WATER RESOURCES RESEARCH, VOL. 46, W10544, doi:10.1029/2010WR009502, 2010
Decision support for dam release during floods using a distributed
biosphere hydrological model driven by quantitative
precipitation forecasts
Oliver C. Saavedra Valeriano,1,2,3 Toshio Koike,1 Kun Yang,4 Tobias Graf,5 Xin Li,6
Lei Wang,1 and Xujun Han6
Received 29 April 2010; accepted 24 May 2010; published 30 October 2010.
This study proposes a decision support system for real‐time dam operation during heavy
rainfall. It uses an operational mesoscale quantitative precipitation forecast (QPF) to force a
hydrological model and considers the forecast error from the previous time step, which
is introduced as a perturbation range applied to the most recent QPF. A weighting module
accounts for the location, intensity, and extent of the error. Missing precipitation intensities
within contributing areas and information from surrounding areas can both be considered.
Forecast error is defined as the ratio of QPF to the observed precipitation within an
evaluation zone (sub‐basin, basin, buffer, or total domain). An objective function is
established to minimize the flood volume at control points downstream and to maximize
reservoir storage. The decision variables are the dam releases, which are constrained to the
ensemble streamflow’s information. A prototype was applied to one of the most important
river basins in Japan, the Tone reservoir system. The efficiency of the approach was
evident in reduced flood peaks downstream and increased water storage. The results from
three events indicate that the developed decision support system is feasible for real‐life dam
operation.
[1]
Citation: Saavedra Valeriano, O. C., T. Koike, K. Yang, T. Graf, X. Li, L. Wang, and X. Han (2010), Decision support for dam
release during floods using a distributed biosphere hydrological model driven by quantitative precipitation forecasts, Water
Resour. Res., 46, W10544, doi:10.1029/2010WR009502.
1. Introduction
[2] Heavy precipitation events are very likely to continue
and become more frequent as a result of global warming
[Intergovernmental Panel on Climate Change (IPCC), 2007].
These events can be attributed to large variations of the water
cycle at global [e.g., Huntington, 2006] and regional scales
[e.g., Kyselý and Beranová, 2009], while flood damage occurs at the basin scale [Kojiri et al., 2008]. Actually, the
frequency of these events has increased over most land areas,
which is consistent with increases in land surface temperature. This is particularly evident in humid regions affected by
tropical cyclones, such as Southeast Asia. These typhoons
often bring heavy rainfall but can cause severe flooding. To
1
Department of Civil Engineering, University of Tokyo, Tokyo,
Japan.
2
Now at Department of Civil Engineering, Tokyo Institute of
Technology, Tokyo, Japan.
3
Also at Energy Resources and Environmental Engineering
Program, Egypt-Japan University of Science and Technology, New
Borg El-Arab, Egypt.
4
TEL, Institute of Tibetan Plateau Research, Chinese Academy of
Sciences, Beijing, China.
5
GAF, AG, Arnu, Munich, Germany.
6
Cold and Arid Regions Environmental and Engineering Research
Institute, Chinese Academy of Sciences, Gansu, China.
Copyright 2010 by the American Geophysical Union.
0043‐1397/10/2010WR009502
reduce flood damage caused by heavy rainfall, a system is
needed to support reservoir operators in flood management
using real‐time observations and weather forecast data.
[3] The accuracy of weather forecasting at the basin scale
has improved in the last few years as a result of more reliable
numerical weather prediction models. Precipitation is one of
the most difficult weather variables to predict because the
atmosphere is highly unstable. However, advanced techniques have enabled reasonable predictions at the regional
scale [e.g., Golding, 2000; Krzysztofowicz and Collier, 2004;
Honda et al., 2005]. Because precipitation makes up the
main input data for hydrological models, the accuracy of a
quantitative precipitation forecast (QPF) is reflected in the
streamflow forecast. Recent works [Jasper et al., 2002;
Collischonn et al., 2005; Saavedra et al., 2009] have shown
the feasibility of forcing distributed hydrological models
(DHMs) with mesoscale QPFs.
[4] A flood peak can be reduced by appropriate dam
operation, assuming the dam has sufficient storage capacity.
