Complex Alpine Terrain Research Articles

Fine‐scale alpine plant community assembly: Relative roles of environmental sorting, dispersal processes and species interactions

Abstract Besides environmental sorting, other processes like biotic interactions and dispersal limitation are vital for the assembly of plant communities in high mountains and their re‐assembly under changing climatic conditions. Nevertheless, studies that compare the impact of these factors on plant community assembly above the tree line are largely lacking so far. We analysed occurrence changes in vascular plant communities of 492 permanent 1‐m2 plots in the alpine‐nival ecotone of Mt. Schrankogel, Austrian Alps by comparing resurvey data from 2014 with data from the initial survey in 1994. We combined these data with species inventories from 899 additional plots sampled in 2021 and 2022 across a larger landscape above the tree line covering an elevational range of 1700 m, which we used for fine‐scale habitat suitability modelling. We assessed the relative effects of projected habitat suitability, propagule pressure from surrounding populations and biomass density of neighbours on 1532 colonization and 372 extirpation events of 31 species observed on the permanent plots. We found that all three factors are significantly related to both colonisations and extirpations, with habitat suitability having the strongest, propagule pressure a slightly weaker, and vegetation density the weakest effect. Colonisations can be better explained by the three process proxies than extirpations. Our results indicate a crucial role of dispersal limitation besides the predominant effect of environmental filtering on the (re‐)assembly of the alpine‐nival plant community, while competitive/facilitative effects between plants tend to play a minor role. The strong imprint of nearby source plant populations on colonization/extirpation events suggests that recent plant migrations predominantly occur in small steps. This implies that while the topographically complex alpine terrain offers climatic microrefugia for plants, it may also pose potential barriers, hindering species from following their suitable climatic niches upwards. Synthesis: Besides filtering by environmental conditions dispersal limitation had a strong effect on the observed changes in a local alpine plant community over two decades. Limited dispersal capacities of plant species may counteract the ability of isolated cold areas to effectively shelter high alpine plants from the effects of climate warming.

The observed evolution of sub‐daily to multi‐day heavy precipitation in Switzerland

AbstractHeavy precipitation is a major natural hazard in the Alps. Understanding the possible changes due to climate change is a prerequisite for effective climate adaptation and protection. In this study, we revisit the long‐term (1901–2023) evolution of daily and multi‐day heavy precipitation intensity and frequency, discuss trends for sub‐daily to multi‐day events in the recent period 1981–2023 and investigate elevation dependencies in the complex topography of Switzerland. We analyze station measurements from MeteoSwiss' dense operational network covering the whole country and a wide range of elevation levels. We find that daily maximum precipitation and the frequency of precipitation events exceeding the 99th all‐day percentile have increased since 1901 with a peak in the 1980s and decreases thereafter. For the recent period 1981–2023, positive trends in summer heavy precipitation intensity are detected for short (10‐min to 3‐h) events, but no changes are found for the frequency of these moderate extreme events. For longer (1‐ to 5‐day) events on the other hand, decreases in intensity and frequency are found, especially for the winter half‐year. We hypothesize that the opposing trends on the centennial (1901–2023) vs. decadal (1981–2023) time scales are caused by the interaction between thermodynamics, reflecting the primary influence of human‐induced climate change, and the internal variability of atmospheric dynamics. Moreover, we observe a small negative elevation dependency of the daily long‐term trends up to 2300 m above sea level. For the 1981–2023 trends, no strong elevation dependencies are found for sub‐daily events. For daily events, we find small opposing negative summer and positive winter elevation dependencies for both intensities and frequencies. The reason for these tendencies remains unclear. Our results underscore the need to further investigate the interplay between climate change, internal variability of large‐scale dynamics and elevation to better understand heavy precipitation variability in the complex Alpine terrain.

