DSpace at VNU: Nutrient Dynamics During Flood Events in Tropical Catchments: A Case Study in Southern Vietnam

CSAWAC 43 (5) 621-786 (2015) · Vol. 43 · No. 5 · May 2015

CLEAN
Soil Air Water
Renewables
Sustainability
Environmental Monitoring

Focus Issue:
Water Management for Agriculture
and Energy Security in Asia

5 | 2015

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652
Nguyen Hong Quan 1
Günter Meon 2
1

Institute for Environment and
Resources, Vietnam National
University, Ho Chi Minh City, Vietnam

2

Leichtweiss Institute for Hydraulic
Engineering and Water Resources,
University of Braunschweig,

Braunschweig, Germany

Research Article
Nutrient Dynamics During Flood Events in Tropical
Catchments: A Case Study in Southern Vietnam
Assessing surface water quality variation as well as chasing water pollution sources is
essential for water quality management. However, for existing conditions in developing
countries, this assessment may not be done properly in many affected catchments due to
limited data and lacking of tools. In particular, pollutant transport from the catchment
to its river system during flood events needs quantification and is the aim of the study.
For this, a combined water quality monitoring and modeling approach is proposed. The
study was exemplarily performed for a typical ungauged medium scale catchment
located in the southern area of Vietnam. The available budget allowed at least a limited
monitoring of nutrients and driven parameters (e.g., flow, sediment). These data were
used to, in total, successfully calibrate the complex Hydrological Simulation ProgramFortran (HSPF) model. The results lead to three main conclusions: (1) the contributions of
point and diffuse sources to nutrient loadings could clearly be identified with the help of
monitoring; (2) water quality sampling during flood events is critical to assess pollution
sources, especially, diffuse ones. However, just a monitoring of data alone is not
adequate to interpret the observed concentrations; modeling is required. (3) Despite of
the limited amount of data, which could be recorded and processed during the
study, a representative catchment modeling during floods could be performed. It
delivered essential information for linking pollution sources with water quality data.
Furthermore, the limits of an application of the complex HSPF model under given
conditions were shown.
Keywords: HSPF model; Point and diffuse sources; Tapioca; Tropical regions; Water quality
monitoring
Received: April 9, 2013; revised: August 27, 2013; accepted: September 18, 2013
DOI: 10.1002/clen.201300264

1 Introduction

Water pollution is one of the challenging problems in water
resources management. At different impact levels, this problem still
exists in both developed and developing countries [1, 2]. For example,
in Vietnam, surface water pollution at river basin scale stays at the
highest priority to be concerned, for example, said by Mr. K. N. Pham,
Minister of the Ministry of Environment and Resources, that “the
Vietnam Environment Administration must concentrate well in
management and pollution control” (www.vea.gov.vn). National and
international agencies, scientists as well as other water resource
stakeholders have been looking for water pollution reduction
solutions. Typically, for example, is the Clean Water Act 1972 for
the implementation of the total maximum daily load [3] in the

Correspondence: Dr. N. H. Quan, Department of Natural Resources
Management, Institute for Environment and Resources (IER), Vietnam
National University of Ho Chi Minh City, 142 To Hien Thanh, District 10,
Ho Chi Minh City, Vietnam
E-mail:
Abbreviations: HSPF, Hydrological Simulation Program-Fortran; LSUR,
length of overland flow plane; LZSN, lower zone nominal storage; NSE,
Nash–Sutcliffe efficiency; PBIAS, percent bias; RMSE, root mean square
error; SLSUR, slope of overland flow plane; TSS, total suspended solid; US
EPA, United State Environmental Protection Agency; UZSN, upper zone
nominal storage
© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

United States. Other examples are the Drinking Water Directive, the
Water Framework Directive in European countries [4], the Vietnam
Law on Environment Protection [5]. However, achievement to
proposed objectives (i.e., good water quality status) is questioned

due to, for instance, complexity of diffuse pollution [6].
Research in nutrient dynamics during flood events is limited in
literature, especially at medium-sized catchments ranging from 10
to 100 km2. The most concerned reason is the lack of monitoring
data [7, 8]. This problem is increasingly recognized in the scientific
community, for example, during the Predictions in Ungauged Basins
program [9]. In the developing countries, for example, Vietnam,
available observations from responsible agencies are often limited in
temporal scale (e.g., from four to twelve times per year). Finer data
resolution is sometimes available at field scale (few hectares) or at a
few small catchments (<5 km2), which are equipped mostly for
research purposes, for example, in Mai [10]. Furthermore, analysis on
the nutrient dynamics during a flood event is often based on
statistical analysis [11, 12] without quantification of complex
anthropogenic impacts [8]. Predictions based on such a few
observations are uncertain. Models can be used to compensate this
gap, not only for prediction but also for assessing different scenarios
including wastewater allocation, change of land use, variation of
climate, etc. [13–15].
Watershed water modeling has been developed extensively in the
last decades. Popular models with focus on pollutant transport are

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Nutrient Dynamics During Flood Events

