Advances in hydrology using remote sensing

1. Advances in hydrology using remote sensing Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington Presentation to NRC Committee on Scientific Accomplishments of Earth Observations from Space Washington D.C. January 24, 2007

2. Question: is the premise (of earth discovery from space) correct, or is what we do from space (mostly) monitoring? Example: (from NY Times): "NASA will soon launch a fleet of five spacecraft in hopes of solving the mystery of how the greenish auroras above the Earths poles suddenly burst into shimmering multicolor lights. NASA will launch five identical satellites, above, to look for what causes the green glow of the northern and southern lights to burst suddenly into pulsing color. The quintet of identical satellites, NASAs first attempt to launch so many satellites on a single rocket, will be positioned in orbits inside the magnetic field surrounding Earth to look for the origin of sudden energy outbursts that enliven the northern and southern lights. The space probes are part of a mission called Themis, short for Time History of Events and Macroscale Interactions during Substorms, which is designed to find the trigger point of geomagnetic substorms that can spring up within minutes to brighten auroras and release bursts of potentially damaging radiation."

3. Charging ahead nonetheless … Boundary conditions: • Land hydrology only (noting that many related areas have important implications for hydrology, such as those below) • Ephemeral snow included, but not glaciers and ice sheets • Land cover (change) only as it directly affects hydrologic processes • Land surface topography not included • Precipitation at the land surface (but not precipitation processes) included

4. Given these boundaries, list of major breakthroughs in hydrology is a short one: • Early interest in remote sensing in hydrology lagged somewhat behind other fields, in part due to applications heritage (e.g., land cover) • Perhaps due to this history, there has never been a satellite mission dedicated to hydrology (Hydros/SMOS come closest)

5. Examples of “discoveries” that would (might) not have occurred without remote sensing • Receding Arctic lakes • CO2 evasion estimates for inundated areas (based on JERS imagery for the Amazon) • Seasonal and interannual variations in surface and subsurface storage from GRACE

6. Example 1: Disappearing Arctic lakes Inventory of ~10,000 large Siberian lakes (1973-1998) reveals lake growth in continuous permafrost but disappearance in discontinuous, isolated and sporadic permafrost (“Disappearing Arctic Lakes,” Smith et al., Science, 2005)

7. 1973 2002 http://earthobservatory.nasa.gov/Newsroom Visual courtesy Larry Smith

8. Lakes would not disappear altogether, but would be substantially reduced: Within all glaciated/lowland terrain (including non-permafrost, ~27M km2): Lake count reduces from 191,583 to 102,844 (-46%) and total inundation area reduces from 560,205 to 324,812 km2 (-42%) Within all current permafrost-influenced terrain (~13M km2):Lake count reduces from 140,500 to 51,761 (-63%), and total inundation area reduces from 396,231 to 160,838 km2 (-59%).

9. Example 2: CO2 evasion from Amazon surface water (visuals courtesy J. Richey)

10. 1.77 x 106 km2 3 0 ~ 1.2 Mg C ha-1 y-1 (basin ~.5 Gt y-1) 25 T (>100m) 2 5 % 20 15 2 0 10 CO2 Evasion (Tg C mo-1) 1 5 1 0 MF S (<100 m) Richey et al 2002 5 MC 0 J F M A M J J A S O N D CO2 “Outgassing” from Waters of the Central Amazon Richey et al. Nature 2002

11. “…that leaks out of the forest in forms that are not usually measured, … Although this C leakage is thought to be relatively small, surprises cannot be ruled out.” Malhi and Grace (2000) VOC (??) CO2 Outgassing: ~ 1. 2 Mg C ha-1 y-1 10x Amazon Mainstem 15 Tg y-1 TOC Export (to the Sea) Global .4 - .8 Pg y-1 Amazon 36 Tg y-1 (.06 Mg C ha-1 y-1) Dissolved C ARE THE HUMID TROPICS A SOURCE OR SINK OF C? Uptake (Towers) ~ 6 Mg C ha-1 y-1 Deforestation 1-2 Pg y-1(?) Biomass Accumulation (long-term plots) .7 ± .4 Soil Accumulation (long-term plots) .6 ± .3

12. Example 3: Large river basin interseasonal and interannual moisture storage dynamics From Crowley et al, GRL, 2006

13. Mississippi River total moisture storage seasonal and interannual variations Source: Lettenmaier and Famiglietti, 2006

14. Other variables and applications – land surface water budget at the scale of continental river basins (o(500,000 km2)) • Precipitation • Evapotranspiration • Streamflow • Storage terms: • Soil moisture change • Snow water change • Groundwater change • Lake, wetland, reservoir change • Ignore for these purposes glaciers and ice sheets

15. Precipitation • IR-based (typically geostationary) methods • Passive microwave (various satellites) • Radar (TRMM) • Various composite approaches

16. From Rudolf and Rubel, 2000

17. TRMM 3B42 precipitation product evaluation, La Plata basin Rrmse=60% Rrmse=13% Rrmse=134% Rrmse=15% Rrmse=44% Scattergramsof daily basin-averaged precipitation estimated from gauged and TRMM data.

18. Monthly Basin-averaged Precipitation Scattergrams of monthly basin-averaged precipitation estimated from gauged and TRMM data.

