Spatial Variability Of Snow Cover Research Articles

Impact of distributed meteorological forcing on simulated snow cover and hydrological fluxes over a mid-elevation alpine micro-scale catchment

Abstract. From the micro- to the mesoscale, water and energy budgets of mountainous catchments are largely driven by topographic features such as terrain orientation, slope, steepness, and elevation, together with associated meteorological forcings such as precipitation, solar radiation, and wind speed. Those topographic features govern the snow deposition, melting, and transport, which further impacts the overall water cycle. However, this microscale variability is not well represented in Earth system models due to coarse resolutions. This study explores the impact of precipitation, shortwave radiation, and wind speed on the water budget distribution over a 15.28 ha small, mid-elevation (2000–2200 m) alpine catchment at Col du Lautaret (France). The grass-dominated catchment remains covered with snow for 5 to 6 months per year. The surface–subsurface coupled distributed hydrological model ParFlow-CLM is used at a very high resolution (10 m) to simulate the impacts on the water cycle of meteorological variability at very small spatial and temporal scales. These include 3D simulations of hydrological fluxes with spatially distributed forcing of precipitation, shortwave radiation, and wind speed compared to 3D simulations of hydrological fluxes with non-distributed forcing. Our precipitation distribution method encapsulates the spatial snow distribution along with snow transport. The model simulates the dynamics and spatial variability of snow cover using the Common Land Model (CLM) energy balance module and under different combinations of distributed forcing. The resulting subsurface and surface water transfers are computed by the ParFlow module. Distributed forcing leads to spatially heterogeneous snow cover simulation, which becomes patchy at the end of the melt season and shows a good agreement with the remote sensing images (mean bias error (MBE) = 0.22). This asynchronous melting results in a longer melting period compared to the non-distributed forcing, which does not generate any patchiness. Among the distributed meteorological forcings tested, precipitation distribution, including snow transport, has the greatest impact on spatial snow cover (MBE = 0.06) and runoff. Shortwave radiation distribution has an important impact, reducing evapotranspiration as a function of the slope orientation (decreasing the slope between observed and simulated evapotranspiration from 1.55 to 1.18). For the primarily east-facing catchment studied here, distributing shortwave radiation helps generate realistic timing and spatial heterogeneity in the snowmelt at the expense of an increase in the mean bias error (from 0.06 to 0.22) for all distributed forcing simulations compared to the simulation with only distributed precipitation. Distributing wind speed in the energy balance calculation has a more complex impact on our catchment, as it accelerates snowmelt when meteorological conditions are favorable but does not generate snow patches at the end of our test case. This shows that slope- and aspect-based meteorological distribution can improve the spatio-temporal representation of snow cover and evapotranspiration in complex mountain terrain.

Optimizing embedded sensor network design for catchment-scale snow-depth estimation using LiDAR and machine learning

