By accumulating precipitation at high elevations, snow and ice change the hydrologic response of a watershed. Water stored in the snow pack and in glaciers thus represents an important component of the hydrological budget in many regions of the world and a sustainment to life during dry seasons. Predicted impacts of climate change in headwater catchments (including a shift from snow to rain, earlier snowmelt and a decrease in peak snow accumulation) will affect both water resources distribution and water uses at multiple scales, with potential implications for energy and food production.
Our knowledge about snow/ice accumulation and melt patterns is highly uncertain, because of both limited availability and inherently large spatial variability of hydrological and weather data in remote areas at high elevations. This translates into limited process understanding, especially in a warming climate. The objective of this session is to integrate specialists focusing on snow accumulation and melt within the context of catchment hydrology and snow as a source for glacier ice and melt, hence streamflow. The aim is to integrate and share knowledge and experiences about experimental research, remote sensing and modelling.
Contributions addressing the following topics are welcome:
- experimental research on snowmelt runoff processes and potential implementation in hydrological models;
- development of novel strategies for snowmelt runoff modelling in various (or changing) climatic and land-cover conditions;
- evaluation of remote-sensing (time-lapse imagery, laser scanners, radar, optical photography, thermal and hyperspectral technologies) or in-situ snow products (albedo, snow cover or depth, snow water equivalent) and application for snowmelt runoff calibration, data assimilation, streamflow forecasting or snow and ice physical properties quantification;
- observational and modelling studies that shed new light on hydrological processes in glacier-covered catchments, e.g., impacts of glacier retreat on water resources and water storage dynamic or the application of techniques for tracing water flow paths;
- studies on cryosphere-influenced mountain hydrology, such as landforms at high elevation and their relationship with streamflow, water balance of snow/ice-dominated, mountain regions.
vPICO presentations: Thu, 29 Apr
Glaciers in the Indian Himalayan Region (IHR) are sensitive to climatic changes. Rivers originating from Himalaya have higher water yields in the ablation season due to large inputs from the melting of snow and glaciers, which is critical for sustaining downstream ecosystem, agricultural practices, hydroelectric power generation, and urban water supplies. Integrated investigations are frequently unavailable at a regional scale over a longer period, which is hampered due to the non-availability of data caused by harsh weather conditions, difficult terrain, as well as difficulty in maintaining the instruments at such high altitudes (> 3000 m asl). The hydrological understanding of melting processes from glacierized basins requires a network of reliable meteorological and hydrological observations. In absence of such reliable meteorological data, most of the hydrological simulation studies are forced to extrapolate air temperature from nearby basins, lower elevations, or consider satellite-based observations, which often deviate or differ from the actual ground conditions and lead to large uncertainty in model outputs. Therefore, an integrated approach for collecting hydrological and meteorological data along with other data like snow-cover, suspended sediment transfer and stable isotopic signatures of different components of the hydrograph were conceptualized for glacierized river basins in Garhwal Himalaya (Bhagirathi and Alaknanda). Our results suggest that the annual distribution of temperature lapse rates (TLR) established exhibits a bimodal pattern and the TLR’s are significantly lower than the adiabatic lapse rate. The major components of the streamflow are derived from snow and glacier melt, while rainfall contributes little during the Indian Summer Monsoon (ISM). Westerlies significantly feed the glacier with snow, while rainfall is dominant during the Indian Summer Monsoon (ISM). Precipitation and temperature are the dominant meteorological factors controlling melting processes and sediment delivery. Climate and topography control the distribution of seasonal snow cover/ snowline in the region. Extreme events like heavy rainfall, flash floods, glacial lake outbursts floods, etc. can be traced using hydrometeorological and isotopic data at high altitude stations. Therefore, in light of the challenges and potential research gaps, the study produces actionable knowledge in the Garhwal Himalaya for better understanding and modeling of glacio-hydrological processes by incorporating ground-based observations.
How to cite: Kumar, A., Verma, A., Tiwari, S. K., and Rai, S. K.: In-situ hydro-meteorological records in conjunction with stable isotope systematics to understand the hydrological processes in Glaciers of Garhwal Himalaya, India, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12032, https://doi.org/10.5194/egusphere-egu21-12032, 2021.
Due to the rise in global temperature, changes in precipitation patterns are predicted particularly in Arctic regions. Such changes in patterns and modifications in typical snow to precipitation ratios will affect the snowpack thickness and the timing of snow accumulation and snow melting. Stable water isotopes (δ2H, δ18O) are one of the latest tools in exploring and tracing such changes, however, snow isotope and particularly snowmelt isotope datasets are rarely available which hamper the high-resolution isotope based hydrological investigations in Arctic regions. In this study, we perform an investigation for evaluating spatiotemporal variations in stable isotopes of snow and snowmelt water. Our Pallas research catchment is located in a subarctic setting in northern Finland. The measurements were made at 11 locations along a 2 km snow survey, which is established on the transect of the catchment, comprising of different landscape features (i) forested hillslope, (ii) mixed forest and (iii) open mires. We sampled depth-integrated bulk snowpack and fixed 5 cm incremental snow stratigraphy profile in snowpits. For snowmelt sampling, we used a system of snowmelt lysimeter, deployed at 11 locations. The bulk snowpack samples were collected biweekly, fixed 5 cm incremental stratigraphic snowpit samples during the period of maximum snowcover thickness and during the start of peak melting, during the peak melting and after the peak melting. Snowmelt samples were collected daily during the spring season until the complete disappearance of snow with complementary measurements of snowmelt flux, snow density and snow water equivalent. Our results indicate the higher mean values of snowmelt isotopes relative to the bulk snowpack and surface snow isotopes. The snow isotope profiles in snowpack reveal that the isotopes at the snow-air and snow-ground interfaces are enriched in heavier isotopes as compared to the middle of the snowpack. The snowmelt isotopes show that the isotopes are initially depleted in heavier isotopes but with the progress of melting, they start to become enriched. A well defined depleted to enriched pattern is observed at different locations in the forested hillslope area, while a relatively dispersed depleted to enriched pattern is observed at different locations in the mixed forested area. Our unique high-resolution dataset of snow and snowmelt isotopes will be useful in many applications; such as for evaluating post-depositional isotope modification in the seasonal snowpack, developing tracer-aided mass and energy based snow models. The establishment of snowmelt isotope dataset, showing spatiotemporal variability of snowmelt isotopes, is an important step forward in isotope based plant-water uptake studies and hydrological analyses in snow-influenced catchments.
How to cite: Noor, K., Ala-Aho, P., Marttila, H., and Kløve, B.: Spatiotemporal variations of isotopes in snow and snowmelt in the subarctic setting at Pallas catchment, Finland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7584, https://doi.org/10.5194/egusphere-egu21-7584, 2021.
