Displays

This study analyses regional differences in annual snow cover duration as quantified by the annual number of days with snow cover (D_sc) and investigates differences in sensitivity of D_sc to climatic variability across the Greater Alpine Region over the 2000-2018 period. MODIS snow cover data were used to estimate D_sc based on the Regional Snowline Elevation (RSLE) method, a spatial filter technique for large-scale cloud cover reduction.

D_sc over the study period closely follows the relief, with a mean D_sc of ~10–60 days at elevations of 500 m that increase to about 100–150 days at 1500m. South of the main alpine ridge, D_sc is, at the same elevation, consistently lower than north of it with differences of ΔD_sc  ~25–50 days. Similarly, the eastern part of the study region experiences longer snow cover duration than the western part. This difference is particularly pronounced at elevations below 1500m where ΔD_sc ~25 days. Throughout the study period, a general upward shift of the RSLE was observed for most parts of the Greater Alpine Region. This upward shift, characterized by later onset of snow accumulation (∆D_start ~14–30 d) and earlier melt-out at the end of the snow season (∆D_end ~10–20 d), translates into reductions of the annual number of snow-covered days by up to ΔD_sc = -46 days over the study period. The data suggest that, in particular, low-elevation  (< 600m.a.s.l.) regions in the north-eastern part of the Greater Alpine Region, as well as elevations between 1400 and 2000 m in the north-western part of the study region experienced the most pronounced reductions of D_sc., whereas ΔD_sc remained very limited south of the main Alpine ridge. The spatially integrated MODIS-derived estimates of D_sc correspond well with D_sc estimates derived from longer-term point-scale observations at >500 ground station observations across the region. In the majority of regions, the temporal evolution of D_sc over the 2000-2018 study period also reflects the longer-term D_sc trends as estimated from these point-scale observations (1970-2014). This provides supporting evidence that the widespread decline of D_sc across the Greater Alpine Region as estimated based on MODIS data is largely not caused by isolated short-term climatic variability but coincides with multi-decadal fluctuations. A comparison of the sensitivities of D_sc to climatic variability indicates that neither mean winter temperatures T_w nor annual solid precipitation totals P_s, are consistent first order controls on D_sc across  elevations and regions. Rather, the data highlight the importance of the interaction between the two variables: depending on the respective sensitivities of D_sc to changes in either variable, T_w or P_s, respectively, the interplay between them can reinforce or largely off-set potential effects on D_sc in different regions in the Greater Alpine Region. The regional differences in ΔD_sc with a less pronounced decline south of the main Alpine ridge are largely a consequence of this interplay: while T_w evolved similarly North and South of the Alpine ridge, many southern regions, unlike the northern regions, experienced an increase in P_s that offsets the effects of positive temperature trends.

How to cite: Hrachowitz, M., Fugger, S., and Schulz, K.: Regional pattern of annual snow cover duration in the Greater Alpine Region (2000 – 2018), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4863, https://doi.org/10.5194/egusphere-egu2020-4863, 2020.

Previous investigations have reported that the performance of the traditional snow cover mapping algorithms based on the Normalized Difference Snow Index (NDSI), derived from a multispectral optical airborne/spaceborne sensor, significantly degrades on transitioning from non-forested to forested landscapes. The thick canopy cover in forested landscapes obscures both the upwelling and the downwelling radiance and hence impairs the detection of the underlying snow cover on the forest floor via NDSI thresholding due to the shift in the apparent threshold. Although NDSI has been reported to be an ineffective index for extracting snow information from forested areas, this investigation presents contrary views. A novel perspective is introduced on exploiting the temporal NDSI-NDVI statistics for extracting snow information under the canopy, as has been also reported important in the past literature when considered together, to reconstruct the actual snow cover scenario over the mixed landscape, comprising both forested areas of varying densities and open vegetation-free patches. The Black Forest (Schwarzwald) is a large forested mountainous terrain at about 200-1500 m above sea level situated in the Federal State of Baden-Württemberg in the southwest corner of Germany. The region is bounded by the Rhine river valley to the west and south stretching in an oblong manner with a length of about 160 km and breadth of up to 50 km. The Black Forest consists of approximately 80% coniferous (spruce, fir, and pine) and 20% deciduous (beech, birch, and oak), with about 70% of the region under forest cover. Seasonal snowmelt water and natural springs originating in this region sources major European rivers like the Danube and the tributaries of the Rhein like the Murg and the Neckar. Therefore, it is essential to monitor snow accumulation under the canopy to accurately forecast and investigate the influence of the snowmelt runoff in such major catchments. One of the test sites is situated in the Murg catchment at Hundseck near the town of Baden-Baden at the north-western border of the Black Forest mountain range. This investigation employs Sentinel-2 multispectral optical data from the previous season in order to test the proposed approach. The proposed method is tested with the European Space Agency's open-access Sentinel-2 multispectral optical satellite data, over the Hundseck test site in the Black Forest. The snow extent map is validated with the Normalized Difference Forest Snow Index (NDFSI), which was proposed as an alternative for NDSI to map the canopy underlying snow in evergreen forests. The proposed algorithm is simple and computationally frugal. Temporal NDSI-NDVI statistics in conjunction with mathematical morphological operation has resulted in significant improvement in the detection of under canopy snow cover. It is noteworthy that the performance of the algorithm inherently shows a dependence on the forest LAI. An improvement of more than 50% is achieved in the under-canopy snow cover mapping. A priori knowledge regarding the LAI of forests will enable adaptive tuning of the algorithm locally for better performance under dense canopy conditions.

How to cite: Muhuri, A.: A Novel Perspective on Mapping Snow Cover Under Forest Canopy With Sentinel-2 Multispectral Optical Satellite Sensor Over Black Forest Germany, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5617, https://doi.org/10.5194/egusphere-egu2020-5617, 2020.

Publicly available webcam images offer an enormous potential to study the variability of snow cover on a high spatio-temporal scale. Such cameras allow detailed analyses of snow cover on steep slopes due to their oblique view on the mountains and can provide snow cover information even under cloudy weather conditions. Our webcam-based snow cover monitoring network comprises several hundreds of webcams and enables to gather snow cover information over a large area with a minimum amount of manual user input. This information can serve as a reference for improved validation of satellite-based approaches, as well as complement satellite-based snow cover retrieval, in particular under cloudy weather conditions. Here, we present a framework to estimate the regional snow line elevation in the Swiss Alps. The snow line elevation is an important indicator of snow cover in mountainous regions and can be used, for example, as an input for hydrological modeling or to study the seasonality of river discharge. We compare and combine snow line retrieval from Sentinel-2 snow cover maps and webcam-based snow cover information to analyze regional differences in the spring snowmelt period. Since cloud cover is an important factor that affects the quality of satellite-based snow cover products, the combination with snow information from webcams can improve the accuracy and can fill temporal gaps, especially during recurrent cloud cover. Furthermore, we present a method to detect cloud cover in webcam images and discuss limitations of webcam-based snow cover monitoring.

How to cite: Portenier, C., Hasler, M., Gascoin, S., and Wunderle, S.: Estimating snow line elevation using publicly available webcam images and Sentinel-2 snow cover maps, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14513, https://doi.org/10.5194/egusphere-egu2020-14513, 2020.

The current study started by examining the three most established snow indices, namely the NDSI (normalized difference snow index), S3, and NDSII-1 (normalized difference snow and ice index), based on their capabilities to differentiate snow pixels from cloud, debris, vegetation, and water pixels. Furthermore, considering the limitations of these indices, a new spectral index called the snow water index (SWI) is proposed. SWI uses spectral characteristics of the visible, SWIR (shortwave infrared), and NIR (near infrared) bands to achieve significant contrast between snow/ice pixels and other pixels including water bodies. A three-step accuracy assessment technique established the dominance of SWI over NDSI, S3, and NDSII-1. In the first step, image thresholding using standard value (>0), individual index theory (fixed threshold), histogram, and GCPs (ground control points) derived threshold were used to assess the performance of the selected indices. In the second step, comparisons of the spectral separation of features in the individual band were made from the field spectral observations collected using a spectroradiometer. In the third step, GCPs collected using field surveys were used to derive the user’s accuracy, producer’s accuracy, overall accuracy, and kappa coefficient for each index. The SWI threshold varied between 0.21 to 0.25 in all of the selected observations from both ablation and accumulation time. Spectral separability plots justify the SWI’s capability of extraction and removal of the most critical water pixels in integration with other impure classes from snow/ice pixels. GCP enabled accuracy assessment resulted in a maximum overall accuracy (0.93) and kappa statistics (0.947) value for the SWI. Thus, the results of the accuracy assessment justified the supremacy of the SWI over other indices. The study revealed that SWI demonstrates a considerably higher correlation with actual snow/ice cover and is prominent for spatio-temporal snow cover studies globally.

How to cite: Dixit, A. and Goswami, A.: Development and Evaluation of a New “Snow Water Index (SWI)” for Accurate Snow Cover Delineation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10724, https://doi.org/10.5194/egusphere-egu2020-10724, 2020.

The synergistic use of data from different satellites of the Sentinel series offers excellent capabilities for generating high quality products on key parameters of the global climate system and environment. A main parameter for climate monitoring, hydrology and water management is the seasonal snow cover. In the frame of the ESA project SEOM S1-4-SCI Snow, led by ENVEO, we developed, implemented and tested a novel approach for mapping the total extent and melting areas of the seasonal snow cover by synergistically exploiting Sentinel-1 SAR and Sentinel-3 SLSTR data and apply these tools for snow monitoring over the Pan-European domain.

Whereas data of medium resolution optical sensors are used for mapping the total snow extent, data of the Copernicus Sentinel-1 mission in Interferometric Wide Swath (IW) mode at co- and cross-polarizations are used for mapping the extent of snowmelt areas applying change detection algorithms. In order to select an optimum procedure for retrieval of snowmelt area, we conducted round-robin experiments for various algorithms over different snow environments, including high mountain areas in the Alps and in Scandinavia, as well as lowland areas in Central Europe covered by grassland, agricultural plots, and forests. In mountain areas the tests show good agreement between snow extent products during the melting period derived from SAR data and from Sentinel-2 and Landsat-8 data. In lowlands ambiguities may arise from temporal changes in backscatter related to soil moisture and agricultural activities. Dense forest cover is a major obstacle for snow detection by SAR because the surface is masked by the canopy layer which is a major scattering source at C-band. Therefore, areas with dense forest cover are masked out. Based on this results we selected for the retrieval of snowmelt area a change-detection algorithm using dual-polarized backscatter data of S1 IW acquisitions. The algorithm applies multi-channel speckle filtering and data fusion procedures for exploiting VV- and VH-polarized multi-temporal ratio images. The binary SAR snowmelt extent product at 100 m grid size is combined with the Sentinel-3 SLSTR and MODIS snow products in order to obtain combined maps of total snow area and melting snow. The optical satellite images provide information on snow extent irrespective of melting state but are impaired by cloud cover. For generating a fractional snow extent product from MODIS and Sentinel-3 SLSTR data we apply multi-spectral algorithms for cloud screening, the discrimination of snow free and snow covered regions and the retrieval of fractional snow extent. In order to fill gaps in the optical snow extent time sequence due to cloud cover we apply a data assimilation procedure using a snow pack model driven by numerical meteorological data of ECMWF, simulating daily changes in the snow extent. We present the results of the Pan-European snow cover and melt extent product derived from optical and SAR data. The performance of this product is evaluated in different environments using independent validation data sets including in-situ snow and meteorological measurements, snow products from Sentinel-2 and Landsat images, as well as high resolution numerical meteorological data.

How to cite: Nagler, T., Keuris, L., Rott, H., Schwaizer, G., Small, D., Malnes, E., Luojus, K., Metsaemaeki, S., and Pinnock, S.: Towards a Pan-European snow cover and melt extent product from Sentinel-1 SAR and Sentinel-3 SLSTR Data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11904, https://doi.org/10.5194/egusphere-egu2020-11904, 2020.

