inorganic properties (sand, silt and clay) which may in any combination give soil its physical characteristics. Organic substances plus clay provide the binding material which tends to hold the soil separates together and form aggregates. Aggregate formation and stability of these aggregate are the resistant properties of soil against erosion. Unstable aggregates or single grain soil materials can be very erodible. 3- Climate and Runoff Climate factors are paramount in soil and vegetal development and determine the quantity and discharge rate of runoff. The same factors constitute the forces that cause erosion and resultant sediment yield. Likewise, temperature, precipitation, and particularly the distribution of precipitation during the growing season, affect the quantity and quality of the ground cover as well as soil development. The quantity and intensity of precipitation determine the amount and discharge rates of runoff and resultant detachment of soil and the transport media for sediment yield. The intensity of prevailing and seasonal winds affects precipitation pattern, snow accumulation and evaporation rate. Snow appears to have a minor effect on upland slope erosion since raindrop impact is absent and runoff associated with snow melt is generally in resistant mountain systems. High runoff of rare frequency may cause an impact on average annual sediment yield for a long period of time in a watershed that is sensitive to erosion, or it may have little effect in an insensitive watershed. For example, sediment that has been collected in the bottom of a canyon and on side slopes for many years of low and moderate flows may be swept out during the rare event, creating a large change in the indicated sediment yield rate for the period of record. 4- Ground Cover Ground cover is described as anything on or above the surface of the ground which alters the effect of precipitation on the soil surface and profile. Included in this factor are vegetation, litter, and rock fragments. A good ground cover dissipates the energy of rainfall before it strikes the soil surface, delivers water to the soil at a relatively uniform rate, impedes the flow of water, and promotes infiltration by the action of roots within the soil. Conversely, absence of ground cover, whether through natural growth habits or the effect of overgrazing or fire, leave the land surface open to the worse effects of storms. In certain areas, small rocks or rock fragments may be so numerous on the surface of the ground that they afford excellent protection for any underlying fine material. These rocks absorb the energy of falling rain and are resistant enough to prevent cutting by flowing water.
25 5- Land Use The use of land has a widely variable impact on sediment yield, depending largely on the susceptibility of the soil and rock to erosion, the amount of stress exerted by climate factors and the type and intensity of use. 6- Upland Slope Erosion This erosion form occurs on sloping watershed lands beyond the confines of valleys. Sheet erosion involves the removal of a thin layer of soil over an extensive area is usually not visible to the eye. This erosion form is evidenced by the formation of rills. Experience indicated that soil loss from rill erosion can be seen if it amounts to about 5 tons or more per acre. This is equivalent in volume per square mile to approximately 2 acre- feet. 7- Catchment area size The peak sediment flow per unit area decreases as the area increases while the period of surface runoff increases with area. The reason behind this case is that; a catchment of larger area has greater time of concentration (reaching time from remotest point to the outlet), as a result a more time is being available to the water for leaching into the soil. Ultimately there is a reduction in the runoff and soil loss or sediment yield. In small size catchments there is a reverse trend. (Al-Mahamid, 2007)
3.7 Estimating Sediment Yield Universal Soil Loss Equation (USLE) was introduced at a series of regional workshops on soil-loss prediction in 1959-62 and by a U.S. Department of Agriculture special report (USDA, 1961). Several years of trial use by Soil Conservation Service (SCS) and others plus extensive interaction between the developer and users results in improved factors values and the evaluation of additional conditions. Finally, USLE was presented in Agriculture Handbook No.282 (Wischmeier and smith 1965). Widespread acceptance of USLE took time, but came progressively as more regions and groups began to use this equation. During the same period, important improvement in USLE expanded its usefulness by providing techniques for estimating site values of its factors for additional land uses, climatic conditions, and management practices. These improvements were incorporated in an updated version of USLE, published as Agriculture Hand book No. 537 (Wischmeier & smith 1978). The Modified Universal Soil Loss Equation (MUSLE), which will be used in this study, was developed to relate empirically storm-period sediment yields to upland soil loss rates indexed by Universal Soil Loss Equation (USLE) (Wischmeier & Smith, 1978). The erodibility factor and transport efficiency of surface runoff indexed by a function of the product of total storm runoff volume and peak runoff rate (Williams, 1975). When applied to average annual sediment yield estimates, however, MUSLE
26 becomes computationally cumbersome and generally requires daily (or hourly) hydrologic simulation. Williams (1975) developed the MUSLE by replacing the rainfallenergy factor in the USLE with a runoff energy factor. The equation was developed using individual storm data from 18 basins in Texas and Nebraska and subsequently validated on102 basins throughout the United States using runoff data generated by the hydrology component of theSimulator for Water Resources in Rural Basins (SWRRB) model (Williams, 1982). The MUSLE is:
0.56 Sed. = 11.8 * (Qsurf* qpeak*A) *K *C *P *LS *CFRG (3.2) Where; Sed.: Sediment yield (metric ton) Qsurf: Surface Runoff volume (mm/ha) qpeak: The peak runoff rate (m3/s) A:is the area of the region (ha) K: is the USLE soil erodibility factor (0.013 metric ton m2 hr/ (m3-metric ton cm)) C: The cover and management factor P:The USLE support practice factor LS:The USLE topographic factor CFRG:The coarse fragment factor
The main advantages of MUSLE are its simplicity, the directconceptual and physical relevance of its factors, the large database upon which the empirical relationship was developed andthe capability to insert management considerations into factorselection. (Simonton et al., 1980; Renard, 1980) The factors of the Modified Soil Loss Equation can be divided into hydrological factors and the physical character of the catchment area, each factor will be discussed in the next section.
3.7.1 Hydrological Factors 3.7.1.1 Surface Runoff Runoff is the quantity of water that is discharged from a drainage basin during a giventime period. Runoff data may be presented as volumes in acre-feet,as mean discharges per unit ofdrainage area in cubic feet per second per square mile, or as depthsof water on the drainage basin ininches. It is measured by establishing stream gauges at selectedplaces of the river courses. The termrunoff refers to the overland flow of water, after every rainfall orsnowmelt. The overland flow startswhen the rate of rainfall is greater than the rate of infiltration of thesoil and the increase ofthe amount ofslope. Initially, Runoff starts as small streams and the water get added from many such streams. (A.Balasubramanian, 2017).
27 The SCS curve number equation as quoted by (V.Chow. et al, 1988) is: (3.3) 2 (푅푑푎푦−퐼푎) Where; 푄푠푢푟푓.= (푅푑푎푦−퐼푎)+푆 Qsurf:is the accumulated runoff or rainfall excess (mm). Rday: is the daily rainfall depth (mm). Ia: is the initial abstractions which includes surface storage, interception and infiltration prior to runoff (mm). S: is the maximum potential retention parameter (mm). An empirical relation was developed showed that the initial abstractions, Ia, is equal to 0.2S and equation 3.3 becomes:
2 (3.4) (푅푑푎푦−0.2 푆) 푠푢푟푓. The retention parameter푄 = varies(푅푑푎푦 +0.8spatially 푆) due to changes in soils, land use, management and slope and temporally due to changes in soil water content. The retention parameter is defined as: 1000 S = 25.4 . − 10 (3.5) CN Where; CN: the curve number of the watershed.
3.7.1.2 The Peak Runoff Rate The peak runoff rate is the maximum runoff flow rate that occurs with a given rainfall event. The peak runoff rate is an indicator of the erosive power of a storm and is used to predict sediment loss. (S.L.Neitsch et al, 2009) The rational method is based on the assumption that if a rainfall of intensity I begins at time t = 0 and continues indefinitely, the rate of runoff will increase until the time of concentration, t = tconc, when the entire sub basin area is contributing to flow at the outlet(S.L.Neitsch et al, 2009). The Rational formula is:
(3.6) 퐶 ∗퐼 ∗ 퐴푟푒푎 Where; 푞푝푒푎푘 = 3.6 3 -1 qpeak: is the peak runoff rate (m .s ). C: is the runoff coefficient. I: is the rainfall intensity (mm/hr). Area: is the sub basin area (Km2). 3.6 is a unit conversion factor.
28 A- Time of Concentration The time of concentration is the amount of time from the beginning of a rainfall event until the entire sub basin area is contributing to flow at the outlet. In the other words, the time of concentration is the time for a drop of water to flow from the remotest point hydraulically in the sub-basin to the sub basin outlet. (S.L.Neitsch. et al, 2009). It is not possible to point to a particular point ona watershed and say, “The time of concentration is measured from this point.” Neither is it possible to measure the time of concentration. Instead, the concept of tc is useful for describing the time response of a watershed to a driving impulse, namely that of watershed runoff. If the chosen storm duration is larger than tc, then the rainfall intensity will be less than that at tc. Therefore, the peak discharge estimated using the rational method will be less than the optimal value. If the chosen storm duration is less than tc, then the watershed is not fully contributing runoff to the outlet for that storm length, and the optimal value will not be realized. Therefore, we choose the storm length to be equal to tc for use in estimating peak discharges using the rational method. There are many methods for estimating the time of concentration. All of them are empirical.In this study the formula proposed by Soil Conservation Service presented by Equation (3.7) was used:
Tlag = Tc *0.6 (3.7) Where; Tlag: the lag time in hours. Tc: the time of concentration in hours.
The lag time is the time from the centroid of rainfall excess to the centroid of corresponding runoff hydrograph. In order to calculate the lag time for Al-Mujeb catchment area, the Soil Conservation Service formula was used:
Tlag = 0.8 1000 0.7(3.8) (L∗3.28∗1000) ∗(CNaw−9) Where; 0.5 T lag:lag time in hours1900∗Y L: Hydraulic length of the catchment in Km; CNaw: Weighted Curve Number; Y: average catchment slope in percents.
B- Runoff Coefficient The runoff coefficient (C) is a dimensionless ratio intended to indicate the amount of runoff generated by a watershed given average intensity of precipitation for a storm. The runoff coefficient represents the fraction of rainfall converted to runoff.
29 The runoff coefficient represented by this equation (David B. Thompson, 2009):
(3.9) Where; 푅 R: total depth of runoff 퐶 = 푃 P: total depth of precipitation Its values are classified on the basis of land uses and soil types, its range between 0-1; a low C value means that the rainfall water is retained for a time on the ground surface and drains into the ground or forms puddles such as well vegetated areas, where a high C value means that most of rainfall water runoff rapidly such as paved or impermeable surface. (V.Chow. et al, 1988). C- Rainfall Intensity The rainfall intensity is the average rainfall rate during the time of concentration. It can be calculated with the equation:
(3.10) Rtc c I = Wheret ;I is the rainfall intensity (mm/hr), Rtc is the amount of rain falling during the time of concentration (mm), and tc is the time of concentration for the sub basin (hr).Many studies on rainfall data for different durations and frequenciesindicate that (Rtc) is proportional to the daily rainfall (p); a procedure toapproximate its value has been produced by (Neitsch et al, 2005). So Equation (3.7) will be:
(3.11) Area∗Qsurf. Where; qpeak = Tc∗3.6 3 qpeak: the peak runoff rate for the catchment area (m /s) Area: Al-Mujeb catchment area (Km2) Qsurf.: the accumulated runoff or rainfall excess (mm). Tc: the time of concentration for Al-Mujeb catchment area (hr). 3.6: is a unit conversion factor
3.7.2 The physical Characteristic of the Catchment Area
1- The Soil Erodibility Factor (KUSLE) Soil erodibility is a complex property. It is also through of as the ease with which soil is detached by splash during rainfall or by surface flow or both. Thus; soil erodibility should be viewed as the change in the soil perunitof applied external force or energy.(K.G.Renard et al. 1997).
