Snow Fraction Research Articles

Response of Land Surface Albedo to Fire Disturbance in the Sierra Nevada Seasonal Snow Zone Over the MODIS Record

AbstractWildfires in the snow zone can brighten winter and spring landscapes by removing forest canopy, revealing underlying snow cover. Land surface albedo (LSA) alterations associated with transitioning from a canopied, snow‐hiding vegetation regime to a snow‐revealing landscape have impacts on the surface energy balance, with implications for climate and water supply. Forest fires are increasing in frequency, size, and elevation, but the change in LSA due to fire in the seasonal snow zone (SSZ) is poorly understood. This study addresses this knowledge gap for the Sierra Nevada, where recent climatic changes have contributed to droughts, earlier and more rapidly declining snowpacks, and worsening wildfire impacts. Remotely sensed snow fraction and LSA data from Moderate Resolution Imaging Spectrometer were used to assess the impact of wildfire on landscapes in the Sierra Nevada SSZ by comparing LSA in burn scars to unburned control areas and the historical average LSA, then quantifying the surface radiative forcing (RF) associated with change in LSA. Among high and moderate burn severity fires, winter LSA varied depending on snow cover, land characteristics, and burn severity, ranging from 0.12 in low‐snow fire scars to 0.47 in snow‐covered fire scars. This study adds to understanding of how landscapes respond to wildfires and the subsequent impacts on the surface energy balance.

An accurate snow cover product for the Moroccan Atlas Mountains: Optimization of the MODIS NDSI index threshold and development of snow fraction estimation models

In semi-arid Mediterranean areas, a significant proportion of the population living downstream depends on water resources from snowmelt and precipitation as their main source of water. Consequently, snow-covered mountain regions can be considered as a vital water tower, providing a steady supply of water, and contributing significantly to streamflow and groundwater recharge. Given the scarcity of ground-based hydroclimatic measurements, remote sensing could be an effective technique for mapping and monitoring snow cover. This study evaluates the last version of MODIS (version 6, called V6) snow cover product, optimizing the NDSI threshold for accurate snow cover mapping and developing models for local fractional snow cover estimation in the southern Mediterranean region, particularly in the Moroccan Atlas Mountains. For this purpose, 448 Sentinel-2 (S2) scenes from six different regions across the Atlas Range were used to adjust the NDSI threshold and to develop FSC estimation models. In addition, a total of 8419 MOD10A1 images from March 2000 to June 2023, and 7561 MYD10A1 images from September 2002 to June 2023, were processed to improve cloud filtering and to develop a highly accurate daily snow cover product suitable for the Moroccan Atlas Mountains. The cloud correction approach significantly reduced the number of cloud-covered pixels, from 25.7% to 0.4% after filtering. Two schemes for selecting the MODIS NDSI threshold were tested: (1) the global reference of 0.4 and (2) the locally optimal threshold of 0.2. The average snow cover estimation errors using the optimal and global NDSI thresholds for Terra are an average overestimation of 0.34% and a significant underestimation of 6.13%, respectively. For Aqua, the corresponding errors are an overestimation of 1.4% and an underestimation of 6.8%. Thus, the optimal NDSI threshold of 0.2 could be more appropriate than the threshold of 0.4 for use in the southern Mediterranean region. The new FSC estimation models developed showed satisfactory performance with significant correlation coefficients (mean of 0.85 for Terra and 0.83 for Aqua), and with low RMSE and MAE values (mean of 0.17 and 0.12 for Terra and mean of 0.19 and 0.14 for Aqua) when comparing FSC derived from high-resolution S2 data with predicted FSC from MODIS NDSI. The daily snow cover product developed was compared with the high-resolution snow maps obtained from S2 satellite imagery in different regions of the Moroccan Atlas. On average, the product showed a mean correlation coefficient of 0.96, a mean absolute error of 0.22%, and a mean reasonable negative bias of −0.17%. This research concludes that the enhanced daily snow cover product could improve the understanding of spatiotemporal dynamics of snow extent and, therefore, contribute to quantifying the snowmelt contribution to the water budget through modeling approaches in the southern Mediterranean region.

