Available online at ScienceDirect. Aquatic Procedia 4 (2015 )

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1 Available online at ScienceDirect Aquatic Procedia 4 (2015 ) INTERNATIONAL CONFERENCE ON WATER RESOURCES, COASTAL AND OCEAN ENGINEERING (ICWRCOE 2015) Snowfall and Snowmelt Variability over Himalayan region in Inter- Annual Timescale Sarita Tiwari a,b, Sarat C. Kar a* and R. Bhatla b a National Centre for Medium Range Weather Forecast, Noida, India b Banaras Hindu University, Varanasi, India Abstract The Himalayan mountain system is the source of one of the world's largest supplies of freshwater, as all the major south Asian rivers originate in the Himalayas and their upper catchments are covered with snow and glaciers. There have been no studies describing large-scale pattern of snowmelt over this region. In order to develop a physically based distributed snowmelt-runoff model, a study on the variability of snowfall and snowmelt in the Himalayas is essential. Therefore, for the first time, a study on the variability of snow accumulation and ablation has been undertaken for this region. Remotely sensed snow water equivalent (SWE) from the National Snow and Ice data Centre (NSIDC) and surface temperature data from the Climate Forecast System Reanalysis (CFSR) have been used to study the snowmelt process in the Himalayas. It is seen that, in April, snowmelt mostly occurs in Afghanistan and northern parts of Jammu and Kashmir. Along the Pir Panjal range, snowfall continues in April. Maximum runoff from snowmelt in Kashmir, Himachal and Uttarakhand occurs in the months of June and July. The degree day factor (snowmelt coefficient) indicating snowmelt rate has been computed and it is found that there is large spatial variations in snowmelt rate over the Himalayas due to variations in surface temperatures, accumulated snow amount and elevation. The melt rate ranges from 0.02 to 0.6 cm/ o C/day in June. Around the Pir Panjal range and the Satluj basin, the melt rate is higher than other places The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license 2015 The Authors. Published by Elsevier B.V. ( Peer-review under responsibility of organizing committee of ICWRCOE Peer-review under responsibility of organizing committee of ICWRCOE 2015 Keywords: snowfall, snowmelt, interannual variability, mechanism *Corresponding Author: Tel address: sckar@ncmrwf.gov.in X 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of organizing committee of ICWRCOE 2015 doi: /j.aqpro

2 Sarita Tiwari et al. / Aquatic Procedia 4 ( 2015 ) Introduction All the major south Asian rivers originate in the Himalayas. A substantial proportion of the annual precipitation in the Hindu Kush-Himalayas is in the form of snow at high altitudes (above 3,000 m) in both winter and summer. Estimation of the volume of water released from the snow and glaciers is needed for the efficient management of water resources, including flood forecasting, reservoir operation etc. Very few studies have been carried out on the magnitudes of interannual variations of snow melt process and its impact on water and energy cycles in the Himalayas. The winter precipitation over the Himalayas shows considerable interannual variability mainly due to large interannual variability in the frequency of western disturbances (WDs) during this season. Yadav et al. (2009) found a positive precipitation anomaly over northwest India is typically found in correspondence to subdued convection over warm pool region. Kar and Rana (2014) have documented the relative role of the North Atlantic Oscillation / Arctic Oscillation and El Nino Southern Oscillation in modulating the Asian jet stream in the Northern Hemisphere winter and their relative impact on the precipitation variability over the region. Importance of climate variability and change on the water resources has been highlighted in India s National Action Plan on Climate Change (NAPCC, 2008). A study on region-wise accumulation and ablation pattern of snow cover in Pir Panjal and Shamshawari ranges of Kashmir valley was carried out by Negi et al. (2009). Shekhar et al. (2010) examined the trend in the snowfall patterns in the western Himalayan range. Tiwari et al (2014) have examined snowfall variability in winters over Himalayas and proposed a mechanism of snowfall and snow accumulation in the region. The Satluj River originates from the Tibetan Plateau in the southern slopes of Mount Kailash at an elevation of more than 4500m above mean sea level and flows generally west and southwest entering India in Himachal Pradesh. About 65% of the Satluj basin area is covered with snow during winter and about 12% of the basin is covered with permanent snowfields and glaciers (Singh and Bengtsson, 2004). The Spiti catchment of Satluj river basin experiences extensive snowfall due to western disturbances in the winter months (Singh and Kumar, 1997). Jain et al. (1998) found that the spring season (March-April-May-June) flow in Satluj comes mainly from the runoff generated by snowmelt in the greater Himalayas. During April to June, snowmelt is the predominant source of runoff and during July to September it forms a significant constituent besides ice melt and rain. The digital elevation model of the Satluj basin is shown in Fig 1. Few studies exist on individual river basins in the Himalayas on snowmelt and runoff process. However, due to lack of observed data on a