1
The role of rain-on-snow in flooding over the conterminous United States 2
3
Dongyue Li1,2, Dennis P. Lettenmaier1, Steven A. Margulis2, Konstantinos Andreadis3 4
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1 Department of Geography, University of California Los Angeles 6
2 Department of Civil and Environmental Engineering, University of California Los Angeles 7
3 Department of Civil and Environmental Engineering, University of Massachusetts Amherst 8
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Corresponding author: Dongyue Li ([email protected]) 10
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Key Points: 12
1. Rain-on-snow relates to a large portion of historical flood events, but its runoff contribution to 13
the total flood runoff is modest. 14
2. Rain-on-snow frequency exerts a first order control on the role of rain-on-snow in hydrologic 15
extremes. 16
3. Future role of rain-on-snow in floods will increase in high-elevation areas and will decrease in 17
areas with moderate elevation. 18
Abstract 19
Based on a process-level characterization of historical and future rain-on-snow (ROS) events, we 20
quantify the runoff contribution from ROS to extreme floods and the source of runoff in large 21
ROS events within the conterminous U.S. (CONUS). We find that the regions impacted most 22
heavily by ROS include the West Coast, the major mountain ranges of the western interior, the 23
Upper Midwest, the Northeast, and the lower Appalachians. While 70% of extreme (upper 0.1%) 24
runoff events in these regions have some contribution from ROS, the runoff generated during 25
these ROS events accounts for less than 10% of the total extreme flood runoff; the much larger 26
fraction of extreme runoff is derived either directly from intense rainfall or from clear-sky 27
snowmelt. Rainfall is the dominant source of runoff in ROS events along the West Coast and 28
over the west-facing slopes of the Cascades and Sierra Nevada, while snowmelt dominates ROS 29
runoff in the other regions in the CONUS. Historically, the role of ROS in streamflow extremes 30
is most significant in mid-elevation areas, but this “significant influence zone” will shift to 31
higher elevations in a warmer future; ROS will account for more of the extreme runoff in the 32
high elevations of the mountainous West and the Upper Midwest, but less in areas with low and 33
moderate elevations in the West and almost the entire East. The future ROS frequency changes 34
exert a first order control on the future change of the runoff contribution from ROS to extreme 35
floods. 36
1. INTRODUCTION 37
Rain-on-snow (ROS) occurs during periods when liquid precipitation falls on a pre-existing 38
snowpack. ROS is common in seasonally snow covered areas and has significant hydrologic and 39
geomorphologic impacts. Intense rainfall and snowmelt that contribute to runoff during ROS 40
events erode soil [Swanston 1974] and redistribute sediment and organic debris [Fredriksen 1965, 41
Sandersen et al., 1997], leading to landslides in areas of high relief [Harr 1981] and contribute to 42
river channel morphology [Bergman 1987]. ROS is also a major trigger of avalanches in 43
maritime climates when rainfall weakens the bond between snow grains and reduces the 44
structural strength of the snowpack [Singh et al. 1997, Conway et al., 1988, Heywood, 1988; 45
Conway and Raymond, 1993]. ROS initiates melting of snowpacks that would otherwise stay 46
frozen, leading to earlier melt onset and longer exposure of soil to the sun and warmer air, which 47
in turn leads to drier soils in the summer that can have broad environmental and ecological 48
implications [Cohen et al., 2015]. Hydrologically, floods associated with ROS events have a 49
long history of causing costly property damage and loss of life [Musselman et al., 2018]. For 50
example, the farmland, cities, and transportation systems of western Oregon have been impacted 51
by ROS flooding numerous times with recorded accounts as early as the mid-1800s when the 52
region was first settled [Harr 1981]. The December 1964 ROS flood in Oregon and Northern 53
California caused over $120 M (1960s dollars) of damage in that region [Waananen et al., 1971]. 54
More recently, the February 2017 ROS events in the Sacramento-San Joaquin Delta caused 55
extensive damage to Oroville Dam’s spillways and led to a massive evacuation of 188,000 56
downstream residents. Kattelmann [1997] and Brunengo [1990] found that most of the largest 57
floods in coastal regions of western North America have been associated to some degree with 58
ROS. 59
Both observation-based (e.g. Sui and Koehler [2001], Garvelmann et al. [2004]) and model-60
based (e.g. Marks et al., 1998, Musselman et al., 2018) studies have explored the mass and 61
energy balance processes associated with ROS events and the mechanisms that produce ROS 62
floods. Snowmelt and rainfall contribute to ROS runoff simultaneously and it is difficult to 63
distinguish the two, although some studies have attempted to quantify the contributions 64
separately. For example, Wayand et al. [2015] studied three mountainous river basins along the 65
U.S. west coast, and found that snowmelt was responsible for only about one-third of the runoff 66
during ROS events. Musselman et al. [2018] found that the portion of ROS runoff attributable to 67
snowmelt generally was higher in the interior of the U.S. West than along the coast, and could be 68
as high as two-thirds in the Rocky Mountains. Studies of other regions around the world have 69
reported that the contribution of snowmelt to runoff in ROS events ranges from 4% to 75% [e.g. 70
Harr, 1981; Singh et al., 1997; Marks et al., 1998, 2001; Sui and Koehler, 2001; Garvelmann et 71
al., 2015] (it should be noted that the definition of what constitutes an ROS event differs among 72
these studies). Other studies have investigated the mechanisms that are responsible for the large 73
runoff during major ROS events. Singh et al. (1997) found that in addition to intense rainfall and 74
snowmelt, large runoff in ROS events also occurs due to the enhanced movement of liquid water 75
through saturated snowpacks (at rates as high as 6 m/hour) as compared with percolation of 76
clear-sky snowmelt (typical rates less than 1.0 m/hour), as a result of snow microstructure 77
metamorphism and the formation of preferential flow pathways in saturated snowpacks. Some 78
studies have also investigated snowpack energy fluxes and their impacts on the snowmelt in ROS, 79
and found that turbulent heat fluxes (latent and sensible) played a more important role in melting 80
snow in ROS than in non-rainy conditions, especially in non-forested areas where snow has large 81
wind exposure that enhances heat transfer [Marks et al. 1998, Garvelman et al., 2014, Wayand et 82
al. 2015, Van Heeswijk et al. 1996]. However, in terms of the dominant energy flux for 83
snowmelt in ROS events, conclusions vary. For example, Marks et al. [1998] studied the 84
February 1996 ROS event in Oregon and found that snowmelt from turbulent fluxes accounted 85
for 60-90 percent of the total snowmelt in the 5-day ROS event, while Mazurkiewicz et al. [2008] 86
studied ROS events from 1996-2003 in the Pacific Northwest (PNW), and found that net 87
radiation (particularly the enhanced longwave radiation in overcast weather [Garvelmann et al. 88
2014]) was the largest contributor to snowmelt during ROS. Wayand et al. [2015] explored other 89
factors that affect ROS, including the specific basin hypsometry, forest cover, and characteristics 90
of individual ROS events such as the elevational distribution of antecedent SWE and the 91
available energy for melt; their work indicates that snowmelt and runoff generation processes 92
can vary substantially among ROS events. 93
Most ROS studies share similar characteristics. First, they are event-based and area-specific. 94
The majority rely on measured hydrological and meteorological data from a specific ROS event 95
or from a specific area that is frequently affected by ROS; large-scale ROS analyses have been 96
limited by a lack of consistent measurements and thus are rare [Cohen et al., 2015]; one 97
exception is the work by Musselman et al. [2018] focusing on the western U.S. Second, 98
compared with studies of the physical processes associated with individual ROS events, there are 99
few studies of ROS in the broader context of hydrologic extremes and predictions of future 100
changes in ROS events and their role in hydrologic extremes. Furthermore; relatively little is 101
known about the relative importance of ROS and non-ROS events to hydrologic extremes, 102
notwithstanding the importance of such information for flood risk prediction. For example, even 103
in areas with frequent ROS-induced floods, possible increases in ROS intensity do not 104
necessarily imply increased flood risk [Sharma et al. 2018]. Therefore, it is critical to 105
characterize both ROS floods and the flood risk from other sources (e.g. intense rainfall) in areas 106
that are susceptible to ROS floods. 107
Here, we examine model-based results that provide a basis for estimating the role of ROS in 108
extreme flood events in space and time over the conterminous United States (CONUS). We also 109
examine, based on model results, how ROS floods are likely to change in the future. The model 110
