Development of a macro scale distributed hydrological modelusing an object-oriented hydrological modeling system
Y. Tachikawa, Y. Ichikawa, K. Takara
Disaster Prevention Research Institute, Kyoto University, Uji 611-0011, Japan

M. Shiiba
Department of Civil Engineering Systems, Kyoto University, Kyoto 606-8501, Japan

ABSTRACT: A macro scale distributed hydrological modeling system using OHyMoS, Object-oriented Hydrological Modeling System (Takasao et al., 1996, Ichikawa
et al., 2000) is developed. When a channel network data set and grid information
on a meso-scale atmospheric model are given, the system automatically builds a distributed hydrological simulation model through a series of procedures. A watershedbasin is divided into grid boxes; a sub-model composed of a runoﬀ element modeland a ﬂow routing element model is set up for each grid box; sub-models are put
together to build a total runoﬀ simulation model by using functions of OHyMoS.
The system is applied to the Chao Phraya River basin in Thailand (110,000 km2 )and a macro scale distributed hydrological model is constructed. River discharges
at many hydrological stations are simulated and model performances are evaluated.

1 INTRODUCTION
To predict ﬂoods, droughts and futurewater resources for a large river basin,a macro scale distributed hydrologicalmodel is an indispensable tool. For modeling water movement of a large river
basin, modeling procedures such as basin
partitioning, hydrological process modeling for a sub-basin, linking sub-basinmodels together to make a total runoﬀ
model require heavy tasks. So, automatic procedures for processing hydrologicalmodeling are necessary so that a model istransferable to various large catchments.
In automatic modeling procedures, processing channel network linkages shouldbe also included to incorporate a river
ﬂow routing model eﬃciently.

To satisfy such modeling requirements, a macro scale grid based distributed hydrological modeling system using
OHyMoS, Object-oriented HydrologicalModeling System (Takasao et al., 1996,
Ichikawa et al., 2000) is developed andapplied to the Chao Phraya River basinin Thailand (110,000km2 ). In the system, a watershed basin is subdivided into grid boxes according to a grid systemof a meso-scale atmospheric model to incorporate atmospheric model outputs.
At ﬁrst, a channel network data set
is generated from the river network data
sets stored in the DCW (Digital Chartof the World) and the GLOBE (Global
Land One-km Base Elevation) data sets.
The generated data set has a newly deﬁned data format for eﬃciently building

Proc. of 4th International Conference Hydroinformatics 2000, Iowa, USA, 2000, CD-ROM, 8 pp.

20N

P20

Discharge
Stations
P1
P12
P14
P19A
P20
W3A
W4A
W16
Y3A
Y20
N12A
N35
N40
N60
C2

19N
W16

P1

Y20
P19A
P14

N35

18N
N12A

W3A
P12

W4A

N60
Y3A
N40

17N

16N
C2 (Nakhon Sawan)

CatchmentArea(km2 )
6,355
26,363
3,853
14,023
1,355
8,985
10,507
1,284
13,583
5,410
15,718
10,335
4,340
18,692
110,569

15N
98E

99E

100E

101E

Figure 1: Channel network and grid division for the Chao Phraya River basin. Dotted lines
show the channel network above Nakhon Sawan (C2 station).

up a river ﬂow routing model.
When the channel network data set
and locations and sizes of a grid system of a meso-scale atmospheric model
are given, a watershed basin is subdivided into grid boxes; a channel network is
separated according to the grid boxes;for each grid box hydrological processes
are modeled as a lumped runoﬀ element
model and ﬂow routing is modeled as a
lumped ﬂow routing element model; andﬂow routing element models are linked
together to make a total runoﬀ simulation model. Hydrological modeling procedures are automatically processed byusing functions of OHyMoS.
This paper is organized as follows. In
section 2, a data structure and its format for representing channel networks
are presented. In section 3, a method forbuilding a hydrological simulation model for a large river basin based on thechannel network data structure is shown.
In section 4, some simulation results for
the Chao Phraya River basin are presented. Finally, conclusions and further
works are drawn in section 5.
2

