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Meeting challenges on the calibration of the global hydrological model WGHM with GRACE data input

S. Werth A. Güntner with input from R. Schmidt and J. Kusche. Meeting challenges on the calibration of the global hydrological model WGHM with GRACE data input.

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Meeting challenges on the calibration of the global hydrological model WGHM with GRACE data input

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  1. S. Werth A. Güntner with input from R. Schmidt and J. Kusche Meeting challenges on the calibrationof the global hydrological modelWGHM with GRACE data input

  2. Time-Variable Gravity and Surface Mass Processes: Validation, Processing and First Application of New Satellite Gravity Data (TIVAGAM) Introduction Terrestrial water balance ΔS = P - R - E • S: Water storage changeP: Precipitation • E: Evaporation • R: Runoff 2

  3. The WaterGAP Global Hydrology Model (WGHM) • Conceptual waterbalance model • 0.5° spatial resolution • Daily time-step • Climate forcing data from CRU, GPCC, ECMWF • Human water use accounted for • Calibration for river dischargeat 1200 stations worldwide Total continental storage change: ΔS =ΔScanop+ΔSsnow+ΔSsoil+ ΔSgw+ΔSlakes+ΔSwetl+ΔSriver 3

  4. mm w.eq. Correspondence between GRACE and WGHM Mean maximum annual storage change (Gaussian filtering, 500 km) GRACE WGHM Aim: Improve WGHM model results by a new calibration with GRACE data. 4

  5. Work plan for model calibration: • Analyze model properties • Identification sensitive parameters • Model uncertainty • Calibration test runs 2) Select adequate GRACE data and filter tools 3) Perform multi-objective model calibration 5

  6. Work plan for model calibration: • Analyze model properties • Identification sensitive parameters • Model uncertainty • Calibration test runs 2)Select adequate GRACE data and filter tools 3) Perform multi-objective model calibration 5

  7. Ob -2.0 -1.5 -1.0 WGHM Monte-Carlo run Standard WGHM Nash-Sutcliffe coefficient for river discharge -0.5 WGHM single-objective, one-parameter calibration 0.0 0.5 1.0 1.0 0.5 0.0 -0.5 Nash-Sutcliffe coefficient for water storage change 1c) Single-objective calibration perfect modelsimulation 6

  8. single model simulation error discharge Pareto Frontier 0 0 error total storage change 0 Calibration approach initial parameter sets GRACE total storage variation RunoffMeasurementdata Model simulation current parametersets Evaluationof error Parameter-variation Optimalsolution no parameter set ranking yes stop ? 7

  9. Work plan for model calibration: • Analyze model properties • Identification sensitive parameters • Model uncertainty • Calibration test runs 2) Select adequate GRACE data and filter tools 3) Perform multi-objective model calibration 8

  10. 2) GRACE filter tool evaluation worldwide 22 largest WGHM river basins Filter type Parameter Source Gaussian filter (GF) filter width Jekeli, 1981 Optimized for basin shape (OF) max. satellite error Swenson and Wahr, 2002 Optimized for exp. signal model (MF) correlation length, signal varianceSwenson and Wahr, 2002 GRACE signal-noise-ratio optimized (SF) factor of formal errors Seo et al, 2005 Correlation Error Filter (CEF) filter window propertiesSwenson and Wahr, 2006 Decorrelation Filter (DDK) covariance matrix parameter Kusche, 2007 9

  11. 1.0 OF MF SF GF CEF decorrelated CEF decorrelated CEF decorrelated CEF decorrelated 0.9 σs = 250 mm DDK decorrelated 150 0.8 100 0.7 50 w = 3 0 , n = 3 0 w = 3 0 , n = 3 0 a e a e 0.6 w = 2 0 , n = 3 0 w = 2 0 , n = 3 0 a e a e K w S = w 0.5 w N a 3 = 3 0 = 0 w a , a w , 2 w = 1 0 , n = 3 0 20 n w = 1 0 , n = 3 0 = n 0 2 = , 0 a e 3 , = a 0 a e e n w n e 3 = 3 = 0 = 0 0.4 1 e e 3 a 0 0 , w n = 1 0 , = n a 3 = e 3 0 0 e 0.3 0.2 Gaussian filter (GF) Optimized for basin shape (OF) Optimized for exp. signal model (MF) GRACE signal-noise-ratio optimized (SF) Correlation Error Filter (CEF) Decorrelation Filter (DDK) 0.1 0.0 1000 800 600 400 200 0 5 10 15 20 500 1000 1500 20 15 10 5 filterwidth (km) max. Satellite error (mm) corrlation length (km) error factor 2) GRACE filter tool evaluation: Amazon 10

  12. 1.0 OF MF SF GF CEF decorrelated CEF decorrelated CEF decorrelated CEF decorrelated 0.9 DDK decorrelated 0.8 0.7 w = 2 0 , n = 3 0 a e w = 3 0 , n = 3 0 a e 0.6 ð = 300 mm K w = 1 0 , n = 3 0 250 S w = 3 0 , n = 3 0 a e 0.5 N a e w w = 3 0 , n = 3 0 a e w 0.4 w = w = 2 a 2 0 0 a = , , a n 3 n = w 0 3 0 = = e , 0.3 1 e 3 0 n a , 0 n w 20 50 = = 3 e 0 = 3 e a 150 1 0 0 , 0.2 n = 100 e 3 0 200 0.1 0.0 500 1000 1500 20 15 10 5 1000 800 600 400 200 0 5 10 15 20 corrlation length (km) error factor filterwidth (km) max. Satellite error (mm) Gaussian filter (GF) Optimized for basin shape (OF) Optimized for exp. signal model (MF) GRACE signal-noise-ratio optimized (SF) Correlation Error Filter (CEF) Decorrelation Filter (DDK) 2) GRACE filter tool evaluation: Lena 11

  13. Gaussian filter (GF) Optimized for basin shape (OF) Optimized for exp. signal model (MF) GRACE signal-noise-ratio optimized (SF) Correlation Error Filter (CEF) Decorrelation Filter (DDK) 2) GRACE filter tool evaluation Filter Amazon wNSC values and filter parameter for different filter types Optimal filter for 5 basin examples 12

  14. Work plan for model calibration: • Analyze model properties • Identification sensitive parameters • Model uncertainty • Calibration test runs 2) Select adequate GRACE data and filter tools 3) Perform multi-objective model calibration 13

  15. Work plan for model calibration: 3) Calibration Realization Implementation of Multi-objective calibration algorithms into WGHM: DDS Dynamically Dimension Search ► single-objective calibration algorithm extended for mutli-objective problems NSGA-II Non-dominated Sorting Genetic Algorithm ► evolutionary multi objective calibration algorithm 14

  16. Summary and Outlook fulfilled steps: Model studies for selected river basins Analyses of GRACE filter tools Implementation of calibration algorithm next steps: Multi-objective calibration runs Use of differently processed GRACE data, e.g. signal proportions from analysis of Schmidt et. al. 2007 15

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