we use (Variable Infiltration Capacity, or VIC, see Liang et al., 1994 for details) resolves 111
precipitation states, elevational temperature and precipitation gradients, and snowpack 112
persistence and melt that all can affect how ROS events manifest as runoff. We also conducted a 113
systematic basin-wide flood risk analysis over the CONUS to evaluate the connection between 114
ROS and predicted 100-year floods. Our motivation is to answer the following questions: 115
Q1. How much does ROS contribute to flood runoff across the CONUS domain? How do 116
(spatially distributed) ROS runoff contributions relate to basin-scale flooding? 117
Q2. Is rain or snowmelt the dominant source of ROS runoff in a spatially distributed sense? 118
What are their relative contributions to ROS runoff? 119
Q3. What snow processes and associated energy fluxes are primarily responsible for snowmelt 120
during ROS? How much does each process contribute to the snowmelt generated during 121
ROS? 122
Q4. In a warmer climate, how will the contribution of ROS to large runoff events change, and 123
what will be the implications of these changes for flood risk over the CONUS? 124
2. STUDY AREA 125
This study was carried out over the CONUS (Figure 1). High-elevation regions are clustered 126
in the Western U.S., mostly in the Cascades, Sierra Nevada, and Rocky Mountain ranges. In 127
comparison, topographic relief in the eastern part of the domain is less; the Appalachians are the 128
only major mountain range in this part of the domain (Figure 1a). Temperature changes over the 129
period 1991-2012 compared to the 1901-1960 average, based on data from The Third National 130
Climate Assessment [Melillo et al. 2014] (Figure 1b). Warming has occurred in all regions, but is 131
less pronounced in the Southeast. The average temperature increase for 1991-2012 as compared 132
with 1901-1960 was 1.2 , with temperature increases in the major mountain areas (especially 133
the West) that average about 2 . 134
3. METHODS 135
The experiments we report are grouped as follows. First, we report a set of model runs that 136
are intended to provide insight into the physical mass and energy exchange processes in ROS 137
events. Second, we report model runs that are intended to characterize flood risk associated with 138
ROS events. Third, we evaluate the spatial patterns of these two model runs to explore the 139
connection between ROS and floods across the CONUS. 140
3.1 Hydrologic modeling 141
We used the Variable Infiltration Capacity (VIC) hydrologic model (Version 4.2d) to 142
simulate SWE and runoff over the entire CONUS. The VIC model has been described in 143
numerous publications, e.g. Liang et al. [1994], and Andreadis et al. [2009]. In this study we 144
used the VIC snow model to simulate the detailed snow mass and energy balance processes, 145
including mass and energy transfers to and from the atmosphere and the overlying vegetation via 146
snow accumulation on vegetation and the underlying snowpack, drip and mass release of snow 147
intercepted by vegetation, and sublimation from both snow in the vegetation canopy and the 148
underlying pack. In addition, we subdivided each VIC grid-cell into up to five elevation bands 149
and further subdivided each elevation band into up to twelve vegetation tiles to characterize the 150
effects of terrain and vegetation on sub-pixel snow variability; we activated this partial snow 151
coverage function to better capture snow spatial variability. The VIC model as we implemented 152
it requires four primary time-varying forcings: daily precipitation, maximum and minimum 153
temperature, and wind speed. The other time-varying forcing fields required by the model 154
(downward shortwave and longwave radiation, atmospheric pressure and vapor pressure) are 155
calculated internally based on MTCLIM algorithms [Hungerford et al., 1989]. VIC output 156
includes time-series of hydrologic variables (e.g. soil moisture, runoff, and evapotranspiration), 157
snow states (e.g. SWE), and energy fluxes (e.g. reflected shortwave radiation and emitted 158
longwave radiation, latent and sensible heat). 159
We used the Livneh et al. [2015] data set (hereafter referred to as L15) as the VIC forcing 160
data. L15 is available daily at 1/16° (~6 km) resolution over North America (south of 53° N) for 161
the period 1950–2013. We used only the CONUS part of L15, which included ~210,000 grid 162
cells. L15 makes orographic adjustments to precipitation and temperature using PRISM (Daly et 163
al., 1997) climatology from 1961 to 1990. We also adopted the L15 VIC parameterization. The 164
L15 VIC forcing and the model parameterization have been calibrated together over major river 165
basins of the CONUS as a part of L15 study, so that the combined application of the L15 VIC 166
forcing and parameterization have been shown to be able to simulate both snow (discussed in 167
Section 4) and streamflow that agree well with observations, as reported in Livneh et al. (2015). 168
L15 forcings and the associated VIC parameterization have been applied in numerous studies 169
(e.g. Barnhart et al. 2016, Henn et al. 2018). We did not perform additional calibration to the 170
model in this study. The VIC modeling was carried out at an hourly time step so as to better 171
simulate the diurnal characteristics of the energy exchange and the snowpack evolution. While 172
L15 forcings are daily, the MTCLM module automatically adjusted the daily forcing inputs for 173
each hour to reflect the diurnal cycle while maintaining the prescribed daily average for each 174
forcing variable. We conducted the hourly simulations for the 64-year period of 1950 to 2013. In 175
the interest of streamlining data storage and analysis, we aggregated hourly model results to 176
daily for output. 177
We used the Geospatial Attributes of Gages for Evaluating Streamflow version II (GAGES II, 178
Falcone et al., 2010) dataset from USGS to evaluate the VIC streamflow estimates. Of the total 179
9322 GAGE II streamflow gages over the U.S., 2057 are classified as reference gages (generally 180
meaning no upstream regulation or diversions); we only used the data at the 311 reference gages 181
that have over 50 years of data between 1950 and 2009; USGS streamflow measurements were 182
collected for these 311 gages. The published GAGES II data include the boundaries of the 183
upstream drainage areas for each gage. At each selected reference gage, we compared the peak 184
flood each year (i.e. the annual maximum series, or AMS) from the streamflow measurements 185
and from the VIC model, since the AMS is used later as the input for the flood risk analysis. 186
We calculated model output streamflow using the direct aggregation of the runoff and 187
baseflow over the drainage area for each gage, i.e., not routed through the stream network. We 188
made this simplification because most routing models (e.g. the VIC routing model [Lohmann et 189
al., 1998]) suitable for large-scale applications are based on the unit hydrograph and/or the 190
linearized St Venant’s Equation. Therefore, once routing parameters are set, the difference 191
between the routed streamflow and the aggregated streamflow is mostly a timing shift that will 192
minimally affect the magnitude of AMS (because the daily AMS series are dominated by the 193
total runoff and baseflow available within a basin). Note that the T-year flood risk analysis (e.g. 194
the 100-year flood magnitude in this study) needs only the magnitude of AMS and is irrelevant 195
with the its timing. We performed comparisons of routed streamflow and aggregated runoff in 196
several test basins with varying drainage areas (see supplemental material section S1), which 197
showed that runoff routing vs aggregation differences in both streamflow timing and magnitude 198
at the daily time scale are modest. 199
We used the Sierra Nevada SWE reanalysis (SNSR) dataset [Margulis et al., 2016] to 200
evaluate the modeled SWE. The SNSR data were produced by assimilating Landsat snow-201
covered area observations into a snow model via a particle batch smoother [Margulis et al., 202
2016]. SNSR has a high spatial-resolution (90 m) and is available daily for the period 1985 to 203
2015 over the portion of the Sierra Nevada above 1500 m elevation, and the data well reproduce 204
the space-time variations of in-situ observed SWE, with a mean-squared error less than 3 cm and 205
a correlation with 9000 station-years SWE observations greater than 0.95 [Margulis et al. 2016]. 206
3.2 Flood risk estimation 207
We used the Generalized Extreme Value (GEV) distribution to assess flood risk based on 208
VIC-simulated runoff. Specifically, we estimated the GEV distribution with time-varying 209
parameters using the Nonstationary Extreme Value Analysis (NEVA) package [Cheng et al. 210
2014] to characterize the flood risk in a changing climate. NEVA is a generalized framework for 211
estimating flood Intensity-Duration-Frequency curves for both the nonstationary and stationary 212
hydrologic responses to the climate change. It first tests for the presence of trends in the AMS 213
using the Mann-Kendall trend test (Kendall 1975; Mann 1945). Upon detection of a trend at the 214
5% significance level (α=0.05), the GEV distribution is estimated with time-varying parameters. 215
Absent a trend at the 5% significance level, the standard stationary GEV distribution parameters 216