30 Wischmeier and smith (1978) define the soil erodibility factor as the soil loss rate per erosion index unit for a specified soil as measured on a unit plot. A unit plot is 22.1 m (72.6 ft) long. With a uniform length wise slope of 9 percent, in continuous fallow, tilled up and down the slope. Continuous fallow is defined as land that has been tilled and kept free of vegetation for more than 2 years. The units for the USLE soil erodibility factor in MUSLE are numerically equivalent to the traditional English units of 0.01 (ton acre hr)/ (acre ft-ton inch). Wischmeier and smith (1978) noted that a soil type usually becomes less erodible with decrease in silt fraction; regardless of whether the corresponding increases in is the sand fraction or clay fraction. Wischmeier et al. (1971) developed a general equation to calculate the soil erodibilty factor when the silt and very fine sand content makes up less than 70% of the soil particle size distribution
1.14 0.00021. M . (12 − OM) + 3.25. (csoilstr − 2) + 2.5 . (cperm − 3) kUSLEWhere= K is the soil erodibility factor, M is the particle-size (3.12) USLE 100 parameter, OM is the percent organic matter (%), csoilstr is the soil structure code used in soil classification, and cperm is the profile permeability class. The particle size parameter can calculate by:
(3.13)
Where푀 msilt = is (푚 the푠푖푙푡 percent+ 푚 푣푓푠of the). ( silt100 content− 푚푐 (0.002-) 0.05 mm diameter particles), mvfs is the percent of the very fine sand content (0.05 – 0.10 mm diameter particles), and mc is the percent of the clay content (˂0.002 mm diameter particles). The percent organic matter content (OM) of a layer can be calculated:
OM = 1.72. orgC (3.14)
Where orgC is the percent organic carbon content of the layer (%).(S.L.Neitsch. et al, 2009). Williams (1995) proposed an alternative equation, which will be used in this study:
KUSLE = fcsand* fcl-si* forg*fhisand (3.15)
Where fsand is a factor that gives low soil erodibility factors for soils with high coarse-sand contents and high values for soil with little sand, fcl-si is a factor that gives low soil erodibility factors for soils with high clay to silt ratios, forg is a factor that reduces soil erodibility for soils with high organic carbon content, and fhisand is a factor that reduces soil erodibility for soils with extremely high sand. The factors are calculated:
31 (3.16) 푚푠푖푙푡 푓푐푠푎푛푑 = (0.2 + 0.3. 푒푥푝 [−0.256. 푚푠. (1 − 100 )]) (3.17) 푚푠푖푙푡 0.3 푓푐푙−푠푖 = [푚푐+푚푠푖푙푡] (3.18) 0.25.표푟푔퐶 푓표푟푔 = [1 − 표푟푔퐶+푒푥푝[3.72−2.95.표푟푔퐶]] 푚푠 (3.19) 0.7.(1−100) ℎ푖푠푎푛푑 푚푠 푚푠 푓 = [1 − (1−100)+푒푥푝[−5.51+22.9.(1−100)]] Where ms is the percent sand content (0.05-2.00 mm diameter particles), msilt is the percent silt content (0.002-0.05 mm diameter particles), mc is the percent clay content (˂0.002 mm diameter particles, and orgC is the percent organic carbon content of the layer (%). (S.L. Neitsch. et al, 2009). 2- The Cover and Management Factor (CUSLE) Wischmeier and smith (1978) defined the cover and management factor (CUSLE) as the ratio of soil loss from land cropped under specified conditions to the corresponding loss from clean-tilled, continuous fallow.(S.L.Neitsch. et al, 2009). It varies from 1 on bare soil to 1/1000 under forest, 1/100 under grasslands and cover plants, and 1–9/10 under root and tuber crops (Al- Zitawi 2006; Roose 1996). The plant canopy affects erosion by reducing the effective rainfall energy of intercepted raindrops. Water drops falling from the canopy may regain appreciable velocity but will be less than the terminal velocity of free falling rain drops. The average fall height of drops from the canopy and the density of the canopy will determine the reduction in rainfall energy expended at the soil surface. (S.L.Neitsch. et al, 2009). C-values is directly proportional to soil erosion as noted in the USLE, therefore the value of C is sensitive to soil erosion. So it can be used as a good calibration parameter in sedimentation modeling and soil erosion studies, this will be verified during this study. 3- The Support Practice Factor (PUSLE) The support practice factor (P) is the ratio of soil loss with a specific support practice to the corresponding loss with upslope and down-slope tillage. These practices principally affect erosion by modifying the flow pattern, grade, or direction of surface runoff and by reducing the amount and rate of runoff (Renard and Foster, 1983). For cultivated land, the support practices include contouring (tillage and planting on or near the contour), strip-cropping, terracing, and subsurface drainage. On dry-land or rangeland areas, soil-disturbing practices oriented on or near the contour
32 that results in storage of moisture and reduction of runoff are also used as support practices. The effect of contour tillage on soil erosion by water is described by the contour P factor in the universal soil loss equation (USLE). If erosion by flow occurs, a network of small eroded channels or rills develops in the areas of the deepest flow. On relatively smooth soil surfaces, the flow pattern is determined by random natural micro-topography. (Renard and Foster, 1983) Contouring is most effective on slopes of 3 to 8 percent. Values for PUSLE and slope-length limits for contour support practices are given in the Table (3.1). Table (3.1) P factor values and slope-length limits for contouring (Wishmeier and smith, 1978) Maximum length Land slope (%) PUSLE (m) 1 to 2 0.60 122 3 to 5 0.50 91 6 to 8 0.50 61 9 to 12 0.60 37 13 to 16 0.70 24 17 to 20 0.80 18 21 to 25 0.90 15
In this study there are no data about any conservation measures, so P is assumed to be 1. 4- The Topographic Factor ( LS ) The topographic factor (LS) is a combined of the slope length factor (L) and the slope steepness factor (S).the combined LS factor represents the ratio of soil loss on a given slope length and steepness to soil loss from a slope that has a length of 72.6 ft and a steeoness of 9%, where all other conditions are the same. LS values are not absolute values but are referenced to a value of 1.0 at a 72.6 ft slope legth and 9% steepness. (Wischeier and smith, 1978). Wischeier and smith (1965) suggested a very common formula in estimating the topographic factor (LS) for any watershed. Verification of the (LS) values provided by equation is corrected for steep slopes by Israelson (1980) using a rainfall simulator.
33 (3.20) 2 65.41 s 4.56 s l m 2 2 Where;LS = ( s +10,000 + √s +10,000 + 0.065). (72.5) l : field slope length, ft s: slope gradient in percent m: exponent dependent on the slope gradient : 0.2 for s ≤ 1.0% : 0.3 for 1.0% ˂ s ≤ 3.5% : 0.4 for 3.5% ˂ s ≤ 5.0% : 0.5 for s > 5.0% Foster and Wischmeier (1974) proposed an approach for determining the topographic factor (LS) when dealing with irregular slopes. True (1974) has used the following simplified topographic factor equation:
(3.21 Where; 0.6 1.4 l : the slope length in퐿푆 ft. = (푙⁄75) . (푠⁄ 9) ) s: the slope in percent. 5- The Coarse Fragment Factor (CFRG) The coarse fragment factor is calculated:
(3.22)
퐶퐹푅퐺Where = exp rockis (−0.053. the percent 푟표푐푘) rock in the first soil layer (%) (S.Neitsch. et al, 2009).
34 Chapter Four Results, Conclusions and Recommendations
4.1 Preface In this chapter the results will be discussed and analyzed for the time of concentration, soil erodibilty factor, coarse fragment factor and the topographic factor. Also, simulate the daily sediment yield by the Modified Universal Soil Loss Equation (MUSLE) for Al-Mujeb dam catchment area during different periods. The purpose and results for this simulation were emphasized in this chapter as a continuation of the common modeling protocol, which implies that any model should be calibrated, verified, then validated for prediction purposes.
4.2 The Time of Concentration ( Tc) The Soil Conservation Service formula parameters in Equation (3.8 ) in the previous chapter were obtained by ArcGIS 10.3.Software. In the first, a Digital Elevation Map (DEM) with resolution of 30 m was developed. The spatial modeling of the slope (%) within Al-Mujeb catchment area was performed by the slope tool Figure (4.1), the average slope value of 6.3% was obtained. The hydraulic length of the catchment area Figure (4.2) was obtained by processing the DEM gradually through Hydrology tool from Spatial Analyst toolset in ArcGIS 10.3, the hydraulic length of Al-Mujeb catchment area was obtained to be 79.297 Km. Weighted curve number within the catchment area was calculated from (M. Al-Mahameed, 2007) study since it was about the same study area, the average curve number was 89.07.Then Equation (4.1) was applied to estimate the lag time for the study area to be 7.90 hrs. According to the formula proposed by Soil Conservation Service: Tlag = Tc *0.6, results that:
Tc = (4.1) 푇푙푎푔 The time of concentration for Al-Mujeb0.6 catchment area was 13.15 hrs (789 min.) after applying Equation (4.1).
35
Figure (4.1) The Slope in Percent Map for Al-Mujeb Catchment Area
Figure (4.2) The Hydraulic Length of Al-Mujeb Catchment Area
36 4.3Soil Erodibility Factor (KUSLE) A 21 sample werecollected randomly from several locationsin Al- Mujeb catchment area. Four tests were conducted on the samples, which are: the specific gravity test (ASTM test designation D-854), Hydrometer analysis (ASTM test designation D-422), the sieve analysis test (ASTM test designation D-421) and the water content test (ASTM test D-2216) to analysis the soil samples into sand, silt and clay percent as shown in Table (4.1). To find the organic carbon percent (Org.C), the soil samples were dried in an oven at temperature of (103 to 105 C)ͦ thenburned in an oven with temperature of 550±50 ͦc for 5 hours. The percent of organic carbon (Org.C) was calculated by the equations: Weight of dry sample = Weight of dry sample and the Container – Weight of the Container Mass of Organic matter = Weight of dry sample – Weight of dry sample after burn Org. C % = *100% mass of organic matterTable (4.1) Gravel,mass Sand, of dry Silt sample and Clay percent for the soil samples Sample The Coordinates of % % % Silt % Number the sample Gravel Sand Clay Latitude Longitude 1 31.44729 35.82206 4.22 6.11 71.54 18.14 2 31.44771 35.82578 4.62 34.22 46.81 14.35 3 31.44189 35.81418 28.91 23.19 22.53 25.38 4 31.44058 35.81588 12.42 6.27 56.15 25.16 5 31.43413 35.80151 0.02 27.36 60.41 12.21 6 31.44765 35.8296 32.22 12.56 37.23 17.99 7 31.44773 35.8297 29.13 59.08 9.69 2.1 8 31.44924 35.83294 30.41 11.45 38.58 19.56 9 31.44297 35.83558 17.25 20.57 52.18 10 10 31.43873 35.85425 0.361 33.68 57.07 8.89 11 31.43751 35.85478 55.77 21.53 9.81 12.9 12 31.43854 35.8512 49.33 20.41 3.99 26.28 13 31.43939 35.81323 16.93 14.76 60.32 8 14 31.43689 35.81392 30.17 15.29 30 24.53 15 31.43205 35.82215 47.25 23.02 7.755 21.97 16 31.43252 35.80347 33.97 16.17 26.92 22.94 17 31.43365 35.80034 0.882 18.54 65.54 15.04 18 31.37184 36.07399 24.92 13.11 45.72 16.25 19 31.23138 36.02804 5.88 21.05 41.79 31.28 20 31.18311 35.78434 6.75 5.701 69.66 17.89 21 31.09263 35.7115 10.15 22.26 45.6 21.99
37 Equations (3.13- 3.17) in the previous chapter were used to calculate the erodibility factor (KUSLE) for Al-Mujeb catchment area as shown in Table (4.2). Table (4.2) Org C %, f csand, f cl-si, f org, f hisand and KUSLE for the soil samples Sample Org. C f csand f cl-si f org f hisand KUSLE Number % 1 2.166 0.392 0.934 0.758 1 0.278 2 24.471 0.203 0.923 0.75 1 0.14 3 1.984 0.203 0.797 0.764 1 0.124 4 2.133 0.348 0.895 0.759 1 0.237 5 3.679 0.219 0.946 0.75 1 0.155 6 2.784 0.240 0.888 0.751 1 0.16 7 4.009 0.2 0.943 0.75 0.994 0.141 8 2.832 0.250 0.884 0.751 1 0.166 9 6.930 0.224 0.949 0.75 1 0.16 10 2.087 0.207 0.958 0.76 1 0.151 11 1.527 0.202 0.777 0.808 1 0.127 12 1.118 0.202 0.544 0.894 1 0.098 13 3.174 0.267 0.963 0.75 1 0.193 14 2.053 0.219 0.836 0.761 1 0.14 15 0.716 0.201 0.668 0.969 1 0.13 16 1.463 0.215 0.831 0.818 1 0.146 17 5.540 0.258 0.94 0.75 1 0.182 18 5.235 0.249 0.913 0.75 1 0.17 19 2.943 0.213 0.846 0.751 1 0.135 20 2.151 0.393 0.934 0.758 1 0.278 21 5.128 0.214 0.889 0.75 1 0.142
The average erodibility factor (KUSLE) for the soil samples was obtained to be 0.164; this value has been adopted in calculating the sediment yield using the MUSLE for the catchment area. In order to estimate the soil properties; it must be identified for each soil sample using Figure (4.3)by applying the silt, sand, and clay percentagesand finding the point of intersection of the three lines, then the soil type represent the zone where the intersection point located (G.Das, 2000). This method is applied for each soil sample in the catchment, the soil sample classification shown in Table (4.3).
38
Figure (4.3) Soil Classification Triangle Based on silt%, sand%, and clay %
Table (4.3) Soil Classification for the soil samples in the catchment area Sample No. Soil Sample Classification 1 Silt Loam 2 Loam 3 Loam 4 Silt Loam 5 Silt Loam 6 Loam 7 Sandy Loam 8 Loam 9 Silt Loam 10 Silt Loam 11 Silt Loam 12 Silt Loam 13 Silt Loam 14 Loam 15 Loam 16 Silt Loam 17 Silt Loam 18 Loam 19 Silt Loam 20 Silt Loam 21 Loam
39 4.4 The Topographic Factor ( LS ) In this study the topographic factor was developed by ArcMap 10.3 software, by usingUnit Stream Power Erosion and Deposition model (USPED) since it could be done with the tools included in a normal ArcMap installation. In comparison to the USLE and RUSLE, the USPED is a physically based model that incorporates a spatial component. In the USLE and RUSLE, is dependent on linear distance , which is the horizontal length from the start of sediment transport to point on the slope. Thus, they are inherentlyL a single dimensional function.λ푖 The USPED instead uses the area of upland contributing flow to point . (J. Pelton푖 et al, 2014) The L calculation for point on a slope is shown in Equation푖 (4.3).
푖 (4.3) 휆퐴 푚 퐿Where, = (푚 + 1) (22.1) : The slope length factor at some point on the landscape, : Thearea of upland flow, 퐿 : An adjustable value depending on the soil’s susceptibility to erosion, 휆퐴 The unit plot length. 푚 22.1The ∶ comes from the fact that, in order to get a value for that is considerate of the area of contributing upland flow on 푚푚 + 1 the slope휆 up to point , we must integrate over the interval . 퐿 = (22.1) 푖 퐿 (4.4)[0 − 푖] 휆퐴푖 푖 But, the extra changes the property of that it achieves ∫푖=0 퐿(푖)푑퐿 ∕ 푑푖 = (푚+1)22.1| . unity when slope length (in this case slope area)0 is 22.1. Provisioning L with the extra 1 ∕term(푚 +removes 1) the equal term from theL denominator. The S calculation: 푚 + 1 (4.5) 푛 Where;sin (0.01745×휃) 푆 : = The ( slope0. in09 degrees,) : The slope gradient constant, and 휃: An adjustable value depending on the soil’s susceptibility to erosion. 0.09 푛
40 The steps that were used to develop the topographic factor (LS) for Al- Mujeb catchment area according to (J. Pelton et al, 2014): Step 1: A depression less Digital Elevation Map (DEM) is required to perform the subsequent steps in finding the LS factor. A depression less DEM is one in which there are no sinks present as shown in Figure(4.4). Step 2: Sinks (and peaks) are often errors due to the resolution of the data or rounding of elevations to the nearest integer value. Sinks should be filled to ensure proper delineation of basins and streams. If the sinks are not filled, a derived drainage network may be discontinuous. The fill tool as presented in Figure (4.5) should be usedto ensure the continuity of the drainage network.
Figure (4.4) Digital Elevation Map (DEM)
41
Figure (4.5) The Fill Tool
Step 3: Calculate the flow direction from the filled DEM by using the Flow Direction toolas shown inFigure (4.6). The output raster presented in Figure (4.7).
Figure (4.6) The Flow Direction Tool
42
Figure (4.7) Flow Direction Raster (output)
Step 4: Creates a raster of accumulated flow by using flow accumulation tool presented in Figure (4.8) with input raster of flow direction that calculated by the step previous as shown in Figure (4.9).
43
Figure (4.8) The Flow Accumulation tool
Figure (4.9) The Flow accumulation raster (output)
44 Step 5: Calculate slope of watershed in degrees using Slope Tool as shown in Figure (4.10)with DEM as the input layer. The output raster presented in Figure (4.11). Make sure that Output Measurement dropdown menu is set to DEGREES.
