The theme for this week’s sustainability research is CLIMATE CHANGE
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Research in Details
Research #1
Assessment of the impact of climate change on cities livability in China
Highlights
Assessed the livability index of 288 Chinese cities over the past decade
Analyze the spatial distribution characteristics of the livability index of Chinese cities
Identified the impact of extreme climate events on the change of urban livability
Authors: Li Liang, Xiangzheng Deng, Pei Wang, Zehao Wang, Lishuang Wang
Date of publication: 15 JULY 2020
Summary
As global warming worsens, climatic conditions in many regions are undergoing profound change, which could influence certain industries, such as agriculture and transportation, and affect the livability of cities. In this study, cities statistics and meteorological station data of the past decade in China were used to analyze the effect of climate change on cities livability. The livability of 288 Chinese cities during 2006–2016 was assessed using an analytic hierarchy process method. Results showed the mean cities livability index in China has improved about 12% throughout the past decade. Moreover, the Moran's I statistic and local indicators of spatial association revealed that the distribution of cities livability reflects a trend of gradual spatial agglomeration. In addition, geostatistical analysis was used to evaluate the impact of extreme weather events on cities livability. It was established that heatwaves and extreme precipitation events have significant impact on the livability of cities in southern China, whereas freezing weather drives the change of urban livability in northern areas. Combinations of different extreme weather conditions will have greater impact on urban livability. Based on mobile phone user data, the relationship between livability change and climate change in Chinese cities was elucidated to provide reference for urban environmental management.
Keywords: Climate change, Livability, Spatial distribution, Geostatistical analysis, Extreme weather
Research #2
Trophism, climate and paleoweathering conditions across the Eocene-Oligocene transition in the Massignano section (northern Apennines, Italy)
Highlights:
Clay minerals reflect climate variations in the Alpine area during the Late Eocene.
Clay minerals reflect the interplay between climate and Alpine evolution across the EOT.
δ13C record of Massignano GSSP matches with the global curve.
The late Priabonian negative δ13C trend indicates atmospheric CO2 lowering.
Marine productivity pulses are linked to cooling phases in the early Priabonian.
Authors: Luca Aldega, Marco Brandanoa, Irene Cornacchia
Date of publication: 15 JULY 2020
Summary
The Eocene-Oligocene transition represents the latest greenhouse-icehouse shift faced by Earth, a major global climate change associated with carbon cycle perturbation. In this paper, we investigate the Massignano stratigraphic section (northern Apennines, Italy), GSSP of the Eocene-Oligocene boundary, by X-ray diffraction analysis of clay minerals and carbon and oxygen stable isotopes to explore the link among climate, paleoweathering and runoff, and carbon cycle in the Neotethys across this major climatic transition. We examine the interplay between global climate forcing and orogen evolution controlling the Massignano hemipelagic sedimentation. The late Eocene clay mineral assemblages indicate that the Neotethys was sensitive to global eustatic changes as well as changes in composition and rates of weathered sediments accumulating into the basin. The carbon isotope record matches with the global signal, showing productivity pulses linked to cooling phases and sea-level drops before 34.8 Ma. Subsequently, a constant negative trend is recorded, which is consistent with the global carbon isotope curve, and attests for a decrease of primary productivity linked to decreasing atmospheric CO2. This negative trend ends at the Eocene-Oligocene boundary, when the onset of the global carbon isotope positive excursion is recorded also within the Massignano hemipelagic succession. On the contrary, the clay mineral assemblage is quite constant across the Eocene-Oligocene boundary reflecting the complex interplay among fluvial discharge, sea level changes and orogen dynamics which clouded the global climate shift. In this context, the enhanced fluvial discharge likely contributed to sustain high trophic conditions in the Adriatic waters and, in turn, the Oligocene positive carbon isotope shift. These results highlight how clay minerals proved to be a useful proxy to identify the interplay between global and regional controlling factors on hemipelagic sedimentation and, their integration with the carbon isotope record, provides insights into carbon cycle dynamics.
Keywords: Carbon and oxygen isotopes, Clay minerals, Climate and paleoweathering conditions, Eocene-Oligocene boundary, Massignano GSSP section, Trophism
Research #3
Impacts of climate variations on crime rates in Beijing, China
Highlights
Daily crime rates and climate variables are decomposed into three components, namely, long-term trends, seasonality and daily variations.
There are strong positive linear correlations between the seasonality of temperature and that of minimal violent robbery, assault and rape.
The correlations between the seasonality of haze and that of both MVR and rape are negative.
Daily variations of temperature and rainfall are positively associated with those of crime rates.
