Snow-covered Surfaces Research Articles

Influence of modeling conditions on the estimation of the dry deposition velocity of aerosols on highly inhomogeneous surfaces

An approach to estimating the dry deposition velocity of aerosol particles on the surfaces of Arctic regions, where snow-covered surfaces, open water surface, tundra and coniferous forest predominate, is proposed and numerically investigated. Optimal modeling conditions are proposed, taking into account the characteristic sizes and densities of aerosol particles involved in transport in the planetary boundary layer, and the interaction of air flows with the surface through the parameter u*, calculated using the WRF-ARW model. The proposed approach is compared with other known models and experimental data. The dependence of the dry deposition velocity obtained by the proposed approach on the diameter, density of aerosol particles and dynamic velocity u* for the surfaces in the Far North is estimated.

A snow-dwelling tropical butterfly? An unprecedented discovery of a new genus of the Pedaliodes clade in an extreme, high-altitude Andean environment (Lepidoptera: Nymphalidae: Satyrinae)

Abstract A new genus of satyrine butterflies, Nivaliodes gen. nov., is described for three species, all new: Nivaliodes negrobueno sp. nov., Nivaliodes viracocha sp. nov., and Nivaliodes puriq sp. nov. (Lepidoptera: Nymphalidae), with the support of molecular data and adult morphology. A target enrichment-based phylogeny indicates that Nivaliodes gen. nov. is sister to the genus Pherepedaliodes within an extremely diverse Pedaliodes clade of the predominantly Andean subtribe Pronophilina. Although an overwhelming majority of species of this group occur in tropical montane forests, N. negrobueno sp. nov. was discovered in a central Peruvian desert puna at some 4600–4800 m a.s.l., the highest elevation reported for any species of the Pronophilina. Individuals were observed overflying rocky slopes and resting directly on snow-covered surfaces, which is an exceptionally unusual behaviour among butterflies. The other two species of the new genus were found at lower elevations, some 3300–4200 m a.s.l., at the timberline and in puna grassland.

Quantifying the influencing factors of the thermal state of permafrost in Northeast China

In Northeast China, permafrost is controlled by a combination of biotic, climatic, physiographic, and anthropogenic factors. Due to the complexity of these governing or influencing factors, it is challenging to exactly describe the features of the Xing’an permafrost in Northeast China. By integrating remote sensing (RS) and geographic information system (GIS) technologies, we have quantified these influencing factors of permafrost changes as an important approach to understanding the nature of latitudinal and mountain permafrost in Northeast China at the mid-latitudes in the Northern Hemisphere. In this study, we combine Geographical Detector (Geodetector) model, trend analysis, and multi-source RS data to quantify the controlling or influencing factors of permafrost thermal state and of permafrost changes, and explain the interactions among permafrost, environment, and climate. The results indicate that, at the regional scale, changes in the thermal state of permafrost are primarily governed or influenced by mean annual land surface temperature (MALST), precipitation, and snow cover duration (SCD). Topographic factors also affect the spatial patterns of permafrost development. Additionally, in the context of climate warming, the insulation effect of snow cover on the permafrost is weakened, or has been weakening. Moreover, the interactive effects among various factors significantly enhance their explanatory power for changes in the thermal state of permafrost. The study emphasizes the complexity of the interactions among permafrost, climate, and the environment, and highlights the significance of understanding these interactions for regional socio-economic development, ecological management, carbon pool stabilization, and research on future climate change in Northeast China.

Assimilating Satellite-Derived Snow Cover and Albedo Data to Improve 3-D Weather and Photochemical Models

During wintertime temperature inversion episodes, ozone in the Uinta Basin sometimes exceeds the standard of 70 ppb set by the US Environmental Protection Agency. Since ozone formation depends on sunlight, and less sunlight is available during winter, wintertime ozone can only form if snow cover and albedo are high. Researchers have encountered difficulties replicating high albedo values in 3-D weather and photochemical transport model simulations for winter episodes. In this study, a process to assimilate MODIS satellite data into WRF and CAMx models was developed, streamlined, and tested to demonstrate the impacts of data assimilation on the models’ performance. Improvements to the WRF simulation of surface albedo and snow cover were substantial. However, the impact of MODIS data assimilation on WRF performance for other meteorological quantities was minimal, and it had little impact on ozone concentrations in the CAMx photochemical transport model. The contrast between the data assimilation and reference cases was greater for a period with no new snow since albedo appears to decrease too rapidly in default WRF and CAMx configurations. Overall, the improvement from MODIS data assimilation had an observed enhancement in the spatial distribution and temporal evolution of surface characteristics on meteorological quantities and ozone production.

