Challenges of Hydrological Engineering Design in Degrading Permafrost Environment of Russia

Abstract

The study shows that the current network of hydrometeorological observation in the permafrost zone of Russia is insufficient to provide data for the statistical approaches adopted at the state level for engineering surveys and calculations. The alternative to the financially costly and practically impossible expansion of the monitoring network is the development of hydrological research stations and the implementation of new methods for calculating streamflow characteristics based on mathematical modeling. The data of the Kolyma Water-Balance Station, the first research basin in the world in a permafrost environment (1948–1997), and the process-based hydrological model Hydrograph are applied to simulate streamflow hydrographs in remote mountainous permafrost basins. The satisfactory results confirm that mathematical modeling may substitute or replace statistical approaches in the conditions of extreme data insufficiency. The improvement of the models in a changing climate requires the renewal of historical observations at currently abandoned research stations in Russian permafrost regions. The study is important for forming the state policy in climate change adaptation and mitigation measures.

Full text

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Citation: Makarieva, O.;
Nesterova, N.; Haghighi, A.T.;
Ostashov, A.; Zemlyanskova, A.
Challenges of Hydrological
Engineering Design in Degrading
Permafrost Environment of Russia.
Energies 2022,15, 2649. https://
doi.org/10.3390/en15072649
Academic Editor: Alban Kuriqi
Received: 28 February 2022
Accepted: 30 March 2022
Published: 4 April 2022
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4.0/).
energies
Article
Challenges of Hydrological Engineering Design in Degrading
Permafrost Environment of Russia
Olga Makarieva 1, 2, * , Nataliia Nesterova 1,2,3, Ali Torabi Haghighi 4, Andrey Ostashov 1
and Anastasiia Zemlyanskova 1,2
1Melnikov Permafrost Institute, Magadan 677010, Russia; nnesterova1994@gmail.com (N.N.);
andrey.ostashov@gmail.com (A.O.); anastasiazemlanskova@gmail.com (A.Z.)
2Institute of Earth Sciences, Saint Petersburg University, Saint Petersburg 199034, Russia
3State Hydrological Institute, Saint Petersburg 199004, Russia
4Water Energy and Environmental Engineering Research Unit, University of Oulu, 90570 Oulu, Finland;
ali.torabihaghighi@oulu.fi
*Correspondence: o.makareva@spbu.ru or omakarieva@gmail.com; Tel.: +7-911-213-2657
Abstract:
The study shows that the current network of hydrometeorological observation in the
permafrost zone of Russia is insufficient to provide data for the statistical approaches adopted at
the state level for engineering surveys and calculations. The alternative to the financially costly
and practically impossible expansion of the monitoring network is the development of hydrological
research stations and the implementation of new methods for calculating streamflow characteristics
based on mathematical modeling. The data of the Kolyma Water-Balance Station, the first research
basin in the world in a permafrost environment (1948–1997), and the process-based hydrological
model Hydrograph are applied to simulate streamflow hydrographs in remote mountainous per-
mafrost basins. The satisfactory results confirm that mathematical modeling may substitute or replace
statistical approaches in the conditions of extreme data insufficiency. The improvement of the models
in a changing climate requires the renewal of historical observations at currently abandoned research
stations in Russian permafrost regions. The study is important for forming the state policy in climate
change adaptation and mitigation measures.
Keywords:
degrading permafrost; streamflow; hydrological engineering design; deteriorating net-
work of observations; hazards; risks; modeling; research stations
1. Introduction
Global warming has impacted world natural and anthropogenic systems in recent
decades [
1
], and the permafrost zone has undergone the strongest climatic changes that
affect all components of the environment, including the transformation of the hydrological
regime [
2
]. Understanding the interactions between changing hydrology and degrading
permafrost is essential to reducing uncertainties in predicting the responses of water
resources and aquatic ecosystems to climate change in high altitude/latitude regions [
3
].
Numerous studies show an increase in the total water flow of large rivers of the permafrost
zone in the second half of the 20th century [
4
–
6
], a shift in the timing of floods and
significant changes in the intra-annual runoff distribution [
7
], including the growth of
maximum streamflow characteristics [8].
The increasing probability of the occurrence of natural hazards in climate change
conditions is stated elsewhere in the world [
9
]. In the permafrost zone, dangerous phe-
nomena are mostly associated with changes in the characteristics of frozen ground. The
infrastructure is affected by the degrading of ice-rich permafrost, which may lead to the
loss of mechanical strength, subsidence and foundation failure [
10
]. Piped systems are
especially susceptible to settlement and subsequent leakage [11].
Energies 2022,15, 2649. https://doi.org/10.3390/en15072649 https://www.mdpi.com/journal/energies

Energies 2022,15, 2649 2 of 16
The risks caused by the changes in the functioning of the entire natural system,
including the hydrological cycle, are increasing. The average annual total (direct and
indirect) damage from floods in Russia is currently estimated at over RUB 40 billion (about
USD 500 million) per year, and this value is constantly increasing [
12
]. A significant
part of it is associated with the damage to transport infrastructure—the erosion of road
sections, flooding, flushing of bridge constructions and destruction of hydraulic structures.
Emergencies are often caused because culverts and hydraulic structures cannot cope with
the release of floods of rare probability. They can be associated with improper operation
and errors at the stage of engineering and hydrological surveys, design and construction,
including the uncertainty of the methods used to calculate hydrological characteristics in
the absence of streamflow observations.
The opening of the Nadym–Salekhard road took place in December 2020 and became
a significant event for the residents of the Yamal-Nenets Autonomous region. However,
the highway was closed for repairs due to the impact of high water already in the spring of
2021 [13].
The statistics on the Magadan region (north-east of Russia) show that hazard floods in
this region have occurred annually over the past ten years. Thus, 74 km of roads and 15
bridges were damaged, including at the Kolyma federal highway; the damage amounted
to more than RUB 600 million (USD 8.7 million) due to the flood in August 2013 [
14
]. The
regional road “Magadan–Balagannoe–Talon” was closed, and the damage was estimated at
RUB 700 million (USD 9.4 million) in 2014. The flood damage in the region reached RUB
250 million (USD 3.4 million) in August 2016. In 2019, the intensity of flood inflow to the
Kolyma and Ust-Srednekan reservoirs of the Magadan region was the highest in the last 80
years [15].
Active expansion of socio-economic infrastructure has been implemented by state
programs of Arctic development, allowing the extraction, processing and transportation of
natural resources despite the observed changes in permafrost territory [
16
]. They include
the large-scale overland transport construction projects in permafrost regions of Russia,
such as the railway line “Severnyy shirotnyy khod”, 707 km length in Yamal-Nenets
Autonomous Region, connecting the Obskaya station on the left bank of the Ob River and
New Urengoy through Salekhard and Nadym. In the future, it is planned to continue
this railway to Norilsk through Igarka and Dudinka (connecting the Ob and Yenisei River
basins by a land transport corridor) (Figure 1).
Figure 1.
Distribution of permafrost type in the departments of Russian Hydrometeorological Service
(DHS) located in permafrost zone (the number of DHS corresponds with Tables 1and 2) and * the
meteorological stations of Russian Hydrometeorological Service monitoring ground temperature at

