The evaluation of the potential of global data products for snow hydrological modelling in ungauged high alpine catchments

Abstract. Worldwide, there is a strong discrepancy between the importance of high alpine catchments for the water cycle and the availability of meteorological and snow hydrological in situ measurements. Good knowledge on the timing and quantity of snow meltwater is crucial for numerous hydrological applications, also far way downstream. For several decades, the number of global data sets of different meteorological and land surface parameters has been increasing, but their applicability in modelling high alpine regions has been insufficiently investigated so far. We tested such data for a 10-year period with the physically-based Cold Regions Hydrological Model (CRHM). Our study site is the gauged high alpine Research Catchment Zugspitze (RCZ) of 12 km2 in the European Alps. We used a selection of nine different meteorological driver data setups including data transferred from another alpine station, data from an atmospheric model and hybrid data, whereof we investigated data for all meteorological parameters and substituting precipitation only. For one product, we applied an advanced downscaling approach to test the advantage of such methods. The range between all setups is high at 3.5 °C for the mean decadal temperature and at 1510 mm for the mean decadal precipitation sum. The comparison of all model results with measured snow depth and reference simulations driven with in situ meteorological data demonstrates that the setup with the transferred data performs best, followed by the substitution of precipitation only with hybrid data. All other setups were unrealistic or showed plausible results only for some parts of the RCZ. As a second goal, we investigated potential differences in model performance resulting from topographic parameterization according to three globally available digital elevation models (DEMs); two with 30 m and one with 1 km resolution. As reference, we used a 2.5 m resolution DEM. The simulations with all DEM setups performed well at the snow depth measurement sites and on catchment scale, even if they indicate considerable differences. Differences are mainly caused by product specific topography induced differences in solar radiation. Surprisingly, the setup with the coarsest DEM performed best in describing the catchment mean due to averaged out topographic differences. However, this was not the case for a finer resolution. For the two plausible meteorological setups and all DEM setups, we additionally investigated the maximum quantity and the temporal development of the snowpack as well as the runoff regime. Even those quite plausible setups revealed differences of up to 20 % in snowpack volume and duration, which consequently lead to considerable shifts in runoff. Overall, we could demonstrate that global data are a valuable source to substitute single missing meteorological variables or topographic information, but the exclusive use of such driver data does not provide sufficiently accurate results for the RCZ. For the future, however, we expect an increasing role of global data in modelling ungauged high alpine basins due to further product improvements, spatial refinements and further steps regarding assimilation with remote sensing data.

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The evaluation of the potential of global data products for snow hydrological modelling in ungauged high-alpine catchments

Hydrology and Earth System Sciences ◽

10.5194/hess-25-2869-2021 ◽

2021 ◽

Vol 25 (5) ◽

pp. 2869-2894

Author(s):

Michael Weber ◽

Franziska Koch ◽

Matthias Bernhardt ◽

Karsten Schulz

Keyword(s):

