scholarly journals Validation of snow extent time series derived from AVHRR GAC data at Himalaya-Hindukush

2021 ◽  
Author(s):  
Xiaodan Wu ◽  
Kathrin Naegeli ◽  
Carlo Marin ◽  
Stefan Wunderle
Keyword(s):  
Land Cover ◽  
Snow Cover ◽  
Environmental Changes ◽  
Slope Aspect ◽  
Adaptation Studies ◽  
Snow Cover Extent ◽  
Coarse Spatial Resolution ◽  
Increasing Demand

Abstract. Long-term monitoring of snow cover is crucial for climate and hydrology studies. To meet the increasing demand for a long-term, consistent snow product, an exceptional snow cover climatology was generated dating back to the 1980s using AVHRR GAC data. However, the retrieval of snow extent is not straightforward due to artifacts introduced during data processing, which are partly caused by the coarse spatial resolution of AVHRR GAC data, but also heterogeneous land cover/topography. Therefore, the accuracy and consistency of this long-term AVHRR GAC snow cover climatology needs to be carefully evaluated prior to its application. Here, we extensively validate the AVHRR GAC snow cover extent dataset for the Hindu Kush Himalaya (HKH) region. The mountainous HKH region is of high importance for climate change, impact and adaptation studies. Additionally, the influences of snow depth, land cover type, elevation, slope, aspect, and topographical variability, as well as the sensor-to-sensor consistency have been explored using a snow dataset based on long-term in situ stations and high-resolution Landsat TM data. Moreover, the performance of the AVHRR GAC snow cover dataset was also compared to that of MODIS (MOD10A1 V006). Our analysis shows an overall accuracy of 94 % in comparison with in situ station data. Using a ±3 days temporal filter caused a slight decrease in accuracy (from 94 to 92 %), which is still comparable to MOD10A1 V006 (93.6 %). Validation against Landsat5 TM data over region of P140-R40/41 indicated overall RMSEs of about 13 % and 16 % and overall Biases of about −1 % and −2 % for the AVHRR GAC raw and gap-filled snow datasets, respectively. It can be concluded that the here validated AVHRR GAC snow cover climatology is a highly valuable and powerful dataset to assess environmental changes in the HKH due to its good quality, unique temporal coverage (1982–2018), and inter-sensor/satellite consistency.

scholarly journals Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas

2021 ◽  
Vol 15 (9) ◽  
pp. 4261-4279
Author(s):  
Xiaodan Wu ◽  
Kathrin Naegeli ◽  
Valentina Premier ◽  
Carlo Marin ◽  
Dujuan Ma ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Land Cover ◽  
Snow Cover ◽  
Area Coverage ◽  
Slope Aspect ◽  
Hindu Kush ◽  
Very High

Abstract. Long-term monitoring of snow cover is crucial for climatic and hydrological studies. The utility of long-term snow-cover products lies in their ability to record the real states of the earth's surface. Although a long-term, consistent snow product derived from the ESA CCI+ (Climate Change Initiative) AVHRR GAC (Advanced Very High Resolution Radiometer global area coverage) dataset dating back to the 1980s has been generated and released, its accuracy and consistency have not been extensively evaluated. Here, we extensively validate the AVHRR GAC snow-cover extent dataset for the mountainous Hindu Kush Himalayan (HKH) region due to its high importance for climate change impact and adaptation studies. The sensor-to-sensor consistency was first investigated using a snow dataset based on long-term in situ stations (1982–2013). Also, this includes a study on the dependence of AVHRR snow-cover accuracy related to snow depth. Furthermore, in order to increase the spatial coverage of validation and explore the influences of land-cover type, elevation, slope, aspect, and topographical variability in the accuracy of AVHRR snow extent, a comparison with Landsat Thematic Mapper (TM) data was included. Finally, the performance of the AVHRR GAC snow-cover dataset was also compared to the MODIS (MOD10A1 V006) product. Our analysis shows an overall accuracy of 94 % in comparison with in situ station data, which is the same with MOD10A1 V006. Using a ±3 d temporal filter caused a slight decrease in accuracy (from 94 % to 92 %). Validation against Landsat TM data over the area with a wide range of conditions (i.e., elevation, topography, and land cover) indicated overall root mean square errors (RMSEs) of about 13.27 % and 16 % and overall biases of about −5.83 % and −7.13 % for the AVHRR GAC raw and gap-filled snow datasets, respectively. It can be concluded that the here validated AVHRR GAC snow-cover climatology is a highly valuable and powerful dataset to assess environmental changes in the HKH region due to its good quality, unique temporal coverage (1982–2019), and inter-sensor/satellite consistency.

scholarly journals A Century of Forest Regrowth and Snow Loss Alters the Cooling Effect of Historical Land Use in the Upper Midwest