SWAT [16, 17], HSPF [18], AGNPS [19, 20], SHETRAN [21], and

GLEAMS [22]. The models have been used extensively not only for
research purposes but also for practical application such as water
balance analysis, nutrient variation, and best management practice.
However, as pointed out by Borah and Bera [23], adopting a suitable
model for a particular objective is rather limited. They selected
the DWSM model as the most suitable one among 11 models for
the simulation of the nutrient variation during flood events [24].
Given extensive agricultural activities, a main contribution to
diffuse pollution, are dominant in the Vietnam, responsible
authorities, farmers, and concerned public should be aware of the
diffuse pollution and its adverse impact on water quality best
possibly [25–28]. For example, overland runoff during storms can
bring enormous loads of pollutants to water body systems from the
agriculture areas [29]. Especially, in tropical region, for example, in
Vietnam, extreme flood events occur very often during the rainy
season. However, this aspect does not get enough attention even in
the most recent years and is ignored in most legal documents
with regard to both water quality monitoring programs and
modeling efforts [25, 30]. In addition, the control of point sources
is still difficult because of limited management capacity. For
example, illegal wastewater disposal during flood events is often
reported by local residents, no official evidence is available
because of limited observations (see also VEDAN company, www.
nea.gov.vn/Sukien_Noibat/Tinkhac/Thang%201-2009/sggp_3-1.htm).
These circumstances finally result in an inadequate water quality
management.
Combining water quality monitoring and water quality modeling
is not a common practice yet [8], in this study, the combined
approach is implemented in order to investigate its implementation
in water quality management. Effects of both point and diffuse

653

sources during flood events at a medium-sized Vietnamese
catchment are examined and compositions of inorganic nutrients
(i.e., phosphorus phosphate [P-PO4], nitrogen ammonium [N-NH4],
and nitrogen nitrate [N-NO3]) are addressed based on three flood
events.

2 Materials and methods
2.1 Introduction to study areas
The study catchment, namely Tra Phi (Fig. 1), is a tributary to the Tay
Ninh canal (river) catchment which is one of the most polluted spots
within the Dong Nai River basin, the biggest national river basin in
Southern Vietnam [25]. The study is linked to an joint Vietnamese/
German research project named “Tapioca” funded by the German
Ministry of Education and Research and the Vietnamese Ministry of
Science and Technology which had been completed in 2012 [31]. An
increase of water hyacinths in the river during rainy season has been
observed. Therefore, this study focuses on the high flow period while
the model-based management system of the Tapioca project is based
on long-term simulations on a daily time step.
The catchment is affected by the tropical monsoon climate with
two distinguished seasons (rainy season from May to November, dry
season from December to April). Extreme rainfall events often occur
in the area whose maximum daily rainfall observed during the last
decade was in the range of 180 mm [32]. The catchment is
characterized by highly topography variation (ranging from 2 to
30 m above sea level (m a.s.l.) in low land areas, to 1000 m a.s.l. at the
water head. The catchment covering about 21 km2 is identified by its

corner coordinates of 11°190 5800 N, 106°50 3000 E and 11°230 3000 N, 106°
100 1500 E. There are three main land-use units including agriculture

Figure 1. Study area in 3D view by draping with LandsatTM (Source: Landsat.org, Global Observatory for Ecosystem Services, Michigan State University
()) composite bands 1, 2, 3 (up right) in relation to Dong Nai–Sai Gon river basin [25] (down right, where sub-catchments are in color,
provincial borders are in black line) and Vietnam (left).
© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

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654

N. H. Quan and G. Meon

(the agriculture is classified according to their specific plants
including rice (20%); “tree” like rice, sugar cane, cassavas (15%) as
short-term plants; “high trees” are mainly rubber (31%) as long-term
plants) (66%), forest areas (11%), wetland (“wetland” is river and
existing canals, ponds) (3%), semi-urban areas (semi-urban area is
defined here as mainly pervious areas except main roads and houses
are classified as urban areas) (13%), and urban areas (7%) (Fig. 2). Gray
soil (acrisols) covers uniformly the catchment [32]. The only one
identified point source comes from a factory which produces tapioca
starch from cassava (tapioca) roots. The company produces averagely
30 tons of tapioca starch per day which generates about 360–600 m3
wastewater per day [33]. Normally, from April to July each year, the
company often stops working due to a limited supply of tapioca roots.

Wastewater from the company is kept inside linked ponds;
nevertheless, a part of the wastewater which occurred during the
washing of tapioca, is obviously discharged directly and continuously
into river since the pond of washing wastewater is always full during
the operating period (estimated as 3 m3/h:3 m3/h was estimated by
random observations; regular monitoring was not possible due to
private sector).
There were two samples of wastewater collected in 2007 and
2008 [34]. In addition, it was observed from the field that the
wastewater can be easily increased during flood events. Company
restrictions did not allow taking measurements of released
wastewater during rainfall.

