Large-sample hydrology–a few camels or a whole caravan?
F Clerc-Schwarzenbach, G Selleri… - Hydrology and Earth …, 2024 - hess.copernicus.org
Large-sample datasets containing hydrometeorological time series and catchment attributes
for hundreds of catchments in a country, many of them known as “CAMELS”(Catchment …
for hundreds of catchments in a country, many of them known as “CAMELS”(Catchment …
[HTML][HTML] Establishing performance criteria for evaluating watershed-scale sediment and nutrient models at fine temporal scales
Watershed water quality models are mathematical tools used to simulate processes related
to water, sediment, and nutrients. These models provide a framework that can be used to …
to water, sediment, and nutrients. These models provide a framework that can be used to …
[HTML][HTML] Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures
Hydrological models are evaluated by comparisons with observed hydrological quantities
such as streamflow. A model evaluation procedure should account for dominantly epistemic …
such as streamflow. A model evaluation procedure should account for dominantly epistemic …
Streamflow prediction at the intersection of physics and machine learning: A case study of two Mediterranean‐climate watersheds
S Adera, D Bellugi, A Dhakal… - Water Resources …, 2024 - Wiley Online Library
Accurate streamflow predictions are essential for water resources management. Recent
studies have examined the use of hybrid models that integrate machine learning models …
studies have examined the use of hybrid models that integrate machine learning models …
Investigating the model hypothesis space: Benchmarking automatic model structure identification with a large model ensemble
Selecting an appropriate model for a catchment is challenging, and choosing an
inappropriate model can yield unreliable results. The Automatic Model Structure …
inappropriate model can yield unreliable results. The Automatic Model Structure …
To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization
E Acuña Espinoza, R Loritz… - Hydrology and Earth …, 2024 - hess.copernicus.org
Hydrological hybrid models have been proposed as an option to combine the enhanced
performance of deep learning methods with the interpretability of process-based models …
performance of deep learning methods with the interpretability of process-based models …
Time series predictions in unmonitored sites: A survey of machine learning techniques in water resources
Prediction of dynamic environmental variables in unmonitored sites remains a long-standing
challenge for water resources science. The majority of the world's freshwater resources have …
challenge for water resources science. The majority of the world's freshwater resources have …
Stream nitrate dynamics driven primarily by discharge and watershed physical and soil characteristics at intensively monitored sites: Insights from deep learning
We developed a suite of models using deep learning to make hindcast predictions of the 7‐
day average backward‐looking nitrate concentration at 46 predominantly agricultural sites …
day average backward‐looking nitrate concentration at 46 predominantly agricultural sites …
Learning extreme vegetation response to climate drivers with recurrent neural networks
F Martinuzzi, MD Mahecha… - Nonlinear Processes …, 2024 - npg.copernicus.org
The spectral signatures of vegetation are indicative of ecosystem states and health. Spectral
indices used to monitor vegetation are characterized by long-term trends, seasonal …
indices used to monitor vegetation are characterized by long-term trends, seasonal …
[HTML][HTML] Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain
This paper introduces a novel approach for hyperparameter optimization of long short-term
memory networks (LSTMs) to achieve highly accurate hourly streamflow and water level …
memory networks (LSTMs) to achieve highly accurate hourly streamflow and water level …