ESSDEarth System Science DataESSDEarth Syst. Sci. Data1866-3516Copernicus PublicationsGöttingen, Germany10.5194/essd-10-303-2018Daily gridded datasets of snow depth and snow water equivalent for the
Iberian Peninsula from 1980 to 2014Alonso-GonzálezEstebane.alonso@ipe.csic.comhttps://orcid.org/0000-0002-1883-3823López-MorenoJ. IgnacioGascoinSimonhttps://orcid.org/0000-0002-4996-6768García-Valdecasas OjedaMatildehttps://orcid.org/0000-0001-9551-8328Sanmiguel-ValleladoAlbaNavarro-SerranoFranciscohttps://orcid.org/0000-0002-2975-6472RevueltoJesúshttps://orcid.org/0000-0001-5483-0147CeballosAntonioEsteban-ParraMaría Jesúshttps://orcid.org/0000-0003-1350-6150EsseryRichardInstituto Pirenaico de Ecología, Consejo Superior de
Investigaciones Científicas (IPE-CSIC), Zaragoza, SpainCentre d'Etudes Spatiales de la Biosphère (CESBIO),
UPS/CNRS/IRD/CNES, Toulouse, FranceDepartamento de Física Aplicada, Facultad de Ciencias,
Universidad de Granada, Granada, SpainMétéo-France – CNRS, CNRM (UMR3589), Centre d'Etudes de la
Neige, Grenoble, FranceDept. Geografía, Universidad de Salamanca, Salamanca, SpainSchool of GeoSciences, University of Edinburgh, Edinburgh, UKEsteban Alonso-González (e.alonso@ipe.csic.com)20February201810130331511September201720October201728December20178January2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://essd.copernicus.org/articles/10/303/2018/essd-10-303-2018.htmlThe full text article is available as a PDF file from https://essd.copernicus.org/articles/10/303/2018/essd-10-303-2018.pdf
We present snow observations and a validated daily gridded snowpack dataset
that was simulated from downscaled reanalysis of data for the Iberian
Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its
different mountain ranges, and winter snowfall occurs in most of its area.
However, there are only limited direct observations of snow depth (SD) and
snow water equivalent (SWE), making it difficult to analyze snow dynamics
and the spatiotemporal patterns of snowfall. We used meteorological data
from downscaled reanalyses as input of a physically based snow energy
balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to
2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 km × 10 km resolution using the Weather Research and Forecasting (WRF)
model. The WRF outputs were used directly, or as input to other submodels,
to obtain data needed to drive the Factorial Snow Model (FSM). We used
lapse rate coefficients and hygrobarometric adjustments to simulate snow
series at 100 m elevations bands for each 10 km × 10 km grid cell in
the Iberian Peninsula. The snow series were validated using data from MODIS
satellite sensor and ground observations. The overall simulated snow series
accurately reproduced the interannual variability of snowpack and the
spatial variability of snow accumulation and melting, even in very complex
topographic terrains. Thus, the presented dataset may be useful for many
applications, including land management, hydrometeorological studies,
phenology of flora and fauna, winter tourism, and risk management. The data
presented here are freely available for download from Zenodo
(10.5281/zenodo.854618). This paper fully describes the work flow, data
validation, uncertainty assessment, and possible applications and limitations
of the database.
Introduction
Seasonal snowpack exerts an important control on the hydrology and economy
of many mountainous and cold regions worldwide (Barnett
et al., 2005). Snow variability also affects different ecological processes,
such as species composition, distribution, and phenology
(Keller et al., 2000; Wipf et
al., 2009). For example, snowpack on Mediterranean mountains is a crucial
source of water during the dry season
(Fayad et al., 2017; García-Ruiz et al., 2011; Viviroli et al., 2007).
Long-term data are required to analyze the spatiotemporal dynamics of
snowpack, to assess the importance of snow as a resource, and to understand the
effect of climatic fluctuations. However, there are only limited in situ
observations of snowpack for most mountain regions
(Raleigh et al., 2016). Currently
remote-sensing techniques can only reliably provide information about snow
cover based on observations in the visible spectrum (Dietz et al., 2012).
