Mathew Herrnegger

TL;DR: In this paper, a novel data-driven approach, using the Long Short-Term Memory (LSTM) network, a special type of recurrent neural network, was proposed for modeling storage effects in e.g. catchments with snow influence.

TL;DR: In this article, a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts is described. But despite the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work.

TL;DR: In this paper, an out-of-sample Long Short-Term Memory (LSTM) network was used for predicting in ungauged basins, where the model was trained and tested on the CAMELS basins (approximately 30 years of daily rainfall/runoff data from 531 catchments in the US of sizes ranging from 4 km to 2,000 km).

TL;DR: It is argued that the effects of COVID-19 are determined mainly by anthropogenic factors which are becoming obvious as human activity diminishes across the planet, and the impacts on cities and public health will be continued in the coming years.

TL;DR: In this article, the influence of climate change and land development on future flood risk for selected Austrian flood-prone municipalities is analyzed. But, the impact of these factors vary strongly across the different cases.