# Will Evolving Climate Conditions Increase the Risk of Floods of the Large U.S.-Canada Transboundary Richelieu River Basin?

Philippe Lucas-Picher ID, Simon Lachance-Cloutier ID, Richard Arsenault ID, Annie Poulin ID, Simon Ricard ID, Richard Turcotte ID, and François Brissette ID

**Research Impact Statement:** The flood risk of the Richelieu River simulated with a hydrological model forced by three ensembles of climate simulations should decrease with time, especially for the high emission scenario.

**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.

(KEYWORDS: floods; impacts of climate change; Richelieu River; Lake Champlain; hydrological model; climate simulations; regional climate models; CORDEX; HYDROTEL.)

## INTRODUCTION

Over the last century, many floods have occurred along the Richelieu River (some of them have been described by Shanley and Denner (1999) and by Riboust and Brissette (2016)), which is located on the south shore of the Saint Lawrence River in southern Quebec. Despite significant damages, infrastructure directing water away from areas to be protected have

not been put into place to reduce flood risks. Indeed, after the floods of the 1930s, the Fryers dam was built, but the construction of the levees and the dredging of the shoals upstream, near St-Jean-sur-Richelieu, were never completed. The dam, which was abandoned during the Second World War, was never put into service and so the Lake Champlain-Richelieu River (LCRR) Basin is still unregulated (Riboust and Brissette 2015). In the spring of 2011, the stage of the Richelieu River (Figure 1) largely

Paper No. JAWR-19-0117-P of the *Journal of the American Water Resources Association* (JAWR). Received July 26, 2019; accepted September 21, 2020. © 2020 American Water Resources Association. Discussions are open until six months from issue publication.

Department of Construction Engineering (Lucas-Picher, Arsenault, Poulin, Brissette), École de Technologie Supérieure Montréal, Québec, CAN; and Quebec's Ministry of the Environment and the Fight against Climate Change (Lachance-Cloutier; Ricard; Turcotte), Québec, Québec, CAN (Correspondence to Lucas-Picher: p.lucas.picher@gmail.com).

*Citation:* Lucas-Picher, P., S. Lachance-Cloutier, R. Arsenault, A. Poulin, S. Ricard, R. Turcotte, and F. Brissette. 2021. "Will Evolving Climate Conditions Increase the Risk of Floods of the Large U.S.-Canada Transboundary Richelieu River Basin?." *Journal of the American Water Resources Association* 57 (1): 32-56. https://doi.org/10.1111/1752-1688.12891.
<figure><img src="image_1.png" /><figcaption>FIGURE 1. Mean annual, seven-day summer (June-July-August; JJA) minimum, and one-day spring (March-April-May; MAM) maximum discharge of the Richelieu River observed at Fryers station for the period 1938–2017. The significance level (p-value) of the trends was determined using the Mann-Kendall test.</figcaption></figure>

exceeded all the previous floods, with a peak river discharge reaching 20% above the previous record lasting for more than two months above the flood threshold (1,064 m³/s) (Riboust and Brissette 2016). With an estimated return period exceeding 700 years, this flood was caused by the combination of extreme spring precipitation (500-year recurrence), an important snowpack (15-year recurrence) and a persistent mild spell during the freshet (Riboust and Brissette 2016). This flood was unprecedented in terms of intensity and duration, destructing properties and inducing negative impacts on agriculture as well as fish habitats, costing an estimated C$90M (Saad et al. 2016). Ranked third among Canada's 2011 top 10 weather stories, this natural disaster was one of the Québec's longest ever, wearing out residents physically and mentally (Lucas-Picher et al. 2015).

Following the 2011 flood, on March 19, 2012, the Governments of Canada and of the United States (U.S.) asked the International Joint Commission (IJC), a binational organization that approves projects affecting water levels and flows across the Canadian-American border, to draft a plan of study. The motivation was to examine the causes and impacts of the 2011 spring flooding on Lake Champlain and the Richelieu River and to develop possible flood mitigation measures to prevent important floods to reoccur.

The plan of study, submitted one year later (IJC 2013), put forth four recommendations. One of them consisted in a five-year scientific project that would gather the necessary data and support model development to determine the causes and impacts of flooding and evaluate flood mitigation measures along the shores of Lake Champlain and the Richelieu River. In this project, officially financed and finally launched in 2017, one fundamental question was raised to identify flood mitigation measures: Will climate change affect the hydrological regime, and particularly the flood risks, of the Richelieu River? To address this question, a state-of-the-art hydrological model was implemented on the LCRR basin (Lucas-Picher et al. 2020) and driven by three large multi-resolution ensembles of climate projections. One of the ensembles, ClimEx, consists of a large high-resolution regional climate model (RCM) ensemble (Leduc et al. 2019) and is particularly suited to estimate hydrological extremes by robustly covering the natural variability.

Flowing at its outlet into the Saint Lawrence River in southern Quebec, the Richelieu River is the last part of a large and complex watershed of 23,881 km² (Figure 2) that includes the Lake Champlain sub-basin, mainly located in the northeastern U.S. of Vermont and New York. The Lake Champlain is fed by
<figure><img src="image_2.png" /><figcaption>FIGURE 2. Topography (m) and boundaries of the Lake Champlain-Richelieu River (LCRR) Basin (magenta and gray), Lake Champlain subbasin (gray), and Lake Champlain (red) depicting the surrounding region including the southern Quebec province and northeastern United States. The total drainage area of the LCRR basin is 23,881 km² (Riboust and Brissette, 2015).</figcaption></figure>

small streams of the Adirondack and Green Mountains, which have many peaks above 1,200 m. These small streams become important rivers that flow through a valley into Lake Champlain, which is a large lake with an average depth of 20 m that acts as a reservoir and filters short-term upstream river flow fluctuations. Finally, Lake Champlain discharges in the Richelieu River. The outlet of the Lake Champlain is located at the Canadian-American border and 90% of the LCRR drainage area is located on the American side of the basin, whereas most of the damages of the 2011 flood affected the cities along the Richelieu River on the Canadian side.

Due to the long (100 km) and narrow (30 km) valley where the lake is bordered by the Adirondack and Green Mountains, the LCRR basin has a mixture of local weather conditions with snowy, rainy, and cold mountains that contrast with the drier and warmer valley. The climate of the LCRR basin is, according to the Koppen classification, warm summer continental. Mean daily maximum temperature is above 20°C in

summer (June-July-August) and daily minimum temperature is below -10°C in winter (December-January-February) with, on average, slightly more precipitation in summer (3.5 mm/day or 322 mm/summer) than in winter (2.5 mm/day or 225 mm/winter). For more details about the LCRR basin, the reader is referred to Shanley and Denner (1999) and to Lucas-Picher et al. (2020). Since the paper has a special focus toward spring floods, most of the forthcoming analysis will pay attention to winter (December-January-February) and spring (March-April-May) weather conditions that contribute the most to floods.

As reported by Guilbert et al. (2014), a subregion of the LCRR basin (located in northern Vermont state and southern Quebec province) has been getting warmer and wetter over the last decades. Actually, over the LCRR basin, the mean annual temperature has increased by 1°C (mainly affecting daily minimum temperature) and the mean annual precipitation by 0.7 mm/day since 1915 (Figure 3). More precisely, for the same period, the mean annual minimum
<figure><img src="image_3.png" /><figcaption>FIGURE 3. Mean precipitation (top row), minimum temperature (center row), and maximum temperature (bottom row) in winter (December-January-February [DJF]) (left column), in spring (MAM) (center column), and annually (right column) for the period 1915–2015 over the LCRR basin from the dataset of Livneh et al. (2015). The significance level (p-value) of the trends was determined using the Mann–Kendall test.</figcaption></figure>

temperature rose by 0.14°C per decade (0.19°C in winter and 0.13°C in spring) and the mean annual maximum temperature rose nonsignificantly by 0.05°C per decade (0.07°C in winter and 0.11°C in spring, see Figure 3). The significance level (p-value) of all the trends in this paper was determined using the Mann–Kendall test. The mean annual discharge of around 380 m³/s at the Richelieu River outlet significantly increased (p-value = 0) by 14 m³/s per decade during the period 1938–2017 (Figure 1). As seen on Figure 1, the one-day annual maximum Richelieu River discharge (around 900 m³/s), mainly associated to snowmelt, has been nonsignificantly (p-value = 0.66) decreasing by 4.5 m³/s per decade since 1938. The seven-day annual low flow of approximately 175 m³/s (see Figure 1), usually happening in late summer due to large summer evapotranspiration, increased with a significant trend of around 11 m³/s per decade. The increase in mean flow and seven-day low-flows is in agreement with the increase in mean annual precipitation of 0.07 mm/day (p-value 0) per decade since 1915, which is higher in spring (0.07 mm/day per decade) than in winter (0.05 mm/day per decade) (Figure 3).

The Quebec's Ministry of Public Security determined three thresholds to assess different flood intensities of the Richelieu River at Fryers station (minor: 1,064 m³/s, medium: 1,221 m³/s, major: 1,335 m³/s).

Table 1 indicates the empirical probability of exceedance of these three thresholds (computed as the number of years that the river flow exceeded the specified thresholds over the total number of years) during the period 1950–2015 and 1970–1999. During the former period (1950–2015), 25.8% of springs reached the minor flood level, whereas 4.5% of them reached the medium flood level, and only 1.5% of them reached the major flood level.

