iucn water and nature initiative...hydraulic study of lake jipe, nym reservoir and kirua swamps...

IUCN WATER AND NATURE INITIATIVE

PANGANI BASIN WATER BOARD1

PANGANI RIVER BASIN FLOW ASSESSMENT

Hydraulic Study of Lake Jipe, Nyumba ya Mungu Reservoir and Kirua Swamp

T.A Kimaro, S.H. Mkhandi, J. Nobert, P.M. Ndomba, P. Valimba and F.W. Mtalo

August 2008

1 As of 2010, Pangani Basin Water Office is known as Pangani Basin Water Board

Published by: Pangani Basin Water Board (PBWB) International Union for Conservation of Nature (IUCN) Copyright: © 2010 International Union for Conservation of Nature and Pangani Basin Water Board This publication may be produced in whole or part and in any form for education or non-profit uses, without special permission from the copyright holder, provided acknowledgement of the source is made. IUCN would appreciate receiving a copy of any publication which uses this publication as a source. No use of this publication may be made for resale or other commercial purpose without the prior written permission of IUCN. Citation: PWBO/IUCN. 2008. Hydraulic Study of Lake Jipe, Nyumba ya Mungu Reservoir and Kirua

Swamps. Pangani River Basin Flow Assessment. Pangani Basin Water Board, Moshi and IUCN Eastern and Southern Africa Regional Programme. 75 pp.

Available from: IUCN - ESARO Publications Service Unit, P. O. Box 68200 - 00200, Nairobi, Kenya; Telephone ++ 254 20 890605-12; Fax ++ 254 20 890615; E-mail: [email protected] The designations of geographical entities in this book, and the presentation of the material, do not imply the expression of any opinion whatsoever on the part of the participating organizations concerning the legal status of any country, territory, or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries. The opinions expressed by the authors in this publication do not necessarily represent the view of PBWB, EU, UNDP GEF, WANI or IUCN.

Hydraulic Study of Lake Jipe, NYM Reservoir and Kirua Swamps August, 2008

________________________________________________________________________1

Hydraulic Study of Lake Jipe, Nyumba ya Mungu Reservoir and Kirua Swamps

Submitted by Hydraulic Modeling Study Team:

Dr. Tumaini A. Kimaro, Dr Simon H. Mkhandi, Dr Joel Nobert, Mr. Preksedis M. Ndomba, Dr Patrick Valimba and Prof. Felix.W. Mtalo.


________________________________________________________________________2

Figure 01 Location map of study area


________________________________________________________________________3

Executive Summary

Within the Pangani River Basin, trade-offs between benefits provided by the

aquatic ecosystems and the benefits provided through off-stream water use such

as irrigation and hydropower need to be decided by the stakeholders. The trade-

offs are to be analysed by examining the potential consequences of a range of

scenarios regarding the future management of the catchment and its water

resources. As part of these trade-offs the impacts on the fisheries and plants

associated with the various dams, lakes and flood plains in the basin will be

considered. For these resources, the primary determinant of abundance is the

water level (or depth) and inundated area in Lake Jipe, Nyumba ya Mungu

Reservoir and Kirua swamps associated with different inflow regimes. For the

swamps, the primary determinant of fish and plant abundance is also affected by

the timing of inundation. This study is aimed at providing predictive tools which

can be used to determine the extent of inundation at Lake Jipe Nyumba ya

Mungu Reservoir and Kirua swamps for given inflow and outflow scenarios.

During the study Area/elevation and storage/elevation curves for Lake Jipe and

Kirua swamps were developed. These relationships were derived from calibrated

NASA Shuttle Radar Topographic Mission (SRTM) Digital Elevation Models

(DEM). The STRM DEM is available at 90 m resolution. The DEM was projected

to cartesian UTM coordinate system before being used to map the ground

surface at the study area. The topographic sheets (73/2, 73/4, 74/1 and 74/3) at

1:50,000 scale, sourced from Surveys and Mapping Division of the Ministry of

Land were used to calibrate and validate the DEM. The data was processed

using Geographical Information System (GIS) software Arc-view 3.2® with tools

for filling pits, stream flow generation and delineation of water sheds.

It was established that the planimetric surface area of Lake Jipe varies from 21.7

km2 at elevation of 699.6 m.a.s.l. to 31.2 km2 at elevation of 702.0 m.a.s.l.. In the

same range of elevations storage of the lake varies between 3.0 to 63.0 Mm3.


________________________________________________________________________4

Analysis of the cross sectional data and developed storage/elevation/surface

area relationship of Kirua swamps indicated that the swamps geometry is

comprised of three main parts, namely:

• a defined river channel;

• extensive floodplains, and;

• a free board.

The channel terminates at the elevation of 620.0 m.a.s.l. This point is located

10.0 m above the general altitude of the Kirua swamps outlet. Above this point, a

unit increase in elevation increases the inundated area by more than 22 times,

and the storage increases by more than 2 times.

Characterization of groundwater/surface water interaction was done using

qualitative and quantitative tools. The approach entailed activities such as

correlations between water levels in Lake Jipe and daily rainfall amounts for

gauging stations located within the sub-catchments, Lumi River water levels, and

the flow discharges at the Outlet. Besides, the water balance analysis on annual

time scale and in longterm perspectives was done to complement regression

analysis. The hydro-meteorological data were sourced from Ministry of Water and

Water Resources Engineering Department database, University of Dar es

Salaam and Water Development Division in Kenya.

Hydraulic study in Lake Jipe has found that there is a strong positive correlation

(0.971) between water levels of Lake Jipe and water levels of Lumi River at Lumi

gauging station.The results of correlation analysis showed that rainfall is weakly

correlated to base flow into lake Jipe suggesting that catchment rainfall alone

does not account for groundwater flow into the Lake. The analysis suggests that

the main source of inflow to Lake Jipe is the Lumi sub-catchment.

This study has successfully used hydraulic modelling approach to map inundation

in Kirua swamps. At the middle section of the swamp, the river bank gets

overtopped by a 2 year-flood. At the in let to the swamp overtopping of the river

bank can be caused by a 5-year flood. Floodplain inundation model relating

inflows to surface area/storage for entire Kirua Swamps has been developed


________________________________________________________________________5

using a fully fledged hydraulic model. As an example case, a flood of 50.0 m3/s

has been simulated to inundate about fifty percent (50 %) of the floodplain.

Inundation model relating inflows and outflows to surface area for Nyumba ya

Mungu reservoir was developed using a simple water balance model that

generates storage and converts it to elevation and area using storage-elevation

and area-elevation curves. The performance of this model is dependent on the

accuracy of the water balance model, which also reflects the accuracy of outflow

measurement, and inflow modelling. Reasonable results were obtained in

estimation of areas based on the balance inflows and outflows.

The Consultant has four major recommendations to improve the hydraulic

modelling results of Lake Jipe and the Kirua Swamps. For the case of Lake Jipe,

the Consultant recommends a bathymetric survey or spot measurements of bed

elevation of Lake Jipe to be done. Besides, water-level monitoring in Lake Chala

and Jipe should be continued and extended. The Consultant also recommends

further monitoring and modeling of groundwater and surface water interactions in

lake Jipe to study the role of groundwater recharge to the lake.

Proper water balances of Nyumba ya Mungu reservoir is important for mapping

the inundated area associated with different inflow and outflow scenarios. A

comprehensive study involving monitoring of inflows and outflows is

recommended to establish the proper model. In this case the outflows,

evaporation, bypass flows and inflows need to be monitored to give a proper

account of water in the reservoir. The current records have errors that complicate

the development of a proper inundation model, which depends heavily on

accuracy of water balance.


________________________________________________________________________6

Acronyms and Abbreviations

STRM Shuttle Radar Topographic Mission

Mm3 Million cubic metres

IUCN International Union of the Conservation of Nature

GEF Global Environmental Fund

HEC-RAS Hydrologic Engineering Centre-River Analysis

System

PHABSIM Physical Habitat Simulation Model

m.a.s.l. Meters above sea level

WDID Water Development and Irrigation Department


________________________________________________________________________7

Table of Contents Executive Summary .............................................................................................. 3 Acronyms and Abbreviations ................................................................................ 6

Table of Contents .............................................................................................. 7 List of Figures.................................................................................................... 9 List of Tables ................................................................................................... 10

1. INTRODUCTION ......................................................................................... 11 1.0 General................................................................................................. 11 1.2 Objectives of the hydraulic modeling study........................................... 11 1.3 Scope ................................................................................................... 11

2. HYDRAULIC STUDIES FOR LAKE JIPE .................................................... 12 2.0 Approach .............................................................................................. 12 2.1 Storage/elevation/surface area relationship for Lake Jipe .................... 13 2.2 Characterization of groundwater/surface water interaction and the role it plays in water balance of Lake Jipe................................................................. 18

3. HYDRAULIC STUDIES FOR NYUMBA YA MUNGU RESERVOIR................ 24 3.1 Introduction................................................................................................ 24 3.2 Approach ................................................................................................... 24 3.3 Operating rules ..................................................................................... 25 3.4 Characteristics of installed hydropower plants...................................... 26

3.4.1 Nyumba ya Mungu Power Plant .................................................... 26 3.4.2 New Pangani Falls (NPF) Power Plant.......................................... 27 3.4.3 Hale Power Plant........................................................................... 27

3.5 Available storage/elevation/surface area curves .................................. 27 3.5 Sedimentation studies in Nyumba ya Mungu Reservoir ....................... 28 3.6 Hydrometric gauging and data.............................................................. 30

3.6.1 Inflows ........................................................................................... 30 3.6.2 Outflows ........................................................................................ 31 3.6.3 Evaporation ................................................................................... 32 3.6.4 Rainfall .......................................................................................... 33

3.7 Existing Models .................................................................................... 34 3.7.1 HEC-HMS...................................................................................... 34

3.7.2 HEC-ReSIM ...................................................................................... 38 3.7.3 Linear Models ................................................................................... 38 3.8 The WEAP model ................................................................................. 39 3.9 Selection of model for reservoir balance............................................... 40 3.10 Reservoir inundation model .................................................................. 40 3.10.1 Inflow data......................................................................................... 41 3.10.2 Outflow.............................................................................................. 41 3.10.3 Reservoir levels ................................................................................ 42 3.10.4 Water balance ................................................................................... 42 3.9.5 Prediction of Inundated area ............................................................. 46

HYDRAULIC STUDIES FOR KIRUA SWAMPS ................................................. 48 4.1 Approach .............................................................................................. 48 4.2 Flood magnitude return period relationship .......................................... 49


________________________________________________________________________8

4.3 Representation of River Channel and Flood plain geometry ............... 51 4.4 Development of Storage/Elevation/Surface area relationship for Kirua swamp ............................................................................................................. 52 4.6 Model applications..................................................................................... 55

5. CONCLUSIONS AND RECCOMMENDATIONS ............................................ 58 5.1 Conclusions .......................................................................................... 58

5.1.1 Lake Jipe ....................................................................................... 58 5.1.2 Nyumba ya Mungu Reservoir ........................................................ 58 5.1.3 Kirua swamps ..................................................................................... 59

5.2 Recommendations................................................................................ 60 APPENDICES..................................................................................................... 65 A3.1 Flows at Kikuletwa river station 1dd1 1995-2005 ................................. 65 A3.2 Flows at Ruvu River (1dc1) 1995-2005 estimated as sum of (1dc2a, 1d11a, 1dc6 and 1dc3a) ..................................................................................... 66 A3.3 Water levels in Nyumba ya Mungu Reservoir 1995-2005..................... 67 A3.4 Outflows from Nyumba ya Mungu Reservoir 1995-2005 ...................... 68 A3.6 Comparison of inflow outflow and water levels in Nyumba ya Mungu Reservoir 1995-2005 .......................................................................................... 69 A4.1 River Channel Geometric Data at 1d8c ................................................ 70 A4.1.1 cross sections at 1d8c gauging stations .................................................. 70 A4.1.2 Longitudinal bed profiles for 1d8c gauging station................................... 70 A4.1.4 Cross sections profiles at 1d8c gauging station....................................... 72 A4.2 River Channel Geometric Data at 1d18 (WRED 2007)......................... 73 A4.2.2 Longitudinal bed profile details at 1d18 sites ........................................... 73 A4.2.3 Cross-section geometry details near 1d18 site........................................ 74


________________________________________________________________________9

List of Figures Figure 2.1 Lake Jipe catchment area and spatial distribution of regular hydro-

meteorological monitoring stations. (Note: Tz_Jipe and Ky_Jipe represent Tanzanian and Kenyan Side Jipe water levels gauging stations) ................ 13

Figure 2.2 Calibrated model for Lake Jipe catchment DEMs.......................... 14 Figure 2.3 Scatter diagram of Topo_Elevation versus Estimated elevation for

Lake Jipe catchment DEMs ......................................................................... 15 Figure 2.4 Storage/elevation/surface area relationships for Lake Jipe ........... 17 Figure 2.5 Storage/elevation/surface area relationships for Lake Jipe ........... 17 Figure 3.1 Area Elevation Curve for Nyumba ya Mungu Reservoir ................ 28 Source: Moges (2003) ........................................................................................ 28 Figure 3.2 Storage Elevation Curve for Nyumba ya Mungu Reservoir ........... 28 Source: Moges (2003) ........................................................................................ 28 Figure 3.3 Comparison of out flow (machine discharge.) flow at 1d8c and

Water levels at NYM reservoir ..................................................................... 32 Figure 3.4 Set up of HEC-HMS for Nyumba ya Mungu Reservoir (Moges,

2003) 35 Figure 3.5 Observed and estimated storage at Nyumba ya Mungu Reservoir43 Figure 3.6 Comparison of Inflow, Outflow and Water Levels in Nyumba ya

Mungu Reservoir 1995-2006 ....................................................................... 44 Figure 3.7 Observed and estimated storage at Nyumba ya Mungu Reservoir45 Figure 3.8 Estimated and observed levels at Nyumba ya Mungu Reservoir: an

output of a water balance model.................................................................. 47 Figure 3.9 Estimated and observed surface area at Nyumba ya Mungu

Reservoir: an output of a water balance model............................................ 47 Figure 4.1 Kirua swamps and spatial distribution of hydro-meteorological

monitoring stations....................................................................................... 48 Figure 4.2 Annual maximum discharges (MaxQ) arranged by time of

occurrence at 1D8C..................................................................................... 51 Figure 4.3 Calibration model for Kirua swamps DEMs ................................... 53 Figure 4.4 Verification scatter plot for Kirua swamps DEMs........................... 54 Figure 4.5 Typical cross swamp cross section derived from DEM...................... 55 Figure 4.6: Relationship between stream flow and the inundated surface area

56 Figure 4.7: Relationship between stream flow and storage ............................. 57 Figure 4.8: inundated area in Kirua Swamps when stream flow at 1D12 is ........ 57 50.0 m3/s)............................................................................................................ 57 Figure 4.9: inundated area in Kirua Swamps when stream flow at 1D12 is 305.0

m3/s 1:100 flood).......................................................................................... 57