are used. In both the stationary and nonstationary cases, NEVA estimates the GEV parameters 217
with a Bayesian approach implemented using a Differential Evolution Markov Chain (DE-MC, 218
Vrugt et al. [2009]; Ter Braak [2006]) for global optimization over the parameter space [Cheng 219
et al., 2014].We carried out the flood risk analysis for all HUC-6 basins in the CONUS using the 220
AMS derived from the aggregated streamflow. 221
We evaluated the flood risk analysis at the 248 GAGES II reference gages over the CONUS 222
that have over 50-year’s data record from 1950 to 2009 and have drainage areas larger than 100 223
km2. At each of these gages, we calculated the 100-year flood magnitude based on both the VIC 224
modeled streamflow aggregated from the gage’s upstream drainage area and the observed 225
streamflow, using the same NEVA flood risk estimation procedure. 226
3.3 ROS characterization 227
We adopted the criteria in Fruediger et al. [2014] to define ROS days in this study. Fruediger 228
et al. defined a ROS day as one having at least 3 mm of rain falling on a snowpack with at least 229
10 mm SWE, and for which snowmelt makes up at least 20% of the sum of the rainfall and 230
snowmelt for the day. These criteria are designed to identify ROS days that have flood-231
generating potential. Experiments reported in Fruediger et al. [2014] showed that these criteria 232
successfully captured those ROS days that contribute to flood events, and effectively removed 233
spurious ROS days. This ROS definition was also applied with slight adjustments by Musselman 234
et al. [2018] to identify ROS over the Western U.S. In this study the criteria were used to identify 235
every ROS day over the 64-year study period at each grid cell. After identifying the ROS days 236
for all grid cells, we calculated the ROS frequency in days/year, and also calculated the centroid 237
of timing of the ROS days based on the rainfall intensity-weighted average of the ROS timing in 238
days of the water year (i.e., days from October 1). Note that all our analyses are ROS day-based; 239
i.e. there was no attempt to define independent events, where some events could consist of 240
multiple consecutive days. 241
We quantified the relative importance of ROS to both large and extreme runoff over the 242
CONUS. To do so, at each model grid cell, we first selected the 200 days with the largest 243
simulated runoff during the 64-year study period (defined as the large runoff days, or 244
“Day_LARGE_200”), and also selected the 20 days with the largest simulated runoff (defined as the 245
extreme runoff days, or “Day_EXTRM_20”). Note that the two sets are not exclusive of each other 246
(i.e. Day_EXTRM_20 is included in the set of Day_LARGE_200 at each grid cell). The selected 247
Day_LARGE_200 and Day_EXTRM_20 include both ROS days and non-ROS days. Herein we refer to 248
the ROS days among Day_LARGE_200 and Day_EXTRM_20 as “Day_LARGE_ROS” and “Day_EXTRM_ROS”, 249
respectively. We further define the total number of Day_LARGE_200, Day_EXTRM_20, Day_LARGE_ROS, 250
and Day_EXTRM_ROS as ND_LARGE_200, ND_EXTRM_20, ND_LARGE_ROS, and ND_EXTRM_ROS, 251
respectively. Note that ND_LARGE_200 is 200 and ND_EXTRM_20 is 20 by definition, whereas 252
ND_LARGE_ROS and ND_EXTRM_ROS vary among grid cells. We analyzed only the large and extreme 253
runoff days (and the ROS days within them) to focus on days that are likely to contribute to 254
hydrologic extremes; Day_LARGE_200 and Day_EXTRM_20 represent on average about the largest 1% 255
and the largest 0.1% of the daily runoff in the 64-year period, respectively. 256
Hereafter, we refer to the total runoff from Day_LARGE_200 as “Q_LARGE_200”, and the total 257
runoff from Day_LARGE_ROS as “Q_LARGE_ROS”. Similarly, the total runoff from Day_EXTRM_20 is 258
referred to “Q_EXTRM_20”, and the total runoff from Day_EXTRM_ROS is referred to “Q_EXTRM_ROS”. 259
At each model grid cell, we calculated the ratio of ND_LARGE_ROS to ND_LARGE_200, and the ratio 260
of ND_EXTRM_ROS to ND_EXTRM_20 to explore the extent to which Day_LARGE_200 and Day_EXTRM_20 261
are ROS-related. We also calculated the ratio of Q_LARGE_ROS to Q_LARGE_200 and the ratio of 262
Q_EXTRM_ROS to Q_EXTRM_20 at each grid cell to evaluate the overall contribution of the runoff 263
volume from ROS to the large and extreme runoff. 264
We calculated the contribution of rainfall and snowmelt to Q_LARGE_ROS and Q_EXTRM_ROS to 265
determine the dominant hydrologic source of the runoff in the identified ROS days, and to 266
explore the spatial pattern of the dominant source over the CONUS. In the calculation at each 267
grid cell, we summed up the snowmelt and the rainfall in Day_LARGE_ROS and Day_EXTRM_ROS , and 268
divided by Q_LARGE_ROS and Q_EXTRM_ROS, respectively. The snowmelt, rainfall, and runoff 269
required for these calculations are all model output. To better understand the snowmelt processes 270
associated with ROS, we partitioned the snowmelt generated on Day_LARGE_ROS and 271
Day_EXTRM_ROS based on the different energy sources that drive snowmelt. In particular, we 272
investigated the (positive) energy transfer into the snowpack from net radiation, sensible heat, 273
latent heat (condensation), and advection heat transfer from rainfall. These energy fluxes have 274
been identified as the dominant energy sources for ROS snowmelt in previous studies. To 275
quantify the snowmelt associated with each of these energy fluxes, we calculated the ratio of 276
each flux to the total incoming energy to the snowpack that was responsible for the total 277
snowmelt in Day_LARGE_ROS and Day_EXTRM_ROS. The details of the calculation of each energy 278
flux are summarized in S2 in supplemental material. 279
3.4 Characterizing future ROS and floods 280
To explore how ROS is likely to change and how these changes will be reflected in future 281
flood risk, we created a delta-warming by uniformly increasing the air temperature in our VIC 282
forcings from 1950-2013 by 2 while holding the other forcing variables unchanged. Previous 283
studies have found that average 20th century warming over the CONUS has been on the order of 284
1 per century, and the rate of warming in the second half of the century is about double that of 285
the first half century, with much of the observed warming occurring after about 1975 [Melillo, 286
2014]. In contrast to the robust increasing trend in temperature, there is little evidence of large-287
scale precipitation trends in the 20th century, especially for winter season precipitation [Cayan et 288
al., 1998; Mote et al., 2005]. These findings form the basis of our delta-warming set up. The 289
uniform temperature increase over the original L15 temperature data preserves the historical 290
warming trend and thus the non-stationarity of the climate warming contained in the observation-291
based L15 data. Furthermore, insofar is it constitutes a sensitivity test rather than a scenario 292
analysis (as, for instance, would be the case with downscaled climate model projections), it 293
avoids confounding warming effects with, for instance, changes in precipitation and other model 294
forcing variables (such as surface wind) that may well change over time as well. We conducted 295
the same hydrologic modeling and the flood risk modeling as in section 3.1 and 3.2 for the future 296
case with the delta-warming. 297
We compared the spatial pattern of the future change of Q_LARGE_ROS/Q_LARGE_200 with the 298
spatial pattern of the future change of the 100-year flood magnitude to explore the role of ROS in 299
flooding events. We used Q_LARGE_ROS/Q_LARGE_200 in the comparison because the 200 large ROS 300
days roughly correspond to the largest 1% of the runoff steps in the study period. Since the 100-301
year flood is a flood that has 1% chance of occurring in any given year, thus the large runoff and 302
the 100-year flood share a statistical correspondence. We carried out the comparisons at the 303
HUC-6 basin level in the regions that showed significant ROS impact (as revealed from the 304
analysis discussed above). We aggregated the historical and future Q_LARGE_ROS/Q_LARGE_200 at 305
the grid cell level to HUC-6 basins by calculating the weighted average Q_LARGE_ROS/Q_LARGE_200 306
across all the grid cells within each HUC-6 basin, using Q_LARGE_200 at each grid cell as weights. 307
4. RESULTS AND DISCUSSION 308
4.1 Hydrologic and snow model evaluation 309
The VIC modeled SWE captured the details of the snow distribution within the CONUS and 310
agree well with the SNSR SWE in both space and time (Figure 2). Since largest ROS floods are 311
often associated with the rapid melt of anomalous snowpacks [McCabe et al 2007], the spatial 312
pattern of the greater-than-20mm historical mean annual maximum SWE (Figure 2a) provides a 313
general delineation of potentially highly ROS-impacted areas. Our ROS analysis focuses only on 314
these areas with at least 20 mm of mean annual maximum SWE since the low snow 315
accumulation in the other areas is less likely to lead to ROS-related extreme floods. Historically, 316
the western mountains have the largest snow accumulations, where the model grid-averaged 317
SWE can exceed 3 m on mountain peaks and divides. However, the mid-elevation transition 318
zone generally has the largest ROS flood-generating potential [Wayand et al. 2015]. Large snow 319