Energies 2022,15, 2649 3 of 16
0.8–3.2 m depth (N) in 2008. 1—“Bovanenkovo—Sabetta”; 2—railway Korotchaevo—Igarka; 3—
“Belkomur” (program of the Russian Arctic); 4—railway Nizhny Bestyakh—Magadan, 5—Kolyma
highway—Anadyr; 6—the bridge over the Lena river; 7—railway “Severny shirotny khod”; 8—
Baikal-Amur line; 9—Transsib; 10—Kolyma highway and part of the Lena highway.
Table 1.
Distribution of permafrost type (%) in the departments of Russian Hydrometeorological
Service (DHS) located in a permafrost zone and the number of meteorological stations monitoring
ground temperature at 0.8–3.2 m depth (N) in 2019.
DHS Area,
mln km2
Type of Permafrost (%–mln km2–N)
Continuous Discontinuous Sporadic Isolated All
1. Murmansk 0.14 0–0.00–0 1–0.00–1 16–0.02–0 8–0.01–0 26–0.04–1
2. North 1.14 15–0.17–1 2–0.02–0 4–0.05–1 4–0.05–1 26–0.30–3
3. Ob-Irtysh 1.51 21–0.32–0 20–0.30–0 18–0.27–4 14–0.21–6 73–1.10–13
4. West Siberian 0.84 2–0.02–0 3–0.03–3 5–0.04–5 9–0.08–2 19–0.16–10
5. Central Siberian 2.54 56–1.42–5 8–0.20–0 11–0.28–0 14–0.36–9 89–2.26–14
6. Irkutsk 0.77 3–0.18–2 15–0.12–3 24–0.19–7 29–0.22–7 91–0.70–19
7. Transbaikal 0.78 47–0.37–4 15–0.12–6 18–0.14–7 17–0.13–8 97–0.76–25
8. Yakutsk 3.06 93–2.84–16 5–0.15–0 1–0.03–2 1–0.03–2 100–3.06–20
9. Far East 1.18 33–0.39–1 20–0.24–2 13–0.15–2 14–0.17–6 81–0.95–11
10. Kolyma 0.46 87–0.40–1 11–0.05–1 1–0.00–0 1–0.00–0 100–0.46–2
11. Chukotka 0.71 100–0.71–3 0–0.00–0 0–0.00–0 0–0.00–0 100–0.71–3
12. Kamchatka 0.46 30–0.14–0 19–0.09–0 9–0.04–0 11–0.05–1 69–0.32–1
Total 14.3 51–6.96–33 10–1.31–16 9–1.21–28 10–1.31–42 80–10.8–122
km2per station 210,000 69,000 43,000 30,000 87,000
Table 2.
The number of hydrological gauges where streamflow discharge is measured, classified by
basin area.
DHS <200 200–2000 2000–10,000 >10,000 All
1980 2008 2019 1980 2008 2019 1980 2008 2019 1980 2008 2019 1980 2008 2019
1. Murmansk 21 8 5 39 18 15 16 11 10 9 1 1 85 38 31
2. North 18 9 9 91 70 71 63 51 49 34 30 31 206 160 160
3. Ob-Irtysh 7 0 0 35 18 19 36 21 24 54 35 42 132 74 85
4. West Siberian 12 5 4 82 65 64 68 53 55 47 46 41 209 169 164
5. Central Siberian 15 14 14 50 47 45 36 33 34 52 42 38 153 136 131

Energies 2022,15, 2649 4 of 16
Table 2. Cont.
DHS <200 200–2000 2000–10,000 >10,000 All
1980 2008 2019 1980 2008 2019 1980 2008 2019 1980 2008 2019 1980 2008 2019
6. Irkutsk 14 7 7 43 30 31 45 31 30 27 26 27 129 94 95
7. Transbaikal 24 10 8 93 49 41 57 53 44 37 39 34 211 151 127
8. Yakutsk 38 20 16 28 13 12 25 17 13 60 57 55 151 107 96
9. Far East 20 16 15 33 24 28 23 22 19 20 16 17 96 78 79
10. Kolyma 36 12 7 17 5 5 13 2 2 8 3 3 74 22 17
11. Chukotka 7 0 0 6 1 0 4 0 0 12 2 2 29 3 2
12. Kamchatka 37 21 19 38 24 24 17 5 6 10 8 7 102 58 56
Total 249 122 104 555 364 355 403 299 286 370 305 298
1577
1090 1043
The Kolyma–Omsukchan–Omolon–Anadyr highway construction began in 2012. Its
planned length is about 2300 km (Figure 1). This road would unite three regions of
the Far East—Chukotka, Magadan Region and Yakutia, including the federal highway
“Kolyma”. It is also planned to build a new railway line Nizhny Bestyakh–Magadan [
17
].
The engineering studies and design for the bridge’s construction over the Lena River in
Yakutsk have begun. The construction cost will be about RUB 83 billion (USD 1.1 billion).
It was planned to put the bridge into operation in 2026. The Lena bridge will connect
three federal and five regional highways, the Amur–Yakutsk railway, a river port and an
international airport [18].
The planned development program requires scientifically based methods for calcu-
lating the characteristics of river streamflow, forecasting and assessing flood risk for the
projected, industrial and social infrastructure given the high cost of construction projects.
The Russian government draws special attention to the compilation of methodological
recommendations for assessing climate risks and corporate plans according to the approved
national action plan for adapting the economy and the population to climate change [19].
This study aims to show that the current network of hydrometeorological observation
in the permafrost zone of Russia is insufficient to provide data for the statistical approaches
adopted at the state level for engineering surveys and calculations. The alternative to the
financially costly and practically impossible expansion of the hydrometeorological network
is the development of a hydrological research network and the implementation of new
methods for calculating flow characteristics based on mathematical modeling.
2. Study Area and Permafrost Data Availability
Permafrost is distributed mainly in the northern hemisphere of the earth and occupies
65% of the territory of Russia. The permafrost type varies from continuous (thickness up
to 1500 m or more) to isolated (10–20 m thick) at the southern border of the permafrost
distribution (Figure 1) [
20
]. The forecast and assessment of changes in permafrost conditions
and hydrological regime and flow characteristics in Russia are complicated by the rapid
reduction in the observation network. It is still the least provided with data of standard
hydrometeorological measurements, despite the growing interest in the development of
the permafrost zone in Russia. This study analyzes the distribution of ground temperature
stations and hydrological gauges where streamflow discharge is measured along the
Russian permafrost zone. We investigate the dynamic of those gauges sorted by basin area
and DHS in the last several decades.

