Kyagalanyi2

14 June 2012

Eng. Dr. Henry K. NtaleVala Associates (Ltd).P. O. Box 5933, Kampala, UgandaEmail: [email protected]: +256 702 746 384

(2)

Executive Summary

This report presents the preliminary hydrological and hydraulic model

set-up results for Namanve hydrological basin. This exercise involved

several components including: identification of the most sensitive model

parameters, model calibration and sensitivity analysis for Namanve

hydrological basin. The result give a clearer understanding of the

hydrological response of Namanve catchments and the potential

application in terms of hydrological structures’ design in relation to the

fluctuating stream flows of Namanve river site . The Water Resources

Modelling involved conceptualisation and developed of a hydrologic

model and development of a GIS database for Namanve basin. Several

meteorological datasets were acquired for effective modelling; however,

the continued lack of daily stream flows limited the effective calibration

of the delineated hydrological basins. A basin-wide summary of the

simulated water resources components is presented to give a general

insight into the water resources components to the basin. However,

additional data especially daily stream flows are required to improve the

water resources calibration/simulation. A simple sensitivity study helped

reduce the dimensionality of the calibration challenge and for the

delineated basin, evaluation of the hydrological performance of the SWAT

model on a daily/monthly time resolution reveal the hydrological patterns

and the sensitivity of hydrological variables to input rainfall datasets and

parameter estimates. The results of the water resources model presented

represent a first attempt to comprehensive modelling of the water

resources of the Namanve river basin. At present, the basin has limited

capacity in terms of gauge network for hydrolometeorological monitoring

and operations, hence additional model calibration is required given the

availability of reliable flow data to the basin, which may also require the

installation of new data collection stations in the basin. New equipment

for measuring both climatic and hydrologic variables is needed to

complement the limited data available in the regions and once the

network is setup, it should be ensured that all stations have complete

installation of hydrometeorological equipment to guarantee effective

monitoring of hydrological events. The Hydraulic modelling results were

achieved using the TRRL hydraulic model and based on the hydrological

modelling results for Namanve basin.

(i)

Table of Contents

1 Introduction...........................................................1

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

1.2 Assessment objectives....................................................................1

1.3 Scope of analysis............................................................................1

1.4 Outputs...........................................................................................1

2 On Site Conditions...............................................2

2.1 Current drainage status.................................................................2

2.2 Site climate conditions...................................................................2

2.3 Land cover, and vegetation at the site...........................................2

3 Brief overview of the methodology used to compute flood flows..................................................3

4 Flood analysis in the vicinity of Kyagalanyi Plot. 4

4.1 Flood flows in the Namanve channel east of the Kyagalanyi Plot..4

4.2 Impact of land cover area changes to Flood Flow in the Namanve channel....................................................................................................4

4.3 Determination of the flood inundation levels;................................4

4.4 Other drainage considerations.......................................................4

4.4.1 Safe disposal of offsite and onsite storm runoff.......................4

4.4.2 The proposed interventions within the overall NIP drainage master plan..........................................................................................5

5 Conclusions and Recommendations....................6

5.1 Conclusions....................................................................................6

5.2 Recommendations..........................................................................6

(iii)

List of Figures

Figure 2-1 Current Site Map......................................................................2

Figure 2-2 Average Rainfall for the Namanve area....................................2

Figure 4-1 Cross sectional area of the Namanve drainage channel...........4

Figure 4-2 500-year Flood plain.................................................................4

Figure 4-3 Proposed drainage channels for the Kyagalanyi Plot................4

List of Tables

No table of figures entries found.

(iv)

1 INTRODUCTION

1.1 BackgroundKyagalanyi Coffe Ltd plans to construct a warehouses and a

processing plant for a coffee business hence a water resource

modelling exercise is required to the Namanve Basin to assess and

analyse the basin water resources using the spatial distribution of

available natural resources including land use, soils and water

resources' components. The variability in precipitation patterns

influences the hydrological performance in the basin which in turn

may affect several economic variables in the region, including the

drainage levels after construction of the proposed industrial park,

hence the development of suitable water resources models as tool to

investigate the safe construction levels from a 500 Year flood levels is

investigated in this study and the management of the stormwater that

may occur after such floods

1.2 Assessment objectivesThe objective of the water resources modelling exercise was to

conceptualise and develop a hydrologic model for water resources

assessment for the delineated Namanve basin. This involved

developing a GIS-Database to support the water modelling and

provide an archive for the water resources and hydrologic data in the

basin. A physically-based simulation model (as compared to

conceptual models) was desired for the simulation of the water

balance in the basin. This required long term time-series of daily

hydrological and climatic inputs for calibration and validation. A

number of automated calibration and validation routines that exist

were applied for this assignment, however, successful application and

validation of such water resources model structures requires

extensive daily observed datasets.