Relating storm-snow avalanche instabilities to data collected from the Differential Emissivity Imaging Disdrometer (DEID)

Storm-snow avalanches are challenging to forecast due to complex alpine terrain and during rapidly changing weather conditions. They can result in loss of lives and significant economic impact. We describe how a new device that continuously measures with high-frequency snowflake mass, size, density, and type, the Differential Emissivity Imaging Disdrometer (DEID), and show how the DEID can be used to aid avalanche forecasting when coupled with a storm-snow stability model. DEID measurements of snow accumulation, snow water equivalent (SWE), and snow density obtained during seventeen storms taken at the mid-Collins Snow-Study Plot at Alta Ski Area in Utah's Central Wasatch mountain range during winter 2020–2021 show excellent agreement with infrequent manual measurements. Additionally, two new variables, the Shape Density Index (SDI) and Complexity, are proposed and used to classify snowflake habit and estimate storm-snow shear strength. We illustrate how these DEID-derived data can be used to identify layers of concern in the storm snow such as density inversions, in real-time without digging snow pits. Furthermore, the DEID-data are used to run four variations of the SNOw Slope Stability model (SNOSS) for the storms investigated. The results are evaluated with data collected from tilt-board tests, infrasound measurements, and visual observations of avalanches. For a total fourteen storms analyzed, the DEID-driven SNOSS-modeled minimum stability index predicts the general stability of the storm-snow as indicated by observed avalanches, both natural and of unknown cause. The results provide a promising approach for nowcasting instabilities within storm-snow layers with a single instrument.

The role of ice-sheet topography in the Alpine hydro-climate at glacial times

Abstract. In this study, we investigate the sensitivity of the glacial Alpine hydro-climate to northern hemispheric and local ice-sheet changes. Bridging the scale gap by using a chain of global and regional climate models, we perform sensitivity simulations of up to 2 km horizontal resolution over the Alps for the Last Glacial Maximum (LGM) and the Marine Isotope Stage 4 (MIS4). In winter, we find wetter conditions in the southern part of the Alps during LGM compared to present day, to which dynamical processes, i.e. changes in the wind speed and direction, contribute substantially. During summer, we find the expected drier conditions in most of the Alpine region during LGM, as thermodynamics suggests drier conditions under lower temperatures. The MIS4 climate shows enhanced winter precipitation compared to the LGM, which is explained by its warmer climate compared to the LGM – thus, again explained by thermodynamics. The sensitivity simulations of the northern hemispheric ice-sheet changes show that an increase of the ice-sheet thickness leads to a significant intensification of glacial Alpine hydro-climate conditions, which is mainly explained by dynamical processes. Changing only the Fennoscandian ice sheet is less influential on the Alpine precipitation, whereas modifications in the local Alpine ice-sheet topography significantly alter the Alpine precipitation; in particular, we find a reduction of summer precipitation at the southern face of the Alps when lowering the Alpine ice sheet. The findings demonstrate that the northern hemispheric and local ice-sheet topography play an important role in regulating the Alpine hydro-climate and thus permits a better understanding of the precipitation patterns in the complex Alpine terrain at glacial times.

Intercomparison of UAV platforms for mapping snow depth distribution in complex alpine terrain

Unmanned Aerial Vehicles (UAVs) offer great flexibility in acquiring images in inaccessible study areas, which are then processed with stereo-matching techniques through Structure-from-Motion (SfM) algorithms. This procedure allows generating high spatial resolution 3D point clouds. The high accuracy of these 3D models allows the production of detailed snow depth distribution maps through the comparison of point clouds from different dates. In this way, UAVs allow monitoring of remote areas that were not achievable previously. The large number of works evaluating this novel technique has not, to date, conducted a systematic evaluation of concurrent snowpack observations with different UAV devices. Taking into account this, and also bearing in mind that potential users of this technique may be interested in exploiting ready-to-use commercial devices, we conducted an evaluation of the snow depth distribution maps with different commercial UAVs. During the 2018–19 snow season, two multi-rotors (Parrot Anafi and DJI Mavic Pro2) and one fixed-wing device (SenseFly eBee plus) were used on three different dates over a small test area (5 ha) within Izas Experimental Catchment in the Central Pyrenees. Simultaneously, snowpack distribution was retrieved with a Terrestrial Laser Scanner (TLS, RIEGL LPM-321) and was considered as ground truth. Three different georeferencing methods (Ground Control Points, ICP algorithm over snow-free areas and RTK-GPS positioning) were tested, showing equivalent performances under optimum illumination conditions. Additionally, for the three acquisition dates, both multi-rotors were flown at two distinct altitudes (50 and 75 m) to evaluate impact on the obtained snow depth maps. The evaluation with the TLS showed an equivalent performance of the two multi-rotors, with mean RMSE below 0.23 m and maximum volume deviations of less than 5%. Flying altitudes did not show significant differences in the obtained maps. These results were obtained under contrasted snow surface characteristics. This study reveals that under good illumination conditions and in relatively small areas, affordable commercial UAVs provide reliable estimations of snow distribution compared to more sophisticated and expensive close-range remote sensing techniques. Results obtained under overcast skies were poor, demonstrating that UAV observations require clear-sky conditions and acquisitions around noon to guarantee a homogenous illumination of the study area.

