What were the main sources of sediment and associated radiocesium transported during the heavy 2019 typhoons in rivers draining the main Fukushima radioactive plume, Japan ?
Notice bibliographique
Résumé
<p>The Fukushima nuclear accident released large quantities of radionuclides into the environment in March 2011 and generated a 3000-km² plume of soils heavily contaminated with Cs-137. Soil erosion in the region mainly takes place during typhoons generally occurring between July and October (Laceby et al., 2016). During these events, rivers draining the main plume may transport large quantities of sediment and radiocesium. Typhoon Hagibis that occurred in October 2019 was the most intense rainfall event affecting the Fukushima region (rainfall range: 77–558 mm) since the nuclear accident in 2011. It led to extensive landsliding and river overflow.</p><p>The impact of this event on sediment sources and Cs-137 contamination was quantified through the implementation of sediment fingerprinting using geochemistry and spectrocolorimetry as potential input properties. The signature of potential source material (including cropland prepared for recultivation after decontamination, forests and subsurface material originating from landslides and channel bank collapse; n=57) was compared with that of sediment deposits collected in the Mano and Niida River catchments late in October 2019. Results show that cropland supplied the main source of sediment (average: 54%) along with forests (41%). In contrast, the contribution of subsurface material (5%) was much lower, likely because landslides and channel bank erosion mainly took place after the flood peak (Evrard et al., 2020). However, this material that deposited at the foot of hillslopes after the typhoon may be mobilized and delivered to the river network by subsequent rainfall events.</p><p>Overall, this flood did not modify the decreasing trend observed in terms of Cs-137 contamination in sediment transiting these rivers between 2011 and 2019. Concentrations in Cs-137 observed in sediment collected in 2019 were on average 84–93% lower than those measured after the accident in 2011. These results demonstrate the effectiveness of decontamination conducted on agricultural and residential soils in the region (Evrard et al., 2019), although the role of forests – that have not been remediated – as a perennial source of sediment and radiocesium in the region remains to be investigated over the longer term.</p><p>References</p><p>Evrard, O., Durand, R., Nakao, A., Patrick Laceby, J., Lefèvre, I., Wakiyama, Y., Hayashi, S., Asanuma-Brice, C. and Cerdan, O., 2020. Impact of the 2019 typhoons on sediment source contributions and radiocesium concentrations in rivers draining the Fukushima radioactive plume, Japan. Comptes Rendus Géoscience, 352(3): 199-211.</p><p>Evrard, O., Laceby, J.P. and Nakao, A., 2019. Effectiveness of landscape decontamination following the Fukushima nuclear accident: a review. SOIL, 5(2): 333-350.</p><p>Laceby, J.P., Chartin, C., Evrard, O., Onda, Y., Garcia-Sanchez, L. and Cerdan, O., 2016. Rainfall erosivity in catchments contaminated with fallout from the Fukushima Daiichi nuclear power plant accident. Hydrology and Earth System Sciences, 20(6): 2467-2482.</p><p> </p>
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».