1.3 Scope of analysisUsing a suitable climatic datasets, hydrological and hydro-geological

datasets and information related to the Namanve basin, the study was

limited to identification of the hydrological system - upstream of the

proposed development site; simulation and assessment of

Kyagalanyi Plot Flood Analysis Page 1

hydrological basins using an appropriate simulation model to achieve

the project objectives

1.4 Outputs


2 ON SITE CONDITIONS

2.1 Current drainage statusThe delineated hydrological basin the project are lies in central

Uganda towards the North of Lake Victoria and the major sub-basins

that were considered for water resources modelling are presented in

(Figures 2.1).

Figure 2-1 Site Maps of the Hydrological streams location to Namanve Basin, Digital Elevation Model (left) and deliberated

hydrological sub basins and stream network (right).

2.2 Site climate conditions

The proposed site falls within climatic Zone B according to the

Uganda Hydroclimatic Study (2001). The zone receives an average of

1,270 mm of rainfall which is principally spread over 2 rainy seasons:

The long rains of March to May and the short rains of September to

November (Figure 2.2).


Figure 2-2 Average Rainfall for the Namanve area (source: Hydroclimatic study (2001))

2.3 Land cover, and vegetation at the siteThe spatial datasets that were available included the following:

(a) Land use (spatial) datasets: This dataset was available from

several national spatial database including the FAO archives

(http://www.africover.org/system/africover_data.php); and the

USGS Global Land Cover Characterization (GLCC) database

(http://edcsns17.cr.usgs.gov/glcc/glcc.html). The spatial

distributions of land cover (shown in Figure 2.2 ) reveal that the

basins are mainly overlain by dry land and savannah;

(b) Land cover / Land use and Soil data: This information can be

acquired from FAO archives, mainly available as national

Multipurpose Africover Databases on Environmental Resources

(MADE). This may be available from several sources including

http://www.africover.org/system/africover_data.php. The spatial

distribution is shown in shown in Figure 2.2 )

(c) Soil datasets (spatial datasets): This dataset can be acquired from

FAO archives, mainly available as national spatial datasets. The

Food and Agriculture Organization of the United Nations (FAO,

1995) provides almost 5,000 soil types at a spatial resolution of 10

km with soil properties for two layers (0 - 30 cm and 30 - 100 cm

depth). Further soil properties (e.g. particle-size distribution, bulk

density, organic carbon content, available water capacity, and

saturated hydraulic conductivity) can be obtained from Reynolds


et al. (1999) or by using pedotransfer functions implemented in

the model Rosetta (http://www.ars.usda.gov/Services/docs.htm?

docid=8953). The spatial distributions of soil-types is shown in

Figure and the basins are mainly underlain by Af32-2ab-3 and

Fo44-2ab-500 soil systems.

(d) Digital Elevation Model (DEM). This can be acquired from several

public domains including http://www2.jpl.nasa.gov. The other

source for a DEM is the Geological Survey’s (USGS) public domain

geographic database HYDRO1k

(http://edc.usgs.gov/products/elevation/gtopo30/hydro/index.html),

which is derived from a 30 arc-second digital elevation model of

the world GTOPO30. The preferred scale of the DEM should be

higher than "30m". The spatial distribution is shown in Figure 2.2.

(e) Spatial distribution of major water abstraction points: Time-series

of the several water users (abstractions) were required and this

included, water abstraction for water supply, irrigation, etc.

However, the basin is still ungagged and this information was not

readily available.

(f) Climatological time-series of Precipitation, Temperature,

Hydrological flows and other climate and hydrological variables.

This data is mainly archived at the national meteorological and

hydrological institute – the Directorate of Water Development

(DWD) - Uganda.


Figure 2.2. The distribution of Soil types (left) and Land use types

(right).