The application of preestablished rule curves is limited during
extreme flood events [Chang and Chang, 2001]. Optimal
release systems using hydrological models to assist dam
operators have been reviewed [e.g., Yeh, 1985; Labadie,
2004]. It is noted that, because of the increase in computational power and real‐time data availability, simulation
approaches have become feasible and attractive [e.g., Wurbs,
1993]. Some of these studies focused on the optimization of
operating rules for multireservoir systems taking advantage
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Figure 1. Steps of the dam decision support system DRESS for flood control under extreme events.
of real‐coded genetic algorithms [e.g., Oliveira and Loucks,
1997; Chen, 2003; Chang, 2008]. Works on areas affected
by typhoons in Southeast Asia, such as Hoa Binh dam in
Vietnam [Ngo et al., 2007], have focused on the optimal rule
curves. A customized geographical information system was
created to support dam release decisions in Korea [Shim et al.,
2002]. Studies in Taiwan targeted real‐time forecasting for
flood control in Taiwan [e.g., Hsu and Wei, 2007; Chang and
Chang, 2009, Wei and Hsu, 2009]. However, no recent
studies have spatially evaluated QPFs at previous steps in real
time or included them in near‐future dam release decisions.
[5] The present system employs a distributed biosphere
hydrological model (DBiHM) with an embedded dam operation module coupled to a heuristic algorithm for supporting
release instructions. This comprehensive model solves the
energy budget explicitly and can simulate the initial soil
moisture properly, which might be critical for flood forecasting [Wang et al., 2009a]. In normal runs, the model is
forced by real‐time observed radar products calibrated with a
high‐density rain gauge telemetry network. The radar calibration is based on a dynamic window, yielding a high‐
quality product almost free of shadows at high spatial and
temporal resolutions. During heavy rainfall the model is
driven by an operational nonhydrostatic mesoscale QPF. The
study has four distinctive features: (1) forecast error weighting
using a relatively wide domain according to location, intensity, and extent; (2) generation of ensemble flood forecasts
using the evaluation of previous forecast errors; (3) use of a
DBiHM forced by output from a nonhydrostatic mesoscale
model; and (4) use of a heuristic algorithm for multireservoir
operations with a multiobjective approach.
2. System Description
[6] The prototype dam release support system (DRESS) is
summarized in Figure 1. The DBiHM runs in normal mode
forced by observed precipitation data until a heavy precipitation event is detected. At that point, the forecast error is
evaluated. Recent observed precipitation and QPFs are
compared over the previous time step window in terms of
location, intensity, and extent. Next, a weighting method is
used to calculate the weights in each zone. These weights
define the amplitude of QPF perturbation to generate
ensemble precipitation members over the total lead time, as
shown in Figure 2. Then the DBiHM model is run to obtain
the ensemble streamflow forecast. For special dam operations, a priori dam release is calculated for each ensemble
member. Once the suggested optimal dam release schedule is
determined using the QPF signal, the efficiency is evaluated
using the observed precipitation.
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weighting scheme based on that concept but we express it
quantitatively. The forecast error is defined as the averaged
value of the high and mean intensity ratio,
FEi; j ¼ 0:5 Figure 2. Error evaluation window t0 when an extreme
event is detected using recent issued QPF; otherwise, the system continues running with observed precipitation.
2.1. Evaluation of the Forecast Error Over Previous
Time Steps
[7] To account for QPF errors in terms of intensity and
displacement of the main pattern, Ebert and McBride [2000]
proposed contiguous rain areas. However, it would also be
helpful to locate these areas with respect to the targeted basin.
Actually, underestimated or overestimated intensities of the
forecast precipitation could occur in areas surrounding the
basin. DRESS applies an evaluation module to different
zones. The inputs are QPF and real‐time observed precipitation, as illustrated in Figure 2. The outputs are the perturbation weights to be used over the total lead‐time forecast to
generate the ensemble precipitation members.
[8] Because the forecast error bias might fluctuate considerably, the error is defined as the ratio of the forecast to the
observed precipitation within the proposed zones. At the first
level, the zones are defined by the drainage areas of the dams
as sub‐basins. At the second level, the total basin area is
delineated down to the control point. At lower levels, a
number of circular buffers are proposed on the basis of the
watershed’s center of mass. The total domain is defined as a
rectangle that covers the basin and buffers.
