Event and Continuous Hydrologic Modeling with HEC-HMS
Xuefeng Chu, A.M.ASCE1; and Alan Steinman2
Abstract: Event hydrologic modeling reveals how a basin responds to an individual rainfall event 共e.g., quantity of surface runoff, peak,
timing of the peak, detention兲. In contrast, continuous hydrologic modeling synthesizes hydrologic processes and phenomena 共i.e.,
synthetic responses of the basin to a number of rain events and their cumulative effects兲 over a longer time period that includes both wet
and dry conditions. Thus, fine-scale event hydrologic modeling is particularly useful for understanding detailed hydrologic processes and
identifying the relevant parameters that can be further used for coarse-scale continuous modeling, especially when long-term intensive
monitoring data are not available or the data are incomplete. Joint event and continuous hydrologic modeling with the Hydrologic
Engineering Center’s Hydrologic Modeling System 共HEC-HMS兲 is discussed in this technical note and an application to the Mona Lake
watershed in west Michigan is presented. Specifically, four rainfall events were selected for calibrating/verifying the event model and
identifying model parameters. The calibrated parameters were then used in the continuous hydrologic model. The Soil Conservation
Service curve number and soil moisture accounting methods in HEC-HMS were used for simulating surface runoff in the event and
continuous models, respectively, and the relationship between the two rainfall-runoff models was analyzed. The simulations provided
hydrologic details about quantity, variability, and sources of runoff in the watershed. The model output suggests that the fine-scale 共5 min
time step兲 event hydrologic modeling, supported by intensive field data, is useful for improving the coarse-scale 共hourly time step兲
continuous modeling by providing more accurate and well-calibrated parameters.
DOI: 10.1061/共ASCE兲0733-9437共2009兲135:1共119兲
CE Database subject headings: Watersheds; Hydrologic models; Geographic information systems; Runoff.
Introduction
Watershed hydrologic modeling and the associated model calibration and verification require a large set of spatial and temporal
data 共e.g., topography, land use/covers, soils, rainfall, and flow
monitoring data兲. In practice, however, the availability and quality of these data are often an issue one needs to cope with. Sometimes, one has to compromise the overall modeling quality
because of insufficient high-resolution data for developing, calibrating, and validating the model. Under these circumstances, it is
critical to develop an effective modeling strategy that not only
takes full advantage of the available data but also maximizes the
accuracy of modeling.
The goal of the current study is to develop such a strategy by
combining fine-scale event and coarse-scale continuous hydrologic modeling with the Hydrologic Engineering Center’s Hydrologic Modeling System 共HEC-HMS兲 共USACE-HEC 2006兲. This
approach has been applied to the Mona Lake watershed, located
in west Michigan. Event hydrologic modeling for a basin characterizes finer-scale hydrologic processes and reveals how the basin
1
Assistant Professor, Dept. of Civil Engineering, North Dakota State
Univ., 1410 14th Ave. North, Fargo, ND 58105; formerly, Annis Water
Resources Institute, Grand Valley State Univ., Muskegon, MI 49441.
E-mail: xuefeng.chu@ndsu.edu
2
Professor and Director, Annis Water Resources Institute, Grand
Valley State Univ., 740 W. Shoreline Dr., Muskegon, MI 49441. E-mail:
steinmaa@gvsu.edu
Note. Discussion open until July 1, 2009. Separate discussions must
be submitted for individual papers. The manuscript for this technical note
was submitted for review and possible publication on January 31, 2007;
approved on April 17, 2008. This technical note is part of the Journal of
Irrigation and Drainage Engineering, Vol. 135, No. 1, February 1, 2009.
©ASCE, ISSN 0733-9437/2009/1-119–124/$25.00.
responds to an individual rainfall event 共e.g., quantity of surface
runoff, peak, timing of the peak, and detention兲. Thus, event hydrologic modeling is useful for better understanding the underlying hydrologic processes and identifying the relevant parameters.
Also, intensive fine-scale hydrologic monitoring data for certain
rainfall events, which are essential to the calibration of the event
hydrologic model, are easily obtained. In contrast, continuous hydrologic modeling synthesizes hydrologic processes and phenomena 共i.e., synthetic responses of the basin to a number of rain
events and their cumulative effects兲 over a longer time period that
includes both wet and dry conditions. In addition, calibration and
verification of a continuous hydrologic model over a long time
period often require considerable monitoring data. For many
small watersheds, however, such long-term monitoring data may
not be available, may not be “continuous,” or may not have sufficient resolution 共small time-interval data兲. Thus, a combination
of event and continuous hydrologic modeling takes advantage of
the two modeling methods and data availability. In particular, the
parameters that are well calibrated in event models will help improve the continuous hydrologic modeling.
