Precipitation and rainfall erosivity datasets for Northeastern Japan over 2011-2024: 10-minute precipitation records, rainfall event characteristics, and processed summary tables (year, month, typhoon season, single most and three most erosive events) from 58 JMA weather stations located within a 110-km radius around Fukushima Dai-ichi Nuclear Power Plant.

Precipitation and rainfall erosivity datasets for Northeastern Japan over 2011-2024: 10-minute precipitation records, rainfall event characteristics, and processed summary tables (year, month, typhoon season, single most and three most erosive events) from 58 JMA weather stations located within a 110-km radius around Fukushima Dai-ichi Nuclear Power Plant. Thomas Chalaux-Clerguea,b,∗ , Pierre-Alexis Chabochea, Yoshifumi Wakiyamac,d, Olivier Evrarda,c aLaboratoire des Sciences du Climat et de l’Environnement (LSCE-IPSL), Unit´e Mixte de Recherche 8212 (CEA-CNRS-UVSQ), Universit´e Paris-Saclay, Gif-sur-Yvette, France bWater and Soil Ressources Research, Institute of Geography, Universit¨at Augsburg, 86150, Augsburg, Germany cMITATE Lab, International Research Laboratory - IRL 2039 (CNRS, CEA, Fukushima University), 960-1296, Fukushima, Japan dInstitute of Environmental Radioactivity (IER), University of Fukushima, Fukushima, Japan 1. Introduction The Fukushima Dai-ichi Nuclear Power Plant (FDNPP) accident in March 2011 deposited 2.7 PBq of 137Cs onto inland Japan, with 74 % of terrestrial fallout (2.0 PBq) concentrated in this Fukushima region (Kato et al.,2019). Post-deposition, precipitation-driven soil erosion has become a dominant mechanism for radiocaesium redistribution, particularly in areas prone to high-intensity precipitation (Evrard et al.,2014; Laceby et al.,2016;Taniguchi et al.,2019). Quantifying rainfall erosivity is thus critical for contamination risk assessment and land management in affected zones. This dataset provides high-temporal resolution precipitation records and derived erosivity metrics from 58 automated weather stations from Japan Meteorological Agency (JMA) within a 110-km radius of the FDNPP (2011-2024). The study area covers two key regions (Hama-dori and Naka-dori) that received the highest 137Cs deposits and exhibit contrasting hydrological regimes, including typhoon-influenced extreme rainfall (Laceby et al.,2016;Chartin et al.,2017). The dataset includes: •Coordinates of the 58 weather station - TCC2025 58ws coordinates.csv •Raw 10-minute precipitation data - TCC 2025 JMA 58ws 10min 2011-2024 (58 files) •Event-level characteristics for all erosive rainfall events, including: Duration, cumulative precipitation, I30 (max 30-min intensity), and EI30 erosivity - TCC2025 58ws erosive events 2011-2024 (58 files) •Aggregated metrics at station-level for each period and pluriannual erosivity and cumulative precipitation statistics at the scale of the year, month, typhoon season, the single and three most erosive events -TCC2025 58ws summaries (14 files) •The R script used to calculated all proposed metrics - TCC2025 58ws analysis.Rmd ∗Corresponding author: thomasc[email protected] (Thomas Chalaux-Clergue) Database introduction for Zenodo November 13, 2025 2. Materials and Methods 2.1. Study area This dataset focuses on a 17,000 km2area within a 110-km radius of the FDNPP (Figure ??), which received the majority of the initial fallout. The study area encompasses two primary geographical regions of Fukushima Prefecture (Nakao et al.,2014): 1. Hama-dori - eastern coastal region 2. Naka-dori - central region These two regions are separated by the Abukuma mountain range, which runs north-south. The ¯ Ouu mountain range forms the western boundary of Naka-dori. 2.2. Hydrology and climate The two regions exhibit distinct hydrological characteristics: •Naka-dori corresponds primarily to the Abukuma River Basin (5,200 km2). •Hama-dori is drained by smaller coastal catchments (typically ≤700 km2), characterised by steep mountainous upper sections (Abukuma range) transitioning abruptly into flat coastal plains. The study area spans multiple K¨oppen’s climatic classification (Beck et al.,2018): •Coastal plains (Hama-dori): Cfa (temperate, no dry season, hot summer). •Foothills and mountainous areas (Abukuma range and Naka-dori): Dfa (cold, no dry season, hot summer), transitioning to Dfb (warm summer) at higher elevations (e.g., Kawamae, Hibara stations). Precipitation is heavily influenced by tropical cyclones, with 60 % of annual rainfall and 86 % of rainfall erosivity occurring between May and October (Japanese typhoon season), peaking from July to September (Laceby et al.,2016). Over the 1991-2021 period, the region exhibited (Chalaux-Clergue et al.,2024): •Mean annual temperature: 13.4 ±0.5 ◦C (SD) •Temperature range: -1.5 ±0.9 ◦C (January) to 30.5 ±2.1 ◦C (August) •Mean annual precipitation: 1,207 ±216 mm (SD). 