Energies 2022,15, 2649 5 of 16
2.1. Soil Temperature Observation in Permafrost Zone of Russia
Table 1shows the list of twelve territorial departments of the Russian Hydrometeo-
rological Service (DHS) where permafrost occupies more than 20% of the territory. The
total area of those DHS constitutes 13.6 mln km
2
with 10.8 mln km
2
(80%) covered by
permafrost of different types. Continuous permafrost is present in more than half of the
area (51%); other types are distributed evenly (each about 10%). Three departments are
located entirely in the permafrost zone (Chukotka, Yakutsk and Kolyma with 100, 93 and
87% of continuous type, respectively). Permafrost covers more than 80% in Transbaikal,
Irkutsk, Central Siberian and Far Eastern departments, here the proportion of continuous
permafrost type ranges from 23 to 56%. The discontinuous permafrost zone varies from 1
to 20%, sporadic and isolated from 1 to 29% in presented DHS. Table 1contains the data
regarding the number of meteorological stations with the data on the ground temperature
at any depth in the range of 0.8–3.2 m available online at the official website of the Russian
Hydrometeorological Service [21] up to 2008.
At an area of 10.8 mln km
2
, there are 123 stations monitoring ground temperature.
Though the stations are unevenly concentrated close to industrial centers and city agglom-
erations, on average, one station covers about 87,000 km
2
; for continuous permafrost, this
value increases by three times and reaches 210,000 km
2
(for example, together at Chukotka
and Kolyma DHS, the north-east of Russia, with a total area 1.17 mln km
2
there are only five
stations of standard observational network with ground temperature data). As well as the
severe lack of stations, they may also present data of uncertain quality (for example, [22]);
often, the data contains many gaps and presents a minimum number of ground depths.
2.2. Reduction in Hydrological Observation Network in Permafrost Zone of Russia
Streamflow discharge is the main hydrological engineering characteristic. The his-
torical and current hydrological gauges with streamflow measurements were compiled
from [23–26], respectively.
The number of hydrological gauges with streamflow discharge measurements in
the permafrost zone constituted 1577 in 1980 and dropped to 1043 in 2019. The network
density has decreased by about 1.5 times over the past 40 years. The situation is even more
acute with tiny rivers (catchment area <200 km
2
)—the number of observation gauges has
decreased more than two-fold, and for gauges with a catchment area of 200–2000 km
2
—1.3-
fold (Table 3, Figure 2). One may note that the strongest drop in the number of hydrological
gauges occurred between 1980 and 2008. However, the tendency of further decrease is well
seen at most DHS. In total, the number of gauges has decreased by 47 (about 5 %) over
the past ten years (by six gauges for each DHS on average, ranging from 0 to 11 gauges).
The most critical situation is observed in the Kolyma DHS. In 2008–2019, the total number
of discharge gauges had dropped from 22 to 17 (23% decrease), the losses (5 gauges) are
characteristic for the most crucial data—small rivers with basin areas less than 200 km
2
. In
the Chukchi peninsula, the number of gauges dropped from 3 to 2; considering the area
of this DHS (>700 thousand km
2
), the situation in the north-east of Russia is critical. The
main reason for hydrometeorological network reduction is the significant depopulation of
northern territories and the limited funding of the service due to the decline of the Russian
economy after the collapse of the Soviet Union.

Energies 2022,15, 2649 6 of 16
Figure 2.
Total density (number of gauges per 100,000 km
2
) and location of hydrological network with
streamflow observations in the departments of Russian Hydrometeorological Service of permafrost
zone (DHS, the numbers correlate to Tables 1and 2) by 2019.
Figure 2shows the density of hydrological gauges of all area categories in permafrost
DHS. By density, we mean the number of any hydrological gauges with discharge mea-
surements per 100,000 km
2
. Figure 2also depicts the location of hydrological gauges with
different basin areas. Figure 3gives a more exact idea about the density of gauges in each
DHS. Additionally, the percentage of the DHS area covered by the permafrost of any type
is shown.
Most territory in east Siberia and the north-east (DHS 8, 10, 11 with 100% permafrost)
is characterized by the density of fewer than five gauges per 100,000 km
2
(Figure 2). In the
Republic of Yakutia (DHS 8), those values are
≤
0.5 for basin areas less than 10,000 km
2
and 1.8 for larger basins. In the Magadan region (DHS 10), the density varies from 0.4 to 1.5
with a total (all gauges) average of 4.0. In Chukotka, there are no gauges in the category less
than 10,000 km2. The density is 0.3 for the gauges of basin areas more than 10,000 km2. In
DHS 3 and 4 (the Ob’ River basin), most gauges are located beyond the permafrost zone in
the south of the regions. The same situation is characteristic for DHS 5, 6 and 9, where the
gauges’ density is representative of the non-permafrost zone. In DHS 1 and 2, permafrost is
distributed in the north-eastern edges of the regions exactly where no gauges are situated.
A relatively acceptable situation with the network density may be considered in DHS 6
and 7 (Irkutsk and Transbaikal regions), where permafrost covers more than 90 % of the
territory and the density of gauges with basin areas > 200 km2reaches 3.5–5.6.

Energies 2022,15, 2649 7 of 16
Figure 3.
The density of hydrological gauges with streamflow discharge observations is classified by
basin area in different DHS (see Tables 1and 2) with indicated permafrost (%) for DHS territory.
3. Methods
3.1. Modern Methods for Calculating the Hydrological Engineering Characteristics
Any construction projects require engineering field studies and design. Most countries
use statistical estimation methods in civil hydrological engineering. Their base is various
distribution functions that describe observed streamflow data and estimate hydrological
characteristics of low probabilities [27].
Developing cost-effective and sustainable plans requires the assessment of flood
risk. In the United States, that computation is done following guidelines in Bulletin
17C [
28
]. It is recommended that the flow discharge be determined by the method of the
probable maximum flood. The probable maximum flood (PMF) calculation is carried out
during the construction design of hydraulic engineering facilities for particularly critical
structures [29–31].
In Europe, most studies are based on statistical methods applied to individual time
series of extreme precipitation or extreme streamflow; moreover, many assessments are
carried out based on the regional principle. In [
32
], various approaches for producing
climate projections of extreme precipitation and flood frequency, methods for statistical
downscaling and bias correction, and alternative hydrological models are presented.