Assessing the Impact of Climate Change on Snowfall Conditions in Poland Based on the Snow Fraction Sensitivity Index

This study focuses on temperature and snowfall conditions in Poland, both of which were analyzed from 1981 to 2020. A 40-year record of daily snow fraction time series values was reconstructed using a unique and global multi-source weighted-ensemble precipitation (MSWEP) product, which provided a spatially and temporally consistent reference for the assessment of meteorological conditions. The average states and trends in snow fraction and temperature were analyzed across several years, focusing on the 6-month cold season (November–April). The impact of temperature on the snow fraction pattern was assessed by introducing a snow fraction sensitivity index. To predict short-term changes in snow conditions, a proxy model was established; it incorporated historical trends in the snow fraction as well as its mean state. This study provides clear evidence that the snow fraction is principally controlled by increases in temperature. A warming climate will thus cause a decline in the snow fraction, as we observed in vast lowland areas. Given the ongoing global warming, by the 2050s, snow-dominated areas may go from covering 86% to only 30% of the country’s surface; they will be converted into transient rain–snow areas. Our results demonstrate that a decline in snow water resources has already occurred, and these resources are expected to diminish further in the near future. New insights into the sensitivity of the snow fraction to climate warming will expand our collective knowledge of the magnitude and spatial extent of snow degradation. Such widespread changes have implications for the timing and availability of soil and groundwater resources as well as the timing and likelihood of floods and droughts. Thus, these findings will provide valuable information that can inform environmental managers of the importance of changing snowfall conditions, guiding them to include this aspect in future climate adaptation strategies.

Spatial variation and chemical reactivity of dusts from open-pit bitumen mining using trace elements in snow

The chemical reactivity of trace elements (TEs) in dusts from bitumen mining, upgrading and related industrial activities in the Athabasca Bituminous Sands region (ABS), Alberta, Canada, was evaluated using the acid-soluble fraction of snow. Samples were collected at 14 sites along the Athabasca River (AR) and its tributaries, and at 3 remote locations. Following metal-free, ultra-clean procedures for processing and analysis, samples were leached with nitric acid (pH < 1), filtered (<0.45 μm), and analyzed using ICP-MS. Insoluble particles (>0.45 μm) were examined using SEM-EDS. Along the river, acid-soluble concentrations of TEs varied by 6 orders of magnitude, from 1 mg/L (Al) to less than 1 ng/L (Tl). Conservative (Al, Y, La, Th) and mobile (Li, Be, Cs, Sr) lithophile elements, those enriched in bitumen (V, Ni, Mo), and potentially toxic chalcophile elements (As, Cd, Pb, Sb, Tl) showed considerable spatial variation. Normalizing the concentrations of TEs in samples collected near industry to the corresponding concentrations in snow from the reference site (UTK), resulted in enrichments of V and most of the lithophile elements. Dust reactivity, quantified as the ratio of acid-soluble to total concentrations, was less than 50% suggesting limited bioaccessibility. The large differences in behaviour between Cd and Pb versus Ni and V could be due to the occurrence of the former pair in carbonate or sulfide minerals, versus acid-insoluble petcoke particles for the latter couple. Spatial variations in the reactivity of TEs most likely reflect the range in diversity and chemical stability of dust particles, and variations in their abundance in primary source areas. The leaching conditions employed here are extreme (pH < 1) and intended to identify an upper limit of chemical reactivity, with far less dust dissolution expected when these dusts encounter natural waters of the area which range in pH from 4 to 8.