Light and Shadow in Mapping Alpine Snowpack With Unmanned Aerial Vehicles in the Absence of Ground Control Points

AbstractRecent developments in unmanned aerial vehicles (UAV) and structure from motion (SfM) algorithms have shown reliable results for retrieving snow depth distribution. However, their ability to obtain accurate results usually relies on deploying and measuring the exact position of ground control points (GCP) for georeferencing the information. Commercial UAVs can now provide real time kinematic (RTK) positioning of the images with centimetric accuracy. Nonetheless, their operational applicability for observing snow distribution in highly heterogeneous mountain areas has not been evaluated. This study presents a complete assessment of the reliability of snow depth observations from a fixed‐wing UAV working in RTK mode with an RGB camera. During the 2018–2019 season, seven field campaigns (13 UAV flights) were undertaken covering 0.48 km2 in complex alpine terrain in the Pyrenees. The UAV observations obtained under different light conditions and flight block configurations (altitude and image overlaps) in the same day were evaluated with terrestrial laser scanner acquisitions. Two SfM processing options of the UAV images were also compared. When the study area received direct solar light, the results were comparable to previous studies that had used GCPs, with an average root mean squared error of 0.19 m and an average absolute snow volume discrepancy lower than 4%. However, when large areas were under shadow from the terrain, or solar light was affected by clouds, the estimated error tripled. The quality of the snow depth maps was little affected by the snow covered area and the flight mission configurations.

Completing the machine learning saga in fractional snow cover estimation from MODIS Terra reflectance data: Random forests versus support vector regression

This study; i) investigates the suitability of two frequently employed machine learning algorithms in remote sensing, namely, random forests (RFs) and support vector regression (SVR) for fractional snow cover (FSC) estimation from MODIS Terra data, and ii) compares them with the previously proposed artificial neural networks (ANNs) and multivariate adaptive regression splines (MARS) methods over an heterogeneous and complex alpine terrain. The dataset comprises 20 Landsat 8 – MODIS image pairs that belong to European Alps acquired from Apr 2013 to Dec 2016. The fifteen image pairs are used to generate the training dataset necessary to build the models, whereas the remaining five are employed as a separate test dataset. The reference FSC maps are derived from the binary classified Landsat 8 snow/no snow maps at 30 m resolution. In order to assess the effect of sampling type and sample size, nine different training datasets are generated. The RF and SVR models are trained accordingly by using various settings of model tuning parameters. During the training of the models, MODIS top-of-atmosphere reflectance values of bands 1–7, NDSI, NDVI and land cover class are input as independent variables (i.e., predictors) to estimate the dependent variable (i.e., response), i.e., FSC value. The resolution of the generated FSC maps is 500 m. The results indicate that the ANN, MARS, RF and SVR models exhibit high consistency with reference FSC values as indicated by low RMSE (~0.14) and high R (~0.93) values. In order to analyze the effect of using three auxiliary variables, i.e., NDSI, NDVI and land cover class, to the predictive ability of the models; ANN, MARS, RF and SVR models are also trained without these predictor variables, i.e., by only using MODIS bands 1–7. The models trained without three auxiliary variables slightly differ from the ones trained with the full set of predictors by only resulting in a mean decrease in R <0.012 and a mean increase in RMSE <0.009, showing that they perform well in solving the complex functional dependencies by only using MODIS reflectance data. In terms of computational efficiencies of the proposed algorithms measured by the CPU times spent during model training, MARS and RF algorithms outperform ANN and SVR methods.