Energies 2022,15, 2649 8 of 16
In Canada, flood management is primarily the responsibility of the provinces and
territories. Therefore, most flood management activities are executed at the ‘local’ rather
than provincial, territorial or federal levels [
33
]. The statistical frequency analysis is per-
formed on high river flows to obtain a set of design flow values corresponding to selected
frequencies of occurrence, commonly interpreted in terms of return periods or annual
exceedance probabilities [
34
]. The statistical approach is used to develop precipitation
intensity-duration-frequency estimates, then integrated with urban hydrological models
to produce the desired design flow values. The estimated design flows are then used in
hydraulic models to generate flood extents and levels to develop flood inundation, flood
hazard and other flood-related maps and products [34].
In Russia, the Calculation Set of Rules 33-101-2003 (SR) [
35
], based on applying
statistical processing methods of long-term series of streamflow observations, is currently
demanded. The SR is an updated version of the document SNiP 2.01.14-83 [
36
], issued
in 1983, and fundamentally does not differ from its predecessor in terms of methods for
calculating runoff characteristics. The previous edition assumed that hydrological processes
are statistically stationary, and consequently, retrospective observations can be considered
representative. The SR methods recognize climate change and require the use of current
hydrometeorological information when making calculations and clarifying the parameters
of calculation equations based on the generalization of current hydrological data. However,
such recommendations do not offer clearly described methods to consider the influence of
climate on streamflow characteristics [37–39].
In [
40
], the calculations of maximum streamflow characteristics for several rivers
in the permafrost zone of Russia based on the recommendations of SR were conducted.
The calculation was carried out for four very small (up to 200 km
2
) and two small (up to
2000 km2)
river basins located in eastern Siberia and the north-east with different hydro-
logical regimes, provided by long series of streamflow data. Analyzed river basins were
treated as ungauged. The data of other hydrological gauges (following the recommendation
of SR) was used as the analog for conducting the calculations. Calculated characteristics
of maximum discharge of different probabilities were compared with observed values.
The results of the study have shown that the choice of analog rivers provided with recent
observational data (including last 20–25 years) is limited to 2–3 options of watersheds that
have no alternative for the area of up to several hundred thousand km
2
and the require-
ments for the selection of analog rivers are largely wide, leading to large uncertainty of
calculation results.
3.2. The Mathematical Modeling Methods and Special Monitoring of Runoff Formation Processes
in the Permafrost Zone
Hydrological calculations and forecasts in the conditions of vast and remote per-
mafrost territories, where the network of hydrometeorological observations is either very
rare or absent, are related to the use of mathematical models. The permafrost zone imposes
increased requirements on hydrological models [
2
]. Among the hydrological models that
describe the processes of heat and moisture transfer in frozen soil and have been tested in
cold regions of the earth are the TopoFlow model [
41
], Cold Region Hydrological Model
(CHRM) [
42
], Variable Infiltration Capacity (VIC) model [
43
], cryospheric basin hydro-
logical model (CBHM) [
44
], GEOTop model [
45
], SoilWater—Atmosphere—Plants model
(SWAP) [
46
], Ecological model for Applied Geophysics (ECOMAG) [
47
], the Hydrograph
model [48,49] and others.
The complexity of runoff formation processes in permafrost regions requires under-
standing the physical mechanisms of heat and moisture exchange processes to improve
and apply mathematical modeling methods. One of the most important obstacles for such
studies is obtaining full-scale data of special and experimental observations. Stationary
observations at small research catchments are the main source of information about the
physical mechanisms of runoff formation and ongoing hydrological cycle changes. There-
fore, the low density of the standard observation network may be compensated by the

Energies 2022,15, 2649 9 of 16
development of a network of research catchments. Canada and the USA, where the area of
permafrost territories and their inaccessibility are commensurate with the permafrost zone
of Russia, are leading in those studies [
50
–
54
]. Watershed research is accompanied by the
development and application of mathematical modeling methods.
Russia has lagged significantly behind other Arctic countries in instrumental studies
of the hydrological cycle processes over the past 30 years. However, it had the world’s
first system of integrated scientific hydrological stations organized in various climatic
conditions in the USSR. M. Velikanov was the first to propose the organization of special
hydrological stations in various physical and geographical conditions in 1925, and D.
Sokolovsky compiled the plan for the placement of 45 field laboratories on the territory of
the USSR in 1933. There were already 11 stations from 1928 to 1940. However, most of them
were closed entirely during World War II. In 1954, the monography with the first results
of the studies at hydrological research stations, called runoff stations, was published [
55
].
The runoff stations made comprehensive observations of all elements of the water balance
and the factors causing their changes. The objects of the study were small catchments and
runoff sites characteristic of the region. By 1981, there were 16 water balance stations (WBS)
at natural catchments (not subject to reclamation) and nine marsh stations on the territory
of the USSR.
The Kolyma Water Balance Station (KWBS) was the only comprehensive research
station in the permafrost zone with long-term observation. The location of the KWBS
(upper reaches of the Kolyma River, Magadan region) was representative of the vast
mountainous territories of the permafrost zone of eastern Siberia, the north-east and the
Far East of Russia. Detailed observations of the runoff formation and the seasonal thawing
and freezing of soils were carried out at the KWBS from 1947 to 1997 [
56
]. The processes
of formation of water balance [
57
], hydrogeological structure and talik zone [
58
], runoff
in various landscapes (the distribution of precipitation, evaporation and water runoff in
permafrost rocks and mountain relief were studied based on the analysis of observational
data [
59
,
60
]. Another example is the Mogot research station of the Baikal-Amur expedition
of the Russian State Hydrological Institute (1976-1985). The Mogot station was created to
provide design and construction solutions for hydrological calculations in the permafrost
zone of economic development of the Baikal-Amur mainline [61–64].
Historically, the observations at WBS have contributed significantly to the develop-
ment of both applied and fundamental hydrology. Nowadays, there is no permanent state
hydrological research station in the permafrost zone of Russia.
In this study we have used one of the available hydrological models which has shown
the significant potential to be applied in remote permafrost regions. The parameters of
the Hydrograph model were previously elaborated at the base of KWBS data for typical
permafrost landscapes [
49
,
56
,
57
,
59
,
60
] to simulate the runoff formation processes in hard-to
reach river basins of the north-east of Russia. The results are aimed to show that the data
of research basins and appropriate process-based models allowing for regionalization of
their parameters could become a decent alternative to statistical approaches in the poorly
gauged permafrost basins.
3.3. Hydrograph Model
The Hydrograph, a distributed process-based model of runoff formation processes is
applied in the study. The model has proven to be an effective tool for research and projection
of hydrological processes in the permafrost and on poorly gauged river basins [
48
,
49
,
65
,
66
].
The model algorithms combine physically based and conceptual approaches in describing
the processes of the terrestrial hydrological cycle, which allows a balance to be maintained
between the complexity of the design schemes and orientation to limited input information.
Precipitation and interception of rainfall water, compaction and ablation of snow cover,
moisture and heat flux in the snow cover and in soils, including freezing and thawing,
are described in an explicit way in the model. Underground water, slope and channel
flow transformation, snow redistribution by wind and evaporation are calculated by