PUBLICATIONS Water Resources Research RESEARCH ARTICLE 10.1002/2016WR018896 Key Points: A machine-learning algorithm for optimizing snow sensor placements is compared to expert placements in an existing sensor network The spatial and temporal transferability of the algorithm is then assessed in 14 total LiDAR surveys in two uninstrumented catchments The accuracy of the snow depth estimated from the sensor measurements is higher than expert and randomized placements in each of the surveys Correspondence to: C. A. Oroza, coroza@berkeley.edu Citation: Oroza, C. A., Z. Zheng, S. D. Glaser, and D. Tuia (2016), Optimizing embedded sensor network design for catchment- scale snow-depth estimation using LiDAR and machine learning, Water Resour. Res., 52, 8174–8189, doi:10.1002/2016WR018896. Received 11 MAR 2016 Accepted 23 SEP 2016 Accepted article online 27 SEP 2016 Published online 22 OCT 2016 Optimizing embedded sensor network design for catchment-scale snow-depth estimation using LiDAR and machine learning Carlos A. Oroza 1 , Zeshi Zheng 1 , Steven D. Glaser 1 , Devis Tuia 2 , and Roger C. Bales 1,3 Department of Civil and Environmental Engineering, University of California, Berkeley, California, USA, 2 Department of Geography, University of Zurich, Zurich, Switzerland, 3 Sierra Nevada Research Institute, University of California, Merced, California, USA Abstract We evaluate the accuracy of a machine-learning algorithm that uses LiDAR data to optimize ground-based sensor placements for catchment-scale snow measurements. Sampling locations that best represent catchment physiographic variables are identified with the Expectation Maximization algorithm for a Gaussian mixture model. A Gaussian process is then used to model the snow depth in a 1 km 2 area sur- rounding the network, and additional sensors are placed to minimize the model uncertainty. The aim of the study is to determine the distribution of sensors that minimizes the bias and RMSE of the model. We com- pare the accuracy of the snow-depth model using the proposed placements to an existing sensor network at the Southern Sierra Critical Zone Observatory. Each model is validated with a 1 m 2 LiDAR-derived snow-depth raster from 14 March 2010. The proposed algorithm exhibits higher accuracy with fewer sensors (8 sensors, RMSE 38.3 cm, bias 5 3.49 cm) than the existing network (23 sensors, RMSE 53.0 cm, bias 5 15.5 cm) and randomized placements (8 sensors, RMSE 63.7 cm, bias 5 24.7 cm). We then evaluate the spatial and temporal transferability of the method using 14 LiDAR scenes from two catchments within the JPL Airborne Snow Observatory. In each region, the optimized sensor placements are determined using the first available snow raster for the year. The accuracy in the remaining LiDAR surveys is then com- pared to 100 configurations of sensors selected at random. We find the error statistics (bias and RMSE) to be more consistent across the additional surveys than the average random configuration. 1. Introduction A major challenge of hydrologic science in montane regions relates to estimating the spatial variability of snow cover [Bales et al., 2006; Trujillo and Lehning, 2015; McCreight et al., 2014; Guan et al., 2013]. Multiple independent variables govern the distribution of snow cover, including elevation, slope, aspect, and the dis- tribution of canopy [Faria et al., 2000; Musselman et al., 2008; Lehning et al., 2011; Helfricht et al., 2014]. Non- stationary effects such as climate warming and changes in vegetation structure may significantly alter the timing and magnitude of storage and runoff in these watersheds [Goulden and Bales, 2014; Flanner et al., 2009]. Existing regression-based hydrologic models, which use statistical relations from historical hydro- graphs to predict runoff and inform allocation decisions [Perkins et al., 2009; Rosenberg et al., 2011; Rango and Martinec, 1995] will have limited accuracy as conditions deviate from historical norms and thus may prove to be inadequate for predictions in water management. C 2016. American Geophysical Union. V All Rights Reserved. OROZA ET AL. Recent research has focused on improving hydrologic models by assimilating remote-sensing and in situ measurements with distributed energy-balance models to better estimate storage and runoff [Guan et al., 2013]. These methods use well-developed remote-sensing [Painter et al., 2003; Rosenthal and Dozier, 1996; € lli et al., 2002; Egli et al., 2012] and energy-balance models [Marks et al., 1992; Link and Marks, Dozier, 1989; P a 1999; Brubaker et al., 1996] to estimate snow and snowmelt processes across basins. In situ measurements for these methods are presently limited to snow pillows and snow courses, which largely sample flat, open terrain [Molotch and Bales, 2006], yet the distribution of snow cover can vary considerably as a function of topographic features. To address this, in situ sensor measurements can be deployed to capture the mean and variance of the snow depth, which can be used to inform models that use these statistics as inputs [e.g., Essery and Pomeroy, 2004]. Alternatively, individual sensor measurements can be used together with OPTIMIZING SNOW SENSOR PLACEMENTS

An evaluation of high‐resolution regional climate model simulations of snow cover and albedo over the Rocky Mountains, with implications for the simulated snow‐albedo feedback

AbstractThe snow‐albedo feedback (SAF) strongly influences climate over midlatitude mountainous regions. However, over these regions the skill of regional climate models (RCMs) at simulating properties such as snow cover and surface albedo is poorly characterized. These properties are evaluated in a pair of 7 year long high‐resolution RCM simulations with the Weather Research and Forecasting model over the central Rocky Mountains. Key differences between the simulations include the computational domain (regional versus continental) and land surface model used (Noah versus Noah‐MP). Simulations are evaluated against high‐resolution satellite estimates of snow cover and albedo from the Moderate Resolution Imaging Spectroradiometer. Both simulations generally reproduce the observed seasonal and spatial variability of snow cover and also exhibit important biases. One simulation substantially overpredicts subpixel fractional snow cover over snowy pixels (by up to 0.4) causing large positive biases in surface albedo, likely due in part to inadequate representation of canopy effects. The other simulation exhibits a negative bias in areal snow extent (as much as 19% of the analysis domain). Surface measurements reveal large positive biases in snow albedo (exceeding 0.2) during late spring caused by neglecting radiative effects of impurities deposited onto snow. Semi‐idealized climate change experiments show substantially different magnitudes of SAF‐enhanced warming in the two simulations that can be tied to the differences in snow cover in their control climates. More confident projections of regional climate change over mountains will require further work to evaluate and improve representation of snow cover and albedo in RCMs.

