This paper presents a study on short-term
ensemble flood forecasting specifically for small dam catchments in Japan.
Numerical ensemble simulations of rainfall from the Japan Meteorological
Agency nonhydrostatic model (JMA-NHM) are used as the input data to a
rainfall–runoff model for predicting river discharge into a dam. The
ensemble weather simulations use a conventional 10 km and a high-resolution
2 km spatial resolutions. A distributed rainfall–runoff model is
constructed for the Kasahori dam catchment (approx. 70 km
Currently, short-term ensemble flood forecasting based on ensemble numerical weather predictions (NWPs) is gaining more attention in Japan, as evidenced by the commencement of a project for ensemble weather/flood forecasting using the new K computer in Kobe, Japan (Saito, 2013b). Here, short-term flood forecasting means flood forecasts with lead times of half to 1 day. Cloke and Pappenberger (2009) presented a comprehensive review of medium range (2–15 days ahead) ensemble flood forecasts; however, the review focused mainly on European weather/flood forecasting examples using global ensemble predictions.
Precipitation data from NWPs are usually not considered as primary data for
flood forecasting because of their accuracy, especially in the disaster
prevention purpose. In Japan, primary data are obtained using radar
observations of precipitation calibrated by the Japan Meteorological Agency
(JMA) AMeDAS (Automated Meteorological Data Acquisition System) surface rain
gauges (Makihara, 2000) or by the rain gauges of the Ministry of Land,
Infrastructure, Transport and Tourism (MLIT, 2012a). It should be noted that
in Japan, NWP-based weather forecasting has shown success in predicting
synoptic (spatial scale of O(1000 km)) weather systems and associated
precipitation events. The difference between weather and flood forecasting
arises because Japanese river basins are often too small for NWP models to
provide accurate estimations. The largest catchment in Japan is the Tone
river catchment, which is around 17 000 km
In the aforementioned project (Saito, 2013b), the Meteorological Research Institute tested ensemble NWPs with 2 km resolution, finer than used previously for mesoscale ensemble forecasts (e.g., Saito et al., 2010, 2011). With such a resolution, complex topographies and mesoscale convective systems can be better represented. In addition, the atmospheric model does not apply cumulus convective parameterizations, which enables us to reproduce rainfall with more realistic intensities. Therefore, such high-resolution cloud-resolving ensemble weather simulations can produce probabilistic information of intense rainfall systems better than mesoscale models with lower resolutions (Duc et al., 2013). Using ensemble rainfall forecasts produced by the JMA nonhydrostatic model (JMA-NHM), the authors have performed a study on the ensemble flood forecasting for a real extreme flood event in Niigata, Japan, using a rainfall–runoff model, the results of which are presented in this paper.
Flood disasters occurred on 27–30 July 2011 in Niigata and Fukushima prefectures, Japan, following a severe rainstorm, characterized by two rainfall peaks. According to a report by the Niigata Prefecture (Niigata, 2011), the cumulative rainfall from the onset of the rainfall until 13:00 JST (04:00 UTC) on 30 July 2011 reached 985 mm at the Kasahori Dam Observatory. The cumulative rainfall at 68 rainfall observatories managed by MLIT, JMA, and Niigata Prefecture exceeded 250 mm. During this time, JMA announced “record-setting short-term heavy rainfall information” on 30 occasions. The hourly rainfall recorded from 20:00 to 21:00 JST on 29 July at the Tokamachi-Shinko Observatory reached 120 mm, which is an example of extreme record-setting rainfall within the region. Among the many local record-setting rainfall amounts, this paper focuses on the Kasahori dam catchment, which is a small sub-catchment of the Shinano river catchment.
The report by the Japan Weather Association (hereinafter JWA, 2011) indicates
that the discharge forecasting system, operated at the Kasahori dam using
short-term and very-short-term rainfall prediction by a weather model, was
effective for deciding the quantity of water release from the Kasahori dam.