Energies 2022,15, 2649 10 of 16
conceptual methods that have shown their effectiveness in various conditions of cold
regions [
49
,
66
]. Using a limited list of meteorological variables (air temperature and
humidity, precipitation) as the input information allows the model to be applied at remote,
poorly gauged basins. The model parameters are related to runoff formation complexes—
landscapes with similar characteristics of soil and vegetation. The sets of parameters refined
on the studied catchments (analogous watersheds) can be transferred to ungauged basins
with similar surface types. The Hydrograph model is used on watersheds of different
sizes from the soil column to the Lena River basin without changing its structure and
algorithms [
48
]. The results of the studies [
49
,
59
,
65
] have shown that the Hydrograph model
performs satisfactorily in terms of active layer dynamic and soil temperature simulations.
The description of the procedures of basin schematization and model parametrization are
presented in detail in the studies [48,49,59,60,66] and therefore is omitted here.
The processes of groundwater and surface flow interactions are complicated in the
permafrost zone; therefore, the main limitations of the Hydrograph model are related to the
representation of those processes. They include the formation and development of taliks,
the formation of giant groundwater aufeis which are widely distributed in the study region,
and other geocryological processes. Those processes and phenomena should be studied at
research watersheds for the improvement of model algorithms and their parametrization.
4. Results
Four watersheds with an area from 84 to 8290 km
2
located in the mountainous regions
of such river basins as Yana, Indigirka and Kolyma were chosen as the study objects
(Table 3). The following data were used in the modeling process: daily meteorological
and hydrological information from standard hydrometeorological networks, previously
developed model parameters for main permafrost landscapes [
49
,
59
,
60
]. The assignment
of typical landscapes within the watersheds was conducted using a SRTM digital elevation
model and Landsat-8 images using previously developed schemes of landscape distribution
in the mountainous basins of the Kolyma [60] and Indigirka [66].
Table 3. The characteristics of the simulated watersheds and modelling results.
Large
River
Basin
River S * H Pr Yo Ys P E Qo Qs NS
(av)
NS
(max)
Indigirka
Sakharynia 84.4 833 1966–2012 93 113 294 181 14 12 0.32 0.76
Artyk-Yuryah 644 591 1966–1991 82
81.8
274 189 90.3 149 0.14 0.72
Yana Charky 8290 274 1966–2007 216 223 361 120 1424 1490 0.34 0.70
Kolyma
Anmangynda 400 668 1966–1987 273 237 375 125 161 81.1 0.43 0.71
* here, S—watershed area, km
2
; H—average watershed elevation, m; Pr—period of simulation, years; Yo, Ys—
observed and simulated annual streamflow, mm; P and E—simulated annual precipitation and evaporation, mm;
Qo, Qs—observed and simulated maximum discharge (m
3
/s); NS (av) and NS (max)—average and maximum
Nash–Sutcliffe criteria.
The model was run in continuous mode for the period from 22 to 47 years with daily
time step. The results of modeling compared to the observed values are presented in Table 3,
including the distribution of annual water balance, the Nash–Sutcliffe (NS) criteria for
daily streamflow hydrographs and maximum discharges. It is important to mention that
all meteorological stations to which data was applied are located beyond the watershed’s
borders. In mountainous conditions, it plays a significant role in flood modeling results.
Simulated values of annual precipitation and evapotranspiration vary in the ranges
274–375 and 120–189 mm, respectively. The bias between simulated and observed annual
values of streamflow reaches the numbers between 0 to 22%, increasing with the decrease
in watershed size. The difference between simulated and observed maximum discharges is
proportional to the distance of the meteorological station to the watershed which confirms
the limitation of modeling results by input data. One may see observed and simulated
hydrographs with good, average and poor convergence which mainly depends on the

Energies 2022,15, 2649 11 of 16
representativity of the input meteorological data (Figures 4and 5). The average Nash–
Sutcliffe (NS) criteria is not very high varying from 0.14 to 0.43, but in some years, it reaches
up to 0.76.
Figure 4.
Observed (black) and simulated (red) streamflow hydrographs;
1
—Sakharynia,
2
—Artyk-
Yuryah; (a–c)—high, low and average NS criteria.
Figure 5.
Observed (black) and simulated (red) streamflow hydrographs;
1
—Anmangynda,
2
—
Charky; (a–c)—high, low and average NS criteria.
The results confirm that the data of special research basins can be used for model
parameter estimation in remote permafrost basins. The quality of simulations is satisfactory.
The modeling results may substitute or replace statistical approaches in the conditions of

Energies 2022,15, 2649 12 of 16
extreme data insufficiency. The improvement of the models in current climate conditions
require the renewal of historical observation in currently abandoned research stations.
5. Conclusions
Most of the territory of Russia can be classified as unexplored hydrological territories.
Currently, calculations of flow characteristics in the permafrost zone are based on regional
statistical parameters. Their refinement was conducted more than 40 years ago, when
the hydrological data from all over the country were summarized fully, using unified
methods developed in the Russian State Hydrological Institute. One could argue there
are serious scientific and practical problems related to the extreme limitations and poor
quality of observational data, as well as the lack of funding and human resources to
restore a wide hydrological observation network in the permafrost regions of Russia. The
main problems related to hazards and water resources are the following: (1) estimating
streamflow characteristics in the tasks of engineering and survey design; (2) forecasting
the magnitude and frequency of catastrophic floods; (3) predicting the inflow of water into
reservoirs and river systems for the needs of hydropower and water transport.
The annual hazard damages in a permafrost region of Russia are comparable with the
costs of building a modern research station. For example, the cost of the Samoylov Island
Arctic permafrost research site built in the Lena River delta in 2012 was RUB 500 million
(about USD 17 million). This station is located in a remote hard-to-reach place and requires
complicated logistics for its provision due to the need for self-efficiency [
67
,
68
]. Building a
station in a less remote place with access to roads, energy and communication networks
would significantly reduce the costs while providing important data for coping with the
stated problems.
The solution of the tasks set can be achieved only at the state level and should be
carried out in three directions:
1.
The development of a state program to organize a network of representative catch-
ments in various climatic zones of permafrost regions for the comprehensive monitor-
ing of main components of water balance and hydrological processes using modern
equipment with a high time resolution and new research methods. It is also neces-
sary to consider the feasibility of restoring historical stations with a long series of
observations, such as the Kolyma water balance station [
56
]. The development of
such a program should be based on the results of a detailed inventory of historical
data of standard and specialized information on the characteristics of the natural
environment (climate, permafrost, hydrology, hazardous phenomena, landscapes,
etc.). The research stations should be equipped for year-round living and may serve
educational purposes for student field practice and experience in the future.
Nowadays, limited in scope and duration, some hydrological research is carried out
in a permafrost environment by individual research teams on a non-permanent basis of
grant funding and without uniform methods [
69
–
71
]. Obviously, it is impossible to solve
the discussed tasks solely by the research teams in terms of capital infrastructure. This is
due to the lack of resources for construction works, purchase of transport, maintenance of
property, etc. State and business input are required to support such initiatives.
2.
State order for the development of approaches for the estimation of the main hydro-
logical characteristics in engineering and survey design tasks based on mathematical
modeling methods.
3.
Improvement (in particular, expansion) of the standard hydrological observation
network, based on modern modeling and remote sensing methods and accounting
for historical experience, and social and economic development programs [
72
]. The
improvement of the measurements’ quality would require the renewal and expansion
of hydrometeorological education which has been in deep decline for the last 30
years. Implementing these three tasks would require us to solve many problems.
Among them are an acute shortage of qualified specialists in hydrometeorology (from
observers to researchers), the loss of experience in organizing and conducting complex

