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Global Future Population Exposure to Heatwaves

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SSRN 2023年

作者： Wang, Yuwei Zhao, Na Yin, Xiaozhe Wu, Chaoyang Chen, Mingxing Jiao, Yimeng Yue, Tianxiang State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100101 China Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application Nanjing210023 China University of Southern California United States Key Laboratory of Land Surface Pattern and Simulation Institute Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Key Laboratory of Regional Sustainable Development Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China

The increasing exposure to extreme heatwaves in urban areas from both climate change and the urban heat island (UHI) effect is posing multiple threats and challenges to human society. Despite a growing number of studies focusing on extreme exposure, research advances are still limited in some aspects like oversimplifies of some inter-individual factors of human exposure, resulting in unreliable and unrealistic estimates of future results. In addition, little research performed comprehensive and fine-resolution global analysis in future scenarios. In this study, we present the first global fine-resolution projection of future changing urban population exposure to heatwaves by 2100 under four shared socioeconomic pathways (SSPs) by considering the urban development at global, regional, and national scales. Overall, global urban population exposure to heatwaves is rising under the four SSPs. Temperate and tropical zones predictably have the greatest exposure among all climate zones. Coastal cities are projected to have the greatest exposure, followed closely by cities at low altitudes. Middle-income countries have the lowest exposure and the lowest inequality of exposure among countries. Individual climate effects contributed the most (about 46.4%) to future changes in exposure, followed by the interactive effect between climate and urbanization (about 18.5%). Our results indicate that more attention needs to be paid to policy improvements and sustainable development planning of global coastal cities and some low-altitude cities, especially in low- and high-income countries. Meanwhile, this study also highlights the impact of continued future urban expansion on population exposure to heatwaves. © 2023, The Authors. All rights reserved.

关键词： Climate change

Mapping Human Appropriation of Net Primary Production in Agroecosystem in the Heihe River Basin, China

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SSRN 2021年

作者： Liu, Yaqun Song, Wei Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Key Laboratory of Regional Sustainable Development Modeling Chinese Academy of Sciences Beijing100101 China Key Laboratory of Land Surface Pattern and Simulation Chinese Academy of Sciences Beijing100101 China

The Human Appropriation of Net Primary Production (HANPP) of agroecosystem is related to food security and the sustainable use of cropland, and affects biogeochemical processes such as carbon cycle and energy flow. However, current agroecosystem management lacks the support of high-resolution crop-type-specific HANPP estimation. To this end, this study integrated multi-source data of crop type, the Normalized Difference Vegetation Index (NDVI) time-series, irrigation and climate, and multiple methods of the Miami model, the Carnegie-Ames-Stanford Approach (CASA) model and process parameters to map the 30 m resolution spatial distribution of agroecosystem HANPP in the Heihe River Basin (HRB) in 2007 and 2012. We then analyzed the influences of climate, irrigation and planting structure changes on HANPP. The results showed that the average HANPP in the HRB decreased from 762.4 gC/m2 to 712.1 gC/m2 from 2007 to 2012, with a decrease of 6.6%. The HANPP of wheat, barley and oilseed rape decreased by more than -10.0%, while the decrease of corn was only -3.1%. The ratio of HANPP to potential NPP (NPPpot) had dropped from 82.7% to 81.4% in the HRB. The ratio of the land-use-change-induced HANPP (HANPPLUC) to HANPP had dropped from 61.9% to 58.5%, while the ratio of crop-harvest-induced HANPP (HANPPharv) to HANPP had increased from 38.1% to 41.5%. These changes indicated that production capacity has increased, while NPP losses have been reduced in the HRB agroecosystem. The crop type conversion contributed 84.7% of the HANPP change, with a contribution value of -93.6×109 gC. HANPP was positively correlated with both temperature, and the sum of precipitation and irrigation. The crop water consumption of HRB far exceeded the actual water demand with the low irrigation efficiency, resulting in the highest HANPP consuming about 1000 mm of water. Reducing HANPP and water consumption while increasing crop production were the key to food security and sustainable agroecosystem deve

关键词： Climate change

Chinese public participation monitoring and warning system for geological hazards