According to the report, at 03:00 JST on 29 July 2011 the discharge
forecasting system predicted dam inflow of 846 m
The structure of this paper is as follows. Section 2 describes additional details regarding the 2011 Niigata–Fukushima heavy rainfall. Section 3 briefly describes the Kasahori dam catchment and the Kasahori dam. Section 4 addresses the rainfall analysis using rain gauge and radar-derived rainfalls. Section 5 introduces the rainfall–runoff model. Section 6 presents the results of the rainfall–runoff simulations using both observed rainfall and ensemble predictions of rainfall. Section 7 presents the concluding remarks and aspects of future work.
A local heavy rainfall event occurred in July 2011 over Niigata and Fukushima prefectures in northern central Japan. Record-breaking torrential rainfall of more than 600 mm was observed during 3 days from 27 to 30 July, which caused severe damages in the prefectures of Niigata and Fukushima. Six people were killed and more than 13 000 houses damaged by dike breaks, river flooding, and landslides.
Figure 1 (left) indicates a surface weather map at 09:00 JST (00:00 UTC), 29 July 2011. A distinct synoptic-scale stationary front runs from the northwest to the southeast over northern central Japan. The right panel of Fig. 1 shows the 3 h accumulated rainfall from 12:00 to 15:00 JST (03:00 to 06:00 UTC) (radar–rain-gauge precipitation analysis of the Japan Meteorological Agency). Torrential rain exceeding 100 mm per 3 h occurred over the small area along the stationary front. A detailed description of this rainfall event has been published by JMA as a special issue of the JMA Technical Report (JMA, 2013a).
Surface weather map for 09:00 JST, 29 July (left). Three-hour accumulated observed rainfall from 12:00 to 15:00 JST (right).
Figure 2 (left) shows the Shinano and Agano river catchments, Japan,
and Fig. 2 (right) shows an enlarged view of the Kasahori and Otani dam
catchments. These catchment data were obtained from the Digital National Land
Information (hereinafter DNLI) of MLIT (MLIT, 2012b). The Kasahori dam
catchment area is calculated as 72.7 km In the rainy season, the reservoir water level is decreased to the normal
water level for the rainy season (elevation level (EL) 194.5 m). If a flood risk due to extreme rainfall is expected by weather
monitoring/prediction, the water level is further decreased to the
preliminary release water level (EL 192.0 m). When the inflow exceeds 140 m When the reservoir water level reaches EL 206.6 m,
Note that, after the flood event in July 2011, the dam has been under renovation to increase its flood control capacity.
Shinano and Agano river catchments (left). Kasahori dam and Otani dam catchments (right).
The analysis of the rainfall over the Kasahori dam catchment is performed in
this section. The rain gauge (RG) rainfall, JMA radar-composite (RC), and JMA
radar–rain-gauge (RR) analyzed data are used for the investigation. The
descriptions of the RC and RR data are as follows.
The 1 km resolution RC data: the echo intensity, which can be converted to rainfall intensity, is observed
by 20 meteorological radar stations of JMA and is available with 10 min
temporal resolution. The 1 km resolution RR analyzed precipitation data: the rainfall intensity observed by the radar is corrected using rain gauge
data (ground observation data) and they are available with 30 min temporal
resolution. See Nagata (2011) for the further details of the analysis data. Several
previous studies have been published (e.g., Kamiguchi et al., 2010; Sasaki et
al., 2008) using these precipitation analysis data. RG rainfall data:
the time-series data of hourly rainfall of the Otani dam, Otani, Koumyozan,
Kasahori dam, Kasahori, and Dounokubo rainfall observatories, shown in Fig. 3
(right), are used as the ground observation data. A Thiessen polygon is drawn
based on the locations of the observatories, by which each observatory is
assigned a representative area. Then, the hourly rainfall data are given to
each representative area in the calculation. The cumulative and maximum
hourly rainfalls for the period 01:00 JST 28 July to 24:00 JST 30 July
were 955 mm and 83 mm h
Land use of the Kasahori and Otani dam catchments (left). Rainfall observatories and Thiessen polygons of the Kasahori and Otani dam catchments (right).
Catchment-averaged rainfalls of the Kasahori dam catchment.
Spatial patterns of cumulative rainfalls around the Shinano and Agano catchments using radar composite (upper left) and radar–rain gauge (upper right) and around the Kasahori dam catchment using radar composite (lower left) and radar–rain gauge (lower right) for the 2011 rainfall event.