Land Surface ◽

Snow Depth ◽

Snow Water Equivalent ◽

Meteorological Data ◽

Hydrological Modelling ◽

Small Scale ◽

European Alps ◽

Hydrological Parameters ◽

Global Data

Abstract. For many ungauged mountain regions, global datasets of different meteorological and land surface parameters are the only data sources available. However, their applicability in modelling high-alpine regions has been insufficiently investigated so far. Therefore, we tested a suite of globally available datasets by applying the physically based Cold Regions Hydrological Model (CRHM) for a 10-year (September 2000–August 2010) period in the gauged high-alpine Research Catchment Zugspitze (RCZ), which is 12 km2 and located in the European Alps. Besides meteorological data, snow depth is measured at two stations. We ran CRHM with a reference run with in situ-measured meteorological data and a 2.5 m high-resolution digital elevation model (DEM) for the parameterization of the surface characteristics. Regarding different meteorological setups, we used 10 different globally available datasets (including versions of ERA, GLDAS, CFSR, CHIRPS) and additionally one transferred dataset from a similar station in the vicinity. Regarding the different DEMs, we used ALOS (Advanced Land Observing Satellite) and SRTM (Shuttle Radar Topography Mission) (both 30 m) as well as GTOPO30 (1 km). The following two main goals were investigated: (a) the reliability of simulations of snow depth, specific snow hydrological parameters and runoff with global meteorological products and (b) the influence of different global DEMs on snow hydrological simulations in such a topographically complex terrain. The range between all setups in mean decadal temperature is high at 3.5 ∘C and for the mean decadal precipitation sum at 1510 mm, which subsequently leads to large offsets in the snow hydrological results. Only three meteorological setups, the reference, the transferred in situ dataset and the CHIRPS dataset, substituting precipitation only, showed agreeable results when comparing modelled to measured snow depth. Nevertheless, those setups showed obvious differences in the catchment's runoff regime and in snow depth, snow cover, ablation period, the date, and quantity of maximum snow water equivalent in the entire catchment and in specific parts. All other globally available meteorological datasets performed worse. In contrast, all globally available DEM setups reproduced snow depth, the snow hydrological parameters and runoff quite well. Differences occurred mainly due to differences in radiation model input due to different spatial realizations. Even though SRTM and ALOS have the same spatial resolution, they showed considerable differences due to their different product origins. Despite the fact that the very coarse GTOPO30 DEM performed relatively well on the catchment mean, we advise against using this product in such heterogeneous high-alpine terrain since small-scale topographic characteristics cannot be captured. While global meteorological data are not suitable for sound snow hydrological modelling in the RCZ, the choice of the DEM with resolutions in the decametre level is less critical. Nevertheless, global meteorological data can be a valuable source to substitute single missing variables. For the future, however, we expect an increasing role of global data in modelling ungauged high-alpine basins due to further product improvements, spatial refinements and further steps regarding assimilation with remote sensing data.

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In-Situ LED-Based Observation of Snow Surface and Depth Transects

Sensors ◽

10.3390/s20082292 ◽

2020 ◽

Vol 20 (8) ◽

pp. 2292

Author(s):

Celeste Barnes ◽

Chris Hopkinson ◽

Thomas Porter ◽

Zhouxin Xi

Keyword(s):

Snow Depth ◽

Surface Albedo ◽

Surface Condition ◽

Field Of View ◽

Snow Surface ◽

Depth Measurement ◽

Individual Distance ◽

Strong Linear Relationship ◽

Better Than

As part of a new snowpack monitoring framework, this study evaluated the feasibility of using an LED LIDAR (Leddar) time of flight sensor for snowpack depth measurement. The Leddar sensor has two additional features over simple sonic ranging sensors: (i) the return signal is divided into 16 segments across a 48° field of view, each recording individual distance-to-target (DTT) measurements; (ii) an index of reflectance or intensity signal is recorded for each segment. These two features provide information describing snowpack morphology and surface condition. The accuracy of Leddar sensor DTT measurements for snow depth monitoring was found to be < 20 mm, which was better than the 50 mm quoted by the manufacturer, and the precision was < 5 mm. Leddar and independent sonic ranger snow depth measurement showed strong linear agreement (r2 = 0.98). There was also a strong linear relationship (r2 = 0.98) between Leddar and manual field snow depth measurements. The intensity signal response was found to correlate with snow surface albedo and inversely with air temperature (r = 0.77 and −0.77, respectively).

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Multi-site evaluation of the JULES land surface model using global and local data

Geoscientific Model Development ◽

10.5194/gmd-8-295-2015 ◽

2015 ◽

Vol 8 (2) ◽

pp. 295-316 ◽

Cited By ~ 11

Author(s):

D. Slevin ◽

S. F. B. Tett ◽

M. Williams

Keyword(s):

