Progress in Earth andPlanetary Science

Park et al.
Progress in Earth and Planetary Science(2024) 11:13
https://doi.org/10.1186/s40645-024-00617-yOpen Access

RESEARCH ARTICLE

Influence of snowmelt on increasing Arctic
river discharge: numerical evaluation
Hotaek Park1,2* , Youngwook Kim3, Kazuyoshi Suzuki4 and Tetsuya Hiyama2

Abstract
Snow is the most important component of the Arctic climatic and hydrological system and is directly vulnerable
to climate change. In recent decades, observations have indicated significant decreases in the Arctic snow coverand snowfall rate, whereas water discharge from circumpolar Arctic river basins into the Arctic Ocean has increased.
To evaluate the contribution, not well quantified, of snow to the river discharge increase, we conducted sensitivity
simulations with surface air temperature and precipitation as climatic treatment variables, combining a land surface
model and a distributed discharge model. Variables were treated assuming higher climate variations in the Arctic coldseason in 1979–2018. The surface and subsurface runoffs simulated by the land surface model were set as inflows
in the discharge model to estimate river discharge. Snowmelt mostly converted to surface runoff, accounting
for 73.6% of the anomalous surface runoff increase and inducing the simulated peak discharge in spring and earlysummer. This relationship was enhanced by the winter precipitation increase. Snow loss induced by higher air temperature contributed to the decrease in the peak and annual discharges, but caused the peak discharge to occur earlier. Additionally, warmer temperature increased the proportion of rainfall in the partitioning of precipitation, causing
more subsurface runoff, particularly in autumn and winter. These results provide a first separate evaluation of factors
influencing Arctic water discharge, including seasonal hydrographs, and illustrate the influence of climate warminginduced snowfall and rainfall variations on the circumpolar Arctic river discharge.
Keywords Snowmelt, Arctic river discharge, Climate change, Precipitation, Sensitivity simulation
1 Introduction
The Arctic is a key region for climate change, where environmental changes are the most drastic and air temperature is increasing three times faster than the global mean
(IPCC 2022). However, despite its vulnerability to climatechange, snow cover, which is closely linked to climate
and hydrology, remains complete in Arctic circumpolar*Correspondence:
Hotaek Park
park@jamstec.go.jp
1
Japan Agency for Marine-Earth Science and Technology (JAMSTEC),
Yokosuka 237‑0061, Japan
2
Nagoya University, Nagoya 464‑8601, Japan
3
Biology Department, College of Science, United Arab Emirates
University, Al Ain, United Arab Emirates
4
Japan Agency for Marine-Earth Science and Technology (JAMSTEC),
Yokohama 236‑0001, Japanregions during the cold season. Extensive observations
have indicated considerable changes in the snow conditions in recent decades. For example, a retreat of thesnow cover at the pan-Arctic scale (Mudryk et al. 2021;
Robinson 2021) and an earlier onset of melting (Mudryk
et al. 2017) have been observed in spring. Moreover, thesnow water equivalent (SWE) in winter has decreased,
particularly in North America (Pulliainen et al. 2020)
where snow cover variations exhibited regional differences, with a decreasing trend in Alaska but an increasing trend in eastern Canada (Hessilt et al. 2023). Thesechanges are connected to modifications of the circumpolar Arctic river hydrograph, because its spring peak-flow
regime is strongly related to snowpack melt. In fact, earlier snowmelt resulted in a notably earlier river discharge
peak flow in spring (Tananaev et al. 2016; Suzuki et al.
2020) and an increase in the thermal energy inflow at the© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

Park et al. Progress in Earth and Planetary Science(2024) 11:13

soil surface, causing earlier soil thawing (Kim et al. 2012;
Park et al. 2015) and further adding snow meltwater tosoil water storage. Consequently, increasing soil moisturein the thawing active layer enhanced evapotranspiration
and runoff (Park et al. 2020, 2021, 2022). In circumpolar regions, evapotranspiration largely consumes thewater of the upper soil layer, mixed by snow meltwater and rain water, owing to the relatively shallow plantroot depth (Park et al. 2021). In summer, evapotranspiration is greater than precipitation (Park et al. 2008),
possibly leading to seasonally drier soil (Hiyama et al.
2023; Park et al. 2022). Additionally, owing to the warming climate, evapotranspiration has shown a trend ofincrease in recent decades (Helbig et al. 2020; Kim et al.
2023). Enhanced evapotranspiration can weaken the link
between snow meltwater in soil water storage and summer rainfall to runoff, consequently reducing summer
river discharge, as observed in the case of Arctic rivers(Déry et al. 2016; Shiklomanov and Lammers 2009).
Linked to relatively low evapotranspiration in autumn,
increased autumnal precipitation (Box et al. 2019) contributes to greater soil water storage and thus higher
runoff during winter (Hiyama et al. 2023). Additionally,
the recent warming has caused delay in the onset of soil
freezing (Kim et al. 2012; Park et al. 2016), lengthening the period over which soil water is likely connected
to runoff (Evans et al. 2018). During winter, snow coverfunctions as an insulator for the permafrost soil thermal
regime. Anomalously thick snow insulates permafrost
not only during the winter season, but until the following summer, thereby contributing to the deepening of theactive layer (Park et al. 2013, 2015). Active layer development enhances the melting of permafrost ground ice(Hiyama et al. 2023), potentially strengthening subsurface runoff (Evans et al. 2020; Han and Menzel 2022; Walvoord and Striegl 2007; Wang et al. 2021). Such complexhydrological processes, strongly affected by snow covervariability, likely cause anomalous river discharge in the
Arctic. However, the river discharge effectively measured in North America exhibited an increasing trend in
the last decades (Feng et al. 2021), inconsistent with theobserved negative SWE trend (Pulliainen et al. 2020).
Because discharge is the balanced output of precipitation,
evapotranspiration, and soil moisture, its observed trend
cannot be easily correlated with snow variability. However, the spring river flow regime, strongly dependent onsnowmelt, likely reflects snowmelt anomalies. Indeed, aprevious study reported that variations in the maximum
daily discharge and cold season precipitation were consistent for most Russian rivers (Shiklomanov et al. 2007).
As mentioned above, the observed snow cover in Arctic regions reflects regionally different trends in recent
years (Pulliainen et al. 2020). The heterogeneity is likely