High-elevation catchments are rapidly changing as glaciers retreat and permafrost thawing intensifies. Consequently, alpine stream hydrochemistry is shifting but the interaction with complex hydrological and geological settings often confounds the effect of the climatic signal. To evaluate the effect of different glacier coverage and rock glacier presence, our study involves a multi-parameter approach of different tracers in two high-elevation catchments. Both catchments (Schnals and Martell; Eastern Italian Alps) share a comparable metamorphic geology but contrast in their glacier cover (4% and 22%, respectively) and abundance of active rock glaciers (numerous in the Schnals catchment).
Based on these different settings, we hypothesized that i) the glacier melt contribution at the daily and monthly scale in Martell is larger than in Schnals, ii) metamorphic catchments share similar hydrochemical patterns along the river network, and iii) rock glacier meltwaters affect more strongly the hydrochemistry of the main stream in Schnals than in Martell, given the higher abundance of active rock glaciers in the former catchment.
From June 2019 to October 2020, we carried out a monthly sampling of stream water along the main river, major tributaries, springs and a rock glacier. Snowmelt and ice melt (only at Martell) were occasionally sampled as well. Rain was collected on a monthly basis. Electrical conductivity of water samples was measured on-site while stable water isotopes and concentrations of major, minor, and trace elements were measured in the laboratory.
Our results indicate that the isotopic composition of streams and tributaries in Martell mainly originated from snowmelt and ice melt, with a minor contribution from groundwater. In contrast, the contribution of precipitation, shallow groundwater, and rock glaciers was larger in the Schnals catchment. The two catchments showed distinct hydrochemical patterns, based on their different elemental concentrations. Mostly during the glacier ablation period and autumn, alkali elements dominated Schnals hydrochemistry, whereas arsenic and strontium characterized the stream hydrochemistry of Martell. Concentrations of metals and metalloids had a sharp increase during autumn, when thawing permafrost and the subglacial drainage was highest, thus affecting the hydrochemistry of the entire river network. As thawing permafrost increasingly influences the quality of freshwaters in deglaciating catchments, efforts must be dedicated to the long-term monitoring of alpine river networks, given the potential implications for human health and ecosystem quality.
How to cite: Engel, M., Brighenti, S., Tirler, W., Nadalet, R., Mair, V., Tagliavini, M., and Comiti, F.: Alpine stream characterization through the lens of hydrochemistry: a comparison study from two high-elevation catchments (Eastern Italian Alps), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9461, https://doi.org/10.5194/egusphere-egu21-9461, 2021.
The river confluence is one of the most complex processes in river morphology, which plays an important role in riverbed deformation, mixing processes, pollution transport, etc. The area of river confluence can be often visually observed as the relatively thin transition region or mixing zone (MZ) separating two parallel weakly mixing flows. The mixing zone characteristics, in particular width, are important indicators of the turbulent mixing intensity and momentum and substance exchange between two flows, therefore, understanding the physical mechanisms affected on the mixing zone formation and manifestation is an important task in ecological remote monitoring. A typical example of a river confluence is the merging of the Volga and Oka rivers (Russia). In this work satellite radar and optical images of the Oka -Volga MZ during the active ice cover melting were analyzed. The mixing zone of rivers as the formation of wet snow at the initial stages of melting, which further contributes to the formation of open water patches in the area of rivers confluence zone is shown. Such manifestation of the mixing zone can be presumably associated with factories / thermal power plants emissions and turbulent mixing of river flows. An increase of the radar signal backscattering of wet snow was observed, and it can be associated with the predominance of surface scattering (an increase of affection of surface roughness) with an increase of snow and ice cover moisture. In conditions of positive average daily temperatures, intense ice melting led to the appearance of open water patches, which were partially covered with fragmented ice. Although the wind velocity during the observation period was about 3-5 m/s, which significantly exceeds the threshold of wind waves excitation, the latter, was rather weak, in particular, due to the wind wave damping on the water covered with the floes. This led to the manifestation of the MZ as an extended dark band, and also presumably caused weak radar backscattering after the ice opening of the Volga part.
The research was funded by the Russian Foundation for Basic Research (Projects RFBR № 18-45-520004 and № 20-05-00561)
How to cite: Danilicheva, O., Ermakov, S., and Kapustin, I.: Satellite remote sensing of the Oka-Volga confluence zone during the ice melting, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13440, https://doi.org/10.5194/egusphere-egu21-13440, 2021.
The seasonal snow cover in the Altas mountains of Morocco is an important resource, mostly because it provides melt-water runoff for irrigation during the crop growing season. However, the knowledge on physical properties of the snowpack (e.g., snow water equivalent (SWE) and snowmelt) is still very limited due to the scarcity or the lack of ground measurements in the elevated area. In this study we suggest that the recent progresses of meteorological reanalysis data (e.g., MERRA-2 and ERA-5) open new perspectives to overcome this issue. We fed a distributed snowpack evolution model (SnowModel) with downscaled ERA-5 and MERRA-2 reanalyses and evaluate their performance to simulate snow cover. The modeling covers the period 1981 to 2019 (37 water years). SnowModel simulations were assessed using observations of river discharge, snow height and snow cover area derived from MODIS.
For most of hydrological years, the results show a good performance for both MERRA-2 and ERA-5 with a slight superiority of ERA-5, to reproduce the snowpack state.
Key words: snow, snow water equivalent, reanalysis , MERRA-2, ERA-5
How to cite: Baba, W. M., Boudhar, A., Gascoin, S., Hanich, L., Marchane, A., and Chehbouni, A.: Monitoring snowpack evolution with meteorological reanalysis data in the Atlas Mountains, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-486, https://doi.org/10.5194/egusphere-egu21-486, 2021.
Snowmelt runoff predictions in alpine catchments are challenging because of the high spatial variability of the snow cover driven by various snow accumulation and ablation processes. In spring, the coexistence of bare and snow-covered ground engages a number of processes such as the enhanced lateral advection of heat over partial snow cover, the development of internal boundary layers, and atmospheric decoupling effects due to increasing stability at the snow cover. The interdependency of atmospheric conditions, topographic settings and snow coverage remains a challenge to accurately account for these processes in snow melt models.
In this experimental study, we used an Infrared Camera (VarioCam) pointing at thin synthetic projection screens with negligible heat capacity. Using the surface temperature of the screen as a proxy for the air temperature, we obtained a two-dimensional instantaneous measurement. Screens were installed across the transition between snow-free and snow-covered areas. With IR-measurements taken at 10Hz, we capture the dynamics of turbulent temperature fluctuations over the patchy snow cover at high spatial and temporal resolution. From this data we were able to obtain high-frequency, two-dimensional windfield estimations adjacent to the surface.