Figure (4.10) The Slope Tool
Figure (4.11) The Slope Map (output raster)
45 Step 6: By using the raster calculator in the spatial analyst tool type the formula as shown in Figure (4.12):
LS=Power ("Flowaccum."*cellres./22.1, 0.4)*Power((sin"slope"*0.01745)/ 0.09, 1.4)*1.4 Where; Cellres.: Resolution of DEM in meters. Slope: Slope raster in degrees Flowaccum. : Flow accumulation raster
Figure (4.12) The Raster Calculator Tool
The output raster represent the topographic factor for the catchment area as shown in Figure (4.13).
46
Figure (4.13) The Topographic Factor Map for Al-Mujeb Catchment Area
4.5 The Coarse Fragment Factor (CFRG) In this study the percent of rock in the first layer (%) was generated from the percent returned on sieve No. 4 (opening 4.75 mm) and sieve No. 10 (opening 2.00 mm) for the soil samples. The coarse fragment factor (CFRG) for each soil sample was calculated by applying Equation (3.20) in the previous chapter. The coarse fragment factors for each soil sample presented in Table (4.4). The average coarse fragment was obtained to be 0.384; this value has been adopted to be used in the MUSLE to calculate the sediment yield for Al-Mujeb catchment area.
47 Table(4.4) Coarse Fragment Factor (CFRG) for soil samples Sample Return on sieve Return on sieve Rock CFRG Number No. 4 No.10 %
1 4.215 2.138 6.354 0.714 2 4.621 0.845 5.466 0.749 3 28.905 10.378 39.28 0.125 4 12.418 3.161 15.58 0.438 5 0.017 0.207 0.224 0.988 6 32.221 7.988 40.21 0.119 7 29.131 7.984 37.11 0.14 8 30.410 7.564 37.97 0.134 9 17.25 4.631 21.88 0.314 10 0.3611 0.889 1.25 0.936 11 55.765 10.099 65.86 0.03 12 49.3291 25.278 74.61 0.019 13 16.926 5.362 22.29 0.307 14 30.174 12.534 42.71 0.104 15 47.255 18.471 65.73 0.031 16 33.967 8.944 42.91 0.103 17 0.882 1.039 1.921 0.903 18 24.92 5.22 30.14 0.202 19 5.883 2.35 8.233 0.646 20 6.750 0.886 7.637 0.667 21 10.147 6.990 17.14 0.403
4.6 Surface Runoff and Peak Runoff Rate The surface runoff in this study was obtained from the Directorate of dams underlying to Jordan Valley Authority as a daily measured volumes of runoff into Al-Mujeb dam reservoir(See Table II -1 in Appendix).Table (4.5) contains the income and outcome volumes in addition to the maximum, minimum and average reservoir water level for the years from 6/11/2003 until 13/3/2018. While the peak runoff rate was calculated using Equation (3.9) in the previous chapter. (See Table II -2 in Appendix).
48
Table (4.5) The income and outcome volumes, maximum, minimum and average reservoir water level Year Income Outcome Maximum Minimum Average volume volume reservoir reservoir reservoir (m3) (m3) level level Level (masl) (masl) (masl) 2003 5728340.29 0 168.62 145.12 156.27 2004 26563814.7 3132315.33 193.82 168.62 176.05 2005 3087143.47 10438865.7 193.57 186.88 188.80 2006 11514576.7 4090619.51 194 186.82 192.112 2007 3036687.27 16037435 194 181.75 189.597 2008 4573031.91 11563608.9 182.08 172.22 178.45 2009 14713135.1 13728222.5 187.14 172.16 181.12 2010 20763036.7 13690439.3 194 176.15 188.79 2011 3641262.31 12453696 186.71 174.67 181.72 2012 8508885 11843740.2 182.48 169.95 177.44 2013 30552078 14476413 194 169.71 188.79 2014 21814130 14922872 194.2 187.09 190.762 2015 24079569 24116567 196.78 185.32 191.82 2016 17389952 23289799 194 155.05 188.21 2017 7014376 20674016 189 170.41 182.26 2018 25438405 1925281 194 170.1 189.92
Figure (4.14) presents the income and outcome volumes for the years 2003 until 2018, while Figure (4.15) presents the average reservoir water level for the years 2003 until 2018 and Figure (4.16) presents the maximum and minimum reservoir water level for the years 2003 until 2018.
49 Income Volume Outcome Volume 35
30
25
20 MCM 15
10
5
0
2008
2007 2018
2006 2017
2005 2012 2016
2004 2011 2015
2009 2013 2003 2010 2014 Year
Figure (4.14) The Income and Outcome volumes for Al-Mujeb dam reservoir
195 190 185 180 175 170 165 160 155
150
Average water level (masl) level water Average
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004 2003 Year
Figure (4.15) Al-Mujeb dam reservoir average water level for years (2003-2018)
50 Max. level Min. level 200 195 190 185 180 175
170 masl 165 160 155 150 145
140
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004 2003
year
Figure (4.16) The Minimum and Maximum Water Level for Al-Mujeb Dam Reservoir from 2003 until 2018
4.7 Model Calibration The term “model calibration” generally describes a process in which the model structure and parameter values are adjusted, either manually or by using formal mathematical optimization procedures, until the model output satisfactorily matches a set of targets (Zheng and Bennett, 2002). In the most of the hydrological models the inputs are not exactly known therefore they have to be determined by the calibration techniques, either manual or automated. The manual calibration will be used for the current model, the measured and simulated values will be compared and better judgment will used to determine which variables to adjust, how much to adjustment and when the results will be reasonable. (Gassman et al., 2005). To match between the model responses and field observationsa visual comparison by using some statistical measures of goodness of fit such as the mean residual errors (M), variance of residual errors (VAR), root mean of squared residual errors (RMS), the standard deviation of residual errors (SDEV),the mean of absolute residual errors (MA),the linear correlation coefficient (r) which lies between -1
51 and 1, the value of 1 or -1; indicates a perfect linear correlation between observed and simulated values, a value of (r) near zero indicate that simulated and observed values are not correlated (Zheng and Bennett, 2002). In this study the statistical measure (the linear correlation coefficient (r)) in addition to Nash-Sutcliffe model efficiency (Emodel) were used to measure the fit between the MUSLE results (Simulated data) and the measured data by Eco-sounder device (observed data). When (Emodel) equal 1 that indicate a perfect fit, while zero value indicate that the model prediction is not better than using the average of the observed data (Nash and Sutcliffe, 1970). The model efficiency can be presented by this equation:
Emodel = (4.1) 푁 2 ∑푖=1(표푏푠 푖−푠푖푚 푖) 푁 2 4.8 Sediment Yield Calibration1 − ∑푖=1 (표푏푠 푖−표푏푠̅̅̅̅̅̅̅) In the present work, measured accumulated sediments for the periods (2003-2005), (2005-2008), (2008-2009) and (2009-2015) were used to calibrate the Modified Universal Soil Loss Equation (MUSLE) model as shown in Table (4.6). These measurements were obtained by Eco-sounder device exploited by Directorate of dams underlying to Jordan Valley Authority.The reading was provided in summer season (with assumption that the reading was obtained on thefirst of September). The cumulated calculated annual sediment yield quantities were estimated in cubic meter by dividing the sediment yield by the unit weight of the sediment (1.3 ton/ m3).The input parameter is the cover and management factor (C).Because of the shortage of direct measurements of sedimentation in Al-Mujeb catchment area, all the four readings were used for the calibration process. Different (C) values were tried to provide the optimum results for sediment yield for Al-Mujeb catchment area outlet. The (C) value that gives the optimum simulation was 0.058 with statistical indicators of correlation coefficient (r) of 0.965 and model efficiency (E model) of 0.838. Figure (4.17) present a comparison between observed and simulated data for thecalibration period. Table (4.6) Accumulated observed data by Eco-sounder device Year Observed data by Eco-Sounder device (m3) 2003-2005 917823 2005-2008 523087 2008-2009 510166
52 2009-2015 1608008
Simulated data Observed data 2 1.8
MCM 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 2005-2003 2008-2005 2009-2008 2015-2009 Year
Figure (4.17) The Comparison between the Observed and the Simulated Data for the Calibrated Period
The calibration statistics for sediment yield are acceptable and indicate that the Modified Universal Soil Loss Equation (MUSLE) is able to simulate the study area and predict the sediment yield.
4.9 Model Verification The Model verification is a process that tested or verified the parameter that was developed by the calibration against the observed data that was obtained by the Eco-sounder device in different years. The Modified Universal Soil Loss Equation (MUSLE) model has been verified using the calibrated parameter to check its capability of calculating the amount of sediment yields at Al-Mujeb catchment area outlet (Dam site). Unfortunately, the sediment was measured at the reservoir only four times, so there are no points left for the verification process. The verification process was produced by redistribution the cumulated sediments yield that were observed by the Eco-Sounder device in the years (2003-2005), (2005- 2008), (2008-2009) and (2009-2015) according to the inflow distribution in each water year. Table (4.7) presents the sediment yield redistribution for the observed data obtained by the Eco-Sounder device according to the inflow in each year as percent of the total inflow during the period of sediment measurement. After rearrange Table (4.7) Table (4.8) will provide the annual measured sediment yield from 2003 until 2015.
53 Figure (4.18) presents the comparison between the simulated data from the Modified Universal Soil Loss Equation (MUSLE) and the observed data from Eco-Sounder device and as shown in Table (4.9). While Figure (4.19) shows the relationship between the simulated sediment yield that was obtained from the Modified Universal Soil Loss Equation (MUSLE) and the inflow in (m3) that enter the dam reservoir.
Table (4.7) Sediment yields redistribution according to the inflow percents for each year
Water Year Percent of total Distributed runoff during the Annual From To measured sediment Sediment yield (%) (m3) 1-Oct-03 30-Sep-04 26.57% 243884.9 1-Ocr-04 30-Sep-05 73.43% 673938.1 1-Oct-05 30-Sep-06 62.12% 324954.6 1-Oct-06 30-Sep-07 30.41% 159083.2 1-Oct-07 30-Sep-08 7.47% 39049.2 1-Oct-08 30-Sep-09 1 510166 1-Oct-09 30-Sep-10 21.16% 340223.4 1-Oct-10 30-Sep-11 3.71% 59665.77 1-Oct-11 30-Sep-12 8.66% 139194.5 1-Oct-12 30-Sep-13 25.83% 415277.7 1-Oct-13 30-Sep-14 23.47% 377468.2 1-Oct-14 30-Sep-15 17.18% 276178.4
Table (4.8) The annual sediment yield after rearrange Table (4.7) The Year Observed Sediment Yield (m3)
2003 243884.9 2004 673938.1 2005 324954.6 2006 159083.2 2007 39049.2 2008 510166 2009 340223.4 2010 59665.77 2011 139194.5
54 2012 415277.7 2013 377468.2 2014 276178.4
Table (4.9) Simulated and Observed data for the years 2003 – 2015 The Observed sediment yield Simulated sediment yield year (m3) (m3) 2003 243884.9 143244 2004 673938.1 478776.7 2005 324954.6 160980.8 2006 159083.2 70869.78 2007 39049.2 15504.29 2008 510166 302613.1 2009 340223.4 387748.7 2010 59665.77 56408.72 2011 139194.5 131061.6 2012 415277.7 488914.5 2013 377468.2 413387.6 2014 276178.4 289289.9
Observed sediment yield Simulated sediment yield 0.8 0.7 0.6 0.5 0.4 0.3 0.2
Sediment Yield Sediment 0.1 MCM
0
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004 2003 Water Year
Figure (4.18)
55 The Comparison between the Annual Simulated Sediment Yield and the Observed Sediment Yield
The Annual Flow Simulated sediment yield Observed sediment yield
30 0.8 25 0.7 0.6 20 0.5 15 0.4 10 0.3
0.2 Sediment Yield (MCM) Yield Sediment Annual flow (MCM) flowAnnual 5 0.1
0 0
2006 2011
2005 2010
2004 2009 2014
2007 2012 2003 2008 2013
Water Year
Figure (4.19) The Relationship between the Simulated Data and the Annual Inflow that Inter the Dam Reservoir
From Figures (4.18) and (4.19) a good indication and reasonable match can be noticed, which provides more support to utilize MUSLE to model Al-Mujeb catchment area and achieve the intended modeling objectives. The daily sediment yields for Al-Mujeb dam reservoir were calculated using the MUSLE for the period 6/11/ 2003 until 18/2/2018 (See TableII-3 in Appendix), Table (4.10) presents 2004 as a typical water year for the daily sediment yield from by applying the topographic factor (LS), soil erodibility factor (KUSLE) and the cover and management factor (CUSLE)that estimated in the previous sections in the Modified Universal Soil Loss Equation (MUSLE). The annual sediment yields can be presented as shown in Table (4.11) and Figure (4.20).
56 Table (4.10) Simulated sediment yield by using MUSLE for the water year 2004 3 Date Qsurf (mm/ha.) Qp (m /sec) Sediment yield (metric ton) 30-Oct-04 2.455066972 67.98886354 73849.9038 31-Oct-04 0.153310511 4.245671314 3306.12715 01-Nov-04 0.019244508 0.532943599 323.5211064 02-Nov-04 0.006418764 0.177756654 94.58550545 03-Nov-04 0.006420732 0.177811153 94.61798547 10-Nov-04 0.006416804 0.177702366 94.55315252 18-Nov-04 0.07713132 2.136019434 1531.713154 19-Nov-04 0.142147689 3.936536122 3037.718794 20-Nov-04 0.032441198 0.898403253 580.6407235 21-Nov-04 0.012990572 0.359751584 208.3256086 23-Nov-04 9.691227178 268.3818933 343734.5732 24-Nov-04 2.119639306 58.69977038 62645.86193 25-Nov-04 0.063319054 1.753512463 1227.997657 26-Nov-04 0.010569062 0.292692015 165.3483243 27-Nov-04 2.025326995 56.08795289 59532.42292 28-Nov-04 0.513657468 14.22486143 12806.5877 09-Dec-04 0.149144127 4.130290452 3205.663135 06-Jan-05 0.907096156 25.12047022 24213.11777 07-Jan-05 0.486568772 13.47468652 12052.59405 24-Jan-05 0.033071411 0.915855936 593.2886533 25-Jan-05 0.04415357 1.222757288 820.0507108 09-Feb-05 0.192902052 5.342091044 4276.182755 10-Feb-05 0.010754836 0.297836713 168.6068584 10-Mar-05 0.009630275 0.266693916 148.9890326 25-Apr-05 0.408628291 11.31625877 9912.126421 26-Apr-05 0.172972449 4.790174905 3784.540473
57 Table (4.11) The annual sediment yields for the years 2003-2017 Year Annual sediment yield (m3)
2003 143243.9939 2004 478776.6604 2005 160980.7646 2006 70869.77678 2007 15504.28894 2008 302613.0915 2009 387748.7466 2010 56408.71837 2011 131061.5781 2012 488914.516 2013 413387.6263 2014 289289.8581 2015 349790.3439 2016 258302.4986 2017 492613.7355
The annual sediment yield
0.6
0.5
0.4
0.3
0.2
0.1 Sediment Yield (MCM) Yield Sediment 0
Water Year
Figure (4.20) The Annual Sediment Yield for the Water Years 2003-2017
58
4.10Prediction Scenario (2018-2030) In the present study, a liner extrapolation of results for the period 2003-2017 was used for future prediction of sediment yield in Al-Mujeb dam reservoir. It is based on the assumption that present accumulation of sediment will continue in the same direction. In other words, the past is a good indicator of the future. Linear regression has been used for the accumulated data shown in Table (4.12). The regression line with regression coefficient R2 equal 0.9975 is shown in Figure (4.21).