accumulation elsewhere in CONUS primarily occurs in the Northeast, the Upper Midwest, and 320
the Lower Appalachians. The maximum SWE decreases over the entire CONUS if the air 321
temperature was uniformly increased over the entire 1950-2013 period by (Figure 2b); the 322
largest declines would occur in the mountains, and the snow accumulation in many lower-323
elevation areas almost entirely gone as most snowfall would change to rain. Figure 2b illustrates 324
the areas that are most likely to undergo future ROS changes. 325
The total SWE in the Sierra Nevada from the VIC modeling and from the SNSR data are 326
consistent in the comparison period (Figure 2c). The timing of the Sierra Nevada-wide annual 327
maximum SWE from the VIC modeling highly correlates with that from the SNSR SWE (Figure 328
2d), with R=0.958 (p<0.05) and a root mean square difference (RMSD) of 8 days. The largest 329
timing differences occur in water years (WY) 1990 and 2002; aside from those two years the 330
dates of peak SWE are mostly within 2 days. In the winters of WY1990 and WY2002, there 331
were two major SWE accumulation events, as a result the total SNSR SWE accumulation had 332
double peaks with similar values separated by about one-month. While the VIC SWE also 333
captured both peaks, the larger VIC peak was the one further from the SNSR peak, leading to a 334
relatively large difference in the peak timing in those two years. The domain-wide peak SWE 335
volume comparison between the two datasets (Figure 2e) also show high agreement (R=0.959, 336
p<0.05). The RMSD of the peak annual SWE volume is 2.2 km3, which is about 12 percent of 337
the mean peak annual volume of 19 km3. Comparison of the spatial distribution of the peak 338
annual SWE from VIC modeling and from the SNSR data in the highest (WY1993, Figures 2h 339
and 2i) and lowest (WY1990, Figures 2f and 2g) water years in the comparison period shows 340
that the VIC model SWE captures the SWE variability both spatially (e.g. the SWE difference in 341
low elevation vs. high elevation) and temporally (e.g. inter-annual variability, also in Figure 2c). 342
While we did not perform SWE evaluation in regions other than the Sierra Nevada due to the 343
absence of observations that have similar accuracy and spatiotemporal continuity as the SNSR 344
SWE dataset, the Sierra Nevada has a particularly complex terrain and atmospheric variability 345
and we expect the model accuracy to be comparable elsewhere, which is suggested by more 346
limited pointwise model and observation comparisons in Mote et al. (2018). 347
We evaluated the streamflow and flood risk modeling results with those from the 348
observations (Figure 3). The modeled AMS from VIC is unbiased compared with the 349
observations at the 311 reference gages, especially for the gages and years with large streamflow 350
(Figure 3a). The modeled AMS has average uncertainty of 22.4% and a R-value of 0.79 (p<0.05) 351
in comparison with the observed AMS. The well-agreement between the cumulative density 352
functions of the modeled and observed AMS (Figure 3b) suggests that the two AMS series have 353
consistent statistical characteristics. To evaluate the flood risk modeling, we compared the 100-354
year flood magnitude estimated by NEVA from the VIC modeled AMS and from the observed 355
AMS at the 248 GAGES II reference gages that have over 50-years of data from 1950-2009 and 356
an upper stream drainage area larger than 100 km2 (Figure 3c). The 100-year flood magnitude 357
estimate based on VIC modeled AMS is unbiased in comparison with that based on the observed 358
AMS, with R=0.82 (p<0.05), an overall mean weighted relative uncertainty of 6%, and similar 359
statistical characteristics (Figure 3d). Overall, these comparisons show that the VIC modeling 360
generates plausible streamflow estimates, and the default NEVA setup and parameterization 361
yield unbiased flood magnitude estimates with reasonable accuracy based on the VIC modeled 362
streamflow. 363
4.2 Historical ROS characterization 364
The areas with high historical ROS frequency (based on ROS day selection described in 365
section 3.3) over the CONUS include the Western mountains, the Upper Midwest, the Northeast, 366
and the lower the Appalachians (Figure 4a). As discussed, our ROS analysis focuses on areas 367
with at least 20 mm of mean historical maximum annual SWE. In these ROS-impacted regions, 368
ROS occurs multiple times a year and almost every year; ROS is a part of the local seasonal 369
water cycle and a consistent contributor to the local runoff when normal snow accumulation is 370
available. Within the CONUS, the Pacific Northwest (PNW) is the most ROS-impacted region 371
with the largest ROS frequency, mainly due to the deep seasonal snow on the Cascades and the 372
tremendous rainfall caused by the orographic uplift of moist air in warm/moisture-rich 373
atmospheric river events [Ralph and Dettinger, 2011]. The west-facing slopes of both PNW and 374
the Sierra Nevada have more frequent ROS than the east-facing slopes (Figure 4a), because the 375
west-facing slopes are directly exposed to rainfall, while the east-facing slopes are in the 376
orographic rainfall-shadow. Mid-elevation mountains (1500 m-2500 m) are most sensitive to 377
ROS, while other mountainous areas are less ROS-impacted, due to lower-elevation areas having 378
less snow accumulation and higher-elevation areas (e.g. the southern Sierra Nevada) having 379
snow-dominated winter precipitation; both factors tend to constrain ROS. In the East, ROS 380
occurs mainly in the Northeast, the lower Appalachians, and the Upper Midwest. These areas are 381
mostly adjacent to large water bodies. For instance, the Upper Midwest is affected by the Great 382
Lakes, and at least part of the Northeast is affected by Lake Ontario, the Atlantic Ocean, and the 383
St Lawrence River. Humid air from these water bodies can result in large snow accumulation in 384
the winter and rainfall in the early spring, leading to the potential for large ROS events. 385
Generally though, ROS in the East is much less frequent compared with the West, mainly due to 386
the shallower snow accumulations and the shorter period of snow cover. 387
The spatial distribution of the centroid timing of all ROS days (Figure 4b) reflects the spatial 388
variation in the rainfall seasonality. In the western U.S., the majority of ROS days in PNW and 389
the Sierra Nevada are in the late fall and winter seasons, while ROS days in the Rockies occur 390
mostly in early to mid-spring (on average about one month later than the Western coastal ranges). 391
This temporal difference in ROS timing is mainly because of the timing of the rainfall season in 392
the two regions [Knowles et al. 2006, Gergel et al. 2017]. Orographically, ROS occurs later at 393
higher elevations due to the snowfall to rainfall transition occurring later in high elevation areas. 394
The elevational ROS timing difference is most obvious in the comparison between the northern 395
and southern Cascades, and between the northern and southern Sierra Nevada (Figure 4b). ROS 396
in the Eastern U.S. occurs earlier than in the West; large ROS days tend to occur around mid-397
January in the northern tier of the Northeast and Midwest, and mostly in October in the lower 398
Appalachians and the lower Midwest. 399
Figure 5 shows the fraction of the 200 large runoff days and the 20 extreme runoff days that 400
are ROS-related (ND_LARGE_ROS/ND_LARGE_200 in Figures 5a, ND_EXTRM_ROS/ND_EXTRM_20 in 401
Figures 5b), and the fraction of total large and extreme runoff that is attributable to these ROS-402
related days (Q_LARGE_ROS/Q_LARGE_200 in Figures 5c, Q_EXTRM_ROS/Q_EXTRM_20 in Figures 5d). 403
Generally, the spatial extent and the spatial variation of the impacts of the ROS runoff on the 404
large runoff and extreme runoff are similar over the CONUS. On average, 53% of the 405
ND_LARGE_200 and 77% of the ND_EXTRM_20 are ROS-related in the major ROS-impacted areas 406
(including the major mountain ranges of the West, the Upper Midwest, the Northeast, and the 407
lower Appalachian region. We found that large and extreme runoff days are more likely to be 408
ROS-related in higher elevation areas, but in those areas with very high elevations, such as the 409
southern Sierra Nevada and the ridges of the Cascades (see Figures 5a and 5b). This appears to 410
be attributable to 1) lower temperatures at high elevations, so while rain may be occurring at 411
lower elevations, higher elevations are above the snow line and the local precipitation is mostly 412
snowfall (i.e. less rainfall and thus less ROS), and 2) high runoff production in these high 413
elevation areas (especially on the east slopes of the Cascades) is dominated by clear-sky 414
snowmelt in spring, rather than during fall and early winter storms. It is also clear that a 415
significant portion of floods that occur in basins that drain the west facing slopes of the Cascades 416
and the Sierra Nevada are ROS-related. 417
While a substantial fraction of large and extreme runoff days are ROS-related across much of 418
our domain, the runoff contribution from these ROS days to the total large and extreme runoff is 419
comparatively low in most cases (Figures 5c and 5d vs. Figures 5a and 5b). The reason has to do 420
with several factors, including: 1) in regions with shallow snowpacks (low elevations in the West, 421
