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

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

Conceptual water balance model 0.5° spatial resolution Daily time-step Climate forcing data from CRU, GPCC, ECMWF Human water use accounted for Calibration for river discharge at 1200 stations worldwide ΔS = ΔS canop + Δ S snow + Δ S soil + Δ S gw + Δ S lakes + Δ S wetl + Δ S river The WaterGAP Global Hydrology Model (WGHM) Total continental storage change: 3

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

Work plan for model calibration: 1)Analyze model properties a)Identification sensitive parameters b)Model uncertainty c)Calibration test runs 2) Select adequate GRACE data and filter tools 3) Perform multi-objective model calibration 5

Work plan for model calibration: 1)Analyze model properties 1)Identification sensitive parameters 2)Model uncertainty 3)Calibration test runs 2) Select adequate GRACE data and filter tools 3) Perform multi-objective model calibration 5

1c) Single-objective calibration WGHM Monte-Carlo run Standard WGHM WGHM single-objective, one-parameter calibration Ob Nash-Sutcliffe coefficient for water storage change Nash-Sutcliffe coefficient for river discharge 6 perfect model simulation

Calibration approach current parameter sets Evaluation of error stop ? Parameter- variation parameter set ranking Optimal solution yes no initial parameter sets Model simulation GRACE total storage variation Runoff Measurement data 7 0 error discharge Pareto Frontier 0 single model simulation error total storage change 0

Work plan for model calibration: 1)Analyze model properties 1)Identification sensitive parameters 2)Model uncertainty 3)Calibration test runs 2) Select adequate GRACE data and filter tools 3) Perform multi-objective model calibration 8

2) GRACE filter tool evaluation worldwide 22 largest WGHM river basins Filter typeParameterSource 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 variance Swenson and Wahr, 2002 GRACE signal-noise-ratio optimized (SF)factor of formal errors Seo et al, 2005 Correlation Error Filter ( CEF) filter window properties Swenson and Wahr, 2006 Decorrelation Filter ( DDK )covariance matrix parameter Kusche,

2) GRACE filter tool evaluation: Amazon 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) 10

2) GRACE filter tool evaluation: Lena 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) 11

2) GRACE filter tool evaluation BasinWGHMGLDAS AmazonOF, MF GangesMF MississippiGFDDK VolgaSF YukonOF, CEF MF MF Optimal filter for 5 basin examples ParameterParameter ValuewNSC r g [km] ε max [mm] error factor σ s [mm], c l [km]250, w a, w e, n a, n e 30, 3, 2, a, p10 14, Amazon wNSC values and filter parameter for different filter types Filter 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) 12

Work plan for model calibration: 1)Analyze model properties 1)Identification sensitive parameters 2)Model uncertainty 3)Calibration test runs 2) Select adequate GRACE data and filter tools 3) Perform multi-objective model calibration 13

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

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