2.2 Field measurements
Field measurements were mainly done within July and August 2009.
The measurements included water level and water quality parameters at the outlet of the Tra Phi River. The water-level records were
transformed into discharge with the help of a stage-discharge curve.
The stage-discharge (rating) curve was developed during the 2007 and
2008 rainy seasons on the basis of 25 flow measurements using an
acoustic Doppler current profiler. Water samples were collected
before, during and after flood events every 1, 2, or 3 h. Only one
sample was taken for each time in the middle of the stream, 40 cm
below the water surface. Several parameters were analyzed
immediately after sampling including temperature (T0), pH, dissolved oxygen, total dissolved solid using the handheld optical
Instrument (WTW 197) [35] with accuracy of Æ1 digit. P-PO4, N-NH4,
and N-NO3 were analyzed within 24 h using a photometer, here the

Spectroquant1 NOVA 60 is used [36]. The equipment implements
specific methods with accuracy were as follows:
(1)

(2)
(3)

N-NH4: indophenol blue method (reference to: analogous EPA
350.1, APHA 4500-NH3 D, ISO 7150/1, DIN 38406 E5) with
accuracy of Æ0.2;
N-NO3: 2,6-dimethylphenol method (reference to: analogous
ISO 7890/1, DIN 38405 D9) with accuracy of Æ0.5; and
P-PO4: phosphormolybdenum blue method (reference to:
analogous EPA 365.2 þ 3, APHA 4500-P E, DIN EN ISO 6878)
with accuracy of Æ0.02%.

The total suspended solid (TSS) was analyzed at the laboratory of the
Institute for Environment and Resources, Vietnam National University,
Ho Chi Minh City following the Standard Methods [37]. Synthesized
information on these three events is presented in Table 1.
Water sampling was done based on observation of flow discharge as
well as at a certain time interval, for example, 1 or 2 h interval when
water level rises rapidly; 3 or 4 h interval when water level recedes
slowly). It was also consistent with those found literature as “for a good
estimation of load at least eight time-integrated samples are needed per
runoff event to reach the level of accuracy comparable to a single flowcomposite sample and consequently we can lose any advantage over
grab sampling at such high sampling frequency” [38]. As shown in
Table 1, 16, 10, and 12 samples were taken for each event, respectively.
Thus, the water sampling frequency in this study was sufficient for an
event sampling. Only one sample was taken for each time in the middle
of the stream, at a depth of 40 cm from water surface.

2.3 Modeling approach

Research on nutrient dynamic modeling at small catchment scale
during flood event in tropical regions is limited. Modeling
applications in tropical regions mostly focus on stream flow and
sediment dynamics. For example, Campling et al. [39] apply the
TOPMODEL model to simulate rainfall—runoff relationship; Marsik
and Waylen [40] use the CASC2D model to assess the changes of land
cover on hydrological cycles; Millward and Mersey [41] use the RULSE
model with some modifications to adapt tropical conditions; DiazRamirez et al. [42] provide an example of utilization of the
Hydrological Simulation Program-Fortran (HSPF) model to study
the hydrology, soil erosion, and sediment transport for tropical
island watersheds at monthly time steps. Polyakov et al. [43] apply the
AnnAGNPS model to simulate runoff and sediment in a tropical
catchment for daily and monthly time steps. Other works on nutrient
dynamics are based on analyzing sampled data [44, 45] in a statistical
manner or applying model at farm scale, for example, nitrogen
leaching [10]. Consequently, simulation of nutrient dynamics at
small catchment scale during flood events, especially at an hourly
time step, is not common in tropical regions.

Table 1. Summarized information of three observed events

Figure 2. Land cover map of Tra Phi Catchment.
© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Total duration (h)
Total rainfall (mm)
Stream water level variation (cm)
Hourly maximum rainfall
intensity (mm)
Total samples

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Event 1

Event 2

Event 3

36
58.6
68
40.5

36
18.6
9
7.3

24
33.6
80
27.6

16

10

12

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Nutrient Dynamics During Flood Events

HSPF [18] was selected among a range of available models codes
including eight often-cited models. These are two empirical models
(ANGPS [19] and CNS [46]), four conceptual models (SWAT [16],
HSPF [18], HBV [47], and ANSWER-2000 [48]), and two physically based
models (SHE and SHETRAN [49] and DWSM [50]). HSPF is a
comprehensive catchment model, simulating land surface and
subsurface hydrological and water quality processes. Furthermore,
stream flow routing and reservoir operation is integrated into HSPF.
The HSPF model suits to all requirements needed for the study, in
particular Nguyen [34]:
(1)
(2)
(3)
(4)

simulation at hourly step to capture the high dynamics of flow
and nutrient load during flood events;
consideration of point and non-point sources;
consideration of all relevant processes in nutrient transformation, transport in upland areas as well as in the river;
availability of a detailed user manual as well as user friendly
interface.

Above characteristics contributed to the widespread utilization of
HSPF. For example, Bicknell et al. [18] stated that “the HSPF is the
primary catchment model included in the EPA BASINS modeling

system”.
The following data were acquired for model implementation
including: (1) meteorological data which were obtained from the Tay
Ninh meteorological national station which is 1 km away from the
outlet; (2) land use and topographic maps obtained from a local
agency; soil parameters; and (3) point sources loadings.
Since soil data are not available for a long-term simulation (e.g.,
soiltemperature), nutrient transformation processes in the soil were
not considered. Nevertheless, the model was used to assess the
contaminant transport during flood events. During the event,
overland flows may be dominant to groundwater flows and thus
bring most pollutants to the receiving water bodies. It was assumed
that the negligence of these processes may not significantly impact
on the results.

2.3.1 Model discretization
The Tra Phi catchment is discretized in the BASINS system into five
sub-catchments accompanied with five reaches. Physical information
of the catchment and land-use distribution are summarized in
Table 2.