According to a few global climate model (GCM) simulations from the fifth phase Coupled Model Intercomparison Project (CMIP5; Taylor et al. 2012), precipitation over the LCRR basin is projected to increase by around 10 mm/year per decade during the twenty-first century, whereas daily temperature is expected to increase by approximately 0.45°C per decade (Guilbert et al. 2014). However, CMIP5 GCMs that have resolutions between 100 and 300 km are not able to simulate regional climate conditions such as those of the Lake Champlain valley (Lucas-Picher et al. 2015). For more than 25 years, RCMs, which use limited area domains that are driven at their lateral boundaries by GCMs, are able to simulate multi-decadal climate simulations efficiently at finer resolutions (10–50 km) (Giorgi 1990). RCM simulations show added-value with a more realistic simulation of regional and local weather processes (Lucas-Picher et al. 2017), leading to improved flood simulations
<table><caption>TABLE 1. Spring probability of exceedance of the Richelieu River at Fryers Station observed and simulated by HYDROTEL when forced by the gridded meteorological dataset of Livneh et al. (2015) for different periods (1950–2015 and 1970–1999). The threshold levels correspond to the minor, medium, and major flood levels determined by the Quebec Ministry of Public Security. The 2011 flood level is also used as a reference.</caption><caption>Observed and simulated spring probability of exceedance</caption><thead><tr><th rowspan="2">Flood</th><th rowspan="2">Threshold<br>m<sup>3</sup>/s</th><th colspan="2">1950–2015</th><th colspan="2">1970–1999</th></tr><tr><th>OBS</th><th>SIM</th><th>OBS</th><th>SIM</th></tr></thead><tbody><tr><td>Minor</td><td>1,064</td><td>25.8%</td><td>16.7%</td><td>30.0%</td><td>26.7%</td></tr><tr><td>Medium</td><td>1,221</td><td>4.5%</td><td>3.0%</td><td>6.7%</td><td>3.3%</td></tr><tr><td>Major</td><td>1,335</td><td>1.5%</td><td>3.0%</td><td>0.0%</td><td>3.3%</td></tr><tr><td>2011</td><td>1,580</td><td>1.5%</td><td>1.5%</td><td>0.0%</td><td>0.0%</td></tr></tbody></table>

(Castaneda-Gonzalez et al. 2019). Using simulations from the NARCCAP project (Mearns et al. 2013), consisting of an ensemble of six RCMs driven by four GCMs forced with the SRES A2 greenhouse gas (GHG) concentrations over most of North America, Rawlins et al. (2012) studied the agreement between the RCMs and the robustness of the climate change signal over northeastern U.S. They found a good agreement between the RCMs for surface temperature, which is projected to increase significantly (2°C–3°C), and to a lesser extent for precipitation, which is expected to increase by the mid-21st Century (2041–2070). Also using the NARCCAP ensemble over the same region, Fan et al. (2015) showed that, generally, the sign of the RCM biases was in agreement with that of its driving GCM biases and that additional RCM biases may counteract the driving GCM biases resulting in decreased combined biases. To push further, this study will take advantage of the recently available North American CORDEX 25- and 50-km simulations (Giorgi et al. 2009; Mearns et al. 2017), which are forced with CMIP5 simulations using recent representative concentration pathway (RCP) GHG scenarios. Furthermore, a state-of-the-art 50-member ensemble of 12-km RCM simulations (Leduc et al. 2019) will also be used to assess the effects of RCM horizontal resolution on hydrological simulations.

Northeastern U.S and southern Quebec Rivers portray generally dominant flows in spring in response to snowmelt and low flows in late summer and winter in response to high summer evapotranspiration and the accumulation of snow in winter. With the warmer and wetter projected future weather conditions described earlier, the hydrological regime may change considerably, especially because the average winter temperature is only a few degrees below 0°C. In a study using a lumped hydrological model and three GCM simulations, Boyer et al. (2010) revealed that the winter/ spring center-volume date (corresponding to the date

where half of the total water volume is cumulated over the January 1–May 31 period) should take place approximately 20 days earlier in spring in 2070–2099 compared to that of 1970–1999 for the Richelieu River. However, the latter study is based on a rather simple approach to correct climate simulations' biases where observed climate variables are scaled toward the monthly climate change deltas. In another study using the same lumped hydrological model (HSAMI), but forced by GCM simulations from the third phase CMIP3, Guay et al. (2015) obtained similar conclusions for southern Quebec Rivers with earlier and reduced future spring floods. While not specified, it is important to note that the studies of Boyer et al. (2010) and Guay et al. (2015) very likely used the Hydro-Quebec potential evapotranspiration (PET) equation that is based solely on minimum and maximum temperatures. Guay et al. (2015) emphasized the impacts of the reduction in snow accumulation associated with the increase in temperature in southern Quebec. In their study where they modeled the Lake Champlain reservoir through a water budget approach, Riboust and Brissette (2015) showed that future extreme spring floods (100-year return periods) might be reduced by approximately 10% according to hydrological simulations driven with CMIP3 GCMs. Finally, over the past eight years, Quebec's Department of environment and climate change published four hydroclimatic atlases describing the future evolution of river discharges of southern Quebec Rivers. Released in 2018, the fourth version of this atlas (DEH 2018), using a distributed hydrological model HYDROTEL (Fortin et al. 2001) driven with CMIP5 simulations foresees that the water availability from rivers in southern Quebec will be higher in winter and lower in summer affecting hydropower production and drinking water supply. Consequently, flood risks should decrease in southern Quebec in spring due to a smaller snowpack in winter.

Unlike statistical downscaling techniques applied to GCM scenarios, RCMs provide an explicit, physically based simulation of the seasonality, size, location, and intensity of historical (HIST) and future extreme storms that lead to improved assessments of flood risks (Salathé et al. 2014). In their study comparing 4-, 12-, and 36-km RCM simulations, Mendoza et al. (2016) highlighted the effect of the RCM's horizontal resolution on basin-averaged precipitation amounts, which in turn also affected the river flow simulations of three high-elevation catchments of the Colorado River Basin. They concluded that the effect of RCM resolution on projected changes might be larger than the effects of the choice of the hydrological model. More recently, Erler et al. (2019) forced a hydrologic model with RCM simulations at different horizontal resolutions to study climate change
impacts of surface water resources on a large Nordic watershed. They found that 90-km regional climate simulations lead to an underestimation of seasonal variability, but that 30-km resolution appears to be adequate to simulate the main surface flow features, and that 10-km RCM simulations generally lead to the best agreement with observed hydrographs.

In addition, reconstructing the 2011 Richelieu River flood using a suite of RCMs and a hydrological model, Lucas-Picher et al. (2015) showed that precipitation and temperature from two 10-km RCM simulations over the complex LCRR basin were improved when compared with their driving reanalysis. They stressed that RCMs, only driven at their lateral boundaries, can improve variables of the driving field and provide additional variables that are especially useful in such a region with a small density of weather stations. Using an ensemble of ten 18-km RCM simulations driving a hydrological model over the U.S., Naz et al. (2016) underlined that the future reduction in snow water equivalent (SWE) might change the hydrological regime of northeastern U.S. Rivers. They found earlier and lower floods in spring: leading sometimes to a shift in the time of occurrence of annual peak runoff from spring to fall. Troin, Caya, et al. (2015) found large benefits in their hydrological modeling results using raw RCM compared to GCM outputs for a large watershed located in a mountainous region. However, for the same basin, once the RCM and GCM outputs were bias-corrected, little differences were found in the assessment of climate change impacts on the simulated streamflows (Troin, Velázquez, et al. 2015). In a study focusing on the impact of increasing spatial resolutions of RCM simulations on flood simulations, Castaneda-Gonzalez et al. (2019) found that floods simulated with finer climate outputs had higher peak discharges than with coarser outputs.

In order to better understand the role of natural variability and obtain a sufficient sample size to explore extreme events, large GCM ensembles became available in recent years (Kay et al. 2015; Kirchmeier-Young et al. 2017). Lately, for the ClimEx project (Leduc et al. 2019), a 50-member CanESM2 ensemble was downscaled to a 12-km horizontal resolution over northeastern North America with the Canadian Regional Climate Model version 5 (CRCM5) for the period 1950–2099. Comparing the GCM ensemble with the RCM ensemble, Leduc et al. (2019) found that local precipitation extremes were better represented at higher resolution, especially over coastal and mountainous regions.

To address the main question of this paper regarding the effects of climate change on the recurrence of floods over the LCRR basin, this study introduces a state-of-the-art methodology and analysis to determine

the impacts of climate change on river flows with a particular interest on floods. To reach this goal, a high-resolution distributed hydrological model (HYDROTEL) was implemented over the LCRR basin (Lucas-Picher et al. 2020). The hydrological model was calibrated using a high-resolution ~7-km gridded observation reference (Livneh et al. 2015) and then driven with the latest climate simulation ensembles CMIP5, CORDEX North America, and ClimEx for RCP 4.5 and 8.5 scenarios. The climate simulations were bias-corrected using the advanced quantile-based daily translation method. Here we assumed, since the LCRR basin has a complex topography, that hydrological simulations forced with the CORDEX 25-km and ClimEx 12-km simulations would provide an opportunity to verify whether or not more realistic simulated flows and extreme events could be achieved.

In the next section, more details about the ensembles of climate simulations and the bias correction technique are provided. Then, in the section "Results", hydroclimate simulations with special attention to floods are presented and analyzed. Finally, results are discussed in the section "Discussion" and conclusions are drawn in the last section "Conclusion".

