Quan Wang

Professor

University of Melbourne

Australia

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Quan Wang is a Professor at The University of Melbourne in Australia. His research primarily focuses on hydrology, including topics such as seasonal streamflow climatology, precipitation forecasting, and flood monitoring methods. Recent articles highlight his work on improving rainfall forecasts and understanding the implications of climate change on extreme weather events.

Articles (22)

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2025

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2022

Collaborators (6)

Rory Nathan

University of Melbourne

AUSTRALIA

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Wenyan Wu

department of medicine university of melbourne melbourne victoria australia

AUSTRALIA

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danlu guo

Senior Lecturer

AUSTRALIA

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Dongryeol Ryu

Professor

University of Melbourne

AUSTRALIA

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Qichun Yang

University of Melbourne

AUSTRALIA

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Daniel Wright

Assistant Professor

University of Wisconsin-Madison

UNITED STATES

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Quan Wang

Research Interests

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About

Articles (22)

Selective incorporation of trend into seasonal streamflow climatology

Calibration of precipitation forecasts from NWP models for ungauged locations

Flood monitoring: A hydrologically guided method for infilling incomplete flood inundation maps derived from satellite images

Advancing High‐Resolution Precipitation Analysis for Operational Hydrological Applications in Australia: Data Sources, Methodologies, and Recommendations

Dynamics-constrained rainfall projection reveals substantial increase in population exposure to unprecedented floods in the North China Plain

A Distributional Regression Network With Data Transformation for Calibrating Rainfall Forecasts

Considering ensemble spread improves rainfall forecast post‐processing

DSC-YOLOv8n: An advanced automatic detection algorithm for urban flood levels

A multi-objective optimization-based framework for extending reservoir service life in a changing world

Adapting Water Resources Management to Climate Change in Water-Stressed River Basins—Júcar River Basin Case

Assessment of surrogate models for flood inundation: The physics-guided LSG model vs. state-of-the-art machine learning models

Anthropogenic Exacerbation in Dry‐Hot Probability and Consequential Record‐Shattering Droughts in the Middle and Lower Reaches of the Yangtze River

An analysis framework to evaluate irrigation decisions using short-term ensemble weather forecasts

Supercharging hydrodynamic inundation models for instant flood insight

Using Ensemble Streamflow Forecasts to Inform Seasonal Outlooks for Water Allocations in the Murray Darling Basin

Post-processing quantitative precipitation forecasts using the seasonally coherent calibration model

Development of a Fast and Accurate Hybrid Model for Floodplain Inundation Simulations

Deep Learning‐Based Rapid Flood Inundation Modeling for Flat Floodplains With Complex Flow Paths

Upskilling Low‐Fidelity Hydrodynamic Models of Flood Inundation Through Spatial Analysis and Gaussian Process Learning

Calibrating anomalies improves forecasting of daily reference crop evapotranspiration

Introducing long‐term trends into subseasonal temperature forecasts through trend‐aware postprocessing

Evaluation and Statistical Post‐Processing of Two Precipitation Reforecast Products During Summer in the Mainland of China

Collaborators (6)

Rory Nathan

Wenyan Wu

danlu guo

Dongryeol Ryu

Qichun Yang

Daniel Wright

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