2.3.2 Model parameterization
Model calibration follows the hierarchy of catchment model
calibration (BASINS 4 lectures, datasets, and exercises at www.epa.
gov/waterscience/basins/training.htm). Firstly, the hydrological com-

655

ponent is calibrated. Next, the simulated sediment loadings are
compared to observation ones. Finally, nutrients variables like P-PO4,

N-NO3, and N-NH4 are considered. Sensitive parameters were
recommended in BASIN’s lecture notes as well as from Radcliffe
and Lin [51]. Model parameters were manually calibrated using given
values adopted from technical notes [52, 53]. The parameterization of
the HSPF model refers to the following groups: (1) hydraulic, (2)
hydrology, (3) sediment, and (4) nutrients (ammonium, nitrate, and
phosphate).
Hydraulic parameters include mean width, depth of the streams,
rivers, side of floodplain, manning numbers of channel, and
floodplain which were mostly taken from the reference manual [52]
and field observation. Representative parameters for the hydrological, sediment, and nutrients components are explained and
parameterized (calibrated) in Table 3 and are explained in detail
as follows.
Following the detailed instructions, that is, US EPA [52], model
parameterization of the hydrological processes was straightforward.
Some parameters (lower zone nominal storage [LZSN], upper zone
nominal storage [UZSN], length of overland flow plane [LSUR], and
slope of overland flow plane [SLSUR]) were calculated using available
formula or GIS processing, while other parameters were calibrated
based on look-up table or using default values. The initial value LZSN
was calculated based on a formula given in Al-Abed and Whiteley [54]
as:
(
LZSN ¼

100 þ 0:25 P ðSeasonal precipitationÞ
100 þ 0:125 P ðPrecipitation distributed throughout the yearÞ

where P the mean annual precipitation (mm).
UZSN is equal to LZSN multiplied by 0.06 at steep slope areas (i.e.,

forest on rock) and multiplied with 0.08 at moderate slope areas. The
geometry parameters (LSUR and SLSUR) were calculated by means of
GIS processing and kept without calibration. The values of the
calibrated LZSN, INTFW, UZSN, LSUR, SLSUR, and NSUR parameters
stay within the ranges. The infiltration parameter (INFILT) was
calibrated according to the instructions [52] where the soil is most
likely within groups A and B. Thus, the INFILT was rather high after
calibration. This is suitable with the soil characteristics (acrisols) as
well as given observations in the field (except for the wetland and rice
due to clay deposition on the land surface). The AGWRC, IRC was
calibrated after using default values in US EPA [52] until the recession
curve can capture the base flow.
Initial values for sediment parameters were chosen from the
BASIN’s lecture notes and US EPA [53]. Calibration was done based on
the first event observation. The parameters were then kept for the
successive events. Since the catchment is covered by the unique
acrisols soil, the sediment generation parameters were kept similar

Table 2. Physical characteristics of sub-catchments and reaches

Sub-catchments
Parameter
Sub-catchment area (ha)
Drop in water elevation from the upstream
to the downstream (m)
Channel length (km)
Average channel slope (%)

SWS1
(reach 2)

266
19.5

© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

1.74
0.21

SWS2
(reach 3)

SWS3
(reach 4)

SWS4
(reach 5)

811
3

389
1.5

621
0.8

5.54
0.005

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1.54
0.006

2.64
0.006

SWS5
(reach 1)
87.9
1.5
1.11
0.002

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656

N. H. Quan and G. Meon

Table 3. Process and physical parameters used after calibration of the HSPF model for hydrology
Parametric value

Process parameter Description (unit)

Forest

Hydrological parameter
LZSN

Lower zone nominal storage (mm)
381
INTFW
Interflow inflow parameter
1.5
INFILT
Index to soil infiltration capacity (mm/h)
2.44
0.99
AGWRC
Groundwater recession coefficient (dayÀ1)
UZSN
Upper zone nominal storage (mm)
4.5
IRC
Interflow recession parameter
0.3
LZETP
Lower zone ET parameter
0.6
LSUR
Length of overland flow plane (m)
45
SLSUR
Slope of overland flow plane
0.41
NSUR
Manning’s n for the overland flow
0.15
Sediment parameter

KRER
Coefficient in the soil detachment equation
0.3
JRER
Exponent in the soil detachment equation (default values)
2
COVER
The fraction of land surface which is shielded from erosion by rainfall
0.8
AFFIX
Fraction by which detached sediment storage decreases each day as
0.05
a result of soil compaction
NVSI
Rate at which sediment enters detached storage from the atmosphere
0
KSER
Coefficient in the detached sediment wash-off equation [55]
10
JSER
Exponent in the detached sediment wash off equation (default values)
2
Nutrient parameter
SQO
Initial storage of QUALOF on the surface of the PLS (kg/ha)
0.45
POTFW
Wash-off potency factor for a QUALSD (kg/ton)
0.03
POTFS

