Rain-on-snow (ROS) melt events reduce the amount of water stored in the snowpack while also exacerbating flooding. The hydrologic implications of changing ROS events in a warming climate, however, are still uncertain. This research used a calibrated and validated Soil and Water Assessment Tool (SWAT) hydrologic model, modified with energy budget equations to simulate ROS melt and forced with a climate model ensemble representing moderate greenhouse gas concentrations, to simulate changes to ROS melt in the North American Great Lakes Basin from 1960–2069. The changes to ROS events between the historic period (1960–1999) and mid-century (2040–2069) represent an approximately 30 % reduction in melt in warmer, southern subbasins but less than 5 % reduction in melt in colder, northern subbasins. Additionally, proportionally more rainfall reduces the formation of snowpacks, with area-weighted combined winter and spring rain-to-snow ratios rising from approximately 1.5 historically to 1.9 by the mid-21st century. Areas with historic mean combined winter and spring air temperatures lower than
Rain-on-snow (ROS) melt events can have important implications for winter floods because of the combined impacts of rainwater and snowmelt runoff (Suriano and Leathers, 2018; Leathers et al., 1998). In places where ROS events are common, they have contributed to the majority of extreme floods, including locations in the United States northwest, upper Midwest, northeast, and Appalachians (Li et al., 2019). ROS events can occur across a wide swath of North America and Eurasia in areas that have substantial snowpack (Pomeroy et al., 2016; Rennert et al., 2009; Rössler et al., 2014; Sui and Koehler, 2001; Ye et al., 2008; Musselman et al., 2018), but their impact on hydrology extends beyond the cold season because snowpack conditions throughout the winter and spring influence the availability of groundwater and stream water later in the year (Blahušiaková et al., 2020; Jenicek et al., 2016; Myers et al., 2021b). Compared to thermally driven snowmelt rates, rainfall-based melt events are often more short-lived and intense. As a result, ROS melt produces proportionally more runoff compared to temperature-based snowmelt, with lower rates of infiltration and groundwater recharge (Wilson et al., 1980; Earman et al., 2006). Thus, ROS melt events can lead to snow droughts and reduced water availability after the snow season because of the lost water storage (Harpold et al., 2017; Hatchett and McEvoy, 2018; Blahušiaková et al., 2020; Myers et al., 2021b).
In the North American Great Lakes Basin, ROS melt is associated with over 25 % of the most extreme snowmelt events (Suriano, 2020) and has been shown to influence hydrological droughts later in the year (Myers et al., 2021b). ROS events melt an average of 4 cm of snow per event in the Great Lakes Basin but decreased in frequency by 37 % from 1960–2009 (Suriano and Leathers, 2018). ROS melt typically occurs when a midlatitude cyclone takes a more northerly track, transporting warm, moist air into the basin (Suriano, 2018). At the same time, the snow water equivalent (SWE) available in the snowpack is critical, and average snow depths in the basin decreased by 25 % from 1960–2009 (Suriano et al., 2019). However, the hydrological impacts of changing ROS melt amounts and frequencies in a transient climate are uncertain, as a decrease in snowpack could not only limit the amount of ROS melt but also increase surface runoff from rain on bare ground during cold seasons.
This research combines outputs from an ensemble of downscaled climate models with a version of the Soil and Water Assessment Tool (SWAT) hydrologic model (Arnold et al., 1998) that incorporates a ROS melt modification (Myers et al., 2021b) to simulate climate change impacts to watersheds in the Great Lakes Basin. Our research asks the following: “how does ongoing climate change alter ROS melt and hydrology in the Great Lakes Basin by the mid-21st century?” This research contributes to scientific knowledge by advancing our understanding of climate change impacts to watersheds, particularly concerning the impacts of ROS melt, thereby improving our ability to manage water quantity and quality into the future. It is important to understand these climate change impacts to sustainably manage rivers and prepare for risks, both within the Great Lakes Basin and in ROS-prone regions around the world.
