Advances in snow and ice hydrology


Advances in snow and ice hydrology
Convener: Tobias Jonas | Co-Conveners: Melody Sandells, S. McKenzie Skiles, James McPhee, Timothy Link, Elzbieta Wisniewski, Vsevolod Moreydo
| Tue, 31 May, 08:30–12:00|Room Rondelet 2
| Attendance Tue, 31 May, 15:00–16:30|Poster area

Orals: Tue, 31 May | Room Rondelet 2

Chairperson: Tobias Jonas
Adrià Fontrodona-Bach, Joshua Larsen, Ross Woods, Bettina Schaefli, and Ryan Teuling

There is strong evidence that rising temperatures lead to less snow accumulation and to an earlier start of the melt season. This has an effect on the magnitude and timing of streamflow generated by snowmelt in spring. Higher temperatures should intuitively lead to faster snowmelt, but some studies suggest that melt rates might be slower in a warming world because the melt onset occurs earlier in the year when less energy is available for melt. Understanding of these changing snow dynamics is challenged by a lack of observations on water content of the snowpack, the Snow Water Equivalent (SWE). However, high quality observations of snow depth are generally more available in both space and time, even at higher elevations. Here we gather several datasets of long-term observed snow depth time series over the Northern Hemisphere, and convert them to SWE. We then investigate changes in total snowmelt, timing of snowmelt and melt rates for the period 1980-2020 over a ranger of climates and regions. Large decreases in total melt and earlier melt timing are widely observed. However, trends in snowmelt rates are generally weak and spatially inhomogeneous. Slower snowmelt in a warmer world occurs mostly on deep snowpacks that have been heavily depleted, but faster melt or no significant change in melt rate are observed too. We provide an analysis of the causes for the spatial and temporal variability in trends. We find that changes and trends can differ depending on the definition of melt rate and peak SWE. Strong warming generates large melt events during the late accumulation season, challenging the commonly used definition of peak SWE and making it harder to compare the snowmelt dynamics of the past and the current climate. We highlight that focusing only on melt rate change might mask important effects on melt timing and magnitude, because a proportional reduction in total melt and number of melt days can lead to no change in melt rate.

How to cite: Fontrodona-Bach, A., Larsen, J., Woods, R., Schaefli, B., and Teuling, R.: How are snowmelt rates actually changing across the Northern Hemisphere?, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-755, https://doi.org/10.5194/iahs2022-755, 2022.

Denis Ruelland

Modelling snow and glacier melt runoff processes is essential for local water supply, hydropower management and flood forecasting. However, melt runoff modelling in mountainous regions faces two challenges: scarcity of meteorological data and uncertainty in parameter calibration due to limited understanding of the complex hydrological processes. Numerous free parameters have been introduced in most of the snow-accounting routines (SAR) used in operational hydrology in order to compensate for errors in the input data, adapt to local snow processes or deal with scale effects between different snow data.  This talk investigates the degree of liberty required in a SAR to simulate local snow dynamics, distributed snow cover areas and flows at the catchment outlet. To address this issue, a SAR on top of the GR4J model was tested on a dataset covering 17 mountainous catchments (45 to 3580 km²) in the French Alps and Pyrenees. Model calibration and control were based on streamflow series, fractional snow-covered areas (FSC) computed from MODIS snow products and at least one chronicle of local measures of snow water equivalent (SWE) acquired in each catchment over the period 2004−2016. The SAR was applied according to elevation bands of 100 meters and various parametrisation: from ten free parameters (precipitation orographic correction, temperature lapse rate, seasonal variation of the temperature lapse rate, snowfall gauge under-catch correction, thermal inertia of the snow pack, melt degree day factor, variable melt factor, ice degree day factor, 2-parameter hysteresis between SWE and FSC) to only one free parameter (snowfall gauge under-catch correction). Results shows that the one-free-parameter SAR is as efficient as more free structures to simulate both distributed and local snow dynamics as well as runoff. Interestingly, using a SAR without any free parameters by fixing the snowfall gauge under-catch correction to a value of 150% leads only to a deterioration of local SWE dynamics. These findings suggest that it is possible, and even advisable, to limit the number of free parameters in temperature-index models in order to reduce problems of over-parameterisation and equifinality.