## METHODOLOGY AND EXPERIMENTAL SETUP

### The Study Basin, the Hydrological Model (HYDROTEL), and the Calibration and Validation of the Model

All the details related to the LCRR basin, the distributed hydrological model (HYDROTEL; Fortin et al. 2001; Turcotte et al. 2007), and its calibration and validation are provided in Lucas-Picher et al. (2020). To summarize, since the discharge of the Richelieu River relies on the Lake Champlain level, the calibration of HYDROTEL was done over 18 tributaries of the Lake Champlain that contribute to 73% of the total lake inflow. HYDROTEL has several tunable parameters that are linked to the vertical water budget in the soil, the snow accumulation and melt, the PET, and the partition of total liquid precipitation into rain and snow. HYDROTEL was calibrated with seven open parameters using a two-step technique (local followed by a global calibration; Ricard et al. 2013) that allows spatial homogeneity of the parameters. The calibration was performed using a novel hybrid optimization approach (Huot et al. 2019) adapted for computationally intensive models that substantially reduces the number of model evaluations required to converge toward an optimum.
The calibration was performed based on the Kling-Gupta Efficiency (KGE) criterion (Gupta et al. 2009). The KGEs for the local calibration of the 18 tributaries of the Lake Champlain varied between 0.69 and 0.89 for the calibration during the period 1992–2003 and between 0.59 and 0.84 for the validation during the period 2004–2013. The KGEs for the global calibration when a unique set of seven parameters were optimized were between 0.5 and 0.86 for the calibration and between 0.33 and 0.83 for the validation. As indicated in Table 1, the calibrated model forced by the observed meteorological dataset of Livneh et al. (2015) underestimated the probability of exceedance of the minor flood level (16.7%) compared to that observed (25.8%), but was in good agreement with the observations for the other flood levels.

In this paper, the multi-year impacts of climate change are assessed on: (1) the sum of the inflows from the 18 tributaries (sum18) of the Lake Champlain, (2) the net basin supply (NBS) of the Lake Champlain (that includes the lake inflows, direct precipitation on the lake and the evaporation of the lake), and (3) the Richelieu River discharge at Fryers station. For extreme floods, the impacts of climate change are only analyzed for the discharge at the Fryers station where discharge floods thresholds were available.

## Ensembles of Climate Simulations

Three ensembles of climate simulations were used to force HYDROTEL. The first set of simulations was selected from the CMIP5 (Taylor et al. 2012) ensemble of GCM simulations. The list of models, their number of members, their resolutions (in degree), and their number of grid cells within the LCRR basin are indicated in Table 2. A total of 82 simulations covering the HIST period (1950–2005), 54 simulations forced with RCP 4.5 and 53 simulations forced with RCP 8.5 for the future period (2006–2100) from 28 GCMs were selected. The second set of (25- and 50-km) simulations came from the North American CORDEX ensemble of RCM simulations (Mearns et al. 2017). The list of models and the resolution of the simulations are indicated in Table 3. A total of 18 simulations covering the HIST period (1950–2005), seven simulations forced with RCP 4.5, and 18 simulations forced with RCP 8.5 for the future period (2006–2099) from 7 RCMs driven by four GCMs were selected. For the 25- and 50-km simulations, approximately 37 and 12 grid cells, respectively, were located within the boundaries of LCRR basin. The third set of simulations was obtained from the ClimEx ensemble (Leduc et al. 2019) consisting of 50 x 12-km RCM simulations from CRCM5 (Martynov et al. 2013)

<table><caption>TABLE 2. List of CMIP5 GCM used in this study. The number of members for the historical (HIST; 1950–2005), RCP 4.5 (2006–2100), and RCP 8.5 (2006–2100), the resolution in degree (long, lat), and the number of grid cells within the LCRR basin are indicated for each GCM.</caption><thead><tr><th>#</th><th>GCM</th><th>HIST</th><th>RCP 4.5</th><th>RCP 8.5</th><th>Resolution (in degree)</th><th>Cells</th></tr></thead><tbody><tr><td>1</td><td>ACCESS1.0</td><td>2</td><td>1</td><td>1</td><td>1.875 x 1.25</td><td>2</td></tr><tr><td>2</td><td>ACCESS1.3</td><td>2</td><td>1</td><td>1</td><td>1.875 x 1.25</td><td>2</td></tr><tr><td>3</td><td>BCC-CSM1.1</td><td>3</td><td>1</td><td>1</td><td>2.8 x 2.8</td><td>1</td></tr><tr><td>4</td><td>BCC-CSM1.1 (m)</td><td>3</td><td>1</td><td>1</td><td>1.125 x 1.1215</td><td>3</td></tr><tr><td>5</td><td>BNU-ESM</td><td>1</td><td>1</td><td>1</td><td>2.8 x 2.8</td><td>1</td></tr><tr><td>6</td><td>CanESM2</td><td>5</td><td>5</td><td>5</td><td>2.8 x 2.8</td><td>1</td></tr><tr><td>7</td><td>CCSM4</td><td>2</td><td>5</td><td>6</td><td>1.25 x 0.9</td><td>2</td></tr><tr><td>8</td><td>CMCC-CM</td><td>1</td><td>1</td><td>1</td><td>0.75 x 0.75</td><td>3</td></tr><tr><td>9</td><td>CMCC-CMS</td><td>1</td><td>1</td><td>1</td><td>1.875 x 1.87</td><td>2</td></tr><tr><td>10</td><td>CNRM-CM5</td><td>10</td><td>1</td><td>1</td><td>1.4 x 1.4</td><td>2</td></tr><tr><td>11</td><td>CSIRO-Mk3.6.0</td><td>10</td><td>10</td><td>10</td><td>1.875 x 1.86</td><td>2</td></tr><tr><td>12</td><td>FGOALS-g2</td><td>1</td><td>1</td><td>1</td><td>2.8 x 2.8</td><td>1</td></tr><tr><td>13</td><td>GFDL-CM3</td><td>5</td><td>1</td><td>1</td><td>2.5 x 2</td><td>0</td></tr><tr><td>14</td><td>GFDL-ESM2G</td><td>1</td><td>1</td><td>1</td><td>2.5 x 2</td><td>0</td></tr><tr><td>15</td><td>GFDL-ESM2M</td><td>1</td><td>1</td><td>1</td><td>2.5 x 2</td><td>0</td></tr><tr><td>16</td><td>GISS-E2-H</td><td>1</td><td>1</td><td>1</td><td>2.5 x 2</td><td>0</td></tr><tr><td>17</td><td>GISS-E2-R</td><td>1</td><td>1</td><td>1</td><td>2.5 x 2</td><td>0</td></tr><tr><td>18</td><td>INM-CM4</td><td>1</td><td>1</td><td>1</td><td>2 x 1.5</td><td>1</td></tr><tr><td>19</td><td>IPSL-CM5A-LR</td><td>6</td><td>4</td><td>4</td><td>3.75 x 1.875</td><td>0</td></tr><tr><td>20</td><td>IPSL-CM5A-MR</td><td>3</td><td>1</td><td>1</td><td>2.5 x 1.25</td><td>1</td></tr><tr><td>21</td><td>IPSL-CM5B-LR</td><td>1</td><td>1</td><td>1</td><td>3.75 x 1.875</td><td>0</td></tr><tr><td>22</td><td>MIROC5</td><td>5</td><td>3</td><td>3</td><td>1.4 x 1.4</td><td>2</td></tr><tr><td>23</td><td>MIROC-ESM</td><td>3</td><td>1</td><td>1</td><td>2.8 x 2.8</td><td>1</td></tr><tr><td>24</td><td>MIROC-ESM-CHEM</td><td>1</td><td>1</td><td>1</td><td>2.8 x 2.8</td><td>1</td></tr><tr><td>25</td><td>MPI-ESM-LR</td><td>3</td><td>3</td><td>3</td><td>1.875 x 1.85</td><td>2</td></tr><tr><td>26</td><td>MPI-ESM-MR</td><td>3</td><td>3</td><td>1</td><td>1.875 x 1.85</td><td>2</td></tr><tr><td>27</td><td>MRI-CGCM3</td><td>3</td><td>1</td><td>1</td><td>1.125 x 1.125</td><td>3</td></tr><tr><td>28</td><td>NorESM1</td><td>3</td><td>1</td><td>1</td><td>2.5 x 1.875</td><td>1</td></tr><tr><td></td><td>Sum</td><td>82</td><td>54</td><td>53</td><td></td><td></td></tr></tbody></table>

Notes: CMIP5, Coupled Model Intercomparison Project 5; GCM, global climate model.

driven with 50 CanESM2 RCP 8.5 simulations (Kirchmeier-Young et al. 2017). With a 12-km resolution, 157 grid cells of the ClimEx simulations were located within the boundaries of the LCRR basin. With such high spatial resolution, regional climate features characterizing the dry valley and the rainy mountains are better represented in the ClimEx simulations (not shown).

Figure 4 shows the spread of the simulations from the three ensembles (CMIP5, CORDEX, and ClimEx)
<table><caption>TABLE 3. List of the CORDEX RCM simulations and their resolutions (in degree) used in this study. A total of 18 historical (1950–2005), 7 RCP 4.5 (2006–2099) (in black), and 18 RCP 8.5 (2006–2099) (in red) simulations were used. The numbers in parenthesis are used to identify the simulations.</caption><thead><tr><th rowspan="2"></th><th colspan="7">RCM</th></tr><tr><th>CRCM5-UQAM</th><th>CRCM5-Ouranos</th><th>RCA4</th><th>RegCM4</th><th>WRF</th><th>CanRCM4</th><th>HIRHAM5</th><th>RCP</th></tr></thead><tbody><tr><td>CanESM2</td><td>0.44° (1)</td><td></td><td>0.44° (2)</td><td></td><td></td><td>0.22° (3)</td><td></td><td>4.5</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>0.44° (4)</td><td></td><td></td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>0.22° (5)</td><td></td><td>8.5</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>0.44° (6)</td><td></td><td></td></tr><tr><td>MPI-ESM-LR</td><td>0.44° (5)</td><td></td><td></td><td></td><td></td><td></td><td></td><td>4.5</td></tr><tr><td rowspan="2">GCM</td><td>0.22° (7)</td><td></td><td></td><td>0.22° (9)</td><td>0.22° (11)</td><td></td><td></td><td>8.5</td></tr><tr><td>0.44° (8)</td><td></td><td></td><td>0.44° (10)</td><td>0.44° (12)</td><td></td><td></td><td></td></tr><tr><td>EC-EARTH</td><td></td><td></td><td>0.44° (6)</td><td></td><td></td><td></td><td>0.44° (7)</td><td>4.5</td></tr><tr><td></td><td></td><td></td><td>0.44° (13)</td><td></td><td></td><td></td><td>0.44° (14)</td><td>8.5</td></tr><tr><td>GFDL-ESM2M</td><td></td><td>0.22° (15)</td><td></td><td>0.22° (16)</td><td>0.22° (18)</td><td></td><td></td><td>8.5</td></tr><tr><td></td><td></td><td></td><td></td><td>0.44° (17)</td><td></td><td></td><td></td><td></td></tr></tbody></table>

using the mean temperature and precipitation over the LCRR basin for the recent past (HIST; 1970–1999) and long-term future (2070–2099) forced by the RCP 4.5 and RCP 8.5 scenarios. For the sake of brevity, only winter and spring are shown in Figures 4–6 because changes over these two seasons affect the spring flood the most. In winter and spring, most CMIP5 simulations during the HIST period have a cold bias, whereas the CORDEX simulations have a mixed behavior around the observations, and the ClimEx simulations have a warm bias. It is noteworthy that the CMIP5 CanESM2 simulations (#6) that are used to drive the ClimEx ensemble are outliers compared to the other CMIP5 simulations with an important warm bias in winter. In spring and winter, most CMIP5, CORDEX, and ClimEx simulations during the HIST period are too wet over the LCRR basin.

