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River Flow in Cold Climate Environments
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River Flow in Cold Climate Environments

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

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 18137

Special Issue Editors

Dr. Masoud Irannezhad

E-Mail Website
Guest Editor
Department of Process and Environmental Engineering, University of Ouludisabled, Oulu, Finland
Interests: hydrological processes in cold climate regions; atmosphere- climate- and snow hydrology linkages; water-related extreme events; river discharge regime analysis; big data analysis and pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. David Gustafsson

E-Mail Website
Guest Editor
Swedish Meteorological and Hydrological Institute (SMHI), Norrkoping, Sweden
Interests: observations and modelling of hydrological systems; cold climate hydrology; coupled modelling (hydrological-meteorological-climate models); earth observations and data assimilation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In cold climate environments on Earth, river flow plays a crucial role in sustainable development by providing drinking water to over one billion people, strongly supporting crop production, generating hydropower energy, comforting urbanization, and preserving different unique ecosystems. Impacting snow, glacier, and permafrost hydrological processes, both of climate change and anthropogenic activities generally alter cold-regions river flows and thereby poses different ecological, economic, and social challenges for humanity. Hence, improving our knowledge about observed and projected modifications in the river flows in cold climate environments under climate change and/or human interventions is substantially important for addressing such challenges. This special issue aims at bringing together pure theoretical and applied researches on a wide range of topics related to the river flow, but with a focus primarily on cold climate environments. We especially encourage submissions on:

  • Observed and projected changes in river flow
  • Natural and regulated river flow
  • Climate change impacts on river discharge regime
  • Effects of anthropogenic activities (e.g. damming, land use-land cover changes, etc.) on river flows
  • Snowmelt floods
  • Snow droughts
  • Environmental river flow
  • River flow modelling
  • Upstream-downstream interactions in response to river flow changes
  • Influential climate teleconnections (e.g. NAO) for river discharge fluctuations
  • Water quality assessment under river flow alterations
  • Application of remote sensing and data assimilation
  • Changes in river ice regimes

Dr. Masoud Irannezhad
Dr. David Gustafsson
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cold-regions river flow
  • snow, glacier, and permafrost hydrological processes
  • hydrological extremes
  • climate change
  • human interventions
  • remote sensing
  • water quality
  • upstream and downstream
  • basin characteristics
  • climate teleconnections
  • river ice regime

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Published Papers (6 papers)

