姓名职称：张 朝 教授

联系方式：zhangzhao@bnu.edu.cn；

研究方向：国家粮食安全；综合灾害风险评估；资源环境与公共健康

教育经历：

2004.04-2007.03 博士，筑波大学生命环境科学院，生命共存科学

1995.09-1998.07 硕士，中国科学院生态环境研究中心，环境化学

1991.09-1995.07 学士，武汉大学环境科学系，环境生物与生态学

工作经历：

2008.04- 北京师范大学

2007.04-2008.03  筑波大学地理科学系，博士后

1998.08-2004.03 北京市自来水集团公司水质监测中心，工程师

承担课程：

研究生授课：《资源安全评估与资源战略》；《学术伦理和道德》；《农业灾害与粮食安全》；《专业英语写作与实践》；《安全统计学》等

本科生授课：《国家安全导论》(资源安全)；《环境风险与人类社会》等

科研项目：

【1】“气候变化背景下东南亚水稻灾害发生机理、灾害风险和农民生计评估与防灾减灾策略研究” 国际合作，349.2万，2021-2025，主持；

【2】“未来气候极端事件多尺度模拟与危险性评估” 国家重点研发计划子课题“未来气候极端事件对我国农作物生产的影响评估”，36万，2020-2025，子课题主持；

【3】“基于作物模型模拟和多源数据同化技术的区域农业灾害风险研究—以我国北方小麦干旱为例”，面上基金，72.8万，2020-2023，主持；

【4】“自然灾害农作物综合风险评估”财政部专项，2021.01-2021.12，48万，子任务主持

【5】“玉米生产系统对气候变化的响应机制及其适应性栽培途径” 国家重点研发计划子课题“关键气候因子的时空变化规律及其对玉米生产系统影响研究”，78万, 2017-2020；

【6】“多尺度、多灾种和多过程下华北平原冬小麦灾害损失风险评估”，面上基金，74.4万，2016-2019，主持；

【7】“区域农业多灾种辨识、影响评估与风险防范”，地表过程与资源生态国家重点实验室基金，80万，2017-2019，主持。

学术兼职：

《Agricultural  Systems  editor》，《中国农业气象》《灾害学》编委等

奖励荣誉：  

【1】中国气象服务协会科学技术奖二等奖（排名第1，2025）

【2】北京师范大学暑假社会实践“先进工作者”（2025）

【3】自然资源科学技术奖二等奖（排名第1，2024）

【4】灾害防御科学技术奖三等奖（排名第1，2024）

【5】空天数据开放共享年度人物提名奖（2024）

【6】空天数据开放共享十大“最优贡献数据团队”（2024）

【7】北京师范大学优秀研究生指导教师（2024）

【8】北京师范大学高等教育教学成果一等奖（排名第1，2021）

【9】北京师范大学优秀新生导师（2018，2020）

【10】北京师范大学优秀博士学位论文指导导师（2017）

【11】北京师范大学优秀本科生毕业论文指导导师（2012）  

编写著作:

【1】《农业灾害与粮食安全：影响评价与适应对策》张朝主编，2024，科学出版社

https://www.ecsponline.com/goods.php?id=226341

【2】《农业灾害与粮食安全：极端温度和水稻生产》张朝主编，2022，科学出版社

https://www.ecsponline.com/goods.php?id=218147

【3】《地理大数据与公共健康：R语言应用实践》张朝主编，2021，科学出版社

https://www.ecsponline.com/goods.php?id=214823

学术链接：

【1】 https://scholar.google.com/citations?user=rMO_bZ0AAAAJ&hl=en

【2】 https://www.researchgate.net/profile/Zhao-Zhang-zhangchao

【3】 https://nesdc.org.cn/otherProject/index?menuId=team&projectId=1319

【4】 https://noda.ac.cn/datasharing/science/scientists/creator/10954

培养学生：

【1】 刘晓菲，2010-2013，硕士，就业：瑞士再保险

【2】 王  品，2011-2016，直博，就业：杭州师范大学

【3】 陈  一，2012-2017，直博，就业：中科院地理所

【4】 魏  星，2013-2016，硕士，留学：伦敦政经

【5】 冯博彦，2014-2017，硕士，就业：中国再保险

【6】 王琛智，2015-2018，硕士，读博：北京大学

【7】 张  静，2015-2023，直博&国家创新博后，广东地理所

【8】 张领雁，2016-2019，硕士，就业：寒武纪科技

【9】 张亮亮，2017-2022，直博，就业：北京大学博士后

【10】 曹  娟，2017-2023，硕士&博士，中科院地理所博士后

【11】 骆玉川，2018-2023，直博，就业：西南大学特聘副教授

【12】 李子悦，2018-2021，硕士，就业：中国再保险

【13】 韩继冲，2019-2025，硕士&博士，就业：成都理工大学特聘研究员

【14】 吴华清，2021-2024，硕士，读博：厦门大学

【15】 程  飞，2020-2025，直博，就业：黄河水利科学研究院

【16】 宋  杰，2022-2025，硕士，就业：江苏省公务员

【17】 在读博士：庄慧敏，韩书阳，李少坤，梅晴航，陶莹，陈典鹏

【18】 在读硕士：吴欣宇，李思畅，陈怡忻

发表论文：

【一、农业灾害风险】

【1】 Han J. Zhang Z*., Xu J, et al., Threat of low-frequency high-intensity floods to global cropland and crop yields 2024, Nature Sustainability 7：994–1006. https://doi.org/10.1038/s41893-024-01375-x 

【2】 Zhang Z.L., Xu J.L., …Zhang Z., Decadal changes in wind speed have offset and then aggravated the impact of warming on maize production in China since 1980, Nature Communications, 2025, 16: 9739.

【3】 Cao J., Zhang Z*., et al., Forecasting global crop yields based on El Nino Southern Oscillation early signals, Agricultural Systems 2023, 205 103564. https://doi.org/10.1016/j.agsy.2022.103564

【4】 Liu, K., Harrison, M.T., Zhang, Z., ... Silver lining to a climate crisis in multiple prospects for alleviating crop waterlogging under future climates. Nature Communications, 14, 765 (2023). https://doi.org/10.1038/s41467-023-36129-4

【5】 Zhang J., Zhang Z*., Wang C. H., et al, Weather index insurance can offset heat-induced rice losses under global warming, Earth Future, 2022, 10, e2021EF002534. https://doi.org/10.1029/2021EF002534

【6】 Li Z.Y., Zhang Z*., Zhang L.L., Improving regional wheat drought risk assessment for insurance application by integrating scenario-driven crop model machine learning and satellite data, Agricultural Systems, 2021, 191, 103141. https://doi.org/10.1016/j.agsy.2021.103141

【7】 Li Z.Y., Zhang Z*., Zhang L.Y., zhang J., et al., A new framework to quantify maize production risk from chilling injury in Northeast China, Climate Risk Management, 2021, 32, 100299. https://doi.org/10.1016/j.crm.2021.100299

【8】 Zhang J., Zhang Z*. et al., Rainfall-related weather indices for three main crops in China, International Journal of Disaster Risk Science, 2020 11:466–48

【9】 Cao J., Zhang Z.*, Zhang L.L., et al., Damage evaluation of soybean chilling injury based on Google Earth Engine (GEE) and crop modelling, Journal of Geographical Sciences 2020, 30(8): 1249-1265

【10】 Zhang J. Chen Y., Zhang Z.*, A remote sensing-based scheme to improve regional crop model calibration at sub-model component level, Agricultural Systems 2020 181:102814

【11】 Zhang L.L., Zhang Z.*, Chen Y., et al., Exposure, vulnerability, and adaptation of major maizegrowing areas to extreme temperature, Natural Hazards 2018 91:1257–127

【12】 Chen Y., Zhang Z*., et al., Impacts of heat stress on leaf area index and growth duration of winter wheat in the North China Plain, Field Crop Research, 2018 222: 230-237

【13】 Zhang Z., Chen Y., et al., Future extreme temperature and its impact on rice yield in China，International Journal of Climatology, 2017 37：4814–4827

【14】 Zhang J., Zhang Z.* et al., Performance of Temperature-Related Weather Index for Agricultural Insurance of Three Main Crops in China, International Journal of Disaster Risk Science, 2017, 8: 78-90

【15】 Wang P., Zhang Z*., et al., How much yield loss has been caused by extreme temperature stress to the irrigated rice production in China? Climatic Change 2016: 134(4): 635-650

【16】 Chen Y., Zhang Z*., et al., Identifying the impact of multi-hazards on crop yield — A case for heat stress and dry stress on winter wheat yield in northern China. European Journal of Agronomy, 2016 73:55-63

【17】 Shuai J., Zhang Z., et al., How ENSO affects maize yields in China: understanding the impact mechanisms using a process-based crop model, International Journal of Climatology, 2016，36（1）：424-438

【18】 Wang P. Zhang Z*., et al., 2014. Temperature variations and rice yields in China: historical contributions and future trends. Climatic Change, 124(4):777-789

【19】Zhang Z., Wang P, et al., 2014 Global warming over 1960–2009 did increase heat stress and reduce cold stress in the major rice-planting areas across China European Journal of Agronomy 59 (2014) 49–56.