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Journal of Mountain Science 2020年第7期17卷 1553-1564页

作者： WU Sheng-nan LEI Yu CUI Peng CHEN Rong YIN Pi-hua Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing10010China Key Laboratory of Mountain Hazards and Earth Surface Processes Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu610041China University of Chinese Academy of Sciences Beijing100049China CAS Center for Excellence in Tibetan Plateau Earth Sciences Beijing100101China Liangshan Prefecture Geological Environmental Center Liangshan615000China Key Laboratory of Environmental Change and Natural Disaster Ministry of EducationBeijing Normal UniversityBeijing100875China Faculty of Geographical Science Academy of Disaster Reduction and Emergency ManagementBeijing Normal UniversityBeijing100875China

In China,many geological hazards occurred in remote mountainous regions,and it was time-consuming to disseminate disaster information for the responsible parties to make timely ***,only relying on professionals to monitor and manage disasters was demanding and *** Chinese government created a system to engage residents in the process of disaster risk management,namely’Public Participation Monitoring and Warning’(PPMW),to disseminate timely disaster information and bring down management *** objective of this system was to reduce casualties with minimum cost by organizing residents to evacuate from disasters in *** paper introduced the PPMW system,including its structure,operation mechanism by reviewing government documents and research articles,and its implementation by a case study of a landslide at Boli village(E 101°01’,N 27°29’),Yanyuan County,Sichuan Province,China on July 19th ***,this paper analyzed the strengths and limitations of the system and discussed its future *** had the potential to become an affordable disaster risk management tool for other countries facing similar situations to China.

关键词： Geological hazard Monitoring and early warning Disaster risk management Public participation Affordable solution

Fine-Scale Population Mapping on Tibetan Plateau Using the Ensemble Machine Learning Methods and Multisource Data

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SSRN 2024年

作者： Zhang, Huiming Fu, Jingqiao Li, Feixiang Chen, Qian Ye, Tao Zhang, Yili Yang, Xuchao Ocean College Zhejiang University Zhoushan316021 China Beijing Normal University Beijing100875 China Faculty of Geographical Science Beijing Normal University Beijing100875 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China CAS Center for Excellence in Tibetan Plateau Earth Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100049 China Department of Big Data in Health Science

The Tibetan Plateau, characterized by its high elevation and sparse population distribution, relies significantly on gridded population data to bolster disaster prevention and management strategies. This study focuses on utilizing multi-source physical geographic and socio-economic factors to delineate the population distribution across the plateau. Leveraging data from the seventh National Census in 2020, we employed three individual machine learning methods (random forest, GBDT, and XGBoost) and two multi-model ensemble methods (weighted average ensemble and stacking ensemble) to spatialize the population data into a 100-meter grid. The findings revealed that the spatialization results of all models surpassed the accuracy of the WorldPop dataset. Specifically, the Random Forest model (RMSE = 4061.09, nRMSE = 44.71%) and stacking ensemble model (RMSE = 4094.47, nRMSE = 44.26%) demonstrated the highest accuracy among individual and ensemble models, respectively. Highlighting the importance of multi-source big data integration, Tencent location-based services data emerged as a crucial variable across all models. This study underscores the efficacy of ensemble models and multi-source big data in enhancing population mapping accuracy, particularly in regions characterized by complex terrains. © 2024, The Authors. All rights reserved.

关键词： Location based services

Climatic and Biotic Controls of Evapotranspiration Across Grassland Ecosystems on Tibetan Plateau

