Publication Date
5-2018
Date of Final Oral Examination (Defense)
11-2-2016
Type of Culminating Activity
Thesis
Degree Title
Master of Science in Hydrologic Sciences
Department
Geosciences
Supervisory Committee Chair
Alejandro N. Flores, Ph.D.
Supervisory Committee Member
Elowyn Yager, Ph.D.
Supervisory Committee Member
Jennifer Pierce, Ph.D.
Supervisory Committee Member
Nancy Glenn, Ph.D.
Abstract
The distribution of vegetation in water-limited ecosystems is a product of complex and nonlinear interactions between climatic forcings (e.g., precipitation, temperature, solar radiation) and the underlying geomorphic template, which includes topography, geology, and soils. Changes in climate, particularly in precipitation and temperature, can dramatically alter the organization of vegetation. This is especially true in ecotones such as our area of study: the semi-arid transition between Great Basin shrub-steppe ecosystems and the coniferous forests of the Northern Rockies. Understanding and predicting how the spatial composition of terrestrial vegetation communities will change in these ecosystems is critical to predicting important future landscape changes such as landslides, erosion, fires, and water storage capacity. This study promotes understanding of the relative sensitivity of vegetation types to changes in weather and climate in water-limited environments using a land modeling framework. Specifically, we use the Landlab framework to develop and conduct a suite of numerical experiments that use ensemble methods to diagnose how changes in precipitation and temperature regimes affect the organization of plant functional types across varying hillslope aspects. This methodology yielded a broader perspective than previous studies that rely on analysis of deterministic runs, including detailed information about the variation within the results of each climate scenario we modeled. The impact of topographic variation such as changes in elevation or aspect are not not the same for temperature and precipitation, and understanding the relative importance of each is useful when extending the implications of results from this study to varying real-world locations.
We synthesized a watershed using Landlab’s landscape evolution capabilities to produce a topographic setting with contrasting hillslope aspects and randomly seeded vegetation (trees, shrubs, grasses, and bare soil). We then allowed that initial vegetation distribution to equilibrate to climatic conditions broadly consistent with contemporary climate. We then subjected the output distribution of vegetation to a perturbed climate, created by interpolating a group of Coupled Model Inter-Comparison Project 5 (CMIP5) climate projections that were downscaled using the Multivariate Adaptive Constructed Analogs (MACA) method to the approximate elevation of the site. We designed a suite of numerical experiments that investigated the sensitivity of the distribution of vegetation to changes in precipitation and temperature independently, as well as the combined effects of changes in both. To examine the sensitivity of vegetation composition to individual realizations of precipitation and temperature time series, and therefore the robustness of any conclusions about changes in vegetation composition to climate change, we took an ensemble approach with all simulations in which five-hundred realizations of precipitation and temperature forcings consistent with the altered climate were used to drive the climate change scenarios. We then investigated the probability density functions of the distribution of tree, shrub, grass, and bare soil coverage across aspects and simulations.
Regardless of scenario, we find that vegetation patterns on north-facing slopes were constant regardless of changes to precipitation or temperature alone. By contrast, vegetation patterns on south-facing slopes were sensitive to changes in both precipitation and temperature. In climate scenarios with reduced precipitation, the percentage of area covered by trees declined on south-facing slopes, while shrub coverage increased to fill areas vacated by trees. Temperature exacerbated this trend. A climate scenario with low precipitation and high temperatures had the lowest recorded tree cover on south-facing slopes, though high precipitation negated the effects of temperature. Using the Landlab framework allowed us to rapidly develop an effective model of the relative sensitives of vegetation types and conclude that precipitation is the most important variable with regard to forest replacement by grasses and shrubs in response to climate change. It is important to underscore, however, that the modeling framework used does not currently include key biogeochemical processes known to influence semi-arid ecosystems. As such, this study cannot examine nutrient limitations in these semi-arid ecosystems. This suggests a potential avenue for future study that leverages the modeling framework and approach taken here.
DOI
10.18122/td/1391/boisestate
Recommended Citation
Gelb, Lucy, "Using Landlab, a Fine Scale Biogeography Model, to Measure the Sustainability of Semi-Arid Vegetation in a Changing Climate" (2018). Boise State University Theses and Dissertations. 1391.
10.18122/td/1391/boisestate