scholarly journals INVESTIGATION OF CRYOSPHERE DYNAMICS VARIATIONS IN THE UPPER INDUS BASIN USING REMOTE SENSING AND GIS

2020 ◽  
Vol XLIV-3/W1-2020 ◽  
pp. 59-63
Author(s):  
J. Iqbal ◽  
M. Ali ◽  
A. Ali ◽  
D. Raza ◽  
F. Bashir ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Mass Balance ◽  
Snow Cover ◽  
Landsat Imagery ◽  
Low Cost ◽  
Melting Rate ◽  
Indus Basin ◽  
Upper Indus Basin ◽  
Snow Mass

Abstract. Glaciers are storehouses for freshwater. Glaciers Monitoring is one of the most important research areas especially when climate change has been accelerated snowmelt process. The major goal of research was to find snow cover trend for glaciated regions of Pakistan followed by estimation of snow mass balance. The area chosen for it was Upper Indus basin, which includes ranges of Hindukush, Karakoram and Himalayas extended in Pakistan, India and China. This region exhibits high topographic relief and climate change variability. Snow cover trend analysis was performed for eleven years ranging from 2004 to 2014 using Moderate Resolution Imaging Spectroradiometer (MODIS) data imagery product with daily temporal resolution. These results were combined with respective year’s average monthly temperature. Further quantitative analysis was performed to relate presence of greater vegetation as an indication of greater snowmelt using Landsat Imagery for these years. Snow mass balance curves reveal that glaciers are regaining their mass balance after losing mass balance in middle of last decade. In addition to that, only freely available data is used for this study. This purpose behind this approach is to prove RS and GIS has an effective and low-cost tool for snow cover monitoring, also mass balance calculations. Continuous monitoring of snow cover dynamics is effective for prediction and mitigation of hazards associated with areas in proximity of glaciated regions. One common hazard is glacial lake outburst phenomenon, which cause severe flash flooding in downstream areas. Year 2004 has the lowest mass snow balance and 2014 has the highest snow mass balance. These different parameters were analysed and results show that snow start melting in months of May and June and faster melting rate observed in months of July and August. With the advancement in computing technologies, it has been easier for computers to handle and manipulate massive datasets. Remote sensing has proved to be an excellent tool for extraction of data from glaciers, snow and oceans for remote areas. In particular, snow cover/snowmelt can tell us continuously changing melting patterns, which helps concerned authorities to take necessary measures for preserving these storehouses of water and to mitigate effect of global warming.

scholarly journals Addressing Climate Change Risks Influencing Cryosphere-Fed Kuhl Irrigation System in the Upper Indus Basin of Pakistan

2020 ◽  
Vol 9 (2) ◽  
pp. 184-203
Author(s):  
Arshad Ashraf ◽  
Ghani Akbar
Keyword(s):  
Climate Change ◽  
High Risk ◽  
Irrigation System ◽  
Climate Change Impacts ◽  
River Basins ◽  
Snow Avalanche ◽  
Indus Basin ◽  
Irrigated Agriculture ◽  
Lake Area ◽  
Upper Indus Basin

Cryosphere-fed kuhl irrigation system forms a major lifeline for agriculture and livelihood development in the Himalayan region. The system is highly vulnerable to climate change impacts like glacier retreat, glacial lake outburst floods, snow avalanches and landslides especially in the upper Indus Basin (UIB). It is necessary to conduct reassessment of climate change impacts and find coping strategies for sustainable agriculture development in this mountainous region. In the present study, risks of glacier depletion , lakes outburst flood, snow avalanche and landslide hazards impacting cryosphere-fed kuhl irrigation system in 10 river basins of the UIB of Pakistan were analyzed using multi-hazard indexing approach. High risk of glacier depletion was observed in the Astore and Swat river basins likely because of the combined effect of reduced snow precipitation and rising warm temperatures in these basins. The risk of expansion in aggregate lake area was high in the Indus sub-basin, moderate in the five basins (i.e., Hunza, Shigar, Shyok, Shingo and Astore), while it was low in the four basins (i.e., Swat, Chitral, Gilgit and Jhelum). More than 2% areas of Hunza and Shigar basins in the Karakoram range exhibited high risk of snow avalanche and landslide (SAL) hazard, while moderate SAL hazard was found in >40% areas of Chitral, Gilgit, Hunza and Shigar river basins. An effective early warning mechanism and provision of adequate resources for preparedness are essential to cope with negative impacts of climate change on irrigated agriculture in this region in future.

