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Mountain forests affect spatial and temporal variability of snow processes through snow interception and by modifying the energy balance of snowpack. The high sensitivity of snow cover to seasonal temperatures in mid–latitude mountains is well known and is of particular interest with regard to a future warmer climate. The snowpack in the Pyrenees is expected to be the most impacted by climate change in the Mediterranean mountains, where future climate trends project rising temperatures and decreasing precipitation. This study analyzes how changes in temperature and precipitation can affect current forest–snow interactions in four forests, located near each other but under contrasting topographic settings, in the Spanish Pyrenees. This understanding will allow us to anticipate the future hydrological responses of Pyrenean forested mountain basins. The research was accomplished by performing a sensitivity analysis using simulations from the Cold Regions Hydrological Model (CRHM) and by comparing forest canopy sites (F) vs. openings (O). The CRHM platform focuses on the incorporation of physically based descriptions of snow–dominated regions hydrological processes. It was found that forest cover induced different snowpack sensibility to climatic change conditions in the studied forests. Delayed onset of snow accumulation (F: 13 days·°C−1; O: 5 days·°C−1) and reduced snowpack duration (F: 28 %·°C−1; O: 23 %·°C−1) under warmer temperatures were more intense in areas beneath the forest canopy compared to openings. A lower annual peak of snow water equivalent (SWE) (F: 81 mm·°C−1; O: 129 mm·°C−1), earlier melt-out date (F: 8 days·°C−1; O: 10 days·°C−1) and slower melting rates (F: 0.4 mm·day−1·°C−1; O: 0.5 mm·day−1·°C−1) with increasing temperatures were more intense in forest openings. The forest–driven reduction in snowpack duration (40%) was significantly enhanced with warming (10% per °C). Lower precipitation (20% precipitation reduction) could increase the response of

Snow dynamics are key to understanding tree growth in mountain forests and future response to climate change. However, precise monitoring of microclimate conditions and variables related to tree growth and functioning are lacking. To advance on those issues, snow cover and microclimate conditions, tree phenology, xylogenesis, intra-annual radial growth and the concentration of sapwood and needle non-structural carbohydrates were intensively monitored in four Pinus uncinata forests along an altitudinal gradient over three years in a Pyrenean valley (NE Spain). Snow dynamics exerted strong influence on soil temperature and moisture, particularly before and during the early growing season. Soil temperature was the most relevant microclimate variable during the overall xylogenesis, mainly influencing the production of mature tracheids. Large snow accumulation resulted in later snow depletion and a consequent delay in soil warming onset. Low soil temperatures in the spring, related to prolonged snow persistence, retarded cambial reactivation and led to lower growth rate. Despite strong spatial variability among plots, wood production was determined by snow dynamics in three out of the four studied plots. This study highlights the major role played by early and late growing season soil temperatures on radial growth of mountain conifers. The results of this study suggest that a future shallower and more transitory snowpack in the studied forests, together with warmer soil and air temperatures, may increase radial growth and productivity of similar mid-latitude, young mountain forests.

Although the mean environmental lapse rate (MELR) value (a linear decrease of −6.5 °C/km) is the most widely used, near‐surface (i.e., non‐free atmosphere) air temperature lapse rates (NSLRs; measured at ~1.5 m height) are variable in space and time because of their dependence on topography and meteorological conditions. In this study we conducted the first analysis of the spatial and temporal variability of NSLRs for continental Spain and their relationship to synoptic atmospheric circulation (circulation weather types [CWTs]), focusing on major mountain areas including the Pyrenees, Cantabrian, Central, Baetic, and Iberian ranges. The results showed that the NSLR varied markedly at spatial and seasonal scales and depended on the dominant atmospheric conditions. The median NSLR values were weaker (less negative) than the MELR for the mountain areas (Pyrenees −5.17 °C/km; Cantabrian range −5.22 °C/km; Central range −5.78 °C/km; Baetic range −4.83 °C/km; Iberian range −5.79 °C/km) and for the entire continental Spain (−5.28 °C/km). For the entire continental Spain the steepest NSLR values were found in April (−5.80 °C/km), May (−5.58 °C/km), and October (−5.54 °C/km) because of the dominance of northerly and westerly advections of cold air. The weakest NSLR values were found in July (−4.67 °C/km) and August (−4.78 °C/km) because of the inland heating, and in winter because of the occurrence of thermal inversions. As the use of the MELR involves the assumption of large errors, we propose 1 zonal, 12 monthly, 11 CWTs, and 132 hybrid monthly–CWTs NSLRs for each of the mountain ranges and for the entire continental Spain. More regional studies are urgently needed to accurately assess the NSLR as a function of atmospheric circulation conditions.