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linked to regional anomalies in snowmelt-associatedriver discharge, at least in the period of peak flow inspring and early summer. At the onset of snowmelt, river
ice also begins to break, and ice floes combine with runoff and the rapid flood pulsing from surrounding uplands
to cause ice jam flooding (Rokaya et al. 2018). This phenomenon has huge impact on biogeochemical and socioeconomic systems on local–regional scales (Prowse
and Beltaos 2002). Similarly, the impact of snowmelt onriver discharge and on human society has been widely
recognized. Previous studies established that snowmelt
accounted for approximately 60% of the total river discharge for the Kolyma River Basin (Welp et al. 2005) andYukon River Basin (Lachniet et al. 2016). However, suchassessment needs verification using additional observed
tracer data such as stable water isotopes in river discharge. If heterogeneous climate features and the geomorphology of the observed rivers are considered, thenthe observed data are limited in terms of expansion toother Arctic rivers. Consequently, there are no similarassessments for other Arctic regions to apportion theabsolute volume of river discharge inflowing to the Arctic Ocean. These limitations could be partially mitigatedthrough application of numerical modeling.
Additionally, the contribution of snowmelt to river discharge is influenced by climate variations; specifically,
it decreases with increasing air temperature. For example, the observed Arctic terrestrial mean air temperaturein the Arctic cold season (October–May) has increasedby 3.1 °C from 1971 to 2017 (Box et al. 2019), possibly
enhancing snow sublimation in winter and evaporation in spring, and consequently reducing the fraction ofsnowmelt contributing to river discharge (Suzuki et al.
2015). Under climate warming conditions, this decrease
could be strengthened by increasing rainfall rates (Bintanja and Andry 2017). Most climate model projections
indicate stronger warming in the near future (Tebaldi
et al. 2021), which would alter the relationship between
snow and river discharge (Berghuijs et al. 2014).
In this study, we asked the following questions:• How much does snowmelt currently contribute toriver discharge in the Arctic?
• In what regions is river discharge most sensitive tosnow variability?
• How do climate warming-induced snowpack variations influence river discharge?
To answer these questions, we quantify the sensitivityof the circumpolar Arctic river discharge to snow covervariability in 1979–2018 during the Arctic cold season,
using simulations from coupled land surface and distributed discharge models. Model simulations are conductedPark et al. Progress in Earth and Planetary Science(2024) 11:13

for several scenarios, depending on two climatic treatment variables (surface air temperature Ta and precipitation P). Results for important diagnostic variables arecompared against a reference simulation with presentday climate conditions. Thus, anomalous output values
indicate the influence of snow cover or, more generally,
climate variability, on river discharge. Because this study
focuses on the sensitivity of river discharge to climatechange-induced snow variability, not on its amplitude,
evaluation of model results against observations is notincluded in this analysis.

2 Methods
2.1 Model description

In this study, we used the process-based “coupled hydrological and biogeochemical” (CHANGE) land surface
model, based on hydrological, physical, and biogeochemical principles (Park et al. 2011, 2018), to evaluate
the impact of snow and climate on the circumpolar Arctic river discharge. CHANGE simulates the heat, water,
and carbon dioxide fluxes in the Arctic terrestrial atmosphere–vegetation–snow–soil system. The snowpackpreserves snow water and the rain water resulting fromhigher temperatures during winter. The snow meltwaterin spring is divided into soil infiltration and surface runoff, depending on water saturation and freezing/thawing
conditions in the soil surface layer, even during precipitation events. With snow meltwater, the excess waterfrom the surface layers of the soil, frozen during winter,is converted to surface runoff, which is defined as snowmelt in spring. The infiltrated water flows down or up,
depending on the water potential gradient between soil
layers. Excess water in either the permafrost surface layeror the soil bottom boundary layer generates a subsurface
flow. The soil is dried by surface evaporation and planttranspiration and wetted by precipitation. It experiences
phase changes in the Arctic circumpolar region, seasonally alternating between freezing and thawing. Freezing/
thawing phase changes in a soil column (depth: 50.4 m)
are an important physical process in the model to explicitly represent the dynamics of water and heat fluxes inboth permafrost soil and deeper ground.
The CHANGE-simulated surface and subsurface runoffs are set as inflows for the storage-based, distributed
river routing “total runoff integrating pathways version2” (TRIP2) model (Ngo-Duc et al. 2007) to simulate riverdischarge and storage (Park et al. 2016, 2017). TRIP2
represents a more realistic travel time without requiring parameter calibration from observational data. It
calculates riverine water storage and river discharge,in each grid cell and at every time step, with the global
river network TRIP (0.5° spatial resolution; Oki and Sud1998). Water storage in individual grid cells is calculated

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prognostically from the inflow–outflow balance; othervariables are diagnosed from the water storage term. Surface and subsurface runoffs simulated by CHANGE are
directly stored into individual river storage reservoirs in
TRIP2, and then water flows along a prescribed channel
network. The TRIP2 model also includes a river-ice algorithm that simulates seasonal ice formation, growth, andmelt, depending on temperature variability of the riverwater (Park et al. 2016). River water temperature along
the river network is calculated with a one-dimensional
advection–diffusion equation, considering inflows ofheat and water, both from upstream and from tributaries,
and heat exchange at the air–water interface (Park et al.
2017, 2020). In the calculation of heat exchange with theatmosphere, river storage, and ice volume, the river surface area represents a critical variable that is estimatedusing two terms: the river channel length within the gridbox, calculated geomorphologically based on the TRIP
river network data, and the river width, estimated using
a geomorphological relationship with mean annual runoff (Arora and Boer 1999). Reservoir regulation, affecting
downstream discharge in some river basins, was not considered in our simulations.
2.2 Data