The North American Great Lakes Basin is the Earth's largest fresh surface water system (Environment Canada and USEPA, 1995), including portions of eight USA states and one Canadian province (Fig. 1). To the north, near Lake Superior, snow cover lasts an average of 180 d but is as low as 107 d around Lake Erie (Suriano et al., 2019). The Great Lakes Basin is experiencing a rapidly changing climate (Lehner et al., 2006; Environment Canada and USEPA, 1995), with average annual air temperatures having already risen nearly 1
Study area map of the Great Lakes Basin, showing historical and projected climate data grid points and study river systems.
We used the SWAT hydrological model (Arnold et al., 1998) with a modified snowmelt routine to simulate ROS melt. SWAT simulates hydrology with a water balance of inputs (precipitation), exports (evapotranspiration, surface runoff, groundwater flow, and lateral flow), and soil water storage. SWAT partitions precipitation into rainfall or snowfall, based on whether the air temperature is above or below a temperature threshold (Fontaine et al., 2002). Groundwater flow was simulated with a shallow aquifer water balance, and evapotranspiration was simulated using the Penman–Monteith method (Monteith, 1965; Ritchie, 1972).
A full description of the ROS modification, hydrology simulation, calibration, and evaluation can be found in Myers et al. (2021b). In short, the SWAT source code was modified to include an energy budget equation for snowmelt from the SNOW-17 model (Anderson, 1973, 2006) that simulates ROS melt based on a function of air temperature, precipitation, wind, saturated vapor pressure, and atmospheric pressure. For the SWAT ROS model, air temperature and precipitation are based on existing SWAT model inputs, wind effects on ROS are simulated using an average function for ROS melt from turbulent energy transfer, saturation vapor pressure is based on air temperature, and atmospheric pressure is based on elevation, so no additional data inputs are required. Previously, SWAT would simulate snowmelt using a snowpack temperature that was based on air temperature (Fontaine et al., 2002), which would not consider daily ROS melt events. Using a snowmelt module that can simulate ROS melt, such as our SWAT ROS model, reduces error and leads to more accurate hydrological simulations in the Great Lakes Basin due to the more accurate simulation of the timing of snowmelt (Myers et al., 2021b).
Evaluation statistics for simulating historic streamflow and snowpack.
This study used a calibrated version of the SWAT hydrological model for the
Great Lakes Basin previously developed in Myers et al. (2021b), based on historic climate inputs (Maurer et al., 2007). In Myers et al. (2021b), a sensitivity analysis was performed using the PAWN method (Pianosi and Wagener, 2015) that identified 24 sensitive parameters. The model was then calibrated at the daily time step with the algorithm called a multialgorithm, genetically adaptive multiobjective method (AMALGAM; Vrugt and Robinson, 2007), using 99 stations for streamflow and 50 stations for snowpack snow water equivalent (SWE). SWE was estimated from the gridded North American snow depth dataset (Mote et al., 2018), using a function of snow depth, precipitation, temperature, and time of year (Hill et al., 2019). The Nash–Sutcliffe efficiency (NSE; Nash and Sutcliffe, 1970) and the revised Index of Agreement (
The SWAT ROS model for the Great Lakes Basin simulated historic streamflow at the daily time step, with an average NSE of 0.38 (with 29 % of stations greater than 0.5, 48 % greater than 0.4, and a maximum NSE of 0.71) and an
average
The model simulated historic snowpack SWE at the daily time step, with a MAE
of 26 mm (Fig. 2a–c). Daily snowpack SWE error across the basin ranged from
Climate models for the Representative Concentration Pathway (RCP) 4.5 scenario used in the research. Full names for each modeling center can be found in Table S1 in the Supplement.
The calibrated hydrological model was forced with 1950–2099 climate
projections from downscaled and bias-corrected outputs of the Coupled Model
Intercomparison Project Phase 5 (CMIP5) multimodel ensemble (Taylor et al., 2012; US Bureau of Reclamation, 2013; Maurer et al., 2007). These models were downscaled to a 1
Global climate models (GCMs) can be a major source of uncertainty when modeling the hydrological impacts of climate change (Wang et al., 2020; Chegwidden et al., 2019). Thus, 19 climate models for the Representative Concentration Pathway (RCP) 4.5 were used to account for variation in climate projections (Table 1). We chose to include 19 climate models because that was the total number of models using the RCP4.5 scenario that had been downscaled and bias-corrected in the multimodel ensemble (Maurer et al., 2007). RCP4.5 is a moderate greenhouse gas scenario that considers long-term changes in emissions, land cover change, the global economy, and climate change mitigation (Thomson et al., 2011). The mean of this multimodel ensemble was used to represent our projection (Christensen et al., 2010), with the standard deviation of the GCM ensemble shown in Fig. 3.
