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Wetlands are responsible for 20%–31% of global methane (CH₄) emissions and account for a large source of uncertainty in the global CH₄budget. Data‐driven upscaling of CH₄fluxes from eddy covariance measurements can provide new and independent bottom‐up estimates of wetland CH₄emissions. Here, we develop a six‐predictor random forest upscaling model (UpCH4), trained on 119 site‐years of eddy covariance CH₄flux data from 43 freshwater wetland sites in the FLUXNET‐CH4 Community Product. Network patterns in site‐level annual means and mean seasonal cycles of CH₄fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash‐Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH₄emissions of 146 ± 43 TgCH₄ y⁻¹for 2001–2018 which agrees closely with current bottom‐up land surface models (102–181 TgCH₄ y⁻¹) and overlaps with top‐down atmospheric inversion models (155–200 TgCH₄ y⁻¹). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH₄fluxes has the potential to produce realistic extra‐tropical wetland CH₄emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid‐to‐arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ORNLDAAC/2253).

 

Abstract This paper synthesizes the contemporary challenges for the sustainability of the social-environmental system (SES) across a geographically, environmentally, and geopolitically diverse region—the Asian Drylands Belt (ADB). This region includes 18 political entities, covering 10.3% of global land area and 30% of total global drylands. At the present time, the ADB is confronted with a unique set of environmental and socioeconomic changes including water shortage-related environmental challenges and dramatic institutional changes since the collapse of the Union of Soviet Socialist Republics. The SES of the ADB is assessed using a conceptual framework rooted in the three pillars of sustainability science: social, economic, and ecological systems. The complex dynamics are explored with biophysical, socioeconomic, institutional, and local context-dependent mechanisms with a focus on institutions and land use and land cover change (LULCC) as important drivers of SES dynamics. This paper also discusses the following five pressing, practical challenges for the sustainability of the ADB SES: (a) reduced water quantity and quality under warming, drying, and escalating extreme events, (b) continued, if not intensifying, geopolitical conflicts, (c) volatile, uncertain, and shifting socioeconomic structures, (d) globalization and cross-country influences, and (e) intensification and shifts in LULCC. To meet the varied challenges across the region, place-based, context-dependent transdisciplinary approaches are needed to focus on the human-environment interactions within and between regional landscapes with explicit consideration of specific forcings and regulatory mechanisms. Future work focused on this region should also assess the role of the following mechanisms that may moderate SES dynamics: socioeconomic regulating mechanisms, biophysical regulating mechanisms, regional and national institutional regulating mechanisms, and localized institutional regulating mechanisms. 

Evaporation (E) is a critical component of the water and energy budget in lake systems yet is challenging to quantify directly and continuously. We examined the magnitude and changes ofEand its drivers over Lake Erie—the shallowest and most southern lake of the Laurentian Great Lakes. We deployed two eddy‐covariance tower sites in the western Lake Erie Basin—one located nearshore (CB) and one offshore (LI)—from September 2011 through May 2016. MonthlyEvaried from 5 to 120 mm, with maximumEoccurring in August–October. The annualEwas 635 ± 42 (±SD) mm at CB and 604 ± 32 mm at LI. Mean winter (October–March)Ewas 189 ± 61 mm at CB and 178 ± 25 mm at LI, accounting for 29.8% and 29.4% of annualE. Mean dailyEwas 1.8 mm during the coldest month (January) and 7.4 mm in the warmest month (July). MonthlyEexhibited a strong positive linear relationship to the product of wind speed and vapor pressure deficit. Pronounced seasonal patterns in surface energy fluxes were observed with a 2‐month lag inEfromR_n, due to the lake's heat storage. This lag was shorter than reports regarding other Great Lakes. Difference inEbetween the offshore and nearshore sites reflected within‐lake spatial heterogeneity, likely attributable to climatic and bathymetric differences between them. These findings suggest that predictive models need to consider lake‐specific heat storage and spatial heterogeneity in order to accurately simulate lakeEand its seasonal dynamics.