Minimal Models of Human-Nature Interaction

Over the last two centuries, the Human System went from having a small impact on the Earth System to becoming dominant, because both population and per capita consumption have grown extremely fast, especially since about 1950. We therefore argue that Human System Models must be included into Earth System Models through bidirectional couplings with feedbacks. In particular, population should be modeled endogenously, rather than exogenously as done currently in most Integrated Assessment Models. The growth of the Human System threatens to overwhelm the Carrying Capacity of the Earth System, and may be leading to collapse. Earth Sciences should be involved in the exploration of potential mitigation strategies including education, regulatory policies, and technological advances. We describe a human population dynamics model developed by adding accumulated wealth and economic inequality to a predator-prey model of humans and nature. The model structure, and simulated scenarios that offer significant implications, are discussed. Four equations describe the evolution of Elites, Commoners, Nature, and Wealth. The model shows Economic Stratification or Ecological Strain can independently lead to collapse, in agreement with the historical record. The measure ``Carrying Capacity'' is developed and its estimation is shown to be a practical means for early detection of a collapse. Mechanisms leading to two types of collapses are discussed. The new dynamics of this model can also reproduce the irreversible collapses found in history. Collapse can be avoided, and population can reach a steady state at maximum carrying capacity, if the rate of depletion of nature is reduced to a sustainable level, and if resources are distributed equitably. Finally we present a Coupled Human-Climate-Water Model (COWA). Policies are introduced as drivers of the model so that the long-term effect of each policy on the system can be seen as we change its level. We have done a case study for the Phoenix AMA Watershed. We show that it is possible to guarantee the freshwater supply and sustain the freshwater sources through a proper set of policy choices

Local cooling and warming effects of forests based on satellite observations

The biophysical effects of forests on climate have been extensively studied with climate models. However, models cannot accurately reproduce local climate effects due to their coarse spatial resolution and uncertainties, and field observations are valuable but often insufficient due to their limited coverage. Here we present new evidence acquired from global satellite data to analyse the biophysical effects of forests on local climate. Results show that tropical forests have a strong cooling effect throughout the year; temperate forests show moderate cooling in summer and moderate warming in winter with net cooling annually; and boreal forests have strong warming in winter and moderate cooling in summer with net warming annually. The spatiotemporal cooling or warming effects are mainly driven by the two competing biophysical effects, evapotranspiration and albedo, which in turn are strongly influenced by rainfall and snow. Implications of our satellite-based study could be useful for informing local forestry policies

Climate model shows large-scale wind and solar farms in the Sahara increase rain and vegetation

More energy, more rain Energy generation by wind and solar farms could reduce carbon emissions and thus mitigate anthropogenic climate change. But is this its only benefit? Li et al. conducted experiments using a climate model to show that the installation of large-scale wind and solar power generation facilities in the Sahara could cause more local rainfall, particularly in the neighboring Sahel region. This effect, caused by a combination of increased surface drag and reduced albedo, could increase coverage by vegetation, creating a positive feedback that would further increase rainfall.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 ~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.Y.L. acknowledges support from the National Key R&D Program of China (no. 2017YFA0604701). E.K. and S.M. acknowledge Lev Gandin funding (grant 2956713) provided by G. Brin. The authors thank the University of Maryland and the Univ. of Illinois for supercomputing resources—in particular, the Deepthought2 (http://hpcc.umd.edu) and Bluewaters (www.ncsa.illinois.edu/enabling/bluewaters) supercomputers—made available for conducting the research reported in this paper

Modeling sustainability : Population, inequality, consumption, and bidirectional coupling of the Earth and human systems

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 SystemModels must be coupled with Human SystemModels 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

Carrying Capacity of Two-way Coupled Earth–Human Systems

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

2014): "Human and Nature Dynamics (HANDY): Modeling inequality and use of resources in the collapse or sustainability of societies

Abstract There are widespread concerns that current trends in resource-use are unsustainable, but possibilities of overshoot/collapse remain controversial. Collapses have occurred frequently in history, often followed by centuries of economic, intellectual, and population decline. Many different natural and social phenomena have been invoked to explain specific collapses, but a general explanation remains elusive. In this paper, we build a human population dynamics model by adding accumulated wealth and economic inequality to a predator-prey model of humans and nature. The model structure, and simulated scenarios that offer significant implications, are explained. Four equations describe the evolution of Elites, Commoners, Nature, and Wealth. The model shows Economic Stratification or Ecological Strain can independently lead to collapse, in agreement with the historical record. The measure "Carrying Capacity" is developed and its estimation is shown to be a practical means for early detection of a collapse. Mechanisms leading to two types of collapses are discussed. The new dynamics of this model can also reproduce the irreversible collapses found in history. Collapse can be avoided, and population can reach a steady state at maximum carrying capacity if the rate of depletion of nature is reduced to a sustainable level and if resources are distributed equitably