2.2. Weighting According to Location, Intensity,
and Extent
[9] Ebert and McBride [2000] proposed a contingency
table for rain events to evaluate the QPF signal. They
expressed the forecast intensity versus the displacement
of the rain pattern qualitatively. We propose a perturbation
HIi;QPF
HIi;OBS
þ
MIi;QPF
MIi;OBS
;
ð1Þ
where HI represents high intensity and MI represents mean
intensity. The subscripts i and j denote the zone and the
evaluated time step window, respectively; QPF indicates
forecast values, and OBS indicates observations. The mean
intensity ratio term accounts for the error extent because it is
averaged over the zones (sub‐basins, basin, buffers, and total
domain). Thus, the intensity and extent are considered in one
term, allowing two‐dimensionality in further analysis against
the zones. If the forecast error is close to 1, a very accurate forecast is assumed. Values higher than 1 indicate
overestimation in the QPF; values between 0 and 1 indicate
underestimation.
[10] This study assigns weights using a lookup table. For
example, a very good forecast in terms of intensity, location,
and extent would receive the minimum weight for a particular
zone. In that case, the QPF requires almost no perturbation,
indicating low uncertainty at the previous time step. However, overestimation or underestimation is penalized with
higher weights according to the total ratio and location. A
good pattern match within a sub‐basin is rewarded with a
weight close to zero. However, for a good match in wider
areas, the minimum weight increases gradually.
2.3. Ensemble Precipitation Forecast
[11] Direct compensation for forecast errors in the previous step might not always be correct, because shifts
from overestimation to underestimation and vice versa are
possible. Therefore, a range is applied to the current QPF
according to its performance over the previous time step. The
QPF’s signal can then be perturbed to generate ensemble
precipitation members by underestimation and overestimation. The amplitude of perturbation is defined by the weights
reflecting previous QPF performance and a noise perturbation
with a Gaussian or normal distribution [Goovaerts, 1997]. An
ensemble precipitation forecast is generated without assimilation, which is a kind of Monte Carlo simulation [Heuvelink,
1998].
[12] We follow the approach suggested by Turner et al.
[2008] to attempt an appropriate ensemble spread. Semirandom numbers are generated assuming a normal distribution
with zero mean and a standard deviation of unity, N (0, 1).
Precipitation is considered a semirestricted data field because
the lower boundary is constrained to zero. The generated
precipitation at each grid (x, y) for each lead time step k is then
defined as
GPð x; yÞk ¼ maxfQPFð x; yÞf1 þ A" N ð0; 1Þwisub
þ ½ B" N ð0; 1Þwitot g; 0g;
ð2Þ
where " is 0.33, meaning an exceedence probability of 0.0014
[Turner et al., 2008]. The terms wisub and witot denote the
perturbation weights directly within the dam’s subbasins
and the average for the entire basin, buffers, and wider
domain (“total”), respectively. Different weights are proposed
for each time step k within the total QPF lead time. Finally,
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Figure 3. Potential flood volume (PFV) reduced by multireservoir operation at control point downstream. PFV is the
integrated simulated stream flow Qsim that exceeds the threshold Qthre.
A and B are the priority weights, which sum to unity. For
example, values of 0.6 and 0.4 indicate that 60% priority is
given to dam sub‐basins and 40% to wider zones.
2.4. Distributed Biosphere Hydrological Model
[13] DHMs can assimilate rainfall patterns and estimate
fluxes such as a streamflow at selected points. Moreover, they
provide the spatial distributions of soil moisture content,
evaporation, and transpiration. However, detailed energy
exchange between the land surface and lower atmosphere is
neglected in most DHMs [e.g., Yang et al., 2001]. Thus,
combining hydrological and land surface models (LSMs)
improves the representation of the water and energy cycles
within a basin. The simple biosphere model (SiB2) [Sellers
et al., 1996], a well‐known LSM, has been embedded into
a geomorphology‐based hydrological model (GBHM) [Yang
et al., 2002] to produce a water‐ and energy‐budget‐based
DHM (WEB‐DHM) [Wang et al., 2009a].