Current spaceborne sensors do not provide accurate data on snow water
equivalent (SWE) and/or snow depth (SD) in mountainous regions (Dozier et
al., 2016). Microwave imaging has a coarse resolution (grid cell size:
∼ 25 km), so does not characterize snowpack variability in the
Mediterranean mountains, which have a high spatial heterogeneity not captured
with this resolution. There are also spatial and temporal limitations when
attempting to estimate snowpack using close-range remote-sensing techniques
such as lidar (Revuelto et al., 2016).
There are limited in situ snow observations and meteorological data at high
elevations in the Iberian Peninsula. Although the number of monitored sites
has increased in recent years, there are no long-term series and there is
insufficient characterization of snowpack dynamics at a regional scale.
However, snowpack in the Iberian Peninsula is an important hydrological and
also economical resource. An area of 19456.4 km2 in the Iberian
Peninsula lies above 1500 m a.s.l., mostly in the five most important mountain
ranges (Pyrenees, Cantabrian Mountains, Central System, Iberian Range, and
Sierra Nevada). At this elevation, snowpack occurs for at least 4 months
of the year (López-Moreno et al., 2011) making it a critical resource for
water management in the largest hydrological basins (Morán-Tejeda et al.,
2014). Snowpack influences the interannual variability of water resources
(López-Moreno and García-Ruiz, 2004) and
the timing of the winter low flows and spring peak flows (Sanmiguel-Vallelado
et al., 2017). Moreover, winter tourism (mainly skiing) has been increasingly
important to the economy of mountain valleys in recent decades, and the
large interannual fluctuations of snowpack in the different mountain regions
of the Iberian Peninsula affect the economic viability of tourism
(Gilaberte-Búrdalo et al., 2014, 2017).
The importance of snow to the environment and economy of the Iberian
Peninsula, and the lack of data on snowpack in this region, motivated us to
use meteorological outputs from downscaled reanalysis data to simulate
snowpack at different elevations in the Iberian Peninsula. Atmospheric
reanalyses, based on data assimilation and modeling (Saha et al., 2010), can
provide important information about the temporal evolution of the atmosphere.
Meteorological variables obtained from reanalysis data can be used as inputs
for models of snow mass and energy balance which can be applied to describe
the behavior of the snowpack over large areas (Brun et al., 2013; Krogh et
al., 2015; Wegmann et al., 2017). However, the coarse resolution (cell size:
around tens of kilometers) implies these simulations may have insufficient spatial
resolution for characterizing the topographical complexity of mountain areas
(Mass et al., 2002). To overcome this limitation, regional climate models
(RCMs) are often used to obtain better representations of surface
climatology, because they downscale physically reanalysis products
(García-Valdecasas Ojeda et al., 2017; Kryza et al., 2017; Warrach-Sagi et
al., 2013). Previous studies have used RCMs to study SD and SWE dynamics at
finer resolutions (grid cell size: 5 to 11 km) when they are driven with
reanalyses, and the resolution increases further (grid cell size: 1 km) when
using forecasted data (Bellaire et al., 2011; van Pelt et al., 2016;
Quéno et al., 2016; Wu et al., 2016).
van Pelt et al. (2016) used the High Resolution Limited Area Model (HIRLAM)
in Svalbard (Norway), with forcing by ERA-40 and ERA-Interim reanalysis, and
then used the meteorological simulation as driving data for SnowModel (Liston
and Elder, 2006a). Their results support the usefulness of the methodology
extracting snowpack trends from these data. Wu et al. (2016) used a similar
procedure to describe the behavior of snowpack over the Altai Mountains in
China. They coupled outputs from the Weather Research and Forecasting (WRF)
model (Skamarock et al., 2008) driven by NCEP/NCAR reanalysis with a
temperature index model (based on remote sensing), and their results had low
error values. To increase the spatial resolution of the WRF outputs, they
used the MICROMET model (Liston and Elder, 2006b), a submodel of SnowModel in
which WRF outputs are interpolated to a new grid, and then corrected
physically according to topography. Wrzesien et al. (2017) tested the
capability of WRF to estimate SWE over complex terrain concluding that WRF
simulations can be used over areas with few observational data.
We used a different approach, in an effort to make our database more
computationally practicable and to avoid the uncertainties of the
statistical interpolations of climatological variables over complex areas.
More specifically, we projected WRF outputs to different elevation bands to
generate simulations for multiple elevations. This procedure allows one to study
the elevation-dependent characteristics of the snowpack over different
mountain ranges, preserving the WRF output resolution.
