The Role of Snow in High-Mountain Hydrologic Cycle

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 19715

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Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: cold-region hydrology; hydrological modelling; water cycle; remote sensing; satellite image analysis
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Dear Colleagues,

In High-Altitude Mountains (HAM), snow plays a vital role in water resources and the climate system. However, the observation and modeling of snow in HAM is still insufficient, limiting the understanding of snow's role in the HAM hydrologic cycle. Considering this challenge, we call for articles on the following topics: (1) field investigation into snow events and snow hydrologic parameters, such as the snow status, precipitation, snow ablation, blowing snow, and avalanche in the HAM area. (2) Optical and microwave remote sensing of snow cover under complex terrain conditions in mountainous regions. (3) Development of snow hydrological models and snow parameterization schemes in high-altitude mountainous areas. (4)  Responses of snow water resources to climate change in the HAM areas and its impact on the environment and society.

Prof. Dr. Hongyi Li
Guest Editor

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Keywords

  • snow hydrology
  • snowmelt
  • high altitude mountains
  • snow remote sensing
  • snow model
  • climate change
  • snow investigation

Published Papers (11 papers)

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Editorial

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4 pages, 161 KiB  
Editorial
Role of Snow in the High-Mountain Hydrologic Cycle
by Hongyi Li
Water 2024, 16(11), 1525; https://doi.org/10.3390/w16111525 - 25 May 2024
Viewed by 478
Abstract
Snow is a crucial component of the cryosphere and plays a vital role in the hydrological cycle, energy balance, and ecosystem function of mountainous regions [...] Full article
(This article belongs to the Special Issue The Role of Snow in High-Mountain Hydrologic Cycle)