Shortwave Radiative Fluxes on Slopes

AbstractSnow-covered mountain ranges are a major source of water supply for runoff and groundwater recharge. Snowmelt supplies as much as 75% of the surface water in basins of the western United States. Net radiative fluxes make up about 80% of the energy balance over snow-covered surfaces. Because of the large extent of snow cover and the scarcity of ground observations, use of remotely sensed data is an attractive option for estimating radiative fluxes. Most of the available methods have been applied to low-spatial-resolution satellite observations that do not capture the spatial variability of snow cover, clouds, or aerosols, all of which need to be accounted for to achieve accurate estimates of surface radiative fluxes. The objective of this study is to use high-spatial-resolution observations that are available from the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive surface shortwave (0.2–4.0 μm) downward radiative fluxes in complex terrain, with attention on the effect of topography (e.g., shadowing or limited sky view) on the amount of radiation received. The developed method has been applied to several typical melt seasons (January–July during 2003, 2004, 2005, and 2009) over the western part of the United States, and the available information was used to derive metrics on spatial and temporal variability of shortwave fluxes. Issues of scale in both the satellite and ground observations are also addressed to illuminate difficulties in the validation process of satellite-derived quantities. It is planned to apply the findings from this study to test improvements in estimation of snow water equivalent.

Snow density and ground permittivity retrieved from L-band radiometry: Application to experimental data

Abstract The potential of retrieving the bottom layer snow density and soil permittivity under dry snow cover conditions from L-band passive microwave observations was analyzed using multi-angular brightness temperatures measured at horizontal and vertical polarization over two test sites in northern Finland. The near-continuous time series of L-band brightness temperatures covers a total of six winter seasons, over both dry mineral soil in a forest clearing, and organic soil over a wetland site. Detailed measurements of snow and soil conditions are available from both sites. Complementing a previous theoretical study, we show that dry snow cover influences the observed L-band brightness temperatures as a result of both impedance matching and changes in the refraction angle at the snow–soil interface. Exploiting these effects, we demonstrate the retrieval of the bottom layer snow density and the influence of dry snow cover on simultaneously retrieved soil permittivity — a consideration which is currently not accounted for in Soil Moisture and Ocean Salinity (SMOS) retrievals of soil permittivity in the presence of dry snow. Depending on season, the mean bias error between retrieved and in situ snow density measured in the lower snow layers was between − 6 kg m − 3 and 15 kg m − 3 for the forest clearing site, and between 37 kg m − 3 and 90 kg m − 3 for the wetland site, demonstrating the feasibility of the retrieval approach at the plot scale. In winter conditions, the ground permittivity retrieved without considering the impact of dry snow on L-band emission was, on average, 35% lower for both test sites, which indicates possible errors in current SMOS ground permittivity retrievals under dry snow conditions. The application of SMOS data to simultaneously retrieve dry snow density and ground permittivity is a complex task due to heterogeneous land cover and snow/soil conditions within SMOS pixels (≈ 45 km resolution). An approach that considers land cover variations and the spatial variability of snow cover is required to fully determine the feasibility of the methodology to aid e.g. improving estimates snow water equivalent from other sensors, and to take into account effects of dry snow in SMOS-based retrievals of ground permittivities. The results should also be applicable to other L-band sensors in space, such as the recently launched NASA Soil Moisture Active Passive (SMAP) mission.