DATA STRUCTURE AND FORMAT FOR CHANNEL NETWORKThe channel network of the Chao Phraya
River is shown in Figure 1. To makethe channel network data set, locations
and connection relationships of river segments were derived from the DCW data
sets. The GLOBE data sets were used
to add elevation information of river segments to determine directions of water
ﬂow.
When a grid system is set over a watershed basin, the channel network data
set is automatically separated according
to the grid system and channel network

6

6

3
7

9
7

1
1

5
4

2
2

[Comment]
This is a sample dataset.
[Data Format Name]plain format V.2[Coordinate System (map projection name)]
UTM 53[Map Number (relative location of grid box)]
1 2[Reference Coordinates (coordinates of origin)]
0.0 0.0[Vertices of the Map (coordinates of corners of grid box)]
300.0 0.0
800.0 0.0
800.0 500.0
300.0 500.0[Number of Endpoints]
10[Data of Endpoints (coordinates of endpoints)]
1 300.0 210.0 345.0 4
2 400.0 170.0 330.0 03 550.0 60.0 425.0 0
4 330.0 0.0 365.0 1
5 710.0 270.0 369.0 0
6 500.0 500.0 382.0 37 550.0 300.0 410.0 08 580.0 500.0 403.0 39 640.0 400.0 397.0 0
10 800.0 320.0 381.0 2[Number of River Segments]
8[Data]# river segment 1
1
2
1
1
4
300.0
210.0
345.0
0.0
1
2
0
1
-1
340.0
180.0
9999.0
10.0
1
2
-2
1
0
400.0
170.0
330.0
0.0
# river segment 2
1
2
2
2
0
400.0
170.0
330.0
0.0
1
2
0
2
-1
410.0
120.0
9999.0
0.0
1
2
0
2
-1
483.0
85.0
9999.0
12.0
1
2
-3
2
0
550.0
60.0
425.0
0.0
# river segment 5
1
2
3
5
0
550.0
60.0
425.0
0.0
1
2
0
5
-1
420.0
15.0
9999.0
15.0
1
2
-4
5
1
330.0
0.0
365.0
0.0
# river segment 3
1
2
-2
3
0
400.0
170.0
330.0
0.0
1
2
0
3
-1
410.0
200.0
9999.0
11.0
1
2
0
3
-1
450.0
320.0
9999.0
0.0
1
2
0
3
-1
475.0
470.0
9999.0
0.0
1
2
6
3
3
500.0
500.0
382.0
0.0
# river segment 4
1
2
5
4
0
510.0
270.0
369.0
0.0
1
2
0
4
-1
535.0
120.0
9999.0
7.0
1
2
-3
4
0
550.0
60.0
425.0
0.0
# river segment 7
1
2
7
7
0
550.0
300.0
410.0
0.0
1
2
-9
7
0
640.0
400.0
397.0
0.0
# river segment 6
1
2
8
6
0
580.0
500.0
403.0
0.0
1
2
-9
6
0
640.0
400.0
397.0
0.0
# river segment 8
1
2
9
8
0
640.0
400.0
397.0
0.0
1
2
0
8
-1
710.0
360.0
385.0
0.0
1
2
-10
8
2
800.0
320.0
381.0
0.0

8

3

5

8

10

river network
grid box
boundaryi i th end point
j j th river segment

4

Figure 2: Schematic drawing of a channel
network in a grid box.

data sets for each grid box are generated. For example, the Chao Phraya Riverbasin is divided into grid boxes having
1/3 × 1/3 degrees as shown in Figure 1.
Figure 2 shows an example of a grid box
and Figure 3 is the data format of thechannel network shown in Figure 2. The
channel network structure is represented
as combinations of river segments divided by endpoints composed of upper andlower ends of a channel network, conﬂuences, and the intersections of a channel
network and a grid box.
In an example of Figure 2, there areten endpoints and eight river segments.
In Figure 3, [Map Number] speciﬁes relative locations of grid boxes. First column is the horizontal map number andsecond column is the vertical one. The[Data] section stores data of river segments. Each record has horizontal andvertical map number, endpoint number
if the point is an endpoint (a negativevalue means that the endpoint is thelower end of the river segment), riversegment number, identiﬁcation number
which speciﬁes the location of the point
within the grid box, the x, y and z coordinates of the point, and width of thepoint. If the value of the width is missing, zero is inputted.

Figure 3: An example of a channel network
data set.