Table (4.12) Cumulative of simulated sediment Year Annual sediment yield Cumulative of simulated (m3) sediment (m3) 2003 143243.9939 143243.9939 2004 478776.6604 622020.6543 2005 160980.7646 783001.4189 2006 70869.77678 853871.1957 2007 15504.28894 869375.4846 2008 302613.0915 1171988.576 2009 387748.7466 1559737.323 2010 56408.71837 1616146.041 2011 131061.5781 1747207.619 2012 488914.516 2236122.135 2013 413387.6263 2649509.761 2014 289289.8581 2938799.62 2015 349790.3439 3288589.964 2016 258302.4986 3546892.462 2017 492613.7355 4039506.198
59
Figure (4.21) Regression Line for the Cumulative of Simulated Sediment for the Years 2003- 2017
After applying extrapolation technique for the years 2018 until 2030 the Figure (4.22) will be resulted. Table (4.13) presents the predicted cumulative of sediment yields for the years 2018 until 2030. As noticed the amount of cumulated sediment in 2030 will be 7.11 MCM which represents 22.8% of the reservoir capacity.
60
61 Table (4.13) The predicted cumulative sediment yields for the years 2018 until 2030 The year Cumulative of simulated sediment 2018 3966692.152(m3) 2019 4228645.234 2020 4490598.316 2021 4752551.397 2022 5014504.479 2023 5276457.561 2024 5538410.643 2025 5800363.725 2026 6062316.807 2027 6324269.889 2028 6586222.971 2029 6848176.053 2030 7110129.135
4.11 Conclusions Based on the results of this study, we conclude that: 1. The Modified Universal Soil Loss Equation (MUSLE) and the Geographic Information System (GIS) technique provides a powerful tool to evaluated the land degradation and the sediment yield for a watershed. 2. The Geographic Information System (GIS) can be used to simulate the Topographic factor (LS) and the concentration time (tc) for Al- Mujeb catchment area. 3. The soil erodibility factor (KUSLE) and the coarse fragment factor (CFRG) can be estimated by taking a samples from several points in Al-Mujeb catchment area. 4. The Modified Universal Soil Loss Equation (MUSLE) can be used to simulate the sedimentation in Al-Mujeb catchment area to produce a reasonable estimate in monthly or daily stream flow. 5. The Modified Universal Soil Loss Equation (MUSLE) was calibrated by altering the cover and management factor (C) and the optimum (C) factor was obtained to be 0.058. 6. The model calibration was evaluated by using a graphical comparison and statistical measures (the coefficient of correlation (r) and Nash-Sutcliffe model efficiency (Emodel)). The model calibration was showed a good performance for the Modified Universal Soil Loss Equation (MUSLE) to simulate the sediment in Al-Mujed catchment area.
62 7. The model was able to predict the sediment in Al-Mujeb catchment area by using the linear extrapolation technique. The accumulated sediment in 2030 will be 7.11 MCM, which represents 22.8% of the reservoir capacity.
4.12 Recommendations Some of recommendation can be proposed based on the results and conclusion of the study: 1. Estimate and study the sediment bed load and suspended load at Al- Mujeb dam reservoir. 2. Study the water quality of Al-Mujeb dam reservoir because the sediment may contain some chemicals that affect the water quality. 3. Exploitation the sediment that released from the dam reservoir in agriculture where sediments are soil rich in elements. 4. Periodical flushing is recommended during flood periods to minimize the amount of sediment at the bottom of the dam, in addition to providing equipments to measure the amount of sediment and water and survey the dam reservoir bottom. 5. The Modified Universal Soil Loss Equation (MUSLE) has shown a good performance in modeling Al-Mujeb catchment area. So it might be expected to simulate other watersheds in Jordan properly. 6. Sediment management is now more than ever a challenge for the current generation. Three main strategies exist for dealing with reservoir sedimentation: reducing incoming sediment yield, minimizing siltation, and removing deposited sediment from reservoirs. Within each of these strategies various techniques present themselves for certain situations. This review will focus on techniques from the latter two of the three aforementioned strategies. - Minimizing Siltation: The main management methods associated with this particular strategy are construction of sediment bypass structures, sediment pass-through (or sluicing), and venting of the density current. 1. Sediment Bypass structures route high-sediment flows, generally resulting from floods, around the reservoir using canals, pressurized pipelines, or tunnels. Construction of canals is an expensive practice with its viability depending upon local topography, reservoir size and shape, and hydraulics of the river system. 2. Sluicing Sediment pass-through, also known as sluicing, is another way of abating sediment deposition in reservoirs. For this method, the reservoir level is drawn down during the flood season and allowed to flow through the sluice gates to maintain the incoming sediment in suspension. When particles enter the low-velocity area of a reservoir, they settle and form a delta
63 consisting first of the heavier coarse sediments, then further on a more shallow layer of fine sediment. 3. Density Current Venting, a seldom-used technique, involves the discharge of turbid sediment-laden water from a low-level outlet (like a sluice gate) while the surface waters remain clear or unchanged. Turbidity currents develop when water with a high sediment load enters a reservoir and immediately plunges to the bottom, travelling through the original channel until settling near the dam in what is called a “muddy pool”. - Removing Deposits: The main management methods associated with removing sediment from submerged reservoirs are flushing and dredging. Flushing can be further characterized as either drawdown or pressurized. 1. Drawdown flushing is highly similar to sluicing; however, it is not executed during flood season. Rather, it is done when the river is at low-flow conditions so that drawing down the water level takes less effort and does not affect the water supply. 2. Pressurized Flushing Pressurized flushing removes only a fraction of the amount of sediment when compared to drawdown flushing. This method is rarely used and its main purpose is to clear the area immediately surrounding the bottom outlets 3. Dredging an expensive but effective solution to extreme storage loss in reservoirs, dredging is perhaps the most often used sediment-management technique. Dredging removes deposited sediment from the bottom of reservoirs using pumps, hydraulic suction, or clamshell buckets.
4.13 Future work 1- Using Eco-sounder device monthly or annually to get better understanding for sedimentation problem in reservoirs. 2- Numerical modeling using finite element or finite difference is of necessity to be applied for reservoir sedimentation. 3- Taking samples from the sediment in reservoirs to classify the soil types, grain size distribution and estimate the concentration of contaminant. 4- Extending the Modified Universal Soil Loss Equation (MUSLE) model to comprise other watersheds in Jordan. It might be expected to simulate other watersheds in Jordan properly, if this can be accomplished, it will be a very impressive work, which eventually leads to come up with a huge database for the whole Jordan and to produce a remarkable set of country-scale results and maps very advantageous for plenty of objectives.
64 4.14 Limitations 1- The sediment volume was measured at Al-Mujeb dam reservoir only four times, so there are no points left for the verification process. 2- Limited studies related to the vegetation cover, soil erosion and hydrological analysis for Al-Mujeb basin. 3- Lack of studies concerning the reduction of sediment quantities that enter Al-Mujeb dam reservoir.
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70
Appendix I
71 Appendix I Statistical Measures
1. The mean of residual errors (M): a value near zero indicates an overall agreement between observed (obsi) and simulated (simi) values, and the sign indicates any tendency toward overestimation or underestimation. (I.1) N Where N: is total number ∑ofi=1 observations.(simi−obsi) M = N 2. The variance of residual errors (VAR): measures the deviation of data points from their mean (M).
(I.2) N 2 ∑i=1((simi−obsi)−M) VAR3. =The standardN−1 deviation of residual errors (SDEV): expected to approach zero as the accuracy of the simulation improves.
(I.3)
4. The mean of absolute푆퐷퐸푉 residual = √ errors푉퐴푅 (MA): more robust than (M), butdoes not provide any information regarding the trend of overestimation orunderestimation.
(I.4) N 5. The root mean of squared∑i=1 residual |simi−obs errorsi| (RMS): less value indicatesmore agreement.MA = N
(I.5) N 2 ∑i=1 ( simi−obsi) 6. The linear correlationRMS = coefficient√ (r):N ranges from -1 to 1. Both limitsindicate perfect correlation, with (-1) indicates that observed andsimulatedvalues change in opposite directions, and (1) indicatesthat they increase ordecrease together, this case characterizes well-calibrated models. A valuenear zero indicates the case of nocorrelation.
(I.6) N ∑i=1(simi−sim)(obsi−obs) N 2 N 2 r = √∑i=1(simi−sim) .√∑i=1(obsi−obs)
72
Appendix II