3 BRIEF OVERVIEW OF THE METHODOLOGY USED TO COMPUTE FLOOD FLOWS

3.1 Hydrological Simulation:

The objective of the water resources modelling exercise was to

conceptualise and develop a hydrologic model for water resources

assessment for the delineated Namanve basin, which involved

developing a GIS-Database to support the water modelling and

provide an archive for the water resources and hydrologic data in the

basin. A physically-based simulation model (as compared to

conceptual models) was desired for the simulation of the water

balance in the basin, this required long term time-series of daily

hydrological and climatic inputs for calibration and validation. A

number of automated calibration and validation routines that exist

were applied for this assignment, however, successful application and

validation of such water resources model structures requires

extensive daily observed datasets.

A brief description of the methodologies employed for the water

resource modelling is briefly described in the following steps: (a).

Review the available datasets & information on water resources;

Identification of major sub-basins for the Namanve river system -

upstream of the proposed development site; (b). Validation of the

findings and acquisition of supplementary data and information

involved identification of key data collection points for hydrological

model, analysis of existing climatic data, hydrological and hydro-

geological data and information; (c). Simulation and assessment of

hydrological basins for the delineated river basins and aquifer

systems in the Namanve basin involved the Identification of

appropriate simulation model (the Soil Water and Assessment Tool

(SWAT)); pre-processing of input datasets; model set-up; attempt on

model calibration and validation;

In this study, several return period floods have been quantified to size

the structures, check if overtopping conditions occur and evaluate the

proposed Hydraulics structure foundations against scour.


3.2 Flood Analysis MethodsBy definition flood flows are rare events and data availability is a

major issue given that sometimes, the datasets are completely

unavailable, especially for ungauged sites. For situations where

datasets are available, extreme flood conditions may be such that no

flow records can be considered suitable to provide the estimates

required, i.e. extrapolation of rating curves. Consequently careful

considerations have to be made using the available datasets to before

selecting the analysis method to be considered. In order of

preference, Watkins and Fiddes (1984) recommend the following

methods for estimating design floods:

a) Methods based on analysing flow data i.e. Extreme value

analysis, Flood transposition, Slope-area method, Bank full

flows

b) Regional flood formulae like envelope curves

c) Rainfall-runoff models i.e. the rational method, unit hydrograph

techniques and synthetic hydrograph

d) Hybrid methods based on a regionalization of rainfall runoff

models i.e. the ORSTOM method (developed in West Africa),

TRRL method (based on 14 catchments in Kenya and Uganda),

the SCS curve number method and the generalized tropical

flood model.

The choice between these methods depends on whether the detailed

shape of the flood or the probable maximum flood is needed and on

the availability of the reliable flow records at the design site or

nearby sites, whether on the same river or some other catchment and

also depends on availability of datasets required and how suitable and

reliable the datasets are.

For this assignment, statistical analysis using flood frequency

estimation was carried out to derive the design flood magnitudes.

However, no flow data is available onsite, hence the flow model was

developed as prescribed before and the TRRL East African model was

also applied to provide estimates for the design flows. The advantages

of relaying on the TRRL method involve the following:


It can be experimentally derived and tested using measurements

of rainfall and runoff for any representative catchments in and it

is specifically tailored for use in flood estimation for highways

bridges and culverts.

The method has been developed extensively by use of reliable

rainfall records for over 867 stations available in the archives of

the East African Meteorological Department with a record length

of 10 - 40 years. Depth-duration data were obtained for stations

in Kenya, Tanzania and Uganda (Busia, Kasese, Wadelai,

Matuga, Atumatak, Entebbe, Gulu, Kampala, Jinja, Mbarara,

Tororo, and Fort Portal).

It incorporates both the Unit Hydrograph (UH) approaches and

regionalization techniques.

It was designed to provide estimates of peak discharges at

recurrence intervals of 5-25 and up to an upper limit of 50 - 100

Years for small catchments of up to 200 km2.

Areal reduction factors for East African rain gauge networks as

well as variations in vegetation are also incorporated in the

model


3.3 The Flood Frequency Analysis MethodologyThe methodology used for estimating the design flood for different

recurrence intervals using statistical analysis of extremes was as

follows:

From a record of daily historical flows (or simulated flows), the

annual maximum values (the maximum daily flow for each year)

were selected;

From a number of candidate statistical distributions the

distribution that best fits the annual maximum flows was

selected. Four candidate distributions were selected for the

current study namely: Normal, Lognormal, Extreme Value and

Weibull distributions;

The parameters of the distribution were estimated and the

growth curve derived;

The flood flows corresponding to the set return periods at the

gauging stations were estimated;