How to cite: Ruelland, D.: Snowfall gauge under-catch correction: a key parameter for snow-hydrological simulations, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-14, https://doi.org/10.5194/iahs2022-14, 2022.

Jean Odry, Marie-Amélie Boucher, Simon Lachance-Cloutier, Richard Turcotte, and Pierre-Yves St-Louis

Particle filtering is interesting for snow data assimilation because of its minimal assumptions. However, implementing a particle filter over a large spatial domain is challenging for many reasons. For instance, the number of required particles rises exponentially as the domain size increases. Another important issue when spatializing a particle filter for snow data assimilation is the creation of spatial discontinuities when resampling the particles at locations where snow observations are available. In this presentation, we will describe how we implemented a spatialized particle filter for snow data assimilation over a large portion of the province of Quebec, Canada (600 000 km2 ). Two different types of snow observations where assimilated with this particle filter: sporadic manual snow surveys, which measure snow water equivalent directly, and continuous automated snow depth observations, which we converted to snow water equivalent using an ensemble of neural networks. We will then explain how a more frequent data assimilation can create unwanted discontinuities and break the spatial structure of the particles, and how we can remediate that by using an adaptation of the Schaake Shuffle reordering method. We will show that this solution significantly reduces the random noise in the distribution of the particles and decreases the uncertainty associated with the estimation. We emphasize that the proposed spatialized particle framework could also eventually accommodate other types of data, such as citizen science data and gamma monitoring data. Overall, the proposed method allows to obtain improved spatial representation of snow water equivalent compared to the previous operational method used by the government of Quebec. 

How to cite: Odry, J., Boucher, M.-A., Lachance-Cloutier, S., Turcotte, R., and St-Louis, P.-Y.: Assimilation of multiple types of snow observations through a large scale spatialized particle filter, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-122, https://doi.org/10.5194/iahs2022-122, 2022.

David Casson, Wouter Knoben, Louise Arnal, Shervan Gharari, Bart van Osnabrugge, Guoqiang Tang, Hongli Liu, and Martyn Clark

Accurate estimation of seasonal snow mass for streamflow forecasting remains a technical and scientific challenge that requires advances in both physically based modelling and measurement techniques. Data assimilation provides methods to optimally combine modeled and measured information, and can be used to improving snow state estimates used as initial conditions for streamflow forecasting. Several key challenges remain for practical implementation in mountainous snow data assimilation, including quantification of measurement and model uncertainties, connecting point-scale observations to spatially distributed model states in complex terrain and the ability to improve information where measurements are not available.

This research presents recent effort in addressing these challenges through ensemble snow data assimilation in the Canadian Rocky Mountains. Specifically, discretization to improve spatial representation of snow cover, assimilation of in-situ measurements with the Particle Filter and Ensemble Kalman Filter and assessment of the impact on streamflow forecasts.  This is carried out with a dynamic multi-layer, energy balance snow model in the Structure for Unifying Multiple Modeling Alternatives (SUMMA) framework.  This builds on recently developed North American domain hydrological modelling, probabilistic meteorological data generation and forecasting efforts by the Computational Hydrology group at the University of Saskatchewan. Planning for snow sub-grid heterogeneity and the assimilation of remotely sensed fractional snow cover area will also be presented.

How to cite: Casson, D., Knoben, W., Arnal, L., Gharari, S., van Osnabrugge, B., Tang, G., Liu, H., and Clark, M.: Ensemble Data Assimilation Methods for Improved Snow Estimation and Streamflow Prediction in Mountainous Terrain, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-572, https://doi.org/10.5194/iahs2022-572, 2022.