Precipitation is an important meteorological variable
to simulate river discharge. Arctic regions are generally
characterized by strong winds and sparse weather stations, inducing large measurement biases in the observed
precipitation. Thus, using a forcing dataset derived fromsuch data increases uncertainty on the simulated riverdischarge. To minimize this uncertainty, we selected agridded climatic dataset as input for the CHANGE andTRIP2 simulations: the bias-corrected reconstructionof near-surface meteorological variables from the latest
European Centre for Medium-Range Weather Forecasts(ECMWF) atmospheric reanalyses (ERA5), obtainedwith the same methodology as the water, energy andclimate change (WATCH) forcing data. This climatic
dataset is known as the “WATCH Forcing Data methodology applied to ERA5” (WFDE5, https://​doi.​org/https://​doi.​org/​10.​24381/​cds.​20d54​e34). The WFDE5 dataset is provided with a daily time step, aggregated fromhourly-timescale data, from 1979 to 2018, on a regular latitude–longitude grid with a spatial resolution of
0.5° × 0.5°. The original horizontal resolution of the ERA5
data product (0.25° × 0.25°) has been degraded in WDEF5
to facilitate comparisons and bias correction, then corrected for elevation and for monthly biases derived
from the observational dataset of the Climatic Research
Unit of the University of East Anglia, England (Cucchi
et al. 2020). Finally, using the adjusted WFDE5 data as
input for TRIP2, we simulated river discharge and water

Park et al. Progress in Earth and Planetary Science(2024) 11:13

temperature in each grid cell of the circumpolar Arctic
river basins. The circumpolar Arctic river drainage system and river basin boundaries are illustrated in Fig. 1.
2.3 Sensitivity simulations

The WFDE5 dataset includes the daily mean, maximum,and minimum values for Ta, P, water vapor pressure,specific humidity, wind speed, and solar radiation. With
these data, we simulated the water budget in circumpolar Arctic river basins for the 40-year study period. In our
simulations, land cover was fixed to its present-day distribution (Lawrence and Chase 2007), while vegetation phenology was simulated prognostically at every time stepfrom the calculated carbon and nitrogen contents, climate conditions, and soil temperature and moisture. Soil
thermal and hydraulic properties were calculated with
CHANGE using soil texture vertical fractions for sand,silt, and clay, combined with the simulated soil organic
matter content, at hourly time steps and with the nominal WFDE5 spatial resolution.
To diagnose the influence of Ta and P variations during the Arctic cold season (i.e., September to May) onsnow cover and, consequently, on river discharge andsoil thermal and hydrological regimes, we designed ten
scenarios for our numerical simulations. The simulations comprised a reference simulation, using the original WFDE5 product as forcing data, and three sets ofthree treated simulations, also forced by WFDE5 data,

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but with additional anomalous increases in Ta, P, orboth simultaneously (Table 1). The mean value of P incircumpolar Arctic river basins was taken as 46.5, 29.6,
and 31.9 mm in September–November, December–February, and March–May, respectively. Anomalous Ta andP increases in the sensitivity simulations were derived
from previously published results, briefly described here.
Ta is a critical variable for soil freezing/thawing and thus
temperature and snow cover variation, useful in assessing both warming trends in the current climate (Boxet al. 2019) and future warming projections (IPCC 2022).
In the Arctic, increases in terrestrial Ta of 1.5–2 °C overthe previous four decades have been reported (Bekryaev et al. 2017). Increasing Arctic Ta also induces P
increases. In the central Lena River basin in Siberia, large
P increases in 2004–2008, by 30% or more relative to the
1979–2013 average, have been measured (Iijima et al.
2016). In this study, we used these large variation rates as
thresholds to define higher climatic changes, then conducted model simulations with Ta and P as treatment
variables to assess snowpack sensitivity to higher climate
warming and the resulting river discharge. Using suchclimate thresholds in the model simulations allowed us
to evaluate the sensitivity of target diagnostic variables
to the treated meteorological variables (Park et al. 2015).
In the reference simulation, no treatment was applied. In
the first set of three simulations (EXTA_SON, EXTA_DJF, and
EXTA_MAM), only Ta was treated, with a constant increaseof 2 °C in different periods of the Arctic cold season, consistently applied every year throughout the study period.
In the second set of simulations (EXPR_SON, EXPR_DJF, and
EXPR_MAM), only P was treated, with an increase of 30%
in the same three periods as for the first set. Finally, thethird set (EXTA_PR_SON, EXTA_PR_DJF, and EXTA_PR_MAM)

Table 1 Model settings for the sensitivity simulations

Fig. 1 Study area: the circumpolar Arctic river drainage system.
In this region, we conducted model simulations to analyze Arctic
river discharge during the study period (1979–2018). Boundariesof the circumpolar Arctic river basins were derived from the sea
boundaries of Lammers et al. (2001)

Simulation

Air temperature*

Precipitation*

Treatment period

Reference**

n/a

n/a

n/a

EXTA_SON+ 2 °C

No increase

Sep–Nov

EXTA_DJF+ 2 °C

No increase

Dec–Feb

EXTA_MAM+ 2 °C

No increase

Mar–May

EXPR_SONNo increase+ 30%

Sep–Nov

EXPR_DJF

No increase+ 30%

Dec–Feb

EXPR_MAM

No increase+ 30%

Mar–May

EXTA_PR_SON+ 2 °C+ 30%

Sep–Nov

EXTA_PR_DJF+ 2 °C+ 30%

Dec–Feb

EXTA_PR_MAM+ 2 °C+ 30%

Mar–May*The differences adopted in this study for the climatic treatment variables(second and third columns) represent increases relative to the reference
simulation**n/a not applicablePark et al. Progress in Earth and Planetary Science(2024) 11:13

combined both treatments: increases of Ta by 2 °C and
P by 30%, in the same periods. The anomaly calculated
by subtracting the reference simulation results from thetreated simulations output represents the sensitivity ofthe target diagnostic variables to snow cover or climate
modifications.
During precipitation events in the cold season, P partitions into snowfall (Ps) and rainfall (Pr) depending on Ta.
In the CHANGE model, partitioning is achieved relative
to a temperature threshold, as defined by Wigmosta andLettenmaier (1994):

P
Ps =Tmin ≥ Ta
Tmax −TaP
Tmin < Ta < Tmax
Tmax −Tmin0Ta ≥ Tmax

Pr = P − Ps
where Tmax is the maximum air temperature at whichsnow occurs and Tmin is the minimum temperature atwhich rain occurs. Because Tmax and Tmin were fixed to
0.5 °C and − 1.0 °C, respectively, in our model simulations, partitioning of P into Ps and Pr largely depended
on Ta variability. Thus, these equations indicate thatthe influence of a Ta increase on P partitioning shouldbe particularly measurable in spring and autumn, thetransitional seasons between cold and warm climate
conditions.

