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Effects of Climate Change on Mountain Hydrology - Recent Trends...
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Effects of Climate Change on Mountain Hydrology - Recent Trends and Challenges

A special issue of Climate (ISSN 2225-1154).

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 15376

Special Issue Editor

Dr. Daniele Bocchiola

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Polytechnic of Milan, Leonardo da Vinci, 32, 20133 Milan, Italy
Interests: water resources; hydrology; climate change; avalanche risk
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The hydrology of mountain areas is terribly affected today in the face of transient climate change. Increasingly more erratic patterns of precipitation, and temperatures therein result into more rapid and more intense dynamics, falling out upon hydrology, water resources, soil erosion, and flood dynamics. Ice bodies are also largely affected, with ice cover shrinking and seasonal snowpack changing, thus reducing water storage. Permafrost dynamics may also be affected. Hydropower production may also depend on the hydrological cycle in the mountain areas, and climate change may hamper energy supply.

Accordingly, on the one hand, mountains and cold environments are undergoing radical changes, affecting landscapes, ecosystems, and the environment, while on the other hand, ever increasing water-related risk is posed to local populations and users.

As such, scientists are called to investigate a changing mountain hydrology under transient climate change, by monitoring and modeling snow/ice dynamics, and hydrological cycles. The Special Issue thus welcomes contributions covering present and prospective mountain hydrology under present and future climate, including but not limited to:

  • Monitoring techniques for snowpack, and snow dynamics, ice bodies. This includes conventional and innovative methods, such as instruments, devices, methods, for local measurements, and remote sensing of snow cover, ice cover, ice flow, permafrost dynamics, etc.;
  • Monitoring techniques for stream flow measurement in mountain areas, soil erosion, and sediment transport;
  • Assessment of recent trends in mountain hydrology worldwide;
  • Modeling tools for depicting mountan hydrology under present and prospective climate, including water resources availability, flood dynamics, soil erosion, sediment transport, and effects on hydropower production;
  • Scenarios of modified mountain hydrology in response to modified climate hereforth;
  • Models and methods to assess countermeasures for changing mountain hydrology.

Dr. Daniele Bocchiola
Guest Editor

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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. Climate is an international peer-reviewed open access monthly 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 1800 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

  • mountain hydrology
  • water resources
  • floods
  • sediment transport
  • soil erosion
  • snow
  • glaciers
  • permafrost
  • climate change
  • monitoring/modeling
  • hydropower

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Published Papers (3 papers)

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20 pages, 4302 KiB  
Article
Physical Modeling of Snow Gliding: A Case Study in the NW Italian Alps
by Giovanni Martino Bombelli, Gabriele Confortola, Margherita Maggioni, Michele Freppaz and Daniele Bocchiola
Climate 2021, 9(12), 171; https://doi.org/10.3390/cli9120171 - 30 Nov 2021
Cited by 2 | Viewed by 2832
Abstract
Snow gliding, a slow movement downhill of snow cover, is complex to forecast and model and yet is extremely important, because it drives snowpack dynamics in the pre-avalanching phase. Despite recent interest in this process and the development of some studies therein, this [...] Read more.
Snow gliding, a slow movement downhill of snow cover, is complex to forecast and model and yet is extremely important, because it drives snowpack dynamics in the pre-avalanching phase. Despite recent interest in this process and the development of some studies therein, this phenomenon is poorly understood and represents a major point of uncertainty for avalanche forecasting. This study presents a data-driven, physically based, time-dependent 1D model, Poli-Glide, able to predict the slow movement of snowpacks along a flow line at the daily scale. The objective of the work was to create a useful snow gliding model, requiring few, relatively easily available input data, by (i) modeling snowpack evolution from measured precipitation and air temperature, (ii) evaluating the rate and extent of movement of the snowpack in the gliding phase, and (iii) assessing fracture (i.e., avalanching) timing. Such a model could be then used to provide hazard assessment in areas subject to gliding, thereby, and subsequent avalanching. To do so, some simplifying assumptions were introduced, namely that (i) negligible traction stress occurs within soil, (ii) water percolation into snow occurs at a fixed rate, and (iii) the micro topography of soil is schematized according to a sinusoidal function in the absence of soil erosion. The proposed model was then applied to the “Torrent des Marais-Mont de La Saxe” site in Aosta Valley, monitored during the winters of 2010 and 2011, featuring different weather conditions. The results showed an acceptable capacity of the model to reproduce snowpack deformation patterns and the final snowpack’s displacement. Correlation analysis based upon observed glide rates further confirmed dependence against the chosen variables, thus witnessing the goodness of the model. The results could be a valuable starting point for future research aimed at including more complex parameterizations of the different processes that affect gliding. Full article
(This article belongs to the Special Issue Effects of Climate Change on Mountain Hydrology - Recent Trends and Challenges)
►▼ Show Figures