________________________________________________________________________10

List of Tables Table 2.1 Calibration data for Lake Jipe catchment DEMs............................ 14 Table 2.2 Verification data for Lake Jipe catchment DEMs ........................... 15 Table 2.3 Storage/elevation/surface area relationships for Lake Jipe ........... 16 Table 2.4 Correlation between input and Output hydrological variables........ 20 Table 2.5A Correlation between Water levels of Lakes Chala and Jipe for year

2005............................................................................................................. 21 Table 2.5B Annual Water balance analysis between 1976 and 1991 .............. 22 Table 3.1 Minimum Reservoir levels to ensure reliability of Nyumba ya Mungu

Reservoir. .................................................................................................... 26 Table 3.2 Physical characteristics of the Nyumba ya Mungu Reservoir ........ 26 Table 3.3 Sediment rating table for Kikuletwa River...................................... 30 Table 3.4 Flow gauging stations upstream of Nyumba ya Mungu Reservoir. 30 Table 3.5 Rainfall stations in Nyumba ya Mungu Reservoir catchment......... 33 Table 3.6 Monthly Rainfall at station 09337090............................................. 34 Table 3.7 Acceptable ranges of parameters for different component models of

HEC-HMS for Kikuletwa and Ruvu catchments ........................................... 36 Table 3.8 Results of application of HEC-HMS for catchments draining into

Nyumba ya Mungu Reservoir ...................................................................... 37 Table 4.1 Annual Maximum discharges (MaxQ) series as extracted from

natural river flow data series at 1D8c gauging station.................................. 50 Table 4.2 Computation sheet of maximum discharges of various return

periods for 1D8C gauging station using Log-Pearson Type III distribution. . 50 Table 4.3 Calibration data for Kirua swamps DEMs ...................................... 52 Table 4.4 Verification data set for Kirua swamps DEMs ..................................... 53

Hydraulic Study of Lake Jipe, NYM Reservoir and Kirua Swamps August, 2008 ________________________________________________________________________

_______________________________________________________________________________________________

11

________________________________________________________________

1. INTRODUCTION ________________________________________________________________

1.0 General The Pangani Basin Water Office (PWBO is implementing the Pangani River

Basin Management Project, supported by the IUCN, Water & Nature Initiative,

UNDP/GEF and the European Union. Task 6 of the project has made provision

for several studies to provide technical information to assist with setting up

predictive tools for use in water allocation in the basin. The components are:

macroeconomic study, hydro-electric power modeling study, study on climate

change, study on hydraulic modeling, study on fisheries of Pangani Basin, study

on fish and invertebrate life histories, and study on vegetation. This report

addresses hydraulic modeling components.

1.2 Objectives of the hydraulic modeling study • To develop a conceptual understanding of the roles that surface water

inflows and groundwater recharge play in maintaining water levels in Lake

Jipe and the Kirua swamps

• To undertake a preliminary assessment of the water levels (or depth) and

inundated areas in Lake Jipe associated with different antecedent river

flows and seasonal variations in aspects such as evaporation.

• To Liaise with the dam/HEP modeller to provide an indication of the water

level (or depth) and inundated area in Nyumba ya Mungu associated with

different inflow regimes.

• To undertake a preliminary assessment of the magnitude of flows that will

inundate Kirua Swamps and the manner in which this inflow distributes

itself across the system.

1.3 Scope The study mostly involved analysis of historical data and development of

analytical model for lake and reservoir inundation. In most cases the geometric

features of the study area were derived from digital model to supplement few

available field measurements.


_______________________________________________________________________________________________

12

________________________________________________________________

2. HYDRAULIC STUDIES FOR LAKE JIPE ________________________________________________________________

2.0 Approach

Hydraulic study for Lake Jipe entailed three main components, namely:

• development of storage/elevation/surface area relationships for Lake Jipe;

• characterization of groundwater/surface water interaction and the role this

plays in the water balance of Lake Jipe and;

Area/elevation and storage/elevation curves were derived from calibrated NASA

Shuttle Radar Topographic Mission (SRTM) Digital Elevation Models (DEM). The

STRM DEM is available at 90 m resolution at (http://srtm.csi.cgiar.org). The DEM

was projected to Cartesian UTM coordinate system before being used to map the

ground surface at the study area. The topographic sheets (73/2, 73/4, 74/1 and

74/3) at 1:50,000 scale, sourced from Surveys and Mapping Division of the

Ministry of Land were used to calibrate and validate the DEM. The data was

processed using Geographical Information System (GIS) software Arc-view 3.2®

with tools for filling pits, stream flow generation and delineation of water sheds.

Characterization of groundwater/surface water interaction was done using

qualitative and quantitative tools. The latter approach entailed activities such as

correlations between input hydrological variables (i.e. daily rainfall for stations

located within sub-catchments and water levels at the Lumi gauging station) and

output hydrological variables (Lake Jipe water levels and outflows (flow of Ruvu

at Kifaru bridge) ) (Figure 2.1). Besides, the water balance analysis on annual

time scale and in long-term was carried out to complement the regression

analysis. The hydro-meteorological data was sourced from Ministry of Water and

Water Resources Engineering Department Database, University of Dar-es-

Salaam.


_______________________________________________________________________________________________

13

Figure 2.1 Lake Jipe catchment area and spatial distribution of regular hydro-

meteorological monitoring stations. (Note: Tz_Jipe and Ky_Jipe represent

Tanzanian and Kenyan Side Jipe water levels gauging stations)

2.1 Storage/elevation/surface area relationship for Lake Jipe

Storage/elevation/surface area relationships for Lake Jipe were established from

a 90 m DEM. The DEM was first calibrated by establishing a regression equation

between the elevation points extracted from 1:50,000 scale topographical maps

(contours) and the same points in the DEM. The data points were selected to

capture important topographic features such as hills, depressions and plains. The

sample covered the entire range of elevations. The calibration data set and

results are presented in Table 2.1 and Figure 2.2 respectively.

Chala

Tz Jipe

Ky Jipe


_______________________________________________________________________________________________

14

Table 2.1 Calibration data for Lake Jipe catchment DEMs

UTM coordinates ID Y(m) X (m)

DEM (m.a.s.l.)

Topo_Elevation (m.a.s.l.)

1 9602895 352585 1396 1400 2 9605145 354475 1255 1240 3 9605235 354565 1191 1200 4 9605235 354655 1176 1160 5 9604785 355825 999 1000 6 9603075 355195 973 940 7 9605235 355375 962 940 8 9605055 356455 940 910 9 9603525 355285 920 910 10 9605505 355465 900 900 11 9599835 355825 880 860 12 9605595 355555 857 870 13 9602175 354385 844 820 14 9598035 356635 821 821 15 9599115 356185 799 800 16 9605505 356995 780 780 17 9605685 357175 760 760 18 9600555 356275 740 740 19 9605505 359335 720 720

Calibration

Topo_Elevation = 0.9952*DEM - 2.9276R 2 = 0.9949

700

800

900

1000

1100

1200

1300

1400

1500

700 800 900 1000 1100 1200 1300 1400 1500

DEM (masl)

Topo

_Ele

vatio

n (m

asl)

Figure 2.2 Calibrated model for Lake Jipe catchment DEMs

The results were verified using independent data sets. The same sampling

approach as described in calibration data was adopted for the verification data.


_______________________________________________________________________________________________

15

The verification results indicate that Topo_Elevations and Estimated elevations

are comparable (Table 2.2 and Figure 2.3).

Table 2.2 Verification data for Lake Jipe catchment DEMs UTM coordinates ID Northings, Y (m) Eastings, X (m)

Topo_Elevation (m.a.s.l.)

Estimated Elevation (m.a.s.l.)

1 9598575.249 358653.387 720.000 712.621 2 9603631.586 357124.476 740.000 755.415 3 9604274.040 355964.592 820.000 808.160 4 9605752.905 355697.749 840.000 822.093 5 9599182.084 355894.966 860.000 776.314 6 9600558.930 354472.605 1000.000 952.464 7 9602675.404 352424.404 1400.000 1358.506 8 9599244.668 353698.840 1400.000 1443.098 9 9600522.504 352495.820 1400.000 1365.472

Validation(R2=98%)

700800900

100011001200130014001500

700 900 1100 1300 1500Estimated Elevation (masl)

Topo

_Ele

vatio

n (m

asl)

Figure 2.3 Scatter diagram of Topo_Elevation versus Estimated elevation for

Lake Jipe catchment DEMs

Volume (storage) and surface area of the lake is computed within an elevation

band of 699.460 m.a.s.l. and 702.000 m.a.s.l as shown in Table 2.3. The analysis

of recorded water levels indicates that the maximum change in water level is less

than 2 m. A recent minimum observed Lake Jipe water level at Tz_Jipe gauging


_______________________________________________________________________________________________

16

station was 699.460 m.a.s.l, recorded on 6/8/2004 (equivalent to water level

gauge height of 18 cm). The latter elevation corresponds to the critical

hydrological condition (drought year) in the region. The storage and area

elevation relationships are defined from and above 699.460 m.a.s.l. Using the

Triangular Irregular Network (TIN) algorithm under Arcview GIS environment

(Version 3.2a), the storage or volume and surface area were computed at 0.1m

interval between 699.40 and 702.000 m.a.s.l. (Table 2.3). TIN implies a specific

storage structure of surface data. In the TIN data model, the terrain is recorded

as a continuous surface made up of a mosaic of non-overlapping triangular facets

formed by connecting selectively sampled elevation points using a consistent

method of triangular construction (Lo and Yeung, 2002). The TIN algorithm was

used in this study because it has a number of advantages. These are that heights

between nodes can be interpolated thus allowing for the definition of a continuous

surface, and that it can accommodate irregularly distributed as well as selective

data sets. The ability to these two kinds of data makes it possible to represent a

complex and irregular surface with a small data set. The developed

storage/elevation/surface area graph is presented in Figure 2.4 and Figure 2.5

From Table 2.3, the planimetric surface area varies from 21.7 km2 at elevation of

699.6 m.a.s.l. to 31.2 km2 at elevation of 702.0 m.a.s.l.. In the same range of

elevations, storage of the lake is found to vary between 3.0 to 63.0 Mm3. These

results are comparable to the figures reported in literature (Musyoki and

Mwandotto, 1999).

Table 2.3 Storage/elevation/surface area relationships for Lake Jipe S/R Elevation

(m.a.s.l.) Area (Km2)

Volume (Mm3)

S/R Elevation (m.a.s.l.)

Area (Km2)

Volume (Mm3)

1 699.5 0 0.0 14 700.8 24.3 30.4 2 699.6 21.7 3.0 15 700.9 24.7 32.9 3 699.7 21.9 5.2 16 701.0 25.0 35.3 4 699.8 22.1 7.4 17 701.1 25.4 37.9 5 699.9 22.2 9.6 18 701.2 25.8 40.4 6 700.0 22.4 11.8 19 701.3 26.3 43.0 7 700.1 22.6 14.1 20 701.4 26.7 45.7 8 700.2 22.8 16.4 21 701.5 27.2 48.4 9 700.3 23.0 18.6 22 701.6 27.7 51.1 10 700.4 23.2 20.9 23 701.7 29.0 53.9 11 700.5 23.4 23.3 24 701.8 29.8 56.9 12 700.6 23.6 25.6 25 701.9 30.5 59.9


_______________________________________________________________________________________________

17

13 700.7 24.0 28.0 26 702.0 31.2 63.0

The generated data was entered into microsoft excel worksheet to plot curves for

area and storage against elevation. The analytical relationships between storage,

area and elevation were determined by fitting trend lines on the scatter plots as

shown in Figures 2.4 and 2.5.

Area Elevation Curve for Lake Jipe between 699.5 and 702 m.a.s.l

Area = -5E-05h4 + 0.0077h3 - 0.4411h2 + 10.738h + 605.54R2 = 0.9992

699.5

700.0

700.5

701.0

701.5

702.0

702.5

21 22 23 24 25 26 27 28 29 30 31 32

Area km2

Elev

atio

n (m

.a.s

.l)

ElevationPoly. (Elevation)

Figure 2.4 Storage/elevation/surface area relationships for Lake Jipe

Storage Elevation Curve for Lake Jipe between 699.5 and 702 m.a.s.l

Storage = 3E-08h4 - 6E-06h3 + 0.0002h2 + 0.0417h + 699.49R2 = 1

699.5

700.0

700.5

701.0

701.5

702.0

702.5

0 9 18 27 36 45 54 63

Storage MCM

Elev

atio

n (m

.a.s

.l)

elevationPoly. (elevation)

Figure 2.5 Storage/elevation/surface area relationships for Lake Jipe


_______________________________________________________________________________________________

18

There is almost a perfect fit of a four degree polynomial function to area elevation

data as it may be seen in Figure 2.4. Similarly storage a four degree polynomial

function fits perfectly to the storage elevation data. On the basis of analysis

conducted an analytical expression describing area inundated at different levels

in Lake Jipe is described by equation 2.1

)1.2(54.60578.104411.00077.010*5 2345 KKKKKKKKKKKK++−+−= − hhhhA

Where A is the area in km2, h is water surface elevation (m.a.s.l) The analytical expression describing storage of lake Jipe above 699.50 m.a.s.l is described by equation 2.2

)2.2(49.6990417.0002.010*610*3 23648 KKKKKKKKKKKK+++−−= −− hhhhV Where V is the volume above 699.5 m.a.s.l, h is the elevation (m.a.s.l)

2.2 Characterization of groundwater/surface water interaction and the

role it plays in water balance of Lake Jipe

A number of approaches have been used to characterize the ground/surface

water interaction and role it plays in water balance of Lake Jipe. They include

qualitative /correlation analysis of hydrological variables and the water balance

analysis. The hydrological variables used include daily rainfall, Lake Levels, river

stage, and river flow. A strong correlation is confirmed if the computed correlation

coefficient, r, is higher than the corresponding value from the table, r_table, at 5%

probability level of significance, p, and N-2 degrees of freedom (Statsoft, 2006).

2.2.1 Correlation analysis There have been some previous suggestions to track the flows and investigate

the hydraulic connection between Lake Chala, Lake Jipe and Pangani River

using tracers (Ndomba and Gurandsrud, 2004). However, environmental

concerns on water quality and research finances have hindered the

implementation. As a compromise between scarce resources and protection of

the environment, a decision was made by UDSM and NTNU to monitor water

levels at Lake Chala and Lake Jipe and develop a relationship which can explain


_______________________________________________________________________________________________

19

the hydraulic connection between the lakes. The levels in the two lakes have

been monitored for some time following this decision using digital data logger and

manual gauge. However, it should be noted that only portion of the data set was

used in this study because sometimes during the sampling programme the flow

from main contributing tributary (Lumi River) to Lake Jipe was diverted. The

Consultants believe that such a modification of flows could affect the statistical

inferences. Therefore, this analysis excludes portion of paired data set under

modified state.

