A First Attempt at Impact-Based Typhoon Track Ensemble Forecasting in Japan: Evaluating the Role of Typhoon Tracks in Flood Damage for Hagibis (2019)

A First Attempt at Impact-Based Typhoon Track Ensemble Forecasting in Japan: Evaluating the Role of Typhoon Tracks in Flood Damage for Hagibis (2019) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A First Attempt at Impact-Based Typhoon Track Ensemble Forecasting in Japan: Evaluating the Role of Typhoon Tracks in Flood Damage for Hagibis (2019) Xiaoyang Li, Kei Yoshimura, Hironori Fudeyasu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6251998/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Typhoon Hagibis (2019), one of the most powerful storms to strike Japan in recent years, caused widespread flooding and significant damage. Impact-based forecasting is crucial for planning effective mitigation measures and enhancing future disaster responses. This study employs the Integrated Land Simulator (ILS) coupled with the Weather Research and Forecasting (WRF) Model to evaluate flood damage induced by Typhoon Hagibis. Our control (c000) simulation successfully reproduced the spatial distribution and intensity of accumulated rainfall and peak river discharge. However, compared to observations, the simulated rainfall and discharge exhibited a slight westward shift in central Japan and eastward shift in northeastern Japan. These discrepancies are likely due to a slight westward (eastward) shift in the simulated typhoon track before (after) its landfall in Japan. To systematically assess the impact of typhoon tracks on flood damage, we conducted ensemble simulations. The e008 simulation (0.8° eastward shift) resulted in the highest flood damage, totaling 2478.7 billion JPY. A westward shift reduced total flood damage across Japan but increased it in southwestern regions, whereas an eastward shift led to an overall decrease in flood damage nationwide. Regarding the spatial distribution of flood damage caused by the worst typhoon tracks in each region, flood damage was primarily concentrated in floodplain areas along the Pacific Ocean coast in central, southwestern, and northeastern Japan, while in southern Japan, more flood damage was concentrated along the Japan Sea coast. These findings underscore the critical influence of typhoon tracks on flood risk. Impact-based typhoon track ensemble simulation can enhance our understanding of high-risk flood-prone areas and improve disaster preparedness and mitigation strategies. Earth and environmental sciences/Hydrology Earth and environmental sciences/Natural hazards Impact-Based Forecasting Typhoon Hagibis (2019) inundation area Integrated Land Simulator flood damage Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction In recent years, East Asia has increasingly faced severe flood disasters triggered by extreme rainfall events, including heavy typhoon rainfall 1 – 3 , intense Meiyu-Baiu rainfall 4 – 6 , and heavy precipitation from explosive extratropical cyclones 7 – 9 . These extreme rainfall events are expected to intensify under a warmer climate with an accelerated hydrological cycle, leading to greater casualties and heightened socioeconomic losses across household assets, agriculture, forestry, and fisheries 10 – 12 . Global and regional flood forecasting systems are widely utilized to mitigate flood disasters caused by extreme rainfall events worldwide 13 – 15 . Global flood forecasting systems offer longer prediction periods, extending up to one month, though with relatively lower accuracy. Examples include the Global Flood Forecasting and Information System (GLOFFIS) 16 by Deltares and the Global Flood Awareness System (GloFAS) 17 , developed jointly by the European Commission and the European Centre for Medium-Range Weather Forecasts. In contrast, regional flood forecasting systems provide shorter prediction periods, ranging from several hours to a week, with generally higher accuracy. Notable examples include the European Flood Awareness System (EFAS) 18 by the European Commission, the Hydrologic Ensemble Forecasting Service (HEFS) 19 by the U.S. National Weather Service, and Hydrological Predictions for the Environment (E-HYPE) 20 by the Swedish Meteorological and Hydrological Institute. Aside from traditional flood forecasting, impact-based flood forecasting integrates weather forecast data with vulnerability and exposure information to generate a comprehensive risk assessment 21 – 23 . This assessment is crucial for communities and individuals, enabling early action before flood disasters to save lives and minimize socioeconomic losses from extreme rainfall events. As a proven and cost-effective approach, impact-based forecasting plays a vital role in reducing disaster-related deaths and damage by facilitating effective planning, timely response for fund allocation and resource mobilization, and efficient mitigation of disaster impacts. By incorporating real-time hazard predictions with socioeconomic and infrastructure data, impact-based forecasting enhances disaster preparedness and response strategies. Flood damage assessment has been conducted at the city and local levels with limited coverage areas 24 – 27 , as well as at the global scale with low resolution 22 , 28 . However, few studies have focused on impact-based forecasting at the regional and national levels, with some exceptions in East Africa and Philippine 21 , 23 . This type of forecasting is crucial for decision-makers to determine appropriate early actions, including national resource mobilization, weather modification and controllability, and so on. Despite its importance, no national-scale impact-based forecasting has been conducted in East Asia. Storm path ensemble simulations are essential for impact-based forecasting, as the spatial distribution of extreme rainfall and flood damage is largely influenced by the storm path 29 – 31 . Understanding the relationship between storm paths and flood hazards is crucial for improving disaster preparedness and mitigation strategies. Recent advancements in numerical weather prediction have enabled the use of typhoon track ensemble simulation to systematically evaluate the potential impacts of different typhoon tracks. By perturbing the initial and boundary conditions of a storm, ensemble simulations generate multiple plausible scenarios, capturing the inherent uncertainty in track forecasts. These ensemble simulations provide a probabilistic assessment of extreme rainfall and flood risks under various storm paths. Previous studies conducted storm path ensemble simulations to evaluate the potential effects of typhoon tracks on storm surges 2 and strong winds 32 in East Asia. However, the effect of storm paths on flood damage using storm path ensemble simulation has not yet been systematically studied. Typhoon Hagibis (2019), one of the most powerful storms to strike Japan in recent years, caused widespread flooding and significant damage. According to the Japan Meteorological Agency’s report, Typhoon Hagibis formed in the tropical region of the northwest Pacific Ocean at 1800 UTC on October 5, moved northwestward, and made landfall in central Japan at 1000 UTC on October 12 as a large and strong typhoon. After passing through central Japan, it transitioned into an extratropical cyclone east of Japan at 0300 UTC on October 13. During this events, maximum 24-hour accumulated rainfall exceeded 500mm in 15 cities across central Japan, with a maximum recorded value of approximately 1000 mm in Hakone city. In particular, record-breaking heavy rainfall was observed in many locations in central Japan for 3-hour, 6-hour, 12-hour, and 24-hour accumulated rainfall amounts. In response to this extreme rainfall, a special heavy rain emergency warning was issued from 0630 UTC until 2340 UTC on October 12 for one metropolis and 12 prefectures in central Japan, urging the highest level of caution. According to the Ministry of Land, Infrastructure, Transport, and Tourism of Japan, Typhoon Hagibis caused a total damage of 1880 billion Japanese Yen (JPY), including 1422 billion JPY in general asset losses, marking the highest recorded damage amount since the beginning of statistical records. During the passage of Hagibis, 84 fatalities were reported, with 3 people missing, and a total of 81,619 buildings were destroyed or damaged. This study aims to comprehensively understand the relationship between typhoon tracks and flood damage, providing decision-makers with valuable insights to mitigate flood disasters through weather modification and controllability. To achieve this, the Weather Research and Forecasting Model (WRF) is utilized to analyze atmospheric conditions and perform typhoon track ensemble simulations for Typhoon Hagibis (2019), while the Integrated Land Simulator (ILS) is employed to simulate inundation conditions (Fig. S1 ). This research represents the first national-scale attempt at impact-based forecasting in Japan. Besides, the whole Japan is divided into five sub-regions (Fig. 1 ) to further analyze regional characteristics of flood damage. The objectives of this study are three-fold: (i) to assess the accuracy of WRF and ILS simulations by comparing them with JMA radar-observed rainfall and MLIT-observed river discharge; (ii) to quantify flood damage caused by Typhoon Hagibis (2019) across Japan based on simulated inundation condition and household distribution; (iii) to evaluate the impact of typhoon track variations on flood damage and identify regions at heightened risk under different track scenarios. Results and discussion Validation of the model-simulated results According to the Japan Meteorological Agency’s report, the rainfall mainly occurred in Japan from 1200 UTC on October 11 to 0000 UTC on October 13. Rainfall data derived from JMA radar were used to validate the WRF simulation. Compared to the JMA radar observations, the WRF control (c000) simulation accurately reproduced the spatial distribution and intensity of accumulated rainfall during the passage of Hagibis from 1200 UTC on October 10 to 0000 UTC on October 13 (Fig. 2 a and b). The heavy rainfall was primarily concentrated in central Japan and along the Pacific Ocean coastal regions. Additionally, river discharge data observed by the MLIT were used to validate the performance of the ILS. The spatial distributions of MLIT-observed and ILS-simulated maximum river discharge across Japan during the passage of Typhoon Hagibis are shown in Fig. 3 a and b. The ILS control (c000) simulation successfully replicated the spatial distribution of maximum river discharge observed by the MLIT. The correlation coefficient (R) between the observed and simulated maximum river discharge was 0.91 (Fig. S1 ). These results indicate that the WRF and ILS simulations demonstrate strong reproducibility of the atmospheric and hydrological processes associated with Typhoon Hagibis, making them well-suited for assessing flood damage caused by the typhoon in Japan. The simulated total rainfall exhibited a slight westward shift in central Japan and eastward shift in northeastern Japan when compared to observations (Fig. 2 ). Similarly, the maximum river discharge showed comparable spatial discrepancies between the ILS simulation and the MLIT observations (Fig. 3 ). These discrepancies can be attributed to a slight westward (eastward) shift in the simulated typhoon track before (after) its landfall in Japan compared to the observed track (Figs. 1 and 2 ). This suggests that even a small error in atmospheric conditions—such as precipitation distribution and intensity—can propagate through land surface processes, leading to significant deviations in flood predictions. To minimize uncertainties and enhance the accuracy of flood damage assessments, it is crucial to improve both atmospheric and land surface process simulations with greater precision and detail. Our results highlight the significant sensitivity of total rainfall, maximum river discharge, and inundation areas to the typhoon track. To further investigate the influence of typhoon tracks on flood damage in Japan, the results of the typhoon track ensemble simulations will be detailed in the following subsection. Effects of Typhoon track on flood damage assessment Typhoon track ensemble simulations were conducted using the WRF model. Among the 81 ensemble members, five representative simulations were selected for further analysis: the control run (c000), two westward shifts (w020 and w040), and two eastward shifts (e020 and e040). Figure 4 illustrates the spatial distribution of total accumulated rainfall for these five simulations. Heavy rainfall patterns shifted significantly as the typhoon track changed. In the control run (c000), rainfall was most concentrated in central Japan (Fig. 4 c). When Typhoon Hagibis shifted westward, heavy rainfall migrated toward southwestern and southern Japan (Fig. 4 a and b). Conversely, when the typhoon shifted eastward, rainfall moved toward the Pacific coastal regions, decreasing in both extent and intensity (Fig. 4 d and e). A similar spatial variation was observed in inundation patterns during the typhoon east-west shift (Fig. 5 ). In the control run (c000), central Japan experienced the most severe flooding, with the largest inundation depth and area (Fig. 5 c). A westward typhoon shift increased flooding in southwestern and southern Japan (Fig. 5 a and b). While an eastward shift resulted in more localized flooding along the Pacific coast, with reduced severity (Fig. 5 d and e). Flood damage analysis revealed that the control run (c000) caused the greatest total flood damage among the five cases, amounting to approximately 1652.4 billion JPY, with 1530.1 billion JPY concentrated in central Japan (Fig. 6 c and Table 1 ). When Hagibis shifted westward, total flood damage across Japan decreased to 956.0 billion JPY for w020 and 730.0 billion JPY for w040. However, damage in southwestern Japan increased significantly, from 40.2 billion JPY to 606.6-609.8 billion JPY for w020 and w040 (Fig. 6 a, b and Table 1 ). Additionally, flood damage in northern Japan increased slightly due to remote typhoon-related rainfall, reaching 1.6 billion JPY for w020 and 13.7 billion JPY for w040. Conversely, when Hagibis shifted eastward, total flood damage across Japan decreased rapidly, to 886.5 billion JPY for w020 and 13.2 billion JPY for w040, with the majority of the damage still occurring in central Japan. Table 1 Flood damage (billion JPY) in each region and whole Japan during the passage of Typhoon Hagibis from 0000 UTC on 12 October to 0000 UTC on 15 October 2019 for the five representative simulations: (a) w040, (b) w020, (c) c000, (d) e020, (e) e040. Numbers in brackets are for permillage (‰) of flood damaged asset value to total asset value. Total asset value (billion JPY) in each region and whole Japan are shown in the last column. Region w040 w020 c000 e020 e040 Total (billion Yen) Northern Japan 13.7 (0.4) 1.6 (0.0) 0.1 (0.0) 0.0 (0.0) 0.0 (0.0) 38974.7 Northeastern Japan 11.2 (0.2) 71.7 (1.5) 82.1 (1.7) 22.4 (0.5) 0.0 (0.0) 49336.1 Central Japan 26.6 (0.1) 273.0 (0.5) 1530.1 (3.1) 864.1 (1.7) 13.2 (0.0) 497581.7 Southwestern Japan 606.6 (2.8) 609.8 (2.8) 40.2 (0.2) 0.0 (0.0) 0.0 (0.0) 218464.3 Southern Japan 71.8 (0.7) 0.0 (0.0) 0.0 (0.0) 0.0 (0.0) 0.0 (0.0) 100069.9 Whole Japan 730.0 (0.8) 956.0 (1.1) 1652.4 (1.8) 886.5 (1.0) 13.2 (0.0) 904426.7 To examine the typhoon track’s effect on flood damage in more detail, Fig. 7 presents the flood damage in each region for all 81 ensemble members. As Hagibis shifted westward (from e050 to w110), total flood damage across Japan increased rapidly, peaking at 2478.7 billion JPY in the e008 simulation (Table 2 ), before gradually decreasing. Table 2 Same as Table 1 , but for maximum flood damage (bold font) in each region and whole Japan with corresponding typhoon track: w054 for southern Japan, w038 for southwestern Japan, w036 for northern Japan, w002 for northeastern Japan, and e008 for central Japan and whole Japan. Region w054 w038 w036 w002 e008 Northern Japan 9.1 (0.2) 15.8 (0.4) 17.2 (0.4) 0.7 (0.0) 0.0 (0.0) Northeastern Japan 1.1 (0.0) 16.6 (0.3) 13.5 (0.3) 96.0 (1.9) 52.5 (1.1) Central Japan 8.9 (0.0) 39.2 (0.1) 52.4 (0.1) 1358.1 (2.7) 2411.2 (4.8) Southwestern Japan 246.2 (1.1) 724.1 (3.3) 712.3 (3.3) 31.9 (0.1) 15.1 (0.1) Southern Japan 259.3 (2.6) 37.4 (0.4) 23.2 (0.2) 0.0 (0.0) 0.0 (0.0) Whole Japan 524.6 (0.6) 833.2 (0.9) 818.6 (0.9) 1486.7 (1.6) 2478.7 (2.7) Flood damage in each region followed distinct patterns during the westward shift (Fig. 7 and Table 2 ). Central Japan sustained the highest damage, reaching a peak of 2411.2 billion JPY at e008. Meanwhile, moderate damage was observed in southwestern Japan (724.1 billion JPY of maximum damage at w038) and southern Japan (259.3 billion JPY of maximum damage at w054). Additionally, maximum flood damage was relatively small in northeastern Japan (96 billion JPY at w002) and northern Japan (17.2 billion JPY at w036). Regarding the spatial distribution of flood damage in each region corresponding to the worst typhoon tracks, most flood damage occurred in floodplain areas along the Pacific Ocean coast in central, southwestern, and northeastern Japan (Fig. 8 b, d, and e). In contrast, flood damage in southern Japan was primarily concentrated in floodplain areas along the Japan Sea coast (Fig. 8 a). Given the high population density in these floodplain regions (Fig. S3), there is a pressing need for enhanced flood disaster preparedness, including improved flood protection infrastructure and impact-based early warning systems. It is worth noting that even a slight shift of the typhoon, 0.2° from e002 to e004 (or from e006 to e008), resulted in a significant difference of 359.2 (318.2) billion JPY in flood damage. These findings highlight the sensitivity of flood risk to typhoon track variations and emphasize the critical need for precise typhoon track forecasting in flood damage assessments. Furthermore, this study underscores the vast potential of weather modification and controllability. By simulating different typhoon tracks using the ILS and WRF models, we can deepen our understanding of the potential impacts of such events and enhance disaster preparedness and mitigation strategies. Flood risk is generally determined by three major components: heavy rainfall hazard (intensity and distribution of rainfall), household asset exposure (economic value of affected properties), and vulnerability (degree of damage experienced per unit of exposure). This study demonstrates that flood risk is highly sensitive to typhoon track. The shift in