Preliminary results show the formation of a stable internal boundary layer (SIBL), which was temporally highly variable. Our data suggest that the SIBL height is very shallow and strongly sensitive to the mean near-surface wind speed. Only strong gusts were capable of penetrating through this SIBL leading to an enhanced energy input to the snow surface.
With these type of results from our experiments and further measurements this spring we aim to better understand small scale energy transfer processes over patch snow cover and it’s dependency on the atmospheric conditions, enabling to improve parameterizations of these processes in coarser-resolution snow melt models.
How to cite: Haugeneder, M., Jonas, T., Reynolds, D., Lehning, M., and Mott, R.: Experiments on wind-driven heat exchange processes over melting snow, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10182, https://doi.org/10.5194/egusphere-egu21-10182, 2021.
Recent research showed that, snow persistence, defined here as the fraction of time that snow is present on the ground, can play an important role in explaining spatial variability of average annual streamflow in moderately snowmelt-dominated regions. Here, we extend this work and explore the following questions: 1) whether globally available snow persistence data is useful for estimating a suite of streamflow signatures explaining the shape, flashiness and components of streamflow hydrograph, and 2) whether snow persistence could be useful for reconstructing streamflow patterns in ungauged watersheds, both spatially and temporally. We explore these questions across a spectrum of climatic dryness, snowiness, and geological settings. The motivations for the study are the need to understand how loss of snow may affect the components of streamflow in different climatic and geological settings, as well as the need for simple methods to predict components of streamflow in snow-dominated ungauged basins.
How to cite: Le, E., Ameli, A., Janssen, J., Hammond, J., and Krugger, K. E.: Can snow persistence explain the spatial-temporal variabilities in streamflow hydrograph flashiness across snow-dominated regions?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14084, https://doi.org/10.5194/egusphere-egu21-14084, 2021.
Streamflow in Mediterranean Mountain Areas is highly linked to the storage capacity of snowpacks and its seasonal dynamics, these becoming the only water source,during long periods, particularly during dryer seasons such as spring or summer. This fact makes that to have a better understanding of the significant drivers of change in the hydrological regimen in many mountain rivers requires a process-oriented approach to assess the different interacting effects and their propagation from atmospheric conditions to runoff and baseflow generation in these areas. Snow dynamics has a direct and major impact on the partitioning of river flow into baseflow, subsurface flow, and runoff. Moreover, the snowpack is extremely affected by the partitioning of precipitation and water outflows (i.e., rainfall vs snowfall and snowmelt vs evaposublimation) that largely modify the riverflow regime with a stronge nonlinearity of their interactions.
This work presents the characterization of streamflow events in mountain rivers of semiarid areas based on a process-oriented approach from the identification of the major sources/sinks of water in the snow-dominated headwaters of different basins in the Sierra Nevada area, in southern Spain, within an altitudinal range of 1000-3479 m a.s.l. For this, two catchments with available time series of streamflow are analyzed together with meteorological data and the simulation of water fluxes from the snowpack by the physically-based model SNOWMED, validated and operational in this area (www.uco.es/dfh/snowmed). First, the Cadiar River catchment (area of 0.19 km2 and mean elevation of 2034 m, 20-yr daily flow series), which is highly dominated by snow,was chosen as a representative catchment with direct dominant impacts on streamflow from snow-related water fluxes. Secondly, the contributing catchment area upstream the Órgive gauge station, in the Guadalfeo River(area of 1058 km2 and mean elevation of 1418.5 m, 28-yr daily flow series), which includes the previous case, was analized to assess the snow impacts propagation and lamination by other runoff generation conditions downstream the snow-dominated areas..
The resulting streamflow-event series i) shows the variability of the flooding and recession periods in this area on both the seasonal and annual scales due to the variability of the snow regime upstream, and ii) constitutes a key database to assess the impact of climate trends on these rivers and understand how future climate may condition the availability of water during the dry season in the downstream areas. The results not only expand this comprehension of how snowpack-streamflow interacts in semiarid regions, but also provide us with an assessment on predictable events within a short and seasonal forecasting local framework, that can be applied to other Mediterranean mountain rivers after local analyses.
How to cite: Torralbo, P., Pimentel, R., Aparicio, J., Herrero, J., Aguilar, C., and Polo, M. J.: Streamflow event classification in snowfed rivers in Mediterranean catchments: a process-oriented assessment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15739, https://doi.org/10.5194/egusphere-egu21-15739, 2021.
There is strong evidence that rising temperatures mostly lead to less snow accumulation and to an earlier melt onset. However, changes in the frequency and intensity of snowmelt events remain unclear. While higher temperatures should intuitively lead to faster snowmelt, some studies find that melt rates are slower because the melt onset occurs earlier in the year when less energy is available for melt. Modelling of these snow dynamics is challenged by a lack of continuous observations on water content of the snowpack, the highly sought after SWE. However, high quality observations of snow depth can be more available in both space and time, even at higher altitudes. Therefore, an increasing number of models try to estimate SWE from snow depth and other variables. Here we first investigate if these models accurately reproduce the snow accumulation and melt dynamics, and to what extent they can be used for hydrological studies. We then convert a long-term pan-European snow depth dataset to SWE by making use of these models and we assess model performance. Historical trends of snowmelt rates, melt onset, and frequency and intensity of melt events are shown for several seasonal snow locations across Europe. Trends across a variety of timescales are generally weak and spatially inhomogeneous, suggesting local conditions dominate over regional climate trends. However, it seems that under the current climate change conditions, the decrease of snowpack depth over most of Europe causes snowpacks to melt faster (i.e. in less days) than before.
How to cite: Fontrodona-Bach, A., Larsen, J., Woods, R., Schaefli, B., and Teuling, R.: Trends in snowmelt rates over Europe inferred from historical snow depth observations converted to SWE, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12760, https://doi.org/10.5194/egusphere-egu21-12760, 2021.
Climate warming has caused in a significant decrease in snowpack, increase in precipitation intensity and earlier melt onset. Based on earlier work published in 2014 on changes in streamflow resulting from a shift from snow towards rain, we analysed the sensitivity of seasonal streamflow to the average annual snow fraction in 253 catchments in CAMELS dataset, which have a record length more than 28 years and mean annual snow fraction larger than 15%. The result shows that places (or years) with higher mean annual snow fraction tend to have higher seasonal streamflow. We quantified seasonal sensitivity as a ratio of change in seasonal flow to change in annual snow fraction, for a given annual precipitation. There are 91%，57% and 51% catchments which showed a positive sensitivity value for Spring, Summer and Winter streamflow, respectively. According to the results of seasonal sensitivity analysis in climate space, we found the largest seasonal sensitivity normally happens at the same regional climate. Places with higher average annual snow fraction tend to have the largest sensitivity in summer, while for places with lower annual snow fraction this largest sensitivity occurs in spring.
