1. How will SWOT observations inform hydrology models?
Eric Martin and Kostas Andreadis
SWOT Science Definition Team Meeting
17-20 June 2014, Toulouse

2. Background
Models developed with motivation of understanding the water cycle
Budget closure
Reproducing variability of processes
Impact and sensitivity studies
Applications of water resources models can range
Long-term re-analyses
Prediction and forecasting at various scales (seasonal to decadal)
Earth system modeling components for climate change simulations
Some disparity among models that depends on specific application
Number of deficiencies that complicates reconciling models with observations
Trade-offs between resolving processes and model area size

3. Global & regional water balance
Ability to close the water balance as a metric of model improvement
How well do models perform over regional and global scales?
What can we currently observe from remote sensing?

4. Observing each water balance term
TRMM, GPM
MODIS
GRACE
SWOT
JASON, SENTINEL-3
SARAL, MODIS
SMOS, SMAP
Uncertainties exist in each satellite observation
retrieval algorithms, representativeness of observations
Observations of individual components do not close the water budget

5. Can models close the water budget?
Models don’t agree with each other
even when forced by identical data
Example comparison of runoff from PILPS-2

6. Role of river discharge
Discharge acts as a basin-wide “integrator” of water fluxes
Discharge dynamics vary both spatially and temporally
Extensively used to calibrate & validate hydrology models
Level 2.5 data product from SWOT
Indirectly estimated from SWOT observables
Number of candidate “direct estimation” algorithms
Instantaneous estimates at a reach-averaged scale
Through data assimilation into river hydraulic models
Potentially continuous estimates across river network and between observation times

7. State-of-the-art hydrology models
Most large-scale hydrology models are essentially column models
Flow routing schemes are rather simplified

8. Hydrodynamic models
Water levels are usually not represented in hydrology models
Need hydrodynamic models to accurately simulate processes in the river and floodplain
Few models can be used to simulate large areas
Downscaling formulations could allow transforming 1-km to <100-m scales
Example of downscaling

9. What can SWOT improve? SWOT will provide observations of
Water storage changes in lakes and reservoirs
Water inundation and surface elevation
River discharge
Overview of improvements envisioned by SWOT
Model calibration and validation
Modeling and delineating lakes and wetlands
Deriving information on reservoir operations
Indirect estimation of water budget components (e.g. precipitation)
Forecasting using either hydrologic or hydrodynamic models
As with every novel type of observation, there will probably be improvements in models that are not included here
GRACE is a favorite example, especially in hydrology

10. At what scales can SWOT be valuable?
Spatial resolution of discharge observations will vary but on the order of few kilometers
Should be adequate for current state-of-the-art hydrologic models
Hydrodynamic models usually have finer spatial resolution
SWOT will only observe rivers wider than m
SWOT will provide observations at varying temporal frequencies (~7-10 days)
Should be adequate for model calibration
It will be difficult to observe faster processes (e.g. flood wave propagation for most rivers)
Higher latitudes will be better described

11. Spatial and temporal sampling
Example of the Garonne River over an orbit cycle

12. Model calibration and validation
Primarily hydrodynamic models
Water levels are usually used for model calibration and validation
Many examples of using in-situ or altimeter measurements for calibration
Use of SWOT water level observations would be seamless
Spatially-distributed measurements should provide order-of-magnitude improvement
Discharge observations from SWOT can be directly used to calibrate hydrologic models
Calibration for each sub-basin would lead to distributed parameters -> increased realism

13. Model calibration and validation (cont’d)
Despite coarser resolution of discharge relative to hydraulic models
SWOT can provide boundary inflows
Provide a reach-averaged river channel bathymetry and roughness
Adjust floodplain topography based on SWOT observations
Example from the Amazon main stem
Model agreement with Observations
Model over- & under-prediction
Water inundation observations have been used to calibrate against specific flood events

14. Reducing errors in water budget terms
Given the role of discharge, SWOT observations can be assimilated to correct water budget imbalances
Unconstrained
Constrained
Example of Mississippi
Use of assimilation to constrain water budget
Q from gauges – SWOT should further improve technique
Water budget closure error

15. Lake water storage from SWOT
Observations of water storage change
Fine-scale determination of size and location of lakes
Especially important for Arctic lakes
WSE and delineation of lakes is an indirect combination of the water budget and dynamics
Ability to extend information on storage by developing area-storage relationships
Interpretation of the measurements requires models that incorporate lake dynamics
No generic parameterization for lakes exists

16. SWOT observations of reservoir storage
Some large-scale models incorporate reservoirs in their flow routing
Simple linear schemes
Optimizing releases according to reservoir type
Coupling of hydrology models with dedicated water resources management models
Observed WSE could be ingested directly or model parameters can be calibrated
Issues of trans-boundary rivers persist for models of these systems
Forecasting and assessment hydropower production

17. Mapping changes in wetlands
Diversity and complexity of wetlands makes their modeling difficult
Affect local water and energy exchanges due to relatively high ET
Distributed versus areal modeling of wetlands
Probably need to explicitly include groundwater in hydrologic models
Changes in water storage and inundation extent can be used to calibrate model parameters
Inferred ET can be assimilated directly
Implications for eco-hydrologic models

18. Can SWOT help with forecasting?
When there is a SWOT overpass initial conditions for a forecast can be estimated
Estimation can be direct or indirect
Direct example: flood forecasting
Indirect example: hydrologic forecasting by estimating soil moisture that produced observed runoff
Improvement in forecast skill by model calibration or identification of model biases
Example of forecast error reduction when assimilating satellite WSE over the Ohio River

19. Challenges: discrepancy between observations and models
What SWOT observes does not necessarily match the model state variable
Transforming WSE to water depth depends on accuracy (or assumption) of topography
Reach-averaged properties are not directly represented in the models
Need to validate assumptions of aggregation with finer scale measurements and models
Models lack or have simplistic representation of lakes and wetlands
Do we need to modify existing model structures?
What is the best approach for resolving these discrepancies?

20. Challenges: hyper-resolution modeling
New satellite missions including SWOT are starting to provide information at high spatial resolutions
Grand challenge of developing models at 1-km scale globally
Not as simple as changing the grid cell size…
Data assimilation must play key role
Perhaps models need to be restructured with satellite observations in mind

21. Challenges: how to move forward
Coupling of hydrology and hydrodynamic models
Need to represent surface water inundation
Leverage existing work or develop new schemes for
Consumptive use
Reservoir operations
Lake and wetland dynamics
Perform model validation experiments
Continue work on data assimilation of SWOT and AirSWOT observations into both hydraulic and hydrodynamic models
State estimation
Model calibration
Assess the feasibility of closing the water budget, in combination with other satellite observations
Demonstrate value of approach in applications

22. Questions?