Scour potency factor for a QUALSD (kg/ton)
0.02
0.045
ACQOP
Rate of accumulation of QUALOF (kg haÀ1 dayÀ1)
SQOLIM
SQOLIM is the maximum storage of QUALOF, (kg/ha) (recommended value)
0.027
WSQOP
Rate of surface runoff which will remove 90% of stored QUALOF/h (recommended value)
0.5
0.05
MON-ACCUM
Monthly values of accumulation rate of QUALOF at start of each month (kg haÀ1 dayÀ1)
MON-SQOLIM
Monthly values limiting storage of QUALOF at start of each month
0.45
(from July to September) (kg/ha)

for each land-use type except those affected by land covers. The gully
erosion, deposition from atmosphere as well as in-stream sediment
transport processes was ignored here, since gully erosion was not
identified during the fieldwork campaign. Thus, default values
were applied for model parameters of these processes. Similarly to
the hydrological part, most of the sediment parameters were within
the ranges provided in look-up tables or by default values [53]. The
coefficient in the detached sediment wash-off equation was out of
the range (0.01–0.5) as it was observed also in Diaz-Ramirez et al. [55].
The calibration was straightforward and soon delivered to reasonable
conditions.

The calibration of hydrological and sediment parameters followed
standard procedures as given by the US EPA [52, 53]. This is not the
case for nutrient parameters as also mentioned by Radcliffe and
Lin [51] which can be considered as the most challenging part in
parameterization processes. In addition, given the site-specific
problems such as simulation at hourly time steps, limited data do
not allow to simulate nutrient transformations in soil or using
default values for in-stream process parameters. Since the focus of
this simulation was nutrient dynamics during flood event only, an
assumption of the interactions and transformation processes in soil
and river was made. Moreover, it was also observed during model
calibration (e.g., the changes of in-stream parameters affect model
results to a minor extent only). In order to cope with the problem of
nutrient transformation during normal condition (e.g., continuous
simulation during dry days) as well as management practice such as
fertilizing the soil, the monthly nutrient accumulation rate and
nutrient storage (MON-SQOLIM and MON-ACCUM, respectively) in the
© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Urban
Cropland
and road and pasture

Rice

Tree

Wetland

482

1.5
1.22
0.99
7.7
0.3
0.3
50
0.02
0.1

482
1.5
12.69
0.99
7.7
0.5
0.6
45
0.02
0.25

482
1.5
1.71
0.99
7.7
0.5
0.8
37
0.02

0.4

482
1.5
6.34
0.99
7.7
0.5
0.7
55
0.02
0.3

482
1.5
0.24
0.99
7.7
0.5
0.9
15
0.02
0.08

0.3
2
0.9
0.05

0.25

2
0.7
0.05

0.2
2
0.5
0.05

0.25
2
0.6
0.05

0.2
2
0.2
0.05

0
10
2

0.37
0.17
0.002
0.027
0.5
0.05
0.45

0
10
2
0.45
0.07
0.03
0.054
0.027
0.5
0.05
0.45

0
10
2
0.45
0.2
0.09
0.045
0.027
0.5
0.05
0.45

0
10
2
0.45
0.13

0.06
0.045
0.027
0.5
0.05
0.45

0
10
2
0.45
0.2
0.09
0.045
0.027
0.5
0.05
0.45

model has been utilized. Model parameters comprising of SQO,
POTFW, POTFS, ACQOP, SQOLIM, WSQOP, MON-ACCUM, and MONSQOLIM mostly relate to storage and transport processes. In addition,
because of lacking data for identifying contributions from different
land uses, each of the model parameters was kept unchanged. An
example of model parameters for phosphate are explained and
parameterized in Fig. 3, more information on data and parameter is
referred elsewhere [34].
Some parameters are only roughly described in the BASINS’
lectures, for example, POTFW, ACQOP, SQOLIM, and WSQOP. Other
model parameters are calibrated by trial and error. The later ones are
site specific. Some other studies using HSPF do not provide parameter

sets for comparison. For example, the work given by Radcliffe and
Lin [51] implementing HSPF for simulating phosphorus dynamics at
catchment scale provided only parameters related to nutrient
transformation in soil. Therefore, comparative studies that is
applying the HSPF model in other areas in the regions or an upscaling of the model are strongly recommended.

3 Results and discussions
3.1 Monitoring data
Three different magnitudes of rainfall and consequently flow for
each event were observed. The maximum hourly rainfall amounts of
the three recorded events were 7, 40, and 26 mm, while maximum
discharge was 1.3, 4.1, and 4.7 m3/s, respectively (Fig. 3). The
correlation coefficient between the maximum rainfall intensity

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Nutrient Dynamics During Flood Events

657

Figure 3. Three rainfall-runoff events measured at the catchment outlet.

Figure 4. TSS, N-NO3, N-NH4, and P-PO4 measured at catchment outlet for three recorded events, respectively.

and maximum river discharge is very high (0.96). The time lag
between maximum rainfall and maximum discharge is about 2–4 h
and depends on event durations. One should note that there were

also preceding rainfall-runoff events which influenced the soil
moisture of the unsaturated zone and thus contributed to overland
flow.
Behaviors of TSS, P-PO4, N-NH4, and N-NO3 were highly dynamic
during these events (Fig. 4). TSS was the most sensitive parameter to
event durations and as well as event magnitudes. However, P-PO4,
N-NH4, and N-NO3 exhibited partially different behavior during the
first and third events, the concentrations of P-PO4 and N-NH4 varied
in correspondence to flow discharge (i.e., 0.4–1 and 0.1–1.4 mg/L for
P-PO4 and N-NH4, respectively). In contrast, during the second event,
these variables increased in a much higher magnitude (i.e., 0.25–3.6
and 0.1–2.3 mg/L for P-PO4 and N-NH4, respectively), although this was
the smallest event. The concentration of N-NO3 reached the highest
value of 2.4 mg/L during the first event. During the second event, a
strong smell of tapioca starch was noted in the sampled water. It was
assumed that the different behaviors between the second on one
hand and the first and third events on the other hand were highly
influenced by more wastewater disposal.