Mountain areas have raised a lot of attention in the past years, as they are considered sentinel of climate changes. Quantification of snow cover changes and related phenology in global mountain areas can have multiple implications on water resources, ecosystem services, tourism, and energy production [1]. Up to now, several studies have investigated snow cover changes at continental scale and there are several indications of snow cover decline over the Northern Hemisphere [2, 3], despite no study has analyzed snow behavior specifically in mountain areas at global level. In this context, this study investigates the changes in the main snow cover parameters (snow cover area, snow cover duration, snow onset and snow melt) over global mountain areas from 2000 to 2018.

To proper monitor the evolution of snow changes at global mountain areas and interlinkages with meteorological drivers (air temperature, snowfall), automatic procedures were developed based on MODIS imagery in global mountain areas over the period 2000-2018 by exploiting Google Earth Engine where the whole time series of MODIS is available at a global scale. MODIS snow cover products have the highest resolution available, 500 m, and with daily global acquisitions. From MODIS snow cover areas (MOD10v6), snow phenology parameters were derived, namely snow cover duration, snow onset and snow melt. Together with snow cover and phenology changes, snow albedo changes were assessed, especially in relation to snow onset and melt variability.

The results of the trend analysis carried with Man-Kendall statistics indicate that around 78% of the global mountain areas present a snow decline. In average, snow cover duration has decreased up to 43 days, and a snow cover area up to 13%. Significant snow cover duration changes can be linked in 58% of the areas to both delayed snow onset, and advanced melt. Few areas show positive changes, mainly during winter time and located in the Northern Hemisphere.

Considering the relationship with meteorological parameters and albedo, air temperature is detected as the main driver for snow onset and melt, while a mixed effect of air temperature and precipitation dominates the winter season. Moreover, snowmelt timing is strongly related to significant changes in snow albedo during March and April in the Northern Hemisphere. Regarding snow onset changes, it has been detected a latitude amplification for the dependency con air temperature, indicating that the sensitivity of snow onset on temperature changes is amplified going from higher to lower latitude.

References

[1] Barnett, T.P., Adam J.C., Lettenmaier D.P. Potential impact of a warming climate on water availability in snow-dominated regions, Nature 438 (2005).

[2] Bormann, K. J., Brown, R. D., Derksen, C., Painter, T. H. Estimating snow-cover trends from space, Nat. Clim. Change 8, 924–928 (2018).

[3] Ye. K. H., & Wu, R. G. Autumn snow cover variability over northern Eurasia and roles of atmospheric circulation. Adv. Atmos. Sci. 34(7), 847–858 (2017) doi: 10.1007/s00376-017-6287-z.

How to cite: Notarnicola, C.: How is snow cover in global mountain area changing? Detection of snow cover and snow phenology changes by using MODIS imagery over 2000 - 2018, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20482, https://doi.org/10.5194/egusphere-egu2020-20482, 2020.

One of the factors that can most influence climate changes on a global scale is the albedo decrease, associated with a temperature increase and a snow cover decrease, mainly in the polar areas, where the remote sensing data are essential because there is much difficulty access to obtain measurements in situ. Therefore, evaluations of satellite measurements are essential.

The daily MOD10A1 snow product provides daily measurements of albedo. Version 6 is currently available. In Antarctica, and more specifically on Livingston Island (South Shetland Archipelago), where one of the Spanish Antarctic bases is located, the daily snow albedo product of MODIS (MOD10A1) has been evaluated using version 5 data (Calleja et al. 2019). However, several authors have recommended updating the analyses based on version 6 data (Box et al. 2012, Casey et al. 2017), as they are more accurate.

In this work, we have analyzed the albedo behavior using MOD10A1 version 6 data between 2006 and 2015 and we have seen an increasing trend of albedo. Version 5 showed an increase of 0.07 per decade. However, version 6 data show less variability (0.04 per decade), and its results are closer to those obtained in the measurements in situ (0.03 per decade). In addition, the results obtained allow us to affirm that the MOD10A1 daily albedo product (v. 6) can be used to determine the albedo in the study area.

References:

Box, J. E., Fettweis, X., Stroeve, J. C., Tedesco, M., Hall, D. K., & Steffen, K. (2012). Greenland ice sheet albedo feedback: thermodynamics and atmospheric drivers. The Cryosphere, 6(4), 821-839.

Calleja, J. F., Corbea-Pérez, A., Fernández, S., Recondo, C., Peón, J., & de Pablo, M. Á. (2019). Snow Albedo Seasonality and Trend from MODIS Sensor and Ground Data at Johnsons Glacier, Livingston Island, Maritime Antarctica. Sensors, 19(16), 3569.

Casey, K. A., Polashenski, C. M., Chen, J., & Tedesco, M. (2017). Impact of MODIS sensor calibration updates on Greenland Ice Sheet surface reflectance and albedo trends. The Cryosphere, 11(4), 1781-1795.

How to cite: Corbea-Pérez, A., Fernández-Calleja, J., Recondo, C., and Fernández, S.: Evaluation the MOD10A1 daily snow albedo product (v. 6) on Livingston Island, Antarctica, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7530, https://doi.org/10.5194/egusphere-egu2020-7530, 2020.

Rapid glacier recession related to recent climate change in Alpine regions is exposing large areas of previously ice-covered till and bedrock. These newly created proglacial areas are composed of poorly sorted sediments and debris of mixed subglacial (till), englacial and supraglacial origin. They are subject to rapid geomorphological and ecological modifications. They also constitute potential new groundwater reservoirs for rain, snowmelt and ice melt. The hydrology of such glaciated catchments is therefore evolving, but the connectivity between glacier meltwater and other paraglacial structures such as talus slopes, outwash plains or small lakes to these areas remains unclear. We propose a conceptual model of water connectivity and storage based on the Otemma glacier, one of the largest Swiss glaciers, which summarizes the key geomorphological structures and their hydrological functions. In particular, we combine multiple field data such as water table fluctuations, river discharge, isotopic analysis and geophysical studies from the proglacial area of the Otemma glacier to show the growing importance of the outwash plain for storing water and maintaining baseflow in these headwater catchments. We show that the accumulation of reworked subglacial till and exported sediments from the glacier create new reservoirs for the storage and release of water which may become larger in regions where the subglacial bedrock has a low slope and where ice is rapidly retreating. These fluvioglacial aquifers are mainly recharged by ice-melt at present but could store more snowmelt and precipitation in the future. The processes influencing sediment export and aggradation combined with future snow and ice melt dynamics are therefore key to understanding the future hydrological functioning of these catchments. River and groundwater dynamics will eventually shape the biodiversity and vegetation succession of these areas that are hotspots for many endemic species and where soil stabilization and development will create a clear feedback on the future sediment and water budget of high Alpine environments.

How to cite: Müller, T., Schaefli, B., and Lane, S. N.: What happens when the ice is gone? A hydrological journey into the glacier forefield subsurface, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10165, https://doi.org/10.5194/egusphere-egu2020-10165, 2020.

The Indian Summer Monsoon (ISM) shapes the melt and accumulation patterns of glaciers in large parts of High Mountain Asia (HMA) in complex ways due to the interaction of persistent cloud-cover, large temperature amplitudes, high atmospheric water content and high precipitation rates. While the ISM dominates in the southern and eastern regions, it progressively loses influence westward towards the Karakoram, where the influence of westerlies is predominant. Previous applications of energy- and mass-balance models for glaciers in HMA have been limited to single study sites (in Khumbu, Langtang and Parlung) and a few attempted to link model results to large-scale weather patterns. While these studies have helped to understand the energy- and mass-balance of glaciers in HMA under specific local climates, a regional perspective is still missing. In this study, we use a full energy- and mass-balance model together  with eight on-glacier AWS datasets around HMA to investigate how ISM conditions influence glacier-surface energy and mass balance. In particular, we look at how debris-covered and debris-free glaciers respond differently to the ISM, validating our results against independent in-situ measurements. This work is fundamental to the development of parameterizations of glacier melt for long-term hydrological studies and to the understanding of the present and future HMA cryosphere and water budget evolution.

How to cite: Fugger, S., Miles, E., McCarthy, M., Fyffe, C., Kneib, M., Fatichi, S., Yang, W., and Pellicciotti, F.: Understanding monsoon controls on the energy- and mass-balance of glaciers in High Mountain Asia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9690, https://doi.org/10.5194/egusphere-egu2020-9690, 2020.

Switzerland is often referred to as Europe’s Water Tower. During the melt season, water stored in the Alps as snow and ice feeds large European rivers such as the Rivers Rhine and Rhone. Under climate change conditions, snow and glacier melt contributions to discharge are expected to change dramatically. These changes might be very important during dry periods, when snow and glacier melt are the main sources of water. Assessing water availability in the future is essential for sustainable management of our water resources. Understanding how much melt water contributes to the discharge at different locations along the rivers is therefore necessary.

In this study, we used a customized version of the bucket-type hydrological model HBV-light, specially developed to assess the daily contribution of snow and glacier melt to discharge in a transient way. We assess the discharge components for 195 glacierized headwater catchments covering the entire Swiss Alps from 1973 to 2099. Hydrological processes in the Alps are spatially and temporally highly variable. Snow and glacier melt modelling are also challenged by data scarcity. Heterogeneously distributed meteorological measurement stations in high elevated and remote regions further complicate the representativity of the data. We show the advantages and challenges of using datasets from various sources as meteorological input data and for model calibration and validation of discharge, snow and glacier cover. In a second step, we applied a regionalization approach to defining model parameters for the ungauged catchments. A multi-criteria calibration was used to ensure that all hydrological processes are correctly represented within the model.

For future climate projections, we used the newly generated precipitation and temperature gridded products from MeteoSwiss for 45 climate models and for three emissions scenarios (RCP 2.6, RCP 4.5 and RCP 8.5). The results show that glacier peak water is already reached by most of the catchments and will be reached by all catchments during the first half of the Century for all three emissions scenarios. Under RCP 8.5, total glacier contribution summarized over all headwater catchments is 8% of total discharge under current climate and less than 2% at the end of the century. Snow melt will still be an important contribution to discharge during the first half of the century. In the second half of the century, however, snow melt contribution will significantly decrease from 34% (current climate) to 25% +/- 10% (2070-2099) of the total discharge. In contrary, rainfall contribution will increase from 58% to 72% +/- 15% of total discharge. Overall, the total annual discharge is expected to decrease slightly. The intensity of these changes in discharge contributions depends on the catchment elevation and large regional differences can be observed. The effects are much smaller under emission scenario RCP 2.6.

How to cite: Freudiger, D., Kohn, I., Stahl, K., Weiler, M., and Seibert, J.: What is the contribution of snow and glacier to discharge in Swiss alpine headwater catchments under climate change?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18192, https://doi.org/10.5194/egusphere-egu2020-18192, 2020.

Extreme warm and dry summer conditions often cause low-flow situations due to the precipitation deficit and increased evapotranspiration. In glacierized catchments, however, the same extreme weather conditions can lead to a very different hydrological response, namely increased streamflow because of increased glacier melt. In larger combined rainfed and glacierfed catchments, meltwater from glaciers can, thus, buffer the adverse hydrological effects of warm and dry spells. The question is how much glacier cover in a catchment is needed to counterbalance the hydrological processes that cause a decline in streamflow. Moreover, due to climate change, glaciers have been retreating, which affects the hydrological response and the buffering effect of glaciers. In this study, we analysed long-term streamflow records of around 60 glacierized catchments in Switzerland, Austria, Norway and Canada with varying glacier coverage. In addition, a few catchments were modelled to analyse some extreme events in more detail and perform sensitivity tests. Warm and dry spells were selected based on weather data for the catchments and the corresponding hydrological responses were investigated. The events were analysed taking into account catchment characteristics, such as glacier cover and elevation information, and antecedent conditions, such as snowfall in winter and precipitation amounts in the period before the warm and dry event. Results show that during extreme warm and dry spells small glacier cover fractions (< 10%) can already alleviate the otherwise emerging streamflow drought. Moreover, we see a clustering of warm and dry periods in recent years and a decreasing trend of summer streamflow in many catchments. Antecedent conditions appear to shape the individual summer streamflow responses. Overall, understanding the hydrological responses to warm and dry spells is essential due to projected increases in weather extremes. Especially in glacierized catchments, our results imply that with changing glacier cover due to global warming, changes in the buffering capacity of glacierized catchments during warm and dry periods can be expected.