Land Surface ◽

Meteorological Data ◽

Land Surface Model ◽

Surface Model ◽

Model Parameters ◽

Data Sets ◽

Local Data ◽

Area Index ◽

Phenology Model ◽

Global Data

Abstract. This study evaluates the ability of the JULES land surface model (LSM) to simulate photosynthesis using local and global data sets at 12 FLUXNET sites. Model parameters include site-specific (local) values for each flux tower site and the default parameters used in the Hadley Centre Global Environmental Model (HadGEM) climate model. Firstly, gross primary productivity (GPP) estimates from driving JULES with data derived from local site measurements were compared to observations from the FLUXNET network. When using local data, the model is biased with total annual GPP underestimated by 16% across all sites compared to observations. Secondly, GPP estimates from driving JULES with data derived from global parameter and atmospheric reanalysis (on scales of 100 km or so) were compared to FLUXNET observations. It was found that model performance decreases further, with total annual GPP underestimated by 30% across all sites compared to observations. When JULES was driven using local parameters and global meteorological data, it was shown that global data could be used in place of FLUXNET data with a 7% reduction in total annual simulated GPP. Thirdly, the global meteorological data sets, WFDEI and PRINCETON, were compared to local data to find that the WFDEI data set more closely matches the local meteorological measurements (FLUXNET). Finally, the JULES phenology model was tested by comparing results from simulations using the default phenology model to those forced with the remote sensing product MODIS leaf area index (LAI). Forcing the model with daily satellite LAI results in only small improvements in predicted GPP at a small number of sites, compared to using the default phenology model.

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ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?

Hydrology and Earth System Sciences ◽

10.5194/hess-22-3515-2018 ◽

2018 ◽

Vol 22 (6) ◽

pp. 3515-3532 ◽

Cited By ~ 74

Author(s):

Clement Albergel ◽

Emanuel Dutra ◽

Simon Munier ◽

Jean-Christophe Calvet ◽

Joaquin Munoz-Sabater ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Land Surface ◽

Snow Depth ◽

Land Surface Model ◽

Surface Model ◽

Area Index ◽

Model Simulations ◽

In Situ Observations

Abstract. The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released the first 7-year segment of its latest atmospheric reanalysis: ERA-5 over the period 2010–2016. ERA-5 has important changes relative to the former ERA-Interim atmospheric reanalysis including higher spatial and temporal resolutions as well as a more recent model and data assimilation system. ERA-5 is foreseen to replace ERA-Interim reanalysis and one of the main goals of this study is to assess whether ERA-5 can enhance the simulation performances with respect to ERA-Interim when it is used to force a land surface model (LSM). To that end, both ERA-5 and ERA-Interim are used to force the ISBA (Interactions between Soil, Biosphere, and Atmosphere) LSM fully coupled with the Total Runoff Integrating Pathways (TRIP) scheme adapted for the CNRM (Centre National de Recherches Météorologiques) continental hydrological system within the SURFEX (SURFace Externalisée) modelling platform of Météo-France. Simulations cover the 2010–2016 period at half a degree spatial resolution. The ERA-5 impact on ISBA LSM relative to ERA-Interim is evaluated using remote sensing and in situ observations covering a substantial part of the land surface storage and fluxes over the continental US domain. The remote sensing observations include (i) satellite-driven model estimates of land evapotranspiration, (ii) upscaled ground-based observations of gross primary production, (iii) satellite-derived estimates of surface soil moisture and (iv) satellite-derived estimates of leaf area index (LAI). The in situ observations cover (i) soil moisture, (ii) turbulent heat fluxes, (iii) river discharges and (iv) snow depth. ERA-5 leads to a consistent improvement over ERA-Interim as verified by the use of these eight independent observations of different land status and of the model simulations forced by ERA-5 when compared with ERA-Interim. This is particularly evident for the land surface variables linked to the terrestrial hydrological cycle, while variables linked to vegetation are less impacted. Results also indicate that while precipitation provides, to a large extent, improvements in surface fields (e.g. large improvement in the representation of river discharge and snow depth), the other atmospheric variables play an important role, contributing to the overall improvements. These results highlight the importance of enhanced meteorological forcing quality provided by the new ERA-5 reanalysis, which will pave the way for a new generation of land-surface developments and applications.

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57 years (1960–2017) of snow and meteorological observations from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.)

10.5194/essd-2018-84 ◽

2018 ◽

Cited By ~ 2

Author(s):

Yves Lejeune ◽

Marie Dumont ◽

Jean-Michel Panel ◽

Matthieu Lafaysse ◽

Philippe Lapalus ◽

...