Figure 1

Figure 1
<p>Location of the study area and position of the glide shoes in reference to the automatic weather stations (AWS) and the avalanche site area. Focus on the area of the glide shoes, with the glade direction and the different probe positions highlighted (snow and soil temperature and soil water content measurements).</p> Full article ">Figure 2
<p>Snowpack (HS) evolution in time, with total precipitation (reconstructed) and temperature for the station of Courmayeur Mont de la Saxe. (<b>a</b>) 2010. (<b>b</b>) 2011.</p> Full article ">Figure 3
<p>Cumulative glide measured and modeled, with information regarding solid and liquid precipitation, temperature of air, interface temperature (the snow temperature at the snow and soil interface), and snowpack depth. (<b>a</b>) A1. (<b>b</b>) A2. (<b>c</b>) B1. (<b>d</b>) B2. (<b>e</b>) A12, virtual glide shoe.</p> Full article ">Figure 3 Cont.
<p>Cumulative glide measured and modeled, with information regarding solid and liquid precipitation, temperature of air, interface temperature (the snow temperature at the snow and soil interface), and snowpack depth. (<b>a</b>) A1. (<b>b</b>) A2. (<b>c</b>) B1. (<b>d</b>) B2. (<b>e</b>) A12, virtual glide shoe.</p> Full article ">Figure 3 Cont.
<p>Cumulative glide measured and modeled, with information regarding solid and liquid precipitation, temperature of air, interface temperature (the snow temperature at the snow and soil interface), and snowpack depth. (<b>a</b>) A1. (<b>b</b>) A2. (<b>c</b>) B1. (<b>d</b>) B2. (<b>e</b>) A12, virtual glide shoe.</p> Full article ">Figure 4
<p>Cumulative glide measured and modeled, with information of solid and liquid precipitation, temperature of air and of soil and snowpack depth for the season of 2011. (<b>a</b>) A1. (<b>b</b>) B1. (<b>c</b>) B2.</p> Full article ">Figure 4 Cont.
<p>Cumulative glide measured and modeled, with information of solid and liquid precipitation, temperature of air and of soil and snowpack depth for the season of 2011. (<b>a</b>) A1. (<b>b</b>) B1. (<b>c</b>) B2.</p> Full article ">Figure 5
<p>Correlation analysis of gliding speed, <math display="inline"><semantics> <mi>U</mi> </semantics></math>, against chosen variables: air temperature, <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>a</mi> </msub> </mrow> </semantics></math>, interface temperature, <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>s</mi> <mo>,</mo> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math>, soil water content, <math display="inline"><semantics> <mi>S</mi> </semantics></math>, solid precipitation, <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>s</mi> </msub> </mrow> </semantics></math>, liquid precipitation, <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>l</mi> </msub> </mrow> </semantics></math>, total precipitation, <math display="inline"><semantics> <mi>P</mi> </semantics></math>, snow depth, <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mi>S</mi> </msub> </mrow> </semantics></math> and snow temperature, <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>S</mi> </msub> </mrow> </semantics></math>.</p> Full article ">
16 pages, 2349 KiB  
Article
Glacio-Nival Regime Creates Complex Relationships between Discharge and Climatic Trends of Zackenberg River, Greenland (1996–2019)
by Karlijn Ploeg, Fabian Seemann, Ann-Kathrin Wild and Qiong Zhang
Climate 2021, 9(4), 59; https://doi.org/10.3390/cli9040059 - 8 Apr 2021
Cited by 4 | Viewed by 3978
Abstract
Arctic environments experience rapid climatic changes as air temperatures are rising and precipitation is increasing. Rivers are key elements in these regions since they drain vast land areas and thereby reflect various climatic signals. Zackenberg River in northeast Greenland provides a unique opportunity [...] Read more.
Arctic environments experience rapid climatic changes as air temperatures are rising and precipitation is increasing. Rivers are key elements in these regions since they drain vast land areas and thereby reflect various climatic signals. Zackenberg River in northeast Greenland provides a unique opportunity to study climatic influences on discharge, as the river is not connected to the Greenland ice sheet. The study aims to explain discharge patterns between 1996 and 2019 and analyse the discharge for correlations to variations in air temperature and both solid and liquid precipitation. The results reveal no trend in the annual discharge. A lengthening of the discharge period is characterised by a later freeze-up and extreme discharge peaks are observed almost yearly between 2005 and 2017. A positive correlation exists between the length of the discharge period and the Thawing Degree Days (r=0.52,p<0.01), and between the total annual discharge and the annual maximum snow depth (r=0.48,p=0.02). Thereby, snowmelt provides the main source of discharge in the first part of the runoff season. However, the influence of precipitation on discharge could not be fully identified, because of uncertainties in the data and possible delays in the hydrological system. This calls for further studies on the relationship between discharge and precipitation. The discharge patterns are also influenced by meltwater from the A.P. Olsen ice cap and an adjacent glacier-dammed lake which releases outburst floods. Hence, this mixed hydrological regime causes different relationships between the discharge and climatic trends when compared to most Arctic rivers. Full article
(This article belongs to the Special Issue Effects of Climate Change on Mountain Hydrology - Recent Trends and Challenges)
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Figure 1

