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Qianfeng Wang   Dr.  Institute, Department or Faculty Head 
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Qianfeng Wang published an article in November 2018.
Top co-authors
Bin He

15 shared publications

College of Global Change and Earth System Science; Beijing Normal University; Beijing China

Honglin Wang

1 shared publications

Publication Record
Distribution of Articles published per year 
(2011 - 2018)
Total number of journals
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Article 0 Reads 0 Citations Metal accumulation in Asiatic clam from the Lower Min River (China) and implications for human health Yue Zeng, Zhongtao Li, Qianfeng Wang, Changcheng Xu, Yunqin ... Published: 19 November 2018
Frontiers of Earth Science, doi: 10.1007/s11707-018-0724-x
DOI See at publisher website
Article 0 Reads 0 Citations BP neural networks and random forest models to detect damage by Dendrolimus punctatus Walker Zhanghua Xu, Xuying Huang, Lu Lin, Qianfeng Wang, Jian Liu, ... Published: 08 November 2018
Journal of Forestry Research, doi: 10.1007/s11676-018-0832-1
DOI See at publisher website
Article 0 Reads 3 Citations A comprehensively quantitative method of evaluating the impact of drought on crop yield using daily multi-scale SPEI and... Qianfeng Wang, Hongkui Zhou, Jianhua Yang, Guangpo Geng, Xue... Published: 25 November 2016
International Journal of Biometeorology, doi: 10.1007/s00484-016-1246-4
DOI See at publisher website PubMed View at PubMed
Article 3 Reads 5 Citations Contrasting Responses of Planted and Natural Forests to Drought Intensity in Yunnan, China Hui Luo, Tao Zhou, Hao Wu, Xiang Zhao, Qianfeng Wang, Shan G... Published: 01 August 2016
Remote Sensing, doi: 10.3390/rs8080635
DOI See at publisher website ABS Show/hide abstract
In recent decades, the area and proportion of planted forests have increased; thus, understanding the responses of planted and natural forests to drought are crucial because it forms the basis for forest risk assessments and management strategies. In this study, we combined the moderate-resolution imaging spectroradiometer (MODIS) enhanced vegetation index (EVI), meteorological aridity indices, and standardized precipitation evapotranspiration indices (SPEI) to identify the drought responses of planted and natural forests. In particular, we used the EVI standard anomaly (ESA) as a physiological drought indicator and analyzed the applicability of SPEIs at time scales of 1–30 months, thereby determining the optimal time scale for the SPEI (SPEIopt), i.e., the SPEI that best represents the drought responses of forests in Yunnan. Next, we employed the optimal SPEI and the ESA as indices to statistically analyze the response characteristics of planted and natural forests under different drought intensities. The results indicated the following: (1) The SPEI in June and a time scale of five months (i.e., SPEIJun,5) comprise the optimal meteorological aridity indicator for forests in Yunnan Province, which had the strongest correlation with the EVI standard anomaly (ESAJun). (2) All forest types were affected by drought in Yunnan, but their responses varied according to the forest type, elevation, and drought intensity. In general, natural forests are more vulnerable and sensitive to drought than planted forests, especially natural coniferous forests at low (0–2000 m) and moderate (2000–4000 m) altitudes, and natural mixed forest at low altitudes (0–2000 m). (3) The remote sensing-based ESA (ESAJun) is sensitive to the intensity of water stress, which makes it a good indicator for drought monitoring. In addition, the forests’ inventory survey revealed that 8.05% of forests were affected by drought; thus, we used this as a guide to estimate an approximate threshold to map forest responses to drought across the region. Below this approximate threshold (i.e., ESAJun < −3.85), severe drought-induced effects on forests may occur. Given that natural forests are more vulnerable and sensitive to drought than the planted forests, natural forests need more careful management, especially in the context of projected increases in extreme drought events in the future.
Article 0 Reads 7 Citations Variations in water storage in China over recent decades from GRACE observations and GLDAS X. Mo, J. J. Wu, Qianfeng Wang, H. Zhou Published: 17 February 2016
Natural Hazards and Earth System Sciences, doi: 10.5194/nhess-16-469-2016
DOI See at publisher website ABS Show/hide abstract
We applied Gravity Recovery and Climate Experiment (GRACE) Tellus products in combination with Global Land Data Assimilation System (GLDAS) simulations and data from reports, to analyze variations in terrestrial water storage (TWS) in China as a whole and eight of its basins from 2003 to 2013. Amplitudes of TWS were well restored after scaling, and showed good correlations with those estimated from models at the basin scale. TWS generally followed variations in annual precipitation; it decreased linearly in the Huai River basin (−0.56cm yr−1) and increased with fluctuations in the Changjiang River basin (0.35cm yr−1), Zhujiang basin (0.55cm yr−1) and southeast rivers basin (0.70cm yr−1). In the Hai River basin and Yellow River basin, groundwater exploitation may have altered TWS's response to climate, and TWS kept decreasing until 2012. Changes in soil moisture storage contributed over 50% of variance in TWS in most basins. Precipitation and runoff showed a large impact on TWS, with more explained TWS in the south than in the north. North China and southwest rivers region exhibited long-term TWS depletions. TWS has increased significantly over recent decades in the middle and lower reaches of Changjiang River, southeastern coastal areas, as well as the Hoh Xil, and the headstream region of the Yellow River in the Tibetan Plateau. The findings in this study could be helpful to climate change impact research and disaster mitigation planning.
Article 0 Reads 6 Citations A new framework for evaluating the impacts of drought on net primary productivity of grassland Tianjie Lei, Jianjun Wu, Xiaohan Li, Guangpo Geng, Changlian... Published: 01 December 2015
Science of The Total Environment, doi: 10.1016/j.scitotenv.2015.06.138
DOI See at publisher website PubMed View at PubMed