Snowpacks and forests have complex interactions throughout the large range of altitudes where they co‐occur. However, there are no reliable data on the spatial and temporal interactions of forests with snowpacks, such as those that occur in nearby areas that have different environmental conditions and those that occur during different snow seasons. This study monitored the interactions of forests with snowpacks in four forest stands in a single valley of the central Spanish Pyrenees during three consecutive snow seasons (2015/2016, 2016/2017 and 2017/2018). Daily snow depth data from time‐lapse cameras were compared with snow data from field surveys that were performed every 10–15 days. These data thus provided information on the spatial and temporal changes of snow–water equivalent (SWE). The results indicated that forest had the same general effects on snowpack in each forest stand and during each snow season. On average, forest cover reduced the duration of snowpack by 17 days, reduced the cumulative SWE of the snowpack by about 60% and increased the spatial heterogeneity of snowpack by 190%. Overall, forest cover reduced SWE total accumulation by 40% and the rate of SWE accumulation by 25%. The forest‐mediated reduction of the accumulation rate, in combination with the occasional forest‐mediated enhancement of melting rate, explained the reduced duration of snowpacks beneath forest canopies. However, the magnitude and timing of certain forest effects on snowpack had significant spatial and temporal variations. This variability must be considered when selecting the location of an experimental site in a mountainous area, because the study site should be representative of surrounding areas. The same considerations apply when selecting a time period for study.

The data correspond to snow bulk density and snow depth measured with different snow core sampler in three field campaigns carried out in mountains of Turkey, Iceland and Finland in order to assess the uncertainty of using different snow core samplers and different observers. The field campaigns were carried out in the frame of the COST project HARMOSNOW ES1404 http://harmosnow.eu

Climate warming is responsible for observed reduction in snowpack depth and an earlier and faster melt-out in many mountains of the Northern Hemisphere. Such changes in mountain hydroclimate could negatively affect productivity and tree growth in high-elevation forests, but few studies have investigated how and where recent warming trends and changes in snow cover influence forest growth. A network comprising 36 high-elevation Pinus uncinata forests was sampled in the NE Iberian Peninsula, mainly across the Spanish Pyrenees, using dendrochronology to relate tree radial growth to a detailed air temperature and snow depth data. Radial growth was negatively influenced by a longer winter snow season and a higher late-spring snowpack depth. Notably, the effect of snow on tree growth was found regardless the widely reported positive effect of growing-season air temperatures on P. uncinata growth. No positive influence of moisture from spring snowmelt on annual growth of P. uncinata was detected in sampled forests. Tall trees showed a lower growth responsiveness to snow than small trees. Decreasing trends in winter and spring snow depths were detected at most Pyrenean forests, suggesting that the growth of high-elevation P. uncinata forests can beneficiate for a shallower and of shorter duration snowpack associated with warmer conditions. However, water-limited sites located on steep slopes or on rocky substrates, with poor soil-water holding capacity, could experience drought stress because of early depleted snow-related soil moisture.

High mountain areas are poorly represented by official weather observatories. It implies that new instruments must be evaluated over snow-covered and strongly insolated environments (i.e. mid-latitude mountain areas). We analyzed uncertainty sources over snow covered areas including: 1) temperature logger accuracy and bias of two widely used temperature sensors (Tinytag and iButton); 2) radiation shield performance under various radiation, snow, and wind conditions; 3) appropriate measurement height over snow covered ground; and 4) differences in air temperature measured among nearby devices over a horizontal band. The major results showed the following. 1) Tinytag performance device (mean absolute error: MAE ≈ 0.1–0.2 °C in relation to the reference thermistor) was superior to the iButton (MAE ≈ 0.7 °C), which was subject to operating errors. 2) Multi-plate radiation shield showed the best performance under all conditions (> 90% samples has bias between ±0.5 °C). The tube shield required wind (> 2.5 m s −1 ) for adequate performance, while the funnel shield required limited radiation (< 400 W m −2 ). Snow cover causes certain overheating. 3) Air temperatures were found to stabilize at 75–100 cm above the snow surface. Air temperature profile was more constant at night, showing a considerable cooling on near surface at midday. 4) Horizontal air temperature differences were larger at midday (0.5 °C). These findings indicate that to minimize errors air temperature measurements over snow surfaces should be carried out using multi-plate radiation shields with high-end thermistors such as Tinytags, and be made at a minimum height above the snow covered ground. © 2019 Elsevier B.V.