Research

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22 pages, 3977 KiB  
Article
An Improved Xin’anjiang Hydrological Model for Flood Simulation Coupling Snowmelt Runoff Module in Northwestern China
by Yaogeng Tan, Ningpeng Dong, Aizhong Hou and Wei Yan
Water 2023, 15(19), 3401; https://doi.org/10.3390/w15193401 - 28 Sep 2023
Viewed by 788
Abstract
The Xin’anjiang hydrological model (XHM) is the practical tool for runoff simulation and flood forecasting in most regions in China, but it still presents some challenges when applied to Northwest China, where the river runoff mostly comes from high-temperature snowmelt, as the model [...] Read more.
The Xin’anjiang hydrological model (XHM) is the practical tool for runoff simulation and flood forecasting in most regions in China, but it still presents some challenges when applied to Northwest China, where the river runoff mostly comes from high-temperature snowmelt, as the model lacks such a functional module. In this study, the improved XHM coupling snowmelt module is presented to complete the existing XHM for better suitability for flood simulation in areas dominated by snowmelt. The improved model includes four sub-models: evapotranspiration, runoff yield, runoff separation, and runoff routing, where the snowmelt runoff module is introduced in both the runoff yield and separation sub-models. The watershed is divided into two types, non-snow areas with lower altitudes and snow-covered areas with higher altitudes, to study the mechanism of runoff production and separation. The evaluation index, determination coefficients (R2), mean square error (MSE), and Nash efficiency coefficients (NSE) are used to assess the improved XHM’s effect by comparing it with the traditional model. Results show that the R2 of the improved XHM coupled with snowmelt are around 0.7 and 0.8 at the Zamashk and Yingluoxia stations, respectively, while the MSE and NSE are also under 0.4 and above 0.6, respectively. The absolute value of error of both flood peaks in the Yingluoxia station simulated by improved XHM is only 10% and 6%, and that of traditional XHM is 32% and 40%, indicating that the peak flow and flood process can be well simulated and showing that the improved XHM coupled with snowmelt constructed in this paper can be applied to the flood forecasting of the Heihe River Basin. The critical temperature of snow melting and degree-day factor of snow are more sensitive compared with other parameters related to snow melting, and the increasing trend of peak flow caused by both decreased critical temperature and increased degree-day factor occurs only when the value of the model’s state (snow reserve) is higher. These results can expand the application scope in snow-dominated areas of the XHM, providing certain technical references for flood forecasting and early warning of other snowmelt-dominated river basins. Full article
(This article belongs to the Special Issue The Role of Snow in High-Mountain Hydrologic Cycle)
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17 pages, 5476 KiB  
Article
Changes in Snow Surface Albedo and Radiative Forcing in the Chilean Central Andes Measured by In Situ and Remote Sensing Data
by Luis Figueroa-Villanueva, Lina Castro, Tomás R. Bolaño-Ortiz, Raúl P. Flores, Diego Pacheco-Ferrada and Francisco Cereceda-Balic
Water 2023, 15(18), 3198; https://doi.org/10.3390/w15183198 - 8 Sep 2023
Viewed by 1279
Abstract
Snow-covered regions are the main source of reflection of incident shortwave radiation on the Earth’s surface. The deposition of light-absorbing particles on these regions increases the capacity of snow to absorb radiation and decreases surface snow albedo, which intensifies the radiative forcing, leading [...] Read more.
Snow-covered regions are the main source of reflection of incident shortwave radiation on the Earth’s surface. The deposition of light-absorbing particles on these regions increases the capacity of snow to absorb radiation and decreases surface snow albedo, which intensifies the radiative forcing, leading to accelerated snowmelt and modifications of the hydrologic cycle. In this work, the changes in surface snow albedo and radiative forcing were investigated, induced by light-absorbing particles in the Upper Aconcagua River Basin (Chilean Central Andes) using remote sensing satellite data (MODIS), in situ spectral snow albedo measurements, and the incident shortwave radiation during the austral winter months (May to August) for the 2004–2016 period. To estimate the changes in snow albedo and radiative forcing, two spectral ranges were defined: (i) an enclosed range between 841 and 876 nm, which isolates the effects of black carbon, an important light-absorbing particle derived from anthropogenic activities, and (ii) a broadband range between 300 and 2500 nm. The results indicate that percent variations in snow albedo in the enclosed range are higher than in the broadband range, regardless of the total amount of radiation received, which may be attributed to the presence of light-absorbing particles, as these particles have a greater impact on surface snow albedo at wavelengths in the enclosed band than in the broadband band. Full article
(This article belongs to the Special Issue The Role of Snow in High-Mountain Hydrologic Cycle)
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20 pages, 7467 KiB  
Article
Snow Albedo Reduction in the Colombian Andes Mountains Due to 2000 to 2020 Saharan Dust Intrusions Events
by Tomás R. Bolaño-Ortiz, Viverlys L. Díaz-Gutiérrez, Andrés M. Vélez-Pereira, Eliana L. Vergara-Vásquez and Yiniva Camargo-Caicedo
Water 2023, 15(17), 3150; https://doi.org/10.3390/w15173150 - 3 Sep 2023
Viewed by 1361
Abstract
This article investigates the snow albedo changes in Colombian tropical glaciers, namely, Sierra Nevada de Santa Marta (SNSM), Sierra Nevada del Cocuy (NSC), Nevado del Ruíz (NDR), Nevado Santa Isabel (NDS), Nevado del Tolima (NDT), and Nevado del Huila (NDH). They are associated [...] Read more.