The Watershed Modeling System 共WMS 1999兲 and the latest
version of HEC-HMS 共USACE-HEC 2006兲 are used in this
hydrologic modeling study. The Watershed Modeling System
is a comprehensive modeling environment for watershed-scale
hydrologic analysis that incorporates several commonly used hydrologic models 关e.g., Hydrologic Engineering Center 共HEC-1兲,
Technical Release 共TR兲-20, TR-55, National Flood Frequency
共NFF兲, and Hydrological Simulation Program–FORTRAN
共HSPF兲兴 and facilitates processing of various geographic information system 共GIS兲 data, automated watershed delineation, computation of hydrologic parameters, and hydrologic modeling.
HEC-HMS, the successor to HEC-1, is a precipitation-runoffrouting model that represents a drainage basin as an intercon-
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Fig. 1. Location of the Mona Lake watershed
The SCS-CN model is available in HEC-HMS for simulating
direct surface runoff from a storm event 共i.e., event rainfall-runoff
model兲. To facilitate continuous hydrologic modeling, a soil
moisture accounting 共SMA兲 model has been incorporated in
HEC-HMS 共USACE-HEC, 2000, 2006兲. Basically, SMA in
HEC-HMS is a lumped bucket-type model that represents a subbasin with well-linked storage layers/buckets accounting for
canopy interception, surface depression storage, infiltration,
evapotranspiration, as well as soil water and groundwater percolation. In this study, the SCS-CN and SMA models were used for
event and continuous hydrologic modeling, respectively. The
event modeling focused on understanding how the hydrologic
system responded to individual storm events on a fine time scale
and identifying relevant hydrologic parameters. The SMA continuous hydrologic modeling operated over a longer period, which
included a series of rainfall events and dry time periods on a
coarse time scale. The main purpose of implementing joint
SCS-CN event and SMA continuous modeling is to strengthen the
overall modeling capability. Parameters that are well calibrated in
the event modeling are further used to improve the continuous
hydrologic modeling. The relationship between the SCS-CN and
SMA methods can be analyzed as follows:
The SCS-CN model can be expressed as 共USDA 1986兲
P2e
Pe + S
共1兲
Pe = P − Ia
共2兲
Ia = ␣S
共3兲
R=
nected system of hydrologic and hydraulic components and
simulates the surface runoff response of the basin to precipitation
共USACE-HEC 2006兲.
The Mona Lake watershed covers 191.64 km2 and is dominated by forest, residential, and agricultural cropland land-use
types. The soils are mostly Rubicon and Au Gres sands 共RsB,
AsB, and Ra兲 共Steinman et al. 2003兲, which possess relatively low
runoff potential and high infiltration capability. Mona Lake itself
covers an area of 2.65 km2 and receives inflows from a number
of tributaries and storm drains 共Steinman et al. 2006兲; the lake
connects directly to Lake Michigan via a small channel. Major
tributaries include Black Creek 共BC兲, Little Black Creek 共LBC兲,
Cress Creek 共CC兲, and Ellis Drain 共ED兲 共Fig. 1兲. Black Creek
drains the largest area and it also receives discharge of treated
wastewater from the Muskegon County Wastewater Management
System 共WWMS兲.
in which
S=
2,540
− 25.4
CN
Methods and Model Development
The Soil Conservation Service curve number method 关共SCS-CN兲,
USDA 1986兴 is essentially an empirical, one-parameter 共CN兲,
event rainfall-runoff model. The dimensionless curve number
takes into account, in a lumped way, the effects of land use/cover,
soil types, and hydrologic conditions on surface runoff, and relates direct surface runoff to rainfall. The SCS-CN method has
been widely used for estimating rainfall-generated surface runoff
in watershed hydrologic modeling. In spite of the popularity of
the SCS-CN method, it is controversial; its limitations, application conditions, abuses, and future directions have been discussed
by many researchers 共e.g., Ponce and Hawkins 1996; Garen and
Moore 2005兲. The SCS-CN method is selected in this study primarily because it allows one to fully utilize the available spatially
distributed GIS data for the Mona Lake watershed that also can be
easily processed by using the Windows-based tools in WMS.
