Arctic Waterbodies have Consistent Spatial and Temporal Size Distributions

The Science

Arctic lowlands are characterized by large numbers of small waterbodies, which are known to affect surface energy budgets and the global carbon cycle. Further, waterbody distributions are changing rapidly in the warming Arctic, and Earth System Models (ESMs) do not currently represent these dynamics. In this study, a new high-resolution (<5 m) circum-Arctic waterbody database (Permafrost Region Pond and Lake; PeRL) was used to create the first high-resolution estimation of Arctic waterbody size distributions, with surface areas ranging from 0.0001 km² (100 m²) to 1 km². Surprisingly consistent relationships were found between mean waterbody size across a region and both the variance and skewness of the distributions. Further, these relationships held in two regions where multidecadal repeat photography was available.

The Impact

Characterizing the size distributions of Arctic waterbodies is a critical missing piece in assessing 21st century changes in hydrological and biogeochemical cycles and exchanges with the atmosphere. The results from this study provide important information for how these fine-resolution dynamics can be represented in ESMs, which is a goal for this study’s Next-Generation Ecosystem Experiments (NGEE)–Arctic work in the Energy Exascale Earth System Model (E3SM) land model (ELMv1).

Summary

In 2017, NGEE–Arctic DOE scientists worked with a group of collaborators to create an open-source database (PeRL) of high-resolution (<5 m) Arctic waterbody sizes [surface areas ranging from 0.0001 km² to 1 km²; Muster et al. (2017)]. The current study (Muster et al. 2019) analyzed that database over 30 study regions and found large variation in waterbody size distributions and that no single size distribution function was appropriate across all the study regions. However, close relationships between the statistical moments (mean, variance, and skewness) of the waterbody size distributions from different study regions clearly emerged: the spatial variance increased linearly with mean waterbody size (R² = 0.97, p < 2.2e-16) and the skewness decreased hyperbolically. These relationships (1) hold across the 30 Arctic study regions covering a variety of (bio)climatic and permafrost zones, (2) hold over time in two of the regions for which multidecadal satellite imagery is available, and (3) can be reproduced by simulating rising water levels in a high-resolution digital elevation model. The consistent spatial and temporal relationships between the statistical moments of the waterbody size distributions underscore the dominance of topographic controls in lowland permafrost areas. These results provide motivation for further analyses of the factors involved in waterbody development and spatial distribution and for how these fine-resolution dynamics can be represented in ESMs, such as E3SM land model (ELMv1).

Principal Investigator(s)

William Riley
Lawrence Berkeley National Laboratory
[email protected]

Funding

This research was supported by the Office of Biological and Environmental Research, within the U.S. Department of Energy Office of Science, as part of the Next-Generation Ecosystem Experiments (NGEE)–Arctic project and the Energy Exascale Earth System Model (E3SM) project.

References

Muster, S., W. J. Riley, K. Roth, M. Langer, F. Cresto-Aleina, C. D. Koven, S. Lange, A. Bartsch, G. Grosse, C. J. Wilson, B. M. Jones, and J. Boike. “Size distributions of Arctic waterbodies reveal consistent relations in their statistical moments in space and time.” Frontiers in Earth Science 7, 5 (2019). DOI:10.3389/feart.2019.00005

Muster, S., K. Roth, M. Langer, S. Lange, F. C. Aleina, A. Bartsch, A. Morgenstern, G. Grosse, B. Jones, B. K. Sannel, Y. Sjöberg, F. Gunther, C. Andresen, A. Veremeeva, P. R. Lindgren, F. Bouchard, M. J. Lara, D. Fortier, S. Charbonneau, T. A. Virtanen, G. Hugelius, J. Palmtag, M. B. Siewer, W. J. Riley, C. D. Koven, and J. Boike. “PeRL: A circum-Arctic permafrost region pond and lake database, Earth System Science Data 9 (2017). DOI:10.5194/essd-9-317-2017