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SSRN 2024年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Hainan Baoting Tropical Rainforest Ecosystem Observation and Research Station School of Ecology Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu 210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu 210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Comprehending the spatial variations of evapotranspiration (ET) and its components on Tibetan Plateau (TP) is crucial for grasping the dynamics of the regional water cycle and energy balance. However, the influence of various environmental factors on ET and its components remains unclear. This study addresses this gap by utilizing observational data from 14 sites and employing the Shuttleworth-Wallace-Hu (SWH) model to examine the spatial relationships between ET, its components, and influencing factors. The SWH model parameters were optimized using the Monte Carlo method, achieving a satisfactory simulation performance with an average R2 value of 0.75. Our analysis indicates that in alpine meadow, the ratio of transpiration to ET is generally higher (0.55) compared to alpine steppe (0.39). In terms of spatial distribution, both precipitation and leaf area index significantly impact ET and transpiration. Precipitation emerges as the primary driver of interannual spatial variations in ET, while leaf area index plays a pivotal role in regulating proportion of transpiration. Furthermore, our results reveal a transition from water-dominated to energy-dominated in TP grassland ecosystem at an aridity index of 0.73. This finding is significant as it challenges the common application of an aridity index of 0.65 to distinguish arid from humid regions. It suggests that TP grassland, within the 0.65-0.73 range, remain primarily water-driven. These findings enhance our comprehension of the intricate interactions within the water cycle. © 2024, The Authors. All rights reserved.

关键词： Evapotranspiration

Representing Model Uncertainty by Multi-Stochastic Physics Approaches in the GRAPES Ensemble

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Advances in Atmospheric Sciences 2020年第4期37卷 328-346页

作者： Zhizhen XU Jing CHEN Zheng JIN Hongqi LI Fajing CHEN Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences Fudan UniversityShanghai 200438China Chinese Academy of Meteorological Sciences China Meteorological AdministrationBeijing 100081China Numerical Weather Prediction Center China Meteorological AdministrationBeijing 100081China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China

To represent model uncertainties more comprehensively,a stochastically perturbed parameterization(SPP)scheme consisting of temporally and spatially varying perturbations of 18 parameters in the microphysics,convection,boundary layer,and surface layer parameterization schemes,as well as the stochastically perturbed parameterization tendencies(SPPT)scheme,and the stochastic kinetic energy backscatter(SKEB)scheme,is applied in the Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System(GRAPES-REPS)to evaluate and compare the general performance of various combinations of multiple stochastic physics *** experiments are performed for a summer month(1-30 June 2015)over China and multiple verification metrics are *** results show that:(1)All stochastic experiments outperform the control(CTL)experiment,and all combinations of stochastic parameterization schemes perform better than the single SPP scheme,indicating that stochastic methods can effectively improve the forecast skill,and combinations of multiple stochastic parameterization schemes can better represent model uncertainties;(2)The combination of all three stochastic physics schemes(SPP,SPPT,and SKEB)outperforms any other combination of two schemes in precipitation forecasting and surface and upper-air verification to better represent the model uncertainties and improve the forecast skill;(3)Combining SKEB with SPP and/or SPPT results in a notable increase in the spread and reduction in outliers for the upper-air wind *** directly perturbs the wind field and therefore its addition will greatly impact the upper-air wind-speed fields,and it contributes most to the improvement in spread and outliers for wind;(4)The introduction of SPP has a positive added value,and does not lead to large changes in the evolution of the kinetic energy(KE)spectrum at any wavelength;(5)The introduction of SPPT and SKEB would cause a 5%-10%and 30%-80%change in the KE of mesosc

关键词： ensemble prediction model uncertainty stochastically perturbed parameterization multi-stochastic physics approaches

Climatic and Biotic Controls of Evapotranspiration Across Grassland Ecosystems on the Tibetan Plateau

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SSRN 2024年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Hainan Baoting Tropical Rainforest Ecosystem Observation and Research Station School of Ecology Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Study region: The Tibetan Plateau in China, the Third Pole of the *** focus: Accurate descriptions of spatial variation in evapotranspiration (ET) and its components on the Tibetan Plateau (TP) are crucial for understanding the dynamics of the regional water cycle and energy balance. However, how various environmental factors affect ET and its components remains unclear. This study addresses this gap using observational data from 14 sites and employing the Shuttleworth-Wallace-Hu (SWH) model to examine the spatial relationships between ET, its components, and underlying *** hydrological insights for the region: The SWH model parameters were optimized using the Monte Carlo method, achieving a satisfactory model performance with an average R² of 0.75. The ratio of transpiration to ET was generally higher in alpine meadows (0.55) than alpine steppes (0.39). Across the TP, precipitation and the leaf area index together shaped spatial variation in ET and transpiration. Precipitation emerged as the primary driver of interannual variation in ET, while the leaf area index strongly regulated the proportion of ET accounted for by transpiration. Furthermore, above aridity index values of 0.73, grassland ecosystems transitioned from water to energy. This finding is significant as it challenges the common application of an aridity index value of 0.65 to distinguish arid from humid regions. It suggests that TP grasslands, with an AI of 0.65-0.73, remain primarily water-limited. © 2024, The Authors. All rights reserved.

