Snow Cover Area Research Articles

STAR NDSI collection: a cloud-free MODIS NDSI dataset (2001–2020) for China

Abstract. Snow dynamics are crucial in ecosystems, affecting radiation balance, hydrological cycles, biodiversity, and human activities. Snow areas with notably diverse characteristics are extensively distributed in China, mainly including Northern Xinjiang (NX), Northeast China (NC), and the Qinghai–Tibet Plateau (QTP). Spatiotemporal continuous snow monitoring is indispensable for ecosystem maintenance. Nevertheless, the formidable challenge of cloud obscuration severely impedes data collection. In the past decades, abundant binary snow cover area (SCA) maps have been retrieved from moderate resolution imaging spectroradiometer (MODIS) datasets. However, the integrated normalized difference snow index (NDSI) maps containing additional details on snow cover extent are still extremely scarce. In this study, a recent 20-year stretch seamless Terra–Aqua MODIS NDSI collection in China is generated using a Spatio-Temporal Adaptive fusion method with erroR correction (STAR), which comprehensively considers spatial and temporal contextual information. Evaluation tests confirm that the cloud-free STAR NDSI collection is superior to the two baseline datasets. The omission error decreased by 10 % in NX compared to the snow cover extent product, and the average correlation coefficient increased by 0.11 compared to the global cloud-gap-filled MODIS NDSI product. Consequently, this collection can serve as a basic dataset for hydrological and climatic modeling to explore various critical environmental issues in China. This collection is available from https://doi.org/10.5281/zenodo.5644386 (Jing et al., 2021).

Retrieval of Fractional Snow Cover over High Mountain Asia Using 1 km and 5 km AVHRR/2 with Simulated Mid-Infrared Reflective Band

Accurate long-term snow-covered-area mapping is essential for climate change studies and water resource management. The NOAA AVHRR/2 provides a unique data source for long-term, large-spatial-scale monitoring of snow-covered areas at a daily scale. However, the value of AVHRR/2 in mapping snow-covered areas is limited, due to its lack of a shortwave infrared band for snow/cloud discrimination. We simulated the reflectance in the 3.75 µm mid-infrared band with a radiative transfer model and then developed three fractional-snow-cover retrieval algorithms for AVHRR/2 imagery at 1 km and 5 km resolutions. These algorithms are based on the multiple endmember spectral mixture analysis algorithm (MESMA), snow index (SI) algorithm, and non-snow/snow two endmember model (TEM) algorithm. Evaluation and comparison of these algorithms were performed using 313 scenarios that referenced snow-cover maps from Landsat-5/TM imagery at 30 m resolution. For all the evaluation data, the MESMA algorithm outperformed the other two algorithms, with an overall accuracy of 0.84 (0.85) and an RMSE of 0.23 (0.21) at the 1 km (5 km) scale. Regarding the effect of land cover type, we found that the three AVHRR/2 fractional-snow-cover retrieval algorithms have good accuracy in bare land, grassland, and Himalayan areas; however, the accuracy decreases in forest areas due to the shading of snow by the canopy. Regarding the topographic effect, the accuracy evaluation indices showed a decreasing and then increasing trend as the elevation increased. The accuracy was worst in the 4000–5000 m range, which was due to the severe snow fragmentation in the High Mountain Asia region; the early AVHRR/2 sensors could not effectively monitor the snow cover in this region. In this study, by increasing the number of bands of AVHRR/2 1 km data for fractional-snow-cover retrieval, a good foundation for subsequent long time series kilometre- resolution snow-cover monitoring has been laid.

