Changing mechanism of global water scarcity events: Impacts of socioeconomic changes and inter-annual hydro-climatic variability

Changes in available fresh water resources, together with changes in water use, force our society to adapt continuously to water scarcity conditions. Although several studies assess the role of long-term climate change and socioeconomic developments on global water scarcity, the impact of inter-annual climate variability is less understood and often neglected. This paper presents a global scale water scarcity assessment that accounts for both temporal changes in socioeconomic conditions and hydro-climatic variability over the period 1960–2000. We thereby visualized for the first time possible over- and underestimations that may have been made in previous water scarcity assessments due to the use long-term means in their analyses. Subsequently, we quantified the relative contribution of hydro-climatic variability and socioeconomic developments on changing water scarcity conditions. We found that hydro-climatic variability and socioeconomic changes interact and that they can strengthen or attenuate each other, both regionally and at the global scale. In general, hydro-climatic variability can be held responsible for the largest share (>79%) of the yearly changes in global water scarcity, whilst only after six to ten years, socioeconomic developments become the largest driver of change. Moreover, our results showed that the growth in the relative contribution of socioeconomic developments to changing water scarcity conditions stabilizes towards 2000 and that the impacts of hydro-climatic variability remain significantly important. The findings presented in this paper could be of use for water managers and policy makers coping with water scarcity issues since correct information both on the current situation and regarding the relative contribution of different mechanisms shaping future conditions is key to successful adaptation and risk reduction.     

Use of multi-temporal Landsat images for analyzing forest transition in relation to socioeconomic factors and the environment

Recently there have been reports of forest regrowth occurring in different regions across the world. There is also a growing recognition of the potential beneficial impact that secondary forests may have on the global environment: providing crucial ecosystem services such as soil conservation, stabilization of hydrological cycles, carbon sequestration, and support for forest dependent communities. Consequently, there is a growing awareness of the need to recognize that landscapes are complex shifting mosaics wherein forest clearing and reforestation take place. In this study, the rates of reforestation, deforestation, forest regrowth and degradation were measured using multi-temporal Landsat images of Danjiangkou, China. Landsat data from 1990, 1999 and 2007 were (1) classified as dense forest, open forest and non-forest areas and (2) compared between years to identify forest cutting, regeneration and degradation. The results showed that there was a net gain of 29,315 ha of forest area (including dense and open forest) from 1990 to 2007, showing a clear trend of reforestation in the study area. Forest modification (degradation and regrowth) and change categories (deforestation and reforestation) occurred simultaneously during the observation time period. Socioeconomic data from public statistics and environmental attributes allowed the assessment of the socioeconomic factors and the environmental conditions that caused these changes using non-metric multidimensional scaling (NMDS). The research showed that the socioeconomic factors due to different policies were major driving forces of forest transition, whereas environmental attributes of the underlying landscape constrained forest cover changes. These findings have led to a better understanding of forest transition at a local scale in our study region. Comprehensive knowledge of these relationships may be useful to reconstruct past forest transitions and predict future changes, and may help to enhance sustainable management practices aimed at preserving essential ecological functions.     

An assessment of the performance of scenarios against historical global emissions for IPCC reports

Long-term emissions scenarios have served as the primary basis for assessing future climate change and response strategies. Therefore, it is important to regularly reassess the relevance of emissions scenarios in light of changing global circumstances and compare them with long-term developments to determine if they are still plausible, considering the newest insights. Four scenario series, SA90, IS92, SRES, and RCP/SSP, were central in the scenario-based literature informing the five Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC) and the sixth assessment cycle. Here we analyze the historical trends of carbon dioxide (CO2) emissions from fossil fuel combustion and industry and emissions drivers between 1960 and 2017. We then compare the emission scenario series with historical trends for the period 1990–2017/2018. The results show that historical trends are quite consistent with medium scenarios in each series. As a result, they can be regarded as valid inputs for past and future analyses of climate change and impacts. Global CO2 emissions 1960–2018 (and 1990–2018) comprised six (and three) overall subperiods of emissions growth significantly higher and lower than average. Historically, CO2 emissions (in absolute numbers and growth rate) are tightly coupled with primary energy and indirectly with GDP. Global emissions generally followed a medium-high pathway, captured by “middle-of-the-road” scenario narratives in the earlier series, and by combinations of “global-sustainability” and “middle-of-the-road” narratives in the most recent series (SRES and SSP-baselines). Historical non-OECD trends were best captured by “rapid-growth” and “regional-competition” scenarios, while OECD trends were close to regional-sustainability and global-sustainability scenarios. Areas where the emissions scenarios captured the historical trends less well, are renewable and nuclear primary energy supply. The fact that the actual historical development is consistent with rapid-growth narratives in the non-OECD regions might have important implications for future greenhouse gas emissions and associated climatic change.     