and much of the rest of the snow-affected domain, e.g. almost all of the upper Midwest and 422
Northeast), relatively small SWE at the beginning of ROS days leads to lower ROS frequencies 423
and contributions to runoff. Also, Q_LARGE_200 and Q_EXTRM_20 in these areas with relatively low 424
elevation often relate to intense rainfall occurred in non-ROS days. 2) Q_LARGE_ROS/Q_LARGE_200 425
and Q_EXTRM_ROS/Q_EXTRM_20 are also limited at very high elevations in the West, e.g. as 426
discussed, the southern Sierra Nevada and the highest elevations of the Cascades as compared 427
with lower elevations in the same general areas (Figures 5c). 428
The comparison between Figure 5c and Figure 5d shows the role of ROS runoff in large 429
runoff is more significant than that in the extreme runoff. For instance, average 430
Q_LARGE_ROS/Q_LARGE_200 is ~23% (Figure 5c), whereas Q_EXTRM_ROS/Q_EXTRM_20 is only 5% in 431
average (Figure 5d) over the ROS affected areas of the western U.S. Over the most ROS-affected 432
mountains in CONUS (including Cascades, the northern Rockies, and the northern 433
Appalachians), Q_LARGE_ROS/Q_LARGE_200 is ~40% while Q_EXTRM_ROS/Q_EXTRM_20 is only 7%. The 434
large difference between the role of ROS runoff in large runoff as contrasted with extreme runoff 435
could be a result of several factors: (1) heavy rainfall and clear-sky melt that occur in non-ROS 436
conditions are more dominant in the extreme runoff than in the large runoff, and these factors 437
dilute the ROS runoff contribution ratio; (2) Extreme runoff mostly occurs in the late winter and 438
early summer (overall later than large runoff), so the antecedent SWE that is available to melt 439
and contribute to the runoff in extreme runoff days is generally less than in large runoff days, 440
especially at low elevations. The areas with large Q_LARGE_ROS/Q_LARGE_200 and 441
Q_EXTRM_ROS/Q_EXTRM_20 (Figures 5c and 5d) coincide with areas with high ROS frequency (see 442
Figure 4a) and high fractions of ROS-related runoff days (Figures 5a and 5b). 443
Figures 6 shows the source of the runoff generated during ROS days. Rainfall accounts for 444
up to 70% of Q_LARGE_ROS in the PNW and the west-facing slopes of the Sierra Nevada and 445
Cascades (Figure 6a). The coastal areas is impacted by the large moisture transport primarily by 446
atmospheric rivers [Chen et al., 2018]), leading to very high precipitation along the west slopes 447
of the mountain barriers. In this region, intense rainfall outweighs the snowmelt and accounts for 448
most of the ROS runoff. In comparison, snowmelt dominates Q_LARGE_ROS in the Rockies, the 449
Northeast, and the Upper Midwest, where snowmelt in Day_LARGE_ROS accounts for an average of 450
65% of the runoff in these days (Figure 6b). Snowmelt dominates the ROS runoff in these 451
regions for two reasons. First, rainfall magnitude and frequency in these regions are 452
comparatively mild, and thus the role of rainfall in Q_LARGE_ROS is not as strong as it is along the 453
West Coast. Second, Day_LARGE_ROS in the Rockies and the Northeast are mostly coincident with 454
the snowmelt season and hence later in the (water) year than along the West Coast (as in Figure 455
4b); warmer temperatures, increased solar radiation, and longer daylight hours later in this time 456
of a year all favor snowmelt. The reduced effects of rainfall and enhanced snowmelt augment the 457
role of snowmelt in Q_LARGE_ROS in these areas. This finding is consistent with Mazurkiewicz et 458
al. 2007. Furthermore, the quantity and spatial pattern of the contribution of snowmelt and 459
rainfall to Q_LARGE_ROS over the Western U.S. agree well with those in Wayand et al. [2015] and 460
Musselman et al. [2018]. The contribution of rainfall and snowmelt in Day_EXTRM_ROS to 461
Q_EXTRM_ROS (Figure 6c and 6d) are similar with their contributions to Q_LARGE_ROS (Figure 6a 462
and 6b) 463
Figure 7 dissects the snowmelt generated during Day_LARGE_ROS (Figures 7 a-d) and 464
Day_EXTRM_ROS (Figures 7 e-h) into components that are driven by net-radiation, condensation, 465
sensible heat, and rainfall advection. The magnitude and the spatial pattern of the contribution of 466
each of these energy sources to the snowmelt in Day_LARGE_ROS and in Day_EXTRM_ROS are very 467
similar. In both large ROS and extreme ROS cases, net-radiation is the dominant energy source 468
for ROS snowmelt in the mountains of the West (Figure 7a and 7e), explaining an average of 68% 469
of ROS snowmelt in the Cascades, Sierra Nevada and the Rockies. Figure 7b and 7f show that in 470
the coastal west, net-radiation accounts for slightly more ROS snowmelt than do turbulent heat 471
fluxes (sensible heat and the condensation-dominant latent heat). Sensible heat is responsible for 472
over 70% of the snowmelt in ROS days in the Great Basin and the Colorado Plateau (Figure 7c 473
and 7g), where snow cover is shallow and ROS days occur later in a water year when air 474
temperature that drives the sensible heat flux is higher. In the major ROS-impacted regions in the 475
eastern U.S., turbulent heat fluxes are about as important as net-radiation in ROS snowmelt. 476
Rainfall advection makes up less than 5% of ROS melt across the entire CONUS domain (Figure 477
7d and 7h), i.e., rainfall does not directly melt much snow in ROS events. This contradicts a 478
common perception that snowmelt in ROS is caused in large part by energy infusion from the 479
warmer rainfall. In fact, ROS snowmelt directly resulting from rainfall energy (i.e. via advection) 480
is limited by (1) the relatively small temperature difference between the rainfall and the freezing 481
point that controls the maximum amount of advection energy that a unit volume of rainfall can 482
provide to melt snow, and (2) the fact that advection is much less efficient in energy transfer in 483
comparison with processes such as condensation and rainfall water refreeze, because the heat 484
capacity of water, which controls the energy transfer rate via advection, is much less than both 485
the latent heat of vaporization and the latent heat of fusion that control the energy transfer rate in 486
condensation and rainfall water re-freeze, respectively. 487
Our finding that net-radiation is responsible for slightly more ROS snowmelt in the Western 488
U.S. than turbulent heat fluxes is consistent with the conclusion in Mazurkiewicz et al. [2007], 489
but differs with other studies (e.g. Marks et al. [1998], Corripio and López-Moreno, [2017]) that 490
find that turbulent heat fluxes dominate. The difference between our study and those referenced 491
above may be explained by the fact that we investigate ROS with a different perspective. In 492
particular, Marks et al. [1998] and Corripio and López-Moreno [2017] explored the energy 493
balance in single ROS events that lead to unprecedented flooding, with exceptionally high wind 494
and humid air which tremendously enhanced the turbulent heat fluxes and allowed them to 495
dominate snowmelt in the ROS events. In comparison, Mazurkiewicz et al. [2007] and the results 496
presented herein examined all the ROS events across a long period at a larger scale, so (1) not 497
many ROS events have exceptionally high wind (and hence very large turbulent heat transfer) as 498
in the two aforementioned papers; and (2) ROS events occurring late in winter or early spring 499
coincident with high solar radiation for a longer period during daytime than the ROS events in 500
mid-winter (e.g. as in Marks et al. [1998]), which enhance the effects of the radiation on ROS 501
snowmelt. 502
4.3 Future change of ROS and its effects on future hydrologic extremes 503
In a warmer future, more ROS will occur in high-elevation areas due to increased rainfall 504
(transitioned from snowfall), whereas ROS in low-elevation and mid-elevation areas will 505
decrease or diminish due to reduced SWE overall (Figure 8a). The elevation at which ROS 506
frequency transitions from decreasing to increasing mostly is around 1500 m to 2000 m. The 507
elevation-dependent ROS frequency change is most apparent in the Cascades and the Sierra 508
Nevada (Figure 8a), which generally have warmer winter temperatures than the interior of the 509
West. In the eastern U.S., ROS frequency decreases over all ROS-impacted areas, especially in 510
the southern Appalachians. The reduced ROS frequency in these areas is driven by declines in 511
snow accumulation at the onset of ROS. Temporally (Figure 8b), ROS will occur earlier by 512
about a month overall in almost the entire CONUS due to the shift from snowfall to rain earlier 513
in the year. Figure 8 has more white areas than the historical case (Figure 4) because more areas 514
fall below the SWE threshold used for ROS identification in the future and thus the ROS with 515
flood-generating potential diminishes. 516
Figure 9 is indicative of how the role of ROS in future hydrologic extremes will change. In 517
the warmer scenario, large and extreme runoff will be more ROS-related in high elevation areas 518
(where ND_LARGE_ROS/ND_LARGE_200 and ND_EXTRM_ROS/ND_EXTRM_20 increase) and less ROS-519
related in moderate and low elevation areas (where ND_LARGE_ROS/ND_LARGE_200 and 520
ND_EXTRM_ROS/ND_EXTRM_20 decrease), as in Figures 9a and 9b. The largest increases in the 521
Western U.S. occur in the high-elevation mountains that have not been affected by ROS in the 522