【20】 Zhang Z*., Chen Y., et al., 2014. Spatial pattern and decadal change of agro-meteorological disasters in main wheat production area of China during1991-2009. Journal of Geographic Sciences 24(3):387-396.

【21】 Zhang Z*., Chen Y., Wang P. et al., 2014. Spatial and temporal changes of agro-meteorological disasters affecting maize production in China since 1990. Natural Hazards71(3):2087-2100.

【22】 Zhang Z.*, Liu X. F., Wang P, et al., 2014. The heat deficit index depicts the responses of rice yield to climate change in the northeastern three provinces of China, Regional Environmental Change 14(1):27-38

【23】 Shuai J.B., Zhang Z.*, Sun D.Z., et al., 2013. ENSO, climate variability and crop yields in China. Climate Research 58:133-148.

【24】 Shuai J.B., Zhang Z.* Liu X.F., et al., 2013. Increasing concentrations of aerosols offset the benefits of climate warming on rice yields during 1980–2008in Jiangsu Province, China Reg Environ Change 13(2):287-297  

【25】 Liu, X., Zhang, Z*., Shuai, J., et al., 2013. Impact of Chilling injury and global warming on rice yield in Heilongjiang province. Journal of Geographical Sciences, 23, 85-97

【26】 张静， 张朝*，张亮亮等基于作物模型与机器学习的水稻障碍型冷害脆弱性研究，中国农业气象，2024,45(9):1053-1066

【27】 张亮亮，张朝*，等 GEE环境下基于遥感和作物模型的低温冷害损失评估—以鄂伦春自治旗玉米为例，遥感学报，2020，24（10）1206-1220.

【28】 曹娟，张朝* 等基于Google Earth Engine（GEE）和作物模型快速评估低温冷害对大豆生产的影响，地理学报 2020 75(9):1879-1892

【29】 张亮亮，张朝*，等．基于CERES-Rice模型的湖南省一季稻极端高温损失评估及适应性措施．生态学报，2019，39(17): 6293-6303.

【30】 张静，张朝*,等中国南方双季稻区天气指数保险的选择分析, 保险研究, 2017(7):13-21.

【31】 王品, 张朝*,等 湖南省暴雨洪涝灾害及其农业灾情评估. 北京师范大学学报 2014 51(1):75-79.

【32】 王品，魏星，张朝* 等气候变化背景下水稻低温冷害和高温热害的研究进展，2014，资源科学36(11):2316-2326.

【33】 魏星，王品，张朝*等温度三区间理论评价气候变化对作物产量的影响，2015，自然资源学报 30（3）：470-479.

【34】 刘晓菲，张朝*,等，黑龙江省冷害对水稻产量的影响，2012, 地理学报67（9）：1223-1232

【35】 张朝，王品，陈一等，2013. 1990年以来中国小麦农业气象灾害时空变化特征地理学报68(11):1453-1460.

【36】 王佳津，孟耀斌，张朝，云南省Palmer旱度模式的建立—2010 年干旱灾害特征分析2012，自然灾害学报，21（1）：190-197

 

【二、粮食安全】

【1】 Wu H.Q., Zhang Z.*, Xu J.L., et al., Food consumption away from home had divergent impacts on diet nutrition quality across urban and rural China, Food security, (2025) 17:41–56  

【2】 Zhuang H.M, Zhang Z.*, et al., 2025, Overcoming wheat yield stagnation in China depends more on cultivar improvements than water and fertilizer management, Field Crop Research 333:110089

【3】 Han J.C., Luo Y.C., Zhang Z., et al., 2025 Planting area and production decreased for winter-triticeae crops but increased for rapeseed in Ukraine with climatic impacts dominating Geography and Sustainability, 6(2):100226

【4】 Cao J. Zhang Z., Luo X.Z., et al, 2025 Mapping global yields of four major crops at 5-minute resolution from 1982 to 2015 using multi-source data and machine learning, Scientific Data, 12:357.

