Establishing a Collaborative Effort to Assess the Role of Glaciers and Seasonal Snow Cover in the Hydrology of the Mountains of High Asia (a.k.a. CHARIS)
R. Armstrong, M. Williams, A. Barrett, M. J. Brodzik, F. Fetterer, Siri Jodha S. Khalsa, A. Racoviteanu, A. Rasmussen, B. Raup, A. Wilson, U. Horodyskyj http://nsidc.org/charis 1
THE NATIONAL SNOW AND ICE DATA CENTER (NSIDC) UNIVERSITY OF COLORADO, BOULDER
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The National Snow and Ice Data Center‌ Manages and distributes scientific data
Researches the cryosphere and data science
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Supports data users
Provides tools for data access
Supports local and traditional knowledge
Educates the public about the cryosphere
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instaar.colorado.edu @instaar 4
• Conducts interdisciplinary (cryosphere, climate, hydrology, ecology and biochemistry) research in polar and alpine regions
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CHARIS Project Outline • • • • • •
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Geographic coverage Goals Asian Partners Data sources Methods Validation
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Geographic Coverage = “High Asia”
River Basins:
Brahmaputra Ganges Indus Amu Darya Syr Darya Partner Countries:
Bhutan Nepal India Pakistan Afghanistan Kazakhstan Tajikistan Kyrgyzstan Uzbekistan 6
HMGWP Workshop, Huaraz, Peru, July 2013
CHARIS Project Goals CHARIS: Contribution to High Asia Runoff from Ice and Snow • In collaboration with CHARIS’ Asian Partners, this project will make fundamental contributions to an improved understanding of the water resources of High Asia. • The amount, timing and spatial patterns of snow and ice melt are important in providing water for downstream irrigation, hydropower generation and general consumption.
• The specific goal is to perform a systematic assessment of the individual contributions of seasonal snow melt and glacier ice melt to water resources across the study region, using the best available data sources and methods. 7
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CHARIS Project Goals CHARIS: Contribution to High Asia Runoff from Ice and Snow
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Glacier ? Snow ? Rain? Groundwater ?
t
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Data Sources • Elevation: SRTM, SPOT, ASTER, Topographical Maps • Glacier coverage: GLIMS, MODICE • Seasonal snow cover: Gapfilled daily MODIS • Catchment basin outlines: GRDC, watershed analysis • River discharge data: GRDC and Project Partners • Reanalysis products (NASA MERRA, ECMWF ERA-Interim, NCEP CFSR): T, q, P • Surface Albedo: MODIS, in situ
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Digital Elevation Model (DEM) Evaluation • SRTM • Global 90m resolution • Voids common in mountainous terrain • Several groups have released using different strategies for void filling and hydrologic conditioning • Stereoscopy from satellite image pairs • Individual scenes (ASTER, SPOT, Corona. Etc.) • Global ASTER-based DEM • 30m resolution • Composite of many scenes • Automated, has undetected errors • ICESat data for vertical biases and horizontal offsets
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Elevation – GDEM2
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GDEM Hillshade
"mole runs"
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Elevation – GDEM2
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Elevation – CGIAR version of SRTM final
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Elevation – HydroSHEDs version of SRTM
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SRTM Voids
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Methods • Glaciers: In addition to using glacier outlines from the GLIMS database (http://nsidc.org/glims), we determine the annual residual coverage of ice across the study area using a consistent, automated remote sensing method, MODICE. • Seasonal snow cover: We generate daily gap-filled snow cover for the study region derived from the MODIS standard 500m snow product. • We estimate the separate contributions of seasonal snow melt and glacier ice melt to basin discharge across the study region using enhanced degree-day, ablation gradient, and other melt models. 17
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Annual snow cover frequency: 2001 to 2011 MODIS Tiles h23v05 h24v05 h25v06
Barrett, et al., American Geophysical Union Annual Fall Meeting, 2012 HMGWP Workshop, Huaraz, Peru, July 2013
Time series of snow covered area in select basins across HKH
Barrett, et al., American Geophysical Union Annual Fall Meeting, 2012
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Inter-annual variability of snow melt runoff Â
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Melt Model Validation • Compare regional scale results with small sub-basin studies based on local scale meteorological inputs and more sophisticated energy balance modeling approaches. • Compare regional scale assessments with results in selected sub-basins using isotopic and geochemical tracers in hydrologic mixing models; also estimate the contribution of groundwater and rainfall to these rivers using the same mixing models.
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Snow and Ice Albedo • Surface energy input, and thus melt rate, are functions of albedo • Can vary from .9 for new snow to .2 for old or dirty snow • Variability in region being investigated • MOD43 time series at locations across Himalaya • Spatial variations mapped with field spectroradiometer • Temporal variations monitored at a weather station on Ngozumpa Glacier in the Khumbu region o o o
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Time‐lapse camera Up and down-looking pyranometers Air temperature and humidity
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MOD43 Albedo time series - Eastern Himalaya
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MOD43 Albedo time series - Western Himalaya
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CHARIS Partner Workshop in Almaty – May 2013
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Summary • CHARIS is leading a trans-national effort to improve our understanding of regional water resources in High Asia. • While transferring knowledge and skills to Project Partners we also engage in joint research activities, such as cooperative field studies and model development. • We will freely share the tools and techniques that are developed by CHARIS.
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The CHARIS Team thanks you for your attention –
http://nsidc.org/charis
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