Quantitative determination of environmental factors governing the snow melting: a geodetector case study in the central Tienshan Mountains

Because of the distinctive vertical climate and topography gradients in the alpine region, the snow cover of the Tienshan Mountains possesses complex spatiotemporal heterogeneity, particularly during the melting process. Quantifying the environmental factors is therefore crucial to understanding the melting process and for predicting and managing snowmelt runoff. Herein, the snow cover area, grain size, and contamination extent were determined to characterize the detailed melting status based on surface reflectance data of MOD09A1 in the central Tienshan Mountains from 2013 to 2017. The environmental factors collected include relief (elevation, slope, and aspect); meteorology (surface air temperature, land surface temperature, solar radiation, and wind speed); and land surface vegetation. Analysis of the geodetector results indicated the following. (1) Patterns of changes in the overall dominant environmental variables were consistent for the pre-, mid-, and post-melting periods defined according to the decline of snow cover area over five years. (2) The overall major environmental factors were wind speed and radiation (pre-period), land surface temperature and elevation (mid-period), and elevation and land surface types (post-period), respectively. (3) Regional distinctions were detected of the dominant environmental factors. In the pre-melting period, the effects of solar radiation and wind speed were noticeable in the north and south regions, respectively. The effects of elevation, land surface temperature, and land cover types became more prominent in all regions during the mid- and post-melting periods. (4) Interaction between the major environmental factors was significantly enhanced on both the overall and regional scales, thus affecting the snow-melting process. Finally, the energy distribution mismatch resulted in the snowmelt. Multiple environmental factors substantially affect heat redistribution at different spatiotemporal scales, resulting in the snowmelt as a complex manifestation of the factors and their interactions. The findings highlight regional differences in various environmental factors of the melting process and offer a theoretical foundation for the melting process at various scales over multiple years.

Evaluation of Snow and Streamflows Using Noah-MP and WRF-Hydro Models in Aroostook River Basin, Maine

Snow influences land–atmosphere interactions in snow-dominated areas, and snow melt contributes to basin streamflows. However, estimating snowpack properties such as the snow depth (SD) and snow water equivalent (SWE) from land surface model simulations remains a challenge. This is, in part, due to uncertainties in the atmospheric forcing variables, which propagate into hydrological model predictions. This study implements the Weather Research and Forecasting (WRF)-Hydro framework with the Noah-Multiparameterization (Noah-MP) land surface model in the NOAA’s National Water Model (NWM) version 2.0 configuration to estimate snow in a single column and subsequently the streamflow across the Aroostook River’s sub-basins in Maine for water years (WY) 2014–2016. This study evaluates how differences between two atmospheric forcing datasets, the North American Land Data Assimilation version 2 (NLDAS-2) and in situ (Station), translate into differences in the simulation of snow. NLDAS-2 was used as the meteorological forcing in the retrospective NWM 2.0 simulations. The results from the single-column study showed that differences in the simulated SWE and SD were linked to differences in the 2 m air temperature (T2m), which influenced the precipitation partitioning of rain and snow, as parameterized in Noah-MP. The negative mean bias of −0.7 K (during the accumulation period) in T2m for NLDAS-2, compared to the Station forcing, was a major factor that contributed to the positive mean bias of +52 mm on average in the peak SWE in the NLDAS-2-forced Noah-MP simulation during the study period. The higher T2m values at the Station led to higher sensible heat fluxes towards the snowpack, which led to a higher amount of net energy at the snow’s surface and melt events during the accumulation season in Station-forced Noah-MP simulations. The results from the retrospective NWM version 2.0′s simulation in the basin showed that the streamflow estimates were closer to the United States Geological Survey gage observations at the two larger sub-basins (NSE = 0.9), which were mostly forested, compared to the two smaller sub-basins (NSE ≥ 0.4), which had more agricultural land-use. This study also showed that the spring snowmelt timing was captured quite well by the timing of the decline in the simulated SWE and SD, providing an early indication of melt in most sub-basins. The simulated fractional snow cover area (fSCA) however provided less information about the changes in snow or onset of snowmelt as it was mostly binary (full snow cover in winter), which differed from the more realistic fSCA values shown by the Moderate Resolution Imaging Spectroradiometer.

