Topography and Land Cover Effects on Snow Water Equivalent Estimation Using AMSR-E and GLDAS Data
- 3 Downloads
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.
KeywordsAMSR-E sensor Aqua satellite GLDAS model Passive microwave Snow water equivalent
The authors would like to thank the National Snow and Ice Data Center (NSIDC) for the AMSR-E data.
Compliance with Ethical Standards
Conflict of Interest
- Adelzadeh A (2015) Diagnostic of the temperature in Northwest Iran and its relationship with geopotential height. Appl Climatol 2(2):17–32Google Scholar
- Asakereh H, Tarkarani F, Soltani S (2013) On tempo-spatial characters of extreme daily precipitation of northwest of Iran. Iran-Water Resour Res 8(3):39–53Google Scholar
- Clark GE, Ahn KH, Palmer RN (2017) Assessing a regression-based regionalization approach to ungauged sites with various hydrologic models in a forested catchment in the northeastern United States. J Hydrol Eng 22(12):05017027-1–05017027-14. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001582 CrossRefGoogle Scholar
- Cohen J (1994) Snow cover and climate. Weather 49:150–156. https://doi.org/10.1002/j.1477-8696.1994.tb05997.x CrossRefGoogle Scholar
- Fang H, Beaudoing H, Rodell M, Tengl W, Vollmer B (2009) Global land data assimilation (GLDAS) products, services and application from Nasa hydrology data and information services center (HDISC). ASPRS 2009 Annual Conference 8–13 March. Baltimore, Maryland, United States, pp 1–9Google Scholar
- Johnson RH, Young GS, Toth JJ, Zehr RM (1984) Mesoscale weather effects of variable snow cover over Northeast Colorado. Mon Weather Rev 112:1141–1152. https://doi.org/10.1175/1520-0493(1984)112<1141:MWEOVS>2.0.CO;2 CrossRefGoogle Scholar
- Kawanishi T, Sezai T, Ito Y, Imaoka K, Takeshima T, Ishido Y, Shibata A, Miura M, Inahata H, Spencer RW (2003) The advanced microwave scanning radiometer for the earth observing system (AMSR-E), NASDA’s contribution to the EOS for global energy and water cycle studies. IEEE Trans Geosci Remote Sens 41(2):184–194. https://doi.org/10.1109/TGRS.2002.808331 CrossRefGoogle Scholar
- Lobl E, Spencer WR, Shibata A, Imaoka K, Sasaki M, Kachi M (2002) Global climate monitoring with the advanced microwave scanning radiometer (AMSR and AMSR-E). SPIE 3rd Int’l Asia-Pacific environmental remote sensing symposium, 23 October. Hangzhou, China. https://doi.org/10.1117/12.466518
- Mirabassi R, Dinpazhooh Y (2013) Trend analysis of precipitation of NW of Iran over the past half of the century. J Irrig Sci Eng 35(4):59–73Google Scholar