River ice breakup classification using dual- (HH&HV) or compact-polarization RADARSAT Constellation Mission data

Ice jams and associated flooding during river ice breakup are seasonal hazards for many northern communities. Synthetic Aperture Radar (SAR) data enable timely monitoring of river ice conditions in inclement weather. River ice types and open water are discriminated during breakup using dual-polarized (HH&HV; DPH) and compact polarimetric (CP) C-band SAR imagery from the RADARSAT Constellation Mission (RCM). Study areas comprise nineteen locations on rivers in northern Ontario, the Northwest Territories, and Alberta, Canada. Rubble ice, sheet ice, and open water areas are sampled in RCM imagery using corroborating evidence from shoreline cameras and oblique airborne photography. Samples are obtained in the incidence angle range 19° to 48°, for open water with various wind speeds and flow conditions, and for rubble ice and sheet ice with varying surface roughness, snow cover, and wetness. Discrimination relies on a primary classification of rubble ice, sheet ice, and open water, and a secondary classification of ice roughness. The primary classification model development uses a recursive-partitioning machine-learning technique. Overall accuracies for the best DPH models are 83.8% to 89.6%, depending on image noise floor. DPH models use both HH and HV polarizations for low noise floor image modes, whereas only HH is used for higher noise floor image modes. The best CP model accuracy is 90.2%, using the RL, RR, and RVRH parameters. Secondary classification associates increasing ice roughness with increasing HH backscatter for DPH data, and with increasing RH backscatter for CP data. The angular dependencies of rubble ice or sheet ice are used to normalize backscatter, which is then divided into roughness categories. The DPH and CP classification models are named IceBC-DP and IceBC-CP, respectively. Qualitative analysis illustrates good overall ice type and open water classification. Confounding situations are mixed pixel issues, whitewater and wind roughening of water, moist snow microwave absorption, and superficial water on sheet ice. The development of robust methods for monitoring ice jam formation with RCM is an operational concern for departments within the Government of Canada and within Provincial and Territorial Governments.

Shortwave and longwave components of the surface radiation budget measured at the Thule High Arctic Atmospheric Observatory, Northern Greenland