2.3. Weather stations data High-resolution (10-minute interval) precipitation and temperature records were obtained from 58 automated weather stations within the 110-km radius of the FDNPP, covering the period from 1 January 2011 to 31 December 2024 available on Japan Meteorological Agency website (JMA,2024). Data sources included: •Automated Meteorological Data Acquisition System (AMeDAS) operated by the Japan Meteorological Agency (JMA). •Prefectural Authority stations. The 10-minute rainfall dataset from JMA and Prefectural Authority has been validated in prior studies, confirming its reliability without requiring further correction (Shiono et al.,2013;Duan et al.,2015;Laceby et al.,2016;Chartin et al.,2017). 2 2.4. Precipitation and rainfall erosivity calculation 2.4.1. Erosive event identification Erosive events were defined using two criteria: 1. Minimum cumulative precipitation: ¿ 12.7 mm per event (Wischmeier & Smith,1978;Panagos et al., 2015). 2. Inter-event separation: ¡ 1.27 mm of precipitation over a 6-hour period (Wischmeier & Smith,1978; Renard & Freimund,1994;Yin et al.,2017). To exclude snowfall, precipitation records with temperatures bellow 0 ◦C were removed, when temperature data were available (Meusburger et al.,2012). 2.4.2. Rainfall erosivity calculation The event rainfall erosivity (EI30) was computed for each erosive event in MJ mm ha−1h−1as the product of the total kinetic energy (E) of the event (MJ ha−1) and maximum 30-minutes intensity (I30) (mm h−1) (Brown & Foster,1987;Renard & Freimund,1994;Yin et al.,2017). The kinetic energy (EI30) was derived using the RUSLE2-corrected formula (Brown & Foster,1987;Yin et al.,2017): EI30 = l X r=1 ervr!I30 (1) where: •er= unit energy per mm of precipitation (MJ ha−1mm−1), •vr= precipitation volume (mm) during the rth 10-minute interval, erwas calculated as: er= 0.29 1−0.72 e−0.082 ir(2) where iris the precipitation intensity (mm h−1, i.e., 6×vi). The R-factor for a given period (year, month, or season) was computed as the sum of EI30s values for all erosive events during that period: R= n X i=1 EI30 i(3) where iis the ith erosive event out on nevents that occurred during the period. Erosive event identification and erosivity calculations were performed in R using the RainErosivity package (Chalaux-Clergue,2025, version 1.1.0). 3 2.5. Hierarchical aggregation All analyses were conducted at the station level before spatial/temporal aggregation. For each station, annual, monthly, and seasonal (June-October) totals were computed for: •Precipitation (mm) •Rainfall erosivity (MJ mm ha−1h−1) Station level metrics were derived by calculating the median, interquartile range (IQR: 25th-75th quantiles), mean, and standard deviation (SD) across all operational stations for each temporal resolution. Long-term pluriannual metrics were obtained by aggregating all station-level data over the 2011-2024 period. All processing, analyses and modelling were performed using the R programming environment (R Core Team, 2025, version 4.5.1) in RStudio (Posit team,2025, version 2025.09.1+401). 3. Author contribution Thomas Chalaux-Clergue: Conceptualization, Software, Formal analysis, Data Curation, Writing - Original Draft, Visualization; Pierre-Alexis Chaboche: Conceptualization, Writing - Review & Editing; Yoshifumi Wakiyama: Writing - Review & Editing; Olivier Evrard: Conceptualization, Writing - Review & Editing, Resources, Funding acquisition. 4. Financial support The support of CEA (Commissariat `a l’Energie Atomique et aux Energies Alternatives, France), CNRS (Centre National de la Recherche Scientifique, France) and JSPS (Japan Society for the Promotion of Science) through the funding of PhD fellowships (Thomas Chalaux-Clergue) and collaboration projects (grant no. 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