How to cite: Van Tiel, M., Van Loon, A. F., Seibert, J., and Stahl, K.: Hydrological response to warm and dry extremes in glacierized catchments: when and how are glaciers compensating?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-859, https://doi.org/10.5194/egusphere-egu2020-859, 2020.

Warming of the polar ice sheets causes changes in the hydrological regime of surface layers of firn and ice. Surface meltwater may undergo perennial storage of liquid water above the firn-ice transition, which could slow sea level rise or cause sudden release events, when storage capacity is reached. Firn aquifers have been commonly observed within the lower percolation zone of the southeastern Greenland ice sheet during the past decade, and more recently, across some Antarctic ice shelves. Knowledge of the geographic extent and fractional liquid water content (and storage) of such aquifers will enable a better understanding of their effects on the sub- and en-glacial hydrologic system and is crucial for accurate predictions of the contribution of meltwater discharge to global sea level rise.

Quantitative geophysical analysis from surface observations can be used to infer hydrological properties of the firn and ice without time intensive direct drilling, providing an efficient spatial distribution of properties along with an estimate of their uncertainty. Furthermore, by combining multiple types of geophysical observations, joint inversions allow ambiguities of one methodology to be mitigated by resolution in the other.

Here, we demonstrate that this joint approach is a powerful complement to the conventional geophysical analysis of firn aquifers, by combining seismic, ground penetrating radar and borehole data to characterise aquifer properties, using the ‘MuLTI’ algorithm. In particular, we incorporate seismic shear wave velocities (Vs), derived from surface (Rayleigh) waves offering a promising means of distinguishing zones containing liquid water, into independent compressional wave velocity, density, and radar soundings of the water table. We find Vs decreases from 1600 m/s in the unsaturated firn above the water table at around 15 m depth, to 800 m/s through saturated ‘clean’ firn aquifer at around 25 m depth. However, at lower elevations, Vs increases to 1250 m/s through thicker, older firn aquifer where there are many ice lenses, which are interpreted to correspond with episodes of refreezing aquifer water as the system has evolved through time. With access to multiple seismic wave velocities (compressional and shear) through the aquifer, a more accurate estimate of liquid water content can be derived. Thus, the application of the MuLTI algorithm to this pressing new problem can deliver an accurate assessment of firn aquifer properties, and provide clear uncertainty limits which will be valuable for predictive modelling.

How to cite: Killingbeck, S., Schmerr, N., Montgomery, L., Booth, A., Livermore, P., Guandique, J., Miller, O., Burdick, S., Forster, R., Koenig, L., Legchenko, A., Ligtenberg, S., Miège, C., Solomon, K., and West, L.: Deriving water content from multiple geophysical properties of a firn aquifer in Southeast Greenland, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8820, https://doi.org/10.5194/egusphere-egu2020-8820, 2020.

Seasonally frozen ground (SFG) occurs on ~25% of the Northern Hemisphere’s land surface, and the influence of SFG on water, energy, and solute fluxes is important in cold climate regions.  The hydrological role of permafrost is now being actively researched, but the influence of SFG has been receiving less attention. Intuitively, water movement in frozen ground is blocked by ice forming in soil pores that were open to water flow prior to freezing. However, it has been shown that the hydrological influence of SFG is insignificant in some cases, with soil remaining permeable to water even when frozen. There is a clear knowledge gap concerning (1) how intensively and (2) under what physiographical and climate conditions SFG influences hydrological fluxes. We conducted a systematic literature review examining the hydrological importance of SFG we found reported in 143 publications. We found a clear hydrological influence of frozen ground in small-scale laboratory measurements, but a more ambiguous effect when the spatial scale under study increased to hillslopes, catchments, or watersheds. We also found that SFG may be hydrologically less important in forested areas or in regions with deep snow cover. Our systematic review suggests that hydrological influence of SFG may become more important in a future warmer climate with less snow and intensified land use in high-latitude areas.

How to cite: Ala-aho, P., Autio, A., Bhattacharjee, J., Isokangas, E., Kujala, K., Marttila, H., Menberu, M., Meriö, L.-J., Postila, H., Rauhala, A., Ronkanen, A.-K., Rossi, P. M., Saari, M., Torabi Haghighi, A., and Klöve, B.: Influence of seasonally frozen ground on hydrological partitioning – a global systematic review, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8441, https://doi.org/10.5194/egusphere-egu2020-8441, 2020.

Climate warming induces shrub expansion on Arctic herb tundra, with effects on snow trapping and hence snow depth. We have used UAV-borne LiDAR and Terrestrial Laser Scanning (TLS) to investigate the impact of shrub height on snow depth at two close sites near Umiujaq, eastern Canadian low Arctic, where dwarf birch and willow shrubs are expanding on lichen tundra. The first site features lichen and high shrubs (50-100 cm), a moderate relief, and a snowpack averaging 95 cm in spring. The second site consists of lichen and low shrubs (20-60 cm), more pronounced topography, and a deeper snowpack (101 cm). Digital Terrain and Surface Models were acquired in early fall to obtain topography and vegetation height. A Digital Surface Model obtained in spring produced snow depth maps at peak depth. TLS over a 400 m² area produced time series of snow depth throughout the winter. TLS data show preferential snow accumulation in shrubs, but also preferential melting in shrubs during fall warm spells and in spring. UAV data at the first site show a strong correlation between vegetation height and snow depth, even after snow depth has exceeded vegetation height. This correlation is not observed at the second site, probably because snow depth there is much greater than vegetation height. These data show the need to reconsider some paradigms on snow-vegetation interactions, for example that vegetation does not affect snow accumulation beyond its height.

How to cite: Lamare, M., Arnaud, L., Picard, G., Pelletier, M., and Domine, F.: Combining UAV LiDAR and Terrestrial Laser Scanning to investigate the impact of shrub expansion on local-scale Arctic snowpack distribution., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17714, https://doi.org/10.5194/egusphere-egu2020-17714, 2020.

The snow dynamics in alpine systems governs the hydrological cycle in these regions. However, snow data are usually limited due to poor accessibility and limited funds. On the other hand, the majority of scientific studies about snow resources are carried out at mountain slope or basin scale. The main goal of this work is to propose a parsimonious methodology to estimate snow water equivalent (SWE) at mountain range scale. A regression model that includes non-steady explanatory variables is proposed to assess snow depth dynamic based on the information coming from snow depth point observations, a digital elevation model, snow cover area from satellite and a precipitation index representative of the area. The main advantages of the method are its applicability in cases with limited information and in mountain ranges scales. In the proposed methodology different regression model structures with different degrees of complexity are calibrated combining steady and non-steady explanatory variables (elevation, slope, longitude, latitude, eastness, northness, maximum upwind slope, radiation, curvature, accumulated snow cover area and precipitation in a temporal window) and four basic mathematical transformations of these variables (square, root square, inverse and logarithm). In the case of the temporal variables different time windows to define the accumulated values of the explanatory indices have been tested too. We have applied the methodology in a case study, the Sierra Nevada mountain range (Southern Spain), where the calibration has been performed by using the snow depth data observation provided by the ERHIN program which have a very low temporal frequency (2 or 3 measurement per year). When only non-steady explanatory variables are considered, the coefficient of determination of the global spatial estimation model is 0.55. When we also include non-steady variables we obtain an approach with a coefficient of determination of 0.62. We have also calibrated a new regression approach by using, in addition to the ERHIN program information, data coming from a detailed temporal series of snow depth in a new specific location, which has allow to obtain models with R² of 0.59 (for steady explanatory variables) and 0.64 (including also non-steady explanatory variables). The dynamic of the snow density in the mountain range has been estimated by means of a physically based simulation driven by WRF data. Combining the snow depth and the density approaches we have estimated the final SWE in Sierra Nevada. 

This research has been partially supported by the SIGLO-AN project (RTI2018-101397-B-I00) from the Spanish Ministry of Science, Innovation and Universities (Programa Estatal de I+D+I orientada a los Retos de la Sociedad).

How to cite: Collados-Lara, A.-J., Pulido-Velazquez, D., Pardo-Igúzquiza, E., Alonso-González, E., and López-Moreno, J. I.: Estimation of snow water equivalent in a mountain range by using a dynamic regression approach, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13112, https://doi.org/10.5194/egusphere-egu2020-13112, 2020.

Snow cover is a critical geophysical parameter for Earth climate and hydrological systems. It contributes to regulate the Earth surface temperature and represents an important water storage that is slowly released during the melting season and contributes to the river discharge.

The parameter that characterizes the hydrological importance of snow cover is the snow water equivalent (SWE). An accurate estimation of the spatial and temporal distribution of SWE in mountain environments is still a relevant challenge for the scientific community, due to the complex topography that causes a high spatial heterogeneity in snow distribution, by reducing the representativeness of traditional pointwise in situ measurements.

Several efforts have been done to develop new methods for estimating snow-related parameters. In particular, the large-scale monitoring of the Earth’s surface from space-borne sensors has proven to be very effective, by improving the spatialization of land surface parameters. In the last decades, scientists have extensively investigated the potential of Synthetic Aperture Radar (SAR) data for deriving SWE. Unlikely to visible sensors, microwave sensors do not depend on the presence of sunlight and are not affected by the presence of clouds.

In this context, the main objective of this work is to exploit the already demonstrated sensitivity of the X-band SAR to snow [1] for estimating the SWE in the mountainous area of South Tyrol, in north-eastern Italy. For this purpose, the information derived from X-band SAR imagery acquired by the Italian Space Agency (ASI)’s COSMO-SkyMed constellation in StripMap HIMAGE mode at 3 m ground resolution is exploited together with ground measurements of SWE, which have been chosen by selecting the dates corresponding to the satellite acquisitions in the study period (2013-2015). In order to increase the training dataset, further backscattering coefficients have been simulated by using an implementation of the Dense Media Radiative Transfer (DMRT) theory, based on the Quasi-Crystalline Approximation (QCA) of Mie scattering of densely packed Sticky spheres [2]. Moreover, to optimize the satellite acquisition and use as much corresponding SWE data as possible, we integrated the ground dataset with other SWE values obtained as explained in [3] by means of a data fusion approach involving the snow model AMUNDSEN.

This work is carried out by EURAC, CNR/IFAC and ASI in the framework of the 2019-2021 project ‘Development of algorithms for estimation and monitoring of hydrological parameters from satellite and drone’, funded by ASI under grant agreement n.2018-37-HH.0.

References

[1] Pettinato, S. et al. (2012). The potential of COSMO-SkyMed SAR images in monitoring snow cover characteristics. IEEE Geoscience and Remote Sensing Letters, 10(1), 9-13.

[2] Tsang, L. et al. (2007). Modeling active microwave remote sensing of snow using dense media radiative transfer (DMRT) theory with multiple-scattering effects. IEEE Transactions on Geoscience and Remote Sensing, 45(4), 990-1004.

[3] De Gregorio, L. et al. (2019). Improving SWE Estimation by Fusion of Snow Models with Topographic and Remotely Sensed Data. Remote Sensing, 11(17), 2033.

How to cite: De Gregorio, L., Cigna, F., Cuozzo, G., Jacob, A., Paloscia, S., Pettinato, S., Santi, E., Tapete, D., and Notarnicola, C.: Exploitation of X-band SAR images and ground data for SWE retrieval through a machine learning technique, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19350, https://doi.org/10.5194/egusphere-egu2020-19350, 2020.

