Power law scaling of topographic depressions and their hydrologic connectivity

Phong V.V. Le, Praveen Kumar

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

Abstract

Topographic depressions, areas of no lateral surface flow, are ubiquitous characteristics of the land surface that control many ecosystem and biogeochemical processes. High density of depressions increases the surface storage capacity, whereas lower depression density increases runoff, thus influencing soil moisture states, hydrologic connectivity, and the climate-soil-vegetation interactions. With the widespread availability of high-resolution lidar-based digital elevation model (lDEM) data, it is now possible to identify and characterize the structure of the spatial distribution of topographic depressions for incorporation in ecohydrologic and biogeochemical studies. Here we use lDEM data to document the prevalence and patterns of topographic depressions across five different landscapes in the United States and quantitatively characterize the probability distribution of attributes, such as surface area, storage volume, and the distance to the nearest neighbor. Through the use of a depression identification algorithm, we show that these probability distributions of attributes follow scaling laws indicative of a structure in which a large fraction of land surface areas can consist of high number of topographic depressions of all sizes and can account for 4 to 21 mm of depression storage. This implies that the impacts of small-scale topographic depressions in the landscapes on the redistribution of material fluxes, evaporation, and hydrologic connectivity are quite significant. Key Points Geometric attributes of topographic depressions follow power law distributions Scaling laws have implications for the transport of water and material fluxes Short distance among depressions can affect hydrologic connectivity

Original languageEnglish
Pages (from-to)1553-1559
Number of pages7
JournalGeophysical Research Letters
Volume41
Issue number5
DOIs
StatePublished - Mar 16 2014
Externally publishedYes

Funding

FundersFunder number
National Science Foundation0930643, 0930731, EAR 11-40198, EAR 13-31906, CBET 12-09402
National Science Foundation1331906, 1209427

    Keywords

    • geometric attributes
    • lidar
    • scaling law
    • topographic depressions

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