A suitable factor to convert mean daily flows into peak flow

values was applied. The factor takes into account the shape of

the flood hydrograph and depends on, among others, the

catchment size, time taken to route the flow through channels

and available storage (in lakes and swamps). The factor can vary

between 1 and 2.5 for large catchment. For smaller catchments,

a much higher factor may be needed;

Where the gauging station location is different from the bridge

site the flows were transferred to the bridge site using the Flood

Transposition method. In this method, it is assumed that the

catchment characteristics for the catchment contributing the

two (gauging station and bridge site) do not vary considerably

and the flood generation mechanisms are similar. In this case,

the flows at the two points are proportional to the areas of their

catchments. Therefore, the flow at the bridge site is simply

estimated as the flow at the gauging site multiplied by the ratio

of the two areas.

3.4 The TRRL model methodology


The steps involved in estimating the design flood for different

recurrence intervals using the TRRL Model were derived from

Watkins and Fiddes (1984) by estimating the following:

a) Catchment area (A) to the basin, upstream to the bridge site;

the catchment slope and channel slope;

b) Catchment type and the surface cover flow time (TS). This may

be computed using equation 7.27 and Table 7.16;

c) The soil type, either by geotechnical investigation or by use of

available soil maps. Soil permeability class and slope class were

established using Table 7.10 and 7.11 and, in connection with

Table 7.12 or 7.13, the basic runoff coefficient (CS) was

established;

d) The land use factor (CL) and catchment wetness factor (CW)

from Tables 7.14 and 7.15

e) The runoff coefficient (CA) using equation 7.22;

f) The base time (TB) is computed from equation 7.29;

g) The ‘Kampala Equation’ (equation 4.11) was used to estimate

the areal reduction factor to take into account that tropical

catchments that rarely receive rainfall uniformly over the

entire catchment;

h) The design storm rainfall (P) for each recurrence interval, for a

selected base time;

i) The average flow (Q̄)

during base time was estimated from the

following relationship:

Q̄=CA PA

360T B

j) The design peak (Q̂)

was the computed from

Q̂=F Q̄

Where and appropriate value of F is taken from Table 7.17

3.6 Flood estimatesGiven that a Frequency analysis approach is followed in relationship

with the TRRL method, a method with higher estimates was selected

for use in hydraulic design. This approach was used for Namanve


proposed bridge site and the simulated hydrological basin data to the

delineated hydrological basin at the proposed Site.


4 FLOOD ANALYSIS IN THE VICINITY OF KYAGALANYI PLOT

4.1 Flood flows in the Namanve channel east of the Kyagalanyi Plot

Applying the methodology described in the precious section, the 50, 100, 500 year flood estimates that will pass through the unlined Namanve channel east of the Kyagalanyi plot have been estimated in the Table 4.1.

Table 4.1. Flood estimates that will pass through the unlined Namanve channel

Parameter Description AbrevReturn Period (n)- years

10 25 50 100 500

Area km2 A 1 1 1 1 1

Catchment slope Average Sr 9% 9% 9% 9% 9%

Slope class Table 7.11 S 4 4 4 4 4

Surface cover flow time

Forest, very steep (Table 7.16)

Ts (hr) 2 2 2 2 2

Soil class Permeable (Table 7.10) I 3 3 3 3 3

Basic runoff coefficient

Table 7.12 Cs 45% 45% 45% 45% 45%

Land use factorDense vegetation - Table 7.14

CL 1 1.0 1.0 1.0 1.0

Catchment wetness factor

Dry zone, perennial streams - Table 7.15

Cw 0.50 0.50 0.50 0.50 0.50

Percentage of runoff

Equation 7.22 Ca 23% 23% 23% 23% 23%

Base time Equation 7.29 TB 3.9 3.9 3.9 3.9 3.9

2yr, 24 hr rainfall millimeters - Figure 3.6 70 70 70 70 70

10:2 year ratio Table 3.6 1.64 1.64 1.64 1.64 1.64

Return period (Years)