Seasonal snow accumulation and melt dominates the hydrology in high latitude areas, providing water storages for both ecological and human needs. However, until recent years there has been a lack of cost-efficient way to measure the spatiotemporal variability of the snow depth and cover in high resolution. Unmanned aircraft systems (UAS) can offer spatial resolutions up to few centimeters, depending on the weather and light conditions, camera quality and drone specification. We used multiple different quadcopters and a fixed wing UAS to determine and analyze the spatiotemporal variability of snow depth and cover in three test plots with different land-cover types (forested slope, open peatland, and peatland-forest) in subarctic northern Finland, where weather and light conditions are challenging. Five measurement campaigns were conducted during winter 2018/2019 and a snow-free bare ground survey after snowmelt. Snow depth maps were constructed using Structure from Motion (SfM) photogrammetry technique and by differentiating the acquired models from snow-covered and snow-free surveys. Due to poor sub-canopy penetration with UAS-SfM method, tree masks were utilized to remove canopy effects prior to analysis. The snow depth maps produced with different UAS were compared to in situ snow course and an automatic ultrasonic measurement data. We highlight the difficulties of working in subarctic winter conditions and discuss the accuracy of UAS-derived snow depth maps. We show that the UAS-derived snow depth measurements agree well with manual snow survey measurements and UAS are suitable method for extending the spatial snow data coverage, whereas a continuous point snow depth measurement is unable to accurately present sub-catchment scale snow depth variability. Furthermore, the spatiotemporal variability of snow accumulation and melt between and within different land cover types is presented.

How to cite: Rauhala, A., Meriö, L.-J., Ala-aho, P., Korpelainen, P., Kuzmin, A., Kumpula, T., Heikkilä, R., Kløve, B., and Marttila, H.: Spatiotemporal Variability of Snow Depth in Subarctic Environment Using Unmanned Aircraft Systems (UAS), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22457, https://doi.org/10.5194/egusphere-egu2020-22457, 2020.

Yamagata prefecture, facing the Japan Sea, is one of the heavy snow fall regions of the world. Around half of the annual precipitation of around 3000 mm falls in winter as snow, producing snow covers of more than three meters depth.  However, air temperature is around 0°C in winter and therefore relatively warm. Hence, snow density becomes 0.5 g/cm³ already early in the snow accumulation phase. To qualify and quantify interactions, three spots on a slope, forested with Japanese cedar (Cryptomeria japonica), have been selected to compare relationships on top, at the middle and at the bottom of snow covered slopes. The site represents the majority of mountain forests in north-eastern Japan. Monitoring soil and air temperature as well as precipitation and soil moisture we found strong interactions between the three hydrological regimes (precipitation, snow cover and soil) in winter. Soil did not freeze and hence volumetric soil moisture content changed during the winter season. Several sharp significant increases of soil moisture have been measured before the snow melt period even started. High rates of soil moisture increase together with an increase of Snow Water Equivalent (SWE) have been found to be caused by rain-on-snow events. In contrast, smaller rates of soil moisture increase in peaks were correlated with a decrease in SWE and therefore a snowmelt process. The interactions of snow cover and soil have been found to be different in the three different spots at the slope. Soil at the bottom of a slope reacts significantly to the highest number of events; soil on the slope reacts only to some events, but more intensively. Thus, most of the water is moving within the snowpack down the slope, increasing the SWE. Thereafter water reaches the soil surface and infiltrates it. This has been found to be also one reason for the formation of depth hoars and therefore the risk of avalanches.

To conclude, hydrological regimes in north-eastern Japan interact during the whole year due to winter air temperatures around 0°C and soil which does not freeze. The shape of peaks in soil moisture can be used to distinguish between rain and snowmelt causing the soil moisture increase. Various preferential flow patterns at different spots on a slope are an excellent basis for further studies and a basis for further monitoring and modelling.

How to cite: Brandt, A., Zhang, Q., Lopez Caceres, M. L., and Murayama, H.: Soil moisture dynamics in winter under heavy snowfall conditions in Shonai (Japan), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-157, https://doi.org/10.5194/egusphere-egu2020-157, 2020.

Trends of the Degree-Day Factors in the mountainous regions

Muhammad Fraz Ismail^{1, 2}, Prof. Dr. –Ing. Markus Disse¹, Prof. Dr. –Ing. Wolfgang Bogacki²,

Alexander Brandt³, M. Larry Lopez C.³

¹ Chair of Hydrology and River Basin Management, Department of Civil, Geo and Environmental Engineering, Technical University of Munich.

² Department of Civil Engineering, Koblenz University of Applied Sciences.

³ Faculty of Agriculture, Yamagata University, Tsuruoka, Japan.

Melt generated through snow and glaciers are considered to be a vital fresh water resource because they store the solid winter precipitation as then act as a reservoir to provide water when it is mostly needed i.e. during the summer season. Recently, a lot of studies based on hydrological modelling showed that the changing climate will adversely affect the snow and glacial melt patterns around the globe. Considering this situation it is quite critical to know more about these melting processes and the factors driving them.

Degree-day approach for simulating the flows generated through the snow and glacial melt has proved to be a handsome one because it uses the temperatures as an index variable to address the complex energy balances as well as its only dependency over the air temperatures to generate the melt make it feasible especially for the high mountainous data scare regions (e.g. Upper Indus Basin). Degree-day models use the Degree-Day Factor (DDF) as a ‘key’ parameter which transforms one degree-day [°C.day^-1] into daily melt depth [mm.day^-1]. Literature enlightens that the DDF is not a constant parameter but it changes with the ripening of the snowpack.

In the present research, snow measurement datasets from three different locations e.g. Japan (Enshurin 173m a.s.l.), Germany (Brunnenkopfhütte 1602m a.s.l.), and Pakistan (Deosai 4149m a.s.l.) have been collected and evaluated for the estimation of the DDFs. Initial findings show that there exists a considerable spatio-temporal variation of the DDFs. Which ranges from 0.3 – 6.8 [mm°C^-1 day^-1] in the German Alps, 0.2 – 7.9 [mm°C^-1 day^-1] in Yamagta Forest Japan and reaches ≥10 [mm°C^-1 day^-1] in the Himalayan ranges during the snowmelt season.

In general, the DDFs show an increasing trend during the snowmelt season at different elevations, which not only demonstrates the altitude influence on the variability of the DDFs but also links to changing snow densities. Latter suggests that the DDFs should not be taken as constant because it changes with the location and needs to be estimated for different regions.

KEYWORDS: Degree-Day Factor, Snow and glacial melt, Measurements

How to cite: Ismail, M. F.: Trends of the Degree-Day Factors in the mountainous regions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7995, https://doi.org/10.5194/egusphere-egu2020-7995, 2020.

Mountain regions represent a particular challenge for critical zone modelling as snowpack interacts with soils, vegetation, surface water and atmosphere and plays a primary role on the water transfers but also on the carbon and nitrogen cycles. Indeed, in these environments ecosystems are adapted to a snow regime under change due to the rise in the 0°C isotherm. In addition, atmospheric nitrogen deposition, a product of industrial activity carried by valley winds and mesoscale atmospheric circulation, already impacts some high-altitude ecosystems by modifying nutrient flows (nitrogen and carbon in particular). These combined forcings could lead to major ecosystem changes (distribution of water, carbon and nitrogen flows, growth rates, species, etc.). Anticipating this evolution, and the associated flows (CO2, nitrogen, water) under this double constraint, remains problematic due to the lack of adapted models.

In this study, we use the Parflow/CLM/Ecoslim model on a small (17ha) nival subalpine catchment close to Lautaret Pass (French Alps) where meteorological and hydrological parameters are measured together with snowpack survey and chemical concentrations measurements in the air, the rivers, the snowpack the vegetation and the ground. Simulations are constrained by a spatially distributed forcing and evaluated from snow pack dynamic and ET measurements. The simulations allow us to estimate the Nitrogen quantities that can be processed by vegetation and those drained in river flows. The estimation of the residence times is then calculated from the velocity field in the catchment. The wide snow cover time distribution leads to wide distribution resident time for any particle deposit. This can impact nitrogen chemical history and any other chemical compounds in the snow pack and the ground even for such small scales.

How to cite: Cohard, J.-M., Reverdy, A., Voisin, D., Hector, B., Gupta, A., and Biron, R.: Residence time of nitrogen deposit in a nival subalpine catchment using the hyper-resolution ParFlow-CLM-EcoSLIM critical zone model., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13207, https://doi.org/10.5194/egusphere-egu2020-13207, 2020.

In mountainous areas, detailed snowpack models are essential to capture the high spatio-temporal variability of the snowpack. This task is highly challenging, and models suffer from large simulation errors. In these regions, in-situ observations are scarce, while remote sensing observations are generally patchy owing to complex physiographic features (steep slopes, forests, shadows,...) and weather conditions (clouds). This point is stressing the need for a spatially coherent data assimilation system able to propagate the informations into unobserved locations.

In this study, we present CRAMPON (CRocus with AssiMilation of snowPack ObservatioNs), an ensemble data assimilation system ingesting snowpack observations in a spatialized context. CRAMPON quantifies snowpack modelling uncertainties with an ensemble and reduces them using a Particle Filter. Stochastic perturbations of meteorological forcings and the multi-physical version of Crocus snowpack model (ESCROC) are used to build the ensemble. Two variants of the Sequential Importance Resampling Particle Filter (PF) were implemented to tackle the common PF degeneracy issue that arises when assimilating a large number of observations. In a first approach (so-called global approach), the observations information is spread across topographic conditions by looking for a global analysis. Degeneracy is mitigated by inflating the observation error covariance matrix, with the side effect of reducing the impact of the assimilation. In a second approach (klocal), we propagate the information and mitigate degeneracy by a localisation of the PF based on background correlation patterns between topographic conditions.

Here, we investigate the ability of CRAMPON to globally benefit from partial observations in a conceptual semi-distributed domain which accounts for the main features of topographic-induced snowpack variability. We compare simulations without assimilation with experiments assimilating synthetic observations of the Height of Snow and VIS/NIR reflectance. This setup demonstrates the ability of CRAMPON to spread the information of various snow observations into unobserved locations.

How to cite: Cluzet, B., Lafaysse, M., Dumont, M., Cosme, E., and Albergel, C.: CRAMPON: A Particle Filter to assimilate sparse snowpack observations into a semi-distributed geometry, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9037, https://doi.org/10.5194/egusphere-egu2020-9037, 2020.

Himalayan mountain region lying in the northern piece of Indian sub-continent is among those zones which bears the most geologically fragile situations and are additionally a vault of biodiversity, new water stockpiling and environment administrations. The Himalaya is one of the world’s largest and mostly inaccessible area of glaciers outside the polar regions and provides glacier-stored water to the major perennial rivers of India throughout the year and to their river basins also. Glacier is a large ice mass formed by accumulation, compaction and re-crystallization of snow and firn due to stress of its own weight. Glacier with steep slopes of bedrock may retreat with slower rate or may even advance because downslope movement of glacier will continuously feed ice to lower altitude. Increased retreating rate of glaciers can be considered as an indicator of climate change. In the course of recent three decades, the occurred changes can be explained with exploitative land utilization which is among the primary drivers of changing snow cover, vegetation covers and profitability in western Himalayas locale. In a region where field-based research is tiring because of heterogenous and high elevation, measuring the changes in aforesaid using Remote Sensing techniques can give basic data regarding variating patterns of Snowfall and Precipitation. This paper studies the trend analysis of changing Snowfall and Precipitation patterns using SWAT and MODIS data (1979–2014 and 1999 to Present) over Uttarakhand Himalaya and its association with altitudinal gradient. This paper investigates the trends in maximum (P_max), minimum (P_min) & mean (P_mean) Snowfall and Precipitation in the annual, seasonal and monthly time-scales for 54 stations in the 5 regions of Uttarakhand’s Western Himalayan region which are categorized on the basis of elevation, from year 1979-2014. Statistical approaches are used to examine the effect of change in pattern of snowfall and precipitation upon the phenology of vegetation, fresh water ecosystems, agricultural productivity, decreasing snow line, increasing tree line & change in duration of the seasons etc of the study area.