3

BUILDING OF MACRO SCALE
DISTRIBUTED HYDROLOGICALSIMULATION MODEL

For each grid box, a sub-model composed of a runoﬀ element model and a
ﬂow routing element model is constructed. The runoﬀ element model representshydrological processes within a grid box,
which computes discharge to the channel
network within the grid box. The ﬂow
routing element model gets discharge
from the runoﬀ element model and river

i
im
P-E

Qi



i(A) = im

Qp
i0
W

0

bution of soil water storage capacity iover a basin is deﬁned as

Ai

1.0

A

Sg

R = Qi + K g Sg2

Figure 4: Schematic drawing of a simpliﬁed
Xinanjiang model.

inﬂows from the upper reaches in the adjacent grid boxes, and it calculates river outﬂows to lower reaches within theadjacent grid boxes. When the channel
network data set is separated into subnetwork data sets for each grid box, connection relationships of sub-networks between grid boxes are also generated. By
using the connection information, submodels are linked together and simulated river discharges are passed throughthe sub-networks.
3.1 Hydrological process modelingwithin a grid box (runoﬀ element
modeling)
A simple hydrologic model (Nirupama etal., 1996) based on the Xinanjiang model(Zhao, 1992) was used to model hydrological processes within a grid box, whichtakes into account a spatial distributionof soil water capacity.
As shown in Figure 4, runoﬀ generation happens when the soil water storage
reaches its capacity. The spatial distri-A − Ai1− 1−
1 − Ai1/b 

(1)

when Ai ≤ A ≤ 1.0. If 0 ≤ A ≤ Ai , i =0, where im is the maximum soil water
storage height, A is the areal fraction of abasin which takes a value between 0 and
1, Ai is the impervious proportion of abasin, and b is a model parameter which
determines the shape of the distributionof soil water storage capacity. From theimpervious area runoﬀ depth Qi occurs,
which is represented as
Qi = Ai (P − E)(2)where P is precipitation, E is evapotranspiration. Runoﬀ depth from the pervious area Qp is given by the following
equation when im ≤ io + P − E,
Qp = (P − E)(1 − Ai ) − Wm + W (3)If im ≥ io + P − E,
Qp = (P − E)(1 − Ai ) − Wm + W
+Wm 1 −

io + P − E
im

1+b

(4)

where W is current soil water storage (indepth unit), i0 is current storage heightin unsaturated area, and Wm is the
maximum water storage capacity over abasin (in depth unit) expressed asWm =im(1 − Ai )1+b(5)

W is considered to be soil water storage
which only contributes to evapotranspiration. Qp is added to groundwater storage Sg and total runoﬀ R is computed by
R = Qi + kg Sg2(6)

Model parameters of the runoﬀ element
model are Ai , b, Wm and kg .

3.2 Flow routing modeling within agrid box (ﬂow routing element
modeling)
To model ﬂow routing within a grid box,
the lumped stream kinematic wave model developed by Shiiba et al. (1996) wasapplied. The model computes discharges
from outlets without calculating ﬂows ofeach computational cross-section by setting the assumption that river dischargevaries linearly along each river segment
at each time step.
Let q0 (t) be a discharge change ratein space along river segments in a gridbox, then the discharge of the j th riversegment at a distance x from its upper
end is given by
Qj (x, t) = Qj (0, t) + q0 (t)x(7)
where Qj (0, t) is the inﬂow at the upper end of the river segment. The crosssectional area at x is given by
Aj (x, t) = Kj {Qj (0, t) + q0 (t)x}Pj (8)where Kj and Pj are constants, so thewater storage in the river segments within the grid box is expressed as
S(t) =N  LjAj (x, t)dx

