Spatial Variability of Snow Density and Its Estimation in Different Periods of Snow Season in the Middle Tianshan Mountains, China

Snow density is one of the essential properties to describe snowpack characteristics. To obtain the spatial variability of snow density and estimate it accurately in different periods of snow season still remain as challenges, particularly in the mountains. This study analyzed the spatial variability of snow density with in-situ measurements in three different periods (i.e. accumulation, stable, melt period) of snow seasons 2017/2018 and 2018/2019 in the middle Tianshan Mountains, China. The performance of multiple linear regression model (MLR) and three machine learning models (i.e. Random Forest (RF), Extreme Gradient Boosting (XGB) and Light Gradient Boosting Machine (LGBM)) to simulate snow density has been evaluated. It was found that the snow density in melt period (0.27 g cm-3) was generally greater than that in stable (0.20 g cm-3) and accumulation period (0.18 g cm-3), and the spatial variability of snow density in melt period was slightly smaller than that in other two periods. The snow density in mountainous areas was generally higher than that in plain or valley areas, and snow density increased significantly (p < 0.05) with elevation in the accumulation and stable periods. Besides elevation, latitude and ground surface temperature also had critical impacts on the spatial variability of snow density in the middle Tianshan Mountains, China. In this work, the machine learning model, especially RF model, performed better than MLR on snow density simulation in three periods. Compared with MLR, the determination coefficients of RF promoted to 0.61, 0.51 and 0.58 from 0.50, 0.1 and 0.52 in accumulation period, stable period and melt period respectively. This study provide a more accurate snow density simulation method for estimating regional snow mass and snow water equivalent, which allows us to achieve a better understanding of regional snow resources.

Improved Northern Hemisphere Snow Water Equivalent product from passive microwave remote sensing and in situ data

&lt;p&gt;The European Space Agency Snow CCI+ project provides global homogenized long time series of daily snow extent and snow water equivalent (SWE). The Snow CCI SWE product is built on the Finish Meteorological Institute's GlobSnow algorithm, which combines passive microwave data with in situ snow depth information to estimate SWE. The CCI SWE product improves upon previous versions of GlobSnow through targeted changes to the spatial resolution, ancillary data, and snow density parameterization.&lt;/p&gt;&lt;p&gt;Previous GlobSnow SWE products used a constant snow density of 0.24 kg m&lt;sup&gt;-3&lt;/sup&gt; to convert snow depth to SWE. The CCI SWE product applies spatially and temporally varying density fields, derived by krigging in situ snow density information from historical snow transects to correct biases in estimated SWE. Grid spacing was improved from 25 km to 12.5 km by applying an enhanced spatial resolution microwave brightness temperature dataset. We assess step-wise how each of these targeted changes acts to improve or worsen the product by evaluating with snow transect measurements and comparing hemispheric snow mass and trend differences.&lt;/p&gt;&lt;p&gt;Together, when compared to GlobSnow v3, these changes improved RMSE by ~5 cm and correlation by ~0.1 against a suite of snow transect measurements from Canada, Finland, and Russia. Although the hemispheric snow mass anomalies of CCI SWE and GlobSnow v3 are similar, there are sizeable differences in the climatological SWE, most notably a one month delay in the timing of peak SWE and lower SWE during the accumulation season. These shifts were expected because the variable snow density is lower than the former fixed value of 0.24 kg m&lt;sup&gt;-3&lt;/sup&gt; early in the snow season, but then increases over the course of the snow season. We also examine intermediate products to determine the relative improvements attributable solely to the increased spatial resolution versus changes due to the snow density parameterizations. Such systematic evaluations are critical to directing future product development.&lt;/p&gt;

Quantification of the radiative impact of light-absorbing particles during two contrasted snow seasons at Col du Lautaret (2058 m a.s.l., French Alps)