Recursive cross-entropy downscaling model for spatially explicit future land uses: A case study of the Heihe River Basin

Downscaling methods assist decision makers in coping with the uncertainty regarding sustainable local area developments. In particular, they allow investigating local heterogeneities regarding water, food, energy, and environment consistently with global, national, and sub-national drivers and trends. In this paper, we develop a conceptual framework that integrates a partial equilibrium Global Biosphere Management Model (GLOBIOM) with a dynamic cross-entropy downscaling model to derive spatially explicit projections of land uses at 1-km spatial resolution from 2010 to 2050 relying on aggregate land demand projections. The fusion of the two models is applied in a case study in Heihe River Basin to analyze the extent of potential cropland, grassland, and unused land transformations, which may exacerbate already extensive water consumption caused by rapid expansion of irrigated agriculture in the case study region. The outcomes are illustrated for two Shared Socioeconomic Pathway scenarios. The kappa coefficients show that the downscaling results are in agreement with the land use and land cover map of the Heihe River Basin, which indicates that the proposed approach produces realistic local land use projections. The downscaling results show that under both SSP scenarios the cropland area is expected to increase from 2010 to 2050, while the grassland area is projected to increase sharply from 2010 to 2030 and then gradually come to a standstill after 2030. The results can be used as an input for planning sustainable land and water management in the study area, and the conceptual framework provides a general approach to creating high-resolution land-use datasets.     

共享社会经济路径(SSPs)的应用与展望

共享社会经济路径(Shared Socioeconomic Pathways, SSPs)是新一代气候变化情景的重要组成部分。SSPs从提出至今已有10年的发展,对于推动气候变化预估与影响研究、支撑气候政策决策的作用逐渐凸显。文中基于179篇主题检索文献分析了SSPs发展和应用的进展,以及在当前气候变化研究中的应用特点。研究发现,次国家和部门层面的SSPs故事线拓展开始兴起,水资源、土地和健康是影响评估领域的关注焦点,方法学上强调模型间耦合与多模型比较。当前SSPs在中国的发展与应用集中于基本要素的预估及气候影响评估,路径对各省间及城乡间社会经济发展差异的刻画有待加强。基于情景发展和应用的现状,最后从加强与气候建模团队的合作、支持影响与脆弱性研究、拓展全球情景、加强模型间比较、提高决策支持力5个方面讨论了SSPs的未来研究展望。     

Global urbanization projections for the Shared Socioeconomic Pathways

The new scenario process for climate change research includes the creation of Shared Socioeconomic Pathways (SSPs) describing alternative societal development trends over the coming decades. Urbanization is a key aspect of development that is relevant to studies of mitigation, adaptation, and impacts. Incorporating urbanization into the SSPs requires a consistent set of global urbanization projections that cover long time horizons and span a full range of uncertainty. Existing urbanization projections do not meet these needs, in particular providing only a single scenario over the next few decades, a period during which urbanization is likely to be highly dynamic in many countries. We present here a new, long-term, global set of urbanization projections at country level that cover a plausible range of uncertainty. We create SSP-specific projections by choosing urbanization outcomes consistent with each SSP narrative. Results show that the world continues to urbanize in each of the SSPs but outcomes differ widely across them, with urbanization reaching 60%, 79%, and 92% by the end of century in SSP3, SSP2, and SSP1/SSP4/SSP5, respectively. The degree of convergence in urbanization across countries also differs substantially, with largely convergent outcomes by the end of the century in SSP1 and SSP5 and persistent diversity in SSP3. This set of global, country-specific projections produces urbanization pathways that are typical of regions in different stages of urbanization and development levels, and can be extended to further elaborate assumptions about the styles of urban growth and spatial distributions of urban people and land cover occurring in each SSP.     

Spatial spillover and the socio-ecological determinants of diabetes-related mortality across US counties

The spatial structure of diabetes-related mortality in US counties is evident from previous studies. However, it is not clear if spatial variation in diabetes-related mortality is associated with spatial variation in socioecological factors. We analyze the spatial spillover effect of changes in socioeconomic gradients (education, employment, household income), retail food environments, and access to health care, on diabetes-related mortality rates across the United States. Seven-year aggregates of multiple cause mortality data from the CDC WONDER compressed mortality database were merged with several sources of county-level data to examine mortality clusters, factors associated with the clusters, and spatial spillover effects in 3109 continuous US counties. The results suggest that high diabetes-related mortality cluster counties are located throughout the Southern Plains, Southeastern, and Appalachian regions. Lower socioeconomic status, a high density of fast food restaurants, a lack of access to grocery stores, a high proportion of Blacks, and low physical activity characterize high diabetes-related mortality rates clusters. The impacts from improvements in socioeconomic gradients and the retail food environment in neighboring counties spill over, and reduce the diabetes-related mortality rate in a particular county. This result implies that improvements in socioeconomic status and access to healthy food would significantly reduce diabetes-related mortality rates in contiguous US counties.     

