Yuyu Zhou

A global map of urban extent from nightlights

Urbanization, a major driver of global change, profoundly impacts our physical and social world, for example, altering not just water and carbon cycling, biodiversity, and climate, but also demography, public health, and economy. Understanding these consequences for better scientific insights and effective decision-making unarguably requires accurate information on urban extent and its spatial distributions. We developed a method to map the urban extent from the defense meteorological satellite program/operational linescan system nighttime stable-light data at the global level and created a new global 1 km urban extent map for the year 2000. Our map shows that globally, urban is about 0.5% of total land area but ranges widely at the regional level, from 0.1% in Oceania to 2.3% in Europe. At the country level, urbanized land varies from about 0.01 to 10%, but is lower than 1% for most (70%) countries. Urbanization follows land mass distribution, as anticipated, with the highest concentration between 30° N and 45° N latitude and the largest longitudinal peak around 80° W. Based on a sensitivity analysis and comparison with other global urban area products, we found that our global product of urban areas provides a reliable estimate of global urban areas and offers the potential for producing a time-series of urban area maps for temporal dynamics analyses.

Carbon Geography: The Political Economy of Congressional Support for Legislation Intended to Mitigate Greenhouse Gas Production

Stringent regulation for mitigating greenhouse gas emissions will impose different costs across geographical regions. Low-carbon, environmentalist states, such as California, would bear less of the incidence of such regulation than high-carbon Midwestern states. Such anticipated costs are likely to influence Congressional voting patterns. This paper uses several geographical data sets to document that conservative, poor areas have higher per-capita carbon emissions than liberal, richer areas. Representatives from such areas are shown to have much lower probabilities of voting in favor of anti-carbon legislation. In the 111th Congress, the Energy and Commerce Committee consists of members who represent high carbon districts. These geographical facts suggest that the Obama Administration and the Waxman Committee will face distributional challenges in building a majority voting coalition in favor of internalizing the carbon externality.

21st century United States emissions mitigation could increase water stress more than the climate change it is mitigating

Significance Devising sustainable climate change mitigation policies with attention to potential synergies and constraints within the climate–energy–water nexus is the subject of ongoing integrated modeling efforts. This study employs a regional integrated assessment model and a regional Earth system model at high spatial and temporal resolutions in the Unites States to compare the implications of two of the representative concentration pathways under consistent socioeconomics. The results clearly show, for the first time to our knowledge, that climate change mitigation policies, if not designed with careful attention to water resources, could increase the magnitude, spatial coverage, and frequency of water deficits. The results challenge the general perception that mitigation that aims at reducing warming also would alleviate water deficits in the future. There is evidence that warming leads to greater evapotranspiration and surface drying, thus contributing to increasing intensity and duration of drought and implying that mitigation would reduce water stresses. However, understanding the overall impact of climate change mitigation on water resources requires accounting for the second part of the equation, i.e., the impact of mitigation-induced changes in water demands from human activities. By using integrated, high-resolution models of human and natural system processes to understand potential synergies and/or constraints within the climate–energy–water nexus, we show that in the United States, over the course of the 21st century and under one set of consistent socioeconomics, the reductions in water stress from slower rates of climate change resulting from emission mitigation are overwhelmed by the increased water stress from the emissions mitigation itself. The finding that the human dimension outpaces the benefits from mitigating climate change is contradictory to the general perception that climate change mitigation improves water conditions. This research shows the potential for unintended and negative consequences of climate change mitigation.

Extraction of Impervious Surface Areas from High Spatial Resolution Imagery by Multiple Agent Segmentation and Classification

In recent years impervious surface areas (ISA) have emerged as a key paradigm to explain and predict ecosystem health in relationship to watershed development. The ISA data are essential for environmental monitoring and management in coastal State of Rhode Island. However, there is lack of information on high spatial resolution ISA. In this study, we developed an algorithm of multiple agent segmentation and classification (MASC) that includes submodels of segmentation, shadow-effect, MANOVA-based classification, and postclassification. The segmentation sub-model replaced the spectral difference with heterogeneity change for regions merging. Shape information was introduced to enhance the performance of ISA extraction. The shadow-effect sub-model used a split-and-merge process to separate shadows and the objects that cause the shadows. The MANOVA-based classification sub-model took into account the relationship between spectral bands and the variability in the training objects and the objects to be classified. Existing GIS data were used in the classification and post-classification process. The MASC successfully extracted ISA from high spatial resolution airborne true-color digital orthophoto and space-borne QuickBird-2 imagery in the testing areas, and then was extended for extraction of high spatial resolution ISA in the State of Rhode Island.

Evaluation of global onshore wind energy potential and generation costs.