Abstract. The Arctic climate is influenced by the interaction of shortwave (SW) and longwave (LW) radiation with the atmosphere and the surface. The comprehensive evolution of the Surface Radiative Fluxes (SRF) on different time scales is of paramount importance to understanding the complex mechanisms governing the Arctic climate. However, only a few sites located in the Arctic region provide long-term time series of SRF allowing for capturing of the seasonality of atmospheric and surface parameters and for carrying out validation of satellite products and/or reanalyses. This paper presents the daily and monthly SRF record collected at the Thule High Arctic Atmospheric Observatory (THAAO, 76.5∘ N, 68.8∘ W), in North-Western Greenland. The downwelling components of the SW (DSI) and the LW (DLI) irradiances have been measured at THAAO since 2009, whereas the collection of the upwelling SW (USI) and LW (ULI) irradiance was started in 2016, together with additional measurements, such as meteorological parameters and column water vapour. The datasets of DSI (Meloni et al., 2022a; https://doi.org/10.13127/thaao/dsi), USI (Meloni et al., 2022b; https://doi.org/10.13127/thaao/usi), DLI (Meloni et al., 2022c; https://doi.org/10.13127/thaao/dli), ULI (Meloni et al., 2022d; https://doi.org/10.13127/thaao/uli), and near-surface air temperature (Muscari et al., 2018; https://doi.org/10.13127/thaao/met), can be accessed through the THAAO web site (https://www.thuleatmos-it.it/data, last access: 16 January 2024). The DSI is absent (solar zenith angle ≥90∘) from 29 October to 13 February, assuming maxima in June (monthly mean of 277.0 Wm−2), when it is about half of the total incoming irradiance. The USI maximum occurs in May (132.4 Wm−2) owing to the combination of moderately high DSI values and high albedo. The shortwave surface albedo (A) assumes an average of 0.16 during summer, when the surface is free of snow. Differently, during months of snow-covered surface, when solar radiation allows A to be estimated, its values are greater than 0.6. A large interannual variability is observed in May and September, months characterized by rapidly changing surface conditions, having a link with air temperature anomalies. The DLI and ULI maxima occur in July and August, and the minima in February and March. ULI is always larger than DLI and shows a wider annual cycle. ULI is well described by a fourth-order polynomial fit to the air temperature (R2>0.99 for monthly data and R2>0.97 for daily data). The Surface Radiation Budget (SRB) is positive from April to August, when absorption of solar radiation exceeds the infrared net cooling, with a maximum value of 153.2 Wm−2 in June. From November to February, during the polar night, the LW net flux varies between −34.5 and −43.0 Wm−2. In March and September, the negative LW net flux overcomes the positive SW contribution, producing a negative SRB. The THAAO measurements show clear evidence of the influence of several regional weather/climate events, that appear strongly linked with SRF anomalies. These anomalies were found, for example, during summer 2012, when a large ice melting event took place over Greenland, and during winter 2019–2020, which was extraordinarily cold in the Arctic region.

Impact of spring land-surface conditions over the Tibetan Plateau on the early summer Asian monsoon using an AGCM large ensemble

Influence of land-surface memory effects over the Tibetan Plateau on the Asian summer monsoon has long been studied, but not quantified because of the difficulty of extracting only these effects from observational data. This study examines the impact of spring land-surface conditions, including surface air temperature (SAT) and snow cover, over the Tibetan Plateau and its surrounding regions on the early stage of the Asian summer monsoon using large-ensemble experiments produced by an atmospheric global climate model (AGCM). The results show that the above-normal SAT over and around the Tibetan Plateau in May can enhance June monsoon circulation without the sea surface temperature (SST) forcing. The physical mechanism behind this involves warmed surface air over the plateau enhancing the north–south SAT gradient, leading to strengthening the Asian summer monsoon circulation and precipitation. Inter-ensemble correlation analysis indicated that SAT warming over the plateau is clearly associated with upper-tropospheric anti-cyclonic circulation anomalies in May. Also, although winter land-surface signals do not persist until late spring, SAT anomalies in March tend to persist until May, explaining 10–20% of the total SAT variance in May. Interestingly, the spring SAT impacts on June monsoon circulations vary interannually, indicating that the contribution of the land–atmosphere (L–A) coupling process to interannual monsoon variability differs from year to year. Active L–A coupling years tend to coincide with cooler SSTs with anomalous low-level divergence and upper-level convergence over and around the Maritime continent, implying that SST forcing is associated with the L–A coupling strength. Specifically, in May of the developing stage of monsoon circulation, the weak Walker circulation over the equatorial and northern Indian Ocean and undeveloped monsoon circulation are suitable for the L–A coupling to influence the development of the monsoon circulation effectively. However, the well-known oceanic events, such as El Nin~\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\widetilde{n}}$$\\end{document}o, are found to be less connected with the interannual L–A coupling variations.

