D422Lot1.SMHI.5.1.1B: Detailed workflows of each case-study on … · 2019-02-18 · Tokyo area...

C3S_422_Lot1_SMHI – D5.1.1B |

Copernicus Climate Change Service

D422Lot1.SMHI.5.1.1B:

Detailed workflows of each case-study

on how to use the CDS for CII

production and climate adaptation

Full Technical Report:

Hydrological extremes in the megacity

of Tokyo

Hideo Amaguchi1, Jonas Olsson2, Lennart Simonsson1 and Akira Kawamura2

1Tokyo Metropolitan University (TMU)

2Swedish Meteorological and Hydrological Institute (SMHI)

REF.: C3S_422_Lot1_SMHI D5.1.1B

C3S_422_Lot1_SMHI – D5.1.1B |


Summary

The Tokyo case study focused on future changes in local sub-daily rainfall extremes and urban flood risk in metropolitan Tokyo. Together with the client (Tokyo Metropolitan Government), a series of experiments and analyses were

designed and performed in order to assess to which degree the Copernicus Climate Change Service (C3S) platform can support this kind of assessment.

As a reference, high-resolution simulations with an RCM (Non-Hydrostatic RCM (NHRCM) by the Japanese Meteorological Agency) and a hydraulic model (Tokyo Storm Runoff (TSR) model) were used. Concerning future changes in

local sub-daily rainfall extremes, changes in daily rainfall extremes from GCMs of the Climate Data Store catalogue agreed rather well with the NHRCM

projection. Concerning future changes in urban flood risk, changes in maximum discharge from WW-HYPE agreed rather well with the TSR results. These results have increased the client’s confidence in the reference

simulations and they suggest that the C3S platform may be used for similar assessment elsewhere, although more work is needed to verify this

possibility.


C3S_422_Lot1_SMHI – D5.1.1B |

Contents 1- Case study description ........................................................................................................ 4

1.1 Issue to be addressed ......................................................................................................... 4

1.2 Decision support to client .................................................................................................. 4

1.3 Temporal and spatial scale ............................................................................................... 4

1.4 Knowledge brokering ........................................................................................................... 4

2- Potential adaptation measures ........................................................................................ 5

2.1 Lessons learnt ........................................................................................................................ 5

2.2 Importance and relevance of adaptation .................................................................... 5

2.3 Pros and cons or cost-benefit analysis of climate adaptation ............................. 5

2.4 Policy aspects ......................................................................................................................... 5

3- Contact ...................................................................................................................................... 6

3.1 Purveyors ................................................................................................................................. 6

3.2 Clients/users ........................................................................................................................... 6

4- Data production and results ............................................................................................. 6

4.1 Step 1: Data collection ....................................................................................................... 6

4.2 Step 2: Empirical analysis ................................................................................................. 8

4.3 Step 3: RCM/GCM evaluation ........................................................................................... 9

4.4 Step 4: Flood risk assessment ...................................................................................... 12

4.5 Step 5: Adaptation support ............................................................................................ 16

4.6 Step 6: Outreach ................................................................................................................ 18

5- Conclusion of full technical report ................................................................................ 19

References ......................................................................................................................................... 20


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1- Case study description Firstly, we emphasize that this is an in-kind contribution to the

C3S_422_Lot1_SMHI contract. Thus the work has had to be aligned with ongoing activities at TMU, which naturally limits the range of possibilities to design the case study as well as the resources available. We have tried to

carry out the work in a way that to the degree possible benefits both the ongoing activities at TMU and the C3S.

Secondly, the set-up of this case study is that we perform reference simulations of future changes of rainfall extremes and urban flood risk in

Tokyo by using a high-resolution RCM and a detailed hydraulic model. These simulations are the basis for assessing adaptation measures. We then analyze

the C3S_422_Lot1_SMHI contract data produced to assess to which degree results from this global service match the results from the local, high-resolution simulations. Thus, the C3S global impacts contract will not provide

any fundamentally new information to the client, but it will primarily help the client assessing the confidence in the local results obtained.

1.1 Issue to be addressed

Extreme rainfall has increased in various places recently due to the influence

of global warming, and the relationship between the magnitude and frequency will change in the future. This will, in turn, increase the risk of urban flooding. In this case study, climate change indicators that the stakeholders in Tokyo

can use for climate adaptation of flood risk are assessed. Among CIIs, 1) extreme rainfall, 2) discharge/water level extremes, and 3) inundation

frequency are included.