Initially, correlation analysis was conducted between input hydrological variables

(rainfall on Lake Jipe catchment and water level at Lumi gauging station) and

output hydrological variables (flow discharges at station 1DC2A (Lake Jipe outlet)

and Lake Jipe water levels at Ky_Jipe). The analysis was conducted to identify

location of water sources and to understand the main lake water content

contributing processes (surface runoff, SURF, and baseflow, BASE). SURF time

series was obtained by filtering the total flow using a baseflow filter developed by

Arnold and Allen (1999). The analysis was carried out in a period between

1/1/1981 and 31/12/1981.

It should be noted also that only six rainfall stations (09337006, 09337031,

09337045, 09337075, 09337132, 09337110) and 2 flow gauging stations (1DC2A

and Lumi gauging station) could be sourced for this analysis. Nevertheless, the

rainfall stations were considered to represent the main runoff contributing sub

catchments (Lumi, Pare Mt. and Intervening catchments and Taveta) (Figure 2.1

and Table 2.4). In order to understand the effect of delay in runoff delivery to the

lake and the outlet of the catchment (1DC2A), the flow discharges were lagged

by 15 days (Table 2.4). This approach was expected to capture delay in

groundwater flow delivery to the Lake.


_______________________________________________________________________________________________

20

Table 2.4 Correlation between input and Output hydrological variables

Input Hydrological variables Output Hydrological variables

r_table at p=5% 9337006 9337031 9337045 9337075 9337132 9337110 LUMI

WL Lumi Intervening Intervening Pare Mt. Lumi Taveta

JIPE WL 0.057 0.065 0.237 0.221 0.015 0.064 0.354

T/Flow 0.075 0.089 0.234 0.246 0.033 0.083 0.395

BASE 0.021 0.088 0.197 0.117 -0.030 0.065 0.297

Unl

agge

d

1DC

2A

flow

SURF

0.050

0.204 0.106 0.249 0.484 0.169 0.139 0.495

JIPE WL -0.091 0.005 0.147 -0.107 -0.130 -0.027 0.087

T/Flow -0.035 0.057 0.213 -0.021 -0.082 0.022 0.180

BASE -0.057 0.033 0.145 -0.077 -0.085 0.016 0.110

Lagg

ed b

y 15

da

ys1D

C2A

flo

w

SURF

0.053

0.024 0.070 0.267 0.109 -0.041 0.020 0.244

One would note from Table 2.4 that there is a strong positive correlation between

water levels at Lumi gauging station and Lake Jipe at 5% probability level of

significance, for hydrological variables which are not lagged. Independent

correlation analysis between outflow at 1DC2A and Lake Jipe water levels

indicates that they are strongly correlated with correlation coefficient, r of 0.971.

Probably, from this result one would suggest that lake storage is small compared

to inflows (i.e. it does not have a significant flood regulation function). The

consultants would like to agree with such an assertion because Lumi River joins

Lake Jipe near its outlet. That means regulation effect of the lake Jipe is minimal.

Besides, the analysis indicates that Lake Jipe water levels are highly correlated

with BASE than SURF. Rainfall from Lumi catchment is poorly correlated with

BASE, Rainfall from Lumi catchment is strongly correlated with Lumi water levels.

On the other hand rainfall from Lumi catchment is strongly correlated with SURF.

All rainfall stations, except 9337132 of Lumi subcatchment are strongly correlated

with Lake Jipe water levels. Lagging output hydrological variables such as flow

discharges at the outlet and Lake Jipe water levels by 15 days decreases the

correlation between BASE and rainfalls (Table 2.4). In particular Lumi catchment

rainfalls become poorly correlated with Lake Jipe WL and BASE. There is strong

positive correlation between Lumi water levels and all output hydrological

variables, i.e., Lake Jipe water levels, total flow, BASE and SURF for both un-

lagged and lagged experiments.


_______________________________________________________________________________________________

21

Secondly, independent correlation analysis between Lakes Chala and Jipe water

levels was also conducted (Table 2.5A). The data used was concurrent daily

water levels measurements from the Lakes between April and December, 2005.

Table 2.5A Correlation between Water levels of Lakes Chala and Jipe for year 2005

Season r_table at p=5%

Water Levels at Lake Chala

Apr. 26 - May 29 0.33 -0.81 Unlagged Nov. 1-Dec. 20 0.28 -0.59

May 11 – May 29 0.47 -0.83 Lagged by 15 days Nov.16 - Dec. 20 0.32 -0.79

Wat

er L

evel

s at

La

ke J

ipe

Lagged by 30 days

Dec.1 - Dec. 20 0.45 -0.66

From Table 2.5A above, one would note that water levels of the two Lakes are

strongly negatively correlated. Another notable observation is that lagging of

water levels at Lake Jipe does not improve or change the statistical inferences in

the table. Probably, this result suggests that Lake Chala and Lake Jipe are not

hydraulically connected, instead they have inverse relationships. It should be

noted however that, concurrent data set for other environmental variables such

as Lumi Water levels as presented in Table 2.4 was not available for such an

analysis. The results from both Tables 2.4 and 2.5A suggest that rainfall alone

does not account for BASE or groundwater flow into Lake Jipe. Therefore, other

sources of water other than Lake Chala and rainfall could explain baseflow or

groundwater contribution to the Lake.

2.2.2 Water balance analysis Another method of characterizing the role of groundwater/surface runoff to water

contents in Lake Jipe was based on catchment water balance analysis. A typical

lake water balance could not be done because the inflow data to the lake is not

available, but some estimates have been made. An annual water balance


_______________________________________________________________________________________________

22

analysis was conducted between 1976 and 1991 using equation 2.3. The

components incorporated into the water balance model are areal precipitation, P

(mm), actual evaporation, E (mm), total outflow runoff, Q (mm), and the error

term. The pan evaporation data was used to estimate the actual evaporation, E. It

should be noted that the Evaporation, E, estimation is based on average crop

coefficients of 0.8 (kc*ks). The outflow runoff, Q, was estimated from stream flow

runoff data at 1D2A gauging station, the outlet of Jipe catchment.

)3.2()( KKKKKKKKKKKKKKKKKKKKKKKKtermErrorQEP =+−

This analysis assumes that natural systems such as catchment, restores/re-

stabilizes itself as a function of time. Therefore, the water balance analysis both

on annual basis and in a longer term perspectives, would register zero error term

in Equation 2.3 above. Otherwise, negative error term indicates that rainfall as

input in the Equation above does not account for the entire output, i.e. E plus Q.

In the latter case external source of water other than rainfall could be associated

to sustaining lake water levels. The results of water balance analysis are

presented in Table 2.5B below. The error term in the table, -149.2 mm, was

computed as the difference between long term annual aerial precipitation, 1406

mm, and sum of evaporation and runoff (i.e. 1496 + 59 = 1555 mm). The percent

error in Q (253 %) was computed as the percentage ratio of absolute error term (-

149.2 mm) to long term stream flow runoff, Q (59 mm).

Table 2.5B Annual Water balance analysis between 1976 and 1991

Water balance components Water balance Error terms P (mm) E (mm) Q (mm) Error (mm) %Error in

Q % Error in P

1406 1496 59 -149.2 253 11

Similarly, the percentage error in P (11 %) was computed as the percentage ratio

of absolute error term (-149.2 mm) to long term annual aerial precipitation, (1406

mm). The negative error term (i.e. -149.2 mm) as computed in Table 2.5B above

supports the contention that rainfall alone does not account for the output (E plus


_______________________________________________________________________________________________

23

Q) on annual basis or in the longer term. This result compares favorably with the

correlation analysis findings. Probably, this suggests that there exists an external

source of inflow to the system as has been reported by Birhanu (2005) working in

neighboring Kikuletwa catchment (1DD1). Birhanu (2005) showed that springs

yield of about 11 m3/s in 1DD1 catchment could not be explained by rainfall

amount alone.


_______________________________________________________________________________________________

24

________________________________________________________________

3. HYDRAULIC STUDIES FOR NYUMBA YA MUNGU RESERVOIR ________________________________________________________________

3.1 Introduction Nyumba ya Mungu Reservoir is owned by Tanzania government and managed

by Pangani Basin Water Office (PBWO). The reservoir has a regulation capacity

of one year. The reservoir was built in 1965 primarily to regulate river flows for

Hale hydropower plant further downstream but the irrigation potential was

recognized and incorporated into plans. Irrigation takes place between Nyumba

ya Mungu Reservoir and Buiko and uses about 4.7m3/s (SIDA, 2005). Nyumba ya

Mungu Reservoir has a catchment area of 9,320 km2 (Mulungu, 1997). Currently

there is huge demand for irrigation upstream of the reservoir, which creates

conflict between irrigation and power generation. Nyumba ya Mungu dam provide

a head of approximately 25 m for the generation of electricity at NYM hydropower

station. The maximum depth of the reservoir is 29 m and live storage capacity is

871.5 Mm3. As stated in the Water Master Plan of Kilimanjaro Region, the

reservoir was designed for 100 % regulation (MoW, 1977). On average, the

estimated flow into NYM reservoir is around 37m3/s out of which 24m3/s is

released through the turbines for power generation. The installed capacity of

Nyumba ya Mungu power plant is 8 MW. Besides the power station at NYM dam

there are two power plants downstream at Hale and New Pangani Falls (NPF)

with installed capacities of 22 MW and 66 MW respectively. In total the power

generated by these plants amounts to 14% of the total electricity produced in the

country. The capacities of the plants are larger than what they produce due to

water shortage (SIDA, 2005).

3.2 Approach The study involved collection of data and information from previous studies and

their analysis. Documents from Tanesco and the Water Resources Engineering

Department at the University of Dar es Salaam were reviewed. These documents

included reports of previous studies, and masters and PhD theses relevant to the


_______________________________________________________________________________________________

25

work. The review covered operating rules for the reservoir, reservoir

sedimentation, water balance and modelling studies.

3.3 Operating rules The operating rules have been formulated to maximize power generation in the

Pangani Hydropower System and do not take seriously irrigation requirements

downstream. About four operating rules have been proposed for the reservoir

(Moges, 2003). The first rule may be identified as TANESCO policy. This involves

trade off between maximum draw down of live storage to supply downstream

power stations (Hale and NPF) and maintenance of high head in Nyumba ya

Mungu Reservoir for power generation at Nyumba ya Mungu power station.

Another operating rule was proposed by NORPLAN. This rule proposed a

constant release of 30 m3/s when the reservoir is above the minimum

conservation level (683.91 m.a.s.l.) and a constant release of 19.8 m3/s when the

reservoir is below this level.

Moges (2003) proposed to include the concept of probability of failure in

operating rule for the reservoir. The proposal creates two new operating rules

(polices) for the reservoir by varying the minimum conservation level on monthly

step in the TANESCO and NORPLAN rules. The minimum conservation level for

each month is determined by analyzing historical operational data of the reservoir.

For each month there is a minimum level above which failure cannot occur within

the year at a given level of confidence. From the analysis conducted by Moges

(2003) the minimum levels for each month for given confidence interval are

shown in Table 3.1 below.


_______________________________________________________________________________________________

26

Table 3.1 Minimum Reservoir levels to ensure reliability of Nyumba ya Mungu Reservoir.

95% reliability 99% reliability Month

CRC as levels (m.a.s.l)

CRC as Storage (Mm3)

CRC as levels (m.a.s.l)

CRC as storage (Mm3)

January 680.86 88.27 681.35 116.22 February 680.37 61.75 680.86 88.27 March 680.37 61.75 681.80 143.33 April 680.37 61.75 682.91 217.27 May 682.32 176.64 684.27 324.26 June 683.30 245.96 684.27 324.26 July 682.81 210.16 684.27 324.26 August 682.81 210.16 684.27 324.26 September 682.32 176.64 683.79 284.22 October 681.80 143.33 683.30 245.96 November 681.35 116.22 682.81 210.16 December 680.36 88.27 681.80 143.33 Source: Moges (2003): CRC=critical rule curve: water may be released from the reservoir provided that levels are not allowed to go below given levels for each month. It should be noted that despite of these proposals the reservoir is not strictly

operated by any of these rules. Information gathered from TANESCO (directorate

of research) indicated that there is no strict rule governing releases from the

reservoir. The releases seem to be guided by power demands in the system or

the need to spill during high flows.

3.4 Characteristics of installed hydropower plants The Nyumba ya Mungu Reservoir supplies water to Nyumba ya Mungu (8 MW),

New Pangani Fall (66 MW) and Hale (22 MW) power plants. The characteristics

of these power plants are discussed in the following sections.

3.4.1 Nyumba ya Mungu Power Plant The Nyumba ya Mungu power plant is located at the dam wall. The maximum

head of the plant is 25 m. Other information on the reservoir is produced below

as presented in Ng’ondya (2006).

Table 3.2 Physical characteristics of the Nyumba ya Mungu Reservoir S/no Property Value, units 1 Length of crest 400m 2 Length of spillway 400m 3 Width of the spillway crest 183m 4 Diameter of the intake tower 5.5m 5 Highest water level 688.91 m.a.s.l (at


_______________________________________________________________________________________________

27

S/no Property Value, units Tanga)

6 Lowest operation level water level 679.15 m.a.s.l. (at Tanga)

7 Height of Intake tower 33m 8 Max. design flood capacity of the spillway 920 m3/s 9 Storage at HWL 871 *106 m3 10 Type of the dam Inclined rock fill 11 Minimum statutory release 21.3 m3/s

Source: Ng’ondya (2006) and Moges (2003)

3.4.2 New Pangani Falls (NPF) Power Plant New Pangani Falls Power plant is located downstream of the Nyumba ya Mungu

Reservoir. The design discharge of the plant is 45 m3/s and it consists of two

units. The total installed capacity of the plant in 66 MW. Power contribution to the

national grid depends on water availability but during favorable years it is up to

17% (SIDA, 2005). The power plant is fed by a pond with a capacity of 0.8 Mm3.

3.4.3 Hale Power Plant The Hale Power Plant, 21 MW, was commissioned in 1964. It utilizes a natural

head of 70 m to generate power with a maximum discharge 42 m3/s. It has a

storage reservoir of total volume of about 1.8 Mm3 and live storage 1.13 Mm3;

and an intake pond of total volume 136,000 m3 and live storage 127,000 m3. The

discharge capacity of the intake spillway is 608 m3/s. The storage reservoir was

meant for weekly regulation and the intake for daily. The Hale power plant is

located 8 km upstream of Pangani Falls Plant (SIDA, 2005).

