Planning for the Next Generation of Water Cycle Missions

1. Planning for the Next Generation of Water Cycle Missions Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington Chapman Conference on Remote Sensing of the Terrestrial Water Cycle Kona, Hawaii February 20, 2012

2. Outline of this talk Water cycle perspective What we can do now What will the next generation missions do? What’s missing

3. 1. Water cycle perspective

4. What we can do now:Precipitation

5. Comparison of seasonal cycle of satellite precipitation products over major global river basins, 2003-2006 From Sahoo et al, RSE 2011

6. Propagation of rainfall errors in streamflow simulations Visual courtesy Ming Pan, Princeton University

7. Evaluating TMPA-RT with TMPA-RP over gauged locations NORTH WEST SOUTHERN

8. TMPA research product (gauge-adjusted) and calibrated and uncalibrated real time (no gauge adjustment) products over three regions of Africa

9. Streamflow (Qs) No current remote sensor provides a basis for estimating streamflow (SWOT will) – currently has to be estimated by difference (or in situ, model, etc)

10. Groundwater flux (Qg) No current remote sensor; most estimates at the scale of large river basins are small relative to stream discharge, although estimation methods are indirect.

11. Evapotranspiration • Satellite-based methods are essentially indirect • Three general approaches: a) estimate sensible heat, get ET by difference from Rn-G; b) combination of vegetation index and radiometric temperature; c) use method like PM; estimate forcings from satellite (and other) sources • All methods requires some in situ data (typically wind, VPD) or many assumptions

12. Estimating the evaporative fraction (EF) over a diverse landscape - Temperature on vegetation: Tveg (S) - Incoming solar radiation: PAR (T) - radiative transfer of atmosphere(T) • VI-Ts diagram (S) Qveg Qbare EF = fveg EFveg + (1 –fveg)  EFbare QQ We want this! - Vegetation Index …NDVI or EVI (S) Note: (S) …. Derived from Satellite (T) …. Estimated theoretically Visual courtesy Steve Running

13. EstimatingEFbare VI-Ts diagram (Nemani & Running, 1989; 1993) satellite image Ts Tbare max Warm Edge Wind speed Tbare Window Tbare max – Tbare EFbare=  Tbare max – Tbare min Tveg=Tbare min Air temperature VI VImin VImax VI Qbare0– ET Tbare =  + Ta 4εσTa3 (1- CG) +  Cp/ra bare Visual courtesy Steve Running

14. KL04 Klamath, OR Evaluation of MODIS ET Bias: -7% for clear sky; -12% for all days from April 1 to October 31, 2004 Flux tower observed and satellite-based ETday at KL04 (an irrigated site) Tang et al. 2009 JGR Acknowledgement: flux tower data are from Oregon State University

15. Klamath Reclamation Project MODIS ET vs Landsat ET KL04 KL03 MODIS ET Landsat METRIC Irrigated Irrigated Bias: ~1 mm/day Non-irrigated METRIC: Mapping Evapotranspiration at high Resolution and with Internalized Calibration

16. ET Evaluation – AmeriFlux At 61 Sites ISCPP radiation is close to SRB radiation. ► MODIS/ISCPP ET is an acceptable approximation to MODIS/SRB ET. ▼

17. Scope of the problem: Initial Landflux ET comparisons (1993, total) 135 120 105 90 75 60 45 30 15 0 OXUNI MPI-BG GWSP MAUNI MERRA ISBA OBSPM NCEP WATCH PRUNI ERA JULES Remote sensing: OXUNI, MAUNI, OBSPR, PRUNI Fluxnet: MPI-BG Reanalysis: MEERA, NCEP, ERA Land Surface Models: GSWP, ISBA, WATCH, JULES (Figure from C. Jimenez)

18. Groundwater flux • No current remote sensor; most estimates at the scale of large river basins are small relative to stream discharge, although estimation methods are indirect.

19. Snow cover extent and water equivalent • SCE can be reasonably well observed from optical sensors (subject to cloud cover, land cover, and subgrid (partial coverage) constraints) • SWE remains elusive except for relatively thin cold snowpacks with minimal vegetation (esp. forest) and topography (typically wind, VPD) or many assumptions

20. Mean (1972-06) northern hemisphere snow cover, April-May-June, from NOAA satellite product (upper row) compared with model (lower row) from Shi et al, in review, J Clim

21. MODIS-based SCE estimates over Scandinavia, April 12, 2004 Comparison of (a) SnowStar operational SCE algorithm over Scandinavia with (b) MODIS MOD10_L2. Panels (c) and (d) are same as (a) and (b) except with composite cloud algorithm.