73 Appendix II Table II.1 The daily surface runoffs as a real measurement for Al-Mujeb dam reservoir.From (6-Nov.-2003) to (13-March-2018) Income - Income Outcome Level Reservoir outcome Date Volume Volume (masl) Volume (m³) Volume (m³) (m³) (m³) 06-Nov-03 145.12 6.01 6 6 0 08-Nov-03 145.42 12.16 6 6 0 09-Nov-03 145.67 19.7 8 8 0 10-Nov-03 145.85 46.8 27 27 0 11-Nov-03 146.00 88.01 41 41 0 12-Nov-03 146.20 160.3 72 72 0 13-Nov-03 146.35 227.45 67 67 0 14-Nov-03 146.47 289.13 62 62 0 15-Nov-03 146.59 357.86 69 69 0 16-Nov-03 146.70 428.41 71 71 0 17-Nov-03 146.80 504.98 77 77 0 18-Nov-03 146.90 604.73 100 100 0 19-Nov-03 147.00 730.58 126 126 0 20-Nov-03 147.11 899.39 169 169 0 21-Nov-03 147.21 1,080.72 181 181 0 22-Nov-03 147.33 1,333.64 253 253 0 23-Nov-03 147.44 1,599.98 266 266 0 24-Nov-03 147.55 1,901.41 301 301 0 25-Nov-03 147.66 2,240.45 339 339 0 26-Nov-03 147.80 2,730.47 490 490 0 27-Nov-03 147.93 3,248.62 518 518 0 28-Nov-03 149.10 15,925.66 12,677 12,677 0 29-Nov-03 150.00 44,183.58 28,258 28,258 0 30-Nov-03 152.20 230,226.84 186,043 186,043 0 01-Dec-03 152.50 266,917.27 36,690 36,690 0 02-Dec-03 152.63 283,523.83 16,607 16,607 0 03-Dec-03 156.95 1,065,288.9 781,765 781,765 0 04-Dec-03 157.95 1,303,933.76 238,645 238,645 0 05-Dec-03 158.15 1,354,867.85 50,934 50,934 0 06-Dec-03 158.36 1,409,330.39 54,463 54,463 0 07-Dec-03 158.42 1,425,077.74 15,747 15,747 0 08-Dec-03 158.46 1,435,623.41 10,546 10,546 0 09-Dec-03 158.49 1,443,558.13 7,935 7,935 0 10-Dec-03 158.51 1,448,860.35 5,302 5,302 0 11-Dec-03 158.53 1,454,172.75 5,312 5,312 0 12-Dec-03 158.54 1,456,832.84 2,660 2,660 0 13-Dec-03 158.56 1,462,160.92 5,328 5,328 0 14-Dec-03 158.57 1,464,828.95 2,668 2,668 0 15-Dec-03 161.77 2,641,094.88 1,176,266 1,176,266 0 16-Dec-03 168.09 5,416,788.22 2,775,693 2,775,693 0
74 ContinueTable II.1 Income - Income Outcome Level Reservoir outcome Date Volume Volume (masl) Volume (m³) Volume (m³) (m³) (m³) 17-Dec-03 168.36 5,574,550.62 157,762 157,762 0 18-Dec-03 168.40 5,598,091.1 23,540 23,540 0 19-Dec-03 168.41 5,603,983 5,892 5,892 0 20-Dec-03 168.49 5,651,215.96 47,233 47,233 0 21-Dec-03 168.55 5,686,754.7 35,539 35,539 0 22-Dec-03 168.56 5,692,687.3 5,933 5,933 0 23-Dec-03 168.57 5,698,622.73 5,935 5,935 0 26-Dec-03 168.58 5,704,560.8 5,938 5,938 0 27-Dec-03 168.60 5,716,445.1 11,884 11,884 0 28-Dec-03 168.61 5,722,391.3 5,946 5,946 0 29-Dec-03 168.62 5,728,340.29 5,949 5,949 0 02-Jan-04 168.63 5,734,291.97 5,952 5,952 0 03-Jan-04 168.64 5,740,246.38 5,954 5,954 0 05-Jan-04 168.67 5,758,125.97 17,880 17,880 0 06-Jan-04 168.68 5,764,091.3 5,965 5,965 0 07-Jan-04 168.69 5,770,059.37 5,968 5,968 0 08-Jan-04 168.70 5,776,030.17 5,971 5,971 0 09-Jan-04 168.72 5,787,979.98 11,950 11,950 0 10-Jan-04 168.74 5,799,940.75 11,961 11,961 0 11-Jan-04 168.75 5,805,925.25 5,985 5,985 0 12-Jan-04 168.76 5,811,912.5 5,987 5,987 0 13-Jan-04 168.79 5,829,890.73 17,978 17,978 0 14-Jan-04 169.33 6,157,741.16 327,850 327,850 0 15-Jan-04 172.03 7,933,941.73 1,776,201 1,776,201 0 16-Jan-04 173.38 8,910,115.78 976,174 976,174 0 17-Jan-04 173.48 8,984,734.2 74,618 74,618 0 18-Jan-04 173.51 9,007,182 22,448 22,448 0 19-Jan-04 173.53 9,022,162.88 14,981 14,981 0 20-Jan-04 173.55 9,037,156.22 14,993 14,993 0 21-Jan-04 173.56 9,044,657.54 7,501 7,501 0 22-Jan-04 173.57 9,052,161.95 7,504 7,504 0 23-Jan-04 173.59 9,067,180.01 15,018 15,018 0 24-Jan-04 173.60 9,074,693.64 7,514 7,514 0 26-Jan-04 173.61 9,082,210.33 7,517 7,517 0 27-Jan-04 173.62 9,089,730.07 7,520 7,520 0 28-Jan-04 173.63 9,097,252.84 7,523 7,523 0 29-Jan-04 173.64 9,104,778.65 7,526 7,526 0 30-Jan-04 173.65 9,112,307.47 7,529 7,529 0 31-Jan-04 173.66 9,119,839.36 7,532 7,532 0 01-Feb-04 173.67 9,127,374.15 7,535 7,535 0 02-Feb-04 173.69 9,142,452.79 15,079 15,079 0 03-Feb-04 173.70 9,149,996.57 7,544 7,544 0 04-Feb-04 173.71 9,157,543.31 7,547 7,547 0 05-Feb-04 173.72 9,165,093 7,550 7,550 0
75 ContinueTable II.1 Income - Income Outcome Level Reservoir outcome Date Volume Volume (masl) Volume (m³) Volume (m³) (m³) (m³) 06-Feb-04 173.73 9,172,645.84 7,553 7,553 0 07-Feb-04 173.74 9,180,201.19 7,555 7,555 0 09-Feb-04 173.75 9,187,759.66 7,558 7,558 0 11-Feb-04 173.76 9,195,321.05 7,561 7,561 0 13-Feb-04 173.77 9,202,885.33 7,564 7,564 0 14-Feb-04 173.78 9,210,452.5 7,567 7,567 0 15-Feb-04 173.79 9,218,022.55 7,570 7,570 0 16-Feb-04 173.81 9,233,171.24 15,149 15,149 0 17-Feb-04 173.82 9,240,749.58 7,578 7,578 0 18-Feb-04 173.83 9,248,331.31 7,582 7,582 0 19-Feb-04 173.84 9,255,915.59 7,584 7,584 0 21-Feb-04 173.85 9,263,502.68 7,587 7,587 0 22-Feb-04 173.87 9,278,685.28 15,183 15,183 0 24-Feb-04 173.88 9,286,280.76 7,595 7,595 0 25-Feb-04 173.89 9,293,879.02 7,598 7,598 0 27-Feb-04 173.90 9,301,480.03 7,601 7,601 0 29-Feb-04 173.91 9,309,083.78 7,604 7,604 0 02-Mar-04 173.92 9,316,690.28 7,607 7,607 0 04-Mar-04 173.93 9,324,299 7,609 7,609 0 07-Mar-04 173.95 9,339,526.1 15,227 15,227 0 09-Mar-04 173.96 9,347,143.47 7,617 7,617 0 12-Mar-04 173.97 9,354,763.55 7,620 7,620 0 15-Mar-04 173.98 9,362,386.33 7,623 7,623 0 04-Apr-04 173.99 9,370,011.81 7,625 7,625 0 30-Oct-04 176.65 11,485,972.26 3,218,593 3,218,593 0 31-Oct-04 176.88 11,686,962.34 200,990 200,990 0 01-Nov-04 176.91 11,712,191.89 25,230 25,230 0 02-Nov-04 176.92 11,720,606.89 8,415 8,415 0 03-Nov-04 176.93 11,729,024.47 8,418 8,418 0 10-Nov-04 176.91 11,712,191.89 8,412 8,412 0 18-Nov-04 177.02 11,804,898.62 101,119 101,119 0 19-Nov-04 177.24 11,991,254.24 186,356 186,356 0 20-Nov-04 177.29 12,033,784.65 42,530 42,530 0 21-Nov-04 177.31 12,050,815.29 17,031 17,031 0 23-Nov-04 189.34 24,756,014.12 12,705,199 12,705,199 0 24-Nov-04 191.45 27,534,861.25 2,778,847 2,778,847 0 25-Nov-04 191.51 27,617,872.53 83,011 83,011 0 26-Nov-04 191.52 27,631,728.57 13,856 13,856 0 27-Nov-04 193.37 30,286,932.26 2,655,204 2,655,204 0 28-Nov-04 193.82 30,960,337.2 673,405 673,405 0 09-Dec-04 193.80 30,930,174.26 195,528 195,528 0 06-Jan-05 193.14 29,946,973.67 1,189,203 1,189,203 0 07-Jan-05 193.57 30,584,865.33 637,892 637,892 0 24-Jan-05 192.65 29,232,074 43,357 43,357 0
76 ContinueTable II.1 Income - Income Outcome Level Reservoir outcome Date Volume Volume (masl) Volume (m³) Volume (m³) (m³) (m³) 25-Jan-05 192.69 29,289,959.33 57,885 57,885 0 09-Feb-05 191.96 28,246,873.72 252,895 252,895 0 10-Feb-05 191.97 28,260,973.31 14,100 14,100 0 10-Mar-05 189.49 24,944,873.11 12,625 12,625 0 25-Apr-05 189.34 24,756,014.12 535,712 535,712 0 26-Apr-05 189.52 24,982,781 226,767 226,767 0 22-Nov-05 187.01 21,936,484.98 70,069 70,069 0 23-Nov-05 187.02 21,948,175.2 11,690 11,690 0 26-Dec-05 186.91 21,819,770.62 34,950 34,950 0 28-Jan-06 187.00 21,924,798.29 209,789 209,789 0 03-Feb-06 188.26 23,424,349.16 1,522,914 1,522,914 0 04-Feb-06 188.73 23,998,261.31 573,912 573,912 0 05-Feb-06 188.75 24,022,869.76 24,608 24,608 0 06-Feb-06 188.76 24,035,179.86 12,310 12,310 0 07-Feb-06 188.79 24,072,133.68 36,954 36,954 0 09-Feb-06 188.80 24,084,459.46 12,326 12,326 0 14-Feb-06 188.82 24,109,122.81 36,989 36,989 0 15-Feb-06 188.86 24,158,496.63 49,374 49,374 0 16-Feb-06 190.84 26,703,599.86 2,545,103 2,545,103 0 17-Feb-06 192.01 28,317,424.25 1,613,824 1,613,824 0 18-Feb-06 192.15 28,515,665 198,241 198,241 0 02-Apr-06 194.00 31,232,788.36 2,858,828 2,858,828 0 12-Aug-06 193.04 29,850,045.52 35,331 35,331 0 30-Oct-06 192.11 28,458,919.64 70,813 70,813 0 31-Oct-06 192.16 28,529,929.49 71,010 71,010 0 06-Nov-06 192.15 28,515,665 14,194 14,194 0 28-Dec-06 192.45 28,943,933.64 1,339,911 1,339,911 0 29-Dec-06 192.61 29,174,275.8 230,342 230,342 0 30-Dec-06 192.65 29,232,074.54 57,799 57,799 0 06-Jan-07 192.96 29,682,885.88 465,269 465,269 0 07-Jan-07 192.98 29,712,143.98 29,258 29,258 0 08-Jan-07 193.00 29,741,423.27 29,279 29,279 0 09-Jan-07 193.04 29,800,045.52 58,622 58,622 0 21-Jan-07 194.00 31,232,788.36 1,681,303 1,681,303 0 23-Jan-07 194.00 31,232,788.36 106,164 106,164 0 31-Jan-07 193.91 31,096,340.92 45,384 45,384 0 04-Feb-07 193.99 31,217,605.55 136,398 136,398 0 05-Feb-07 194.00 31,232,788.36 15,183 15,183 0 10-Feb-07 194.00 31,232,788.36 75,859 75,859 0 16-Feb-07 194.00 31,232,788.36 91,014 91,014 0 27-Feb-07 193.96 31,172,090.15 90,883 90,883 0 16-Mar-07 194.00 31,232,788.36 196,948 196,948 0 01-Apr-07 193.89 31,066,079.58 15,122 15,122 0 31-Jan-08 181.30 15,785,681.24 166,622 166,622 0
77 ContinueTable II.1 Income - Income Outcome Level Reservoir outcome Date Volume Volume (masl) Volume (m³) Volume (m³) (m³) (m³) 01-Feb-08 181.84 16,321,225.75 535,545 535,545 0 02-Feb-08 181.96 16,441,476.91 120,251 120,251 0 03-Feb-08 181.98 16,461,561.89 20,085 20,085 0 15-Feb-08 181.95 16,431,439.07 210,086 210,086 0 16-Feb-08 182.07 16,552,097.6 120,659 120,659 0 17-Feb-08 182.08 16,562,172.36 10,075 10,075 0 25-Oct-08 174.59 9,836,238.14 1,768,323 1,768,323 0 26-Oct-08 176.09 11,057,265.14 1,221,027 1,221,027 0 27-Oct-08 176.13 11,090,703.41 33,438 33,438 0 30-Oct-08 176.15 11,107,437.16 58,526 58,526 0 31-Oct-08 176.37 11,292,152.34 184,715 184,715 0 15-Nov-08 175.99 10,972,839.9 123,681 123,681 0 11-Feb-09 178.50 13,145,836.04 5,120,352 5,120,352 0 12-Feb-09 181.74 16,221,353.34 3,075,517 3,075,517 0 13-Feb-09 181.82 16,301,226.78 79,873 79,873 0 14-Feb-09 181.86 16,341,236.95 40,010 40,010 0 15-Feb-09 181.87 16,351,247.14 10,010 10,010 0 22-Feb-09 182.92 17,420,352.84 1,139,113 1,139,113 0 23-Feb-09 182.99 17,493,030 72,677 72,677 0 28-Feb-09 183.47 17,996,360.33 524,114 524,114 0 01-Mar-09 183.76 18,304,426 308,066 308,066 0 02-Mar-09 185.95 20,715,649 2,411,223 2,411,223 0 03-Mar-09 186.13 20,920,459.88 204,811 204,811 0 04-Mar-09 186.15 20,943,278.81 22,819 22,819 0 05-Mar-09 186.16 20,954,692.95 11,414 11,414 0 24-Mar-09 186.63 20,080,707.98 948,966 948,966 0 25-Mar-09 187.11 20,639,538 558,830 558,830 0 26-Mar-09 187.14 20,674,719.54 35,182 35,182 0 30-Mar-09 187.12 20,661,262.11 45,161 45,161 0 14-Apr-09 186.89 20,499,115.1 104,998 104,998 0 18-Jan-10 176.17 9,710,180.65 16,743 16,743 0 19-Jan-10 183.67 16,794,516 7,084,335 7,084,335 0 20-Jan-10 184.42 17,601,624.37 807,108 807,108 0 21-Jan-10 184.48 17,666,936.79 65,312 65,312 0 22-Jan-10 184.50 17,688,731.54 21,795 21,795 0 05-Feb-10 185.69 19,007,644.29 1,568,830 1,568,830 0 06-Feb-10 186.54 19,976,759.49 969,115 969,115 0 07-Feb-10 187.25 20,803,974 827,215 827,215 0 08-Feb-10 187.34 20,910,031 106,057 106,057 0 09-Feb-10 187.36 20,933,637.53 23,607 23,607 0 26-Feb-10 187.29 20,851,077.47 305,209 305,209 0 27-Feb-10 194.00 29,818,788.36 8,967,711 8,967,711 0 01-Feb-11 183.59 16,709,488.73 74,219 74,219 0 02-Feb-11 185.55 18,850,129.65 2,140,641 2,140,641 0
