||Assessment of Cryosphere Dynamics in Upper Indus Basin using Integration of Remote Sensing Data and Hydrological Model
||Department of Geomatics
Snow Cover Area
Upper Indus Basin
Terrestrial Water Storage
A major progress has been made for the assessment of cryosphere dynamics globally, however regional and basin scale response that differs from global response of cryosphere remain poorly understood. In this study, we present a comprehensive analysis of cryosphere dynamics in Upper Indus Basin (UIB) by using Spaceborn remote sensing satellites data along with hydrological model. The Indus River, which flows through China, India, and Pakistan, is mainly fed by melting snow and glaciers that are spread across the Hindukush, Karakoram and Himalaya (HKH) Mountains. The downstream population of the Indus Plain heavily relies on this water resource for drinking, irrigation, and hydropower generation. Therefore, its cryosphere dynamics and river runoff variability must be properly monitored. Gilgit Basin, the northwestern part of the Upper Indus Basin, is selected for studying cryosphere dynamics and its implications on river runoff. In this study, 8−day snow products (MOD10A2) of moderate resolution imaging Spectroradiometer, from 2001 to 2015 are selected to access the snow−covered area in the catchment. A non−parametric Mann–Kendall test and Sen’s slope are calculated to assess whether a significant trend exists in the Snow cover area (SCA) (%) time series data. Then, data from ground observatories for 1995–2013 are analyzed to demonstrate annual and seasonal signals in air temperature and precipitation. Results indicate that the annual and seasonal mean of SCA show a non−significant decreasing trend, but the autumn season shows a statistically significant decreasing SCA with a slope of−198.36 km2/year. The annual SCA shows a decreasing trend with the slope of−0.04 km2/year, however a sharp decline trend observed from 2010–2015. Seasonal analysis of SCA indicates that spring season has the greatest SCA compared to winter season with 59.15% SCA for winter, spring (65.33 %), summer (30.43%), and autumn (50.76%). The annual mean temperature and precipitation show an increasing trend with highest values of slope 0.05 °C/year and 14.98 mm/year, respectively. Furthermore, Pearson correlation coefficients are calculated for the hydro−meteorological data to demonstrate any possible relationship. The SCA is affirmed to have a highly negative correlation with mean temperature and runoff. Meanwhile, SCA has a very weak relation with precipitation data. The Pearson correlation coefficient between SCA and runoff is −0.82, which confirms that the Gilgit River runoff largely depends on the melting of snow cover rather than direct precipitation. The study indicates that the SCA slightly decreased for the study period, which depicts a possible impact of global warming on this mountainous region.
Gravity Recovery and Climate Experiment (GRACE) and satellite altimetry are suitable for the precise measurement of terrestrial water storage (TWS) and lake water level variations from space. In this study, two GRACE solutions, namely, spherical harmonics (SH) and mascon (MSC), are utilized with the Global Land Data Assimilation System (GLDAS) model to estimate the spatial and temporal variations of TWS in the UIB for the study period of January 2003 to December 2016. The TWS estimated by SH, MSC, and the GLDAS model are consistent and generally show negative trends of −4.47 ± 0.38 mm/year, −4.81 ± 0.49 mm/year, and −3.77 ± 0.46 mm/year, respectively. Moreover, we use the GLDAS model data to understand the roles of variations in land surface state variables (snow water equivalent (SWE), soil moisture, and canopy water storage) in enhancing or dissipating the TWS in the region. Results indicate that SWE, which has a significant contribution to GRACE TWS variability, is an important parameter. Spearman’s rank correlations are calculated to demonstrate the relationship of the GLDAS land surface state variables and the GRACE signals. A highly positive correlation between SWE with TWS is estimated by SH and MSC as 0.691and 0.649, respectively, indicating that the TWS signal is mainly reliant on snow water in the study region. The analysis of seasonal TWS indicates that TWS is high in spring and summer season while it is low in winter and autumn. In addition, the ground water storages estimated by SH and MSC solutions are nearly stable with slight increasing trends of 0.63 ± 0.48 mm/year and 0.29 ± 0.51 mm/year, respectively. Furthermore, we take advantage of the potential of satellite altimetry in measuring lake water level variations in Attabad Lake, and our result indicates that Crysot−2 SARin mode altimetry data can be used in estimating small water bodies accurately in the high mountainous region of the UIB. Our study indicates that the water level in the lake is decreasing. However, a sharp decrease in lake level was observed from 2011 to 2014, that is, −29.65 m possibly due to opening of spillway to reduced lake water level. Moreover, the climate indices data of El−Niño Southern Oscillation and Pacific Decadal Oscillation are analyzed to determine the influence of pacific climatic variability on TWS. The assessment of cryosphere dynamics in UIB probably has an importance for better management of water resource and forecasting of natural hazards.
TABLE OF CONTENTS
LIST OF FIGURES ix
LIST OF TABLES xi
LIST OF ABBREVIATIONS xii
1. CHAPTER−I INTRODUCTION 1
1.1. Background 1
1.2. Upper Indus Basin (UIB) 3
1.3. Theoretical Framework 5
1.3.1. Multi−Mission Satellite Observations 5
1.3.2. Hydrological Model Data 7
1.4. Objectives of the Study 8
1.5. Dissertation Outline 9
2. CHAPTER II: MODERATE RESOLUTION IMAGING SPECTRORADIOMETER (MODIS) FOR SNOW COVER AND HYDROLOGICAL CHARACTERISTICS OF THE GILGIT BASIN 11
2.1. Introduction 11
2.2. Materials and Methods 14
2.2.1. Gilgit River Basin 14
2.2.2. MODIS Snow Cover 17
2.2.3. Topographic Reference 20
2.2.4. SCA and In−Situ Data 21
2.3. Results 22
2.3.1. Snow Cover Dynamics 22
2.3.2. Climatic Variability Analysis 27
2.3.3. Correlation between Snow Dynamics, Climate Variables, and Streamflow 31
2.3.4. Hydrological Behavior of the Gilgit River Basin 34
2.4. Discussion 37
2.5. Chapter Summary 40
3. CHAPTER III: SPATIAL AND TEMPORAL VARIATIONS OF TERRESTRIAL WATER STORAGE IN UPPER INDUS BASIN USING GRACE MISSION AND SATELLITE ALTIMETRY 42
3.1. Introduction 42
3.2. Materials 45
3.2.1. GRACE Data 45
3.2.2. Cryosat−2 Altimetry Data 46
3.2.3. GLDAS Model Data 47
3.2.4. Tropical Rainfall Measuring Mission (TRMM) Data 47
3.2.5. ENSO and PDO index Data 47
3.2.6. In−situ Data 48
3.3. Methodology 48
3.3.1. GRACE Data Processing 48
3.3.2. Hydrological Model 50
3.3.3. Altimetry Data Processing 51
3.4. Results and Discussion 53
3.4.1. TWS Variations 53
3.4.2. Impact of Climate Variability on TWS Variation 57
3.4.3. Impact of Soil Moisture, Snow and Canopy Water on TWS 59
3.4.4. Spatial Variation in TWS 62
3.4.5. Ground Water Storage (GWS) Anomalies from GRACE 64
3.4.6. Lake Water Level Changes 65
3.4.7. Inter-annual and Decadal Changes and Pacific Climate Variability 69
3.5. Chapter Summary 71
4. CHAPTER−IV: CONCLUSIONS AND FUTURE WORK 73
5. REFERENCES 78
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