1.2 Decision support to client

The final result will aid the Tokyo Metropolitan Government (TMG) in their

planning and decisions about policy on how to adapt small and medium-sized urban areas in Tokyo to increased flood risk. Furthermore, the results will

help to make information on the expected impacts of climate change easier and more accessible to various stakeholders (e.g. policy makers in Tokyo).

1.3 Temporal and spatial scale

Adaptations will be assessed from about 30 to about 80 years in the future, focusing on small and medium-sized urban river basin in Tokyo. The spatial

scale for urban storm runoff and inundation analysis is about 10 km² and the temporal scale is 1 h (or shorter). The C3S global impacts data will be

evaluated at its highest possible resolutions, i.e. 1 day in time and 50×50 km² (rainfall) or WW-HYPE sub-basin level (discharge) in space.

1.4 Knowledge brokering

Tokyo Metropolitan University (TMU) is founded by TMG. There is an agreement between Department of Civil and Environmental Engineering, TMU,

and Civil Engineering Support and Training Center, Bureau of Construction,


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TMG, for research partnership on the social infrastructure related problems.

We regularly had meetings with each other, about once a month. Under the agreement, it has been possible for us to promote this case study as

cooperative research.

2- Potential adaptation measures

2.1 Lessons learnt

The main added value of the C3S global impacts contract can be summarized as follows. - Future changes in daily rainfall and discharge extremes estimated from the

C3S_422_Lot1_SMHI contract overall support the results from a high-resolution RCM and a detailed hydraulic model

- This increases the confidence in the local modelling results and also adds some information about the uncertainty involved (although this has not been much further explored in the case study).

- In other megacities, the corresponding contract’s results can potentially be used to obtain rough numbers on the expected climate change impact on

urban flood risk, although more work is needed to validate this prospect.

2.2 Importance and relevance of adaptation

It is important to evaluate the climate adaptability of current flood control measures by understanding the changes in the frequency of heavy rain in the future and the worsening of the river water level and river flooding

accompanying it. We will continue to discuss with clients about the importance of climate adaptation on measures to control floods in small and

medium rivers.

2.3 Pros and cons or cost-benefit analysis of climate adaptation

Already today urban flooding is a problem in Tokyo (see e.g. the summary of

the 2005-09-05 event in the Interactive Atlas). Thus reducing flood risk is a key task even without considering climate change, but of course the latter

increases the need. Improved flood protection is however difficult in this very densely populated area and any cost-benefit analysis has been beyond the

scope of this study. Our detailed assessment of different impact aspects (maximum inundation, flood frequency, etc.), with and without a concrete adaptation, measure (new underground reservoir), has provided new

knowledge and decision support that will be used by TMG in their planning.

2.4 Policy aspects

The basic countermeasure for heavy rainfall currently underway by the Tokyo metropolitan government does not consider the impact of climate change. It is necessary to take measures other than river improvement in order to deal

with the increase in risk of flooding associated with future climate change, and it is important to promote countermeasures against rainwater outflow

together with municipalities.


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3- Contact

3.1 Purveyors

Akira Kawamura and Hideo Amaguchi Department of Civil and Environmental Engineering Tokyo Metropolitan University, Hachioji, Japan

3.2 Clients/users

Tadakatsu Takasaki Civil Engineering Support and Training Center

Bureau of Construction, Tokyo Metropolitan Government, Koto-ku, Japan

4- Data production and results

The main objective of the Tokyo case study was to investigate the possibility of using the C3S_422_Lot1_SMHI data to support urban flood risk assessment, which generally requires higher resolutions in time and space

than what the contract can offer. The study included analyses of extreme values of precipitation and discharge from the C3S_422_Lot1_SMHI contract,

available in the Climate Data Store (CDS) at its highest resolutions (time: 1 day; space: sub-basin), and comparing with results from higher-resolution RCM data and local flood modelling. The following steps were included:

- Data collection: compilation of rainfall data from long-term historical observations, a high-resolution RCM projection and several GCM

projections from the C3S_422_Lot1_SMHI contract, available in the Climate Data Store (CDS).