large-scale, the studies are mostly patchy and a comprehensive view on the snow accumulation and ablation process in the Himalayas can not be taken. Fig 1. The study domain and the digital elevation model of the Satluj river basin. There have been no studies on mechanism of interannual variation of snowmelt in the Himalayan region. Remotely sensed snow water equivalent (SWE) and snow cover area (SCA) are two important parameters based on which a better understanding of snow accumulation and melt processes over the Himalayas can be studied. Remotely sensed SWE data on a monthly scale has now become available for a longer period from National Snow and Ice Centre (NSIDC) of National Oceanic and Atmospheric Administration (NOAA), USA, (Armstrong et al 2005). Several global reanalysis projects such as Climate Forecast System Reanalysis (CFSR) from National

3 944 Sarita Tiwari et al. / Aquatic Procedia 4 ( 2015 ) Centers of Environmental Prediction (NCEP), USA (Saha et al 2010) provide long-term consistent atmospheric and surface reanalysis datasets. The main objective of the present study is to examine the interannual variability of snow melt in the Himalayas. While large-scale aspects of variability are addressed, the focus area is the region surrounding the Satluj river basin. Monthly snowmelt amounts using remotely sensed data have been estimated. A method has been developed to estimate the snowmelt coefficient based on the degree-day using surface temperature from reanalysis data. Spatial and temporal variations of the snowmelt coefficient have been evaluated. The Section 2 of this paper describes the dataset used in the study and methodologies employed. Results of the study are described in Section 3. The study is concluded in Section 4 by highlighting the main results. 2. Data and Methodologies Monthly mean remotely sensed SWE data from 1979 to 2007 have been used in the study to represent snow amount. These data are from National Snow and Ice Data Centre (NSIDC), USA. The NSIDC SWE data is derived from Scanning Multichannel Microwave Radiometer (SMMR) and selected Special Sensor Microwave/Imagers (SSM/I), Armstrong et al (2005). Data is gridded at 25 km equal area Scalable Earth Grids (EASE-Grids). The data has been interpolated to regular latitude-longitude (0.25 o x0.25 o ) for the present study. Several global reanalysis project have been undertaken in past. An important goal of the reanalysis efforts is to provide comprehensive, consistent, and reliable long-term datasets of temperatures, precipitation, winds, and other variables that characterize the state of the climate system. Among the recent global reanalysis projects, the CFSR reanalysis (Saha et al., 2010) used global atmosphere resolution of ~38 km (T382) with 64 levels and data assimilation is carried out using a three-dimensional variational analysis using grid-point statistical interpolation scheme. Some of the surface variables in the reanalysis are updated daily, weekly or monthly depending on frequency of observations received. However, there are large uncertainties in such forecasts for surface variables due to coarse resolution to represent surface topography and inadequate physical parameterization schemes of the models producing the forecasts. SWE in CFSR is updated using analysis data from the Air Force Weather Agency s SNODEP model (Kopp et al. 1996) and the NOAA/NESDIS Interactive Multisensor Snow and Ice Mapping System (IMS) (Helfrich et al. 2007). SNODEP uses in situ observations, an SSM/I-based detection algorithm, and its own climatology to produce a global analysis of physical snow depth, once per day at 47 km resolution (Saha et al., 2010). In simple snowmelt modelling system such as the snowmelt runoff model (SRM, Martinec et al, 1994), the degree-day factor (DDF) a [cm o C -1 d -1 ] converts the number of degree-days T [ o C d] into the daily snowmelt depth M [cm]: M = a T (1) Degree-day ratios can be evaluated by comparing degree-day values with the daily decrease of the snow water equivalent. However, there exists a considerable variability of degree-day factor from day to day, month to month and locations depending on altitude. This is because the degree-day method does not take specifically into account radiative and heat flux components of the energy balance. Singh, et al., (2000) has determined the degreeday factor of snow and ice at an altitude of about 4000m in the Garhwal Himalayan region. They found that the average DDF for clean snow and dusted snow was 5.7 and 6.4 mm/ºc/day respectively. However, estimation of large-scale variations of DDF over Himalayas has not been carried out and researchers using SRM type of model use only a constant degree day factor in their studies. Surface temperature at 6hrly interval has been used from CFSR to estimate degree-day in this study and the computed degree-day along with the SWE data from NSIDC to estimate snowmelt coefficient (DDF) over the Himalayas. For the computation purpose, the monthly SWE data has been bilinearly interpolated to daily values. After estimating the daily values of degree-day factor, monthly mean and then the climatological values of the snowmelt coefficient have been computed. In this study, surface fields such as SWE, air temperature near the surface and runoff etc have been used from CFSR reanalysis data to explain the interannual variability of snow obtained from the remotely-sensed SWE from NSIDC.