past, especially the Upper Cascades in the Pacific Northwest, which is consistent with the finding 523
in Yan et al. (2019). The greatest decreases occur in the coastal Western U.S., especially the 524
middle and low elevation west-facing barriers of the Sierra Nevada and the Cascades, where 525
declines in snow accumulation and increases in rainfall intensity (which dilute ROS runoff 526
contribution ratio) will occur in the future. In most of the eastern U.S., future large and extreme 527
runoff will be less ROS-related, due primarily to reduced snow accumulation, except in the 528
northern part of the upper Midwest, and some high peaks in the Upper Appalachian region. 529
Changes in the ratios of Q_LARGE_ROS/Q_LARGE_200 (Figure 9c) are similar to changes in 530
ND_LARGE_ROS/ND_LARGE_200 (Figure 9a); Q_LARGE_ROS/Q_LARGE_200 shows elevation-dependence 531
in the West; it increases by an average of 11% in high-elevation mountains and decreases by 7% 532
at intermediate and low elevations. In the East, Q_LARGE_ROS/Q_LARGE_200 reduces by 6% 533
compared with the historical case, and the role of ROS increases only at a few mountains in 534
northern Michigan where seasonal snow exists. The spatial pattern of Q_EXTRM_ROS/Q_EXTRM_20 535
changes (Figure 9d) is similar to that of Q_LARGE_ROS/Q_LARGE_200 (Figure 9c), but with a 536
comparatively lower magnitude. While ND_EXTRM_ROS/ND_EXTRM_20 changes more significantly 537
than ND_LARGE_ROS/ND_LARGE_200 does (Figure 9b vs 9a), Q_EXTRM_ROS/Q_EXTRM_200 changes less 538
than Q_LARGE_ROS/Q_LARGE_200 (Figure 9d vs 9c), because of the extreme runoff is more 539
dominated by intense rainfall and clear-sky melt, so the number of ROS day (or ROS frequency) 540
changes have less effects on the ROS runoff contribution change in the extreme runoff case than 541
in the large runoff case. 542
Comparing Figures 9a and 9b with Figures 9c and 9d, it is clear that in a warmer future, ROS 543
will be involved in more flood events in mountainous areas, especially for extreme flood events 544
in many areas that are headwaters of large rivers. Also, the spatial patterns shown in Figure 9a 545
and 9b are highly consistent with those in Figures 9c and 9d, respectively, i.e. in areas with more 546
frequent ROS days, the role of ROS in large and extreme runoff increases, and vice versa. This 547
demonstrates that ROS frequency change is a first order control on the changes in the 548
contribution of ROS runoff to total runoff in large and extreme floods. 549
We calculated changes in Q_LARGE_ROS/Q_LARGE_200 and changes in the 100-year flood 550
magnitude at the HUC-6 level over the entire CONUS (as shown in Figure S4), and compared 551
these changes in the major ROS-impacted regions of the CONUS (Figure 10) to explore the 552
connection between ROS and the hydrologic extremes. Note that the ROS contribution was 553
originally calculated at grid-cell level (e.g. Figures 5c and 5d, Figures 9c and 9d), here we 554
aggregated it to each HUC-6 basin by calculating the runoff-weighted average of 555
Q_LARGE_ROS/Q_LARGE_200 for all the grid-cells within the basin. Figures 10a-d show the spatial 556
patterns of future flood risk changes and changes in Q_LARGE_ROS/Q_LARGE_200; the two changes 557
are largely consistent in the ROS-impacted regions in the eastern U.S. and they both show 558
decreases. Since the large runoff in these regions is usually driven by rainfall, whose intensity is 559
likely to increase in the future as a result of the increased temperature and the resulting increased 560
atmospheric water holding ability (based on Clausius-Claperyon Equation, and increased 561
atmospheric moisture amount is likely to lead to more intense rainfall when saturated), but on the 562
other hand the flood risk in these regions decreases, therefore the indication is the future 563
decreases in snow accumulation and ROS is likely to be a factor that lead to the decreased flood 564
risk in these areas. Indeed, the total water entering the soil column is about the same, but since 565
the snowmelt becomes less (because of reduced snow), so more rainfall water supplies to the soil 566
and reduces the amount of direct rainfall runoff, ultimately resulting in reduced flood risk in 567
areas where local floods are heavy rainfall-dominated. A few basins in the Upper Midwest, 568
where deep snow accumulates and future ROS contributes more runoff to large and extreme 569
runoff events are the exception; in these basins, the flood risk increases. 570
Over the mountains of the West, the Cascades and the northern Rockies have spatially-571
consistent decreases in Q_LARGE_ROS/Q_LARGE_200 and spatially-consistent increases in the flood 572
risk, but Q_LARGE_ROS/Q_LARGE_200 changes and the flood risk changes in some of the basins in the 573
southern Rockies show spatially inconsistent (Figures 10e and 10f). Rainfall is a major part of 574
the floods along the west coast and in the Cascades and the northern Sierra Nevada. The flood 575
risk increases in these regions is primarily due to the more intense rainfall caused by the 576
atmospheric rivers and the increased water amount in the atmosphere; the more intense rainfall 577
directly increases the chance of hydrologic extremes and also dilutes the impacts of ROS runoff 578
to the large floods. The reduced snow accumulations in the future also contributes to the reduced 579
role of ROS in this region. Inconsistency between the change in Q_LARGE_ROS/Q_LARGE_200 and the 580
change in flood risk occurs in a few mid-elevation basins in the southern Rockies, where ROS 581
contribution slightly increases and the flood risk decreases. The peak streamflow in these basins 582
is mostly controlled by clear-sky melt, and floods occur as a result of intense clear-sky snowmelt. 583
Musselman et al (2016) found that in the future, the warmer temperature will shift the snowmelt 584
onset earlier in this area, but the earlier snowmelt will occur in a time of year with lower solar 585
radiation, shorter days, and colder temperatures. As a result, the snowmelt process in these mid-586
elevation areas will become longer and less intense, which will reduce the overall flood risk. 587
Therefore, despite the fact that ROS effects will increase, reduced clear-sky melt will outweigh 588
this, and these areas will have an overall reduced flood risk. 589
Figure 11 compares the flood risk changes and the Q_LARGE_ROS/Q_LARGE_200 changes in the 590
HUC-6 basins shown in Figure 10, with the basins color coded by their median basin elevation. 591
For the basins whose median basin elevation is below 1700 m (Figure 11a), the flood risk and the 592
contribution of large ROS runoff to total large runoff can either increase or decrease, but the two 593
changes are positively correlated, implying that flood risk in these basins is related with ROS. In 594
comparison, for basins higher than 1700m (Figure 11b), Q_LARGE_ROS/Q_LARGE_200 all increase, 595
but the increases in the ROS runoff contribution in these higher basins are not correlated with the 596
flood risk change, likely because floods in these high elevation basins are dominated by clear-597
sky melt, and the ROS effect on local extreme runoff are limited. 598
5. CONCLUSIONS 599
We have quantitatively characterized historical and future ROS conditions across the 600
CONUS, and explored the role of ROS in hydrologic extremes. We find that: 601
1. The main ROS-impacted regions in the CONUS are the major western mountain ranges 602
(including the Cascades, the Sierra Nevada, and the Rockies), the Upper Midwest, the 603
Northeast, and the lower Appalachian region. Historically, the contribution of ROS to 604
extreme runoff in the western U.S. has been greatest in mid-elevation areas (1500 m to 2300 605
m); this “significant influence zone” will shift higher in the future. ROS in the coastal West 606
and in the Eastern U.S. mostly occur in fall and winter, while ROS in the high mountains in 607
the West occurs mostly in early spring. 608
2. While a significant portion of large and extreme runoff days in the historical record are ROS-609
related in the most ROS-affected regions, total runoff from ROS days accounts for a modest 610
part of the runoff from large runoff days (upper 1%), and a small (mostly less than 10 percent) 611
part of the runoff from extreme runoff days (upper 0.1%), indicating that most extreme 612
runoff is a result of either intense rainfall or radiation-driven snowmelt even on ROS-days. 613
3. The runoff generated during ROS days is dominated by rainfall along the west coast and is 614
dominated by snowmelt in the rest of ROS-impacted regions in the CONUS. Net-radiation 615
dominates the snowmelt in ROS days in the high mountains in the West, while net-radiation 616
and turbulent heat flux (including the condensation latent heat and sensible heat) are equally 617
dominant in the rest of the ROS-impacted regions in CONUS. The amount of snow directly 618
melt by rainfall (through heat advection) is negligible. 619
4. In a warmer future, the role of ROS in local hydrologic extremes will increase in high 620
elevation mountains while decreasing at low and moderate elevation areas (especially in the 621
West), and its role will decrease over almost the entire Midwest and the eastern U.S.; the 622
future change of ROS frequency exerts a first order control on the future change of the runoff 623