10 25 50 100 500

n:2 year ratio Figure 3.11 1.65 1.96 2.20 2.44 2.99

Constant b Table 4.6 b 0.3 0.3 0.3 0.3 0.3

Constant n Table 4.5 n 0.95 0.95 0.95 0.95 0.95

Area reduction factor

Equation 4.11 (T=8) ARF 0.96 0.96 0.96 0.96 0.96

Rainfall ratio Equation 4.3 RR 0.85 0.85 0.85 0.85 0.85

n-yr 24-hr storm depth

94.3 112.0 125.6 139.2 170.9

Average flow during base time

Equation 7.31 2 2 2 2 3

Peak factor Forest zone - Table 7.17 1.7 1.7 1.7 1.7 1.7


n-yr peak flow m3/s 3 3 3 4 5

4.2 Impact of land cover area changes to Flood Flow in the Namanve channel

This should address the following issue: How will this value change when the rest of Namanve area is built up? (again another set of points or curves) The Flood values we get here are the ones we are going for the design

4.3 Determination of the flood inundation levels;

Should answer the following: What is the likely flood level (inundation) of say 100, or 500 year flood level? (In meters above sea level). Therefore what is the best level to which the foundation should be set in order to be protected from the 500 year flood level? (after the getting the flood magnitude, assume steady flow in the channel rich whose cross-section we already have and get the maximum depth using the simple mannings equation!).

Figure 4-3 Cross sectional area of the Namanve drainage channel

Figure 4-4 500-year Flood plain

4.4 Other drainage considerations

4.4.1 Safe disposal of offsite and onsite storm runoff

Deal with the following: You recall the culvert crossing the road going direct into the Kyagalanyi plot. Assuming a 100 year flood, what should we expect to cross at this point? What are the sizing of the open channel drainage which should be put up to get rid of this discharge and the runoff generated internally [in the Plot] safely into the Namanve channel?


Figure 4-5 Proposed drainage channels for the Kyagalanyi Plot

4.4.2 The proposed interventions within the overall NIP drainage master plan

5. How do the proposed interventions in (4) above fit in with the overall Namanve Industrial Park? That’s why I want you to visit the NIV office to find out this information


5 CONCLUSIONS AND RECOMMENDATIONS

5.1 ConclusionsThe hydrological and hydraulic modelling work presented here involved data

collection, hydrological model set-up, identification of the most sensitive model

parameter, model calibration and sensitivity analysis for the Namanve basin. The

main output of the strategy was a clearer understanding of the hydrological and

hydrological response of Namanve catchments and the potential use of these

hydrological responses to facilitate the design of the hydraulic structure being

proposed in the basin. The Water Resources Model results presented facilitate the

conceptualisation and development of additional foundation for hydraulic

assessment in the Navamve region. Several meteorological datasets were acquired

for effective modelling; however, the continued lack of daily stream flows to the

Namanve still limited the effective calibration of the delineated hydrological basin.

However, several attempts were carried out to ensure effective estimation of the

Water resources for the basin. A basin-wide summary of the simulated water

resources' components was presented and most of the results from this study

provide a foundation for water resources estimates and further work.

Limitations faced were mainly due to the limited hydrologic data availability, hence

additional data collection for Namanve basin is essential for improving the

estimates and the effectiveness of hydrological simulations. Additionally, use of

remote sensing products can also be useful in the short term and the results of this

study create a basis for additional investigations in water resource modelling in

terms of model set-up and simulation methodology. Additional data especially daily

stream flows at several locations in the hydrological region is required to improve

the water resources calibration/simulation. The available hydrological and

climatological data could be sparse and not free of errors. The climate in the region

is rather complex and for accurate use of hydrological models, representative

precipitation sequences are required for additional calibration. Evaluation of the

hydrological performance of the SWAT model on a daily/monthly time resolution

should reveal the hydrological patterns and the sensitivity of hydrological variables

to input rainfall datasets and parameter estimates. A simple sensitivity study helped

reduce the dimensionality of the calibration challenge. The TRRM model applied for


the hydraulics modelling presented reliable results for Flood computation for

reliable designs of the proposed park at different design return periods.

5.2 Recommendations

The present hydrological/hydraulic modelling results present a baseline for the

proposed structural developments, however, additional modelling is required given

additional datasets are made available for effective hydrological simulations and

there is also need to intensify the set-up of hydrometeorological monitoring

network to enhance the capacity in terms of gauge network for

hydrolometeorological monitoring and operations to ensure effective operation of

the designed hydrometeorological networks. It should be ensured that installed

stations in the basin have complete installation of equipment to guarantee effective

monitoring of meteorological and hydrological events. Given the significant lack of

hydrological and Meteorological data required for water resources monitoring and

modelling, it is recommended that weather stations should be deployed after an

appropriate study to provides a background for further details hydraulic studies for

the proposed industrial development in the Namanve region.


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