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Research

23 pages, 11304 KiB  
Article
Predicting Daily Streamflow in a Cold Climate Using a Novel Data Mining Technique: Radial M5 Model Tree
by Ozgur Kisi, Salim Heddam, Behrooz Keshtegar, Jamshid Piri and Rana Muhammad Adnan
Water 2022, 14(9), 1449; https://doi.org/10.3390/w14091449 - 1 May 2022
Cited by 10 | Viewed by 2341
Abstract
In this study, the viability of radial M5 model tree (RM5Tree) is investigated in prediction and estimation of daily streamflow in a cold climate. The RM5Tree model is compared with the M5 model tree (M5Tree), artificial neural networks (ANN), radial basis function neural [...] Read more.
In this study, the viability of radial M5 model tree (RM5Tree) is investigated in prediction and estimation of daily streamflow in a cold climate. The RM5Tree model is compared with the M5 model tree (M5Tree), artificial neural networks (ANN), radial basis function neural networks (RBFNN), and multivariate adaptive regression spline (MARS) using data of two stations from Sweden. The accuracy of the methods is assessed based on root mean square errors (RMSE), mean absolute errors (MAE), mean absolute percentage errors (MAPE), and Nash Sutcliffe Efficiency (NSE) and the methods are graphically compared using time variation and scatter graphs. The benchmark results show that the RM5Tree offers better accuracy in predicting daily streamflow compared to other four models by respectively improving the accuracy of M5Tree with respect to RMSE, MAE, MAPE, and NSE by 26.5, 17.9, 5.9, and 10.9%. The RM5Tree also acts better than the M5Tree, ANN, RBFNN, and MARS in estimating streamflow of downstream station using only upstream data. Full article
(This article belongs to the Special Issue River Flow in Cold Climate Environments)
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15 pages, 3419 KiB  
Article
Peak Spring Flood Discharge Magnitude and Timing in Natural Rivers across Northern Finland: Long-Term Variability, Trends, and Links to Climate Teleconnections
by Masoud Irannezhad, Saghar Ahmadian, Amin Sadeqi, Masoud Minaei, Behzad Ahmadi and Hannu Marttila
Water 2022, 14(8), 1312; https://doi.org/10.3390/w14081312 - 18 Apr 2022
Cited by 8 | Viewed by 2904
Abstract
In northern regions, like Finland, peak river discharge is principally controlled by maximum snowmelt runoff during spring (March–May). Global warming and climate change extensively influence both the quantity and temporal characteristics of peak discharge in northern rivers by altering snowpack accumulation and melt [...] Read more.
In northern regions, like Finland, peak river discharge is principally controlled by maximum snowmelt runoff during spring (March–May). Global warming and climate change extensively influence both the quantity and temporal characteristics of peak discharge in northern rivers by altering snowpack accumulation and melt processes. This study analyzed peak spring flood discharge (PSFD) magnitude (PSFDM) and timing (PSFDT) in four natural rivers (Simojoki, Kuivajoki, Kiiminkijoki, and Temmesjoki) across northern Finland, in terms of long-term (1967–2011) variability, trends, and links to large-scale climate teleconnections. The PSFDM significantly (p < 0.05) declined in the Simojoki, Kuivajoki, and Kiiminkijoki rivers over time. Both the Simojoki and Kuivajoki rivers also experienced significant decreasing trends of about −0.33 and −0.3 (days year−1), respectively, in the PSFDT during 1967–2011. In these two rivers, the less and earlier PSFDs were principally attributable to the warmer spring seasons positively correlated with the North Atlantic Oscillation (NAO) in recent decades. Moreover, daily precipitation time series corresponding to the PSFD events showed no considerable effects on PSFDM and PSFDT changes in all the natural rivers studied. This suggests that less and earlier historical PSFDs in natural rivers at higher latitudes in northern Finland were primarily induced by warmer springtime temperatures influencing snowpack dynamics. Full article
(This article belongs to the Special Issue River Flow in Cold Climate Environments)
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14 pages, 7332 KiB  
Article
Streamflow Changes of Small and Large Rivers in the Aldan River Basin, Eastern Siberia
by Liudmila Lebedeva and David Gustafsson
Water 2021, 13(19), 2747; https://doi.org/10.3390/w13192747 - 3 Oct 2021
Cited by 3 | Viewed by 2499
Abstract
The flow of large northern rivers has increased, but regional patterns of changes are not well understood. The aim of this study is the estimation of monthly discharge changes of the 11 river catchments in the Aldan River basin in Eastern Siberia, the [...] Read more.