Snow Cover in the Three Stable Snow Cover Areas of China and Spatio-Temporal Patterns of the Future

In the context of global warming, relevant studies have shown that China will experience the largest temperature rise in the Qinghai–Tibet Plateau and northwestern regions in the future. Based on MOD10A2 and MYD10A2 snow products and snow depth data, this study analyzes the temporal and spatial evolution characteristics of the snow cover fraction, snow depth, and snow cover days in the three stable snow cover areas in China, and combines 15 modes in CMIP6 snow cover data in four different scenarios with three kinds of variables, predicting the spatiotemporal evolution pattern of snow cover in China’s three stable snow cover areas in the future. The results show that (1) the mean snow cover fraction, snow depth, and snow cover days in the snow cover area of Northern Xinjiang are all the highest. Seasonal changes in the snow cover areas of the Qinghai–Tibet Plateau are the most stable. The snow cover fraction, snow depth, and snow cover days of the three stable snow cover areas are consistent in spatial distribution. The high values are mainly distributed in the southeast and west of the Qinghai–Tibet Plateau, the south and northeast of Northern Xinjiang, and the north of the snow cover area of Northeast China. (2) The future snow changes in the three stable snow cover areas will continue to decline with the increase in development imbalance. Snow cover fraction and snow depth decrease most significantly in the Qinghai–Tibet Plateau and the snow cover days in Northern Xinjiang decrease most significantly under the SSPs585 scenario. In the future, the southeast of the Qinghai–Tibet Plateau, the northwest of Northern Xinjiang, and the north of Northeast China will be the center of snow cover reduction. (3) Under the four different scenarios, the snow cover changes in the Qinghai–Tibet Plateau and Northern Xinjiang are the most significant. Under the SSPs126 and SSPs245 scenarios, the Qinghai–Tibet Plateau snow cover has the most significant change in response. Under the SSPs370 and SSPs585 scenarios, the snow cover in Northern Xinjiang has the most significant change.

SAFETY REQUIREMENTS FOR SNOW LOAD ON UNIQUE TRANSPORT INFRASTRUCTURE FACILITIES

Introduction: Wind-induced snow drift is the main reason behind the non-uniform snow load on a snow-covered area. As known, snow load poses a serious hazard to the roofing of buildings and structures. According to the applicable regulatory documents, snow loads for non-standard roofs must be determined based on the results of model tests in wind tunnels. Purpose of the study: We aimed to study snow load on a unique transport infrastructure facility — the world’s first crossborder cable car connecting Blagoveshchensk in Russia to Heihe in China. Methods: We performed model tests to study snow accumulation and drifting with the use of a unique research setup — the Large Gradient Wind Tunnel, courtesy of the National Research Moscow State University of Civil Engineering. Results: Based on climate analysis and tests under different wind attack angles, we obtained distribution patterns for snow deposits on the roofing of the unique transport infrastructure facility under consideration and derived the values of the coefficient μ (the coefficient of transition from the weight of the snow cover on the ground to the snow load on roofing).

Subpixel Snow Mapping Using Daily AVHRR/2 Data over Qinghai–Tibet Plateau

Based on a linear spectral mixture model and multiple endmember spectral mixture analysis, using daily advanced very-high-resolution radiometer (AVHRR/2) data of the Qinghai–Tibet Plateau, a subpixel snow mapping algorithm was proposed in this paper, for prolonging the historical time series of the fractional snow cover data to 40 years. In particular, the normalized difference vegetation index (NDVI), and channels 1 and 2 of AVHRR/2 data were used to automatically select the end-members directly, from a certain AVHRR/2 image. A look-up table of sample spectra of mixed pixels and their respective snow cover percentages was introduced for one AVHRR/2 image. According to the established look-up tables, the fractional snow cover of each mixed pixel can then be extracted from the AVHRR/2 images. Before the subpixel snow mapping, the cloud pollution of the AVHRR/2 images was mitigated, with both the thick and thin clouds almost removed from the AVHRR/2 images. It turns out that the processing speed of the subpixel snow mapping is three times faster than the process not using the look-up table. The mapping algorithm was validated against the snow-covered area from Thematic Mapper (TM) data, with the root-mean-square errors (RMSEs) well below 0.12. Results show that the proposed algorithm for subpixel snow mapping is both effective and efficient, especially in such a mountainous region as the Qinghai–Tibet Plateau.