How to cite: Pandey, A. and Palni, S.: Trend analysis of Changing Pattern of Snowfall and Precipitation over the Time Period of 1979 to 2014 in Alpine region of Uttarakhand, Western Himalaya, India , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-565, https://doi.org/10.5194/egusphere-egu2020-565, 2020.

Seasonal snow cover extent (SCE) is a critical component not only for the global radiation balance and climatic behavior but also for water availability of mountainous and arid regions, vegetation growth, permafrost, and winter tourism. However, due to the effects of the global warming, SCE has been observed to behave in much more irregular and extreme patterns in both temporal and spatial aspects. Therefore, a continuous SCE monitoring strategy is necessary to understand the effect of climate change on the cryosphere and to assess the corresponding impacts on human society and the environment. Nevertheless, although conventional optical sensor-based sensing approaches are mature, they suffer from cloud coverage and illumination dependency. Consequently, spaceborne Synthetic Aperture Radar (SAR) provides a pragmatic solution for achieving all-weather and day-and-night monitoring at low cost, especially after the launch of the Sentinel-1 constellation. 

In the present study, we propose a new global SCE mapping approach, which utilizes dual-polarization intensity-composed bands, polarimetric H/A/α decomposition information, topographical factors, and a land cover layer to detect the SCE. By including not only amplitude but also phase information, we overcome the limitations of previous studies, which can only map wet SCE. Additionally, a layer containing the misclassification probability is provided as well for measuring the uncertainty. Based on the validation with in-situ stations and optical imagery, around 85% accuracy of the classification is ensured. Consequently, by implementing the proposed method globally, we can provide a novel way to map high resolution (20 m) and cloud-free SCE even under cloud covered/night conditions. Preparations to combine this product with the optical-based DLR Global SnowPack are already ongoing, offering the opportunity to provide a daily snow mapping service in the near future which is totally independent from clouds or polar darkness.

How to cite: Tsai, Y.-L., Uereyen, S., Dietz, A., Kuenzer, C., and Oppelt, N.: Global Snow Cover Extent Mapping Using Sentinel-1, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1383, https://doi.org/10.5194/egusphere-egu2020-1383, 2020.

Precipitation enhancement along elevation gradients is the result of complex interactions between synoptic-circulation patterns and local topography. Since precipitation measurements at high elevation are often biased and sparse, predicting precipitation distribution in mountain regions is challenging, despite this being a key step of hydrologic-forecasting procedures and of water management in general. By acting as a natural precipitation gauge, the snowpack can provide useful information about precipitation orographic enhancement, but the information content of snow-course measurements in this regard has been generally underappreciated. We leveraged 70,000+ measurements upstream five reservoirs in Valle d’Aosta, Italy, to show how manual and radar snow courses can be used to estimate precipitation lapse rates and consequently improve predictions of hydrologic models. Snow Water Equivalent above 3000 m ASL can be more than 4-5 times cumulative seasonal precipitation below 1000 m ASL, with elevational gradients up to 1000 mm w.e. / km ASL. Enhancement factors estimated by blending precipitation-gauge and snow-course data are highly seasonal and spatially variable, with exponential or linear profiles with elevation depending on the year. Blended gauge - snow-course precipitation lapse rates can be used to infer precipitation in ungauged areas and compensate for elevation gradients in an iterative, two-step distribution procedure of precipitation based on modified Kriging. Coupling this precipitation-distribution procedure with a snow model (S3M) shows promising improvements in Snow Water Equivalent estimates at high elevations.

How to cite: Avanzi, F., Ercolani, G., Gabellani, S., Cremonese, E., Morra di Cella, U., Pogliotti, P., Filippa, G., Ratto, S., and Stevenin, H.: Insights into precipitation orographic enhancement from snow-course data and their value for improved hydrologic predictions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1285, https://doi.org/10.5194/egusphere-egu2020-1285, 2020.

The Himalayan and Karakorum (H-K) region comprise the highest amount of snow and ice cover outside the Polar Regions. The H-K region is grouped into four-part, i.e., the Karakorum (KK), Western (WH), Central (CH), and Eastern Himalayas (EH), based on climate and geographic location. The EH and CH mainly feed by summer-monsoon snowfall, whereas the KK and WH are winters accumulated. This regional variability of climate will affect the water availability for hydropower generation, agriculture, and ecosystem. Therefore, the mapping and monitoring of snow cover change over the study area played an essential role in the context of climate change. The snow cover area (SCA) was observed using Moderate-resolution Imaging Spectroradiometer (MODIS) daily snow cover products version 6 during 2000-2019. Different cloud removal techniques (e.g., multi-sensor, temporal, spatial, regional snow line, multiday backward) are applied to reduce the cloud cover pixels over snow pixels of the MODIS data. The mean annual SCA of the H-K region is ∼26.4% of the total geographical area during the study period. The statistical trend analysis of mean monthly, seasonal, and annual SCA is examined using Mann-Kendal and Sen’s slope test. The mean yearly SCA of the H-K region shows an increasing trend during 2000-2009 and start decreasing significantly during 2009-2019. Similar results are observed in the KK, WH, CH, and EH, which shows a decreasing trend of mean annual SCA since 2009. The mean seasonal SCA shows a significant decreasing trend in summer (June to September) and winter (December to February) since 2009, suggesting a seasonal shift or change in snow cover. Overall, the winter shows an insignificant decreasing trend in comparison to the other seasons during 19 hydrological years (2000-01 to 2018-19). The mean monthly minimum SCA observed in August for the KK and WH, July for the CH, and June for the EH. However, the mean maximum SCA in February for the KK, WH, CH, and March for the EH. The snow cover depletion curve suggests that the maximum SCA in February and minimum in August of the entire region during the study period. The seasonal variation of SCA can be highly related to the influence of monsoonal patterns in the region.

How to cite: Dharpure, J. K., Goswami, A., and Kulkarni, A. V.: Spatial and temporal variation of snow cover in the Himalayan and Karakorum region using MODIS data (2000-2019), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3421, https://doi.org/10.5194/egusphere-egu2020-3421, 2020.

Budyko framework has been widely used to estimate the partitioning of precipitation into evapotranspiration and runoff as a function of an aridity index (i.e., ratio of potential evapotranspiration to precipitation) in catchments where snow or glaciers are absent. Where snow or glaciers exist, meltwater from either may considerably affect the performance of the Budyko framework. However such effects have not been investigated in the Xinjiang territory of Northwest China, which features many meltwater-dependent river systems. To analyze the effects of meltwater on hydrological cycles in Xinjiang, we utilized a calibrated hydrological model (Soil and Water Assessment Tool, SWAT) to estimate meltwater from snow or glaciers. The water budgets of 21 catchments across three major mountain ranges of Xinjiang showed that normalized contributions of meltwater to river runoff were respectively 89.9%, 77.0%, and 55.6% in the catchments of Altay, Kunlun and Tienshan Mountains. The results showed that the catchments of Altay Mountains with the highest meltwater ratio (defined as the ratio of meltwater to the sum of meltwater and rainfall, 0.572 ± 0.075) had the lowest Budyko parameter ω (1.238), while those of Tienshan Mountains with the lowest meltwater ratio (0.239 ± 0.143) had the highest ω value (1.348). This indicated that the Budyko parameter ω was negatively correlated to meltwater ratio across three mountains. Incorporating meltwater from snow and glaciers into the Budyko framework significantly increased the values of ω in all three mountain ranges, indicating that the Budyko framework without inclusion of meltwater could under-estimate evapotranspiration in Xinjiang, China. There results derived from this research also implied that both surface runoff and evapotranspiration may increase under a warming climate in mountain areas.

How to cite: Bai, J., Li, J., Liu, T., and Bao, A.: Significance of meltwater in estimating runoff using Budyko framework in Northwest Xinjiang, China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5456, https://doi.org/10.5194/egusphere-egu2020-5456, 2020.

How to cite: Balasubramanian, S., Hoelzle, M., Lehning, M., Wangchuk, S., Oerlemans, J., and Keller, F.: A first attempt to model an Artificial Ice Reservoir (Ice Stupa) using a simple energy balance approach, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5277, https://doi.org/10.5194/egusphere-egu2020-5277, 2020.

Recent climate change is causing rapid retreat of alpine glaciers around the globe. As ice melts and glaciers thin, glacier motion and subglacial processes will change. One of the most relevant aspects for down-valley environments, settlements and infrastructure is the potential change in flow discharge and sediment output.

Here we present the results of an ongoing monitoring program at the Gorner Glacier, Switzerland, the second-largest glacier system in the European Alps.  During the melt season of 2018 and 2019, stage and turbidity were monitored with a 5 minute frequency along a turbulent section of the glacial river, located approximately 1 km downstream of the glacier terminus. For calibration of the turbidity measurements, daily water samples were obtained with an automated pump sampler, supported by additional intermittent manual sampling. The data is complemented by a discharge time series that also contains information on the flushing of a bedload trap at the hydro power weir located about 2 km downstream of the glacier terminus. The discharge and flushing data have a resolution of 15 minutes.  Turbidity and discharge allow estimation of the output of suspended load, while the flushing data inform about bedload. We further measured total organic carbon content of the water samples to infer the water and sediment source.

Data suggest a clear seasonal pattern, not only in discharge and sediment output, but also in suspended sediment concentration (SSC). While SSC is high during snow melt and in early summer, it decreases rapidly in July and stays at similar levels until September. This may indicate exhaustion of sediment storage beneath the glacier, but could also result from a change in subglacial regime, e.g. from a decrease in subglacial water pressure due to the progressive opening of subglacial cavities during the melt season. High fractions of organic carbon, presumably due to lateral sediment input from hillslopes, occur during storms throughout the entire season.

How to cite: Prasicek, G., Mettra, F., Lane, S., and Herman, F.: Recent patterns of discharge and sediment output of the Gorner Glacier, Switzerland, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9242, https://doi.org/10.5194/egusphere-egu2020-9242, 2020.

The assimilation of different satellite and in-situ products generally improves the hydrological model predictive skill. Most studies have focused on assimilating a single product at a time with the ensemble size subjectively chosen by the modeller. In this study, we use the European-scale Hydrological Predictions for the Environment hydrological model in the Umeälven catchment in northern Sweden with the stream discharge and local reservoir inflow as target variables to objectively choose an ensemble size that optimises model performance. We further assess the effect of assimilating different satellite products namely snow water equivalent, fractional snow cover, and actual and potential evapotranspiration; as well as in situ measurements of river discharge and local reservoir inflows. We finally investigate the combinations of those products that improve model predictions of the target variables and how the model performance varies through the year for those combinations. We found that an ensemble size of 50 was sufficient for all products except the reservoir inflow, which required 100 members and that in situ products outperform satellite products when assimilated. In particular, potential evapotranspiration alone or as combinations with other products did not generally improve predictions of our target variables. However, assimilating combinations of the snow products, discharge and local reservoir without ET products improves the model performance.

How to cite: Musuuza, J. L., Crochemore, L., Gustafsson, D., Pimentel, R., and Pechlivanidis, I.: Impact of satellite and in situ data assimilation on hydrological predictions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9305, https://doi.org/10.5194/egusphere-egu2020-9305, 2020.