共4兲
where R⫽cumulative runoff; P⫽cumulative rainfall; Pe⫽cumulative effective rainfall 共Pe ⬎ 0; otherwise, R = 0兲; S = potential
maximum retention; Ia = initial abstraction 共all initial losses: surface depression storage, vegetation interception, and infiltration兲;
␣ = initial abstraction coefficient; and CN= curve number. For a
default value of ␣ = 0.2, Eq. 共1兲 becomes
R=
SCS-CN Event Model versus SMA Continuous Model
in HEC-HMS
共SI unit system,cm兲
共P − 508/CN + 5.08兲2
P + 2032/CN − 20.32
共5兲
According to the SMA method in HEC-HMS, rainfall contributes first to the canopy interception storage 共Sc兲. Then, rainwater
is available for infiltration, which is determined by infiltration
capacity and soil storage 共Ss兲. Any excess rainwater sequentially
fills the surface depression storage 共Ssf兲 and eventually becomes
surface runoff. The potential infiltration rate is given by 共USACEHEC 2000兲
i共t兲 = im
Ssd共t兲
Ss max
共6兲
in which
Ssd共t兲 = Ss max − Ss共t兲
共7兲
where i共t兲 = potential infiltration rate at time t 共the actual infiltration rate also depends on the water available for infiltration at
time t兲; im = maximum infiltration rate; Ssmax = maximum soil water
storage; Ss共t兲 = soil water storage at time t; and Ssd共t兲 = soil water
storage deficit at time t. The infiltration rate equals zero when
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Ssd共t兲 = 0 关i.e., Ss共t兲 = Ssmax兴 and reaches the maximum rate im
when Ssd共t兲 = Ssmax 关i.e., Ss共t兲 = 0兴.
From the definition of initial abstraction in the SCS-CN model
and the computation procedures of the SMA model 共no evaporation during rainfall兲, one has
Ia = Sc + Ssf + F0
共8兲
F 0 = i 0t 0
共9兲
in which
where F0 = cumulative infiltration before surface runoff starts;
i0 = average infiltration rate before surface runoff starts; and
t0 = initial time.
Substituting the expression of Ia 关Eqs. 共3兲 and 共4兲兴 into Eq. 共8兲,
we have
F0 = ␣共2,540/CN − 25.4兲 − Sc − Ssf
共10兲
For ␣ = 0.2, Eq. 共10兲 becomes
F0 = 共508/CN − 5.08兲 − Sc − Ssf
共11兲
Fig. 2. HEC-HMS conceptual model for the Mona Lake watershed
For the entire rainfall event, total runoff can be expressed as
R = P − Sc − Ssf − F0 − Fr
共12兲
F = F0 + Fr = 关共508/CN − 5.08兲 − Sc − Ssf兴
+
i.e.,
R = Pe − Fr
共13兲
F r = i rt r
共14兲
in which
where Fr = cumulative infiltration after surface runoff initiates;
ir = average infiltration rate after surface runoff starts; and
tr = postrunoff time 共note that the entire time of the rainfall event
T = t0 + tr兲.
According to Eqs. 共1兲 and 共13兲, one has
P2e
= Pe − Fr
Pe + S
共15兲
thus,
Fr =
P eS
Pe + S
共16兲
Substituting Eqs. 共2兲 and 共3兲 into Eq. 共16兲, one has
Fr =
共P − ␣S兲S
共P − ␣S兲 + S
共17兲
RS
Pe
共18兲
or
Fr =
共P − 508/CN + 5.08兲共2,540/CN − 25.4兲
共P + 2,032/CN − 20.32兲
共20兲
The average infiltration rate over the entire event can be written
as
i=
F
T
共21兲
Although SCS-CN and SMA utilize dissimilar methods for
simulating surface runoff, infiltration, and other related hydrologic processes, the above-presented derivations do provide a way
to better estimate the involved parameters in the SMA continuous
model based on those well calibrated in the SCS-CN event model.
Hydrologic Monitoring and Field Data Collection
In this watershed study, eight sites 共Fig. 1兲 were selected for
hydrologic monitoring 关three BC sites 共S1–S3兲; three LBC sites
共S4–S6兲; one CC site 共S7兲; and one ED site 共S8兲兴. The specific
location of each site was determined based on a number of factors, such as shape and stability of stream channels, flow conditions, and accessibility. An Odyssey pressure and temperature
recording system was installed for collecting stream water level
and temperature data at each site. Streamflow also was manually
measured and processed by using the Windows-based hydrologic
software, HYDROL-INF 共Chu and Mariño 2006兲. Then, rating
curves were developed and observed hydrographs 共Q – t兲 were
computed for all monitoring sites, which were further utilized for
model calibration.
