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1. Characterization of coastal wetland soil organic matter: Implications for wetland submergence

   https://doi.org/10.1016/j.scitotenv.2019.04.405  

   Steinmuller, Havalend E.; Chambers, Lisa G. (August 2019, Science of The Total Environment)
2. Wetland management using microbial indicators

   https://doi.org/10.1016/j.ecoleng.2017.07.022  

   Urakawa, Hidetoshi; Bernhard, Anne E. (November 2017, Ecological Engineering)
3. Wetland Removal Mechanisms for Emerging Contaminants

   https://doi.org/10.3390/land12020472  

   Overton, Olivia Celeste; Olson, Leif Hans; Majumder, Sreemala Das; Shwiyyat, Hani; Foltz, Mary Elizabeth; Nairn, Robert William (February 2023, Land)

   In recent decades, previously unobserved trace compounds have become more widely detected in wastewater treatment effluents and freshwater ecosystems. Emanating from various sources and presenting potential human health and ecological risks at much lesser concentrations than traditional contaminants, detection of “emerging contaminants” has increased with improvements in analytical techniques. The behavior of emerging contaminants in wetlands is a topic of increasing interest, as natural wetlands are known to transform and sequester pollutants and constructed or treatment wetlands are widely utilized to address elevated concentrations of constituents of concern. Both natural and constructed wetlands are complex biogeochemical systems with interrelated abiotic and biotic mechanisms leading to the removal of emerging contaminants. A literature review was performed to assess the current state of knowledge of various wetland mechanisms involved in removing these contaminants from surface waters and effluents. The primary mechanisms discussed in the literature are sorption, photodegradation, microbial biodegradation and phytoremediation. The most influential mechanisms are dependent on the properties of the contaminants and wetland systems studied. Common trends exist for different constructed wetland designs to leverage various mechanisms based on hydrology, substrate and vegetation plantings. Much remains to be understood about the various processes occurring in wetlands as they relate to emerging contaminant removal. Improving the understanding of the potential role of wetland mechanisms can help manage this environmental challenge more effectively. 

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4. Wetland Classification, Attribute Accuracy, and Scale

   https://doi.org/10.3390/ijgi13030103  

   Carlson, Kate; Buttenfield, Barbara P; Qiang, Yi (March 2024, ISPRS International Journal of Geo-Information)

   Quantification of all types of uncertainty helps to establish reliability in any analysis. This research focuses on uncertainty in two attribute levels of wetland classification and creates visualization tools to guide analysis of spatial uncertainty patterns over several scales. A novel variant of confusion matrix analysis compares the Cowardin and Hydrogeomorphic wetland classification systems, identifying areas and types of misclassification for binary and multivariate categories. The specific focus on uncertainty in the paper refers to categorical consistency, that is, agreement between the two classification systems, rather than comparing observed data to ground truth. Consistency is quantified using confusion matrix analysis. Aggregation across progressive focal windows transforms the confusion matrix into a multiscale data pyramid for quick determination of where attribute uncertainty is highly variant, and at what spatial resolutions classification inconsistencies emerge. The focal pyramids summarize precision, recall, and F1 scores to visualize classification differences across spatial scales. Findings show that the F1 scores appear most informative on agreement about wetlands misclassification at both coarse and fine attribute scales. The pyramid organizes multi-scale uncertainty in a single unified framework and can be “sliced” to view individual focal levels of attribute consistency. Results demonstrate how the confusion matrix can be used to quantify the percentage of a study area in which inconsistencies occur reflecting wetland presence and type. The research provides confusion metrics and display tools to focus attention on specific areas of large data sets where attribute uncertainty patterns may be complex, thus reducing land managers’ workloads by highlighting areas of uncertainty where field checking might be appropriate, and improving analytics by providing visualization tools to quickly see where such areas occur. 

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5. The Boreal–Arctic Wetland and Lake Dataset (BAWLD)

   https://doi.org/10.5194/essd-13-5127-2021  

   Olefeldt, David; Hovemyr, Mikael; Kuhn, McKenzie A.; Bastviken, David; Bohn, Theodore J.; Connolly, John; Crill, Patrick; Euskirchen, Eugénie S.; Finkelstein, Sarah A.; Genet, Hélène; et al (January 2021, Earth System Science Data)

   Abstract. Methane emissions from boreal and arctic wetlands, lakes, and rivers areexpected to increase in response to warming and associated permafrost thaw.However, the lack of appropriate land cover datasets for scalingfield-measured methane emissions to circumpolar scales has contributed to alarge uncertainty for our understanding of present-day and future methaneemissions. Here we present the Boreal–Arctic Wetland and Lake Dataset(BAWLD), a land cover dataset based on an expert assessment, extrapolatedusing random forest modelling from available spatial datasets of climate,topography, soils, permafrost conditions, vegetation, wetlands, and surfacewater extents and dynamics. In BAWLD, we estimate the fractional coverage offive wetland, seven lake, and three river classes within 0.5 × 0.5∘ grid cells that cover the northern boreal and tundra biomes(17 % of the global land surface). Land cover classes were defined usingcriteria that ensured distinct methane emissions among classes, as indicatedby a co-developed comprehensive dataset of methane flux observations. InBAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain)with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetlandclasses, covering ∼ 28 % each of the total wetland area,while the highest-methane-emitting marsh and tundra wetland classes occupied5 % and 12 %, respectively. Lakes, defined to include all lentic open-waterecosystems regardless of size, covered 1.4 × 106 km2(6 % of domain). Low-methane-emitting large lakes (>10 km2) and glacial lakes jointly represented 78 % of the total lakearea, while high-emitting peatland and yedoma lakes covered 18 % and 4 %,respectively. Small (<0.1 km2) glacial, peatland, and yedomalakes combined covered 17 % of the total lake area but contributeddisproportionally to the overall spatial uncertainty in lake area with a95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12  × 106 km2 (0.5 % of domain), of which 8 % was associated withhigh-methane-emitting headwaters that drain organic-rich landscapes.Distinct combinations of spatially co-occurring wetland and lake classeswere identified across the BAWLD domain, allowing for the mapping of“wetscapes” that have characteristic methane emission magnitudes andsensitivities to climate change at regional scales. With BAWLD, we provide adataset which avoids double-accounting of wetland, lake, and river extentsand which includes confidence intervals for each land cover class. As such,BAWLD will be suitable for many hydrological and biogeochemical modellingand upscaling efforts for the northern boreal and arctic region, inparticular those aimed at improving assessments of current and futuremethane emissions. Data are freely available athttps://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021). 

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