This article investigates the snow albedo changes in Colombian tropical glaciers, namely, Sierra Nevada de Santa Marta (SNSM), Sierra Nevada del Cocuy (NSC), Nevado del Ruíz (NDR), Nevado Santa Isabel (NDS), Nevado del Tolima (NDT), and Nevado del Huila (NDH). They are associated with the possible mineral dust deposition from the Sahara Desert during the June and July months using snow albedo (SA), snow cover (SC), and land surface temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Terra and Aqua satellites. And mineral dust (MD) from The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), both of them during 2000–2020. Results show the largest snow albedo reductions were observed at 39.39%, 32.1%, and 30.58% in SNC, SNSM, and NDR, respectively. Meanwhile, a multiple correlation showed that the glaciers where MD contributed the most to SA behavior were 35.4%, 24%, and 21.4% in NDS, NDC, and NDR. Results also display an increasing trend of dust deposition on Colombian tropical glaciers between 2.81 × 10−3 µg·m−2·year−1 and 6.58 × 10−3 µg·m−2·year−1. The results may help recognize the influence of Saharan dust on reducing snow albedo in tropical glaciers in Colombia. The findings from this study also have the potential to be utilized as input for both regional and global climate models. This could enhance our comprehension of how tropical glaciers are impacted by climate change. Full article
(This article belongs to the Special Issue The Role of Snow in High-Mountain Hydrologic Cycle)
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20 pages, 5894 KiB  
Article
Performance of Frequency-Corrected Precipitation in Ungauged High Mountain Hydrological Simulation
by Hongyi Li, Jiapei Ma, Yaru Yang, Liting Niu and Xinyu Lu
Water 2023, 15(8), 1461; https://doi.org/10.3390/w15081461 - 8 Apr 2023
Cited by 2 | Viewed by 1480
Abstract
Accurate precipitation data are essential for understanding hydrological processes in high mountainous regions with limited observations and highly variable precipitation events. While frequency-corrected precipitation data are expected to aid in understanding hydrological processes, its performance in ungauged high mountain hydrological simulation remains unclear. [...] Read more.
Accurate precipitation data are essential for understanding hydrological processes in high mountainous regions with limited observations and highly variable precipitation events. While frequency-corrected precipitation data are expected to aid in understanding hydrological processes, its performance in ungauged high mountain hydrological simulation remains unclear. To clarify this issue, we conducted a numerical experiment that used reanalysis precipitation, frequency-corrected precipitation, and gridded precipitation to drive a distributed cold region hydrological model. We selected an ungauged basin in high mountain Asia (Manas River Basin in China) as the study area and employed a statistical parameter optimization method to avoid subjectivity in parameter selection. Our findings indicate that the frequency information from the few existing stations can aid in correcting the reanalysis precipitation data. The frequency correction approach can reduce the total volume of errors in the reanalysis precipitation data, especially when severe biases occur. Our findings show that frequency-corrected precipitation performs better in modeling discharge, runoff depth, and evaporation. Furthermore, the improvement in precipitation using frequency correction bears clear altitude differences, which implies that having more stations at different altitudes is necessary to measure precipitation accurately in similar areas. Our study provides a feasible flow for future precipitation preparation for similar ungauged high mountain areas. Frequency correction, instead of direct interpolation, may be a viable option for precipitation preparation. Our work has reference implications for future hydrological simulations in similar ungauged high mountains. Full article
(This article belongs to the Special Issue The Role of Snow in High-Mountain Hydrologic Cycle)
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18 pages, 3852 KiB  
Article
Can Remotely Sensed Snow Disappearance Explain Seasonal Water Supply?
by Kaitlyn Bishay, Nels R. Bjarke, Parthkumar Modi, Justin M. Pflug and Ben Livneh
Water 2023, 15(6), 1147; https://doi.org/10.3390/w15061147 - 15 Mar 2023
Viewed by 1894
Abstract
Understanding the relationship between remotely sensed snow disappearance and seasonal water supply may become vital in coming years to supplement limited ground based, in situ measurements of snow in a changing climate. For the period 2001–2019, we investigated the relationship between satellite derived [...] Read more.
Understanding the relationship between remotely sensed snow disappearance and seasonal water supply may become vital in coming years to supplement limited ground based, in situ measurements of snow in a changing climate. For the period 2001–2019, we investigated the relationship between satellite derived Day of Snow Disappearance (DSD)—the date at which snow has completely disappeared—and the seasonal water supply, i.e., the April—July total streamflow volume, for 15 snow dominated basins across the western U.S. A Monte Carlo framework was applied, using linear regression models to evaluate the predictive skill—defined here as a model’s ability to accurately predict seasonal flow volumes—of varied predictors, including DSD and in situ snow water equivalent (SWE), across a range of spring forecast dates. In all basins there is a statistically significant relationship between mean DSD and seasonal water supply (p ≤ 0.05), with mean DSD explaining roughly half of the variance. Satellite-based model skill improves later in the forecast season, surpassing the skill of in-situ-based (SWE) models in skill in 10 of the 15 basins by the latest forecast date. We found little to no correlation between model error and basin characteristics such as elevation and the ratio of snow water equivalent to total precipitation. Despite a relatively short data record, this exploratory analysis shows promise for improving seasonal water supply prediction, in particular for snow dominated basins lacking in situ observations. Full article
