ｆｃHYDROLOGIC SIMULATION TOWARDS REDUCTION OF POLLUTANTLOADS IN THE KOISE WATERSHED

Boon-Hon LEE1, Jia-Yun Li2, Tsuyoshi KINOUCHI3 and Tadaharu ISHIKAWA4
1,2

Student, Dept. of Environmental Science and Technology, Tokyo Institute of Technology, Japan
E-mail: lee.b.ab@m.titech.ac.jp, li.j.ag@m.titech.ac.jp
3
Associate Professor, Dept. of Environmental Science and Technology, Tokyo Institute of Technology, Japan
E-mail: kinouchi.t.ab@m.titech.ac.jp
4
Professor, Dept. of Environmental Science and Technology, Tokyo Institute of Technology, Japan
E-mail: riversky@depe.titech.ac.jp

Abstract: There is a need to study both water and pollutant transport system of a watershed to reduce amount of pollutant
loads, hence finding solution for watershed environmental problems. My research uses WEP (Water and Energy transfer
Processes) model to simulate water and nitrogen cycle of Koise watershed. Various data such as land covers, meteorology,
underground and rivers’ properties of Koise watershed were processed as inputs to WEP model. As hydrograph is successfully
being simulated, WEP model is confirmed to be applicable to Koise watershed. Next, inorganic nitrogen cycle is simulated
and the result was compared with observed data and continuous simulation result from Artificial Neural Network (ANN)model. Analysis on the sensitivity of uncertain parameters had been conducted to achieve better simulation result.

Key words: Hydrologic simulation, pollutant loads, WEP model, parameter sensitivity
1

INTRODUCTION

Pollutant loads generated by human activities inwatersheds have caused eutrophication in many closed water
bodies such as lakes, reservoirs, and inner bay. In recent
years, pollutant loads from point sources has beensuccessfully reduced by lawful regulation. However there
are still a huge amount of polluted effluents being
discharged from non-point sources such as agricultural lands,
livestock, poultry farms and urban areas. Therefore
management and regulation of non-point sources is key issue
in solving eutrophication problem. As pollutant loads from
non-point sources rapidly increases during flood period,
water quality analysis of monthly or even weekly sampled
water does not have sufficient time resolution (Richards
1998).
Therefore, there is a need to accurately simulate both waterand pollutant transport system of a watershed to findsolution for watershed’s eutrophication problem. Our
research uses WEP (Water and Energy transfer Processes)
model to simulate the hourly time series of water andnitrogen cycle in Koise watershed. Although Koise
watershed is an important agricultural area and designated asa water quality control area, there were no significant water
quality improvements for decades. Because of that, it isessential to improve its water quality. Time series simulation
results from WEP model significantly help to better
understand behavior of river outflow and pollutant loads
discharge. This knowledge will greatly assist our effort infinding solution to reduce pollutant loads from non point
sources in Koise watershed.
In this study, reliability of simulation results by WEPmodel is being tested and verified. As there are limitations incurrent WEP model, for pollutant loads only inorganic
Nitrogen (NO2-N, NO3N, NH4+) is being considered.
Simulation results of WEP model is being compared tosimulation results by ANN (Artificial Neural Network)
model and observed data to testify its reliability. WEP model
consists of many uncertain parameters that may influence its
simulation results. Therefore, analysis of uncertain
parameters had been done to check its sensitivity.

2

DESCRIPTION OF KOISE WATERSHED

Koise watershed lies in the east of Japan (Fig. 1) and has anarea of 222.2 km2 and population of 69,279. It is a part of
Kasumigaura watershed which is located in Ibaraki
prefecture and about 64km northeast of Tokyo.
Koise watershed consists of 19.9% paddy field, 23.4%
agriculture land, 39.3% forests, 9.7% urban area, 2.0% golfcourse, 1.7% barren land, 1.3% rivers, 0.6% highway and2.1% others (Fig. 2). It has four seasons where temperature
ranges from -6℃ to 38℃.Average precipitation per year
is 1250mm, higher compared to world average of 900mm.
The main river in Koise watershed is Koise River. It
discharges into Kasumigaura Lake. Land surface type isbeing categorized into Kanto loam, alluvium or rockfoundation. Each type has different properties such aspermeability coefficient, porosity and layer depth. Numerous
streams and sub basins are connected to the main river. In
our simulation, Koise watershed is being divided into 3 parts(Fig. 1) and 11 rivers are being modeled. Water flow
direction in Koise watershed is delineated by considering
difference in height (Fig. 3).

sectional face of all rivers is in trapezium shape andriverbed’s roughness, thickness at 0.0035 and 1.0m
respectively. The sewage treatment percentage in sub basin 1,
2, 3 is 24%, 0% and 3% where all is very low. It is assumed
that all treated wastewater discharges out of Koise watershed.
3.2 Inorganic nitrogen cycle simulation

Fig. 1 Map of Koise watershed (black line) in
Kasumigaura watershed (dotted line). Black dot
represents observation point.

