Yuhang Zhang, Research Fellow

Department of Infrastructure Engineering, The University of Melbourne

yuhang.zhang12@unimelb.edu.au

About Me

Hey Yuhang (Chinese: 张宇航) here! Welcome to my personal website. I am currently a Postdoctoral Research Fellow in the Department of Infrastructure Engineering at the University of Melbourne, collaborating closely with Prof. QJ Wang, Prof. Dongryeol Ryu, and other colleagues. Before joining the University of Melbourne, I completed my PhD in Physical Geography at the Faculty of Geographical Science, Beijing Normal University, China.

The motto I practice in my life: "Work hard and play hard. Stay true to principles. Experience new things. Live passionately, fully, and well. Love and be loved if at all possible."

Research Interests

Precipitation analysis and forecasting
Hydrological modeling and uncertainty analysis
Climate change and its impact
Hydroclimate extreme events

Feb 16-20, 2026

AMOS 2026 in Hobart

Oral presentation: "BRAIN - A national multi-source rainfall blending system for improving sub-daily rainfall estimates and hydroclimate applications".

Nov 30-Dec 4, 2025

MODSIM 2025 in Adelaide

Oral presentation: "BRAIN: Building a national, hourly, kilometre-scale precipitation analysis product for Australia".

Featured Publications

WIREs Water

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

Yuhang Zhang, Quan J. Wang, Andrew J. Frost, Blair Trewin, Jayaram Pudashine, Carlos Velasco-Forero, Chun-Hsu Su, Vincent Villani

WIREs Water, e70037, 2025

Abstract: This comprehensive review examines available precipitation data sources in Australia and evaluates current blending techniques for developing multi-source high-resolution precipitation analysis products for operational hydrological applications.

GRL

New insights into error decomposition for precipitation products

Yuhang Zhang, Aizhong Ye, Phu Nguyen, Bita Analui, Soroosh Sorooshian, Kuolin Hsu

Geophysical Research Letters, 48, e2021GL094092, 2021

Abstract: This study presents a novel four-component error decomposition (4CED) framework for analyzing errors in precipitation products, providing new insights into positive and negative biases detected in precipitation estimates.

WRR

QRF4P-NRT Probabilistic Post-processing of Near-real-time Satellite Precipitation Estimates Using Quantile Regression Forests

Yuhang Zhang, Aizhong Ye, Phu Nguyen, Bita Analui, Soroosh Sorooshian, Kuolin Hsu

Water Resources Research, 58, e2022WR032117, 2022

Abstract: This study develops a quantile regression forest framework (QRF4P-NRT) for probabilistic post-processing of satellite precipitation estimates in near-real-time applications, improving uncertainty quantification for operational use.

JoH

Improve streamflow simulations by combining machine learning pre-processing and post-processing

Yuhang Zhang, Aizhong Ye, Jinyang Li, Phu Nguyen, Bita Analui, Kuolin Hsu, Soroosh Sorooshian

Journal of Hydrology, 630, 130815, 2025

Abstract: This study presents a combined pre-processing and post-processing framework for improving streamflow simulations, quantifying the individual and joint contributions of each approach to overall simulation accuracy.

HESS

Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations

Yuhang Zhang, Aizhong Ye, Bita Analui, Phu Nguyen, Soroosh Sorooshian, Kuolin Hsu, Yuxuan Wang

Hydrology and Earth System Sciences, 27, 4529-4550, 2023

Abstract: This study provides a systematic comparison of quantile regression forest (QRF) and mixture density long short-term memory (CMAL-LSTM) models for probabilistic post-processing of satellite precipitation-driven streamflow simulations across 522 sub-basins.