Snow Water Equivalent Estimation Using AMSR-E and GLDAS Model (Case study: Basins of Northwestern Iran)

Document Type : Research Article

Authors

• hadi ansari
• safar marofi

Bu-Ali Sina University, Hamedan

https://doi.org/10.22067/jsw.v31i5.58484

Abstract

Introduction: Snow water equivalent (SWE) provides important information for water resources management and recently has attracted the attention of many researchers using remote sensing. Remote sensing presents a possibility for observation of snow characteristics, like water equivalent, over larger areas. Validation of remote sensing data of snow water equivalent (SWE) has always been an important issue for the researchers. Previous studies have assessed the global SWE data. Although it has been tried by using large-scale models of the world to estimate SWE, but regional effects such as snow density, topography and local meteorological conditions may lead to uncertainty.
Materials and Methods: The Northwestern Iran was selected as the study area in this research. Reasons for choosing this area are being mountainous with much snowfall. Also this region compared to the other parts of Iran, has more dense snow survey stations. In this study the AMSR-E sensor data and Global Land Data Assimilation System (GLDAS) was used to estimate SWE in the basins of the northwestern Iran. After processing AMSR-E sensor data and GLDAS model with related software, SWE was estimated in the snow survey stations and evaluated with observed data. To specify the snow density effect on SWE data in AMSR-E sensor from the snow density data, the stations were used. To determine the accuracy of estimation of SWE at different heights, snow survey stations is arranged by considering height and were divided into four height classes that contain enough observational data to evaluate computational data in each height class. To verify SWE obtained estimations in the stations, Root Mean Square Error (RMSE) and Pearson correlation coefficient (r) assessment criteria were used. After evaluating, the SWE data of AMSR-E sensor and GLDAS model for the GLDAS model monthly data to estimate SWE was used for the period 2000 to 2015. With calculating average annual SWE from monthly data, SWE trend changes in mentioned period, the moving averages graphs 3, 5 and 7-year-old was drawn.
Results and Discussion: According to the obtained results, SWE computational data with observational data had significant correlation at the 1% level. Using in situ snow densities, the correlation coefficient between AMSR-E and situ SWE increased from 0.27 to 0.55. The results showed that the best estimation of SWE is in the stations, which have the height of 1,350 to 1600 meters. Also with increasing altitude, the estimation accuracy is significantly reduced. In most years maximum of the SWE was obtained in January and February and in the period of June to September, the area was out of snow storage. According to the average annual SWE and moving averages graphs 3, 5 and 7-years old, the SWE of Northwestern Iran basins in period 2015-2001 has a reducing trend.
Conclusions: In the regions like the Northwestern Iran mountainous where snowfall constitutes a significant fraction of total precipitation, the snowpack delays the resulting runoff into the time of year where water demand is greater. So measurement of snow on the ground has been an important component of hydrologic forecasting for a century. Various remotely sensed snow data have been widely utilized for cold regions to explore the relationships between snow distribution, river discharge, and climate change. The accuracy of remotely sensed snow products should be well understood and incorporated in any investigations using such data. The main objective of the present study was to quantitatively compare the AMSR-E and GLDAS model for an understudied region of the earth. AMSR-E global SWE data and GLDAS data were compared by situ SWE measurements performed in the snow courses. The results showed that the snow density is an effective factor in derived algorithm for the SWE AMSR-E data. Also with increasing height, precision of the estimation significantly decreased. The determination of SWE from satellite imagery in progress updated with new learning. The obtained results from passive microwave in smooth terrain are promising, but involvement of different mechanisms become more complicated as the terrain gets more complex. Nevertheless, it is believed that if the above discussions are taken into account, AMSR-E would provide valuable SWE information even for a mountainous region like Northwestern Iran. It is also hoped that this study would be a starting point in the water scarce, developing Iran to plan and use the limited supply in a suitable manner.

Keywords

• Aqua satellite
• Moving Average
• Snow Density
• Topography

20.1001.1.20084757.1396.31.5.18.3

References

1- Akhavan Karbasi F., Aref M.R., and Sarkargar S.A. 2015. Estimate the volume of snow water equivalent of cover with MODIS data processing and ground data (case study: Yazd Province). P. 19. National Conference on Application of spatial analysis advanced models (remote sensing and GIS) in spatial planning, 2425 February. 2015. Islamic Azad University, Yazd, Iran. (in Persian)

2- Andreadis K.M., and Lettenmaier D.P. 2006. Assimilating remotely sensed snow observations into amacroscale hydrology model. Advances in Water Resources, 29:872–886.