Using CERES SYN Surface Irradiance Data as Forcing for Snowmelt Simulation in Complex Terrain*

Abstract The benefit of using solar and longwave surface irradiance data from NASA’s Clouds and the Earth’s Radiant Energy System (CERES) synoptic (SYN) satellite product in simulations of snowmelt has been examined. The accuracy of the SYN downwelling solar and longwave irradiances was first assessed by comparison to measurements at NOAA’s Surface Radiation Network (SURFRAD) reference stations and to remote mountain observations. Typical shortwave (longwave) biases had magnitudes less than 30 (10) W m−2, with most standard deviations below 140 (30) W m−2. The performance of a range of snow models of varying complexity when using SYN irradiances as forcing data was then evaluated. Simulated snow water equivalent and runoff from cases using SYN data fell in the range of those from simulations forced with irradiances from well-maintained surface observation sites as well as empirical methods that have been shown to perform well in mountainous terrain. The SYN irradiances are therefore judged to be suitable for use in snowmelt modeling. It is also noted that the SYN upwelling shortwave irradiances, and hence albedos derived from them, are frequently not representative of individual monitoring stations because of the high spatial variability of snow cover and other surface properties in mountainous regions. In addition, adjusting the SYN downwelling longwave irradiances to reflect the exact elevation of the point of interest relative to the mean altitude of the satellite grid box is recommended.

Simulation of the melt season using a resolved sea ice model with snow cover and melt ponds

A three-dimensional sea ice model is presented with resolved snow thickness variations and melt ponds. The model calculates heating from solar radiative transfer and simulates the formation and movement of brine/melt water through the ice system. Initialization for the model is based on observations of snow topography made during the summer melt seasons of 2009, 2010, and 2012 from a location off the coast of Barrow, AK. Experiments are conducted to examine the importance of snow properties and snow and ice thickness by comparing observed and modeled pond fraction and albedo. One key process simulated by the model is the formation of frozen layers in the ice as relatively warm fresh water grid cells freeze when cooled by adjacent, cold brine-filled grid cells. These layers prevent vertical drainage and lead to flooding of melt water commonly observed at the beginning of the melt season. Flooding persists until enough heat is absorbed to melt through the frozen layer. The resulting long-term melt pond coverage is sensitive to both the spatial variability of snow cover and the minimum snow depth. For thin snow cover, initial melting results in earlier, reduced flooding with a small change in pond fraction after drainage of the melt water. Deeper snow tends to generate a delayed, larger peak pond fraction before drainage.

Permafrost and snow monitoring at Rothera Point (Adelaide Island, Maritime Antarctica): Implications for rock weathering in cryotic conditions

In February 2009 a new permafrost borehole was installed close to the British Antarctic Survey Station at Rothera Point, Adelaide Island (67.57195°S 68.12068°W). The borehole is situated at 31masl on a granodiorite knob with scattered lichen cover. The spatial variability of snow cover and of ground surface temperature (GST) is characterised through the monitoring of snow depth on 5 stakes positioned around the borehole and with thermistors placed at three different rock surfaces (A, B and C). The borehole temperature is measured by 18 thermistors placed at different depths between 0.3 and 30m. Snow persistence is very variable both spatially and temporally with snow free days per year ranging from 13 and more than 300, and maximum snow depths varying between 0.03 and 1.42m. This variability is the main cause of high variability in GST, that ranged between −3.7 and −1.5°C. The net effect of the snow cover is a cooling of the surface. Mean annual GST, mean summer GST, and the degree days of thawing and the n-factor of thawing were always much lower at sensor A where snow persistence and depth were greater than in the other sensor locations. At sensor A the potential freeze–thaw events were negligible (0–3) and the thermal stress was at least 40% less than in the other sensor locations. The zero curtain effect at the rock surface occurred only at surface A, favouring chemical weathering over mechanical action. The active layer thickness (ALT) ranged between 0.76 and 1.40m. ALT was directly proportional to the mean air temperature in summer, and inversely proportional to the maximum snow depth in autumn. ALT temporal variability was greater than reported at other sites at similar latitude in the Northern Hemisphere, or with the similar mean annual air temperature in Maritime Antarctica, because vegetation and a soil organic horizon are absent at the study site. Zero annual amplitude in temperature was observed at about 16m depth, where the mean annual temperature is −3°C. Permafrost thickness was calculated to range between 112 and 157m, depending on the heat flow values adopted. The presence of sub-sea permafrost cannot be excluded considering the depth of the shelf around Rothera Point and its glacial history.

