Qi Feng

ORCID: 0000-0002-5469-1738
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About
Contact & Profiles
Research Areas
  • Plant Water Relations and Carbon Dynamics
  • Hydrology and Watershed Management Studies
  • Cryospheric studies and observations
  • Climate change and permafrost
  • Groundwater and Isotope Geochemistry
  • Climate variability and models
  • Plant Ecology and Soil Science
  • Tree-ring climate responses
  • Soil Carbon and Nitrogen Dynamics
  • Environmental and Agricultural Sciences
  • Rangeland Management and Livestock Ecology
  • Geology and Paleoclimatology Research
  • Land Use and Ecosystem Services
  • Ecology and Vegetation Dynamics Studies
  • Soil and Unsaturated Flow
  • Hydrological Forecasting Using AI
  • Soil erosion and sediment transport
  • Urban Heat Island Mitigation
  • Flood Risk Assessment and Management
  • Environmental Changes in China
  • Remote Sensing in Agriculture
  • Remote Sensing and Land Use
  • Urban Stormwater Management Solutions
  • Random lasers and scattering media
  • Plant responses to elevated CO2

Northwest Institute of Eco-Environment and Resources
2016-2025

Chinese Academy of Sciences
2016-2025

Guangdong University Of Finances and Economics
2024

Guangdong University of Finance
2024

University of Chinese Academy of Sciences
2009-2024

Institute of Hydrobiology
2024

Shandong Jianzhu University
2024

Zhejiang Gongshang University Hangzhou College of Commerce
2024

Zhejiang Gongshang University
2024

Gansu Qilian Mountains Water Conservation Forest Research Institute
2020-2023

Abstract The ecological water diversion project in the Heihe River Basin is first successful case China which systems a river basin have been rescued. This serves as valuable example for management of ecosystems other inland basins. paper reviews integrated studies water–ecosystem–economy relationship and concludes that sustainable development basins requires to be considered whole, with relationships between upstream, midstream downstream areas coordinated appropriately. Successful these...

10.1093/nsr/nwu017 article EN cc-by National Science Review 2014-06-17

Abstract Desertification is the result of complex interactions among various factors, including climate change and human activities. However, previous research generally focused on either meteorological factors associated with or activities lacked quantitative assessments their interaction combined long-term monitoring. Thus, roles in desertification remain uncertain. To understand that determine whether mitigation programs can contribute to control vegetation cover improvements desertified...

10.1038/srep15998 article EN cc-by Scientific Reports 2015-11-03

Abstract Climate change and land use/cover (LUCC) are two factors that produce major impacts on hydrological processes. Understanding quantifying their respective influence is of great importance for water resources management socioeconomic activities as well policy planning sustainable development. In this study, the Soil Water Assessment Tool (SWAT) was calibrated validated in upper stream Heihe River Northwest China. The reliability SWAT model corroborated terms Nash–Sutcliffe efficiency...

10.1002/hyp.11098 article EN Hydrological Processes 2016-12-09

In light of Harris (2010) finding insufficient evidence to assert a causal linkage between any the seven previously proposed causative factors and grassland degradation on Qinghai-Tibetan Plateau (QTP), more recent empirical studies QTP were explored ascertain whether, in fact, these are casually linked degradation. The mischaracterization underlying causes among policymakers has continues be an obstacle sustainable regional management practices. Accumulating suggests that privatization...

10.1016/j.rama.2019.06.001 article EN cc-by Rangeland Ecology & Management 2019-08-09

Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of underlying soils plays a critical role in planning and managing water resources sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface (SSM) forecasts, employing gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), convolutional neural network...

10.3390/rs13040554 article EN cc-by Remote Sensing 2021-02-04
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