3 Results
3.1 Snowfall and rainfall

In the reference simulation, annual mean Ps and Pr
derived from the WFDE5 product (spatial distribution of
the annual mean trends, Fig. 2a, b, respectively; interannual trend variability, Fig. 2c), averaged over all grid cellsof the circumpolar Arctic river basins (Fig. 1), accounted
for 42% and 58%, respectively, of the annual mean P(Fig. 2c). Annual mean Ps exhibited a small increasing trend, not statistically significant (0.03 mm ­year−1,
p < 0.41). Conversely, Pr exhibited a strong significant
increasing trend (0.87 mm ­year−1, p < 0.01). There werealso clear differences between Ps and Pr in the spatial distribution of their annual mean trends. Annual mean Ps
trends showed values consistently within ± 3 mm ­year−1,
without large regional differences except in a few small
regions with larger decreasing trends (mainly easternmost Russia and southwestern Norway, Fig. 2a). Overall,regions with positive (but not statistically significant) Ps
trends accounted for 58.2% of the combined circumpolar Arctic river basin area, markedly more than the negative trend regions (41.9%). On the contrary, Pr displayedstrong regional discrepancies, with strong increasing
trends of up to 8 mm ­year−1 on the Eurasian continent,

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but smaller, mostly negative trends in northeastern Canada (Fig. 2b).
3.2 River discharge

Although model-simulated discharges have previously
been compared to measurements from major Arctic riverwatersheds under different climate conditions (Park et al.
2016, 2017; Hiyama et al. 2023), the simulated discharges
for the six major Arctic river watersheds in this study
were compared against the measurements for model
validation (Additional file 1: Fig. S1). The model generally well simulated the seasonal variation in discharge
observed at the mouth of each watershed, particularly
for the hydrograph of the spring peak discharge, fractioning the larger amount of the annual discharge. However,
the model considerably overestimated the volume ofthe spring peak discharge in the Ob and Yenisey rivers.
The water flow in the upstream regions of the two riverwatersheds was disturbed by anthropogenic regulationfrom several artificial reservoirs (McClelland et al. 2004),
for which a deficiency exists in the model representation,
as mentioned in Sect. 2.1, which likely resulted in theoverestimations. In contrast, the model underestimatedthe observed summer discharge of the Yukon River, forwhich the upstream region is covered by a mountain glacier and where melted glacier water accounts for a considerable fraction of summer discharge (Chesnokova
et al. 2020). This glacial process was not considered in themodel simulation; thus, the deficiency was attributed tothe bias in the simulated discharge. The degree of overestimation or underestimation of the discharge of the threerivers (i.e., Ob, Yenisey, and Yukon) was determined by
the lowest Nash–Sutcliffe coefficients, which were compared to the coefficients with values of > 0.65 in the simulations of the remaining watersheds (i.e., Lena, Kolyma,
and Mackenzie).
As indicated in Sect. 2.1, surface and subsurface runoffs simulated by CHANGE were stored to individual
reservoirs of the TRIP2 river channel then flowed downalong a prescribed channel network. Figure 3 illustrates
the seasonal variations, averaged over the study period,of the simulated daily runoff (Fig. 3a) flowing from thecircumpolar Arctic river basins (Fig. 1) into the Arctic
Ocean, and the corresponding seasonal trends (Fig. 3b).
The total discharge (sum of the surface and subsurface
runoffs) followed a typical hydrograph, with a marked
seasonal peak in spring and early summer, followed by aquick decrease, then a smaller secondary peak in autumn(Fig. 3a). Peak discharge originated nearly exclusively
from surface runoff, which accounted for 90% of theApril–July total discharge. Conversely, subsurface runoff
exhibited a different seasonality, increasing from August
to its peak value in October, and represented the primary

Park et al. Progress in Earth and Planetary Science(2024) 11:13

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Fig. 2 Spatial distribution and interannual variability of precipitation (snowfall and rainfall). Spatial distributions of the mean annual trends
during the study period are represented in the study area (illustrated in Fig. 1) for a snowfall and b rainfall, in which hatching indicates statisticallysignificant trends at the 90% confidence level. Panel c corresponding interannual variability for total precipitation, snowfall, and rainfall. The meanannual trends (values indicated in the figure with the associated p values) were calculated as area-weighted averages in each grid cell of the Arctic
circumpolar river basins

contribution to river discharge in the Arctic cold season(September–May).
Daily surface runoff, the primary contributor to totalsummer discharge in the Arctic, exhibited sharply positive than negative trends in the early summer season(May–June). Surface runoff greatly increased in spring,with a maximum value of 0.5 ­km3 ­year−1 in late May(Fig. 3b), likely because of the Ta increase. Increasing Ta resulted in earlier spring snowmelt, with fasterinflow of snowmelt into the circumpolar Arctic riverdischarge (Hiyama et al. 2023). This modification of thesnow cycle seasonality has already been identified inobservational data from the Eurasian and North American river basins (Mudryk et al. 2017, 2021). Then, thePark et al. Progress in Earth and Planetary Science(2024) 11:13

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Fig. 4 Interannual discharge variability. Annual mean discharge
values were calculated in the study area for the surface
and subsurface runoffs routed separately in TRIP2, then combined
to derive temporal variations of the total discharge. Corresponding
trends, calculated over the study period, are also indicatedFig. 3 Seasonal variability of the discharge volume and trends. Daily
mean values calculated for the reference simulation over the study
period are indicated in the study area for a the total, surface,and subsurface runoff discharges from the circumpolar Arctic river
basins, and b the corresponding annual trends. Discharges associatedwith surface and subsurface runoffs simulated by CHANGE were
calculated separately by TRIP2 along the prescribed river route
network, then combined to derive the total discharge

