Warming-driven erosion and sediment transport in the world’s cold regions
Notice bibliographique
Résumé
The world’s cryospheric regions, ranging from high mountains to polar regions, have experienced unprecedented atmospheric warming, glacier melting and permafrost thawing since the mid-20th century. This rapid cryosphere degradation has dramatically altered terrestrial/coastal landscapes, characterized by creating debuttressed valleys and thermokarst hillslopes, expanding unstable landscapes, and increasing the access to sub-/pro-glacially stored sediment. Such rapid landscape changes have resulted in increases in erosion and sediment loads, posing threats to riverine and near-shore marine environments and triggering cascading impacts on water-food-energy securities which support the livelihoods of a quarter of the global population.Here, we present a global inventory of cryosphere degradation-driven increases in erosion and sediment yield, including 76 locations from the high Arctic, European mountains, High Mountain Asia and Andes, and 18 Arctic permafrost-coastal sites, collected from over 80 publications. This inventory confirms the widespread increase in sediment transport from cold regions in response to modern deglaciation. Moreover, we identify two to eight-fold increases in sediment fluxes and more than doubled coastal erosion rates in many cold regions between the 1950s and 2010s.Such increases in sediment evacuation from deglaciating/thermokarst regions have been blamed for introducing large amounts of carbon, nitrogen, and pollutants into aquatic ecosystems, impacting primary productivity, river biodiversity, and water quality. In high-mountain areas, increased sediment fluxes have also hampered hydropower exploitation through reservoir sedimentation and turbine abrasion. Meanwhile, accelerated erosion along ice-rich Arctic permafrost coasts has caused an irreversible land loss, costing billions of dollars for relocating or protecting coastal infrastructure.With continuous cryosphere degradation, sediment fluxes are likely to increase in the next decades until reaching a maximum (“Peak Sediment”). Theoretically, the timing of peak sediment can lag decades to hundreds of years behind the peak meltwater due to the remobilization of paraglacial and subglacial sediment legacy. Thereafter, sediment fluxes will decline as glacier/permafrost erosion ceases and active sediment contributing area shrinks. We predict that sediment-transport regimes will shift through three stages, from the ongoing temperature-dominated regime to a temperature-precipitation jointly controlled regime, eventually shifting toward a rainfall-dominated regime roughly between 2100-2200.However, the understanding of sediment dynamics in cold regions is still limited by the lack of long-term observations and the inherent complexity of geomorphic processes, such as episodic events, scale/threshold effects in sediment transport, and positive/negative feedbacks of geomorphic responses. To underpin the forward-looking mitigation strategies for climate-sensitive and fragile cold regions, we call for the enhancement of multi-source sediment monitoring programs, fully distributed physics-based sediment-yield models, and interdisciplinary-international scientific collaborations.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».