Typhoon Hagibis’s route altered the spatial distribution of rainfall and exposed assets, which in turn significantly influenced total flood damage. Furthermore, our results indicate that even a slight shift in a typhoon’s track can lead to substantial differences in flood damage. This underscores the potential feasibility and importance of weather control and weather modification as a means to mitigate flood risks. These findings provide fundamental insights for disaster preparedness, policy-making, and future advancements in weather modification techniques. Additionally, during the Typhoon Hagibis event, 77% (67 people) of the victims were aged 60 or older, highlighting a significant concentration of casualties among the elderly due to difficulties in escaping flooded areas 33 . Flood risks in East Asia are expected to increase under a warmer future climate. Simultaneously, aging populations and declining birth rates are intensifying demographic challenges, particularly in Japan, China, and South Kerea. Impact-based forecasting is essential for reducing casualties and increasing the evacuation rate among the elderly and people with disabilities. By providing more detailed risk information before flood hazards occur, early actions can be taken, enhancing disaster preparedness for vulnerable populations. Methods Atmospheric Model and Typhoon Track Ensemble Simulation The Weather Research and Forecasting Model (WRF V4.3.3 34 ) was employed to simulate Typhoon Hagibis (2019). A one-way nested configuration was used, with the inner domain (121.37° E–152.48° E, 21.70° N–44.81° N) nested within the outer domain (120.02° E–153.98° E, 18.09° N–45.76° N). These domains encompassed the Japanese archipelago and the surrounding seas. The outer domain had a horizontal resolution of 15 km, while the inner domain had a finer resolution of 5 km. The grid points were configured as 220 × 215 for the outer domain and 601 × 541 for the inner domain. The model used 48 vertical layers for both domains, with integration time steps of 60 seconds and 20 seconds for the outer and inner domains, respectively. The simulation period for the outer domain spanned from 1800 UTC on 9 October to 0000 UTC on 13 October 2019, while for the inner domain, it covered from 1200 UTC on 10 October to 0000 UTC on 13 October 2019. Model outputs were generated every 30 minutes. The primary physics packages used in the WRF model for both domains included the RRTMG shortwave and longwave radiation schemes 35 (Iacono et al., 2008), the WSM 6-class graupel cloud microphysics scheme 36 (Hong & Lim, 2006), the Kain-Fritsch cumulus convection scheme 37 (Kain 2004), and the Yonsei University planetary boundary layer (PBL) scheme 38 (Hong et al., 2006). The Typhoon bogus scheme was applied exclusively to the outer domain. The initial and lateral boundary conditions for the outer domain were derived from the Japanese 55-year Reanalysis 39 (JRA-55). For the inner domain, the initial and lateral boundary conditions were interpolated from the outer domain results. This simulation setup is hereinafter referred to as the control (c000) run. An ensemble simulation of typhoon tracks 40 based on the WRF model was also performed. JRA-55 initial and boundary atmospheric conditions were systematically shifted longitudinally at 0.2° intervals, extending up to 11° westward (designated as w110) and 5° eastward (designated as e050). In total, 81 typhoon tracks were simulated for Typhoon Hagibis (2019) to investigate the effects of typhoon track variations on flood damage in Japan. Land Surface Model and Flood Damage Assessment The Integrated Land Simulator 41 (ILS; Nitta et al. 2020) was employed to simulate flood extent and floodplain water depth for the 81 typhoon tracks. The ILS integrates two key models: MATSIRO 42 , 43 (Minimal Advanced Treatments of Surface Interaction and Runoff; Takata et al. 2003; Nitta et al. 2014) a physical land surface model; and CaMa-Flood 44 (Catchment-based Macro-scale Floodplain model; Yamazaki et al. 2011), a hydrodynamic river routing model. MATSIRO incorporates a bulk formula for transpiration with a single-layer canopy and simulates soil processes using six soil layers. To address potential instabilities in skin temperature and surface flux calculations, MATSIRO employs the Newton–Raphson iterative method. Additionally, it features a tile-scheme design that allows users to select specific tiles (e.g., land cover, soil type, elevation) tailored to their research objectives. The CaMa-Flood model incorporates several advanced physical schemes, including the Stabilized Local Inertial Equation for simulating water flow dynamics 45 (Bates et al., 2010), the Adaptive Time Step Scheme to enhance computation efficiency and stability 46 (Hunter et al., 2005), the Floodplain Inundation Scheme for accurate modelling of floodplain water distribution 44 (Yamazaki et al., 2011), the Levee and Reservoir Scheme to account for human-made infrastructure effects 47 (Hanazaki et al., 2022), the MERIT DEM/MERIT Hydro Baseline Topography to providing high-resolution elevation and hydrography data 48 , 49 (Yamazaki et al., 2017; Yamazaki et al., 2019). The ILS simulations were performed from October 1 to 16, 2019, with a horizontal resolution of 1-arc minute (~ 1.5km) to estimate inundation depth and area. Atmospheric forcing data for the simulations were derived from the JMA MSM, which provided hourly data on rainfall, temperature, wind speed, specific humidity, and surface pressure, as well as daily shortwave and longwave radiation. During the passage of Typhoon Hagibis, from 1200 UTC on October 10 to 0000 UTC on October 13, 2019, the atmospheric forcing data were replaced by the-WRF simulated outputs corresponding to each ensemble member of the Hagibis track simulations. The ILS-simulated inundation results were further downscaled from a 1-arc minute (~ 1.5km) resolution to a 1-arc second (~ 30m) resolution using the MERIT DEM topographic information to enable detailed flood damage assessment. Flood damage was estimated following the guidelines outlined in the Manual for Economic Evaluation of Flood Control Investment 50 , published by the River Bureau of Ministry of Land, Infrastructure, Transport and Tourism of Japan. For each grid cell, flood damage was calculated by multiplying the total asset values by the flood damage rate. The total asset values were determined by multiplying the number of households by the values of assets per household, including housing and houseware. The flood damage rate within each grid was derived from the downscaled inundation depth using an empirical relationship. Household distribution data and asset values for houses and housewares were obtained from the National Population Census by the Statistic Bureau and the Manual for Economic Evaluation of Flood Control Investment, respectively. Declarations Data availability The JRA-55 data is available on the JRA-55 website (http://jra.kishou.go.jp/JRA-55/index_en.html). River discharge data is available on the MLIT website (http://www1.river.go.jp/). The JMA radar data is collected and distributed by Research Institute for Sustainable Humanosphere, Kyoto University (http://database.rish.kyoto-u.ac.jp/arch/jmadata/data/jma-radar/synthetic/original/). Code availability The source code for the Weather Research and Forecasting (WRF) Model is available on the GitHub website (https://github.com/wrf-model). Similarly, the source code for the Integrated Land Simulator (ILS) can also be accessed on the GitHub website (https://github.com/integrated-land-simulator). Acknowledg ments This research was supported by the JST-Moonshot Program (JPMJMS2282-08), JSPS KAKENHI (grant numbers 21H05002, 22H04938, and JP23K19068), JST-Mirai Program (JPMJMI21I6), JST-eASIA Joint Research Program (JPMJSC22E4), MEXT program for the advanced studies of climate change projection SENTAN (JPMXD0722680395, JPMXD1420318865), the Environment Research and Technology Development Fund S-20 of the Environmental Restoration and Conservation Agency of Japan (JPMEERF21S12020), Earth Observation Research Center, Japan Aerospace Exploration Agency (JX-PSPC-533980). Author contributions K.Y., and X.L. developed research concepts. X.L., K.Y., and H.F., contributed to numerical simulation. X.L., and K.Y. analyzed and interpreted the data. X.L. wrote the paper and all the authors reviewed the paper and contributed to the final manuscript. C ompeting interest s The authors declare no competing interests. Correspondence and requests for materials should be addressed to Xiaoyang Li. 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Liu, L. et al. Ensemble-based sensitivity analysis of track forecasts of typhoon In-fa (2021) without and with model errors in the ECMWF, NCEP, and CMA ensemble prediction systems. Atmos Res 309 , 107596 (2024). Yamada, Y. et al. Large Ensemble Simulation for Investigating Predictability of Precursor Vortices of Typhoon Faxai in 2019 With a 14‐km Mesh Global Nonhydrostatic Atmospheric Model. Geophys Res Lett 50 , (2023). Tamamadin, M., Lee, C., Kee, S.-H. & Yee, J.