The presence of light-absorbing particles (LAPs) in snow leads to a decrease in short-wave albedo affecting the surface energy budget. However, the understanding of the impacts of LAPs is hampered by the lack of dedicated datasets, as well as the scarcity of models able to represent the interactions between LAPs and snow metamorphism. The present study aims to address both these limitations by introducing a survey of LAP concentrations over two snow seasons in the French Alps and an estimation of their impacts based on the Crocus snowpack model that represents the complex interplays between LAP dynamics and snow metamorphism. First, a unique dataset collected at Col du Lautaret (2058 m a.s.l., above sea level, French Alps) for the two snow seasons 2016–2017 and 2017–2018 is presented. This dataset consists of spectral albedo measurements, vertical profiles of snow specific surface area (SSA), density and concentrations of different LAP species. Spectral albedos are processed to estimate SSA and LAP absorption-equivalent concentrations near the surface of the snowpack. These estimates are then compared to chemical measurements of LAP concentrations and SSA measurements. Our dataset highlights, among others, large discrepancies between two measurement techniques of black carbon (BC) concentrations in snow (namely thermal-optical and laser-induced incandescence). Second, we present ensemble snowpack simulations of the multi-physics version of the detailed snowpack model Crocus, forced with in situ meteorological data, as well as dust and BC deposition fluxes from an atmospheric model. The temporal variations of near-surface LAP concentrations and SSA are most of the time correctly simulated. The simulated seasonal radiative forcing of LAPs is 33 % higher for the 2017–2018 snow season than for the 2016–2017 one, highlighting a strong variability between these two seasons. However, the shortening of the snow season caused by LAPs is similar with 10 ± 5 and 11 ± 1 d for the first and the second snow seasons, respectively. This counter-intuitive result is attributed to two small snowfalls at the end of the first season and highlights the importance in accounting for meteorological conditions to correctly predict the impact of LAPs. The strong variability of season shortening caused by LAPs in the multi-physics ensemble for the first season (10 ± 5 d) also points out the sensitivity of model-based estimations of LAP impact on modelling uncertainties of other processes. Finally, the indirect impact of LAPs (i.e. the enhancement of energy absorption due to the acceleration of the metamorphism by LAPs) is negligible for the 2 years considered here, which is contrary to what was found in previous studies for other sites.

Diagnosis of future changes in hydrology for a Canadian Rockies headwater basin

Climate change is anticipated to impact the hydrology of the Saskatchewan River, which originates in the Canadian Rockies mountain range. To better understand the climate change impacts in the mountain headwaters of this basin, a physically based hydrological model was developed for this basin using the Cold Regions Hydrological Modelling platform (CRHM) for Marmot Creek Research Basin (∼9.4 km2), located in the Front Ranges of the Canadian Rockies. Marmot Creek is composed of ecozones ranging from montane forests to alpine tundra and alpine exposed rock and includes both large and small clearcuts. The model included blowing and intercepted snow redistribution, sublimation, energy-balance snowmelt, slope and canopy effects on melt, Penman–Monteith evapotranspiration, infiltration to frozen and unfrozen soils, hillslope hydrology, streamflow routing, and groundwater components and was parameterised without calibration from streamflow. Near-surface outputs from the 4 km Weather Research and Forecasting (WRF) model were bias-corrected using the quantile delta mapping method with respect to meteorological data from five stations located from low-elevation montane forests to alpine ridgetops and running over October 2005–September 2013. The bias-corrected WRF outputs during a current period (2005–2013) and a future pseudo global warming period (PGW, 2091–2099) were used to drive model simulations to assess changes in Marmot Creek's hydrology. Under a “business-as-usual” forcing scenario, Representative Concentration Pathway 8.5 (RCP8.5) in PGW, the basin will warm up by 4.7 ∘C and receive 16 % more precipitation, which will lead to a 40 mm decline in seasonal peak snowpack, 84 mm decrease in snowmelt volume, 0.2 mm d−1 slower melt rate, and 49 d shorter snow-cover duration. The alpine snow season will be shortened by almost 1.5 months, but at some lower elevations there will be large decreases in peak snowpack (∼45 %) in addition to a shorter snow season. Declines in the peak snowpack will be much greater in clearcuts than under mature forest canopies. In alpine and treeline ecozones, blowing snow transport and sublimation will be suppressed by higher-threshold wind speeds for transport, in forest ecozones, sublimation losses from intercepted snow will decrease due to faster unloading and drip, and throughout the basin, evapotranspiration will increase due to a longer snow-free season and more rainfall. Runoff will begin earlier in all ecozones, but, as a result of variability in surface and subsurface hydrology, forested and alpine ecozones will generate the greatest runoff volumetric increases, ranging from 12 % to 25 %, whereas the treeline ecozone will have a small (2 %) decrease in runoff volume due to decreased melt volumes from smaller snowdrifts. The shift in timing in streamflow will be notable, with 236 % higher flows in spring months and 12 % lower flows in summer and 13 % higher flows in early fall. Overall, Marmot Creek's annual streamflow discharge will increase by 18 % with PGW, without a change in its streamflow generation efficiency, despite its basin shifting from primarily snowmelt runoff towards rainfall-dominated runoff generation.