Fig. 2 Land use cover of Koise
watershed. Agriculture activities
cover more than 43% .

3Fig. 3 Map of flow
direction.

Extended version of WEP model (Iizumi et al. 2005) iscapable of simulating inorganic nitrogen cycle in awatershed. The model was first developed for simulation in
Ushikunuma watershed which is also in Ibaraki prefecture.
The soil properties and weather condition of Koise
watershed are almost the same and similarly agriculture
activities are dominant in Ushikunuma watershed too. Basedon that, this extended version of WEP model is considered tobe applicable to Koise watershed too.
As the main purpose of this conceptual model is to simulate
behavior of inorganic nitrogen in soil surface, undergroundwater and river flow, less important behavior of organic
nitrogen is being left out. Although inorganic nitrogenconsists of ammonia nitrogen, nitrite nitrogen and nitrate
nitrogen, the model calculates all those as inorganic nitrogen.
The model mainly considers nitrogen uptake by plant,
nitrogen used in fertilizers, nitrogen concentration in rainfall,
the decay of remnants and nitrogen outflow to rivers. An
illustration of nitrogen cycle process is shown at Fig. 5.
Monthly average of inorganic nitrogen concentration in
rainfall is obtained from Ministry of Environment. Inorganic
nitrogen concentration of wastewater from untreated area,
water quality of irrigation water, initial condition ofunderground water is being considered as constant at
5.0mgN/L, 1.0 mgN/L and 3.0 mgN/L.

APPLICATION OF WEP MODEL

3.1 Hydrologic simulation
WEP model (Jia et al. 2001) is a distributed water andenergy transfer model that solves physical equations ofprecipitation, infiltration, surface flow, underground water
flow, subsurface flow, artificial water system and heattransfer process in a watershed basin mesh by mesh. The
illustration of those processes is shown in Fig. 4. Unlike
other hydrological models, WEP model can deal withcomplex land cover type. As calculation is done on every
mesh, hydrological output data at any one point can beeasily obtained.
Data of land covers, meteorology, underground and rivers’
properties etc of Koise watershed were processed as inputs
to WEP model. Information were collected from various
references and related organizations. ArcGIS software wasused to process these input data according to each mesh.
Mesh size is 500m x 500m. Complex land cover data werereclassified into water body, vegetation land, barren land andimpervious body. Changes in water flow during irrigation
period are being modeled as well (Jia et al. 2005). Rate of
paddy field which is irrigated by Kasumigaura Lake waterfor each sub basin is 0.336, 0.375 and 0.288. Else it isdirectly supplied by nearby river. We assume that the crossFig. 4 Illustration of WEP modelFig. 5 Illustration of nitrogen cycle

SIMULATION RESULTS

The simulation results of inorganic nitrogen cycle in Koise
watershed from 2007/01/01 00:00 to 2007/12/31 24:00 isshown in Fig. 12. Comparison is made with simulation result
from ANN (artificial neural network) model and observed
data. ANN is a model that predict water quality data by
fixing the empirical correlations between data from opticalsensors such as Chl-a, D-COD with water qualities obtained
from occasional sample analysis (Yoshimi et al. 2007).
In Fig. 12, inorganic nitrogen discharges simulated by WEPmodel and observed inorganic nitrogen data represents thetotal of NO2-N,NO3-N and NH4-N. Simulation results by
ANN model is different as it does not consider NH4-N.
Both results from WEP model and ANN model shows
similar changing pattern of water quality data. We can judge
from Fig. 12 that concentration of inorganic nitrogendecreases during irrigation period. It suggests that quality
and quantity of discharged irrigation water from paddy fieldhas big influence.
When uncertain parameters are calibrated, it is found thatsome parameters greatly affect the simulation result by WEPmodel. In order to indentify the significance of changes ineach uncertain parameter, analysis on sensitivity of uncertain
parameters was done. The result is discussed in following
parts. 5 uncertain parameters were selected for our
preliminary study.