78 ContinueTable II.1 Income - Income Outcome Level Reservoir outcome Date Volume Volume (masl) Volume (m³) Volume (m³) (m³) (m³) 03-Feb-11 186.20 19,586,380.33 736,251 736,251 0 04-Feb-11 186.30 19,700,819.86 114,440 114,440 0 05-Feb-11 186.32 19,723,745.37 22,926 22,926 0 06-Feb-11 186.34 19,746,683.48 22,938 22,938 0 07-Feb-11 186.36 19,769,634.2 22,951 22,951 0 12-Feb-11 186.37 19,781,114.29 11,480 11,480 0 22-Feb-11 186.65 20,103,842.79 425,936 425,936 0 23-Feb-11 186.67 20,126,990.25 23,147 23,147 0 24-Feb-11 186.68 20,138,569.25 11,579 11,579 0 25-Feb-11 186.71 20,173,324.26 34,755 34,755 0 14-Jan-12 174.16 8,086,056 23,006 48,446 25,440 23-Jan-12 175.20 8,911,321 1,039,041 1,064,518 25,477 24-Jan-12 175.46 9,122,708 211,387 238,258 26,871 25-Jan-12 175.50 9,155,384 32,676 58,096 25,420 26-Jan-12 175.49 9,147,211 -8,173 16,883 25,056 28-Jan-12 175.45 9,114,546 -8,162 16,894 25,056 29-Jan-12 175.43 9,098,229 -16,317 9,414 25,731 30-Jan-12 175.40 9,073,772 -24,457 24,457 0 01-Feb-12 175.54 9,188,100 130,620 130,620 0 02-Feb-12 177.14 10,534,420 1,346,320 1,346,320 0 03-Feb-12 177.45 10,803,005 268,585 268,585 0 04-Feb-12 177.46 10,811,712 8,707 8,707 16,320 05-Feb-12 177.44 10,794,302 -17,410 15,611 33,021 06-Feb-12 177.42 10,776,902 -17,400 6,920 24,320 07-Feb-12 177.40 10,759,513 -17,389 2,591 19,980 18-Feb-12 178.98 12,166,892 1,580,685 1,580,685 0 19-Feb-12 181.60 14,668,053 2,501,161 2,501,161 0 20-Feb-12 181.95 15,017,439 349,386 349,386 0 21-Feb-12 182.05 15,117,956 100,517 100,517 0 22-Feb-12 182.05 15,117,956 0 9,000 9,000 26-Feb-12 182.03 15,097,828 10,059 10,059 0 27-Feb-12 182.03 15,097,828 0 11,717 11,717 01-Mar-12 182.00 15,067,659 10,050 10,050 0 03-Mar-12 182.21 15,279,432 241,915 259,230 17,315 04-Mar-12 182.40 15,472,238 192,806 202,915 10,109 05-Mar-12 182.42 15,492,602 20,364 31,785 11,421 06-Mar-12 182.48 15,553,774 61,172 61,172 0 23-Jun-12 179.17 12,341,080 82,658 110,718 28,060 12-Nov-12 171.84 6387090 -13,932 14,170 13,932 07-Jan-13 169.71 4,979,434 31,217 31,217 0 08-Jan-13 170.20 5,289,479 310,045 310,045 0 09-Jan-13 179.80 12,927,101 7,637,622 7,637,622 0 10-Jan-13 190.93 25,410,739 12,483,638 12,483,638 0 11-Jan-13 192.08 27,002,415 1,591,676 1,591,676 0
79 ContinueTable II.1 Income - Income Outcome Level Reservoir outcome Date Volume Volume (masl) Volume (m³) Volume (m³) (m³) (m³) 12-Jan-13 192.38 27,429,581 427,166 427,166 0 13-Jan-13 192.48 27,573,020 143,439 143,439 0 14-Jan-13 192.49 27,587,392 14,372 14,372 0 15-Jan-13 192.50 27,601,770 14,378 14,378 0 29-Jan-13 192.33 27,358,059 71,391 113,791 42,400 31-Jan-13 192.40 27,458,227 143,019 143,019 0 01-Feb-13 192.68 27,861,480 403,253 403,253 0 02-Feb-13 194.00 29,818,788 1,957,308 1,957,308 0 02-Sep-13 186.87 20,475,759 58,332 58,332 0 28-Oct-13 184.42 17,601,624 456,470 456,470 0 04-Nov-13 184.74 17,951,205 512,391 512,391 0 12-Dec-13 185.25 19,643,561 2,667,657 2,667,657 0 13-Dec-13 186.92 20,417,427 773,866 773,866 0 14-Dec-13 187.20 20,745,172 327,745 327,745 0 15-Dec-13 187.50 21,099,260 354,088 354,088 0 16-Dec-13 187.59 21,206,088 106,828 106,828 0 17-Dec-13 187.61 21,229,865 23,777 23,777 0 11-Mar-14 187.16 20,698,190 82,089 82,089 0 12-Mar-14 187.26 20,815,745 117,555 117,555 0 13-Mar-14 188.95 22,855,818 2,040,073 2,040,073 0 14-Mar-14 190.45 26,120,861 3,265,043 3,265,043 0 15-Mar-14 194.02 29,818,788 3,697,927 9,003,043 2,425,840 16-Mar-14 194.01 29,818,788 0 51,840 51,840 17-Mar-14 194.00 29,818,788 0 18,144 0 18-Mar-14 194.00 29,818,788 0 6,048 6,048 09-May-14 194.20 29,818,788 1,899,360 3,211,200 1,311,840 12-May-14 194.20 29,818,788 0 18,144 18,144 04-Nov-14 188.86 22,744,496 74,037 111,038 37,001 05-Nov-14 189.33 23,329,463 584,967 584,967 0 06-Nov-14 189.35 23,354,569 25,106 25,106 0 07-Nov-14 189.35 23,354,569 0 25,106 25,106 09-Nov-14 189.30 23,291,841 -37,622 111,039 37,622 14-Dec-14 190.80 25,235,933 2,982,376 2,982,376 0 15-Dec-14 190.92 25,397,252 161,319 161,319 0 08-Jan-15 190.80 25,235,933 306,638 306,638 0 09-Jan-15 191.26 26,356,610 1,120,677 1,120,677 0 10-Jan-15 191.74 26,524,010 167,400 167,400 0 11-Jan-15 191.90 26,748,386 224,376 224,376 0 12-Jan-15 192.25 27,243,896 495,510 495,510 0 13-Jan-15 192.32 27,343,770 99,874 99,874 0 14-Jan-15 192.34 27,372,353 28,583 28,583 0 15-Jan-15 192.37 27,415,266 42,913 42,913 0 16-Jan-15 192.56 27,688,148 272,882 272,882 0 17-Jan-15 192.58 27,716,983 28,835 28,835 0
80 ContinueTable II.1 Income - Income Outcome Level Reservoir outcome Date Volume Volume (masl) Volume (m³) Volume (m³) (m³) (m³) 18-Jan-15 192.59 27,731,408 14,425 14,425 0 20-Jan-15 192.60 27,745,839 14,431 14,431 0 17-Feb-15 192.24 27,229,649 142,179 142,179 0 20-Feb-15 192.29 27,300,953 114,013 114,013 0 21-Feb-15 194.00 29,818,788 2,517,835 2,517,835 0 22-Feb-15 194.00 29,818,788 0 6,894,360 6,894,360 23-Feb-15 194.00 29,818,788 0 264,960 264,960 24-Feb-15 194.00 29,818,788 0 51,840 51,840 25-Feb-15 194.00 29,818,788 0 51,840 51,840 27-Oct-15 190.30 24,573,283 5,980,493 5,980,493 0 28-Oct-15 193.20 28,621,386 4,048,103 4,048,103 0 29-Oct-15 193.98 29,788,422 1,167,036 1,167,036 0 30-Oct-15 194.00 29,818,788 30,366 30,366 0 02-Jan-16 191.650 26,398,386 1,469,091 1,469,091 0 03-Jan-16 191.780 26,579,979 181,593 181,593 0 10-Jan-16 192.55 27,673,738 1,358,853 1,358,853 0 28-Mar-16 192.05 26,959,959 2,556,677 2,556,677 0 29-Mar-16 193.58 29,185,818 2,225,859 2,225,859 0 30-Mar-16 193.89 29,652,079 466,261 466,261 0 31-Mar-16 194.00 29,818,788 166,709 166,709 0 14-Apr-16 193.800 29,667,207 436,495 436,495 0 15-Apr-16 193.93 29,712,264 45,057 45,057 0 30-Oct-16 181.32 14,391,348 419,815 419,815 0 15-Dec-16 184.35 17,525,562 6,213,204 6,213,204 0 16-Dec-16 184.88 18,105,120 579,558 579,558 0 17-Dec-16 184.91 18,138,179 33,059 33,059 0 21-Dec-16 185.30 18,570,492 564,386 564,386 0 22-Dec-16 185.39 18,670,933 100,441 100,441 0 24-Dec-16 185.35 18,626,261 22,317 22,317 0 25-Dec-16 185.85 18,883,818 257,557 257,557 0 26-Dec-16 185.61 18,917,536 33,718 33,718 0 28-Dec-16 185.65 18,962,580 67,526 67,526 0 29-Dec-16 185.70 19,018,910 56,330 56,330 0 30-Dec-16 185.82 19,154,356 135,446 135,446 0 28-Jan-17 186.99 20,499,115 2,745,845 2,745,845 0 29-Jan-17 187.80 21,456,451 957,336 957,336 0 30-Jan-17 188.05 21,756,541 300,090 300,090 0 31-Jan-17 188.07 21,780,645 24,104 24,104 0 01-Feb-17 188.11 21,828,895 48,250 48,250 0 02-Feb-17 188.12 21,840,967 12,072 12,072 0 03-Feb-17 188.17 21,901,379 60,412 60,413 0 04-Feb-17 188.19 21,925,569 24,190 41,190 0 17-Feb-17 187.99 21,684,315 168,025 168,025 0 03-Mar-17 187.89 21,564,226 203,335 203,335 0
81 ContinueTable II.1 Income - Income Outcome Level Reservoir outcome Date Volume Volume (masl) Volume (m³) Volume (m³) (m³) (m³) 14-Apr-17 186.99 20,499,115 35,029 35,029 0 15-Apr-17 187.51 21,111,116 612,001 612,001 0 16-Apr-17 188.51 22,314,590 1,203,474 1,203,474 0 17-Apr-17 188.91 22,806,302 491,712 491,712 0 18-Apr-17 188.98 22,892,996 86,694 86,694 0 19-Apr-17 189.00 22,917,802 24,806 24,806 0 06-Jan-18 179.92 13,040,237 7,814,599 7,814,599 0 07-Jan-18 181.95 15,017,439 1,977,202 1,977,202 0 08-Jan-18 181.99 15,057,609 40,170 104,080 63,910 09-Jan-18 182.00 15,067,659 10,050 46,210 36,160 10-Jan-18 182.03 15,097,828 30,169 30,169 0 11-Jan-18 182.06 15,128,025 30,197 30,197 0 12-Jan-18 182.09 15,158,250 30,225 30,225 0 13-Jan-18 182.12 15,188,503 30,253 30,253 0 14-Jan-18 182.13 15,198,594 10,091 10,091 0 15-Jan-18 182.12 15,188,503 -10,091 10,091 0 19-Jan-18 184.72 17,929,266 2,801,241 2,801,241 0 20-Jan-18 194.00 29,818,788 11,889,522 11,889,522 0 26-Jan-18 194.00 29,818,788 45,532 45,532 0 14-Feb-18 194.00 29,818,788 543,134 543,134 0 18-Feb-18 194.00 29,818,788 75,859 75,859 0
82 Table II.2
The peak runoff rates (Qpeak) after applying Equ.(3.10), From (6-Nov.- 2003) to (13-March-2018) The peak runoff The peak runoff Date Date rate, m3/sec rate, m3/sec 06-Nov-03 0.000127 12-Dec-03 0.056 08-Nov-03 0.000130 13-Dec-03 0.113 09-Nov-03 0.000159 14-Dec-03 0.056 10-Nov-03 0.000572 15-Dec-03 24.847 11-Nov-03 0.000871 16-Dec-03 58.633 12-Nov-03 0.001537 17-Dec-03 3.333 13-Nov-03 0.001418 18-Dec-03 0.497 14-Nov-03 0.001303 19-Dec-03 0.124 15-Nov-03 0.001452 20-Dec-03 0.998 16-Nov-03 0.001490 21-Dec-03 0.751 17-Nov-03 0.001617 22-Dec-03 0.125 18-Nov-03 0.002107 23-Dec-03 0.125 19-Nov-03 0.002658 26-Dec-03 0.125 20-Nov-03 0.003566 27-Dec-03 0.251 21-Nov-03 0.003830 28-Dec-03 0.126 22-Nov-03 0.005343 29-Dec-03 0.126 23-Nov-03 0.005626 02-Jan-04 0.126 24-Nov-03 0.006367 03-Jan-04 0.126 25-Nov-03 0.007162 05-Jan-04 0.379 26-Nov-03 0.010351 06-Jan-04 0.126 27-Nov-03 0.010945 07-Jan-04 0.126 28-Nov-03 0.267787 08-Jan-04 0.126 29-Nov-03 0.596914 09-Jan-04 0.252 30-Nov-03 3.929938 10-Jan-04 0.253 01-Dec-03 0.775041 11-Jan-04 0.126 02-Dec-03 0.350793 12-Jan-04 0.126 03-Dec-03 16.51384 13-Jan-04 0.380 04-Dec-03 5.041083 14-Jan-04 6.925 05-Dec-03 1.075921 15-Jan-04 37.520 06-Dec-03 1.150455 16-Jan-04 20.620 07-Dec-03 0.332644 17-Jan-04 1.576 08-Dec-03 0.22276 18-Jan-04 0.474 09-Dec-03 0.167611 19-Jan-04 0.316 10-Dec-03 0.112003 20-Jan-04 0.317 11-Dec-03 0.112218 21-Jan-04 0.158