Table 4 summarizes the derived coefficient of determination (R2)
based on an exponential fitting curve between constituents and flow
as well as between constituents and TSS, and those are presented as R2
(Q), R2 (TSS), respectively. Between TSS and flow discharge (Q), the
highest R2 was 0.78 for event 3 and R2 was 0.41 and 0.19 for events 1
and 2, respectively. It should be noted that during event 3 it was not
affected by point sources disposal since the factory did not produce
tapioca starch at that time. Therefore, the sediment (TSS) was in very
high correlation with the river discharge. The highest correlation
between P-PO4 and Q was obtained in event 1 (R2 ¼ 0.48) and the
coefficient remained low in events 2 and 3; while the highest

correlation between P-PO4 and TSS was 0.47 (event 3) and the
coefficient was 0.38 and 0.1 for events 2 and 1. The correlation
between N-NH4 and Q, as well as between N-NH4 and TSS was slightly
different compared to the correlation between P-PO4 and Q, TSS,
especially during the first event. There was no significantly clear
relation between N-NO3 and Q as well as between N-NO3 and TSS. The
correlation was 0.33, 0.3, and 0.38 (events 1, 2, and 3, respectively) and
with TSS was 0.29, 0.24, and 0.26 for events 1, 2, and 3, respectively. In
addition, another similar test was given to the observed nutrient
parameters and results are shown in Table 5. Among the three

Table 4. Correlation between TSS, discharge, and P-PO4, N-NH4, N-NO3 during the three events

TSS

Event 1
Event 2
Event 3

P-PO4

N-NH4

N-NO3

R2 (Q)Ã

R2 (TSS)Ã

R2 (Q)

R2 (TSS)

R2 (Q)

R2 (TSS)

R2 (Q)

0.41
0.19
0.78

0.10
0.38
0.47

0.48
0.12
0.12

0.30
0.41
0.21

0.81
0.19
0.01

0.29

0.24
0.26

0.33
0.03
0.38

à 2

R (Q): correlation of determination based on exponential fitting curve with flow discharge; R2 (TSS): correlation of determination based on
exponential fitting curve with TSS.

© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

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658

N. H. Quan and G. Meon

Table 5. Correlation of determinations among P-PO4, N-NH4, and
N-NO3

P-PO4
and N-NH4

P-PO4

and N-NO3

N-NO3
and N-NH4

0.49
0.98
0.48

0.27
0.2
0.38

0.23
0.25
0

Event 1
Event 2
Event 3

nutrients, P-PO4 and N-NH4 are best correlated, especially during the
second event (R2 was 0.98) where the contribution of point sources
was expected. This correlation was also observed in the wastewater
sample. Correlation between N-NO3 and P-PO4 as well as between
N-NO3 and N-NH4 was not clear: with P-PO4, R2 was 0.29, 0.24, and
0.26 (events 1, 2, and 3, respectively); with N-NH4, R2 was 0.33, 0.3,
and 0.38 (events 1, 2, and 3, respectively). These data indicate a
poor correlation between flow (as well as suspended sediment)
and nutrient constituents (i.e., <0.5). However, the suspended solid

(sediment) is considerably correlated with flow discharge because of
little point source effects. The correlation coefficient can go up to
0.78. A relation between nutrient constituents and discharge or
sediment is hardly visible. However, it was observed that the
correlation between P-PO4 and N-NH4 is quite high as compared to
N-NO3, especially for the second event where there was also a good
agreement between these two parameters in the wastewater samples.
Although data availability for statistical analysis is quite limited
(only three events because of the project budget), it is obvious that
monitoring alone is not enough to explain the variation of water
quality owing to system complexity and various anthropogenic
impacts. Therefore, other tools (i.e., modeling) are needed for
reasoning the nutrient dynamics.

3.2 HSPF results
Model results are compared with measured data for the three events.
The model results are assessed by both qualitative and quantitative
means. Qualitatively, model results versus observed variables are
presented in Figs. 5–7. Quantitatively, model results are assessed by a
number of criteria including agreement index (d), coefficient of
determination (R2), root mean square error (RMSE), Nash–Sutcliffe
efficiency (NSE), and percent bias (PBIAS).
The agreement index (d) can be given as:

Figure 6. Observed and simulated TSS using HSPF model for three
recorded events, respectively.

 average of the
where Oi, observed value; Pi, predicted value; O,
 average of the predicted value.

observed value; P,
The NSE was estimated by:
Pn
ðOi À P i Þ2
NSE ¼ 1:0 À Pi¼1
n
 2
i¼1 ðOi À OÞ
 average of the
where Oi, observed value; Pi, predicted value; O,
observed value.
The PBIAS was calculated with:
Pn
ðOi À P i Þ Â 100
PBIAS ¼ i¼1 Pn
i¼1 ðOi Þ
 average of the
where Oi, observed value; Pi, predicted value; O,
observed value.
The coefficient of determination R2 was predicted:
(
)2
Pn
2
2
i¼1 ðOi À P i Þ
R ¼ ÂP
à ÂPn
Ã0:5
n

 2 0:5
ðOi À OÞ
ðP i À 
PÞ2
i¼1

i¼1

 average of the
where Oi, observed value; Pi, predicted value; O,
 average of the predicted value.
observed value; P,
The RMSE can be estimated by:
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
n
X
RMSE ¼
ðOi À P i Þ2
i¼1

where Oi, observed value; Pi, predicted value.