To get a better impression of the climate change signal from the simulations of the ensembles, Figure 5 shows the mean temperature and precipitation deltas over the LCRR basin computed by subtracting the mean recent past values (1970–1999) from the long-term mean future values (2070–2099). Most of the CMIP5 simulations and all the CORDEX and ClimEx simulations projected more precipitation in winter and spring, with wetter conditions for RCP 8.5 compared to RCP 4.5. The increase in mean temperature in winter is between 2°C and 6°C for RCP 4.5 and between 4°C and 8°C for RCP 8.5 for the CMIP5 and CORDEX simulations, whereas the ClimEx simulations projected an increase in mean temperature between 4°C and 7°C. In spring, the increases in temperatures of the CMIP5, CORDEX, and ClimEx simulations are similar to those in winter. In spring, more precipitation is projected in the future by the CMIP5 and CORDEX simulations (between 0% and 30%) compared to the ClimEx simulations, with most of them projecting a reduction in precipitation (between 0% and 20%).

## Bias Correction

The bias correction was done on the grids of the climate models. Thus, the gridded observations of Livneh et al. (2015) were initially scaled to each grid of the climate simulations. Then, the simulations described earlier were bias-corrected before forcing HYDROTEL. The quantile-based daily translation bias correction method described in Mpelasoka and Chiew (2009) was selected. In this method, the biases of the climate variables when comparing simulations and observations over the control period (1970–1999) are computed using 100 quantiles day by day using a 30-day moving window to avoid temporal discontinuities. Assuming that the bias during the control period is preserved in the future, the future and HIST simulations are adjusted according to the bias during the control period. The correction of temperature is additive, whereas that of precipitation is multiplicative. For the models with many members, all the members were pooled together to find a unique set of correction factors that were applied to all members for the same model in order to preserve the natural variability. To prevent the bias correction to be affected by the GCM drizzle problem, daily precipitations below 0.1 mm/day were set to zero before correction.

In Figure 6, the spreads of the CMIP5, CORDEX, and ClimEx simulations in Figure 4 are plotted again, but using the bias-corrected variables. All climate simulations during the HIST period are now close to the observations. Accordingly, the future period was also corrected. The remaining differences are linked to the natural variability of the members of the same model and to the fact that the bias corrections are performed on the grid of the climate models where the observations are scaled. The deltas were slightly affected by the bias correction, but most of the climate change signal remained. To provide an
<figure><img src="image_4.png" /><figcaption>FIGURE 4. Precipitation against mean temperature for the CMIP5 (left column), CORDEX (center column), and ClimEx (right column) simulations in winter (DJF; top row) and spring (MAM; bottom row) over the LCRR basin. The HIST (1970–1999) (black), RCP 4.5 (2070–2099) (blue), and RCP 8.5 (2070–2099) (red) simulations and the gridded observations (1970–1999) (green) are shown. The numbers refer to those of the models in Table 2 for CMIP5 and Table 3 for CORDEX.</figcaption></figure>

overview of the mean climate change signals from the different ensembles of simulations after bias correction, Figures 7 and 8 show the ensemble mean (using only the member #1 of each model for CMIP5) of the mean annual cycle of minimum and maximum temperatures, and precipitation for the CMIP5, CORDEX, and ClimEx simulations for RCP 8.5 and RCP 4.5, respectively. Figure 8 does not show ClimEx at RCP 4.5 as the model was only driven by the RCP 8.5 CanESM2 model. As seen previously, the projected increases in maximum temperature from ClimEx are above the ones from CORDEX and CMIP5, whereas the mean increases in precipitation in winter and spring (a bit drier for ClimEx) from the three ensembles are generally in agreement. However, ClimEx projected a larger decrease in mean precipitation in summer and fall, than the other two ensembles. As expected, the warming signal is much larger for RCP 8.5 than for RCP 4.5 for the CMIP5 and CORDEX simulations.

## RESULTS

### Analysis of the Effects of Climate Change on the LCRR Hydrological Regime

Figures 9 and 10 show the ensemble-mean of the mean annual cycle for the sum18, the Lake Champlain NBS, and the flows of the Richelieu River at Fryers station for the recent past (HIST: 1970–1999), short-term (H30: 2020–2049), medium-term (H50: 2040–2069), and long-term (H80: 2070–2099) time horizons forced by each ensemble for RCP 8.5 and RCP 4.5 scenarios, respectively. The observations for the sum18 come from the sum of the observed river discharges recorded by the U.S. Geological Survey for the 18 Lake Champlain tributaries used during the calibration of HYDROTEL, whereas the observed Lake Champlain NBSs were estimated by Environment and Climate Change Canada (ECCC; Boudreau
<figure><img src="image_5.png" /><figcaption>FIGURE 5. Relative delta precipitation (ΔP/P) against delta mean temperature (ΔT) for the CMIP5 (left column), CORDEX (center column), and ClimEx (right column) simulations in winter (DJF; top row) and spring (MAM; bottom row) over the LCRR basin. The delta (Δ) is computed as follows: mean (2070–2099) — mean (1970–1999). The numbers refer to those of the models in Table 2 for CMIP5 and Table 3 for CORDEX.</figcaption></figure>

et al. 2018). Meanwhile, the observed Richelieu River discharges at Fryers station were measured by ECCC. The river discharges simulated using the gridded observations (Livneh et al. 2015) are added to show the ability of HYDROTEL to reproduce the HIST river discharges. As shown in Lucas-Picher et al. (2020), HYDROTEL forced by the gridded observations is able to reproduce the sum18. Then, the simulation of the NBSs, which account for all the tributaries of the Lake Champlain, and also the evaporation and precipitation over the lake, are also well simulated by HYDROTEL when forced by the gridded observations. Finally, the simulation of the Richelieu River discharges at Fryers station, downstream of the Lake Champlain, is reproduced well.

For the three ensembles, the HIST hydrological simulations are in good agreement with the observations highlighting the ability of HYDROTEL to reproduce the mean recent past conditions of sum18, NBS, and Richelieu River discharge forced by bias-corrected HIST climate simulations. The HIST

simulations that are the closest to the observations are those obtained by the hydrological simulations forced by ClimEx and CORDEX, possibly because of the higher spatial resolutions of the climate simulations. In all cases, there is a lack of water in May and June during the transition from flood conditions in spring to low flows in summer. However, the mean high flow at the beginning of April and mean low flow in July and August are well captured by the hydrological simulations for the sum18 and NBS, giving confidence in HYDROTEL to reproduce the annual cycle. As for the validation, when HYDROTEL is forced by the gridded observations (Lucas-Picher et al. 2020), the mean spring flows of the Richelieu River at Fryers station simulated by HYDROTEL when forced by HIST from CORDEX and ClimEx are underestimated by 10%, whereas the mean low flows are overestimated by 30%. The ability to simulate low flows could have been improved using a multi-objective function, but this would have been done at the expense of the ability to reproduce peak flows, which
<figure><img src="image_6.png" /><figcaption>FIGURE 6. Precipitation against mean temperature for the CMIP5 (left column), CORDEX (center column), and ClimEx (right column) bias-corrected simulations in winter (DJF; top row) and spring (MAM; bottom row) over the LCRR basin. The HIST (1970–1999) (black), RCP 4.5 (2070–2099) (blue), and RCP 8.5 (2070–2099) (red) simulations, and the gridded observation (1970–1999) (green) are shown. The numbers refer to those of the models in Table 2 for CMIP5 and Table 3 for CORDEX.</figcaption></figure>

is the main concern in this study. Moreover, the simplified lake parameterization that allows the reproduction of the lake storage and routing effects in HYDROTEL likely affects the ability to simulate low flows (Lucas-Picher et al. 2020).

Paying attention to the future time horizons for RCP 8.5, the reduction in the multi-year mean peak flow in spring forced by all the ensembles is clear with approximately 20%, 30%, and 40% decreases (with respect to HIST) for H30, H50, and H80, respectively, which is in general consistent for the three ensembles. The projected summer low flows differ between the ensembles. The largest streamflow reduction for H80 (>50%) is obtained when HYDROTEL is forced by ClimEx. This behavior could be explained by the fact that the ClimEx simulations project the largest future temperature increase in summer and fall of all the ensembles and a decrease in precipitation in summer and fall compared to the other ensembles that projected almost no change (see

Figure 7). Furthermore, the simulated streamflows forced by CMIP5 and CORDEX at the Fryers station in the future are increased in February and March, probably because of their warmer temperatures and larger percentage of liquid precipitations (not shown). Another interesting feature in the future is the extended mean low flow conditions at the Fryers station that last from July to November for H80 compared to August to September for HIST. The changes in mean flood peak and low flow are smaller for RCP 4.5 than those of RCP 8.5, likely due to the smaller increase in temperature for RCP 4.5 (see Figure 8).