- Empirical analysis: investigation of how differences in rainfall extremes

between two climatological periods at daily resolution are related to corresponding changes at shorter durations.

- RCM/GCM evaluation: evaluation of short-duration rainfall extremes RCM and GCM projections for historical and future periods.

- Flood risk assessment: local flood risk impact modelling (reference

simulations) and comparison with corresponding results from C3S_422_Lot1_SMHI.

- Adaptation support: performance evaluation of a “hard measure” (underground reservoir) to reduce future flood risk, for decision support.

- Outreach: development of Interactive Atlas and external presentations.

4.1 Step 1: Data collection

The study focuses on the upper Kanda River basin in the north-western part of central Tokyo (Figure 1a). Three types of precipitation data were collected:

- Observations: hourly observations during the period 1901-2018 from a station located in Tokyo.


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- Regional Climate Model (RCM): Output from a high-resolution RCM

projection for periods 1980-1999, 2016-2035 and 2076-2095 covering the Tokyo area (Figure 1c). The RCM is the Non-Hydrostatic RCM run by Japan

Meteorological Agency (JMA, 2018). The temporal resolution is 10 min and the spatial resolution is 5×5 km².

- Global Climate Model (GCM): Output from three GCM projections available

in the CDS catalogue for periods 1980-1999, 2016-2035 and 2076-2095 covering the Tokyo area (Figure 1b). The GCM projections are GFDL-

ESM2M, HadGEM2-ES and NorESM1-M (two more Japanese projections were included in the study; MIROC5 and MRI-CGCM3). The temporal resolution is 1 day and the spatial resolution is 50×50 km².

a b

c

Figure 1. Location of Kanda River basin (a). Location of GCM grid cells used in the study with

Tokyo Prefecture marked in (dark) pink (b). Tokyo Prefecture (divided into east and west) with upper Kanda River basin and the NHRCM grid points (c).


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4.2 Step 2: Empirical analysis

One way to assess how future changes as estimated at daily scale is related to changes at shorter time steps is to study historical observations and differences between different periods. In this step, we studied how differences

in extreme daily rainfall between two 20-year periods are related to the corresponding differences in sub-daily extremes. Rainfall extremes were

characterized in terms of the commonly used Depth-Duration-Frequency (DDF) or Intensity-Duration-Frequency (IDF) statistics (e.g. Chow et al., 1988).

The workflow was as follows:

1. For each coherent 20-year period in the observations from Tokyo (section 4.1), extract the annual maximum rainfall for durations (i.e. accumulation periods) between 1 hour and 1 day.

2. Fit the Gumbel distribution to the annual maxima for the different durations.

3. From the distribution fitted to each duration, calculate the rainfall depth for different return periods (this step generates the DDF/IDF statistics).

4. For each possible combination of two (non-overlapping) 30-year periods,

calculate the (relative) differences in 20-year rainfall for the different durations. Return period 20 years was requested by the client (TMG).

5. Plot the differences in 20-year depths for sub-daily durations as functions of the difference in 20-year depths for daily duration.

Some examples of the results are shown in Figure 2. The results indicate that difference in daily rainfall extremes between two 30-year periods are rather

strongly related to the difference in 18-hour extremes between the same periods (Figure 2a). Then, with gradually decreasing duration, the relationship

with daily extremes gets weaker. However, even at duration 6 hours the relationship is still rather distinct (Figure 2c), implying that some information about the expected difference in 6-hour extremes can be derived from the

difference in daily extremes. At duration 1 hour, however, the relationship collapses (Figure 2d). This pattern likely reflect the fact that short-duration

extremes (up to 1-2 hours) are generally produced by small-scale primarily convective systems, whereas longer-duration extremes are rather related to large-scale stratiform systems. The slope of the regression line is generally

close to 1, implying that the differences in daily extremes are similar to differences at shorter durations. The results are used in the following section

to characterize the high-resolution NHRCM projection.


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a b

c d

Figure 2. Differences in 20-year depths for sub-daily durations (a: 18 hours; b: 12 hours; c: 6 hours; d: 1 hour) as functions of the difference in 20-year depths for daily duration in the

Tokyo gauge. The solid red line is the best linear fit and the dashed green lines represent the prediction interval.

4.3 Step 3: RCM/GCM evaluation

In this step, the RCM and GCM projections are evaluated in terms of two aspects; historical performance and future changes.