Our procedure uses the physically based Factorial Snow Model (FSM; Essery,
2015), which is fed by ERA-Interim reanalysis (Berrisford et al., 2011), and
downscaled by the WRF model. The final products of our analysis are simulated
daily time series of SD and SWE at different elevations from 1980 to 2014.
Data and methods
Digital elevation model (DEM) of the Iberian Peninsula and locations of
the telenivometers, Cotos Pass SD sensor, and MODIS study areas.
We used an existing WRF simulation (cell size: 10 km) for the whole Iberian
Peninsula (Fig. 1), with a 3 h time step from January 1979 to November 2014,
as input data for the FSM. Most inputs of the FSM were extracted directly
from the WRF simulation, but some were calculated using other submodels. We
projected the WRF outputs and derived variables to different elevation bands,
from 500 to 2900 m a.s.l. at steps of 100 m, from the WRF pixel elevation
using several hygrometric and psychrometric formulas and elevation lapse
rates. FSM outputs were aggregated at a daily time step in order to increase
the manageability of the data. Validation was performed at different steps of
the workflow using different observational data sources. Figure 2 shows the
workflow completely, which is described in more detail below. Appendix A
lists all the abbreviations used in this study.
Simulation workflow. Squared boxes represent modeling steps and
rounded boxes represent meteorological variables. Variables that are not
inputs or outputs of a model are indicated by dotted lines (see a glossary of
abbreviations used in Appendix A).
Meteorological driving data
The meteorological variables were calculated using the WRF model, a mesoscale
climate model. Previous researchers used this model to simulate climate at
regional scales for analysis of past, present, and future conditions (Chen et
al., 2011; Heikkilä et al., 2011). The spatial resolution
of our simulation is 0.088∘ (∼ 10 km) and the time step is
3 h. ERA-Interim reanalysis (Berrisford et al., 2011) was used as driving
data for the WRF model. With this procedure all meteorological variables for
running snowpack models were generated for the whole Iberian Peninsula. The WRF
configuration was described in detail by García-Valdecasas Ojeda et
al. (2017). This simulation provided the following variables: wind speed
(Ua), surface temperature (T), precipitation (Pr), relative humidity (RH),
shortwave incoming radiation (SW), and atmospheric pressure (Ps).
Snow energy and mass balance model
SD and SWE time series were obtained using a mass and energy balance snowpack
model. The FSM is a multi-physics snow model that
simulates the accumulation and melting of snow (Essery, 2015). This model
allows selection of two options for parameterizations of five different
process, thereby enabling 32 different model configurations. The
configuration used to develop our simulations decreases snow albedo and
increases snow density at different rates for cold and melting snow,
calculates thermal conductivity as a function of snow density, adjusts the
turbulent exchange coefficient as a function of the bulk Richardson number,
and allows retention and refreezing of liquid water inside the snowpack.
The model works with different numbers and thicknesses of layers, depending
on snowpack depth. Thus, it assumes a single layer when snow depth is less
than 0.2 m, and a maximum of three layers when the depth is greater than
0.5 m. This configuration allows the model to characterize the highly
variable climatological conditions of the Iberian mountains. In addition to
the variables provided by the WRF simulation (listed in Sect. 2.1), the FSM
also needs estimates of snow rate (Sf), rain rate (Rf), and longwave
incoming radiation (LW). To avoid the expense of rerunning WRF in this study,
these variables have been reconstructed from available WRF simulation
outputs.
To calculate Sf and Rf, we used a psychrometric energy balance method
(PPPm; Harder and Pomeroy, 2013), which uses relative humidity and air
temperature to calculate the surface temperature of falling hydrometeors.
From this value, the fraction of liquid precipitation is as follows:
frTi=11+bcTi,
where fr is the percentage of liquid precipitation, Ti is the
temperature (∘C) of the falling hydrometeor, and b and c are
derived from statistical fits (2.50286 and 0.125006, respectively, for hourly
time intervals). Ti is calculated from Eq. (2), which we solved
numerically using the method described by Brent and Richard (1972):
Ti=Ta+DλtLρTa-ρsatTi
where Ta is the temperature (K), D is the diffusivity of water
vapor in air (m2 s-1), λt is the thermal
conductivity of air (W m-1 K-1), L is the latent heat of
sublimation or vaporization (J kg-1), and ρTa and
ρsatTi (kg m-3) are respectively
the vapor densities in free atmosphere and at the saturated hydrometeor
surface. This methodology gives the percentage of liquid precipitation; the
percentage of solid precipitation is directly calculated from fr.