3- Ashrafzadeh Afshar A., Joodaki G R., and Sharifi M A. 2016. Evaluation of Groundwater Resources in Iran Using GRACE Gravity Satellite Data. Journal of Geomatics Science And Technology, 5 (4):73–84. (in Persian)

4- Byun K., and Choi M. 2014. Uncertainty of snow water equivalent retrieved from AMSR-E brightness temperature in northeast Asia. Hydrological Processes, 28:3173–3184.

5- Chang A.T.C., Foster J.L., and Hall D.K. 1987. Nimbus-7 Derived Global Snow Cover Parameters. Annals of Glaciology, 9:39–44.

6- Chang A. T. C., Foster J.L.,. Hall D.K., Goodison B.E., Walker A.E., and Metcalfe J.R. 1997. Snow Parameters Derived from Microwave Measurements During the BOREAS Winter Field Experiment. Journal of Geophysical Research, 102:29663–29671.

7- Cohen J., and Entekhabi D. 1999. Eurasian snow cover variability and Northern Hemisphere climate predictability. Geophysical Research Letters, 26:345–348.

8- Dai L., Che T., Wang J., and Zhang P. 2012. Snow depth and snow water equivalent estimation from AMSR-E data based on a priori snow characteristics in Xinjiang, China. Remote Sensing of Environment, 127:14–29

9- Dehghani M., Morid S., and Norouzi A.A. 2010. Runoff Simulation in Snowbound Catchments, Using SRM and ANN Models to Estimate Hydropower Potentials in Data Scarcity Situations. Iran-Water Resources Research, 3:12–24. (in Persian)

10- Delavar M., Morid S., and Nikbakht N. 2012. Distributed Snowmelt Simulation in Ungauged Mountainous Catchments, Case Study: Imamzadeh Davoud Catchment. Iran-Water Resources Research, 4:41–50. (in Persian with English abstract)

11- Derksen C., Walker A., and Goodison B. 2005. Evaluation of passive microwave snow water equivalent retrievals across the boreal forest/tundra transition of western Canada. Remote Sensing of Environment, 96:315–327.

12- Davis R.E., McKenzie J.C., and Jordan R. 2006. Distributed snow process modeling: an image processing approach. Hydrological Processes, 9:865–875.

13- Dong J., Jeffrey P., and Houser P. 2005. Factor affecting remote sensed snow water equivalent uncertainty. Remote Sensing of Environment 97:68–82.

14- Dziubanski D.J., and Franz K.J. 2016. Assimilation of AMSR-E snow water equivalent data in a spatiallylumped snow model. Journal of Hydrology, 540:26–39.

15- Fang H., Beaudoing H.K., Rodell M., Teng W.L., and Vollmer B.E. 2009. Global land data assimilation (GLDAS) products,services and application from nasa hydrology data and information services center (HDISC). P. 1–9. ASPRS 2009 Annual Conference. 8–13 March, 2009. Baltimore, Maryland, United States.

16- Fathzade A., and Zare Bidaki R. 2012. Estimating the Distribution of Snow Melt Equivalent at the Peak of Snow Accumulation, through Degree – Day Model. Iranian Journal of Soil and Water Research, 43:171–177. (In Persian)

17- Farokhnia A., and Morid S. 2014. Assessment of GRACE and GLDAS Capabilities for Estimation of Water Balance in Large Scale Areas, a Case Study of Urmia Lake Watershed. Iran-Water Resources Research, 1:51–62. (in Persian with English abstract)

18- Foster J.L., Hall D.K., Chang A.T.C., and Rango A. 1984. An overview of passive microwave snow research and results. Reviews of Geophysics & Space Physics, 22:195–208.

19- Foster J.L., Sun C., Walker J.P., Kelly R., Chang A., Dong J., and Powell H. 2005. Quantifying the uncertainty in passive microwave snow water equivalent observations. Remote Sensing of Environment, 94: 187–203.

20- Frei A., and Robinson D.A. 1999. Northern Hemisphere snow extent: Regional variability 1972–1994. International Journal of Climatology, 19:1535–1560.

21- Jiang L M., Wang P., Zhang L.X., Yang H., and Yang J.T. 2014. Improvement of snow depth retrieval for FY3B-MWRI in China. Science China Earth Sciences, 57(6):1278–1292.

22- Kawanishi T., Sezai T., Ito Y., Imaoka K., Takeshima T., Ishido Y., Shibata A., Miura M., Inahata H., and Spencer R.W. 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 Transactions on Geoscience and Remote Sensing, 41:184–194.

23- Kelly R.E., Chang A.T., Tsang L., and Foster J.L. 2003. A prototype AMSR-E global snow area and snow depth algorithm. IEEE Transactions on Geoscience and Remote Sensing, 41:230–242.