19. Daily time series of precipitation for basin 3861 1998 1999 mm/dy mm/dy 1 2 3 1 2 3 4 5 6 4 5 6 7 8 9 7 8 9 10 11 12 10 11 12 Month Month Gauged TRMM

20. Relative RMSE in daily precipitation from a TRMM-like radar averaged over 2500 km2 for 1, 3, and 6-hour overpasses (from Nijssen and Lettenmaier, 2004, based on error model of Steiner et al, 2003)

21. Evapotranspiration • Satellite-based methods are essentially indirect • Two general approaches: a) estimate sensible heat, get ET by difference from Rn-G; b) combination of vegetation index and radiometric temperature • All methods requires some in situ data (typically wind, VPD) or many assumptions

22. 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

23. 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

24. Visual courtesy Steve Running Only NDVI Full algorithm Validation for each landcover type (surface data required) Test of simplified algorithm (no surface data)

25. River discharge • Satellite altimetry-based methods can give good (errors within several cm) river stage measurements • Problem is need for rating curve (which requires either river velocity measurements, or in situ data), and river width • Other complications include footprint (large for ocean altimeters, but susceptible to reduction), repeat interval, river orientation relative to satellite track • Altimetry methods are applicable to storage estimation as well (discussed later)

26. TOPEX-POSEIDON altimetry applications to the Amazon (from Birkett et al, JGR, 2002)

27. River Velocity & Width & Slope Measurements Concept by Ernesto Rodriguez of JPL Measure -Doppler Velocity Measure Topography Example of measurement of the radial component of surface velocity using along-track interferometry Measure +Doppler Velocity Basic configuration of the satellite

28. Soil moisture • Relatively long wavelengths (L band) are needed to avoid confounding with vegetation moisture • Observation depth a few cm (hence need for indirect methods to estimate total column moisture) • Wavelengths of existing sensors are all suboptimal (too short) for soil moisture applications, but some (AMSR, TMI) have some promise in areas of sparse vegetation • Spatial resolution is relatively coarse for all existing and planned sensors, although passive/active approaches may provide subpixel information

30. Washita’92 • Long term-large scale-hydrologic focus • TB sequence and soil moisture product • Demonstration of mapping and ESTAR • Soil moisture products-Infiltration, scaling, geostatistics

31. Area-averaged ESTAR and in situ near-surface soil moisture in Oklahoma, SGP 97 and 99 (from Jackson et al, 1999 and Guha et al, 2003) SGP 97 SGP 99

32. Satellite vs. model surface soil moisture: Variability (79-87) (courtesy Rolf Reichle and Randy Koster) Global patterns similar (R2=0.30 and 0.35), but absolute numbers very different.

33. Snow (water equivalent) • Snow spatial extent is amenable to visible band sensors (e.g. AVHRR, MODIS) subject to cloud cover • SWE retrieval algorithms based on passive microwave sensors (e.g. SSM/I 18 & 37 GHz bands) have been demonstrated, and are used operationally over Canadian prairies • Passive microwave algorithms are limited by sensitivity to liquid water in snowpack, and grain size dependence • Algorithms work best with relatively homogeneous vegetation and topography, and thin to moderate depth snowpacks

34. From MSC

35. From Derksen et al, ESC, 2002

36. From Derksen et al, ESC, 2002

37. Lake and reservoir storage • Lake and reservoir (and to lesser extent wetland) Stage is well suited to altimetry measurements (existing technology) • Signal processing currently limits to large lakes (> 1-10 km2) • Storage change (vs stage) has received less attention, but should be suitable for estimation via various visible and SAR instruments • Extension to smaller scales via scaling arguments could be pursued.

38. Lake levels of large African Lakes from TOPEX/POSEIDON and JASON (from www.aviso.oceanobs.com) Lake Victoria

39. Groundwater • No known method of observing directly via R/S • Microgravity sensors (e.g. GRACE) provide basis for estimating total mass variations (consisting mostly of water) at more or less monthly intervals • Groundwater storage change can perhaps be recovered by difference (after removing variations in atmospheric moisture, soil moisture, snow, surface water). • Spatial scale is large for small errors (o(105-106 km2 for ~ 1 cm error), but geometry can be more or less arbitrary (i.e., need not be approximated by coarse grid mesh)

40. Simulated GRACE recoveries of total water storage for Mississippi and Ohio River basins (from Swenson et al, 2003)

41. The potential (and obstacles)? • Precipitation – GPM/constellation approach is constrained by sampling error– progress may well require assimilation approaches • ET – some improvements possible but observation is very indirect, and susceptible to accumulated errors from various modeling steps • Stream discharge: Most likely will be via assimilation of observed water surface elevation into hydrodynamics models • Soil moisture: Retrieving the profile from near-surface obs (inevitably involves modeling) • Snow: What to do about wet snow, deep snow, and sensitivity to microstructure? • Groundwater: Will GRACE 2 substantially improve spatial resolution? • Lakes and reservoirs: Large ones can be estimated with existing altimetry, smaller ones will require swath altimetry

42. Other thoughts • Remote sensing is not an option, it is the only viable option if we want to produce coherent estimates of the global/continental water balance • As a basic premise – we should attempt to measure all the terms in the water balance as directly as possible (i.e., not relying on data assimilation or closure) • For these (continental) applications, spatial resolution is not an issue, but accuracy is! • For some variables (e.g. P) with well developed in situ networks, R/S solutions will have the greatest payoff in the underdeveloped world. • Importance of a community supporting the scientific basis and need for measurements