Keyword(s):

Spatial Variability ◽

Snow Depth ◽

Meteorological Data ◽

Long Period ◽

Several Variables ◽

Mountain Site ◽

Snow Water ◽

The Mean ◽

Meteorological Observations ◽

Measurements Errors

Abstract. In this paper, we introduce and provide access to a daily (1960–2017) and hourly (1993–2017) dataset of snow and meteorological data measured at the Col de Porte site, 1325 m a.s.l, Charteuse, France. Site metadata and ancillary measurements such as soil properties and masks of the incident solar radiation are also provided. Weekly snow profiles are made available from September 1993 to April 2015. A detailed study of the uncertainties originating from both measurements errors and spatial variability within the measurement site is provided for several variables. We show that the estimates of the ratio of diffuse to total shortwave broadband irradiance is affected by an uncertainty of ± 0.21. The estimated root mean squared deviation, that can be mainly attributed to spatial variability, is ± 10 cm for snow depth, ± 25 kg m−2 for snow water and ± 1 K for soil temperature (± 0.4 K during the snow season). The daily dataset can be used to quantify the effect of climate change at this site with a reduction of the mean snow depth (Dec. 1st to April 30th of 39 cm from 1960–1990 to 1990–2017 and an increase in temperature of + 0.90 K for the same periods. Finally, we show that the daily and hourly datasets are useful and appropriate for driving and evaluating a snowpack model over such a long period. The data are placed on the repository of the Observatoire des Sciences de l'Univers de Grenoble (OSUG) datacenter: https://doi.org/10.17178/CRYOBSCLIM.CDP.2018.

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Detection of precipitation and snow cover trends in the the European Alps over the last century using model and observational data

10.5194/egusphere-egu2020-18274 ◽

2020 ◽

Author(s):

Julien Beaumet ◽

Martin Menegoz ◽

Hubert Gallée ◽

Vincent Vionnet ◽

Xavier Fettweis ◽

...

Keyword(s):

Climate Change ◽

Snow Cover ◽

Observational Data ◽

Snow Depth ◽

European Alps ◽

Temperature Changes ◽

In Situ Observations ◽

Precipitation Changes ◽

High Elevations

The European Alps are particularly sensitive to climate change. Compared to temperature, changes in precipitation are more challenging to detect and attribute to ongoing anthropic climate change mainly as a result of large inter-annual variability, lack of reliable measurements at high elevations and opposite signals depending on the season or the elevation considered. However, changes in precipitation and snow cover have significant socio-environmental impact mostly trough water resource availability. These changes are investigated within the framework of the Trajectories initiative (). The variability and changes in precipitation and snow cover in the European Alps has been simulated with the MAR regional climate model at a 7 km horizontal resolution driven by ERA20C (1902-2010) and ERA5 (1979-2018) reanalyses. For precipitation, MAR outputs were compared with EURO-4M, SAFRAN, SPAZM and E-OBS reanalyses as well as in-situ observations. The model was shown to reproduce correctly seasonal and inter-annual variability. The spatial biases of the model have the same order of magnitude as the differences between the three observational data sets. Model experiment has been used to detect precipitation changes over the last century. An increase in winter precipitation is simulated over the North-western part of the Alps at high altitudes (>1500m). Significant decreases in summer precipitation were found in many low elevation areas, especially the Po Plain while no significant trends where found at high elevations. Because of large internal variability, precipitation changes are significant (pvalue<0.05) only when considering their evolution over long period, typically 60-100 years in both model and observations.Snow depth and water equivalent (SWE) in the French Alps simulated with MAR have been compared to the SAFRAN-Crocus reanalyses and to in-situ observations. MAR was found to simulate a realistic distribution of SWE as function of the elevation in the French Alpine massifs, although it underestimates SWE at low elevations in the Pre-Alps. Snow cover over the whole European Alps is evaluated using MODIS satellite data. Finally, trends in snow cover and snow depth are highlighted as well as their relationships with the precipitation and temperature changes over the last century.