[14] DRESS employs WEB‐DHM validated in humid
areas [Wang et al., 2009b], which can predict the evolution of
land surface temperature and soil moisture. The SiB2 submodel calculates turbulent fluxes between the lower atmosphere and land surface independently. It then updates
the surface runoff and subsurface flow, which become the
lateral inflow to the main river channel. Next, the GBHM
submodel simulates water flow within the river network using
one‐dimensional kinematic wave equations solved by finite
difference approximation. This simulation sequence is performed for each sub‐basin using the Pfafstetter scheme
[Verdin and Verdin, 1999]. Moreover, every sub‐basin is
divided into a number of flow intervals based on the flow
distance to the outlet. The flow intervals are linked by the
river network according to the geomorphology. The flow
downstream might be affected by the operation of upstream
dams. A dam function based on the mass balance approach
was used to express the change in volume. Inflows to the
dams are simulated by the GBHM submodel.
2.5. A Priori Dam Release for Selected Members
[15] The ensemble streamflow is calculated using WEB‐
DHM at a selected control point downstream. The system is
activated only when the simulated streamflow with unregulated releases surpasses the threshold flow. This flow is
defined as the average of the observed values exceeding the
mean annual discharge at the control point [Saavedra et al.,
2010]. The simulated streamflow with closed gates is then
calculated to check the effect downstream. The integrated
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value between the two simulations over time is the potential
flood volume (PFV) for a specific dam, as shown in Figure 3.
This procedure shows the effect of inflow at the control point
according to dam capacity. The system defines a priori release
Q from n dams based on the PFV at the control point over the
present time step. Thus, the release implicitly becomes a
function of inflow.
[16] For the rest of the total lead‐time forecast, the gates are
assumed to be totally closed to replenish the reservoirs to
levels comparable to initial levels [Saavedra et al., 2010].
Essentially, the system releases only a water volume that
could be re‐stored after the event according to the QPF. This
preventive measure reduces flood volumes directly and
reduces flood peaks as a consequence. The mean and standard
deviation of dam release for the participating members are
then calculated to express the uncertainty. They are used as
the initial guess and boundary condition for the decision
variables in the optimization, respectively.
2.6. Optimization of Dam Release
[17] Defining a proper objective function is crucial to
optimal dam operation. We propose to extend the application
of shuffled complex evolution (SCE) [Duan et al., 1992] in
multireservoir operation using the QPF during heavy rainfall.
2.6.1. Objective
[18] The flood peak should be minimized at control points
downstream and the available free volume at dams should be
reduced (i.e., water storage should be encouraged).
2.6.2. Strategy
[19] A trade‐off is made between water allocation at the
control point, which might result in flood damage, and stored
water at the reservoirs for potential water use.
2.6.3. Objective Function
[20] Minimize Z, which is defined as
Z¼
n
X
PFVi þ
i¼1
n
X
RFVi ;
ð3Þ
i¼1
where PFVi and RFVi are the potential flood and reservoir
free volumes for n reservoirs, represented on the left and right,
respectively, in Figure 4. PFVi was calculated as shown in
equation (3); RFVi can be estimated as
n
X
i¼1
RFVi ¼
n
X
ðMXVi SVi Þ;
ð4Þ
i¼1
where MXVi is the maximum storage and SVi is the simulated volume.
2.6.4. Optimization
[21] The decision variables are the dam releases. Their
initial values are the mean a priori dam release obtained
previously. Their upper and lower boundaries are the mean
plus and minus one standard deviation, respectively. The
heuristic algorithm SCE drives WEB‐DHM, seeding different combinations of decision variables until the objective
function is fulfilled.
3. Application of the System to the Upper Tone
River
[22] To validate DRESS, it was applied to the upper Tone
reservoir system in Japan. The northern headwaters of the
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Figure 4. Objective function to reduce PFV at (left) the control point while attempting to reduce the (right)
reservoir free volumes (RFVs) at the end the time step.
Tone River basin are about 130 km northwest of Tokyo. The
river supplies water for irrigation and power generation to
the Tokyo metropolitan area and surroundings. Therefore,
its management is crucial for the region. The watershed and
drainage sub‐basins are shown on the right in Figure 5.