Figure 1
<p>Location of Zackenberg Research Station and the drainage basin of Zackenberg River in Greenland, own representation based on data provided by GADM [<a href="#B30-climate-09-00059" class="html-bibr">30</a>] and ZERO [<a href="#B31-climate-09-00059" class="html-bibr">31</a>].</p> Full article ">Figure 2
<p>Walter climate diagram of Zackenberg based on climate data [<a href="#B40-climate-09-00059" class="html-bibr">40</a>,<a href="#B41-climate-09-00059" class="html-bibr">41</a>] between 1996–2019.</p> Full article ">Figure 3
<p>Hydrographs consisting of discharge measurements between 1996 and 2019. The date is displayed as the day of the month. Extreme discharge events are labelled with their estimated maximum peak discharge. Note the missing data after a flood in 2005.</p> Full article ">Figure 4
<p>Linear trends calculated for the different parameters with their corresponding <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> values and <span class="html-italic">p</span>-values. (<b>a</b>) Annual discharge between 1996 and 2019, including the trend between 1996 and 2003 as found by Mernild et al. [<a href="#B27-climate-09-00059" class="html-bibr">27</a>]. (<b>b</b>) Mean annual air temperature between 1996 and 2019. (<b>c</b>) Thawing Degree Days between 1996 and 2019. (<b>d</b>) Annual precipitation between 1996 and 2019.</p> Full article ">Figure 5
<p>Bars indicating the period of running water in Zackenberg River between 1996 and 2019. Start and end of the period, respectively river break-up and freeze-up, indicated by the day of the year.</p> Full article ">Figure 6
<p>Max. snow depth in comparison to the total discharge (1996–2019). The blue bars mark the max. snow depth measured during a year, while the black line shows the total discharge of Zackenberg River.</p> Full article ">
24 pages, 6619 KiB  
Article
Future Hydrology of the Cryospheric Driven Lake Como Catchment in Italy under Climate Change Scenarios
by Flavia Fuso, Francesca Casale, Federico Giudici and Daniele Bocchiola
Climate 2021, 9(1), 8; https://doi.org/10.3390/cli9010008 - 6 Jan 2021
Cited by 15 | Viewed by 7268
Abstract
We present an assessment of climate change impact on the hydrology of the Lago di Como lake catchment of Italy. On one side, the lake provides water for irrigation of the Po valley during summer, and on the other side its regulation is [...] Read more.
We present an assessment of climate change impact on the hydrology of the Lago di Como lake catchment of Italy. On one side, the lake provides water for irrigation of the Po valley during summer, and on the other side its regulation is crucial to prevent flood risk, especially in fall and winter. The dynamics of lake Como are linked to the complex cryospheric hydrology of its Alpine contributing catchment, which is in turn expected to change radically under prospective global warming. The Poli-Hydro model is used here to simulate the cryospheric processes affecting the hydrology of this high-altitude catchment. We demonstrated the model’s accuracy against historical hydrological observations, available during 2002–2018. We then used four Representative Concentration Pathways scenarios, provided by three Global Circulation Models under the AR6 of IPCC, to project potential climate change until 2100. We thereby derived daily series of rainfall and temperature, to be used as inputs for hydrological simulations. The climate projections here highlight a substantial increase in temperature at the end of the century, between +0.61° and +5.96°, which would lead to a decrease in the total ice volume in the catchment, by −50% to −77%. Moreover, there would be a decrease in the contribution of snow melt to the annual lake inflow, and an increase in ice melt under the worst-case scenarios. Overall, the annual Lake inflows would increase during autumn and winter and would decrease in summer. Our study may provide a tool to help policy makers to henceforth evaluate adaptation strategies in the area. Full article
(This article belongs to the Special Issue Effects of Climate Change on Mountain Hydrology - Recent Trends and Challenges)
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Figure 1

