Hydrology Virtual Mission for WATERHM

1. Hydrology Virtual Mission for WATERHM • Doug Alsdorf, Ohio State U. • Mike Durand, Jim Hamski, Brian Kiel, Gina LeFavour, Jon Partsch • Dennis Lettenmaier, U. Washington • Kostas Andreadis, Liz Clark • Delwyn Moller, JPL

2. What is the VM? • Goal: to define the spatial and temporal trade-offs when measuring storage changes and discharge. • Goal accomplished by answering 3 key questions: • What are the spatial and temporal samplings of ΔS and Q required to accurately constrain weather and climate models? • Storage change plays a hydrologic role in many basins, but is knowing ΔS sufficient, or is Q also required to accurately constrain the water balance? • Can reach-to-reach discharge variations be accurately measured from space?

3. How will the VM work? • We will conduct the following tasks to address the 3 questions: • Various orbital tracks will be overlaid on a global VIC model-based mapping of ΔS and Q to determine the percentages of ΔS and Q that can potentially be sampled. Rather than only knowing the percentages of water bodies missed, this will demonstrate the percentages of ΔS and Q measured for each basin’s water balance as dictated by various orbits and sampling technologies. • VM-I creates model h surfaces with errors expected from a spaceborne technology. These simulated h values will be used to construct storage changes in three selected basins, and resulting ΔS values will be compared to VIC model supplied Q values (and Q values from Task 3). • Our initial SRTM-based Manning’s-method of estimating Amazon discharge will be expanded to other rivers and channel cross-sectional geometries will be investigated for constraining Q. An alternative method of estimating discharge will be constructed in a data assimilation of instrument simulated h surfaces (i.e., from Task 2 above). Spatial and temporal sampling resolutions and errors will be investigated in both Q methods. • The instrument simulator of VM-I will be enhanced (and applied in the Tasks 2 and 3) with layover identification, assessment of height accuracies as bounded by the use of the SRTM DEM for instrument calibration, and quantification of h averaging schemes.

4. ( ) Q2 ¶ ¶ z ¶ Q g + + ¶ t ¶ x A ¶ t 1 1 zw A A 2/3 ( ) zw n 2z+w ¶ Q ¶ h q - w = ( ) 1/2 ¶ x ¶ t ¶ h ¶ x The VM Concept Simple Moderate Q = z = (h-bathymetry) • The VM uses both model and real data, and uses complex and simple techniques. Complex = g(S0-Sf) S0 = bathymetric slope Sf = friction or energy slope, i.e., dh/dx Synthetic World Real World VIC LISFLOOD Instrument Simulator SRTM Altimetry InSAR Imagery • Use data assimilation to introduce “errors” (P, Z) and varying sampling resolutions to determine their impacts on estimating Q and measuring S. • Use Manning’s n approach to estimate Q and measure S. • Use DA to estimate Q • Manning’s equation, Continuity to estimate Q These are designed to assess Q from existing satellite measurements. Key: all parameters can be measured from space, but not depth (n is empirical).

5. We can measure width • Uses classification based on 30m Landsat data 69 meters Pavelsky & Smith, algorithm in review

6. What does SRTM tell us? • SRTM h accuracy is too coarse for reaches < 100s kms, but Q is not too bad! Q m3/s Observed SRTM Error Tupe 63100 62900 -0.3% Itapeua 74200 79800 7.6% Manacapuru 90500 84900 -6.2%

7. What does repeat-pass InSAR tell us? • Floodplains have complex patterns of water flow, use continuity to get flux

8. First Results from Data Assimilation • See next presentation on Ohio River data assimilation • Preliminary feasibility test shows successful estimation of discharge by assimilating satellite water surface elevations • Nominal 8 day overpass frequency gives best results; effect of updating largely lost by ~ 16 days • Results are exploratory and cannot be assumed to be general -- additional experiments with more realistic hydrodynamic model errors (Manning’s coefficient, channel width etc), hydrologic model errors, and more topographically complex basins (e.g. Amazon River) are needed. • Assumption that “truth” and filter models (both hydrologic and hydrodynamic) are identical needs to be investigated