Non-Lambertian snow surface reflection models for simulated top-of-the-atmosphere radiances in the NIR and SWIR wavelengths

Accurately modeling snow reflectance is one way to improve satellite observations in the high-latitude regions. Snow surfaces are known to be challenging for atmospheric retrievals in the short-wave infrared (SWIR) wavelength regime due to their low reflectance. For example, current algorithms for satellite-based remote sensing of atmospheric carbon dioxide (CO2) do not take into account the unique reflective properties of snow surfaces. In this paper, we present a measurement-based snow surface reflectance model in the near-infrared (NIR; 755–775 nm) and SWIR (1590–1620 nm, 2040–2080 nm) bands used in remote sensing of atmospheric CO2. We study snow reflectance in detail using a novel atmospheric radiative transfer model (RTM) software and a measurement-based model for snow bi-directional reflectance distribution function (BRDF) to identify how the observations could be optimized in regards of observation geometry and wavelengths. The novel simulation software for NIR-SWIR atmospheric radiative transfer, Raysca, is presented and validated. Top-of-the-atmosphere radiance simulations show that forward-viewing geometries over snow-covered surfaces yield higher radiances than the traditional nadir-viewing geometries, which could indicate the preferability of forward-viewing observation modes in the retrieval of atmospheric CO2. Similarly, atmospheric observations in the 1.6 μm CO2 absorption band might be preferable to the 2.0 μm CO2 absorption band due to higher radiances in the 1.6 μm band.

Spatial Estimation of Snow Water Equivalent for Glaciers and Seasonal Snow in Iceland Using Remote Sensing Snow Cover and Albedo

Efficient water resource management in glacier- and snow-dominated basins requires accurate estimates of the snow water equivalent (SWE) in late winter and spring and melt onset timing and intensity. To understand the high spatio-temporal variability of snow and glacier ablation, a spatially distributed energy balance model combining satellite-based retrievals of albedo and snow cover was applied. Incoming short-wave energy, contributing to daily estimates of melt energy, was constrained by remotely sensed surface albedo for snow-covered surfaces. Fractional snow cover was used for non-glaciated areas, as it provides estimates of snow cover for each pixel to better constrain snow melt. Thus, available daily estimates of melt energy in a given area were the product of the possible melt energy and the fractional snow cover of the area or pixel for non-glaciated areas. This provided daily estimates of melt water to determine seasonal snow and glacier ablation in Iceland for the period 2000–2019. Observations from snow pits on land and glacier summer mass balance were used for evaluation, and observations from land and glacier-based automatic weather stations were used to evaluate model inputs for the energy balance model. The results show that the interannual SWE variability was generally high both for seasonal snow and glaciers. For seasonal snow, the largest SWE (>1000 mm) was found in mountainous and alpine areas close to the coast, notably in the East- and Westfjords, Tröllaskaga, and in the vicinity of glacier margins. Lower SWE values were observed in the central highlands, flatter inland areas, and at lower elevations. For glaciers, more SWE (glacier ablation) was associated with lower glacier elevations while less melt was observed at higher elevations. For the impurity-rich bare-ice areas that are exposed annually, observed SWE was more than 3000 mm.

Snow Persistence and Snow Line Elevation Trends in a Snowmelt-Driven Basin in the Central Andes and Their Correlations with Hydroclimatic Variables

The mountain cryosphere is crucial for socio-economic processes, especially during the dry seasons. However, anthropogenic climate change has had a detrimental impact on the cryosphere due to its sensitivity. Over the past two decades, there has been a decline in precipitation and a temperature rise, leading to a substantial reduction in the timing and extent of snow cover. This increase in temperature also elevates the snow line elevation (SLE), further diminishing the volume of available freshwater in the snow-driven basins of the Andes. In this study, we use 22 years (2000–2021) of 8-day snow product (MOD10A2) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to analyze the annual and seasonal variability of snow cover area, SLE, and snow persistence (SP, an indicator of the duration of snow) in the Yeso River basin in Central Chile and the correlation of SP and SLE with hydrometeorological variables and climatic indices. We introduce a new approach called the Maximum Dissimilarity Method to obtain the SLE even on cloudy days. The results are as follows: (1) Snow cover area reductions of 34.0 km2 at low elevations in spring and 86.5 km2 at mid elevations in summer were found when comparing the period 2016–2021 to 2000–2004; (2) SP trends at the annual scale revealed a significant decrease in 89% of its area and an average of 3.6 fewer days of snow cover per year; (3) an upward and significant trend of 21 m‧year−1 in the annual SLE was found; and (4) annual SP and SLE were highly correlated with annual hydrometeorological variables, and spring and summer snow variables were significantly correlated with dry streamflow. This methodology can potentially serve as a valuable tool for detecting trends in snow-covered surfaces, and thereby associate these changes with climate change or other anthropogenic effects in future research.

