Hydrological ensemble forecasting using a multi-model framework - Dion, Patrice; Martel, Jean-Luc; Arsenault, Richard (Journal of Hydrology, 2021)

Understanding the influence of “hot” models in climate impact studies: a hydrological perspective - Rahimpour Asenjan, Mehrad; Brissette, Francois; Martel, Jean-Luc; Arsenault, Richard (Hydrology and Earth System Sciences, 2023)
Abstract. Efficient adaptation strategies to climate change require the estimation of future impacts and the uncertainty surrounding this estimation. Over- or underestimating future uncertainty may lead to maladaptation. Hydrological impact studies typically use a top-down approach in which multiple climate models are used to assess the uncertainty related to the climate model structure and climate sensitivity. Despite ongoing debate, impact modelers have typically embraced the concept of “model democracy”, in which each climate model is considered equally fit. The newer Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations, with several models showing a climate sensitivity larger than that of Phase 5 (CMIP5) and larger than the likely range based on past climate information and understanding of planetary physics, have reignited the model democracy debate. Some have suggested that “hot” models be removed from impact studies to avoid skewing impact results toward unlikely futures. Indeed, the inclusion of these models in impact studies carries a significant risk of overestimating the impact of climate change. This large-sample study looks at the impact of removing hot models on the projections of future streamflow over 3107 North American catchments. More precisely, the variability in future projections of mean, high, and low flows is evaluated using an ensemble of 19 CMIP6 general circulation models (GCMs), 5 of which are deemed hot based on their global equilibrium climate sensitivity (ECS). The results show that the reduced ensemble of 14 climate models provides streamflow projections with reduced future variability for Canada, Alaska, the Southeast US, and along the Pacific coast. Elsewhere, the reduced ensemble has either no impact or results in increased variability in future streamflow, indicating that global outlier climate models do not necessarily provide regional outlier projections of future impacts. These results emphasize the delicate nature of climate model selection, especially based on global fitness metrics that may not be appropriate for local and regional assessments.

Using the ERA5 and ERA5-Land reanalysis datasets for river water temperature modelling in a data-scarce region - Gatien, Philippe; Arsenault, Richard; Martel, Jean-Luc; St-Hilaire, André (Canadian Water Resources Journal / Revue canadienne des ressources hydriques, 2023)

Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models - Arsenault, Richard; Martel, Jean-Luc; Brunet, Frédéric; Brissette, François; Mai, Juliane (Hydrology and Earth System Sciences, 2023)
Abstract. 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 model-dependent regionalization methods are applied to 148 catchments in northeast North America and compared to an LSTM model that uses the exact same available data as the hydrological models. While conceptual model-based methods attempt to derive parameterizations at ungauged sites from other similar or nearby catchments, the LSTM model uses all available data in the region to maximize the information content and increase its robustness. Furthermore, by design, the LSTM does not require explicit definition of hydrological processes and derives its own structure from the provided data. The LSTM networks were able to clearly outperform the hydrological models in a leave-one-out cross-validation regionalization setting on most catchments in the study area, with the LSTM model outperforming the hydrological models in 93 % to 97 % of catchments depending on the hydrological model. Furthermore, for up to 78 % of the catchments, the LSTM model was able to predict streamflow more accurately on pseudo-ungauged catchments than hydrological models calibrated on the target data, showing that the LSTM model's structure was better suited to convert the meteorological data and geophysical descriptors into streamflow than the hydrological models even calibrated to those sites in these cases. Furthermore, the LSTM model robustness was tested by varying its hyperparameters, and still outperformed hydrological models in regionalization in almost all cases. Overall, LSTM networks have the potential to change the regionalization research landscape by providing clear improvement pathways over traditional methods in the field of streamflow prediction in ungauged catchments.
Keywords: Brunet_Alain; Infolettre; Infolettre_Fevrier_2023

Impacts of climate change on flood volumes over North American catchments - Ionno, Alexandre; Arsenault, Richard; Troin, Magali; Martel, Jean-Luc; Brissette, François (Journal of Hydrology, 2024)

Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years - Troin, Magali; Arsenault, Richard; Wood, Andrew W.; Brissette, François; Martel, Jean‐Luc (Water Resources Research, 2021)
Abstract Ensemble forecasting applied to the field of hydrology is currently an established area of research embracing a broad spectrum of operational situations. This work catalogs the various pathways of ensemble streamflow forecasting based on an exhaustive review of more than 700 studies over the last 40 years. We focus on the advanced state of the art in the model‐based (dynamical) ensemble forecasting approaches. Ensemble streamflow prediction systems are categorized into three leading classes: statistics‐based streamflow prediction systems, climatology‐based ensemble streamflow prediction systems and numerical weather prediction‐based hydrological ensemble prediction systems. For each ensemble approach, technical information, as well as details about its strengths and weaknesses, are provided based on a critical review of the studies listed. Through this literature review, the performance and uncertainty associated with the ensemble forecasting systems are underlined from both operational and scientific viewpoints. Finally, the remaining key challenges and prospective future research directions are presented, notably through hybrid dynamical ‐ statistical learning approaches, which obviously present new challenges to be overcome in order to allow the successful employment of ensemble streamflow forecasting systems in the next decades. Targeting students, researchers and practitioners, this review provides a detailed perspective on the major features of an increasingly important area of hydrological forecasting. , Key Points This work summarizes the 40 years of research in the generation of streamflow forecasts based on an exhaustive review of studies Ensemble prediction systems are categorized into three classes: statistics‐based, climatology‐based and numerical weather prediction‐based hydrological ensemble prediction systems For each ensemble forecasting system, thorough technical information is provided