Radiatively Active Hydrometeor Frequencies From CloudSat–CALIPSO Data for Evaluating Cloud Fraction in Global Climate Models

AbstractThis study derives radiatively active hydrometeor frequencies (HFs) from CloudSat–CALIPSO satellite data to evaluate modeled cloud fraction in recent historical period simulations by the fifth phase of the Coupled Model Intercomparison Project (CMIP5) models. Most CMIP5 models do not consider precipitating and convective core hydrometeors in radiative transfer calculation except for CESM1‐CAM5 with precipitating ice (snow). They do not provide radiatively active cloud fractions for all precipitating hydrometeor types such as snow fraction while the satellite‐retrieved HFs include mixed profiles of stratiform clouds and precipitation as well as convective cores. A filtering method is applied to produce estimates of nonprecipitating and nonconvective core HF (NPCHF) from the total HF (THF) excluding rain, which is defined here as the sum of NPCHF, precipitating ice (snow, graupel, and hail), and convective core HF. We determine the optimal reference HF data for model comparison that include estimates of liquid‐phase NPCHF from CloudSat radar‐only data (2B‐CWC) and ice‐phase THF from CloudSat–CALIPSO 2C‐ICE combined radar/lidar data. The zonal‐mean at selected heights and regionally averaged profiles of CMIP5 multi‐model‐mean cloud fraction show good agreements in comparison with NPCHF estimates with biases within 5%, whereas cloud fraction is significantly underestimated (up to 30%) against the THF estimates. The biases appear below the midtroposphere over the extratropics, in the upper troposphere over the midlatitude lands and a few tropical convective regions, where precipitating ice and convective core hydrometeors contribute greatly. This comparison reveals some underestimates of thick cloud HFs resulting from measurement limitations due probably to attenuation of radar/lidar signals, which cannot be qualified in this study.

Impacts of abiotic and biotic factors on tundra productivity near Utqiaġvik, Alaska

Earlier snowmelt, warmer temperatures and herbivory are among the factors that influence high-latitude tundra productivity near the town of Utqiaġvik in northern Alaska. However, our understanding of the potential interactions between these factors is limited. MODIS observations provide cover fractions of vegetation, snow, standing water, and soil, and fractional absorption of photosynthetically active radiation by canopy chlorophyll (fAPARchl) per pixel. Here, we evaluated a recent time-period (2001–2014) that the tundra experienced large interannual variability in vegetation productivity metrics (i.e. fAPARchl and APARchl), which was explainable by both abiotic and biotic factors. We found earlier snowmelt to increase soil and vegetation cover, and productivity in June, while warmer temperatures significantly increased monthly productivity. However, abiotic factors failed to explain stark decreases in productivity during August of 2008, which coincided with a severe lemming outbreak. MODIS observations found this tundra ecosystem to completely recover two years later, resulting in elevated productivity. This study highlights the potential roles of both climate and herbivory in modulating the interannual variability of remotely retrieved plant productivity metrics in Arctic coastal tundra ecosystems.

Why the 2022 Po River drought is the worst in the past two centuries.

The causes of recent hydrological droughts and their future evolution under a changing climate are still poorly understood. Banking on a 216-year river flow time series at the Po River outlet, we show that the 2022 hydrological drought is the worst event (30% lower than the second worst, with a six-century return period), part of an increasing trend in severe drought occurrence. The decline in summer river flows (-4.14 cubic meters per second per year), which is more relevant than the precipitation decline, is attributed to a combination of changes in the precipitation regime, resulting in a decline of snow fraction (-0.6% per year) and snowmelt (-0.18 millimeters per day per year), and to increasing evaporation rate (+0.013 cubic kilometers per year) and irrigated areas (100% increment from 1900). Our study presents a compelling case where the hydrological impact of climate change is exacerbated by local changes in hydrologic seasonality and water use.

A warming-induced reduction in snow fraction amplifies rainfall extremes.