Freshwater shortage is one of the global problems of our time. Glaciers contain a large amount of freshwater on the Earth. Nowadays mountain glaciation is decreasing almost throughout the world (Panov, 1993; Duethmann et al., 2016; Fausto et al. 2016). This effect leads to an increase in the water content of mountain rivers, but also cause a decrease in glaciers freshwater reserves (Trenberth et al., 2007; Sorg et al., 2012). This impact is already felt in the arid regions of our planet. Recently in Central Asia was observed a shortage of water resources. According to the estimates, the total area and mass decrease of the Tien Shan glaciers, from 1961 to 2012, amountes to 18 ± 6% and 27 ± 15% (Farinotti et al., 2015). The degradation of the area and volume of the Tien Shan glaciers, in the period from 1961 to 2012, was 18 ± 6% and 27 ± 15% (Farinotti et al., 2015). About 15% of the runoff in the Republic of Kyrgyzstan is fed by glacial nutrition, but this contribution may even be 1.5-3 times greater during the warm season (Dikikh et al., 1995; Kemmerikh, 1972). The average annual rivers runoff in the Republic of Kyrgyzstan increased from 47.1 km3 (~ 1947–1972) to 50 km3 (1973–2000) (Mamatkanov et al., 2006). The representative glacier of the Central Caucasus - Dzhankuat can serve as an example of depletion of freshwater in the glaciers of the Caucasus. Over the past decades, since 1974, the Dzhankuat glacier has lost large volumes - almost twice, and at the time of 2013 it is equal to 0.077 ± 0.002 km³. From 2006 to 2015 the volume of the Dzhankuat glacier decreased by 25%, as a consequence, there is an increase in the rate of degradation (Lavrentiev et al., 2014).

In this article is presented mathematical simulation, which allows to solve a number of problems. One of the most important problem is the calculation of the water supply into the river network because of snow and ice melting in mountain areas. Weather conditions are taken into account in the simulation calculation of snow and ice melting over the entire glacier surface.

This work is supported by the Presidential Russian Federation grant №MK-2936.2019.5

How to cite: Belozerov, E., Rets, E., and Popovnin, V.: Mathematical simulation of melting mountain glaciers, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10440, https://doi.org/10.5194/egusphere-egu2020-10440, 2020.

Seasonal snowpack is an important part of the water cycle and it has a large influence on runoff regime in mountain catchments of Central Europe. However, snow water equivalent (SWE) is decreasing in many mountain regions over the last decades and spring snowmelt occurs earlier in the year. This study aimed 1) to analyse long-term changes and trends in selected snowpack characteristics, such as SWE, snow cover duration, snowmelt onset and melt-out in 40 mountain catchments in Czechia in the period 1965–2014 and 2) to relate the detected changes to changes in air temperature and snowfall fraction at different elevations. Since the availability of time series of measured SWE at a catchment scale is limited, a conceptual semi-distributed hydrological model HBV-light was used to simulate daily SWE for defined elevation zones. Besides SWE, the model simulated other water balance components, such as runoff, soil moisture and groundwater recharge. The integrated multi-variable model calibration procedure was used to calibrate the model. Both observed runoff and SWE were used for evaluation of the model performance. Seasonal and monthly mean of SWE, as well as snow cover duration, snowmelt onset, snowmelt rates and melt-out were calculated for individual catchments and elevation zones. The non-parametric Mann-Kendall test was used to detect potential trends in simulated time series. The results showed significant decreasing trends in snowfall fraction for all catchments and elevations in the study period mostly due to an increase in air temperature. This resulted in a decrease in snow storages in most of catchments, especially in western parts of Czechia. However, a lot of regional differences exists and no trends in SWE were detected in some catchments. Decreasing trends in snow cover duration were detected as well, mostly because of earlier snowmelt onset and melt-out.

How to cite: Nedelcev, O. and Jenicek, M.: Changes in seasonal snowpack in mountain catchments in Czechia , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9999, https://doi.org/10.5194/egusphere-egu2020-9999, 2020.

Snow water equivalent is highly heterogeneous due to the spatial distribution of precipitation, local topographic characteristics, effects of vegetation, and wind. In particular, the latter has important effects on such distribution, controlling the preferential deposition of snowfall, transport (either by saltation or suspension) on the ground, and sublimation of blowing snow. In this work, we analyze the effects of incorporating redistribution by wind transport when modeling the seasonal water balance in two experimental catchments: (i) the Izas catchment (0.33 km²), located in the Spanish Pyrenees, with an elevation range of 2000-2300 m a.s.l., and (ii) Las Bayas catchment (2.45 km²), located in the extratropical Andes Cordillera (Chile) and elevation between 3400 and 3900 m a.s.l. After assessing model simulations using time series of snow depth and terrestrial lidar scans, we examine the water balance at the annual and seasonal scales, quantifying the different fluxes that govern snow accumulation and melting with a spatially distributed model that considers the physics of transport and the sublimation of blowing snow. Moreover, we characterize the sensitivity of dominant processes to changes in precipitation and temperature. The results of this investigation have important implications on current and future research, allowing to contrast wind effects in the spatio-temporal patterns of accumulation and melting in alpine and subalpine areas, identifying those processes that will be most affected under projected climatic conditions.

How to cite: Pinto Escobar, F., Mendoza, P. A., Shaw, T. E., Revuelto, J., Musselman, K., and McPhee, J.: Wind effects on the spatial distribution of snow and seasonal water balance in two Mediterranean basins, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10994, https://doi.org/10.5194/egusphere-egu2020-10994, 2020.

Snow is a key component of the hydrological cycle in many regions of the world, providing a natural storage of water by accumulating snow in winter and releasing it in spring. Many ecosystems, societies and economies rely on this mechanism as a water resource. There is strong evidence in the literature that global warming leads to decreasing snowfall and snow accumulation and shifts the onset of the melt season to earlier in the year. However, little is known about how rising temperatures affect snowmelt rates and timing, and how these can have an impact on water resources for instance by changing the time and magnitude of streamflow. Some studies predict slower snowmelt rates in a warmer world, due to the onset of melt being earlier when there is less energy available for melt, but there is not yet an observation-based study showing such trends. As a first step, here we present preliminary results of observed long term trends in snowmelt rates from different climates. We use a dataset that has already shown strong decreasing signals for winter snow accumulation. Here we also present potential avenues to investigate the sensitivity of snowpacks and snowmelt regimes in different climatic settings to further rising temperatures using modeled snow dynamics. A few possibilities on how to link the snowpack dynamics to impacts in water resources are also discussed, for instance by comparing modelled dynamics to hydrological models and observations.

How to cite: Fontrodona Bach, A., Larsen, J., Woods, R., Schaefli, B., and Teuling, R.: Investigating global changes in snow dynamics and the impact on water resources, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11810, https://doi.org/10.5194/egusphere-egu2020-11810, 2020.

Snow heights have been measured at lots of places over many years and decades, often at daily resolution. In many cases the data series have no gaps and are of high quality. In recent times, remote sensing provides more and more maps of snow heights, sometimes at high temporal frequency as well. However, most of these snow height data series lack information about snow water equivalents (SWEs), and they often come without sufficient meteorological data to run sophisticated, process-based snow models to simulate SWEs. Statistical SWE models, on the other hand, are subject to regional calibration parameters and cannot model SWEs of distinct days. Nevertheless, for many applications (hydrology, climatology, structural design,…) SWE-series are very valuable.

The ΔSNOW.MODEL presented, is a semi-empirical layer-model that simulates SWEs exclusively from snow heights and their temporal changes. It is computationally cheap and is provided as an easy-to-use R-package. Like statistical snow models, the ΔSNOW.MODEL does not need any meteorological input, but simulates more accurate SWE values: Statistical models typically show root mean square differences between observations and model values of 20-50 kg/m², biases of maximum seasonal SWE of +50 to +100 kg/m², and timing offsets for seasonal maximum SWE of -15 to 0 days. The ΔSNOW.MODEL reaches 15-30 kg/m², -20 to +20 kg/m², and -3 to +5 days, respectively. These scores are comparable with those of process based models, though they are calculated without the need of further meteorological or geographical data except snow height. Therefore, the ΔSNOW.MODEL can be used to assign highly reliable means and maxima of SWE as well as durations of high snow loads to long-term and historic snow height data, and it can simulate SWEs of distinct days with a comparatively high precision. In some (promising) respect the ΔSNOW.MODEL bridges the gap between process-based and statistical snow models.

How to cite: Winkler, M. and Schellander, H.: Snow Water Equivalents exclusively from Snow Heights and their temporal Change: The ΔSNOW.MODEL, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11298, https://doi.org/10.5194/egusphere-egu2020-11298, 2020.

Spatio-temporal variation of snowmelt affects the Earth’s radiation budget hence serves as a proxy of climate change and global warming. Ablation zone including blue ice and wetsnow has a low surface albedo and melt water ponding ice shelf surface during summer that enhances crevasse propagation then poses a threat the stability of ice shelves. The lack of high spatial and temporal ablation product limits the in-depth exploration of the mechanism and spatio-temporal characteristics of ablation in Antarctica. Here an ablation area detection method based on the modified normalized difference water index adapted for ice (MNDWI_ice ) is developed to determine and characterize ablation variationsbased on Landsat-8 images of the Dalk glacier, East Antarctica, between September 2016 and March 2017.. The results showed that the Landsat-8 reflectance data can be used to extract seasonal ablation using a uniform MNDWI_ice threshold (0.136), and the average extraction accuracy is 81.5%, and varies between 67.7% and 94.2% in case of the thin cloud and fractional topographic shadow.The ablation area and the mean value of MNDWI_ice in the ablation zone show obvious seasonal spatio-temporal variation characteristics. The ablation area in the Dalk glacier appears no later than the earliest time (early September) of the observation. The earliest appearance of ablation is mainly distributed at the eastern grounding line where the terrain changes drastically. Brightness temperature and air temperature of Zhongshan Station show a strong correlation, which can be used as a mechanism analysis of the ablation zone distribution.

Key words Antarctica, Dalk glacier, ablation, MNDWI_ice

How to cite: Qu, Y. and Liu, Y.: Extraction of seasonal surface ablation zone in DALK glacier based on Landsat-8, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13049, https://doi.org/10.5194/egusphere-egu2020-13049, 2020.

Remote sensing products have been widely applied in hydrological modeling for more realistic representations of hydrological processes. In this study, in addition to gauged discharge, the combined MODIS snow cover maps and ERS scatterometer based soil moisture products were added to constrain a semi-distributed conceptual hydrological model. The latest version of MODIS snow cover images provides a daily Normalized Difference Snow Index (NDSI) in a 500-meter resolution. We derived the snow cover maps by using a new NDSI thresholding method from the MODIS Aqua (MYD10A1) and Terra (MOD10A1) daily snow cover products. Furthermore, the newest ERS soil moisture product also provided a finer spatial resolution of 500-meter over Austria. The semi-distributed TUW-model was tested in 213 catchments using both single and multiple object calibration methods. We found that the semi-distributed TUW-model performed well in discharge modeling. Moreover, applying the MODIS snow cover maps improved the accuracy in the snow-melt season, while the soil moisture product helped the discharge simulation in the no-snow period.

How to cite: Tong, R., Parajka, J., Komma, J., and Blöschl, G.: Calibration of a semi-distributed hydrological model adding constrains from remotely sensed snow cover and soil moisture products, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11985, https://doi.org/10.5194/egusphere-egu2020-11985, 2020.

Seasonal snowpack significantly influences the catchment runoff and thus represents an important input for the hydrological cycle. Changes in the precipitation distribution and intensity, as well as a shift from snowfall to rain is expected in the future due to climate changes. As a result, rain-on-snow events, which are considered to be one of the main causes of floods in winter and spring, may occur more frequently.