The elevation varies from 100 m to about 2500 m with a
mean of about 1020 m. The long‐term average precipitation
is about 1500 mm per year. Heavy rainfall, commonly associated with typhoons and seasonal front activities, occurs
from July to October. Typhoons bring the heaviest rainfall.
After a catastrophic flood in 1947 [Takahasi and Okuma,
1979], development of the upper reservoir system, as well as
embankment improvements in the middle and lower reaches,
were emphasized.
3.1. Spatial Data Preparation
[23] The geomorphology was obtained using a digital
elevation model with 50 m resolution and aggregated into
500 m grids. Land use, soil type, and geological maps were
also prepared for the study area. Land use data were
reclassified according to the predefined SiB2 categories, and
broadleaf deciduous trees were found to be dominant in the
basin. Thematic maps, such as those for surface terrain slope,
topsoil depth, and hillslope length, were derived from the
basic data described above using a geographic information
system. Dynamic vegetation parameters, such as the leaf area
index and fraction of photosynthetically active radiation,
were obtained from satellite data. The air temperature, wind
speed, and sunshine duration were obtained from available meteorological sites managed by the automated meteorological data acquisition system (AMeDAS). Relative
humidity and air pressure were obtained from three radiation
stations maintained by the Japan Meteorological Agency
(JMA). Downward solar radiation was estimated from sunshine duration, temperature, and humidity as measured by
Yang et al. [2006]. Longwave radiation was then estimated
from the temperature, relative humidity, pressure, and solar
radiation as measured by Crawford and Duchon [1999].
High‐quality observed radar precipitation, calibrated using a
dense rain gauge network based on a dynamic window, was
also obtained from AMeDAS. The temporal resolution was
1 h at 2.5 km until 2002, 30 min at 1 km from 2003 to 2004,
and 10 min at 1 km later. The AMeDAS data are transmitted
through advanced telemetry almost in real time.
Figure 5. Location of upper Tone River system, northwest of Tokyo, which encompasses 3304 km2. The
map on the left shows the proposed zones around the basin to evaluate the forecast error. The drawing on the
right depicts the dams’ arrangement in squares and the streamflow gauging stations in triangles.
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Table 1. Characteristics of Upper Tone Reservoir System
2
3
Name of Dam
Complete (year)
Drainage (km )
Capacity (Mm )
Fujiwara
Aimata
Sonohara
Yamba
1958
1958
1966
Not yet
401.5
111.2
494.2
703.8
90
25
20.3
107.5
3.2. Mesoscale QPF Data
[24] QPF was obtained from the operational nonhydrostatic JMA model [Saito et al., 2007], which has a 10 km
horizontal resolution and was issued four times a day until
February 2006; the data are available at the University of
Tokyo’s website (http://gpv.tkl.iis.u‐tokyo.ac.jp/GPV/). The
mesoscale‐model grid point values (MSM‐GPV) data set was
downloaded and clipped to the study area, which covers all of
Japan (2900 km × 3600 km); the most relevant heavy rainfall
from 2002 to 2004 was targeted. The data set has a total lead
time of up to 18 h updated every 6 h. Thus, for each hour
of observed precipitation, three different precipitation forecast values are available: those issued 1–6, 7–12, and 13–18 h
previously, as illustrated in Figure 2.
3.3. Reservoir Characteristics
[25] Three dams, labeled 1, 2, and 3 in Figure 5, were
selected for flood control to protect downstream plains. Their
main characteristics and capacities are summarized in
Table 1. The location of the embankments was placed within
the river network in the grid‐based model. The reservoirs are
normally operated using mutually independent rule curves. A
release is decided according to the actual water level of the
reservoir, inflow to the reservoir, and water demand [Ministry
of Construction, 1995]. However, during extreme events,
reservoir release is determined according to the judgment of
the director of the reservoir operation office, according to the
reservoir’s status and downstream flood conditions. Normal
operation for each reservoir was simulated by Yang et al.
[2004], and optimal operation was simulated using radar
data by Saavedra et al. [2010].
4. Results
[26] To validate the efficiency of DRESS, the highest
yearly flood peaks between 2002 and 2004 were selected.