In order to explore the mechanism(s) by which snow fraction change affects seasonal streamflow, we summarized four hypothesised mechanisms from the literature: water-energy synchrony (Mechanism I), inputs exceed threshold (Mechanism II), demand-storage competition (Mechanism III), and energy partitioning (Mechanism IV). Most of the catchments in the western part of the contiguous US can be explained by the mechanism I, II, III and IV, while for catchments in the central US can be explained by mechanism II, III and IV. Catchments in the eastern part (and some scattered in the northern part) can be explained by mechanism III. Other types of evidence are required to further distinguish between mechanisms in much of the USA. in further research we will use detailed data or hydrologic model to reproduce the hydrological process to find what are the hydrological processes responsible for precipitation phase partitioning changing with climate warming to influence catchment response. These findings would provide an evidence for how does snow affect hydrology, which may help to understand the effect of climate warming on future water resources in snow-dominated regions.
How to cite: Wang, L. and Woods, R.: The mechanism of snow shift affect seasonal streamflow in the contiguous US, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16479, https://doi.org/10.5194/egusphere-egu21-16479, 2021.
The river discharge of recently deglaciated headwater catchments shows strong variations at daily, seasonal and annual time-scales. These cycles are susceptible to changes in the near future due to rapid glacier retreat combined with warmer winters and earlier snowmelt. Low discharges, in particular, are not only driven by climatic conditions but are filtered by a number of geomorphological features, which are also rapidly evolving with glacier debuttressing, sediment transport and vegetation onset. Future water availability as well as discharge extremes of future deglaciated forefields can therefore only be explained by considering the co-evolution of both local climate and catchment geomorphology. Many hydrological predictions for high Alpine catchments fail to take into account such combined effects, which are relevant in better predicting winter low flow, as well as the recession behavior in autumn and potentially even peak flow events induced by later summer rainfall. These extremes are however of significant importance for ecosystem development on proglacial margins as well as for the management of hydropower, in particular of high elevation water. In this study, we propose a detailed analysis of the yearly groundwater and river stage fluctuations of the proglacial zone of the Otemma glacier, one of the largest glaciers of the Swiss Alps. By decomposing the water fluctuations and comparing them with climate forcing, we analyze the role of key landscape features in smoothing and delaying the different water inputs. Using additional datasets of daily water electrical conductivity and water isotopes a finer description of the hydrological functions of these features is achieved. A perceptual model is then proposed, showing how the different water signals driven by climate are modified by the local geomorphology. We finally propose a new metric which encapsulates the hydrological effects of these landscape features and should allow for a better assessment of the filtering effect at the catchment-scale of the different water input signals. Using such a simple metric and the perceptual model should help in building or assessing more realistic hydrological models where the complex hydro-geomorphological interactions are better represented.
How to cite: Müller, T., Schaefli, B., and Lane, S.: Assessing the effect of the geomorphological complexity of glacier forefields on the multi-temporal water dynamics will provide better future models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7182, https://doi.org/10.5194/egusphere-egu21-7182, 2021.
Distributed, physically based modelling of runoff routing in highly glacierized river basins is an extremely complicated task as glacier drainage systems functioning is very sophisticated, close to karst river systems but also dynamically developing within very short time periods. Accordingly, runoff routing of glacier melt water is most often based on the concept of linear storage. The number of reservoirs generally vary from 1 to 3. For example, one ‘fast’ reservoir for melt and rain in glacierized grid cells in GERM model, three parallel different linear reservoirs representing snow, firn and ice in GSM-SOCONT model.
Here we test applicability of different machine learning techniques (gradient boosting, random forest, LSTM) for runoff routing in a highly glacierized river basin. We use the data from Djankuat alpine research catchment located in the North Caucasus (Russia) for the period of 2007-2019. The dataset contains different parameters measured with an hourly or sub-daily time step: water runoff, conductivity, turbidity, temperature, 18O, D content at the main gauging station; measurements of precipitation amount, standard meteorological parameters and radiation fluxes. Results of snow and ice melting modelling in the Djankuat river basin over a regular net with an hourly time step using energy-balance distributed A-Melt model are also used as input data.
Total runoff from the Djankuat river basin (1) and meltwater runoff according to isotopic hydrograph separation (2) were chosen as target functions. Different sets of features to predict the target functions were generated from the original time series using different combinations of the input parameters as well as variable lag times. To score different machine learning techniques and sets of features to predict target function we use correlation coefficient, Nash-Sutcliff efficiency index (NSE), root mean square error (RMSE).
The study was supported by the Russian Foundation for Basic Research, grant No. 20-35-70024
How to cite: Rets, E., Fomichev, S., and Belozerov, E.: Testing different machine learning techniques for runoff routing in a highly glacierized Djankuat river basin (the North Caucasus, Russia)., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4567, https://doi.org/10.5194/egusphere-egu21-4567, 2021.
Extreme dry (and hot) summers, such as observed in Europe in the years 2003, 2015, 2018 and 2019, cause meteorological, hydrological and soil moisture droughts. These different types of droughts can have a range of negative impacts on, for instance, agricultural yield, water supply and hydropower production. The European Alps, also known as Europe’s water tower, potentially play an important role during such extreme summers by providing (extra) water from the melt of snow and ice. In this research study, we analyzed the streamflow conditions from a large sample of glacierized headwater catchments in the Swiss Alps during low flow years (1976, 1985, 1991, 2003, 2011, 2015, 2018 and 2019) observed downstream. Streamflow and its components (snow, ice and rain) were modelled with the HBV-light model for the period 1973-2020. These simulated streamflow components, together with observed total streamflow records, were compared between different catchments and different drought years. For each drought year, the winter conditions were examined, and the development of the drought situation over the summer was evaluated. To estimate the streamflow contribution of headwaters in the European Alps in such drought years in a situation without any glaciers, we also performed a model simulation where glaciers were assumed to have disappeared entirely. The results showed that glacier- and snowmelt contributed substantially to streamflow during these extreme years in the glacierized headwater catchments. In some cases, glacier ice provided up to three times as much melt than usually during summer. Catchments with a high glacier cover fraction usually showed a positive streamflow anomaly during these years. In 1991 and 2003, all catchments had an increased ice melt contribution, while in the other extreme years some catchments provided less ice melt contribution than average. Overall, this study shows that glaciers play an important role during low flow years and that a reduced glacier cover, or even completely retreated glaciers, will substantially reduce the buffer capacity of Europe’s water tower in the situation of meteorological drought.