Ecosystems ◽  
2019 ◽  
Vol 23 (5) ◽  
pp. 1056-1074
Author(s):  
Bethany J. Blakely ◽  
Adrian V. Rocha ◽  
Jason S. McLachalan
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Snow Cover ◽  
Forest Canopy ◽  
Forest Cover ◽  
Cooling Effect ◽  
Forest Regrowth ◽  
Historical Land Use ◽  
Over Time

AbstractAnthropogenic land use affects climate by altering the energy balance of the Earth’s surface. In temperate regions, cooling from increased albedo is a common result of historical land-use change. However, this albedo cooling effect is dependent mainly on the exposure of snow cover following forest canopy removal and may change over time due to simultaneous changes in both land cover and snow cover. In this paper, we combine modern remote sensing data and historical records, incorporating over 100 years of realized land use and climatic change into an empirical assessment of centennial-scale surface forcings in the Upper Midwestern USA. We show that, although increases in surface albedo cooled through strong negative shortwave forcings, those forcings were reduced over time by a combination of forest regrowth and snow-cover loss. Deforestation cooled strongly (− 5.3 Wm−2) and mainly in winter, while composition shift cooled less strongly (− 3.03 Wm−2) and mainly in summer. Combined, changes in albedo due to deforestation, shifts in species composition, and the return of historical forest cover resulted in − 2.81 Wm−2 of regional radiative cooling, 55% less than full deforestation. Forcings due to changing vegetation were further reduced by 0.32 Wm−2 of warming from a shortened snow-covered season and a thinning of seasonal snowpack. Our findings suggest that accounting for long-term changes in land cover and snow cover reduces the estimated cooling impact of deforestation, with implications for long-term land-use planning.

Global Snow Cover Extent Mapping Using Sentinel-1

2020 ◽  
Author(s):  
Ya-Lun Tsai ◽  
Soner Uereyen ◽  
Andreas Dietz ◽  
Claudia Kuenzer ◽  
Natascha Oppelt
Keyword(s):  
Snow Cover ◽  
Low Cost ◽  
Global Radiation ◽  
Radiation Balance ◽  
Human Society ◽  
Vegetation Growth ◽  
Winter Tourism ◽  
Snow Cover Extent ◽  
Near Future

<p>Seasonal snow cover extent (SCE) is a critical component not only for the global radiation balance and climatic behavior but also for water availability of mountainous and arid regions, vegetation growth, permafrost, and winter tourism. However, due to the effects of the global warming, SCE has been observed to behave in much more irregular and extreme patterns in both temporal and spatial aspects. Therefore, a continuous SCE monitoring strategy is necessary to understand the effect of climate change on the cryosphere and to assess the corresponding impacts on human society and the environment. Nevertheless, although conventional optical sensor-based sensing approaches are mature, they suffer from cloud coverage and illumination dependency. Consequently, spaceborne Synthetic Aperture Radar (SAR) provides a pragmatic solution for achieving all-weather and day-and-night monitoring at low cost, especially after the launch of the Sentinel-1 constellation. </p><p>In the present study, we propose a new global SCE mapping approach, which utilizes dual-polarization intensity-composed bands, polarimetric H/A/α decomposition information, topographical factors, and a land cover layer to detect the SCE. By including not only amplitude but also phase information, we overcome the limitations of previous studies, which can only map wet SCE. Additionally, a layer containing the misclassification probability is provided as well for measuring the uncertainty. Based on the validation with in-situ stations and optical imagery, around 85% accuracy of the classification is ensured. Consequently, by implementing the proposed method globally, we can provide a novel way to map high resolution (20 m) and cloud-free SCE even under cloud covered/night conditions. Preparations to combine this product with the optical-based DLR Global SnowPack are already ongoing, offering the opportunity to provide a daily snow mapping service in the near future which is totally independent from clouds or polar darkness.</p>

Links between spring snow cover and long-term alpine vegetation change

2020 ◽  
Author(s):  
Shengwei Zong ◽  
Christian Rixen
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Plant Species ◽  
Climate Warming ◽  
Vegetation Change ◽  
Alpine Vegetation ◽  
Alpine Ecosystems ◽  
Snow Cover Extent ◽  
The Relationship

