IUCN WATER AND NATURE INITIATIVE PANGANI BASIN WATER BOARD 1

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1 IUCN WATER AND NATURE INITIATIVE PANGANI BASIN WATER BOARD 1 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 As of 2010, Pangani Basin Water Office is known as Pangani Basin Water Board

2 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 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 , Nairobi, Kenya; Telephone ; Fax ; earo@iucn.org 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.

3 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. 1

4 Figure 01 Location map of study area 2

5 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 tradeoffs 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 km 2 at elevation of m.a.s.l. to 31.2 km 2 at elevation of m.a.s.l.. In the same range of elevations storage of the lake varies between 3.0 to 63.0 Mm 3. 3

6 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 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 4

7 using a fully fledged hydraulic model. As an example case, a flood of 50.0 m 3 /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. 5

8 Acronyms and Abbreviations STRM Mm 3 IUCN GEF HEC-RAS PHABSIM m.a.s.l. WDID Shuttle Radar Topographic Mission Million cubic metres International Union of the Conservation of Nature Global Environmental Fund Hydrologic Engineering Centre-River Analysis System Physical Habitat Simulation Model Meters above sea level Water Development and Irrigation Department 6

9 Table of Contents Executive Summary... 3 Acronyms and Abbreviations... 6 Table of Contents... 7 List of Figures... 9 List of Tables INTRODUCTION General Objectives of the hydraulic modeling study Scope HYDRAULIC STUDIES FOR LAKE JIPE Approach Storage/elevation/surface area relationship for Lake Jipe Characterization of groundwater/surface water interaction and the role it plays in water balance of Lake Jipe HYDRAULIC STUDIES FOR NYUMBA YA MUNGU RESERVOIR Introduction Approach Operating rules Characteristics of installed hydropower plants Nyumba ya Mungu Power Plant New Pangani Falls (NPF) Power Plant Hale Power Plant Available storage/elevation/surface area curves Sedimentation studies in Nyumba ya Mungu Reservoir Hydrometric gauging and data Inflows Outflows Evaporation Rainfall Existing Models HEC-HMS HEC-ReSIM Linear Models The WEAP model Selection of model for reservoir balance Reservoir inundation model Inflow data Outflow Reservoir levels Water balance Prediction of Inundated area HYDRAULIC STUDIES FOR KIRUA SWAMPS Approach Flood magnitude return period relationship

10 4.3 Representation of River Channel and Flood plain geometry Development of Storage/Elevation/Surface area relationship for Kirua swamp Model applications CONCLUSIONS AND RECCOMMENDATIONS Conclusions Lake Jipe Nyumba ya Mungu Reservoir Kirua swamps Recommendations APPENDICES A3.1 Flows at Kikuletwa river station 1dd A3.2 Flows at Ruvu River (1dc1) estimated as sum of (1dc2a, 1d11a, 1dc6 and 1dc3a) A3.3 Water levels in Nyumba ya Mungu Reservoir A3.4 Outflows from Nyumba ya Mungu Reservoir A3.6 Comparison of inflow outflow and water levels in Nyumba ya Mungu Reservoir A4.1 River Channel Geometric Data at 1d8c A4.1.1 cross sections at 1d8c gauging stations A4.1.2 Longitudinal bed profiles for 1d8c gauging station A4.1.4 Cross sections profiles at 1d8c gauging station A4.2 River Channel Geometric Data at 1d18 (WRED 2007) A4.2.2 Longitudinal bed profile details at 1d18 sites A4.2.3 Cross-section geometry details near 1d18 site