James McPhee, Thomas Shaw, Simon Gascoin, César Deschamps-Berger, Pablo Mendoza, Álvaro Ayala, and Francesca Pellicciotti

Information about end-of-winter spatial distribution of snow depth is important for seasonal forecasts of spring/summer streamflow in high-mountain regions. However, obtaining detailed information about high mountain snowpacks is often limited by insufficient ground-based observations and uncertainty in the (re)distribution of solid precipitation. We utilize high-resolution optical images from Pléiades satellites to generate a 4 m snow depth map for a high mountain catchment of central Chile and test the potential of this high-resolution information for improving the representation of snow depth initial conditions (SDICs) in a glacio-hydrological model (TOPKAPI-ETH). We calibrate model parameters controlling glacier mass balance and snow cover evolution using ground-based and satellite observations, and consider the relative importance of SDICs compared to model parameters and forcings. Satellite-based estimates are negatively biased (median difference of −0.22 m) when compared against subdomain observations from a terrestrial LiDAR scan, though they replicate general snow depth variability well. However, the Pléiades dataset is subject to data gaps (17% of total pixels), negative values for shallow snow (12%), and noise on slopes >40–50° (2%). Snow depths (with an estimated error of ~0.36 m) relate well to topographical parameters such as elevation and northness in a similar way to previous studies. However, estimations of snow depth based upon topographic characteristics, physically based modeling or model spin-up cannot resolve localized processes (i.e., avalanching or wind scouring) that are detected by Pléiades, even when forced with locally calibrated data. We find that Pléiades SDICs improve the simulation of snow-covered area, glacier mass balance, and monthly streamflow compared to alternative SDICs for our glacio-hydrological model. Model simulations are found to be sensitive to SDICs in the early spring (up to 48% variability in modeled streamflow compared to the best estimate model), and to temperature gradients in all months due to its control on albedo and melt rates over a large elevation range (>2,400 m). Therefore, optical stereo-photogrammetry offers an advantage for obtaining SDICs that aid both the timing and magnitude of streamflow simulations, process representation (e.g., snow cover evolution) and has the potential for upscaling to larger spatial domains.

How to cite: McPhee, J., Shaw, T., Gascoin, S., Deschamps-Berger, C., Mendoza, P., Ayala, Á., and Pellicciotti, F.: The Applicability of Optical Satellite Photogrammetry for Snow Depth Derivation and Streamflow Estimates, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-441, https://doi.org/10.5194/iahs2022-441, 2022.

Roseanna Neupauer, Alexi Lainis, Joshua Koch, and Michael Gooseff

In river channels in artic environments, groundwater that discharges to the land surface during winter can freeze in the channel, forming large sheet-like masses of layered ice called aufeis.  Water stored in aufeis is released slowly as the aufeis melt during summer, providing a critical course of water to the Arctic river ecosystems late into the summer when other water resources are reduced.  Under warming conditions in the Arctic, the quantity of water stored in aufeis may be reduced and the release of water by melting of the aufeis may end earlier in the summer.  The processes that lead to the formation of aufeis are not well understood; however, an understanding of these processes is necessary to predict how rising air temperature may affect aufeis formation and the availability of water in Arctic river ecosystems.  This work uses numerical simulation to evaluate a conceptual model of subsurface hydrogeothermal conditions that can lead to the formation of aufeis in the Kuparuk aufeis field on the North Slope of Alaska.  At this site, groundwater flows year-round through a talik above the permafrost and beneath the seasonally-frozen active layer just beneath the land surface. Groundwater in this talik discharges to the land surface through unfrozen gaps in the active layer, where it can freeze and form aufeis.  We developed a 2-D heterogeneous vertical profile model to show that subsurface water can discharge to the land surface through subvertical high permeability pathways during winter months while the lower permeability soils near the land surface remain frozen, thus providing a source of water for aufeis formation.  We investigate the effects of the warming conditions on the magnitude and timing of these discharges, which are surrogates for the mass of aufeis and the timing of aufeis formation, respectively.  Aufeis formation and ablation are both sensitive to climatic conditions. The sensitivity analyses presented here form a basis for future investigations of aufeis dynamics across the Arctic.

