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Author: Ilias Pechlivanidis

50 shades of green: lessons about business models for climate services

50 shades of green: lessons about business models for climate services

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Contributed by Francesca Larosa (Euro-Mediterranean Center on Climate Change (CMCC), Ca’ Foscari University, and  UCL Energy Institute | UCL Institute for Sustainable Resources) Climate services are essential for adaptation to climate variability and change The transition towards a zero-carbon economy is profoundly reshaping the business-as-usual. If, on one hand, we must cut down our emissions and restructure the way our economies work, on the other hand we need to use the science we have and the tremendous technological progresses we…

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Machine learning for probabilistic hydrological forecasting

Machine learning for probabilistic hydrological forecasting

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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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Celebrating the new hydrological year with a new HEPEX blog year: Let’s co-generate the HEPEX blog global pattern

Celebrating the new hydrological year with a new HEPEX blog year: Let’s co-generate the HEPEX blog global pattern

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Happy New Hydrological Year!! According to USGS and based on meteorological and geographical factors, the hydrological year is defined as the period between October 1st of one year and September 30th of the next year. Driven by this, HEPEX will set for this year a new interactive approach for scheduling the blogs with and for the community. The blog has been our channel to communicate scientific achievements, insights and developments. As a blogger, you do not need to be outstanding…

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Hydrological Forecasting at EGU 2020: (5+1) things not to miss

Hydrological Forecasting at EGU 2020: (5+1) things not to miss

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The EGU 2020 Annual General Assembly will take place from 4–8 May 2020; however this time we will not be able to physically meet in Vienna and the event will instead take place remotely. Nevertheless there are ways to get updated on the recent scientific insights and developments and also to virtually socialize with friends and colleagues. To make EGU 2020 and the hydrological forecasting subdivision a big success this year too, here are 5+1 HEPEX activities we initiated that…

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Uncertainty in operational hydrological forecasting: Insights from SMHI’s services

Uncertainty in operational hydrological forecasting: Insights from SMHI’s services

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Contributed by Ilias Pechlivanidis  (SMHI), member of the SMHI Guest Columnist Team Background The production of hydrological forecasts generally involves the selection of model(s) and their setup, calibration and initialization, verification and updating, generation and evaluation of forecasts. However, the precision of hydrological forecasts is often subject to both epistemic and aleatory uncertainties, with the former being related to various components of the production chain and the data used. Aleatory uncertainty refers to quantities or natural phenomena that are inherently variable over time and…

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