## Analysis of Future SWE

As mentioned earlier, the cyclic spring flood of the Richelieu River is majorly due to the melt of the snowpack. With warmer temperatures in the future, the SWE of the snowpack should diminish, resulting
<figure><img src="image_7.png" /></figure>

FIGURE 7. Mean annual cycle of mean minimum temperature (left column), mean maximum temperature (center column), and mean precipitation (right column) over the LCRR basin for the bias-corrected CMIP5 (top row), CORDEX (center row), and ClimEx (bottom row) simulations for RCP 8.5. The periods for HIST, H30, H50, and H80 correspond to 1970–1999, 2020–2049, 2040–2069, 2070–2099, respectively. Only the first members of each CMIP5 model are presented here.

<figure><img src="image_8.png" /></figure>

FIGURE 8. Mean annual cycle of mean minimum temperature (left column), mean maximum temperature (center column), and mean precipitation (right column) over the LCRR basin for the bias-corrected CMIP5 (top row), and CORDEX (bottom row) simulations for RCP 4.5 and different time horizons. Only the first members of each CMIP5 model are presented here.

in a reduction in the spring flows seen in Figures 9 and 10. The ensemble means of SWE simulated by HYDROTEL over the Lake Champlain subbasin for

the different time horizons and three ensembles are shown in Figure 11. The simulated mean maximum SWE based on the CMIP5 ensemble is slightly above
<figure><img src="image_9.png" /><figcaption>FIGURE 9. Mean annual cycle of the sum of the river discharge of the 18 subbasins (sum18) of Lake Champlain (left column), the net basin supply (NBS) of Lake Champlain (center column), and the river discharge at Fryers station (right column) simulated by HYDROTEL when driven by the CMIP5 (top row), CORDEX (center row), and ClimEx (bottom row) simulations for RCP 8.5 for different time horizons. Only the first members of each CMIP5 model are presented here. When indicated, the observations and the HYDROTEL simulations forced with the gridded observations are smoothed with a 30-day moving average to ease comparisons. [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>FIGURE 10. Mean annual cycle of the sum18 of Lake Champlain (left column), the NBS of Lake Champlain (center column), and the river discharge at Fryers station (right column) simulated by HYDROTEL when driven by the CMIP5 (top row) and CORDEX (bottom row) simulations for RCP 4.5 for different time horizons. Only the first members of each CMIP5 model are presented here. When indicated, the observations and the HYDROTEL simulations forced with the gridded observations are smoothed with a 30-day moving average to ease comparisons. [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

that of SNODAS estimates (Barrett, 2003), whereas those obtained using the CORDEX and ClimEx ensembles are slightly below. The mean reduction in SWE in the future over the Lake Champlain

subbasin is striking. For RCP 8.5 (RCP 4.5), the mean maximum simulated SWE using the CMIP5 ensemble proceed from 90 mm for the HIST period, to 50 (60) mm for H30, to 35 (45) mm for H50, and 15
<figure><img src="image_11.png" /><figcaption>FIGURE 11. Mean annual cycle of mean snow water equivalent simulated by HYDROTEL when driven by the CMIP5 (top row), CORDEX (center row), and ClimEx (bottom row) simulations for RCP 8.5 (left column) and RCP 4.5 (right column) over the Lake Champlain subbasin for different time horizons. The HYDROTEL simulated with gridded observations and Snow Data Assimilation System data cover the period 2004-2013. Only the first member of each CMIP5 model is used for the multimodel mean to give equal weights to each GCM.</figcaption></figure>

(40) mm for H80. The maximum mean SWE is also reached earlier (mid-February for RCP 8.5), indicating a sooner snowmelt period. Results are similar for CORDEX and ClimEx, except that the SWE during the HIST period is smaller.

Impacts of Climate Change on Floods

**Time Series.** The above results give a general impression on the change in the mean hydrograph in the future, but do not provide any indications about the change in variability and extremes, and the intermember variability. In order to investigate extreme hydrological conditions and the variability between the models of an ensemble, Figure 12 depicts the ensemble mean, minimum, maximum, and one

standard deviation of the one-day spring maximum Richelieu River discharge at Fryers station as simulated by HYDROTEL when forced by the three ensembles for each year and for the two GHG scenarios (RCP 4.5 and RCP 8.5). Furthermore, the observed discharge and the HYDROTEL simulation forced by the gridded observation (Livneh et al. 2015) are also indicated to provide context on the 2011 extreme flood and the other observed conditions. First, the intermodel (intermember for ClimEx) variability and extremes simulated by HYDROTEL forced by the CMIP5 simulations are much larger than those forced by ClimEx and CORDEX, likely due to the large variety of models (28). Moreover, with their coarse resolutions, GCMs simulate unrealistic homogeneous weather conditions over large regions and are not capable of capturing the north-south and
<figure><img src="image_12.png" /><figcaption>FIGURE 12. Ensemble mean, minimum, maximum, and ±STDEV of the one-day spring maximum simulated Richelieu River discharge at Fryers station when forced by CMIP5 (top row), CORDEX (center row), and ClimEx (bottom row) simulations for the period 1950–2100 for RCP 8.5 (left column) and RCP 4.5 (right column). Since years 1950 (1955 for ClimEx) and 2006 were used as spin up, their associated values are not displayed in the above graphs. Only the first members of each CMIP5 model are used here.</figcaption></figure>

high-low topography temperature gradients leading to quick snowmelt that generate exaggerated runoff at the watershed scale, artificially enhancing river streamflow variability. Except for year 2011, observations are generally within the standard deviation of the models, showing realistic simulations forced by the ensembles. For the simulations forced by CORDEX and ClimEx, a few maximum discharges during the HIST period (1950–2005) are similar to that observed in 2011. The general trend is toward lower one-day maximum Richelieu River discharge. The reduction in the one-day spring maximum is larger for RCP 8.5 than for RCP 4.5. For the long-term future, extremes such as the one observed in 2011 become less frequent than for the HIST period. This makes sense since the main driver of the spring flood, the SWE, is projected to be largely reduced in the

future. In Figure 13, the analysis of Figure 12 is repeated, but for the mean annual discharge. The negative trend of the mean annual discharge emphasizes the reduction in the annual runoff volume due to an increase in evapotranspiration with warmer conditions in the future that is not compensated by the small projected increase in precipitation. Most of the observed time series of the mean annual discharge falls within the range of those simulated except in the 1960s, where the observations lie beyond the range.

**Return Periods.** Another way to determine the probability of occurrence of specific extremes in a given interval consists of the computation of return periods through a frequency analysis. To perform such an analysis, generalized extreme value (GEV)
<figure><img src="image_13.png" /><figcaption>FIGURE 13. Ensemble mean, minimum, maximum, and ±STDEV of the mean annual simulated Richelieu River discharge at Fryers station when forced by CMIP5 (top row), CORDEX (center row), and ClimEx (bottom row) simulations for the period 1950–2100 for RCP 8.5 (left column) and RCP 4.5 (right column). Since years 1950 (1955 for ClimEx) and 2006 were used as spin up, their associated values are not displayed in the above graphs. Only the first members of each CMIP5 model are used here.</figcaption></figure>

distributions are fitted to the one-day spring maximum Richelieu River discharge for the four periods described earlier (HIST, H30, H50, H80) of each simulation of the three ensembles for the RCP 4.5 and RCP 8.5 scenarios. Stationary conditions were assumed for the fitting of the GEV since the 30-year periods used are relatively short. This approach was used by Martel et al. (2020) who showed that for short time windows, the null hypothesis of an absence of trend for precipitation for large computational domains over North America and Europe was accepted with the same frequency as would be expected from random data. Figure 14 (top) shows the 20-year return period of the one-day maximum Richelieu River discharge in spring (corresponding to a medium flood) for the different ensembles and the

different time horizons using box plots. The same analysis is also performed for the observations (OBS) and the simulations (SIM) forced with the gridded meteorological observations. For the HIST period, the simulations using CMIP5 overestimate the 20-year discharge return period (hereafter 20-year discharge) compared to that observed, likely because of the coarse resolution of the GCMs. The 20-year discharges using the high-resolution CORDEX and ClimEx simulations (with a median close to 1,200 m³/s) are realistic and in agreement with that observed (1,275 m³/s). For the three ensembles, the 20-year discharges are decreasing in the future, especially for the CMIP5-forced simulations (comparing H80 with HIST for the RCP 8.5 scenario, the reduction in the median is 8% for CORDEX and 18% for the ClimEx-
<figure><img src="image_14.png" /><figcaption>FIGURE 14. Box plots of the 20-year return period of the one-day maximum in spring (top row) and mean annual (bottom row) of the HYDROTEL simulated Richelieu River discharge at Fryers station forced by CMIP5 (CM), CORDEX (CO), and ClimEx (CL) simulations for the HIST, H30, H50, and H80 periods for RCP 8.5 (left column) and RCP 4.5 (right column). Results using the observed (OBS) and simulated (SIM) discharge with the gridded observation are also indicated. Only the first members of each CMIP5 model are presented here. Note that the y-axis does not start at zero. In the box plots, the median is in red, the 25th and 75th percentiles are, respectively, at the bottom and top edges of the box, the whiskers represent the most extreme data points not considered as outliers, and the outliers are indicated with the red plus symbols. [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

forced HYDROTEL simulations, whereas for those of CMIP5, the reduction is 35%). The negative trend of the 20-year return period river discharge is smaller for the RCP 4.5 and is less robust for CORDEX-forced simulations since only seven simulations were computed (comparing H80 with HIST for the RCP 4.5 scenario, the reduction is 0% for CORDEX and 23% for CMIP5). The same analysis was repeated for the annual mean Richelieu River discharge (bottom of Figure 14) illustrating the large reduction in the annual runoff volume in the future seen in Figure 13. The largest reduction (40%) of the mean annual discharge is obtained for the simulations forced by ClimEx, which simulated the largest increase in temperature (Figure 7) and a reduction in precipitations in summer and fall in the future.

