Phenology is the dominant control of methane emissions in a tropical non-forested wetland

Tropical wetlands are a significant source of atmospheric methane (CH4), but their importance to the global CH4 budget is uncertain due to a paucity of direct observations. Net wetland emissions result from complex interactions and co-variation between microbial production and oxidation in the soil, and transport to the atmosphere. Here we show that phenology is the overarching control of net CH4 emissions to the atmosphere from a permanent, vegetated tropical swamp in the Okavango Delta, Botswana, and we find that vegetative processes modulate net CH4 emissions at sub-daily to inter-annual timescales. Without considering the role played by papyrus on regulating the efflux of CH4 to the atmosphere, the annual budget for the entire Okavango Delta, would be under- or over-estimated by a factor of two. Our measurements demonstrate the importance of including vegetative processes such as phenological cycles into wetlands emission budgets of CH4.

Improving Forest Baseline Maps in Tropical Wetlands Using GEDI-Based Forest Height Information and Sentinel-1

Remote Sensing-based global Forest/Non-Forest (FNF) masks have shown large inaccuracies in tropical wetland areas. This limits their applications for deforestation monitoring and alerting in which they are used as a baseline for mapping new deforestation. In radar-based deforestation monitoring, for example, moisture dynamics in unmasked non-forest areas can lead to false detections. We combined a GEDI Forest Height product and Sentinel-1 radar data to improve FNF masks in wetland areas in Gabon using a Random Forest model. The GEDI Forest Height, together with texture metrics derived from Sentinel-1 mean backscatter values, were the most important contributors to the classification. Quantitatively, our mask outperformed existing global FNF masks by increasing the Producer’s Accuracy for the non-forest class by 14%. The GEDI Forest Height product by itself also showed high accuracies but contained Landsat artifacts. Qualitatively, our model was best able to cleanly uncover non-forest areas and mitigate the impact of Landsat artifacts in the GEDI Forest Height product. An advantage of the methodology presented here is that it can be adapted for different application needs by varying the probability threshold of the Random Forest output. This study stresses that, in any application of the suggested methodology, it is important to consider the UA/PA trade-off and the effect it has on the classification. The targeted improvements for wetland forest mapping presented in this paper can help raise the accuracy of tropical deforestation monitoring.

Comparison of Various Spectral Indices for Optimum Extraction of Tropical Wetlands Using Landsat 8 OLI

This research specifically aims to investigate the most accurate spectral indices in extracting wetlands geospatial information taking South Kalimantan, Indonesia, as an example of wetlands in tropical areas. Ten spectral indices were selected for testing their ability to extract wetlands, those are NDVI, NDWI, MNDWI, MNDWIs2, NDMI, WRI, NDPI, TCWT, AWEInsh, andAWEIsh. Tests were performed on Landsat 8 OLI path/row 117/062 and 117/063. The threshold method which was used to separate the wetland features from the spectral indices imagery is Otsu method. The results of this research showed that generally MNDWIs2 was the most optimal spectral indices in wetlands extraction. Especially tropical wetlands that rich with green vegetation cover. However, MNDWIs2 is very sensitive to dense vegetation, this feature has the potential to be detected as wetlands. Furthermore, to improve the accuracy and prevent detection of the dryland vegetation as wetlands, the threshold value should be determined carefully.

Accelerating methane growth rate from 2010 to 2017: leading contributions from the tropics and East Asia

After stagnating in the early 2000s, the atmospheric methane growth rate has been positive since 2007 with a significant acceleration starting in 2014. While the causes for previous growth rate variations are still not well determined, this recent increase can be studied with dense surface and satellite observations. Here, we use an ensemble of six multi-species atmospheric inversions that have the capacity to assimilate observations of the main species in the methane oxidation chain – namely, methane, formaldehyde, and carbon monoxide – to simultaneously optimize both the methane sources and sinks at each model grid. We show that the surge of the atmospheric growth rate between 2010–2013 and 2014–2017 is most likely explained by an increase of global CH4 emissions by 17.5±1.5 Tg yr−1 (mean ± 1σ), while variations in the hydroxyl radicals (OH) remained small. The inferred emission increase is consistently supported by both surface and satellite observations, with leading contributions from the tropical wetlands (∼ 35 %) and anthropogenic emissions in China (∼ 20 %). Such a high consecutive atmospheric growth rate has not been observed since the 1980s and corresponds to unprecedented global total CH4 emissions.

Inundation prediction in tropical wetlands from JULES-CaMa-Flood global land surface simulations

Wetlands play a key role in hydrological and biogeochemical cycles and provide multiple ecosystem services to society. However, reliable data on the extent of global inundated areas and the magnitude of their contribution to local hydrological dynamics remain surprisingly uncertain. Global hydrological models and Land Surface Models (LSMs) include only the most major inundation sources and mechanisms, therefore quantifying the uncertainties in available data sources remains a challenge. We address these problems by taking a leading global data product on inundation extents (GIEMS) and matching against predictions from a sophisticated global hydrodynamic model (CaMa-Flood) that uses runoff data generated from the JULES land surface model. The ability of the model to reproduce patterns and dynamics showed by the observational product is assessed in a number of case studies across the tropics (including the Sudd, Pantanal, Congo and Amazon), which show that it performs well in large wetland regions, with a good match between corresponding seasonal cycles. However, at finer spatial scale, water inputs (e.g. groundwater inflow to wetland) may become underestimated in comparison to water outputs (e.g. infiltration and evaporation from wetland); or the opposite may occur, depending on the wetland concerned. Additionally, some wetlands display a clear spatial displacement between observed and simulated inundation as a result of over- or under-estimation of overbank flooding upstream. This study provides timely data that can contribute to our current ability to make critical predictions of inundation events at both regional and global levels.

Methane fluxes from Zambian tropical wetlands

<p>Airborne measurements of methane (CH<sub>4</sub>) were recorded over three major wetland areas in Zambia in February 2019 during the MOYA (Methane Observations and Yearly Assessments) ZWAMPS field campaign. Enhancements of up to 600 ppb CH<sub>4</sub> were measured over the Bangweulu (11°36’ S, 30°05’ E), Kafue (15°43’ S, 27°17’ E), and Lukanga (14°29’ S, 27°47’ E) wetlands. Three independent methods were used to quantify methane emission fluxes; aircraft mass balance, aircraft eddy covariance, and atmospheric inversion modelling. Results yielded methane emission fluxes of 10-20 mg CH<sub>4</sub> m<sup>-2</sup> hr<sup>-1</sup>, which were up to an order of magnitude greater than the emission fluxes simulated by various wetland process models (WetCHARTs ensemble and LPX-Bern). Independent column CH<sub>4</sub> observations from the TROPOMI instrument were used to verify these fluxes, and investigate their applicability for timescales longer than the duration of the MOYA flight campaign.</p>