simulated surface runoff trend became negative, with aminimum value of − 0.2 ­km3 ­year−1 at the end of June
(Fig. 3b). We attribute this negative trend to a decreasein soil moisture caused by enhanced evapotranspiration, directly because of the increasing Ta values.
Evapotranspiration presented a trend of 0.8 k­ m3 ­year−1
larger than that of precipitation in June over the circumpolar Arctic river basin (Additional file 1: Fig. S2).
Like its seasonal variations, the total river discharge
trends were also determined primarily by surface runoff
in spring and summer (Fig. 3b). In contrast, subsurface
runoff exhibited a positive trend during the Arctic coldseason, particularly after October (Fig. 3b), with a maximum value of 0.05 k­ m3 ­year−1. The subsurface runoff
increase likely resulted from large positive Pr trends

in summer and autumn (Additional file 1: Fig. S3). In
previous studies, we had also discussed a groundwater(subsurface) contribution increase to the anomalous
river discharge during the Arctic cold season, inducedby the deepening of the active layer under warming climate conditions (Hiyama et al. 2023).
Annual mean discharge from the circumpolar Arctic river basins into the Arctic Ocean was 5 273 ­km3,
increasing continuously at an annual mean rate of 12.2
­km3 ­year−1 (Fig. 4) during the study period. Corresponding increasing trends were also derived for the surface
and subsurface runoffs. Interestingly, although surface
runoff represented 71% of the total river discharge (notably more than subsurface runoff ), we calculated a smaller
positive trend for surface runoff than for subsurface runoff, primarily explained by the markedly different rates ofincrease for Ps and Pr (Fig. 2c, see also next section).
Additional analysis for river discharge was conducted,
breaking down the pan-Arctic scale to the watershedscale. Targeted to the major six Arctic river watersheds,
the separated annual discharges were averaged for the
study period, and their trends were analyzed (Table 2).
As identified at the Arctic scale, the largest contribution of surface runoff to total river discharge, fractioned
from 62 to 79% over the six river basins, was also consistent at the watershed scale. However, different to thedischarge of the other five rivers that exhibited a trendof increase, the Yukon River discharge revealed a negative trend (− 0.22 ­km3 ­year−1), although it was statistically insignificant. The negative trend in Yukon River
discharge was likely influenced by the reduction in surface runoff discharge (− 0.36 ­km3 ­year−1) linked to thedecrease in snow water (Fig. 2a), as observed by satellite

Park et al. Progress in Earth and Planetary Science(2024) 11:13

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Table 2 Summary of discharge budget for the six major Arctic
river basins
Watershed Total

Surface runoff

Subsurface runoffOb

583.7 ± 77.9(+ 2.68*)460.4 ± 64.9(+ 1.66)

123.2 ± 37.8(+ 1.02*)

Yenisey

737.4 ± 155.1(+ 1.44)518.3 ± 93.1(+ 1.28)

219.1 ± 62.0 (+ 0.16)

Lena

547.3 ± 69.4(+ 1.96*)369.7 ± 34.7(+ 0.77)

177.6 ± 56.2 (+ 1.19)

Kolyma117.1 ± 21.2(+ 0.73*)

86.4 ± 16.2(+ 0.38*)30.7 ± 10.8 (+ 0.35*)

Mackenzie

322.6 ± 39.3(+ 1.50*)244.4 ± 32.4(+ 0.98*)78.2 ± 19.4 (+ 0.53*)

Yukon

131.2 ± 21.5(− 0.22)82.0 ± 17.2(− 0.36)49.3 ± 12.7 (+ 0.14)

Values represent the annual mean volume ­(km3, with standard deviation) during
the study period 1979–2018. Parentheses indicate trends of discharge variables
during the study period, in which asterisks represent statistical significance atthe 95% confidence level

remote sensing (Pulliainen et al. 2020). Additionally, theabsolute value of the trend in subsurface runoff of the
Lena River was larger than that of the surface runoff, but
it deviated from the results derived for the other rivers.
As previously documented by Hiyama et al. (2023), theincrease in subsurface runoff of the Lena River is attributable to the influence of increased summer rainfall andpermafrost thawing. Comparison of the discharge budget
between the six river basins reveals regionally differentchanges in seasonal processes under the background ofclimate change.
3.3 Impact of snowmelt on the circumpolar Arctic riverdischarge

To evaluate the sensitivity of the surface and subsurface
runoffs to altered climate conditions during the Arctic
cold season, particularly to snowmelt variations, we calculated discharge anomalies by subtracting the reference
simulation from the treated simulations (with Ta and P
as treatment variables, Table 1). Figure 5 illustrates theseasonal anomalous discharge variations for each simulation. In the three P-treated simulations (i.e., EXPR_SON,
EXPR_DJF, and EXPR_MAM), larger P values during the coldseason, inducing larger Ps, clearly resulted in larger surface runoff in spring and early summer, because of theincreased snowmelt relative to the reference simulation(Fig. 5a). The largest anomalous surface runoff among thethree P-treated simulations resulted from the EXPR_DJF
simulation, while that of the EXPR_SON simulation waslowest. The P increase in the mid-winter season with thecoldest temperature was linked to larger snowpack andthen higher snowmelt in spring. The variance of the seasonal P increments resulted in the discrepancy in annual

Fig. 5 Seasonal variability of anomalous discharges in the sensitivity
simulations. The anomalies represent discharge differences associatedwith the a surface and b subsurface runoffs between the treatedsimulations (with increases in surface air temperature, precipitation,
or both simultaneously) and the reference simulation (Fig. 3)

river discharge between the three P-treated experiments,
where the anomaly of the amount of evapotranspirationis negligible. Conversely, higher Ta values during thecold season mainly induced a decrease in the April–Julysurface runoff, as shown by the EXTA_SON and EXTA_DJF
simulations. Under warmer conditions, the contributionof Ps to P was reduced, resulting in a thinner snow cover
during the Arctic cold season and therefore less snowmelt in spring. The negative effect of Ta on snowmelt was
further evident in the EXTA_SON simulation with a low Ps
rate, relative to that in the EXTA_DJF simulation. However,the seasonality of the anomalous surface runoff discharge
in the EXTA_MAM simulation (treated Ta values in spring)
was inconsistent with the other Ta-treated simulations(EXTA_SON and EXTA_DJF); the temperature increase firstcaused an earlier (April–May) snowmelt-induced positive surface runoff anomaly, and then a strong negative
anomaly (mid-June) that was probably the result of soil