(This article belongs to the Special Issue The Role of Snow in High-Mountain Hydrologic Cycle)
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13 pages, 2200 KiB  
Article
Interannual Variability of Snowiness and Avalanche Activity in the Ile Alatau Ridge, Northern Tien Shan
by Akhmetkal Medeu, Viktor Blagovechshenskiy, Tamara Gulyayeva, Vitaliy Zhdanov and Sandugash Ranova
Water 2022, 14(18), 2936; https://doi.org/10.3390/w14182936 - 19 Sep 2022
Cited by 2 | Viewed by 1811
Abstract
Snowiness and avalanche activity are very important natural characteristics of mountain areas. They have a great influence on the possibility of areas’ development, especially regarding winter recreation. This article considers the interannual variability of snowiness and avalanche activity in the Ile Alatau Ridge [...] Read more.
Snowiness and avalanche activity are very important natural characteristics of mountain areas. They have a great influence on the possibility of areas’ development, especially regarding winter recreation. This article considers the interannual variability of snowiness and avalanche activity in the Ile Alatau Ridge (Northern Tien Shan), which belongs to the areas with a continental snow climate. The sum of winter precipitation and snow depth are used as snowiness indices, and the indices of avalanche activity are the total avalanche volume, maximum avalanche volume and number of avalanches. The work uses archival data for the period from 1966 to 2022. Interannual variability of snowiness and avalanche activity indices and long-term temporal trends were assessed, correlation between these indices was studied, and extreme values with different return periods were calculated. The relationship between years with a high snowiness and years with a high avalanche activity, as well as years with a high avalanche activity and years with a large number of avalanche victims and high avalanche damage has been studied. Similar studies have not been previously carried out for the areas with a continental snow climate. Snowiness indices have weak, non-significant, increasing temporal trends. The total avalanche volume has a non-significant decreasing temporal trend, and the maximum avalanche volume has a significant decreasing one. The number of avalanches has a significant increasing temporal trend. This study could be relevant for understanding the features of temporal variability of snowiness and avalanche activity in the mountainous regions with a continental snow climate. Full article
(This article belongs to the Special Issue The Role of Snow in High-Mountain Hydrologic Cycle)
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19 pages, 2623 KiB  
Article
Contribution of Spring Snowmelt Water to Soil Water in Northeast China and Its Dynamic Changes
by Wenshuai Zhang, Chen Du, Lijuan Zhang, Yulong Tan, Yutao Huang and Meiyi Jiang
Water 2022, 14(9), 1368; https://doi.org/10.3390/w14091368 - 22 Apr 2022
Cited by 1 | Viewed by 1656
Abstract
Snowmelt water in spring is an important source of soil water, which is critical to supporting crop growth. Quantifying the contribution of snowmelt water to soil water and its dynamic changes is essential for evaluating soil moisture and allocating agricultural water resources. In [...] Read more.
Snowmelt water in spring is an important source of soil water, which is critical to supporting crop growth. Quantifying the contribution of snowmelt water to soil water and its dynamic changes is essential for evaluating soil moisture and allocating agricultural water resources. In this paper, through controlled outdoor experiments, different snow depths and soil depth gradients were set; and snow, precipitation, and soil samples were collected regularly. To analyze the contribution of snowmelt water to soil water and its dynamic changes, the MAT-253 stable isotope ratio mass spectrometer was adopted for hydrogen and oxygen isotope analyses. The results showed that the snowmelt water for snow depths of 10 cm, 30 cm, and 50 cm all contributed to the 0–30 cm soil layer. The contribution increased with soil depth, contributing 8.13%, 8.55%, and 11.24%, respectively. The contribution of the snow cover at the same depth to the soil moisture at different depths also varied, i.e., the contribution increased with increasing soil depth. The snowmelt water retention time at depths of 10 cm, 30 cm, and 50 cm was inconsistent, i.e., it was the longest at 0–10 cm (average of 69 days), followed by 20–30 cm (average of 59 days), and the shortest at 10–20 cm (average of 54 days). The greater the snow depth, the shorter the retention time of the snowmelt water in the different soil layers. For surface soil, the contribution of the snowmelt water at greater depths was significantly different; while for deep soil, the contribution was more sensitive to the snow depth. Regardless of snow depth, soil contributions at different depths were significantly different. Precipitation also affected the contribution of the snowmelt water to the soil water, exhibiting different effects at different depths. Full article
(This article belongs to the Special Issue The Role of Snow in High-Mountain Hydrologic Cycle)
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14 pages, 7207 KiB  
Article
Classification of Snow Cover Persistence across China
by Hongxing Li, Xinyue Zhong, Lei Zheng, Xiaohua Hao, Jian Wang and Juan Zhang
Water 2022, 14(6), 933; https://doi.org/10.3390/w14060933 - 16 Mar 2022
Cited by 8 | Viewed by 2461
Abstract
In this study, we classified the variability in snow cover persistence across China by using a novel method; continuous snow cover days and variability of snow cover were used as the evaluation indicators based on a long-term Advanced Very High Resolution Radiometer (AVHRR) [...] Read more.
