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MODSNOW-Tool: an operational tool for daily snow cover monitoring using MODIS data

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Abstract

Spatially distributed snow cover information is important for the assessment of climate-related variability of water resources and for calibration and validation of hydrological models in snow-dominated regions. Near-real-time snow cover data can be valuable for short term to seasonal streamflow prediction. Such information can be extracted using remote sensing techniques with good accuracy. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data, meanwhile available for more than 15 years, have been shown to be useful for monitoring snow cover extent in remote areas. This data can be processed and used with only 2-day delay, which is sufficient for many water resources management purposes. However, processing remote sensing data require knowledge and computational skills to handle large amounts of data. Moreover, cloud obscuration in optical remote sensing such as MODIS may lead to data gaps. These limitations impede the use of the freely available MODIS data for water resources management in developing countries, particularly in snow-dominated mountainous regions. To overcome this, we present the all-in-one software package MODSNOW-Tool. It processes raw MODIS data and eliminates cloud cover using advanced cloud removal algorithms. The ready-to-use output of the MODSNOW-Tool is a cloud-free snow cover map and a daily report, which includes spatiotemporal snow statistics for pre-defined river basins. The accuracy of cloud-eliminated MODSNOW snow cover maps was validated for 84 almost cloud-free days in the Karadarya river basin in Central Asia, and an average accuracy of 94 % was achieved. The MODSNOW-Tool can be used in operational and non-operational mode. In the operational mode, the tool is set up as a scheduled task on a local computer allowing automatic execution without user interaction and delivers snow cover maps on a daily basis. In the non-operational mode, the tool can be used to process historical time series of snow cover maps from MODIS.

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Acknowledgments

This work was carried out in the frame of the CAWa (Central Asian Water) project (www.cawa-project.net, Contract No. AA7090002), funded by the German Federal Foreign Office as part of the German Water Initiative for Central Asia (“Berlin Process”).

Author information

Authors and Affiliations

  1. GFZ German Research Centre for Geosciences, Section 5.4: Hydrology, Telegrafenberg, 14473, Potsdam, Germany

    A. Gafurov, S. Lüdtke, K. Unger-Shayesteh, S. Vorogushyn, S. Schmidt & B. Merz

  2. GFZ German Research Centre for Geosciences, Section 1.2: Global Geomonitoring and Gravity Field, Potsdam, Germany

    T. Schöne

  3. Central Asian Institute for Applied Geosciences (CAIAG), Bishkek, Kyrgyzstan

    O. Kalashnikova

Authors
  1. A. Gafurov
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  2. S. Lüdtke
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  3. K. Unger-Shayesteh
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  4. S. Vorogushyn
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  5. T. Schöne
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  6. S. Schmidt
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  7. O. Kalashnikova
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  8. B. Merz
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Corresponding author

Correspondence to A. Gafurov.

Additional information

This article is part of a Topical Collection in Environmental Earth Sciences on ‘‘Water in Central Asia’’, guest edited by Daniel Karthe, Iskandar Abdullaev, Bazartseren Boldgiv, Dietrich Borchardt, Sergey Chalov, Jerker Jarsjö, Lanhai Li and Jeff Nittrouer.

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Gafurov, A., Lüdtke, S., Unger-Shayesteh, K. et al. MODSNOW-Tool: an operational tool for daily snow cover monitoring using MODIS data. Environ Earth Sci 75, 1078 (2016). https://doi.org/10.1007/s12665-016-5869-x

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