Retinder Kour

The present study was undertaken in the Chenab basin, western Himalayas to explore the effect of shadow on atmospherically and topographically corrected normalized difference snow index (NDSI) values. The research was conducted using the Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) datasets of two time periods i.e., February 2015 and February 2018. Our results suggested that total snow cover area (SCA) computed using NDSI from raw satellite images, under estimated SCA (viz. 10,938.66 and 7739.97 Km 2); relative to atmospherically corrected (AC) (14,819.01 and 11,199.7 Km 2) and topographically corrected (TC) (14,882.07 and 11,188.18 Km 2) images during both the time periods (i.e., 2015 and 2018, respectively). This is mainly due to the non-identification of snow pixels in the shadow regions of raw imageries. Maximum SCA lies in the higher NDSI range in case of AC and TC images, unlike raw image where maximum SCA lies in lower NDSI range for both sunlit and shadow test sites. In the sunlit and shadow terrains, minimum NDSI values were found to be higher for TC images as compared to AC images, except few observations which exhibited similar values for both AC and TC images. In case of maximum NDSI values, most of the observations exhibited similar values for both AC and TC images, except few observations which depicted higher maximum NDSI value in AC images as compared to TC images in both the terrains. This observation ascertains the narrow range of NDSI values for TC images as compared to AC images, which could be attributed to reduced background reflection and low atmospheric scattering in the TC images. Comparison among shadow and sunlit snow terrains, reveals low values of minimum and maximum NDSI in the shadow site, which is ascertained by significant (p < 0.001) t-values of means difference between shadow and sunlit terrains. Moreover, snow surface temperature (SST) values computed for both the time periods, reveal low SST values for shadow sites (t-values − 5.1 and − 10.57 for minimum SST, and − 9.1 and − 12.61 for maximum SST for the years 2015 and 2018, respectively) as compared to sunlit sites, and this observation validates that minimum amount of solar radiation reaches the shadow sites. Thus, it is concluded that AC and TC are helpful for delineation of snow pixels under shadow, but shadow has a significant impact on the spectral reflectance of snow, even in the AC and TC images. The influence of shadow should be taken into account for accurate estimation of physical properties and broadband albedo of the snow.

The changes in maximum and minimum temperature, potential evapotranspiration (PET) and precipitation of future bi-decades, i.e., 2020-2040, 2040-2060, 2060-2080 and 2080-2100 with the corresponding observed baseline datasets (1991–2016) were analyzed for the Jhelum catchment, western Himalayas, using Representative
Concentration Pathways (RCPs) 4.5 and 8.5 of Regional Climate Model version 4 (RegCM4). Maximum and minimum temperature projections indicated a rise of 1.18 ◦C to 3.29 ◦C and 1.30 ◦C to 2.92 ◦C under RCP 4.5, and 3.58 ◦C to 7.22 ◦C and 3.38 ◦C to 6.92 ◦C under RCP 8.5, respectively, relative to the baseline period. Precipitation shows a decline of 14.34 mm, 9.60 mm, 4.19 mm and 15.62 mm under RCP 4.5, whereas RCP 8.5 shows an incline of 7.45 mm, 12.37 mm, 22.19 mm, and 6.86 mm during 2040s, 2060s, 2080s and 2100s, respectively, as compared to the baseline period. For the months of May to September, both the RCPs indicated a decrease in PET values. Snow cover area (SCA) computed from MODIS Snow Cover 8-Day L3 Global 500 m Grid (MOD10A2) datasets over a period of 2002 to 2016, depicted a negative relationship with river runoff, with strong negative coefficient of -0.88 for the year 2012.
The impact of these climate change projections were computed on the hydrology of Jhelum catchment using Variable Infiltration Capacity (VIC) model. Comparison among the future bi-decades, show increasing trend of precipitation under both the RCPs from 2020 to 2080, then slightly decreases during 2080–2100. Similar trend was exhibited by runoff projections, viz. runoff increases till 2080 and decreases afterwards under both the RCP emission scenarios. Temperature and runoff shows relatively higher values under RCP 8.5, than RCP 4.5. Overall, results indicated a strong influence of precipitation projections on runoff of Jhelum catchment.