Authors: Bing Shena, Xiaofeng Hu, Huanggang Wu
Date of publication: 10 JULY 2020
Summary
Previous studies suggest that climate variability and change impact both violent and property crimes. To investigate the mechanism, time series of crime rates (crime incidents per million people) and climate variables (temperature, relative humidity, rainfall, wind speed and haze) are decomposed into three components– long-term trends, seasonality and daily variations (or “noise”). Based on a 12-year dataset of daily crime (robbery, minimal violent robbery (MVR), assault, rape and homicide) numbers in Beijing, China, the correlation between climate variability and crime rate is examined for each component. The results show that in terms of seasonality, strong positive relations are observed for temperature-to-MVR, temperature-to-assault, temperature-to-rape, and relative-humidity-to-MVR but negative relations for haze-to-MVR, haze-to-assault, and haze-to-rape, which can be explained by Routine Activity Theory. In terms of daily variations, temperature, rainfall and haze are key factors. The correlations are all positive for temperature-to-assault, temperature-to-rape, temperature-to-homicide, rainfall-to-MVR, rainfall-to-robbery and rainfall-to-homicide. However, the relations between haze and crimes are more complicated. Specifically, the correlations are negative for haze-to-MVR and haze-to-robbery, but positive for haze-to-homicide, which can be interpreted by Crime Pattern Theory and also influenced by offenders' bounded rationality.
Keywords: Climate variations, Crime rates, Long-term trends, Seasonality, Daily variations
Research #4
VARENN: graphical representation of periodic data and application to climate studies
Highlights:
Convolutional neural networks (CNNs) is a powerful tool in computer vision for object identification and classification in various fields.
VARENN (visually augmented representation of environment for neural networks) was employed to efficiently summarize monthly observations of climate data.
Similarities between the input and target variables were observed to have an effect on model accuracy
Authors: Takeshi Ise & Yurika Oba
Date of publication: 06 JULY 2020
Summary
Analyzing and utilizing spatiotemporal big data are essential for studies concerning climate change. However, such data are not fully integrated into climate models owing to limitations in statistical frameworks. Herein, we employ VARENN (visually augmented representation of environment for neural networks) to efficiently summarize monthly observations of climate data for 1901–2016 into two-dimensional graphical images. Using red, green, and blue channels of color images, three different variables are simultaneously represented in a single image. For global datasets, models were trained via convolutional neural networks. These models successfully classified the rises and falls in temperature and precipitation. Moreover, similarities between the input and target variables were observed to have a significant effect on model accuracy. The input variables had both seasonal and interannual variations, whose importance was quantified for model efficacy. We successfully illustrated the importance of short-term (monthly) fluctuations in the model accuracy, suggesting that our AI-based approach grasped some previously unknown patterns that are indicators of succeeding climate trends. VARENN is thus an effective method to summarize spatiotemporal data objectively and accurately.
Keywords: VARENN, Climate change
Research #5
Climate change impact on nutrient loading in a water supply watershed
Highlights
The SWAT-HS model was used to simulate stream water quality.
Future scenarios of hydrology and water quality are presented using 20 GCMs.
Large number of future scenarios capture a wide range of possible future climate.
Nutrient loading is influenced by large storms and a seasonal shift in streamflow.
Authors: Rajith Mukundan, Linh Hoang, Rakesh K Gelda, Myeong-Ho Yeo, Emmet M Owen
Date of publication: JULY 2020
Summary
In this study we investigated the impact of climate change on nutrient loading in the Cannonsville Reservoir watershed of the New York City (NYC) water supply system where management practices have reduced nutrient inputs in the last 25 years. A modified version of the SWAT hydrological and water quality model (SWAT-HS) that had been previously tested and verified for streamflow and phosphorus for this watershed was calibrated for nitrate to estimate contributions from point and nonpoint sources. Model simulations show that forests that occupy 64% of the watershed area contribute the greatest proportion of nitrate at 39%, while pastures that are in close proximity to streams and runoff generating areas contribute the greatest proportion of total nitrogen at 34%. Point sources contribute less than 5% of the annual nitrate load. Stream export accounts for only about 23% of the annual total N input to the watershed from anthropogenic sources, suggesting significant storage or loss from the landscape. We assumed stationary land use and management practices to assess the change in nutrient loading from baseline (2001–2010) to middle of the century (2051–2060) period due to a change in climate. Results indicated no change to moderate increase in the annual loading of dissolved forms of nutrients (N and P) whereas particulate forms of nutrients and sediment loadings are projected to increase due to an increase in the frequency and magnitude of large storm events. A seasonal shift in streamflow due to warmer winter temperatures, greater amounts of precipitation falling as rain, and earlier melting of snowpack may play an important role in controlling the seasonal pattern of nutrient loading. The methodology used in this study can be adapted in other watersheds to estimate the relative importance and partition contributions from various nonpoint sources to water quality, and to investigate the impacts of climate change.
Keywords: Nutrients, Sediment, Nonpoint source pollution, Climate change, Large storms