The objective of this study is 1) to evaluate the frequency, inter-annual variability and extremity of rain-on-snow events in the past based on existing measurements and 2) to simulate the effect of predicted increase in air temperature on the occurrence of rain-on-snow events in the future. We selected 59 near-natural mountain catchments in Czechia with significant snow influence on runoff and with available long-time series (>35 years) of daily hydrological and meteorological variables. A semi-distributed conceptual model, HBV-light, was used to simulate the individual components of the water cycle at a catchment scale. The model was calibrated for each of study catchments by using 100 calibration trials which resulted in respective number of optimized parameter sets. The model performance was evaluated against observed runoff and snow water equivalent. Rain-on-snow events definition by threshold values for air temperature, snow depth, rain intensity and snow water equivalent decrease allowed us to analyze inter-annual variations and trends in rain-on-snow events during the study period 1980-2014 and to explain the role of different catchment attributes.

The preliminary results show that a significant change of rain-on-snow events related to increasing air temperature is not clearly evident. Since both air temperature and elevation seem to be an important rain-on-snow drivers, there is an increasing rain-on-snow events occurrence during winter season due to a decrease in snowfall fraction. In contrast, a decrease in total number of events was observed due to the shortening of the period with existing snow cover on the ground. Modelling approach also opened further questions related to model structure and parameterization, specifically how individual model procedures and parameters represent the real natural processes. To understand potential model artefacts might be important when using HBV or similar bucket-type models for impact studies, such as modelling the impact of climate change on catchment runoff.

How to cite: Hotovy, O. and Jenicek, M.: Changes in snow storages and their impact on occurrence and extremity of runoff caused by rain-on-snow events, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13431, https://doi.org/10.5194/egusphere-egu2020-13431, 2020.

A multi-objective calibration of the parameters of conceptual hydrologic models has the potential to improve the consistency of the simulated model states, their representativeness with respect to catchment states and thereby to reduce the uncertainty in the estimation of hydrological model outputs. Observed in-situ or remotely sensed state variables, such as the snow cover distribution, snow depth, snow water equivalent and soil moisture were often considered as additional information in such calibration strategies and subsequently utilized in data assimilation for operational streamflow forecasting. The objective of this paper is to assess the effects of the inclusion of MODIS products characterizing soil moisture and the snow water equivalent in a multi-objective calibration strategy of an HBV type conceptual hydrological model under the highly variable physiographic conditions over the whole territory of Austria.

The methodology was tested using the Technical University of Vienna semi-distributed rainfall-runoff model (the TUW model), which was calibrated and validated in 213 Austrian catchments. For calibration we use measured data from the period 2005 to 2014. Subsequently, we simulated discharges, soil moisture and snow water equivalents based on parameters from the multi-objective calibration and compared these with the respective MODIS values. In general, the multi-objective calibration improved model performance when compared to results of model parametrisation calibrated only on discharge time series. Sensitivity analyses indicate that the magnitude of the model efficiency is regionally sensitive to the choice of the additional calibration variables. In the analysis of the results we indicate ranges how and where the runoff, soil moisture and snow water equivalent simulation efficiencies were sensitive to different setups of the multi-objective calibration strategy over the whole territory of Austria. It was attempted to regionalize the potential to increase of the overall model performance and the improvement in the consistency of the simulation of the two-state variables. Such regionalization may serve model users in the selection which remotely sensed variable or their combination is to be preferred in local modelling studies.

How to cite: Kubáň, M., Sleziak, P., Brziak, A., Hlavčová, K., and Szolgay, J.: regionalization of the potential to increase rainfall-runoff model performance by multi-objective calibration using modis data over Austria, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13804, https://doi.org/10.5194/egusphere-egu2020-13804, 2020.

Forest canopies strongly affect snowpack energetics during wintertime. In discontinuous forest stands, spatio-temporal variations in radiative and turbulent fluxes create complex snow distribution and melt patterns, with further impacts on the hydrological regimes and on the land surface properties of seasonally snow-covered forested environments.

As increasingly detailed canopy structure datasets are becoming available, canopy-induced energy exchange processes can be explicitly represented in high-resolution snow models. We applied the modelling framework FSM2 to obtain spatially distributed simulations of the forest snowpack in subalpine and boreal forest stands at high spatial (2m) and temporal (10min) resolution. Modelled sub-canopy radiative and turbulent fluxes were compared to detailed meteorological data of incoming irradiances, air and snow surface temperatures. These were acquired with novel observational systems, including 1) a motorized cable car setup recording spatially and temporally resolved data along a transect and 2) a handheld setup designed to capture temporal snapshots of 2D spatial distributions across forest discontinuities.

The combination of high-resolution modelling and multi-dimensional datasets allowed us to assess model performance at the level of individual energy balance components, under various meteorological conditions and across canopy density gradients. We showed which canopy representation strategies within FSM2 best succeeded in reproducing snowpack energy transfer dynamics in discontinuous forests, and derived implications for implementing forest snow processes in coarser-resolution models.

How to cite: Mazzotti, G., Essery, R., Malle, J., Webster, C., and Jonas, T.: Small-scale processes with large-scale impacts: Investigating canopy structure controls on energy fluxes to the forest snowpack , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19027, https://doi.org/10.5194/egusphere-egu2020-19027, 2020.

Recently, snow disasters have been increased in South Korea due to the unexpected heavy snow in a region where winter gives little snow. For instance, 10 people were dead by the collapsed roof due to the unusual heavy snow. Many local governments do not have enough snow removal equipment because of little snow in winter season. Therefore, it has been important to estimate the amount of snow damage to prepare heavy snow disaster. There are not many researches to estimate damage of snow disaster in South Korea. In this study, historical snow damage data from 1994~2018 recorded in National Disaster Report were used to predict the future snow disaster damage using a statistical equation. However, it was not easy to predict the amount of snow damage when the heavy snow is happened in the area where no snow during the winter in history. Therefore, the relationship between the snow depth and damaged area were analyzed using the historical damage data. Principal multiple regression method was applied to develop the snow damage estimation function using the damaged area. The developed model could be applied to plan the budget for the snow removal equipment or snow damage reduction.

Acknowledgement:

This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Intelligent Management Program for Urban Water Resources Project, funded by Korea Ministry of Environment(MOE) (2019002950002).

How to cite: Chung, G. and Park, H.: Historical Relationship between Snow Depth and Damaged Area in South Korea, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19549, https://doi.org/10.5194/egusphere-egu2020-19549, 2020.

On the 22^nd of March 2019, PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission has been launched by the Italian Space Agency (ASI). Since then, the spacecraft has been collecting on demand hyperspectral data of the Earth surface. The imaging spectrometer features 239 bands covering the visible, near infrared and shortwave infrared wavelengths (400-2500 nm) with a spectral resolution <12nm. PRISMA acquires hyperspectral images with a spatial resolution of 30m and a swath of 30 km.

The satellite mission is still in the initial commissioning phase. During this period, the acquisition of field spectroscopy data contemporary to satellite observation is fundamental. With the aim of calibrating and validating PRISMA observations on snow fields, we organized field campaigns at a high altitude (2160 m) experimental site (Torgnon, Aosta Valley) in the European Alps. During these campaigns, we measured spectral reflectance of snow with a Spectral Evolution spectrometer (350-2500 nm), snow grain size, and snow density. Among different instruments operating at the site (e.g. net radiometer, webcam, sensors for snow depth, snow water equivalent, snow surface temperature etc.), we recently installed an unattended spectrometer acquiring continuous measurements of snow reflectance. This instrument covers part of the visible and near infrared spectral range (400-900 nm) and it was used to analyze the daily evolution of snow reflectance during the snow season.

In this contribution, we present a preliminary comparison between field and satellite hyperspectral reflectance data of snow. This comparison is fundamental for the future development of algorithms for the estimation of snow physical variables (snow grain size, snow albedo, and concentration of impurities) from satellite hyperspectral data.

How to cite: Di Mauro, B., Garzonio, R., Bramati, G., Cogliati, S., Cremonese, E., Julitta, T., Panigada, C., Rossini, M., and Colombo, R.: PRISMA hyperspectral satellite mission: first data on snow in the Alps, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19825, https://doi.org/10.5194/egusphere-egu2020-19825, 2020.

Gauges modify wind fields, producing important undercatch in solid precipitation.  For this reason, solid precipitation measurements show significant bias with respect to real values, especially under windy conditions. In this work we propose a methodology that combines geostatistical and hydrological models to perform a preliminary assessment of global undercatch and precipitation patterns (distribution between solid and liquid phase and spatial gradient with elevation) in alpine regions. It is based on the available information about daily natural streamflow and daily climatic data (precipitation and temperature) in the catchment. We want to analyse long time periods in order to take into account the stochastic behaviour of natural streamflow and climatic variables. A preliminary assessment of temperature and precipitation fields is performed by applying various geostatistical approaches assuming some hypothesis about the relationship between climatic fields and altitude. The generated fields are then employed as inputs of conceptual hydrological models, which includes two parameters to correct the solid and liquid precipitation, respectively. We have considered different hydrological approaches (SRM, HBV and a Témez model with a simple degree-day approach). The parameters are calibrated by minimizing the difference between the simulated and historical natural streamflows and/or snow cover area. It allows us to identify the best combination of geostatistical and hydrological models to approximate streamflow, to perform a global preliminary assessment of the undercatch of solid and liquid precipitation and their precipitation patterns by analysing spatial gradients with elevation. The methodology was applied in the Canales Basin, an alpine catchment of the Sierra Nevada (Spain).

This research has been partially supported by the SIGLO-AN project (RTI2018-101397-B-I00) from the Spanish Ministry of Science, Innovation and Universities (Programa Estatal de I+D+I orientada a los Retos de la Sociedad) and by the GeoE.171.008-TACTIC project from GeoERA organization funded by European Union’s Horizon 2020 research and innovation program

How to cite: Jimeno-Sáez, P., Collados-Lara, A. J., Alvarado-Montero, R., Pulido-Velazquez, D., Pardo-Igúzquiza, E., and Senent-Aparicio, J.: Combined use of geostatistical and conceptual hydrological models for a preliminary assessment of “undercatch” of precipitation in The Canales Basin (Sierra Nevada, Spain). , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20004, https://doi.org/10.5194/egusphere-egu2020-20004, 2020.

The Andean cryosphere is a vital water resource for downstream populations. In recent years, it has been in steep decline as a whole, but shown strong spatio-temporal variability due to climatic events such as the current mega drought in central Chile. Glacio-hydrological models are necessary to understand and predict changes in water availability as a result of changes to the cryosphere. However, due to a lack of data for initialisation, forcing, calibration and validation, they are rarely used, especially in the Andes, for periods longer than a few years or decades. While useful insights can be gained from short-term modelling, there is a gap in our understanding of how glaciers impact hydrology on longer timescales, which may prevent local communities and governments from achieving effective planning and mitigation. Here we use the glacio-hydrological model TOPKAPI-ETH – initialised, forced, calibrated and validated using unique and extensive field and remote sensing datasets – to investigate glacier contributions to the streamflow of the high-elevation Rio Yeso catchment, Chile, over the past 50 years. We focus in particular on: 1) fluctuations in glacier surface mass balance and runoff and associated climatic variability; 2) if peak water has already occurred and when; 3) the effect of supraglacial debris cover on seasonal and long-term hydrographs. We offer insights into some of the challenges of running glacio-hydrological models on longer timescales and discuss the implications of our findings in the context of a shrinking Andean cryosphere.

How to cite: McCarthy, M., Burger, F., Ayala, A., Fugger, S., Shaw, T. E., Miles, E., MacDonell, S., Bhattacharya, A., Bolch, T., McPhee, J., and Pellicciotti, F.: The impact of glaciers on the long-term hydrology of a high-elevation Andean catchment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21463, https://doi.org/10.5194/egusphere-egu2020-21463, 2020.

Reliable representations of spatial distribution of snow and subsequent snow-melt are critical challenges for hydrological estimations, given their crucial relevance in mountainous regimes especially because of the high sensitivity to climate change. Relatively accurate physically based models are data intensive while in-situ measurements of snow-depth are prone to be non-representative due to local influences. Likewise, lack of snow-depth information and to some extent, cloud cover in the mountains limit the usage of Remote-sensing images in snow estimation. Against this backdrop, this work presents a methodology incorporating available remotely-sensed images (MODIS Snow-cover products) and simple distributed snow-melt models to estimate a time-continuous spatial snow extent in snow dominated regimes. 