Estimation of anthropogenic and volcanic SO2 emissions from satellite data in the presence of snow/ice on the ground

Abstract. Early versions of satellite nadir-viewing UV SO2 data products did not explicitly account for the effects of snow/ice on retrievals. Snow-covered terrain, with its high reflectance in the UV, typically enhances satellite sensitivity to boundary layer pollution. However, a significant fraction of high-quality cloud-free measurements over snow is currently excluded from analyses. This leads to increased uncertainties of satellite emission estimates and potential seasonal biases due to the lack of data in winter months for some high-latitudinal sources. In this study, we investigated how Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) satellite SO2 measurements over snow-covered surfaces can be used to improve the annual emissions reported in our SO2 emissions catalogue (version 2; Fioletov et al., 2023). Only 100 out of 759 sources listed in the catalogue have 10 % or more of the observations over snow. However, for 40 high-latitude sources, more than 30 % of measurements suitable for emission calculations were made over snow-covered surfaces. For example, in the case of Norilsk, the world's largest SO2 point-source, annual emission estimates in the SO2 catalogue were based only on 3–4 summer months, while the addition of data for snow conditions extends that period to 7 months. Emissions in the SO2 catalogue were based on satellite measurements of SO2 slant column densities (SCDs) that were converted to vertical column densities (VCDs) using site-specific clear-sky air mass factors (AMFs), calculated for snow-free conditions. The same approach was applied to measurements with snow on the ground whereby a new set of constant, site-specific, clear sky with snow AMFs was created, and these were applied to the measured SCDs. Annual emissions were then estimated for each source considering (i) only clear-sky and snow-free days, (ii) only clear-sky with snow days, and (iii) a merged dataset (snow and snow-free conditions). For individual sources, the difference between emissions estimated for snow and snow-free conditions is within ±20 % for three-quarters of smelters and oil and gas sources and with practically no systematic bias. This is excellent consistency given that there is typically a factor of 3–5 difference between AMFs for snow and snow-free conditions. For coal-fired power plants, however, emissions estimated for snow conditions are on average 25 % higher than for snow-free conditions; this difference is likely real and due to larger production (consumption of coal) and emissions in wintertime.

Evaluation of Seasonal Differences among Three NOAA Climate Data Records of Precipitation

Abstract Three satellite precipitation datasets—CMORPH, PERSIANN-CDR, and GPCP—from the NOAA/Climate Data Record program were evaluated in their ability to capture seasonal differences in precipitation for the period 2007–18 over the conterminous United States. Data from the in situ U.S. Climate Reference Network (USCRN) provided reference precipitation measurements and collocated atmospheric conditions (temperature) at the daily scale. Satellite precipitation products’ (SPP) performance with respect to cold season precipitation was compared to warm season and full-year analysis for benchmarking purposes. Considering an ensemble of typical performance metrics including accuracy, false alarm ratio, probability of detection, probability of false detection, and the Kling–Gupta efficiency (KGE) that combines correlation, bias, and variability, we found that the three SPPs displayed better performances during the warm season than during the cold season. Among the three datasets, CMORPH displayed better performance—smaller bias, higher correlation, and a better KGE score—than the two other SPPs on an annual basis and during the warm season. During the cold season, CMORPH showed the worst performance at higher latitudes over areas experiencing recurring snow or frozen and mixed precipitation. CMORPH’s performances were further degraded compared to PERSIANN-CDR and GPCP when considering freezing temperatures (T < 0°C) due to the inability to microwave sensors to retrieve precipitation over snow-covered surface. However, for cold rainfall events detected simultaneously by the satellite and the USCRN stations (i.e., conditional case), CMORPH performance noticeably improved but remained inferior to the two other datasets. The quantification of seasonal precipitation errors and biases for three satellite precipitation datasets presented in this work provides an objective basis for the improvement of rainfall retrieval algorithms of the next generation of satellite precipitation products.