The intensity of extreme precipitation events is projected to increase in a warmer climate1-5, posing a great challenge to water sustainability in natural and built environments. Of particular importance are rainfall (liquid precipitation) extremes owing to their instantaneous triggering of runoff and association with floods6, landslides7-9 and soil erosion10,11. However, so far, the body of literature on intensification of precipitation extremes has not examined the extremes of precipitation phase separately, namely liquid versus solid precipitation. Here we show that the increase in rainfall extremes in high-elevation regions of the Northern Hemisphere is amplified, averaging 15 per cent per degree Celsius of warming-double the rate expected from increases in atmospheric water vapour. We utilize both a climate reanalysis dataset and future model projections to show that the amplified increase is due to a warming-induced shift from snow to rain. Furthermore, we demonstrate that intermodel uncertainty in projections of rainfall extremes can be appreciably explained by changes in snow-rain partitioning (coefficient of determination 0.47). Our findings pinpoint high-altitude regions as 'hotspots' that are vulnerable to future risk of extreme-rainfall-related hazards, thereby requiring robust climate adaptation plans to alleviate potential risk. Moreover, our results offer a pathway towards reducing model uncertainty in projections of rainfall extremes.

The classification of atmospheric hydrometeors and aerosols from the EarthCARE radar and lidar: the A-TC, C-TC and AC-TC products

Abstract. The EarthCARE mission aims to probe the Earth's atmosphere by measuring cloud and aerosol profiles using its active instruments, the Cloud Profiling Radar (CPR) and ATmospheric LIDar (ATLID). The correct identification of hydrometeors and aerosols from atmospheric profiles is an important step in retrieving the properties of clouds, aerosols and precipitation. Ambiguities in the nature of atmospheric targets can be removed using the synergy of collocated radar and lidar measurements, which is based on the complementary spectral response of radar and lidar relative to atmospheric targets present in the profiles. The instruments are sensitive to different parts of the particle size distribution and provide independent but overlapping information in optical and microwave wavelengths. ATLID is sensitive to aerosols and small cloud particles, and CPR is sensitive to large ice particles, snowflakes and raindrops. It is therefore possible to better classify atmospheric targets when collocated radar and lidar measurements exist compared to using a single instrument. The cloud phase, precipitation and aerosol type within the column sampled by the two instruments can then be identified. ATLID-CPR target classification (AC-TC) is the product created for this purpose by combining the ATLID target classification (A-TC) and CPR target classification (C-TC). AC-TC is crucial for the subsequent synergistic retrieval of cloud, aerosol and precipitation properties. AC-TC builds upon previous target classifications using CloudSat and CALIPSO synergy while providing richer target classification using the enhanced capabilities of EarthCARE's instruments, specifically CPR's Doppler velocity measurements to distinguish snow and rimed snow from ice clouds and ATLID's lidar ratio measurements to objectively discriminate between different aerosol species and optically thin ice clouds. In this paper, we first describe how the single-instrument A-TC and C-TC products are derived from ATLID and CPR measurements. Then the AC-TC product, which combines the A-TC and C-TC classifications using a synergistic decision matrix, is presented. Simulated EarthCARE observations based on combined cloud-resolving and aerosol model data are used to test the processors generating the target classifications. Finally, the target classifications are evaluated by quantifying the fractions of ice and snow, liquid clouds, rain, and aerosols in the atmosphere that can be successfully identified by each instrument and their synergy. We show that radar–lidar synergy helps better detect ice and snow, with ATLID detecting radiatively important optically thin cirrus and cloud tops, while CPR penetrates most deep and highly concentrated ice clouds. The detection of rain and drizzle is entirely due to C-TC, while that of liquid clouds and aerosols is due to A-TC. The evaluation also shows that simple assumptions can be made to compensate for when the instruments are obscured by extinction (ATLID) or surface clutter and multiple scattering (CPR); this allows for the recovery of the majority of liquid cloud not detected by the active instruments.

