Drought risk and crisis management cambodia


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Drought risk and crisis management cambodia

  1. 1. Drought Risk and Crisis Management Cambodia<br />AbolfazlAbesht<br />Master of Engineering <br />(Disaster Preparedness, Mitigation and Management)<br />
  2. 2. Terminology<br />Drought: A temporary abnormality in precipitation (quantity, timing and effectivness); it differs from aridity, which is restricted to low rainfall regions and is a permanent feature of climate<br /><ul><li>Normal: Long-term average condition of balance between precipitation and evapotranspiration.
  3. 3. Timing: delays in the start of the rainy season, (occurrence of rains in relation to principal crop growth stages)
  4. 4. Effectiveness: rainfall intensity, number of rainfall events</li></li></ul><li>Background<br />Agricultural drought is quite common either due to precipitation shortfall or late monsoon.<br />More than 95% of the total arable land is rain-fed and likely to be affected by drought<br />
  5. 5. Objectives<br />The overall objective of this study is to provide decision making support information for Drought Risk and Crisis Management.<br />1- To identify and classify drought prone areas using Tropical Rainfall Measuring Mission (TRMM) data<br />2- To analyze areas, relatively under water stress using Temperature Vegetation Dryness  Index (TVDI) derived from MODIS data<br />3- To assess feasibility of using remote sensing data for drought monitoring<br />
  6. 6. Study Area<br />
  7. 7. Data<br />1- Tropical Rainfall Measuring Mission (TRMM)<br />2- Moderate Resolution Imaging Spectroradiometer (MODIS)<br /> 2.1 Normalized Difference Vegetation Index (NDVI)<br /> 2.2 Land Surface Temperature (LST)<br />3- Monthly Precipitation data (Meteorological stations)<br />4- WFP study on drought (2003)<br />
  8. 8. Methodology<br />Monthly Rainfall (TRMM)<br />Monthly Rainfall (Met-Stations)<br />MODIS Data<br />NDVI-LST <br />Long-term Rainfall Anomaly <br />STDV<br />Normalize<br />SPI Model<br />TVDIModel<br />Meteorological Drought Prone Areas <br />Standardized Precipitation Index (SPI)<br />Soil Moisture Index (SMI)<br />Identify Drought Prone Areas (Preparedness and Mitigation)<br />Drought Monitoring (Response, preparedness and Mitigation)<br />
  9. 9. Whatis TRMM?<br />TRMM is the acronym for the Tropical Rainfall Measuring Mission.<br />TRMM is focusing on measuring the precipitation globally (Kummerow et al., 1998)<br />It was launched in November 1997 with the data archive from December 1997 to date<br />Spatial resolution of the data is<br /> 0.25 degree to 5.0 degree. <br />Precipitation Radar (PR) <br />Visible and Infrared Scanner (VIRS) <br />TRMM Microwave Imager (TMI)<br />
  10. 10. Correlation between Ground Data and TRMM (Microwave data)<br />
  11. 11. Correlation between Ground Data and TRMM (Microwave data)<br />
  12. 12. Identify Drought Prone Areas <br />Common to all types of drought is the fact that they originate from a deficiency of precipitation resulting from an unusual weather pattern (Peterson et al., 1998)<br />
  13. 13. Rainfall Stability Analysis(Identify Drought Prone Areas)<br />s: sample standard deviation<br />Cv: Coefficient of variation<br />: Observed Values<br />: Standard Deviation<br />: Mean<br />: Mean Value<br />
  14. 14. Annual Rainfall Distribution (TRMM)<br />
  15. 15. Classified Drought Prone Areas<br />
  16. 16. Drought Prone Areas Based on TRMM data vs. Ground data (WFP 2003)<br />
  17. 17. MODIS Products Table<br />
  18. 18. Concept of Temperature vegetation Dryness Index (TVDI)<br />
  19. 19. Soil Moisture Index (SMI)<br />
  20. 20. SMI VS. SPI of different time spansSiemReab<br />
  21. 21. Soil Moisture Index (2003-2005)<br />
  22. 22. Soil Moisture Index (2000-2009)<br />
  23. 23. Conclusion<br />TRMM data can provide valuable information to identify Drought Prone Areas which can be used for Drought Risk Management<br />TVDI model can provide useful information to identify drought affected areas which can be used for Drought Crisis Management as well as Drought Risk Reduction.<br />Development of accurate ground data is a priority to validate and improve the accuracy of remote sensing data analysis which together can be promising in Drought Management.<br />