Impact of Seasonal Snow‐Cover Change on the Observed and Simulated State of the Atmospheric Boundary Layer in a High‐Altitude Mountain Valley

AbstractThe structure and evolution of the atmospheric boundary layer (ABL) under clear‐sky fair weather conditions over mountainous terrain is dominated by the diurnal cycle of the surface energy balance and thus strongly depends on surface snow cover. We use data from three passive ground‐based infrared spectrometers deployed in the East River Valley in Colorado's Rocky Mountains to investigate the response of the thermal ABL structure to changes in surface energy balance during the seasonal transition from low to high snow cover. Temperature profiles were retrieved from the infrared radiances using the optimal estimation physical retrieval Tropospheric Remotely Observed Profiling via Optimal Estimation. A nocturnal surface inversion formed in the valley during clear‐sky days, which was subsequently mixed out during daytime with the development of a convective boundary layer when snow cover was low. Over high snow cover, a very shallow convective boundary layer formed, above which the inversion persisted through the daytime hours. We compare these observations to NOAA's operational High‐Resolution‐Rapid‐Refresh model and find large warm biases on clear‐sky days resulting from the model's inability to form strong nocturnal inversions and to maintain the stable stratification in the valley during daytime when there was snow on the ground. We suggest several factors contributing to the large model errors. These are (a) the inability of the model to represent well‐developed thermally driven flows likely due to the too coarse horizontal grid spacing (3 km), (b) too much convective mixing during daytime, and (c) too strong vertical coupling between the valley atmosphere and the free troposphere.

Characteristics and Source Apportionment of Black Carbon Over the Eastern Tibetan Plateau

As the most important absorbing aerosol, black carbon (BC) can affect radiation, clouds, and surface snow cover over the Tibetan Plateau. In this study, the BC mass concentrations were measured using a seven-channel aethalometer (AE-33) in Litang County over the eastern Tibetan Plateau from July 5 to September 5, 2017. The aethalometer model, potential source contribution function (PSCF), and concentration-weighted trajectory (CWT) models were used to analyze the variation characteristics, potential sources, and affecting areas of BC. The results showed that the mass concentration of ρ(BC) in Litang ranged from 0.4 to 4699.8 ng·m-3, with an average value of 816.4 ng·m-3, accounting for 5.96% of PM2.5. The average mass concentrations of ρ(BCliquid) and ρ(BCsolid) in Litang were 486.1 ng·m-3 and 398.5 ng·m-3, respectively, with a C of 0.51. The ρ(BC) mass concentration was mainly distributed from 0-2000 ng·m-3, which accounted for 92.5% of the total observation period. The diurnal variation in BC, BCliquid, and BCsolid showed a bimodal distribution, with the peaks appearing at 08:00 and 20:00, respectively. The first peak was mainly related to traffic sources and incomplete combustion of carbonaceous materials, whereas the second peak was mainly related to incomplete combustion of carbonaceous materials. The potential sources and affecting areas of PM2.5 and BC were different. Imports from abroad had a greater impact on the concentrations of PM2.5 and BC in Litang, and the affecting areas were mainly transmitted to the northeast in China. The high-value centers were mainly concentrated in the surrounding areas of Litang.

Spatiotemporal dynamics analysis of surface water body and snow cover area to climate change in Gilgit Baltistan, Pakistan

ABSTRACT Surface water and snow cover in mountainous areas are sensitive to climate change. Pakistan’s irrigation water supply primarily depends on the Indus River. Identifying the spatio-temporal dynamics, response patterns of snow cover area (SCA) and surface water bodies (SWB) to climate change are vital for understanding the changes in the hydrological process of the Indus basin. Therefore, the SWB area was investigated using surface reflectance data (MOD09A1) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and SCA changes were examined using MODIS snow cover data (MOD10A2) from 2009 to 2018 in Gilgit Baltistan with respect to climatic variables. According to results the SWB area in Gilgit Baltistan, for annual, summer and winter, changes at the rate of −0.00057% yr−1 (p > 0.05), −0.00042% yr−1 (p > 0.05) and 0.00037% yr−1 (p > 0.05), respectively. Moreover, annual mean temperature and the SWB exhibited a negative correlation (r = −0.244, p > 0.05) and correlation between SWB and total annual precipitation was positive (r = 0.535, p > 0.05). However, SCA of Gilgit Baltistan changed at the rate of −0.396% yr−1 (p < 0.05), −0.940% yr−1 (p < 0.05) and −0.253% yr−1 (p > 0.05) for annual, summer and winter, respectively. Furthermore, annual mean temperature and SCA exhibited a negative correlation (r = −0.731, p < 0.05), whereas annual total precipitation and SCA showed a positive correlation (r = 0.636, p < 0.05). These results indicated that climate had been significantly affected the SCA and SWB of the Gilgit Baltistan.