Accuracy of UAV Photogrammetry in Glacial and Periglacial Alpine Terrain: A Comparison With Airborne and Terrestrial Datasets

Unoccupied Aerial Vehicles (UAVs) equipped with optical instruments are increasingly deployed in high mountain environments to investigate and monitor glacial and periglacial processes. The comparison and fusion of UAV data with airborne and terrestrial data offers the opportunity to analyse spatio-temporal changes in the mountains and to upscale findings from local UAV surveys to larger areas. However, due to the lack of gridded high-resolution data in alpine terrain, the specific challenges and uncertainties associated with the comparison and fusion of multi-temporal data from different platforms in this environment are not well known. Here we make use of UAV, airborne, and terrestrial data from four (peri)glacial alpine study sites with different topographic settings. The aim is to assess the accuracy of UAV photogrammetric products in complex terrain, to point out differences to other products, and to discuss best practices regarding the fusion of multi-temporal data. The surface geometry and characteristic geomorphological features of the four alpine sites are well captured by the UAV data, but the positional accuracies vary greatly. They range from 15 cm (root-mean-square error) for the smallest survey area (0.2 km2) with a high ground control point (GCP) density (40 GCPs km−2) to 135 cm for the largest survey area (>2.5 km2) with a lower GCP density (<10 GCPs km−2). Besides a small number and uneven distribution of GCPs, a low contrast, and insufficient lateral image overlap (<50–70%) seem to be the main causes for the distortions and artefacts found in the UAV data. Deficiencies both in the UAV and airborne data are the reason for horizontal deviations observed between the datasets. In steep terrain, horizontal deviations of a few decimetres may result in surface elevation change errors of several metres. An accurate co-registration and evaluation of multi-temporal UAV, airborne, and terrestrial data using tie points in stable terrain is therefore of utmost importance when it comes to the investigation of surface displacements and elevation changes in the mountains. To enhance the accuracy and quality of UAV photogrammetry, the use of UAVs equipped with multi-spectral cameras and high-precision positioning systems is recommended, especially in rugged terrain and snow-covered areas.

An estimation and multi-step ahead prediction study of monthly snow cover area, based on efficient atmospheric-oceanic dynamics

An estimation and multi-step ahead prediction study of monthly snow cover area, based on efficient atmospheric-oceanic dynamics

On the Detection of Snow Cover Changes over the Australian Snowy Mountains Using a Dynamic OBIA Approach

This study detected the spatial changes in Snow Cover Area (SCA) over the Snowy Mountains in New South Wales, Australia. We applied a combination of Object-Based Image Analysis (OBIA) algorithms by segmentation, classification, and thresholding rules to extract the snow, water, vegetation, and non-vegetation land covers. For validation, the Maximum Snow Depths (MSDs) were collected at three local snow observation sites (namely Three Mile Dam, Spencer Creek, and Deep Creek) from 1984 to 2020. Multiple Landsat 5, 7, and 8 imageries extracted daily MSDs. The process was followed by applying an Estimation Scale Parameter (ESP) tool to build the local variance (LV) of object heterogeneity for each satellite scene. By matching the required segmentation parameters, the optimal separation step of the image objects was weighted for each of the image bands and the Digital Elevation Model (DEM). In the classification stage, a few land cover classes were initially assigned, and three different indices—Normalized Differential Vegetation Index (NDVI), Surface Water Index (SWI), and a Normalized Differential Snow Index (NDSI)—were created. These indices were used to adjust a few classification thresholds and ruleset functions. The resulting MSDs in all snow observation sites proves noticeable reduction trends during the study period. The SCA classified maps, with an overall accuracy of nearly 0.96, reveal non-significant trends, although with considerable fluctuations over the past 37 years. The variations concentrate in the north and south-east directions, to some extent with a similar pattern each year. Although the long-term changes in SCA are not significant, since 2006, the pattern of maximum values has decreased, with fewer fluctuations in wet and dry episodes. A preliminary analysis of climate drivers’ influences on MSD and SCA variability has also been performed. A dynamic indexing OBIA indicated that continuous processing of satellite images is an effective method of obtaining accurate spatial–temporal SCA information, which is critical for managing water resources and other geo-environmental investigations.

