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Challenges in modeling and predicting floods and droughts: A review
Predictions of floods, droughts, and fast drought‐flood transitions are required at different
time scales to develop management strategies targeted at minimizing negative societal and …
time scales to develop management strategies targeted at minimizing negative societal and …
[HTML][HTML] Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management
Hydroclimatic extremes such as intense rainfall, floods, droughts, heatwaves, and wind or
storms have devastating effects each year. One of the key challenges for society is …
storms have devastating effects each year. One of the key challenges for society is …
Reconciling disagreement on global river flood changes in a warming climate
An intensified hydrological cycle with global warming is expected to increase the intensity
and frequency of extreme precipitation events. However, whether and to what extent the …
and frequency of extreme precipitation events. However, whether and to what extent the …
Caravan-A global community dataset for large-sample hydrology
High-quality datasets are essential to support hydrological science and modeling. Several
CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) datasets exist …
CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) datasets exist …
[HTML][HTML] Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual …
Long short-term memory (LSTM) models are recurrent neural networks from the field of deep
learning (DL) which have shown promise for time series modelling, especially in conditions …
learning (DL) which have shown promise for time series modelling, especially in conditions …
[HTML][HTML] Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models
This study investigates the ability of long short-term memory (LSTM) neural networks to
perform streamflow prediction at ungauged basins. A set of state-of-the-art, hydrological …
perform streamflow prediction at ungauged basins. A set of state-of-the-art, hydrological …
[HTML][HTML] RF-MEP: A novel Random Forest method for merging gridded precipitation products and ground-based measurements
The accurate representation of spatio-temporal patterns of precipitation is an essential input
for numerous environmental applications. However, the estimation of precipitation patterns …
for numerous environmental applications. However, the estimation of precipitation patterns …
[HTML][HTML] Bias correction of global high-resolution precipitation climatologies using streamflow observations from 9372 catchments
Bias Correction of Global High-Resolution Precipitation Climatologies Using Streamflow
Observations from 9372 Catchments in: Journal of Climate Volume 33 Issue 4 (2020) Jump …
Observations from 9372 Catchments in: Journal of Climate Volume 33 Issue 4 (2020) Jump …
Anthropogenic drying in central-southern Chile evidenced by long-term observations and climate model simulations
The socio-ecological sensitivity to water deficits makes Chile highly vulnerable to global
change. New evidence of a multi-decadal drying trend and the impacts of a persistent …
change. New evidence of a multi-decadal drying trend and the impacts of a persistent …
[HTML][HTML] Progressive water deficits during multiyear droughts in basins with long hydrological memory in Chile
Abstract A decade-long (2010–2020) period with precipitation deficits in central–south Chile
(30–41∘ S), the so-called megadrought (MD), has led to streamflow depletions of larger …
(30–41∘ S), the so-called megadrought (MD), has led to streamflow depletions of larger …