Spatial patterns of cumulative rainfalls around the Shinano and Agano catchments using radar composite (upper left) and radar–rain gauge (upper right) and around the Kasahori dam catchment using radar composite (lower left) and radar–rain gauge (lower right) for the 2004 rainfall event.
The catchment-averaged rainfalls are calculated using RC, RR, and RG (Fig. 4). The catchment-averaged rainfalls of RG and RR are similar, whereas those of RC are smaller than the other two. The catchment-averaged cumulative rainfall during the period, based on the RG, RR, and RC, reaches 765.0, 762.8, and 568.5 mm, respectively. In other words, the cumulative rainfall by the RC is 0.74 times that of the ground observation, whereas the value by the RR is almost similar to the RG. Figure 5 shows the spatial distributions of the cumulative rainfall for the 2011 rainfall event around the Shinano and Agano river catchment by RC (upper left) and RR (upper right), while Fig. 5, lower left and right panels, shows those of the Kasahori dam catchment. It is apparent from Fig. 5 that the distributions by RC and RR show similar patterns in the mesoscale. However, it becomes slightly different when focusing on the small-scale Kasahori dam catchment. To verify whether the RC precipitation in this region is always smaller than RR, Fig. 6 show the rainfall patterns for another rainfall event in 2004, when flooding also occurred in the region. The damage by the flooding due to the 2004 event was even greater than that caused by the 2011 rainfall, although the total amount of rainfall in 2011 was larger. Figure 6 shows that the RC rainfall is larger than RR rainfall for the 2004 rainfall. The RR rainfall is obtained by correcting the RC using RG rainfall. Thus, the magnitude of the relation between the RC and RR rainfalls depends on the magnitude of the RG rainfall compared with the RC. The precipitation by RC is occasionally larger than the RR rainfall when the RG rainfall is smaller than RC and sometimes vice versa. As the RC can be obtained at 10 min interval with greater spatial coverage, it is considered more reasonable for use in future real-time purposes, though the authors do not carry out the operation. Thus, the calibration of the rainfall–runoff model is performed using RC rainfall.
Schematic of the 10 and 2 km EPSs.
A DRR model was applied to the
Kasahori dam catchment. The DRR model applied is that originally developed by
Kojima and Takara (2003) called CDRMV3. The details of this DRR model can be
seen in the work by Apip et al. (2011). In the DRR model, the surface and
river flows are simulated using a 1-D kinematic wave model. The subsurface
flow is simulated using a
Equivalent roughness coefficient of the forest, Manning's coefficient of the river, and soil-related parameters identified by the radar composite.
Two 11-member ensemble forecasts with different horizontal resolutions (10 and 2 km) were conducted for the 2011 Niigata–Fukushima heavy rainfall event using JMA-NHM (Saito et al., 2006; Saito, 2012) as the forecast model. The 10 km ensemble prediction system (EPS) uses the JMA's operational mesoscale 4D-Var analysis of 12:00 UTC (21:00 JST) on 28 July and the JMA's global spectral model (GSM) forecast from the same time as the initial and boundary conditions of the control run, respectively. As for the initial and lateral boundary conditions, perturbations from the JMA's 1-week global ensemble prediction from 12:00 UTC (21:00 JST) on 28 July were employed, whose detailed procedures are given in Saito et al. (2010, 2011). The 2 km EPS is a downscaling of the 10 km EPS with a 6 h time lag, using the forecasts of the 10 km EPS as the initial and boundary conditions (Fig. 8).
Three-hour accumulated rainfall from 12:00 to 15:00 JST (03:00 to 06:00 UTC) on 29 July by the control run of the 10 km EPS (upper left). Same as in the left figure, but the forecast by each member of the 10 km EPS (upper right). The figures on the lower left and right are the same as in the upper figures but for the forecasts by the 2 km EPS.
The bulk method that predicts the mixing ratios of six water species (water vapor, cloud water, rainwater, cloud ice, snow, and graupel) and the number density of cloud ice was adopted as the cloud microphysical process. The 10 km EPS applied the modified Kain–Fritsch convective parameterization scheme, while the 2 km EPS did not use convective parameterization. Other physical processes of the two systems were almost the same to those of the operational mesoscale model and the local forecast model of JMA (JMA, 2013b). The verification of the statistical performance of similar double-nested EPSs has been given by Duc et al. (2013).
