Ensemble Flash Flood Predictions Using a High-Resolution Nationwide Distributed Rainfall-Runoff Model: Case Study of the Heavy Rain Event of July 2018 and Typhoon Hagibis in 2019

DOI: https://doi.org/10.21203/rs.3.rs-40714/v2

Abstract

The heavy rain event of July 2018 and Typhoon Hagibis in October 2019 caused severe flash flood disasters in numerous parts of western and eastern Japan. Flash floods need to be predicted over a wide range with long forecasting lead time for effective evacuation. The predictability of flash floods caused by the two extreme events are investigated by using a high-resolution (~150 m) nationwide distributed rainfall-runoff model forced by ensemble precipitation forecasts with 39-h lead time. Results of the deterministic simulation at nowcasting mode with radar and gauge composite rainfall could reasonably simulate the storm runoff hydrographs at many dam reservoirs over western Japan for the case of heavy rainfall in 2018 (F18) with the default parameter setting. For the case of Typhoon Hagibis in 2019 (T19), a similar performance was obtained by incorporating unsaturated flow effect in the model applied to Kanto region. The performance of the ensemble forecast was evaluated based on the bias ratios and the relative operating characteristic curves, which suggested the higher predictability in peak runoff for T19. For the F18, the uncertainty arises due to the difficulty in accurately forecasting the storm positions by the frontal zone; as a result, the actual distribution of the peak runoff could not be well forecasted. Overall, this study showed that the predictability of flash floods was different between the two extreme events. The ensemble spreads contain quantitative information of predictive uncertainty, which can be utilized for the decision making of emergency responses against flash floods.

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Tables

Table 1. The default model parameters (Def) and the calibrated ones (Cal). The default model parameters represent only saturated subsurface and surface runoff, while the calibrated ones used only in the Kanto Region for T19 represent also the effect of unsaturated subsurface flow.

Parameters

Def

Cal

n [m-1/3s]

0.4

0.4

da [m]

0.471

0.471

dm [m]

-

0.05

ka [m/s]

0.1

0.03

b [-]

-

7.0

nriver [m-1/3s]

0.03

 


 

 

Table 2. Values of the verification metrics for the F18 simulation at each dam reservoir.

Dam Name

Region

Peak Runoff

[mm/h]

r

a

b

KGE

NSE

PE

Kuzuryu

Kinki

14.0

0.93

1.14

0.90

0.81

0.68

0.81

Hiyoshi

Kinki

15.3

0.97

1.14

1.14

0.80

0.90

1.08

Hitokura

Kinki

18.8

0.94

1.02

1.15

0.84

0.75

1.01

Tomata

Chugoku

10.8

0.95

1.15

1.26

0.70

0.74

1.01

Hattabara

Chugoku

12.4

0.96

1.06

1.30

0.69

0.74

0.91

Haizuka

Chugoku

16.7

0.94

0.83

1.10

0.79

0.80

0.71

Nagayasuguchi

Shikoku

20.9

0.98

1.02

0.74

0.74

0.79

0.89

Nomura

Shikoku

34.4

0.90

0.70

0.81

0.63

0.61

0.58

Shingu

Shikoku

14.5

0.81

0.79

0.94

0.72

0.53

0.80

Tomisato

Shikoku

19.5

0.95

1.01

0.84

0.83

0.76

0.68

Nakasujigawa

Shikoku

22.2

0.95

1.45

1.26

0.48

0.56

1.13

Odo

Shikoku

10.8

0.97

1.10

0.92

0.87

0.88

1.02

Samerura

Shikoku

22.3

0.93

0.94

0.82

0.80

0.69

0.75

Terauchi

Kyushu

24.3

0.97

1.08

0.99

0.91

0.84

0.95

Egawa

Kyushu

25.2

0.97

0.95

0.84

0.83

0.84

0.79

Ryumon

Kyushu

24.6

0.98

1.12

1.05

0.87

0.85

0.94

Shimouke

Kyushu

23.5

0.98

1.00

0.99

0.97

0.87

0.86

Yabakei

Kyushu

21.7

0.92

0.84

1.03

0.81

0.72

0.69

Mean

19.5

0.94

1.02

1.00

0.78

0.75

0.87

Median

20.2

0.95

1.02

0.99

0.81

0.75

0.87

Standard Deviation

5.9

0.04

0.17

0.16

0.11

0.10

0.15

 


 

 

Table 3. Values of the verification metrics for the T19 simulation at each dam reservoir.

Dam Name

Region

Peak Runoff

[mm/h]

r

a

b

KGE

NSE

PE

Aimata

Kanto

11.4

0.91

1.13

1.28

0.68

0.73

0.82

Kusaki

Kanto

23.0

0.95

1.07

1.21

0.77

0.86

1.09

Shimokubo

Kanto

20.5

0.99

1.09

1.09

0.87

0.97

1.09

Kawamata

Kanto

21.9

0.91

0.90

1.07

0.85

0.82

0.85

Urayama

Kanto

29.3

0.98

1.25

1.14

0.71

0.88

1.21

Takizawa

Kanto

23.1

0.99

0.99

1.03

0.97

0.98

0.99

Ninose

Kanto

19.8

0.97

0.89

0.93

0.86

0.93

0.71

Yunishikawa

Kanto

16.1

0.97

1.38

1.47

0.40

0.67

1.26

Kawafusa

Tohoku

18.3

0.98

1.20

1.22

0.70

0.88

1.11

Mean

20.3

0.96

1.10

1.16

0.76

0.86

1.01

Median

20.5

0.97

1.09

1.14

0.77

0.88

1.09

Standard Deviation

4.7

0.03

0.15

0.15

0.16

0.10

0.17