Compiling and Mapping an International Climate Change Database: Worlds of Gaps and Unevenness

Daily and weekly reporting events of climate change and impacts on populations, cultures, economies and politics at local, national, regional and international scales suggest the need to construct databases that will be useful in future scientific inquiry and global human/environmental policies. That need is evident in constructing a geographic or locational knowledge base that examines countries, regions and cities. This study constructs a database on the impacts of climate change using Google Scholar entries for 200 countries and capital cities. A series of maps reveal the vast unevenness in the database, especially between Global North and Global South countries. The discussion explores these sharp differences and suggests future research topics for much-needed global, interdisciplinary and international research.

Assessment of drought hazard,vulnerability and risk in Iran using GIS techniques

The drought has enormous adverse effects on agriculture,water resources and environment,and causes damages around the world.Drought risk assessment and prioritization of drought management can help decision makers and planners to manage the adverse effects of drought.This paper aims to determine the risk of drought in Iran.At the first stage,standardized precipitation index(SPI)was calculated for the period 1981–2016.Then the probability map of different drought classes or drought hazard probability map were prepared.After that the indicator-based vulnerability assessment method was used to determine the drought vulnerability index.Five indices including climate,topography,waterway density,land use and groundwater resources were chosen as the most critical factors of drought in Iran and followed by the analytical hierarchy process questionnaire,the weights of each index were obtained based on expert opinions.Fuzzy membership maps of each index and sub-index were prepared using ArcGIS software.The drought vulnerability map of Iran was plotted using these weights and maps of each indicator.Finally,the drought risk map of Iran was provided by multiplying drought hazard and vulnerability maps.According to the 43-completed questionnaires by experts,climate index has the highest vulnerability to drought.Climate does not have an important role in drought hazard index,but it is the most crucial factor to classified drought vulnerability index.The results showed that central,northeast,southeast and west parts of Iran are at high risks of drought.There are regions with different risks in Iran due to unusual weather and climatic conditions.We realized that the climate and the groundwater situation is almost the same in the central,east and south parts of Iran,because the land use plays a crucial role in the drought vulnerability and risk in these areas.The drought risk decreases from the center of Iran to the southwest and northwest.

Coping with Multi-scale Climate Change:Study on the Application of Urban Environmental Climate Maps of Stuttgart,Germany

The development of modern cities has brought about tremendous changes in the climate environment.Faced with complex climate conditions,research on multi-scale climate change in cities is of great significance.The urban environmental climate maps and the application of climate atlas tool in Stuttgart,Germany were studied,and the multi-scale application of urban environmental climate maps in Stuttgart,Germany was summarized through the analysis of the pre-planning,current construction situation,and landscape reconstruction of the German"Stuttgart 21"plan case.Besides,other important measures to cope with climate change in German were proposed,and finally multi-scale practical strategies to cope with urban climate and environment were summarized to provide ideas and methods for improving China’s future urban climate environment.

Generating high-resolution climate maps from sparse and irregular observations using a novel hybrid RBF network

Sparse and irregular climate observations in many developing countries are not enough to satisfy the need of assessing climate change risks and planning suitable mitigation strategies.The wideused statistical downscaling model(SDSM)software tools use multi-linear regression to extract linear relations between largescale and local climate variables and then produce high-resolution climate maps from sparse climate observations.The latest machine learning techniques(e.g.SRCNN,SRGAN)can extract nonlinear links,but they are only suitable for downscaling low-resolution grid data and cannot utilize the link to other climate variables to improve the downscaling performance.In this study,we proposed a novel hybrid RBF(Radial Basis Function)network by embedding several RBF networks into new RBF networks.Our model can well incorporate climate and topographical variables with different resolutions and extract their nonlinear relations for spatial downscaling.To test the performance of our model,we generated high-resolution precipitation,air temperature and humidity maps from 34 meteorological stations in Bangladesh.In terms of three statistical indicators,the accuracy of high-resolution climate maps generated by our hybrid RBF network clearly outperformed those using a multi-linear regression(MLR),Kriging interpolation or a pure RBF network.

Assessment of Urban Climate Environment and Configuration of Ventilation Corridor:A Refined Study in Xi'an

Integrating urban spatial landscape(USL) parameters into refined climate environment assessment is important. By taking the central urban area(CUA) of Xi’an, China as an example, this study develops an evaluation method based on Urban Climatic Map(UCMap) technology. We define surface urban heat island intensity(SUHI) and surface ventilation potential coefficient(VPC), which can effectively reflect local urban climate. Based on SUHI and VPC,we analyze the influences of seven typical USL metrics including building height(BH), building density(BD), floor area ratio(FAR), sky view factor(SVF), frontal area index(FAI), surface roughness length(RL), and vegetation cover(VC). Then, we construct a comprehensive evaluation model and create an urban climate zoning map on a 100-m resolution. The climate optimization on the map is performed for configuration of possible ventilation corridors and identification of associated control indicators. The results show that the main factors affecting SUHI in the CUA of Xi’an are VC and BD, which explain 87.9% of the variation in SUHI, while VPC explains 50% of the variation in SUHI. The main factors affecting VPC are BH, FAR, FAI, and RL, all of which contribute to more than 95% of the variation in VPC. The evaluation model constructed by SUHI, VPC, and VC can divide the CUA into climate resource spaces, climate preservation spaces, climate sensitive spaces, and climate restoration spaces. On this basis, a ventilation corridor network of 3 level-1 corridors(each over 500 m wide), 6 level-2 corridors(each over 500 m wide) and 13 level-3 corridors(each over 50 m wide) is established. Meanwhile, the main quantitative control indicators selected from the USL metrics are proved to be capable of ensuring smooth implementation of the planned corridors at different levels.