Application of a High‐Resolution Distributed Hydrological Model on a U.S.‐Canada Transboundary Basin: Simulation of the Multiyear Mean AnnualHydrograph and 2011 Flood of theRichelieu River Basin - Lucas‐Picher, Philippe; Arsenault, Richard; Poulin, Annie; Ricard, Simon; Lachance‐Cloutier, Simon; Turcotte, Richard (Journal of Advances in Modeling Earth Systems, 2020)
Abstract During spring 2011, an extreme flood occurred along the Richelieu River located in southern Quebec, Canada. The Richelieu River is the last section of the complex Richelieu basin, which is composed of the large Lake Champlain located in a valley between two large mountains. Previous attempts in reproducing the Richelieu River flow relied on the use of simplified lumped models and showed mixed results. In order to prepare a tool to assess accurately the change of flood recurrences in the future, a state‐of‐the‐art distributed hydrological model was applied over the Richelieu basin. The model setup comprises several novel methods and data sets such as a very high resolution river network, a modern calibration technique considering the net basin supply of Lake Champlain, a new optimization algorithm, and the use of an up‐to‐date meteorological data set to force the model. The results show that the hydrological model is able to satisfactorily reproduce the multiyear mean annual hydrograph and the 2011 flow time series when compared with the observed river flow and an estimation of the Lake Champlain net basin supply. Many factors, such as the quality of the meteorological forcing data, that are affected by the low density of the station network, the steep terrain, and the lake storage effect challenged the simulation of the river flow. Overall, the satisfactory validation of the hydrological model allows to move to the next step, which consists in assessing the impacts of climate change on the recurrence of Richelieu River floods. , Plain Language Summary In order to study the 2011 Richelieu flood and prepare a tool capable of estimating the effects of climate change on the recurrence of floods, a hydrological model is applied over the Richelieu basin. The application of a distributed hydrological model is useful to simulate the flow of all the tributaries of the Richelieu basin. This new model setup stands out from past models due to its distribution in several hydrological units, its high‐resolution river network, the calibration technique, and the high‐resolution weather forcing data set used to drive the model. The model successfully reproduced the 2011 Richelieu River flood and the annual hydrograph. The simulation of the Richelieu flow was challenging due to the contrasted elevation of the Richelieu basin and the presence of the large Lake Champlain that acts as a reservoir and attenuates short‐term fluctuations. Overall, the application was deemed satisfactory, and the tool is ready to assess the impacts of climate change on the recurrence of Richelieu River floods. , Key Points An advanced high‐resolution distributed hydrological model is applied over a U.S.‐Canada transboundary basin The simulated net basin supply of Lake Champlain and the Richelieu River discharge are in good agreement with observations of the 2011 flood The flow simulation is challenging due to the topographic and meteorological complexities of the basin and uncertainties in the observations

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)

Pricing Flood Insurance with a Hierarchical Physics-Based Model - Boudreault, Mathieu; Grenier, Patrick; Pigeon, Mathieu; Potvin, Jean-Mathieu; Turcotte, Richard (North American Actuarial Journal, 2020)
Floods account for a large part of global economic losses from natural disasters. As a result, the private insurance sector is increasingly participating in the financial risk sharing, thus expanding the role of actuaries to flood risk management. In this article, we investigate pricing and spatial segmentation of flood risk in the context of private insurance, meaning that individual risk assessment should minimize adverse selection. As such, we design a hierarchical flood risk model that allows an assessment at the individual level. Our model relies on a chain of physics-based climate, hydrological, and hydraulics modules combined with civil engineering methods to map the distribution of individual flood losses at high resolution. Building on such approach, we design pricing and segmentation methods tailored for flood risk management. We then apply the methods to study flood risk in a small city in the province of Quebec. We calculate premiums, analyze the impacts of risk sharing, set pricing territories consistent with the spatial flood risk, and finally, quantify the impact of greenhouse gas emission scenarios on individual and aggregate losses, premiums, and tail risk measures.
Publisher: Routledge _eprint: https://doi.org/10.1080/10920277.2019.1667830
Keywords: Site_internet_RIISQ