How to cite: Neupauer, R., Lainis, A., Koch, J., and Gooseff, M.: Aufeis Formation and Climate Change, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-144, https://doi.org/10.5194/iahs2022-144, 2022.

Coffee break
Chairperson: Tobias Jonas
Anthony Lemoine, Isabelle Gouttevin, Thomas Condom, Sophie Cauvy-Fraunié, Juliette Becquet, Jordi Bolibar, and Antoine Rabatel

The rapid evolution of glaciers and snow cover in mountain areas reflects the impacts of climate change on these regions. The decrease of the water stock in solid form induces irreversible changes on the hydrological regimes of alpine rivers and affects their ecological functioning. In addition, there are numerous anthropogenic pressures, mainly due to the use of water in mountain areas (artificial snow, water supply, agriculture, hydropower production, tourism). To better understand the consequences of glacial retreat and snow cover changes on water resources and help decision makers, we have set up a modeling chain composed of a dynamic glacier module, based on the ALPGM glaciological model (Bolibar et al., 2020), as well as the J2000 semi-distributed hydrological model (Branger et al., 2016) running at daily time step. In the present contribution, this modelling chain is applied to 3 catchments of the French Northern Alps characterized by contrasting ranges of altitudes and glacier cover. The evolution of hydrological regimes over the last sixty years is first examined using meteorological reanalysis products adapted to the Alpine regions (SPAMZ; Gottardi, 2009 and S2M; Vernay et al., 2021). Then, we envisage hydrological projections until 2100 from regionalized climate projections (Verfaillie et al., 2017) according different greenhouse gas emission scenarios. We also study of the evolution of evapotranspiration as a function of altitude to characterize its impact on the catchment’s water balance. The ALPGM-J2000 modelling chain allows us to discriminate the contributions of different inputs to the riverflow (glacial melt, snow melt, precipitation runoff), highlighting the shift in time of the parts of the catchment under glacial or more nival influence. We illustrate the use of this modelling chain through the production of indicators to characterize the impacts of glacier melt on aquatic ecosystems at different temporal scales, with particular emphasis on the relationship between changes in hydrological conditions and changes in aquatic invertebrate habitat.

References : https://cloud.univ-grenoble-alpes.fr/index.php/s/Gz9iLzd9ZRtrTjt

How to cite: Lemoine, A., Gouttevin, I., Condom, T., Cauvy-Fraunié, S., Becquet, J., Bolibar, J., and Rabatel, A.: Nivo-glaciological changes in Alpine catchments: impacts on hydrological regimes and aquatic ecosystems, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-386, https://doi.org/10.5194/iahs2022-386, 2022.

Tom Müller, Bettina Schaefli, Stuart N. Lane, and Mauro Fischer

Glaciated catchments in the Swiss Alps are subject to intense changes due to rapid glacier retreat. Combined with earlier snowmelt, water availability in summer and especially during drought periods will become limited, which will have a strong influence on the local ecology but also for downstream water usages.

Water stable isotopes have become a commonly used tracer in hydrological studies to separate the contribution of snow and rain, due to their distinct signature and conservative behaviour, but also due to their simple sampling procedure and low analysis costs. In high alpine catchments, their application is however challenging because the temporal and spatial isotopic composition of ice and snow varies strongly (which requires intense sampling) and because their isotopic signal range is often strongly overlapping

Based on a case study of a glaciated catchment in the Swiss Alps, we propose in this study to compare the estimation of snow and ice melt contribution using two separate approaches: a glacier surface mass balance modelling approach, and a separation using a large database of isotopic observations. We show the large spatial and temporal variation of the snow isotopic signal at the catchment scale and propose a simple modelling approach to reconstruct its temporal evolution. On this basis, we show that snow and ice melt isotopes show very similar ranges of values during the summer due to the effect of rain on snow and snow fractionation. As a result, it appears that the estimation of their respective contribution is very sensitive to the accuracy of the estimated snow melt end-member. In comparison, the mass balance modelling approach may provide more robust results but requires a more field intensive work in order to measure snow accumulation and ice ablation. Finally, we insist that water stable isotopes should only be used with a proper statistical assessment of their spatial variability and a proper characterization of their temporal evolution in order to provide any realistic estimations in glaciated catchments.

