Continental-scale high resolution terrestrial hydrologic modeling - - PowerPoint PPT Presentation

Continental-scale high resolution terrestrial hydrologic modeling over Europe using COSMO- REA6 reanalysis dataset.

Bibi S. Naz1,2, Stefan Kollet1,2, Anne Springer5, Harrie-Jan Hendricks Franssen1,2, Carsten Montzka1, Klaus Goergen1,2, Carina Furusho1,2

1Research Centre Jülich, Institute of Bio- and Geosciences: Agrosphere (IBG-3), Jülich 52425, Germany 2Centre for High-Performance Scientific Computing in Terrestrial Systems, Geoverbund ABC/J, Jülich

52425, Germany

3Research Centre Jülich, Jülich Supercomputing Centre, Jülich 52425, Germany 4Laboratory of Hydrology and Water Management, Ghent University, Ghent 9000, Belgium 5Institute of Geodesy and Geoinformation, Bonn University, Nussallee 17, Bonn 53115, Germany

International Symposium on Regional Reanalysis, July 17 - 19, 2018

(The Energy oriented Centre of Excellence in computing applications)

• Hydropower production simulations (Regional to site-

specific)

– Refine spatial resolution for hydrologic simulation (~3km) – Routing of streamflow at locations of interest (e.g., hydropower plants in Alpine Region)

• How to quantify the

uncertainty of hydrological predications?

– Ensemble-based data assimilation

• Need to have reasonable

model performance for hydrologic states and fluxes across different scales

Background

• Water for Energy

Reservoirs, hydropower plants and gauging stations in the Italian Alpine Region.

Community Land Model (CLM-PDAF)

Parallel Data Assimilation Framework (PDAF)

Nerger et al. (2005)

CLM3.5 for the European CORDEX Domain

TerrSysMP-PDAF Interface: Kurtz et al. (2016)

CCI-SM

CLM-PDAF

Naz et al. 2018, HESSD

CLM 3.5

Oleson et al. 2008 Satellite soil moisture

Model setup

Domain Extent and resolution: Ø EU-CORDEX at 0.03° x 0.03°(~ 3km) Land surface inputs: Ø Topography (GMTED2000 elevation) Ø Soil characteristics (FAO global dataset) Ø Vegetation LAI (global LAI product) Ø Land surface classification (MODIS)

Meteorological forcings

• COSMO-REA6 reanalysis at

6km resolution (Bollmeyer et al., 2015),

• Temperature, precipitation,

Wind speed, humidity, short and longwave radiation, pressure

• Compare precipitation with

DWD station data (166 stations for 1997 – 2006 time period),

Model Validation

Ø The Global Runoff Data Centre (GRDC) gauge stations Streamflow (2001 – 2015) Ø MODIS Snow Cover (2001 – 2015) Ø ESACCI satellite soil moisture

• bservation (2000 – 2006) at 0.25o

resolution, Ø E-RUN v1 gridded monthly runoff for 1950 - 2015 (Gudmundsson et al., 2016), Ø Total Water Storage from GRACE satellite (2003 – 2006)

GRDC stations (4105) E-RUN v1 gridded monthly runoff

CLM River Transport Model

Continental scale high resolution river discharge simulations at 3Km resolution

CLM Streamflow comparison with GRDC stations

Gave Dòssau, France (NSE: 0.8) Rhine River (NSE: 0.6) Danube River (NSE: -0.5)

Nash–Sutcliffe efficiency for 884 stations Monthly flow (2001 – 2015) An NSE of 1 corresponds to a perfect match of modeled discharge to the observed data. Red: Observation Blue: Simulated

Soil moisture is a key component to control the water and energy exchange between the atmospheres and the land surface. Large scale estimates of soil moisture are available from: ESA CCI surface soil moisture Upper few cm, ~25km, ~daily. Land surface model forced w/

• bserved meteorology. Complete

space/time coverage, incl. root zone.

Model Soil Moisture (subject to error)

RS Soil Moisture (subject to data gaps)

Assimilation

Optimal Soil Moisture/other hydrologic predictions

Weights based on uncertainties

Data assimilation of soil moisture

Ø ESACCI satellite soil moisture

• bservation (2000 – 2006) at 0.25o

resolution. Ø Data coverage of observation is low in northern Europe and in winter. Ø For data assimilation 100 grid cells were randomly selected. Ø CCI-SM soil moisture: cross- validation over grid cells that were not used in the data assimilations Simulation period: Ø 1 January 2000 – 31 December 2006. Simulation Scenarios: Ø Open-loop (no data assimilation) Ø State update (soil moisture)

Data assimilation of soil moisture

Ensemble generation: Ø 12 realization of perturbed precipitation and soil texture.