From Patch to Catchment: A Statistical Framework to Identify and Map Soil Moisture Patterns Across Complex Alpine Terrain

Climate warming in alpine regions is changing patterns of water storage, a primary control on alpine plant ecology, biogeochemistry, and water supplies to lower elevations. There is an outstanding need to determine how the interacting drivers of precipitation and the critical zone (CZ) dictate the spatial pattern and time evolution of soil water storage. In this study, we developed an analytical framework that combines intensive hydrologic measurements and extensive remotely-sensed observations with statistical modeling to identify areas with similar temporal trends in soil water storage within, and predict their relationships across, a 0.26 km2 alpine catchment in the Colorado Rocky Mountains, U.S.A. Repeat measurements of soil moisture were used to drive an unsupervised clustering algorithm, which identified six unique groups of locations ranging from predominantly dry to persistently very wet within the catchment. We then explored relationships between these hydrologic groups and multiple CZ-related indices, including snow depth, plant productivity, macro- (102-&amp;gt;103 m) and microtopography (&amp;lt;100-102 m), and hydrological flow paths. Finally, we used a supervised machine learning random forest algorithm to map each of the six hydrologic groups across the catchment based on distributed CZ properties and evaluated their aggregate relationships at the catchment scale. Our analysis indicated that ~40–50% of the catchment is hydrologically connected to the stream channel, lending insight into the portions of the catchment that likely dominate stream water and solute fluxes. This research expands our understanding of patch-to-catchment-scale physical controls on hydrologic and biogeochemical processes, as well as their relationships across space and time, which will inform predictive models aimed at determining future changes to alpine ecosystems.

Random forests as a tool to understand the snow depth distribution and its evolution in mountain areas

AbstractThe small scale distribution of the snowpack in mountain areas is highly heterogeneous, and is mainly controlled by the interactions between the atmosphere and local topography. However, the influence of different terrain features in controlling variations in the snow distribution depends on the characteristics of the study area. As this leads to uncertainties in high spatial resolution snowpack simulations, a deeper understanding of the role of terrain features on the small scale distribution of snow depth is required. This study applied random forest algorithms to investigate the temporal evolution of snow depth in complex alpine terrain using as predictors various topographical variables and in situ snow depth observations at a single location. The high spatial resolution (1 m x 1 m) snow depth distribution database used in training and evaluating the random forests was derived from terrestrial laser scanner (TLS) devices at three study sites, in the French Alps (2 sites) and the Spanish Pyrenees (1 site). The results show the major importance of two topographic variables, the topographic position index and the maximum upwind slope parameter. For these variables the search distances and directions depended on the characteristics of each site and the TLS acquisition date, but are consistent across sites and are tightly related to main wind directions. The weight of the different topographic variables on explaining snow distribution evolves while major snow accumulation events still take place and minor changes are observed after reaching the annual snow accumulation peak. Random forests have demonstrated good performance when predicting snow distribution for the sites included in the training set with R2values ranging from 0.82 to 0.94 and mean absolute errors always below 0.4 m. Oppositely, this algorithm failed when used to predict snow distribution for sites not included in the training set, with mean absolute errors above 0.8 m.

Combined Use of Terrestrial Laser Scanning and UAV Photogrammetry in Mapping Alpine Terrain

Airborne and terrestrial laser scanning and close-range photogrammetry are frequently used for very high-resolution mapping of land surface. These techniques require a good strategy of mapping to provide full visibility of all areas otherwise the resulting data will contain areas with no data (data shadows). Especially, deglaciated rugged alpine terrain with abundant large boulders, vertical rock faces and polished roche-moutones surfaces complicated by poor accessibility for terrestrial mapping are still a challenge. In this paper, we present a novel methodological approach based on a combined use of terrestrial laser scanning (TLS) and close-range photogrammetry from an unmanned aerial vehicle (UAV) for generating a high-resolution point cloud and digital elevation model (DEM) of a complex alpine terrain. The approach is demonstrated using a small study area in the upper part of a deglaciated valley in the Tatry Mountains, Slovakia. The more accurate TLS point cloud was supplemented by the UAV point cloud in areas with insufficient TLS data coverage. The accuracy of the iterative closest point adjustment of the UAV and TLS point clouds was in the order of several centimeters but standard deviation of the mutual orientation of TLS scans was in the order of millimeters. The generated high-resolution DEM was compared to SRTM DEM, TanDEM-X and national DMR3 DEM products confirming an excellent applicability in a wide range of geomorphologic applications.