How to cite: Müller, T., Schaefli, B., Lane, S. N., and Fischer, M.: On the use of water stable isotopes to estimate snow and ice melt contribution in a glaciated catchment, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-652, https://doi.org/10.5194/iahs2022-652, 2022.

Susanne Schmidt, Stanzin Passang, Dagmar Brombierstäudl, and Marcus Nüsser

In the semi-arid Trans-Himalayan region of Ladakh, meltwater supply from the cryosphere is essential for irrigated agriculture. Different meltwater sources are used during the agricultural production period. Apart from high altitude glaciers above 5200 m a.s.l. and permafrost which meltwaters become available in summer, runoff from seasonal snow cover and aufeis is mostly used to bridge recurrent water scarcity in spring. While glaciers and snow cover are “visible” sources, easily detectable by optical remote sensing data, permafrost and aufeis, a seasonal ice body created by successive freezing of flowing water onto the already frozen surface is mainly located along rivers and streams, are almost neglected in current research. The aim of the study is to inventory different components of the cryosphere and to analyse their seasonal variability and their general dynamics in the context of climate change. Based on different multi-temporal and multi-scale remote sensing data and techniques glacier changes are documented over long observation periods. The investigation of the seasonal snow cover is based on MODIS data and for the mapping of aufeis fields a time series analysis of Landsat data was conducted.

How to cite: Schmidt, S., Passang, S., Brombierstäudl, D., and Nüsser, M.: Glaciers, snow and neglected forms of frozen water: Cryosphere components in the Trans-Himalaya of Ladakh, India, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-682, https://doi.org/10.5194/iahs2022-682, 2022.

Nicolas Champollion, Lucille Gimenes, and Romain Millan

The effects of climate change on water resources are partly determined by the size and the spatial distribution of ice reservoirs around the world. While mountain glaciers represent only 1% of today’s global ice volume, they have contributed around 25% to sea-level rise during the last decades, and are expected to contribute within the same proportion during the rest of the 21st century [SROCC, 2019]. Mountain glaciers also represent sources of drinking water for millions of people. The glacierized drainage basins cover around 26% of the global land surface and are populated by more than two billions of people [Huss et Hock, 2018]. However, mountain glacier ice thickness estimates are largely uncertain due to the use of simplified retrieval approaches [Rabatel et al., 2018]. Furthermore, nearly 50% of the global glacier evolution uncertainty comes from the glacier model itself and the initial glacier representation in space [Marzeion et al., 2019]. The objective of this study is to understand the effects of initial glacier conditions, and specifically the ice thickness, on simulations of glacier evolution and their contribution to river runoff.

For that purpose, we used the Open Global Glacier Model [OGGM, Maussion et al., 2019], that simulates the surface mass balance and ice dynamics to estimate the evolution of any glacier in the world. We used three new different ice thickness dataset and proposed a framework to assimilate them within OGGM. We then focused on sensitivity analysis of future glacier evolution using different climate scenarios, initial conditions of ice thickness and internal model parameters such as the creep parameter, the spatial resolution, the spin-up initialization. These experiments were performed for different types of glaciers in terms of location, size and geometry. The results helped us to assess the importance of model initialization with respect to other model parameters, in the glacier evolution during the 21st century and specifically the changes in surface and volume. We also explored the differences induced in terms of glacier contribution to river runoff and peak water timing, which is of great importance for freshwater resources management.

How to cite: Champollion, N., Gimenes, L., and Millan, R.: Influence of initial glacier conditions to the future evolution of river runoff, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-340, https://doi.org/10.5194/iahs2022-340, 2022.