关键词： Biotic

Climatic and Biotic Controls of Evapotranspiration Across Grassland Ecosystems on Tibetan Plateau

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SSRN 2024年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Hainan Baoting Tropical Rainforest Ecosystem Observation and Research Station Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation School of Ecology and Environment Hainan University Haikou570228 China Center for Eco-Environment Restoration Engineering of Hainan Province Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Understanding the spatial variations of evapotranspiration (ET) and its components on Tibetan Plateau (TP) is pivotal for grasping the regional water cycle and energy balance dynamics. Yet it is unclear how the changes of the intricate synergy among diverse environment factors significantly impacts the patterns of ET and its transformation state. Addressing this gap, this study employed observational data from 14 sites, utilizing the Shuttleworth-Wallace-Hu (SWH) model to investigate the spatial relationships between ET, its components, and influencing factors. Model parameters of SWH were optimized by Monte Carlo method, achieving a commendable simulation performance with a mean R2 value of 0.75. Our analysis reveals that in alpine meadow, the ratio of transpiration (T) to ET (T/ET) is generally higher (0.55) compared to alpine steppe (0.39). The T/ET values for hover around or below 0.5, indicating a balanced contribution of various ET components. Spatially, both PPT and LAI have an impact on ET and T/ET. PPT emerges as the primary driver of interannual spatial variations in ET, while LAI plays a crucial role in regulating T/ET. Our results further demonstrate that a transition from water-dominated to energy-dominated systems in TP grassland ecosystem at an approximately aridity index of 0.73. This is noteworthy considering the general application of an aridity index of 0.65 to delineate arid from humid regions. It implies that TP grasslands, within the 0.65-0.73 range, remain primarily water-driven. This insight holds substantial implications for comprehending water-energy dynamics in similar ecosystems, challenging the conventional binary categorization of arid and humid areas. Such findings are of immense significance in enhancing understanding of the intricate interactions of the water cycle between land and the atmosphere. © 2024, The Authors. All rights reserved.

关键词： Water supply

Crop Yield Forecasting by Integrating Data Assimilation, Crop Model, and Machine Learning

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SSRN 2023年

作者： Zhuang, Huimin Zhang, Zhao Cheng, Fei Han, Jichong Luo, Yuchuan Zhang, Liangliang Cao, Juan Zhang, Jing He, Bangke Xu, Jialu Tao, Fulu Academy of Disaster Reduction and Emergency Management Minsitry of Emergency Management Ministry of Education School of National Safety and Emergency Management Beijing Normal University Beijing China State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University Aerospace Information Research Institute Chinese Academy of Sciences Faculty of Geographical Science Beijing Normal University Beijing100875 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China College of Resources and Environment University of Chinese Academy of Sciences Beijing China Helsinki Finland

Timely and reliable regional crop yield forecasting before harvest is critical for managing climate risk, adjusting agronomic management, and making food trade policy. Although various methods exist for crop yield forecasting, including process-based crop models and machine learning techniques, the potential of integrating these methods for early-season yield forecasts has not been well investigated. In this study, we proposed a hybrid framework for crop yield forecasting that firstly assimilated remote sensing leaf area index and soil moisture into a crop model and then combined the data-assimilated crop model with machine learning techniques to improve the prediction skill further. The proposed framework was demonstrated in winter wheat yield forecasting in the North China Plain during 2009–2015. Results showed that assimilating multi-source data into crop model improved wheat yield predictions significantly, with the anomaly correlation coefficient (ACC) increasing by 0.27 and the mean absolute percentage error (MAPE) decreasing by 6.12% compared to unassimilated results. Incorporating weather forecasts enabled reliable winter wheat yield forecasts up to 1–3 months in advance (median ACC > 0.79, MAPE © 2023, The Authors. All rights reserved.

关键词： Soil moisture