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On the Ability of LIDAR Snow Depth Measurements to Determine or Evaluate the HRU Discretization in a Land Surface Model

Hydrology ◽

10.3390/hydrology7020020 ◽

2020 ◽

Vol 7 (2) ◽

pp. 20

Author(s):

Michael Weber ◽

Moritz Feigl ◽

Karsten Schulz ◽

Matthias Bernhardt

Keyword(s):

Snow Cover ◽

Land Surface ◽

Snow Depth ◽

Water Cycle ◽

Land Surface Model ◽

Surface Model ◽

Discretization Scheme ◽

Dominant Component ◽

Alpine Research ◽

Alpine Catchments

To find the adequate spatial model discretization scheme, which balances the models capabilities and the demand for representing key features in reality, is a challenging task. It becomes even more challenging in high alpine catchments, where the variability of topography and meteorology over short distances strongly influences the distribution of snow cover, the dominant component in the alpine water cycle. For the high alpine Research Catchment Zugspitze (RCZ) a new method for objective delineation of hydrological response units (HRUs) using a time series of high resolution LIDAR derived snow depth maps and the physiographic properties of the RCZ is introduced. Via principle component analysis (PCA) of these maps, a dominant snow depth pattern, that turned out to be largely defined during the (winter) accumulation period was identified. This dominant pattern serves as a reference for HRU delineations on the basis of cluster analyses of the catchment’s physiographic properties. The method guarantees for an appropriate, objective, spatial discretization scheme, which allows for a reliable and meaningful reproduction of snow cover variability with the Cold Regions Hydrological Model — at the same time avoiding significant increase of computational demands. Different HRU schemes were evaluated with measured snow depth and the comparison of their model results identified significant differences in model output and best performance of the scheme which best represents measured snow depth distribution.

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The method ADAMONT v1.0 for statistical adjustment of climate projections applicable to energy balance land surface models

10.5194/gmd-2017-135 ◽

2017 ◽

Author(s):

Deborah Verfaillie ◽

Michel Déqué ◽

Samuel Morin ◽

Matthieu Lafaysse

Keyword(s):

Land Surface ◽

Snow Depth ◽

Meteorological Data ◽

Grid Point ◽

Climate Projections ◽

Point Selection ◽

Selection Technique ◽

Observational Dataset ◽

Statistical Adjustment ◽

Time Periods

Abstract. We introduce the method ADAMONT v1.0 to adjust and disaggregate daily climate projections from a regional climate model against an observational dataset at hourly time resolution. The method uses a refined quantile mapping approach for statistical adjustment and an analogous method for sub-daily disaggregation. The method produces ultimately adjusted hourly time series of temperature, precipitation, wind speed, humidity, and short- and longwave radiation, which can in turn be used to force any energy balance land surface model. While the method is generic and can be employed on any appropriate observation time series, here we focus on the description and evaluation of the method in the French mountainous regions. The observational dataset used here is the SAFRAN meteorological reanalysis, which covers the entire French Alps split into 23 massifs, within which meteorological conditions are provided for several 300 m elevation bands. In order to evaluate the skills of the method itself, it is applied to the ALADIN-Climate v5 RCM using the ERA-Interim reanalysis as boundary conditions, for the time period from 1980 to 2010. Results of the ADAMONT method are compared to the SAFRAN reanalysis itself. Various evaluation criteria are used for temperature, precipitation, but also snow depth, which is computed by the SURFEX/ISBA-Crocus model using the meteorological driving data from either the adjusted RCM data, or the SAFRAN reanalysis itself. The evaluation addresses in particular the time transferability of the method (using various learning/application time periods), the impact of the RCM grid point selection procedure for each massif/altitude band configuration, and the inter-variable consistency of the adjusted meteorological data generated by the method. Results show that the performance of the method is satisfactory, with similar or even better evaluation metrics than alternative methods. However, results for air temperature are generally better than for precipitation. Results in terms of snow depth are satisfactory, which can be viewed as indicating a reasonably good inter-variable consistency of the meteorological data produced by the method. In terms of temporal transferability (evaluated over time periods of 15 years only), results depend on the learning period. In terms of RCM grid point selection technique, the use of a complex RCM grid points selection technique, taking into account horizontal but also altitudinal proximity to SAFRAN massif centre points/altitude couples, generally degrades evaluation metrics for high altitudes, compared to a simpler grid point selection method based on horizontal distance.

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Discharge simulation in the sub-basins of the Amazon using ORCHIDEE forced by new datasets

Hydrology and Earth System Sciences ◽

10.5194/hess-16-911-2012 ◽

2012 ◽

Vol 16 (3) ◽

pp. 911-935 ◽

Cited By ~ 58

Author(s):

M. Guimberteau ◽

G. Drapeau ◽

J. Ronchail ◽

B. Sultan ◽

J. Polcher ◽

...