Quantifying the evolution of ensemble water temperature forecasts as a function of weather forecast lead-time: case study on the Nechako River watershed - Charles, Paul; Arsenault, Richard; Martel, Jean-Luc; Gatien, Philippe; St-Hilaire, André (Canadian Water Resources Journal / Revue canadienne des ressources hydriques, 2023)

Climate Change and Rainfall Intensity–Duration–Frequency Curves: Overview of Science and Guidelines for Adaptation - Martel, Jean-Luc; Brissette, François P.; Lucas-Picher, Philippe; Troin, Magali; Arsenault, Richard (Journal of Hydrologic Engineering, 2021)

Improved historical reconstruction of daily flows and annual maxima in gauged and ungauged basins - Martel, Jean-Luc; Arsenault, Richard; Lachance-Cloutier, Simon; Castaneda-Gonzalez, Mariana; Turcotte, Richard; Poulin, Annie (Journal of Hydrology, 2023)

Comparing a long short-term memory (LSTM) neural network with a physically-based hydrological model for streamflow forecasting over a Canadian catchment - Sabzipour, Behmard; Arsenault, Richard; Troin, Magali; Martel, Jean-Luc; Brissette, François; Brunet, Frédéric; Mai, Juliane (Journal of Hydrology, 2023)
Keywords: Brunet_Alain

Future flow and water temperature scenarios in an impounded drainage basin: implications for summer flow and temperature management downstream of the dam - Khorsandi, Mostafa; St-Hilaire, André; Arsenault, Richard; Martel, Jean-Luc; Larabi, Samah; Schnorbus, Markus; Zwiers, Francis (Climatic Change, 2023)

Limits of Blue and Green Infrastructures to Adapt Actual Urban Drainage Systems to the Impact of Climate Change - Benoit, Thomas; Martel, Jean-Luc; Bilodeau, Émilie; Brissette, François; Charron, Alain; Brulé, Dominic; Rivard, Gilles; Deslauriers, Simon (Journal of Irrigation and Drainage Engineering, 2025)
Keywords: Infolettre; Infolettre_Mars_2025

Exploring the ability of LSTM-based hydrological models to simulate streamflow time series for flood frequency analysis - Martel, Jean-Luc; Arsenault, Richard; Turcotte, Richard; Castañeda-Gonzalez, Mariana; Brissette, François; Armstrong, William; Mailhot, Edouard; Pelletier-Dumont, Jasmine; Lachance-Cloutier, Simon; Rondeau-Genesse, Gabriel; Caron, Louis-Philippe (Hydrology and Earth System Sciences, 2025)
Abstract. An increasing number of studies have shown the prowess of long short-term memory (LSTM) networks for hydrological modelling and forecasting. One drawback of these methods is the requirement for large amounts of training data to properly reproduce streamflow events. For maximum annual streamflow, this can be problematic since they are by definition less common than middle or low flows, leading to under-representation in the model's training set. This study investigates six methods to improve the peak-streamflow simulation skill of LSTM models used for flood frequency analysis (FFA) in ungauged catchments. These include adding meteorological data variables, providing streamflow simulations from a distributed hydrological model, oversampling peak-streamflow events, adding multi-head attention mechanisms, adding data from a large set of “donor” catchments, and combining some of these elements in a single model. Furthermore, results are compared to those obtained by the distributed hydrological model HYDROTEL. The study is performed on 88 catchments in the province of Quebec using a leave-one-out cross-validation implementation, and an FFA is applied using observations, as well as model simulations. Results show that LSTM-based models are able to simulate peak streamflow as well as (for a simple LSTM model implementation) or better than (with hybrid LSTM–hydrological model implementations) the distributed hydrological model. Multiple pathways forward to further improve the LSTM-based model's ability to predict peak streamflow are provided and discussed.
Keywords: Infolettre_Novembre_2025

The PAVICS-Hydro platform: A virtual laboratory for hydroclimatic modelling and forecasting over North America - Arsenault, Richard; Huard, David; Martel, Jean-Luc; Troin, Magali; Mai, Juliane; Brissette, François; Jauvin, Christian; Vu, Long; Craig, James R.; Smith, Trevor J.; Logan, Travis; Tolson, Bryan A.; et al. (Environmental Modelling & Software, 2023)