4 Sarita Tiwari et al. / Aquatic Procedia 4 ( 2015 ) Results and discussion 3.1 Spatial pattern of snow ablation Tiwari et al (2014) have made a detailed analysis of the mechanism of snowfall and accumulation of snow during winter seasons using observed remotely sensed data. Climatological annual cycle of SWE from NSIDC suggests that snowfall starts in the region in October/November and continues till March with some increase in April in some areas. Fig 2(a) shows the climatological mean of observed SWE from remotely sensed data (NSIDC) over the Himalayas in March (averaged over 29 years). It is found that in addition to a region of snow around 65E and 35N, a broad region of snow is seen from northern parts of Afghanistan, Pakistan and J&K which extends south-eastwards up to Himachal Pradesh and Uttarakhand. The amount of snow decreases from west to east. SWE maximum is seen between 70E to 78E (about mm) in the observed data. In the region of interest surrounding Satluj river, accumulated snow in March is about mm. Fig 2(b to f) show the monthly climatology of snowmelt amount over the region. a b d e f Fig 2. Spatial pattern (monthly) of snowmelt over the Himalayas In April, snowmelt mostly occurs in Afghanistan and northern parts of Jammu and Kashmir. Along the Pir Panjal range, snowfall continues in April and it is seen that the snow amount is more in April than in May over this region. There is a north-west south-east band of additional snow covering J&K, Himachal and Uttarakhand. In this region, snowmelt in April is less than additional snow accumulated. From May onwards drastic decrease in snow is seen with melt of snow increasing with increase in temperature as spring and summer set in. However, certain pockets in this region also receive fresh snow in May. As the summer season progresses, the snowmelt contribution increases continuously. In June and July, most of the snow in the region melts and is available as snowmelt runoff. Thus, it is found that maximum runoff from snowmelt in J&K, Himachal and Uttarakhand occurs in the months of June and July, whereas maximum snowmelt occurs in Afghanistan and adjoining regions in April and May.

5 946 Sarita Tiwari et al. / Aquatic Procedia 4 ( 2015 ) Composite analysis of snow in June In order to further study the interannual variability of snowmelt process in the region, year-wise SWE values over the region of interest (Satluj basin) has been examined. Four years when more SWE was available over Himalayas in June were selected along with another four years with less SWE. Excess snow years chosen for the study are 1982, 1983, 1987 and The deficient snow years for June are 1984, 1996, 1988 and ac b c d Fig 3 Composite analysis of SWE and Ts for excess and deficient snow years in June and their differences A composite analysis of SWE for these two sets of years was made. Fig 3a shows the composite analysis of SWE in excess years while Fig 3b shows the same for deficient SWE years. It is seen that in the Satluj basin in excess years, the SWE values range from 60mm to 80mm in June. At the same time, the SWE values over J&K and to the north of it are about 100mm. During the years with less snow in June, the Satluj basin has only about 10-20mm of SWE. At the same time, the SWE over the other regions are also drastically reduced and SWE ranges from 30-50mm. The difference of the composite data (Fig 3c) shows that the difference in SWE over the Himalayas for these two sets of years is quite large (20-70mm) and statistically significant. This difference is due to following two reasons. The first reason is that in the deficient years, snowfall amount in the preceding winters is less than that in excess years. Another main reason is that the accumulated snow has melted in summer due to warmer conditions in spring and summer. In order to further examine the surface conditions in June, the daily mean surface temperature (Ts) from CFSR data has been examined for these two sets of years. The difference in temperature between deficient and excess years is shown in Fig 3d. It is seen that during deficient snow years, the surface temperature is warmer than that of excess years. The magnitude of warming is as high as 2 o C or more over the region of interest. However, at the same time, it is seen that in the region where the snow remains till the end of June, the surface temperature is warmer only by about 0.5 o C. Wherever temperature differences are large, large amount of snowmelt is noticed as shown in Fig Estimation of snowmelt rate As mentioned earlier, in the temperature-index based snowmelt runoff models, snowmelt is computed using degree-day. Based on the degree day and beyond a threshold temperature, snowmelt coefficient is computed. This