3.5 Available storage/elevation/surface area curves Storage elevation and area elevation curves for Nyumba ya Mungu Reservoir

have been compiled from previous reports (Mulungu, 1997; Moges, 2003;

Ng’ondya, 2006). The Nyumba ya Mungu Reservoir was constructed in 1965, the

original surveys were done in empirical units and curves for storage and elevation

were provided in feet (elevation) and acre-feet (volume). The curves have since

then been changed to metric units and equations fitted to describe the curves

digitally. The existing curves are presented in Figures 3.1 and 3.2.


_______________________________________________________________________________________________

28

672674676678680682684686688690

0 20 40 60 80 100 120 140 160

Area (Km^2)

Elev

atio

n (m

.a.s

.l.)

A=1.2442*(h-672.132)**1.62 +17.6

Figure 3.1 Area Elevation Curve for Nyumba ya Mungu Reservoir Source: Moges (2003)

679

681

683

685

687

689

0 100 200 300 400 500 600 700 800 900 1000

Live Storage(MM^3)

ELev

atio

n (m

.a.s

.l.)

S=49.24h+(h-679.15)**2.62-33441.35

Figure 3.2 Storage Elevation Curve for Nyumba ya Mungu Reservoir Source: Moges (2003)

3.5 Sedimentation studies in Nyumba ya Mungu Reservoir Sedimentation in Nyumba ya Mungu Reservoir is contributed by erosion resulting

from agricultural and other human activities on the slopes of Mount Kilimanjaro

and North Pare Mountains. There is scanty sedimentation data for Kikuletwa and

Ruvu Rivers, the main tributaries draining into Nyumba ya Mungu Reservoir.

Sediment rating curves are also available from various studies (Philipo, 2006,


_______________________________________________________________________________________________

29

IVO, 1997) but they are based on few spot measurements and may be highly

unreliable.

Modeling work on sedimentation upstream of the reservoir has also been

conducted by Ndomba (2006) Patrick (2006). Ndomba (2001) used the Universal

Soil Loss Equation (USLE) to estimate sediment deposition rates in Nyumba ya

Mungu Reservoir. Data used in the study included soil types, land use and

farming systems, rainfall, and digital elevation model. Soil types were derived

from the physiographic map of Tanzania (De Pauw, 1984), land use and farming

systems data was obtained from the soil research service of the Ministry of

Agriculture Food Security and Cooperatives (MAFC). Other inputs including

rainfall and sedimentation data for Ruvu and Kikuletwa Rivers were collected

from the Ministry of Water. The study estimated that potential soil erosion in the

basin is 24 t/ha/yr and deposition rate in Nyumba ya Mungu Reservoir is 13

t/ha/yr. By these results the sediment delivery ratio is 54%, which means about

half of the sediment eroded in the basin is delivered into the reservoir. From the

computed sedimentation deposition rate, it will take 455 years of reservoir

operation for the reservoir to be filled with sediments.

IVO international and NORPLAN have also reported sedimentation rates

upstream of Nyumba ya Mungu Reservoir (TANESCO, 1997). A conservative

estimate of sedimentation rates in Kikuletwa River was made while ignoring

totally the input from Ruvu River. The two rivers contributes 65 and 30% of inflow

to the reservoir but Ruvu River has very low sediments as it flows through a

swampy area upstream of the dam, where sediment deposition occurs. The

conservative curve (Table 3.3) was adopted for estimation of sediment flow rate

in Kikuletwa and hence sediment deposition rate into Nyumba ya Mungu

Reservoir.


_______________________________________________________________________________________________

30

Table 3.3 Sediment rating table for Kikuletwa River

Flow m3/s Conc. Mg/l Susp. Load (t/day) 50 200 864 100 500 4320 150 1500 19,440 200 4000 69,120 250 10,000 216,000

Source: (TANESCO, 1997)

Based on these data it is estimated that it will take up to 1000 years of reservoir

operation for the reservoir to be filled with sediments. This means sedimentation

is of relatively low importance for operation of the reservoir.

3.6 Hydrometric gauging and data

3.6.1 Inflows Inflow into Nyumba ya Mungu Reservoir is not measured as the case is for many

other reservoirs. The volume of water entering the reservoir can only be

estimated from gauging stations located upstream in Kikuletwa (1dd1) and Ruvu

(1dc1) Rivers. Existing gauging stations upstream of the reservoir are shown in

Table 3.4.

Table 3.4 Flow gauging stations upstream of Nyumba ya Mungu Reservoir

location Station code Name of river longitude Latitude

1dd1 Kikuletwa 3.516667 31.010281dc2a Kifaru 3.525000 31.51028

1dc3a Rau 3.510833 30.660281dc6 Mue 3.883333 53.01028

1dc11a Himo 3.500000 30.01028

1d8c Pangani 3.800000 48.01028

Station 1dc1 effectively became un-operational after impoundment of Nyumba ya

Mungu Reservoir because of backwater effect. To estimate inflow into Nyumba

ya Mungu using this station one has to estimate first the flows at these stations

using a model. Ng’odya (2006) used a sum of flows at 1dc2a, 1dc6, 1dc3a and

1dc11a to estimate the flow at 1dc1. The inflow to Nyumba ya Mungu Reservoir

were then estimated as the sum of 1dc1 and 1dd1.


_______________________________________________________________________________________________

31

3.6.2 Outflows Outflows from Nyumba ya Mungu Reservoir can occur through turbines, when

generating power, or through the spillway. Nyumba ya Mungu Reservoir has a

spillway that is 400 m long and 183 m wide. The profile of the spillway is curved

such that it allows spillage at a water surface elevation of 688.50 m.a.s.l. This

level is slightly below that specified in the design drawings (688.91; Ng’ondya,

2006). Discharge through the turbines is made via 2 outlets with a maximum

capacity for discharging 56 m3/s. The discharge is controlled by butterfly valves

installed in the penstocks. The outflow rate depends on the level of opening and

may be determined by the discharge elevation curves. Outflow data for Nyumba

ya Mungu Reservoir has been compiled for the year 1995-2005 by Ng’ondya

(2006). Average outflow from the reservoir for the period is 20.2 m3/s and

maximum release was 56.00 m3/s, which occurred on 21st February, 2002.

Estimation of spills out of Nyumba ya Mungu Reservoir can be made using the

spillway rating curve, which was digitized by Ng’ondya (2006). However the

equation for this curve was not fitted.

Alternatively spills can be estimated from flows recorded at station 1d8c, which is

downstream of the dam and discharge through turbines. It may be assumed that

the difference in flow at 1d8c and discharge through turbines is the spill. The

problem with this approach is that if spills occur during or shortly after a rainfall

event, the flow from the small river, that joins the main river downstream of the

dam may increases the flow leading to over estimation of the spill. Nyumba ya

Mungu Reservoir may also be drained through a by-pass gate when power is not

being generated. This happens infrequently and water flowing out through the by

pass way is not easily accounted for. The outflow series recorded through the

turbines and flow measured downstream at 1d8c are compared in Figure 3.3.


_______________________________________________________________________________________________

32

Comparizon of outflow from NYM reservoir and flow at down stream station 1d8c

0

20

40

60

80

100

120

140

160

180

20010

/1/1

995

2/1/

1996

6/1/

1996

10/1

/199

6

2/1/

1997

6/1/

1997

10/1

/199

7

2/1/

1998

6/1/

1998

10/1

/199

8

2/1/

1999

6/1/

1999

10/1

/199

9

2/1/

2000

6/1/

2000

10/1

/200

0

2/1/

2001

6/1/

2001

10/1

/200

1

2/1/

2002

6/1/

2002

10/1

/200

2

2/1/

2003

6/1/

2003

10/1

/200

3

2/1/

2004

6/1/

2004

10/1

/200

4

2/1/

2005

6/1/

2005

10/1

/200

5

Dis

char

ge m

3 /s

676

678

680

682

684

686

688

690

692

Wat

er L

evel

(m.a

.s.l)

Machine Discharge flow at 1d8c WaterLevel

Figure 3.3 Comparison of out flow (machine discharge.) flow at 1d8c and

Water levels at NYM reservoir

It is expected that flow at 1d8c will always be higher than flow measured through

the turbines, however there are few cases as may be seen in Figure 3.3 that flow

through the turbines is higher than flow measured at 1d8c. For example in

February 2001 there was a spill of 56 m3/s, which was not recorded at 1d8c.

Such inconsistence in measurement complicates water balance studies and

makes it difficulty to interpret the results of water balance study. The Consultant

proposes to investigate further the outflow series to improve the accuracy of the

data.

3.6.3 Evaporation Estimation of evaporation from the reservoir can be made using pan evaporation

data from a meteorological station located close to the reservoir. The station

09337090 (Nyumba ya Mungu) is located adjacent to the reservoir and is suitable

for estimating evaporation from the reservoir. Other data monitored at this station

includes rainfall, temperature, humidity, sunshine hours, and wind speed.


_______________________________________________________________________________________________

33

3.6.4 Rainfall Rainfall data is collected by both Pangani Basin Water Office and the Tanzania

Meteorological Agency (TMA). The Consultant identified the following rainfall

stations in the vicinity of the reservoir which are useful for water balance study.

All the stations in Table 3.5 have recent data from 1995-2005.

Table 3.5 Rainfall stations in Nyumba ya Mungu Reservoir catchment

Location s/n Station code Station name longitude Latitude

1 09337090 NYM reservoir -3.783333 37.4500002 09337031 Himo Sisal Estate -3.383333 37.5500003 09337091 W.D & I.D Moshi -3.350000 37.3333334 09337028 TPC Langasani -3.500000 37.3166675 09337116 Uru West -3.166667 37.2500006 09337115 KIA -3.416667 37.0666677 09337120 Kilema Forest -3.250000 37.450000 For water balance calculation contribution of direct rainfall into the reservoir may

be estimated using station 09337090 located close to the dam site. This station

has data from 1971 to 2005. There are missing data in 2000, 2001 and 2002 as

well as 1985, 1998 and 1999. The monthly data compiled by the Consultant is

presented in Table 3.6.


_______________________________________________________________________________________________

34

Table 3.6 Monthly Rainfall at station 09337090 YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1971 55.8 0.1 72.5 135.9 51.0 17.5 0.0 0.0 0.0 0.0 4.6 71.4 1972 278.3 43.3 124.6 31.9 110.2 0.0 2.8 11.6 3.2 32.5 97.4 18.8 1973 85.3 98.1 10.4 55.7 18.4 109.3 0.0 1.5 0.0 16.1 39.9 21.7 1974 3.2 18.7 114.0 206.7 15.3 20.3 5.9 45.7 0.0 0.0 13.4 4.0 1975 55.1 4.3 42.4 38.4 39.1 2.8 4.3 0.0 55.6 3.6 16.5 16.8 1976 21.6 15.2 89.2 49.5 52.1 10.4 0.8 4.3 47.2 3.8 23.6 27.2 1977 41.9 45.2 51.1 68.6 6.1 11.4 0.0 7.1 9.7 95.8 22.1 36.3 1978 125.7 42.4 163.8 115.8 18.3 4.8 0.0 0.0 0.0 19.6 130.0 123.21979 86.6 53.8 49.5 183.6 113.3 30.5 7.9 0.0 1.3 m 21.6 18.5 1980 23.4 6.4 57.4 63.8 5.6 0.0 0.0 18.8 1.0 69.9 22.9 16.3 1981 0.5 0.0 128.0 42.9 56.4 0.0 0.0 11.4 2.0 58.2 0.0 44.2 1982 1.5 16.5 20.8 86.1 84.6 21.6 46.2 2.7 12.3 145.8 215.8 82.5 1983 7.1 28.3 59.3 10.8 12.8 7.1 0.0 0.0 0.0 1.0 9.0 53.6 1984 30.3 0.3 25.0 101.9 14.6 9.6 7.1 0.0 0.0 5.3 96.0 72.9 1986 108.6 4.0 988.1 784.9 67.5 20.4 0.0 3.7 0.2 64.3 37.4 34.2 1987 43.3 60.9 11.7 66.0 81.8 0.0 1.0 13.8 0.0 m 58.4 9.9 1988 42.8 18.5 150.9 69.6 5.8 3.6 0.3 5.2 16.8 23.0 58.4 34.7 1990 54.7 25.9 189.6 m 8.1 0.0 0.0 0.0 0.0 12.0 39.3 55.9 1991 19.6 6.4 89.7 149.9 143.8 0.0 7.9 8.6 0.0 25.9 62.2 68.3 1993 483.1 206.5 123.2 91.4 78.7 0.0 0.0 7.1 0.0 43.9 20.8 17.0 1995 17.0 23.0 147.7 117.0 28.9 0.0 0.0 0.0 0.0 24.8 6.4 1.0 1996 61.8 63.5 59.4 62.6 40.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

3.7 Existing Models This section gives a review of reservoir models that have been configured for

Nyumba ya Mungu. Several modeling studies have been conducted in Nyumba

ya Mungu Reservoir catchment through research students and consultants.

Despite of many studies undertaken, there is no single model that is

recommended and used for operational purposes. This means the reservoir is

most probably being operated without knowledge of inflows. Models that have

been applied in Nyumba ya Mungu Reservoir catchment are presented and

discussed below. The discussion covers only details of configuration/set up and

application of the models. For complete review of model assumptions and

representation of the hydrological process within the models relevant references

are cited.

3.7.1 HEC-HMS This model has been applied by Moges (2003). The model is normally applied as

a semi distributed conceptual model but can also be fully distributed. The model


_______________________________________________________________________________________________

35

consist of interconnected functions describing the movement of water on the

surface, through the soil horizon, channel, ground water and other water facilities

in a distributed manner. This model has an advantage that it can take simulate

artificial control to water flow including irrigation abstractions of which many exist

upstream of Nyumba ya Mungu reservoir. HEC-HMS model was applied to two

catchments upstream of the reservoir namely Kikuletwa at 1DD1 and Ruvu at

1DC2A. These two catchments drain directly into Nyumba ya Mungu Reservoir.

The set up of the model is shown in Figure 3.4.

Figure 3.4 Set up of HEC-HMS for Nyumba ya Mungu Reservoir (Moges,

2003)

The model was applied at 1-day time step since the data is only available at this

resolution. The HEC-HMS has different components with a range of models for

each component of which the user is required to select. The selection depends

on data availability and performance factors. Moges (2003) has provided the

selected process models and their parameters as presented in Table 3.7


_______________________________________________________________________________________________

36

Table 3.7 Acceptable ranges of parameters for different component models of HEC-HMS for Kikuletwa and Ruvu catchments

Model Parameter Unit Minimum Maximum

Initial Loss mm 0 500Initial and constant-rate loss Constant loss rate Mm/hr 0 300

Initial deficit mm 0 500

Maximum deficit mm 0 500

Runoff Volume

Deficit and Constant rate loss Deficit recovery factor - 0.1 5

Lag hour 0.1 hr 500Snyder's UH Cp 0.1 1

Direct Runoff Transformation

SCS UH Lag minute 0.1 30000

Initial base flow m3/s 0 100000

Recession factor 0.000011 -

Base Flow

Recession Flow-to-peak ratio 0 1

K hour 0.1 150

X - 0 0.5Muskingum Number of steps - 1 100

Channel Routing

Lag routing Lag - 0 30000Source: Moges (2003)

The model was applied between 1977 and 1986. Six years of data from 1977 to

1982 were used for calibration of both Kikuletwa and Ruvu sub catchments.

