Annualmeeting:2017 CSDMS meeting-070: Difference between revisions
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{{CSDMS meeting personal information template-2014 | {{CSDMS meeting personal information template-2014 | ||
|CSDMS meeting first name= | |CSDMS meeting first name=Prashant | ||
|CSDMS meeting last name= | |CSDMS meeting last name=Baral | ||
|CSDMS meeting institute= | |CSDMS meeting institute=NIIT University | ||
|CSDMS meeting city= | |CSDMS meeting city=Neemrana, Alwar, Rajasthan | ||
|CSDMS meeting country= | |CSDMS meeting country=India | ||
|CSDMS meeting email address=prashant.baral@st.niituniversity.in | |||
|CSDMS meeting email address= | |CSDMS meeting phone=00919784019298 | ||
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|CSDMS_meeting_select_clinics2= | |CSDMS_meeting_select_clinics2=1) Modeling Permafrost toolbox | ||
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{{CSDMS meeting select clinics3 | {{CSDMS meeting select clinics3 | ||
|CSDMS_meeting_select_clinics3= | |CSDMS_meeting_select_clinics3=1) Parflow groundwater modeling | ||
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|CSDMS meeting scholarships= | |CSDMS meeting scholarships=Yes | ||
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|CSDMS meeting abstract submit= | |CSDMS meeting abstract submit=Yes | ||
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|CSDMS meeting abstract title=Understanding the current state and predictable future changes in the state of permafrost distribution in North-Western Himalayas, India | |||
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|CSDMS meeting coauthor first name abstract=M.Anul | |||
|CSDMS meeting coauthor last name abstract=Haq | |||
|CSDMS meeting coauthor institute / Organization=NIIT University | |||
|CSDMS meeting coauthor town-city=Neemrana, Alwar, Rajasthan | |||
|CSDMS meeting coauthor country=India | |||
|CSDMS meeting coauthor email address=MohdAnul.Haq@niituniversity.in | |||
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|CSDMS meeting abstract=The impacts of climate change on extent of permafrost degradation in the Himalayas are not well understood due to lack of historical ground-based observations. The area of permafrost exceeds that of glaciers in almost all Hindu Kush Himalayan (HKH) countries. However, very little is known about permafrost in the region as only a few local measurements have been conducted which is not sufficient to produce the fundamental level of knowledge of the spatial existence of permafrost. We intend to simulate permafrost conditions in Western Himalayas in India using Hyperspectral and Microwave remote sensing methods and computational models for the quantitative assessment of the current state of permafrost and the predictions of the extent and impacts of future changes. We also aim to identify the strength and limitations of remotely sensed data sets when they are applied together with data from other sources for permafrost modelling. We look forward to modelling ground temperatures using remote sensing data and reanalysis products as input data on a regional scale and support our analysis with measured in situ data of ground temperatures. Overall, we approach to model the current state and predictable future changes in the state of permafrost in Western Himalayas and also couple our results with similar research outcomes in atmospheric sciences, glaciology, and hydrology in the region. | |||
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Revision as of 17:17, 9 March 2017
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Understanding the current state and predictable future changes in the state of permafrost distribution in North-Western Himalayas, India
[[Image:|300px|right|link=File:]]The impacts of climate change on extent of permafrost degradation in the Himalayas are not well understood due to lack of historical ground-based observations. The area of permafrost exceeds that of glaciers in almost all Hindu Kush Himalayan (HKH) countries. However, very little is known about permafrost in the region as only a few local measurements have been conducted which is not sufficient to produce the fundamental level of knowledge of the spatial existence of permafrost. We intend to simulate permafrost conditions in Western Himalayas in India using Hyperspectral and Microwave remote sensing methods and computational models for the quantitative assessment of the current state of permafrost and the predictions of the extent and impacts of future changes. We also aim to identify the strength and limitations of remotely sensed data sets when they are applied together with data from other sources for permafrost modelling. We look forward to modelling ground temperatures using remote sensing data and reanalysis products as input data on a regional scale and support our analysis with measured in situ data of ground temperatures. Overall, we approach to model the current state and predictable future changes in the state of permafrost in Western Himalayas and also couple our results with similar research outcomes in atmospheric sciences, glaciology, and hydrology in the region.