Park et al. Progress in Earth and Planetary Science(2024) 11:13

dryness caused by temperature-enhanced evaporationfrom the soil surface. A similar pattern was identified in
the EXTA_PR_MAM simulation, in which the combined Ta
and P increases resulted in larger early-spring surface
runoff than in the EXTA_MAM simulation, mitigating thenegative influence of Ta on the surface runoff. The positive influence of P on the surface runoff in early summer
was also identified in the other Ta- and P-treated simulations (EXTA_PR_SON and EXTA_PR_DJF), in which the anomalous surface runoff exhibited similar seasonality to that in
the three P-treated simulations (i.e., EXPR_SON, EXPR_DJF,
and EXPR_MAM).
Our results also indicated significant sensitivity ofthe subsurface runoff to cold season climate variations
when P increased in autumn (i.e., simulations EXPR_
SON and EXTA_PR_SON, Fig. 5b). Evapotranspiration inautumn, when atmospheric demand is lowest, is typicallyextremely low (Park et al. 2008; Kim et al. 2023). Under
such conditions, excess precipitation effectively contributes to soil wetting, thus enhancing subsurface runoff.
In contrast, the higher temperature in autumn increases
evapotranspiration and results in negative anomalous
subsurface runoff (i.e., the EXTA_SON simulation). In theremaining seven simulations other than the EXPR_SON
and EXTA_PR_SON simulations, subsurface runoff sensitivity to cold season climate variations was moderate, withanomalous discharge values consistently within ± 20 ­km3
(Fig. 5b). The thicker snow cover caused by a winterincrease in P produces higher insulation, thus a deeper
active layer (Park et al. 2015), in which more thawed soil
water is available for subsurface runoff. However, thedependence of summer subsurface runoff on the wintersnow conditions was limited, as identified by the EXPR_DJF
and EXPR_MAM simulations (Fig. 5b). This suggests that
subsurface runoff depended primarily on summer andautumn rainfall, as reported in a previous study (Hiyama
et al. 2023). Meanwhile, the Ta increase in the cold season
resulted in slightly negative anomalous subsurface runoff,
particularly in late summer and autumn (i.e., the EXTA_
SON and EXTA_MAM simulations), which was probably due
to the influence of higher evapotranspiration-induced
soil dryness.
To investigate our hypothesis on the possible relationship between the surface/subsurface runoffs and
Ps/Pr, we compared the anomalous annual mean surface and subsurface runoffs with Ps and Pr anomalies,
respectively, for all simulations (Fig. 6). The impactof snowmelt on the runoff anomalies persisted until
summer and was not limited to spring (Fig. 5); therefore, the anomalous annual mean runoff values wereused for comparison purposes. The comparison indicated a strong, statistically significant linear correlation

Page 9 of 15(correlation coefficient r = 0.98, p < 0.001) between Ps
and surface runoff. On the basis of this linear relationship, we estimated directly the contribution of Ps tothe surface runoff: a Ps increase of 10 mm, equivalent
to a water volume of 211 k­ m3, would account for 73.6%of the surface runoff discharge anomaly (286.8 k­m3;
Fig. 6). Moreover, a Ta increase of 2 °C reduced Ps by(3–13) mm, corresponding to surface runoff decreasesof (89–240) k­ m3. The second comparison between subsurface runoff and Pr anomalies also yielded a statistically significant linear correlation, but with more data
scatter and a lower correlation coefficient (r = 0.75,
p < 0.01). Thus, the contribution of Pr to subsurface
runoff was more difficult to evaluate. However, fromsimulations EXPR_SON and EXTA_PR_SON, we estimatedthat Pr increases of (20–38) mm in autumn could
induce a subsurface runoff increase of approximately
328 ­km3. Finally, the total anomalous circumpolar Arctic river discharge was significantly correlated to thetotal annual P anomaly (Fig. 6e), suggesting that P wasthe main contributor to the yearly total circumpolar
Arctic river discharge. Additional comparisons showedthat there were no significant correlations between Ps
and subsurface runoff (Fig. 6c) or Pr and surface runoff(Fig. 6b).
We also investigated the spatial distribution of thesurface and subsurface runoffs to determine the mainareas of circumpolar Arctic river discharge. For thispurpose, we calculated correlations between annual
mean Ps and Pr and CHANGE-simulated surface andsubsurface runoffs, respectively, in each grid cell forall simulations (Fig. 7). The surface runoff distribution yielded statistically significant correlations withPs (r > 0.6, p < 0.05) in all Arctic river basin grid cells(Fig. 7a), implying that snowmelt was the largest contributor to surface runoff. As snow melts in spring,soil surface layers remain initially frozen (Park et al.
2015), effectively generating mainly surface runoff
from the snow meltwater by preventing soil infiltration. Significant correlations (p < 0.05) were also calculated between subsurface runoff and Pr, but they wereregionally localized, mainly at the southern permafrost
boundary and in non-permafrost circumpolar regionsof the study area (Fig. 7b), in good correspondence with
the increasing Pr trend regions of Fig. 2b. Interestingly,
these regions were also characterized by large standard
deviations (over all simulations) associated with active
layer thickness and soil moisture in the upper layers(Fig. 8), indicating larger variability of the diagnostic
variables in the treated simulations. When the active
layer deepens under the influence of stronger snow
insulation or higher temperatures, the soil water storage capacity also increases (Suzuki et al. 2021).

