Hydrological Modelling and data assimilation of Satellite Snow Cover Area using a Land Surface Model, VIC

Abstract

NBSSLUP soil map and land use land cover map of ISRO-GBP project for year 2014 were used for generating the soil parameters and vegetation parameters respectively. The threshold temperature i.e. the minimum rain temperature is -0.5°C and maximum snow temperature is about +0.5°C at which VIC can generate snow fluxes. Hydrological simulations were done using both NCEP and IMD based meteorological Forcing datasets, but very few snow fluxes were obtained using IMD data met forcing, whereas NCEP based met forcing has given significantly better snow fluxes throughout the simulation years as the temperature resolution as given by IMD data is 0.5°C and rainfall resolution of 0.25°C. The simulated discharge has been validated using observed data from BBMB (Bhakra Beas Management Board) and coefficient of Correlation(R 2 ) measured for (2003)(2004)(2005)(2006) was 0.67 and 0.61 for the year 2006.But as VIC does not consider snowmelt runoff as a part of the total discharge, snowmelt runoff has been estimated for the simulation both with and without D.A. The snow fluxes as generated from VIC gives basin average estimates of Snow Cover, SWE, Snow Depth and Snow melt. It has been observed to be overestimated when model predicted snow cover is compared with MODIS SCA of 500 m resolution from MOD10A2 for each year. So MODIS 8-day snow cover area has been assimilated directly into the model state as well as by using EnKF after every 8 days for the year 2006.D.I Technique performed well as compared to EnKF. R 2 between Model SCA and MODIS SCA is estimated as 0.73 after D.I with Root Mean Square Error (RMSE) of +0.19. After direct Insertion of D.A, SCA has been reduced comparatively which resulted in 7% reduction of annual snowmelt contribution to total discharge.The assimilation of MODIS SCA data hence improved the snow cover area (SCA) fraction and finally updated other snow components.

Snow Cover and Glacier Dynamics Study Using C-and L-Band Sar Datasets in Parts of North West Himalaya

“…Therefore, Remote Sensing (RS) remain one of most important and feasible technology to map and monitor the SSC and its physical properties at various spatio-temporal scales, as proven by many operational and research studies (Kulkarni et al ;Thakur et al, 2012Thakur et al, , 2017aJain et al, Negi et al, Sharma et * Corresponding author al., Nikam et al, 2017). The RS based snow cover and its properties have direct use as input to snowmelt runoff models (Jain et al, 2009;Prasad and Roy, 2005;Thakur et al, 2009;Aggarwal et al, 2013;Thakur, 2014;Wulf et al, 2016), and as validation and data assimilation in process based hydrological models (Naha et al, 2016;Agarwal et al, 2016). The optical data is capable of giving good quality snow cover maps, but synthetic aperture radar (SAR) data is able to provide, dry/wet snow and snow physical properties as well, due to penetration capability and sensitivity to snow wetness Venkataraman 2007, 2009;Thakur et al, 2012;Snapir et al, 2019).…”

Current Practice and Recommendations for Modelling Global Change Impacts on Water Resource in the Himalayas

“…This could suggest that the use of complex process-based models is unnecessary in the Himalayan region. For example, the conceptual HBV model [99,118] outperformed the process-based VIC model [119] when applied in a 1-km grid in the upper Beas catchment in the Western Himalayas; the empirical SRM and conceptual GR4J models [50] had similar levels of performance to the process-based model J2000 [52] when applied to the Dudh Koshi catchment in Nepal. However, good baseline model performance does not necessarily mean that a model will produce appropriate hydrological behaviour when applied outside of the historical hydro-meteorological conditions against which it has been calibrated [49,120].…”

“…Possibly due to the fact that air temperature data are available and persistent across large areas [65], the most common approach to representing snow and ice melt in the reviewed literature are simple temperature-index models (Figure 3) that describe meltwater production as a linear function of air temperature at a rate defined by a constant-degree day factor that translates temperatures above a melting threshold to the melt snow water equivalent [42,48,87,88,91,99,114,119,123]. Detailed energy balance models are the least common [79,94,107,115,[124][125][126] despite their more detailed process representation, most likely due to their much greater data requirements.…”

“…When more than two sources of uncertainty are considered, they mostly refer to soil and snow parameters, combined with climate change uncertainty [52,113,130,146], hydrological model uncertainty [77,78] or spatial distribution of meteorological variables [60,80]. The least-regarded uncertainty source refers to structural aspects with few studies analysing the variability introduced by different hydrological [50,77,98] or evapotranspiration models [49], although the focus again is on snow/ice melt processes [78,118,119,126]. The main technique used to analyse uncertainty in the reviewed literature is the deterministic sensitivity analysis [48,49,91,111,114,126], but Monte Carlo simulations [42,52,60,77,79,128], and Latin Hypercube sampling [113] have also been applied.…”

Estimation of Hydro-Meteorological Extremes in Beas Basin Over Historic, Present and Future Scenario

“…North Western Himalaya has always been prone to hydro-meteorological extremes and among in it, the Beas Basin is more prone to such flood events. (Aggarwal et al, 2016;Gupta et al, 1982;Naha et al, 2016;Prasad & Roy, 2005) It also receives the highest annual rainfall with most number of rainfall extreme events. The high hilly terrain does not allow flood water to retain, instead the heavy rainfall triggers the occurrence of flash flood which causes huge damage to the basin.…”