11 List of Figures Figure 2.1 Lake Jipe catchment area and spatial distribution of regular hydrometeorological monitoring stations. (Note: Tz_Jipe and Ky_Jipe represent Tanzanian and Kenyan Side Jipe water levels gauging stations) Figure 2.2 Calibrated model for Lake Jipe catchment DEMs Figure 2.3 Scatter diagram of Topo_Elevation versus Estimated elevation for Lake Jipe catchment DEMs Figure 2.4 Storage/elevation/surface area relationships for Lake Jipe Figure 2.5 Storage/elevation/surface area relationships for Lake Jipe Figure 3.1 Area Elevation Curve for Nyumba ya Mungu Reservoir Source: Moges (2003) Figure 3.2 Storage Elevation Curve for Nyumba ya Mungu Reservoir Source: Moges (2003) Figure 3.3 Comparison of out flow (machine discharge.) flow at 1d8c and Water levels at NYM reservoir 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 Reservoir 43 Figure 3.6 Comparison of Inflow, Outflow and Water Levels in Nyumba ya Mungu Reservoir Figure 3.7 Observed and estimated storage at Nyumba ya Mungu Reservoir 45 Figure 3.8 Estimated and observed levels at Nyumba ya Mungu Reservoir: an output of a water balance model Figure 3.9 Estimated and observed surface area at Nyumba ya Mungu Reservoir: an output of a water balance model Figure 4.1 Kirua swamps and spatial distribution of hydro-meteorological monitoring stations Figure 4.2 Annual maximum discharges (MaxQ) arranged by time of occurrence at 1D8C Figure 4.3 Calibration model for Kirua swamps DEMs Figure 4.4 Verification scatter plot for Kirua swamps DEMs Figure 4.5 Typical cross swamp cross section derived from DEM Figure 4.6: Relationship between stream flow and the inundated surface area 56 Figure 4.7: Relationship between stream flow and storage Figure 4.8: inundated area in Kirua Swamps when stream flow at 1D12 is m 3 /s) Figure 4.9: inundated area in Kirua Swamps when stream flow at 1D12 is m 3 /s 1:100 flood)

12 List of Tables Table 2.1 Calibration data for Lake Jipe catchment DEMs Table 2.2 Verification data for Lake Jipe catchment DEMs Table 2.3 Storage/elevation/surface area relationships for Lake Jipe Table 2.4 Correlation between input and Output hydrological variables Table 2.5A Correlation between Water levels of Lakes Chala and Jipe for year Table 2.5B Annual Water balance analysis between 1976 and Table 3.1 Minimum Reservoir levels to ensure reliability of Nyumba ya Mungu Reservoir Table 3.2 Physical characteristics of the Nyumba ya Mungu Reservoir Table 3.3 Sediment rating table for Kikuletwa River Table 3.4 Flow gauging stations upstream of Nyumba ya Mungu Reservoir. 30 Table 3.5 Rainfall stations in Nyumba ya Mungu Reservoir catchment Table 3.6 Monthly Rainfall at station Table 3.7 Acceptable ranges of parameters for different component models of HEC-HMS for Kikuletwa and Ruvu catchments Table 3.8 Results of application of HEC-HMS for catchments draining into Nyumba ya Mungu Reservoir Table 4.1 Annual Maximum discharges (MaxQ) series as extracted from natural river flow data series at 1D8c gauging station 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 Table 4.4 Verification data set for Kirua swamps DEMs

13 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. 11

14 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 ( 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. 12

15 Chala Tz Jipe Ky Jipe Figure 2.1 Lake Jipe catchment area and spatial distribution of regular hydrometeorological 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. 13

16 Table 2.1 Calibration data for Lake Jipe catchment DEMs ID UTM coordinates DEM Topo_Elevation Y(m) X (m) (m.a.s.l.) (m.a.s.l.) Topo_Elevation (masl) Calibration Topo_Elevation = *DEM R 2 = DEM (masl) 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. 14

17 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 Topo_Elevation Estimated Elevation ID Northings, Y (m) Eastings, X (m) (m.a.s.l.) (m.a.s.l.) Topo_Elevation (masl) Validation (R2=98%) Estimated Elevation (masl) 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 m.a.s.l. and 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 15

18 station was 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 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 and 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 km 2 at elevation of m.a.s.l. to 31.2 km 2 at elevation of m.a.s.l.. In the same range of elevations, storage of the lake is found to vary between 3.0 to 63.0 Mm 3. 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 (Km 2 ) Volume (Mm 3 ) S/R Elevation (m.a.s.l.) Area (Km 2 ) Volume (Mm 3 )

19 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 and 702 m.a.s.l Elevation (m.a.s.l) Area = -5E-05h h h h R 2 = Elevation Poly. (Elevation) Area km 2 Figure 2.4 Storage/elevation/surface area relationships for Lake Jipe Storage Elevation Curve for Lake Jipe between and 702 m.a.s.l Elevation (m.a.s.l) Storage = 3E-08h 4-6E-06h h h R 2 = 1 elevation Poly. (elevation) Storage MCM Figure 2.5 Storage/elevation/surface area relationships for Lake Jipe 17

20 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 A = 5*10 5 h h h h KKKKKKKKKKKK (2.1) Where A is the area in km 2, h is water surface elevation (m.a.s.l) The analytical expression describing storage of lake Jipe above m.a.s.l is described by equation 2.2 V = 3*10 8 h 4 6 *10 6 h h h KKKKKKKKKKKK (2.2) Where V is the volume above 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) 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 18

