Open Journal of Modern Hydrology, 2013, 3, 75-78
http://dx.doi.org/10.4236/ojmh.2013.32010 Published Online April 2013 (http://www.scirp.org/journal/ojmh) 75
Calibration of Hydrognomon Model for Simulating the
Hydrology of Urban Catchment
Haruna Garba1, Abubakar Ismail2, Folagbade Olusoga Peter Oriola1
1Department of Civil Engineering, Nigerian Defence Academy, Kaduna, Nigeria; 2Department of Water Resources and Environ-
mental Engineering, Ahmadu Bello University, Zaria, Nigeria.
Received August 8th, 2012; revised September 15th, 2012; accepted September 25th, 2012
Copyright © 2013 Haruna Garba et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
Due to chaotic nature of flow in natural open channels and the physical processes and the unknown in a river basin vari-
able, the parameters to be used in studying the behavior of river basin to a given rainfall data cannot be measured di-
rectly for this reason, a hydrological model was calibrated and applied to simulate the hydrology of Kaduna River (7112
sq miles) North West Nigeria. Prior to the model calibration, a sensitivity analysis of the hydrognomon model to the
parameters was carried out to gain a better understanding of the correspondence between the data and the physical proc-
esses being modeled.
Keywords: Calibration; Model; Optimization; Flood; Stream Flow; Parameter
1. Introduction
Across the globe, human populations are becoming in-
creasingly urban with approximately fifty percent of the
worlds population r esiding in urban areas as observed by
[1]. Continued land development and land use changes
within cities present considerable challenges for envi-
ronmental management. [2] observed that hydrologic
changes including increase impervious area, soil com-
paction and increased drainage efficiency generally lead
to increase direct runoff, decreased ground water re-
charge and increase flooding cited by [3]. Increase
stream stage and discharge variability are the common
responses to urbanization. [4] observed that urban strea ms
are susceptible to the occurrence of extreme flow event
than their rural counterpart. Furthermore, according to [4]
changes in peak flow due to urbanization vary with the
degree of urban development, recurrence interval of the
peak flow and location within the water shed.
Hydrological models especially the simple rainfall-
runoff types are widely used in understanding and quan-
tifying the impacts of land use changes and provide in-
formation that can be used in land use decision making.
Furthermore [5], observed that the frequency of occur-
rences of extreme flood due to climate changes have in-
creased the need for bett e r understanding of flo o ds.
Understanding the behavior of a river basin to a given
rainfall volume depends on the analysis of rainfall data
which helps to provide information about data with re-
gards to the variable under study. The probability distri-
bution is a hydrological tool most widely used in flood
estimation and prediction Probability distribution func-
tions have been used to model phenomenon characterized
by significant variability such as rainfall in contrast to
deterministic approach determin ed by physical principles.
Parameters of distribution functions are characteristics of
rainfall data which helps to provide information about
the data with regards to the variable under study. Due to
parametization of the physical processes and the un-
known in the basin characteristics parameters to be used
cannot be measured directly; they are therefore obtained
by a process of model calibration.
Model calibration according to [6] is the procedure of
demonstrating that the model can produce field measured
quantities such as hydrau lic heads and flows. It is carried
out by finding set of parameters and boundary co nditions
that produce simulation results that is in agreement with
field measured data. The objective of model calibration is
maximize the coefficient of efficiency by reducing the
percentage difference in mean, standard deviation and
coefficient of variation between the predicted and ob-
served stream flow. Calibration of a rainfall-runoff mo-
del with respect to local observational data will help to
improve model predictability as observed by [3]. When

Calibration of Hydrognomon Model for Simulating the Hydrology of Urban Catchment 76
model results match observed values from stream flow
measurement, there is greater confidence in the reliability
of the model.
The model was calibrated and applied to simulate the
hydrology of Kaduna River under various climatic con-
ditions as part of a PhD study by [5].
2. Materials and Methods
2.1. The Model
Hydrognomon is an open sources software tool used for
the processing of hydrological data (Figure 1). Data are
usually imported through standard text files, spread
sheets or by typing. The available processing techniques
for the tool includes time step aggregation and regulari-
zation, interpolation, regression analysis and infilling of
missing values, consistency test, data filtering, graphical
and tabular visualization of time series. Hydrognomon
support several time step from the finest minutes scales
up to decades. The programme also include common
hydrological application such as evapotranspiration mo-
deling, stage discharge analysis, homogeneity test, areal
integration of point data series, processing of hydromet-
ric data as well as lumped hydrological modeling with
automatic calibration facilities as contained in the hy-
drognomon user manual (http:www.hydrognomon.org) [7].
2.2. The Study Area
Kaduna Township located between latitudes 10˚27'15''N -
10˚13'5''N and Longitudes 7˚21'48''E - 7˚29'36''E in the
high plains of North western Nigeria region (Figure 2).