scholarly journals Comparative Assessment of Spatial Variability and Trends of Flows and Sediments under the Impact of Climate Change in the Upper Indus Basin

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 730
Author(s):  
Waqas Ul Hussan ◽  
Muhammad Khurram Shahzad ◽  
Frank Seidel ◽  
Anna Costa ◽  
Franz Nestmann
Keyword(s):  
Climate Change ◽  
Power Plants ◽  
Kendall Test ◽  
Comparative Assessment ◽  
Indus Basin ◽  
Hydroelectric Power ◽  
Indus River ◽  
Impact Of Climate Change ◽  
Upper Indus Basin ◽  
The Impact

Extensive research of the variability of flows under the impact of climate change has been conducted for the Upper Indus Basin (UIB). However, limited literature is available on the spatial distribution and trends of suspended sediment concentrations (SSC) in the sub-basins of UIB. This study covers the comparative assessment of flows and SSC trends measured at 13 stations in the UIB along with the variability of precipitation and temperatures possibly due to climate change for the past three decades. In the course of this period, the country’s largest reservoir, Tarbela, on the Indus River was depleted rapidly due to heavy sediment influx from the UIB. Sediment management of existing storage and future planned hydraulic structures (to tap 30,000 MW in the region) depends on the correct assessment of SSC, their variation patterns, and trends. In this study, the SSC trends are determined along with trends of discharges, precipitation, and temperatures using the non-parametric Mann–Kendall test and Sen’s slope estimator. The results reveal that the annual flows and SSC are in a balanced state for the Indus River at Besham Qila, whereas the SSC are significantly reduced ranging from 18.56%–28.20% per decade in the rivers of Gilgit at Alam Bridge, Indus at Kachura, and Brandu at Daggar. The SSC significantly increase ranging from 20.08%–40.72% per decade in the winter together with a significant increase of average air temperature. During summers, the SSC are decreased significantly ranging from 18.63%–27.79% per decade along with flows in the Hindukush and Western–Karakorum regions, which is partly due to the Karakorum climate anomaly, and in rainfall-dominated basins due to rainfall reduction. In Himalayan regions, the SSC are generally increased slightly during summers. These findings will be helpful for understanding the sediment trends associated with flow, precipitation, and temperature variations, and may be used for the operational management of current reservoirs and the design of several hydroelectric power plants that are planned for construction in the UIB.

scholarly journals Predicting hydrologic responses to climate changes in highly glacierized and mountainous region Upper Indus Basin

2020 ◽  
Vol 7 (8) ◽  
pp. 191957 ◽  
Author(s):  
Muhammad Izhar Shah ◽  
Asif Khan ◽  
Tahir Ali Akbar ◽  
Quazi K. Hassan ◽  
Asim Jahangir Khan ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Climate Model ◽  
Assessment Tool ◽  
Model Simulation ◽  
Global Climate ◽  
Meteorological Data ◽  
Swat Model ◽  
Indus Basin ◽  
Upper Indus Basin

The Upper Indus Basin (UIB) is a major source of supplying water to different areas because of snow and glaciers melt and is also enduring the regional impacts of global climate change. The expected changes in temperature, precipitation and snowmelt could be reasons for further escalation of the problem. Therefore, estimation of hydrological processes is critical for UIB. The objectives of this paper were to estimate the impacts of climate change on water resources and future projection for surface water under different climatic scenarios using soil and water assessment tool (SWAT). The methodology includes: (i) development of SWAT model using land cover, soil and meteorological data; (ii) calibration of the model using daily flow data from 1978 to 1993; (iii) model validation for the time 1994–2003; (iv) bias correction of regional climate model (RCM), and (v) utilization of bias-corrected RCM for future assessment under representative concentration pathways RCP4.5 and RCP8.5 for mid (2041–2070) and late century (2071–2100). The results of the study revealed a strong correlation between simulated and observed flow with R 2 and Nash–Sutcliff efficiency (NSE) equal to 0.85 each for daily flow. For validation, R 2 and NSE were found to be 0.84 and 0.80, respectively. Compared to baseline period (1976–2005), the result of RCM showed an increase in temperature ranging from 2.36°C to 3.50°C and 2.92°C to 5.23°C for RCP4.5 and RCP8.5 respectively, till the end of the twenty-first century. Likewise, the increase in annual average precipitation is 2.4% to 2.5% and 6.0% to 4.6% (mid to late century) under RCP4.5 and RCP8.5, respectively. The model simulation results for RCP4.5 showed increase in flow by 19.24% and 16.78% for mid and late century, respectively. For RCP8.5, the increase in flow is 20.13% and 15.86% during mid and late century, respectively. The model was more sensitive towards available moisture and snowmelt parameters. Thus, SWAT model could be used as effective tool for climate change valuation and for sustainable management of water resources in future.