-J. Regional Typhoon Track Prediction Using Ensemble k-Nearest Neighbor Machine Learning in the GIS Environment. Remote Sens (Basel) 14 , 5292 (2022). Yamasaki, S., Fudeyasu, H., Kato, M., Takemi, T. & Kiyohara, Y. Assessing Typhoon Wind Hazard: Development of Typhoon Nomogram. Journal of Wind Engineering 42 , 121–133 (2017). Ushiyama, M., Honma, M., Yokomaku, S. & Sugimura, K. Characteristics of victims caused by heavy rainfall disaster caused by typhoon No.1919. Journal of Japan Society for Natural Disaster Science 40 , 81–102 (2021). Skamarock, W. C. et al. A Description of the Advanced Research WRF Version 3 . NCAR Technical Note. NCAR/TN-475+STR (2008). Iacono, M. J. et al. Radiative forcing by long‐lived greenhouse gases: Calculations with the AER radiative transfer models. Journal of Geophysical Research: Atmospheres 113 , (2008). Hong, S.-Y. & Lim, J.-O. The WRF Single-Moment 6-Class Microphysics Scheme (WSM6). Asia Pac J Atmos Sci 42 , 129–151 (2006). Kain, J. S. The Kain–Fritsch Convective Parameterization: An Update. Journal of Applied Meteorology 43 , 170–181 (2004). Hong, S.-Y., Noh, Y. & Dudhia, J. A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes. Mon Weather Rev 134 , 2318–2341 (2006). KOBAYASHI, S. et al. The JRA-55 Reanalysis: General Specifications and Basic Characteristics. Journal of the Meteorological Society of Japan. Ser. II 93 , 5–48 (2015). OTAKI, T. et al. Investigation of Characteristics of Maximum Storm Surges in Japanese Coastal Regions Caused by Typhoon Jebi (2018) Based on Typhoon Track Ensemble Simulations. Journal of the Meteorological Society of Japan. Ser. II 100 , 2022–034 (2022). Nitta, T., Arakawa, T., Hatono, M., Takeshima, A. & Yoshimura, K. Development of Integrated Land Simulator. Prog Earth Planet Sci 7 , 68 (2020). Takata, K., Emori, S. & Watanabe, T. Development of the minimal advanced treatments of surface interaction and runoff. Glob Planet Change 38 , 209–222 (2003). Nitta, T. et al. Representing Variability in Subgrid Snow Cover and Snow Depth in a Global Land Model: Offline Validation. J Clim 27 , 3318–3330 (2014). Yamazaki, D., Kanae, S., Kim, H. & Oki, T. A physically based description of floodplain inundation dynamics in a global river routing model. Water Resour Res 47 , (2011). Bates, P. D., Horritt, M. S. & Fewtrell, T. J. A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling. J Hydrol (Amst) 387 , 33–45 (2010). Hunter, N. M., Horritt, M. S., Bates, P. D., Wilson, M. D. & Werner, M. G. F. An adaptive time step solution for raster-based storage cell modelling of floodplain inundation. Adv Water Resour 28 , 975–991 (2005). Hanazaki, R., Yamazaki, D. & Yoshimura, K. Development of a Reservoir Flood Control Scheme for Global Flood Models. J Adv Model Earth Syst 14 , (2022). Yamazaki, D. et al. A high‐accuracy map of global terrain elevations. Geophys Res Lett 44 , 5844–5853 (2017). Yamazaki, D. et al. MERIT Hydro: A High‐Resolution Global Hydrography Map Based on Latest Topography Dataset. Water Resour Res 55 , 5053–5073 (2019). River Bureau of MLIT in Japan. Manual for Economic Evaluation of Flood Control Investment . (2005). Additional Declarations There is NO Competing Interest. Supplementary Files NCSupplementarydata250318.docx Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6251998","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":430431400,"identity":"d4d05efa-5f71-4fc8-8e52-b81d659cd970","order_by":0,"name":"Xiaoyang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIie2RMUvEMBiGUwJxiXa9QfAvtBRyg7X9IS4pgbtJcLzhkIhQF39Af4RCQRDcvhJwCnYt3OLtN1w3QRRzUdCl6SqYZ3qH9+FN+BDyeP4gBxgh+IqBjIqPdBcuwaWQHwXD+ZbMrOtWfkXeV0TZ6Fb2aAyvC3U0DW/je0rb7O5amZVlejr8MMKbG63ix2qTJHSyEg+6MMrT7EwOKhhgv1RB3WmW0GglGBglkMqhBLJ5L1VulOkb5c+CtesxBYMyK0XdljyuADLWja4Qrg71XNQdhqiXgrPOrHDHX8JQJ/1mcXxSt405pcxy1s7XL9tlOqh8c4XQhNtU2CZ313dcmD2wKR8vezwez3/jEzXZahBK2PWxAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-1377-4913","institution":"Institute of Industrial Science, The University of Tokyo","correspondingAuthor":true,"prefix":"","firstName":"Xiaoyang","middleName":"","lastName":"Li","suffix":""},{"id":430431401,"identity":"c68ea2ba-8236-46d9-8017-8d4f1cea4d7d","order_by":1,"name":"Kei Yoshimura","email":"","orcid":"https://orcid.org/0000-0002-5761-1561","institution":"The University of Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Kei","middleName":"","lastName":"Yoshimura","suffix":""},{"id":430431402,"identity":"5d2807ee-655d-4028-8efd-452db9fbbcb5","order_by":2,"name":"Hironori Fudeyasu","email":"","orcid":"","institution":"Typhoon Science and Technology Research Center, Yokohama National University; and Graduate School of Education, Yokohama National University","correspondingAuthor":false,"prefix":"","firstName":"Hironori","middleName":"","lastName":"Fudeyasu","suffix":""}],"badges":[],"createdAt":"2025-03-18 10:10:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6251998/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6251998/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78824714,"identity":"fcb64bba-5c63-4c16-bf5e-e4ead9614e3e","added_by":"auto","created_at":"2025-03-19 12:18:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2178054,"visible":true,"origin":"","legend":"\u003cp\u003eThe simulated and observed typhoon tracks with\u003cstrong\u003e \u003c/strong\u003etopography. Shading denotes the surface height (m) from sea level. The short dash black lines indicate tracks of the five representative simulations (w040, w020, c000, e020, and e040). The solid black line indicates the observed track of Typhoon Hagibis from the best track archives of the Regional Specialized Meteorological Centers–Tokyo Typhoon Center. The purple boxes indicate the five regions: northern Japan (NJ), northeastern Japan (NEJ), central Japan (CJ), southwestern Japan (SWJ), southern Japan (SJ).\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6251998/v1/8673d00c1d5bd78913bdf6e7.png"},{"id":78824717,"identity":"5c15e141-7266-4565-82b3-491be9bdd614","added_by":"auto","created_at":"2025-03-19 12:18:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3854093,"visible":true,"origin":"","legend":"\u003cp\u003eWRF-simulated (a), JMA radar-observedaccumulatedprecipitation (b), and the difference between simulation and observation (c) during the passage of Typhoon Hagibis from 1200 UTC on 10 October to 0000 UTC on 13 October 2019. The short dash (solid) line indicates the simulated (observed) track of typhoon Hagibis.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6251998/v1/e99851525223648fa9eba194.png"},{"id":78824718,"identity":"f2b8edca-c1e0-417f-bfdf-79a08c9d1043","added_by":"auto","created_at":"2025-03-19 12:18:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6882577,"visible":true,"origin":"","legend":"\u003cp\u003eThe ILS-simulated (a), MLIT-observed (b) maximum river discharge (m\u003csup\u003e3\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e) and the difference between simulation and observation (c) during the Hagibis passage from 0000 UTC on 12 October to 0000 UTC on 15 October 2019.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6251998/v1/8963a96eaa69f0a6dc9b16e6.png"},{"id":78826762,"identity":"9d8f24b4-b46c-455d-a854-d30241d59df3","added_by":"auto","created_at":"2025-03-19 12:42:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3637905,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of total\u003cstrong\u003e \u003c/strong\u003eaccumulated rainfall (mm) during the passage of Typhoon Hagibis from 1200 UTC on 10 October to 0000 UTC on 13 October 2019 for the five representative simulations: (a) w040, (b) w020, (c) c000, (d) e020, (e) e040. The short dash black line indicates the corresponding typhoon track for each simulation.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-6251998/v1/2f2f522709849734ac2a8c8f.png"},{"id":78824726,"identity":"e0cd33a6-1160-4172-9738-5eb558347558","added_by":"auto","created_at":"2025-03-19 12:18:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2896752,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of maximum inundation depth (m) during the passage of Typhoon Hagibis from 0000 UTC on 12 October to 0000 UTC on 15 October 2019 for the five representative simulations: (a) w040, (b) w020, (c) c000, (d) e020, (e) e040. The short dash black line indicates the corresponding typhoon track for each simulation.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-6251998/v1/0f1bd51c35887eec0b8a6f8a.png"},{"id":78824727,"identity":"c9203228-b5fd-4edf-9db5-c0529f9c275d","added_by":"auto","created_at":"2025-03-19 12:18:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":8566049,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig.5, but for flood damage of the five representative simulations.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-6251998/v1/041a239939f160ad0d1aaec1.png"},{"id":78826008,"identity":"d737dde8-45f6-48e3-a470-4c6798e20353","added_by":"auto","created_at":"2025-03-19 12:34:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":273508,"visible":true,"origin":"","legend":"\u003cp\u003eTotal flood damage (billion JPY)in each region during the passage of Typhoon Hagibis for the all-ensemble members.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-6251998/v1/bb35939167b872ca04d111fd.png"},{"id":78824730,"identity":"89261837-10f2-4992-97c3-0d692600cc63","added_by":"auto","created_at":"2025-03-19 12:18:57","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":9465213,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig.6, but for the five simulations caused maximum flood damage in each region.