AcknowledgmentIt has taken the efforts of many people and the support of their organization during the last several years to allow us to reach this new milestone in snowmelt runoff modeling. The following organizations and people were particularly helpful and supportive: It is now possible to divide a basin into as many as 16 elevation or other zones in order to refine the modeling, while Version 4 only allowed 8. These improvements facilitate new developments in SRM applications which are already taking place: runoff modeling by using different land use zones, separating satellite mapping of snow and glaciers, runoff modeling in very large basins with an extreme elevation range, and others. The specific features of WinSRM Version 1.11 are explained in detail in this document in Sections 8.5, 8.6, 9, and 10.WinSRM Version 1.11 has been developed without sacrificing the advantages of the SRM Version 4, in particular the speed of getting results. Both versions are available on the Internet by accessing http://www.ars.usda.gov/Services/docs.htm?docid=8872. Should this link not be "current" for the reader, one can "search" on "SRM home" or "WinSRM" to locate a "current site".So far, four SRM workshops (in 1992, 1994, 1996, and 1998) have been organized at the University of Bern, Switzerland, with about 130 participants from 20 countries taking part. A fifth SRM workshop was organized in 2005 at New Mexico State University. In addition, the authors are available to assist users in overcoming special problems which may be encountered. INTRODUCTIONThe Snowmelt-Runoff Model (SRM) is designed to simulate and forecast daily streamflow in mountain basins where snowmelt is a major runoff factor. Most recently, it has also been applied to evaluate the effect of a changed climate on seasonal snow cover and runoff. SRM was developed by Martinec (1975) in small European basins. Thanks to the progress of satellite remote sensing of snow cover, SRM has been applied to larger and larger basins. Recently, the runoff was modelled in the basin of the Ganges River, which has an area of 917,444 km 2 and an elevation range from 0 to 8,840 m a.s.l. Contrary to the original assumptions, there appear to be no limits for application with regard to the basin size and the elevation range. Also, a dominant role of snowmelt does not seem to be a necessary condition. It is, however, advisable to carefully evaluate the formula for the recession coefficient.Runoff computations by SRM appear to be relatively easily understood. To date the model has been applied by various agencies, institutes and universities in over 100 basins, situated in 29 different countries as listed in Table 1. More than 80% of these applications have been performed by independent users, as is evident from 80 references to pertinent publications. Some of the localities are shown in Figure 1. SRM also successfully underwent tests by the World Meteorological Organization with regard to runoff simulations (WMO, 1986) and to partially simulated conditions of real ti...

Abstract. The local weather and climate of the Himalayas are sensitive and interlinked with global-scale changes in climate, as the hydrology of this region is mainly governed by snow and glaciers. There are clear and strong indicators of climate change reported for the Himalayas, particularly the Jammu and Kashmir region situated in the western Himalayas. In this study, using observational data, detailed characteristics of long- and short-term as well as localized variations in temperature and precipitation are analyzed for these six meteorological stations, namely, Gulmarg, Pahalgam, Kokarnag, Qazigund, Kupwara and Srinagar during 1980–2016. All of these stations are located in Jammu and Kashmir, India. In addition to analysis of stations observations, we also utilized the dynamical downscaled simulations of WRF model and ERA-Interim (ERA-I) data for the study period. The annual and seasonal temperature and precipitation changes were analyzed by carrying out Mann–Kendall, linear regression, cumulative deviation and Student's t statistical tests. The results show an increase of 0.8 ∘C in average annual temperature over 37 years (from 1980 to 2016) with higher increase in maximum temperature (0.97 ∘C) compared to minimum temperature (0.76 ∘C). Analyses of annual mean temperature at all the stations reveal that the high-altitude stations of Pahalgam (1.13 ∘C) and Gulmarg (1.04 ∘C) exhibit a steep increase and statistically significant trends. The overall precipitation and temperature patterns in the valley show significant decreases and increases in the annual rainfall and temperature respectively. Seasonal analyses show significant increasing trends in the winter and spring temperatures at all stations, with prominent decreases in spring precipitation. In the present study, the observed long-term trends in temperature (∘Cyear-1) and precipitation (mm year−1) along with their respective standard errors during 1980–2016 are as follows: (i) 0.05 (0.01) and −16.7 (6.3) for Gulmarg, (ii) 0.04 (0.01) and −6.6 (2.9) for Srinagar, (iii) 0.04 (0.01) and −0.69 (4.79) for Kokarnag, (iv) 0.04 (0.01) and −0.13 (3.95) for Pahalgam, (v) 0.034 (0.01) and −5.5 (3.6) for Kupwara, and (vi) 0.01 (0.01) and −7.96 (4.5) for Qazigund. The present study also reveals that variation in temperature and precipitation during winter (December–March) has a close association with the North Atlantic Oscillation (NAO). Further, the observed temperature data (monthly averaged data for 1980–2016) at all the stations show a good correlation of 0.86 with the results of WRF and therefore the model downscaled simulations are considered a valid scientific tool for the studies of climate change in this region. Though the correlation between WRF model and observed precipitation is significantly strong, the WRF model significantly underestimates the rainfall amount, which necessitates the need for the sensitivity study of the model using the various microphysical parameterization schemes. The potential vorticities in the upper troposphere are obtained from ERA-I over the Jammu and Kashmir region and indicate that the extreme weather event of September 2014 occurred due to breaking of intense atmospheric Rossby wave activity over Kashmir. As the wave could transport a large amount of water vapor from both the Bay of Bengal and Arabian Sea and dump them over the Kashmir region through wave breaking, it probably resulted in the historical devastating flooding of the whole Kashmir valley in the first week of September 2014. This was accompanied by extreme rainfall events measuring more than 620 mm in some parts of the Pir Panjal range in the south Kashmir.