Incoming longwave radiation (W m-2) was estimated from the
Stefan–Boltzmann law:
L↓=εσTa4,
where σ is the Stefan–Boltzmann constant and ε is the
emissivity of the atmosphere.
Emissivity was calculated as a function of elevation and cloud cover, as
proposed by Liston and Elder (2006b), who used a variation of the methodology
described by Iziomon et al. (2003). Thus, emissivity is calculated as follows:
ε=1.0831+Zscc21-Xsexp-YseeTaTa,
where e (Pa) is the atmospheric vapor pressure, cc is the fractional cloud
cover, and Xs, Ys, and Zs are coefficients that are corrected with
elevation:
CS=C1z<200ma.s.l.,CS=C1+z-z1C2-C1z2-z1200ma.s.l.≤z≤3000ma.s.l.,CS=C23000ma.s.l.<z,
where zm is the elevation above sea
level, and Xs, Ys, and Zs can be substituted for C, with
X1=0.35,X2=0.51,Y1=0.100 K Pa-1,
Y2=0.130 K Pa-1, Z1=0.224, Z2=1.100,
z1=200 m a.s.l., and z2=3000 m a.s.l.
Different parameterizations using SW were tested to estimate cc, from
potential SW, a more accurate approach than the parameterization proposed by
Liston and Elder (2006b), according to Gascoin et al. (2013). This approach uses
the relationship between SW and potential SW radiation that is restricted to
daylight hours. Thus, in this work, we used the parameterization proposed by
Walcek (1994) for cc estimation, which is the original parameterization
proposed by Liston and Elder (2006b).
cc=0.832expRH700-10041.6,
where RH700 is the relative humidity at 700 mb.
The methodology used to project RH to 700 mb elevation is described below.
To scale the snow simulations to different elevations, we first used the
internationally accepted standard air temperature lapse rate (β=0.0065 K m-1; Barry and Chorley, 1987; ISO, 1975) to project the
surface air temperature. For RH, the methodology proposed by Liston and Elder (2006b) was used, in which a lapse rate is applied to the dew point
temperature (HRm). First, we calculated the dew point temperature from RH and
the saturation vapor pressure. Then, we applied the standard air temperature
lapse rate to the dew point temperature, and recalculated the RH at the
target elevation from the scaled dew point temperature and the saturation
vapor pressure. Once we rescaled temperature and RH, we calculated the
precipitation phase and LW radiation at the different elevations.
Finally, to estimate the scaled surface air pressure we used a generalization
of the barometric formula for scenarios that consider air temperature
lapse rates (Berberan-Santos et al., 1997):
pz=p01-β⋅zTamgmgRβRβ,
where p0 is the surface air pressure, z is the
elevation difference (m), m is the molecular mass of air
(0.0289644 kg mol-1), and R is the universal gas constant
(8.31432 J K-1 mol-1).
Validation procedure
Validation was performed at different resolutions and at different steps of
the workflow, using all available observational data (Fig. 2). Previous
studies
(Argüeso
et al., 2012; García-Valdecasas-Ojeda et al., 2016) simulated
temperature and precipitation using WRF at different timescales compared
with the grids, based on observations from Spain02
(Herrera et al., 2012) and PT02
(Belo-Pereira et al., 2011), high-resolution precipitation and
temperature gridded datasets for Spain and Portugal, respectively. The
results indicated proper simulation of the major patterns of precipitation
and temperature, even for extreme events. Subsequent research showed that
the downscaling made by WRF provided improved accuracy compared to
ERA-Interim data, due to the higher resolution
(García-Valdecasas Ojeda et al., 2017).
Comparison between modeled (red) and observed (black) SD time series
for each telenivometer and the Cotos SD sensor.
In this work, we used the Moderate-Resolution Imaging Spectroradiometer
(MODIS) satellite sensor to validate our snow cover product for the period
September 2000 to November 2014. Similarly, we used data from telenivometers,
which were available in the Pyrenees from October 2009 to June 2014.