Quantification of the radiative impact of light-absorbing particles during two contrasted snow seasons at Col du Lautaret (2058 m a.s.l., French Alps)

The presence of light-absorbing particles (LAPs) in snow leads to a decrease in shortwave albedo, affecting the surface energy budget. Precisely quantifying the impacts of LAPs on snowpack evolution is crucial to characterise the spatio-temporal variability of snowmelt and assess snow albedo feedbacks in detail. However, the understanding of the impacts of LAPs is hampered by the lack of dedicated datasets, as well as the scarcity of models able to represent the interactions between LAPs and snow metamorphism. The present study aims to address both these limitations by introducing a survey of LAP concentrations over two snow seasons in the French Alps, as well as an estimation of their impacts based on the Crocus snowpack model that represents the complex interplays between LAP dynamics and snow metamorphism. First, we present a unique dataset collected at the Col du Lautaret (2058 m a.s.l; French Alps) for the two snow seasons 2016–2017 and 2017–2018. This dataset consists of spectral albedo measurements (manual and automated), vertical profiles of snow specific surface area (SSA), density, and concentrations of refractive Black Carbon (rBC), Elemental Carbon (EC) and mineral dust. Spectral albedo data are processed to estimate near-surface SSA and LAP absorption-equivalent concentrations near the surface of the snowpack. These estimates are then compared to chemical measurements of dust and BC concentrations, as well as to SSA measurements acquired by near-infrared reflectometry. Our dataset highlights large discrepancies between the two measurement techniques of BC concentrations, with EC concentrations being one order of magnitude higher than rBC measurements. In view of LAP absorption inferred from albedo measurements, the mass absorption efficiency (MAE) of BC used in our study (11.25 g m−2 at 550 nm) is more appropriate for EC measurements than for rBC ones. Second, we present ensemble snowpack simulations of ESCROC – the multi-physics version of the detailed snowpack model Crocus – forced with in-situ meteorological data as well as dust and BC deposition fluxes from the ALADIN-Climate atmospheric model. The results of these simulations are compared to the near-surface properties estimated from automatic albedo measurements, showing that the temporal variations of near-surface LAP concentration and SSA are correctly reproduced. The impact of dust and BC on our simulations is estimated by comparing this ensemble to a similar ensemble that does not account for LAPs. The seasonal radiative forcing of LAPs is 1.33 times higher for the 2017–2018 snow season than for the 2016–2017 one, highlighting a strong variability between these two seasons. However, the shortening of the snow season caused by LAPs are similar with 10 ± 5 and 11 ± 1 days for the first and the second snow seasons respectively. This counter-intuitive result is attributed to two small snowfalls at the end of the first season and highlights the importance to account for meteorological conditions to correctly predict the impact of LAPs. The strong variability of season shortening caused by LAPs in the multi-physics ensemble for the first season also points out the sensitivity of model-based estimations of LAP impact to modelling uncertainties of other processes. Finally, the indirect impact of LAPs (i.e. the enhancement of energy absorption due to acceleration of the metamorphism by LAPs) is negligible for the two years considered here, contrary to what was found in previous studies for other sites. This finding is mainly attributed to the meteorological conditions of the two studied snow seasons.

