Degree Type

Thesis

Date of Award

2017

Degree Name

Master of Science

Department

Ecology, Evolution, and Organismal Biology

Major

Environmental Science

First Advisor

William G. Crumpton

Abstract

An algorithm developed to identify, delineate, and derive the morphology of drained depressional features within a landscape was applied to the Iowa portion of the Des Moines Lobe (DML-IA) geomorphic sub-region of the Prairie Pothole Region of North America (PPR), using high resolution LiDAR derived Digital Elevation Models (DEMs). Nearly 240,000 unique upland depressions were identified and their individual morphologies determined. Testing of our algorithm against an algorithm designed to integrate over triangulated surface representations of 975 randomly selected depressions from the DML-IA dataset reveals that our computational process produces morphology results to within 0.3 and 2% of those obtained using the latter process, and is nearly 3 orders of magnitude faster. Maximum areas of inundation, maximum depths, and maximum storage volumes were determined to follow a power-law distribution. Maximum volume was determined to be strongly related to maximum area through a power-law model, the coefficients of which appear to vary significantly from other areas of the PPR, but are in close agreement with values obtained for small sub-areas of the DML-IA, and for a large river basin in North Saskatchewan, CN. While the majority (80%) of depressions within the DML-IA are less than 1 ha in area, these only comprise 9.8% of the total potential depressional storage and 25.6% of the total depressional wetland area of this landscape. More than half of the potential storage capacity is provided by depressions between 1 and 30 ha.

DOI

https://doi.org/10.31274/etd-180810-5191

Copyright Owner

Samuel Marcus McDeid

Language

en

File Format

application/pdf

File Size

42 pages

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