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Storage change science and algorithms

Storage change science and algorithms. Hyongki Lee 1 , Sylvain Biancamaria 2,3 , Michael Durand 4 , Hahn Chul Jung 1,4 , Doug Alsdorf 1,4 , C.K. Shum 1,4 , Yongwei Sheng 5 , Nelly Mognard 2. 1 School of Earth Sciences, Ohio State University 2 LEGOS

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Storage change science and algorithms

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  1. Storage change science and algorithms Hyongki Lee1, Sylvain Biancamaria2,3, Michael Durand4, Hahn Chul Jung1,4, Doug Alsdorf1,4, C.K. Shum1,4, Yongwei Sheng5, Nelly Mognard2 1 School of Earth Sciences, Ohio State University 2 LEGOS 3 Dpt. of Civil & Env. Eng., University of Washington 4 Byrd Polar Research Center, Ohio State University 5 Department of Geography, UCLA

  2. Global lake storage change seen by SWOT • SWOT storage change accuracy for Arctic lakes

  3. Global lakestorage change seen by SWOT • SWOT storage change accuracy for Arcticlakes

  4. Methodology • Purpose : • estimate a global relationship between lake area and lake storage change, • estimate % of storage change SWOT could see. • Global relationship between lake area (A) and the number of lake with this area (N) from a power law: N=α.Aβ(Downing et al., 2006). Number of lakes Lake area (km2)

  5. Methodology • Lake yearly water height variation (dh) (~200 lakes) CDF of observed lake water height variations Cumulative Density Function (CDF) Water height variation (m) Log-normal distribution CDF Observation Log-normal dist Water height variation (m) Lake/drainage area (km2) -> Lake water height variation follows a log-normal distribution • Total storage change (dSi) for lakes with area=Ai: - Ni = number of lakes with area Ai - dHi from log-normal distribution

  6. Results • Nadir altimeters miss more than 60% of lakes and can see area>100 km2 -> see only 15% of the global lake storage change • SWOT = global coverage and see area>250mx250m -> see 65% of the global lake storage change Current capabilities ~15% SWOT science requirement ~50% Cumulative lake storage change (%) SWOT science goal ~65% 10km x 10km 1km x 1km 250mx250m Lake area (km2) Biancamaria et al., IEEE JSTARS, 2010

  7. Global lakestorage change seen by SWOT • SWOT storage change accuracy for Arcticlakes

  8. Study Areas and satellite ground tracks Peace-Athabasca Delta (PAD): ENVISAT SWOT Credit: Tamlin Pavelsky Alaskan Lakes:

  9. Study Areas and satellite ground tracks West Siberian Lakes:

  10. Procedure to estimate SWOT storage change error 35-day repeat Envisat measurement High-frequency component from daily gauge data Daily hTruth SWOT orbit overlay Spatio-temporal sampling Water Mask Alake + N(0,σA) Add SWOT height error, N(0,σh) ΔhSWOT ASWOT “Worst” Case Scenario 50 m by 50 m pixel size 50 cm height accuracy ΔSSWOT

  11. Created “True” Height Anomaly Water elevation anomaly at Day 120 (or April 30, 2002) over PAD

  12. SWOT Ability to Observe Storage Change Results from PAD Height errors and areal errors vary with lake area

  13. SWOT Ability to Observe Storage Change Results from PAD

  14. SWOT Storage Change Error with Different Orbits

  15. Results • SWOT storage change accuracy is controlled by lake size. monthly error < 5 % for lakes larger than 1 km2 • ~20% for 1 hectare sized lakes • SWOT storage change measurements are relatively • insensitive to orbital inclination or orbital • repeat period. Lee et al., IJRS 2010

  16. Conclusions • More than 65% of yearly global storage change seen by SWOT. • SWOT storage accuracy: monthly errors < 5% for lakes > 1 km2.

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