Historical performance

Rainfall extremes in the NHRCM output are compared with observed extremes in the historical period, to assess the climate model’s realism and the impact of temporal and spatial resolution. In this comparison, rainfall extremes for

different durations and return periods, estimated from 20-year periods, were calculated as described in section 4.2 (step 1-3). Further, rainfall extremes in

the NHRCM and GCM projections’ historical period (1980-1999) were compared, in order to assess their agreement.

Figure 3 shows that 10-min rainfall extremes in the NHRCM projection are underestimated, as compared with the observations (Figure 3a). Some

underestimation is expected as the NHRCM data represent a spatial average


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of 25 km² and spatial extremes will always be lower than point extremes from

a single gauge (e.g. Chow et al., 1988). The underestimation in Figure 3a is however larger than the expected spatial effect, implying an inability of the

model to fully reproduce observed extremes. This is hardly surprising as local 10-min rainfall extremes are extremely difficult to simulate. At duration 60 min (Figure 3b) the extremes match very well. Generally, the

effect of spatial averaging decreases with increasing duration, thus no substantial underestimation by the NHRCM projection is expected here.

Overall Figure 3 shows that the NHRCM does a good job with reproducing local short-duration rainfall extremes in Tokyo.

Figure 3. Short-duration rainfall depths with different return periods (equivalent to IDF curve

statistics) estimated for period 1980-1999 in observations and NHRCM projection, durations 10 min (a) and 60 min (b).

Figure 4a compares rainfall extremes in the NHRCM and GCM projections in

the historical period. In this comparison, the NHRCM projection was spatially aggregated to the GCM grid, in order to eliminate effects of spatial resolution. Further, the NHRCM projection was analyzed both at 1-hour and 1-day

temporal resolution to assess the impact of temporal resolution. For example, daily extremes will be higher when estimated from sub-daily data, if using a

“moving time window” to identify the maximum (which is common practice). Figure 4a indicates that the impact of time resolution is rather small. Mainly Figure 4a shows that the extremes in the NHRCM projection (aggregated to

GCM resolutions) is substantially higher than in the GCM projections. This likely illustrates the added value of regional modelling, with e.g. better

reproduction of topography and other geographical characters that affect the processes behind rainfall extremes.


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Figure 4. 20-year rainfall intensities for durations between 1 hour and 3 days in the NHRCM and GCM projections, periods 1980-1999 (a), 2016-2035 (b) and 2076-2095 (c). The NHRCM projection was spatially aggregated to the GCM grid.

Future changes

Changes in rainfall extremes in the NHRCM and GCM output for future periods are compared, to assess the agreement of the climate change signals in the

two types of climate models. Similarly to the historical performance step, rainfall extremes for different durations and return periods, estimated from

20-year periods, were calculated as described in section 4.2 (step 1-3). Further, relative changes between periods were calculated as also described in section 4.2 (step 4).

Figures 4b and 4c show how the 20-year rainfall in Tokyo is expected to

change in the future, as estimated in the NHRCM and GCM projections. For sub-daily durations in the (hourly) NHRCM projection there is a clear increase, but for durations ≥ 1 day the increase is less evident.

Figure 5 shows the relative change in the 20-year rainfall between periods

1980-1999 and 2076-2095, i.e. essentially the difference between Figures 4a and 4c (but for 20-year instead of 10-year rainfall). The NHRCM projection indicates an increase between 3% and 21% without clear dependence on

duration, even though the increase is generally somewhat higher for short than for long durations. The GCM projections vary rather widely between an

increase up to 40% and a slight decrease. If considering only duration 1 day (24 h), the mean change in the GCM ensemble is 8% increase. At duration 1

day the change in the mean change is a 13% increase. Thus, the mean change from the GCM projections at duration 1 day is in relatively good agreement with the mean change at durations ≥ 1 day in the NHRCM

projection.


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Figure 5. Change in 20-year rainfall between periods 1980-1999 and 2076-2095 in the NHRCM and GCM projections. The grey triangles represent the results from the empirical analysis

(section 4.2).

The grey triangles in Figure 5 schematically illustrate the results from the

empirical analysis (section 4.2). The vertical width of the triangles is an approximation of the width of the prediction intervals (Figure 2). If further

developed, this approach could potentially be developed into a method to estimate the pdf of the change in sub-daily rainfall extremes, based on the pdf of the change in daily rainfall extremes. This development is, however,

beyond the scope of this study.