The present investigation was conducted to analyze the temporal patterns of snow cover area% (SCA%), air temperature, snowfall and river discharge in parts of Chenab basin, western Himalayas. The relationship of mean SCA% with mean air temperature and river discharge was also tested using Pearson's product-moment correlation at 95% confidence limit and further sensitivity analysis of river discharge to SCA and SCA to air temperature was performed. Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day surface reflectance product MOD09A1 was used to delineate SCA during the period 2000-2013. Moreover, variation in the lowest elevation from where snow cover area starts (LESCA) was also analyzed and its relationship with mean air temperature was also studied. Non-parametric method, Mann-Kendall test was employed to determine the trend in the SCA%, air temperature, snowfall and river discharge. The investigation carried out for three meteorological stations i.e. Batote, Reasi and Tandi revealed significant findings. At Batote and Reasi, statistically significant decreasing trends were observed over the period 2000 to 2012, for maximum, minimum and mean air temperature. Mean minimum SCA% exhibited a significant upward trend during 2000-2013 which is corroborated by the significantly increasing trend of mean annual snowfall (Tandi station) from 2000 to 2010. Further, significant decreasing trend of river discharge for the winter season at Batote station from 2000 to 2011 and decreasing trends in the maximum, minimum and mean air temperature at Batote and Reasi stations are also consistent with the increasing trend of SCA%. At both Batote and Reasi stations, mean SCA% exhibited significant negative relationship with the mean air temperature. On the other hand, LESCA exhibited positive correlation with the mean air temperature except in a few months, where negative relationship was seen. Sensitivity analysis of river discharge to SCA exhibited very low values of sensitivity coefficient in most of the months, indicating less sensitivity of river discharge to SCA. On the other hand, sensitivity coefficient of SCA to air temperature exhibited comparatively higher values which indicate SCA is more sensitive to air temperature.

Snow cover characteristics play a vital role in hydrological and climatological analyses. Snow characteristics have been retrieved using different techniques but no study has been conducted hitherto to determine its relationship with snow cover indices. In the present study the relationship of snow cover characteristics i.e., snow grain size index (SGI) and snow contamination index (SCI) with the snow cover indices viz. normalized difference snow index (NDSI) and S3 index is investigated using LANDSAT 8 OLI data in parts of Chenab Basin, western Himalayas. This task has been accomplished through comparative assessment of the relationship of snow cover characteristics with NDSI and S3: first, over two distinct illumination conditions i.e., sunlit snow cover and snow cover under shadow and second, for two different time periods i.e., November 2013 and February 2014 respectively. The results reveal the following observations. First, in the sunlit snow cover, there occurs positive correlation of both NDSI and S3 with SGI whereas they are negatively correlated with SCI, but in the snow cover under shadow, both NDSI and S3 exhibit negative correlation with SGI and SCI each. Second, S3 shows higher correlation with SGI and SCI than NDSI in the respective illumination conditions and time periods. Third, SGI and SCI portray highly positive correlation between them in the shadow side and a smaller negative correlation in the sunlit side. The results provide improved understanding regarding the relationship of the snow cover characteristics with the snow cover indices.

Terrain variables are the main factors affecting the spatial distribution of snow cover. This paper aims to find a relationship between snow-cover area (SCA) and topographic variables (elevation, slope and aspect), using MODIS Terra data (MOD09A1) in parts of the Chenab basin, western Himalayas. The inter-annual variability of SCA% for each month has been analysed for the years 2000 to 2011. The analysis reveals that mean annual SCA value was maximum (37.89%) in 2005 and minimum (32.07%) in 2001. The slope classes with maximum and minimum SCA% are 5°-10°a nd 30°-35°, respectively. Among the 16 aspect classes, the ESE-facing slope evinces maximum SCA%. During the snow accumulation period, the expanse at 3600-4300 m elevation, and in the depletion period, 4300-5000 m elevation are found to have maximum rate of change in SCA% per 100 m rise in elevation, i.e. 3.37% and 3.67%, respectively.

