Mechanistic Modeling of Microtopographic Impacts on CO2 and CH4 Fluxes in an Alaskan Tundra Ecosystem Using the CLM-Microbe Model

Spatial heterogeneities in soil hydrology have been confirmed as a key control on CO2 and CH4 fluxes in the Arctic tundra ecosystem. In this study, we applied a mechanistic ecosystem model, CLM-Microbe, to examine the microtopographic impacts on CO2 and CH4 fluxes across seven landscape types in Utqiaġvik, Alaska: trough, low-centered polygon (LCP) center, LCP transition, LCP rim, high-centered polygon (HCP) center, HCP transition, and HCP rim. We first validated the CLM-Microbe model against static-chamber measured CO2 and CH4 fluxes in 2013 for three landscape types: trough, LCP center, and LCP rim. Model application showed that low-elevation and thus wetter landscape types (i.e., trough, transitions, and LCP center) had larger CH4 emissions rates with greater seasonal variations than high-elevation and drier landscape types (rims and HCP center). Sensitivity analysis indicated that substrate availability for methanogenesis (acetate, CO2 + H2) is the most important factor determining CH4 emission, and vegetation physiological properties largely affect the net ecosystem carbon exchange and ecosystem respiration in Arctic tundra ecosystems. Modeled CH4 emissions for different microtopographic features were upscaled to the eddy covariance (EC) domain with an area-weighted approach before validation against EC-measured CH4 fluxes. The model underestimated the EC-measured CH4 flux by 20% and 25% at daily and hourly time steps, suggesting the importance of the time step in reporting CH4 flux. The strong microtopographic impacts on CO2 and CH4 fluxes call for a model-data integration framework for better understanding and predicting carbon flux in the highly heterogeneous Arctic landscape

Mapping Arctic Plant Functional Type Distributions in the Barrow Environmental Observatory Using WorldView-2 and LiDAR Datasets

Multi-scale modeling of Arctic tundra vegetation requires characterization of the heterogeneous tundra landscape, which includes representation of distinct plant functional types (PFTs). We combined high-resolution multi-spectral remote sensing imagery from the WorldView-2 satellite with light detecting and ranging (LiDAR)-derived digital elevation models (DEM) to characterize the tundra landscape in and around the Barrow Environmental Observatory (BEO), a 3021-hectare research reserve located at the northern edge of the Alaskan Arctic Coastal Plain. Vegetation surveys were conducted during the growing season (June–August) of 2012 from 48 1 m × 1 m plots in the study region for estimating the percent cover of PFTs (i.e., sedges, grasses, forbs, shrubs, lichens and mosses). Statistical relationships were developed between spectral and topographic remote sensing characteristics and PFT fractions at the vegetation plots from field surveys. These derived relationships were employed to statistically upscale PFT fractions for our study region of 586 hectares at 0.25-m resolution around the sampling areas within the BEO, which was bounded by the LiDAR footprint. We employed an unsupervised clustering for stratification of this polygonal tundra landscape and used the clusters for segregating the field data for our upscaling algorithm over our study region, which was an inverse distance weighted (IDW) interpolation. We describe two versions of PFT distribution maps upscaled by IDW from WorldView-2 imagery and LiDAR: (1) a version computed from a single image in the middle of the growing season; and (2) a version computed from multiple images through the growing season. This approach allowed us to quantify the value of phenology for improving PFT distribution estimates. We also evaluated the representativeness of the field surveys by measuring the Euclidean distance between every pixel. This guided the ground-truthing campaign in late July of 2014 for addressing uncertainty based on representativeness analysis by selecting 24 1 m × 1 m plots that were well and poorly represented. Ground-truthing indicated that including phenology had a better accuracy ( R 2 = 0.75 , R M S E = 9.94 ) than the single image upscaling ( R 2 = 0.63 , R M S E = 12.05 ) predicted from IDW. We also updated our upscaling approach to include the 24 ground-truthing plots, and a second ground-truthing campaign in late August of 2014 indicated a better accuracy for the phenology model ( R 2 = 0.61 , R M S E = 13.78 ) than only using the original 48 plots for the phenology model ( R 2 = 0.23 , R M S E = 17.49 ). We believe that the cluster-based IDW upscaling approach and the representativeness analysis offer new insights for upscaling high-resolution data in fragmented landscapes. This analysis and approach provides PFT maps needed to inform land surface models in Arctic ecosystems

Analysis of tundra vegetation developement using a time series of ortoimages in the Krkonoše Mountains

Analysis of tundra vegetation developement using a time series of ortoimages in the Krkonoše Mountains Abstract The aim of this study is to analyse changes in arctic-alpine tundra vegetation in the Krkonoše Mountains using archival and current aerial imagery with red, green and blue bands and spatial resolution of 0.5 m. Three small areas of interest (cca 100 100 m) with different types of vegetation and a one larger area of the eastern tundra were studied. Several classification methods (Maximum likelihood classification, Random forest and object-based classification) were tested to obtain the best classification results. For more detailed analysis of grass species development, unsupervised classification and extended time series (5 orthoimages) were used for the area of Bílá louka. Classification were executed in softwares ENVI 5.5 and R 4.2.1. The highest overall accuracy of the 2020 image classifications were over 70% in all study areas, in some cases over 80%. With the exception of the Luční hora area (58%), the best overall accuracies for 2004 image were above 65%. After comparing classification results between years 2004 and 2020, a possible development trend was revealed. But due to low accuracy of the 2004 data classifications, this cannot be reliably demonstrated. Key words: classification,...Analýza vývoje vegetace krkonošské tundry s využitím časové řady Cílem této studie je analyzovat změny vegetac alpínské tundry v Krkonoších s využitím archivních a současných leteckých snímků s červeným, zeleným a modrým pásmem a prostorovým rozlišením 0,5 m. tři rozdílnými typy vegetace a dále širší území východní tundry epších výsledků klasifikace bylo testováno několik klasifikačních objektově orientovaná klasifikace) podrobnější analýzu travních druhů byla pro oblast použita neřízená a rozšířená časová řada ortofot (5 časových horizontů) Nejvyšší celková přesnost byla ve všech studovaných oblastech přes 70 %, v některých případech přes 80 %. výjimkou Luční hory (58 %), byly p nejlepší celkové přesnosti vyšší než 65 % Po porovnání výsledků klasifikací mezi lety 2004 a 2020 se ukázal možný trend vývoje důvodu malé přesnosti klasifikací dat z nelze spolehlivě doložit Klíčová slova: tundra, dálkový průzkum Země, KrkonošskýDepartment of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografiePřírodovědecká fakultaFaculty of Scienc

2016 International Land Model Benchmarking (ILAMB) Workshop Report

As earth system models (ESMs) become increasingly complex, there is a growing need for comprehensive and multi-faceted evaluation of model projections. To advance understanding of terrestrial biogeochemical processes and their interactions with hydrology and climate under conditions of increasing atmospheric carbon dioxide, new analysis methods are required that use observations to constrain model predictions, inform model development, and identify needed measurements and field experiments. Better representations of biogeochemistryclimate feedbacks and ecosystem processes in these models are essential for reducing the acknowledged substantial uncertainties in 21st century climate change projections