A Study on Flood Forecasting in the Upper Indus Basin Considering Snow and Glacier Meltwater

2017 ◽  
Vol 12 (4) ◽  
pp. 793-805 ◽  
Author(s):  
Tong Liu ◽  
Morimasa Tsuda ◽  
Yoichi Iwami ◽  
◽  
Keyword(s):  
Snow Cover ◽  
Monsoon Season ◽  
Indus Basin ◽  
Maximum Magnitude ◽  
Upper Indus Basin ◽  
Moderate Resolution ◽  
Daily Discharge ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Tarbela Dam

This study considered glacier and snow meltwater by using the degree–day method with ground-based air temperature and fractional glacier/snow cover to simulate discharge at Skardu, Partab Bridge (P. Bridge), and Tarbela Dam in the Upper Indus Basin during the monsoon season, from the middle of June to the end of September. The optimum parameter set was determined and validated in 2010 and 2012. The simulated discharge with glaciermelt and snowmelt could capture the variations of the observed discharge in terms of peak volume and timing, particularly in the early monsoon season. The Moderate Resolution Imaging Spectroradiometer (MODIS) daily and eight-day snow cover products were applied and recommended with proper settings for application. This study also investigated the simulations with snow packs instead of daily snow cover, which was found to approach the maximum magnitude of observed discharge even from the uppermost station, Skardu.This study estimated the glacier and snow meltwater contribution at Skardu, Partab Bridge, and Tarbela as 43.2–65.2%, 22.0–29.3%, and 6.3–19.9% of average daily discharge during the monsoon season, respectively. In addition, this study evaluated the main source of simulation discrepancies and concluded that the methodology proposed in the study worked well with proper precipitation.

scholarly journals Snow depth derived from passive microwave remote-sensing data in China

2008 ◽  
Vol 49 ◽  
pp. 145-154 ◽  
Author(s):  
Tao Che ◽  
Xin Li ◽  
Rui Jin ◽  
Richard Armstrong ◽  
Tingjun Zhang
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Snow Depth ◽  
Temporal Distribution ◽  
Remote Sensing Data ◽  
Passive Microwave ◽  
Spatial And Temporal Distribution ◽  
Sensing Data ◽  
Seasonal Snow ◽  
Snow Mass

AbstractIn this study, we report on the spatial and temporal distribution of seasonal snow depth derived from passive microwave satellite remote-sensing data (e.g. SMMR from 1978 to 1987 and SMM/ I from 1987 to 2006) in China. We first modified the Chang algorithm and then validated it using meteorological observation data, considering the influences from vegetation, wet snow, precipitation, cold desert and frozen ground. Furthermore, the modified algorithm is dynamically adjusted based on the seasonal variation of grain size and snow density. Snow-depth distribution is indirectly validated by MODIS snow-cover products by comparing the snow-extent area from this work. The final snow-depth datasets from 1978 to 2006 show that the interannual snow-depth variation is very significant. The spatial and temporal distribution of snow depth is illustrated and discussed, including the steady snow-cover regions in China and snow-mass trend in these regions. Though the areal extent of seasonal snow cover in the Northern Hemisphere indicates a weak decrease over a long period, there is no clear trend in change of snow-cover area extent in China. However, snow mass over the Qinghai–Tibetan Plateau and northwestern China has increased, while it has weakly decreased in northeastern China. Overall, snow depth in China during the past three decades shows significant interannual variation, with a weak increasing trend.

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