【5】 Mei, Q.H.; Zhang, Z.*; Han, J.C.; et al. Cultivar shifts have offset climate warming impacts on soybean phenology in China since 1981, Agricultural Systems, 2025, 224:104260. https://doi.org/10.1016/j.agsy.2024.104260

【6】 Zhuang H.M., Zhang Z.*, Han J.C., Cheng F., et al. 2024, Stagnating rice yields in China need to be overcome by cultivars and management improvements, Agricultural Systems, 221, 104134. https://doi.org/10.1016/j.agsy.2024.104134

【7】 Zhang Z.*, Luo Y.C., Han J.C., et al., Estimating Global Wheat Yields at 4 km Resolution during 1982–2020 by a Spatiotemporal Transferable Method, Remote Sensing, 2024, 16: 2342. https://doi.org/10.3390/rs16132342

【8】 Zhuang H., Zhang Z*., Cheng F., Han J., et al. 2024. Integrating data assimilation, crop model, and machine learning for winter wheat yield forecasting in the North China Plain, Agricultural and Forest Meteorology, 347, 109909. https://doi.org/10.1016/j.agrformet.2024.109909

【9】 Mei, Q.H.; Zhang, Z.*; Han, J.C.; et al., ChinaSoyArea10m: a dataset of soybean-planting areas with a spatial resolution of 10 m across China from 2017 to 2021, Earth Syst. Sci. Data,16, 3213-3231, 2024. https://doi.org/10.5194/essd-16-3213-2024

【10】 Wu, H., Zhang, J., Zhang, Z.*, et al., 2023. AsiaRiceYield4km: Seasonal Rice Yield in Asia from 1995 to 2015, Earth Syst. Sci. Data 15, 791–808, 2023. https://doi.org/10.5194/essd-15-791-2023

【11】 Cheng, F., Zhang, Z.*, Zhuang, H., et al., ChinaCropSM1 km: a fine 1 km daily soil moisture dataset for dryland wheat and maize across China during 1993–2018, Earth Syst. Sci. Data, 2023 15, 395–409. https://doi.org/10.5194/essd-15-395-2023

【12】 Zhang, J., Wu, H.Q., Zhang, Z.*, et al., 2022. Asian Rice Calendar Dynamics Detected by Remote Sensing and Their Climate Drivers. Remote Sensing, 14, 4189. https://doi.org/10.3390/rs14174189

【13】 Luo Y.C., Zhang Z.*, Zhang L.L., et al., Weakened Maize phenological response to climate warming in China over 1981-2018 due to cultivar shifts, Advances in climate change research 2022, 13(5): 710-720. https://doi.org/10.1016/j.accre.2022.08.007

【14】 Han J.C.，Zhang Z.*, Luo Y.C, et al., (2022) Annual paddy rice planting area and cropping intensity datasets and their dynamics in the Asian monsoon region from 2000 to 2020, Agricultural Systems, 2022: 200, 103437. https://doi.org/10.1016/j.agsy.2022.103437

【15】 Zhang L.L., Zhang Z.*, Zhang J., et. al., Response of rice phenology to climate warming weakened across China during 1981–2018: did climatic or anthropogenic factors play a role? Environmental Research Letters,2022: 17, 064029. DOI 10.1088/1748-9326/ac6dfb

【16】 Zhang, L.L., Zhang, Z.*, Tao, F.L., Luo, Y.C., Zhang, J., & Cao, J. (2022). Adapting to climate change precisely through cultivars renewal for rice production across China: When, where, and what cultivars will be required? Agricultural and Forest Meteorology, 316, 108856. https://doi.org/10.1016/j.agrformet.2022.108856

【17】 Luo Y.C., Zhang Z.*, Cao J. et al., Accurately mapping global wheat production system using deep learning algorithms, International Journal of Applied Earth Observations and Geoinformation, 2022: 110, 102823. https://doi.org/10.1016/j.jag.2022.102823

【18】 Luo Y.C., Zhang Z.*, Zhang L.L., et al. Developing High-Resolution Crop Maps for Major Crops in the European Union Based on Transductive Transfer Learning and Limited Ground Data, Remote Sensing, 2022, 14:1809. https://doi.org/10.3390/rs14081809

【19】 Han J.C., Zhang Z.*, Luo Y.C., et al., NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019，Earth Syst. Sci. Data, 2021, 13, 5969–5986. https://doi.org/10.5194/essd-13-5969-2021