(9)

j=1 0

where N is the number of river segments
within the grid box and Lj is the lengthof the j th river segment. Aj (x, t) is afunction of q0 (t), so S(t) is a function ofq0 (t).
The continuity equation of river ﬂow
in the grid box isMIMOdS(t) Ij (t)+R(t)−Oj (t) (10)
=
dt
j=1
j=1

where Ij (t) is inﬂow into the j th inlet of
the grid box, Oj (t) is outﬂow from thej th outlet, M I is the number of inlets,
M O is the number of outlets, and R(t) isdischarge from the runoﬀ element model. Here, the summation of outﬂows fromthe grid box can be written as
M
O

j=1

Oj (t) =MI
j=1

Ij (t) + q0 (t)NLj (11)

j=1

Substituting Equation 11 into Equation
10
N

dS(t)
Lj
= R(t) − q0 (t)
dt
j=1

(12)

is obtained. From Equations 9 and 12,
the value of q0 (t + ∆t) is obtained andoutﬂow discharges are calculated using
q0 (t + ∆t) and Equation 11.
Model parameters of the ﬂow routing
element model are K and P for each river segment. We assume a wide rectangular cross section, so K is a functionof Manning’s roughness coeﬃcient n andchannel width B.
3.3

Building a total runoﬀ simulation
model
OHyMoS, Object-oriented HydrologicalModeling System (Takasao et al., 1996,
Ichikawa et al., 2000) is designed to connect element models and build a totalhydrological simulation model systematically by using object-oriented programming techniques. In OHyMoS, basic
common functions such as giving model
parameter values, initialization of computational variables, data exchanges between element models and setting computation time steps for each element
model are implemented as functions of
the abstract base class. It is possible
to set diﬀerent time steps for each element model depending on the rate of ahydrological response. The system automatically accumulates calculated values
before they are passed from one element
model to another element model and calculated values are transfered when thelower element model requests the values.

inflow data
total system

the element
model Athe element
model Bgrid-1a port for
sending dataa port forreceiving datagrid-2

Figure 5: Data sending and receiving ports.
grid-4grid-3

rainfallevapotranspiration
output data 1

runoff element model

output data 2

output data 3

Figure 7: Connections of sub-system models
to build up a total simulation model.

lateral inflow

flow routing
element model

Figure 6: Structure of a sub-system model.

To use the functions of OHyMoS, a
program code of an element model ismade by using “inheritance” from theabstract base class. Each element model
has a “data sending port” and a “datareceiving port” through which computed values are sent or received. In Figure 5, the element model A sends data through a “data sending port” and B
receives them through a “data receiving
port”. OHyMoS automatically controls
data transfer according to the port connection relationships.
By using the functions of OHyMoS,
rainfall-runoﬀ processes within a gridbox are modeled as a sub-system mod-el shown in Figure 6. For each grid box,
grid mean precipitation and evapotranspiration are inputted to the runoﬀ element model through a “receiving data
port”, and discharge computed by the
runoﬀ element model is sent to the ﬂow
routing element model through a “sending data port”. The ﬂow routing element model receives the discharge fromthe runoﬀ element model and inﬂows
from the upstream ﬂow routing element
models in the adjacent grid boxes, andit computes the outﬂows to the downstream ﬂow routing element models in
the adjacent grid boxes.
A total runoﬀ simulation model ismade up of the sub-system models. As
shown in Figure 7, sub-models are linked
together according to the channel network connection relationships to transfer the computed river discharge. Whenbuilding the total simulation model, we
do not need to consider the calculation order of the sub-system models be-45

35
Rainfall (mm/day)

Ai
0.0

runoﬀ element modelb
Wm (mm) kg (mm-s unit)
1.0
620.0
0.00011average rainfall

40

Table 1: Values of model parameters.

30
25
20
15
10
5
0
0

ﬂow routing element model
B (m)n (m-s unit)
P
100.0
0.05
0.6

30

60

90

120

150

180
210
Time (day)

240

270

300

330

360

Figure 8: Basin average precipitation.
1400
p19a (14,023 km^2) computed
observed

The Chao Phraya River basin was divided into grid boxes with 1/3 × 1/3 degrees
as shown in Figure 1. Table 1 shows thevalues of model parameters used in theelement models. Model parameters wereidentiﬁed at the watershed having theP20 station(1,355km2 ) and applied to all
the element models. A reason to determine model parameters at the watershedis that its catchment area is nearly equal
to the size of one grid box.
By using observed precipitation data, model performances were evaluated.
Precipitation values given to each gridbox were obtained by simply averaging
the precipitation values observed withineach grid box. There were no observed or
simulated evapotranspiration data sets,so evapotranspiration values were determined from precipitation and discharge
data sets and supposed to be the samevalue every day.
A simulation period is from April 1,
1987 to March 31, 1988. The basin average rainfall is shown in Figure 8. Somesimulation results are shown from Figure
9 to Figure 12. The results at P19A sta-