First, we compared MODIS data with the SD and SWE time series (10 km
resolution). MODIS snow maps were generated using the same workflow for each
mountain range in the study area (Pyrenees, Cantabrian Mountains, Central
System, Iberian Range, and Sierra Nevada). We downloaded all the available
MOD10A1 and MYD10A1 products (version 5) from the National Snow and Ice Data
Center (Hall et al., 2006). The original granules were mosaicked and
re-projected from the sinusoidal system to the Universal Transverse Mercator
(UTM) reference system. Then, we ran a gap-filling algorithm, using the
binary snow product to avoid data losses due to cloud cover
(Gascoin et al., 2015). This provided gap-free daily maps showing the presence and
absence of snow in each mountain range from 2000 to 2014. From these maps,
the probability of snow was calculated as follows:
PSnow=NsN×100,
where PSnow is the probability of snow (%),
Ns
is the number of days with snow, and N is the total number of days of the
period.
Snow probability maps were also calculated from the FSM snow cover maps. In
this work, we chose a threshold of 0.11 m for SD and a threshold of
40 mm for SWE (Gascoin et al., 2015) in the FSM time series. This allowed us to
generate snow cover maps from FSM outputs. Then, we aggregated the MODIS
pixels (500 m) to the simulation grid (∼ 10 km), with averaging of
the values of MODIS pixels to make them comparable.
We also used data from 11 telenivometers, which measure sub-hourly SWE and
SD using gamma ray attenuation and acoustic sensors. These data were
provided by the ERHIN program (Estimación de Recursos Hídricos
Procedentes de la Nieve) of the Hydrological Ebro River Basin Authority
(Navarro-Serrano and López-Moreno, 2017). Ten telenivometers were located in the Pyrenees, and one in the Cantabrian Mountains. A complete description of the telenivometers and
their locations can be found at www.saihhebro.com. We also used an SD sensor in
the Central System mountain range (Durán et al., 2017), which is from the
National Meteorological Agency of Spain (AEMET). We projected the
meteorological variables from the WRF simulation to elevations of the
different telenivometers for simulations. Figure 3 shows a comparison of the
modeled and observed SD time series at these 10 sites.
Correlation between the long-term (2000–2015) mean probability of
snow depth (a) and snow water equivalent (b) from MODIS data and from
FSM output. Box plot insets show the frequency distributions of errors (%),
with the central red lines indicating average errors, boxes indicating the
25th and 75th percentiles, bars indicating the 10th and 90th percentiles, and
dots indicating the 5th and 95th percentiles.
Kappa values derived from comparison of observed and simulated
series for different percentiles of snow depth (a) and snow water
equivalent (b), and periods of the year (blue) when snowpack exceeds
the 90th, 75th, and 50th percentiles (c). In (c), each pair
of bands shows the times when the different percentiles in the observed (OBS)
and simulated (SIM) series at each telenivometer exceeded the indicated
percentile.
It must be noted that it is challenging to validate gridded products from
ground-based data (Snauffer et al., 2016). Snowpack can have large
variability over small distances (López-Moreno et al., 2015; Meromy et
al., 2013). This implies that punctual measurements may not be representative
of the 10 km resolution data, even when comparing a simulation at the same
elevation as the telenivometer. In addition, snow measurements always include
biases from the different measuring devices (Kinar and Pomeroy, 2015). Thus,
we focused on the temporal patterns of snowpack during the season. More
specifically, we compared the accumulation patterns during the season,
assuming that accumulation and melting rates were similar in the simulated
and observational data, but that SD and SWE likely differ between the
telenivometer and the simulation.
Thus, we first compared different percentiles of SD and SWE in the
telenivometer and the simulated time series. Then, using each percentile as a
threshold for snow presence, we converted the series into binary data,
allowing use of the kappa test (Cohen, 1960) for each percentile. The kappa
coefficient ranges from 1 and < 0, but it is difficult to assign an
agreement criterion based on kappa value. Thus, we used the thresholds
proposed by Landis and Koch (1977), which basically agree with values
proposed by Fleiss et al. (1969; < 0.00: poor; 0.00–0.20: slight;
0.21–0.4: fair; 0.41–0.60: moderate; 0.61–0.80: substantial; and
0.81–1.00: almost perfect). We examined percentile values between 10 and
90 %, as more representative of snow accumulation during the season.