In this study, we classified the variability in snow cover persistence across China by using a novel method; continuous snow cover days and variability of snow cover were used as the evaluation indicators based on a long-term Advanced Very High Resolution Radiometer (AVHRR) snow cover extent (SCE) product. The product has been generated by the snow research team in the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. There were obvious differences in snow cover classification in three snow cover areas (northern Xinjiang, northeast China, and the Tibetan Plateau): northern Xinjiang was dominated by persistent snow cover, most regions of northeast China were covered by persistent and periodic variable snow cover. There was the most abundant snow cover classification in the Tibetan Plateau. The extents of persistent and periodic variable snow cover were gradually shrinking due to rising temperatures and decreasing snowfall during 1981–2019. In contrast, non-periodic variable snow cover areas increased significantly. This method takes into account the stability, continuity, and variability of snow cover, and better captures the characteristics and changes of snow cover across China. Based on our research, we found that snow disasters in ephemeral-type (belong to non-periodic variable snow cover) regions cannot be well prevented because of the unfixed snow cover timing. Therefore, we recommend that monitoring and forecasting of snow cover in these snow cover regions should be strengthened. Full article
(This article belongs to the Special Issue The Role of Snow in High-Mountain Hydrologic Cycle)
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17 pages, 46797 KiB  
Article
Simulation of Daily Snow Depth Data in China Based on the NEX-GDDP
by Hongju Chen, Jianping Yang, Yongjian Ding, Qingshan He and Qin Ji
Water 2021, 13(24), 3599; https://doi.org/10.3390/w13243599 - 15 Dec 2021
Viewed by 2454
Abstract
In this study, a backpropagation artificial neural network snow simulation model (BPANNSIM) is built using data collected from the National Climate Reference Station to obtain simulation data of China’s future daily snow depth in terms of representative concentration pathways (RCP4.5 and RCP8.5). The [...] Read more.
In this study, a backpropagation artificial neural network snow simulation model (BPANNSIM) is built using data collected from the National Climate Reference Station to obtain simulation data of China’s future daily snow depth in terms of representative concentration pathways (RCP4.5 and RCP8.5). The input layer of the BPANNSIM comprises the current day’s maximum temperature, minimum temperature, snow depth, and precipitation data, and the target layer comprises snow depth data of the following day. The model is trained and validated based on data from the National Climate Reference Station over a baseline period of 1986–2005. Validation results show that the temporal correlations of the observed and the model iterative simulated values are 0.94 for monthly cumulative snow cover duration and 0.88 for monthly cumulative snow depth. Subsequently, future daily snow depth data (2016–2065) are retrieved from the NEX-GDPP dataset (Washington, DC/USA: the National Aeronautics and Space Administration(NASA)Earth Exchange/Global Daily Downscaled Projections data), revealing that the simulation data error is highly correlated with that of the input data; thus, a validation method for gridded meteorological data is proposed to verify the accuracy of gridded meteorological data within snowfall periods and the reasonability of hydrothermal coupling for gridded meteorological data. Full article
(This article belongs to the Special Issue The Role of Snow in High-Mountain Hydrologic Cycle)
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16 pages, 5183 KiB  
Article
Altitudinal Gradient Characteristics of Spatial and Temporal Variations of Snowpack in the Changbai Mountain and Their Response to Climate Change
by Yongming Chen, Zehua Chang, Shiguo Xu, Peng Qi, Xiaoyu Tang, Yang Song and Dongmei Liu
Water 2021, 13(24), 3580; https://doi.org/10.3390/w13243580 - 14 Dec 2021
Cited by 2 | Viewed by 2166
Abstract
The variations in the snowpack in water towers of the world due to climate change have threatened the amount and timing of freshwater supplied downstream. However, it remains to be further investigated whether snowpack variation in water towers exhibits elevational heterogeneity at different [...] Read more.
The variations in the snowpack in water towers of the world due to climate change have threatened the amount and timing of freshwater supplied downstream. However, it remains to be further investigated whether snowpack variation in water towers exhibits elevational heterogeneity at different altitude gradients and which climatic factors mainly influence these differences. Therefore, Changbai Mountain, a high-latitude water tower, was selected to analyze the changes in the snowpack by the methods of modified Mann–Kendall based on the daily meteorological data from the China Meteorological Data Service Centre. Meanwhile, the responses of snowpack change to climatic factors over recent decades were assessed and generalized using additive models. The results showed that the snow depth was greater in the higher altitude areas than in the lower elevation areas at different times. Areas with a snow depth of over 70 mm increased significantly in the 2010s. Increasing trends were shown at different altitudes from December to March of the next year during 1960~2018. However, a significant decreasing trend was shown in April, except for altitudes of 600–2378 m. The snow cover time at different altitudes showed a trend of first increasing and then decreasing during 1960~2018. The date of maximum snow depth appears to be more lagged as the altitude increases. In addition, the spring snowpack melted significantly faster in the 2010s than that in the 1960s. The snowpack variation in low-altitude regions is mainly influenced by ET and relative humidity. However, the mean temperature gradually became an important factor, affecting the snow depth variation with the increase in altitude. Therefore, the results of this study will be beneficial to the ecological protection and sustainable development of water towers. Full article
(This article belongs to the Special Issue The Role of Snow in High-Mountain Hydrologic Cycle)
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