83 ContinueTable II.2 The peak runoff The peak runoff Date Date rate, m3/sec rate, m3/sec 22-Jan-04 0.1595 31-Oct-04 4.246 23-Jan-04 0.3172 01-Nov-04 0.533 24-Jan-04 0.1587 02-Nov-04 0.178 26-Jan-04 0.15880 03-Nov-04 0.178 27-Jan-04 0.1588 10-Nov-04 0.178 28-Jan-04 0.1589 18-Nov-04 2.137 29-Jan-04 0.1590 19-Nov-04 3.937 30-Jan-04 0.1590 20-Nov-04 0.898 31-Jan-04 0.1591 21-Nov-04 0.360 01-Feb-04 0.1592 23-Nov-04 268.382 02-Feb-04 0.3185 24-Nov-04 58.70 03-Feb-04 0.1594 25-Nov-04 1.754 04-Feb-04 0.1594 26-Nov-04 0.293 05-Feb-04 0.1595 27-Nov-04 56.088 06-Feb-04 0.1595 28-Nov-04 14.225 07-Feb-04 0.15960 09-Dec-04 4.130 09-Feb-04 0.15970 06-Jan-05 25.120 11-Feb-04 0.15970 07-Jan-05 13.475 13-Feb-04 0.15979 24-Jan-05 0.916 14-Feb-04 0.1598 25-Jan-05 1.223 15-Feb-04 0.1599 09-Feb-05 5.340 16-Feb-04 0.3400 10-Feb-05 0.2978 17-Feb-04 0.1601 10-Mar-05 0.2670 18-Feb-04 0.1602 25-Apr-05 11.316 19-Feb-04 0.1602 26-Apr-05 4.790 21-Feb-04 0.1603 22-Nov-05 1.480 22-Feb-04 0.3207 23-Nov-05 0.247 24-Feb-04 0.1604 26-Dec-05 0.738 25-Feb-04 0.1605 28-Jan-06 4.432 27-Feb-04 0.1606 03-Feb-06 32.170 29-Feb-04 0.1606 04-Feb-06 12.123 02-Mar-04 0.1607 05-Feb-06 0.520 04-Mar-04 0.1607 06-Feb-06 0.260 07-Mar-04 0.3217 07-Feb-06 0.781 09-Mar-04 0.1609 09-Feb-06 0.260 12-Mar-04 0.1609 14-Feb-06 0.780 15-Mar-04 0.1610 15-Feb-06 1.043 04-Apr-04 0.1611 16-Feb-06 53.762 30-Oct-04 67.9890 17-Feb-06 34.090
84 ContinueTable II.2 The peak runoff The peak runoff Date Date rate, m3/sec rate, m3/sec 18-Feb-06 4.1880 11-Feb-09 108.161 01-Apr-06 2.11238E-05 12-Feb-09 64.967 02-Apr-06 60.389 13-Feb-09 1.6877 16-Apr-06 2.11238E-05 14-Feb-09 0.8457 17-Apr-06 2.11238E-05 15-Feb-09 0.2117 12-Aug-06 0.746 22-Feb-09 24.0627 30-Oct-06 1.496 23-Feb-09 1.5357 31-Oct-06 1.500 28-Feb-09 11.0717 06-Nov-06 0.300 01-Mar-09 6.508 28-Dec-06 28.304 02-Mar-09 50.934 29-Dec-06 4.866 03-Mar-09 4.326 30-Dec-06 1.221 04-Mar-09 0.482 06-Jan-07 9.828 05-Mar-09 0.241 07-Jan-07 0.618 24-Mar-09 20.046 08-Jan-07 0.618 25-Mar-09 11.805 09-Jan-07 1.238 26-Mar-09 0.7435 21-Jan-07 35.520 30-Mar-09 0.954 23-Jan-07 2.243 14-Apr-09 2.218 31-Jan-07 0.959 18-Jan-10 0.354 04-Feb-07 2.880 19-Jan-10 149.648 05-Feb-07 0.321 20-Jan-10 17.049 10-Feb-07 1.6021 21-Jan-10 1.380 16-Feb-07 1.923 22-Jan-10 0.460 27-Feb-07 1.920 05-Feb-10 33.140 16-Mar-07 4.160 06-Feb-10 20.471 01-Apr-07 0.319 07-Feb-10 17.474 31-Jan-08 3.520 08-Feb-10 2.240 01-Feb-08 11.312 09-Feb-10 0.499 02-Feb-08 2.540 26-Feb-10 6.447 03-Feb-08 0.424 27-Feb-10 189.432 15-Feb-08 4.438 01-Feb-11 1.568 16-Feb-08 2.550 02-Feb-11 45.218 17-Feb-08 0.213 03-Feb-11 15.552 25-Oct-08 37.354 04-Feb-11 2.417 26-Oct-08 25.793 05-Feb-11 0.484 27-Oct-08 0.706 06-Feb-11 0.485 30-Oct-08 1.236 07-Feb-11 0.485 31-Oct-08 3.902 12-Feb-11 0.243 15-Nov-08 2.613 22-Feb-11 8.990
85 ContinueTable II.2 The peak runoff The peak runoff Date Date rate, m3/sec rate, m3/sec 23-Feb-11 0.4890 14-Jan-13 0.3036 24-Feb-11 0.2446 15-Jan-13 0.3038 25-Feb-11 0.7342 29-Jan-13 2.4037 14-Jan-12 1.0234 31-Jan-13 3.02117 23-Jan-12 22.4866 01-Feb-13 8.51827 24-Jan-12 5.0329 02-Feb-13 41.3458 25-Jan-12 1.2272 02-Sep-13 1.2322 26-Jan-12 0.3566 28-Oct-13 9.6424 28-Jan-12 0.3569 04-Nov-13 10.8236 29-Jan-12 0.1989 12-Dec-13 56.3510 30-Jan-12 0.5166 13-Dec-13 16.3470 01-Feb-12 2.7592 14-Dec-13 6.9232 02-Feb-12 28.4394 15-Dec-13 7.4797 03-Feb-12 5.6735 16-Dec-13 2.2566 04-Feb-12 0.1839 17-Dec-13 0.5023 05-Feb-12 0.3298 11-Mar-14 1.7340 06-Feb-12 0.1462 12-Mar-14 2.4832 07-Feb-12 0.0547 13-Mar-14 43.0941 18-Feb-12 33.3901 14-Mar-14 68.9701 19-Feb-12 52.8340 15-Mar-14 190.1783 20-Feb-12 7.38035 16-Mar-14 1.0951 21-Feb-12 2.1233 17-Mar-14 0.3833 22-Feb-12 0.19013 18-Mar-14 0.1278 26-Feb-12 0.2123 09-May-14 67.8330 27-Feb-12 0.2475 12-May-14 0.38330 01-Mar-12 0.2123 04-Nov-14 2.3455 03-Mar-12 5.4760 05-Nov-14 12.3567 04-Mar-12 4.2863 06-Nov-14 0.53033 05-Mar-12 0.6714 07-Nov-14 0.5303 06-Mar-12 1.2922 09-Nov-14 2.3456 23-Jun-12 2.3388 14-Dec-14 62.9991 12-Nov-12 0.2993 15-Dec-14 3.4077 07-Jan-13 0.6594 08-Jan-15 6.4774 08-Jan-13 6.5493 09-Jan-15 23.6730 09-Jan-13 161.3355 10-Jan-15 3.5361 10-Jan-13 263.7017 11-Jan-15 4.7397 11-Jan-13 33.6222 12-Jan-15 10.467 12-Jan-13 9.0234 13-Jan-15 2.1097 13-Jan-13 3.0300 14-Jan-15 0.6038
86 ContinueTable II.2 The peak runoff The peak runoff Date Date rate, m3/sec rate, m3/sec 15-Jan-15 0.9065 31-Jan-17 0.50917 16-Jan-15 5.76435 01-Feb-17 1.01927 17-Jan-15 0.6091 02-Feb-17 0.25501 18-Jan-15 0.30471 03-Feb-17 1.2762 20-Jan-15 0.30481 04-Feb-17 0.8701 17-Feb-15 3.0034 17-Feb-17 3.54931 20-Feb-15 2.4084 03-Mar-17 4.29521 21-Feb-15 53.18621 14-Apr-17 0.74000 22-Feb-15 145.6350 15-Apr-17 12.9300 23-Feb-15 5.5970 16-Apr-17 25.4220 25-Feb-15 1.0951 17-Apr-17 10.3870 27-Oct-15 126.3307 18-Apr-17 1.8313 28-Oct-15 85.5113 19-Apr-17 0.5240 29-Oct-15 24.652 06-Jan-18 165.0740 30-Oct-15 0.6414 07-Jan-18 41.7660 02-Jan-16 31.0328 08-Jan-18 2.19860 03-Jan-16 3.8359 09-Jan-18 0.9761 10-Jan-16 28.7041 10-Jan-18 0.6373 28-Mar-16 54.0067 11-Jan-18 0.6379 29-Mar-16 47.0186 12-Jan-18 0.6385 30-Mar-16 9.8492 13-Jan-18 0.6391 31-Mar-16 3.52153 14-Jan-18 0.2132 14-Apr-16 9.2204 15-Jan-18 0.2132 15-Apr-16 0.9518 19-Jan-18 59.1728 30-Oct-16 8.8681 20-Jan-18 251.1517 15-Dec-16 131.2464 26-Jan-18 0.96181 16-Dec-16 12.2425 14-Feb-18 11.473 17-Dec-16 0.6983 18-Feb-18 1.6024 21-Dec-16 11.9220 22-Dec-16 2.12170 24-Dec-16 0.47142 25-Dec-16 5.4406 26-Dec-16 0.7123 28-Dec-16 1.4264 29-Dec-16 1.1900 30-Dec-16 2.8611 28-Jan-17 58.0030 29-Jan-17 20.223 30-Jan-17 6.339
87 Table II.3
The daily sediment yields after applying the Modified Universal Soil Loss Equation (MUSLE) from 6/11/2003 until 1/9/2015. Surface Peak runoff rate Sediment Yields Date runoff(mm) (m3/sec) (metric ton) 06-Nov-03 4.58E-06 0.000127 0.0283 08-Nov-03 4.69E-06 0.000130 0.0291 09-Nov-03 5.75E-06 0.000160 0.03651 10-Nov-03 2.06E-05 0.000570 0.1530 11-Nov-03 3.1E-05 0.000871 0.2447 12-Nov-03 5.51E-05 0.00153 0.45913 13-Nov-03 5.12E-05 0.00142 0.4227 14-Nov-03 4.71E-05 0.00130 0.3843 15-Nov-03 5.24E-05 0.001452 0.4339 16-Nov-03 5.38E-05 0.00149 0.4468 17-Nov-03 5.84E-05 0.00162 0.4897 18-Nov-03 7.609E-05 0.00211 0.6585 19-Nov-03 9.600E-05 0.00266 0.8543 20-Nov-03 0.0001288 0.00357 1.1870 21-Nov-03 0.0001388 0.00383 1.2860 22-Nov-03 0.000193 0.00534 1.8668 23-Nov-03 0.000203 0.00563 1.9781 24-Nov-03 0.000230 0.00637 2.2722 25-Nov-03 0.000259 0.00716 2.5921 26-Nov-03 0.000374 0.01035 3.9156 27-Nov-03 0.000400 0.01095 4.1683 28-Nov-03 0.009670 0.26779 149.6732 29-Nov-03 0.021600 0.5969 367.3170 30-Nov-03 0.141910 3.92994 3032.0170 01-Dec-03 0.027990 0.7750 492.1110 02-Dec-03 0.012670 0.3508 202.5243 03-Dec-03 0.596310 16.5140 15135.940 04-Dec-03 0.182030 5.0411 4007.250 05-Dec-03 0.038900 1.0759 710.581 06-Dec-03 0.0415 1.1505 765.938 07-Dec-03 0.01201 0.3326 190.825 08-Dec-03 0.00804 0.223 121.789 09-Dec-03 0.00605 0.1676 88.560 10-Dec-03 0.00404 0.1120 56.384 11-Dec-03 0.00405 0.11222 56.5054
88 ContinueTable II.3 Surface Peak runoff rate Sediment Yields Date runoff(mm) (m3/sec) (metric ton) 12-Dec-03 0.00203 0.0562 26.040 13-Dec-03 0.00406 0.1126 56.6920 14-Dec-03 0.00204 0.0564 26.1270 15-Dec-03 0.89723 24.847 23918.292 16-Dec-03 2.1172 58.633 62566.24 17-Dec-03 0.12034 3.3325 2520.740 18-Dec-03 0.01796 0.4973 299.362 19-Dec-03 0.00450 0.12446 63.453 20-Dec-03 0.03603 0.9977 653.008 21-Dec-03 0.02711 0.7507 474.843 22-Dec-03 0.00453 0.1253 63.944 23-Dec-03 0.00453 0.1254 63.978 26-Dec-03 0.00453 0.1254 64.010 27-Dec-03 0.00907 0.2510 139.231 28-Dec-03 0.00454 0.12561 64.1081 29-Dec-03 0.00454 0.12567 64.1420 02-Jan-04 0.00454 0.12577 64.1742 03-Jan-04 0.004542 0.12578 64.207 05-Jan-04 0.013640 0.3777 219.991 06-Jan-04 0.004550 0.12607 64.340 07-Jan-04 0.00455 0.1261 64.372 08-Jan-04 0.00455 0.1261 64.405 09-Jan-04 0.00912 0.2524 140.090 10-Jan-04 0.009123 0.2527 140.234 11-Jan-04 0.004565 0.1264 64.571 12-Jan-04 0.004567 0.1265 64.604 13-Jan-04 0.01371 0.3798 221.351 14-Jan-04 0.250077 6.925 5719.010 15-Jan-04 1.3548441 37.520 37948.535 16-Jan-04 0.74460 20.620 19410.40 17-Jan-04 0.05692 1.5760 1089.812 18-Jan-04 0.01712 0.4742 283.84 19-Jan-04 0.01143 0.3165 180.45 20-Jan-04 0.01144 0.31672 180.622 21-Jan-04 0.00571 0.15846 83.161 22-Jan-04 0.00572 0.1585 83.1991 23-Jan-04 0.01145 0.31724 180.960 24-Jan-04 0.0057 0.1587 83.3136 26-Jan-04 0.0057 0.15878 83.352
89 ContinueTable II.3 Surface Peak runoff rate Sediment Yields Date runoff(mm) (m3/sec) (metric ton) 27-Jan-04 0.005736 0.1588 83.389 28-Jan-04 0.00576 0.1589 83.427 29-Jan-04 0.00576 0.1590 83.464 30-Jan-04 0.00576 0.1590 83.502 31-Jan-04 0.00575 0.1591 83.540 01-Feb-04 0.005751 0.1592 83.576 02-Feb-04 0.01155 0.3190 181.773 03-Feb-04 0.0058 0.15940 83.690 04-Feb-04 0.00576 0.1594 83.725 05-Feb-04 0.00579 0.1595 83.762 06-Feb-04 0.005769 0.1595 83.801 07-Feb-04 0.005769 0.1596 83.832 09-Feb-04 0.00577 0.1597 83.872 11-Feb-04 0.00577 0.1597 83.907 13-Feb-04 0.00577 0.1599 83.943 14-Feb-04 0.0058 0.1598 83.979 15-Feb-04 0.0058 0.1600 84.015 16-Feb-04 0.01156 0.3200 182.720 17-Feb-04 0.0058 0.1601 84.120 18-Feb-04 0.0058 0.1602 84.160 19-Feb-04 0.0058 0.1602 84.190 21-Feb-04 0.0058 0.1603 84.226 22-Feb-04 0.0116 0.3207 183.177 24-Feb-04 0.0058 0.1604 84.331 25-Feb-04 0.0058 0.1605 84.3651 27-Feb-04 0.0058 0.1606 84.4000 29-Feb-04 0.0058 0.1606 84.4340 02-Mar-04 0.0059 0.1607 84.470 04-Mar-04 0.0058 0.16077 84.4954 07-Mar-04 0.0116 0.3217 183.7790 09-Mar-04 0.0058 0.16097 84.6030 12-Mar-04 0.0058 0.16097 84.6370 15-Mar-04 0.0058 0.16102 84.6702 04-Apr-04 0.00582 0.16108 84.7040 30-Oct-04 2.455066972 67.9890 73849.904 31-Oct-04 0.153310511 4.24567 3306.13 01-Nov-04 0.019244508 0.5329 323.521 02-Nov-04 0.006418764 0.1778 94.586 03-Nov-04 0.006420732 0.1778 94.618