An Adaptive Method for the Estimation of Snow-Covered Fraction with Error Propagation for Applications from Local to Global Scales

Snow can cover over 50% of the landmass in the Northern Hemisphere and has been labelled as an Essential Climate Variable by the World Meteorological Organisation. Currently, continental and global snow cover extent is primarily monitored by optical satellite sensors. There are, however, no large-scale demonstrations for methods that (1) use all the spectral information that is measured by the satellite sensor, (2) estimate fractional snow and (3) provide a pixel-wise quantitative uncertainty estimate. This paper proposes a locally adaptive method for estimating the snow-covered fraction (SCF) per pixel from all the spectral reflective bands available at spaceborne sensors. In addition, a comprehensive procedure for root-mean-square error (RMSE) estimation through error propagation is given. The method adapts the SCF estimates for shaded areas from variable solar illumination conditions and accounts for different snow-free and snow-covered surfaces. To test and evaluate the algorithm, SCF maps were generated from Sentinel-2 MSI and Landsat 8 OLI data covering various mountain regions around the world. Subsequently, the SCF maps were validated with coincidentally acquired very-high-resolution satellite data from WorldView-2/3. This validation revealed a bias of 0.2% and an RMSE of 14.3%. The proposed method was additionally tested with Sentinel-3 SLSTR/OLCI, Suomi NPP VIIRS and Terra MODIS data. The SCF estimations from these satellite data are consistent (bias less than 2.2% SCF) despite their different spatial resolutions.

Detecting surface freeze/thaw states in Northeast China with passive microwave data using an improved standard deviation method

Passive microwave remote sensing datasets are widely used to observe surface freeze/thaw (F/T) states. However, current algorithms are highly affected by snow cover and complex land cover types, compromising their performance. Therefore, this study proposes an improved algorithm for daytime detection of diurnal F/T states by using Advanced Microwave Scanning Radiometer 2 data. In the daytime F/T discrimination algorithm, a microwave spectral gradient index is applied to divide the surface into snow-covered and snow-free areas. In the snow-free area, the surface temperature index is optimised to improve the accuracy of the standard deviation method (SDM) in evaluating the accuracy of the F/T state. For the nighttime dataset, the microwave standard deviation index difference values between day and night are used to detect the F/T states based on the daytime results. The accuracy of the improved algorithm reaches 88.6% and 84.5% in the daytime and at nighttime, respectively. Compared with the SDM, the accuracy is improved by 10.2% in the daytime and 5.4% at nighttime. The results demonstrate that the proposed model is able to effectively distinguish the F/T states of snow-covered surfaces. Optimising the surface temperature index can significantly improve the accuracy of the SDM. The results reveal that the proposed surface F/T detection algorithm can be applied to regions with complex land cover types.

Modeling Permafrost Distribution Using Geoinformatics in the Alaknanda Valley, Uttarakhand, India