Basin-wide ensemble-averaged
Hydrological outputs from the SWAT model were aggregated to the boundaries of regulatory river basins for the USA (Hydrologic Unit Code (HUC) 8; USGS, 2022) and Canada (tertiary-level watersheds; Government of Ontario, 2022) using spatial averaging. Aggregating our subbasins into the regulatory major river basins from the USGS HUC 8 and Ontario tertiary-level watersheds data allowed us to compare SWAT outputs with the existing basin structure and facilitate the comparison and discussion of our results with other studies.
Results were analyzed by comparing the averages and extreme high events among
historic (1960–1999) and mid-21st century (2040–2069) time periods at the subbasin scale, based on water years (1 October to 30 September). The mid-21st century period was the focus for informing water resources management and because of better agreement among the models. Simulations of
ROS changes generally agree across CMIP5 RCPs (4.5 and 8.5) until the mid-century but then diverge by the late century (Musselman et al., 2021a). Calculations of basin-wide averages were weighted by subbasin area. Seasons were defined as winter (December, January, and February), spring (March, April, and May), summer (June, July, and August), and fall (September, October, and November). ROS melt events were defined as days with
Pearson's correlation was used to evaluate the strength of the linear relationships, with significant relationships defined as
Across the Great Lakes Basin, the CMIP5 ensemble average annual precipitation and air temperatures are projected to increase between the historic (1960–1999) period and mid-21st century using RCP4.5. Spatially averaged annual precipitation increases by 53 mm (6.3 %) from
Historic and projected climate changes for the Great Lakes Basin
between the historic (1960–1999) period and mid-21st century (2040–2069) for RCP4.5 ensemble mean combined winter and spring.
Between the historic (1960–1999) period and mid-21st century, winter months generally see an increase in the amount of ROS melt for individual subbasins due to the increased amount of rainfall (not snowfall) under RCP4.5 projections. For instance, the area-weighted median (value of a ranked set, where half of the total area is ranked lower; Willmott et al., 2007) amount of January ROS melt among subbasins increases 59 % from 3.2 mm historically to 5.1 mm by mid-21st century (Fig. 5a). Similarly, area-weighted median February ROS melt rises 50 % from 7.0 mm historically to 10.5 mm in the mid-21st century. In the spring, the amount of ROS melt decreases due to the reduction in snowpack. For instance, the area-weighted median amount of April ROS melt among subbasins decreases 56 % from 52.3 mm historically to 22.9 mm by the mid-21st century.
Changes in RCP4.5 ensemble mean monthly
Mean monthly snowmelt (including temperature-based melt and ROS melt) among individual Great Lakes Basin subbasins is projected to experience a decrease and shift to earlier timing in the spring by the mid-21st century (Fig. 5b) using the RCP4.5 pathway. Historically, the maximum snowmelt overall has been in April, with an area-weighted median of 85.3 mm, while the March median snowmelt has been less at 44.8 mm. By the mid-21st century, the median amount of monthly snowmelt among subbasins reaches a maximum at 44.8 mm in March but drops to only 39.5 mm in April, which is a 54 % decrease during April between the two periods.
Changes in the amount of monthly snowmelt among individual subbasins are affected by changes to ROS melt amounts because days that have ROS melt occurrences account for more than 50 % of the total snowmelt for most subbasins from December through April (Fig. 5c). Temperature-based snowmelt is usually a slower process, while ROS melt events combined with temperature-based melt on these days can rapidly melt snowpack. However, the proportion of melt occurring during December ROS days (compared with all December melt) decreases from an area-weighted median of 71 % historically (1960–1999) to 59 % by mid-21st century (a decrease of 12 %). With warmer temperatures, temperature-based melt can have more of an influence on total snowmelt. The proportion of total annual snowmelt from ROS tends to increase in the northern and eastern parts of the Great Lakes Basin but decrease in the south and west between the historic (1960–1999) period and the mid-21st century by about 5 % in each direction as temperatures warm (Fig. 6a and b). Additionally, snowpack SWE decreases throughout the winter and spring. For instance, by March in the mid-21st century, only 61.6 mm of the area-weighted median snowpack SWE is left in the basin, compared to a median of 104.0 mm historically (a decrease of 41 %; Fig. 5d).