Mapping the annual evolution of snow depth in a small catchment in the Pyrenees using the long-range terrestrial laser scanning

This paper presents the methodology used to develop snow depth distribution maps for a small catchment in the Central Spanish Pyrenees covering 55 ha in a 1:10,000 scale. The Main Map was obtained using LiDAR (light detection and ranging) technology from a long-range Terrestrial Laser Scanner (TLS) in six field surveys undertaken during the 2012 winter–spring period. This technique enabled the acquisition of information at a very high resolution concerning the spatial variability of snow cover, providing snow depth information for remote areas where data acquisition is complex and hazardous. We describe the methodological steps and the quality assessment applied in developing the maps. Comparison with manual measurements confirmed the reliability of the snow depth maps, including areas located at large distances from the scanner (800 m). This method provides a promising tool for future investigations of snow dynamics in mountainous environments.

Small-scale horizontal variability of snow, sea-ice thickness and freeboard in the first-year ice region north of Svalbard

AbstractVariability of sea-ice and snow conditions on the scale of a few hundred meters is examined using in situ measurements collected in first-year pack ice in the European Arctic north of Svalbard. Snow thickness and surface elevation measurements were performed in the standard manner using a snow stick and a rotating laser. Altogether, 4109 m of measurement lines were surveyed. The snow loading was large, and in many locations the ice freeboard was negative (38.8% of snowline measurements), although the modal ice and snow thickness was 1.8 m. The mean of all the snow thickness measurements was 36 cm, with a standard deviation of 26 cm. The mean freeboard was only 3 cm, with a standard deviation of 23 cm. There were noticeable differences in snow thickness among the measurement sites. Over the undeformed ice areas, the mean snow thickness and freeboard were 23 and 2.4 cm, respectively. Over the ridged ice areas, the mean freeboard was only –0.3 cm due to snow accumulation on the sails of ridges (average thickness 54 cm). These findings imply that retrieval algorithms for converting freeboard to ice thickness should take account of spatial variability of snow cover.

THE IMPACT OF CLIMATE CHANGE ON THE SNOW COVER PATTERN IN ESTONIA

The paper deals with problems of temporal and spatial variability of snow cover duration, of correlation between snow cover and winter mean air temperature patterns and of the impact of climate change on the snow cover pattern in Estonia. Snow cover fields are presented in form of IDRISI raster images. Snow cover duration measured at ca 100 stations and observation points have been interpolated into raster cells. On the base of time series of raster images, a map of mean territorial distribution of snow cover duration is calculated. Estonia is characterized by a great spatial variability of snow cover mostly caused by the influence of the Baltic Sea. General regularities of snow cover pattern are determined. A 104-year time series of spatial mean values of snow cover duration is composed and analyzed. A decreasing trend and periodical fluctuations have detected. Standardized principal component analysis is used for the time series of IDRISI raster images. It enables to study the influence of different factors on the formation of snow cover fields and territorial extent of coherent fluctuations. Correlation between snow cover duration and winter mean air temperature fields is analyzed. A spatial regression model is created for estimation of the influence of climate change on snow cover pattern in Estonia. Using incremental climate change scenarios (2 °C, 4 °C and 6 °C of warming in winter) mean decrease of snow cover duration in different regions in Estonia is calculated. According to results of model calculation, the highest decrease of snow cover duration will be take place on islands and in the coastal region of West Estonia. A permanent snow cover may not form at all. In the areas with maximum snow cover duration in North-East and South-East Estonia, that decrease should be much lower.

Predicting Snowmelt Runoff on Sagebrush Rangeland Using a Calibrated Spur Hydrology Model

ABSTRACT This study evaluates the ability of the SPUR (Simulation of Production and Utilization of Rangelands) model to predict monthly runoff on sagebrush watersheds where runoff is generated by spring snowmelt. Four watersheds in southwest Idaho were used to evaluate the model. Predicted monthly runoff was compared to actual runoff using a different part of the runoff record than what was used to calibrate the model. Results indicate that the calibrated SPUR hydrology model adequately simulates the volume and timing of monthly runoff for watersheds that have a relatively uniform snow cover. Transferability of the model for uncalibrated watersheds with uniform snow cover was also demonstrated. For most rangeland watersheds where snowmelt runoff is important, however, the spatial variability of snow cover is high; runoff is supplied mainly by one or more isolated drifts. The model works poorly under these kinds of conditions.