Research ArticleCLIMATOLOGY

Changes in seasonal snow water equivalent distribution in High Mountain Asia (1987 to 2009)

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Science Advances  17 Jan 2018:
Vol. 4, no. 1, e1701550
DOI: 10.1126/sciadv.1701550
  • Fig. 1 Study area.

    (A) Topographic map of HMA with major catchment boundaries (black) derived from SRTM data (68) and names of major mountain ranges. Inset map shows political boundaries, as well as wind direction of major weather systems (WWD, ISM, and EASM). (B) Twenty-two–year average DJF daily SWE volume across the study area, as derived from SSMI data. (C) DJF SWE standard deviation. Each point represents a 0.25 × 0.25 dd grid cell.

  • Fig. 2 Seasonality in SWE trends.

    Significant (P < 0.05) (A) DJF, (B) MAM, (C) JJA, and (D) SON trends in SWE volume (1987 to 2009), as derived from SSMI data, with major catchments (black outlines, see Fig. 1A). We limit our analysis to regions where the seasonal average SWE is greater than 5 mm to remove spurious results in areas with shallow or infrequent snow cover. MAM and JJA trends across HMA are overwhelmingly negative, except a few isolated regions. DJF trends are more widely positive and are also present in SON in the western Himalaya, the Tien Shan, and the Kunlun Shan. Yearly aggregated SWE trends available in fig. S2.

  • Fig. 3 SWE contribution and SWE trend synthesis.

    (A) Elevation distribution of SWE in each catchment, where each point shows the percentage of total catchment SWE at each five-percentile elevation bin. (B) Mean SWE trend at each five-percentile elevation bin. In the majority of catchments, maximum SWE occurs below the maximum catchment elevation, despite differences in catchment hypsometry. Each catchment is characterized by a unique elevation-trend relationship. The Indus, Amu Darya, and Tibetan Plateau catchments see the most negative SWE trends at their mid-elevations. The Ganges in the central Himalaya sees the most negative trends at the highest elevations.

  • Fig. 4 Differences between the snow distribution of the Ganges and Indus catchments.

    (A) The Ganges and (B) Indus catchments showing catchment hypsometry (gray) (68), percentage glaciated area (red) (69), and SWE elevation distribution (blue). Dashed lines indicate catchment elevation percentiles. Both catchments show SWE maxima below their elevation peaks, despite differences in their SWE distributions. The altitude of SWE maxima are also minimally overlapping with glacier areas, indicating that snow and glacier meltwaters are often distinct and are affected by different climatic processes.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/4/1/e1701550/DC1

    Catchment-averaged SWE trend characteristics

    By-catchment seasonal trends

    Seasonal trend decomposition coefficients

    fig. S1. APHRODITE station density (13).

    fig. S2. Annual trends in SWE volume (1987 to 2009), as derived from SSMI data, with major regional watersheds (black outlines).

    fig. S3. Syr Darya seasonal trends.

    fig. S4. Amu Darya seasonal trends.

    fig. S5. Tarim seasonal trends.

    fig. S6. Tibetan Plateau seasonal trends.

    fig. S7. Ganges seasonal trends.

    fig. S8. Indus seasonal trends.

    fig. S9. Impact of removal of glacier areas on trend patterns.

    fig. S10. Percentage of time by season above 150-mm SWE.

    fig. S11. Percentage of time by season above 120-mm SWE.

    fig. S12. DJF and MAM SWE trends with areas impacted by PM signal saturation removed.

    fig. S13. Impact of weighted regression on full-year SWE trends.

    fig. S14. Impact of weighted regression on DJF SWE trends.

    fig. S15. Changes in SWE trends when an uncertainty margin is introduced.

    table S1. Full-year catchment-aggregated SWE trends above 500 m asl.

    table S2. DJF catchment-aggregated SWE trends above 500 m asl.

    table S3. MAM catchment-aggregated SWE trends above 500 m asl.

    table S4. JJA catchment-aggregated SWE trends above 500 m asl.

    table S5. SON catchment-aggregated SWE trends above 500 m asl.

  • Supplementary Materials

    This PDF file includes:

    • Catchment-averaged SWE trend characteristics
    • By-catchment seasonal trends
    • Seasonal trend decomposition coefficients
    • fig. S1. APHRODITE station density (13).
    • fig. S2. Annual trends in SWE volume (1987 to 2009), as derived from SSMI data, with major regional watersheds (black outlines).
    • fig. S3. Syr Darya seasonal trends.
    • fig. S4. Amu Darya seasonal trends.
    • fig. S5. Tarim seasonal trends.
    • fig. S6. Tibetan Plateau seasonal trends.
    • fig. S7. Ganges seasonal trends.
    • fig. S8. Indus seasonal trends.
    • fig. S9. Impact of removal of glacier areas on trend patterns.
    • fig. S10. Percentage of time by season above 150-mm SWE.
    • fig. S11. Percentage of time by season above 120-mm SWE.
    • fig. S12. DJF and MAM SWE trends with areas impacted by PM signal saturation removed.
    • fig. S13. Impact of weighted regression on full-year SWE trends.
    • fig. S14. Impact of weighted regression on DJF SWE trends.
    • fig. S15. Changes in SWE trends when an uncertainty margin is introduced.
    • table S1. Full-year catchment-aggregated SWE trends above 500 m asl.
    • table S2. DJF catchment-aggregated SWE trends above 500 m asl.
    • table S3. MAM catchment-aggregated SWE trends above 500 m asl.
    • table S4. JJA catchment-aggregated SWE trends above 500 m asl.
    • table S5. SON catchment-aggregated SWE trends above 500 m asl.

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