Park et al. Progress in Earth and Planetary Science(2024) 11:13

Page 10 of 15

Fig. 6 Relationship between anomalous discharge components and anomalous precipitation variables. Integrated annual anomalous
values, calculated in each treated simulation, are plotted for the discharge components (surface and subsurface runoff, total discharge)
against the precipitation variables (snowfall, rainfall, and total precipitation) to illustrate the relationships between a surface runoff–snowfall; bsurface runoff–rainfall; c subsurface runoff–snowfall; d subsurface runoff–rainfall; and e total discharge–total precipitation. Anomalies represent
annual mean differences between each treated simulation and the reference simulation (Fig. 5). Values for the precipitation variables are calculated
as area-weighted averages in each grid cell of the circumpolar Arctic river basins. Numbers in symbols represent experimental scenarios: 1 EXTA_SON,
2 EXPR_SON, 3 EXTA_PR_SON, 4 EXTA_DJF, 5 EXPR_DJF, 6 EXTA_PR_DJF, 7 EXTA_MAM, 8 EXPR_MAM, and 9 EXTA_PR_MAM

Park et al. Progress in Earth and Planetary Science(2024) 11:13

Page 11 of 15

Fig. 7 Spatial distribution of correlations between runoff and precipitation. Correlation coefficient values are represented for a surface runoff–
snowfall and b subsurface runoff–rainfall correlations. The correlation coefficient was calculated in each grid cell of the study area from the annual
mean values in each treated simulation (nine values per grid cell). It represents the influence of snowfall on surface runoff (Panel a) and of rainfall
on subsurface runoff (Panel b). Correlations are only plotted in the grid cells where statistical significance exceeds 5%

4 Discussion
This study investigated the influence of snowmelt on circumpolar Arctic river discharge, on the basis of treatedmodel simulations that incorporated climate modifications, with Ta and P as treatment variables, in the Arctic cold season. Snowmelt in spring generally separated
into two flows: surface runoff and soil infiltration. The
onset of snowmelt occurred earlier than soil thawing (Park et al. 2015). Frozen soil limited snow meltwater infiltration, therefore meltwater mainly generated
surface runoff, particularly at the initial stage of thawing. Surface runoff accounted for 90% or more of thepeak Arctic river discharge in spring and early summer(Fig. 3a). The strong relationship between snowmelt andsurface runoff was demonstrated by high correlations in
all grid cells of the Arctic river basins (Fig. 7). This suggested that Ps was the primary contributor to surface
runoff change. We also calculated a small, but not statistically significant, increasing Ps trend over the study
period (0.03 mm ­year−1, p < 0.41; Fig. 2c), insufficient
to explain the strong surface runoff increasing trend(Fig. 4). The missing contribution was likely provided by
Pr increases in autumn of the previous year. Part of theautumn rainfall, captured and frozen in winter in upper
soil layers, thawed during the following spring and mixed

with infiltrated snow meltwater, thereby also contributing to surface runoff. This is commonly referred to as the“memory effect” of hydrological processes in the Arctic circumpolar regions (Park et al. 2021; Hiyama et al.
2023).
Precipitation over the circumpolar Arctic river basins
exhibited an increasing trend over the study period(Fig. 2c) under warming climate conditions. Variationsin Ta critically affect the partitioning of P into Ps and Pr,
with a stronger influence in spring and autumn whentransitions between freezing and thawing occur. However, the measured Ps and Pr trends were negative andpositive, respectively, in spring and autumn (Additional
file 1: Fig. S2). The Pr increase in spring possibly mitigated the surface runoff decreasing trend induced by a
Ps decrease. Conversely, decreasing Ps values in autumn,
when snow starts accumulating, corresponded to lower
winter SWE values, thus to a lower contribution to spring
surface runoff. Previous climate model studies have also
projected a Ps decrease in the Arctic circumpolar regions
under warming climate conditions (Bintanja and Selten2014), with reduced snow cover and shorter snowing
period (Mudryk et al. 2021; Robinson 2021). This implies
that the contribution of snowmelt to runoff generation
will decrease under warming climate conditions, as also

Park et al. Progress in Earth and Planetary Science(2024) 11:13

Page 12 of 15

Fig. 8 Spatial distribution of standard deviations for a active layer thickness and b surface soil moisture. Surface soil moisture is definedas the water content in a soil column extending from the surface to a depth of 1.5 m. The standard deviations, calculated from the anomalous
values representing the difference between the treated simulations and the reference simulation, illustrate the sensitivity of the active layer
and the soil moisture to climatic variations during the Arctic cold season

demonstrated by our Ta-treated simulations (Fig. 6a). In
the present-day climate simulation, summer Ps exhibiteda decreasing trend (Additional file 1: Fig. S2), likely inducing a reduction in the mountainous snowpack. However,
it is difficult to directly attribute the snow loss in mountainous regions to a decreasing river flow, because of
the replenishment effect induced on Pr by a Ta increase.
Assessing these warming-induced effects is also complicated by competing factors such as higher evapotranspiration, altered vegetation composition, and changes
in wildfire propagation. However, a warmer climate will
demonstrably alter the dependence of runoff on snowmelt in the Arctic. Similar events have already occurredon Earth, as indicated by historical trends established at
the global scale in mountainous regions (Huss et al. 2017;
Immerzeel et al. 2020; Siirila-Woodburn et al. 2021).
To analyze the sensitivity of the circumpolar Arctic
river discharge to snowmelt, we conducted model simulations in the Arctic cold season, with Ta and P as treatment variables. The treated variables were sequentially
updated following modifications of other meteorologicalvariables. For example, higher Ta altered specific humidity, thereby inducing a P increase that, in turn, resulted
in a lower Ta value. The simple treatment implemented

in our simulations only altered the diagnostic variables
but excluded internal feedbacks between meteorologicalvariables. This simplification causes uncertainties on thesimulated results: an increase in Ta yields a higher saturated vapor pressure, thus a larger vapor pressure deficit,
leading to an overestimation of snow surface sublimation,
thereby introducing errors in SWE calculations. Furthermore, in real environmental conditions, frozen andthawed soil often coexist at the regional scale, a likely
characteristic of transitional landscapes at the boundaries
between continuous and discontinuous (sporadic or isolated) permafrost regions (Kim et al. 2012). In our model,
the spatial resolution is coarse (0.5° × 0.5°) and simulated
soil in each horizontal grid cell is assumed completely
frozen or fully thawed, with direct implications for surface runoff and soil infiltration. Therefore, the limited
model configuration cannot fully reproduce hydrological processes at the soil surface in transitional boundary
regions, causing errors on the simulated surface runoff.
For example, our simulation results indicated that thePs increase accounted for 73.6% of the total Arctic riverdischarge variations (Fig. 6), larger than the 60% contribution previously measured in the Kolyma and Yukon
river basins (Welp et al. 2005; Lachniet et al. 2016). These

Park et al. Progress in Earth and Planetary Science(2024) 11:13

discrepancies likely result from limitations in the experimental design and model representation. Snowmelt in
spring linked to snow meltwater, and soil surface water
attributable to Pr in autumn of the previous year, are contributors to surface runoff. The estimated value of 73.6%
could include a partial contribution of Pr, indicating alimitation of the current model in that it cannot separate the individual contributions of snow meltwater andrain water to surface runoff. However, results obtained inthis study were consistent with observed historical snow
loss trends induced by climate warming, despite the simple simulation settings. Therefore, we believe that oursimulation results are useful to quantify regional water
budgets and analyze the influence of snow conditions onwater discharges in circumpolar Arctic river basins underwarming climate conditions.