Changes in
ROS melt is affected by the changing climate, and there will be different intensities and frequencies of ROS melt events in the future using the RCP4.5 pathway. The northernmost subbasins near Lake Superior experience the fewest changes in the annual amount of ROS melt between the historic (1960–1999) period and mid-21st century, with less than a 5 % change. However, the central and southern areas of the basin experience large decreases in annual ROS melt, with the greatest reduction in southern subbasins in Michigan and southern Ontario, with a
Additionally, northern and central subbasins around eastern Lake Huron and
the southern shore of Lake Superior tend to see a slight increase in the annual frequency of ROS events of
Following the earlier timing of ROS melt, the center of volume for ROS melt (the day of the water year when half of the total annual ROS melt is passed) decreases between the historic (1960–1999) period and mid-21st century. Historically, the ROS melt center of volume ranged from day 145 (23 February) in the southern part of the Great Lakes Basin to day 207 (26 April) in the northern part (Fig. 7a). By the mid-21st century, the ROS melt center of volume becomes earlier and ranges from day 134 (12 February) to day 198 (16 April), which is approximately 2 weeks earlier (Fig. 7b).
The cause of the reduction in annual ROS melt across the basin is largely from a reduction in snowpack SWE due to the rising air temperatures. Although an increase in ensemble average combined winter and spring precipitation in major river basins by
Changes in
Changes in the annual amount of ROS melt are strongly correlated with historic combined winter and spring snowpack SWE (
Relationships between the historic (1960–1999) conditions and the
percent change from the historic period to mid-21st century for RCP4.5
ensemble means.
Mean historic winter and spring air temperatures have a strong relationship
with changes to ROS melt. Subbasins that have colder combined winter and spring air
temperatures during the historic (1960–1999) period have weaker changes to the amount of ROS melt (
The earlier center of volume for ROS melt has a lagged response on monthly
water yields for the major river basins that lasts throughout the spring but
becomes obscured by summer. There is a positive correlation between changes
in the ROS melt center of volume and March (
Correlations of the change in the COV for ROS melt events between the historic (1960–1999) period and mid-21st century, with the percent change in monthly water yield during that time for spring and summer months. Markers represent projections of the RCP4.5 ensemble mean for individual major river basins. Colors are coded with the latitudinal gradient. The water year lasts from 1 October to 30 September.
It is important to understand the factors affecting spatial variability in ROS changes in the Great Lakes Basin to realize the impacts of this variability on aquatic resources. Spatial variability in ROS melt changes has previously been shown to occur because of differences in latitude, elevation, and atmospheric processes (Pan et al., 2018; Ye et al., 2008; Jeong and Sushama, 2018; Cohen et al., 2015), although there were not large elevation changes in the Great Lakes Basin to observe elevation-based variability at our scale. There are particularly large increases in ROS melt runoff predicted for northeastern North America but decreases in more southern latitudes due to a decrease in snow cover (Jeong and Sushama, 2018). The frequency of ROS melt events can be affected by latitude because of its association with air temperature, precipitation type (rain or snow), and snow cover (Suriano, 2022). This aligns with our findings for the Great Lakes Basin that the change in ROS melt amounts decreases by mid-century and is strongly related to latitude, with the greatest decreases in southern subbasins, where snowpack becomes exhausted, as latitude affects whether mean winter air temperatures will be near the threshold around the freezing and melting points, where ROS is most sensitive to changes in climate.