The methodology employs relatively cloud-free MODIS composite images to calibrate the spatial distribution of snow simulated by different distributed degree-day models. These variants of models are run in a domain of 500m x 500m grids, and incorporate daily precipitation, daily min-, max- and mean temperatures, and daily radiation data interpolated onto the aforementioned grids. Variations in the models include a simple degree model followed by incorporation of different aspects governing snow hydrology such as precipitation induced melt, radiation, topography, and land use.  The modeled snow depths in each grid are reclassified to ‘1’ (snow depths above a threshold) and ‘0’ (no snow), and calibrated against MODIS snow-cover for cloud-free days with snow. Snow-melt parameters are then estimated for the region of interest. The result is a spatial snow-cover distribution time-series. This approach is replicated in different regions viz. Baden-Württemberg and Bavaria in Germany, and in Switzerland. Results suggest good agreement with MODIS data and the parameters show relative stability across the time domain at the same sites and are transferrable to other regions. Calibration using readily available images used in this method offers adequate flexibility, albeit the simplicity, to calibrate snow distribution in mountainous areas across a wide geographical extent with reasonably accurate precipitation and temperature data. The final validated spatial snow-distribution data can be, as a stand-alone input, coupled with distributed hydrological models to reliably estimate streamflow in data-scarce mountainous catchments.

How to cite: Gyawali, D. R. and Bárdossy, A.: Modeling spatial snow-cover distribution using snow-melt models and MODIS images, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21740, https://doi.org/10.5194/egusphere-egu2020-21740, 2020.

We have recently investigated the use of SAR interferometry for the mapping of Snow Water Equivalent (SWE) temporal variations using Sentinel-1 data [1]. Maps of temporal changes of SWE, measured with a sub-centimetre accuracy and updated every six days have been obtained over a study area in Finland. This methodology relies on the shift in the interferometric phase caused by the refraction of the microwave signal penetrating the snow layer. In this work, we investigate phase inconsistencies [2] of a sets of three interferograms obtained from three Sentinel-1 images acquired along the same orbit at different acquisition times to study the snow melt. We find that while phase inconsistencies are not expected to be present in case of examining surfaces covered with frozen snow, the scattering mechanism of microwave in the snow layer during the melting phase affects both the interferometric phase and coherence.

This work was supported, in part, by the Academy of Finland project no. 325806.

References:

[1] V. Conde, G. Nico, P. Mateus, J. Catalão, A. Kontu, M. Gritsevich, On the estimation of temporal changes of snow water equivalent by spaceborne SAR interferometry: a new application for the Sentinel-1 mission, J. Hydrol. Hydromech., 67, 2019, 1, 93–100. DOI: 10.2478/johh-2018-0003

[2] F. De Zan, M. Zonno, P. López-Dekker, Phase inconsistencies and multiple scattering in SAR interferometry, IEEE Transactions on Geoscience and Remote Sensing, 53(12), 6608-6616, 2015

How to cite: Gritsevich, M., Nico, G., Conde, V., Mateus, P., and Catalao, J.: On the computation of phase inconsistencies of Sentinel-1 interferograms over snow-covered areas, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21946, https://doi.org/10.5194/egusphere-egu2020-21946, 2020.

In this work we propose a simple technique to derive snow and atmosphere properties from satellite top-of-atmosphere spectral reflectance observations using asymptotic radiative transfer theory valid for the case of weakly absorbing and optically thick media. The following snow properties are derived and analyzed: ice grain size, snow specific surface area, snow pollution load, snow spectral and broadband albedo. The developed retrieval technique includes both atmospheric correction and cloud screening routines and is based on Ocean and Land Colour Instrument (OLCI) measurements on board Sentinel-3A, B. The spectral aerosol optical thickness, total ozone and water vapour column are derived fitting the measured and simulated OLCI-registered spectral reflectances at 21 OLCI channels.

The derived results are validated using ground - based observations. It follows that satellite observations can be used to study time series of spectral and broadband albedo over Greenland. The deviations of satellite and ground observations are due to problems with cloud screening over snow and also due to different spatial scale of satellite and ground observations (Kokhanovsky et al., 2020).

Acknowledgements

The work has been supported by the European Space Agency in the framework of ESRIN contract No. 4000118926/16/I-NB ‘Scientific Exploitation of Operational Missions (SEOM) Sentinel-3 Snow (Sentinel-3 for Science, Land Study 1: Snow’) and ESRIN contract 4000125043 – ESA/AO/1-9101/17/I-NB EO science for society ‘Pre-operational Sentinel-3 snow and ice products’.

References

Kokhanovsky, A.A., et al. (2020), The determination of snow albedo from satellite observations using fast atmospheric correction technique, Remote Sensing, 12 (2), 234,  https://doi.org/10.3390/rs12020234.

How to cite: Kokhanovsky, A., Box, J., Vandecrux, B., and Kern, M.: Remote sensing of snow and atmosphere properties using Ocean and Land Colour Instrument on board Copernicus Sentinel-3 mission, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21774, https://doi.org/10.5194/egusphere-egu2020-21774, 2020.

Water storage availability of semiarid regions is closely linked to the snow reservoir and its changes. The change of hydrological regime in mountain rivers is strongly affected by the snowpack’s dynamics, which plays a crucial role during spring and/or summer season in Mediterranean areas, becoming one of the major water sources to streamflow. This influence can be analyzed from different approaches; however, due to the concurrence of different processes, whose interaction and propagation undoubtedly affect runoff and baseflow generation, a process-oriented approach is required for further understanding the ultimate reasons behind the observed changes. Hence, the partitioning of river flow into baseflow, subsurface flown, and runoff, is a key step in hydrograph analysis and for better understanding snowfed rivers and how climate variability can influence their regime.

This work presents an assessment of different baseflow separation methods in mountain rivers of semiarid areas in the framework of a process-oriented approach for identifying the major sources/sinks of water. The study area comprises the headwaters of the different basins in the Sierra Nevada area, in southern Spain, within an altitudinal range of 1000-3479 m a.s.l., high slopes, and different facing. For this, a 20-yr series of daily flow in a gauged point in the Guadalfeo River that drains the southwestern area of Sierra Nevada is analyzed. Five standard baseflow separation methods, together with the simulation by the physically-based hydrological model WiMMed, which includes the module SNOWMED developed from an energy-water balance approach and validated in the study site, were selected and their results compared. Discussion on the effects of the final baseflow series on the descriptors of the direct-runoff hydrograph (daily time step) series is also included, considering snowmelt- and rainfall-driven events, and their combination.

The results not only provide a better understanding of baseflow separation in snowfed rivers in semiarid regions, but also assess hydrograph analysis in a process-oriented approach.  

How to cite: Torralbo, P., Pimentel, R., Pérez-Palazón, M. J., Aparicio, J., Herrero, J., Aguilar, C., and Polo, M. J.: Baseflow separation methods in snowfed rivers in Mediterranean catchments: a process-oriented assessment for hydrograph analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20038, https://doi.org/10.5194/egusphere-egu2020-20038, 2020.

Glacier-wide mass balances and catchment-wide runoffs are reconstructed over 1979-2018 for Dokriani Glacier catchment in the Garhwal Himalaya (India). A glacio-hydrological model including temperature-index, accumulation, rain and evapotranspiration modules is used for the reconstruction using daily air-temperature and precipitation fields from ERA5 data. Model is calibrated using 6 years of observed annual glacier-wide mass balances (1993-1995 and 1998-2000) and observed summer mean monthly runoff (1994, 1998-2000) data. Modelled mass wastage on Dokriani Glacier is moderate with annual loss of −0.28±0.38 m w.e. a^–1 over 1979-2018. The mean winter glacier-wide mass balance is 0.62±0.38 m w.e. a^–1 while mean summer glacier-wide mass balance is −0.91±0.38 m w.e. a^–1 over 1979-2018. The mean annual catchment-wide runoff is 1.38±0.11 m³ s^–1 over 1979-2018. Maximum runoff is produced during summer-monsoon months with a peak in August (5.35 m³ s⁻¹). Rainfall contributes the maximum to the total mean annual runoff with 44% share while snow melt and ice melt contribute 35% and 22%, respectively. The loss through evapotranspiration is only around 2% of the total runoff. The heterogeneous debris-cover distribution over lower ablation area (<5000 m a.s.l.) protects the glacier for higher melting. Decadal mass balances suggest that Dokriani Glacier was close to steady-state conditions over 1989-1997 because of negative temperature anomaly and positive precipitation anomaly over this period. Mass balance and runoff are the most sensitive for threshold temperature for melt with sensitivities of −0.71 m w.e. a^–1oC^–1 and 0.18 m³ s^{–1 o}C^–1, respectively.

How to cite: Azam, M. F. and Srivastava, S.: Glacio-hydrological modelling of partially debris-covered Dokriani Glacier in monsoon-dominated Garhwal Himalaya (India), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2060, https://doi.org/10.5194/egusphere-egu2020-2060, 2020.

Blowing snow potential is diagnosed for typical cases in roads around Sapporo, Japan, as snow concentration and visibility based on dynamically downscaled data with 1-km resolution. The results are consistent with the blowing-snow records on time and place of traffic disruption, when the dynamical downscaling (DDS) reproduced wind speed well for a case. Moreover, the DDS-based diagnosis had a strength on the onset and cease of blowing snow in the event. The diagnosis with mesoscale model analysis with 5-km resolution does not reproduce the blowing snow events in most area, however. Hence, the DDS potentially, not perfectly, adds the value to estimate blowing snow potential, despite a large scale-gap from an explicit representation of small-scale turbulence related to blowing snow. The meteorological forecast with 1-km resolution might improve the estimate of blowing snow potential.

How to cite: Tanji, S. and Inatsu, M.: Case Study of Blowing Snow Potential Diagnosis with Dynamical Downscaling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2123, https://doi.org/10.5194/egusphere-egu2020-2123, 2020.

Climate change will modify the availability of snow resources in the future. Thus developing methodologies to assess impacts of potential future climate change scenarios on snow variables is a key subject. In this work we combine several previous developed methodologies (downscaling climate change scenarios to local scale, cellular automata models, and stochastic weather generators) to assess impacts of future climate change scenarios and its uncertainty on snow cover area through a Montecarlo simulation. The cellular automata model uses climatic indices (precipitation and temperature) as driving variables to estimate snow cover area. Future scenarios of these variables can be generated using bias correction and delta change approaches and different regional climate models. The stochastic weather generators allow us to produce multiple series of precipitation and temperature based on the statistical characteristics of the future local scenarios generated. These multiple series can be used as inputs of the cellular automata model in order to assess the future snow cover area and its uncertainty. The main advantages of the proposed methodology are its applicability in cases with limited information and in mountain ranges scales. The methodology has been applied to the Sierra Nevada mountain range in southern Spain. This area has a Mediterranean climate very sensitive to climate change. Using the future precipitation and temperature scenarios generated considering the Representative Concentration Pathways 8.5 (RCP8.5) for the period 2071–2100, we obtain a significant reduction in snow cover area, with mean values of 59.0% for the local scenarios generated with a delta change approach, and 61.7% for those one generated with the bias correction approach.

This research has been partially supported by the SIGLO-AN project (RTI2018-101397-B-I00) from the Spanish Ministry of Science, Innovation and Universities (Programa Estatal de I+D+I orientada a los Retos de la Sociedad).

How to cite: Pulido-Velazquez, D., Collados-Lara, A.-J., and Pardo-Igúzquiza, E.: Assessing impacts of future potential climate change scenarios on snow cover area by using cellular automata models and Montecarlo simulations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13636, https://doi.org/10.5194/egusphere-egu2020-13636, 2020.