Deriving long term snow cover extent dataset from AVHRR and MODIS data: Central Asia case study

Zhou, Hang; Aizen, Elena M.; Aizen, Vladimir

doi:10.1016/j.rse.2013.04.015

Cited by 63 publications

(47 citation statements)

References 48 publications

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“…The second aim-to compare the obtained results with a similar study about snow cover changes within Amu Darya catchment-is yet to be answered. Some of the conclusions drawn by [15] differ slightly from what we found during our investigations. A general negative trend of SCD that was identified by the authors of this study could not be confirmed for Central Asia.…”

Section: Discussioncontrasting

confidence: 99%

“…These rivers form the lifelines of the whole region [12] and therefore, information about ongoing changes on a sub-catchment scale is desirable. The presented results tie to an earlier research about Central Asian snow cover characteristics between 2000 and 2011 [14] and follow a similar study about snow cover changes between 1986 and 2008 in the Amu Darya river catchment [15]. The results of the latter study already show a decreasing trend in Snow Cover Duration (SCD) especially for the high elevation regions with a shift towards earlier snow cover start and melt.…”

Section: Introductionsupporting

confidence: 89%

“…We conducted Mann-Kendall tests on the time series of snow cover data. The same tests were applied in a similar study for the Amu Darya River basin [15]. We decided to apply the same analysis in order to make the results from both studies comparable.…”

Section: Resultsmentioning

confidence: 99%

“…The results of the latter study already show a decreasing trend in Snow Cover Duration (SCD) especially for the high elevation regions with a shift towards earlier snow cover start and melt. Therefore, the secondary aim of our study is to investigate whether the findings of [15] can be confirmed also for the Syr Darya catchment and whether the results for Amu Darya would conform. This is of particular interest as especially the delineation of snow cover from Advanced Very High Resolution Radiometer (AVHRR) data can pose a challenge.…”

Section: Introductionmentioning

confidence: 98%

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Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data

et al. 2014

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Central Asia consists of the five former Soviet States Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, therefore comprising an area of ~4 Mio km 2 . The continental climate is characterized by hot and dry summer months and cold winter seasons with most precipitation occurring as snowfall. Accordingly, freshwater supply is strongly depending on the amount of accumulated snow as well as the moment of its release after snowmelt. The aim of the presented study is to identify possible changes in snow cover characteristics, consisting of snow cover duration, onset and offset of snow cover season within the last 28 years. Relying on remotely sensed data originating from medium resolution imagers, these snow cover characteristics are extracted on a daily basis. The resolution of 500-1000 m allows for a subsequent analysis of changes on the scale of hydrological sub-catchments. Long-term changes are identified from this unique dataset, revealing an ongoing shift towards earlier snowmelt within the Central Asian Mountains. This shift can be observed in most upstream hydro catchments within Pamir and Tian Shan Mountains and it leads to a potential change of freshwater availability in the downstream regions, exerting additional pressure on the already tensed situation.

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Section: Discussioncontrasting

confidence: 99%

Section: Introductionsupporting

confidence: 89%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data

et al. 2014

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“…By consulting the published literature, most previous studies on land use/cover mapping at large scale commonly used the low and medium spatial resolution imagery, such as NOAA/AVHRR, TERRA/MODIS and Landsat TM or ETM+ [7,8]. As a result, several global and regional land use/cover products such as FROM-GLC [9], GLC2000 [10], GlobCover [11] and NLCD-China [12], were derived from remotely sensed data and made available to the public.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

et al. 2013

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Google Earth (GE) releases free images in high spatial resolution that may provide some potential for regional land use/cover mapping, especially for those regions with high heterogeneous landscapes. In order to test such practicability, the GE imagery was selected for a case study in Wuhan City to perform an object-based land use/cover classification. The classification accuracy was assessed by using 570 validation points generated by a random sampling scheme and compared with a parallel classification of QuickBird (QB) imagery based on an object-based classification method. The results showed that GE has an overall classification accuracy of 78.07%, which is slightly lower than that of QB. No significant difference was found between these two classification results by the adoption of Z-test, which strongly proved the potentials of GE in land use/cover mapping. Moreover, GE has different discriminating capacity for specific land use/cover types. It possesses some advantages for mapping those types with good spatial characteristics in terms of geometric, shape and context. The object-based method is recommended for imagery classification when using GE imagery for mapping land use/cover. However, GE has some limitations for those types classified by using only spectral characteristics largely due to its poor spectral characteristics. OPEN ACCESSRemote Sens. 2013, 5 6027

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The Value of Hydrograph Partitioning Curves for Calibrating Hydrological Models in Glacierized Basins

Vorogushyn

Unger‐Shayesteh

et al. 2018

Water Resources Research

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This study refines the method for calibrating a glacio‐hydrological model based on Hydrograph Partitioning Curves (HPCs), and evaluates its value in comparison to multidata set optimization approaches which use glacier mass balance, satellite snow cover images, and discharge. The HPCs are extracted from the observed flow hydrograph using catchment precipitation and temperature gradients. They indicate the periods when the various runoff processes, such as glacier melt or snow melt, dominate the basin hydrograph. The annual cumulative curve of the difference between average daily temperature and melt threshold temperature over the basin, as well as the annual cumulative curve of average daily snowfall on the glacierized areas are used to identify the starting and end dates of snow and glacier ablation periods. Model parameters characterizing different runoff processes are calibrated on different HPCs in a stepwise and iterative way. Results show that the HPC‐based method (1) delivers model‐internal consistency comparably to the tri‐data set calibration method; (2) improves the stability of calibrated parameter values across various calibration periods; and (3) estimates the contributions of runoff components similarly to the tri‐data set calibration method. Our findings indicate the potential of the HPC‐based approach as an alternative for hydrological model calibration in glacierized basins where other calibration data sets than discharge are often not available or very costly to obtain.

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Deriving long term snow cover extent dataset from AVHRR and MODIS data: Central Asia case study

Cited by 63 publications

References 48 publications

Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data

Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

The Value of Hydrograph Partitioning Curves for Calibrating Hydrological Models in Glacierized Basins

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