Tinted detail of a photo: Fast-flowing white water rushing over mossy boulders in a rocky stream through a tall green conifer forest.
HS
Hydrological Sciences

Hydrological Sciences

HydroTalks: Dr. Hossein Hashemi on Groundwater Monitoring and Water Management in Arid Regions

Podcast promotional graphic titled HYDRO TALKS EPS. 12, featuring a central silver microphone and three people in yellow circular frames against a blue background with a yellow soundwave.

For this month’s episode of HydroTalks, we’re thrilled to welcome Dr. Hossein Hashemi, an associate professor in the Division of Water Resources Engineering at Lund University in Sweden and a member of the Center for Advanced Middle Eastern Studies. His research focuses on groundwater and surface water management in arid and water-scarce regions, combining remote …[Read More]

Which rain and temperature should drive your model? A guide to select daily forcing datasets , with application to northern Italy

Which rain and temperature should drive your model? A guide to select daily forcing datasets , with application to northern Italy

If you want to run a hydrological model  anywhere, the first question is which meteorological forcing to use. For the Po basin, or anywhere in northern Italy,  there are today at least a dozen gridded products, regional and European, built from different meteorological networks, with different methods and for different purposes. I reviewed them from …[Read More]

Bridging the Gap Between Global Tech and Local Expertise: Why We Open-Sourced Google’s Hydrology Framework

Bridging the Gap Between Global Tech and Local Expertise: Why We Open-Sourced Google’s Hydrology Framework

Dear EGU Hydrology community, Over the past few years, machine learning (ML) and artificial intelligence have fundamentally changed what is possible in large-scale hydrological modeling. At Google, our team has worked to develop machine learning river forecast models that operate globally, extending reliable forecasting to ungauged basins and covering almost two billion people through the …[Read More]

1st International Training School on Urban Water Management and Nature-based Solutions for Sustainable Cities

1st International Training School on Urban Water Management and Nature-based Solutions for Sustainable Cities

From 7-8 September 2026, the 1st International Training School on Urban Water Management and Nature-based Solutions for Sustainable Cities was held in Munich, Germany. Organised by researchers from the Technical University of Munich, the Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, and the University of Cagliari in collaboration with the International Association …[Read More]

eDNA4Hydrology

eDNA4Hydrology

What flows around comes around For centuries, hydrologists have studied the flow of water through landscapes near and far. We measured flow rates, mapped aquifers and followed invisible paths of raindrops through soil and bedrock. Hydrology is a meticulous, beautiful science that has taught us much over the years. From the water cycle, surface water …[Read More]

Drought in Europe

Drought in Europe

Many of us are experiencing it right now: Europe is suffering from a long lasting large scale drought (Fig. 1), with record heat and low-flow levels that probably nobody of us has seen before; and current forecasts do not announce any relieve for the coming weeks. The pictures of record low levels in the river …[Read More]

Has groundwater finally gone mainstream?

Has groundwater finally gone mainstream?

Groundwater has always had an image problem. Throughout my career, I have lost count of how many times we have described groundwater as an “invisible resource” or similar. It is almost a cliché in hydrogeology. We use the phrase because it captures something really fundamental: groundwater is hidden physically, but it has also been hidden …[Read More]

What machine learning can and cannot tell us about floods

Collage based on a picture by Imad Clicks via Pexel

Floods are shaped by complex and nonlinear interactions between weather patterns, rainfall, soil properties, topography, land cover, and how wet a catchment already is. This complexity is one reason machine learning (ML) is increasingly used in hydrology. ML models can learn patterns from large and complex datasets with many variables.  Early in my PhD, while …[Read More]