Coverage inequality and quality of volunteered geographic features in Chinese cities: Analyzing the associated local characteristics using geographically weighted regression

The volunteered geographic information (VGI) gains increasing popularity with the general public and scientific community. However, the optimism about the VGI has been tempered by two critical issues: inequality in data coverage (social justice) and data quality. It therefore requires a better understanding of the mechanism driving VGI contributions and content quality. With a case of China, this paper demonstrates one potential avenue, examining the associations between VGI coverage/quality and local demographic and socioeconomic characteristics. In particular, VGI data are harvested from the OpenStreetMap for 333 cities in China. VGI coverage is measured by the total volume of different geographic features (point, line and polygon); and VGI quality is described from two aspects: completeness and accuracy. Geographically weighted regression (GWR) shows that both demographic and socioeconomic factors have statistically significant influences on VGI coverage and quality. More specifically, densely populous cities with more young, educated and non-agricultural people enjoy higher VGI coverage and quality. Cities with lower VGI coverage and quality are primarily located in the western and southwestern regions where the ethnic minorities concentrate. High VGI coverage and quality are possibly observed in economically developed cities with high marketization degree. Besides, possibility of high VGI coverage and quality occurs in cities with more labor in scientific research and greater percentage of employers in the tertiary industry. The GWR also demonstrates that the strength and nature of the obtained relationships vary across the 333 cities. The spatial non-stationary relationships may partially answer for the controversial empirical conclusions in earlier case studies at different scales. Quantitative analysis (Gini index, Lorenz curve and Moran's I index) further evidences the great inequality in VGI coverage and quality. It can be safely inferred that the differences in engagement and use of VGI, as a new digital divide, can raise troubling concerns on the social justice implications.     

The relationship between obesity and socioeconomic status among Texas school children and its spatial variation

Understanding the relationships between obesity and socioeconomic status (SES) among school children and the spatial variation of such relationship is essential for developing appropriate intervention strategies. In this study, we employed Local Entropy Map (LEM) to explore the spatial patterns of the relationship at school district level in Texas. Children's obesity was measured by Body Mass Index (BMI). The BMI data for this study were obtained from Physical Fitness Assessment Initiative (PFAI) program that has been coordinated by Texas Education Agency (TEA). SES was described by six variables, which were further reduced into two factors, namely Household SES and Neighborhood SES. The study period was 2012–2013 academic year. LEM analyses revealed clear spatial variation of the relationship between obesity and SES at school-district level. In particular, the prevalence of obesity among school children was found to be significantly related to Household SES and Neighborhood SES in four regions in Texas. These four regions are centered in major metropolitan areas in Texas, including San Antonio, Dallas-Fort Worth, Houston, and Lubbock. Further regression analyses showed variation of the relationship across these four regions. Obesity among school children in Texas was found to be more related to Household SES than Neighborhood SES; the relationship was strongest in San Antonio region. These findings may suggest the presence of obesogenic environment in the low SES school districts in these regions. Further studies to examine the particular nature of the obesogenic environment in these school districts are needed in order to support the development of regionalized policy and practice that can be more effective in addressing locale specifics.     

Extending the Shared Socioeconomic Pathways for sub-national impacts,adaptation, and vulnerability studies

The exploration of alternative socioeconomic futures is an important aspect of understanding the potential consequences of climate change. While socioeconomic scenarios are common and, at times essential, tools for the impacts, adaptation and vulnerability and integrated assessment modeling research communities, their approaches to scenario development have historically been quite distinct. However, increasing convergence of impacts, adaptation and vulnerability and integrated assessment modeling research in terms of scales of analysis suggests there may be value in the development of a common framework for socioeconomic scenarios. The Shared Socioeconomic Pathways represents an opportunity for the development of such a common framework. However, the scales at which these global storylines have been developed are largely incommensurate with the sub-national scales at which impacts, adaptation and vulnerability and, increasingly, integrated assessment modeling studies are conducted. The objective of this study was to develop sub-national and sectoral extensions of the global SSP storylines in order to identify future socioeconomic challenges for adaptation for the U.S. Southeast. A set of nested qualitative socioeconomic storyline elements, integrated storylines, and accompanying quantitative indicators were developed through an application of the Factor–Actor–Sector framework. In addition to revealing challenges and opportunities associated with the use of the SSPs as a basis for more refined scenario development, this study generated sub-national storyline elements and storylines that can subsequently be used to explore the implications of alternative sub-national socioeconomic futures for the assessment of climate change impacts and adaptation.