4.4 Step 4: Flood risk assessment

In this step, the Tokyo Storm Runoff (TSR) model (Amaguchi et al., 2011) was used to simulate future flood risk in Upper Kanda River, based on the

NHRCM projection.

The TSR model (Figure 6) aims at describing the urban environment at the highest possible level of detail, thereby faithfully tracing the water fluxes both

on and under the land surface. The model structure as well as the set-up for the upper Kanda River basin (Figure 7) is described in detail in Amaguchi et al. (2011). The model has been previously applied for climate change impact

assessment in e.g. Olsson et al. (2012).


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Figure 6. Schematic of the Tokyo Storm Runoff (TSR) model.

The TSR model was applied to simulate the flood response to single high-intensity rainfall events using two types of rainfall forcing data:

- Design storms. Design storms are idealized time series of rainfall events widely used in urban hydrological engineering (e.g. Chow et al., 1988).

Here, we use a type of design storm typically used at TMU, representing Japanese standard (Tokyo Shuppan, 2007). The design storm is constructed based on historical observations, to represent today’s climate,

and then modified in line with the estimated future changes in DDF statistics (section 4.3). This strategy was considered the most suitable for

comparing the results with C3S_422_Lot1_SMHI discharge output. - Individual events. Also selected individual rainfall events in the NHRCM

projection were used as TSR input directly, both to study differences in

flood frequency and to assess the impact of adaptation measures (see further section 4.5).

The results of the TSR simulations were post-processed in different ways to describe various aspects of flood risk, e.g. maximum discharge, maximum

inundation depth and frequency of inundation over a certain water depth.


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Figure 7. Description of surface and sewers in the set-up of the TSR model to the upper Kanda River basin.

Figure 8 shows the IDF statistics for return period 20 years, based on the NHRCM projection (Figure 8a), as well as the corresponding 3-h design storms calculated based on the IDF statistics (Figure 8b). These design

storms were in turn used as forcing data in the TSR model to simulate the corresponding discharge in upper Kanda River. Figures 8c and 8d show the

resulting discharge in different cross sections along the river. On average, the peak discharge increases by 21% from period 1980-1999 to period 2076-2095.

a b


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c d

Figure 8. 10-year IDF statistics based on the NHRCM projection for periods 1980-1999 (Past), 2016-2035 (Future1) and 2076-2095 (Future2) (a), corresponding design storms (b),

discharge in selected points along the upper Kanda River (denoted No. 14-271) for design storm Past (c) and Future2 (d).

The above results may be compared with results obtained from

C3S_422_Lot1_SMHI directly. The closest corresponding Climate Impact Indicator is “Max water discharge”. In the area of upper Kanda River, and

increase of 10-25% is indicated until the end of the century under RCP8.5. Thus, the change estimated from the high-resolution modelling is within this interval, close to the upper limit.

The results from the TSR model simulations can be analyzed and presented in

many different ways, to highlight different impacts and to facilitate decision support. Figure 9a shows the inundated area resulting from TSR simulations with different design storms (Figure 8b). From the simulations of individual

events, the amount of time (i.e. duration) when the water level is above certain thresholds may be estimated (Figure 9b). It is interesting to note that

whereas the duration above the lower threshold (Warning B) decreases and the duration above the middle threshold (Warning A) increases only slightly until the end of the century, the duration of flooding increases substantially

(>100%). This is likely related to changes in the rainfall patterns but an in-depth investigation was beyond the scope of this study.

a

b


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Figure 9. Inundated area resulting from forcing the TSR model with design storms representing

different periods (b). Duration above different water level thresholds for the different periods (b).

4.5 Step 5: Adaptation support

Tokyo Metropolitan Government (TMG) has a long-term plan for how to improve flood protection by different types of adaptation measures (Figure

10). The plan is illustrated in Figure 9, with an example showing how rivers today can handle a rainfall intensity of 55 mm/h without flooding but in 30 years from now should be able to handle 75 mm/h through different

measures. Analyses from this study indicate that the return period of 75 mm/h will change from today’s 20 years to ~6 years. This type of analysis is

crucial for designing the long-term plan.


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Figure 10. Example of long-term adaptation plan (left box) and supporting rainfall analysis (right diagram).