Similarly, an understanding of the temporal factors affecting variability in ROS changes can provide insights to the timing of hydrological impacts. Temporally, the frequency of ROS events in a warming climate has the potential to increase as more rain falls on snowpack but decline after a warming threshold is reached and the snowpack becomes scarce (Beniston and Stoffel, 2016). For instance, in eastern Russia, the frequency of ROS events has historically increased with warming air temperatures because of more winter rainfall, at a rate of 0.5 to 2.5 events per degree Celsius of air temperature increase, but future increases could be limited by a lack of snow in warmer regions (Ye et al., 2008). Suriano (2022) has found snowfall amounts to be a dominant control on the frequency of North American ROS melt events.
The earlier timing of ROS melt (and earlier passage of its annual center of
volume) has the potential to influence other parts of the hydrologic cycle. In the eastern USA, since 1940, the timing of the center of streamflow volume passing through gages in the winter and spring has become earlier, at a rate of 1.6 d per decade, due to increasing air temperatures in snowmelt-driven regions and earlier snowmelt occurrences (Dudley et al., 2017). A similar trend of earlier snowmelt timing has been found for the western USA as well (Stewart et al., 2004; Musselman et al., 2021b). The earlier timing of snowmelt aligns with what our projections show for the basin overall, due to winter precipitation increases and ROS, supporting that the earlier center of volume for ROS melt could influence spring water yields. Also, following a 2
Changing rain-to-snow ratios can have meaningful impacts on hydrological systems. The rain-to-snow ratio is important because it was previously found to be the primary avenue for changing air temperatures to affect snowpack in the Sierra Nevada of California, USA, exacerbating runoff during early season flooding (Huang et al., 2018). As more precipitation falls as rain rather than snow, the size of floods from rainfall and ROS events can far exceed the size of typical snowmelt-driven floods, due to the rapid contributions of rainfall and ROS runoff, with the largest increases being over 2.5 times in size for the western USA (Davenport et al., 2020). The rain-to-snow ratio can also influence the size and timing of spring snowmelt and summer baseflow (Huntington et al., 2004). Thus, the rain-to-snow ratio could help explain the earlier center of volume (COV) of ROS melt for the Great Lakes Basin by the mid-21st century, since we found that, as the basin-averaged rain-to-snow ratio increases from approximately 1.5 historically to 1.9 by mid-21st century, the COV of ROS melt occurs 2 weeks earlier.
The proportion of total snowmelt from ROS or temperature-based melt also has important hydrological impacts. Research in the western USA has found that climate change can decrease the speed at which snowpack melts, as warmer air temperatures mean that there will be bare ground later in the snowmelt season, while radiative energy fluxes are high, and more snowmelt occurs during the colder part of winter when energy fluxes are low, causing snowmelt to be a slower process (Musselman et al., 2017). This increase in the proportion of temperature-based melt to total snowmelt reflects a transition to slower, earlier snowmelt and helps explain the decrease in high spring streamflow that historically influenced hydrological regimes in the Great Lakes Basin (Hodgkins et al., 2007). The decrease in the proportion of total snowmelt from ROS in the Great Lakes Basin could also contribute to groundwater recharge (Earman et al., 2006; Wilson et al., 1980) and the increases in May water yields in subbasins where the annual amount of ROS decreases (Fig. 9c), as the water would not have been rapidly exported from the subbasins in earlier ROS events.
Previous work by Jeong and Sushama (2018), whose definition of ROS we adopted, produced estimates of historic frequencies of ROS events comparable to ours, with approximately 10–20 ROS days per year in the Great Lakes Basin. Also, Jeong and Sushama (2018) report a 1976–2005 average annual amount of ROS runoff of approximately 100 mm or greater throughout the basin, which is similar to our historic (1960–1999) estimates that were approximately 75 mm annual ROS melt in the southwestern part of the basin and 175 mm in the northeast. Jeong and Sushama (2018) evaluated their model results using observations and found that spatial patterns in ROS were captured reasonably well, although some errors likely were due to data uncertainties rather than model errors. Using a different definition of a ROS event (air temperature
To verify our historic estimates, we identified ROS amounts and frequencies
in observed data using the same approach and definition as our GCM-forced
SWAT model. The historic climate observations were from Maurer et al. (2007), used in Myers et al. (2021b), and our historic SWE observations were from
Myers et al. (2021b), which had been estimated from the daily gridded North American snow depth dataset (Mote et al., 2018), both using the same 1
Climate change is disrupting ROS patterns globally, potentially impacting ecosystems, communities, and economies in regions where these events are prevalent. This study used the Soil and Water Assessment Tool (SWAT) rain-on-snow (ROS) melt model, which builds upon SWAT by incorporating energy budget equations to simulate ROS melt (Myers et al., 2021b), to study the impacts of climate change on ROS melt due to altered snowpack, air temperatures, and precipitation. An ensemble of RCP4.5 climate projections (representing moderate greenhouse gas concentrations) was used to study the relationships. Although combined winter and spring precipitation increases in the Great Lakes Basin by the mid-21st century, compared with historic (1960–1999) amounts, its influence on ROS melt is limited by an exhausted snowpack with warmer air temperatures, particularly for southern subbasins. Combined winter and spring rain-to-snow ratios from the climate input data rise from around 1.5 historically to 1.9 by mid-21st century, so proportionally more rainfall decreases snowpack SWE. Changes in ROS melt are positively correlated with snowpack snow water equivalent and combined winter and spring precipitation.