Recent decreases in snow water storage in western North America

Mountain snowpacks act as natural water towers, storing winter precipitation until summer months when downstream water demand is greatest. We introduce a Snow Storage Index (SSI), representing the temporal phase difference between daily precipitation and surface water inputs—sum of rainfall and snowmelt into terrestrial systems—weighted by relative magnitudes. Different from snow water equivalent or snow fraction, the SSI represents the degree to which the snowpack delays the timing and magnitude of surface water inputs relative to precipitation, a fundamental component of how snow water storage influences the hydrologic cycle. In western North America, annual SSI has decreased (p < 0.05) from 1950–2013 in over 25% of mountainous areas, as a result of substantially earlier snowmelt and rainfall in spring months, with additional declines in winter precipitation. The SSI and associated trends offer a new perspective on hydrologic sensitivity to climate change which have broad implications for water resources and ecosystems.

High Dissolved Carbon Concentration in Arid Rocky Mountain Streams.

Warming in mountains is known to intensify aridity and threaten water availability globally. Its impacts on water quality, however, have remained poorly understood. Here we collate long-term (multi-year to decadal mean), baseline stream concentrations and fluxes of dissolved organic and inorganic carbon, two essential indicators of water quality and soil carbon response to warming, across more than 100 streams in the United States Rocky Mountains. Results show a universal pattern of higher mean concentrations in more arid mountain streams with lower mean discharge, a long-term climate measure. A watershed reactor model revealed less lateral export of dissolved carbon (via less water flow) out of the watersheds in more arid sites, leading to more accumulation and higher concentrations. Lower concentrations typically occur in cold, steep, and compact mountains with higher snow fraction and lower vegetation cover, which generally have higher discharge and carbon fluxes. Inferring from a space-for-time perspective, the results indicate that as warming intensifies, lateral fluxes of dissolved carbon will decrease but concentrations will increase in these mountain streams. This indicates deteriorating water quality and potentially elevated CO2 emission directly from the land (instead of streams) in the Rockies and other mountain areas in the future climate.

Temporal hydrological drought clustering varies with climate and land-surface processes

Recurrent hydrological droughts (streamflow deficits) are highly impactful and challenge water management. Regional studies have provided some evidence of drought-rich periods at specific time scales. However, it is yet unclear where and when droughts cluster in time. Here, we test for significant temporal hydrological drought clustering at subseasonal to multi-year time scales in different climate zones around the world using two different clustering metrics, i.e. the dispersion index and Ripley’s K. We find that (1) only 10% of the catchments show temporal hydrological drought clustering, (2) hydrological droughts cluster from seasonal to 3-year time scales with clustering being strongest at an annual time scale; (3) arid catchments with a low snow fraction are most prone to temporal drought clustering; and (4) temporal clustering is more pronounced for hydrological than for meteorological droughts. These results suggest that besides climatic drivers, land-surface processes importantly influence the temporal clustering behavior of hydrological droughts.

Trace Metals and Polycyclic Aromatic Hydrocarbons in the Snow Cover of the City of Nizhnevartovsk (Western Siberia, Russia).

The city of Nizhnevartovsk is one of the centers of oil production in Western Siberia (Russia). A survey of the contents of trace metals and metalloids (TMMs) and polycyclic aromatic hydrocarbons (PAHs) in the snow cover was conducted there. It was aimed to study insoluble particles in the snow where the predominant fraction of pollutants in urban areas is concentrated. In contrast to the background area, the deposition of TMMs in Nizhnevartovsk increases by 1-2 orders of magnitude. The deposition of V and Mn increases by 37 and 88 times, respectively, and the deposition of W increases at most (by 98 times). Abrasion of spikes of winter tires, abrasion of metal parts of vehicles, and combustion of motor fuels cause the pollution with W, Co, and V, respectively. The total content of 12 EPA PAHs in the particulate fraction of snow in the urban area averaged 148.2ngl-1, and the deposition rate was 17.0μg/m2. In contrast to the background area, the fraction of high molecular weight 5-6-ring PAHs significantly increases in the city, especially dibenzo(a,h)anthracene (DahA). The indicative ratios of PAHs showed that the snowpack composition was influenced by both petrogenic and pyrogenic sources. The proportion of pyrogenic sources is the highest in the low-rise residential area due to fuel combustion to produce heat and burning of household waste. The impact of motor transport is also major and is manifested in the maximum pollution in areas of heavy traffic. No emissions of PAHs from oil spills from the nearby Samotlor oil field have been identified. It is concluded that the hydrocarbon pollution of the atmosphere from the field weakens during the winter period compared to the warm season. Application of the integral TDF index characterizes the majority (72%) of the studied samples as lowly polluted, 24% of the observation sites are classified as moderately polluted, and one (4%), as highly polluted. The maximum TDF values are observed in the industrial area. The data obtained during the study allowed us to identify the central areas and sites along the roads with the heaviest traffic as the most contaminated areas of the city. This study can be a reference for air pollution monitoring in Nizhnevartovsk.