How to cite: Van Tiel, M., Freudiger, D., Kohn, I., Weiler, M., Seibert, J., and Stahl, K.: Glacier melt contribution to streamflow during extremely dry summers, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5299, https://doi.org/10.5194/egusphere-egu21-5299, 2021.
Winter regimes affect significantly the long-term water and energy balance of mountainous areas in Central Europe. A recently developed numerical model is used to study near-surface fluxes of water and energy in the Liz catchment — a small headwater catchment of the Otava River, situated in the Southern Bohemia. The results of the numerical simulations are compared with high-resolution data recorded at the site of interest. The forest floor of the catchment is mostly covered by snow during winter. However, the snowpack is usually exposed to several snowmelt episodes over the season. The intensity, duration and frequency of these episodes is irregular and seems to be highly sensitive to changing climate. Increasing frequency of winter periods with limited or missing snow cover affects both water flow and heat transport in the catchment. Changes in the temporal distribution of snowmelt are reflected in changing runoff patterns.
How to cite: Vogel, T., Dohnal, M., Votrubova, J., Dusek, J., and Tesar, M.: Modeling winter season fluxes of water and energy in a temperate montane forest, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9464, https://doi.org/10.5194/egusphere-egu21-9464, 2021.
Forest snow cover dynamics affect hydrological regimes, ecosystem processes, and climate feedbacks, and thus need to be captured by model applications that operate across a wide range of spatial scales. At large scales and coarse model resolutions, high spatial variability of the processes shaping forest snow cover evolution creates a major modelling challenge. Variability of canopy-snow interactions is determined by heterogeneous canopy structure and can only be explicitly resolved with hyper-resolution models (<5m).
Here, we address this challenge with model upscaling experiments with the forest snow model FSM2, using hyper-resolution simulations as intermediary between experimental data and coarse-resolution simulations. When run at 2-m resolution, FSM2 is shown to capture the spatial variability of forest snow dynamics with a high level of detail: Its accurate performance is verified at the level of individual energy balance components based on extensive, spatially distributed sub-canopy measurements of micrometeorological and snow variables, obtained with mobile multi-sensor platforms. Results from hyper-resolution simulations over a 150,000 m2 domain are then compared to spatially lumped, coarse-resolution runs, where 50m x 50m grid cells are represented by one model run only. For the spatially lumped simulations, we evaluate alternative upscaling strategies, aiming to explore the representation of forest snow processes at model resolutions coarser than the spatial scales at which these processes vary and interact.
Different upscaling strategies exhibited large discrepancies in simulated (1) distribution of snow water equivalent at peak of winter, and (2) timing of snow disappearance. Our results indicate that detailed canopy structure metrics, as included in hyper-resolution runs, are necessary to capture the spatial variability of forest snow processes even at coarser resolutions. They further demonstrate the relevance of accounting for unresolved sub-grid variability in snowmelt calculations even at relatively small spatial aggregation scales. By identifying important model features, which allow coarse-resolution simulations to approximate spatially averaged results of corresponding hyper-resolution simulations, this work provides recommendations for modelling forest snow processes in medium- to large-scale applications.
How to cite: Mazzotti, G., Webster, C., Essery, R., Malle, J., and Jonas, T.: Improved representation of forest snow processes in coarse-resolution models: lessons learnt from upscaling hyper-resolution simulations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15226, https://doi.org/10.5194/egusphere-egu21-15226, 2021.
Changes in rain/snowfall apportionments are already being observed in mountain environments because of climate change. Increases in temperatures are leading to the displacement of rain-snow transition zones towards higher elevations, and are impacting snowpack storage, discharge timing and magnitude and low-flow patterns. To assess sensitivity of discharge to such changes, we investigated variability in surface water inputs (SWI = snowmelt + rainfall) in a semi-arid, 1.8 km2 headwater catchment in the rain-snow transition zone in Idaho (USA). We used a spatially distributed snowpack model (iSnobal/Automated Water Supply Model, AWSM) to investigate catchment SWI during four years (2005, 2010, 2011, 2014) with contrasting climatological conditions, and compared these results to measured streamflow and soil moisture. Results are evaluated using continuous measurements of snow depths at eleven weather stations, one lidar snow depth survey, and high-resolution satellite imagery (PSScene4Band) used to quantify the persistence of the snowpack across the catchment. We found that the model results agreed well with the spatial (r2: 0.86 in 2009 compared to lidar-derived snow depths) and temporal (median Nash-Sutcliffe Efficiency for normalized snow depths: 0.76 compared to weather station snow depth measurements) variations of the snowpack. The model results suggested that simulated snow-covered area was a good predictor for simulated SWE (range r2: 0.60 to 0.78 for all modeled years) during most of the snow-covered season, which indicates the usefulness of snow-covered area to quantify SWE at the rain-snow transition zone. We found that snow drifting and aspect-controlled processes caused large differences in snow depths across the watershed, with some snowdrifts producing SWI that was 3x greater than from nearby low elevation, south-facing slopes. In years with a lower snow fraction of total precipitation, the spatial distribution of SWI was much more homogeneous and stream discharge in spring time was lower, even though significant rainstorms occurred during that time. Indeed discharge response to SWI varied by season: in late spring/early summer, discharge was produced when basin-wide shallow subsurface storage exceeded ~150mm whereas in late fall/early winter, discharge was most responsive to precipitation after the shallow subsurface storage exceeded 250-300 mm. This indicates the importance of contributions from other, possibly deeper, flow paths, and is also consistent with the observation that years with a lower snow fraction did not have lower discharge nor earlier stream drying in summer. Nonetheless, the dry-out date at the catchment outlet was positively correlated to the last day at which there was snow present in the catchment as derived from the model results for the simulated years, and for four additional years (2016-2019) for years in which the high-resolution satellite imagery was available. This indicates the importance of snowdrifts for sustaining streamflow and the need for spatially-distributed modeling of the snowpack at the rain-snow transition zone, rather than using basin-average values. While extensive data may be required to understand the breadth of catchment responses in rain-snow transition zone, some critical parameters such as dry-out date can be determined from high-resolution satellite images.
How to cite: Kiewiet, L., Hale, K., Havens, S., Trujillo, E., Hedrick, A., Seyfried, M., Kampf, S., and Godsey, S. E.: How sensitive is discharge at the rain-snow transition zone to the spatial and temporal distribution of surface water inputs?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2920, https://doi.org/10.5194/egusphere-egu21-2920, 2021.
Runoff from seasonal snow- and glacier melt is critical for hydropower production and reservoir storage in Iceland as the energy system is strongly dependent on summer inflow. The isolation and high natural climate variability can pose a risk to the energy security of the power system as drought conditions and low-flow periods are usually not foreseen in great advance. Forecasting the timing, spatial distribution and magnitude of seasonal melt is a challenge and influences the operational control of energy infrastructure and long-term resource planning. As hydropower generation provides over 72% of the total average energy produced in Iceland, accurate forecasting of seasonal melt is essential for the operation of the national power system.