The general relief of the area is an undulating plain land
at a height of between 582 m to 640 m [8]. Kaduna state
experiences a typical continental climate with distinct
seasonal regimes. The spatial and temporal distribution
of rainfall varies over Kaduna. The soils and vegetation
Figure 1. Structure of the simulation/calibration module
(http: www.hydrogn o mon. org).
area typical of red brown to red yellow tropical soils and
savanna grass land comprising of scattered trees and
woody scrubs.
Kaduna river is the main tributary of Niger river in
central Nigeria. It rises on the Jos plateau south west of
Jos town in a North West direction to the northeast of
Kaduna town (Figure 3). It then adopts a south westerly
and southerly course before completing its flow to the
Niger River at Mureji. Most of its course passes through
open savanna woodlands but its lower section cut several
gorges including the granite ravine at Shiroro above its
entrance into the extensive Niger flood plains.
2.3. Data Used
The historical data used for the calibration were recorded
rainfall and gauge height levels from 1975-2000 (26
years) of record at a gauging point (Datum at 582.96 m)
located in the study area at Kaduna south water works.
The data are totals on monthly basis spanning the cali-
bration period. The steps of the data collection process
Figure 2. Location of Kaduna state on map of Nigeria.
Figure 3. Location map of the study area (Kaduna River).

Calibration of Hydrognomon Model for Simulating the Hydrology of Urban Catchment 77
involve the following:
 The daily stream flow was read as gauge height while
the daily rainfalls were read for each of the stations.
 The monthly maximum stream flow values and rain-
fall values were extracted from the daily values.
 The gauged levels measured were used to scale the
flow to runoff volume of the watershed by using the
expression below [9] in calibrating the model.
Q = ICA
where
Q = calculated runoff;
I = gauged w ater levels;
C = a factor (distribution coefficient) the ratio maximum
gauge level at a point tothe mean gauge levels of Kaduna
river. A = draina g e area of Kadu n a river.
3. Results and Discussions
Before calibrating, the sensitivity analysis of the hy-
drognomon model to the flow parameters was tested. The
hydrognomon model is most sensitive to the flow reces-
sion parameters in both the initial soil storage and the
initial ground water storage. The recession parameters
control the ability of the soil to retain water in the two
storages, the model is insensitive to the capillary flow
and the coefficient of base flow is insignificant in simu-
lating the flow.
The physical processes on the basin scale and the un-
knowns in the basin characteristics consist of too many
parameters which cannot be measured directly, for this
reason the model was calibrated. Figure 4 shows the ob-
served and simulated runoff. The actual runoff was cali-
brated with the calculated runoff at an objectiv e function
of 0.993 [10]. Prior to the model calibration, a sensitivity
analysis of the hydrognomon model to the soil parame-
ters have been tested. For the test of each parameter, all
the others were fixed and the tested parameter was ch ange
from the lower to th e upper boundary on a discrete valu e
and applied to the model (Figure 4). The table below
shows parameters calibrated in the study together with
their upper and lower boundaries. The model was cali-
brated at an objective function of 0.390 with 8001 itera-
tions. The parameter calibrated in this study with lower
and upper boundaries is in the Table 1 below.
4. Conclusion
Due to the fact that the physical processes at the river
basin scale and the unknown in the basin characteristics
consist of too many parameters which cannot be meas-
-ured directly the model was thus calibrated. The model
is most sensitive to the flow recession parameters of both
the lower and the upper tanks. The calibrated results
showed the Nash and Sutcliffe efficiency of >9. The pa-
Figure 4. Pr edicte d and simulate d monthly flow for Kaduna
River.
Table 1. Parameters calibrated in the study with their mini-
mum and maximum values.
Parameter Lower
boundary Upper boundary Sensitivity level
Soil storage
capacity (κ) Insensitive
Coefficient for
inner flow (λ) 0.01 0.990
Highly
sensitive
Percolation
coefficient (μ) 0.01 0.975
Highly
sensitive
Evaporation (ε) 0.01 0.035 Insensitive
Coefficient for
out flow (υ) 0.00 0.010 Insensitive
Coefficient for
surface runoff
flow (κ) 5 599.95
Highly
sensitive
H1/κ 0.01 0.01 Insensitive
Threshold for
creating base flow
(H2) 5.0 5.0 Insensitive
S0/κ 0.00 0.990
Highly
sensitive
Initial ground
storage (γο) 5 300.00
Highly
sensitive
Coefficient for
base flow (ξ) 0.01 0.990
Highly
sensitive
rameters of λ (0.99), μ (0.975), κ (599.95), S0/κ (0.990),
γο (300) and ξ (0.990) can be transferred to the model and
applied to simulate the hydrology of Kaduna River for
hydrological investigation.
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