contribution from ROS to extreme floods. The mean timing of ROS will shift earlier across 624
the entire CONUS by an average about a month in a +2 warmer scenario. 625
Acknowledgements 626
This study was performed under supports from the Strategic Environmental Research and 627
Development Program (SERDP) – Project #RC-2513 granted to the University of California, Los 628
Angeles, and from the U.S.–China Clean Energy Research Center for Water-Energy 629
Technologies/California Energy Commission Grant 300-15-006. The authors declare no real or 630
perceived financial conflicts of interests. All data used in this study are publicly available online 631
at the following URLs: Livneh meteorological forcings: 632
https://data.nodc.noaa.gov/thredds/catalog/nodc/archive/data/0129374/daily/catalog.html, 633
GAGES II data: 634
https://water.usgs.gov/GIS/metadata/usgswrd/XML/gagesII_Sept2011.xml#stdorder, SNSR 635
SWE: https://ucla.app.box.com/v/SWE-REANALYSIS. USGS streamflow observations: 636
https://waterdata.usgs.gov/nwis/uv/?referred_module=sw. Both the VIC hydrologic model and 637
the NEVA flood risk estimation scheme are open source and can be downloaded from 638
https://github.com/UW-Hydro/VIC/releases/tag/VIC.4.2.d and http://amir.eng.uci.edu/neva.php. 639
References 640
Andreadis, K. M., Storck, P., & Lettenmaier, D. P. (2009). Modeling snow accumulation and 641
ablation processes in forested environments. Water Resources Research, 45(5). 642
Barnhart, T. B., Molotch, N. P., Livneh, B., Harpold, A. A., Knowles, J. F., & Schneider, D. 643
(2016). Snowmelt rate dictates streamflow. Geophysical Research Letters, 43(15), 8006-644
8016. 645
Bergman, J.A. Rain-on-snow and soil mass failure in the Sierra Nevada of California. In 646
Landslide Activity in the Sierra Nevada during 1982 and 1983; DeGraff, J.V., Ed.; USDA 647
Forest Service: San Francisco, CA, USA, 1987; pp. 15–26. 648
Brunengo, M. J. (1990). A method of modeling the frequency characteristics of daily snow 649
amount, for stochastic simulation of rain-on-snowmelt events. In Proc. Western Snow Conf 650
(Vol. 58, pp. 110-121). 651
Cayan, D. R., M. D. Dettinger, H. F. Diaz, and N. Graham (1998), Decadal variability of 652
precipitation over western North America, J. Clim.,11(12),3148–3166 653
Chen, X., Leung, L. R., Gao, Y., Liu, Y., Wigmosta, M., & Richmond, M. (2018). Predictability 654
of Extreme Precipitation in Western US Watersheds Based on Atmospheric River 655
Occurrence, Intensity, and Duration. Geophysical Research Letters, 45(21), 11-693. 656
Cheng, L., AghaKouchak, A., Gilleland, E., & Katz, R. W. (2014). Non-stationary extreme value 657
analysis in a changing climate. Climatic change, 127(2), 353-369. 658
Cohen, J., Ye, H., & Jones, J. (2015). Trends and variability in rain on snow events. 659
Geophysical Research Letters, 42(17), 7115-7122. 660
Conway, H., Breyfogle, S., Wilbour, C.R., 1988. Observations Relating to Wet Snow Stability. 661
International Snow Science Workshop, ISSW ’88 Commission, Whistler, B.C., Canada. 662
Conway, H., Raymond, C.F., 1993. Snow stability during rain. Journal of Glaciology 39, 635–663
642. 664
Corripio, J. G., & López-Moreno, J. I. (2017). Analysis and Predictability of the Hydrological 665
Response of Mountain Catchments to Heavy Rain on Snow Events: A Case Study in the 666
Spanish Pyrenees. Hydrology, 4(2), 20. 667
Daly, C., Taylor, G. H., & Gibson, W. P. 1997. The PRISM approach to mapping precipitation 668
and temperature. In Proc., 10th AMS Conf. on Applied Climatology pp20-23. 669
Falcone, J. A., Carlisle, D. M., Wolock, D. M., & Meador, M. R. (2010). GAGES: A stream 670
gage database for evaluating natural and altered flow conditions in the conterminous United 671
States. Ecology, 91(2), 621-621. 672
Fredriksen, R. L. (1965). Christmas storm damage on the HJ Andrews Experimental Forest (p. 673
11). Pacific Northwest Forest and Range Experiment Station, US Department of Agriculture. 674
Freudiger, D., Kohn, I., Stahl, K., & Weiler, M. (2014). Large-scale analysis of changing 675
frequencies of rain-on-snow events with flood-generation potential. Hydrology and Earth 676
System Sciences, 18(7), 2695-2709. 677
Garvelmann, J., Pohl, S., & Weiler, M. (2014). Variability of observed energy fluxes during rain-678
on-snow and clear sky snowmelt in a midlatitude mountain environment. Journal of 679
Hydrometeorology, 15(3), 1220-1237. 680
Garvelmann, J., Pohl, S., & Weiler, M. (2015). Spatio temporal controls of snowmelt and runoff 681
generation during rain on snow events in a mid latitude mountain catchment. Hydrological 682
Processes, 29(17), 3649-3664. 683
Gergel, D. R., Nijssen, B., Abatzoglou, J. T., Lettenmaier, D. P., & Stumbaugh, M. R. (2017). 684
Effects of climate change on snowpack and fire potential in the western USA. Climatic 685
Change, 141(2), 287-299. 686
Harr, R. D. (1981). Some characteristics and consequences of snowmelt during rainfall in 687
western Oregon. Journal of Hydrology, 53(3-4), 277-304. 688
Henn, B., Newman, A. J., Livneh, B., Daly, C., & Lundquist, J. D. (2018). An assessment of 689
differences in gridded precipitation datasets in complex terrain. Journal of Hydrology, 556, 690
1205-1219. 691
Heywood, L., 1988. Rain on snow avalanche events—some observations. Proceedings of the 692
International Snow Science Workshop. ISSW ’88 Comm., Whistler, B.C., Canada. 693
Hungerford, R. D., Nemani, R. R., Running, S. W., & Coughlan, J. C. (1989). MTCLIM: a 694
mountain microclimate simulation model. Res. Pap. INT-RP-414. Ogden, UT: US 695
Department of Agriculture, Forest Service, Intermountain Research Station. 52 p., 414. 696
Kattelmann, R. (1997). Flooding from rain-on-snow events in the Sierra Nevada. IAHS 697
Publications-Series of Proceedings and Reports-Intern Assoc Hydrological Sciences, 239, 698
59-66. 699
Kendall, M.G. 1975. Rank Correlation Methods, 4th edition, Charles Griffin, London. 700
Knowles, N., Dettinger, M. D., & Cayan, D. R. (2006). Trends in snowfall versus rainfall in the 701
western United States. Journal of Climate, 19(18), 4545-4559. 702
Liang, X., Lettenmaier, D. P., Wood, E. F., & Burges, S. J. (1994). A simple hydrologically 703
based model of land surface water and energy fluxes for general circulation models. Journal 704
of Geophysical Research: Atmospheres, 99(D7), 14415-14428. 705
Livneh, B., Bohn, T. J., Pierce, D. W., Munoz-Arriola, F., Nijssen, B., Vose, R., ... & Brekke, L. 706
(2015). A spatially comprehensive, hydrometeorological data set for Mexico, the US, and 707
Southern Canada 1950–2013. Scientific data, 2, 150042. 708
Lohmann, D., Raschke, E., Nijssen, B., & Lettenmaier, D. P. (1998). Regional scale hydrology: I. 709
Formulation of the VIC-2L model coupled to a routing model. Hydrological Sciences 710
Journal, 43(1), 131-141. 711
Margulis, S. A., Girotto, M., Cortés, G., & Durand, M. (2015). A particle batch smoother 712
approach to snow water equivalent estimation. Journal of Hydrometeorology, 16(4), 1752-713
1772. 714
Margulis, S. A., Cortés, G., Girotto, M., & Durand, M. (2016). A Landsat-era Sierra Nevada 715
snow reanalysis (1985–2015). Journal of Hydrometeorology, 17(4), 1203-1221. 716
Marks, D., Kimball, J., Tingey, D., & Link, T. (1998). The sensitivity of snowmelt processes to 717
climate conditions and forest cover during rain on snow: A case study of the 1996 Pacific 718
Northwest flood. Hydrological Processes, 12(10 11), 1569-1587. 719
Marks, D., Link, T., Winstral, A., & Garen, D. (2001). Simulating snowmelt processes during 720
rain-on-snow over a semi-arid mountain basin. Annals of Glaciology, 32, 195-202. 721
Mazurkiewicz, A. B., D. G. Callery, and J. J. McDonnell (2008), Assessing the controls of the 722
snow energy balance and water available for runoff in a rain-on-snow environment, J. 723
Hydrol., 354(1-4), 1–14, doi:10.1016/j.jhydrol.2007.12.027. 724
McCabe, G. J., Clark, M. P., & Hay, L. E. (2007). Rain-on-snow events in the western United 725
States. Bulletin of the American Meteorological Society, 88(3), 319-328. 726
Melillo, Jerry M., Terese Richmond, and Gary W. Yohe, Eds., 2014: Highlights of Climate 727
Change Impacts in the United States: The Third National Climate Assessment. U.S. Global 728
Change Research Program, 148 pp. 729
Mote, P. W., A. F. Hamlet, M. P. Clark, and D. P. Lettenmaier (2005), Declining mountain 730
snowpack in western North America, Bull. Am. Meteorol. Soc.,86(1), 39–49 731
Mote, P. W., Li, S., Lettenmaier, D. P., Xiao, M., & Engel, R. (2018). Dramatic declines in 732
snowpack in the western US. npj Climate and Atmospheric Science, 1(1), 2. 733
Musselman, K.N., Lehner, F., Ikeda, K., Clark, M.P., Prein, A.F., Liu, C., Barlage, M. and 734
Rasmussen, R., 2018. Projected increases and shifts in rain-on-snow flood risk over western 735
North America. Nature Climate Change, 8(9), p.808. 736
Ralph, F. M., & Dettinger, M. D. (2011). Storms, floods, and the science of atmospheric rivers. 737
Eos, Transactions American Geophysical Union, 92(32), 265-266. 738
Sandersen, F.; Bakkehøi, S.; Hestnes, E.; Lied, K. (1997), The influence of meteorological 739
factors on the initiation of debris flows, rockfalls, rockslides and rockmass stability. Publ. 740
Nor. Geotek. Inst., 201, 97–114. 741
Sharma, A., Wasko, C., & Lettenmaier, D. P. (2018). If Precipitation Extremes Are Increasing, 742
Why Aren't Floods?. Water Resources Research, 54(11), 8545-8551. 743
Singh, P., Spitzbart, G., Hübl, H., & Weinmeister, H. W. (1997). Hydrological response of 744