6 Sarita Tiwari et al. / Aquatic Procedia 4 ( 2015 ) coefficient is also known as degree-day factor and it represents the rate of snowmelt. So far, the melt factor has been prescribed in the snowmelt runoff models as a constant based on some observational studies made elsewhere. Literature suggests that the melt factor varies with season, altitude and geographical locations. In this study, snow melt rate for June has been computed using observed SWE and surface temperature from CFSR. 6hrly surface temperature data for 29 years have been used to estimate the degree day factor. The melt rate (cm per degree per day) averaged over 29 years in June is shown in Fig 4. It is found that there are large spatial variations in melt rate over the Himalayas. The melt rate ranges from 0.02 to 0.6 cm/ o C/day. Around the Pir Panjal range and the Satluj basin, the melt rate is higher than other places. The Melt rate has been computed for other months (March, April, May and July) also. It is seen that the snowmelt rate over this region varies with month. Over the Hindukush and Afghanistan, the snowmelt rate is large in March and April. Fig 4. Snowmelt Coefficient for June (cm/ o C/day) Variation of the melt factor according to altitude has been examined. For this purpose, topography data has been interpolated to the snow grid. The topography plot for the Himalayan region is shown in Fig 5. At this coarse resolution, finer details of rugged topography of Himalayas are not seen. Maximum terrain height obtained at this grid is about 5500m. By comparing the snowmelt coefficient and orography, it is seen that the snowmelt factor is large in June over the regions with higher topography. Melt coefficient of 0.5 cm/ o C/day or more is seen only over the region at an elevation of more than 4000m. This is because, due to higher elevation and also due to the fact that the snowfall season extends up to late spring in this region, amount of snow available at this height in June is larger than other regions. In summer, when surface warms, the snowmelt rate is enhanced. Whereas at some other locations snowmelt coefficient is smaller in June at lower elevations. It is also important to note that use of a constant degree-day factor in snowmelt runoff models will not lead to accurate snowmelt amount. Moreover, the snowmelt coefficient obtained in this study is smaller than that assumed in many studies using snowmelt runoff model (Martinec et al, 1998). With further high-resolution datasets, a more detailed and comprehensive knowledge on snowmelt coefficient shall be obtained. Therefore, this study recommends that proper values of the snowmelt coefficients should be used to estimate snowmelt amount and snowmelt runoff. Snowmelt amount estimated using the coefficient obtained in this study agrees well with the observed snowmelt amount.

7 948 Sarita Tiwari et al. / Aquatic Procedia 4 ( 2015 ) Runoff Fig 5. Topography (25kmx25km grid) The melted snow flows along the mountain slopes, along the streams and channels to feed the rivers in Himalayas. There are several methods to estimate snowmelt runoff. In CFSR, the Global Forecast System model uses NOAH land surface scheme for surface processes and runoff. This scheme is particularly suitable for estimating runoff in large-scale. However, this scheme does not consider existence of stream network and river flow routing. The average of runoff estimated by the CFSR (mm/day) for 29 years period for June is shown in Fig 6. Seen along with the snow covered area (Fig 2d), and snowmelt coefficient (Fig 4), it is noted that the runoff amount is large in CFSR where there is large amount of snow in June and where the magnitude of snowmelt coefficient is large. Fig 6 shows that the runoff amount is substantial (3-4 mm/day) in the Satluj river basin. The runoff amount seen in Nepal is due to snowmelt in that region as well as due to rainfall runoff. Nevertheless, the snowmelt runoff amount simulated by CFSR is consistent with that of computed snowmelt coefficient and SWE amount as seen in remote sensing data. 