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 …
Climate and land management accelerate the Brazilian water cycle
Increasing floods and droughts are raising concerns of an accelerating water cycle,
however, the relative contributions to streamflow changes from climate and land …
however, the relative contributions to streamflow changes from climate and land …
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] A retrospective on hydrological catchment modelling based on half a century with the HBV model
J Seibert, S Bergström - Hydrology and Earth System Sciences, 2022 - hess.copernicus.org
Hydrological catchment models are important tools that are commonly used as the basis for
water resource management planning. In the 1960s and 1970s, the development of several …
water resource management planning. In the 1960s and 1970s, the development of several …
A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting
G Papacharalampous, H Tyralis - Frontiers in Water, 2022 - frontiersin.org
Probabilistic forecasting is receiving growing attention nowadays in a variety of applied
fields, including hydrology. Several machine learning concepts and methods are notably …
fields, including hydrology. Several machine learning concepts and methods are notably …
LamaH | Large-Sample Data for Hydrology and Environmental Sciences for Central Europe
C Klingler, K Schulz… - Earth System Science Data …, 2021 - essd.copernicus.org
Very large and comprehensive datasets are increasingly used in the field of hydrology.
Large-sample studies provide insights into the hydrological cycle that might not be available …
Large-sample studies provide insights into the hydrological cycle that might not be available …
CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia
This paper presents the Australian edition of the Catchment Attributes and Meteorology for
Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS comprises data for 222 …
Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS comprises data for 222 …
Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins
The distribution of flowmeter data and basin characteristic information exhibits substantial
disparities, with most flow observations being recorded at a limited number of well …
disparities, with most flow observations being recorded at a limited number of well …