The flow of large northern rivers has increased, but regional patterns of changes are not well understood. The aim of this study is the estimation of monthly discharge changes of the 11 river catchments in the Aldan River basin in Eastern Siberia, the largest Lena River tributary and the sixth largest river in Russia. We considered the trend dependence on month, number of years in the sample, finish and start years, and basin area. The median fraction of samples with no trend, positive and negative trends are 70.5%, 28.5%, and 1%, respectively. Longer samples tend to show more positive trends than shorter ones. There is an increasing fraction of samples with positive trends as a function of later sample end year, whereas the start year does not result in a similar pattern. The larger basins, with one exception, have more positive trends than smaller ones. The trends in monthly streamflow have prominent seasonality with absence of positive trends in June and increasing fraction of samples with positive trends from October till April. The study reports the recent streamflow changes on the rarely analyzed rivers in Eastern Siberia, where air temperature rises faster than in average on the globe. The study results are important for water resources management in the region and better understanding of current environmental changes. Full article
(This article belongs to the Special Issue River Flow in Cold Climate Environments)
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17 pages, 6478 KiB  
Article
Streamflow Changes in the Headwater Area of Yellow River, NE Qinghai-Tibet Plateau during 1955–2040 and Their Implications
by Qiang Ma, Changlei Dai, Huijun Jin, Sihai Liang, Victor F. Bense, Yongchao Lan, Sergey S. Marchenko and Chuang Wang
Water 2021, 13(10), 1360; https://doi.org/10.3390/w13101360 - 14 May 2021
Cited by 8 | Viewed by 2850
Abstract
Human activities have substantially altered present-day flow regimes. The Headwater Area of the Yellow River (HAYR, above Huanghe’yan Hydrological Station, with a catchment area of 21,000 km2 and an areal extent of alpine permafrost at ~86%) on the northeastern Qinghai-Tibet Plateau, Southwest [...] Read more.
Human activities have substantially altered present-day flow regimes. The Headwater Area of the Yellow River (HAYR, above Huanghe’yan Hydrological Station, with a catchment area of 21,000 km2 and an areal extent of alpine permafrost at ~86%) on the northeastern Qinghai-Tibet Plateau, Southwest China has been undergoing extensive changes in streamflow regimes and groundwater dynamics, permafrost degradation, and ecological deterioration under a warming climate. In general, hydrological gauges provide reliable flow records over many decades and these data are extremely valuable for assessment of changing rates and trends of streamflow. In 1998–2003, the damming of the Yellow River by the First Hydropower Station of the HAYR complicated the examination of the relations between hydroclimatic variables and streamflow dynamics. In this study, the monthly streamflow rate of the Yellow River at Huanghe’yan is reconstructed for the period of 1955–2019 using the double mass curve method, and then the streamflow at Huagnhe’yan is forecasted for the next 20 years (2020–2040) using the Elman neural network time-series method. The dam construction (1998–2000) has caused a reduction of annual streamflow by 53.5–68.4%, and a more substantial reduction of 71.8–94.4% in the drier years (2003–2005), in the HAYR. The recent removal of the First Hydropower Station of the HAYR dam (September 2018) has boosted annual streamflow by 123–210% (2018–2019). Post-correction trends of annual maximum (QMax) and minimum (QMin) streamflow rates and the ratio of the QMax/QMin of the Yellow River in the HAYR (0.18 and 0.03 m3·s−1·yr−1 and −0.04 yr−1, respectively), in comparison with those of precorrection values (−0.11 and −0.004 m3·s−1·yr−1 and 0.001 yr−1, respectively), have more truthfully revealed a relatively large hydrological impact of degrading permafrost. Based on the Elman neural network model predictions, over the next 20 years, the increasing trend of flow in the HAYR would generally accelerate at a rate of 0.42 m3·s−1·yr−1. Rising rates of spring (0.57 m3·s−1·yr−1) and autumn (0.18 m3·s−1·yr−1) discharge would see the benefits from an earlier snow-melt season and delayed arrival of winter conditions. This suggests a longer growing season, which indicates ameliorating phonology, soil nutrient availability, and hydrothermal environments for vegetation in the HAYR. These trends for hydrological and ecological changes in the HAYR may potentially improve ecological safety and water supplies security in the HAYR and downstream Yellow River basins. Full article
(This article belongs to the Special Issue River Flow in Cold Climate Environments)
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18 pages, 1535 KiB  
Article
Short-Term River Flow Forecasting Framework and Its Application in Cold Climatic Regions
by Chiara Belvederesi, John Albino Dominic, Quazi K. Hassan, Anil Gupta and Gopal Achari
Water 2020, 12(11), 3049; https://doi.org/10.3390/w12113049 - 30 Oct 2020