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-6251998/v1/86ff9fdc29a62408596e37d9.png"},{"id":79338167,"identity":"8e677d56-9244-49d9-bda1-bfcad3f9f614","added_by":"auto","created_at":"2025-03-27 08:14:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":36743310,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6251998/v1/4d389579-e274-4b83-ad55-b2758918bd4e.pdf"},{"id":78824728,"identity":"00e6d44b-8118-4ddb-8d09-6e0ffe2222e7","added_by":"auto","created_at":"2025-03-19 12:18:57","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":654589,"visible":true,"origin":"","legend":"","description":"","filename":"NCSupplementarydata250318.docx","url":"https://assets-eu.researchsquare.com/files/rs-6251998/v1/9e887f0abe9e4feca1bea643.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"A First Attempt at Impact-Based Typhoon Track Ensemble Forecasting in Japan: Evaluating the Role of Typhoon Tracks in Flood Damage for Hagibis (2019)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, East Asia has increasingly faced severe flood disasters triggered by extreme rainfall events, including heavy typhoon rainfall\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, intense Meiyu-Baiu rainfall\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, and heavy precipitation from explosive extratropical cyclones\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. These extreme rainfall events are expected to intensify under a warmer climate with an accelerated hydrological cycle, leading to greater casualties and heightened socioeconomic losses across household assets, agriculture, forestry, and fisheries\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGlobal and regional flood forecasting systems are widely utilized to mitigate flood disasters caused by extreme rainfall events worldwide\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Global flood forecasting systems offer longer prediction periods, extending up to one month, though with relatively lower accuracy. Examples include the Global Flood Forecasting and Information System (GLOFFIS)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e by Deltares and the Global Flood Awareness System (GloFAS)\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, developed jointly by the European Commission and the European Centre for Medium-Range Weather Forecasts. In contrast, regional flood forecasting systems provide shorter prediction periods, ranging from several hours to a week, with generally higher accuracy. Notable examples include the European Flood Awareness System (EFAS)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e by the European Commission, the Hydrologic Ensemble Forecasting Service (HEFS)\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e by the U.S. National Weather Service, and Hydrological Predictions for the Environment (E-HYPE)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e by the Swedish Meteorological and Hydrological Institute.\u003c/p\u003e \u003cp\u003eAside from traditional flood forecasting, impact-based flood forecasting integrates weather forecast data with vulnerability and exposure information to generate a comprehensive risk assessment\u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. This assessment is crucial for communities and individuals, enabling early action before flood disasters to save lives and minimize socioeconomic losses from extreme rainfall events. As a proven and cost-effective approach, impact-based forecasting plays a vital role in reducing disaster-related deaths and damage by facilitating effective planning, timely response for fund allocation and resource mobilization, and efficient mitigation of disaster impacts. By incorporating real-time hazard predictions with socioeconomic and infrastructure data, impact-based forecasting enhances disaster preparedness and response strategies.\u003c/p\u003e \u003cp\u003eFlood damage assessment has been conducted at the city and local levels with limited coverage areas\u003csup\u003e\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, as well as at the global scale with low resolution\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. However, few studies have focused on impact-based forecasting at the regional and national levels, with some exceptions in East Africa and Philippine \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. This type of forecasting is crucial for decision-makers to determine appropriate early actions, including national resource mobilization, weather modification and controllability, and so on. Despite its importance, no national-scale impact-based forecasting has been conducted in East Asia.\u003c/p\u003e \u003cp\u003eStorm path ensemble simulations are essential for impact-based forecasting, as the spatial distribution of extreme rainfall and flood damage is largely influenced by the storm path\u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Understanding the relationship between storm paths and flood hazards is crucial for improving disaster preparedness and mitigation strategies. Recent advancements in numerical weather prediction have enabled the use of typhoon track ensemble simulation to systematically evaluate the potential impacts of different typhoon tracks. By perturbing the initial and boundary conditions of a storm, ensemble simulations generate multiple plausible scenarios, capturing the inherent uncertainty in track forecasts. These ensemble simulations provide a probabilistic assessment of extreme rainfall and flood risks under various storm paths. Previous studies conducted storm path ensemble simulations to evaluate the potential effects of typhoon tracks on storm surges\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and strong winds\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e in East Asia. However, the effect of storm paths on flood damage using storm path ensemble simulation has not yet been systematically studied.\u003c/p\u003e \u003cp\u003eTyphoon Hagibis (2019), one of the most powerful storms to strike Japan in recent years, caused widespread flooding and significant damage. According to the Japan Meteorological Agency\u0026rsquo;s report, Typhoon Hagibis formed in the tropical region of the northwest Pacific Ocean at 1800 UTC on October 5, moved northwestward, and made landfall in central Japan at 1000 UTC on October 12 as a large and strong typhoon. After passing through central Japan, it transitioned into an extratropical cyclone east of Japan at 0300 UTC on October 13. During this events, maximum 24-hour accumulated rainfall exceeded 500mm in 15 cities across central Japan, with a maximum recorded value of approximately 1000 mm in Hakone city. In particular, record-breaking heavy rainfall was observed in many locations in central Japan for 3-hour, 6-hour, 12-hour, and 24-hour accumulated rainfall amounts. In response to this extreme rainfall, a special heavy rain emergency warning was issued from 0630 UTC until 2340 UTC on October 12 for one metropolis and 12 prefectures in central Japan, urging the highest level of caution.\u003c/p\u003e \u003cp\u003eAccording to the Ministry of Land, Infrastructure, Transport, and Tourism of Japan, Typhoon Hagibis caused a total damage of 1880\u0026nbsp;billion Japanese Yen (JPY), including 1422\u0026nbsp;billion JPY in general asset losses, marking the highest recorded damage amount since the beginning of statistical records. During the passage of Hagibis, 84 fatalities were reported, with 3 people missing, and a total of 81,619 buildings were destroyed or damaged.\u003c/p\u003e \u003cp\u003eThis study aims to comprehensively understand the relationship between typhoon tracks and flood damage, providing decision-makers with valuable insights to mitigate flood disasters through weather modification and controllability. To achieve this, the Weather Research and Forecasting Model (WRF) is utilized to analyze atmospheric conditions and perform typhoon track ensemble simulations for Typhoon Hagibis (2019), while the Integrated Land Simulator (ILS) is employed to simulate inundation conditions (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This research represents the first national-scale attempt at impact-based forecasting in Japan. Besides, the whole Japan is divided into five sub-regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) to further analyze regional characteristics of flood damage.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe objectives of this study are three-fold: (i) to assess the accuracy of WRF and ILS simulations by comparing them with JMA radar-observed rainfall and MLIT-observed river discharge; (ii) to quantify flood damage caused by Typhoon Hagibis (2019) across Japan based on simulated inundation condition and household distribution; (iii) to evaluate the impact of typhoon track variations on flood damage and identify regions at heightened risk under different track scenarios.