ORCID Profile
0000-0001-7751-3814
Current Organisation
Wageningen University
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Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-12760
Abstract: & & There is strong evidence that rising temperatures mostly lead to less snow accumulation and to an earlier melt onset. However, changes in the frequency and intensity of snowmelt events remain unclear. While higher temperatures should intuitively lead to faster snowmelt, some studies find that melt rates are slower because the melt onset occurs earlier in the year when less energy is available for melt. Modelling of these snow dynamics is challenged by a lack of continuous observations on water content of the snowpack, the highly sought after SWE. However, high quality observations of snow depth can be more available in both space and time, even at higher altitudes. Therefore, an increasing number of models try to estimate SWE from snow depth and other variables. Here we first investigate if these models accurately reproduce the snow accumulation and melt dynamics, and to what extent they can be used for hydrological studies. We then convert a long-term pan-European snow depth dataset to SWE by making use of these models and we assess model performance. Historical trends of snowmelt rates, melt onset, and frequency and intensity of melt events are shown for several seasonal snow locations across Europe. Trends across a variety of timescales are generally weak and spatially inhomogeneous, suggesting local conditions dominate over regional climate trends. However, it seems that under the current climate change conditions, the decrease of snowpack depth over most of Europe causes snowpacks to melt faster (i.e. in less days) than before.& & &
Publisher: Copernicus GmbH
Date: 23-06-2023
DOI: 10.5194/ESSD-15-2577-2023
Abstract: Abstract. Ground-based datasets of observed snow water equivalent (SWE) are scarce, while gridded SWE estimates from remote-sensing and climate reanalysis are unable to resolve the high spatial variability of snow on the ground. Long-term ground observations of snow depth, in combination with models that can accurately convert snow depth to SWE, can fill this observational gap. Here, we provide a new SWE dataset (NH-SWE) that encompasses 11 071 stations in the Northern Hemisphere (NH) and is available at 0.5281/zenodo.7515603 (Fontrodona-Bach et al., 2023). This new dataset provides daily time series of SWE, varying in length between 1 and 73 years, spanning the period 1950–2022, and covering a wide range of snow climates including many mountainous regions. At each station, observed snow depth was converted to SWE using an established snow-depth-to-SWE conversion model, with excellent model performance using regionalised parameters based on climate variables. The accuracy of the model after parameter regionalisation is comparable to that of the calibrated model. The key advantages and strengths of the regionalised model presented here are its transferability across climates and the high performance in modelling daily SWE dynamics in terms of peak SWE, total snowmelt and duration of the melt season, as assessed here against a comparison model. This dataset is particularly useful for studies that require accurate time series of SWE dynamics, timing of snowmelt onset, and snowmelt totals and duration. It can, for ex le, be used for climate change impact analyses, water resources assessment and management, validation of remote sensing of snow, hydrological modelling, and snow data assimilation into climate models.
Publisher: Copernicus GmbH
Date: 26-01-2023
DOI: 10.5194/ESSD-2023-31
Abstract: Abstract. Ground-based datasets of observed Snow Water Equivalent (SWE) are scarce, while gridded SWE estimates from remote-sensing and climate reanalysis are unable to resolve the high spatial variability of snow on the ground. Long-term ground observations of snow depth, in combination with models that can accurately convert snow depth to SWE, can fill this observational gap. Here, we provide a new SWE dataset (NH-SWE) that encompasses 11,071 stations in the Northern Hemisphere, and is available at 0.5281/zenodo.7515603 (Fontrodona-Bach et al., 2023). This new dataset provides daily time series of SWE, varying in length between one and seventy-three years, spanning the period 1950–2022 and covering a wide range of snow climates including many mountainous regions. At each station, observed snow depth was converted to SWE using an established snow-depth-to-SWE conversion model, with excellent model performance using regionalised parameters based on climate variables. The accuracy of the model after parameter regionalisation is comparable to that of the calibrated model. The key advantages and strengths of the regionalised model presented here are its transferability across climates and the high performance in modelling daily SWE dynamics in terms of peak SWE, total snowmelt and duration of the melt season, as assessed here against a comparison model. This dataset is particularly useful for studies that require accurate time series of SWE dynamics, timing of snowmelt onset, and snowmelt totals and duration. It can e.g. be used for climate change impact analyses, water resources assessment and management, validation of remote sensing of snow, hydrological modelling and snow data assimilation into climate models.
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-11810
Abstract: & & 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.& &
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-11859
Abstract: & & Although warming temperatures should intuitively lead to faster snowmelt, some studies suggest that melt rates might be slower in a warming world. This assumes that typically deep snowpacks are thinning and become isothermal earlier in the season when less solar radiation is available for melt. Investigating these changing snow dynamics is challenged by a lack of observations on water content of the snowpack, the Snow Water Equivalent (SWE). However, high quality observations of snow depth are generally more available in both space and time, even at higher elevations. Here we present a new dataset of historical SWE time series over the Northern Hemisphere, including a wide variety of climates. These time series are obtained converting historical ground-based snow depth time series to SWE by using the DeltaSNOW model. For the conversion to work over a range of climates, we apply a regional calibration of model parameters based on climatological data and provide model performance and uncertainty estimates. For & .000 sites characterised by seasonal snow, we investigate changes in total snow accumulation, timing of snowmelt and melt rates for the period 1980-2020. Large decreases in total melt and earlier melt timing are widely observed. However, trends in snowmelt rates are generally weak and spatially inhomogeneous. Slower snowmelt in a warmer world occurs mostly on deep snowpacks that have been heavily depleted and where the number of days with melt has not significantly changed, making melt rates slower. However, both faster and slower melt are observed on sites where both the amount of melt and number of melt days have decreased. We provide an analysis of the causes for the spatial and temporal variability in trends. We find that trends can differ depending on the definition of melt rate and peak SWE, and that the drivers of the trends differ over different climates. Strong warming generates large melt events during the late accumulation season, challenging the commonly used definition of peak SWE and making it harder to compare the snowmelt dynamics of the past and the current climate. We note that focusing on melt rate change might mask important effects on melt timing and magnitude, because a proportional reduction in total melt and number of melt days can lead to no change in melt rate.& &
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-12620
Abstract: Ground-based observation datasets of Snow Water Equivalent (SWE) are scarce. In contrast, numerous long-term and good quality ground observations of snow depth are available. Furthermore, an increasing number of models can accurately convert snow depth to SWE. We present a novel dataset of SWE time series over the Northern Hemisphere based on in-situ observations of snow depth. We convert snow depth to SWE using the DeltaSNOW model and we present a method to generalise the conversion model for global use. We calibrate the model over a wide range of climates with the SNOTEL dataset and we regionalise the model parameters based on climate variables. We evaluate this approach on independent datasets such as the Canadian SWE dataset and other European SWE datasets. The key strengths of the modelling approach and the SWE dataset are the excellent performance of peak SWE and timing of snowmelt season onset. The final SWE dataset contains 11,003 stations with daily SWE and snow density time series distributed across the Northern Hemisphere, including mountain regions, at the point scale, and spanning the period 1950-2022. The dataset is available and free to access. It can be used for a variety of applications including validation of remote sensing of snow, hydrological modelling, water resources assessment and climate change impact analyses.
Location: United Kingdom of Great Britain and Northern Ireland
Location: Switzerland
No related grants have been discovered for Adrià Fontrodona-Bach.