WEB‐DHM’s soil‐ and vegetation‐associated parameters
were calibrated and validated by Wang et al. [2009b] using
the data observed between 2001 and 2004. The simulated
river discharge and land surface temperature were compared
with values recorded in situ and by satellite, respectively. The
WEB‐DHM model had a 1 h time step, whereas the optimization horizon had an 18 h time step according to the QPF
lead time. The initial conditions were set to those several
hours before the event using observed data. Three multipurpose dams arranged in parallel, Fujiwara, Aimata, and
Sonohara, were selected to examine flood reduction at the
Maebashi gauging station (see Figure 5). Once an extreme
event was identified, the system verified the situation
downstream. The spatial distribution of the 7–12 h MSM‐
GPV series was compared to the AMeDAS radar observations, as shown in Figures 6a and 6b (2002 event), Figures 6c
and 6d (2003 event), and Figures 6e and 6f (2004 event). In
these particular examples, a close match between the 2002
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and 2003 events is evident. However, the results for 2004
indicate that this might not always be the case. The spatial
distribution of precipitation shows a high resolution and
quality of AMeDAS and MSM‐GPV data.
4.1. Forecast Error Evaluation
[27] After the forecast error was evaluated within different
zones, the perturbation weights for the three MSM‐GPV
series were estimated using Table 2. The six proposed zones
are displayed in Figure 6. The weights oscillate from 0 to 5.
The approximate forecasts were defined for ratios between
0.9 and 1.1. The intensity and extent are included in the
magnitude of the forecast error (columns). The location is
expressed by the evaluated zones (rows).
[28] Three cases must be carefully considered when using
ratios to express the forecast error:
[29] 1. If only the observed rainfall tends to zero, the ratio
tends to infinity, giving an abnormal overestimation. In this
case, an eligible weight from a larger zone should be assigned
until the ratio no longer tends to infinity.
[30] 2. If only the forecast rainfall tends to zero, the ratio
tends to zero, giving an abnormal underestimation. This
should be distinguished from cases where the weight is zero
(indicating a very good forecast); thus, it is penalized by being
assigned the highest weight in the evaluation zone.
[31] 3. If the observed and forecast precipitations are both
zero, which is a good match, the ratio is mathematically
undetermined, and the minimum weight should be assigned.
[32] The averaged ratios of the MSM‐GPV data to the radar
AMeDAS observations over the sub‐basins are presented in
Figure 7, which shows the temporal and spatial variability of
the forecast error. The ratio can shift from overestimation to
underestimation and vice versa. This confirms the need to use
the ensemble approach. In general, the MSM‐GPV signal
tended to increase as the climax of a heavy rainfall event
approached, as seen in the last time steps of the graphs in
Figure 7.
4.2. Ensemble Streamflow Forecast
[33] By applying the perturbation weights from Table 2, an
ensemble of 50 MSM‐GPV members (i.e., recently issued
QPFs) were generated using equation (2). They were given
60% priority in the contributing areas (i.e., sub‐basins) and
40% in wider zones. The ensemble precipitation forced the
WEB‐DHM model, generating an ensemble streamflow.
Then, the system verified whether the members exceeded the
threshold flow. An ensemble size of 50 members was found
suitable for the three events considered here and might also
be appropriate in similar studies. The threshold flow that
triggers special operation was calculated as 500 m3 s−1 for the
Maebashi gauging station using historical data following the
approach of Saavedra et al. [2010].
4.3. A Priori Release
[34] Most ensemble members during the three events
suggested activating the system for a priori release, except
during the first step of the October 2004 event. At this step,
96% (48 of the 50 members) suggested that the system be
activated. This indicates that the ensemble amplitude using
the perturbation weights was efficient for those events. The a
priori release was obtained using the PFVs at the Maebashi
gauging station for each member for the three dams.
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Figure 6. Spatial distribution of the accumulated precipitation over 6 h using forecast QPF and observed
radar: July 2002 event issued at 10.09z (local time) using (a) MSM‐GPV and (b) AMeDAS radar; August
2003 event issued at 09.09z (local time) using (c) MSM‐GPV and (d) AMeDAS radar; and October 2004
event issued at 20.09z (local time) using (e) MSM‐GPV and (f) AMeDAS radar.