40
20
0

2007/01/01

2007/05/31

2007/10/28

Fig. 6 Simulated hydrograph of year 2007. Enlarged image
in dotted line bracket are shown in Fig. 7, 8.
90
60

70

50

flow rate (m3/s)

80
60
50
40
30
20

40
30
20
10

10

0

0
7/14

7/16

7/18

10/24

11/3

11/13

Fig. 8 Enlarged image of Fig. 7 Enlarged image offlood event’s hydrograph ① flood event’s hydrograph ②
120
simulated

100

observed

80
60
40
20
0
2008/1/1

2008/4/10

2008/7/19

2008/10/27

Fig. 9 Simulated hydrograph of year 2008. Enlarged image
in dotted line bracket are shown in Fig. 12, 13.
120

60

flow rate (m3/s)

4.2 Inorganic nitrogen cycle simulation result

flow rate (m 3 /s)

Hydrologic simulation had been run starting from 1 January
00:00 to 31st December 24:00 for year 2007 by WEP model.
The calibrated simulation result is shown in Fig.6.
Compared to observed data, there are some differences inabsolute value of flow rate especially during flood period
but in overall almost identical hydrograph pattern isachieved. Further analyses of hydrograph during flood
period are done to check on the reliability of calibrated
simulation result. Fig. 7 and 8 show two examples ofenlarged flood event’s hydrographs for year 2007. We
compared simulated peak flow rate during flood events withobserved data. High reproducibility rate of 0.975 is
confirmed.
Next, for verification purpose, we used the same
parameters to simulate hydrograph of year 2008. The
validated simulation result is shown in Fig. 9. Hydrographpattern is fairly well simulated but errors are still visible
especially during flood event. Fig. 10 and 11 show two
examples of enlarged flood event’s hydrographs for year
2008. Similarly to year 2007 analysis, peak flow rate ofobserved and simulated results are being compared. There isa drop in correlation coefficient value but it is still high at
0.9057 (Fig. 13).Therefore it can be concluded that WEPmodel is applicable to Koise watershed as well. It can bederived that extended version of WEP model are applicable
to Koise watershed as well.

60

flow rate (m3/s)

stflow rate (m3/s)

4.1 Hydrologic simulation results

simulated
observed

80

flow rate (m3/s)

4

100
80
60
40

50
40
30
20

20

10

0

0
8/27

9/1

Fig. 10 Enlarged imageofflood
event’shydrograph ③

10/20

10/25

Fig. 11 Enlarged imageofflood
event’shydrograph ④

simulated

4

observed day average data

ANN

flow

120
100

3

80
2

60
40

1

flow rate (m3/s)

concentration ( mgN/L)

140

20

0

0

2007/1/1 0:00

2007/3/22 0:00

2007/6/10 0:00

2007/8/29 0:00

2007/11/17 0:00

Fig. 12 Inorganic nitrogen cycle simulation result for year 2007.
Simulated （m3/s)

80
y = 0.9855x + 1.1227
R² = 0.975

60
40

5.3
y = 0.8787x + 1.1336
R² = 0.9057

2007
2008

20
0
0

20

40

60
80
100
Observed （m3/s)

Fig. 13 Comparison of peak flow rate between observed
data and simulated results during flood event.

5

ANALYSIS OF UNCERTAIN PARAMETERS

Rainfall’s inorganic nitrogen concentration

Monthly average of inorganic nitrogen concentration in
rainfall is set as input data. However in reality, the inorganic
nitrogen concentration differs in each rainfall event. As theaim of WEP model simulation is to accurately simulate
amount of discharged pollutant loads especially during flood
event, the uncertainties of concentration in rainfall is of ourconcern. The inorganic nitrogen concentration value in
rainfall is being calibrated from -50% to 80%.
Besides nitrogen input during rainfall events, there are also
increase of nitrogen content into earth surface by dry
deposition. Therefore, it can be said that nitrogen input into
earth surface from atmosphere is being down rated.

5.1 Initial underground water value

5.4

Without sufficient observation data, we assumed that
inorganic nitrogen concentration in underground water to beconstant at 3.0mgN/L for the whole area of Koise watershed
despite the fact that nitrogen content in underground water is
uncertain and has huge influences on nitrogen content that
outflows into river.
In order to check this uncertain parameter’s impacts, wecalibrated the initial constant value in the range of 1.0
mgN/L to 5.0 mgN/L. Compare to initial setting of 3.0
mgN/L, it was a change from -66.7 % to 66.7 %. Other
parameters were not altered.

Current model assumes that inorganic nitrogenconcentration of irrigated water is equivalent to the nitrogenconcentration of Kasumigaura Lake. Uncertainty still exists
in the percentage of inorganic components of the water
irrigated from Kasumigaura Lake. Irrigation period is set
from 15th April to 31st August.
Inorganic nitrogen is only a part of nitrogen which also
consists of organic nitrogen. As concentration of inorganic
nitrogen will not exceed the default constant value ofnitrogen, calibration was made by decreasing the nitrogenconcentration value from 1.0mgN/L till 0.2mgN/L.