Cheick Doumbia and Alain N. Rousseau

In the Upper Yukon Watershed (UYW, 20,000 km2), seasonal melt from glaciers contribute significantly to annual runoff and operation of the Whitehorse power plant. This study aims to analyze the impact of climate change on the contribution of glaciers, which covers 5% of the UYW, to the annual runoff using hydrological modelling, GRACE data and machine learning algorithms.

The spatial resolution of GRACE data remains too low to discriminate changes in glacier mass signal at the scale of the UYW. Thus, here we applied a spatial concentration function approach to build high resolution monthly time-series of glaciers mass changes over the UYW. To estimate glaciers mass changes, we decomposed four GRACE TWS solutions with different processing assumptions using monthly data from LSM GLDAS v2.1 (Rodell et al., 2004) and WGHM v2.2d (Müller Schmied et al., 2020). Spatial concentration functions were derived from two heterogeneous a priori of different resolutions and sources (Hugonnet et al., 2021; Larsen et al., 2015) and the leakage was subtracted by using glaciers over the Gulf Of Alaska (GOA). To analyze the accuracy of our assessments, we compared the trends resulting from the spatial concentration functions and the constrained forward approach (Doumbia et al., 2020) over the GOA and the Saint-Elias Mountains. To extend/reconstruct glacier mass change up to GRACE-FO (i.e. 2003-2020), we used the Automated Machine Learning (AML) H2O-AutoML (LeDell and Poirier, 2020). Then, glacier mass anomalies were used to calibrate the hybrid (i.e., degree-day/thermal energy balance) glacier melt model of HYDROTEL over UYW.

For the period of 2003 to 2016, the trends in glaciers mass losses over the GOA and Saint-Elias Mountains varied from 41.61 to 53.43Gt/yr and 19.31 to 28.88Gt/yr, respectively. Our results compared well with the glaciers mass losses reported in other studies. The AML algorithms performed well with NSE values varying from 0.75 to 0.99; correlation coefficients from 0.93 to 0.99; P-bias from -2.4 to 4.8 and NMRSE from 0.8 to 49.6. The use of the glacier mass changes, in addition to stream flows, improved the calibration of HYDROTEL. 

How to cite: Doumbia, C. and N. Rousseau, A.: Impact of climate change on the contribution of glaciers to the Upper Yukon River, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-449, https://doi.org/10.5194/iahs2022-449, 2022.

Gonzalo Navarro, Shelley MacDonell, and Rémi Valois

Rock glaciers play an important hydrological role in the semiarid Andes (SA; 27°-35°S). They cover about three times the area of uncovered glaciers and represent important contribution to streamflow when water is needed most. For this reason, their role in freshwater production, transfer and storage is likely to be of primary importance. Their internal composition (i.e. ice, water, debris and air) and distribution (e.g. ice lenses, massive ice, hydrological channels) determine how water is transfer through the geoform. To understand such processes, geophysical surveys were conducted in the Tapado glacier complex in the Chilean SA (30°S; above 4200 masl), which could be regarded as the biggest and most relevant ‘water tower’ of the Elqui River catchment. Electrical Resistivity Tomography (ERT) and Ground Penetrating Radar (GPR) were used and qualitatively combined to delineate potential hydrological routing pathways on the debris-covered and rock glacier, which are part of the complex glacier. The media is characterized by high resistivities and a general pattern of increasing resistivities with depth, associated with the presence of buried ice. Radargrams shows diffraction linked to boulders presence, but strong and diffuse reflectors indicative of ice lenses or massive ice were also recognized. The results suggest that while the covered glacier shows probable hydrological routing zones exclusively in the area above the massive ice, the rock glacier would have more hydrological transfer routes downstream due to its fragmented ice structure, with vertical passages that could conduct water channeled from the thawing ice or from water coming from upstream. This work reinforces the valuable use of geophysical methods in exploring the internal structure of geoforms with ice contained below rock blocks. In addition, it allows to improve the understanding of headwaters in snow and ice melt driven hydrological system, where rock glaciers represent an important unknown when developing models of energy and mass flow (e.g. water), and how these flows interact with adjacent geoforms (e.g. moraines, valleys, peatland).