In this study, we present results from a spatially-distributed energy-balance model combined with gap-filled satellite-based time series of fractional snow cover and surface albedo from MODIS. The model reconstructs seasonal snow and glacier melt for the Icelandic highlands providing insight into the spatio-temporal distribution of snow water equivalent over the study period. The reconstruction method uses daily, satellite-derived estimates of fractional snow cover and albedo to scale the melt flux at every pixel. Modeled snow melt was integrated over time, reconstructing the maximum snowpack/glacier melt for each year. The model runs at a 500 m spatial resolution, with a daily timestep from 1 March to 30 September during 2000 to 2019 spanning the general seasonal snow and glacier melt period.
Energy-balance components were validated with in-situ observations from the Icelandic highlands and a network of stations operated annually at various Icelandic glaciers. Ground-based measurements of snow water equivalent (snow pits, surface mass balance) were used to validate the model performance as well as discharge observations. Simulations indicate a good performance compared with summer glacier mass balance records from Vatnajökull, Hofsjökull, Langjökull and Mýrdalsjökull. Sparse and discontinuous measurements of seasonal snow water equivalent from snow pillows or transects from snow courses were available from a few location, providing limited capabilities for direct validation for seasonal snow. Discharge observations in highland catchments indicate acceptable performance.
The results allow for quantification of the spatial distribution of snow water equivalent, relationships to elevation and other topographical parameters as well as between basins and years. Discrimination between seasonal snow and glacier melt on a catchment scale is valuable to analyze the annual variability in these two critical hydrological water sources and how they are related.
How to cite: Gunnarsson, A., Garðarsson, S. M., Jóhannesson, T., and Pálsson, F.: Spatial estimation of snow water equivalent by modeling of the melting of seasonal snow and glacier in Iceland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10721, https://doi.org/10.5194/egusphere-egu21-10721, 2021.
Snow in the mountains is the result of the interplay between meteorological conditions, e.g., precipitation, wind and solar radiation, and landscape features, e.g., vegetation and topography. For this reason, it is highly variable in time and space. It represents an important water storage for several sectors of the society including tourism, ecology and hydropower. The estimation of the amount of snow stored in winter and available in the form of snowmelt runoff can be strategic for their sustainability. In the hydropower sector, for example, the occurrence of higher snow and snowmelt runoff volumes at the end of the spring and in the early summer compared to the estimated one can substantially impact reservoir regulation with energy and economical losses. An accurate estimation of the snow volumes and their spatial and temporal distribution is thus essential for spring flood runoff prediction. Despite the increasing effort in the development of new acquisition techniques, the availability of extensive and representative snow and density measurements for snow water equivalent estimations is still limited. Hydrological models in combination with data assimilation of ground or remote sensing observations is a way to overcome these limitations. However, the impact of using different types of snow observations on snowmelt runoff predictions is, little understood. In this study we investigated the potential of assimilating in situ and remote sensing snow observations to improve snow water equivalent estimates and snowmelt runoff predictions. We modelled the seasonal snow water equivalent distribution in the Lake Överuman catchment, Northern Sweden, which is used for hydropower production. Simulations were performed using the semi-distributed hydrological model HYPE for the snow seasons 2017-2020. For this purpose, a snowfall distribution model based on wind-shelter factors was included to represent snow spatial distribution within model units. The units consist of 2.5x2.5 km2 grid cells, which were further divided into hydrological response units based on elevation, vegetation and aspect. The impact on the estimation of the total catchment mean snow water equivalent and snowmelt runoff volume were evaluated using for data assimilation, gpr-based snow water equivalent data acquired along survey lines in the catchment in the early spring of the four years, snow water equivalent data obtained by a machine learning algorithm and satellite-based fractional snow cover data. Results show that the wind-shelter based snow distribution model was able to represent a similar spatial distribution as the gpr survey lines, when assessed on the catchment level. Deviations in the model performance within and between specific gpr survey lines indicate issues with the spatial distribution of input precipitation, and/or need to include explicit representation of snow drift between model units. The explicit snow distribution model also improved runoff simulations, and the ability of the model to improve forecast through data assimilation.
How to cite: Clemenzi, I., Gustafsson, D., Zhang, J., Norell, B., Marchand, W., Pettersson, R., and Pohjola, V.: Exploring the use of gpr and satellite-based snow data for snowmelt runoff predictions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14972, https://doi.org/10.5194/egusphere-egu21-14972, 2021.
In many of the world’s mountainous regions, river discharge is largely influenced by the seasonal melt of snow. Therefore, accurate information on the amount of water stored as snow is essential for water management and flood forecasting. However, there are large uncertainties in model simulations of snow depth, partly due to uncertain precipitation estimates in mountain regions with complex topography. A study by Lievens et al. (2019) showed the potential of Sentinel-1 (S1) satellite observations to provide snow depth estimates at 1 km spatial and ~weekly temporal resolution in mountain regions. In this study, we assimilated these retrievals into the Noah Multiparameterization (Noah-MP) v3.6 land surface model for the western Alps using an ensemble Kalman filter. The land surface model was coupled to the Hydrological Modeling and Analysis Platform (HyMAP) routing scheme to also provide estimates of river discharge. With S1 data assimilation, the snow depth estimates improved, reducing the bias from 0.23 m to 0.05 m compared to in situ measurements. Preliminary results also show improved discharge simulations mainly in mountain catchments at high elevations that are less prone to regulations (e.g., by dams). This study demonstrates the capability of the S1 snow depth retrievals to improve not only snow depth estimates, but also the estimation of snow melt water contributions to river discharge.
How to cite: Brangers, I., Lievens, H., Getirana, A., Kumar, S., and De Lannoy, G.: Sentinel-1 snow depth assimilation improves river discharge simulations in the western Alps, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2554, https://doi.org/10.5194/egusphere-egu21-2554, 2021.
In France, flash floods are responsible for a significant proportion of damages caused by natural hazards, either human or material. Hence, advanced modeling tools are needed to perform effective predictions. However for mountainous catchments snow modeling components may be required to correctly simulate river discharge.