Figure 9 (upper left) shows the 3 h accumulated rainfall from 12:00 to 15:00 JST (21:00 to 24:00 JST) by the control run of the 10 km EPS. Although the maximum value of the predicted rainfall (74 mm) is somewhat weaker than the observation (right panel of Fig. 1), the region of intense rainfall is simulated well. The upper right panel of Fig. 9 indicates the forecast by each member of the 10 km EPS. Seemingly, the result of each ensemble member resembles the others, and the basic characteristic features of the observed rainfall are simulated well. The maximum rainfall was obtained by member p02 (89 mm). A common feature seen in these figures is that weak fake rainfall appears over the coastal region facing the Sea of Japan, which is likely produced by the Kain–Fritsch convective parameterization.
Figure 9 lower panels shows the corresponding results by the 2 km EPS. The concentration of intense precipitation is produced more clearly, the maximum rainfall of which reaches 237 mm. The areas of weak rainfall over the western coastal region, appearing in Fig. 9 upper panels, no longer develop because of the removal of the convective parameterization. A detailed analysis of the two EPSs (ensemble spread and fraction skill scores) and the result of a sensitivity experiment to the orography have been presented by Saito et al. (2013a).
First, the DRR model is verified by performing experiments with the observed rainfall data. Next, the ensemble rainfall forecast data are used to perform an ensemble flood forecast. Finally, additional experiments are performed to consider position errors of rainfall.
The inflow to the Kasahori dam is simulated using the DRR model. The RG, RC, and RR data are used as the inputs to the runoff simulations. The three hydrographs with the parameters identified by the RC are shown in Fig. 10. The simulated hydrograph with the RC rainfall is in relatively good agreement with the observations, which is to be expected because the model parameters are calibrated against the RC rainfall. Using a straight line method for the base flow separation, the total discharge with RC in mm becomes 556.3 mm while the total rainfall is 568.5 mm.
Dam inflows for three rainfalls using the parameters identified with radar composite.
Flowchart of the overall procedure for the ensemble weather/flood simulation.
The simulated hydrographs for the other two rainfalls are larger than the observations. We do not address the magnitude of the relationship in this paper because it is not possible to determine more accurate rainfall data. The RG, RC, and RR measurements all have strengths and weaknesses; however, we focus on the consideration of RC for use because of the frequency of the data, i.e., 10 min interval.
Using the ensemble rainfalls from JMA-NHM, explained in Sect. 5.2, the ensemble flood simulation focusing on the Kasahori dam catchment was performed. A flowchart is shown in Fig. 11 to explain briefly again the overall procedure of the methodology for the ensemble simulations used in the paper.
The catchment-averaged ensemble rainfalls obtained from the 10 and 2 km resolution NHM are shown in Fig. 12. Figure 12 (upper) shows the control run and five negatively perturbed members, m01–m05 (m indicates minus), and five positively perturbed members, p01–p05 (p indicates positive), for the 10 km resolution.
Catchment-averaged rainfalls with JMA-NHM 10 km resolution ensemble simulation (upper) and with JMA-NHM 2 km resolution ensemble simulation (lower).
It is apparent from the figure that the magnitude of the 10 km resolution ensemble rainfall is basically lower than the RC rainfall. Thus, the dam inflows, obtained from the RC parameters in Table 1 with the 10 km resolution ensemble rainfall, lead to lower magnitude discharge compared with the ground observations (shown later in the paper).