Pn

ðOi À P i Þ2
d ¼ 1:0 À Pn

 2
i¼1 ðjP i À Oj þ jOi À OjÞ
i¼1

Figure 5. Observed and simulated hydrographs using HSPF model for
three recorded events, respectively.
© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Figure 7. Observed and simulated P-PO4 using HSPF model for three
recorded events, respectively (simulated results on N-NO3, N-NH4 can be
found in MERCK [36]).

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Clean – Soil, Air, Water 2015, 43 (5), 652–661


Nutrient Dynamics During Flood Events

Table 6. Model evaluation based on different criteria

Flow
Event
Event
Event
TSS
Event
Event
P-PO4
Event
Event
N-NH4
Event

Event
N-NO3
Event
Event

PBIAS

d

R2 (1:1)

RMSE

1
2
3

À34.52
À12.05
50.71

0.90
0.61
0.76

0.64
À0.12
À1.00

0.84

0.29
0.78

0.48
À1.70
0.18

1
2

6.62
À217.50

0.83
0.23

0.02
0.30

223.78
254.01

0.52
À85.30

1
2

À29.66
À2.00

0.64
0.77

0.17
0.86

0.38
0.45

À2.95
0.24

1
2

À120.88
À5.49

0.57
0.84

À1.47
0.91

0.41
0.26

À3.82
0.48

1
2

À7.92
À16.87

0.57
0.58

À0.32
0.83

0.71
0.16

À0.59
0.01

NSE

A summary of the evaluation parameters is shown in Table 6. The
observed values in the third events are not the same to the model in
term of recorded time (measured and predicted values are shifted a
30 min). Therefore, in the evaluation table, the contaminants like
TSS, nutrients are only applied for the first and second events, and
evaluation for event 3 is limited to graphical aspect.
Figure 5 shows that flow discharge variations are captured in the
model simulation although the variations are very large (e.g., nearly
four times between the extreme condition and the normal ones). In

Table 6, it can be seen that the simulated values agree well with
observed ones (d, approach to 1). However, the model performance is
rather limited with regard to the R2, RMSE and NSE values, except the
first event. The PBIAS values show that the model overestimates for
the first and second events and underestimates for the last events
(event 3). Nevertheless, the peaks of flow discharge were well
represented. This aspect is very critical since it is assumed that diffuse
contaminants are transported during these times. One reason for
above over- and under-estimations is the impact of the of rainfall data
error. Data from only one meteorological station were used in this
study. Although the catchment is small, its highly topological
differences can induce variations of rainfall in time and space, for
example, by orthographic lifting [56]. Event 2 is a clear example of
errors in rainfall data. Given observation in the catchment outlet
(nearby the rainfall station) that the rain was heavy (18.6 mm in 2 h),
rainfall observed in a daily rainfall station (Nui Ba station, upstream
of the catchment) was only 5 mm. This leads to a simulated flood
hydrograph, which is higher than the hydrograph induced by rainfall
in reality.
The simulation results of sediment transport (TSS, Fig. 6) seem
similar to those observed in the flow discharge. Considering the
uncertainties involved in the input data, in the model algorithms,
and in model parameters; the predicted and observed values have a
moderate to good agreement especially during the high flow.
However, the model cannot reproduce well the observed contaminant during low flow conditions (event 2). In addition, errors caused
by sampling may contribute to these differences.
For nutrients simulation, in the first simulation runs, the model
could not yet represent the values observed in the field. Based on own
observations in the field (at the tapioca company), it could be possible
that the pollutant loadings during the flood events were increased

because of the filled-up settling pond or illegal wastewater disposal.
Consequently, the input data were modified by increasing wastewa© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

659

ter loadings from the company. After several times of “trial”, the
model catches the observed nutrient patterns at different magnitudes higher than the normal wastewater loadings. For the first and
second events, the expected loadings were three and twelve times
higher than the normal one. In the last event, no point sources
existed since the company was closed for renovation. It is observed
that the behavior of P-PO4 (Fig. 7), N-NH4 is quite similar, while N-NO3
is not. The N-NO3 is quite sensitive to flood events, whereas P-PO4 and
N-NH4 were strongly related to point sources and extreme events
only. With the introduction of “illegal” wastewater disposal (denoted
as simulation 2 in Fig. 7), for the first events, only the simulation of PPO4 is improved; the simulation of N-NO3 does not change much, and
the simulation of N-NH4 is even worse. For the second event, the
simulation of N-NO3 has a similar behavior, while the simulation of PPO4, N-NH4 is much improved. The model simulates well the peaks of
nutrient variations. However, during the receding flow, the model
generally underestimates the concentration. This can be explained by
retention effects in the agricultural fields, especially rice fields being
covered by earth-dykes.
Flow discharge and TSS were rather well simulated during high
flood events, especially the peaks. Reproduction failed for the low
flow period. One common reason is because of rainfall data errors.
The different rainfall distribution in time and space can lead to overand under-estimation of the real flow by the model. The nutrient
dynamics during flood events are well captured by the model. In spite
of the abnormal observations, the model was adapted by introducing
higher wastewater loadings given the fact that strong smell of tapioca
starch wastewater was observed during monitoring, as well as during
investigating the sewage discharge system of the tapioca company.