This contribution investigates the implementation and constrain of snow components in the spatially distributed SMASH* platform (Jay-Allemand et al. 2020). The goal is to upgrade model structure and spatially distributed calibration strategies for snow-influenced catchments, as well as to investigate parametric sensitivity and equifinality issues. First, the implementation of snow modules of varying complexity is addressed based on Cemaneige (Valery et al. 2010) in the spatially distributed framework. Next, tests are performed on a sample of 55 catchments in the French North Alps. Numerical experiments and global sensitivity analysis enable to determine pertinent combinations of flow components (including a slow flow one) and calibration parameters. Spatially uniform or distributed calibrations using a variational method (Jay-Allemand 2020) are performed and compared on the dataset, for different model structures and constrains. These tests show critical improvements in outlet discharge modeling by adding slow flow and snow modules, especially considering spatially varying parameters. Current and future works focus on testing and improving the constrains of snow modules and calibration strategy, as well as potential validation and multiobjective calibration with snow signatures gained from in situ or satellite data.
*SMASH: Spatially-distributed Modelling and ASsimilation for Hydrology, platform developped by INRAE-Hydris corp. for operational applications in the french flood forecast system VigicruesFlash
How to cite: Colleoni, F., Fouchier, C., Garambois, P.-A., Javelle, P., Jay-Allemand, M., and Organde, D.: Performance and sensitivity of a spatially distributed hydrological conceptual flood model with snow components., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15300, https://doi.org/10.5194/egusphere-egu21-15300, 2021.
Considering the snow effect on land and atmospheric processes, accurate representation of seasonal snow evolution including the distribution and melt volume, is highly imperative to strengthen water resources development trajectories in mountainous regions. However, along with the high sensitivity to climate change, the limitation of reliable snow-melt estimation in these regions is further exacerbated with data scarcity. This study thus attempts to develop relatively simpler degree-day snow-models driven by freely available gridded datasets for data scarce snow-fed regions. The methodology uses readily available MODIS imageries to calibrate the snow-melt models on snow-distribution instead of snow-amount. In addition, freely available cloud masks from geostationary satellites are also used to complement the snow-melt models. The major advantage of this approach is the possibility of regional calibration using freely available reasonably accurate climate data, without the need of direct snow depth measurements. These models offer relative simplicity and plausible alternatives to data intensive physically based model as well as in-situ measurements and have a wide scale applicability allowing immediate verification with point measurements.
Bavaria region in Germany is selected for this study. E-OBS (European Observations) gridded precipitation and temperature datasets (0.25 degrees) are considered here instead of the ground measured data to replicate “a data scarce scenario” as in most of the mountainous regions around the globe. The coarser meteorological inputs are downscaled applying the delta method using WorldClim monthly climate surfaces to 0.0833 degrees (~1km) grids. MODIS images are also resampled and upscaled to 1km resolution for uniformity. The qualitative pixel-to-pixel comparison suggest a very good agreement with MODIS data and the calibrated parameter sets depict plausible temporal stability.
The snow-melt volume will be further used in HBV hydrological model as standalone input to simulate the streamflow in one of the snow-fed catchments in Bavaria and to evaluate the performance of this approach in streamflow. The abstract will the updated as soon as the results are available.
How to cite: Gyawali, D. R. and Bárdossy, A.: Performance evaluation of gridded climate data in snow-melt models calibrated by spatial snow-cover observations from MODIS, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16376, https://doi.org/10.5194/egusphere-egu21-16376, 2021.
Mountains are often called as “water towers” because they substantially affect hydrology of downstream areas. However, snow storages are decreasing and snow melts earlier mainly due to air temperature increase. These changes largely affect seasonal runoff distribution, including summer low flows and thus influence the water availability. Therefore, it is important to investigate the future change in relation between snow and summer low flows, specifically to assess a wide range of hydrological responses to different climate predictions. Therefore, the main objectives of this study were 1) to simulate the future changes in snow storages for a large set of mountain catchments representing different elevations and to 2) analyse how the changes in snow storages will affect streamflow seasonality and low flows in the future reflecting a wide range of climate predictions. The predictions of the future climate from EURO-CORDEX experiment for 59 mountain catchments in Czechia were considered. These data were further used to drive a bucket-type catchment model, HBV-light, to simulate individual components of the rainfall-runoff process for the reference period and three future periods.
Future simulations showed a dramatic decrease in snow-related variables for all catchments at all elevations. For example, annual maximum SWE decreased by 30%-70% until the end of the 21st century compared to the current climate. Additionally, the snow will melt on average by 3-4 weeks earlier in the future. The results also showed the large variability between individual climate chains and indicated that the increase in air temperature causing the decrease in snowfall might be partly compensated by the increase in winter precipitation. Expected changes in snowpack will cause by a month earlier period with highest streamflow during melting season in addition to lower spring runoff volume due to lower snowmelt inputs. The future climate scenarios leading to overall dry conditions in summer are associated with both lowest summer precipitation and seasonal snowpack. The expected lower snow storages might therefore contribute to more extreme low flow periods. The results also showed considerably smaller changes for the RCP 2.6 scenario compared to the RCP 4.5 and RCP 8.5 both in terms snow storages and seasonal runoff. The results are therefore important for mitigation and adaptation strategy related to climate change impacts in mountain regions.
How to cite: Jenicek, M., Hnilica, J., Nedelcev, O., and Sipek, V.: Future changes in snow and its influence on seasonal runoff and low flows in Czechia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-80, https://doi.org/10.5194/egusphere-egu21-80, 2021.
Hydrological regimes of alpine catchments are expected to be strongly influenced by climate change due to their dependence on snow dynamics. While seasonal changes have been studied extensively, studies on changes in the timing and magnitude of annual extremes remain rare. This study investigates the effects of climate change on runoff patterns in six alpine catchments in Austria by using a topography-driven semi-distributed hydrological model and 14 climate projections for RCP 4.5 and RCP 8.5. The study catchments represent a range of alpine catchments, from pluvial-nival to nivo-glacial, as the study focuses on providing a comprehensive picture of future runoff changes on catchments at different altitudes. Simulations of 1981-2010 are compared to projections of 2071-2100 by examining changes in timing and magnitude of annual maximum and minimum flows as well as monthly discharges.
Our results indicate a substantial shift to earlier occurrences in annual maximum flows by 9 to 31 days on average and an extension of the potential flood season by 1 to 3 months for high elevation catchments. For lower elevation catchments, changes in timing of annual maximum flows are less pronounced. Magnitudes of annual maximum flows are likely to increase, with four catchments exhibiting larger increases under RCP 4.5 compared to RCP 8.5. The timing of minimum annual discharges shifts to earlier in the winter months for high elevation catchments, whereas for lower elevation catchments a shift from winter to autumn is observed. While all catchments show an increase in mean magnitude of minimum flows under RCP 4.5, this is not the case for two low elevation catchments under RCP 8.5.
Our results suggest a relationship between the altitude of catchments and changes in timing of annual maximum and minimum flows and magnitude of low flows, whereas no relationship between altitude and magnitude of annual maximum flows could be distinguished.