The Importance of Near-Surface Winter Precipitation Processes in Complex Alpine Terrain

Abstract In this study, near-surface snow and graupel dynamics from formation to deposition are analyzed using WRF in a large-eddy configuration. The results reveal that a horizontal grid spacing of ≤50 m is required to resolve local orographic precipitation enhancement, leeside flow separation, and thereby preferential deposition. At this resolution, precipitation patterns across mountain ridges show a high temporal and spatial variability. Simulated and observed event-mean snow precipitation across three mountain ridges in the upper Dischma valley (Davos, Switzerland) for two precipitation events show distinct patterns, which are in agreement with theoretical concepts, such as small-scale orographic precipitation enhancement or preferential deposition. We found for our case study that overall terrain–flow–precipitation interactions increase snow accumulation on the leeward side of mountain ridges by approximately 26%–28% with respect to snow accumulation on the windward side of the ridge. Cloud dynamics and mean advection may locally increase precipitation on the leeward side of the ridge by up to about 20% with respect to event-mean precipitation across a mountain ridge. Analogously, near-surface particle–flow interactions, that is, preferential deposition, may locally enhance leeward snow precipitation on the order of 10%. We further found that overall effect and relative importance of terrain–flow–precipitation interactions are strongly dependent on atmospheric humidity and stability. Weak dynamic stability is important for graupel production, which is an essential component of solid winter precipitation. A comparison to smoothed measurements of snow depth change reveals a certain agreement with simulated precipitation across mountain ridges.

Spatial variability in snow precipitation and accumulation in COSMO–WRF simulations and radar estimations over complex terrain

Abstract. Snow distribution in complex alpine terrain and its evolution in the future climate is important in a variety of applications including hydropower, avalanche forecasting and freshwater resources. However, it is still challenging to quantitatively forecast precipitation, especially over complex terrain where the interaction between local wind and precipitation fields strongly affects snow distribution at the mountain ridge scale. Therefore, it is essential to retrieve high-resolution information about precipitation processes over complex terrain. Here, we present very-high-resolution Weather Research and Forecasting model (WRF) simulations (COSMO–WRF), which are initialized by 2.2 km resolution Consortium for Small-scale Modeling (COSMO) analysis. To assess the ability of COSMO–WRF to represent spatial snow precipitation patterns, they are validated against operational weather radar measurements. Estimated COSMO–WRF precipitation is generally higher than estimated radar precipitation, most likely due to an overestimation of orographic precipitation enhancement in the model. The high precipitation amounts also lead to a higher spatial variability in the model compared to radar estimates. Overall, an autocorrelation and scale analysis of radar and COSMO–WRF precipitation patterns at a horizontal grid spacing of 450 m show that COSMO–WRF captures the spatial variability normalized by the domain-wide variability in precipitation patterns down to the scale of a few kilometers. However, simulated precipitation patterns systematically show a lower variability on the smallest scales of a few hundred meters compared to radar estimates. A comparison of spatial variability for different model resolutions gives evidence for an improved representation of local precipitation processes at a horizontal resolution of 50 m compared to 450 m. Additionally, differences of precipitation between 2830 m above sea level and the ground indicate that near-surface processes are active in the model.

Measuring snow ablation rates in alpine terrain with a mobile multioffset ground‐penetrating radar system

AbstractGround‐penetrating radar (GPR) has become a promising technique in the field of snow hydrological research. It is commonly used to measure snow depth, density, and water equivalent over large distances or along gridded snow courses. Having built and tested a mobile lightweight set‐up, we demonstrate that GPR is capable of accurately measuring snow ablation rates in complex alpine terrain. Our set‐up was optimized for efficient measurements and consisted of a multioffset radar with four pairs of antennas mounted to a plastic sled, which was small enough to permit safe and convenient operations. Repeated measurements at intervals of 2 to 7 days were taken during the 2014/2015 winter season along 10 profiles of 50 to 200 m length within two valleys located in the eastern Swiss Alps. Resulting GPR‐based data of snow depth, density, and water equivalent, as well as their respective change over time, were in good agreement with concurrent manual measurements, in particular if accurate alignment between repeated overpasses could be achieved. Corresponding root‐mean‐square error (RMSE) values amounted to 4.2 cm for snow depth, 17 mm for snow water equivalent, and 22 kg/m3 for snow density, with similar RMSE values for corresponding differential data. With this performance, the presented radar set‐up has the potential to provide exciting new and extensive datasets to validate snowmelt models or to complement lidar‐based snow surveys.