800
600
400

0
0

30

60

90

120

150

180
210
Time (day)

240

270

300

330

360

Figure 9: Simulated discharge at the P19A
station.
350
n40 (4,340 km^2) computed
observed

300
Discharge (m^3/sec)

4 APPLICATION AND DISCUSSION

1000

200

250
200
150
100
50
0
0

30

60

90

120

150

180
210
Time (day)

240

270

300

330

360

Figure 10: Simulated discharge at the N40
station.
1200
y3a (13,583 km^2) computed
observed

1000
Discharge (m^3/sec)

cause OHyMoS controls accumulationand transfer of computed values andthey are passed depending on the requests of the sub-system models.

Discharge (m^3/sec)

1200

800
600
400
200
0
0

30

60

90

120

150

180
210
Time (day)

240

270

300

330

360

Figure 11: Simulated discharge at the Y3A
station.

tion (Figure 9), N40 station (Figure 10)
and Y3A station (Figure 11) show good
agreement with the observed discharges.
These discharge stations are located at
the upper reaches of the Bhumiphol dam
and the Sirikit dam. Simulations for other stations at upper reaches of the dams

6000

Discharge (m^3/sec)lation system. The next version of themodel will include the eﬀect of humanactivities on a hydrological cycle, such as
dam controls, reservoir regulations, water intakes for irrigation.

c2 (110,569 km^2) computed
observed

5000
4000
3000
2000
1000
0
0

30

60

90

120

150

180
210
Time (day)

240

270

300

330

360

Figure 12: Simulated discharge at the C2station.

also showed good results. However at
the stations located at the lower reachesof the dams, there were signiﬁcant diﬀerences between simulated discharges andobserved ones. Figure 12 shows computed discharge at the outlet of the study
area. This shows that dam controls givea great eﬀect on a hydrological cycle.
5 CONCLUSIONS AND FURTHER
WORKS
A macro scale distributed hydrologicalmodeling system using OHyMoS was developed. When a channel network data set and grid information on a mesoscale atmospheric model are given, thesystem divides a watershed basin into
grid boxes; a sub-model is set up foreach grid box; and a total runoﬀ simulation model is built by using functionsof OHyMoS. The system was applied to
the Chao Phraya River basin in Thailand (110,000 km2 ) and a macro scale
distributed hydrological model was constructed.
The simulation results showed good
agreements with the observed discharges
at the discharge stations along the upper
reaches of the dams. The diﬀerence between simulated discharge and observed
one at the lower reaches of dams suggeststhat it is quite important to incorporate
the eﬀect of dam control to the simu-

Acknowledgments :The hydrologic
data used in this paper were obtained
through the GAME-Tropics project. A
part of this work has been supportedby the Grants-in-Aid for Scientiﬁc Research, Nos. 10044156 (PI: Prof. S.
Ikebuchi) and 11694141 (PI: Prof. K.
Takeuchi) by the Ministry of Education,
Science, Sports and Culture of Japanese
Government (Monbusho).
REFERENCES
Ichikawa, Y., M. Shiiba, Y. Tachikawa
and K. Takara, “Object-oriented hydrological modeling system”, Submitted to HydroInformatics 2000.
Nirupama, Y. Tachikawa, M. Shiiba and
T. Takasao, “A simple water balance model for a mesoscale catchment
based on heterogeneous soil water
storage capacity”, Bulletin of Disaster Prevention Research Institute,
Kyoto Univ., 45, pp. 61-83, 1996.
Shiiba, M., Y. Ichikawa, S. Ikebuchi, Y.
Tachikawa and T. Takasao, “Scaleup of a runoﬀ model using GIS andan object-oriented hydrological modeling system”, IAHS Publ. 235, pp.
63–70, 1996.
Takasao, T., M. Shiiba and Y. Ichikawa,“A runoﬀ simulation with structuralhydrological modeling System”, Journal of Hydroscience and HydraulicEngineering, Japan Society of Civil
Engineers, 14(2), pp. 47–55, 1996.
Zhao, R. J., “The Xinanjiang model applied in China”, Journal of Hydrology,
135, pp. 371–381, 1992.