Spatial and Temporal Patterns of Global NDVI Trends: Correlations with Climate and Human Factors

Changes in vegetation activity are driven by multiple natural and anthropogenic factors, which can be reflected by Normalized Difference Vegetation Index (NDVI) derived from satellites. In this paper, NDVI trends from 1982 to 2012 are first estimated by the Theil–Sen median slope method to explore their spatial and temporal patterns. Then, the impact of climate variables and human activity on the observed NDVI trends is analyzed. Our results show that on average, NDVI increased by 0.46 × 10−3 per year from 1982 to 2012 globally with decadal variations. For most regions of the world, a greening (increasing)–browning (decreasing)–greening (G-B-G) trend is observed over the periods 1982–2004, 1995–2004, and 2005–2012, respectively. A positive partial correlation of NDVI and temperature is observed in the first period but it decreases and occasionally becomes negative in the following periods, especially in the Humid Temperate and Dry Domain Regions. This suggests a weakened effect of temperature on vegetation growth. Precipitation, on the other hand, is found to have a positive impact on the NDVI trend. This effect becomes stronger in the third period of 1995–2004, especially in the Dry Domain Region. Anthropogenic effects and human activities, derived here from the Human Footprint Dataset and the associated Human Influence Index (HII), have varied impacts on the magnitude (absolute value) of the NDVI trends across continents. Significant positive effects are found in Asia, Africa, and Europe, suggesting that intensive human activity could accelerate the change in NDVI and vegetation. A more accurate attribution of vegetation change to specific climatic and anthropogenic factors is instrumental to understand vegetation dynamics and requires further research

Causality Analysis: Identifying the Leading Element in a Coupled Dynamical System.

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

Case IV: experiments on signals of cases II and III with imposed additive Gaussian noise.

<p>We consider noise levels at 5% and 10% of the standard deviation of the original signals in cases II and III. (a) Δ<i>ρ</i> at <i>L</i> = 500 as a function of </p><p><math><mrow><msub><mi>η</mi><mo>¯</mo><mi>N</mi></msub><mo>/</mo><msub><mi>μ</mi><mo>¯</mo><mi>N</mi></msub></mrow></math></p> for signals of case II. (b) Δ<i>ρ</i> at <i>L</i> = 500 as a function of <p><math><mrow><msub><mi>η</mi><mo>¯</mo><mi>N</mi></msub><mo>/</mo><msub><mi>μ</mi><mo>¯</mo><mrow><mi>W</mi><mi>b</mi></mrow></msub></mrow></math></p> for signals of case III. Δ<i>ρ</i> = 0 and <p><math><mrow><msub><mi>η</mi><mo>¯</mo><mi>N</mi></msub><mo>/</mo><msub><mi>μ</mi><mo>¯</mo><mi>N</mi></msub><mo>=</mo><mn>1</mn></mrow></math></p> are shown by dashed, bold lines.<p></p

Case III: mixed normal–nonnormal coupling coefficients.

<p>(a) </p><p><math><mrow><mo>Δ</mo><mi>ρ</mi><mo>=</mo><msub><mi>ρ</mi><mrow><mi>X</mi><mo>̂</mo><mo>∣</mo><mi>Y</mi></mrow></msub><mo>−</mo><msub><mi>ρ</mi><mrow><mi>Y</mi><mo>̂</mo><mo>∣</mo><mi>X</mi></mrow></msub></mrow></math></p> at <i>L</i> = 500 as a function of <p><math><mrow><msub><mi>η</mi><mo>¯</mo><mi>N</mi></msub><mo>/</mo><msub><mi>μ</mi><mo>¯</mo><mrow><mi>W</mi><mi>b</mi></mrow></msub></mrow></math></p>. (b) 1000 independent realizations of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131226#pone.0131226.e069" target="_blank">Eq (12)</a> and the corresponding Δ<i>ρ</i> at <i>L</i> = 500 as a function of <p><math><mrow><msub><mi>η</mi><mo>¯</mo><mi>N</mi></msub><mo>/</mo><msub><mi>μ</mi><mo>¯</mo><mrow><mi>W</mi><mi>b</mi></mrow></msub></mrow></math></p>. <p><math><mrow><msub><mi>η</mi><mo>¯</mo><mi>N</mi></msub></mrow></math></p> and <p><math><mrow><msub><mi>μ</mi><mo>¯</mo><mrow><mi>W</mi><mi>b</mi></mrow></msub></mrow></math></p> are the mean values of <i>η</i>_<i>N</i> and <i>μ</i>_<i>Wb</i> over the span of the time–series. Δ<i>ρ</i> = 0 and <p><math><mrow><msub><mi>η</mi><mo>¯</mo><mi>N</mi></msub><mo>/</mo><msub><mi>μ</mi><mo>¯</mo><mi>N</mi></msub><mo>=</mo><mn>1</mn></mrow></math></p> are shown by dashed, bold lines.<p></p