【20】 Luo Y.C., Zhang Z.*, Zhang L.L., et al., Spatiotemporal patterns of winter wheat phenology and its climatic drivers based on an improved pDSSAT model, Science China Earth Sciences, 2021, 12, 2144-2160. https://doi.org/10.1007/s11430-020-9821-0

【21】 Zhang L.L., Zhang Z.*, et. al., Planning maize hybrids adaptation to future climate change by integrating crop modelling with machine learning, Environmental Research Letters, 2021, 16:124043. DOI 10.1088/1748-9326/ac32fd

【22】 Han J.C., Zhang Z.*, Luo Y.C., et al., The RapeseedMap10 database annual maps of rapeseed at a spatial resolution of 10m based on multi-source data, Earth Syst. Sci. Data, , 2021, 13, 2857–2874. https://doi.org/10.5194/essd-13-2857-2021

【23】 Zhang L.L., Zhang Z.*, Luo Y.C., et al., Integrating satellite-derived climatic and vegetation indices to predict smallholder maize yield using deep learning, Agricultural and Forest Meteorology 2021, 311:108666. https://doi.org/10.1016/j.agrformet.2021.108666

【24】 Han J.C., Zhang Z.*, Cao J., et al., Developing a New Method to Identify Flowering Dynamics of Rapeseed Using Landsat 8 and Sentinel-1/2, Remote Sensing, 2021, 13, 105. https://doi.org/10.3390/rs13010105

【25】 Cao J., Zhang Z.*, et al., Integrating multi-source data for rice yield prediction across China using machine learning and deep learning approaches, Agricultural and Forest Meteorology 2021, 297: 108275. https://doi.org/10.1016/ j.agrformet.2020.108275

【26】 Cao J., Zhang Z.*, Luo Y.C., et al., Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine, European Journal of Agronomy, 2021, 123,126204. https://doi.org/10.1016/j.eja.2020.126204

【27】 Luo Y.C., Zhang Z.*, Li Z.Y. et al., Identifying the spatiotemporal changes of annual harvesting areas for three staple crops in China by integrating multi-data sources Environmental Research Letters 2020, 15, 074003 (39). DOI 10.1088/1748-9326/ab80f0

【28】 Zhang L.L., Zhang Z., Cao J., et al., Optimizing genotype-environment-management interactions to ensure silage maize production in the Chinese Maize Belt, Climate Research, 2020 80:133–146. https://www.jstor.org/stable/26926484

【29】 Zhang Z., Li Z.Y., et al.. Improving regional wheat yields estimations by multi-step-assimilating of a crop model with multi-source data. Agricultural and Forest Meteorology, Agricultural and Forest Meteorology 2020, 290:107993

【30】 Luo Y.C., Zhang Z.*, et al. ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products, Earth System Science Data, 2020, 12, 197–214

【31】 Luo Y.C., Zhang Z.*, et al.. Drivers of planting area and yield shifts for three staple crops across China, 1950−2013 Climate Research,2020 80:73–84

【32】 Zhang L.L., Zhang Z.*, et al., Optimizing genotype-environment-management interactions for maize farmers to adapt to climate change in different agro-ecological zones across China, Science of the Total Environment 2020 728:138614

【33】 Luo Y.C., Zhang Z.*, et al. Identifying the spatiotemporal changes of annual harvesting areas for three staple crops in China by integrating multi-sources data, Environment Research Letters 2020 15, 074003

【34】 Zhang L.L., Zhang Z.*, Cao J., et al., Optimizing genotype-environment-management interactions to ensure silage maize production in the Chinese Maize Belt, Climate Research, 2020 80:133–146

【35】 Zhang L.L., Zhang Z.*, et al., Combining optical fluorescence thermal satellite and environmental data to predict county-level maize yield in China using machine learning approaches, Remote Sensing, 2020,12,21

【36】 Han J.C., Zhang Z*, Cao J. et al., Prediction of winter wheat yield based on multi-source data and machine learning in China Remote Sensing, 202012, 236

【37】 Cao J., Zhang Z.*, et al. Identifying the contributions of Multi-Source Data for Winter Wheat Yield Prediction in China, Remote Sensing,2020 12,750

【38】 Zhang J., Zhang Z.*, et al., Double-rice system simulation in a topographically diverse region—a remote-sensing-driven case study in Hunan Province of China, Remote Sensing, 2019 11 I577