Verification of Gridded Wind Forecasts in Complex Alpine Terrain: A New Wind Verification Methodology Based on the Neighborhood Approach

AbstractA novel wind verification methodology is presented and analyzed for six surface wind cases in the greater Alpine region as well as an idealized setup. The methodology is based on the idea of the fractions skill score, a neighborhood-based spatial verification metric frequently used for verifying precipitation. The new score avoids the problems of traditional nonspatial verification metrics (the “double penalty” problem and the failure to distinguish between a “near miss” and much poorer forecasts) and can distinguish forecasts even when the spatial displacement of wind patterns is large. Moreover, the time-averaged score value in combination with a statistical significance test enables different wind forecasts to be ranked by their performance.

HoloGondel: in situ cloud observations on a cable car in the Swiss Alps using a holographic imager

Abstract. In situ observations of cloud properties in complex alpine terrain where research aircraft cannot sample are commonly conducted at mountain-top research stations and limited to single-point measurements. The HoloGondel platform overcomes this limitation by using a cable car to obtain vertical profiles of the microphysical and meteorological cloud parameters. The main component of the HoloGondel platform is the HOLographic Imager for Microscopic Objects (HOLIMO 3G), which uses digital in-line holography to image cloud particles. Based on two-dimensional images the microphysical cloud parameters for the size range from small cloud particles to large precipitation particles are obtained for the liquid and ice phase. The low traveling velocity of a cable car on the order of 10 m s−1 allows measurements with high spatial resolution; however, at the same time it leads to an unstable air speed towards the HoloGondel platform. Holographic cloud imagers, which have a sample volume that is independent of the air speed, are therefore well suited for measurements on a cable car. Example measurements of the vertical profiles observed in a liquid cloud and a mixed-phase cloud at the Eggishorn in the Swiss Alps in the winters 2015 and 2016 are presented. The HoloGondel platform reliably observes cloud droplets larger than 6.5 µm, partitions between cloud droplets and ice crystals for a size larger than 25 µm and obtains a statistically significantly size distribution for every 5 m in vertical ascent.

Error Analysis of Satellite Precipitation-Driven Modeling of Flood Events in Complex Alpine Terrain

The error in satellite precipitation-driven complex terrain flood simulations is characterized in this study for eight different global satellite products and 128 flood events over the Eastern Italian Alps. The flood events are grouped according to two flood types: rain floods and flash floods. The satellite precipitation products and runoff simulations are evaluated based on systematic and random error metrics applied on the matched event pairs and basin-scale event properties (i.e., rainfall and runoff cumulative depth and time series shape). Overall, error characteristics exhibit dependency on the flood type. Generally, timing of the event precipitation mass center and dispersion of the time series derived from satellite precipitation exhibits good agreement with the reference; the cumulative depth is mostly underestimated. The study shows a dampening effect in both systematic and random error components of the satellite-driven hydrograph relative to the satellite-retrieved hyetograph. The systematic error in shape of the time series shows a significant dampening effect. The random error dampening effect is less pronounced for the flash flood events and the rain flood events with a high runoff coefficient. This event-based analysis of the satellite precipitation error propagation in flood modeling sheds light on the application of satellite precipitation in mountain flood hydrology.

From runoff to rainfall: inverse rainfall–runoff modelling in a high temporal resolution

Abstract. Rainfall exhibits a large spatio-temporal variability, especially in complex alpine terrain. Additionally, the density of the monitoring network in mountainous regions is low and measurements are subjected to major errors, which lead to significant uncertainties in areal rainfall estimates. In contrast, the most reliable hydrological information available refers to runoff, which in the presented work is used as input for an inverted HBV-type rainfall–runoff model that is embedded in a root finding algorithm. For every time step a rainfall value is determined, which results in a simulated runoff value closely matching the observed runoff. The inverse model is applied and tested to the Schliefau and Krems catchments, situated in the northern Austrian Alpine foothills. The correlations between inferred rainfall and station observations in the proximity of the catchments are of similar magnitude compared to the correlations between station observations and independent INCA (Integrated Nowcasting through Comprehensive Analysis) rainfall analyses provided by the Austrian Central Institute for Meteorology and Geodynamics (ZAMG). The cumulative precipitation sums also show similar dynamics. The application of the inverse model is a promising approach to obtain additional information on mean areal rainfall. This additional information is not solely limited to the simulated hourly data but also includes the aggregated daily rainfall rates, which show a significantly higher correlation to the observed values. Potential applications of the inverse model include gaining additional information on catchment rainfall for interpolation purposes, flood forecasting or the estimation of snowmelt contribution. The application is limited to (smaller) catchments, which can be represented with a lumped model setup, and to the estimation of liquid rainfall.

