Dissertation/Thesis Abstract

Carrying Capacity of Two-way Coupled Earth–Human Systems
by Motesharrei, Safa, Ph.D., University of Maryland, College Park, 2018, 320; 10977350
Abstract (Summary)

Over the last two centuries, the impact of the Human System has grown dramatically, becoming strongly dominant within the Earth System in many different ways. Consumption, inequality, and population have increased extremely fast, especially since about 1950, threatening to overwhelm the many critical functions and ecosystems of the Earth System. Changes in the Earth System, in turn, have important feedback effects on the Human System, with costly and potentially serious consequences. However, current models do not incorporate these critical feedbacks. We argue that in order to understand the dynamics of either system, Earth System Models must be coupled with Human System Models through bidirectional couplings representing the positive, negative, and delayed feedbacks that exist in the real systems. In particular, key Human System variables, such as demographics, inequality, economic growth, and migration, are not coupled with the Earth System but are instead driven by exogenous estimates, such as United Nations population projections. This makes current models likely to miss important feedbacks in the real Earth--Human system, especially those that may result in unexpected or counterintuitive outcomes, and thus requiring different policy interventions from current models. The importance and imminence of sustainability challenges, the dominant role of the Human System in the Earth System, and the essential roles the Earth System plays for the Human System, all call for collaboration of natural scientists, social scientists, and engineers in multidisciplinary research and modeling to develop coupled Earth--Human system models for devising effective science-based policies and measures to benefit current and future generations.

The official UMD Press Release for the Modeling Sustainability paper is available at: \noindent \url{https://umdrightnow.umd.edu/news/its-more-just-climate-change}

Existing studies of freshwater systems generally focus on hydrological flows without considering impacts from human feedbacks on these natural flows. However, the human system has become the major driver of changes in freshwater systems. The Coupled Human--Climate--Water model (COWA) is a minimal dynamic model that aims to capture these bidirectional positive, negative, and delayed feedbacks. The results show remarkable differences between simulation outputs of two-way and one-way coupled models, showing that projecting both human and natural system variables without feedbacks over long periods of time yields unrealistic results. COWA shows that Carrying Capacity is not static but rather evolves in response to the dynamic interactions and feedbacks in a changing system of many human and natural factors. COWA allows to determine the Water Carrying Capacity (WCC) of a region, i.e., the level of population and water consumption that a region's natural hydrological regime can support over the long term. Additionally, it shows that implementing effective water management policies --- such as recycling and conservation technologies --- can expand WCC of a region. An unexpected result of including bidirectional coupling is that expanding reservoir size and water collection capacity produces short-term population growth without expanding WCC, but because groundwater stocks are depleted more rapidly, it leads to an earlier and steeper collapse of the water resources and population. Lack of a dynamic understanding of a system can lead to the opposite conclusion about its behavior. COWA shows the critical importance of long-term policies for sustaining water resources, especially when demand rises and when estimates of available groundwater or potential for contamination of freshwater sources are highly uncertain. Significant signals of potential collapse (or unsustainability) can be missed if we limit the horizon to short term and neglect bidirectional feedbacks.

Physical systems with time-varying internal couplings are abundant in nature. While the full governing equations of these systems are typically unknown due to insufficient understanding of their internal mechanisms, there is often interest in determining the leading element. Here, the leading element is defined as the sub-system with the largest coupling coefficient averaged over a selected time span. Previously, the Convergent Cross Mapping (CCM) method has been employed to determine causality and dominant component in weakly coupled systems with constant coupling coefficients. In this study, CCM is applied to a pair of coupled Lorenz systems with time-varying coupling coefficients, exhibiting switching between dominant sub-systems in different periods. Four sets of numerical experiments are carried out. The first three cases consist of different coupling coefficient schemes: I) Periodic--constant, II) Normal, and III) Mixed Normal/Non-normal. In case IV, numerical experiment of cases II and III are repeated with imposed temporal uncertainties as well as additive normal noise. Our results show that, through detecting directional interactions, CCM identifies the leading sub-system in all cases except when the average coupling coefficients are approximately equal, i.e., when the dominant sub-system is not well defined.

Wind and solar farms offer a major pathway to clean, renewable energies. However, these farms would significantly change land surface properties, and, if sufficiently large, the farms may lead to unintended climate consequences. In this study, we used a climate model with dynamic vegetation to show that large-scale installations of wind and solar farms covering the Sahara lead to a local temperature increase and more than a twofold precipitation increase, especially in the Sahel, through increased surface friction and reduced albedo. The resulting increase in vegetation further enhances precipitation, creating a positive albedo--precipitation--vegetation feedback that contributes \mytilde 80\% of the precipitation increase for wind farms. This local enhancement is scale dependent and is particular to the Sahara, with small impacts in other deserts.

The official UMD Press Release for the Sahara paper is available at: \noindent \url{https://umdrightnow.umd.edu/news/large-scale-wind-and-solar-farms-sahara-would-increase-rain-and-vegetation}

Indexing (document details)
Advisor: Yorke, James A.
Commitee: Yakovenko, Victor M., Kalnay, Eugenia E., Dorland, William D., Srebric, Jelena
School: University of Maryland, College Park
Department: Physics
School Location: United States -- Maryland
Source: DAI-B 81/1(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Physics, Environmental science, Systems science
Keywords: Carrying capacity, Causality analysis, Coupled human–climate–water model, Earth–human systems, Modeling sustainability, Wind and solar energy
Publication Number: 10977350
ISBN: 9781085562508
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