5 Conclusions
The total discharge inflow from circumpolar Arctic river
basins into the Arctic Ocean has increased continuously
in the last decades. This study quantitatively evaluatedthe contribution of snowmelt, sensitive to climate variations, to the observed increase in Arctic river discharge.
Using model simulation results for 1979–2018, we established that snowmelt, through surface runoff, was a major
contributor to both the spring peak and total annual riverdischarge. Correlation between snowmelt and surface
runoff was strong in nearly all grid cells of the circumpolar Arctic river basins. Treated simulations, initializedwith fixed increases in Ta, P, or both to simulate climate
warming variations, indicated that an increase in Ta
enhanced snowmelt, thereby reducing surface runoff andriver discharge, but also induced larger rainfall and subsurface runoff, particularly in the autumn season. These
results synthetically demonstrate that climate warming will limit the contribution of snowmelt to the total
Arctic river discharge while increasing the influence ofrainfall on the discharge. However, the simplified model
assumption did not account for feedbacks between meteorological variables; therefore, our results also include
the uncertainty generated by this simplification. Usingclimate model projections to represent the near-future
warming climate conditions might be beneficial to reduce
the uncertainty caused by the model settings’ simplification. One of the biggest problems in the field of cold
land hydrology is to determine the changes in seasonal
processes under the background of a warming climate.
Therefore, improving the model to separate the variations in source waters attributable to the seasonal hydrology is a priority for our future work.
Abbreviations
CHANGE	Coupled hydrological and biogeochemical land surface model

Page 13 of 15

ECMWF	European Centre for Medium-Range Weather Forecasts
ERA5	ECMWF Reanalysis v5
P	Precipitation
Pr	Rainfall
Ps	Snowfall
SWE	Snow water equivalentTa	Surface air temperature
Tmax	The maximum air temperature at which snow occurs
Tmin	The minimum temperature at which rain occurs
TRIP	Total runoff integrating pathways
WATCH	The water, energy and climate change
WFDE5	WATCH Forcing Data methodology applied to ERA5

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40645-​024-​00617-y.
Additional file 1: Fig. S1. Daily discharge simulated by CHANGE (red)compared to observations (black) at the mouths of the six major Arctic
rivers. Daily discharge was averaged from 1979 to 2008, except for Kolyma,
which was averaged from 1979 to 1994. Blue and red shading denotes
one temporal standard deviation range. NSC in each panel means theNash–Sutcliffe coefficient. Observed daily river discharge records for the
major river watersheds from 1979 to 2008 were obtained from the University of New Hampshire data repository of the Arctic Rapid Integrated
Monitoring System (ArcticRIMS; http://​rims.​unh.​edu). Fig. S2. Seasonalvariability of the monthly averaged mean annual trend in (a) precipitationand (b) evapotranspiration over the circumpolar Arctic river basin in thereference simulation. The monthly values represent mean annual trends
of precipitation and evapotranspiration over the study period, calculated
each month as area-weighted averages in each grid cell of the circumpolar Arctic river basins, in which asterisks indicate statistically significant
trends at the 95% confidence level. Fig. S3. Monthly trends of precipitation in the reference simulation, partitioned into snowfall and rainfall.
Monthly values were calculated as area-weighted averages in each gridcell of the circumpolar Arctic river basins.
Acknowledgements
We thank Eric Dupuy, Ph.D., from Edanz (https://​jp.​edanz.​com/​ac) for editing a
draft of this manuscript.
Author contributions
HP was involved in conceptualization; data curation; methodology; originaldraft preparation. YK helped in formal analysis; validation; writing, reviewing,
and editing of the manuscript. KS contributed to validation; writing, reviewing,
and editing of the manuscript. TH assisted in validation; writing, reviewing,
and editing of the manuscript.
Funding
This study was partly supported by the Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (KAKENHI) funding program,Grant Numbers 19H05668, 21H04934, and 22H03758. It was also supportedby the Belmont Forum of the Japan Science and Technology Agency, Grant
Number JPMJBF2003, and the “Arctic Challenge for Sustainability II” project
funded by the Ministry of Education, Culture, Sports, Science and Technology,
Japan, Grant Number JPMXD1420318865.
Availability of data and material
The datasets supporting the conclusions of this article are available on
request.

Declarations
Competing interests
The authors declare that they have no competing interest.

Park et al. Progress in Earth and Planetary Science(2024) 11:13

Received: 20 October 2023 Accepted: 4 March 2024

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Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Hotaek Park received the Ph.D. degree in agricultural sciencefrom Nagoya University in 2000. He is currently a Senior Scientist at
JAMSTEC and a Visiting Professor at the Institute for Space-Earth Environmental Research, Nagoya University, involved in the “Pan-Arctic
Water-Carbon Cycles” project funded by the JSPS.
Youngwook Kim received the Ph.D. degree in soil, water, andenvironmental science from the University of Arizona in 2007. He isan Assistant Professor of environmental remote sensing at the United

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Arab Emirates University. His research interests include monitoring
the vegetation response to global climate change and estimating thecarbon, water, and energy cycles in the terrestrial ecosystem.
Kazuyoshi Suzuki holds a Ph.D. in agricultural Science from Iwate
University. He was hired as a Researcher for the Frontier Research System for Earth Observation at JAMSTEC in 2000. He has participatedin field observations in Siberia and other cold regions. He is actively
involved in satellite data analysis and data assimilation, with a special
focus on atmosphere–land surface coupling.
Tetsuya Hiyama received the Ph.D. degree in science from the
University of Tsukuba in 1995. He is the Principal Investigator of the
JSPS KAKENHI project “Pan-Arctic Water-Carbon Cycles.”