David McInerney

University of Adelaide — Australia

Environmental Science · Physical Sciences

24h-index2.0kcitations93works6.52yr avg
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Research Topics

Hydrology and Watershed Management Studies(46), Flood Risk Assessment and Management(30), Hydrology and Drought Analysis(25), Hydrological Forecasting Using AI(24), Climate variability and models(19)

Publications93 total

Software library to quantify the value of forecasts for decision-making: Case study on sensitivity to damages
Environmental Modelling & Software·2025Open Access
Virtual Hydrological Laboratories: Developing the Next Generation of Conceptual Models to Support Decision Making Under Change
Water Resources Research·2024· 16 citedOpen Access
Neglecting hydrological errors can severely impact predictions of water resource system performance 
2024Open Access
Virtual Hydrological Laboratories to develop the next generation of conceptual models and support decision-making under change
2024Open Access
Neglecting hydrological errors can severely impact predictions of water resource system performance
Journal of Hydrology·2024· 22 citedOpen Access
Software Library to Quantify the Value of Forecasts for Decision-Making: Case Study on Sensitivity to Damages
SSRN Electronic Journal·2024· 1 citedOpen Access
RGN: Robust-Gauss Newton (RGN) Optimization of Sum-of-Squares Objective Function
2023Open Access
A climate stress testing method for changes in spatially variable rainfall
Journal of Hydrology·2023· 6 citedOpen Access
Floods and Heavy Precipitation at the Global Scale: 100‐Year Analysis and 180‐Year Reconstruction
Journal of Geophysical Research Atmospheres·2023· 3 citedOpen Access
Flexible forecast value metric suitable for a wide range of decisions: application using probabilistic subseasonal streamflow forecasts
Hydrology and earth system sciences·2023· 3 citedOpen Access
Seamless streamflow forecasting at daily to monthly scales: MuTHRE lets you have your cake and eat it too
Hydrology and earth system sciences·2022· 8 citedOpen Access
Can Gauss‐Newton Algorithms Outperform Stochastic Optimization Algorithms When Calibrating a Highly Parameterized Hydrological Model? A Case Study Using SWAT
Water Resources Research·2022· 13 cited
Reply on RC1
2022Open Access
Reply on RC2
2022Open Access
Predicting wildfire induced changes to runoff: A review and synthesis of modeling approaches
Wiley Interdisciplinary Reviews Water·2022· 42 cited
Comment on hess-2021-589
2022Open Access
High-quality probabilistic predictions for existing hydrological models with common objective functions    
2022Open Access
Improving sub-seasonal forecasts of high and low flows using a flow-dependent nonparametric model
2022Open Access
A Global-Scale Analysis of Hydrologic Extremes using Hidden Climate Indices
2022Open Access
Flexible forecast value metric suitable for a wide range of decisions: application using probabilistic subseasonal streamflow forecasts
2022· 4 citedOpen Access
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Frequent Co-authors

Mark Thyer(27), Dmitri Kavetski(24), George Kuczera(11), Seth Westra(10), Klaus Keller(9), Richard Laugesen(7), E. J. Moyer(6), Julien Lerat(6), Fitsum Woldemeskel(6), Narendra Tuteja(6), Holger R. Maier(6), Robert J. Lempert(5), Jim W. Hall(4), Andrew Hackbarth(4), Christophe Mijere(4), Michael L. Stein(4), Bree Bennett(4), Michael Leonard(4), Matthew S. Gibbs(3), Margaret Shanafield(3)