The abnormal nutrient variation during low flow (event 2) is
explainable by considering illegal wastewater disposal (12 times
more than under normal conditions). The improvement of model
simulation is clearly seen for phosphate phosphorus and ammonium
nitrogen. This is not the case for the performance of nitrate nitrogen.
It is concluded that the point sources contribute significantly to
phosphate phosphorus and ammonium nitrogen but the diffuse
sources control the nitrate nitrogen. Nutrient dynamics were well
simulated during the rising flow; it is not the case during receding
flow. This could be an effect of water retention in rice fields where
water was released from the after event by farmers in order to keep
water level stay at a certain level.
Regarding the uncertainty aspects in implementing the HSPF
model, several facts should be kept in mind: (1) the model is complex.
Some parameters can interfere with each other during the
calibration processes. Therefore, the problem of equi-finality cannot
be avoided; (2) despite of the monitoring campaign, the study
catchment can be regarded as a “nearly ungauged” catchment since
data are still limited. Many model parameters were estimated from
literature (e.g., soil data), not from the real catchment. In addition,
input data such as rainfall, pollutant sources (e.g., point sources,
nutrient storages in soil – most related to management practice) were
also to some extent uncertain. Therefore, for further study (e.g., upscaling), the estimated model parameters have to be checked with
care; (3) data are limited for model calibration; no data are available
for model validation. Thus, model results for long-term prediction
have to be re-assessed by collecting more data. In addition, soil data
were not available for a long-term simulation (e.g., several years).
Furthermore, the contribution of contaminants from groundwater
was ignored in this model application. Thus, the model applied here
is limited for short-term simulation of single events.

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660

N. H. Quan and G. Meon

The comprehensive HSPF model has been implemented. The model
can be used for (extreme) event-based simulations of tropical
catchments being exposed to various anthropogenic impacts (point
and diffuse sources). The model parameterization is difficult and
requires high-level expertise. In addition, given a number of
uncertainty sources, especially from model input and model
parameters, collecting of more data as well as implementing
comparative studies are recommended. It became evident, that a
simpler and more robust model is – in total – more reasonable for the
simulation of nutrient dynamics during flood events for given
tropical conditions and a poor database. Details about such a robust
model, which had been developed by the first author, and its
application to the same catchment is given in elsewhere [34].

4 Concluding remarks
In this study, the field measurement of river discharge and water
quality (focusing on nutrients parameters) being relevant to the
research project offers an example of how data can be collected in a
small catchment in Vietnam being exposed to anthropogenic
impacts. Data quality as well as limitations of data monitoring

was also emphasized. The data showed that there are significant
correlations between N-NH4 and P-PO4 in connection to point sources
while N-NO3 is typically related to diffuse sources in the catchment.
However, monitoring alone is not enough to explain the variation of
water quality owing to system complexity and various anthropogenic
impacts. Therefore, the HSPF model was used to investigate the
nutrient dynamics besides monitoring activities. However, the
uncertain and limited data are typical issues when implementing
the model. For example, when point sources are hardly controlled,
consequently, wastewater discharge is highly uncertain and it can be
increased significantly during flood event by either overloading the
wastewater pond or discharging illegally. For this aspect, water
quality modeling becomes an important tool for a sound planning of
wastewater allocation. Furthermore, it is an important tool for the
virtual reconstruction of disastrous historical wastewater discharge
scenarios as applied in several cases in Vietnam where monitoring
data are not available for inspection [57, 58]. The results show that
although the model was implemented, there are still a number of
open issues that makes the model difficult, but not impossible, to be
used as an operational tool for the region. Regarding the group of
available highly complex models like HSPF, comparative studies
using several tropical catchments are recommended. A data bank of
parameter sets for the HSPF model under tropical conditions is
needed if it is used to support water quality management in the
developing countries. Based on the monitoring data and the HSPF
model, further research can be developed, for example, long-term
studies about nutrient transport mechanism (e.g., at rice field),
groundwater and surface water interaction, water quality management schemes (best management practice, wastewater allocation,
and reduction). Furthermore, for the given conditions, simpler
models as, for example, the developed by Nguyen [34], may replace

the HSPF model.

Acknowledgments
The study was performed within the IPSWAT PhD Scholarship
Program of the German Ministry of Education and Research (BMBF).
Furthermore, the study was linked to the Vietnamese-German
research project “TAPIOCA” funded by the German BMBF and

© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

the Vietnamese Ministry of Science and Technology MOST and
coordinated by the Leichtweiss Institute for Hydraulic Engineering and Water Resources, University of Braunschweig, Germany
(www.tu-braunschweig.de/lwi/hywa/forschung-projekte/vietnam).

The authors have declared no conflict of interest.

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