Energies 2022,15, 2649 13 of 16
hydrological research, a lag in the development of modern hydrometeorological
devices’ domestic production, financing of the industry on a residual basis,
and others.
Author Contributions:
Conceptualization, O.M., N.N. and A.T.H.; formal analysis, A.O. and A.Z.;
data curation, A.O. and A.Z.; writing—original draft preparation, O.M. and N.N.; writing—review
and editing, O.M.; visualization, A.O. and A.Z. All authors have read and agreed to the published
version of the manuscript.
Funding: The study was carried out with the support of RFBR (19-55-80028), Russian Geographical
Society (“Water resources of the north-east of Russia in the conditions of global and regional changes”)
and St. Petersburg State University (project 75295776).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: All data is available upon request to the corresponding author.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or
in the decision to publish the results.
References
1.
The Intergovernmental Panel on Climate Change (IPCC). Available online: https://www.ipcc.ch/reports/ (accessed on
20 October 2021).
2. Walvoord, M.A.; Kurylyk, B.L. Hydrologic Impacts of Thawing Permafrost—A Review. Vadose Zone J. 2016,15, 6. [CrossRef]
3.
Bring, A.; Fedorova, I.; Dibike, Y.; Hinzman, L.; Mård, J.; Mernild, S.H.; Prowse, T.; Semenova, O.M.; Stuefer, S.L.; Woo, M.-K.
Arctic terrestrial hydrology: A synthesis of processes, regional effects, and research challenges. J. Geophys. Res. Biogeosciences
2016
,
121, 621–649. [CrossRef]
4.
Kattsov, V.M.; Kallen, E.; Cattle, H.; Christensen, J.; Drange, H.; Hanssen-Bauer, I.; Johannesen, T.; Karol, I.; Raisanen, J.; Svensson,
G.; et al. Future climate change: Modeling and scenarios for the Arctic. In ACIA; Cambridge University Press: Cambridge, UK,
2005; pp. 99–150.
5.
Rawlins, M.A.; Serreze, M.C.; Schroeder, R.; Zhang, X.; McDonald, K.C. Diagnosis of the record discharge of Arctic-draining
Eurasian Rivers in 2007. Environ. Res. Lett. 2009,4, 045011. [CrossRef]
6.
Alekseevskii, N.I.; Magritskii, D.V.; Mikhailov, V.N. Anthropogenic and natural changes in hydrological restrictions for the use of
natural resources in the deltas of the Russian Arctic. Water Manag. Russ. Probl. Technol. Manag. 2015,1, 14–31.
7.
Yang, D.; Kane, D.L.; Hinzman, L.; Zhang, X.; Zhang, T.; Ye, H. Siberian Lena River hydrologic regime and recent change. J.
Geophys. Res. 2002,107, ACL-14. [CrossRef]
8.
Tananaev, N.I.; Makarieva, O.M.; Lebedeva, L.S. Trends in annual and extreme flows in the Lena River basin, Northern Eurasia.
Geophys. Res. Lett. 2016,43, 10764–10772. [CrossRef]
9.
Ridder, N.N.; Pitman, A.J.; Westra, S.; Ukkola, A.; Do, H.X.; Bador, M.; Hirsch, A.L.; Evans, J.P.; Di Luca, A.; Zscheischler, J. Global
hotspots for the occurrence of compound events. Nat. Commun. 2020,11, 5956. [CrossRef]
10. Anisimov, O.; Streletskiy, D. Geocryological Hazards of Thawing Permafrost. Arctika XXI Century 2015,2, 60–74. (In Russian)
11.
Kolokolova, N.A.; Garris, N.A. About the choice of method of laying pipelines in permafrost. J. THNP
2013
,1, 13–17. (In Russian)
12.
Akimov, V.A.; Sokolov, Y.I.; Sosunov, I.V. Global and National Priorities for Disaster Risk Reduction; VNII GOChS (FC) All-Russian
Research Institute for Civil Defence of the EMERCOM of Russia (the Federal Science and High Technology Center): Moscow,
Russia, 2016; 396p. (In Russian)
13.
Pravdaurfo.ru. A New Section of the Destroyed Salekhard-Nadym Highway Is Blocked in YANAO. Available online:
https://pravdaurfo.ru/novost/v-yanao-perekryvayut-novyj-uchastok- razrushennoj-trassy- salekhard-nadym/ (accessed on
20 November 2021). (In Russian)
14.
Ria.ru. Traffic Was Closed on the Kolyma Highway Section due to Flooding. Available online: https://ria.ru/society/20170717/1
498659385.html (accessed on 19 April 2018). (In Russian)
15.
Newizv.ru. The flood in the Magadan region broke the record of 1939. Available online: https://newizv.ru/news/incident/06-0
8-2019/navodnenie-v-magadanskoy-oblasti-pobilo-rekord-1939-goda (accessed on 20 November 2021). (In Russian)
16.
Gosudarstvennaja Programma Rossijskoj Federacii «Social’no-Jekonomicheskoe Razvitie Arkticheskoj Zony Rossijskoj Federacii»,
Utverzhdena Postanovleniem Pravitel’stva Rossijskoj Federacii ot 21 Aprelja 2014 g.
№
366 (v Redakcii Postanovlenija Pravitel’stva
Rossijskoj Federacii ot 31 Avgusta 2017 g.
№
1064). The State Program of the Russian Federation “Socio-Economic Development
of the Arctic Zone of the Russian Federation”. Approved by the Government of the Russian Federation of April 21, 2014, No. 366
(as Amended by Government Decree No. 1064 of 31 August 2017). Available online: http://static.government.ru/media/files/
GGu3GTtv8bvV8gZxSEAS1R7XmzloK6ar.pdf (accessed on 19 April 2018). (In Russian)