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3.00
3.50
4.00
4.25
4.50
5.00
Dam drainage
Basin
First Buffer
Second Buffer
Third Buffer
Total Domain
2.50
3.00
3.50
3.75
4.00
4.50
0.01–0.05
2.00
2.50
3.00
3.25
3.50
4.00
0.05–0.075
1.50
2.00
2.50
2.75
3.00
3.50
0.075–0.1
1.00
1.50
2.00
2.25
2.50
3.00
0.1–0.3
0.75
1.25
1.75
2.00
2.25
2.75
0.3–0.5
0.50
1.00
1.50
1.75
2.00
2.50
0.5–0.7
0.25
0.75
1.25
1.50
1.75
2.25
0.7–0.9
0.00
0.50
1.00
1.25
1.50
2.00
0.9–1.1
Approx.
Correct
0.25
0.75
1.25
1.50
1.75
2.25
1.1–1.3
From left to right, column values indicate underestimation through overestimation of the forecast error magnitude; rows indicate zone.
a
0–00.1
Total ratio
Underestimation
Table 2. Perturbation Weights According to the Forecast Error Magnitude and Zonea
0.50
1.00
1.50
1.75
2.00
2.50
1.3–1.5
0.75
1.25
1.75
2.00
2.25
2.75
1.5–1.7
1.00
1.50
2.00
2.25
2.50
3.00
1.50
2.00
2.50
2.75
3.00
3.50
1.9–2.5
Overestimation
1.7–1.9
2.00
2.50
3.00
3.25
3.50
4.00
2.5–5.0
2.50
3.00
3.50
3.75
4.00
4.50
5.0–10
3.00
3.50
4.00
4.25
4.50
5.00
>10
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[35] The mean and standard deviation of the dam release
were then obtained, as shown in Figure 8. The lengths of the
bars denote the amplitudes of the perturbation (i.e., uncertainty) according to the QPF at the previous step. Comparing
Figures 7 and 8 for the same steps, we find that the performance
Figure 7. Area precipitation ratio MSM‐GPV over
AMeDAS at local time: (a) 9–10 July 2002, (b) 8–9 August
2003, and (c) 20 October 2004.
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of the MSM‐GPV data is consistent with the a priori release.
According to Figure 8, for those steps in which the MSM‐
GPV was overestimated, the mean release tended to be
higher. For example, consider the partial overestimation for
the Aimata sub‐basin in steps 09.03z (2003) and 20.15z
(2004). These overestimations might lead to water loss, as
seen in Table 3, if we evaluate the system’s performance only
for this sub‐basin. However, decision makers could consider
the uncertainty of the dam release using the information in
Figure 8.
4.4. Optimization of Multireservoir Operation
[36] The upper and lower boundaries of the bars in Figure 8
were used to optimize dam network operation. The initial
guess was the mean dam release (red squares in Figure 8).
After the iteration of the SCE algorithm was completed,
the suggested optimized variables were obtained. The dam
release tended to be closer to the lower boundary in most
cases because water storage was preferred. However, this
would not be the case if the initial water levels before the
event were higher (i.e., more volume were available). Then,
higher dam release magnitudes would be expected.
4.5. Real‐Time Operation
[37] The process described above was applied every 6 h
with the issue of new MSM‐GPV data. Thus, the simulation
used only the most recent available data as would be done in a
real‐time basis. Integrating the suggested dam release over
the evaluated steps yielded a release schedule per event. Then
the WEB‐DHM model was run in evaluation mode using the
observed AMeDAS precipitation. The flood reduction at the
Maebashi gauging station using the suggested release
schedule for the three dams is shown in Figure 9 (2002 event),
Figure 10 (2003 event), and Figure 11 (2004 event). The
efficiencies in flood peak reduction were 18%, 28%, and 7%,
respectively, for the three events. Early release from the three
dams is evident for the 2002 and 2004 events. During the
2003 event, the total suggested release was lower than the
observed release (except for the Aimata dam, due to MSM‐
GPV overestimation for this sub‐basin, as discussed previously). Underestimation in the precipitation forecast was
found to increase water storage but to decrease flood control
efficiency. Such cases should be examined carefully when
attempting to reduce flood damage. The total gain and loss
of water at each reservoir for each event is summarized in
Table 3. Overall, the results indicate gains in water storage,
demonstrating the system’s efficiency.