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eValidation of the model-simulated results\u003c/h2\u003e \u003cp\u003eAccording to the Japan Meteorological Agency\u0026rsquo;s report, the rainfall mainly occurred in Japan from 1200 UTC on October 11 to 0000 UTC on October 13. Rainfall data derived from JMA radar were used to validate the WRF simulation. Compared to the JMA radar observations, the WRF control (c000) simulation accurately reproduced the spatial distribution and intensity of accumulated rainfall during the passage of Hagibis from 1200 UTC on October 10 to 0000 UTC on October 13 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and b). The heavy rainfall was primarily concentrated in central Japan and along the Pacific Ocean coastal regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, river discharge data observed by the MLIT were used to validate the performance of the ILS. The spatial distributions of MLIT-observed and ILS-simulated maximum river discharge across Japan during the passage of Typhoon Hagibis are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and b. The ILS control (c000) simulation successfully replicated the spatial distribution of maximum river discharge observed by the MLIT. The correlation coefficient (R) between the observed and simulated maximum river discharge was 0.91 (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These results indicate that the WRF and ILS simulations demonstrate strong reproducibility of the atmospheric and hydrological processes associated with Typhoon Hagibis, making them well-suited for assessing flood damage caused by the typhoon in Japan.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe simulated total rainfall exhibited a slight westward shift in central Japan and eastward shift in northeastern Japan when compared to observations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, the maximum river discharge showed comparable spatial discrepancies between the ILS simulation and the MLIT observations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These discrepancies can be attributed to a slight westward (eastward) shift in the simulated typhoon track before (after) its landfall in Japan compared to the observed track (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis suggests that even a small error in atmospheric conditions\u0026mdash;such as precipitation distribution and intensity\u0026mdash;can propagate through land surface processes, leading to significant deviations in flood predictions. To minimize uncertainties and enhance the accuracy of flood damage assessments, it is crucial to improve both atmospheric and land surface process simulations with greater precision and detail. Our results highlight the significant sensitivity of total rainfall, maximum river discharge, and inundation areas to the typhoon track. To further investigate the influence of typhoon tracks on flood damage in Japan, the results of the typhoon track ensemble simulations will be detailed in the following subsection.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEffects of Typhoon track on flood damage assessment\u003c/h3\u003e\n\u003cp\u003eTyphoon track ensemble simulations were conducted using the WRF model. Among the 81 ensemble members, five representative simulations were selected for further analysis: the control run (c000), two westward shifts (w020 and w040), and two eastward shifts (e020 and e040).\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the spatial distribution of total accumulated rainfall for these five simulations. Heavy rainfall patterns shifted significantly as the typhoon track changed. In the control run (c000), rainfall was most concentrated in central Japan (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). When Typhoon Hagibis shifted westward, heavy rainfall migrated toward southwestern and southern Japan (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and b). Conversely, when the typhoon shifted eastward, rainfall moved toward the Pacific coastal regions, decreasing in both extent and intensity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed and e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA similar spatial variation was observed in inundation patterns during the typhoon east-west shift (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In the control run (c000), central Japan experienced the most severe flooding, with the largest inundation depth and area (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). A westward typhoon shift increased flooding in southwestern and southern Japan (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea and b). While an eastward shift resulted in more localized flooding along the Pacific coast, with reduced severity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed and e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFlood damage analysis revealed that the control run (c000) caused the greatest total flood damage among the five cases, amounting to approximately 1652.4\u0026nbsp;billion JPY, with 1530.1\u0026nbsp;billion JPY concentrated in central Japan (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). When Hagibis shifted westward, total flood damage across Japan decreased to 956.0\u0026nbsp;billion JPY for w020 and 730.0\u0026nbsp;billion JPY for w040. However, damage in southwestern Japan increased significantly, from 40.2\u0026nbsp;billion JPY to 606.6-609.8\u0026nbsp;billion JPY for w020 and w040 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, b and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, flood damage in northern Japan increased slightly due to remote typhoon-related rainfall, reaching 1.6\u0026nbsp;billion JPY for w020 and 13.7\u0026nbsp;billion JPY for w040. Conversely, when Hagibis shifted eastward, total flood damage across Japan decreased rapidly, to 886.5\u0026nbsp;billion JPY for w020 and 13.2\u0026nbsp;billion JPY for w040, with the majority of the damage still occurring in central Japan.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFlood damage (billion JPY) in each region and whole Japan during the passage of Typhoon Hagibis from 0000 UTC on 12 October to 0000 UTC on 15 October 2019 for the five representative simulations: (a) w040, (b) w020, (c) c000, (d) e020, (e) e040. Numbers in brackets are for permillage (\u0026permil;) of flood damaged asset value to total asset value. Total asset value (billion JPY) in each region and whole Japan are shown in the last column.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ew040\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ew020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ee020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ee040\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(billion Yen)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthern Japan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.7 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38974.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNortheastern Japan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.2 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.7 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.4 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49336.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Japan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.6 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e273.0 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1530.1 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e864.1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.2 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e497581.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthwestern Japan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e606.6 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e609.8 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.2 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e218464.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern Japan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.8 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e100069.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole Japan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e730.0 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e956.0 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1652.4 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e886.5 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.2 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e904426.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo examine the typhoon track\u0026rsquo;s effect on flood damage in more detail, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the flood damage in each region for all 81 ensemble members. As Hagibis shifted westward (from e050 to w110), total flood damage across Japan increased rapidly, peaking at 2478.7\u0026nbsp;billion JPY in the e008 simulation (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), before gradually decreasing.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSame as Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, but for maximum flood damage (bold font) in each region and whole Japan with corresponding typhoon track: w054 for southern Japan, w038 for southwestern Japan, w036 for northern Japan, w002 for northeastern Japan, and e008 for central Japan and whole Japan.