ResultsValidation
Our analysis of the probability of snow presence from MODIS and FSM shows
that the outputs had good correlations (Fig. 4). This analysis compared the
probability of snow at each pixel (∼ 10 km × 10 km) from MODIS
and FSM outputs for the SWE and SD time series from September 2000 to
November 2014. The mean coefficient (R2) was 0.76, and a mean absolute
error was 6.3 %. This analysis also shows the correlations for each
mountain range, and the distribution of errors for SWE and SD (simulated less
observed).
These results also show there are no significant differences in the errors of
PSnow for the different mountain ranges.
However, the correlation was not strong for the Sierra Nevada range, probably
due to its limited snow cover, although this remained inside the variability
of the scatter plot.
Validation of these results with telenivometers indicated kappa values for
thresholds in the 10th to 90th percentiles of each season (Fig. 5). The
kappa values were mostly above 0.6, although accuracy declined for the
highest percentiles.
Long-term (1980–2014) average maximum SWE and SD grids at 1500,
2000, and 2500 m a.s.l.
Long-term (1980–2014) average number of snowfall events and
percentage of snow presence at 1500, 2000, and 2500 m a.s.l.
Comparison of SWE time series at 1500, 2000, and 2500 m a.s.l. at
Aneto Peak.
The kappa coefficient does not account for the displacement magnitude of the
different percentiles, and a difference of a few days at the time of peak
accumulation may cause a sharp decrease in the kappa value. This is the
reason for the loss of accuracy at the highest percentiles. Thus, we further
analyzed these data to determine the time of the year when snowpack exceeded
the 90th, 75th, and 50th percentiles at each telenivometer in the observed
(OBS) and simulated (SIM) series (Fig. 5c). This analysis shows that, despite
small temporal shifts, the simulated snow series accurately represents the
temporal patterns when different snow percentiles are exceeded.
The biggest shift in the position of the 90th and 75th percentiles was during
the 2011/2012 season. This season was extremely dry on the Iberian Peninsula,
and there were very few snowfall events (Fig. 3). Thus, a small bias in the
simulation of a single event during this time could lead to a large error in
prediction of the magnitude and timing of SD and SWE maxima.
Gridded snow dataset: applications and limitations
The final products of the models are daily gridded datasets (resolution:
0.088∘, ∼ 10 km) of SD and SWE at elevations from 500 to
2900 m a.s.l. (100 m intervals) from 1980 to 2014. The datasets (ncdf4
format) cover the entire Iberian Peninsula, including the north side of the
Pyrenees in France. Each dataset contains information of the entire Iberian
Peninsula and a mask that covers pixels that do not present areas at the
elevations of the simulation estimated from a 250 m resolution DEM.
This snow database provides new opportunities for studies of snow in the
Iberian Peninsula. In particular, the temporal resolution and the duration of
the series show significant improvements over previous observational data.
Also, the geographic data generated on SD and SWE provides the opportunity to
obtain more snow and hydrologically relevant information than available
from remote sensing alone. It is also possible to develop different snow
products at different elevations, allowing for comparison of different elevations
and different regions. For example, Fig. 6 shows the long-term average
interannual maximum SD and SWE at three different elevations.
Figure 7 shows examples of other snow variables that can be derived from the
database: average number of snowfall events and percentage of days with snow cover
at three elevations. These analyses are particularly useful for the
development of different snow climatologies for the whole Iberian Peninsula,
or for specific areas, in studies that rely on ecological data (e.g.,
phenology or distribution of plants and animals, forest growth),
studies that require hydrological parameters for different catchments, and
studies that determine risk maps for snow-related events.
It is also possible to extract daily time series for different areas or
elevations at each pixel. For example, Fig. 8 compares SWE series at three
elevations in the pixel at the highest peak of the Pyrenees (Aneto Peak,
3404 m a.s.l.). Thus, these series allows for the study of different annual snow
accumulation and melting patterns at a specific location and how elevation
influences snow evolution. Similarly, it enables one to study the existence of
temporal trends or the occurrence of extreme snowfall and melting events.
