What is he best known for?
The fields of study he is best known for:
- Hydrology
- Meteorology
- Climate change
His primary areas of study are Precipitation, Climatology, Hydrology, Structural basin and Drainage basin.
His biological study spans a wide range of topics, including Global warming and Climate change.
The Global warming study combines topics in areas such as Permafrost, Arctic, Physical geography, Water cycle and Atmospheric temperature.
The study incorporates disciplines such as Snow, Wind speed, Meteorology and Trend surface analysis in addition to Climatology.
His work deals with themes such as Streamflow and Snowmelt, which intersect with Hydrology.
Daqing Yang is involved in the study of Hydrology that focuses on Surface runoff in particular.
His most cited work include:
- Siberian Lena River hydrologic regime and recent change (275 citations)
- Analysis of the Arctic System for Freshwater Cycle Intensification: Observations and Expectations (216 citations)
- Changes in Lena River streamflow hydrology: Human impacts versus natural variations (213 citations)
What are the main themes of his work throughout his whole career to date?
Daqing Yang spends much of his time researching Precipitation, Climatology, Snow, Hydrology and Drainage basin.
His work carried out in the field of Precipitation brings together such families of science as Spice, Wind speed and Atmospheric sciences.
His work is dedicated to discovering how Climatology, Climate change are connected with Monsoon and other disciplines.
His study focuses on the intersection of Snow and fields such as Arctic with connections in the field of Physical geography and Evaporation.
In his research, Watershed and Snow cover is intimately related to Structural basin, which falls under the overarching field of Hydrology.
His Drainage basin research integrates issues from Warm season, Atmospheric circulation and Tributary.
He most often published in these fields:
- Precipitation (58.25%)
- Climatology (46.60%)
- Snow (37.86%)
What were the highlights of his more recent work (between 2017-2021)?
- Arctic (23.30%)
- Climatology (46.60%)
- Precipitation (58.25%)
In recent papers he was focusing on the following fields of study:
Arctic, Climatology, Precipitation, Snow and Hydrology are his primary areas of study.
His Arctic study combines topics from a wide range of disciplines, such as Ecosystem, Seasonality and The arctic.
His Climatology research includes elements of Remote sensing and Climate change.
His Precipitation study incorporates themes from Ancillary data, Wind speed, Atmospheric sciences and Numerical weather prediction.
His Snowmelt and Snow line study, which is part of a larger body of work in Snow, is frequently linked to Elevation, bridging the gap between disciplines.
Streamflow and Surface runoff are subfields of Hydrology in which his conducts study.
Between 2017 and 2021, his most popular works were:
- Recent third pole's rapid warming accompanies cryospheric melt and water cycle intensification and interactions between monsoon and environment : Multidisciplinary approach with observations, modeling, and analysis (118 citations)
- Spatiotemporal variation of snow cover over the Tibetan Plateau based on MODIS snow product, 2001–2014 (34 citations)
- Using GRACE to Estitmate Snowfall Accumulation and Assess Gauge Undercatch Corrections in High Latitudes (18 citations)
In his most recent research, the most cited papers focused on:
- Hydrology
- Meteorology
- Climate change
Daqing Yang mostly deals with Climatology, Snow, Climate change, Arctic and Drainage basin.
His Snow study frequently draws connections between related disciplines such as Remote sensing.
Daqing Yang has included themes like Monsoon and Water cycle in his Climate change study.
His Arctic research incorporates themes from Thermohaline circulation, Hydrology, Surface water, Permafrost and Global warming.
His research integrates issues of Precipitation, Snow line, Snowmelt and Moderate-resolution imaging spectroradiometer in his study of Drainage basin.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
