Ashish Sharma

Professor

University of New South Wales

Australia

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Ashish Sharma is a Professor at the University of New South Wales in Australia. His research focuses on climate modeling and hydrology, with recent articles exploring topics such as flood risk due to deforestation, seasonal flood forecasting using hydroclimatic covariates, and the impact of climate change on precipitation extremes. Professor Sharma's work includes advancements in decadal climate predictions and the assessment of climate-induced changes in flood hazards.

Articles (26)

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2025

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2023

Collaborators (13)

Rory Nathan

University of Melbourne

AUSTRALIA

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Venkataraman Lakshmi

University of Virginia

UNITED STATES

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Francesco Marra

University of Padua

ITALY

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Jason Evans

Professor

University of New South Wales

AUSTRALIA

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Conrad Wasko

AUSTRALIA

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Fiona Johnson

University of New South Wales

AUSTRALIA

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Hayley J. Fowler

Newcastle University

UNITED KINGDOM

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Hyunglok Kim

Assistant Professor

Gwangju Institute of Science and Technology

SOUTH KOREA

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Venkatesh Merwade

Purdue University

UNITED STATES

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Seokhyeon Kim

Assistant Professor

Kyungpook National University

SOUTH KOREA

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Bruno Merz

GERMANY

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Lucy Marshall

Macquarie University

AUSTRALIA

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Daniel Wright

Assistant Professor

University of Wisconsin-Madison

UNITED STATES

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Ashish Sharma

Research Interests

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About

Articles (26)

Spectrally Transformed Hydroclimatic Covariates Improve Seasonal Flood Forecasting

Interception reduction from deforestation and forest fire increases large-scale fluvial flooding risk

Spectrally transformed CMIP6 decadal projections improve interannual rainfall forecasts

Decadal drought prediction via spectral transformation of projected Sea Surface Temperatures

The role of regional water vapor dynamics in creating precipitation extremes

Characterizing Satellite Soil Moisture Drydown: A Bivariate Filtering Approach

A simple and robust approach for adapting design storms to assess climate-induced changes in flash flood hazard

Unveiling the Evolution of Extreme Rainfall Storm Structure Across Space and Time in a Warming Climate

Pyraingen: A python package for constrained continuous rainfall generation

Revisiting model complexity: Space-time correction of high dimensional variable sets in climate model simulations

Correcting Multivariate Biases in Regional Climate Model Boundaries: How Are Synoptic Systems Impacted Over the Australian Region?

Separating Storm Intensity and Arrival Frequency in Nonstationary Rainfall Frequency Analysis

A software for correcting systematic biases in RCM input boundary conditions

Changing Storm Temporal Patterns with Increasing Temperatures across Australia

Characterizing Errors Using Satellite Metadata for Eco‐Hydrological Model Calibration

Correcting biases in regional climate model boundary variables for improved simulation of high-impact compound events

How Is Spatial Homogeneity in Precipitation Extremes Changing Globally?

Correcting systematic bias in derived hydrologic simulations – Implications for climate change assessments

Do Derived Drought Indices Better Characterize Future Drought Change?

Rectifying low-frequency variability in future climate sea surface temperature simulations: are corrections for extreme change scenarios realistic?

Improved Extreme Rainfall Frequency Analysis Using a Two‐Step Kappa Approach

Beyond river discharge gauging: hydrologic predictions using remote sensing alone

Temporal Gap‐Filling of 12‐Hourly SMAP Soil Moisture Over the CONUS Using Water Balance Budgeting

Can Sub‐Daily Multivariate Bias Correction of Regional Climate Model Boundary Conditions Improve Simulation of the Diurnal Precipitation Cycle?

Hydrological sustainability of international virtual water trade

Water Supply Sustainability Revisited: Assessment Methodology for Multiple Water Resources

Collaborators (13)

Rory Nathan

Venkataraman Lakshmi

Francesco Marra

Jason Evans

Conrad Wasko

Fiona Johnson

Hayley J. Fowler

Hyunglok Kim

Venkatesh Merwade

Seokhyeon Kim

Bruno Merz

Lucy Marshall

Daniel Wright

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