**Probability of Exceedance.** A last way to analyze the evolution of flood risks in the future is by computing the probability of exceedance of the different flood level thresholds (minor, medium, and major) reported in the introduction. Table 4 indicates these probabilities of exceedance for the HYDROTEL simulations forced by the different ensembles of climate

projections (CMIP5, CORDEX, and ClimEx), time horizons (HIST, H30, H50, and H80), and scenarios (RCP 4.5 and RCP 8.5). The CMIP5 ensemble simulations exhibit higher probability of exceedance than those estimated in Table 1 using observations likely because of the coarse resolutions and the large variety of GCMs. For the CMIP5 ensemble under RCP 8.5, the probability of exceedance for the minor flood levels decreases from 46% (HIST) to 31% (H30), 20% (H50), and 10% (H80). The probabilities are also decreasing for RCP 4.5 but less rapidly to 31% (H30), 25% (H50), and 22% (H80). Still, for the CMIP5 ensemble, the reduction in the probabilities for the other flood levels is also steady and mainly more important for RCP 8.5 than RCP 4.5. For the horizon H30, the probabilities for the RCP 4.5 and 8.5 are comparable, and then the differences between the two scenarios become more important as the time horizons go further in time.

For the HYDROTEL simulations forced by CORDEX and ClimEx ensembles, the probabilities for the HIST period are in better agreement with those observed than for the CMIP5 ensemble, but in general, the probabilities of exceeding the minor flood
<table><caption>TABLE 4. Ensemble mean spring probability of exceedance of the Richelieu River at Fryers Station for the HYDROTEL simulations forced by the CMIP5, CORDEX, and ClimEx ensembles for the periods HIST (1970–1999), H30 (2020–2049), H50 (2040–2069), and H80 (2070–2099) for the RCP 4.5 and RCP 8.5 scenarios. The threshold levels correspond to the minor, medium, major, and 2011 floods. Only the first members of each CMIP5 model are used in this analysis.</caption><thead><tr><th rowspan="2">Flood</th><th rowspan="2">Threshold<br>m³/s</th><th rowspan="2">1970–1999<br>HIST</th><th colspan="2">2020–2049</th><th colspan="2">2040–2069</th><th colspan="2">2070–2099</th></tr><tr><th>RCP 4.5</th><th>RCP 8.5</th><th>RCP 4.5</th><th>RCP 8.5</th><th>RCP 4.5</th><th>RCP 8.5</th></tr></thead><tbody><tr><td colspan="9"><strong>CMIP5 ensemble mean spring probability of exceedance</strong></td></tr><tr><td>Minor</td><td>1,064</td><td>46.2%</td><td>30.5%</td><td>30.7%</td><td>25.2%</td><td>20.3%</td><td>21.8%</td><td>9.5%</td></tr><tr><td>Medium</td><td>1,221</td><td>29.1%</td><td>16.2%</td><td>16.0%</td><td>12.9%</td><td>9.8%</td><td>10.7%</td><td>3.0%</td></tr><tr><td>Major</td><td>1,335</td><td>19.4%</td><td>9.7%</td><td>9.9%</td><td>8.0%</td><td>4.4%</td><td>5.9%</td><td>1.4%</td></tr><tr><td>2011</td><td>1,580</td><td>7.9%</td><td>2.6%</td><td>3.6%</td><td>2.4%</td><td>0.9%</td><td>2.2%</td><td>0.3%</td></tr><tr><td colspan="9"><strong>CORDEX ensemble mean spring probability of exceedance</strong></td></tr><tr><td>Minor</td><td>1,064</td><td>18.0%</td><td>14.8%</td><td>15.0%</td><td>13.8%</td><td>13.0%</td><td>11.9%</td><td>8.5%</td></tr><tr><td>Medium</td><td>1,221</td><td>7.2%</td><td>6.7%</td><td>6.3%</td><td>6.2%</td><td>5.0%</td><td>6.7%</td><td>2.6%</td></tr><tr><td>Major</td><td>1,335</td><td>5.4%</td><td>4.3%</td><td>3.5%</td><td>2.4%</td><td>1.9%</td><td>3.3%</td><td>1.3%</td></tr><tr><td>2011</td><td>1,580</td><td>1.3%</td><td>1.0%</td><td>0.9%</td><td>0.5%</td><td>0.4%</td><td>1.9%</td><td>0.2%</td></tr><tr><td colspan="9"><strong>ClimEx ensemble mean spring probability of exceedance</strong></td></tr><tr><td>Minor</td><td>1,064</td><td>14.5%</td><td></td><td>6.9%</td><td></td><td>5.3%</td><td></td><td>3.4%</td></tr><tr><td>Medium</td><td>1,221</td><td>5.1%</td><td></td><td>2.3%</td><td></td><td>1.6%</td><td></td><td>1.2%</td></tr><tr><td>Major</td><td>1,335</td><td>2.6%</td><td></td><td>0.6%</td><td></td><td>0.3%</td><td></td><td>0.3%</td></tr><tr><td>2011</td><td>1,580</td><td>0.7%</td><td></td><td>0.0%</td><td></td><td>0.0%</td><td></td><td>0.0%</td></tr></tbody></table>

level are underestimated, whereas those of medium and major levels are slightly overestimated. As for the CMIP5-driven simulations, the probabilities related to CORDEX and ClimEx decrease with time, and the largest decreases are for the most part seen for RCP 8.5. The decrease in probabilities is more important for the ClimEx-driven simulations. For example, the probabilities of exceeding the minor flood levels proceed from 15% (HIST) to 7% (H30), 5% (H50), and 3% (H80) for the ClimEx-forced simulations, and from 18% (HIST) to 15% (H30), 13% (H50), and 9% (H80) for the CORDEX RCP 8.5-forced simulations. For the H30 horizon, there is almost no difference between the RCP 4.5 and RCP 8.5 CORDEX-driven simulations and the decrease for the RCP 4.5 simulation is small, especially for the medium and major flood levels where there is a slight increase from H50 to H80. This behavior is likely a consequence of the small sample of simulations (only seven RCP 4.5 CORDEX simulations).

DISCUSSION

This paper addresses the general question: Will climate change impact the hydrological regime, and particularly the flood risks, of the Richelieu River? The answer to this question will help in identifying resilient flood mitigation measures while accounting for climate change. In a related study of the impacts of climate change on the flooding of the endorheic Devils Lake located close to the U.S.-Canada border, Kharel

and Kirilenko (2018) used CMIP3 and CMIP5 ensembles of climate projections to force a hydrological model to estimate future flood risks and evaluate current mitigation measures. They found out that lake overspill risks will likely increase in the future and that more frequent water releases through artificial outlets would be required to mitigate flood consequences.

On the contrary, the results of our study indicate a reduction in flood probabilities, especially in the long-term future and for the RCP 8.5 scenario for which the increase in temperature is the most important. The reduction in floods is smaller for the RCP 4.5 scenario and sometimes almost none for the CORDEX-driven simulations. In the near-term future (2021–2050), the probability of floods is lower than for the HIST period, but given the natural variability, it is still possible to get an extreme flood comparable to that observed in 2011. However, in the long-term future, the probabilities of reaching the 2011 level become very low for RCP 8.5. The main reason behind the reduction in the flood probability is that the projected warmer temperature in the future severely affects the accumulation of SWE in the LCRR basin in winter. Despite a clear trend toward less frequent floods in the future, many uncertainties regarding the chosen hydrological model and methodology will be detailed in the discussion in the following section.

Uncertainties in Hydrological Impacts of Climate Change

An important aspect of the modeling of the hydrological impacts of climate change consists of a
comprehensive consideration of all sources of uncer-
tainties. Generally, the RCM (or GCM) forcing the
hydrological model is the most important source of
uncertainty in hydroclimatic studies (Teutschbein
and Seibert 2010). However, Poulin et al. (2011) and
Velazquez et al. (2013) found that the impact of the
hydrological model structure (lumped, semi-dis-
tributed, and distributed model) can be significant. In
a comprehensive study of many sources of uncer-
tainty, Chen et al. (2011) showed that the choice of a
GCM is consistently a major contributor to the uncer-
tainty. However, the choice of the bias correction
technique and natural variability provides a compara-
ble uncertainty for some hydrological variables.
Finally, still according to Chen et al. (2011), uncer-
tainties linked to the GHG scenario and hydrological
model structure are smaller than those related to the
GCMs and downscaling techniques. In their compar-
ison of bias correction techniques on an ensemble of
RCM simulations, Teutschbein and Seibert (2012)
found that all bias correction techniques improved
hydrological simulations and that distribution map-
ping was the best correction method. In short, all
these studies underline the importance to adequately
cover all sources of uncertainty, and failure to do so
may result in severely biased climate change impact
studies (Chen et al. 2011).

In this study, only one hydrological model was
used and it would have been interesting to use many
of them to estimate the uncertainties related to the
hydrological model. However, HYDROTEL is a recog-
nized and advanced hydrological model, which is well
known in the province of Quebec and used regularly
for operational purposes (DEH 2018). Thus, most
users are aware of the benefits and drawbacks of this
model and it has proven to be quite useful for both
hydrological forecasting and climate change assess-
ments. Furthermore, the resolution of the river net-
work was pushed to a high order to take advantage
of the distributed nature of the model in the rugged
terrain of the LCRR basin. First and foremost,
HYDROTEL was robustly calibrated using 18 tribu-
taries of the Lake Champlain, ensuring a good simu-
lation of the streamflows all over the basin (Lucas-
Picher et al. 2020). Equally, only one bias correction
technique (daily translation) was used in this study.
It would have been preferable to explore many bias
correction techniques, but the daily translation is a
quantile-based method, which is advanced and well
known (Teutschbein and Seibert 2012). It is similar
to the quantile mapping method that provided the
best results in Teutschbein and Seibert (2012). One
should keep in mind that bias correction breaks the
spatial and inter-variable coherence, and assumes
that the bias remains constant over time (Chen et al.
2015).