STUDY ON SNOW COVER CHANGE BASED ON THE FUSION OF SENTINEL-2 AND MODIS IMAGES

Abstract. Snow cover is one of the features that change rapidly on the surface, and remote sensing data with high spatial and temporal resolution is an important means to monitor the dynamic changes of snow. However, due to the limitation of satellite conditions, it is difficult to acquire remote sensing images with both high temporal and spatial resolution at the same time. The classical Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) breaks through the limitations of a single sensor and effectively brings into play the complementary advantages of different observation platforms. In this paper, we take the Babao River Basin within the Qinghai-Tibet Plateau as the study area to investigate the effect of fusion of Sentinel-2 and MODIS (Moderate-resolution Imaging Spectroradiometer) based on the ESTARFM and analyze the fusion-supplemented snow accumulation period data. The results show that the fusion effect of Sentinel-2 and MODIS is good in terms of visual and multiple quantitative indicators. The snow accumulation area of the fused image is also close to the real image. By fusing to supplement the missing Sentinel-2 data, a variation map of snow NDSI (Normalized Difference Snow Index) in the study area was obtained. It was found that the snowfall process in the watershed appeared first in the surrounding uplands and then in the flat areas, and the maximum rate of snow cover area change was 116.17 km2. Therefore, the spatiotemporal data fusion of Sentinel-2 and MODIS based on ESTARFM algorithm can provide reliable satellite remote sensing data with a higher spatiotemporal resolution for snow accumulation change monitoring.

Trends in land surface temperature and its drivers over the High Mountain Asia

The present study estimates spatial and temporal (seasonal/annual) trends in daytime land surface temperature (LST) and its drivers (elevation, cloud fraction (CF), atmospheric water vapor (AWV), normalized difference vegetation index (NDVI), and snow cover area (SCA)) over the High Mountain Asia (HMA) during 2001–2019 using MODIS remote sensing data. The findings of the study showed mixed trends in LST in the region with intra-regional variations. Overall, the warming in the region is attributed to increasing AWV and NDVI and declining CF and SCA. The study also found evidence of elevation-dependent warming (EDW) (4500–5500 m AMSL) in some regions. However, the same conclusion cannot be drawn in all the sub-regions of HMA, suggesting the need to explore the effects of additional natural and anthropogenic factors on LST with fine resolution data to improve the understanding of its spatio-temporal dynamics.

Statistical Analysis of Time Series of Snow-Covered Area Obtained from Satellite Images in Mazandaran Province

Statistical Analysis of Time Series of Snow-Covered Area Obtained from Satellite Images in Mazandaran Province

Controlling Factors and Characteristics of Peak Runoff in an Alpine Headwater Under the Asian Monsoon Climate

Understanding the mountain hydrological cycle, including runoff processes, is important for water-related disaster prevention. Although the process of peak runoff generation is closely related to water hazards, this process has not been clarified in alpine headwaters with large amounts of precipitation. In this study, we conducted hydrological observations to clarify runoff characteristics and factors that determine peak runoff in an alpine headwater under the Asian monsoon climate. Total precipitation during the summer period (3 months) was 1581.4 mm, and the water runoff responded quickly and clearly to rainfall events. Focusing on baseflow, the runoff was terminated when the snow cover area decreased. This suggested that snowmelt water plays an important role in maintaining baseflow in alpine headwaters under the Asian monsoon climate, like other alpine areas worldwide. In addition, peak runoff was not significantly correlated with soil wetness (as indicated by the antecedent precipitation index), whereas it was correlated with the amount of rainfall just before the generation of peak runoff. Therefore, the amount of rainfall before peak runoff in a single event was important in determining peak runoff. Focusing on the snowmelt season, we confirmed that the runoff increased even during small rainfall events. This indicated that snowpack melting is another factor determining peak runoff when the snowpack remains in the catchment. Considering the immediate runoff generation after rainfall events, direct observation of hydrometeorological data in situ is crucial to predict water-related disasters and consider countermeasures in alpine regions.