Energies 2022,15, 2649 14 of 16
17.
Regnum.ru. A Railway from Yakutia to Magadan Can Be Built along the Kolyma Highway. Available online: https://regnum.ru/
news/economy/3352653.html (accessed on 20 November 2021). (In Russian)
18.
Interfax.ru. The Construction of a Bridge across the Lena River in Yakutia Will Be Half Paid by the State. Available online:
https://www.interfax.ru/russia/757608 (accessed on 20 November 2021). (In Russian)
19.
Prikaz Ministerstva Ekonomicheskogo Razvitiya Rossijskoj Federacii (MINEKONOMRAZVITIYA Rossii)
№
267 ot 13 Maya 2021
g. Ob Utverzhdenii Metodicheskih Rekomendacij i Pokazatelej po Voprosam Adaptacii k Izmeneniyam Klimata. Order of the
Ministry of Economic Development of the Russian Federation No. 267 Dated May 13, 2021 On Approval of Methodological
Recommendations and Indicators on Adaptation to Climate Change. Available online: https://www.economy.gov.ru/material/
file/b3cc582c24e7367170b5605f1199c6a9/267_13052021.pdf (accessed on 9 October 2021). (In Russian)
20.
Brown, J.; Ferrians, O.J., Jr.; Heginbottom, J.A.; Melnikov, E.S. 211 Circum-Arctic Map of Permafrost and Ground-Ice Conditions.
Available online: https://nsidc.org/212data/ggd318.html (accessed on 1 December 2021).
21.
Hydrometeorological Information—World Data Center. Available online: http://meteo.ru/ (accessed on 5 September 2021).
(In Russian)
22.
Makarieva, O.M.; Nesterova, N.V.; Beldiman, I.N.; Lebedeva, L.S. Actual Problems of Hydrological Assessments in the Arctic Zone
of Russian Federation and Adjacent Permafrost Territories. Πрoблемы Aрктики и Aнтaрктики 2018,64, 101–118. [CrossRef]
23.
USSR State Committee for Hydrometeorology and Environmental Control. State Water Cadastre. Long-Term Data on the Regime and
Resources of Land Surface Waters; 1 (15); Gidrometeoizdat: Leningrad, USSR, 1986. (In Russian)
24.
USSR State Committee for Hydrometeorology and Environmental Control. State Water Cadastre. Long-Term Data on the Regime and
Resources of Land Surface Waters; 1 (16); Gidrometeoizdat: Leningrad, USSR, 1987. (In Russian)
25.
USSR State Committee for Hydrometeorology and Environmental Control. State Water Cadastre. Long-Term Data on the Regime and
Resources of Land Surface Waters; 1 (17); Gidrometeoizdat: Leningrad, USSR, 1985. (In Russian)
26.
Automated Information System of State Monitoring of Water Bodies. Available online: https://gmvo.skniivh.ru/ (accessed on 5
September 2021). (In Russian)
27.
Gelder, P.H.A.J.M. Statistical Estimation Methods in Hydrological Engineering. In Proceedings of the International Scientific
Seminar, Irkutsk, Russia, 16–23 June 2003; Korytny, L.M., Luxemburg, W.M., Eds.; Publishing House of the Institute of Geography:
Moscow, Russia, 2004; p. 11.
28.
England, J.F., Jr.; Cohn, T.A.; Faber, B.A.; Stedinger, J.R.; Thomas, W.O.; Veilleux, A.G., Jr.; Kiang, J.E.; Mason, R.R., Jr. Guidelines
for determining flood flow frequency—Bulletin 17C. In Techniques and Methods; Book 4; U.S. Geological Survey: Reston, VA, USA,
2019; Chapter 5; p. 148. [CrossRef]
29.
World Meteorological Organization. Technical Note, No. 98. Estimation of Maximum Floods: Report of a Working Group; Secretariat of
the World Meteorological Organization: Geneva, Switzerland, 1969; Volume 233, p. 208.
30.
Asquith, W.H.; Slade, R.M. Documented and potential extreme peak discharges and relation between potential extreme peak
discharges and probable maximum flood peak discharges in Texas. In U.S. Geological Survey Water-Resources Investigations Report
95–4249; U.S. Geological Survey: Reston, VA, USA, 1995; p. 58. Available online: https://pubs.er.usgs.gov/publication/wri954249
(accessed on 1 December 2021).
31.
Singh, A.; Singh, V.P.; Byrd, A.R. Computation of probable maximum precipitation and its uncertainty. Int. J. Hydrol.
2018
,2,
504–514. [CrossRef]
32.
Madsen, H.; Lawrence, D.; Lang, M.; Martinkova, M.; Kjeldsen, T.R. (Eds.) A Review of Applied Methods in Europe for Flood Frequency
Analysis in a Changing Environment; Centre for Ecology & Hydrology on behalf of COST: Swindon, UK, 2012; p. 189.
33.
Federal Hydrologic and Hydraulic Procedures for Flood Hazard Delineation Version 1.0 2019 Natural Resources Canada General Information
Product 113e.; Natural Resources Canada—Public Safety Canada: Ottawa, Canada, 2019; p. 72.
34.
Khaliq, M.N. An Inventory of Methods for Estimating Climate Change-Informed Design Water Levels for Floodplain Mapping; Technical
Report; National Research Council of Canada, Ocean, Coastal and River Engineering: Ottawa, Canada, 2019. [CrossRef]
35.
SP 33-101-2003; Determination of Basic Calculated Hydrological Characteristics. Gosstroy of Russia: Moscow, Russia, 2004; p. 73.
(In Russian)
36.
SNiP 2.01.14-83; Determination of Calculated Hydrological Characteristics. Stroyizdat: Moscow, Russia, 1983; p. 97. (In Russian)
37.
State Hydrological Institute. Methodological Recommendations for Determining the Calculated Hydrological Characteristics in the Presence
of Hydrometric Observations; Vector-TiS: Nizhny Novgorod, Russia, 2007; p. 134. (In Russian)
38.
State Hydrological Institute. Methodological Recommendations for the Determination of Computed Hydrological Characteristics when
Hydrometric Observations Are Inadequate; AARI: Saint Petersburg, Russia, 2008; p. 66. (In Russian)
39.
State Hydrological Institute. Methodological Recommendations for Determining the Calculated Hydrological Characteristics in the Absence
of Hydrometric Observations; Nestor-History: Saint Petersburg, Russia, 2009; p. 193. (In Russian)
40.
Makarieva, O.M.; Bel’diman, I.N.; Lebedeva, L.S.; Vinogradova, T.A.; Nesterova, N.V. To the question of the validity of the
recommendations of SP 33-101-2003 for calculating the characteristics of the maximum runoff of small rivers in the zone of
permafrost area (in the order of discussion). Eng. Surv. 2017,6–7, 50–63. (In Russian) [CrossRef]
41.
Schramm, I.; Boike, J.; Bolton, W.R.; Hinzman, L.D. Application of TopoFlow, a spatially distributed hydrological model, to the
Imnavait Creek watershed, Alaska. J. Geophys. Res. 2007,112, G04S46. [CrossRef]