Impacts on seasonal surface soil moisture

Ø CLM-OL has higher soil water content (upper two soil layers) in all seasons over most part

• f Europe compared to

the CLM-DA simulations and CCI-SM. Ø The SWC in the summer and autumn is better reproduced in the CLM-DA simulations than in the CLM-OL when compared with the CCI-SM

CCI-SM CLM-OL CLM-DA

Winter Spring Summer Autumn

Daily surface soil moisture evaluation over Prudence regions

Evaluation of monthly runoff over PRUDENCE regions

Impacts on Total Water Storage

Ø Total water storage anomaly from GRACE satellite was compared with CLM TWS for 2003 – 2006 time period. Ø Total water storage from CLM was calculated through vertical aggregation

• f:

Ø Snow water, Ø Canopy water, Ø Soil ice, Ø Soil water, Ø Aquifer water. Ø Horizontal aggregation to ½ degree to compare with GRACE data

1) Using COSMO-REA6 data, CLM 3km model realistically simulates hydrological states and fluxes 2) Assimilating daily satellite SM improved near-surface soil moisture simulations over most parts of Europe relative to open-loop simulations. 3) CLM-DA underestimated runoff in the summer and autumn seasons particularly in the mid and south Europe. 4) Assimilating SM data showed slightly reduced correlation with GRACE TWS compared to open-loop estimates. Future work will focus: Ø Calibrate model parameters/ ensemble size and parameter updates Ø Joint assimilation of SM and GRACE data

Summary and Outlook

EU 3km model Interface RS Data

Thank you

Acknowledgements: Ø The Energy oriented Centre of Excellence in computing applications (EoCoE) (Horizon 2020 programme of the European Commission) Ø The Jülich Supercomputing Centre Naz, B. S., Kurtz, W., Montzka, C., Sharples, W., Goergen, K., Keune, J., Gao, H., Springer, A., Hendricks Franssen, H.-J., and Kollet, S.: Improving soil moisture and runoff simulations

• ver Europe using a high-resolution data-assimilation modeling framework, Hydrol. Earth
• Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-24, in review, 2018.

Extra slides

CLM Snow Cover comparison with MODIS

Ø Snow cover fraction from MODIS data was compared with CLM snow cover for 2001 – 2015 time period. Ø Snow cover fraction from CLM was extracted for area where MODIS data is available. Ø Horizontal aggregation to 0.05o degree to compare with MODIS data

Improvements in monthly runoff estimates

• Improvements in

terms of RMSE are more pronounced in the SC,AL and EA regions and in the spring season

• Negative bias error

in CLM-DA

Impacts on seasonal runoff

Ø CLM-OL estimated greater magnitude of runoff over most parts of Europe compared to CLM-DA in all seasons. Ø The overestimation in the CLM-OL runs is more pronounced in the summer and spring seasons. Ø CLM-DA underestimated runoff in the summer and autumn seasons particularly in the mid and south Europe.

Winter Spring Summer Autumn

Improvements in Soil Moisture

• Improvements in

terms of RMSE are more pronounced in the SC,AL,MD and EA regions

• Negative bias error

in summer in CLM- DA

Evaluation of monthly runoff over PRUDENCE regions

Soil texture parameter update impacts on soil moisture and runoff

Mean annual soil water content (2000 – 2006)

Soil texture parameter update impacts on soil moisture and runoff

Mean annual Runoff (2000 – 2006)

CCI soil moisture data interpolation (25 km vs. 3km)

CLM-PDAF: The Global Ensemble Kalman Filter

ensemble of state vectors ensemble of updated state vectors Kalman Gain matrix ensemble of perturbed

• bservations
• bservation
• perator

‘weight matrix’ Ø Determines by model and observation covariance matrices

Ensemble generation: Ø 12 realization of perturbed precipitation and soil texture. Ø Soil moisture updates was set to 1 day.

CLM Snow cover fraction

CLM 3km FSNO is aggregated to 5km resolution. SCF (fraction of a grid covered by snow) is calculated as a function of snow density and snow depth CLM SCF