Small scale spatial variability of snow density and depth over complex alpine terrain: Implications for estimating snow water equivalent

This study analyzes spatial variability of snow depth and density from measurements made in February and April of 2010 and 2011 in three 1–2km2 areas within a valley of the central Spanish Pyrenees. Snow density was correlated with snow depth and different terrain characteristics. Regression models were used to predict the spatial variability of snow density, and to assess how the error in computed densities might influence estimates of snow water equivalent (SWE).The variability in snow depth was much greater than that of snow density. The average snow density was much greater in April than in February. The correlations between snow depth and density were generally statistically significant but typically not very high, and their magnitudes and signs were highly variable among sites and surveys. The correlation with other topographic variables showed the same variability in magnitude and sign, and consequently the resulting regression models were very inconsistent, and in general explained little of the variance. Antecedent climatic and snow conditions prior to each survey help highlight the main causes of the contrasting relation shown between snow depth, density and terrain. As a consequence of the moderate spatial variability of snow density relative to snow depth, the absolute error in the SWE estimated from computed densities using the regression models was generally less than 15%. The error was similar to that obtained by relating snow density measurements directly to adjacent snow depths.

Relationship between Radar-Estimated Precipitation and Synoptic Weather Patterns in the European Alps

AbstractA 9-yr (2000–08) analysis of precipitation characteristics for the central and western European Alps has been generated from ground-based operational weather radar data provided by the Swiss radar network. The radar-based precipitation analysis focuses on the relationship between synoptic-scale weather patterns and mesoscale precipitation distribution over complex alpine terrain. The analysis divides the Alps into six regions (each approximately 200 × 200 km2 in size)—one on the northern side, two each on the western and southern sides of the Alps, and one in the Massif Central—representing various orographic aspects and localized climates within the radar coverage area. For each region, estimated precipitation rate derived from radar data is analyzed on a seasonal basis for total daily precipitation and frequency of high-precipitation-rate events. The summer season has the highest total daily precipitation for all regions in the study, whereas median values of daily precipitation in winter are less than one-half of median daily precipitation for summer. For all regions, high-precipitation-rate events occur most frequently in the summer. Daily synoptic-scale weather patterns are associated with total daily precipitation and frequency of high precipitation rate to show that an advective synoptic-scale pattern with southerly midtropospheric flow results in the highest median and 90th-quantile values for total daily precipitation and that a convective synoptic-scale pattern results in elevated frequency of extreme-precipitation-rate events.

Improved interpolation of meteorological forcings for hydrologic applications in a Swiss Alpine region

This paper presents a comparative study on the mapping of temperature and precipitation fields in complex Alpine terrain. Its relevance hinges on the major impact that inadequate interpolations of meteorological forcings bear on the accuracy of hydrologic predictions regardless of the specifics of the models, particularly during flood events. Three flood events measured in the Swiss Alps are analyzed in detail to determine the interpolation methods which best capture the distribution of intense, orographically-induced precipitation. The interpolation techniques comparatively examined include: Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Kriging with External Drift (KED). Geostatistical methods rely on a robust anisotropic variogram for the definition of the spatial rainfall structure. Results indicate that IDW tends to significantly underestimate rainfall volumes whereas OK and KED methods capture spatial patterns and rainfall volumes induced by storm advection. Using numerical weather forecasts and elevation data as covariates for precipitation, we provide evidence for KED to outperform the other methods. Most significantly, the use of elevation as auxiliary information in KED of temperatures demonstrates minimal errors in estimated instantaneous rainfall volumes and provides instantaneous lapse rates which better capture snow/rainfall partitioning. Incorporation of the temperature and precipitation input fields into a hydrological model used for operational management was found to provide vastly improved outputs with respect to measured discharge volumes and flood peaks, with notable implications for flood modeling.