4. Conclusion Fig 6. Runoff (mm/day) in CFSR for June Considering the importance of snowmelt process in river runoff and water cycle in the Himalayas, a detailed distributed snowmelt runoff model is being developed. In order to examine the characteristics of snowmelt variability in Himalayas, monthly climatology and variability of snowmelt amount has been analyzed using observed (remote sensing) and reanalysis data. It may be noted that this is for the first time such a study has been undertaken for the Himalayas. It is seen that, in April, snowmelt mostly occurs in Afghanistan and northern parts of Jammu and Kashmir. Along the Pir Panjal range, snowfall continues in April and it is seen that the snow amount is

8 Sarita Tiwari et al. / Aquatic Procedia 4 ( 2015 ) more in April than in May over this region. Maximum runoff from snowmelt in J&K, Himachal and Uttarakhand occurs in the months of June and July. The composite analysis for excess and deficit snow years for June indicates that large difference in SWE over the Himalayas (20-70mm) is due to warmer (2 o C or more) surface temperature in deficient snow years than that in excess year. It is found that there is large spatial variations in snowmelt rate over the Himalayas due to variations in surface temperatures and accumulated snow amount. The degree day factor (snowmelt coefficient) indicating snowmelt rate has been computed for the Himalayas using CFSR surface temperature and SWE from remote sensing data. The melt rate ranges from 0.02 to 0.6 cm/ o C/day. Around the Pir Panjal range and the Satluj basin, the melt rate is higher than other places. Melt coefficient of 0.5 cm/ o C/day or more is seen only over the region at a height of more than 4000m. The snowmelt coefficient obtained in this study is smaller than that assumed in many studies using snowmelt runoff model. Examination of runoff in June from CFSR model data shows that the runoff amount is substantial (3-4 mm/day) in the Satluj river basin. References Armstrong, R., M. Brodzik, K. Knowles, and M. Savoie Global Monthly EASE-Grid Snow Water Equivalent Climatology. [indicate subset used]. Boulder, Colorado USA: NASA DAAC at the National Snow and Ice Data Center. Helfrich, S. R., D. McNamara, B. H. Ramsay, T. Baldwin, and T. Kasheta, 2007: Enhancements to, and forthcoming developments in the Interactive Multisensor Snow and Ice Mapping System (IMS). Hydrol. Processes, 21, , doi: / hyp Jain, S. K., Kite, G. W., Kumar, N. and Ahmad, T., 1998: SLURP model and GIS for estimation of runoff in a part of Satluj catchment, India, Hydrolog. Sci. J. 43(6), December Kar S. C., S. Rana, 2014: Interannual variability of winter precipitation over. northwest India and adjoining region: impact of global forcings, Theoretical and Applied Climatology 116 (3-4), Kopp, T. J., and R. B. Kiess, 1996: The Air Force Global Weather Central snow analysis model. Preprints, 15th Martinec, J., Rango, A. & Roberts, R. (1998) Snowmelt Runoff Model (SRM) User s Manual (Version 4.0). cgi-bin/srmhome/srm4.pdf Conf. on Weather Analysis and Forecasting, Norfolk, VA, Amer. Meteor. Soc., NAPCC, 2008: National Action Plan for Climate Change, Government of India, 52pp. Negi H.S., N.K Thakur, R Kumar, M Kumar, 2009: Monitoring and evaluation of seasonal snow cover in Kashmir valley using remote sensing, GIS and ancillary data. Journal of Earth System Science 118 (6), Han, Lidia Cucurull, Richard W. Reynolds, Glenn Rutledge, and Mitch Goldberg, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, Shekhar MS, Chand H, Kumar S, Srinivasan K, Ganju A, 2010: Climate change studies in the western Himalaya. Ann Glaciol 51, Singh P., Bengtsson L., 2004: Hydrological sensitivity of a large Himalayan basin to climate change. Hydrological Processes, 18, Singh, P., Kumar, N., and Arora, M., 2000: Degree-day factors for snow and ice for Dokriani glacier, Garhwal Himalayas, J. Hydrol., 235, Singh P. and Kumar N., 1997: Impact assessment of climate change on the hydrological response of a snow and glacier melt runoff dominated Himalayan river. Journal of Hydrology, 193: Tiwari Sarita, S.C. Kar and R. Bhatla, 2014: Interannual Variability of Snowfall over Western Himalayas, Pure Appl. Geoph, under review. Yadav R K, Rupa Kumar K and Rajeevan M, 2009: Out-of-phase relationships between convection over north-west India and warm-pool region during winter season; Int. J. Climatol , doi: /joc.1783.