Verification of the model was done using four years of data (1983-1986). The

model was also applied for short term events to simulate seasonal flows over a

period of several months.

For each sub-catchment the model has 11 parameters. There is a need to control

the spatial scale in order to reduce the total number of parameters to be

optimized (Moges, 2003). The model may be calibrated by the univariate gradient

and the Nelder and Mead search algorithm. The final parameters for HEC-HMS

for for Kikuletwa and Ruvu Rivers are given in Table 3.8.

Several combinations of the model were used in simulation. The combinations

were made by selecting different models for the major components of the HEC-

HMS watershed model namely the direct runoff, base flow and channel flow


_______________________________________________________________________________________________

37

components (Moges, 2003). The best combination of model selected for Ruvu

catchment (1dc1) for seasonal forecast is the Initial and Constant loss- SCS unit

hydrograph and recession. This combination gave an overall efficiency (R2) of

89.2 in calibration (March-June, 1977) and 63.0 % in verification (March-June,

1982). The volumetric fit was also close to unity in calibration period. In the case

of the Kikuletwa watershed (1dd1), the model performance during calibration and

verification was 73.5 and 60.2% respectively with similar trend in volumetric fit as

that of 1dc1. The best model cocktail was found to be Deficit and Constant,

Snyder unit hydrograph and recession (Moges, 2003).

The best model cocktail for long term simulation at 1dc1 was found to be the

Initial and Constant Loss, Snyder unit hydrograph and recession models. The

performance efficiency (R2) in calibration (1977-1982) and verification (1983-

1987) periods were 56.2 and 7.2 % respectively. While the model reproduced

better hydrograph fit in terms of shape and volume during calibration, verification

results underestimated the low flow values consistently. The results of modelling

study are presented in Table 3.8

Table 3.8 Results of application of HEC-HMS for catchments draining into

Nyumba ya Mungu Reservoir

Calibration (1977-82) Verification (1983-86)

Catchment Modelling mode R2 VIF

Total Residual (mm)

% In Volume R2 VIF

Total Residual

(mm)

% In Volume

Seasonal 89.2 0.979 0.9 2.1 63.0 0.783 8.6 27.7

Ruvu (1dc1) Long term 56.2 0.937 40.2 6.7 7.1 1.169 -46.3 -14.4

Seasonal 73.5 0.941 3.6 6.3 60.2 0.687 22.2 45.7 Kikuletwa Long-term 63.6 0.924 60.6 8.2 49.6 0.940 29.4 6.4 Upstream

Nyumba ya Mungu

Reservoir

Long term 63.3 0.84 137 19.7 49.5 0.83 84.3 20.4

Source: Moges (2003)


_______________________________________________________________________________________________

38

For the case of 1dd1, model cocktail containing Deficit and Constant, Snyder Unit

hydrograph and recession was found to perform better than other set of

combinations. The R2 in calibration and verification was 63 and 49% respectively

and the IVF was closed to unity in calibration period. The shape of the

hydrograph and volumetric fit is better reproduced for 1dd1 than 1dc1. The Index

of volumetric fit (IVF) indicates that the model underestimates the verification

results of 1dc1 and overestimates the same for 1dd1.

3.7.2 HEC-ReSIM Ngo’ndya (2006) applied the HEC-ReSIM model for reservoir optimization study

at Nyumba ya Mungu Reservoir between 1995 and 2005. Station 1dd1 and 1dc1

were used to estimate inflows into the reservoir. The study did not estimate

inflows into the reservoir directly but used measured releases, water levels and

calculated evaporation to estimate inflows. The flows at 1dc1 were estimated as

the sum of flows at 1dc2a, 1dc6, 1dc3a, and 1dc11a. Outflow data, including

spillage, were considered in the water balance as well as evaporation and rainfall.

Evaporation was estimated using pan evaporation data from Nyumba ya Mungu

met station (station code 09337090). Evaporation from the reservoir was

estimated by multiplying recorded pan evaporation data by a factor of 0.7. Inflows

were estimated by trial and error reducing flows at 1dd1 and 1dc1 by a constant

fraction and checking the agreement between observed and estimated water

levels. Water levels were calculated using the HEC-ReSIM model using the

storage elevation curve. A good agreement between estimated and observed

water levels was achieved with a Nash and Sutcliffe efficiency of 61.4%

(Ng’ondya 2006).

3.7.3 Linear Models A suite of linear models developed at the Department of Engineering Hydrology

University College Galway (UCG), Ireland (Kachroo, 1992) and later modified at

the Department of Water Resources Engineering, University of Dar es Salaam

has been applied in different watersheds in Tanzania since mid 1990’s. Mulungu

(1997) applied a version of this model known as the Multiple Inputs Linear

Perturbation Model (MILPM) to estimate inflows into Nyumba ya Mungu


_______________________________________________________________________________________________

39

Reservoir. This is a rainfall-runoff model that uses rainfall and/or stream flow

upstream to estimate flow at a point downstream. The advantage of this model is

that it is simple and does not require extensive inputs while maintaining fairly high

accuracy in estimation.

Estimation of flow at a given point using the MILPM involves identification of

inputs to the model, calibration, verification and application. The model was set

up to use the flow at 1dd1 and 1dc2a. The model was calibrated at 1d8c (a

station 1 km downstream of the dam at Nyumba ya Mungu) with data recorded

between 1959 and 1964. The model efficiency, measured by Nash and Sutcliffe

criteria (Nash and Sutcliffe, 1970), was 87.14% (Mulungu, 1997). The model was

used to estimate inflows into the reservoir between 1964 and 1987 as an

extension of natural flows at 1d8c. Mulungu (1997) attempted to use the

estimated flows to perform water balance study of the reservoir between 1971

and 1987 but the results were unsatisfactory.

This approach for estimating inflows into NYM reservoir has several

disadvantages:

• The model parameters must be established prior to 1965 when the natural

flow data exist at 1d8c. Most probably the rainfall-runoff relationships have

changed over time and the model may fail to estimate accurately inflow

into the reservoir;

• there is no possibility of including irrigation abstractions occurring

downstream of 1dd1 and 1dc2a stations before entering the reservoir;

• data by Mulungu (1997) were not available for cross-checking of the

balance between the estimated inflows estimated by MILPM and recorded

outflow.

3.8 The WEAP model More recently WEAP model have been implemented in Pangani River basin by

the Pangani Basin Water Office. This model approved by a panel of experts in

the basin for flow assessment. WEAP model uses the Pitman model for modeling

hydrological processes normally at monthly time step. The model has advantage


_______________________________________________________________________________________________

40

that it can also simulate the impact of abstractions and features like swamps

which is highly desirable in flow assessment. An ongoing study has applied this

model for Kikuletwa catchment at 1dd1 and seventeen other catchments in the

basin with reasonable accuracy.

3.9 Selection of model for reservoir balance Ideally a model for a water balance study of Nyumba ya Mungu Reservoir should

be able to estimate the inflows into the reservoir as accurately as possible. From

the models reviewed it seems that a simple linear model estimates inflows more

accurately than more comprehensive and data demanding models. The MILPM,

for example, obtained an efficiency of 87% compared to HEC-HMS and HEC-

ReSIM, which obtained efficiencies of 63 and 61%, respectively. MILPM is not

without some short-comings, however:

• it cannot account for abstractions upstream that reduce actual inflows into

the reservoir;

• It requires calibration with longterm data and forecasting into the future

has large uncertainty due to changing rainfall runoff relationships.

The possibility of including abstractions upstream is also not easily realized

because the difficulty in getting information on location and amount of

abstractions. The techniques used by Ng’odya (2006) and adopted in this study

seem more promising in estimating inflows into the reservoir. The Consultant

proposes that inflows can be estimated by simple models relating the flows at

1dd1, 1dc1 and the observed water balance of the reservoir. Currently this can

be implemented in the HEC-ReSIM model, simple spreadsheet program or

custom codes. Alternatively inflows in the reservoir can simply be estimated using

the WEAP model which takes into account abstractions as cites in section 3.8.

3.10 Reservoir inundation model This section describes the development of relationships between water level or

depth and inundated area in Nyumba ya Mungu associated with different inflow

and release regimes. Inundation area is determined from the elevation, which is

calculated from a water balance model. A series of reservoir storage is calculated


_______________________________________________________________________________________________

41

from a series of inflows and outflows from the reservoir. The values of storage

volumes were converted into elevations using storage-elevation curve. The

reservoir surface area was then determined from the area elevation curves.

Inflows into the reservoir were estimated using flows of Kikuletwa and Ruvu rivers,

which drain into the reservoir. Outflow from the reservoir was obtained from the

dam operators (Ng’ondya, 2006).

3.10.1 Inflow data

Inflow into Nyumba ya Mungu Reservoir was estimated using the most

downstream stations on the Kikuletwa and Ruvu Rivers which are located just

before draining into the Reservoir. Kikuletwa River is gauged at station 1dd1

located upstream of the reservoir. On the Ruvu River the station that is closest to

the reservoir is 1dc1. This station is affected by backwater since impoundment of

the reservoir and the data are not useful for water balance study. In the current

study inflows into the reservoir were determined as a fraction of flows at 1dd1

and 1dc1 obtained by summing flows at 1dc6, 1d11a and 1dc3a. The average

flow of Kikuletwa (1dd1) for the same range is 1.72 times the flow of Ruvu (sum

of 1d2a, 1dc6, 1d11a and 1dc3a). This factor was used to estimate the flows of

Ruvu and Kikuletwa Rivers to fill the missing data in the series. The flow series

used for Kikuletwa had 29 values missing which were filled using this approach.

Time series of flows in Kikuletwa and Ruvu Rivers and inflow into Nyumba ya

Mungu Reservoir are shown in Appendix A3.1

3.10.2 Outflow

Outflow from the reservoir was determined as records of observed flow through

the penstocks feeding the turbines. The maximum discharge capacity through the

penstocks is 56 m3/s (Moges, 2003). Outflow may also occur as a spill when

there is excessive inflow into the reservoir. The maximum capacity of the spillway

at Nyumba ya Mungu dam is 920 m3/s (Ng’ondya 2006). The Consultant has

compiled outflow data for Nyumba ya Mungu reservoir between 1995 and 2005.

The maximum outflow (56 m3/s) was recorded twice in 2002 (Feb, 21 and Dec

27) and in 2003 (Oct 15th and Dec 8th). Outflow pattern is shown in appendix A3.4.


_______________________________________________________________________________________________

42

Ngondya 2006 has reported that spillage occurred at NYM between February and

May 1998. This is the only period when spillage occurred between 1995 and

2005. Outflow data at Nyumba ya Mungu Reservoir is estimated from the power

produced, there is therefore possibility for errors if the machines are not working

at optimum and use more water than expected. Spilling starts when the reservoir

reaches a level of 688.15 (Ng’ondya, 2006).

3.10.3 Reservoir levels

Water levels are recorded daily at the reservoir. Water levels show the variation

of storage in the reservoir and reveals how inflows and outflows affect the

reservoir storage. The Consultant has compiled reservoir levels for a period

between 1995 and 2005. The data compiled shows that reservoir levels have

been dropping continuously since May 1998 when a maximum level of 688.94

was attained. Reservoir level dropped by around 2m between December 31,

2002 and January 17, 2003 due high discharge rates. Variation of reservoir levels

between 1995 and 2005 is presented in appendix A3.3.

3.10.4 Water balance

A simple water balance accounting for inflow and outflow was adopted for

preliminary analysis and for setting up the inundation model. This model was

used to generate a series of reservoir storages which were converted to

elevations using storage-elevation curve and then converted to area using area

elevation curve. The adopted water balance model is of the form presented in

equation 3.1

)1.3(1 KKKKKKKKKKKKKKKKKKKKKKKKKKKKtttt OISS −+=+

Where S is the storage, I is the inflow and O is the outflow. Inflow in equation 3.1

was determined from a fractional sum of flows at 1dd1, 1dc2a, 1d3a, 1dc6 and

1d11a. The outflow O in equation 3.1 is difficulty to estimate at Nyumba ya

Mungu Reservoir. This is mainly comprised of the flow discharge through the

turbines which is well recorded but it also includes the spill and bypass flow which

are not well monitored and reported. The Consultant has also monitored flows at

gauging station 1d8c downstream of Nyumba ya Mungu Reservoir as a cross


_______________________________________________________________________________________________

43

check for recorded outflows from the reservoir. This data shows random errors in

measurement of the outflow scattered throughout the period of record which can

not easily be removed. It was finally decided that outflow from the reservoir be

taken as the flow recorded at 1d8c which captures all releases from the reservoir

including spills and by pass. When

the outflow is assumed to be the flow recorded at 1d8c and the inflow set at 73%

of the sum of flows of 1dd1 and 1dc1 the computed water balance slightly

resemble the observed one as shown in Figure 3.5. Observed storage series was

calculated using observed water levels and equation 3.2 (the storage elevation

equation).

)2.3(35.33441)15.679(*24.49 62.2 KKKKKKKKKKKKKKKKKK−−+= hhS

Where S is the storage [in Mm3] and h is water elevation [m.a.s.l]. Equation 3.2

gives the live storage of the reservoir estimated between the elevation of 679.15

[m.a.s.l] (S=0.0) and 688.15 m.a.s.l (S=871 Mm3) (Moges, 2003). It may be seen

that the predicted storage follows the general pattern of the observed storage.

