1 / 27

Contributions of Different Water Storage Compartments to Total Storage Change - Multi-Sensor Analysis

This research explores the impact of various water storage compartments on total storage change using multi-sensor analysis. The study delves into the signals of climate variability in continental hydrology through space and in-situ observations and hydrological modeling. It focuses on the role of gravimetry, surface water, groundwater, soil moisture, snow water, and ice in understanding total water storage changes. The analysis includes principles of Fresnel reflection equation and the importance of dielectric constant in determining water content. Limitations and potential of different sensors like AMSR-E are also discussed.

milt
Télécharger la présentation

Contributions of Different Water Storage Compartments to Total Storage Change - Multi-Sensor Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Contributions of different water storage compartments to total storage change from multi-sensor analysis

  2. Introduction Sarah Abelen / CLIVAR-Hydro

  3. CLIVAR-Hydro • Signals of Climate Variability in Continental Hydrology from Multi-Sensor Space and In-situ Observations and Hydrological Modeling Object Mean Target Researchers Multi-Sensor Space Observations Signals of Climate Variability + 3 PhD Students Continental Hydrology In-situ Observations Hydrological Modeling Sarah Abelen / CLIVAR-Hydro

  4. Gravimetry Surface Water Ground Water Soil Moisture Snow Water and Ice Concept Schönheinz D., BTU-Cottbus Sarah Abelen / CLIVAR-Hydro

  5. Soil Moisture Sarah Abelen / CLIVAR-Hydro

  6. Theory • Basic Principle: Fresnel reflection equation Reflectivity of the ground (r) for a certain polarization (H or V) depends on: • the viewing angle of the sensor (Ѳ) • the dielectric constant (k)  depends on the constituents of the ground (air, soil, water) Example: dry soil: k = 5 water: k = 80 Jackson 2005 Jackson 2002 Sarah Abelen / CLIVAR-Hydro

  7. Passive vs. Active Sensors • AMSR-E = Advanced Microwave Scanning Radiometer for EOS • Time span: 2002 – present • Pixel size: 25 km x 25 km(of data products) • Largest wavelength: 4.3 cm = 6.9 GHz (C-Band) Njoku et al. 2003 Sarah Abelen / CLIVAR-Hydro

  8. AMSR-E Data Sarah Abelen / CLIVAR-Hydro

  9. Monthly Soil Moisture Sarah Abelen / CLIVAR-Hydro

  10. Selection of the Test-Site Sarah Abelen / CLIVAR-Hydro

  11. Limitations • Gravimetric changes (GRACE) can be identified in regions with: • High soil moisture (≥ 20 kg/m2) Example: 20 kg/m2 = 0.2 g/cm2 and 10 cm depth • Strong variation in soil moisture • Large spatial extend (> 300 km x 300 km) • Soil moisture (AMSR-E) can be acquired in regions with: • Low vegetation water content (< 1.5 kg/m2) Sarah Abelen / CLIVAR-Hydro

  12. Variation of Soil Moisture Paraná River large changes in river basins no changes in the deserts Sarah Abelen / CLIVAR-Hydro

  13. Variation of Soil Moisture with Quality Mask mainly the deserts remain with small variations only few areas with larger variations remain Sarah Abelen / CLIVAR-Hydro

  14. Mean of Soil Moisture with Quality Mask variation is high but the mean value is low Sarah Abelen / CLIVAR-Hydro

  15. Summary and Outlook Sarah Abelen / CLIVAR-Hydro

  16. Summary and Outlook • Gravimetry  total change in water storage • Remote Sensing + In-situ  compartments (e.g. soil moisture) • Principle for Soil Moisture: Reflection  dielectric constant  water content • Soil moisture products exist for active and passive sensors • Problems AMSR-E: • vegetation water content (< 1.5 kg/m2)  mostly related to low variability / magnitude of soil moisture • Problems GRACE: • mass changes are small for soil moisture • lack of knowledge on depth of soil moisture (≤ 1 cm for C-Band) Sarah Abelen / CLIVAR-Hydro

  17. References Jackson, T., 2002. Remote sensingofsoilmoisture: implicationsforgroundwaterrecharge. Hydrogeology Journal, 10(1), 40-51. Jackson, T., 2005. SoilMoisture. In Encyclopediaofsoils in theenvironment. Elsevier, pp. 392-398. Njoku, E. et al., 2003. Soilmoistureretrievalfrom AMSR-E. Geoscienceand Remote Sensing, IEEE Transactions on, 41(2), 215-229. Njoku, E. 2004, updateddaily. AMSR-E/Aqua L3 SurfaceSoilMoisture, Interpretive Parameters, & QC EASE-GridsV002, 01.07.2002 - 31.07.2010. Boulder, Colorado USA: National Snow andIce Data Center. Digital media. Sarah Abelen / CLIVAR-Hydro

  18. Sarah Abelen / CLIVAR-Hydro

  19. AMSR-E • AMSR-E = Advanced Microwave Scanning Radiometer for EOS (AMSR-E) • Space Agency: NASA • Satellite: Aqua (precipitation, evaporation, water cycle) • Data products: • Time span: 2002 – present • Spatial resolution: 25 km2 (of data products) • Largest Wavelength: 4.3 cm = 6.9 GHz (C-Band) • Soil Moisture • Vegetation Water • content • Land-cover (10 types) http://en.wikipedia.org/wiki/File:Aqua_satellite_simulation.jpg Sarah Abelen / CLIVAR-Hydro

  20. Variation of Valid Soil Moisture • No pronounced temporal variation • Vegetation Water Content is mostly below 1.5 kg/m2 Sarah Abelen / CLIVAR-Hydro

  21. Integration of Other Data Sources 2000 Sarah Abelen / CLIVAR-Hydro

  22. Test-Site Proposal 35° Lat 35°- 65° Long Sarah Abelen / CLIVAR-Hydro

  23. Annual Cycle Sarah Abelen / CLIVAR-Hydro

  24. Ancillary Data: Quality Control Flags Sarah Abelen / CLIVAR-Hydro

  25. Selection of one Grid Cell Sarah Abelen / CLIVAR-Hydro

  26. Soil Moisture: Time Variation Errorbar of 0.06 g/cm3 Validity for Vegetation Water Content < 1.5 kg/m2 • Soil Moisture goes up to 0.2 g/cm2 • Errorbar is relatively high • Limitation is the Vegetation Water Content Njoku et al. 2003 Sarah Abelen / CLIVAR-Hydro

  27. Object Mean Researchers Target Signals of Climate Variability 2 Continental Hydrology 1 Signals of Climate Variability 3 Multi-Sensor Space Observations + 3 PhD Students 4 In-situ Observations 5 Hydrological Modeling Sarah Abelen / CLIVAR-Hydro

More Related