In return, the uncertainty related to the climate
forcing, known to be one of the most important, is
largely covered in this study with many climate sim-
ulations at different horizontal resolutions and dri-
ven by two GHG scenarios (RCP 4.5 and RCP 8.5).
The hydrological simulations forced by the high-reso-
lution CORDEX and ClimEx simulations provided
the best comparison for the recent past in terms of
variability and mean. These results are likely more
robust in the future, but the ClimEx simulations
driven by CanESM2 are among the ones showing
the warmest future (highest climate sensitivity) and
are generated with only one RCM and one scenario
(RCP 8.5). Overall, according to all climate simula-
tions, the future evolution of climate drivers (less
snow, more evaporation, little increase in precipita-
tion) is conducive to a reduction in flood intensity
and flood frequency. The choice of the hydrological
model and bias correction technique remains impor-
tant and the use of many of them would allow to
sample uncertainties. However, based on the clear
evolution of flood forcings from most climate scenar-
ios, it is likely that the selection of a certain hydro-
logical model or bias-correction technique will only
affect the scale of the reduction, but not the general
conclusions.

The large variability obtained for hydrological
simulations forced by the CMIP5 simulations is
likely due to the large variety of climate models in
this ensemble and to the fact that those simulations
were not downscaled to a higher resolution. Several
reasons explain why no spatial disaggregation was
performed on the CMIP5 ensemble of simulations.
First, when climate simulations are used, spatial
downscaling is performed by HYDROTEL to provide
climate information at each of the many hydrological
units available given the high-resolution river net-
work selected. The spatial downscaling only affects
temperature according to the rate at which air tem-
perature falls with increasing altitude (wet adiabatic
lapse rate). Second, the bias correction was per-
formed on the grid of the climate models, allowing
to keep the spatial consistency from the model,
which is important for hydrological modeling. Third,
since many high-resolution dynamically downscaled
RCM simulations are used in this study, the need to
downscale the CMIP5 GCM simulations became less
necessary. Fourth, the reduction in floods due to cli-
mate change when forced with the GCMs is large
and comparable to that obtained with high-resolu-
tion forcing. Finally, the direct use of the bias-cor-
rected GCM simulations without further
downscaling reduces the cascade of uncertainty and
allows an estimation of the sensitivity related to the
use high-resolution forcing from CORDEX and Cli-
mEx.
## Limitations in the Simulation of the Lake Storage and Routing Effect by HYDROTEL

As specified in Lucas-Picher et al. (2020), Lake Champlain behaves as a large reservoir that dampens short-term fluctuations from upstream rivers through a process known as the lake storage and routing effect. The level of the lake is dependent on the water balance between the inflows and sole outflow toward the Richelieu River. The discharge of the Richelieu River is directly related to the lake level that is likely to decrease in the future due to increased evapotranspiration over the LCRR basin and evaporation of the lake that will not be compensated by additional precipitation over the basin. In HYDROTEL, the lake storage and routing effect is simply simulated by a flow-level relationship where two parameters (C and k) need to be calibrated (Lucas-Picher et al. 2020). The parameter C considers that the outflow of the lake is done through a rectangular section that remains constant in time. A precise simulation of the flow-level relationship would only have been possible through computationally intensive 2D or 3D hydraulic models. Such an analysis is not computationally affordable for the large LCRR basin for climate change studies where hundreds of century-long climate simulations are used to force a hydrological model. The latter does not interfere with the focus of this study on the ability of HYDROTEL to simulate Richelieu River spring floods, and the changes of the amplitude and frequency of those simulated spring floods remain reliable.

## Uncertainties Related to the Choice of the Evapotranspiration Formula

Evapotranspiration is an essential component of the hydrological cycle and is very difficult to measure,

especially over a large area. Seiller and Anctil (2016) evaluated the sensitivity of hydrological projections to 24 PET formulas on a southern Quebec Basin. They found that the choice of PET formula influences hydrological projections and climate change conclusions, particularly in the magnitude of changes during spring and autumn high flows. According to Lofgren et al. (2013), the PET formulas based only on temperature overestimate the evapotranspiration for high temperature and likely its change in climate change studies. Indeed, in one of their examples, the PET unrealistically increased by a factor of 10 during the 21st Century. During the calibration of HYDROTEL, two available PET formulas (Hydro-Québec and Linacre) were tested. The calibration with the Linacre formula outperformed that with the Hydro-Quebec formula, explaining why the former was selected. An effective calibration with a PET formula does not necessarily translate into robust results for climate change studies with warmer temperatures, especially when the PET formula is based almost exclusively on temperature. Ideally, it would have been preferable to test many PET formulas, but only two of them were available in the HYDROTEL model.

In order to evaluate the change in the simulated future evapotranspiration, a simple water budget can be computed. Table 5 shows the water budget using the ensemble mean simulated river discharge by HYDROTEL forced by the CMIP5, CORDEX, and ClimEx simulations and the projected precipitation over the basin from the same ensembles for the HIST and H80 periods for the RCP 8.5 GHG scenario. Then, using a drainage area of 22,000 km² at Fryers station, it is possible to compute the total volume of precipitation over the LCRR basin. After, subtracting the water leaving the basin (the river discharge) from the incoming total precipitation, the total annual amount of water evaporated over the basin can be

<table><caption>TABLE 5. Water budget for the CMIP5, CORDEX, and ClimEx ensemble mean over the LCRR basin during the HIST and H80 periods for the RCP 8.5 greenhouse gas scenario. The values observed and simulated by HYDROTEL when driven by the gridded observations of Livneh et al. (2015) are also indicated.</caption><thead><tr><th></th><th>PCP<br>(mm/day)</th><th>PCP<br>(m³/year)</th><th>Disch.<br>(m³/s)</th><th>Disch.<br>(m³/year)</th><th>Evap<br>(mm/day)</th><th>Evap<br>(mm/year)</th><th>Evap/<br>(m³/year)</th><th>Evap/<br>PCP</th></tr></thead><tbody><tr><td>Observed</td><td>2.91</td><td>2.34E + 10</td><td>386</td><td>1.22E + 10</td><td>1.39</td><td>508.84</td><td>1.12E + 10</td><td>0.48</td></tr><tr><td>Simulated gridded<br>observations</td><td>2.9</td><td>2.33E + 10</td><td>402</td><td>1.27E + 10</td><td>1.32</td><td>482.25</td><td>1.06E + 10</td><td>0.46</td></tr><tr><td>CMIP5 HIST</td><td>2.94</td><td>2.36E + 10</td><td>434</td><td>1.37E + 10</td><td>1.24</td><td>450.98</td><td>9.92E + 09</td><td>0.42</td></tr><tr><td>CMIP5 H80 RCP 8.5</td><td>3.21</td><td>2.58E + 10</td><td>341</td><td>1.08E + 10</td><td>1.87</td><td>682.84</td><td>1.50E + 10</td><td>0.58</td></tr><tr><td>CORDEX HIST</td><td>2.95</td><td>2.37E + 10</td><td>411</td><td>1.30E + 10</td><td>1.34</td><td>487.6</td><td>1.07E + 10</td><td>0.45</td></tr><tr><td>CORDEX H80 RCP 8.5</td><td>3.2</td><td>2.57E + 10</td><td>329</td><td>1.04E + 10</td><td>1.91</td><td>696.39</td><td>1.53E + 10</td><td>0.6</td></tr><tr><td>CLIMEX HIST</td><td>2.9</td><td>2.33E + 10</td><td>405</td><td>1.28E + 10</td><td>1.31</td><td>477.95</td><td>1.05E + 10</td><td>0.45</td></tr><tr><td>CLIMEX H80 RCP 8.5</td><td>2.95</td><td>2.37E + 10</td><td>252</td><td>7.95E + 09</td><td>1.96</td><td>715.52</td><td>1.57E + 10</td><td>0.66</td></tr></tbody></table>

Notes: Disch., discharge; Evap, evapotranspiration; PCP, precipitation.
estimated, assuming steady-state water storage conditions in the basin. The annual evapotranspiration and the fraction of precipitation loss to evapotranspiration for the HIST period simulated by HYDROTEL when forced by ClimEx (477.95 mm/year and 0.45), CORDEX (487.6 mm/year and 0.45), and CMIP5 (450.98 mm/year and 0.42) are in good agreement with those estimated by Sanford and Selnick (2013) (approximately 450 mm/year and 0.45) close to Lake Champlain. The evapotranspiration computed with the observed precipitation and discharge listed in Table 5 for comparison is also in good agreement with that of Sanford and Selnick (2013).

*Comparing the Projected Decrease in the Richelieu River Floods Estimated in This Study with Those of Neighboring Basins Simulated in Another Study*

Based on hydrological simulations forced by 16 bias-corrected, statistically downscaled, and temporally and spatially disaggregated GCM simulations, Demaria et al. (2016) found, for basins near the LCRR basin, a mixture of positive and negative trends of three-day peak flows in the future for the RCP 8.5 scenario. Moreover, in an analysis of the 100-year return period, they found a reduction in the three-day peak flows in the future. These results are generally in agreement with the results of this study, but the trends are less important, likely because of their future time interval around the mid-century (2028–2082). Demaria et al. (2016) revealed that the uncertainty in GCM projections associated with their coarse resolution is not reduced with statistical downscaling and encouraged the use of RCM projections. They also recognized that the bias-correction and temporal disaggregation can contribute to the underestimation of streamflow extremes, which implied that the magnitude of their trends can be conservative. This study addressed those issues using RCM and GCM ensembles and forcing the hydrological model directly with daily outputs.