Watershed Delineation and Parameter Computation
Substituting ␣ = 0.2 and Eq. 共4兲 into Eq. 共17兲, one has
Fr =
共P − 508/CN + 5.08兲共2,540/CN − 25.4兲
共P + 2,032/CN − 20.32兲
共19兲
Thus, the cumulative infiltration over the entire rainfall event time
T is given by
Using WMS, overland flow directions and accumulations were
computed and the drainage network and subbasin boundaries
were determined. The entire Mona Lake watershed was divided
into thirteen subbasins: five BC subbasins 共Basins 1–5兲, five LBC
subbasins 共Basins 6–10兲, one CC subbasin 共Basin 11兲, one ED
subbasin 共Basin 12兲, and a subbasin adjacent to Mona Lake
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共Basin 13兲 共Figs. 1 and 2兲. Further, all geometric parameters, such
as subbasin area, overland flow length, basin slope, and stream
channel length and slope were computed. Curve numbers were
computed for all subbasins based on their land use/covers, soil
types, and hydrologic soil groups. Other parameters 共e.g., lag time
and time of concentration兲 also were estimated by using appropriate approaches in WMS.
Event Hydrologic Modeling
A 5 min time step was selected in the event hydrologic modeling.
The SCS-CN loss method, the Clark transform method, and
the recession baseflow method were selected for all subbasins.
The SCS-CN method simulates rainfall excess and losses; the
Clark unit hydrograph method 共Clark 1945兲 transforms the computed rainfall excess to direct runoff at the outlet of a subbasin;
and the recession method utilizes an exponential recession model
共Chow et al. 1988兲 to represent baseflow from a subbasin. To
better represent the distinct flow characteristics of BC and smaller
tributaries 共LBC, CC, and ED兲, three reach routing methods—
straddle-stagger 共progressive average-lag兲 method 共USACE
1960兲, Muskingum method, and lag method—were utilized in the
modeling.
Selection of rainfall events is a critical step for event hydrologic modeling and model calibration/verification. Selection depends on many factors, such as rainfall characteristics
共magnitude, duration, intensity, and temporal and spatial variability兲, watershed properties 共size, land use/covers, soil types, etc.兲,
and availability and completeness of rainfall and stream monitoring data. Four rainfall events were selected for calibration and
verification of the event model. Two methods—normalized objective function 共NOF兲 共Ibbitt and O’Donnell 1971兲 and modeling
efficiency 共EF兲 共Nash and Sutcliffe 1970兲—were used to quantify
the goodness of fit between the simulated and observed flows.
The values of NOF and EF, respectively, are given by
NOF =
1
Q̄obs
冑兺
EF = 1 −
n
1
共Qobs,i − Qsim,i兲2
n i=1
n
兺i=1
共Qobs,i − Qsim,i兲2
n
兺i=1
共Qobs,i − Q̄obs兲2
Table 1. Normalized Objective Function 共NOF兲 and Modeling Efficiency
共EF兲 for the Event Model
Event
Watershed
Outlet/site
NOF
EF
8/10-8/15
BC
8/26-8/30
共Calibration兲
BC
Outlet 2 共S1兲
Outlet 3 共S2兲
Outlet 4 共S3兲
Average
Outlet 2 共S1兲
Outlet 3 共S2兲
Outlet 4 共S3兲
Average
Outlet 7 共S4兲
Outlet 9 共S5兲
Outlet 10 共S6兲
Outlet 11 共S7兲
Outlet 12 共S8兲
Average
Outlet 9 共S5兲
Outlet 10 共S6兲
Outlet 7 共S4兲
Outlet 10 共S6兲
Average
Outlet 11 共S7兲
Outlet 12 共S8兲
Outlet 11 共S7兲
Outlet 12 共S8兲
Average
Overall average
0.136
0.068
0.082
0.095
0.084
0.069
0.066
0.073
0.425
0.212
0.238
0.115
0.182
0.234
0.186
0.300
0.355
0.135
0.244
0.159
0.242
0.248
0.207
0.214
0.185
0.641
0.897
0.922
0.820
0.852
0.753
0.810
0.805
0.793
0.962
0.961
0.887
0.932
0.907
0.954
0.921
0.930
0.971
0.944
0.723
0.861
0.380
0.877
0.710
0.844
9/28-10/1
共Verification兲
LBC
CC
ED
8/26-8/30
共Calibration兲
LBC
6/30-7/3
LBC
8/26-8/30
共Verification兲
CC, ED
6/30-7/3
CC, ED
共Verification兲
the suggestions provided in the HEC-HMS User’s Manual
共USACE-HEC 2006兲. As in the event modeling, the NOF and EF
methods also were applied to quantify the fit of the simulated
hydrographs to the observed ones at the eight sites.