Energies 2022,15, 2649 15 of 16
42.
Pomeroy, J.W.; Gray, D.M.; Brown, T.N.; Hedstrom, R.; Quinton, W.L.; Granger, R.J.; Carey, S.K. The cold regions hydrological
model: A platform for basing process representation and model structure on physical evidence. Hydrol. Process.
2007
,21,
2650–2667. [CrossRef]
43.
Cuo, L.; Zhang, Y.; Bohn, T.J.; Zhao, L.; Li, J.; Liu, Q.; Zhou, B. Frozen soil degradation and its effects on surface hydrology in the
northern Tibetan Plateau. J. Geophys. Res. Atmos. 2015,120, 8276–8298. [CrossRef]
44.
Rensheng, C.; Wang, G.; Yang, Y.; Liu, J.; Chuntan, H.; Song, Y.; Liu, Z.; Kang, E. Effects of Cryospheric Change on Alpine
Hydrology: Combining a Model With Observations in the Upper Reaches of the Hei River, China. J. Geophys. Res. Atmos.
2018
,
123, 3414–3442.
45.
Dall’Amico, M.; Endrizzi, S.; Gruber, S.; Rigon, R. A robust and energy-conserving model of freezing variably-saturated soil.
Cryosphere 2011,5, 469–484. [CrossRef]
46.
Gusev, Y.; Nasonova, O.; Dzhogan, L. Reproduction of Pechora runoff hydrographs with the help of a model of heat and water
exchange between the land surface and the atmosphere (SWAP). Water Resour. 2010,37, 182–193. [CrossRef]
47.
Motovilov, Y.G. ECOMAG: Distributed model of runoff formation and pollution transformation in river basins. IAHS Publ.
2013
,
361, 227–234.
48.
Vinogradov, Y.B.; Semenova, O.M.; Vinogradova, T.A. An approach to the scaling problem in hydrological modelling: The
deterministic modelling hydrological system. Hydrol. Processes 2011,25, 1055–1073. [CrossRef]
49.
Semenova, O.; Lebedeva, L.; Vinogradov, Y. Simulation of subsurface heat and water dynamics, and runoff generation in
mountainous permafrost conditions, in the Upper Kolyma River basin, Russia. Hydrogeol. J. 2013,21, 107–119. [CrossRef]
50.
National Science Foundation Long-Term Ecological Research (LTER). Available online: https://lternet.edu (accessed on
1 April 2018).
51.
NPR—A Hydrology, Water and Environmental Research Centre. Available online: http://ine.uaf.edu (accessed on 1 April 2018).
52.
Arctic Observatory Network (AON). International Study of Carbon, Water, and Energy Balances in the Terrestrial Arctic. Available
online: http://aon.iab.uaf.edu (accessed on 1 April 2018).
53. Global Water Future. Available online: https://gwf.usask.ca/ (accessed on 15 October 2021).
54.
Global Water Future. Datasets Generated by Previous Projects Changing Cold Regions Network (CCRN). Available online:
https://gwf.usask.ca/outputs-data/data.php (accessed on 15 October 2021).
55. Management of Streamflow Stations; Gidrometeoizdat: Leningrad, USSR, 1954; p. 287. (In Russian)
56.
Makarieva, O.; Nesterova, N.; Lebedeva, L.; Sushansky, S. Water balance and hydrology research in a mountainous permafrost
watershed in upland streams of the Kolyma River, Russia: A database from the Kolyma Water-Balance Station, 1948–1997. Earth
Syst. Sci. Data 2018,10, 689–710. [CrossRef]
57.
Lebedeva, L.S.; Makarieva, O.M.; Vinogradova, T.A. Spatial variability of the water balance elements in mountain catchments in
the North-East Russia (case study of the Kolyma Water Balance Station). Meteorol. Hydrol. J. 2017,4, 90–101. (In Russian)
58. Mikhailov, V.M. Floodplain Taliks of Northeast of Russia; Geo: Novosibirsk Russia, 2013; p. 244. (In Russian)
59.
Lebedeva, L.S.; Semenova, O.M.; Vinogradova, T.A. Hydrological modeling: Seasonal thaw depths in different landscapes of the
Kolyma Water Balance Station (Part 2). Earth’s Cryosphere 2015,2, 35–44. (In Russian)
60.
Makarieva, О.М.; Lebedeva, L.S.; Vinogradova, T.A. Modelling of runoff formation processes at small mountain watersheds in
the permafrost zone (by the data of the Kolyma Water Balance Station). Earth’s Cryosphere 2020,1, 43–56. (In Russian)
61.
Vasilenko, N.G. Hydrology of the Rivers of the BAM Zone: Expeditionary Research; Nestor-History: Saint Petersburg, Russia, 2013;
p. 672. (In Russian)
62.
Practical Recommendations on the Calculation of Hydrological Characteristics in the Zone of Economic Development of the Baikal-Amur
Mainline; Gidrometeoizdat: Leningrad, USSR, 1986; p. 180. (In Russian)
63.
Vasilenko, N.G.; Khersonskii, E.S. Calculation of the maximum discharge of rain floods in the area of the BAM trail. Proc. SHI
1986,312, 93–104. (In Russian)
64.
Nesterova, N.V.; Makarieva, O.M.; Vinogradova, T.A.; Lebedeva, L.S. Modelling of runoff formation processes in the zone of
Baikal-Amur Main line based on the data of the Mogot research site. Water Sect. Russ. Probl. Technol. Manag.
2017
,1, 18–36.
(In Russian)
65.
Semenova, O.; Vinogradov, Y.; Vinogradova, T.; Lebedeva, L. Simulation of Soil Profile Heat Dynamics and their Integration into
Hydrologic Modelling in a Permafrost Zone. Permafr. Periglac. Process. 2015,25, 257–269. [CrossRef]
66.
Nesterova, N.; Makarieva, O.; Post, D.A. Parameterizing a hydrological model using a short-term observational dataset to study
runoff generation processes and reproduce recent trends in streamflow at a remote mountainous permafrost basin. Hydrol. Process.
2021,35, e14278. [CrossRef]
67.
Research Station Samoylov Island. Available online: https://eu-interact.org/field-sites/research-station-samoylov-island/
(accessed on 15 October 2021).
68.
Boike, J.; Nitzbon, J.; Anders, K.; Grigoriev, M.; Bolshiyanov, D.; Langer, M.; Lange, S.; Bornemann, N.; Morgenstern, A.; Schreiber,
P.; et al. A 16-year record (2002–2017) of permafrost, active-layer, and meteorological conditions at the Samoylov Island Arctic
permafrost research site, Lena River delta, northern Siberia: An opportunity to validate remote-sensing data and land surface,
snow, and permafrost models. Earth Syst. Sci. Data 2019,11, 261–299. [CrossRef]
69.
Gartsman, B.I.; Shamov, V.V. Field studies of runoff formation in the Far East region based on modern observational instruments.
Water Resour. 2015,6, 589–599. [CrossRef]

Energies 2022,15, 2649 16 of 16
70.
Tananaev, N.I.; Teisserenc, R. Building a Multi-Disciplinary Observatory in the Lower Yenisei Region (Igarka Geocryology Lab).
In Proceedings of the Arctic Science Summit Week 2015, Toyama, Japan, 23–30 April 2015; p. 1.
71.
Lebedeva, L.S.; Bazhin, K.I.; Khristoforov, I.I.; Abramov, A.A.; Pavlova, N.A.; Efremov, V.S.; Ogonerov, V.V.; Tarbeeva, A.M.;
Fedorov, M.P.; Nesterova, N.V.; et al. Suprapermafrost subaerial taliks, central Yakutia, Shestakovka River basin. Earth’s Cryosphere
2019,23, 40–50. [CrossRef]
72.
Frolova, N.L.; Alekseevskii, N.I.; Zhuk, V.A. Monitoring of hydrological processes and ensuring the safety of water use. Environ.
Manag. 2014,3, 66–68. (In Russian)