Morphological and microphysical characteristics associated with the lifecycle of a stationary cloud cluster during the Indian Summer Monsoon: A comparative study with numerical simulations and radar observation

The simulated morphological, dynamical, and microphysical characteristics, by the three microphysics schemes with increasing complexities, during the lifecycle of a tropical continental mesoscale cloud cluster observed during the monsoon over the peninsular Indian region are illustrated in this study. Observations from a polarimetric C-Band radar have set the benchmark for the evaluation of the performances of the three microphysics schemes. Results show that all the simulations have a tendency to produce intense convection in comparison to observations, where the least complex scheme projected the maximum value. The contrasting dynamical characteristics by each microphysics scheme resulted in different cloud structure. The simulated aggregation/clustering properties of convection by the microphysics schemes were different, where the most sophisticated scheme produced the convective aggregation/clustering closer to radar-derived products. Significant differences in upper-level cloud fractions are noted among the simulations, which are manifested by the variations in the upper-level relative humidity and distribution of ice-phase hydrometeors. Intercomparison of radar-derived and simulated hydrometeor fractions suggests that all the simulations have a tendency to produce lesser snow fraction, higher graupel fraction, and higher ice fraction. Overall analyses suggest that the most sophisticated microphysics scheme produced a more realistic simulation of the cloud system.

Design flood estimation using extreme Gradient Boosting-based on Bayesian optimization

• Proposed XGB-based ML model for RFFA and flood estimation. • CAMELs data for 671 catchments from the USA were used to test the efficacy of the approach. • Multiple Linear Regression methods and Artificial Neural Networks were used to compare the output of the eXtreme Gradient Boost-based ML model. • The importance of each catchment feature is characterized using Gini Impurity, Permutation, and Dropout Loss Feature Ranking methods. Regional Flood Frequency Analysis (RFFA) is one of the widely used approaches for estimating design floods in the ungauged basins. We developed an eXtreme Gradient Boost (XGB) machine learning model for RFFA and flood estimation. Our approach relies on developing a regression model between flood quantiles and the commonly available catchment descriptors. We used CAMELs data for 671 catchments from the USA to test the approach's efficacy. The results were compared with the traditional Multiple Linear Regression methods and Artificial Neural Networks. Results revealed that the XGB-based approach estimated design flood with the highest accuracy during training and validation with minor mean absolute error, root mean square error values, and percentage bias ranging from −10 to + 10. The importance of each catchment feature is visualized by three different approaches Gini Impurity, Permutation, and Dropout Loss Feature Ranking. We observed that the most dominating variables are rainfall intensity, slope, snow fraction, soil porosity, and temperature. It is observed that the importance of these variables is a function of the hydroclimatic regions and varies with space. In contrast, mean annual areal potential evapotranspiration, mean annual rainfall, fraction forest area, and soil conductivity have low significance in estimating design flood for an ungauged catchment. Indeed, the proposed XGB-based approach has broader applicability and replicability.

Modeling the small-scale deposition of snow onto structured Arctic sea ice during a MOSAiC storm using snowBedFoam 1.0.