5.2 Denitrification constant

5.5

Before out flowing into river, nitrate nitrogen goes throughdenitrification process. A constant number is being
multiplied to consider the loss of nitrate nitrogen bydenitrification. We assumed the constant number as 0.8.
The constant value was changed from the range of 0.2 to
1.0. A smaller constant represents higher loss of nitrate
nitrogen by denitrification process.
The process is expressed by equation (1) where K is thedenitrification constant, RNO3GW is nitrate concentration in
unconfined ground water and NO3GW is the amount of
nitrate that flows into river.

Another theory for nitrate transport in the surface soil isthat nitrate from rainfall infiltrates into first layer of soil andmix with it before outflows into river. It is expressed asfollowing equation:

NO3GW  K  RNO3GW(1)

Inorganic nitrogen concentration of irrigated water

Modeling nitrate transport in the surface soil

C1ROF = CRA + C1T(1 -  )(2)where CRA is concentration of inorganic nitrogen content in
rainfall, C1T is concentration of nitrogen in first layer of soil,
C1ROF is the total of it and  represents how well does
nitrate mixes with soil’s first layer. The smaller it is, thebetter the mixture is. The default value of  is 1 andcalibration was made by decreasing the value of  till 0.2.

5.7

Error calculation

Error evaluation for changes in each uncertain parameter ismade. The error value  is defined by the following
equation: (   )
n

2

(3)

where  is simulated inorganic nitrogen value,  is day
average of observed data and n represents number ofobserved data.
It is found that 10% increase in underground’s inorganic
nitrogen concentration and denitrification constant; 50%
decrease in irrigated water’s nitrogen concentration and 50%
increase in rainfall’s nitrogen concentration gives thesmallest error value (Fig. 16). Meanwhile, for the case of value in surface soil’s nitrate transport system, value 1
has the smallest error which means default model of nitratetransport in the surface soil is the most optimum. As we cansee in Fig. 16, change in inorganic nitrogen concentration ofirrigated water does not have big impact on error value.
Error value varies only from 0.475 to 0.476 for theparameter change of up to -80%.
The error value from combination of all conditions

Change in Concentration (%)

The concentration of inorganic nitrogen outflow into
Kasumigaura Lake at the end of each month for year 2007 is
considered. We normalized the average of those data to
determine the changes in percentage. It is then compared topercentage of changes in parameter to study the relationshipbetween them. Same analysis method was applied to flood
events as well to determine the influences during flood event.
From the comparison of change in concentration at the endof each month (Fig. 14), it is clear that parameter changes ininitial underground water value and denitrification constant
have big impact on the output data. Both show proportional
relation with a gradient of 0.86 and 0.78. In contrast,
parameter changes of inorganic nitrogen concentration in
rainfall and  value in surface soil’s nitrate transportsystem shows minimal effect.
On the other hand, from the comparison of change inconcentration during flood event, it is found that parameterchanges of inorganic nitrogen concentration in rainfall and value in surface soil’s nitrate transport system shows big
influences. One example of such flood event is shown in Fig.
15. This implies that to correctly simulate flood event’s
inorganic nitrogen cycle, accurate data of inorganic nitrogenconcentration in rainfall and how we define surface soil’s
nitrate transport system play an important role.
In both cases, change of parameter in nitrogenconcentration of irrigated water which only has effect during
irrigation period shows no big difference in outflow’s
nitrogen concentration.

mentioned above is 0.539, which is bigger than default
parameter‘s error value of 0.528. This is because bothincreases of denitrification constant and inorganic nitrogenconcentration in underground water give a higher value ofsimulation result. Double positive relation causes thesimulation result to be drifted away from observed data.
By cross examining between these 5 uncertain parameters,
we found that combination of 10% and 50% nitrogenconcentration increase in underground water and rainfallwith decrease of 20%~50% in irrigated water’s nitrogenconcentration gives best result of  at 0.457.This shows an
improvement of 13.56% compared to simulation result by
default parameter. It is assumed as optimum parameter.
From the relation between observed data versus simulateddata, it is found that optimum parameter produces better
result where an improvement of 21.52% from 0.381 to 0.426
in correlation coefficient is achieved.