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ew054\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ew038\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ew036\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ew002\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ee008\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthern Japan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.8 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e17.2 (0.4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNortheastern Japan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.1 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.6 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.5 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e96.0 (1.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52.5 (1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Japan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.9 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.2 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.4 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1358.1 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2411.2 (4.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthwestern Japan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e246.2 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e724.1 (3.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e712.3 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.9 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.1 (0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern Japan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e259.3 (2.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.4 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.2 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole Japan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e524.6 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e833.2 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e818.6 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1486.7 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2478.7 (2.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFlood damage in each region followed distinct patterns during the westward shift (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Central Japan sustained the highest damage, reaching a peak of 2411.2\u0026nbsp;billion JPY at e008. Meanwhile, moderate damage was observed in southwestern Japan (724.1\u0026nbsp;billion JPY of maximum damage at w038) and southern Japan (259.3\u0026nbsp;billion JPY of maximum damage at w054). Additionally, maximum flood damage was relatively small in northeastern Japan (96\u0026nbsp;billion JPY at w002) and northern Japan (17.2\u0026nbsp;billion JPY at w036).\u003c/p\u003e \u003cp\u003eRegarding the spatial distribution of flood damage in each region corresponding to the worst typhoon tracks, most flood damage occurred in floodplain areas along the Pacific Ocean coast in central, southwestern, and northeastern Japan (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb, d, and e). In contrast, flood damage in southern Japan was primarily concentrated in floodplain areas along the Japan Sea coast (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). Given the high population density in these floodplain regions (Fig. S3), there is a pressing need for enhanced flood disaster preparedness, including improved flood protection infrastructure and impact-based early warning systems.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt is worth noting that even a slight shift of the typhoon, 0.2\u0026deg; from e002 to e004 (or from e006 to e008), resulted in a significant difference of 359.2 (318.2) billion JPY in flood damage. These findings highlight the sensitivity of flood risk to typhoon track variations and emphasize the critical need for precise typhoon track forecasting in flood damage assessments. Furthermore, this study underscores the vast potential of weather modification and controllability. By simulating different typhoon tracks using the ILS and WRF models, we can deepen our understanding of the potential impacts of such events and enhance disaster preparedness and mitigation strategies.\u003c/p\u003e \u003cp\u003eFlood risk is generally determined by three major components: heavy rainfall hazard (intensity and distribution of rainfall), household asset exposure (economic value of affected properties), and vulnerability (degree of damage experienced per unit of exposure). This study demonstrates that flood risk is highly sensitive to typhoon track. The shift in Typhoon Hagibis\u0026rsquo;s route altered the spatial distribution of rainfall and exposed assets, which in turn significantly influenced total flood damage.\u003c/p\u003e \u003cp\u003eFurthermore, our results indicate that even a slight shift in a typhoon\u0026rsquo;s track can lead to substantial differences in flood damage. This underscores the potential feasibility and importance of weather control and weather modification as a means to mitigate flood risks. These findings provide fundamental insights for disaster preparedness, policy-making, and future advancements in weather modification techniques.\u003c/p\u003e \u003cp\u003eAdditionally, during the Typhoon Hagibis event, 77% (67 people) of the victims were aged 60 or older, highlighting a significant concentration of casualties among the elderly due to difficulties in escaping flooded areas\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Flood risks in East Asia are expected to increase under a warmer future climate. Simultaneously, aging populations and declining birth rates are intensifying demographic challenges, particularly in Japan, China, and South Kerea. Impact-based forecasting is essential for reducing casualties and increasing the evacuation rate among the elderly and people with disabilities. By providing more detailed risk information before flood hazards occur, early actions can be taken, enhancing disaster preparedness for vulnerable populations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAtmospheric Model and Typhoon Track Ensemble Simulation\u003c/h2\u003e \u003cp\u003eThe Weather Research and Forecasting Model (WRF V4.3.3\u003csup\u003e34\u003c/sup\u003e) was employed to simulate Typhoon Hagibis (2019). A one-way nested configuration was used, with the inner domain (121.37\u0026deg; E\u0026ndash;152.48\u0026deg; E, 21.70\u0026deg; N\u0026ndash;44.81\u0026deg; N) nested within the outer domain (120.02\u0026deg; E\u0026ndash;153.98\u0026deg; E, 18.09\u0026deg; N\u0026ndash;45.76\u0026deg; N). These domains encompassed the Japanese archipelago and the surrounding seas.\u003c/p\u003e \u003cp\u003eThe outer domain had a horizontal resolution of 15 km, while the inner domain had a finer resolution of 5 km. The grid points were configured as 220 \u0026times; 215 for the outer domain and 601 \u0026times; 541 for the inner domain. The model used 48 vertical layers for both domains, with integration time steps of 60 seconds and 20 seconds for the outer and inner domains, respectively. The simulation period for the outer domain spanned from 1800 UTC on 9 October to 0000 UTC on 13 October 2019, while for the inner domain, it covered from 1200 UTC on 10 October to 0000 UTC on 13 October 2019. Model outputs were generated every 30 minutes.\u003c/p\u003e \u003cp\u003eThe primary physics packages used in the WRF model for both domains included the RRTMG shortwave and longwave radiation schemes\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e (Iacono et al., 2008), the WSM 6-class graupel cloud microphysics scheme\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e (Hong \u0026amp; Lim, 2006), the Kain-Fritsch cumulus convection scheme\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e (Kain 2004), and the Yonsei University planetary boundary layer (PBL) scheme\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e (Hong et al., 2006). The Typhoon bogus scheme was applied exclusively to the outer domain.\u003c/p\u003e \u003cp\u003eThe initial and lateral boundary conditions for the outer domain were derived from the Japanese 55-year Reanalysis\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e (JRA-55). For the inner domain, the initial and lateral boundary conditions were interpolated from the outer domain results. This simulation setup is hereinafter referred to as the control (c000) run.\u003c/p\u003e \u003cp\u003eAn ensemble simulation of typhoon tracks\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e based on the WRF model was also performed. JRA-55 initial and boundary atmospheric conditions were systematically shifted longitudinally at 0.2\u0026deg; intervals, extending up to 11\u0026deg; westward (designated as w110) and 5\u0026deg; eastward (designated as e050). In total, 81 typhoon tracks were simulated for Typhoon Hagibis (2019) to investigate the effects of typhoon track variations on flood damage in Japan.