Figure 12 shows the control run, m01–m05, and p01–p05 for the 2 km resolution NHM. The figures reveal that the first peak in the 2 and 10 km resolution ensemble simulations appears 2–4 h earlier than that in the observation. The magnitudes of some 2 km resolution ensemble rainfalls are equivalent to that of the RC rainfall. Thus, dam inflows using the RC parameters in Table 1 with the 2 km resolution ensemble rainfall can indicate discharge with equivalent magnitude (shown later in the paper). Figure 13 shows the spatial patterns of the cumulative ensemble rainfalls from 03:00 JST on 29 July 2011 to 03:00 JST on 30 July 2011 by the 11 ensemble simulations (upper: 10 km resolution; lower: 2 km resolution). The figures indicate that the 2 km resolution NHM rainfalls are apparently larger than the 10 km resolution rainfalls. Tables 2 and 3 show the cumulative and maximum hourly rainfalls from the 10 and 2 km resolution NHMs, respectively, averaged over the Kasahori dam catchment, which show that the 10 km resolution rainfalls are smaller than the 2 km resolution rainfalls. The maximum cumulative rainfall of the 2 km resolution NHM is realized in p02: 175.5 mm. Table 2 also shows the average cumulative rainfalls of both the 10 and 2 km resolution NHMs. The average cumulative rainfall in the 2 km resolution NHM is greater than in the 10 km resolution NHM. With regard to the maximum hourly rainfall in Table 3, p02 shows the highest values in both the 10 and 2 km resolution NHMs. The maximum hourly rainfall in the 2 km resolution NHM is also greater than that in the 10 km resolution NHM. This tendency is also true in the average maximum hourly rainfall shown in Table 3.
Spatial distributions of cumulative ensemble rainfalls (upper: 10 km resolution; lower: 2 km resolution).
Cumulative rainfall of 2 and 10 km resolution ensemble rainfall simulations.
Maximum hourly rainfall of 2 and 10 km resolution ensemble rainfall simulations.
Figure 14 (upper) shows the simulated inflow to the Kasahori dam with the
control run and positively/negatively perturbed rainfalls of the
10 km resolution NHM. Figure 14 (upper) shows that all the inflows to the
Kasahori dam are lower than the observations; however, these inflows exceed
the flood discharge of 140 m
Results of ensemble flood simulations with 10 km resolution rainfall (upper) and with 2 km resolution rainfall (lower).
Inflow volume into the reservoir based on observation and 2 km ensemble simulations.
Figure 14 (lower) shows the simulated discharge with the 2 km resolution
ensemble rainfalls. Figure 14 (lower) shows that at least the first peak of
the dam inflow in p02 shows a comparable value with that of the observed
inflow; the peak discharge of the observation is 843 m
Examples of the position shifts of the ensemble rainfalls.
Spatial distributions of cumulative ensemble rainfalls with position shift (2 km resolution).
Results of ensemble flood simulations with rainfall position shift (upper: control run and negatively perturbed members; lower: control and positively perturbed members).
Inflow volume into the reservoir based on observation and ensemble simulations with rainfall position shift.
In the actual operation of the Kasahori dam, the dam gate opening is fixed
once the inflow exceeds 140 m
All the dam inflow simulations, however, show that the second and third peaks
of the inflow are much smaller than indicated by the observations. In the
actual flood event, the so-called
Numerical weather prediction have inevitable forecast errors. The current case has a large amount of accumulated rainfall within a limited area and is sensitive to the position error. Although ensemble simulation represents the uncertainty to some extent, the ensemble spread tends to be under-dispersive because of imperfect model/initial condition representations and limited ensemble sizes. Duc et al. (2013) verified the spatial–temporal fractions skill score of 10 km/2 km ensemble forecasts for heavy rainfall events occurring over central Japan from 3 July 2010 to 2 August 2010. They showed that a spatial scale of 60 km (positional lag of 30 km) should be considered to obtain a reasonable reliability from a high-resolution ensemble forecast. Thus, it is important to take into account the position error within a reasonable distance before input to the runoff model.