21 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 ( , , , , , ) 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. 19

22 Table 2.4 Output Hydrological variables Correlation between input and Output hydrological variables Input Hydrological variables r_table at p=5% LUMI WL Lumi Intervening Intervening Pare Mt. Lumi Taveta JIPE WL Unlagged 1DC2A flow T/Flow BASE SURF Lagged by 15 days 1DC2A flow JIPE WL T/Flow BASE SURF 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 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 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 unlagged and lagged experiments. 20

23 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, Table 2.5A Correlation between Water levels of Lakes Chala and Jipe for year 2005 Water Levels at Lake Jipe Season r_table at Water p=5% Levels at Lake Chala Apr May Unlagged Nov. 1-Dec May 11 May Lagged by 15 days Nov.16 - Dec Lagged by 30 days Dec.1 - Dec 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 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 21

24 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. P ( E + Q) = Error termkkkkkkkkkkkkkkkkkkkkkkk K(2.3) This analysis assumes that natural systems such as catchment, restores/restabilizes 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, mm, was computed as the difference between long term annual aerial precipitation, 1406 mm, and sum of evaporation and runoff (i.e = 1555 mm). The percent error in Q (253 %) was computed as the percentage ratio of absolute error term ( 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 % Error Q in P Similarly, the percentage error in P (11 %) was computed as the percentage ratio of absolute error term ( mm) to long term annual aerial precipitation, (1406 mm). The negative error term (i.e mm) as computed in Table 2.5B above supports the contention that rainfall alone does not account for the output (E plus 22

25 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 m 3 /s in 1DD1 catchment could not be explained by rainfall amount alone. 23

26 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 4.7m 3 /s (SIDA, 2005). Nyumba ya Mungu Reservoir has a catchment area of 9,320 km 2 (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 Mm 3. 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 37m 3 /s out of which 24m 3 /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 24

27 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 m 3 /s when the reservoir is above the minimum conservation level ( m.a.s.l.) and a constant release of 19.8 m 3 /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. 25

28 Table 3.1 Minimum Reservoir levels to ensure reliability of Nyumba ya Mungu Reservoir. Month 95% reliability 99% reliability CRC as levels (m.a.s.l) CRC as Storage (Mm 3 ) CRC as levels (m.a.s.l) CRC as storage (Mm 3 ) January February March April May June July August September October November December 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 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 m.a.s.l (at 26

29 S/no Property Value, units Tanga) 6 Lowest operation level water level m.a.s.l. (at Tanga) 7 Height of Intake tower 33m 8 Max. design flood capacity of the spillway 920 m 3 /s 9 Storage at HWL 871 *10 6 m 3 10 Type of the dam Inclined rock fill 11 Minimum statutory release 21.3 m 3 /s Source: Ng ondya (2006) and Moges (2003) 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 m 3 /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 Mm Hale Power Plant The Hale Power Plant, 21 MW, was commissioned in It utilizes a natural head of 70 m to generate power with a maximum discharge 42 m 3 /s. It has a storage reservoir of total volume of about 1.8 Mm 3 and live storage 1.13 Mm 3 ; and an intake pond of total volume 136,000 m 3 and live storage 127,000 m 3. The discharge capacity of the intake spillway is 608 m 3 /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

30 Elevation (m.a.s.l.) A=1.2442*(h )** Area (Km^2) Figure 3.1 Area Elevation Curve for Nyumba ya Mungu Reservoir Source: Moges (2003) 689 ELevation (m.a.s.l.) S=49.24h+(h )** Live Storage(MM^3) 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, 28

31 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. 29

32 Table 3.3 Sediment rating table for Kikuletwa River Flow m 3 /s Conc. Mg/l Susp. Load (t/day) , , , ,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 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 Station code Name of river location longitude Latitude 1dd1 Kikuletwa dc2a Kifaru dc3a Rau dc6 Mue dc11a Himo d8c Pangani 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. 30

33 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 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 m 3 /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 by Ng ondya (2006). Average outflow from the reservoir for the period is 20.2 m 3 /s and maximum release was m 3 /s, which occurred on 21 st February, 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

34 Comparizon of outflow from NYM reservoir and flow at down stream station 1d8c /1/1995 2/1/1996 6/1/ /1/1996 2/1/1997 6/1/ /1/1997 2/1/1998 6/1/ /1/1998 2/1/1999 6/1/ /1/1999 2/1/2000 6/1/ /1/2000 2/1/2001 6/1/ /1/2001 2/1/2002 6/1/ /1/2002 2/1/2003 6/1/ /1/2003 2/1/2004 6/1/ /1/2004 2/1/2005 Discharge m 3 /s 6/1/ /1/ Water Level (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 m 3 /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 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 (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. 32