Effect of shadow on atmospheric and topographic processed NDSI values in Chenab basin, western Himalayas

The present study was undertaken in the Chenab basin, western Himalayas to explore the effect of shadow on atmospherically and topographically corrected normalized difference snow index (NDSI) values. The research was conducted using the Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) datasets of two time periods i.e., February 2015 and February 2018. Our results suggested that total snow cover area (SCA) computed using NDSI from raw satellite images, under estimated SCA (viz. 10,938.66 and 7739.97 Km2); relative to atmospherically corrected (AC) (14,819.01 and 11,199.7 Km2) and topographically corrected (TC) (14,882.07 and 11,188.18 Km2) images during both the time periods (i.e., 2015 and 2018, respectively). This is mainly due to the non-identification of snow pixels in the shadow regions of raw imageries. Maximum SCA lies in the higher NDSI range in case of AC and TC images, unlike raw image where maximum SCA lies in lower NDSI range for both sunlit and shadow test sites. In the sunlit and shadow terrains, minimum NDSI values were found to be higher for TC images as compared to AC images, except few observations which exhibited similar values for both AC and TC images. In case of maximum NDSI values, most of the observations exhibited similar values for both AC and TC images, except few observations which depicted higher maximum NDSI value in AC images as compared to TC images in both the terrains. This observation ascertains the narrow range of NDSI values for TC images as compared to AC images, which could be attributed to reduced background reflection and low atmospheric scattering in the TC images. Comparison among shadow and sunlit snow terrains, reveals low values of minimum and maximum NDSI in the shadow site, which is ascertained by significant (p < 0.001) t-values of means difference between shadow and sunlit terrains. Moreover, snow surface temperature (SST) values computed for both the time periods, reveal low SST values for shadow sites (t-values −5.1 and − 10.57 for minimum SST, and − 9.1 and − 12.61 for maximum SST for the years 2015 and 2018, respectively) as compared to sunlit sites, and this observation validates that minimum amount of solar radiation reaches the shadow sites. Thus, it is concluded that AC and TC are helpful for delineation of snow pixels under shadow, but shadow has a significant impact on the spectral reflectance of snow, even in the AC and TC images. The influence of shadow should be taken into account for accurate estimation of physical properties and broadband albedo of the snow.

Mapping snow cover in forests using optical remote sensing, machine learning and time-lapse photography

The accurate spatial information of snow cover is useful for understanding the impact of global warming, and it is of high significance for hydrological disaster prediction, water resources management, and climate change research. The Normalized Difference Snow Index (NDSI) based approach has been used extensively around the world for mapping snow, and they displayed high accuracy in open areas. However, capturing snow cover in forests remains problematic due to the obstruction effects of the forest canopy, which causes the snow cover area to be seriously underestimated. In this paper, we present a new algorithm based on machine learning (ML) technology to improve the accuracy of binary snow cover (BSC) mapping in forests, using the remotely sensed surface reflectance and ground truth data. A time-lapse photography network with a two-hour resolution was established in the eastern Qilian Mountains in northwestern China to obtain the ground truth data both in forests and open areas. We trained Random Forests (RF) with the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data from bands 1–7 to generate BSC results (RF-BSC). Then we evaluated RF-BSC and the NDSI-derived BSC maps with three different NDSI thresholds (i.e., 0.10, 0.29, and 0.40) against ground-truth data. The results indicate that the proposed algorithm has a high performance in forest BSC mapping in this area, compared to the NDSI-threshold approach. The RF-BSC can retrieve 67% of all real forest snow pixels, while the NDSI-based BSC can only detect 8–14%. We also find that the performance of the algorithm seems to be sensitive to changes in solar illumination conditions and forest coverage. This study suggests that machine learning with the fusion of optical remote sensing and ground-based observations is an effective approach for improving the accuracy of forest snow cover mapping at regional scales.