Keyword(s):

Land Surface ◽

Interpolation Method ◽

Land Surface Model ◽

Main Stem ◽

Amazon River ◽

Surface Model ◽

Discharge Simulation ◽

The Mean ◽

Hydrological Station

Abstract. The aim of this study is to evaluate the ability of the ORCHIDEE land surface model to simulate streamflows over each sub-basin of the Amazon River basin. For this purpose, simulations are performed with a routing module including the influence of floodplains and swamps on river discharge and validated against on-site hydrological measurements collected within the HYBAM observatory over the 1980–2000 period. When forced by the NCC global meteorological dataset, the initial version of ORCHIDEE shows discrepancies with ORE HYBAM measurements with underestimation by 15% of the annual mean streamflow at Óbidos hydrological station. Consequently, several improvements are incrementally added to the initial simulation in order to reduce those discrepancies. First, values of NCC precipitation are substituted by ORE HYBAM daily in-situ rainfall observations from the meteorological services of Amazonian countries, interpolated over the basin. It highly improves the simulated streamflow over the northern and western parts of the basin, whereas streamflow over southern regions becomes overestimated, probably due to the extension of rainy spots that may be exaggerated by our interpolation method, or to an underestimation of simulated evapotranspiration when compared to flux tower measurements. Second, the initial map of maximal fractions of floodplains and swamps which largely underestimates floodplains areas over the main stem of the Amazon River and over the region of Llanos de Moxos in Bolivia, is substituted by a new one with a better agreement with different estimates over the basin. Simulated monthly water height is consequently better represented in ORCHIDEE when compared to Topex/Poseidon measurements over the main stem of the Amazon. Finally, a calibration of the time constant of the floodplain reservoir is performed to adjust the mean simulated seasonal peak flow at Óbidos in agreement with the observations.

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Filling the gaps in meteorological continuous data measured at FLUXNET sites with ERA-interim reanalysis

Earth System Science Data Discussions ◽

10.5194/essdd-8-23-2015 ◽

2015 ◽

Vol 8 (1) ◽

pp. 23-55 ◽

Cited By ~ 1

Author(s):

N. Vuichard ◽

D. Papale

Keyword(s):

Standard Deviation ◽

Land Surface ◽

Meteorological Data ◽

Internal Variability ◽

Fair Use ◽

Meteorological Variable ◽

Continuous Data ◽

High Temporal Resolution ◽

In Situ Data

Abstract. Exchanges of carbon, water and energy between the land surface and the atmosphere are monitored by eddy covariance technique at the ecosystem level. Currently, the FLUXNET database contains more than 500 sites registered and up to 250 of them sharing data (Free Fair Use dataset). Many modelling groups use the FLUXNET dataset for evaluating ecosystem model's performances but it requires uninterrupted time series for the meteorological variables used as input. Because original in-situ data often contain gaps, from very short (few hours) up to relatively long (some months), we develop a new and robust method for filling the gaps in meteorological data measured at site level. Our approach has the benefit of making use of continuous data available globally (ERA-interim) and high temporal resolution spanning from 1989 to today. These data are however not measured at site level and for this reason a method to downscale and correct the ERA-interim data is needed. We apply this method on the level 4 data (L4) from the LaThuile collection, freely available after registration under a Fair-Use policy. The performances of the developed method vary across sites and are also function of the meteorological variable. On average overall sites, the bias correction leads to cancel from 10 to 36% of the initial mismatch between in-situ and ERA-interim data, depending of the meteorological variable considered. In comparison to the internal variability of the in-situ data, the root mean square error (RMSE) between the in-situ data and the un-biased ERA-I data remains relatively large (on average overall sites, from 27 to 76% of the standard deviation of in-situ data, depending of the meteorological variable considered). The performance of the method remains low for the wind speed field, in particular regarding its capacity to conserve a standard deviation similar to the one measured at FLUXNET stations. The ERA-interim reanalysis data debiased at FLUXNET sites can be downloaded from the PANGAEA data center (http://doi.pangaea.de/10.1594/PANGAEA.838234).

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