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 snowfalls occur 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 ×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 × 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 purpose of this study was to characterize mountain river regimes in the Spanish Pyrenees and to assess the importance of snow accumulation and snowmelt on the timing of river flows. Daily streamflow data from 9 gauging stations in the Pyrenees were used to characterize river regimes. These data were analyzed by hydrological indices, with a focus on periods when snow accumulation and snowmelt occurred. These results were combined with data on Snow Water Equivalent (SWE) (from measurements of depth and density of snow in the main river basins and also simulated by a process-based hydrological model), snowmelting (simulated by a process-based hydrological model), precipitation (from observations), and temperature (from observations). Longitude and elevation gradients in the Pyrenees explain the transition of river regimes from those that mostly had low nival signals (in the west and at low elevations) to those that mostly had high nival signals (low winter runoff and late spring peakflow, in the east and at high elevations). Although trend analyses indicated no statistically significant changes, there was a trend of decreased nival signal over time in most of the analyzed rivers. Our results also demonstrated that snow processes cannot explain all of the interannual variability of river regimes, because the temporal distribution of liquid precipitation and temperature play key roles in hydrography.

This work describes the snow and meteorological dataset available for the Izas Experimental Catchment, in the Central Spanish Pyrenees, from 2011 to 2016 snow seasons. The experimental site is located in the southern side of the Pyrenees between 2000 and 2300 m above sea level with an extension of 55 ha. The site is a good example of sub-alpine ambient in which snow accumulation and melting dynamics have major importance in many mountain processes. The climatic dataset includes information on different meteorological variables acquired with an Automatic Weather Station (AWS) such as precipitation, air temperature, incoming and reflected short and long-wave radiation, relative humidity, wind speed and direction, atmospheric air pressure, surface temperature (snow or soil surface) and soil temperature; all of them at 10 minute intervals. Snow depth distribution was measured during 23 field campaigns using a Terrestrial Laser Scanner (TLS), and there is also available daily information of the Snow Covered Area (SCA) retrieved from time-lapse photography. The data set (https://doi.org/10.5281/zenodo.579979) is valuable since it provides high spatial resolution information on the snow depth and snow cover distribution, which is particularly useful in combination with meteorological variables to simulate the snow energy and mass balance. This information has already been analyzed in different scientific works studying snow pack dynamics and its interaction with the local climatology or terrain topographic characteristics. However, the database generated till the date has great potential for understanding other environmental processes from a hydrometerological or ecological perspective in which snow dynamics play a determinant role.

Forests in snow-dominated areas have substantial effects on the snowpack and its evolution over time. Such interactions have significant consequences for the hydrological response of mountain rivers. Thus, the impact of forest management actions on the snow distribution, and hence the storage of water in the form of snow during winter and spring, is a major concern. The results of this study provide the first detailed comparison of the small-scale effect of forest characteristics on the snowpack distribution, assessed prior to and following major modification of the structure of the canopy by pruning of the lower branches of the trees to 3 m above the ground. This is a common management practice aimed at reducing the spread of forest fires. The snowpack distribution was determined using terrestrial laser scanning (LiDAR technology) at a high spatial resolution (0.25 m) over a 1000 m2 study area during 23 survey dates over three snow seasons in a small study area in the central Pyrenees. The pruning was conducted during summer following the snow season in the second year of the study (i.e., the study duration encompassed two seasons prior to canopy pruning and one following). Principal component analysis (PCA) was used to identify recurring spatial patterns of snow distribution. The results showed that pruning reduced the average radius of the canopy of trees by 1.2 m, and increased the clearance around the trunks, as all the branches that formerly contacted the ground were removed. However, the impact on the snowpack was moderate. The PCA revealed that the spatial configuration of the snowpack did not change significantly, as the principal components included survey days from different periods of the snow season, and did not discriminate days surveyed prior to and following pruning. Nevertheless, removal of the lower branches reduced the area beneath the canopy by 36%, and led to an average increase in total snow depth of approximately 14%.

Comparamos la diversidad y composición de especies del sotobosque en un pinar de repoblación (Pinus sylvestris) y un robledal o melojar de Quercus pyrenaica. Nuestro principal objetivo fue conocer el efecto de la especie arbórea dominante sobre las características estructurales dentro de nuestra comunidad. La prueba de disimilaridad y posterior clasificación determinó que ambos sotobosques constituyen una única comunidad, sabiendo que el robledal alberga mayor riqueza de especies; esto último guarda relación con la cantidad de radiación solar que llega al sotobosque