<p><span>Snow is an important environmental factor determining distributions of plant species in alpine ecosystems. During the past decades, climate warming has resulted in significant reduction of snow cover extent globally, which led to remarkable alpine vegetation change. Alpine vegetation change is often caused by the combined effects of increasing air temperature and snow cover change, yet the relationship between snow cover and vegetation change is currently not fully understood. To detect changes in both snow cover and alpine vegetation, a relatively fine spatial scales over long temporal spans is necessary. In this study in alpine tundra of the Changbai Mountains, Northeast China, we (1) quantified spatiotemporal changes of spring snow cover area (SCA) during half a century by using multi-source remote sensing datasets; (2) detected long-term vegetation greening and browning trends at pixel level using Landsat archives of 30 m resolution, and (3) analyzed the relationship between spring SCA change and vegetation change. Results showed that spring SCA has decreased significantly during the last 50 years in line with climate warming. Changes in vegetation greening and browning trend were related to distributional range dynamics of a dominant indigenous evergreen shrub <em>Rhododendron aureum</em>, which extended at the leading edge and retracted at the trailing edge. Changes in <em>R. aureum</em> distribution were probably related to spring snow cover changes. Areas with decreasing <em>R. aureum</em> cover were often located in snow patches where probably herbs and grasses encroached from low elevations and adjacent communities. Our study highlights that spring SCA derived from multi-source remote sensing imagery can be used as a proxy to explore relationship between snow cover and vegetation change in alpine ecosystems. Alpine indigenous plant species may migrate upward following the reduction of snow-dominated environments in the context of climate warming and could be threatened by encroaching plants within snow bed habitats.</span></p>

scholarly journals 40 years High Arctic climatological dataset of the Polish Polar Station Hornsund (SW Spitsbergen, Svalbard)

2019 ◽  
Author(s):  
Tomasz Wawrzyniak ◽  
Marzena Osuch
Keyword(s):  
Environmental Changes ◽  
High Arctic ◽  
The Arctic ◽  
Data Series ◽  
Annual Data ◽  
Climate Data ◽  
Atlantic Sector ◽  
Svalbard Archipelago

Abstract. The article presents the climatological dataset from the Polish Polar Station Hornsund located in the SW part of Spitsbergen - the biggest island of the Svalbard Archipelago. Due to a general lack of long-term in situ measurements and observations, the high Arctic remains one of the largest climate‐data deficient regions on the Earth, so described series is of unique value. To draw conclusions on the climatic changes in the Arctic, it is necessary to analyse the long-term series of continuous, systematic, in situ observations from different locations and comparing the corresponding data, rather than rely on the climatic simulations only. In recent decades, rapid environmental changes occurring in the Atlantic sector of the Arctic are reflected in the data series collected by the operational monitoring conducted at the Hornsund Station. We demonstrate the results of the 40 years-long series of observations. Climatological mean values or totals are given, and we also examined the variability of meteorological variables at monthly and annual scale using the modified Mann-Kendall test for trend and Sen’s method. The relevant daily, monthly, and annual data are provided on the PANGAEA repository (https://doi.org/10.1594/PANGAEA.909042, Wawrzyniak and Osuch, 2019).

scholarly journals Assimilation of Sentinel-2 Data into a Snowpack Model in the High Atlas of Morocco

2018 ◽  
Author(s):  
Mohamed Wassim Baba ◽  
Simon Gascoin ◽  
Lahoucine Hanich
Keyword(s):  
Data Assimilation ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Snow Cover Area ◽  
High Atlas ◽  
Data Assimilation Scheme ◽  
Snow Cover Extent ◽  
Assimilation Scheme ◽  
Sentinel 2

The snow melt from the High Atlas is a critical water resource in Morocco. In spite of its importance, monitoring the spatio-temporal evolution of key snow cover properties like the snow water equivalent remains challenging due to the lack of in situ measurements at high elevation. Since 2015, the Sentinel-2 mission provides high spatial resolution images with a 5 day revisit time, which offers new opportunities to characterize snow cover distribution in mountain regions. Here we present a new data assimilation scheme to estimate the state of the snowpack without in situ data. The model was forced using MERRA-2 data and a particle filter was developed to dynamically reduce the biases in temperature and precipitation using Sentinel-2 observations of the snow cover area. The assimilation scheme was implemented using SnowModel, a distributed energy-balance snowpack model and tested in a pilot catchment in the High Atlas. The study period covers 2015-2016 snow season which corresponds to the first operational year of Sentinel-2A, therefore the full revisit capacity was not yet achieved. Yet, we show that the data assimilation led to a better agreement with independent observations of the snow height at an automatic weather station and the snow cover extent from MODIS. The performance of the data assimilation scheme should benefit from the continuous improvements in MERRA-2 reanalyses and the full revisit capacity of Sentinel-2.

scholarly journals Interrelationships between MODIS/Terra remotely sensed snow cover and the hydrometeorology of the Quesnel River Basin, British Columbia, Canada

2009 ◽  
Vol 13 (8) ◽  
pp. 1439-1452 ◽  
Author(s):  
J. Tong ◽  
S. J. Déry ◽  
P. L. Jackson
Keyword(s):  
British Columbia ◽  
Snow Cover ◽  
Correlation Coefficient ◽  
River Basin ◽  
Time Lag ◽  
Spatial Filter ◽  
In Situ Observation ◽  
Cloud Coverage ◽  
Snow Cover Extent

