What is he best known for?
The fields of study he is best known for:
- Statistics
- Hydrology
- Machine learning
Shenglian Guo mainly investigates Hydrology, Flood myth, Climate change, Water resources and Flood forecasting.
His work on Hydrograph as part of general Hydrology research is frequently linked to Current, thereby connecting diverse disciplines of science.
Shenglian Guo focuses mostly in the field of Flood myth, narrowing it down to matters related to Inflow and, in some cases, Standard deviation.
Shenglian Guo has researched Climate change in several fields, including Surface runoff and Precipitation.
His Water resources study integrates concerns from other disciplines, such as Streamflow, Flood control and Hydropower.
His Flood forecasting study combines topics from a wide range of disciplines, such as Artificial neural network and Meteorology.
His most cited work include:
- Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff (192 citations)
- A macro-scale and semi-distributed monthly water balance model to predict climate change impacts in China (155 citations)
- Separating the impacts of climate change and human activities on runoff using the Budyko-type equations with time-varying parameters (145 citations)
What are the main themes of his work throughout his whole career to date?
Shenglian Guo focuses on Hydrology, Flood myth, Water resources, Climate change and Climatology.
The study of Hydrology is intertwined with the study of Hydropower in a number of ways.
His studies in Flood myth integrate themes in fields like Copula, Quantile and Inflow.
His studies deal with areas such as Drainage basin, Streamflow, Water supply and Water resource management as well as Water resources.
His research integrates issues of China, Climate model and Precipitation in his study of Climatology.
His research in Surface runoff intersects with topics in Soil science and Water balance.
He most often published in these fields:
- Hydrology (29.22%)
- Flood myth (22.37%)
- Water resources (22.83%)
What were the highlights of his more recent work (between 2019-2021)?
- Precipitation (14.16%)
- Climatology (17.35%)
- Water resources (22.83%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Precipitation, Climatology, Water resources, Mathematical optimization and Climate change.
The Water resources study combines topics in areas such as Drainage basin, Uncertainty analysis, Soil and Water Assessment Tool and Water supply.
His work deals with themes such as Streamflow and Radial basis function, which intersect with Mathematical optimization.
His Climate change research includes elements of Flood myth, Management system and Natural disaster.
His China course of study focuses on General Circulation Model and Hydrology.
In the field of Hydrology, his study on Flood control overlaps with subjects such as Upstream and downstream.
Between 2019 and 2021, his most popular works were:
- An advanced complementary scheme of floating photovoltaic and hydropower generation flourishing water-food-energy nexus synergies (11 citations)
- Toward Monitoring Short-Term Droughts Using a Novel Daily Scale, Standardized Antecedent Precipitation Evapotranspiration Index (11 citations)
- Flash droughts in the Pearl River Basin, China: Observed characteristics and future changes. (10 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Hydrology
- Machine learning
Precipitation, Climatology, Climate change, Global warming and Flood myth are his primary areas of study.
He has included themes like Hydrological modelling, Evapotranspiration and Scale in his Precipitation study.
His Climatology study incorporates themes from Quantile, Ecosystem, Return period and Representative Concentration Pathways.
Shenglian Guo usually deals with Climate change and limits it to topics linked to Water resources and Resilience, Water use and Water cycle.
His Global warming research is multidisciplinary, incorporating perspectives in Pareto principle, Mathematical optimization and Maximization.
He interconnects Recurrent neural network, Copula, Climate model, Spatial variability and Bivariate analysis in the investigation of issues within Flood myth.
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