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Topography and Land Cover Effects on Snow Water Equivalent Estimation Using AMSR-E and GLDAS Data

Author

Listed:
  • Hadi Ansari

    (Bu-Ali Sina University)

  • Safar Marofi

    (Bu-Ali Sina University)

  • Mohamad Mohamadi

    (Bu-Ali Sina University)

Abstract

Accurate predictions of snow characteristics have an essential function in water resources management, especially in the high mountainous areas. Remote sensing presents a possibility for snow characteristics observation, such as snow water equivalent (SWE), in the large basins. Many studies are focused on the assessment of remote sensing product, especially global SWE data. However, regional effects such as topography, land cover, and meteorological conditions may lead to uncertainty in the estimation of the snow characteristics. In this research, the Advanced Microwave Scanning Radiometer-Eos (AMSR-E) data and the GLDAS model data (2006–2011) were used to estimate SWE in the northwest basins of Iran. The evaluation was performed by the root mean square error (RMSE) and percent bias (PBIAS) criteria. The results indicated a significant correlation (at 1% level) between the observed and estimated SWE data. According to the results, the estimation accuracy decreased with increasing altitude, land slope, and the normalized difference vegetation index (NDVI). The best estimation was detected at altitudes between 1350 and 1600 m. Generally, the SWE products of the AMSR-E and GLDAS data on the north-facing slope shows good accuracy in the SWE estimation compared to the other aspects.

Suggested Citation

  • Hadi Ansari & Safar Marofi & Mohamad Mohamadi, 2019. "Topography and Land Cover Effects on Snow Water Equivalent Estimation Using AMSR-E and GLDAS Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(5), pages 1699-1715, March.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:5:d:10.1007_s11269-019-2200-0
    DOI: 10.1007/s11269-019-2200-0
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    Cited by:

    1. Erhao Meng & Shengzhi Huang & Qiang Huang & Linyin Cheng & Wei Fang, 2021. "The Reconstruction and Extension of Terrestrial Water Storage Based on a Combined Prediction Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5291-5306, December.
    2. Arash Adib & Arash Zaerpour & Ozgur Kisi & Morteza Lotfirad, 2021. "A Rigorous Wavelet-Packet Transform to Retrieve Snow Depth from SSMIS Data and Evaluation of its Reliability by Uncertainty Parameters," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2723-2740, July.

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