Abstract. The remoteness and extreme conditions of the Arctic make it a very difficult environment to investigate. In these polar regions covered by sea ice, the wind is relatively strong due to the absence of obstructions and redistributes a large part of the deposited snow mass, which complicates estimates for precipitation hardly distinguishable from blowing or drifting snow. Moreover, the snow mass balance in the sea ice system is still poorly understood, notably due to the complex structure of its surface. Quantitatively assessing the snow distribution on sea ice and its connection to the sea ice surface features is an important step to remove the snow mass balance uncertainties (i.e., snow transport contribution) in the Arctic environment. In this work we introduce snowBedFoam 1.0., a physics-based snow transport model implemented in the open-source fluid dynamics software OpenFOAM. We combine the numerical simulations with terrestrial laser scan observations of surface dynamics to simulate snow deposition in a MOSAiC (Multidisciplinary Drifting Observatory for the Study of Arctic Climate) sea ice domain with a complicated structure typical for pressure ridges. The results demonstrate that a large fraction of snow accumulates in their vicinity, which compares favorably against scanner measurements. However, the approximations imposed by the numerical framework, together with potential measurement errors (precipitation), give rise to quantitative inaccuracies, which should be addressed in future work. The modeling of snow distribution on sea ice should help to better constrain precipitation estimates and more generally assess and predict snow and ice dynamics in the Arctic.

Climate change is increasing the risk of a California megaflood

Despite the recent prevalence of severe drought, California faces a broadly underappreciated risk of severe floods. Here, we investigate the physical characteristics of “plausible worst case scenario” extreme storm sequences capable of giving rise to “megaflood” conditions using a combination of climate model data and high-resolution weather modeling. Using the data from the Community Earth System Model Large Ensemble, we find that climate change has already doubled the likelihood of an event capable of producing catastrophic flooding, but larger future increases are likely due to continued warming. We further find that runoff in the future extreme storm scenario is 200 to 400% greater than historical values in the Sierra Nevada because of increased precipitation rates and decreased snow fraction. These findings have direct implications for flood and emergency management, as well as broader implications for hazard mitigation and climate adaptation activities.

Divergence of apparent and intrinsic snow albedo over a season at a sub-alpine site with implications for remote sensing

Abstract. Intrinsic albedo is the bihemispherical reflectance independent of effects of topography or surface roughness. Conversely, the apparent albedo is the reflected radiation divided by the incident and may be affected by topography or roughness. For snow, the surface is often rough, and these two optical quantities have different uses: intrinsic albedo is used in scattering equations whereas apparent albedo should be used in energy balance models. Complementing numerous studies devoted to surface roughness and its effect on snow reflectance, this work analyzes a time series of intrinsic and apparent snow albedos over a season at a sub-alpine site using an automated terrestrial laser scanner to map the snow surface topography. An updated albedo model accounts for shade, and in situ albedo measurements from a field spectrometer are compared to those from a spaceborne multispectral sensor. A spectral unmixing approach using a shade endmember (to address the common problem of unknown surface topography) produces grain size and impurity solutions; the modeled shade fraction is compared to the intrinsic and apparent albedo difference. As expected and consistent with other studies, the results show that intrinsic albedo is consistently greater than apparent albedo. Both albedos decrease rapidly as ablation hollows form during melt, combining effects of impurities on the surface and increasing roughness. Intrinsic broadband albedos average 0.056 greater than apparent albedos, with the difference being 0.052 in the near infrared or 0.022 if the average (planar) topography is known and corrected. Field measurements of spectral surface reflectance confirm that multispectral sensors see the apparent albedo but lack the spectral resolution to distinguish between darkening from ablation hollows versus low concentrations of impurities. In contrast, measurements from the field spectrometer have sufficient resolution to discern darkening from the two sources. Based on these results, conclusions are as follows: (1) impurity estimates from multispectral sensors are only reliable for relatively dirty snow with high snow fraction; (2) a shade endmember must be used in spectral mixture models, even for in situ spectroscopic measurements; and (3) snow albedo models should produce apparent albedos by accounting for the shade fraction. The conclusion re-iterates that albedo is the most practical snow reflectance quantity for remote sensing.