Predicted and Observed Storage in Nyumba ya Mungu Reservoir (1995-2005)

0

200

400

600

800

1000

1200

10/1

/199

5

4/1/

1996

10/1

/199

6

4/1/

1997

10/1

/199

7

4/1/

1998

10/1

/199

8

4/1/

1999

10/1

/199

9

4/1/

2000

10/1

/200

0

4/1/

2001

10/1

/200

1

4/1/

2002

10/1

/200

2

4/1/

2003

10/1

/200

3

4/1/

2004

10/1

/200

4

4/1/

2005

10/1

/200

5

Stor

age

mm

3

Estimated Storage Observed storage

Figure 3.5 Observed and estimated storage at Nyumba ya Mungu Reservoir


_______________________________________________________________________________________________

44

Comparizon of inflow, outflow and Water Levels in NYM Reservoir 1995-2005

0

50

100

150

200

250

300

10/1

/199

5

4/1/

1996

10/1

/199

6

4/1/

1997

10/1

/199

7

4/1/

1998

10/1

/199

8

4/1/

1999

10/1

/199

9

4/1/

2000

10/1

/200

0

4/1/

2001

10/1

/200

1

4/1/

2002

10/1

/200

2

4/1/

2003

10/1

/200

3

4/1/

2004

10/1

/200

4

4/1/

2005

10/1

/200

5

Flow

m3/

s

676

678

680

682

684

686

688

690

692

Wat

er L

evel

(mas

l)

Outflow Inflow WaterLevel

Figure 3.6 Comparison of Inflow, Outflow and Water Levels in Nyumba ya Mungu Reservoir 1995-2006

The balance is not achieved at all time because of varying accuracy in

determining the outflows. This variation may be caused by unknown estimation

errors in both inflow and outflows. The Consultant has satisfied himself after

rigorous analysis that based on available data the results presented are the best

achievable. Further discussion of water balance of the reservoir during 1995-

2005 is made with reference to Figure 3.6 and 3.7


_______________________________________________________________________________________________

45

0

10

20

30

40

50

60

10/1

/200

3

10/8

/200

3

10/1

5/20

03

10/2

2/20

03

10/2

9/20

03

11/5

/200

3

11/1

2/20

03

11/1

9/20

03

11/2

6/20

03

12/3

/200

3

12/1

0/20

03

12/1

7/20

03

12/2

4/20

03

12/3

1/20

03

1/7/

2004

1/14

/200

4

1/21

/200

4

1/28

/200

4

2/4/

2004

2/11

/200

4

2/18

/200

4

2/25

/200

4

3/3/

2004

3/10

/200

4

3/17

/200

4

3/24

/200

4

3/31

/200

4

Flow

rate

m3 /s

Machine Discharge Inflow

Figure 3.7 Inflow and outflow from Nyumba ya Mungu Reservoir from October

2003 to March 2004

From Figure 3.6 it may be seen that reservoir tends to respond to a balance

between inflows and releases. The huge drop in level was caused by high

releases between October 2003 and April 2004. The average outflow in this

period was just before the period. The discharge through the turbines was

maximum on 15th October and 8th December 2003. During this period inflow was

less than outflow except for few days Figure 3.7.

Although the main water balance components seem to explain the variation in

reservoir level fairly realistically, a simple sum water balance fails to capture the

trends of water level changes. It is evident from Figure 3.6 that when inflow is

less than outflow reservoir levels drop and vice versa. This indicates that there is

somewhat good agreement between measured outflow, levels and to some

extent the estimated inflow. The quantitative accuracy is however difficulty to

establish due to random errors in both inflow estimation and measured outflow.


_______________________________________________________________________________________________

46

Such errors accumulate in water balance and may cause a significant deviation

of estimated storage from observed.

3.9.5 Prediction of Inundated area

The prediction of inundated area is based on the water balance of the reservoir.

The storage estimate produced by water balance model at any time step using

equation 3.1 is converted to water level and then reservoir surface area is

calculated using area elevation curve (equation 3.3).

)3.3(6.17)132.672(*2442.1 62.1 KKKKKKKKKKKKKKKKKKKKK+−= hA

Where A is the area of reservoir surface in km2 and h is water elevation (m.a.s.l).

Water level is estimated as a root of equation 3.4, which is obtained by

rearranging the storage elevation equation.

)4.3(35.33441)15.679(*24.49)( 62.2 KKKKKKKKKKKKKKKKShhhF −−−+=

In equation 3.4 S is the storage obtained from equation 3.1 and h is the estimated

water level. The roots of equation 3.4 are obtained by iterative procedure using

Newton Rampson Method. The method is embedded into a water balance

program to calculate iteratively the reservoir water level at each time step. It may

be noted that using equation 3.4 the reservoir cannot go below 679.15 m.a.s.l.

The equation therefore determines water levels associated with live storage only.

Below this level, depth is undefined and storage becomes negative.

The results of the inundation model directly reflect the accuracy obtainable with

the water balance model. Estimated and observed surface water areas were

computed with equation 3.3 using estimated and observed levels respectively

and are presented in Figure 3.8 and 3.9.


_______________________________________________________________________________________________

47

674

676

678

680

682

684

686

688

690

692

10/1

/199

5

2/1/

1996

6/1/

1996

10/1

/199

6

2/1/

1997

6/1/

1997

10/1

/199

7

2/1/

1998

6/1/

1998

10/1

/199

8

2/1/

1999

6/1/

1999

10/1

/199

9

2/1/

2000

6/1/

2000

10/1

/200

0

2/1/

2001

6/1/

2001

10/1

/200

1

2/1/

2002

6/1/

2002

10/1

/200

2

2/1/

2003

6/1/

2003

10/1

/200

3

2/1/

2004

6/1/

2004

10/1

/200

4

2/1/

2005

6/1/

2005

10/1

/200

5

Wat

er le

vel (

m.a

.s.l)

Estimated Levels Observed Levels Figure 3.8 Estimated and observed levels at Nyumba ya Mungu

Reservoir: an output of a water balance model

0

20

40

60

80

100

120

140

160

10/1

/199

5

2/1/

1996

6/1/

1996

10/1

/199

6

2/1/

1997

6/1/

1997

10/1

/199

7

2/1/

1998

6/1/

1998

10/1

/199

8

2/1/

1999

6/1/

1999

10/1

/199

9

2/1/

2000

6/1/

2000

10/1

/200

0

2/1/

2001

6/1/

2001

10/1

/200

1

2/1/

2002

6/1/

2002

10/1

/200

2

2/1/

2003

6/1/

2003

10/1

/200

3

2/1/

2004

6/1/

2004

10/1

/200

4

2/1/

2005

6/1/

2005

10/1

/200

5

Wat

er S

urfa

ce A

rea

(km

2 )

Estimated Area Observed Area

Figure 3.9 Estimated and observed surface area at Nyumba ya Mungu Reservoir: an output of a water balance model


_______________________________________________________________________________________________

48

______________________________________________________________

HYDRAULIC STUDIES FOR KIRUA SWAMPS ________________________________________________________________

4.1 Approach Hydraulic modeling study for Kirua swamps (Figure 4.1) entailed three main

components namely:

• determination of flood magnitudes associated with various return periods;

• river channel and floodplain geometry analysis;

• development of flow/stage inundation relationships.

The river cross-sections geometry and flow measurements data were sourced

from Water Development and Irrigation Department (WDID, 1966) and data

available at the Water Resources Engineering Department (WRED, 2003).

Sources and processing methods for, the spatial data (i.e. topographic data) are

similar to those adopted in hydraulic modeling of Lake Jipe. Historical hydro-

meteorological data were sourced from Ministry of Water (MoW) and Water

Resources Engineering Department (WRED) database, University of Dar es

Salaam.

Figure 4.1 Kirua swamps and spatial distribution of hydro-meteorological

monitoring stations


_______________________________________________________________________________________________

49

Inundation mapping was attempted by modeling the entire flood plain

incorporating a river reach beyond the Kirua swamps to capture downstream

boundary condition. The authors would like to note that such an approach was

adopted because of two main reasons. Firstly, the detailed cross section

geometry data for entire river reach of Kirua swamps is not available. Secondly, it

is hypothesized by the consultants that floodplain hydraulics could well be

captured if a fully fledged model for the entire river reach is developed.

Terrestrial/distant downstream hydraulic controls such as meandering rivers,

channel constriction and river bed slope nick points would affect the flood plain

hydraulics. In contrast, site or reach based level hydraulics are mostly affected by

local hydraulic controls such as boulders, rock outcrop, rapids and riffles located

within micro-channel.

It should be noted that some of the gauging stations in these reaches have been

abandoned and there are no stable rating curves. Hydrologic Engineering Centre-

River Analysis System (HEC-RAS) model was used to set fully fledged river

hydraulic models of the Kirua Swamps.

Physical characteristics of the Kirua Swamps were determined by topographic

analysis in a GIS environment. This enabled generation of kirua swamps cross

sections and modeling of the manner in which flows distributed in the flood plain

when inundation occurs.

4.2 Flood magnitude return period relationship

The flood frequency analysis was carried out using annual maximum discharge

series (Table 4.1 and Figure 4.2) of naturalized flows that is before year 1968

(before dam construction). The study of peak flows uses just the largest flow

recorded each year at a gauging station out of the many thousands of values

recorded (Chow et al., 1988). The Log-Pearson Type III distribution was used in

this study.

Log-Pearson Type III model was fitted using excel spread sheet. The frequency

factors for this distribution are dependent on the skewness and return period as


_______________________________________________________________________________________________

50

seen in Table 4.2. KT values corresponding to the skewness of the annual

maximum flood series for 1D18 (-0.464) for different return periods were

interpolated linearly. The flood magnitudes for different return periods are

presented in the last column of the Table

Table 4.1 Annual Maximum discharges (MaxQ) series as extracted from natural river flow data series at 1D8c gauging station

S/R YEAR MaxQ 1 1957 63.8 2 1958 118.6 3 1959 82.4 4 1960 141.4 5 1961 182.8 6 1962 120.3 7 1963 143.9 8 1964 255.9 9 1965 107.1

10 1966 54.1 11 1967 38.3 12 1968 139.1

Table 4.2 Computation sheet of maximum discharges of various return

periods for 1D8C gauging station using Log-Pearson Type III distribution.

RETURN PERIOD KT(-0.4) KT(-0.464) KT(-0.5) QT (m3/s) 2 0.066 0.082 0.083 112 5 0.855 0.856 0.856 169 10 1.231 1.217 1.216 205 20 1.481 1.426 1.424 229 25 1.606 1.531 1.528 243 50 1.834 1.779 1.777 277 100 2.029 1.958 1.955 305

The results of fitting indicate that a two year flood is roughly 100m3/s and a ten

year flood is approximately 200m3/s. Such floods can be seen in the time series

of data plotted in Figure 4.2. For example the flood in 1961 was slightly more

than 5 year flood while the one in 1964 exceed a 25 year flood. It is known that in


_______________________________________________________________________________________________

51

1964 widespread flooding occurred in the east African region due to un-usually

high rainfall.

0

50100

150200

250300

350

1957

1958

1959

1960

1961

1962

1963

1964

1965

1966

1967

1968

Year

Max

imum

Q [m

3/s]

Annual Flood 2 -Year Flood 5 Year Flood 25-Year Flood 100-YR Flood

Figure 4.2 Annual maximum discharges (MaxQ) arranged by time of

occurrence at 1D8C The results of flood frequency analysis obtained above were compared to

information obtained key informants who have been farming in Kirua area for long

time. Two farmers were consulted independently to enquire their opinion how

often the Kirua Swamps get flooded. The farmers indicated that Kirua Swamps

were flooded in the year 1942, 1947, 1961, 1962, 1964, 1978, 1989 and 1997.

From the results of this interview it seems most likely that 1:2 year flood is

enough to inundate the swamps in Kirua. The farmers also pointed out that prior

to construction of the Dam at Nyumba ya Mungu, the swamps used to flood more

often than the case is after impoundment. The farmers also correctly pointed to

1964 as the year with the severest floods in history which caused relocation of

people forming a village named Kwa Kombo (named after former secretary

general of Afro Shirazi Party who visited the area to supervise humanitarian aid)

near Makanya in Same district.

4.3 Representation of River Channel and Flood plain geometry

Searches for cross-sections across the Kirua swamps (at gauging stations 1D18

and 1D12) aimed at characterizing the geometry of the Kirua swamps were not

successful. Lack of this information limited to a great extent the development of


_______________________________________________________________________________________________

52

the flow/stage/inundation relationship for the swamps. However, the river channel

geometry for Pangani River downstream of Nyumba ya Mungu Reservoir at

representative reaches were obtained from surveyed cross sections data. Three

to four cross sections (see appendix A4.1 and A4.2) per river reach sites with

length spanning between 71.0 – 174.0 m were sourced. All the cross sections

elevations for a particular site were reduced to local Bench Marks. Additional

information such as location coordinates in degrees and altitudes above mean

seal level in metres was sourced from Hydrological Year Book (URT, 1976).

4.4 Development of Storage/Elevation/Surface area relationship for Kirua swamp

Storage/elevation/ surface area relationships were developed to define the

physical characteristic of the Kirua swamp. The developed relationship helps to

define dynamics of the surface area and storage change as a function of

elevation due to the net effect of input-output hydrological variables. The

automated topographic analysis (e.g. TIN algorithm) of Digital elevation data was

used to develop above-mentioned relationship. Detailed procedures to develop

the relationship are described in Chapter 2.

The data set used for calibrating and validating the DEMs are presented in

Tables 4.3 and 4.4. The developed regressive models between elevations from

contour maps and DEMs and the performance plots are presented in Figures 4.3

and 4.4, respectively.

Table 4.3 Calibration data for Kirua swamps DEMs

ID X(m) Y(m) Topo_Elevation (m.a.s.l.)

DEM (m.a.s.l.)

1 366880.210 9491484.720 610.000 619.000 2 357999.980 9517000.180 620.000 624.000 3 359000.000 9523000.000 640.000 646.000 4 326912.750 9573133.610 680.000 688.000 5 329609.120 9573197.060 680.000 672.000 6 331597.370 9570446.670 680.000 684.000 7 329192.069 9572778.047 680.000 674.000 8 325616.810 9572424.010 700.000 699.000 9 330680.790 9572457.580 700.000 700.000 10 325811.670 9568821.660 700.000 707.000 11 332876.890 9569174.930 700.000 695.000 12 332319.590 9570533.030 700.000 693.000 13 326062.710 9565242.010 700.000 694.000


_______________________________________________________________________________________________

53

ID X(m) Y(m) Topo_Elevation (m.a.s.l.)

DEM (m.a.s.l.)

14 330802.720 9572554.710 700.000 695.000 15 361000.000 9531000.000 700.000 705.000 16 324107.850 9572681.630 720.000 737.000 17 331764.060 9572401.550 720.000 718.000 18 331574.710 9574379.390 720.000 715.000 19 324089.250 9573690.350 740.000 756.000 20 324163.170 9569010.130 740.000 747.000

Calibration

Topo_Elevation = 0.955*DEM + 29.292R2 = 0.95

600

620

640

660

680

700

720

740

760

600 650 700 750 800

DEM (masl)

Topo

_Ele

vatio

n (m

asl)

Figure 4.3 Calibration model for Kirua swamps DEMs

Table 4.4 Verification data set for Kirua swamps DEMs ID X_CENTER Y_CENTER Topo_Elevation

(masl) Estimated_Elevation (masl)

1 326747.780 9577173.780 700.000 702.567 2 324651.410 9576189.800 720.000 726.442 3 332390.140 9576413.070 700.000 693.972 4 331273.590 9576485.250 700.000 702.567 5 333207.330 9577665.600 720.000 712.117 6 329211.430 9575353.460 680.000 670.097 7 327411.400 9574838.110 680.000 688.242 8 327507.020 9576270.930 700.000 694.927


_______________________________________________________________________________________________

54

Validate

675680685690695700705710715720725

675 685 695 705 715 725

Estimated_Elevation (masl)

Topo

_Ele

vatio

n (m

asl)

Figure 4.4 Verification scatter plot for Kirua swamps DEMs

Development of flow/stage/inundation relationships for Kirua Swamps aimed at

preliminary assessment of the magnitude of flows that will inundate Kirua Flood

Plains and the manner in which these flows distributes in the system.