Figure 8. Dam release uncertainties for the events (a) 9–10
July 2002, (b) 8–9 August 2003, and (c) 20 October 2004.
5. Discussion
[38] The effects of overestimation or underestimation in
QPFs were crucial to the system’s responses, leading to water
loss or less effective flood reduction, respectively. As QPF
Table 3. Evaluation of the DRESS System’s Performance in Tone River, Japan
Event
Flood Peak
Reduction at
Control Point (%)
Gain or Loss at
Fujiwara Reservoir
(MCM)
Gain or Loss at
Aimata Reservoir
(MCM)
Gain or Loss at
Sonohara Reservoir
(MCM)
Total Gain or Loss,
Reservoirs
(MCM)
2002.07
2003.08
2004.10
18.14
28.03
7.6
10.45
5.36
5.24
8.28
−1.76
−4.98
17.43
11.85
7.82
17.43
11.85
7.82
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Figure 9. (a) Flood reduction at Maebashi gauging station using suggested release from (b) Fujiwara,
(c) Aimata, and (d) Sonohara dams for July 2002, 10.04z → 11.24z.
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Figure 10. (a) Flood reduction at Maebashi gauging station using suggested release from (b) Fujiwara,
(c) Aimata, and (d) Sonohara dams for August 2003, 09.04z → 10.24z.
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Figure 11. (a) Flood reduction at Maebashi gauging station using suggested release from (b) Fujiwara,
(c) Aimata, and (d) Sonohara dams for October 2004, 20.10z → 21.24z.
accuracy increases, dam release operations using decision
support systems such as DRESS are expected to become
more efficient and reliable.
[39] In real‐life operation, DRESS should be able to suggest water release before the next QPF is issued. The total
processing time should be less than 6 h, which is the QPF
update interval employed. The present application took
around 17 h using a 2 GHz personal computer; the run time
can be reduced by as much as 10 times by using a parallel
computing system. The time could also be reduced as QPFs
become more accurate, because the perturbation range would
narrow, decreasing the ensemble size.
[40] Several modifications are possible for future applications. For example, the objective function can be adjusted
depending on local conditions. A flushing procedure for flood
control is needed in humid mountains and highly vegetated
basins. In drier basins, the objective function can give priority
to maintaining a higher water level in reservoirs as long as
possible. Also, only QPFs were used as real‐time forcing
data; however, other atmospheric model outputs, such as air
humidity and wind profiles, could be included. Since a
DBiHM is used, updates of soil moisture conditions could
eventually interact with land data assimilation systems [e.g.,
Boussetta et al., 2008; Yang et al., 2009].
[41] QPFs in Japan are reasonably useful. However, this
might not be true in countries with limited infrastructure,
technology, and resources. In such cases, satellite‐derived
precipitation could be used in near real time depending on
the time period [e.g., Saavedra et al., 2009]. Priority could
be given to recent satellite‐based measurements (i.e., those
calibrated with available rain gauges) to improve system
reliability.
6. Conclusion
[42] A prototype DRESS was developed for flood control
during heavy rainfall. It employs a DBiHM forced by an
operating mesoscale QPF in Japan, the MSM‐GPV. To
reflect recent forecast errors in the near‐future dam release
schedule, an ensemble forecast approach and optimization
algorithm are employed. An objective function was formulated to reduce flood effects downstream and increase the
total volume in the reservoirs.
[43] DRESS was validated in the upper Tone River reservoir network using the worst heavy rainfall in 2002, 2003,
and 2004. The efficiency of the system was demonstrated for
all events, with some differences depending on the accuracy
of the MSM‐GPV. The objective function successfully
minimized the flood volume at a control point and the free
volume of the reservoirs, taking into account different release
scenarios. The results indicate that DRESS is feasible for real‐
life dam operation.
[44] Acknowledgments. We would like to thank the River Section
Bureau of the Ministry of Land Infrastructure, Transportation, and Tourism
of Japan for providing valuable data from operating dams. This study was supported by the Science and Technology National Project Japan: Data Integration
and Analysis System. Additionally, we would like to thank the three anonymous reviewers who provided constructive suggestions for the final version.
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