Will Evolving Climate Conditions Increase the Risk of Floods of the Large U.S.‐Canada Transboundary Richelieu River Basin? - Lucas‐Picher, Philippe; Lachance‐Cloutier, Simon; Arsenault, Richard; Poulin, Annie; Ricard, Simon; Turcotte, Richard; Brissette, François (JAWRA Journal of the American Water Resources Association, 2021)
Abstract In spring 2011, an unprecedented flood hit the complex eastern United States (U.S.)–Canada transboundary Lake Champlain–Richelieu River (LCRR) Basin, destructing properties and inducing negative impacts on agriculture and fish habitats. The damages, covered by the Governments of Canada and the U.S., were estimated to C$90M. This natural disaster motivated the study of mitigation measures to prevent such disasters from reoccurring. When evaluating flood risks, long‐term evolving climate change should be taken into account to adopt mitigation measures that will remain relevant in the future. To assess the impacts of climate change on flood risks of the LCRR basin, three bias‐corrected multi‐resolution ensembles of climate projections for two greenhouse gas concentration scenarios were used to force a state‐of‐the‐art, high‐resolution, distributed hydrological model. The analysis of the hydrological simulations indicates that the 20‐year return period flood (corresponding to a medium flood) should decrease between 8% and 35% for the end of the 21st Century (2070–2099) time horizon and for the high‐emission scenario representative concentration pathway (RCP) 8.5. The reduction in flood risks is explained by a decrease in snow accumulation and an increase in evapotranspiration expected with the future warming of the region. Nevertheless, due to the large climate inter‐annual variability, short‐term flood probabilities should remain similar to those experienced in the recent past.

Uncertainty sources in flood projections over contrasting hydrometeorological regimes - Castaneda-Gonzalez, Mariana; Poulin, Annie; Romero-Lopez, Rabindranarth; Turcotte, Richard; Chaumont, Diane (Hydrological Sciences Journal, 2022)
Keywords: Infolettre; Infolettre_Fevrier_2023

Mapping SWE in near real time across a large territory using a particle filter - Odry, Jean; Boucher, Marie-Amélie; Lachance Cloutier, Simon; Turcotte, Richard; Saint-Louis, Pierre-Yves (2020)
In snow-prone regions, snowmelt is one of the main drivers of runoff. For operational flood forecasting and mitigation, the spatial distribution of snow water equivalent (SWE) in near real time is necessary. In this context, in situ observations of SWE provide a valuable information. Nonetheless, the high spatial variability of snowpack characteristics makes it necessary to implement some kind of snow modelling to get a spatially continuous estimation. Data assimilation is thus a useful approach to combine information from both observation and modeling in near real-time. For example, at the provincial government of Quebec (eastern Canada), the HYDROTEL Snowpack Model is applied on a daily basis over a 0.1 degree resolution mesh covering the whole province. The modelled SWE is corrected in real time by in situ manual snow survey which are assimilated using a spatial particles filter (Cantet et al., 2019). This assimilation method improves the reliability of SWE estimation at ungauged sites. The availability of manual snow surveys is however limited both in space and time. These measurements are conducted on a bi-weekly basis in a limited number of sites. In order to further improve the temporal and spatial observation coverage, alternative sources of data should be considered. In this research, it is hypothesized that data gathered by SR50 sonic sensors can be assimilated in the spatial particle filter to improve the SWE estimation. These automatic sensors provide hourly measurements of snow depth and have been deployed in Quebec since 2005. Beforehand, probabilistic SWE estimations were derived from the SR50 snow depth measurements using an ensemble of artificial neural networks (Odry et al. 2019). Considering the nature of the data and the conversion process, the uncertainty associated with this dataset is supposed larger than for the manual snow surveys. The objective of the research is to evaluate the potential interest of adding this lower-quality information in the assimilation framework. The addition of frequent but uncertain data in the spatial particle filter required some adjustments in term of assimilation frequency and particle resampling. A reordering of the particles was implemented to maintain the spatial coherence between the different particles. With these changes, the consideration of both manual snow surveys and SR50 data in the spatial particle filter reached performances that are comparable to the initial particle filter that combines only the model and manual snow survey for estimating SWE in ungauged sites. However, the addition of SR50 data in the particle filter allows for continuous information in time, between manual snow surveys. References: Cantet, P., Boucher, M.-A., Lachance-Coutier, S., Turcotte, R., Fortin, V. (2019). Using a particle filter to estimate the spatial distribution of the snowpack water equivalent. J. Hydrometeorol, 20. Odry, J., Boucher, M.-A., Cantet,P., Lachance-Cloutier, S., Turcotte, R., St-Louis, P.-Y. (2019). Using artificial neural networks to estimate snow water equivalent from snow depth. Canadian water ressources journal (under review)

Combining large-scale and regional hydrological forecasts using simple methods - Fontaine, Nicolas; Boucher, Marie-Amélie; Anctil, François; Odry, Jean; Lachance-Cloutier, Simon; Fortin, Vincent; Turcotte, Richard (Canadian Water Resources Journal / Revue canadienne des ressources hydriques, 2024)
Keywords: Infolettre; Infolettre_Novembre_2023

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