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DSD-INT 2018 Can we combine satellite derived Soil Moisture with hydrological models - Schellekens

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Presentation by Jaap Schellekens (Vandersat) at the wflow User Day 2018, during Delft Software Days - Edition 2018. Friday 09 November 2018, Delft.

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DSD-INT 2018 Can we combine satellite derived Soil Moisture with hydrological models - Schellekens

  1. 1. Can we combine satellite derived Soil Moisture with hydrological models?
  2. 2. VanderSat ● Founded in 2015 ● 10, 14 23 employees (scientists ( > 50 % has a phd in EO) and entrepreneurs) ● Transition from startup to SME ● Located in Haarlem ● Commercial EO services ● Prime focus on Microwave data and Soil Moisture ● Mission: Build the best satellite products to solve the global water and food crisis and always keep innovating.
  3. 3. Soil moisture and hydrology 1. Soil moisture ● Indicator for drought ● Indicator for floods ● Derive discharge rating from soil moisture ● Input to models 2. Surface water ● Identify water seasonality ● Post flood analysis ● Flood monitoring/risk The state of the system (now) is the most important factor in determining the quality of modelling results -> Soil moisture helps a LOT in doing that! Inputtomodels Monitoring Also VOD and Temp
  4. 4. Why Soil moisture? 1. Represent a store (not a flux) 2. Indicator for floods and droughts 3. Check the relation between Q and soil moisture and Q and precipitation
  5. 5. Satellite Soil Moisture Case study of a catchment in Australia, Van der Schalie et al., 2015: RSE
  6. 6. 7 Data architecture <12 hr for L1 (7d for L4) Downscaling // LPRM // Corrections // Flagging // L1 L3 + 2 hr Viewer // API connected // < 14 hr
  7. 7. https://maps.vandersat.com/
  8. 8. Success parameters…. 1. The soil moisture to be used should: a. be of sufficient quality and preferably come with defined errors b. have sufficient spatial and temporal resolution c. operational product 2. The model should be able to properly represent (top) soil moisture a. Sometimes derived parameters (discharge) can be used 3. The model should be able to handle the new input data
  9. 9. VanderSat Soil Moisture VS Cosmic Ray
  10. 10. VanderSat Soil Moisture VS Cosmic Ray
  11. 11. VanderSat Soil Moisture VS Cosmic Ray
  12. 12. Calibration with Soil Moisture 1. Demonstrate here in PCR-GLOBWB 2. Can be done globally
  13. 13. Assimilation with Soil Moisture 1. Demonstrate here in PCR-GLOBWB 2. Performance close to a locally calibrated model
  14. 14. Assimilation of Q derived from satellite soil moisture in wflow_sbm
  15. 15. Mozambique 1. Used layered wflow_sbm to match different zones 2. Large uncertainties in: a. Precipi b. Soil c. but also the soil moisture 3. When it matches we have more certainty
  16. 16. Conclusions 1. Satellite soil moisture is available at NRT globally (between 2 and 7 times per week) 2. New layered wflow_sbm can be configured to match the satellite observed soil moisture 3. For models that do not have a surface soil moisture store we can use a Derived Root Zone Soil Moisture or discharge derived from soil moisture/wetness 4. Improvement is large for poorly or uncalibrated models and smaller for well calibrated models 5. If a model is well calibrated but for the wrong reasons adding soil moisture will show this -> recalibration required
  17. 17. jschellekens@vandersat.com www.vandersat.com

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