A Framework for Multivariate Analysis of Land Surface Dynamics and Driving Variables—A Case Study for Indo-Gangetic River Basins

The analysis of the Earth system and interactions among its spheres is increasingly important to improve the understanding of global environmental change. In this regard, Earth observation (EO) is a valuable tool for monitoring of long term changes over the land surface and its features. Although investigations commonly study environmental change by means of a single EO-based land surface variable, a joint exploitation of multivariate land surface variables covering several spheres is still rarely performed. In this regard, we present a novel methodological framework for both, the automated processing of multisource time series to generate a unified multivariate feature space, as well as the application of statistical time series analysis techniques to quantify land surface change and driving variables. In particular, we unify multivariate time series over the last two decades including vegetation greenness, surface water area, snow cover area, and climatic, as well as hydrological variables. Furthermore, the statistical time series analyses include quantification of trends, changes in seasonality, and evaluation of drivers using the recently proposed causal discovery algorithm Peter and Clark Momentary Conditional Independence (PCMCI). We demonstrate the functionality of our methodological framework using Indo-Gangetic river basins in South Asia as a case study. The time series analyses reveal increasing trends in vegetation greenness being largely dependent on water availability, decreasing trends in snow cover area being mostly negatively coupled to temperature, and trends of surface water area to be spatially heterogeneous and linked to various driving variables. Overall, the obtained results highlight the value and suitability of this methodological framework with respect to global climate change research, enabling multivariate time series preparation, derivation of detailed information on significant trends and seasonality, as well as detection of causal links with minimal user intervention. This study is the first to use multivariate time series including several EO-based variables to analyze land surface dynamics over the last two decades using the causal discovery algorithm PCMCI.

Use of satellite based information in Snowmelt run-off studies

This paper aims at evolving a conceptual technique for the computation of water yield from the basin snow cover. It may serve as a useful information to compute the snowmelt driven run-off particularly in the lean summer season. For this purpose, the measurement of snow cover area in catchment of Satluj river using very high resolution imagery received from the meteorological satellite NOAA-9 was undertaken on selected dates during the periods, (i) October 1985 to May 1986, and (ii) January to June 1987. The computed snowmelt water yield have been compared with the available actual run-off data. The study shows that the satellite derived snow cover data are potentially useful in predicting the snowmelt run-off. The importance of this technique is further enhanced for the large watersheds over Himalayas where ground based measurements are too scanty.

Glacier Area and Snow Cover Changes in the Range System Surrounding Tarim from 2000 to 2020 Using Google Earth Engine

Glacier and snow are sensitive indicators of regional climate variability. In the early 21st century, glaciers in the West Kunlun and Pamir regions showed stable or even slightly positive mass budgets, and this is anomalous in a worldwide context of glacier recession. We studied the evolution of snow cover to understand whether it could explain the evolution of glacier area. In this study, we used the thresholding of the NDSI (Normalized Difference Snow Index) retrieved with MODIS data to extract annual glacier area and snow cover. We evaluated how the glacier trends related to snow cover area in five subregions in the Tarim Basin. The uncertainty in our retrievals was assessed by comparing MODIS results with the Landsat-5 TM in 2000 and Landsat-8 OLI in 2020 glacier delineation in five subregions. The glacier area in the Tarim Basin decreased by 1.32%/a during 2000–2020. The fastest reductions were in the East Tien Shan region, while the slowest relative reduction rate was observed in the West Tien Shan and Pamir, i.e., 0.69%/a and 1.08%/a, respectively, during 2000–2020. The relative glacier stability in Pamir may be related to the westerlies weather system, which dominates climate in this region. We studied the temporal variability of snow cover on different temporal scales. The analysis of the monthly snow cover showed that permanent snow can be reliably delineated in the months from July to September. During the summer months, the sequence of multiple snowfall and snowmelt events leads to intermittent snow cover, which was the key feature applied to discriminate snow and glacier.

Meteorological factors associated with July 2005 floods in river Jhelum

The meteorological conditions leading to the July, 2005 floods in river Jhelum in the state of Jammu & Kashmir have been analyzed in the present study. The floods coincided with a spell of heavy rains over the state during second week of July 2005 caused by the interaction of a westward moving monsoon disturbance over the plains of northwest India and an eastward moving trough in middle troposphere over north Pakistan. Further analysis of precipitation over the state during the preceding winter season shows that there was record snowfall at many stations over the state. The estimate from KALPANA-1 satellite also revealed the highest snow cover area over the region since 1998. The higher volume of snowmelt because of the increased snow cover area seems to have significantly contributed towards the floods.

Intercomparison of Partial Least Square Regression-based Feature Selection Techniques for Snow Cover Area Models

Snow cover area on a river basin, affects so many meteorologic and environmental parameters. By growing remote sensing technology, nowadays snow cover area could be measured on a regular basis for scientific purposes. In this study, the monthly average of snow cover area of the Baranduz river basin from West Azerbaijan in Iran had been used for modelling by ANN and SVM. The snow cover area was extracted from MODIS 8-day maximum snow extent products from 2000 to 2019. Also, the 20 meteorologic parameters were collected from Bibakran and Babarud ground hydrometeorological stations and 20 parameters were collected from satellite base data powered by NASA LaRC projects. After BoxCox transformation analysis, the feature selection methods were used to select the modelling subsets. Partial least square regression base filter and wrapper feature selection methods were used to select modelling subsets. LW, RC, SR, VIP, SMC, MRMR, JT filter methods and GA, MCUVE and REP wrapper methods were used to select the best parameters for modelling. By increasing the thresholds of the feature selection methods, the number of the selected parameters in subsets was decreased, and after a certain amount of thresholding value, the number of parameters was fixed in 10 variables. Selected subsets were being evaluated by multicollinearity indexes and by performances of the ANN and the SVM models. 80% of the data used for training models and 20% of the data used for testing the models. The accuracy of all models was high and acceptable but, in some subsets, there was a serious multicollinearity issue. However, the correlation between parameters was so high despite this, the PLSR base feature selection methods have been very successful in reducing a great amount of multicollinearity in selected subsets. Also, the ANN and SVM models have shown very high performance in modelling the monthly snow cover area.