One suggested measure to reduce the future flood risk in the upper Kanda River is to construct an underground reservoir that can handle large volumes

and thereby delay the flood response to extreme rainfalls. In tailored TSR simulations, designed together with the client (TMG), the impact of the

underground reservoir on flood risk was evaluated. In the simulations, the planned reservoir was first implemented in the existing TSR set-up for the upper Kanda River basin. Then the maximum future rainfall event in the

NHRCM projection was used as forcing in the model. This strategy was preferred by the client, rather than using the design storm approach (section

4.4). The result was assessed mainly in terms of maximum inundation depth (Figure 11). By the reservoir, the maximum inundation depth can be reduced by 24% and in total 17% less area will become flooded.


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a

b

Figure 11. Maximum inundation depth for the maximum future rainfall event in the NHRCM projection with (a) and without (b) underground reservoir.

4.6 Step 6: Outreach

Outreach comprises mainly the following three activities:

1. Development of an Interactive Atlas. This atlas introduces the upper Kanda River basin and its historical flooding problems. The expected

changes in rainfall extremes are described as well as the TSR model. Finally, the results of the TSR simulations used for adaptation support are shown and described (see e.g. Figure 10).

2. The Tokyo case study was presented orally at the 2018 World Water Congress & Exhibition of the International Water Association (IWA) (Olsson

et al., 2018). A follow-up scientific paper is being written. 3. The case study is planned to be presented in March 2019 at a seminar of

the Kanto branch of Japan Society of Civil Engineering.


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5- Conclusion of full technical report

It should be emphasized that the scope of this case study – using a global climate service to support urban flood risk assessment – is very challenging in

light of the different scales/resolutions involved. The main issue can be formulated as follows: to assess to which degree future changes of local sub-

daily rainfall extremes and urban discharge extremes can be estimated using the C3S global impacts service at its highest resolutions.

The main, and rather encouraging, results are that changes in both rainfall and discharge extremes derived from the C3S global impacts (GCMs and WW-

HYPE) rather well represented the corresponding changes as estimated from very high-resolution simulations (RCM and TSR). This suggests that the C3S global impacts is potentially applicable for general decision support related to

urban flooding impact assessment. But of course this was just one case study and much more work is needed to verify this possibility. We recommend that

some indicator of maximum rainfall, similar to the “Max water discharge” indicator, is provided in the C3S global impacts.

In this case study the client thus already had adaptation support from local simulations and there was thus no fundamentally new information provided

by the C3S global impacts. Rather the study supported the use of the C3S global impacts for similar assessment elsewhere, where no local simulations are available. Even so, the fact that the results from the C3S global impacts

overall agreed with the results from the local simulations is of course valuable for the client and provides an increased confidence in the results.

Acknowledgement

The DIAS dataset is archived and provided under the framework of the Data Integration and Analysis System (DIAS) funded by Ministry of Education,

Culture, Sports, Science and Technology (MEXT).


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References Amaguchi, H., Kawamura, A., Olsson, J., and T. Takasaki (2012) Development and

testing of a distributed urban storm runoff event model with a vector-based catchment

delineation, J. Hydrol., 420–421, 205-215.

Chow, V.T., Maidment, D.R. and L.W. Mays (1988) Applied Hydrology. McGraw-Hill, NY.

JMA (2018), Global Warming Projection, Volume 8, Japan Meteorological Agency.

Available at http://search.diasjp.net/en/dataset/JMA_GWP (accessed on 2019-01-07).

Olsson, J., Amaguchi, H., Alsterhag, E., Dåverhög, M., Adrian, P.-E., and A. Kawamura

(2013) Adaptation to climate change impacts on urban flooding: a case study in

Arvika, Sweden, Clim. Chang., 116, 231-247.

Olsson, J., Amaguchi, H., Kawamura, A., Takasaki, T., and B. Arheimer (2018) A global

service for tailored hydrological climate change impact assessment: application to

floods and drought in Tokyo, IWA World Water Congress & Exhibition, 16-21

September, Tokyo, Japan.

Tokyo Shuppan (2007) Technical guidelines for on-site storm water storage facilities

(Proposal), Association for Rainwater Storage and Infiltration Technology, Tokyo.

http://search.diasjp.net/en/dataset/JMA_GWP

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