As mentioned in section 4.4, lack of field data characterizing the geometry of

cross-sections across the Kirua swamps, the Consultants opted to use the DEM

(90m) to generate cross-sections in the swamp area using GIS based software

called Hec-GeoRAS Arc view extension. The geometric data extracted was then

exported to one-dimensional, steady state routing hydraulic model, under HEC-

RAS model environment, to simulate flow/flood levels for different flood

magnitudes and the corresponding inundated areas and storage (Figure 4.12).

The process entails cross section points filtering, interpolation, graphical cross

section edit, merging micro-channel geometry where appropriate, and hydraulic

model calibration and simulations. Typical section of Kirua swamp generated

from DEM is shown in figure 4.5. The calibration information used was based on

flood magnitudes and highest water marks data as reported by Mtalo and


_______________________________________________________________________________________________

55

Ndomba (2003). Table 4.6 and Figures 4.6 & 4.7 show the relationship between

stream flows and inundated areas and stream and storage for the Swamp

respectively. In order to map the spatial view of the flood inundations across the

Kirua Swamps, the simulated hydraulics such as water levels was processed in

Hec-GeoRAS model under GIS environment. Figure 4.8 shows Kirua Swamps

Floodplain inundation map at stream flow of 50.0 m3/s and Figure 4.9 presents

Kirua Swamps Floodplain inundation map at stream flow with return period of 100

years (i.e. 305.14 m3/s).

Figure 4.5 Typical cross swamp cross section derived from DEM

4.6 Model applications

Figure 4.8 shows Kirua Swamps Floodplain inundation map at stream flow of

50.0 m3/s. At this flow rate an area of about 534 Km2 which is equivalent to

63.0 % of the Kirua Swamps is inundated (see Table 4.6). Figure 4.9 presents

inundation map at 100 years return period flow (i.e. 305.14 m3/s). An area of

about 668 Km2 which is equivalent to 78.0 % of the Kirua swamps is inundated at

this discharge. Further, the hydraulic model simulations indicate that the average


_______________________________________________________________________________________________

56

depths of inundations for flow discharges of 50.0 and 305.0 m3/s are 2.75 m and

3.0 m, respectively.

Table 4.6 Relationship between stream flow at 1DC12 and inundated

areas/storage at Kirua Swamps

Inundated area Storage Stream flow [m3/s] Area

[km2] % Area Storage

[km2-m] % Storage

15 6.4 0.8 6.3 1.2 24 344.2 40.4 29.7 5.6 30 450.6 52.8 45.1 8.5 50 534.3 62.6 81.5 15.4 112 575.9 67.5 156.3 29.6 169 598.7 70.2 222.0 42.1 205 635.1 74.5 270.4 51.2 229 642.1 75.3 309.2 58.6 243 645.8 75.7 332.6 63.0 277 661.4 77.5 396.3 75.1 305 668.3 78.4 463.2 87.8 500 852.9 100.0 527.8 100.0

Note: 1 km2-m is equivalent to 106 m3 or Mm3

0100200300400500600700800900

0 50 100 150 200 250 300 350 400 450 500

Streamflow, Q, [m3/s]

Inun

date

d su

rfac

e ar

ea[k

m2]

0

20

40

60

80

100

% A

rea

inun

date

d

Inundated surface area in Km2 % Area inundated

Figure 4.6: Relationship between stream flow and the inundated surface area


_______________________________________________________________________________________________

57

0

100

200

300

400

500

600

0 100 200 300 400 500

Streamflow, Q, [m3/s]

Stor

age

[km

2-m

]

0

20

40

60

80

100

% S

tora

ge

Storage in km2-m % Storage

Figure 4.7: Relationship between stream flow and storage

Note: 1 km2-m is equivalent to 106 m3 or Mm3

Figure 4.8: inundated area in Kirua Swamps when stream flow at 1D12 is 50.0 m3/s)

Figure 4.9: inundated area in Kirua Swamps when stream flow at 1D12 is 305.0 m3/s 1:100 flood)

________________________________________________________________


_______________________________________________________________________________________________

58

5. CONCLUSIONS AND RECCOMMENDATIONS ________________________________________________________________

5.1 Conclusions

5.1.1 Lake Jipe Storage/elevation and area/ elevation curves have been derived in the study. It is

established that the planimetric surface area varies from 21.7 km2 at elevation of

699.6 m.a.s.l. to 31.2 km2 at elevation of 702.0 m.a.s.l. In the same range of

elevations storage of the lake is also found to vary between 3.0 to 63.0 Mm3.

In characterization of groundwater/surface water interaction it is found that there

is a strong positive correlation between Lake Jipe water levels and river stage at

Lumi and with outflow from the catchment. Rainfall from Lumi catchment is poorly

correlated with the baseflow and the Lake water levels. The results also show

that, lagging output hydrological variables such as flow discharges at the outlet

and Jipe water levels does not improve the correlation between the baseflow and

rainfalls. The results suggest that rainfall alone does not account for groundwater

flow into the Lake. Therefore, other sources of water such as Lake Chala and

unmonitored springs could explain base flow or groundwater contribution to the

Lake. Comparable results were also deduced from water balance analysis

approach.

5.1.2 Nyumba ya Mungu Reservoir Available models for estimation of inflow into NYM reservoir were reviewed.

There is no operation model for estimation of inflow into the reservoir. Estimation

of inflows into the reservoir is complicated by the fact there is unquantified

irrigation abstraction upstream of the reservoir which can not be modelled easily.

In the past both physical and systems models have been applied to estimate

inflows into the reservoir. The review indicates that simple linear models are more

efficient but they lack the capability to take into account irrigation abstractions

upstream. A simple linear model relating flows of Kikuletwa and Ruvu Rivers

which drains into the reservoir seem to be the best option for water balance study

of the reservoir. A reasonable reproduction of levels and storages of the reservoir


_______________________________________________________________________________________________

59

were obtained using this approach whereby 73% of flows at Ruvu and Kikuletwa

are assumed as inflows to Nyumba ya Mungu Reservoir. There is also a strong

inclination to use the WEAP model which has already been configured by the

hydrology study group in Pangani River basin. The model have capabilities for

accounting for effects of abstraction upstream of the reservoir and may be used

to estimate the inflow into Nyumba ya Mungu reservoir.

A review of reservoir sedimentation studies revealed that the reservoir is too big

to be affected by the sediment load expected. It is estimated that, at current

sedimentation rate, it will take more than 1000 years of reservoir operation to fill

the reservoir. There is however very limited information on sediment flow rates

and more comprehensive studies may be needed to verify existing findings such

as those of IVO-NORPLAN 1997.

Water balance of the reservoir is complicated by errors in measuring the outflows

in addition to the challenge of estimating inflows. A review of available records

reveals errors such as higher machine discharge than flows recorded

downstream. The bypass flow from Nyumba ya Mungu is also not recorded. The

Consultant proposes a comprehensive water balance study where inflows and

outflows will be monitored simultaneously to develop proper water balance and

inundation model.

Inundation model relating inflows and outflows to surface area was developed

using a simple water balance model that generates storage and converts them to

elevation and area using storage-elevation and area-elevation curves. The

performance of this model is dependent on the accuracy of the water balance

model, which also reflects the accuracy of outflow measurement and inflow

modelling. Reasonable results were obtained in estimation of areas and storage

based on the balance inflows and outflows.

5.1.3 Kirua swamps Analysis of the cross sections data and developed storage/elevation/surface area

relationship of Kirua swamps indicate that the swamps geometry consists of three

main parts namely a defined river channel, extensive flood plains and a free


_______________________________________________________________________________________________

60

board. The river channel terminates at the elevation of 620.0 m.a.s.l. This point is

located 10.0 m above the general altitude of the Kirua swamps outlet.

Digital elevation model was used to defined cross sections in the kirua swamp as

a step towards modelling of flow inundation. This made for the lack of cross

section data at the site.

This study has successfully used hydraulic modeling approach to map inundation

within main river channel in Kirua swamps. At the middle section of the swamp,

the river bank gets overtopped by a 2 year-flood. Overtopping of the river bank at

the inlet of the swamps is by a 5-year flood. Interview with key informants in the

site indicate that a two year flood can overtop the banks. Model results showed

that a flow of about 50m3/s at 1dc12 can inundate 50% of the swamp area taken

as the area.

5.2 Recommendations

The Consultant has three major recommendations to improve the hydraulic

modeling results of Lake Jipe and the Kirua Swamps namely:

• For the case of Lake Jipe the consultant recommends a bathymetric

survey or spot measurements of bed elevation of Lake Jipe to be done.

Besides, water levels monitoring in Lake Chala and Jipe should be

extended.

• More and extensive cross sections that represent both main channel and

the flood plain geometry for Kirua swamps need to be surveyed.

• In this study it was found that rainfall alone in Lake Jipe catchment does

not account for groundwater flow into the Lake. However, the analysis

suggests that the source is Lumi sub-catchment. Currently there is lack of

enough information to describe the role of groundwater to Lake Jipe,

further monitoring and modeling should be pursued to understand this

relationship.


_______________________________________________________________________________________________

61

• Proper water balance of Nyumba ya Mungu Reservoir is required for

mapping the inundated area associated with different inflow and outflow

scenarios. A comprehensive study involving monitoring of inflows and

outflows is recommended to establish the model. In this case the outflows,

evaporation, bay pass flows and inflows need to be monitored to give a

proper account of water in the reservoir.


_______________________________________________________________________________________________

62

References

Perzyna, J. G. (1994a). Pangani Basin Water Management: Assessment of inflow to Nyumba ya Mungu reservoir, Tanzania : Ministry of Water, Tanzania, IVO International of Finland & Norplan A/S of Norway. Perzyna, J. G. (1994b). Pangani Basin Water Management: Assessment of inflow to Nyumba ya Mungu reservoir, Tanzania. Mission Report : Ministry of Water, Tanzania, IVO International of Finland & Norplan A/S of Norway SIDA, (2005). Pangani Falls Redevelopment Project, SIDA evaluation 06/09, Department of Infrastructure and Economic Cooperation, Reg. No 2004-2347, Stockholme, Sweden Arnold, J.G., and Allen, P.M., (1999). Automated methods for estimating baseflow and ground water recharge from streamflow records. Journal of the American Water Resources Association 35(2): 411-424. Birhanu, B.Z., (2005). Application of a GIS based SWAT model in simulating the available water resources in a Pangani basin subcatchment. A Dissertation submitted for degree of Master’s of Science in Water Resources Engineering at University of Dar es salaam. Chow, V.T., Maidment, D.R., and Mays, L.W., (1988). Applied Hydrology, p.148, McGraw-Hill International Editions, New York, 1964. De Pauw, E. (1984). Soils, Physiography and Agro ecological zones of Tanzania Publication: Crop monitoring and early warning systems Project GCPS/URT/047/NET. Ministry of Agriculture, Dar es Salaam Food and Agriculture Organization of the United Nations. IVO-NORPLAN (1997). Pangani falls redevelopment, Pangani River training project feasibility study. Pangani Basin water Office. Consultancy report to TANESCO Kachroo R. K. (1992). River Flows Forecasting Part I-V, a discussion of the Principles, Journal of Hydrology, Vol 133, elservier Science Publisher, B.V. Amsterdam Lo, C.P., and Yeung, A.K.W., (2002). Concepts and Techniques of Geographic Information Systems. PH Series in Geographic Information Science, Keith C. Clarke, Series Editor. Moges, S. (2003). Development and application of hydrological decision support tools for Pangani River Basin Tanzania. Tanzania. Unpublished Ph.D thesis, Department of Water Resources Engineering, University of Dar es Salaam 308pp.


_______________________________________________________________________________________________

63

Mulungu, D.M.M. (1997). Application of linear models for inflow forecasting to Nyumba ya Mungu Reservoir in Pangani River basin Tanzania. . Unpublished Master’s thesis, Department of Water Resources Engineering, University of Dar es Salaam, Tanzania Musyoki, M. M. and Mwandotto, B. A. J. (1999). Presentation of results/reports on the assessment of management needs for the watershed wetlands and waters of Lake Jipe. Paper presented at the Lake Jipe Cross-border Workshop, 13th - 15th October, 1999. Mtalo, F. and Ndomba, P. (2003). Hydraulic Modelling of Reservoir-Swamp System for Sustainable Allocation of Water Downstream of the Nyumba ya Mungu Reservoir in the Pangani Basin. Unpublished research report for Pangani Research Project. Ndomba, P.M. (2001). Estimation of Soil Erosion In the Pangani Basin Upstream of Nyumba ya Mungu Reservoir. Dissertation submitted in partial fulfillment of the requirements for the degree of Master of Science in Water Resources Engineering of the University of Dar es Salaam. Ndomba, P. M., and Gurandsrud, Åsta E., (2004). Installations of Water Level Loggers and Gauges in Lake Chala and Lake Jipe, Pangani River Basin. Field Report, for Pangani Basin Research Project at UDSM/NTNU March 4-17, 2004. Ndomba, P.M.(2006). “Modelling of Erosion Processes and Reservoir Sedimentation in the Pangani River Basin, Upstream of Nyumba ya Mungu Reservoir.” Unpublished PhD. Thesis. University of Dar es Salaam, Tanzania. Ng’ondya, R. (2006). Nyumba ya Mungu Reservoir System Simulation using HEC-ResSIM model in Pangani River Basin in Tanzania. Unpublished Master’s thesis, Department of Water Resources Engineering, University of Dar es Salaam 182pp Phillipo, P. (2006). Application of A Gis-Based Channel Network Model (Cche1d) For The Pangani River System. Dissertation submitted in partial fulfillment of the requirements for the degree of Master of Science in Water Resources Engineering of the University of Dar es Salaam Rohr, P.C., (2003). A hydrological study concerning the southern slopes of Mt. Kilimanjaro, Tanzania. A dissertation submitted to the Faculty of Engineering Science and Technology, in partial fulfillment of the requirements for the degree of Doctor Engineer. Trondheim, Norway, 16th June 2003. StatSoft, Inc. (2006). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html. Accessed on October 2006


_______________________________________________________________________________________________

64

WDID (Water Development and Irrigation Division), (1966). Pangani River 1 Mile Below NyM dam: proposed AWL Recorder and Cableway sites. Drawing No. 1D8C. R.C.S. 1D8C, July 15, 1966.

.