How to cite: Hanus, S., Zekollari, H., Schoups, G., Kaitna, R., and Hrachowitz, M.: Timing and magnitude of runoff in Austrian mountain catchments in a warming climate, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2714, https://doi.org/10.5194/egusphere-egu21-2714, 2021.
The Himalayan region has a large hydropower potential due to the natural topographic gradient and abundance of water resource from rainfall, snow and glacier melt. However, future water availability in the Himalayan streams is likely to be altered due to climatic conditions, which necessitates an assessment of hydropower investments, especially for small run-of-the-river projects. Here, we study the future glacio-hydrological changes in a small catchment located in the Upper Beas basin, in Western Himalaya in India, and their impacts on the operation of two small hydropower projects with contrasting hydrological requirements. The Water Evaluation and Planning (WEAP) model is used to integrate and analyse changes in cryosphere, hydrology and hydropower production in the middle and end of the 21st century using multiple climate models representing different types of future scenarios under RCP 4.5 and 8.5. In response to projected climate, the snow and glacier melt contribution to annual discharge declines from 34% in the baseline to 16.5% (RCP4.5) and 13.8% (RCP8.5) by the end of the century. The total streamflow shows broad uncertainty in magnitude and direction of change but shows a noticeable seasonal shift in the hydrological cycle. Of the two hydropower projects, the plant that utilizes high flows with low hydraulic head shows a behaviour similar to streamflow projections resulting in 13% (RCP45) and 19.7% (RCP85) increase in annual power generation by the end of the century arising from the increased hydropower potential of low flows and the rise in precipitation. The second power project that relies on lesser flows with high head maintains its designed power production consistently throughout the century in all the climate change scenarios. The differing sensitivity of the power projects to climate change is influenced by future changes in the runoff as well as by their design. Thus, this study provides insights into the climate-adaptive development and planning of small hydropower projects in the Himalayan region.
How to cite: Shirsat, T., Kulkarni, A., Momblanch, A., Singh Randhawa, S., and Holman, I.: Potential impacts of warming climate on future water resources and hydropower production in a glacierized catchment in Western Himalaya, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11917, https://doi.org/10.5194/egusphere-egu21-11917, 2021.
Glacierized mountain areas are witnessing strong changes in their streamflow generation processes, influencing their capacity to provide crucial water resources to downstream environments. Shifting precipitation patterns, a warming climate, changing snow dynamics and retreating glaciers are occurring simultaneously, driven by complex physical feedbacks. To predict and diagnose future hydrological behaviour in these glacierized catchments, a semi-distributed, physically-based hydrological model including both on and off-glacier process representation was applied to Peyto basin, a 21 km2 glacierized alpine catchment in the Canadian Rockies. The model was forced with bias-corrected outputs from a dynamically downscaled, 4-km resolution Weather and Research Forecasting (WRF) simulation, for the 2000-2015 and 2085-2100 period. The future WRF runs had boundary conditions perturbed using RCP8.5 late century climate. The simulations show by the end-of-century, the catchment shifts from a glacial to a nival regime. The increase in precipitation nearly compensates for the decreased ice melt associated with glacier retreat, with a decrease in annual streamflow of only 7%. Peak flow shifts from July to June and August streamflow is reduced by 68%. Changes in blowing snow transport and sublimation, avalanching, evaporation and subsurface water storage also contribute to the strong hydrological shift in the Peyto catchment. A sensitivity analysis to uncertainty in forcing meteorology reveals that streamflow volume is more sensitive to variations in precipitation whereas streamflow timing and variability are more sensitive to variations in temperature. The combination of the temperature and precipitation variations caused substantial changes both in the future snowpack and in the streamflow pattern. By including high-resolution atmospheric modelling and unprecedented both on and off-glacier process-representation in a physically-based hydrological model, the results provide a particularly comprehensive evaluation of the hydrological changes occurring in high-mountain environments in response to climate change.
How to cite: Aubry-Wake, C. and Pomeroy, J. W.: Exploring the future hydrology of a Canadian Rockies glacierized catchment and its sensitivity to meteorological forcings, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3565, https://doi.org/10.5194/egusphere-egu21-3565, 2021.
Snow is a crucial component of the hydrological cycle in the Western Himalaya, where the warming climate is already impacting precipitation and melt runoff patterns. In this study, we investigated the future evolution of snow cover and snowmelt in the Panjshir catchment (2,210 km2) of Afghanistan. Located in northern Afghanistan, the Panjshir catchment of the Kabul river basin is the westernmost catchment of the transboundary Indus river system. The climate in Panjshir catchment is characterised by warm-dry summer and cold-wet winter with a large spatial and temporal heterogeneity. Water from snowmelt is used in various sectors in downstream regions, and thus plays a critical role in securing the livelihood of millions of people.
In order to analyse the future evolution of snow-related processes under climate change, a few global climate model simulations from CMIP5 climate datasets for RCP4.5 and RCP8.5 which showed reasonable performance when compared with ERA5 data for the historic period (1981-2010) were selected. The selected models were then segregated into two groups: those projecting a cold-wet climate with a 13-28% and 2.5-4.9oC increase in precipitation and temperature respectively, and those projecting a warm-dry climate with a 26-40% decrease in precipitation and a 4.3-7.8oC increase in temperature by the end of the 21st century. These GCMs were downscaled to a higher resolution using empirical statistical downscaling. To simulate the snow processes, we used the distributed cryospheric-hydrological J2000 model.
Results of our analysis show that the J2000 model captures the snow cover dynamics well in the historical period (2003-2018) compared to the improved MODIS-derived snow cover with a coefficient of determination of 0.94. The model was then forced by climate projections from the selected GCMs to quantify the future changes in snow cover area, snow storage and snowmelt. A consistent decrease in decadal snow cover is projected in which the warm-dry models showed a higher decrease than cold-wet models. A 10-18 % reduction in annual snow cover is projected by the cold-wet models whereas a 22-36% reduction is expected from the warm-dry models. At the seasonal scale, across all models and scenarios, the snow cover in autumn and spring seasons are projected to reduce by as much as 25%, with an increase in winter and spring snowmelt and a decrease in summer snowmelt. The projected changes in the seasonal availability of snowmelt-driven water resources in the Panjshir region have direct implications for the water-dependent sectors in the downstream regions and highlight a need for a better understanding of current water usage and future adaptation practices in the Western Himalaya.
How to cite: Nepal, S., Khatiwada, K. R., Pradhananga, S., Kralisch, S., Samyn, D., Bromand, M. T., Dildar, M., Durrani, F., Rassouly, F., Azizi, F., Salehi, W., Malikzooi, R., Krause, P., Koirala, S., and Chevallier, P.: The fate of the snow in the western Himalaya region: A climate change perspective, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16342, https://doi.org/10.5194/egusphere-egu21-16342, 2021.
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