90 ContinueTable II.3 Surface Peak runoff rate Sediment Yields Date runoff(mm) (m3/sec) (metric ton) 10-Nov-04 0.0064 0.17770 94.553 18-Nov-04 0.077 2.1360 1531.71 19-Nov-04 0.14215 3.9365 3037.72 20-Nov-04 0.0324 0.8984 580.641 21-Nov-04 0.0130 0.3598 208.326 23-Nov-04 9.69123 268.382 343734.57 24-Nov-04 2.1196 58.700 62645.862 25-Nov-04 0.0633 1.754 1227.998 26-Nov-04 0.0106 0.293 165.35 27-Nov-04 2.025 56.088 59532.423 28-Nov-04 0.5137 14.225 12806.58 09-Dec-04 0.14917 4.1303 3205.66 06-Jan-05 0.9071 25.1205 24213.12 07-Jan-05 0.4866 13.475 12052.592 24-Jan-05 0.0331 0.9159 593.289 25-Jan-05 0.0442 1.223 820.051 09-Feb-05 0.1929 5.3421 4276.183 10-Feb-05 0.0108 0.2978 168.610 10-Mar-05 0.0096 0.2667 148.989 25-Apr-05 0.40863 11.3163 9912.126 26-Apr-05 0.17300 4.7902 3784.540 22-Nov-05 0.05345 1.4801 1015.664 23-Nov-05 0.00892 0.2470 136.686 26-Dec-05 0.0267 0.7383 466.041 28-Jan-06 0.1600 4.432 3468.660 03-Feb-06 1.16160 32.1697 31941.870 04-Feb-06 0.4378 12.1232 10707.075 05-Feb-06 0.01880 0.520 314.614 06-Feb-06 0.00939 0.260 144.830 07-Feb-06 0.02819 0.781 496.070 09-Feb-06 0.0094 0.2604 145.036 14-Feb-06 0.02821 0.7814 496.600 15-Feb-06 0.0377 1.043 686.247 16-Feb-06 1.94137 53.762 56774.5927 17-Feb-06 1.23099 34.0901 34084.98 18-Feb-06 0.1512 4.1876 3255.52 01-Apr-06 7.63E-07 2.11E-05 0.0038 02-Apr-06 2.1806 60.389 64668.77 16-Apr-06 7.63E-07 2.11E-05 0.0038
91 ContinueTable II.3 Surface Peak runoff rate Sediment Yields Date runoff(mm) (m3/sec) (metric ton) 17-Apr-06 0.00000076 0.000021 0.00380 12-Aug-06 0.0269 0.7463 471.738 30-Oct-06 0.0540 1.4958 1027.764 31-Oct-06 0.0542 1.5000 1030.958 06-Nov-06 0.0108 0.2998 169.875 28-Dec-06 1.0221 28.3040 27675.095 29-Dec-06 0.1757 4.8657 3851.432 30-Dec-06 0.0441 1.2209 818.677 06-Jan-07 0.3549 9.8282 8464.332 07-Jan-07 0.0223 0.6180 381.909 08-Jan-07 0.0223 0.6185 382.219 09-Jan-07 0.0447 1.2383 831.752 21-Jan-07 1.2825 35.5155 35685.141 23-Jan-07 0.0810 2.2426 1617.559 31-Jan-07 0.0346 0.9587 624.443 04-Feb-07 0.1040 2.8812 2141.653 05-Feb-07 0.0116 0.3207 183.180 10-Feb-07 0.0579 1.6024 1110.126 16-Feb-07 0.0694 1.9226 1361.341 27-Feb-07 0.0693 1.9198 1359.135 16-Mar-07 0.1502 4.1603 3231.754 01-Apr-07 0.0115 0.3194 182.365 31-Jan-08 0.1271 3.5197 2679.815 01-Feb-08 0.4085 11.3127 9908.662 02-Feb-08 0.0917 2.5402 1859.790 03-Feb-08 0.0153 0.4243 250.599 15-Feb-08 0.1602 4.4378 3474.146 16-Feb-08 0.0920 2.5488 1866.848 17-Feb-08 0.0077 0.2128 115.714 25-Oct-08 1.3488 37.3537 37760.086 26-Oct-08 0.9314 25.7927 24939.990 27-Oct-08 0.0255 0.7063 443.524 30-Oct-08 0.0446 1.2363 830.215 31-Oct-08 0.1409 3.9019 3007.786 15-Nov-08 0.0943 2.6126 1919.302 11-Feb-09 3.9057 108.1612 124216.630 12-Feb-09 2.3459 64.9666 70183.065 13-Feb-09 0.0609 1.6872 1176.128 14-Feb-09 0.0305 0.8452 542.244
92 ContinueTable II.3 Surface Peak runoff rate Sediment Yields Date runoff(mm) (m3/sec) (metric ton) 15-Feb-09 0.0076 0.2115 114.884 22-Feb-09 0.8689 24.0624 23073.778 23-Feb-09 0.0554 1.5352 1058.107 28-Feb-09 0.3998 11.0713 9672.097 01-Mar-09 0.2350 6.5075 5333.895 02-Mar-09 1.8392 50.9342 53440.417 03-Mar-09 0.1562 4.3264 3376.598 04-Mar-09 0.0174 0.4820 289.104 05-Mar-09 0.0087 0.2411 133.076 24-Mar-09 0.7238 20.0457 18805.481 25-Mar-09 0.4263 11.8046 10392.434 26-Mar-09 0.0268 0.7432 469.501 30-Mar-09 0.0344 0.9540 621.012 14-Apr-09 0.0801 2.2179 1597.664 18-Jan-10 0.0128 0.3537 204.396 19-Jan-10 5.4038 149.6480 178689.477 20-Jan-10 0.6156 17.0492 15686.558 21-Jan-10 0.0498 1.3796 938.770 22-Jan-10 0.0166 0.4604 274.611 05-Feb-10 1.1967 33.1396 33022.415 06-Feb-10 0.7392 20.4714 19253.267 07-Feb-10 0.6310 17.4739 16124.873 08-Feb-10 0.0809 2.2403 1615.727 09-Feb-10 0.0180 0.4987 300.303 26-Feb-10 0.2328 6.4472 5278.522 27-Feb-10 6.8404 189.432 232684.452 01-Feb-11 0.0566 1.5678 1083.287 02-Feb-11 1.6328 45.2184 46770.612 03-Feb-11 0.5616 15.5524 14152.487 04-Feb-11 0.0873 2.4174 1759.419 05-Feb-11 0.0175 0.4843 290.617 06-Feb-11 0.0175 0.4845 290.796 07-Feb-11 0.0175 0.4848 290.975 12-Feb-11 0.0088 0.2425 133.938 22-Feb-11 0.3249 8.9974 7667.071 23-Feb-11 0.0177 0.4890 293.770 24-Feb-11 0.0088 0.2446 135.231 25-Feb-11 0.0265 0.7342 463.131 14-Jan-12 0.0370 1.0234 671.820
93 ContinueTable II.3 Surface Peak runoff rate Sediment Yields Date runoff(mm) (m3/sec) (metric ton) 23-Jan-12 0.8120 22.4866 21388.255 24-Jan-12 0.1817 5.0329 3999.974 25-Jan-12 0.0443 1.2272 823.394 26-Jan-12 0.0129 0.3566 206.304 28-Jan-12 0.0129 0.3569 206.455 29-Jan-12 0.0072 0.1989 107.248 30-Jan-12 0.0187 0.5166 312.446 01-Feb-12 0.0996 2.7592 2040.304 02-Feb-12 1.0269 28.4394 27823.392 03-Feb-12 0.2049 5.6735 4574.415 04-Feb-12 0.0066 0.1839 98.269 05-Feb-12 0.0119 0.3298 188.976 06-Feb-12 0.0053 0.1462 75.977 07-Feb-12 0.0020 0.0547 25.284 18-Feb-12 1.2057 33.3901 33302.033 19-Feb-12 1.9078 52.8340 55677.871 20-Feb-12 0.2665 7.3804 6141.382 21-Feb-12 0.0767 2.1233 1521.501 22-Feb-12 0.0069 0.1901 101.980 26-Feb-12 0.0077 0.2125 115.512 27-Feb-12 0.0089 0.2475 137.037 01-Mar-12 0.0077 0.2123 115.396 03-Mar-12 0.1977 5.4759 4396.342 04-Mar-12 0.1548 4.2863 3341.610 05-Mar-12 0.0242 0.6714 419.037 06-Mar-12 0.0467 1.2922 872.375 23-Jun-12 0.0845 2.3388 1695.463 12-Nov-12 0.0108 0.2993 169.550 07-Jan-13 0.0238 0.6594 410.660 08-Jan-13 0.2365 6.5493 5372.293 09-Jan-13 5.8258 161.3355 194391.430 10-Jan-13 9.5222 263.7017 337028.076 11-Jan-13 1.2141 33.6222 33561.488 12-Jan-13 0.3258 9.0234 7691.876 13-Jan-13 0.1094 3.0300 2265.851 14-Jan-13 0.0110 0.3036 172.260 15-Jan-13 0.0110 0.3037 172.340 29-Jan-13 0.0868 2.4037 1748.255 31-Jan-13 0.1091 3.0211 2258.421
94 ContinueTable II.3 Surface Peak runoff rate Sediment Yields Date runoff(mm) (m3/sec) (metric ton) 01-Feb-13 0.3076 8.5182 7211.256 02-Feb-13 1.4930 41.3458 42307.976 02-Sep-13 0.0445 1.2322 827.141 28-Oct-13 0.3482 9.6424 8285.251 04-Nov-13 0.3908 10.8236 9430.129 12-Dec-13 2.0348 56.3510 59845.233 13-Dec-13 0.5903 16.3470 14964.756 14-Dec-13 0.2500 6.9232 5716.950 15-Dec-13 0.2701 7.4797 6234.025 16-Dec-13 0.0815 2.2566 1628.888 17-Dec-13 0.0181 0.5023 302.733 11-Mar-14 0.0626 1.7340 1212.727 12-Mar-14 0.0897 2.4832 1813.151 13-Mar-14 1.5561 43.0941 44316.675 14-Mar-14 2.4905 68.9701 75044.617 15-Mar-14 6.8673 190.1783 233711.465 16-Mar-14 0.0395 1.0951 724.751 17-Mar-14 0.0138 0.3833 223.638 18-Mar-14 0.0046 0.1278 65.339 09-May-14 2.4494 67.8327 73659.949 12-May-14 0.0138 0.3833 223.638 04-Nov-14 0.0847 2.3455 1700.953 05-Nov-14 0.4462 12.3567 10938.332 06-Nov-14 0.0192 0.5303 321.747 07-Nov-14 0.0192 0.5303 321.747 09-Nov-14 0.0847 2.3456 1700.970 14-Dec-14 2.2749 62.9991 67806.895 15-Dec-14 0.1231 3.4077 2584.472 08-Jan-15 0.2339 6.4774 5306.218 09-Jan-15 0.8548 23.6729 22655.940 10-Jan-15 0.1277 3.5361 2693.829 11-Jan-15 0.1711 4.7397 3739.879 12-Jan-15 0.3780 10.4670 9082.861 13-Jan-15 0.0762 2.1097 1510.604 14-Jan-15 0.0218 0.6038 372.053 15-Jan-15 0.0327 0.9065 586.494 16-Jan-15 0.2081 5.7643 4656.460 17-Jan-15 0.0220 0.6091 375.729 18-Jan-15 0.0110 0.3047 172.971
95 ContinueTable II.3 Surface Peak runoff rate Sediment Yields Date runoff(mm) (m3/sec) (metric ton) 20-Jan-15 0.0110 0.3048 173.052 17-Feb-15 0.1085 3.0034 2243.570 20-Feb-15 0.0870 2.4084 1752.076 21-Feb-15 1.9205 53.1862 56093.755 22-Feb-15 5.2589 145.6350 173331.385 23-Feb-15 0.2021 5.5970 4505.323 24-Feb-15 0.0395 1.0951 724.751 25-Feb-15 0.0395 1.0951 724.751 27-Oct-15 4.5618 126.331 147811.900 28-Oct-15 3.0878 85.5113 95474.131 29-Oct-15 0.8902 24.6522 23708.187 30-Oct-15 0.0232 0.6414 398.142 02-Jan-16 1.1206 31.0328 30680.223 03-Jan-16 0.1385 3.8359 2950.903 10-Jan-16 1.0365 28.7041 28113.645 28-Mar-16 1.9502 54.0067 57063.833 29-Mar-16 1.6978 47.0186 48860.886 30-Mar-16 0.3557 9.8492 8484.545 31-Mar-16 0.1272 3.5215 2681.378 14-Apr-16 0.3329 9.2204 7880.264 15-Apr-16 0.0344 0.9518 619.409 30-Oct-16 0.3202 8.8681 7543.778 15-Dec-16 4.7393 131.2464 154268.573 16-Dec-16 0.4421 12.2425 10825.115 17-Dec-16 0.0252 0.6983 437.893 21-Dec-16 0.4305 11.9220 10508.225 22-Dec-16 0.0766 2.1217 1520.213 24-Dec-16 0.0170 0.4714 281.992 25-Dec-16 0.1965 5.4406 4364.577 26-Dec-16 0.0257 0.7123 447.682 28-Dec-16 0.0515 1.4264 974.477 29-Dec-16 0.0430 1.1899 795.413 30-Dec-16 0.1033 2.8611 2124.918 28-Jan-17 2.0945 58.0026 61813.184 29-Jan-17 0.7302 20.2226 18991.361 30-Jan-17 0.2289 6.3390 5179.474 31-Jan-17 0.0184 0.5092 307.400 01-Feb-17 0.0368 1.0192 668.776 02-Feb-17 0.0092 0.2550 141.696
96 ContinueTable II.3 Surface Peak runoff rate Sediment Yields Date runoff(mm) (m3/sec) (metric ton) 03-Feb-17 0.0461 1.2762 860.261 04-Feb-17 0.0314 0.8701 560.184 17-Feb-17 0.1282 3.5493 2705.096 03-Mar-17 0.1551 4.2952 3349.358 14-Apr-17 0.0267 0.7399 467.222 15-Apr-17 0.4668 12.928 11506.053 16-Apr-17 0.9180 25.4219 24538.787 17-Apr-17 0.3751 10.3868 9004.925 18-Apr-17 0.0661 1.8313 1289.174 19-Apr-17 0.0189 0.5240 317.444 06-Jan-18 5.9608 165.074 199443.311 07-Jan-18 1.5082 41.7660 42789.887 08-Jan-18 0.0794 2.1986 1582.032 09-Jan-18 0.0352 0.9761 637.189 10-Jan-18 0.0230 0.6373 395.250 11-Jan-18 0.0230 0.6379 395.661 12-Jan-18 0.0231 0.6385 396.072 13-Jan-18 0.0231 0.6391 396.483 14-Jan-18 0.0077 0.2132 115.923 15-Jan-18 0.0077 0.2132 115.923 19-Jan-18 2.1367 59.1728 63211.560 20-Jan-18 9.0690 251.1517 319115.645 26-Jan-18 0.0347 0.9618 626.727 14-Feb-18 0.4143 11.4730 10066.067 18-Feb-18 0.0579 1.6024 1110.125
97 Personal Information
Name: Aseel Nayef Al-Nawiseh
Education:
- Bachelor Degree of Civil Engineering/ water and environment, Mutah University, Faculty of engineering. (2006-2011) - Master Degree of Water and Environmental Engineering, Mutah University, Faculty Engineering. (2016-2018)
Contact Information: Mobile: +962(77)2479811 Email: [email protected]
98