22. Partial snow cover issues

23. Snow water equivalent estimates (from SSM/I) over Canadian prairies From MSC

24. From Derksen et al, ESC, 2002

25. Lakes, wetlands, and manmade reservoirs • Altimetry for water surface elevation (large lakes and reservoirs only) • Extent via visible sensors • For reservoirs, gets ~15% of total storage • No coherent data set (yet) for natural lakes or wetlands

26. 4 Reservoir Surface Levels from Altimetry LEGOS: 36 USDA: 15 UW (T/P):20 Total: 62 T/P: Topex/Poseidon (1992-2002)

27. 9 Method: Level-Area Relationship Storage Estimation Vo = Vc – (Ac+Ao)(hc-ho)/2 Fort Peck Reservoir ho Ao Ao ho MODIS Vo = f(ho) or Vo = g(Ao) Variables at capacity from Global Reservoir and Dam database (Lehner et al., 2011) Altimetry

28. 12 Method: Storage Estimation Fort Peck Reservoir altimetry estimated MODIS smoothed MODIS estimated Vo=f(ho) Ao inferred from ho(Altimetry) Vo=g(Ao): ho inferred from Ao(MODIS)

29. Global reservoir storage product, 1992-2010

30. Soil moisture • Existing sensors provide estimates of upper few cm • Work best for low biomass (e.g., grasslands, not forest) • Topographic complications (and mixed pixels, e.g., open water) • Existing sensors not optimized for soil moisture (exception SMOS, products in process)

31. Remote sensing opportunities: 5. Soil moisture TMI soil moisture image for a single orbit visual courtesy Eric Wood

32. TMI Soil Moisture Time Series VIC 10 cm, 1/8th degree Mesonet 5 cm, point data TMI 30 0 Volumetric soil moisture rain (mm) ~0.5 cm, 38km Comparison of TMI soil moisture with Oklahoma Mesonet observations and VIC model output (average of 72 sites) visual courtesy Eric Wood

33. Glaciers and Ice Caps • Visible sensors provide basis for near-global inventory of glaciers and ice sheets (subject to possible confounding with seasonal snow cover, and debris cover) • Notwithstanding ICESAT and other satellite-based estimates of ice sheet surface topography change, in situ observations remain the primary basis for estimation of glacier and ice cap thickness (e.g. Meier et al., 2007)

34. GLIMS (Global Land Ice Measurement from Space) From Raup et al, GPC 2007

35. Groundwater • No current remote sensing-based estimate

36. Total water storage change (d/dt{Ssn + Ssm +Sg +Slw + Sgi]) • GRACE is the best (essentially only) option • Subject to relatively coarse spatial resolution, and some “leakage” issues near coasts

37. GRACE TWSC compared with model-derived estimate over the Mississippi River basin Estimates of Mississippi River basin terrestrial water storage from Variable Infiltration Capacity (VIC) model, and GRACE data. Blue and red shading indicate contribution of seasonal snowpack. from Lettenmaier and Famiglietti, 2006

38. So how well can we close the water budget based on RS alone over major river basins?

39. Approach (from Gao et al., IJRS, 2010) R (observed) ?=?P – ET – ∆S (remote sensing) Three products used for P, ET, ∆S respectively (3*3*3=27 ensemble members)

40. Major River Basins within the U.S. River outlets are marked by white circles

41. Comparison of inferred runoff (remotely sensed P - E + TWSC) with observed runoff over 9 USGS water resource regions from Gao et al.,IJRS 2010

42. GPM – Global Precipitation MeasurementSMAP – Soil Moisture Active PassiveSMOS – Soil Moisture and Ocean SalinityIceSAT2SWOT – Surface Water and Ocean TopographyGRACE-2CoreH2O Planned satellite missions

43. GPM Core Observatory • GPM=Global Precipitation Measurement mission (NASA/JAXA/CNES/ISRO/NOAA/EUMETSAT) • Dual-frequency Precipitation Radar (Ku/Ka-band) • 3-D measurements of precipitation over 125 and 254 km swaths • Multi-channel GPM Microwave Imager • 885-km swath measurements of heavy, moderate and light precipitation • Launch ~mid-2014 Source: http://pmm.nasa.gov/GPM

44. GPM Constellation Source: http://pmm.nasa.gov/GPM

45. SMAP • SMAP = Soil Moisture Active Passive (NASA) • Radiometer and a synthetic aperture radar operating at L-band (1.20-1.41 GHz) • 1000 km swath width • Launch ~2014 • Life time 3 years • Soil moisture and freeze/thaw state maps (3-36 km resolution) http://smap.jpl.nasa.gov/multimediagallery/

46. SMOS (Soil Moisture and Ocean Salinity) ESA mission Launched late 2009 L-band, passive resolution ~50 km revisit 1-3 days accuracy ~4% volumetric SM

47. SMOS Frozen soil depth and extent over Finland 4 days later

48. ICESat-2 • ICESat-2 = Ice, Cloud, and land Elevation Satellite-2 (NASA) • Laser altimeter (micro-pulse, multi-beam) • Launch ~2016 • Along-track sampling ~70 cm, 10-m ground footprints • 91-day repeat, with monthly sub-cycles http://icesat.gsfc.nasa.gov/

49. SWOT • SWOT= Surface Water and Ocean Topography (NASA/CNES) • Wide swath altimeter (KaRIN= Ka-band Radar Interferometer) • Launch ~2019 • Life time 3-5 years • 2 orbits: • Fast sampling phase: 3 day 78° orbit (during 3 months) • Nominal phase: 22 day 78° orbit • Water elevation maps (100m pix. siz.) 60 km 20 km 60 km

50. SWOT Lake Storage Change • 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