Trends in Snow Cover Duration Across River Basins in High Mountain Asia From Daily Gap-Filled MODIS Fractional Snow Covered Area

High Mountain Asia (HMA) has the largest expanse of snow outside of the polar regions and it plays a critical role in climate and hydrology. In situ monitoring is rare due to terrain complexity and inaccessibility, making remote sensing the most practical way to understand snow patterns in HMA despite relatively short periods of record. Here, trends in snow cover duration were assessed using MODIS between 2002 and 2017 across the headwaters of the region’s primary river basins (Amu Darya, Brahmaputra, Ganges, Indus, and Syr Darya). Data limitations, associated with traditional binary mapping and data gaps due to clouds, were addressed with a daily, spatially and temporally complete, snow cover product that maps the fraction of snow in each pixel using spectral mixture analysis. Trends in fractional snow cover duration (fSCD) were calculated at the annual and monthly scale, and across 1,000 m elevation bands, and compared to trends in binary snow cover duration (SCD). Snow cover is present, on average, for 102 days across all basin headwaters, with the longest duration in western basins and shortest in eastern basins. Broadly, snow cover is in decline, which is most pronounced in elevation bands where snow is most likely to be present and most needed to sustain glaciers. Some of the strongest negative trends in fSCD were in the Syr Darya, which has 13 fewer days between 4,000–5,000 m, and Brahmaputra, which has 31 fewer days between 5,000–6,000 m. The only increasing tendency was found in the Indus between 2,000 and 5,000 m. There were differences between fSCD and SCD trends, due to SCD overestimating snow cover area relative to fSCD.

Variation of snow cover in the Nyang River basin of southeastern Tibetan Plateau, China

Snow cover is highly sensitive to global climate change and strongly influences the climate at global and regional scales. Because of limited in situ observations, snow cover dynamics in the Nyang River basin (NRB) have been examined in few studies. Five snow cover indices derived from observation and remote sensing data from 2000 to 2018 were used to investigate the spatial and temporal variation of snow cover in the NRB. There was clear seasonality in the snow cover throughout the entire basin. The maximum snow-covered area was 8,751.35 km2, about 50% of the total basin area, and occurred in March. The maximum snow depth (SD) was 5.35 cm and was found at the northern edge of the middle reaches of the basin. Snow cover frequency, SD, and fraction of snow cover area increased with elevation. The decrease in SD was the most marked in the elevation range of 5,000–6,000 m. Above 6,000 m, the snow water equivalent showed a slight upward trend. There was a significant negative correlation between snow cover and temperature. The results of this study could improve our understanding of changes in snow cover in the NRB from multivariate perspectives. It is better for water resources management.

Analysis of the Snow Cover Area of the Gangotri and Surrounding Glaciers using Remote Sensing and GIS

Himalayas has one in every of the biggest resources of snow and ice, which act as a freshwater reservoir for all of the rivers originating from it. Monitoring of these sources is vital for the assessment of availability of water within the Himalayan Rivers. The mapping of Glaciers could be very tough undertaking due to the inaccessibility and remoteness of the terrain. Faraway sensing techniques are regularly the simplest way to research glaciers in remote mountains and to monitor a large range of glaciers in multitemporal manner. This paper presents the results obtained from the analysis of 5 set of Landsat 8 Band 3 - Green and Band 6 - SWIR 1 images from year 2017 to 2021 for the monitoring and analysis of approx 76% of Gangotri and Surrounding Glaciers (GSG) main snow covered area. It is seen in the analysis that there has been a down fall around 85 sq km of the Snow Cover of the Gangotri and Surrounding Glacier and Surrounding Glaciers (GSG) Area in the years of 2018 and 2019 respectively from the year 2017. In 2020 huge recovery has occurred with a drastic increase in snow cover area by approximately the same amount which has been previously depleted. After 2020, it seems that a gradual drop of 27 sq km occurred in 2021. Calculation shows a dip of 14.91% of snow cover area from 2017 to 2018 of the Gangotri and Surrounding Glaciers (GSG) which was recovered to original level in 2020. Slight dip of around 4.88% occurred in the current year 2021.

Snow Cover Dynamics and its Spatio-Temporal Distribution in the Central Higher Himalaya of India

Retreating glaciers, changing timber line and decreasing accumulation of snow in the Himalaya are considered the indicators of climate change. In this study, an attempt is made to observe the snow cover change in the higher reaches of the Central Himalayas. Investigation of climate change through snow cover is very important to understand the impact and adaptation in an area. Landsat thematic and multi spectral optical data with a spatial resolution of 60m and 30m are considered for the estimation and extraction of snow cover. Total 3,369 Km2 snow cover area is lost since 1972 out of total geographical area i.e. 17,227 Km2. The accumulation of snow during winter is lower than the melting rate during summer. The current study identified the decrease of 19.6 % snow cover in 47 years since 1972 to 2019. Composite satellite imageries of September to December show that the major part of the study area covered with snow lies above 3600m. Overall observation indicates that in 47 years, permanent snow cover is decreasing in Central Himalayas.