To improve the ensemble rainfalls in quantity and timing, the cumulative rainfalls of each ensemble member are calculated and the rain distribution is translated within 30 km from the original position so that the catchment-averaged cumulative rainfall for the Kasahori dam maximizes. The analysis is carried out using the 2 km resolution, 30 h rainfall after the simulation. This position change corresponds to consideration of a 30 km positional lag to detect a risk of the maximum rainfall amount. Figure 16 shows the examples of the position shifts for cntl, m02, p03, and p04. Although the ensemble forecasts produce high cumulative rainfall, the original peak lies to the south of the Kasahori dam in all four members shown in Fig. 16. Figure 17 shows the spatial distribution of the position-shifted cumulative ensemble rainfalls with the 2 km resolution. Comparing Figs. 13 and 17, it is apparent that the rainfall intensity becomes higher. The simulated discharges with these position-shifted rainfalls are shown in Fig. 18. Figure 18 indicates that the first peak discharge simulated becomes high enough compared with the observed discharge. Timing of the first peak is also improved, and, in particular, some members reproduce the exact timing. Figure 18 shows the ensemble mean of the discharge as well since the ensemble mean becomes more informative compared to that in the experiment without position shifting. Figure 19 shows the inflow volume into the reservoir based on the observation and position-shifted ensemble simulations; the simulated inflow volume becomes comparable to the observed inflow volume. These results indicate that the ensemble rainfall simulation with position shift brings better performance although testing with more cases is desirable to confirm that.
As indicated in Sect. 5.2, it is known that ensemble weather simulations can be useful in adding value to weather forecasts. In the current operational weather forecasting, it is not necessarily expected that the weather will be predicted accurately for any specific location. However, accurate prediction over dam catchments is the main concern of river dam administrators. In this regard, this paper shows clearly that although the original 2 km prediction forecast provides much better results than that with the 10 km resolution prediction, greater accuracy is still desirable. For example, in dam/reservoir operations, the reliable prediction of the peak timing, flood duration, and runoff volume is extremely important parameters necessary to avoid erroneous operation. The results with original ensemble rainfalls here do not match the current requirements; however, the position-shifted 2 km resolution ensemble rainfall could be a useful tool for supporting operational decisions after statistical validation with various rainfall events, which would not be possible based on previous simulations with coarser resolutions.
This paper presents an example of short-term (lead times of less than a day)
ensemble flood forecasting for a typical small-scale dam catchment in Japan.
The Kasahori dam catchment (approx. 70 km
One of the strengths of the current study is the use of cloud-resolving
ensemble NWPs. However, the cloud-resolving ensemble forecast is still too
expensive for operational NWPs. Although this limits the number of experiments
and their experimental periods in the current study, some previous studies
also reported experimental use of similar NWP-based quantitative precipitation forecasts (QPFs) in the flood
forecasting. For example, Yu et al. (2015) showed an improvement of rainfall
and flood forecasting by blending NWP-based and radar-based QPFs. Their
target was typhoon Talas of 2011 over the two catchments, Futatsuno
(356.1 km
As far as we recognized, this study is the first trial of applying NWP-based
ensemble QPF to such a small dam catchment of less than 100 km
In any case, overall results are considered on some level helpful for decision-making related to flood control, especially as a supporting tool in addition to discharge observations and forecasting with radars. Likewise, improving the accuracy of original rainfall forecasted by high-resolution state-of-the-art numerical models, dense observation networks, and advanced data assimilation techniques is still essential.
JMA-NHM is available under collaborative framework between MRI and related institute or university.
Likewise, the DRR model is available under collaborative framework between Kobe, Kyoto Universities and related institute or university.
The JMA's operational analyses and forecasts, radar rain gauge analyses, and radar composite analyses can be purchased at
The first author performed the field survey of the region as a member of the investigation group of the Japan Society of Civil Engineers, led by Nobuyuki Tamai, Emeritus at the University of Tokyo. Through these activities, we received much useful information and data from the Niigata Prefecture. The authors would like to thank Tamai and the many other people who offered their help. This study is supported by the MEXT Global COE programme, “Sustainability/Survivability Science for a Resilient Society Adaptable to Extreme Weather Conditions” (GCOE-ARS; programme leader: Kaoru Takara, DPRI, Kyoto University). The authors appreciate the help provided by Takara. The ensemble forecast using JMA-NHM was conducted at the Meteorological Research Institute (MRI) as a part of the Grant-in-Aid for Scientific Research (21244074) and the HPCI Strategic Programs for Innovative Research (SPIRE, hp150214) of MEXT, We thank Seiji Origuchi and Hiromu Seko of MRI for their help in performing the ensemble forecasts. Edited by: M.-C. Llasat Reviewed by: two anonymous referees