We find that relationships with ROS patterns and latitude are strong in the Great Lakes Basin, as ROS amounts in northern subbasins that had mean combined winter and spring air temperatures well below freezing are more resilient to air temperature increases, while southern subbasins that had mean combined winter and spring temperatures around freezing historically are more sensitive to changes in air temperature. The changing temperature directly affects whether snowpack would form or melt or whether precipitation would be snow or rain. We expect this result of increased sensitivity for ROS changes to apply to cold regions around the globe with average combined winter and spring air temperatures around 0
We also find there are temporal relationships with ROS melt timing in the Great Lakes Basin by mid-21st century, as the center of volume (the day of the water year when at least half the total ROS melt volume has passed) becomes earlier by approximately 2 weeks when compared to the historic (1960–1999) period. The temporal scale of impacts from this earlier timing on monthly water yields lasts through the spring (positively correlated in March and April but negative in May), although these relationships can be obscured by summer because of changing summer precipitation. Investigations projecting the response of extreme water yields to changing ROS conditions in future climates are an additional avenue for future research, with meaningful implications for water resources management.
Finally, it is important that future work involve collaborations outside the academic realm so that the findings of climate change impacts to ROS melt can inform management of aquatic resources (Meadow and Owen, 2021) and engage communities with the research (Serreze et al., 2021). Future work could also investigate how changing ROS conditions affect other components of the water balance, including groundwater and soil water storage in the Great Lakes Basin. The implications of this work, specifically involving the influence of changing ROS melt on extreme hydrological events and future water availability, in addition to the climate-related sensitivities to changing ROS melt, could help prepare managers of ecosystems and human water use for the climatic changes in the mid-21st century.
The data and SWAT ROS model used in this study are publicly available from Mendeley Data at
The supplement related to this article is available online at:
DTM was responsible for the conceptualization, methodology, software, validation, formal analysis, investigation, data curation, writing of the original draft, review and editing of the paper, and the visualization. DLF was responsible for the conceptualization, methodology, software, resources, writing of the original draft, review and editing of the paper, supervision, project administration, and funding acquisition. SMR was responsible for the conceptualization, methodology, software, resources, writing of the original draft, review and editing of the paper, and visualization.
The contact author has declared that none of the authors has any competing interests.
Any opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of the National Science Foundation. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
We thank Ram Neupane, Alejandra Botero-Acosta, and Dan Li for guidance with this study. We also thank Indiana University's University Information Technology Services High Performance Computing team for technical support. We acknowledge the World Climate Research Program's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table S1) for producing and making available their model output. For CMIP, the USA 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. We also thank Sandra Akkermans and the Integrated Topics in Earth and Environment course at Wageningen University for valuable comments to improve our paper.
This work has been supported by the Indiana University Geography Department, William R. Black Fellowship, Indiana University Sustainability Research Development Grant, National Science Foundation (grant nos. DBI-1564806 and CNS-0521433), Indiana University Pervasive Technology Institute, Lilly Endowment, Inc., Indiana METACyt Initiative, and Shared University Research Grants from IBM, Inc., to Indiana University.
This paper was edited by Daniel Viviroli and reviewed by two anonymous referees.