Presentation on theme: "How will SWOT observations inform hydrology models?"— Presentation transcript:
1 How will SWOT observations inform hydrology models? Eric Martin and Kostas AndreadisSWOT Science Definition Team Meeting17-20 June 2014, Toulouse
2 BackgroundModels developed with motivation of understanding the water cycleBudget closureReproducing variability of processesImpact and sensitivity studiesApplications of water resources models can rangeLong-term re-analysesPrediction and forecasting at various scales (seasonal to decadal)Earth system modeling components for climate change simulationsSome disparity among models that depends on specific applicationNumber of deficiencies that complicates reconciling models with observationsTrade-offs between resolving processes and model area size
3 Global & regional water balance Ability to close the water balance as a metric of model improvementHow well do models perform over regional and global scales?What can we currently observe from remote sensing?
4 Observing each water balance term TRMM, GPMMODISGRACESWOTJASON, SENTINEL-3SARAL, MODISSMOS, SMAPUncertainties exist in each satellite observationretrieval algorithms, representativeness of observationsObservations of individual components do not close the water budget
5 Can models close the water budget? Models don’t agree with each othereven when forced by identical dataExample comparison of runoff from PILPS-2
6 Role of river discharge Discharge acts as a basin-wide “integrator” of water fluxesDischarge dynamics vary both spatially and temporallyExtensively used to calibrate & validate hydrology modelsLevel 2.5 data product from SWOTIndirectly estimated from SWOT observablesNumber of candidate “direct estimation” algorithmsInstantaneous estimates at a reach-averaged scaleThrough data assimilation into river hydraulic modelsPotentially continuous estimates across river network and between observation times
7 State-of-the-art hydrology models Most large-scale hydrology models are essentially column modelsFlow routing schemes are rather simplified
8 Hydrodynamic modelsWater levels are usually not represented in hydrology modelsNeed hydrodynamic models to accurately simulate processes in the river and floodplainFew models can be used to simulate large areasDownscaling formulations could allow transforming 1-km to <100-m scalesExample of downscaling
9 What can SWOT improve? SWOT will provide observations of Water storage changes in lakes and reservoirsWater inundation and surface elevationRiver dischargeOverview of improvements envisioned by SWOTModel calibration and validationModeling and delineating lakes and wetlandsDeriving information on reservoir operationsIndirect estimation of water budget components (e.g. precipitation)Forecasting using either hydrologic or hydrodynamic modelsAs with every novel type of observation, there will probably be improvements in models that are not included hereGRACE 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 kilometersShould be adequate for current state-of-the-art hydrologic modelsHydrodynamic models usually have finer spatial resolutionSWOT will only observe rivers wider than mSWOT will provide observations at varying temporal frequencies (~7-10 days)Should be adequate for model calibrationIt 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 modelsWater levels are usually used for model calibration and validationMany examples of using in-situ or altimeter measurements for calibrationUse of SWOT water level observations would be seamlessSpatially-distributed measurements should provide order-of-magnitude improvementDischarge observations from SWOT can be directly used to calibrate hydrologic modelsCalibration 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 modelsSWOT can provide boundary inflowsProvide a reach-averaged river channel bathymetry and roughnessAdjust floodplain topography based on SWOT observationsExample from the Amazon main stemModel agreement with ObservationsModel over- & under-predictionWater 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 imbalancesUnconstrainedConstrainedExample of MississippiUse of assimilation to constrain water budgetQ from gauges – SWOT should further improve techniqueWater budget closure error
15 Lake water storage from SWOT Observations of water storage changeFine-scale determination of size and location of lakesEspecially important for Arctic lakesWSE and delineation of lakes is an indirect combination of the water budget and dynamicsAbility to extend information on storage by developing area-storage relationshipsInterpretation of the measurements requires models that incorporate lake dynamicsNo generic parameterization for lakes exists
16 SWOT observations of reservoir storage Some large-scale models incorporate reservoirs in their flow routingSimple linear schemesOptimizing releases according to reservoir typeCoupling of hydrology models with dedicated water resources management modelsObserved WSE could be ingested directly or model parameters can be calibratedIssues of trans-boundary rivers persist for models of these systemsForecasting and assessment hydropower production
17 Mapping changes in wetlands Diversity and complexity of wetlands makes their modeling difficultAffect local water and energy exchanges due to relatively high ETDistributed versus areal modeling of wetlandsProbably need to explicitly include groundwater in hydrologic modelsChanges in water storage and inundation extent can be used to calibrate model parametersInferred ET can be assimilated directlyImplications for eco-hydrologic models
18 Can SWOT help with forecasting? When there is a SWOT overpass initial conditions for a forecast can be estimatedEstimation can be direct or indirectDirect example: flood forecastingIndirect example: hydrologic forecasting by estimating soil moisture that produced observed runoffImprovement in forecast skill by model calibration or identification of model biasesExample 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 variableTransforming WSE to water depth depends on accuracy (or assumption) of topographyReach-averaged properties are not directly represented in the modelsNeed to validate assumptions of aggregation with finer scale measurements and modelsModels lack or have simplistic representation of lakes and wetlandsDo 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 resolutionsGrand challenge of developing models at 1-km scale globallyNot as simple as changing the grid cell size…Data assimilation must play key rolePerhaps models need to be restructured with satellite observations in mind
21 Challenges: how to move forward Coupling of hydrology and hydrodynamic modelsNeed to represent surface water inundationLeverage existing work or develop new schemes forConsumptive useReservoir operationsLake and wetland dynamicsPerform model validation experimentsContinue work on data assimilation of SWOT and AirSWOT observations into both hydraulic and hydrodynamic modelsState estimationModel calibrationAssess the feasibility of closing the water budget, in combination with other satellite observationsDemonstrate value of approach in applications
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