The database contains uncertainties that are not easy to quantify, due to
the limited amount of observational data. Biases may be due to uncertainty
of the boundary conditions from the ERA-Interim reanalysis
(Chaudhuri et al., 2013) since
errors from the WRF downscaling model are difficult to quantify in mountain
areas (Gutmann et al., 2012), and
uncertainties that typically result from simulations of snow mass and energy
balance from meteorological data (Essery et
al., 1999, 2013; Magnusson et al., 2015). The use of the standard air
temperature lapse rate could also be a source of uncertainty. Although other
studies have observed a decrease in the lapse rate during winter months,
this effect is result of thermic inversions that are not considered to be due to
the spatial resolution of the simulation.
Despite these limitations, we had very satisfactory results when testing the
duration and the interannual variability of the snowpack against MODIS and
telenivometer data, which provided reliable observations during several snow
seasons. This way, the database presents a reliable validation for more than
a third of the time period generated. When using this database, it is
important to consider that it was based on the assumption of flat topography
within each 10 km × 10 km pixel. Therefore, this dataset is not
suitable for studies of snow variability due to terrain aspect, slope, and
snow redistribution processes, such as avalanches and wind transport.
The data presented here are freely available for download
from Zenodo (10.5281/zenodo.854618). SD and SWE datasets are
in ncdf4 format, with one file for each elevation band. The observational
information used to validate the main data is also available for download.
All telenivometer data are in CSV format. Daily snow cover (derived from
MODIS) is provided as five multiband GeoTiff files (one file for each mountain
range, each band is a date), and a CSV file indicates the date of each band.
The FSM code is freely available from
https://github.com/RichardEssery/FSM (Essery, 2015).
Conclusions
We presented a new daily gridded database of SD and SWE for the Iberian
Peninsula from 1980 to 2014 period at a resolution of 0.088∘
(∼ 10 km). The database consists of 50 ncdf4 files for SD and SWE
from 500 to 2900 m a.s.l., and another 2 files of WRF simulation DEMs, summing
more than 652 000 maps. A mask label of “no data” is included if the grid
is not found at the elevation of the simulated elevation band.
The scarcity of snow observations in the Iberian Peninsula made it necessary
to couple a dynamic downscaling of ERA-Interim reanalysis using the WRF
model by use of a snow energy and mass balance model (FSM). Input data of
FSM provided directly, or estimated from WRF outputs, were available for the
average elevation of each 10 km × 10 km pixel, and these data were
transformed to achieve an elevation offset at 100 m intervals.
Despite some uncertainties, the database is consistent with available
observational data. More specifically, validation with MODIS data indicated
an error of 6.07 % and an R2 of 0.76 from the analysis of the mean presence
of snow. The database also provides good representation of the temporal
patterns of the telenivometers, with kappa values generally over 0.6, and
above 0.4 for all analyzed percentiles.
This database will be an important resource for studies of many different
hydrological, environmental, and economic processes in Mediterranean areas.
Thus, we expect the database presented here will be useful for future
snow-related studies at regional scales on the Iberian Peninsula, and for a
broad community of researchers and land managers working in areas where
snowfall occurs.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “Hydrometeorological
data from mountain and alpine research catchments”. It is not associated with a
conference.
Acknowledgements
Esteban Alonso-González is supported by the Spanish Ministry of Economy and
Competitiveness (BES-2015-071466). This study was funded by the Spanish Ministry of Economy and Competitiveness projects CGL2014-52599-P 10 (Estudio del manto de nieve en la montaña española y su respuesta a la variabilidad y cambio climatico) and CGL2017-82216-R (HIDROIBERNIEVE) and (with additional
support from the European Community funds, FEDER) CGL2013-48539-R
(Impactos del cambio climático en los recursos hídricos de la cuenca del Duero a alta resolución). Also, the Regional Government
of Andalusia has funded this research with the project P11-RNM-7941
(Impactos del Cambio Climático en la cuenca del Guadalquivir, LICUA). The authors would like to express thanks to Hydrological Ebro
River Basin Authority (CHE) for providing telenivometer data. Development of
FSM is supported by NERC grant NE/P011926/1. Cotos snow data were provided by
Consejería de Medio Ambiente, Administración Local y Ordenación
del Territorio de la Comunidad de Madrid, from the meteorological network of
Parque Natural de Peñalara. The authors sincerely thank Jan Magnusson for his help on the first steps of the use of FSM code. Edited by: Danny
Marks
Reviewed by: Javier Herrero and Benedita Santos
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