_______________________________________________________________________________________________

65

APPENDICES

A3.1 Flows at Kikuletwa river station 1dd1 1995-2005

Flow of Kikuletwa River at 1dd1 1995-2005

0

50

100

150

200

250

10/1

/199

5

2/1/

1996

6/1/

1996

10/1

/199

6

2/1/

1997

6/1/

1997

10/1

/199

7

2/1/

1998

6/1/

1998

10/1

/199

8

2/1/

1999

6/1/

1999

10/1

/199

9

2/1/

2000

6/1/

2000

10/1

/200

0

2/1/

2001

6/1/

2001

10/1

/200

1

2/1/

2002

6/1/

2002

10/1

/200

2

2/1/

2003

6/1/

2003

10/1

/200

3

2/1/

2004

6/1/

2004

10/1

/200

4

2/1/

2005

6/1/

2005

10/1

/200

5

Flow

M3 /s


_______________________________________________________________________________________________

66

A3.2 Flows at Ruvu River (1dc1) 1995-2005 estimated as sum of (1dc2a, 1d11a, 1dc6 and 1dc3a)

Flow of Ruvu River sum(1dc3a, 1dc2a, 1d11a, 1dc3a) 1995-2005

0

10

20

30

40

50

60

70

10/1

/199

5

2/1/

1996

6/1/

1996

10/1

/199

6

2/1/

1997

6/1/

1997

10/1

/199

7

2/1/

1998

6/1/

1998

10/1

/199

8

2/1/

1999

6/1/

1999

10/1

/199

9

2/1/

2000

6/1/

2000

10/1

/200

0

2/1/

2001

6/1/

2001

10/1

/200

1

2/1/

2002

6/1/

2002

10/1

/200

2

2/1/

2003

6/1/

2003

10/1

/200

3

2/1/

2004

6/1/

2004

10/1

/200

4

2/1/

2005

6/1/

2005

10/1

/200

5

Flow

m3 /s


_______________________________________________________________________________________________

67

A3.3 Water levels in Nyumba ya Mungu Reservoir 1995-2005

Nyumba ya Mungu Reservor Water Levels 1995-2005

676

678

680

682

684

686

688

690

692

10/1

/199

5

2/1/

1996

6/1/

1996

10/1

/199

6

2/1/

1997

6/1/

1997

10/1

/199

7

2/1/

1998

6/1/

1998

10/1

/199

8

2/1/

1999

6/1/

1999

10/1

/199

9

2/1/

2000

6/1/

2000

10/1

/200

0

2/1/

2001

6/1/

2001

10/1

/200

1

2/1/

2002

6/1/

2002

10/1

/200

2

2/1/

2003

6/1/

2003

10/1

/200

3

2/1/

2004

6/1/

2004

10/1

/200

4

2/1/

2005

6/1/

2005

10/1

/200

5

Wat

er L

evel

(m.a

.s.l)


_______________________________________________________________________________________________

68

A3.4 Outflows from Nyumba ya Mungu Reservoir 1995-2005

Nyumba ya Mungu Reservoir Outflows 1995-2005

0

10

20

30

40

50

60

10/1

/199

5

2/1/

1996

6/1/

1996

10/1

/199

6

2/1/

1997

6/1/

1997

10/1

/199

7

2/1/

1998

6/1/

1998

10/1

/199

8

2/1/

1999

6/1/

1999

10/1

/199

9

2/1/

2000

6/1/

2000

10/1

/200

0

2/1/

2001

6/1/

2001

10/1

/200

1

2/1/

2002

6/1/

2002

10/1

/200

2

2/1/

2003

6/1/

2003

10/1

/200

3

2/1/

2004

6/1/

2004

10/1

/200

4

2/1/

2005

6/1/

2005

10/1

/200

5

Flow

m3 /s


_______________________________________________________________________________________________

69

A3.6 Comparison of inflow outflow and water levels in Nyumba ya Mungu Reservoir 1995-2005

Comparizon of Water Balance Components at NYM Reservoir 1995-2005

0

20

40

60

80

100

120

140

160

10/1

/199

5

2/1/

1996

6/1/

1996

10/1

/199

6

2/1/

1997

6/1/

1997

10/1

/199

7

2/1/

1998

6/1/

1998

10/1

/199

8

2/1/

1999

6/1/

1999

10/1

/199

9

2/1/

2000

6/1/

2000

10/1

/200

0

2/1/

2001

6/1/

2001

10/1

/200

1

2/1/

2002

6/1/

2002

10/1

/200

2

2/1/

2003

6/1/

2003

10/1

/200

3

2/1/

2004

6/1/

2004

10/1

/200

4

2/1/

2005

6/1/

2005

10/1

/200

5

Flow

m3/

s

676

678

680

682

684

686

688

690

692

Elev

atio

n (m

asl)

Inflow Outflow WaterLevel


_______________________________________________________________________________________________

70

A4.1 River Channel Geometric Data at 1d8c

A4.1.1 cross sections at 1d8c gauging stations

Note: MG Manual gauging station staffs AWL Automatic Water Levels recorder (Electroflo) BM Bench Mark

A4.1.2 Longitudinal bed profiles for 1d8c gauging station

25.626.026.426.827.227.628.0

25 35 45 55 65 75

Distance from Manual Gauge, MG (m) to the upstream direction

Tha

lweg

(mas

d)

Thalweg WSL

CS Lable RCHL CH THALWEG WSL(XS) CS2 25.141 25.141 26.140 27.499 CS3 9.487 34.628 25.945 27.502 CS4 37.000 71.628 26.021 27.545

NB: CS = Cross-section, RCHL = Reach Length, CH = Channel, THALWEG = The lowest bed elevation in a cross-section, WSL = Water Surface Elevation

CS4 CS3 CS2

BM 100.00

71.628 34.628 25.141 0.00 Chainage (m)

Transects

AWL

MG


_______________________________________________________________________________________________

71

A4.1.3 Cross-section (Transects) geometric details for 1d8c site CS4 CS3 CS2 CH (m) RL [masd] CH (m) RL [masd] CH (m) RL [masd] 0.00 30.934 0.00 30.538 0.00 30.538 3.05 30.885 4.57 30.355 3.96 30.099 6.10 30.852 7.32 29.410 6.40 29.502 9.14 30.696 9.45 27.502 8.69 27.499 12.19 29.651 10.06 26.911 9.14 27.182 13.72 29.096 12.19 26.140 10.06 26.911 15.24 27.545 13.72 26.213 12.19 26.707 15.85 27.027 15.24 26.292 13.72 26.606 16.46 26.768 16.76 26.292 15.24 26.576 17.37 26.679 18.29 26.219 16.76 26.542 18.29 26.198 19.81 26.201 18.29 26.600 19.81 26.112 21.34 26.222 19.81 26.600 21.34 26.225 22.86 26.323 21.34 26.576 24.38 26.387 24.38 26.353 22.86 26.588 27.43 26.417 25.91 26.490 24.38 26.551 30.48 26.265 27.43 26.444 25.91 26.505 33.53 26.158 28.96 26.396 27.43 26.484 36.58 26.021 30.48 26.335 28.96 26.499 39.62 26.021 32.00 26.332 30.48 26.484 42.67 26.280 33.53 25.945 32.00 26.393 44.20 26.478 35.05 26.185 33.53 26.332 45.11 26.554 36.58 26.179 35.05 26.234 45.72 26.935 38.10 26.237 36.58 26.228 46.33 27.545 39.62 26.231 38.10 26.143 47.85 28.883 41.15 26.185 39.62 26.140 49.38 29.459 42.67 26.185 41.15 26.179 52.43 30.224 44.20 26.185 42.67 26.167 55.47 30.358 45.72 26.911 44.20 26.191 58.52 30.120 46.33 27.502 45.72 26.240 61.57 30.017 46.94 28.401 47.24 26.545 50.29 30.044 47.85 26.911 52.73 30.056 48.46 27.499 50.90 29.608 53.04 29.575 27.960 27.038 27.042 1.863 1.427 1.244 6.7 5.3 4.6 30.934 30.538 30.538 26.021 25.945 26.140


_______________________________________________________________________________________________

72

A4.1.4 Cross sections profiles at 1d8c gauging station

T4 at 71.628 m

2526272829303132

0 20 40 60

Chainage from LB (m)

Bed

Ele

vatio

n (m

asd)

T3 at 34.628 m

25262728293031

0 20 40 60


Bed

Ele

vatio

n (m

asd)

T2 at 25.141 m

25262728293031

0 20 40 60


Bed

Ele

vatio

n (m

asd)

CS4 CS3

CS2


_______________________________________________________________________________________________

73

A4.2 River Channel Geometric Data at 1d18 (WRED 2007) A4.2.1 Cross-sections near 1D18 gauging station

A4.2.2 Longitudinal bed profile details at 1d18 sites

96.5

97.0

97.5

98.0

98.5

99.0

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180

Distance upstream (m)

Elev

atio

n (m

)

Thalweg WSL

Transect name CH (m) Thalweg WSL Water depth CS1 0 97.482 98.748 1.266 CS2 61.741 97.323 98.774 1.451 CS3 119.012 97.035 98.794 1.759 CS4 173.095 97.332 98.799 1.467

CS1CS2CS3 CS4

173.095 119.012 61.741 0.00 Chainage (m)

Transects

Riff

les

TBM


_______________________________________________________________________________________________

74

A4.2.3 Cross-section geometry details near 1d18 site CS1 CS2 CS3 CS4 CH (m) RL (m) CH (m) RL (m) CH (m) RL (m) CH (m) RL (m) 0.0 100.000 0.00 99.851 0.0 99.762 0.00 99.439 7.6 100.045 8.00 99.857 9.1 99.222 12.30 99.495 12.8 99.804 12.00 99.615 14.0 99.215 14.40 99.470 13.0 98.772 13.00 99.410 16.3 98.821 15.50 98.879 15.0 98.034 13.60 98.797 18.3 98.620 16.30 98.803 17.0 97.482 17.60 98.008 20.3 98.500 24.30 98.562 19.0 97.609 19.60 97.663 22.3 98.380 26.30 98.482 21.0 97.809 21.60 97.548 24.3 98.060 28.30 98.272 23.0 97.952 23.60 97.413 26.3 98.155 30.30 98.022 25.0 98.061 25.60 97.323 28.3 98.385 32.30 97.757 27.0 98.242 27.60 97.533 30.3 98.240 34.30 97.502 29.0 98.247 29.60 97.583 32.3 97.755 36.30 97.452 31.0 98.242 31.60 97.693 34.3 97.580 38.30 97.482 33.0 98.152 33.60 98.313 36.3 97.570 40.30 97.482 35.0 98.187 35.60 98.313 38.3 97.550 42.30 97.432 37.0 98.292 37.60 98.343 40.3 97.380 44.30 97.382 39.0 98.307 39.60 98.348 42.3 97.260 46.30 97.432 41.0 98.122 41.60 98.323 44.3 97.120 48.30 97.397 43.0 98.112 43.60 98.323 46.3 97.035 50.30 97.342 45.0 98.112 45.60 98.263 48.3 97.100 52.30 97.332 47.0 98.032 47.60 98.263 50.3 97.230 54.30 97.447 49.0 98.242 49.60 98.293 52.3 97.115 56.30 97.667 51.0 98.067 51.60 98.273 54.3 97.300 58.30 98.192 53.0 98.002 53.60 98.333 56.3 97.560 59.50 98.805 55.0 98.022 55.60 98.313 58.3 98.085 59.76 98.961 57.0 97.952 57.60 98.283 59.9 98.818 60.87 99.872 59.0 98.142 59.60 98.353 60.2 99.840 63.12 100.031 61.0 98.267 61.60 98.343 64.2 99.952 69.12 100.001 63.0 98.302 63.60 98.343 72.2 99.768 65.0 98.462 65.60 98.643 80.2 99.809 67.0 98.522 67.60 98.573 69.0 98.557 68.60 98.854 71.0 98.577 72.80 98.924 72.6 98.772 79.93 99.007 73.5 99.787 80.15 99.564 76.7 100.132 89.15 99.774 76.7 99.905 77.0 99.838 89.8 99.745 0.735 0.659 0.924 0.878 98.504 98.425 98.185 98.237 0.75 0.67 0.94 0.89 100.132 99.857 99.952 100.031 97.482 97.323 97.035 97.332 61.741 57.271 54.083 0 61.741 119.012 173.095 89.8 89.15 80.2 69.12


_______________________________________________________________________________________________

75

A4.2.4 Cross section profiles at 1d18 site Xsection 1 at 0.0 m

97.0

97.5

98.0

98.5

99.0

99.5

100.0

100.5

0 15 30 45 60 75 90

Chainage (m)

Bed

elev

atio

n (m

)Xsection 2 at 61.741 m

97.0

97.5

98.0

98.5

99.0

99.5

100.0

0 15 30 45 60 75 90

Chainage (m)

Bed

ele

vatio

n (m

)

Xsection 3 at 119.012 m

96.597.097.598.098.599.099.5

100.0100.5

0 20 40 60 80

Chainage (m)

Bed

elev

atio

nl (m

)

Xsection 4 at 173.095 m

97.0

97.5

98.0

98.5

99.0

99.5

100.0

100.5

0 15 30 45 60 75

Chainage (m)

Bed

ele

vatio

n (m

)

IUCN WATER AND NATURE INITIATIVE

PANGANI BASIN WATER BOARD1

PANGANI RIVER BASIN FLOW ASSESSMENT

Hydraulic Study of Lake Jipe, Nyumba ya Mungu Reservoir and Kirua Swamp

T.A Kimaro, S.H. Mkhandi, J. Nobert, P.M. Ndomba, P. Valimba and F.W. Mtalo

August 2008

1 As of 2010, Pangani Basin Water Office is known as Pangani Basin Water Board

Published by: Pangani Basin Water Board (PBWB) International Union for Conservation of Nature (IUCN) Copyright: © 2010 International Union for Conservation of Nature and Pangani Basin Water Board This publication may be produced in whole or part and in any form for education or non-profit uses, without special permission from the copyright holder, provided acknowledgement of the source is made. IUCN would appreciate receiving a copy of any publication which uses this publication as a source. No use of this publication may be made for resale or other commercial purpose without the prior written permission of IUCN. Citation: PWBO/IUCN. 2008. Hydraulic Study of Lake Jipe, Nyumba ya Mungu Reservoir and Kirua

Swamps. Pangani River Basin Flow Assessment. Pangani Basin Water Board, Moshi and IUCN Eastern and Southern Africa Regional Programme. 75 pp.

Available from: IUCN - ESARO Publications Service Unit, P. O. Box 68200 - 00200, Nairobi, Kenya; Telephone ++ 254 20 890605-12; Fax ++ 254 20 890615; E-mail: [email protected] The designations of geographical entities in this book, and the presentation of the material, do not imply the expression of any opinion whatsoever on the part of the participating organizations concerning the legal status of any country, territory, or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries. The opinions expressed by the authors in this publication do not necessarily represent the view of PBWB, EU, UNDP GEF, WANI or IUCN.