In snow-covered mountainous terrains, topography-induced different illumination conditions would cause varying influence on the snow characteristics in the sunlit and shadowed sites owing to albedo variation. Since the thermal bands suffer less by the shadows as compared to the optical bands, it is imperative to investigate the effect of shadow on the optical and thermal snow indices. The investigation comprises determination of (a) difference of means between the sunlit snow cover and snow cover under shadow for thermal snow indices, optical snow cover indices and optical snow cover characteristic indices and (b) correlation coefficient of the thermal snow indices with the respective optical snow cover indices and optical snow cover characteristic indices. The study was conducted in the test sites of the Chenab basin, western Himalayas using the Landsat-8 Operational Land Imager and Thermal Infrared Sensor data. The mean values of different snow indices exhibit significant difference between the sunlit and shadow test sites with Z-values ranging between 68.92 and 1220.39 (p b 0.0001). Shadow significantly increases correlation of the thermal snow indices with the optical snow cover indices with r ≥ 0.81, while r-values lie below 0.29 in the sunlit test site (Student's t-test, p < 0.0001). On the other hand, thermal snow indices exhibit low correlation with both optical snow cover characteristic indices in either site; however, shadow induces negative correlation between them (r = −0.37 to −0.62, p < 0.0001). The results ascertain the varying influence of shadow on the optical and thermal snow indices and their interrelationship, which could be significantly helpful for accurate radiative transfer modelling of snow in the light of the seasonal variation in the earth-sun geometry.

In this study, we have developed a new thermal snow index viz. S3 thermal snow index (S3TSI) and determined its relationship with two existing thermal snow indices (i.e., normalized difference snow thermal index (NDSTI) and normalized difference thermal snow index (NDTSI)) and with snow cover indices (i.e., normalized difference snow index (NDSI) and S3) and snow cover characteristic indices (i.e., snow grain size index (SGI) and snow contamination index (SCI)) for two different sites such as bare snow cover and snow cover mixed with vegetation, respectively, based on Pearson's correlation coefficients. Whereas NDSTI uses blue band, NDTSI incorporates NDSI. With this analogy, we have developed "S3TSI" by substituting S3 for NDSI since S3 better demarcates snow cover mixed with vegetation. The study was conducted in snow cover test sites of the Chenab basin, Western Himalayas using LANDSAT-8 Operational Land Imager and Thermal Infrared Sensor data of November 2013. The investigation revealed that S3TSI exhibits considerably higher correlation with NDTSI and also with NDSI and S3 in either site except in the case of snow cover mixed with vegetation where NDTSI correlates better with NDSI. All the three thermal snow indices exhibit very high correlation with the snow cover indices in snow cover mixed with vegetation as compared to bare snow cover. However, the thermal snow indices show weak correlation with the snow cover characteristic indices as compared to the snow cover indices in either site. The findings demonstrate the significance of the new thermal snow index as compared to NDSTI and NDTSI.

Quantitative knowledge about the impacts of climate change on the hydrological regime is essential in order to achieve meaningful insights to address various adverse consequences related to water such as water scarcity, flooding, drought, etc. General circulation models (GCMs) have been developed to simulate the present climate and to predict future climatic change. But, the coarse resolution of their outputs is inefficient to resolve significant regional scale features for assessing the effects of climate change on the hydrological regimes, thus restricting their direct implementation in hydrological models. This article reviews hierarchy and development of climate models from the early times, importance and intercomparison of downscaling techniques and development of hydrological models. Also recent research developments regarding the evaluation of climate change impact on the hydrological regime have been discussed. The article also provides some suggestions to improve the effectiveness of modelling approaches involved in the assessment of climate change impact on hydrological regime.