共22兲
Analysis of Results
共23兲
where Qobs,i = observed discharge; Qsim,i = simulated discharge;
Q̄obs = mean of the observed discharge; and n = number of the observed or simulated data points. Note that if all observed discharges are the same as the simulated ones, the NOF and EF
values equal 0 and 1, respectively.
Continuous Hydrologic Modeling
In the continuous hydrologic model, the simulation time period
ranged from April 6, 2005 to September 15, 2005 and an hourly
time step was used. As in the event model, the Clark transform
method, the recession baseflow method, as well as the straddle
stagger, Muskingum, and lag routing methods were selected in
the continuous model. The relevant parameters calibrated in the
event model were used. The SMA loss method was utilized for
continuously simulating rainfall excess in this continuous model.
Initial estimates of the parameters involved in the SMA method
were determined primarily based on the relationship between the
SCS-CN and SMA methods and the well-calibrated SCS-CN
model parameters, the actual conditions 共e.g., soil properties兲, and
The event hydrologic model was calibrated and verified using
the observed flow data at the eight monitoring sites 共Sites S1–S8,
Fig. 1兲. According to quantitative evaluation of the performance
of the event hydrologic model 共Table 1兲, the overall NOF and EF
averages for all rainfall events and all monitoring sites are 0.185
and 0.844, respectively. The minimum NOF value is 0.066 for
Event 8 / 26-8 / 30 at Site S3 and the highest EF value is 0.971 for
Event 6 / 30-7 / 3 at Site S6. Thus, good agreement between simulations and field data has been achieved in the event hydrologic
modeling.
The parameters calibrated in the event model were then used
for continuous hydrologic modeling. Comparisons between the
simulated and observed hydrographs at the three BC sites and
five other sites 共three LBC sites, one CC site, and one ED site兲
for the continuous hydrologic modeling are shown in Figs. 3 and
4, respectively. Similarly, performance of the continuous model
was quantitatively evaluated by using the NOF and EF methods
共Table 2兲. The average NOF value for all eight monitoring sites is
0.312 and the average EF value is 0.691, suggesting a fairly good
agreement between the simulated and observed hydrographs. Site
S4 has the highest NOF 共0.663兲 and the lowest EF 共0.341兲 because of the significant variations in the flow measurements in
April 关Fig. 4共a兲兴, which can be attributed to various factors 共e.g.,
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Table 2. Normalized Objective Function 共NOF兲 and Modeling Efficiency
共EF兲 for the Continuous Model
a). Wolf-Lake Site (Outlet 2)
1.0
0.5
0.0
8/1/05
9/1/05
1.0
0.5
0.0
6/1/05
7/1/05
8/1/05
9/1/05
c). US31 Site (Outlet 4)
1.5
1.0
0.5
0.0
6/1/05
7/1/05
Outlet 2 共S1兲
Outlet 3 共S2兲
Outlet 4 共S3兲
Outlet 7 共S4兲
Outlet 9 共S5兲
Outlet 10 共S6兲
Outlet 11 共S7兲
Outlet 12 共S8兲
Average
0.172
0.101
0.200
0.663
0.323
0.351
0.241
0.445
0.312
0.916
0.954
0.605
0.341
0.681
0.715
0.703
0.613
0.691
Simulated
Observed
2.0
5/1/05
EF
8/1/05
9/1/05
Date
Fig. 3. Comparison between simulated and observed hydrographs for
the BC sites 共simulation period: April 6–September 15, 2005兲
diurnal changes in temperature and moisture兲. In addition, minor
diurnal oscillations, induced by the varying discharge of treated
wastewater from the WWMS, were observed at Sites S1 and S2
along Black Creek 关Figs. 3共a and b兲兴, but such oscillations were
not represented in the continuous model because only daily discharge data were available for the simulation time period. Overall, the simulated hydrographs at the eight sites reflect the
dominant trends and variations observed in the field flow data
共Figs. 3 and 4兲.