The Indian Himalayan region is experiencing frequent hazards and disasters related to permafrost. However, research on permafrost in this region has received very little or no attention. Therefore, it is important to have knowledge about the spatial distribution and state of permafrost in the Indian Himalayas. Modern remote sensing techniques, with the help of a geographic information system (GIS), can assess permafrost at high altitudes, largely over inaccessible mountainous terrains in the Himalayas. To assess the spatial distribution of permafrost in the Alaknanda Valley of the Chamoli district of Uttarakhand state, 198 rock glaciers were mapped (183 active and 15 relict) using high-resolution satellite data available in the Google Earth database. A logistic regression model (LRM) was used to identify a relationship between the presence of permafrost at the rock glacier sites and the predictor variables, i.e., the mean annual air temperature (MAAT), the potential incoming solar radiation (PISR) during the snow-free months, and the aspect near the margins of rock glaciers. Two other LRMs were also developed using moderate-resolution imaging spectroradiometer (MODIS)-derived land surface temperature (LST) and snow cover products. The MAAT-based model produced the best results, with a classification accuracy of 92.4%, followed by the snow-cover-based model (91.9%), with the LST-based model being the least accurate (82.4%). All three models were developed to compare their accuracy in predicting permafrost distribution. The results from the MAAT-based model were validated with the global permafrost zonation index (PZI) map, which showed no significant differences. However, the predicted model exhibited an underestimation of the area underlain by permafrost in the region compared to the PZI. Identifying the spatial distribution of permafrost will help us to better understand the impact of climate change on permafrost and its related hazards and provide necessary information to decision makers to mitigate permafrost-related disasters in the high mountain regions.

On the value of satellite remote sensing to reduce uncertainties of regional simulations of the Colorado River

Abstract. As the major water resource in the southwestern United States, the Colorado River is experiencing decreases in naturalized streamflow and is predicted to face severe challenges under future climate scenarios. To better quantify these hydroclimatic changes, it is crucial that the scientific community establishes a reasonably accurate understanding of the spatial patterns associated with the basin hydrologic response. In this study, we employed remotely sensed land surface temperature (LST) and snow cover fraction (SCF) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess a regional hydrological model applied over the Colorado River Basin between 2003 and 2018. Based on the comparison between simulated and observed LST and SCF spatiotemporal patterns, a stepwise strategy was implemented to enhance the model performance. Specifically, we corrected the forcing temperature data, updated the time-varying vegetation parameters, and upgraded the snow-related process physics. Simulated nighttime LST errors were mainly controlled by the forcing temperature, while updated vegetation parameters reduced errors in daytime LST. Snow-related changes produced a good spatial representation of SCF that was consistent with MODIS but degraded the overall streamflow performance. This effort highlights the value of Earth observing satellites and provides a roadmap for building confidence in the spatiotemporal simulations from regional models for assessing the sensitivity of the Colorado River to climate change.

An Improved Spatiotemporal Data Fusion Method for Snow-Covered Mountain Areas Using Snow Index and Elevation Information.

Remote sensing images with high spatial and temporal resolution in snow-covered areas are important for forecasting avalanches and studying the local weather. However, it is difficult to obtain images with high spatial and temporal resolution by a single sensor due to the limitations of technology and atmospheric conditions. The enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) can fill in the time-series gap of remote sensing images, and it is widely used in spatiotemporal fusion. However, this method cannot accurately predict the change when there is a change in surface types. For example, a snow-covered surface will be revealed as the snow melts, or the surface will be covered with snow as snow falls. These sudden changes in surface type may not be predicted by this method. Thus, this study develops an improved spatiotemporal method ESTARFM (iESTARFM) for the snow-covered mountain areas in Nepal by introducing NDSI and DEM information to simulate the snow-covered change to improve the accuracy of selecting similar pixels. Firstly, the change in snow cover is simulated according to NDSI and DEM. Then, similar pixels are selected according to the change in snow cover. Finally, NDSI is added to calculate the weights to predict the pixels at the target time. Experimental results show that iESTARFM can reduce the bright abnormal patches in the land area compared to ESTARFM. For spectral accuracy, iESTARFM performs better than ESTARFM with the root mean square error (RMSE) being reduced by 0.017, the correlation coefficient (r) being increased by 0.013, and the Structural Similarity Index Measure (SSIM) being increased by 0.013. For spatial accuracy, iESTARFM can generate clearer textures, with Robert's edge (Edge) being reduced by 0.026. These results indicate that iESTARFM can obtain higher prediction results and maintain more spatial details, which can be used to generate dense time series images for snow-covered mountain areas.