Notice bibliographique
Résumé
This research aimed to enhance the accuracy of methane (CH₄) emissions estimates from natural wetlands by integrating satellite observations and advanced modeling techniques. Wetlands are among the largest natural sources of CH₄. However, current estimates carry large uncertainties due to the spatial and temporal variability of wetland characteristics. This study focused on reducing those uncertainties using high-resolution datasets and evaluating the role of environmental drivers, particularly wetland extent and soil moisture. Chapter 2 examined how spatial resolution affects CH₄ emissions modeling in the Fennoscandinavian Peninsula, a region with high-quality environmental data. Using a simple but efficient model, CH₄ emissions were simulated with input from a 100 m wetland map, and then the resolution was systematically coarsened. Results showed a threefold increase in estimated emissions at the finest resolution, driven by correlations between soil moisture and soil carbon. This revealed that coarse-resolution models may severely underestimate emissions due to spatial averaging. To mitigate resolution-dependent errors, the study recommended using high-resolution datasets and modeling approaches that preserve spatial correlations, such as multivariate probability density functions. The research also emphasized the need for globally consistent, fine-resolution wetland datasets to improve model accuracy and reduce discrepancies seen in global CH₄ emission inventories. Chapter 3 focused on the role of soil moisture in CH₄ emissions by applying the MeSMOD model, which uses high-resolution satellite and hydrological model soil moisture data. Calibration was done using observations from 13 FLUXNET-CH₄ sites. Simulations with 100 m soil moisture data performed better than those using coarser inputs, demonstrating the critical value of fine-scale moisture information. Upscaling the model to the pan-Arctic region revealed spatial variations tied to differences in wetland maps, but MeSMOD aligned well with other models in key CH₄-emitting regions like western Canada and West Siberia. Seasonal patterns peaked in July–August and dipped during winter, consistent with biogeochemical models. The model also captured CH₄ emission anomalies in 2016 and especially in 2020, which corresponded with record-breaking global CH₄ growth. This was attributed to warm temperatures and early snowmelt in northern latitudes. These findings support the use of high-resolution satellite data in improving CH₄ flux estimates and understanding climatic influences on emissions. Chapter 4 extended previous studies by analyzing CH₄ emissions over the South Sudan Wetlands Region (SSWR) from 2018 to 2022 using TROPOMI satellite data, river altimetry, and outputs from the PCR-GLOBWB hydrological model. CH₄ emissions in SSWR increased by 77.8% during this period, rising from 9.2 to 16.3 Tg CH₄ yr⁻¹. This surge was linked to increased wetness and warmer temperatures driven by ENSO-related climate variability. River altimetry confirmed a strong relationship between rising water levels and CH₄ emissions, particularly in upstream catchments. Time-lag analysis showed that hydrological parameters like soil moisture, groundwater recharge, and capillary rise led CH₄ emissions by days to weeks, indicating their influence on production processes. Capillary rise, in particular, showed a surprisingly strong correlation, suggesting new avenues for research. These results highlight the need for catchment-specific analyses rather than regional averaging, which can obscure critical hydrological-emission relationships. General Conclusions The research underscores the crucial role of high-resolution datasets for wetland CH₄ modeling. In the Fennoscandinavian Peninsula, finer resolutions revealed much higher emissions than coarser ones, mainly due to the nonlinear interaction of soil properties. In boreal and pan-Arctic regions, using high-resolution satellite soil moisture significantly improved emission estimates and allowed clear identification of temporal trends. In tropical wetlands, such as the SSWR, combining satellite observations and hydrological modeling revealed the dynamic interaction between climate, river systems, and CH₄ emissions. Overall, this work provides a framework for improving global CH₄ budgets by integrating satellite data, hydrological modeling, and targeted catchment-scale analysis.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 |
| 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) | 0,002 | 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 ».