Figure 1
<p>Case study. Lake Como catchment. Location of Automatic Weather Stations (AWS), and hydrometric (water stage) stations.</p> Full article ">Figure 2
<p>Poli-Hydro model, hydropower regulation for the control run period, and climate change scenarios simulations. We report the necessary tools as per 7 categories, i.e., domain (e.g., hydrology, reservoir operation), tools (e.g., hydrological model, optimization), functions (e.g., snow melt <span class="html-italic">M<sub>s</sub></span> as a function of temperature <span class="html-italic">M<sub>s</sub></span>(<span class="html-italic">T</span>), etc..), data (weather, terrain models, etc..), model outputs (e.g., snow melt in time and space <span class="html-italic">M<sub>i</sub></span>(<span class="html-italic">t,s</span>)), and model accuracy (e.g., Bias, NSE). Division in present, and future (projections) reported. <span class="html-italic">T(t)</span> is daily temperature, <span class="html-italic">P</span>(<span class="html-italic">t</span>) daily precipitation, <span class="html-italic">Q</span>(<span class="html-italic">t</span>) is daily observed discharge at outlet section, <span class="html-italic">Q<sub>m</sub>(t</span>) is daily modelled discharge at outlet section, <span class="html-italic">M<sub>s</sub></span>(<span class="html-italic">t,s</span>) is daily snow melt in a given place (cell), <span class="html-italic">R<sub>t</sub></span>(<span class="html-italic">t</span>) is reservoirs’ release. Bias is systematic error on average, NSE is Nash-Sutcliffe Efficiency. <span class="html-italic">T<sub>f</sub>’</span>(<span class="html-italic">t</span>), <span class="html-italic">P<sub>f</sub>’</span>(<span class="html-italic">t</span>) are (future/projected) temperature and precipitation from GCMs before downscaling (biased), <span class="html-italic">T<sub>f</sub></span>(<span class="html-italic">t</span>), <span class="html-italic">P<sub>f</sub></span>(<span class="html-italic">t</span>) future daily temperature and precipitation after downscaling (unbiased), <span class="html-italic">Q<sub>f</sub></span>(<span class="html-italic">t</span>) is projected discharge.</p> Full article ">Figure 3
<p>Snow covered area from the <span class="html-italic">Poli-Hydro</span> model vs. MODIS estimates. (<b>a</b>) calibration. (<b>b</b>) validation.</p> Full article ">Figure 4
<p>Snow depth from the model vs. snow depth from four AWS stations (average).</p> Full article ">Figure 5
<p>Inflows to Lake Como obs/mod. 2002–2018. (<b>a</b>) Mean monthly inflows. (<b>b</b>) Monthly inflows.</p> Full article ">Figure 6
<p>Monthly share of low components in Lake Como catchment. 2002–2018.</p> Full article ">Figure 7
<p>Projected mean temperature of the catchment for each socio-economic pathway (SSP) and each Global circulation models (GCM).</p> Full article ">Figure 8
<p>Projected total precipitation on the catchment for each SSP and each GCM.</p> Full article ">Figure 9
<p>Variation of projected average volume of snow water equivalent (<span class="html-italic">SWE<sub>cum</sub></span>) for each SSP respect to the control run (CR) period. (<b>a</b>) April. (<b>b</b>) June. (<b>c</b>) September. (<b>d</b>) December.</p> Full article ">Figure 9 Cont.
<p>Variation of projected average volume of snow water equivalent (<span class="html-italic">SWE<sub>cum</sub></span>) for each SSP respect to the control run (CR) period. (<b>a</b>) April. (<b>b</b>) June. (<b>c</b>) September. (<b>d</b>) December.</p> Full article ">Figure 10
<p>Projected ice volume until 2100 for each SSP, averaged for different GCMs.</p> Full article ">Figure 11
<p>Projected monthly inflow to Lake Como for each SSP and each GCM. 2051–2060 period on the left y axis, 2091–2100 period on the right y axis, upside-down. The black line represents the discharge in the CR period.</p> Full article ">Figure 12
<p>Projected share of flow components for each SSP, and average inflow at Coo lake Q<sub>y</sub> for each SSP (average among models), at half century (solid) and end of century (striped).</p> Full article ">
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