The hydrologic modeling suggests that the overall percentage
of rainfall excess for the entire Mona Lake watershed is 6.8% and
the remaining portion of rainfall 共93.2%兲 is subject to various
losses. The BC subbasin, which is the largest in the watershed
共Basins 1–5兲, has higher loss and lower excess percentages than
the other subbasins. During the simulation period from April 6 to
September 15, 2005, 1.65⫻ 107 m3 of water was generated from
the 13 subbasins, of which 93.9% came from baseflow and 6.1%
was contributed by direct runoff; baseflow was the primary source
of runoff in the Mona Lake watershed.
the event model兲 enabled the writers to refine the model calibration and identify parameters more accurately on a fine time scale,
which sequentially improved the continuous hydrologic modeling
over a much larger time scale. Thus, this modeling study suggests
that a combination of fine-scale event and coarse-scale continuous
hydrologic simulations can be an effective way that not only fully
takes advantage of the characteristics of distinct modeling approaches and the availability of various data, but also enhances
the overall modeling capabilities.
The event hydrologic model was calibrated and validated,
which was then used for improving the continuous modeling over
a longer time period. The quantitative evaluation of the goodness
Discharge (m3/s)
5/1/05
2.5
NOF
a). Roberts Site (Outlet 7)
0.6
0.4
0.2
0.0
Discharge (m3/s)
3
7/1/05
1.5
3.0
Discharge (m /s)
6/1/05
b). Broadway Site (Outlet 3)
3
Discharge (m /s)
5/1/05
2.0
Outlet/site
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
Discharge (m3/s)
1.5
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
Discharge (m3/s)
3
Discharge (m /s)
2.0
0.5
5/1/05
5/1/05
Discharge (m3/s)
In practice, long-term hydrologic monitoring data are not always
available or they may not always be of sufficient frequency and
duration for hydrologic modeling. How to implement effective
and accurate hydrologic modeling when faced with such incomplete data is often an issue for modelers. In this study, both event
and continuous hydrologic models were developed for the Mona
Lake watershed in west Michigan by using WMS and the widely
used HEC-HMS. However, it was found that for some small subbasins, a larger computation time scale 共such as an hourly time
step used in the continuous model兲 prevented the writers from 共1兲
effectively identifying how these basins responded to a storm
event and 共2兲 accurately determining the time-related parameters
共e.g., time of concentration or lag time兲 because their response
time was shorter than the hourly time step. In contrast, smallscale, high-resolution storm event data 共5 min time step used in
7/1/05
8/1/05
9/1/05
8/1/05
9/1/05
8/1/05
9/1/05
b). Airline Site (Outlet 9)
6/1/05
7/1/05
c). Hoyt Site (Outlet 10)
5/1/05
Summary and Conclusions
6/1/05
6/1/05
7/1/05
d). Grand-Haven Site (Outlet 11)
0.4
0.3
0.2
0.1
0.0
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
5/1/05
6/1/05
7/1/05
8/1/05
e). Rood Site (Outlet 12)
5/1/05
6/1/05
7/1/05
8/1/05
9/1/05
Simulated
Observed
9/1/05
Date
Fig. 4. Comparison between simulated and observed hydrographs for
the LBC, CC, and ED sites 共simulation period: April 6–September
15, 2005兲
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of fit by the NOF and EF methods indicated that good agreements
between the simulated and observed flows were achieved in both
event and continuous simulations. As to the practical aspect of
this application study, the following conclusions can be reached:
共1兲 the continuous hydrologic modeling suggested that only 6.8%
of rainwater was available for surface runoff; 共2兲 93.9% of the
runoff from the 13 subbasins originated from baseflow; and 共3兲
Black Creek had hydrologic characteristics distinct from Little
Black Creek, Cress Creek, Ellis Drain, and other small tributaries
flowing into Mona Lake, which can be attributed to their dissimilar sizes, land use/land covers, soil types, and other hydrologic
conditions. Black Creek was supported primarily by subsurface
flow and characterized by a consistent, relatively stable pattern
共Fig. 3兲 and an obvious annual hydrologic cycle. In contrast,
Little Black Creek and other small subbasins were particularly
“sensitive” to rainfall showing a number of high short-duration
peaks dominated by storm events 共Fig. 4兲, which could be attributed primarily to their small size and higher percentage of impervious area.
Acknowledgments
This project was supported by the Michigan Department of
Environmental Quality and Grand Valley State University. The
writers would like to thank Annoesjka Steinman, David Fongers,
David Kendrick, Vivek Singh, Rick Rediske, Kurt Thompson,
Rod Denning, Patrick Womble, Eric Nemeth, and Brain Hanson
for their contributions to various aspects of this research.
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