【39】 Wang C.Z., Zhang Z.*, Zhang J., et al., The effect of terrain factors on rice production—a case study in Hunan Province. Journal of Geographical Science, 2019, 29(2): 287-305

【40】 Wang C.Z., Zhang Z.*, Chen Y., et al., Comparing different smoothing methods to detect double-cropping rice phenology based on LAI product - a case study on double-cropping rice in Hunan Province of China, International Journal of Remote Sensing, 201939(19):6405-6428

【41】 Chen Y., Wang P., Zhang Z.*, et al., Rice yield development and the shrinking yield gaps in China, 1981–2008, Regional Environmental Change, DOI10.1007/s10113-017-1168-7

【42】 Chen Y., Zhang Z.*, et al., Spatio-temporal patterns of winter wheat yield potential and yield gap during the past three decades in North China, Field Crops Research.,2017 206:11-20

【43】 Wei X., Zhang Z*.,et al., Recent patterns of production for the main cereal grains – implications for food security in China, Regional Environmental Change, 2016, doi:10.1007/s10113-016-0977-4

【44】 Zhang Z., Song X., et al., Climate trends and crop production in China at county scale, 1980 to 2008. Theoretical and Applied Climatology. 2016 123(1):291-302DOI 10.1007/s00704-014-1343-4

【45】 Zhang Z*., Feng B.Y., et al., ENSO-Climate Fluctuation-Crop Yield Early Warning System—A Case Study in Jilin and Liaoning Province in Northeast China, Physics and Chemistry of the Earth 2015 87:10-18. DOI: 10.1016/j.pce.2015.09.015

【46】 Wei X., Zhang Z*, 2015. Is yield increase sufficient to achieve food security in China? PlosONE. (2015)10(2):e0116430

【47】 Zhang Z*., Song X., et al. 2015 Dynamic variability of the heading–flowering stages of single rice in China based on field observations and NDVI estimations International Journal of Biometeorology, 2015, 59: 643-655 DOI10.1007/s00484-014-0877-6

【48】 Shi, W., Tao F., Zhang Z.*. 2013. A review on statistical models for identifying climate contributions to crop yields. Journal of Geographical Sciences 23(3):567-576.

【49】 Liu S., Lamberty B., ...Zhang Z., et al., Grand challenges in understanding the interplay of climate and land changes,2017, Earth Interactions 21:1-43.

【50】 吴华清，张朝*，梅晴航等，环境—健康视角下中国城乡膳食结构与数量优化，地理学报，2026, 81

【51】 张朝，蔡宏波，崔雪峰，徐佳路. 国家粮食安全：政治、经济和环境多维探讨，社会治理 ，2025,2:60-72

【52】 梅晴航, 张朝, 骆玉川, 等. 中国三大作物 1 km分辨率种植面积数据集（2009–2015 年）[J/OL]. 中国科学数据, 2023, 8(3). (2023-09-20). DOI: 10.11922/11-6035.csd.2022.0079.zh

【53】 王琛智，张朝*，等.湖南省地形因素对水稻生产的影响, 地理学报, 2018, 73(9):1-17.

【三、资源环境与公共健康】

【1】 Cao J., Zhang Z.*, et al., Multigeohazards susceptibility mapping based on machine learning—a case study in Jiuzhaigou, China, Natural Hazards, 2020, 102:851–871

【2】 Cao J., Zhang Z.*, et al., Susceptibility assessment of landslides triggered by earthquakes in the Western Sichuan Plateau, Catena, 2019, 175:63-76.

【3】 Zhang L.Y., Zhang Z.*, et al., Spatial pattern of surface water quality in China and its driving factors—implication for the environment sustainability, Human and Ecological Risk Assessment: An International Journal,2019 25(7):1789-1801

【4】 Wang C.Z., Zhang Z.*, et al., Different response of human mortality to extreme temperatures (MoET) between rural and urban areas: A multi-scale study across China, Health&Place, 2018, 50:119-129

【5】 Zhang L.Y., Zhang Z.*, et al,. Different Mortality Effects of Extreme Temperature Stress in Three Large City Clusters of Northern and Southern China. International Journal of Disaster Risk Science, 2017, 8(4): 445-456

【6】 Zhang L.Y., Zhang Z.*, et al., Mortality effects of heat waves vary by age and area: a multi-area study in China，Environment Health, 2018,17:54