Cited by 11 | Viewed by 2745
Abstract
Catchments located in cold weather regions are highly influenced by the natural seasonality that dictates all hydrological processes. This represents a challenge in the development of river flow forecasting models, which often require complex software that use multiple explanatory variables and a large [...] Read more.
Catchments located in cold weather regions are highly influenced by the natural seasonality that dictates all hydrological processes. This represents a challenge in the development of river flow forecasting models, which often require complex software that use multiple explanatory variables and a large amount of data to forecast such seasonality. The Athabasca River Basin (ARB) in Alberta, Canada, receives no or very little rainfall and snowmelt during the winter and an abundant rainfall–runoff and snowmelt during the spring/summer. Using the ARB as a case study, this paper proposes a novel simplistic method for short-term (i.e., 6 days) river flow forecasting in cold regions and compares existing hydrological modelling techniques to demonstrate that it is possible to achieve a good level of accuracy using simple modelling. In particular, the performance of a regression model (RM), base difference model (BDM), and the newly developed flow difference model (FDM) were evaluated and compared. The results showed that the FDM could accurately forecast river flow (ENS = 0.95) using limited data inputs and calibration parameters. Moreover, the newly proposed FDM had similar performance to artificial intelligence (AI) techniques, demonstrating the capability of simplistic methods to forecast river flow while bypassing the fundamental processes that govern the natural annual river cycle. Full article
(This article belongs to the Special Issue River Flow in Cold Climate Environments)
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20 pages, 3949 KiB  
Article
Contemporary Snow Changes in the Karakoram Region Attributed to Improved MODIS Data between 2003 and 2018
by Amrit Thapa and Sher Muhammad
Water 2020, 12(10), 2681; https://doi.org/10.3390/w12102681 - 25 Sep 2020
Cited by 16 | Viewed by 4294
Abstract
Snowmelt significantly contributes to meltwater in most parts of High Mountain Asia. The Karakoram region is one of these densely glacierized and snow-covered regions. Several studies have reported that glaciers in the Karakoram region remained stable or experience slight mass loss. This trend [...] Read more.
Snowmelt significantly contributes to meltwater in most parts of High Mountain Asia. The Karakoram region is one of these densely glacierized and snow-covered regions. Several studies have reported that glaciers in the Karakoram region remained stable or experience slight mass loss. This trend has called for further investigation to understand changes in other components of the cryosphere. This study estimates the comparative snow cover area (SCA) and snowline altitude (SLA) changes between 2003 and 2018 in the Karakoram region and its subbasins, including Hunza, Shigar, and Shyok. We used three different 8-day composite snow products of the Moderate Resolution Imaging Spectroradiometer (MODIS) in this study including (1) Original Aqua (MYD10A2), (2) Original Terra (MOD10A2), and (3) Improved Terra-Aqua (MOYDGL06*) snow products from 2003 to 2018. We used Mann–Kendall and Sen Slope methods to assess trends in the SCA and SLA. Our results show that the original snow products are significantly biased when investigating seasonal and annual trends. However, discarding a cloud cover of >20% in the original products improves the results and makes them more comparable to our improved snow product. The original products (without cloud removal) overestimate the SCA during summer and underestimate the SCA during winter and year-round throughout the Karakoram region. The bias in the mean annual SCA between improved and Aqua and Terra cloud threshold products for the Karakoram region is found to be −1.67% and 1.1%, respectively. The improved (MOYDGL06*) product reveals a statistically insignificant decreasing trend of the SCA on the annual scale between 2003 and 2018 in the Karakoram region and all three subbasins. The annual trends decreased at −0.13%, −0.1%, −0.08%, and −0.05% in the Karakoram, Hunza, Shigar, and Shyok, respectively. The monthly trends were slightly positive overall in December. The annual maximum SLA shows a statistically significant upward trend of 13 m above sea level (m a.s.l.) per year for the entire Karakoram region. This finding suggests a significant uncertainty in water resource planning based on the original snow data, and this study recommends the use of the improved snow product for a better understanding. Full article
(This article belongs to the Special Issue River Flow in Cold Climate Environments)
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