ALBERT

Description: Arctic ponds, i. e. water bodies with a surface area equal to or smaller than 10⁴ m² (1 ha), are currently not inventoried on a circum-arctic scale. However, they are a key element of the water, energy, and carbon balance and abundant in Arctic permafrost lowlands. Ponds and lakes have been subject to both wetting and drying in a warming climate yet studies remain ambivalent regarding the causes of these changes. Goals of this study are to (i) investigate the variability of water body size distributions as a function of landscape characteristics, and (ii) assess the vulnerability of water bodies in different landscapes to scenarios of wetting and drying. Ponds and lakes were mapped from high-resolution aerial and satellite imagery with resolutions of 4 m or better in 14 regions in Alaska, Canada, and Siberia covering a total area of ca. 1.6*104 km². Whereas lake distributions are similar, pond distributions in our study regions vary significantly with the area-normalized number of ponds differing up to 3 orders of magnitude. Landscape characteristics that may explain the current water body distributions include climate (eg., precipitation, evapotranspiration, temperature), permafrost (eg., ground ice content, maximum thaw depth) and terrain characteristics (eg., topography, glaciation, landscape age) which we derive from in situ, remote sensing and modeling data sources. Multivariate regression analysis are used to relate landscape characteristics to distribution parameters. This study for the first time allows to quantify the circum-arctic variability of pond distribution. The current maps are the start of a high-resolution circum-arctic water body inventory and present a baseline for future surface inundation mapping and modelling. We present representative regional probability density functions (pdf) and assess the potential to upscale pdfs using spatial landscape characteristics. We then discuss the vulnerability of water bodies to wetting or drying based on the distribution parameters, their correlation with landscape characteristics and the likeliness of both to change in different future climate scenarios.

Description: Ponds and lakes are abundant in Arctic permafrost lowlands. They play an important role in Arctic wetland ecosystems by regulating carbon, water, and energy fluxes and providing freshwater habitats. However, ponds, i.e., waterbodies with surface areas smaller than 1. 0 × 104 m2, have not been inventoried on global and regional scales. The Permafrost Region Pond and Lake (PeRL) database presents the results of a circum-Arctic effort to map ponds and lakes from modern (2002–2013) high-resolution aerial and satellite imagery with a resolution of 5 m or better. The database also includes historical imagery from 1948 to 1965 with a resolution of 6 m or better. PeRL includes 69 maps covering a wide range of environmental conditions from tundra to boreal regions and from continuous to discontinuous permafrost zones. Waterbody maps are linked to regional permafrost landscape maps which provide information on permafrost extent, ground ice volume, geology, and lithology. This paper describes waterbody classification and accuracy, and presents statistics of waterbody distribution for each site. Maps of permafrost landscapes in Alaska, Canada, and Russia are used to extrapolate waterbody statistics from the site level to regional landscape units. PeRL presents pond and lake estimates for a total area of 1. 4 × 106 km2 across the Arctic, about 17 % of the Arctic lowland ( 〈  300 m a.s.l.) land surface area. PeRL waterbodies with sizes of 1. 0 × 106 m2 down to 1. 0 × 102 m2 contributed up to 21 % to the total water fraction. Waterbody density ranged from 1. 0 × 10 to 9. 4 × 101 km−2. Ponds are the dominant waterbody type by number in all landscapes representing 45–99 % of the total waterbody number. The implementation of PeRL size distributions in land surface models will greatly improve the investigation and projection of surface inundation and carbon fluxes in permafrost lowlands. Waterbody maps, study area boundaries, and maps of regional permafrost landscapes including detailed metadata are available at https://doi.pangaea.de/10.1594/PANGAEA.868349.

Description: Sub-grid and small scale processes occur in various ecosystems and landscapes (e.g., periglacial ecosystems, peatlands and vegetation patterns). These local heterogeneities are often important or even fundamental to better understand general and large scale properties of the system, but they are either ignored or poorly parameterized in regional and global models. Because of their small scale, the underlying generating processes can be well explained and resolved only by local mechanistic models, which, on the other hand, fail to consider the regional or global influences of those features. A challenging problem is then how to deal with these interactions across different spatial scales, and how to improve our understanding of the role played by local soil heterogeneities in the climate system. This is of particular interest in the northern peatlands, because of the huge amount of carbon stored in these regions. Land-atmosphere greenhouse gas fluxes vary dramatically within these environments. Therefore, to correctly estimate the fluxes a description of the small scale soil variability is needed. Applications of statistical physics methods could be useful tools to upscale local features of the landscape, relating them to large-scale properties. To test this approach we considered a case study: the polygonal tundra. Cryogenic polygons, consisting mainly of elevated dry rims and wet low centers, pattern the terrain of many subartic regions and are generated by complex crack-and-growth processes. Methane, carbon dioxide and water vapor fluxes vary largely within the environment, as an effect of the small scale processes that characterize the landscape. It is then essential to consider the local heterogeneous behavior of the system components, such as the water table level inside the polygon wet centers, or the depth at which frozen soil thaws. We developed a stochastic model for this environment using Poisson-Voronoi diagrams, which is able to upscale statistical large scale properties of the system taking into account the main processes within the single polygons. We compare the results with available recent field studies and demonstrate that the model captures the main statistical characteristics of the landscape and describes its dynamical behavior under climatic forcings (e.g., precipitation and evapotranspiration). We analyze seasonal dynamics of water table variations and the landscape response under different scenarios of precipitation income. We upscale methane fluxes by using a simple idealized model for methane emission. We also investigate hydraulic interconnectivities and large-scale drainage through percolation properties and thresholds in the Voronoi-Deleaunay graph. The model captures the main statistical characteristics of the landscape topography, such as polygon area and surface properties as well as the water balance. This approach enables us to statistically relate large-scale properties of the system taking into account the main small-scale processes within the single polygons. Overall, the general agreement between field measurements and model results suggests that such statistical methods and simple parameterizations, if accurately tuned with field data, could be a powerful way to consider spatial scale interactions in such heterogenous and complex environments. http://www.earth-syst-dynam-discuss.net/3/453/2012/esdd-3-453-2012.html