Another important aspect consists of the size of the basins. The streamflow of a small basin varies considerably and rapidly to short-term extreme precipitation and is more sensitive to interannual variability of climate forcings. At the opposite, the streamflow of a large basin varies less, but on a longer time scale, and is largely influenced by snowmelt in spring. Furthermore, the Richelieu River discharge is largely controlled by the damping effect of Lake Champlain that acts as a large reservoir. It reacts slowly to varying weather conditions. Thus, despite the proximity of other basins that share similar climate and projected climate changes, the streamflows of the Richelieu River are strongly influenced by the large Lake

Champlain and the impacts of climate change on extreme streamflows depend more on long-term climate indicators than do the small neighboring basins.

## CONCLUSIONS

The LCRR basin is a complex basin owing to the large Lake Champlain, located in a narrow valley bounded by two 1,000-m and above mountain ranges, that dampen short-term upstream river flow fluctuations. An unprecedented flood of the Richelieu River in 2011 motivated a Canadian-American study investigating possible mitigation measures to reduce flood risks and damages (IJC 2013). In order to recommend flood mitigation measures that are resilient to climate change, this paper investigated the impacts of climate change on the recurrence of floods of the Richelieu River. To address this question, a state-of-the-art distributed hydrological model was applied to the LCRR basin (Lucas-Picher et al. 2020). This model stands out from previous studies by the high-resolution river network, an original calibration technique considering the NBS of the Lake Champlain, a global calibration of the Lake tributaries, and the use of a new high-resolution observed gridded meteorological dataset. In order to estimate the impacts of climate change, three state-of-the-art multiresolution ensembles of projected climate change simulations were used to force the hydrological model. The ensembles comprised, in addition to the CMIP5 and CORDEX North America simulations, a new high-resolution large ensemble of climate simulations over northeastern North America (from the ClimEx project). The latter ensemble is well suited to explore the natural variability and extreme events. The simulations from these ensembles were bias-corrected using the quantile-based daily translation method before forcing the hydrological model.

The climate change deltas of the three ensembles emphasized the large variety of projected climate change scenarios. Amongst the three ensembles, the ClimEx simulations indicated the warmest future climate. Most models projected a 20% increase in winter precipitation. In spring, the projected changes in precipitation vary between 0% and 30% for CMIP5 and CORDEX, whereas the ClimEx simulations projected a ~10% decrease in precipitation. The mean annual cycle of the simulated river discharge showed a global decrease in the mean flood and mean annual discharge that is more important for the RCP 8.5 than for the RCP 4.5 scenario. For the HIST simulations, it is noteworthy that the hydrological simulations
forced by the high-resolution CORDEX and ClimEx simulations were closer to the observed flows. The substantial projected warming will severely affect the accumulation of snow with a mean peak of SWE expected to drop by 75% for RCP 8.5 and 50% for RCP 4.5 in the long-term future (2070–2099).

An in-depth analysis of the inter-model (CMIP5- and CORDEX-forced simulations) and intermember (ClimEx-forced simulations) variability indicated that most of the observed time series of the one-day spring maximum and mean annual Richelieu River discharge lies within the range of hydrological simulations. The time series of the hydrological simulations indicated a reduction in extreme floods similar to that observed in 2011 and a marked reduction in the mean annual discharge. The reductions are more important for the long-term horizon and for RCP 8.5 compared to RCP 4.5. A frequency analysis highlighted the reduction in the 20-year return period of the one-day spring maximum simulated discharge (corresponding to a medium flood) in the future. In the frequency analysis, the simulated 20-year floods forced by the CORDEX and ClimEx simulations are comparable to those observed, whereas the ones forced by the CMIP5 simulations are greater, likely because of the large variety of climate models and coarse resolution of the CMIP5 simulations. Finally, an analysis using the probability of exceeding four flood levels indicated that the probability of a flood above that of 2011 is almost vanishing in the long-term future for RCP 8.5 and slightly decreasing for RCP 4.5.

A large majority of hydrological simulations indicate a reduction in the mean annual discharge and a reduction in the recurrence of floods on a warmer planet. The two main factors that reduce spring floods in the future are the reduction in the accumulation of snow and the increased evapotranspiration in spring, summer, and fall over the LCRR basin. According to the climate simulations, there will be more liquid precipitation instead of solid in winter, resulting in larger river discharge in winter and lower peak flood in spring. The increased evapotranspiration in spring, summer, and fall in the future will also reduce inflows to the Lake Champlain and increase the lake evaporation. According to the climate simulations, this loss of water over the LCRR basin will not be compensated by the additional precipitation projected in the future. This should lead to a slow reduction in the Lake Champlain level, and thus to a reduction in the Richelieu River discharge that is linked to the level of the lake.

Therefore, flood risks should slowly diminish with time if greenhouse gases emissions remain high. However, the large interannual (natural) variability of the system should keep flood risks relatively the same in the near-term future. Thus, mitigation

measures recommended by the IJC at the end of the project to reduce flood risks should be optimized to a medium and long-term reduction in flood probabilities. The use of three multiresolution ensembles of climate model simulations for two scenarios covers the uncertainties related to climate forcing, known to be the most important in most hydrological studies, to a level unseen previously in the literature.

Further studies involving different hydrological models, different bias correction methods, and different PET formulas would be interesting to better cover the different sources of uncertainties. The hydrological model HYDROTEL includes different submodels that also deserve to be tested. Furthermore, this study considered essentially the inter-model variability using the HYDROTEL simulations forced only by the first member of each CMIP5 model. This choice was done to give equal weights to each CMIP5 model. If ensemble means or variances would have been computed from all available simulations, the models with many members would have received larger weights and the computed statistics would have been artificially weighted toward these multimember models. Thus, despite the attention given to the multi-member ClimEx ensemble, little attention was given to the intermember variability from the multimember CMIP5 GCMs. Thus, more studies are welcome to explore the potential of assigning larger weights to certain models (Chen et al. 2017) and to take advantage of the multi-member model simulations in estimating the natural variability. Some of the considered mitigation measures such as the construction of dams, the conversion of some river sections to wetlands, the dredging of the Richelieu River, and the use of the Chambyl Canal could be tested by a system computing the NBS of the Lake Champlain with HYDROTEL that would force a hydraulic model. Finally, the findings from this study, and especially the benefits of the multiresolution ensembles of climate simulations forcing, will contribute to the forthcoming Hydroclimatic Atlas of Southern Quebec.

## DATA AVAILABILITY

The observed discharge of the Richelieu River at the Fryers hydrometric station is available from the Water Survey of Canada of ECCC at https://www.ca.nada.ca/en/environment-climate-change/services/water-overview/quantity/monitoring/survey.html. The observed discharge of the Lake Champlain tributaries is available from the U.S. Geological Survey at https://waterdata.usgs.gov/nwis/sw. The North American hydrometeorology daily dataset of Livneh et al. (2015) is available at http://ciresgroups.colorado.edu/livneh/data/daily-observational-hydrometeorology-da
ta-set-north-american-extent. The SWE from the
Snow Data Assimilation System (SNODAS) of the
National Weather Service of the National Oceanic
and Atmospheric Administration is available at
https://nsidc.org/data/g02158. The HYDROTEL model
simulations and the NBS of Lake Champlain are
available at https://drive.google.com/drive/folders/
1F6brWN7MDrz_EqsN_OZR3wzBsDJcSRhJ.

ACKNOWLEDGMENTS

The authors are grateful to the anonymous reviewers for their valuable comments that helped in improving the manuscript. The authors thank Dr. Jean Morin from ECCC and scientists of the IJC for their important scientific advice about the paper. The authors also acknowledge the access to the NBS of Lake Champlain developed by ECCC. This work was supported by the International Joint Commission Lake Champlain and Richelieu River Project and by the Département de l'Expertise Hydrique du Ministère de l'Environnement et de la Lutte contre les Changements Climatiques. The production of ClimEx was funded within the ClimEx project by the Bavarian State Ministry for the Environment and Consumer Protection. The CRCM5 was developed by the ESCER center of Université du Québec à Montréal (UQAM; www.escer.uqa.m.ca) in collaboration with ECCC. We acknowledge ECCC's Canadian Centre for Climate Modelling and Analysis for executing and making available the CanESM2 Large Ensemble simulations used in this study, and the Canadian Sea Ice and Snow Evolution Network for proposing the simulations. Computations with the CRCM5 for the ClimEx project were made on the SuperMUC supercomputer at Leibniz Supercomputing Centre (LRZ) of the Bavarian Academy of Sciences and Humanities. The operation of this supercomputer is funded via the Gauss Centre for Supercomputing (GCS) by the German Federal Ministry of Education and Research and the Bavarian State Ministry of Education, Science and the Arts. We acknowledge the World Climate Research Programme's Working Group on Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5. We also thank the climate modeling groups (listed in Table 3 of this paper) for producing and making available their model output. We also acknowledge the U.S. Department of Defense ESTCP for its support of the NA-CORDEX data archive, where Table 3 lists the models and modeling groups that provided the data. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 2 of this paper) for producing and making available their model output. For CMIP the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This work was partly supported by the French National Research Agency under the future investment program ANR-18-MPGA-0005.

AUTHORS' CONTRIBUTIONS

**Philippe Lucas-Picher:** Conceptualization; data curation; formal analysis; methodology; writing-

original draft. **Simon Lachance-Cloutier:** Conceptualization; data curation; formal analysis; funding acquisition; methodology; project administration; supervision; validation; writing-review & editing. **Richard Arsenault:** Conceptualization; formal analysis; funding acquisition; methodology; project administration; supervision; writing-review & editing. **Annie Poulin:** Conceptualization; formal analysis; funding acquisition; methodology; project administration; supervision; writing-review & editing. **Simon Ricard:** Conceptualization; formal analysis; funding acquisition; methodology; project administration; writing-review & editing. **Richard Turcotte:** Conceptualization; formal analysis; writing-review & editing. **François Brissette:** Conceptualization; formal analysis; writing-review & editing.

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