24- Liang J., Liu X., Huang K., Li X., Shi X.b., Chen Y., and Li J. 2015. Improved snow depth retrieval by integrating microwave brightness temperature and visible/infrared reflectance. Remote Sensing of Environment, 156: 500–509

25- Li D., Durand M., and Margulis S.A. 2012. Potential for hydrologic characterization of deep mountain snowpack via passive microwave remote sensing in the Kern River basin, Sierra Nevada, USA. Remote Sensing of Environment, 125:34–48.

26- Lobl E., Spencer W.R., Shibata A., Imaoka K., Sasaki M., and 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. 2002. Hangzhou, China.

27- Mir Yaghoobzadeh M.H., and Ghanbarpour M.R. 2010. Investigation to MODIS Snow Cover Maps Usage in Snowmelt Runoff Modeling (Case Study: Karaj Dam Basin). Journal of Geoscience, 76:141148. (in Persian)

28- Mizukami N., Perica S., and Hatch D. 2011. Regional approach for mapping climatological snow water equivalent over the mountainous regions of the western United States. Journal of Hydrology, 400:7282.

29- Mizukami N., and Perica S. 2011. Towards improved snow water equivalent retrieval algorithms for satellite passive microwave data over the mountainous basins of western USA. Hydrological Processes. 26:1991–2002.

30- Najafi M., Sharifi M.A., and Mokhtari E. 2011. Using GRACE satellite data for estimation of the total water level variation over Iran. Geospatial Engineering Journal, 1:110. (in Persian with English abstract)

31- Rasouli A.A., and Edhami S. 2008. Calculate the water equivalent of snow cover with image processing MODIS sensor. Geography and Development Iranian Journal, 10:2336. (in Persian)

32- Rodell M., Houser P.R., Jambor U., Gottschalck J., Mitchell K., Meng C.J., Arsenault K., Cosgrove B., Radakovich J., Bosilovich M., Entin J.K., Walker J.P., Lohmann D., and Toll D. 2004. "The Global Land Data Assimilation System. American Meteorological Society, 85(3):381394.

33- Snauffer A M., Hsieh W W., and Cannon A J. 2016. Comparison of gridded snow water equivalent products with in situ measurements in British Columbia, Canada. Journal of Hydrology, 541:714–726.

34- Takala M., Pulliainen J., Metsämäki S., and Koskinen J. 2009. Detection of Snowmelt Using Spaceborne Microwave Radiometer Data in Eurasia from 1979 to 2007. IEEE Transactions on Geoscience and Remote Sensing, 47:29963007.

35- Tedesco M., Kelly R., Foster J.L., and Chang A.T. 2004. AMSR-E/Aqua Daily L3 Global Snow Water Equivalent EASE-Grids. Version 2. [indicate subset used]. Boulder, Colorado USA: NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: 10.5067/AMSR-E/AE_DYSNO.002.

36- Tedesco M., and Narvekar P.S. 2010. Assessment of the NASA AMSR-E SWE product. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3:141–159.

37- Tekeli A.E., Akyurek Z.A., Sormanc A.A., Sensoyc A., and Sorman A.U. 2005. Using MODIS Snow Cover Maps in Modeling Snowmelt Runoff Process in the Eastern Part of Turkey. Remote Sensing of Environment, 97:216 230.

38- Tekeli A.E. 2008. Early findings in comparison of AMSR-E/Aqua L3 global snow water equivalent EASE-grids data with in situ observations for Eastern Turkey. Hydrological Processes, 22:27372747.

39- Tong J., and Velicogna I. 2010. A comparison of AMSR-E/Aqua snow products with in situ observations and MODIS snow cover products in the Mackenzie River Basin, Canada. Remote Sensing, 2:2313–2322.

40- Tong J., Dery S.J., Jackson P.L., and Derksen C. 2010. Snow distribution from SSM/I and its relationships to the hydroclimatology of the Mackenzie River Basin, Canada. Advances in Water Resources, 33:667677.

41- Yang D., Robinson D., Zhao Y., Estilow T., and Ye B. 2003. Streamflow response to seasonal snow cover extent changes in large Siberian watersheds. Journal of Geophysical Research, 108:7-1,7-14.

42- Yang G., Hongjie X., Ning L., Tandong Y., and Tiangang L. 2010. Toward advanced daily cloud-free snow cover and snow water equivalent products from Terra–Aqua MODIS and Aqua AMSR-E measurements. Journal of Hydrology, 385:2335.

43- Yang J., Jiang L., Menard C B., Luojus K., Lemmetyinen J., and Pulliainen J. 2015. Evaluation of snow products over the Tibetan Plateau. Hydrological Processes, 29(15): 3247–3260.