-100

50

0
-50

0
-50

50

100

Underground Water
Denitrification constantRainfall concentration
irrigation water
Surface soil

-100
Change in Parameter (%)

Fig. 14 Comparison of change at the end of the month.
Change in Concentration (%)

Results

80
60
40
20
0
-100

-50

-20 0
-40
-60

50

100

Underground Water
Rainfall concentration
Irrigation water
Denitrification constantSurface soil

-80
Change in Parameter (%)

Fig. 15 Comparison of change during flood event.
1.4
Error value (mgN/L)

5.6

1.2
1
0.8
0.6
0.4
0.2
0

-100

Rainfalldenitrification constant
Underground water
Irrigation water
Surface soil

-50
0
50
change in parameter (%)

100

Fig. 16 Error value compare to change in parameter.

simulated
ANN

observed data
optimum parameter

observed day average data
flow

140
120
100

3

80
2

60
40

1

20
0

flow rate (m3/s)

concentration ( mgN/L)

40

2007/1/1 0:00

2007/3/22 0:00

2007/6/10 0:00

2007/8/29 0:00

2007/11/17 0:00

Fig. 17 Comparison of simulation result by default parameter and by optimum parameter.

6

CONCLUSION

Environment is a very complex system that consists ofmany unknown mechanism. In order to find solution forvarious environmental problems, we need better understandof unknown factors. Due to the complex nature ofenvironment system, it is almost impossible to pursue theperfect or universal knowledge of it.
Therefore, in our aim to reduce pollutant loads in Koise
watershed, we suggested the use of WEP simulation model
to simulate the hydrological and inorganic nitrogen cycle
process. First, calibration and verification of WEP model isdone to confirm its applicability to Koise watershed. Afterthe applicability is confirmed, we used extended version of
WEP model to simulate inorganic nitrogen cycle of Koise
watershed. Major conclusions of this study are as follows.
I.

Comparing the pattern of whole year hydrograph andpeak flow of simulation results with observed data,
WEP model is found to be applicable to Koise
watershed. This fact is confirmed by good result
obtained through verification process.
II. Inorganic nitrogen cycle of Koise watershed could be
simulated by WEP model. This is supported by the factthat its simulation results matches well with observed
data and simulation results from ANN model.
III. Through analysis on sensitivity of uncertain parameters,
initial underground water level is found to have big
influence on simulation results. Therefore, in futurestudies more observation should be done to haveaccurate information about underground water.
IV. From the perspective of whole year, inorganic nitrogenconcentration level in rainfall appears to have minimal
effects but it applies great effect during rainfall event.
V. Up to 50% increase of inorganic nitrogen concentrationin rainfall gives smaller error value and this can be
explained by extra input from dry deposition. 1
VI. Calibration among 5 uncertain parameters as
mentioned in this study, best simulation results can beachieved by increasing 50% ,10% and decreasing
20%~50% of inorganic nitrogen concentration in
rainfall, underground water and irrigated water

respectively. The result is shown in Fig. 17.
VII. From Fig.17, we can see that optimum parametershows better result where it matches well withobserved data and simulation results from ANN model.
However, significant difference is still visible in themonth of August which is also the end of irrigation
period. Further studies are needed to indentify the
specific reasons for this phenomenon.

ACKNOWLEDGMENT
Many input data used for WEP model are acquired from
Kasumigaura River Office; Ministry of Land, Infrastructure
and Transport of Japan (MLIT); Japan Meteorological
Agency (JMA), Ministry of Agriculture, Forestry and
Fisheries of Japan (MAFF), Ibaraki Prefectural Government,
National Statistic Center (NSTAC), and report from previous
related researches. We sincerely offer our appreciation tothem for providing us necessary data. We are also very
grateful to the staff of IDEA Consultants Inc, and all
members in Ishikawa・Kinouchi・Nakamura Laboratory
for their assistance in the observation data collection.

REFERENCES
Iizumi, Y., Kinouchi, T., and Fukami, K.: Calculation
Program of Nitrogen Budget in Farmland and Water
Quality Analysis by Distributed System Model, Civil
Engineering Journal, Vol.47, No.11, pp.44-49, 2005.
Jia, Y., Ni, G., Kawahara, Y., and Suetsugi, T.: Developmentof WEP model and its application to an urban watershed,
Hydrological Processes, 15, 2175-2194, 2001.
Jia, Y. et al., Journal of Hydrologic Eng., ASCE, Vol. 10, No.
4, pp 253-263, 2005.
Richards RP (1998). Estimation of pollutant loads in rivers
and streams: a guidance document from NPS programs.
Project report prepared under Grant X998397-01-0.
USEPA, Region Ⅷ, Denver, 7-12.
Yoshimi, K., Liu, M. and Ishikawa, T., A new practicalmeasurement of pollutant load in rivers by using opticalsensors. Advances in River Engineering, Vol. 13, pp. 219224, 2007.