Statistical analysis of precipitation, temperature and snow cover in Bhagirathi river basin

One of the world’s largest river basins is the Ganga River basin. Bhagirathi basin which is part of this Ganga Basin is important because the glacier (Gangotri) from which river Ganga originates is situated in this basin. The present study presents the changing climate impact on dynamics of snow cover and hydrological regime of the Bhagirathi basin. The remote sensing satellite data from MODIS, TRMM and Cartosat DEM are used and watershed delineation, snow cover area extraction, trend and correlation analysis are performed in ArcGIS and R statistical software respectively. The results of trend analysis show a negative and weak association in precipitation data and positive but weak association in temperature data. However, when correlation analysis was done for different stations in the basin a strong positive correlation was found among the precipitation and temperature data for Kendall (0.93), Spearman (0.99) and Pearson (0.99) methods. A weak correlation was found between inflow and percentage snow cover area in the Bhagirathi basin for Kendall (0.04), Spearman (0.06) and Pearson (0.07) methods. The study findings present an insight into the precipitation, temperature and snow cover area data statistically. Further a better understanding of the data can be established if sub-basin level study is performed.

Analysis of the Potential Impact of Climate Change on Climatic Droughts, Snow Dynamics, and the Correlation between Them

Climate change is expected to increase the occurrence of droughts, with the hydrology in alpine systems being largely determined by snow dynamics. In this paper, we propose a methodology to assess the impact of climate change on both meteorological and hydrological droughts, taking into account the dynamics of the snow cover area (SCA). We also analyze the correlation between these types of droughts. We generated ensembles of local climate scenarios based on regional climate models (RCMs) representative of potential future conditions. We considered several sources of uncertainty: different historical climate databases, simulations obtained with several RCMs, and some statistical downscaling techniques. We then used a stochastic weather generator (SWG) to generate multiple climatic series preserving the characteristics of the ensemble scenario. These were simulated within a cellular automata (CA) model to generate multiple SCA future series. They were used to calculate multiple series of meteorological drought indices, the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and a novel hydrological drought index (Standardized Snow Cover Index (SSCI)). Linear correlation analysis was applied to both types of drought to analyze how they propagate and the time delay between them. We applied the proposed methodology to the Sierra Nevada (southern Spain), where we estimated a general increase in meteorological and hydrological drought magnitude and duration for the horizon 2071–2100 under the RCP 8.5 emission scenario. The SCA droughts also revealed a significant increase in drought intensity. The meteorological drought propagation to SCA droughts was reflected in an immediate or short time (1 month), obtaining significant correlations in lower accumulation periods of drought indices (3 and 6 months). This allowed us to obtain information about meteorological drought from SCA deficits and vice versa.

Evaluating the different responses to climatic factors between snow water equivalent and snow cover area in the Central Tianshan Mountains

Evaluating the different responses to climatic factors between snow water equivalent and snow cover area in the Central Tianshan Mountains

Classification of Snow Cover Persistence across China

In this study, we classified the variability in snow cover persistence across China by using a novel method; continuous snow cover days and variability of snow cover were used as the evaluation indicators based on a long-term Advanced Very High Resolution Radiometer (AVHRR) snow cover extent (SCE) product. The product has been generated by the snow research team in the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. There were obvious differences in snow cover classification in three snow cover areas (northern Xinjiang, northeast China, and the Tibetan Plateau): northern Xinjiang was dominated by persistent snow cover, most regions of northeast China were covered by persistent and periodic variable snow cover. There was the most abundant snow cover classification in the Tibetan Plateau. The extents of persistent and periodic variable snow cover were gradually shrinking due to rising temperatures and decreasing snowfall during 1981–2019. In contrast, non-periodic variable snow cover areas increased significantly. This method takes into account the stability, continuity, and variability of snow cover, and better captures the characteristics and changes of snow cover across China. Based on our research, we found that snow disasters in ephemeral-type (belong to non-periodic variable snow cover) regions cannot be well prevented because of the unfixed snow cover timing. Therefore, we recommend that monitoring and forecasting of snow cover in these snow cover regions should be strengthened.