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Winter (December to March) precipitation is vital to the agriculture and water security of the Western Himalaya. This precipitation is largely brought to the region by extratropical systems, known as western disturbances (WDs), which are embedded in the subtropical jet. In this study, using seventy years of data, it is shown that during positive phases of the North Atlantic Oscillation (NAO+) the subtropical jet is significantly more intense than during negative phases (NAO−). Accordingly, it is shown that the NAO significantly affects WD behaviour on interannual timescales: during NAO+ periods, WDs are on average 20% more common and 7% more intense than during NAO− periods. This results in 40% more moisture flux entering the region and impinging on the Western Himalaya and an average increase in winter precipitation of 45% in NAO+ compared to NAO−. Using empirical orthogonal function (EOF) analysis, North Atlantic variability is causally linked to precipitation over North India—latitudinal variation in the jet over the North Atlantic is linked to waviness downstream, whereas variation in its tilt over the North Atlantic is linked to its strength and shear downstream. These results are used to construct a simple linear model that can skilfully predict winter precipitation over north India at a lead time of one month.

18The Himalaya is very sensitive to climatic variations because of its fragile environmental and Introduction 47 48Climate change is a real Earth's atmospheric and surface phenomenon and the influences of which 49 on all the spheres of life are considered significant everywhere in the world at least in the past few decades. 50Extreme weather events like anomalously large floods and unusual drought conditions associated with al., 2007; Kohler and Maselli 2009;Immerzeel et al., 2010;Romshoo et al., 2015;Romshoo et al., 2017). 57Western disturbances (WD) is considered as one of the main sources of precipitation(in the form of 66(PT) and 200 mb level pressure surface(PS) as they are considered as proxies for Rossby wave activities 67 (Ertel, 1942;Bartels et al., 1998 Observational and model datasets used in this study 167The obtained observational data were carefully analysed for homogeneity and missing values. 168Analyses of ratios of temperature from the neighbouring stations with the Srinagar station were conducted 169 using relative homogeneity test (WMO, 1970 6Chen and Gupta, 2012). In this study, change point in time series of temperature and precipitation was 181 identified using cumulative deviation test (Pettitt, 1979). This method detects the time of significant change 182 in the mean of a time series when the exact time of the change is unknown (Gao et al., 2011). 184The data 189temperature, precipitation and NAO index was determined using Pearson correlation coefficient method. 190To test whether the observed trends in winter temperature and precipitation are enforced by NAO, linear 191regression analysis was performed. 193 WRF Model configuration 195The Advanced Research WRF version 3.9.1 model simulation was used in this study to downscale 235Kokarnag and Kupwara, located at the heights of about 1800-2000m amsl, showed an increase of 0.9°C and 2361°C respectively at S=99% (Fig. 2a). However, Srinagar and Qazigund, located at the heights of about 237 1700m-1600m amsl, exhibited an increase of 0.65°C and 0.44°Crespectively at S=99% (Fig. 2a). 239The analysis of maximum and minimum temperatures ( 256(S=90%) respectively (Fig, 2f). In Autumn, Gulmarg shows an increase of 0.9C while Pahalgam and 257Kupwara shows less than 0.6C (S=95%). On the contrary, Kokarnag and Qazigund shows less than 0.4C 258(S=90%) but Srinagar shows no significant increase ( Fig. 2g and 267exhibit decrease at S=95% (Fig.3a).The analysis of winter precipitation reveals maximum decrease at 268Gulmarg and Kokarnag followed by Kupwara and Pahalgam at S=90%. On the other hand, Srinagar and 269Qazigund display an average insignificant (NS) decrease (Table 2 and Fig. 3b) while the spring season 270 precipitation exhibits decreasing trend at S=95% at Qazigund and Pahalgam (Fig. 3c). Srinagar, Gulmarg 271and Kokarnag show decrease at S=99%, S=99% and S=95% respectively. The lowest decreasing trend of 272 42mm precipitation during 1980-2010was observed at Kupwara at S=99% (Table 2). 274During the summer months, precip...