35 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 Table 3.5 Rainfall stations in Nyumba ya Mungu Reservoir catchment s/n Station code Station name Location longitude Latitude NYM reservoir Himo Sisal Estate W.D & I.D Moshi TPC Langasani Uru West KIA Kilema Forest For water balance calculation contribution of direct rainfall into the reservoir may be estimated using station located close to the dam site. This station has data from 1971 to There are missing data in 2000, 2001 and 2002 as well as 1985, 1998 and The monthly data compiled by the Consultant is presented in Table

36 Table 3.6 Monthly Rainfall at station YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC m m m 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 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 34

37 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

38 Table 3.7 Acceptable ranges of parameters for different component models of HEC-HMS for Kikuletwa and Ruvu catchments Runoff Volume Direct Runoff Transformation Base Flow Model Parameter Unit Minimum Maximum Initial Loss mm Initial and constant-rate loss Constant loss rate Mm/hr Initial deficit mm Deficit and Maximum deficit mm Constant rate loss Deficit recovery factor Lag hour 0.1 hr 500 Snyder's UH Cp SCS UH Lag minute Initial base flow m 3 /s Recession factor Recession Flow-to-peak ratio 0 1 Channel Routing K hour X Muskingum Number of steps Lag routing Lag Source: Moges (2003) The model was applied between 1977 and 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 ( ). 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 36

39 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 (R 2 ) 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 (R 2 ) in calibration ( ) and verification ( ) 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 Catchment Results of application of HEC-HMS for catchments draining into Nyumba ya Mungu Reservoir Modelling mode R 2 VIF Calibration ( ) Verification ( ) Total % In Residua Volume R2 VIF l (mm) Total Residual (mm) % In Volume Seasonal Ruvu (1dc1) Long term Seasonal Kikuletwa Long-term Upstream Nyumba ya Mungu Reservoir Long term Source: Moges (2003) 37

40 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 R 2 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 1dd HEC-ReSIM Ngo ndya (2006) applied the HEC-ReSIM model for reservoir optimization study at Nyumba ya Mungu Reservoir between 1995 and 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 ). 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) 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 38

41 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 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 39

42 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 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 40

43 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) 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 A 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 m 3 /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 m 3 /s (Ng ondya 2006). The Consultant has compiled outflow data for Nyumba ya Mungu reservoir between 1995 and The maximum outflow (56 m 3 /s) was recorded twice in 2002 (Feb, 21 and Dec 27) and in 2003 (Oct 15 th and Dec 8 th ). Outflow pattern is shown in appendix A

44 Ngondya 2006 has reported that spillage occurred at NYM between February and May This is the only period when spillage occurred between 1995 and 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 (Ng ondya, 2006) 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 The data compiled shows that reservoir levels have been dropping continuously since May 1998 when a maximum level of 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 A 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 S t+ 1 = St + I t Ot KKKKKKKKKKKKKKKKKKKKKKKKKKK K(3.1) 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 42

45 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). S = 49.24* h + ( h ) KKKKKKKKKKKKKKKKKK (3.2) Where S is the storage [in Mm 3 ] and h is water elevation [m.a.s.l]. Equation 3.2 gives the live storage of the reservoir estimated between the elevation of [m.a.s.l] (S=0.0) and m.a.s.l (S=871 Mm 3 ) (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 ( ) /1/1995 4/1/ /1/1996 4/1/ /1/1997 4/1/ /1/1998 4/1/ /1/1999 Storage mm 3 4/1/ /1/2000 4/1/ /1/2001 4/1/ /1/2002 4/1/ /1/2003 4/1/ /1/2004 4/1/ /1/2005 Estimated Storage Observed storage Figure 3.5 Observed and estimated storage at Nyumba ya Mungu Reservoir 43

46 300 Comparizon of inflow, outflow and Water Levels in NYM Reservoir /1/1995 4/1/ /1/1996 4/1/ /1/1997 4/1/ /1/1998 4/1/ /1/1999 4/1/ /1/2000 4/1/ /1/2001 4/1/ /1/2002 4/1/ /1/2003 4/1/ /1/2004 4/1/2005 Flow m3/s 10/1/ Water Level (masl) 676 Outflow Inflow WaterLevel Figure 3.6 Comparison of Inflow, Outflow and Water Levels in Nyumba ya Mungu Reservoir 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 is made with reference to Figure 3.6 and