In this study, we develop an updated global estimate of onshore wind energy potential using reanalysis wind speed data, along with updated wind turbine technology performance, land suitability factors, cost assumptions, and explicit consideration of transmission distance in the calculation of transmission costs. We find that wind has the potential to supply a significant portion of the world energy needs, although this potential varies substantially by region and with assumptions such as on what types of land can be used to site wind farms. Total global economic wind potential under central assumptions, that is, intermediate between optimistic and pessimistic, is estimated to be approximately 119.5 petawatt hours per year (13.6 TW) at less than 9 cents/kWh. A sensitivity analysis of eight key parameters is presented. Wind potential is sensitive to a number of input parameters, particularly wind speed (varying by -70% to +450% at less than 9 cents/kWh), land suitability (by -55% to +25%), turbine density (by -60% to +80%), and cost and financing options (by -20% to +200%), many of which have important policy implications. As a result of sensitivities studied here we suggest that further research intended to inform wind supply curve development focus not purely on physical science, such as better resolved wind maps, but also on these less well-defined factors, such as land-suitability, that will also have an impact on the long-term role of wind power.

GCAM 3.0 Agriculture and Land Use: Data Sources and Methods

This report presents the data processing methods used in the GCAM 3.0 agriculture and land use component, starting from all source data used, and detailing all calculations and assumptions made in generating the model inputs. The report starts with a brief introduction to modeling of agriculture and land use in GCAM 3.0, and then provides documentation of the data and methods used for generating the base-year dataset and future scenario parameters assumed in the model input files. Specifically, the report addresses primary commodity production, secondary (animal) commodity production, disposition of commodities, land allocation, land carbon contents, and land values.

Correcting Incompatible DN Values and Geometric Errors in Nighttime Lights Time-Series Images

The Defense Meteorological Satellite Programs Operational Linescan System (DMSP-OLS) nighttime lights imagery has proven to be a powerful remote sensing tool to monitor urbanization and assess socioeconomic activities at large scales. However, the existence of incompatible digital number (DN) values and geometric errors severely limit application of nighttime light image data on multiyear quantitative research. In this paper, we extend and improve previous studies on intercalibrating nighttime lights image data to obtain more compatible and reliable nighttime lights time-series (NLT) image data for China and the U.S. through four steps, namely, intercalibration, geometric correction, steady-increase adjustment, and population data correction. We then use gross domestic product (GDP) data to test the processed NLT image data indirectly and find that sum light (summed DN value of pixels in a nighttime light image) maintains apparent increase trends with relatively large GDP growth rates but does not increase or decrease with relatively small GDP growth rates. As nighttime light is a sensitive indicator for economic activity, the temporally consistent trends between sum light and GDP growth rate imply that brightness of nighttime lights on the ground is correctly represented by the processed NLT image data. Finally, through analyzing the corrected NLT image data from 1992 to 2008, we find that China experienced apparent nighttime lights development in 1992-1997 and 2001-2008, respectively, and the U.S. showed nighttime lights decay in large areas after 2001.

Spatial distribution of net primary productivity and evapotranspiration in Changbaishan Natural Reserve, China, using Landsat ETM+ data

Remote sensing has been a useful tool to monitor net primary productivity (NPP) and evapotranspiration (ET). In this paper, based on field measurements and Landsat enhanced thematic mapper plus (ETM+) data, NPP and ET are estimated in 2001 in the Changbaishan Natural Reserve, China. Maps of land cover, leaf area index, and biomass of this forested region are first derived from ETM+ data. With these maps and additional soil texture and daily meteorological data, NPP and ET maps are produced for 2001 using the boreal ecosystem productivity simulator (BEPS). The results show that the estimated and observed NPP values for forest agree fairly well, with a mean relative error of 8.6%. The NPP of mixed forests is the highest, with a mean of 500 g C m–2·a–1, and that of alpine tundra and shrub is the lowest, with a mean of 136 g C m–2·a–1. Unlike the spatial pattern of NPP, the annual ET changes distinctly with altitude from greater than 600 mm at the foot of the mountain to about 200 mm at the top of the mountain. ET is highest for broadleaf forests and lowest for urban and built-up areas.

Technology and U.S. Emissions Reductions Goals: Results of the EMF 24 Modeling Exercise

This paper discusses Technology and U.S. Emissions Reductions Goals: Results of the EMF 24 Modeling Exercise

A new methodology for quantifying on-site residential and commercial fossil fuel CO2 emissions at the building spatial scale and hourly time scale

In order to advance the scientific understanding of carbon exchange with the land surface, and contribute to quantitative-based US climate change policy interests, quantification of fossil fuel CO2 emissions (the primary greenhouse gas), at fine spatial and temporal scales, is essential. Known as the ‘Hestia Project’, this pilot study has quantified all fossil fuel CO2 emissions down to the scale of individual buildings, road segments and industrial/electricity production facilities on an hourly basis for the greater Indianapolis region, IN, USA. Here, we describe the method used to quantify the on-site fossil fuel CO2 emissions in the residential and commercial sectors. By downscaling the Vulcan Project’s 2002 county-level commercial and residential fossil fuel CO2 emissions, we quantified the CO2 emissions for all building structures using a combination of multiple datasets and energy simulation. At the landscape scale, the spatial variation in CO2 emissions is driven by building density, height and type. Within the urban core, larger emissions are driven by the larger amounts of energy consumed per unit floor area. The resulting dataset and corresponding methods will be of immediate use to city environmental managers and regional planning agencies, enabling the analysis of alternative strategies to lower fossil fuel CO2 emissions. The results obtained here will also be a useful comparison to atmospheric CO2 monitoring efforts aimed at constraining the land surface net carbon exchange via atmospheric sampling.