Diagnosis of future changes in hydrology for a Canadian Rocky Mountain headwater basin

Climate change is anticipated to have impacts on the water resources of the Saskatchewan River, which originates in the Canadian Rocky Mountains. To better understand the climate change impacts in the mountain headwaters of this basin, a physically based hydrological model was developed for this basin using the Cold Regions Hydrological Modelling platform (CRHM) for Marmot Creek Research Basin (~9.4 km2), located in the Front Ranges of the Canadian Rocky Mountain. Marmot Creek is composed of ecozones ranging from montane forests to alpine tundra and alpine exposed rock and includes both large and small clearcuts. The model included blowing and intercepted snow redistribution, sublimation, energy-balance snowmelt, slope and canopy effects on melt, Penman-Monteith evapotranspiration, infiltration to frozen and unfrozen soils, hillslope hydrology, streamflow routing and groundwater components and was parameterised without calibration from streamflow. Near-surface outputs from the 4-km Weather Research and Forecasting (WRF) model were bias corrected using the quantile delta mapping method with respect to meteorological data from five stations located from montane forest to mountaintop during October 2005–September 2013. The bias corrected WRF outputs during current period (CTRL, 2005–2013) and future period (PGW, 2091–2099) were used to drive model simulations to assess changes in Marmot Creek's hydrology. Under a "business as usual" forcing scenario: representative concentration pathway 8.5 (RCP8.5) in PGW, the basin warms up by 4.7 °C and receives 16 % more precipitation, which leads to a 40 mm decline in seasonal peak snowpack, 84 mm decrease in snowmelt volume, 0.2 mm day-1 slower melt rate, and 49 days shorter snowcover duration. The alpine snow season will be shortened by almost one and half month, but at some lower elevations there are large decreases in peak snowpack (~45 %) as well as a shorter snow season. Losses of peak snowpack will be much greater in clearcuts than under forest canopies. In alpine and treeline ecozones blowing snow transport and sublimation will be suppressed by higher threshold wind speeds for transport, in forest canopies sublimation losses from intercepted snow will decrease due to faster unloading and drip, and for all ecozones, evapotranspiration will increase due to longer snow-free seasons and more rainfall. Runoff will begin earlier in all ecozones, but, as result of variability in surface and subsurface hydrology, forested and alpine ecozones generate larger runoff volumes, ranging from 12 % to 25 %, whereas the treeline ecozone has a small (2 %) decrease in runoff volume due to decreased melt volumes from smaller snowdrifts. The shift in timing in streamflow is notable, with 236 % higher flows in spring months and 12 % lower flows in summer and 13 % higher flows in early fall. Overall, Marmot Creek basin annual streamflow discharge will increase by 18 % with PGW without a change in its streamflow generation efficiency, despite the basin shifting from snowmelt runoff towards rainfall-dominated runoff generation.

Snow Depth Retrieval in Farmland Based on a Statistical Lookup Table from Passive Microwave Data in Northeast China

At present, passive microwave remote sensing is the most efficient method to estimate snow depth (SD) at global and regional scales. Farmland covers 46% of Northeast China and accurate SD retrieval throughout the whole snow season has great significance for the agriculture management field. Based on the results of the statistical analysis of snow properties in Northeast China from December 2017 to January 2018, conducted by the China snow investigation project, snow characteristics such as snow grain size (SGS), snow density, snow thickness, and temperature of the layered snowpack were measured and analyzed in detail. These characteristics were input to the microwave emission model of layered snowpacks (MEMLS) to simulate the brightness temperature (TB) time series of snow-covered farmland in the periods of snow accumulation, stabilization, and ablation. Considering the larger SGS of the thick depth hoar layer that resulted in a rapid decrease of simulated TBs, effective SGS was proposed to minimize the simulation errors and ensure that the MEMLS can be correctly applied to satellite data simulation. Statistical lookup tables (LUTs) for MWRI and AMSR2 data were generated to represent the relationship between SD and the brightness temperature difference (TBD) at 18 and 36 GHz. The SD retrieval results based on the LUT were compared with the actual SD and the SD retrieved by Chang’s algorithm, Foster’s algorithm, the standard MWRI algorithm, and the standard AMSR2 algorithm. The results demonstrated that the proposed algorithm based on the statistical LUT achieved better accuracy than the other algorithms due to its incorporation of the variation in snow characteristics with the age of snow cover. The average root mean squared error of the SD for the whole snow season was approximately 3.97 and 4.22 cm for MWRI and AMSR2, respectively. The research results are beneficial for monitoring SD in the farmland of Northeast China.