Abstract. A spatial filter (SF) method is adopted to reduce the cloud coverage from the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day snow products (MOD10A2) between 2000–2007 in the Quesnel River Basin (QRB) of British Columbia, Canada. A threshold of k = 2 cm of snow depth measurements at four in-situ observation stations in the QRB are used to evaluate the accuracy of MODIS snow products MOD10A1, MOD10A2, and SF. Using the MOD10A2 and the SF, the relationships between snow ablation, snow cover extent (SCE), snow cover fraction (SCF), streamflow and climate variability are assessed. Based on our results we are able to draw several interesting conclusions. Firstly, the SF method reduces the average cloud coverage in the QRB from 15% for MOD10A2 to 9%. Secondly, the SF increases the overall accuracy (OA) based on the threshold k = 2 cm by about 2% compared to MOD10A2 and by about 10% compared to MOD10A1 at higher elevations. The OA for the four in-situ stations decreases with elevation with 93.1%, 87.9%, 84.0%, and 76.5% at 777 m, 1265 m, 1460 m, and 1670 m, respectively. Thirdly, an aggregated 1°C rise in average air temperature during spring leads to a 10-day advance in reaching 50% SCF (SCF50%) in the QRB. The correlation coefficient between normalized SCE of the SF and normalized streamflow is −0.84 (p<0.001) for snow ablation seasons. There is a 32-day time lag for snow ablation to impact the streamflow the strongest at the basin outlet. The linear correlation coefficient between SCF50% and 50% normalized accumulated runoff (R50%) attains 0.82 (p<0.01). This clearly demonstrates the strong links that exist between the SCF depletion and the hydrology of this sub-boreal, mountainous watershed.

A Long-Term Land Surface Hydrologic Fluxes and States Dataset for China

2014 ◽  
Vol 15 (5) ◽  
pp. 2067-2084 ◽  
Author(s):  
Xue-Jun Zhang ◽  
Qiuhong Tang ◽  
Ming Pan ◽  
Yin Tang
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Snow Cover ◽  
Land Surface ◽  
Hydrologic Model ◽  
Reliable Estimate ◽  
Time Step ◽  
Snow Cover Extent ◽  
Monthly Streamflow

Abstract A long-term consistent and comprehensive dataset of land surface hydrologic fluxes and states will greatly benefit the analysis of land surface variables, their changes and interactions, and the assessment of land–atmosphere parameterizations for climate models. While some offline model studies can provide balanced water and energy budgets at land surface, few of them have presented an evaluation of the long-term interaction of water balance components over China. Here, a consistent and comprehensive land surface hydrologic fluxes and states dataset for China using the Variable Infiltration Capacity (VIC) hydrologic model driven by long-term gridded observation-based meteorological forcings is developed. The hydrologic dataset covers China with a 0.25° spatial resolution and a 3-hourly time step for 1952–2012. In the dataset, the simulated streamflow matches well with the observed monthly streamflow at the large river basins in China. Given the water balance scheme in the VIC model, the overall success at runoff simulations suggests that the long-term mean evapotranspiration is also realistically estimated. The simulated soil moisture generally reproduces the seasonal variation of the observed soil moisture at the ground stations where long-term observations are available. The modeled snow cover patterns and monthly dynamics bear an overall resemblance to the Northern Hemisphere snow cover extent data from the National Snow and Ice Data Center. Compared with global product of a similar nature, the dataset can provide a more reliable estimate of land surface variables over China. The dataset, which will be publicly available via the Internet, may be useful for hydroclimatological studies in China.

scholarly journals A long-term Northern Hemisphere snow cover extent data record for climate studies and monitoring

2014 ◽  
Vol 7 (2) ◽  
pp. 669-691 ◽  
Author(s):  
T. W. Estilow ◽  
A. H. Young ◽  
D. A. Robinson
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Environmental Data ◽  
Climatic Data ◽  
Climate Data ◽  
Snow Cover Extent ◽  
Data Record ◽  
Climate Data Record ◽  
Climate Studies

Abstract. This paper describes the long-term, satellite-based visible snow cover extent NOAA climate data record (CDR) currently available for climate studies, monitoring, and model validation. This environmental data product is developed from weekly Northern Hemisphere snow cover extent data that have been digitized from snow cover maps onto a Cartesian grid draped over a polar stereographic projection. The data has a spatial resolution of 190.5 km at 60 ° latitude, are updated monthly, and span from 4 October 1966 to present. The data comprise the longest satellite-based CDR of any environmental variable. Access to the data are provided in netCDF format and are archived by the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA) under the satellite climate data record program (doi:10.7289/V5N014G9). The basic characteristics, history, and evolution of the dataset are presented herein. In general, the CDR provides similar spatial and temporal variability as its widely used predecessor product. Key refinements to the new CDR improve the product's grid accuracy and documentation, and bring metadata into compliance with current standards for climate data records.

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