snowpack under rain-on-snow events: a field study. Journal of Hydrology, 202(1-4), 1-20. 745
Sui, J., & Koehler, G. (2001). Rain-on-snow induced flood events in Southern Germany. Journal 746
of Hydrology, 252(1-4), 205-220. 747
Swanston, D. N. (1974). Slope stability problems associated with timber harvesting in 748
mountainous regions of the western United States. Gen. Tech. Rep. PNW-GTR-021. 749
Portland, OR: US Department of Agriculture, Forest Service, Pacific Northwest Research 750
Station. 19 p, 21. 751
Ter Braak, C. J. (2006). A Markov Chain Monte Carlo version of the genetic algorithm 752
Differential Evolution: easy Bayesian computing for real parameter spaces. Statistics and 753
Computing, 16(3), 239-249. 754
Van Heeswijk, M., J. Kimball, and Marks (1996), Simulation of water available for runoff in 755
clearcut forest openings during rain-on-snow events in the western Cascade Range of 756
Oregon and Washington, U.S. Geol. Surv. Water Resour. Invest. Rep., 95-4219, 67 pp. 757
Vrugt, J. A., Ter Braak, C. J. F., Diks, C. G. H., Robinson, B. A., Hyman, J. M., & Higdon, D. 758
(2009). Accelerating Markov chain Monte Carlo simulation by differential evolution with 759
self-adaptive randomized subspace sampling. International Journal of Nonlinear Sciences 760
and Numerical Simulation, 10(3), 273-290. 761
Waananen, A. O., Harris, D. D., & Williams, R. C. (1971). Floods of December 1964 and 762
January 1965 in the Far Western States; Part 1 Description (No. 1866-A). US Geol. Surv., 763
Water Supply Pap. 1866-A, pp265 764
Wayand, N. E., Lundquist, J. D., & Clark, M. P. (2015). Modeling the influence of hypsometry, 765
vegetation, and storm energy on snowmelt contributions to basins during rain on snow 766
floods. Water Resources Research, 51(10), 8551-8569. 767
Yan, H., Sun, N., Wigmosta, M., Skaggs, R., Leung, L. R., Coleman, A., & Hou, Z. (2019). 768
Observed Spatiotemporal Changes in the Mechanisms of Extreme Water Available for 769
Runoff in the Western United States. Geophysical Research Letters. 770
771
Figures 772
773
Figure 1. (a) Elevation of the CONUS (the study domain). (b) Observed temperature change in 774
the 20th century (1991-2012 average compared to the 1901-1960 average). The mean temperature 775
increased by about 1.2 on average over the domain (data source: the Third National Climate 776
Assessment, Melillo et al., 2014). 777
(a) (b)
778
(c)
(d) (e)
(f) (h) (g) (i)
(b) (a)
Figure 2. (a) Historical (1950-2013) mean annual maximum SWE over the CONUS from VIC 779
modeling. Regions that have mean annual maximum SWE less than 20 mm are masked out. (b) 780
The change in the maximum SWE that would occur if the air temperature was uniformly 781
increased over the 1950-2013 record by 2.0 . (c) Comparison of the daily time series of total 782
SWE in the Sierra Nevada from the SNSR dataset (blue, Margulis et al, 2016) and from the VIC 783
model (red). The SSNR SWE is available from water year 1985. (d) Comparison of the timing 784
and (e) the total volume of the annual peak SWE over the Sierra Nevada from the two sources. (f) 785
and (g) compare the spatial distribution of the peak SWE from the VIC model and from the 786
SNSR data in water year 1990, which is the lowest water year in the comparison period. 787
Comparison between (h) and (i) are similar with that in (f) and (g), except the comparison is for 788
WY1993, which is the highest water year in the comparison period. 789
790
Figure 3. Evaluation of the VIC modeled streamflow and the 100-year flood magnitude estimates 791
from the flood risk analysis. (a) comparison of the annual maximum streamflow observed at the 792
GAGES II reference gages that have over 50-years of record from 1950 to 2009 with the 793
modeled annual maximum streamflow at these gages. (b) comparison of the cumulative 794
distribution of the annual maximum streamflow from model and observations at the gages from 795
panel (a). (c) comparison of the 100-year flood magnitude calculated from the annual maximum 796
streamflow from the GAGES II reference gages that have over 50-years of record and at least 797
100 km2 drainage area with that calculated using the VIC-modeled annual maximum streamflow 798
at these gages. (d) comparison of the cumulative distribution of the 100-year flood magnitude 799
calculated from the VIC model and observations as shown in panel (c). 800
(a) (b)
(c) (d)
801
Figure 4. (a) The frequency of historical ROS days defined by the criteria in Fruediger et al. 802
(2015). (b) the centroid of timing of all the historical ROS days. Results are shown only for grid 803
cells with average maximum annual SWE > 20 mm (as in Figure 1(b)). 804
(a) (b)
805
Figure 5. (a) the ratio of the number of large ROS days to the number of large runoff days (200), 806
where large runoff days are defined as the 200 days that have the largest runoff during the 64-807
year study period, and the large ROS days are defined as ROS days among the 200 large runoff 808
days at each model grid cell. (b) the ratio of the number of extreme ROS days to the number of 809
extreme runoff days (20), where extreme runoff days are defined as the 20 days that have the 810
largest runoff during the 64-year study period and the extreme ROS days are defined as ROS 811
days among the 20 extreme runoff days at each model grid cell. (c) the ratio of the large ROS 812
runoff (i.e. the runoff from the large ROS days) to the total large runoff (i.e. the total runoff from 813
the 200 large runoff days); (d) the same as (c) except the ratio is extreme ROS runoff to the total 814
(c) (d)
(a) (b)
extreme runoff. The white areas in the maps have mean annual maximum SWE less than 20 mm 815
(see Figure 1(b)) and are excluded in the analysis. 816
817
Figure 6. Fractional contribution to the large and extreme ROS runoff from rainfall and 818
snowmelt. (a) and (b) show the ratio of the rainfall and snowmelt from the large ROS days (i.e. 819
the ROS days in the 200 large runoff days) to the total large ROS runoff (i.e. the total runoff 820
from the large ROS days), respectively. (c) and (d) show the ratio of the rainfall and snowfall 821
from the extreme ROS days (i.e. the ROS days in the 20 extreme runoff days) to the total 822
extreme ROS runoff (i.e. the total runoff from the extreme ROS days), respectively. White areas 823
in the maps have mean annual maximum SWE less than 20 mm and are excluded from the 824
analysis (see Figure 1(b)). 825
(a) (b)
(c) (d)
826
(a) (e)
(b) (f)
(c) (g)
(d) (h)
Figure 7. Fractional contribution to snowmelt on large and extreme ROS days from different 827
energy sources; panels (a) - (d) show the fraction of the total snowmelt on large ROS days (i.e. 828
the ROS days among the 200 largest runoff days) caused by net radiation (dominated by long-829
wave radiation), condensation, sensible heat, and advection; panels (e) - (h) show the fraction of 830
total snowmelt on extreme ROS days (i.e. the ROS days among the 20 largest runoff days) 831
caused by net radiation, condensation, sensible heat, and advection. White areas have mean 832
annual maximum SWE less than 20 mm and are excluded from the analysis (see Figure 1(b)). 833
834
Figure 8: (a) Change in ROS frequency in the +2 warmer scenario in comparison with the 835
historical ROS frequency. (b) change in the centroid of timing of ROS days in the +2 warmer 836
climate in comparison with the historical ROS timing. The white areas either have historical 837
mean annual maximum SWE less than 20 mm (see Figure 1(b)) or no identified ROS days in the 838
warmer clime due to reduced SWE. 839
840
(a) (b)
841
Figure 9. (a) Change in the ratio of the number of large ROS days to the number of large runoff 842
days (200) in a +2 warmer scenario; large runoff days are the 200 days that have the largest 843
runoff during the study period and the large ROS days are the ROS days among the 200 large 844
runoff days. (b) change in the ratio of the number of extreme ROS days to the number of extreme 845
runoff days (20) in a +2 warmer scenario; extreme runoff days are the 20 days that have the 846
largest runoff during the study period and the extreme ROS days are the ROS days among the 20 847
extreme runoff days. (c) change in the ratio of total runoff during large ROS days to total runoff 848
from the 200 large runoff days. (d) same as (c), except the ratio is extreme ROS runoff to total 849
extreme runoff. White areas have historical mean annual maximum SWE less than 20 mm (see 850
Figure 1(b)) and are excluded in the analysis. 851
(a) (b)
(c) (d)
852
Figure 10: Changes in the 100-yr flood magnitude (left column) and in the contribution of large 853
ROS runoff to the total large runoff (right column) in the +2 warmer climate for each HUC-6 854
basin within the three major most ROS-impacted regions within the CONUS, including the 855
Northeast (panel a and b), the upper Midwest (panel c and d), and the western mountains (panel e 856
and f). 857
(a) (b)
(c) (d)
(f) (e)
- 49 -
858
Figure 11: 100-year flood magnitude change and changes in the ratio of large ROS runoff to the 859
total large runoff in a +2 warmer climate at basins in the most ROS-impacted regions in 860
CONUS (from Figure 11). Each dot represents a basin and is color-coded with the median 861
elevation of the basin. (a) compares at basins whose elevation is below 1700m, and (b) compares 862
basins whose elevation is above 1700m. 863