Hydrological effects of the snow fraction and its ecohydrological explication within the Budyko framework

Understanding the effects of climate and catchment characteristics on hydrological behavior is critical in hydrological sciences. Furthermore, improving the clarity associated with the physical meaning of the Budyko hypothesis on how it can be incorporated into ecohydrological processes remains a significant challenge, while the conciseness it affords in addressing hydrological responses has promoted its application. This study explores the hydrological effects of the snow fraction (fs) and assesses the ecohydrological meaning of the catchment parameter (n) within the Budyko framework, employing 1576 globally investigated catchments. Results showed that the Budyko hypothesis decreased systematic errors (NS = 0.76, RMSE = 181.01 mm a−1) when incorporating the fs compared with no snow fraction incorporated it (NS = 0.54, RMSE = 248.85 mm a−1), especially for catchments with higher fs. In these catchments, a higher fs was generally consistent with a higher normalized mean streamflow (Q), while the opposite was true for normalized evaporation (E/P). Under the Budyko framework, n and fs distribution generally exhibited opposite trends. Moreover, higher n was mainly distributed near water limited and the highest energy limited boundaries, while higher fs represented lower (P-Q)/P (i.e., the mean (P-Q)/P was 0.59, 0.57, and 0.38 for catchments with lower (fs < 0.15), medium (0.15 ≤ fs < 0.50), and higher (fs ≥ 0.50) fs). Statistical relationships among ecohydrological processes and catchment parameter suggested that: (i) higher storm depth (α) generally indicated higher normalized mean Q; (ii) a higher plant-available water holding capacity (κ) resulted in a reduction in Q/P, especially when κ was > 0.12; (iii) the effective rooting depth (Ze) exhibited a complex regulatory effect on P partitioning; and (iv) α, Ze, and κ controlled n by 65%, 22%, and 13%, respectively, in all catchments. The ecohydrological processes incorporated into catchment parameters will broaden the ability of the Budyko hypothesis to describe hydrological behavior in a changing world, especially in the ungauged catchment.

Estimated changes in different forms of precipitation (snow, sleet, and rain) across China: 1961–2016

Different forms of precipitation (snow, sleet, and rain) have divergent effects on the Earth's surface water and energy fluxes. Therefore, discriminating between these forms, and exploring their climatology and variations are of significant importance, especially under a changing climate. In this study, a state-of-the-art parameterization scheme with wet-bulb temperature (Tw), relative humidity (RH), surface air pressure, and elevation as inputs was used to estimate the climatologies and trends of the different forms of precipitation and their temperature thresholds across China mainland from 1961 to 2016. The results indicate that on average, only 9.6% and 0.8% of the total precipitation (602.9 mm) over China during 1961–2016 occurred as snow and sleet. The trends of the different forms of precipitation varied greatly from region to region and from season to season. The regionally averaged annual precipitation, rainfall, and snowfall across China significantly increased at rates of 11.4 mm/decade, 11.0 mm/decade, and 1.0 mm/decade, respectively, whereas the annual sleet decreased at a rate of −0.3 mm/decade. The region-wide snow (rain) fraction significantly increased (decreased) in Northwestern China (NWC) and Northern China (NC) but significantly decreased (increased) on the Tibetan Plateau (TP). Given the heterogeneous RH and complex topography, the Tw values at which snow-sleet and sleet-rain occurred with equal frequency—that is, Ts and Tr, varied significantly across China, averaging 0.1 °C and 0.3 °C, respectively. However, most regions experienced significant decreases in the temperature thresholds (especially Tr), with decreasing RH. Almost all of the regions also experienced significant increases in Tw, especially on the TP and during the winter and spring. The adverse combination of the increase in Tw and the decrease in the temperature thresholds resulted in more precipitation falling as rain, although the snowfall slightly increased in some regions.