【7】 Wang C, Zhang Z*., et al. Nonlinear relationship between extreme temperature and mortality in different temperature zones: A systematic study of 122 communities across the mainland of China, Science of the Total Environment 586 (2017) 96–106

【8】 Chen Y., Zhang Z*., et al., Public perception and responses to environmental pollution and health risks: evaluation and implication from a national survey in China Journal of Risk Research, 2017 20(3): 347–365 DOI:10.1080/13669877.2015.1057199

【9】Song X., Zhang Z.* et al. 2014. Spatiotemporal changes of global extreme temperature events (ETEs) since 1981 and the meteorological causes. Natural Hazards 70:975-994.

【10】 张领雁，张朝*,等.高温热浪致死风险的人群和城市分异及保险费率厘定的研究.气候变化研究进展, 2018, 14(5):1-12

【11】 王琛智，张朝*等低温对中国居民健康影响的空间差异性分析, 地球信息科学 2017 19(3):336-345.

【12】 周阿颖，张朝*等，影响地震救灾效率的因素分析-以汶川8.0级地震和玉树7.1级地震为例，2011，灾害学26(4)：134-138.

 

【四、流域环境】

【1】Sun J.B., Chen Y., Zhang Z.*, et al.,2015 The spatio-temporal variations of surface water quality in China during the “Eleventh Five-Year Plan” Environ Monit Assess (2015) 187: 64 DOI10.1007/s10661-015-4278-z

【2】 Chen Y., Song X., Zhang Z.* et al. 2015 Simulating the impact of flooding events on non-point source pollution and the effects of filter strips in an intensive agricultural watershed in China Limnology 16:91–101 DOI 10.1007/s10201-014-0443-2

【3】 Chen Y., Shuai J.B., Zhang Z.* et al.,2014. Simulating the impact of watershed management for surface water quality protection: A case study on reducing inorganic nitrogen load at a watershed scale. Ecological Engineering 62:61-70.

【4】 Zhang Z*, Chen Y., Wang P., et al. 2014. River discharge, land use change, and surface water quality in the Xiangjiang River, China hydrological Processes 28(13):4130-4140, DOI: 10.1002/hyp.9938

【5】 Guo L.L., Chen Y., Zhang Z* et al. N:P Stoichiometry in a Forested Runoff during Storm Events-Comparisons with Regions and Vegetation types The Scientific World Journal Volume 2012, Article ID 257392, DOI:10.1100/2012/257392

【6】 Chen Y., Zhang Z.*, Du S. Q., et al. 2011. Water quality changes in the world’s first special economic zone, Shenzhen, China Water Resources Research 47: W11515.

【7】 Zhang Z*.,et al., 2010. Surface water quality and its control in a river with intensive human impacts—a case study of the Xiangjiang River, China. Journal of Environmental Management.91:2483-2490.

【8】 Zhang Z*, et al., Characterizing the flush of stream chemical runoff from forested watersheds. Hydrological Processes. 24: 2960-2970, 2010.

【9】 Zhang Z*., FukushimaT., Shi P.J. et al. Seasonal changes of nitrate concentration in baseflow headwaters of Japanese coniferous forests: a significant indicator for N saturation. Catena 76: 63-69, 2008, DOI:10.1016/j.catena.2008.09.007

【10】 Zhang Z*., Fukushima T., Shi P.J., et al. Baseflow concentrations of nitrogen and phosphorus in forested headwaters in Japan. Science of the Total Environment. 402: 113-122, 2008, DOI:10.1016/j.scitotenv.2008.04.045

【11】 Du J., He F., Zhang Z., Shi P.J. Precipitation change and human impacts on hydrologic variables in Zhengshui River Basin, China. Stoch Environ Res Risk Assess, 2011. 25(7) 1013-1025, DOI:10.1007/s00477-010-0453-5

【12】 Zhang Z*., Fukushima T. Onda Y. et al. Characterization of diffuse pollutions from forested watersheds in Japan during storm events: its association with rainfall and watershed features. Science of the Total Environment. 390: 215-226, 2008. DOI:10.1016/j.scitotenv.2007.09.045

【13】 Zhang Z*., Fukushima T. Onda Y. et al. Nutrient runoff from forested watersheds in central Japan during typhoon storms: Implications for understanding runoff mechanisms during storm events. Hydrological Processes. 21: 1167-1187, 2007, DOI: 10.1002/hyp.6677

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