47 /1/ /8/ /15/ /22/ /29/ /5/ /12/ /19/ /26/ /3/ /10/ /17/ /24/ /31/2003 1/7/2004 1/14/2004 1/21/2004 1/28/2004 2/4/2004 2/11/2004 2/18/2004 2/25/2004 Flow rate m 3 /s 3/3/2004 3/10/2004 3/17/2004 3/24/2004 3/31/2004 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 The average outflow in this period was just before the period. The discharge through the turbines was maximum on 15 th October and 8 th December 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. 45

48 Such errors accumulate in water balance and may cause a significant deviation of estimated storage from observed 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). A = * ( h ) KKKKKKKKKKKKKKKKKKKKK (3.3) Where A is the area of reservoir surface in km 2 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. F( h) = * h + ( h ) S KKKKKKKKKKKKKKKK (3.4) 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 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

49 Water level (m.a.s.l) /1/1995 2/1/1996 6/1/ /1/1996 2/1/1997 6/1/ /1/1997 2/1/1998 6/1/ /1/1998 2/1/1999 6/1/ /1/1999 2/1/2000 6/1/ /1/2000 2/1/2001 6/1/ /1/2001 2/1/2002 6/1/ /1/2002 2/1/2003 6/1/ /1/2003 2/1/2004 6/1/ /1/2004 2/1/2005 6/1/ /1/2005 Estimated Levels Observed Levels Figure 3.8 Estimated and observed levels at Nyumba ya Mungu Reservoir: an output of a water balance model /1/1995 2/1/1996 6/1/ /1/1996 2/1/1997 6/1/1997 Water Surface Area (km 2 ) 10/1/1997 2/1/1998 6/1/ /1/1998 2/1/1999 6/1/ /1/1999 2/1/2000 6/1/ /1/2000 2/1/2001 6/1/ /1/2001 2/1/2002 6/1/ /1/2002 2/1/2003 6/1/ /1/2003 2/1/2004 6/1/ /1/2004 2/1/2005 6/1/ /1/2005 Estimated Area Observed Area Figure 3.9 Estimated and observed surface area at Nyumba ya Mungu Reservoir: an output of a water balance model 47

50 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 hydrometeorological 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 48

51 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 49

52 seen in Table 4.2. K T 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 Table 4.2 Computation sheet of maximum discharges of various return periods for 1D8C gauging station using Log-Pearson Type III distribution. RETURN PERIOD K T (-0.4) K T (-0.464) K T (-0.5) Q T (m 3 /s) The results of fitting indicate that a two year flood is roughly 100m 3 /s and a ten year flood is approximately 200m 3 /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 50

53 1964 widespread flooding occurred in the east African region due to un-usually high rainfall Maximum Q [m3/s] Year 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 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 51

54 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 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.)

55 ID X(m) Y(m) Topo_Elevation (m.a.s.l.) DEM (m.a.s.l.) Calibration 740 Topo_Elevation (masl) Topo_Elevation = 0.955*DEM R2 = DEM (masl) 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)

56 Topo_Elevation (masl) Validate Estimated_Elevation (masl) 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 54

57 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 m 3 /s and Figure 4.9 presents Kirua Swamps Floodplain inundation map at stream flow with return period of 100 years (i.e m 3 /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 m 3 /s. At this flow rate an area of about 534 Km 2 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 m 3 /s). An area of about 668 Km 2 which is equivalent to 78.0 % of the Kirua swamps is inundated at this discharge. Further, the hydraulic model simulations indicate that the average 55

58 depths of inundations for flow discharges of 50.0 and m 3 /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 Stream flow Inundated area Storage [m 3 /s] Area [km 2 ] % Area Storage [km 2 -m] % Storage Note: 1 km 2 -m is equivalent to 10 6 m 3 or Mm 3 Inundated surface area [km2] Streamflow, Q, [m3/s] % Area inundated Inundated surface area in Km2 % Area inundated Figure 4.6: Relationship between stream flow and the inundated surface area 56

59 600 0 Storage [km2-m] % Storage Streamflow, Q, [m3/s] Storage in km2-m % Storage Figure 4.7: Relationship between stream flow and storage Note: 1 km 2 -m is equivalent to 10 6 m 3 or Mm 3 Figure 4.8: inundated area in Kirua Swamps when stream flow at 1D12 is 50.0 m 3 /s) Figure 4.9: inundated area in Kirua Swamps when stream flow at 1D12 is m 3 /s 1:100 flood) 57