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLand Surface Model and Flood Damage Assessment\u003c/h3\u003e\n\u003cp\u003eThe Integrated Land Simulator\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e (ILS; Nitta et al. 2020) was employed to simulate flood extent and floodplain water depth for the 81 typhoon tracks. The ILS integrates two key models: MATSIRO\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e (Minimal Advanced Treatments of Surface Interaction and Runoff; Takata et al. 2003; Nitta et al. 2014) a physical land surface model; and CaMa-Flood\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e (Catchment-based Macro-scale Floodplain model; Yamazaki et al. 2011), a hydrodynamic river routing model. MATSIRO incorporates a bulk formula for transpiration with a single-layer canopy and simulates soil processes using six soil layers. To address potential instabilities in skin temperature and surface flux calculations, MATSIRO employs the Newton\u0026ndash;Raphson iterative method. Additionally, it features a tile-scheme design that allows users to select specific tiles (e.g., land cover, soil type, elevation) tailored to their research objectives.\u003c/p\u003e \u003cp\u003eThe CaMa-Flood model incorporates several advanced physical schemes, including the Stabilized Local Inertial Equation for simulating water flow dynamics\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e (Bates et al., 2010), the Adaptive Time Step Scheme to enhance computation efficiency and stability\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e (Hunter et al., 2005), the Floodplain Inundation Scheme for accurate modelling of floodplain water distribution\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e (Yamazaki et al., 2011), the Levee and Reservoir Scheme to account for human-made infrastructure effects\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e (Hanazaki et al., 2022), the MERIT DEM/MERIT Hydro Baseline Topography to providing high-resolution elevation and hydrography data\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e (Yamazaki et al., 2017; Yamazaki et al., 2019).\u003c/p\u003e \u003cp\u003eThe ILS simulations were performed from October 1 to 16, 2019, with a horizontal resolution of 1-arc minute (~\u0026thinsp;1.5km) to estimate inundation depth and area. Atmospheric forcing data for the simulations were derived from the JMA MSM, which provided hourly data on rainfall, temperature, wind speed, specific humidity, and surface pressure, as well as daily shortwave and longwave radiation. During the passage of Typhoon Hagibis, from 1200 UTC on October 10 to 0000 UTC on October 13, 2019, the atmospheric forcing data were replaced by the-WRF simulated outputs corresponding to each ensemble member of the Hagibis track simulations.\u003c/p\u003e \u003cp\u003eThe ILS-simulated inundation results were further downscaled from a 1-arc minute (~\u0026thinsp;1.5km) resolution to a 1-arc second (~\u0026thinsp;30m) resolution using the MERIT DEM topographic information to enable detailed flood damage assessment. Flood damage was estimated following the guidelines outlined in the Manual for Economic Evaluation of Flood Control Investment\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, published by the River Bureau of Ministry of Land, Infrastructure, Transport and Tourism of Japan. For each grid cell, flood damage was calculated by multiplying the total asset values by the flood damage rate.\u003c/p\u003e \u003cp\u003eThe total asset values were determined by multiplying the number of households by the values of assets per household, including housing and houseware. The flood damage rate within each grid was derived from the downscaled inundation depth using an empirical relationship. Household distribution data and asset values for houses and housewares were obtained from the National Population Census by the Statistic Bureau and the Manual for Economic Evaluation of Flood Control Investment, respectively.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe JRA-55 data is available on the JRA-55 website (http://jra.kishou.go.jp/JRA-55/index_en.html). River discharge data is available on the MLIT website (http://www1.river.go.jp/). The JMA radar data is collected and distributed by Research Institute for Sustainable Humanosphere, Kyoto University (http://database.rish.kyoto-u.ac.jp/arch/jmadata/data/jma-radar/synthetic/original/).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe source code for the Weather Research and Forecasting (WRF) Model is available on the GitHub website (https://github.com/wrf-model). Similarly, the source code for the Integrated Land Simulator (ILS) can also be accessed on the GitHub website (https://github.com/integrated-land-simulator).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledg\u003c/strong\u003e\u003cstrong\u003ements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the JST-Moonshot Program (JPMJMS2282-08), JSPS KAKENHI (grant numbers 21H05002, 22H04938, and JP23K19068), JST-Mirai Program (JPMJMI21I6), JST-eASIA Joint Research Program (JPMJSC22E4), MEXT program for the advanced studies of climate change projection SENTAN (JPMXD0722680395, JPMXD1420318865), the Environment Research and Technology Development Fund S-20 of the Environmental Restoration and Conservation Agency of Japan (JPMEERF21S12020), Earth Observation Research Center, Japan Aerospace Exploration Agency (JX-PSPC-533980).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK.Y., and X.L. developed research concepts. X.L., K.Y., and H.F., contributed to numerical simulation. X.L., and K.Y. analyzed and interpreted the data. X.L. wrote the paper and all the authors reviewed the paper and contributed to the final manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003eompeting interest\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCorrespondence\u003c/strong\u003e and requests for materials should be addressed to Xiaoyang Li.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMa, W. \u003cem\u003eet al.\u003c/em\u003e Applicability of a nationwide flood forecasting system for Typhoon Hagibis 2019. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 10213 (2021).\u003c/li\u003e\n\u003cli\u003eOTAKI, T. \u003cem\u003eet al.\u003c/em\u003e Investigation of Characteristics of Maximum Storm Surges in Japanese Coastal Regions Caused by Typhoon Jebi (2018) Based on Typhoon Track Ensemble Simulations. \u003cem\u003eJournal of the Meteorological Society of Japan. 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(2005).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Impact-Based Forecasting, Typhoon Hagibis (2019), inundation area, Integrated Land Simulator, flood damage","lastPublishedDoi":"10.21203/rs.3.rs-6251998/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6251998/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTyphoon Hagibis (2019), one of the most powerful storms to strike Japan in recent years, caused widespread flooding and significant damage. Impact-based forecasting is crucial for planning effective mitigation measures and enhancing future disaster responses. This study employs the Integrated Land Simulator (ILS) coupled with the Weather Research and Forecasting (WRF) Model to evaluate flood damage induced by Typhoon Hagibis. Our control (c000) simulation successfully reproduced the spatial distribution and intensity of accumulated rainfall and peak river discharge. However, compared to observations, the simulated rainfall and discharge exhibited a slight westward shift in central Japan and eastward shift in northeastern Japan. These discrepancies are likely due to a slight westward (eastward) shift in the simulated typhoon track before (after) its landfall in Japan. To systematically assess the impact of typhoon tracks on flood damage, we conducted ensemble simulations. The e008 simulation (0.8\u0026deg; eastward shift) resulted in the highest flood damage, totaling 2478.7\u0026nbsp;billion JPY. A westward shift reduced total flood damage across Japan but increased it in southwestern regions, whereas an eastward shift led to an overall decrease in flood damage nationwide. Regarding the spatial distribution of flood damage caused by the worst typhoon tracks in each region, flood damage was primarily concentrated in floodplain areas along the Pacific Ocean coast in central, southwestern, and northeastern Japan, while in southern Japan, more flood damage was concentrated along the Japan Sea coast. These findings underscore the critical influence of typhoon tracks on flood risk. Impact-based typhoon track ensemble simulation can enhance our understanding of high-risk flood-prone areas and improve disaster preparedness and mitigation strategies.\u003c/p\u003e","manuscriptTitle":"A First Attempt at Impact-Based Typhoon Track Ensemble Forecasting in Japan: Evaluating the Role of Typhoon Tracks in Flood Damage for Hagibis (2019)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-19 12:18:52","doi":"10.21203/rs.3.rs-6251998/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-earth-and-environment","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsenv","sideBox":"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)","snPcode":"","submissionUrl":"","title":"Communications Earth \u0026 Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"700eedac-1516-4a14-8abd-25613131d54d","owner":[],"postedDate":"March 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":45843014,"name":"Earth and environmental sciences/Hydrology"},{"id":45843015,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2025-03-31T16:40:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-19 12:18:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6251998","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6251998","identity":"rs-6251998","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}