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Snow Water Equivalent Anomaly - with Provinces


Daily Snow Water Equivalent (SWE) Anomaly

Daily Period

Product Documentation


The NASA FEWS NET Land Data Assimilation System (FLDAS) (McNally et al., 2022) provides land surface modelling and routinely generates ensembles of soil moisture, snow cover, snow depth, and snow water equivalent (SWE) aiding FEWS NET food security assessments. The FLDAS is a custom instance of the NASA Land Information System (LIS; (Kumar, et al., 2006) and was developed for a global (NcNally et al., 2017) and a Central Asia domain (30-100°E, 21-56°N) specialized for snow modeling. The FLDAS-Central Asia incorporates Noah-Multiparameterization (Noah-MP) Land Surface Model version 4.0.1 and is forced with Global Data Assimilation System (GDAS) 6-hourly atmospheric inputs as well as MOD44w land mask, IGBP-MODIS vegetation, albedo provided by the University of Maryland, SRTM elevation, and STATSGOFAO soil texture parameters (details of the parameters are available at The GDAS input parameters are downscaled to 1km x 1km spatial resolution by utilizing the inherent LIS capability of bilinear interpolation, temperature lapsing, and correction for slope and aspect (Kumar, et al., 2013). The Noah-MP offers a physically based approach of snow modeling by simulating bio-geophysical, hydrological, and energy balance processes that occur at the land surface. The Noah-MP for FLDAS-Central Asia is run at 1km x 1km spatial resolution and fifteen minutes temporal resolution. The model runs on NASA Center for Climate Simulation Discover high performance computing system at NASA Goddard Space Flight Center. After running the Noah-MP model for multiple years as spin up, the final model outputs of soil moisture, snow cover, snow depth, and SWE are produced on a daily timestep since October 1, 2000, to present. These daily outputs are then provided to USGS EROS where specialized snow products are produced and distributed through its Early Warning Data Portal at

Because very few surface reference observations are available for the Central Asia domain, snow data assimilation is not an element of the FLDAS-Central Asia snow modeling system. Consequently, caution is needed when interpreting snow states produced by this system. However, when compared with satellite-derived snow cover products, the FLDAS-Central Asia snow cover has been shown to effectively describe the evolution of snow cover extent throughout the year. Therefore, it was decided that the best way to evaluate snow states produced by FLDAS-Central Asia is to compare the latest results with a retrospective analysis running from October 1, 2000, to present.

Snow Graphics

Snow Depth and SWE map graphics are created for Central Asia, South Central Asia, as well as country specific for Afghanistan, Pakistan, Tajikistan, and Iraq in PNG (*.png) and PDF (*.pdf) formats. The units for graphics showing snow depth and SWE are in meters and units for graphics showing their respective anomalies are in millimeters.


McNally, A., Jacob, J., Arsenault, K., Slinski, K., Sarmiento, D.P., Hoell, A., Pervez, S., Rowland, J., Budde, M., Kumar, S. and Peters-Lidard, C., 2022. A Central Asia hydrologic monitoring dataset for food and water security applications in Afghanistan. Earth System Science Data, 14(7), pp.3115-3135.

Kumar, S.V., Peters-Lidard, C.D., Tian, Y., Houser, P.R., Geiger, J., Olden, S., Lighty, L., Eastman, J.L., Doty, B., Dirmeyer, P. and Adams, J., 2006. Land information system: An interoperable framework for high resolution land surface modeling. Environmental modelling & software, 21(10), pp.1402-1415.

Kumar, S.V., Peters-Lidard, C.D., Mocko, D. and Tian, Y., 2013. Multiscale evaluation of the improvements in surface snow simulation through terrain adjustments to radiation. Journal of Hydrometeorology, 14(1), pp.220-232.

McNally, A., Arsenault, K., Kumar, S., Shukla, S., Peterson, P., Wang, S., Funk, C., Peters-Lidard, C.D. and Verdin, J.P., 2017. A land data assimilation system for sub-Saharan Africa food and water security applications. Scientific data, 4(1), pp.1-19.