Browsed by
Author: Ilias Pechlivanidis

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…

Read More Read More

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

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…

Read More Read More

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

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

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…

Read More Read More

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

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

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…

Read More Read More

High-resolution flood forecasting in Sweden: a status update

High-resolution flood forecasting in Sweden: a status update

Contributed by Jonas Olsson (SMHI), member of the SMHI Guest Columnist Team Traditionally, hydrological activities (observations, modelling, forecasting) at SMHI have mainly focused on Sweden’s large rivers. The largest ones are Göta River with a catchment size of ~50 000 km² and Torne River with ~40 000 km² and then there are many (often regulated) with a catchment size of 20 000 to 30 000 km². The HBV model in combination with comparatively coarse-scale geographical and (in time and space) meteorological data has worked excellently…

Read More Read More