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Category: forecast techniques

Machine learning for probabilistic hydrological forecasting

Machine learning for probabilistic hydrological forecasting

Contributed by Georgia Papacharalampous and Hristos Tyralis We would firstly like to thank HEPEX for giving us the opportunity to set a background on how machine learning can be used in probabilistic hydrological forecasting.  In this blog post, we start by providing the schematic summary of the discussion given below. Hope you will enjoy the reading! Learning practical problems with data: A machine learning algorithm can be explicitly trained for probabilistic hydrological forecasting Let’s suppose one of our most familiar…

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“Are we talking just a bit of water out of bank? Or is it Armageddon?” Front line perspectives on transitioning to probabilistic fluvial flood forecasts in England

“Are we talking just a bit of water out of bank? Or is it Armageddon?” Front line perspectives on transitioning to probabilistic fluvial flood forecasts in England

Contributed by Louise Arnal, Jess Neumann, Liz Stephens and Hannah Cloke This blog post is based on a paper recently published in Geoscience Communication, written in collaboration with Liz Anspoks, Sue Manson, Tim Norton and Louise Wolfenden from the Environment Agency. With the aim to better anticipate future floods, UK policy is seeing an ongoing shift from flood defence towards a forecast-based flood risk management approach, under the Flood and Water Management Act 2010. It is in this context that…

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New water modelling to improve irrigation outlook accuracy

New water modelling to improve irrigation outlook accuracy

Contributed by Yating Tang1, Catherine Norwood2, Q J Wang1, Guy Ortlipp3, Mark Bailey3, and Kirsti Hakala1. Goulburn-Murray Water (GMW), Australia’s largest rural water corporation, has teamed up with researchers from the University of Melbourne to improve water forecasting in northern Victoria, to help irrigators with their business planning, cropping and water trading decisions. GMW is responsible for providing water availability information to local irrigators and other water users during the irrigation season each year. GMW provides two types of information…

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How many zeros is too many for reliable streamflow predictions?

How many zeros is too many for reliable streamflow predictions?

Contributed by Mark Thyer, David McInerney and Dmitri Kavetski, University of Adelaide. Ephemeral catchments, where there are days with zero flow, are common in many parts of the world, particular in areas with highly variable climate such as Australia (see Figure 1). Recent research has established how the number of days with zero flow impacts the reliability of probabilistic streamflow predictions in ephemeral catchments (McInerney et al., 2019). When there exists days with zero flow, producing reliable probabilistic predictions is…

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Can we achieve seasonally coherent forecasts given limited NWP DATA – across a continental domain?

Can we achieve seasonally coherent forecasts given limited NWP DATA – across a continental domain?

Contributed by Kirsti Hakala1, QJ Wang1, Qichun Yang1 and David Robertson2. Reliable weather forecasts are critical for the planning and management of a variety of social and economic activities, such as water management. To make such forecasts, numerical weather prediction (NWP) models have been developed. However, NWP models are limited in their ability to represent certain physical processes and initial conditions, and thus include inaccuracies, which can be improved through calibration. Effective calibration should aim to provide forecasts that are…

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