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SADC THEMA: Drought Service Capacity Building for the Drought Service

SADC THEMA: Drought Service Capacity Building for the Drought Service A. Murwira (PhD), K. S. Murwira (PhD), M. Masocha (PhD), I. Gwitira and M. D. Shekede Department of Geography and Environmental science

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SADC THEMA: Drought Service Capacity Building for the Drought Service

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  1. SADC THEMA:Drought Service Capacity Building for the Drought Service A. Murwira (PhD), K. S. Murwira (PhD), M. Masocha (PhD), I. Gwitira and M. D. Shekede Department of Geography and Environmental science University of Zimbabwe and Scientific and Industrial Research and Development Centre, Geo-information and Remote Sensing Institute (SIRDC-GRSI)

  2. SADC THEMA:Drought Service Drought Concepts & Drought Monitoring DAY II

  3. SADC THEMA:Drought Service Conceptual background I. Gwitira and M. Masocha (PhD), A. Murwira (PhD), K. S. Murwira (PhD), M. D. Shekede Department of Geography and Environmental science University of Zimbabwe

  4. Outline • What is drought? • Types of drought • Drought monitoring, Drought Hazard, Drought Damage Photo credits: http://www.guardian.co.uk/environment/gallery/2009/feb/05/global-drought

  5. Definition of Drought • Drought is one of the major natural hazards yet there is no universally accepted definition of drought but only operational definitions • Drought is any period of moisture deficiency that deviates from climatic normals at a given location or region

  6. Drought vs. Aridity • Unlike aridity which refers to a permanent condition, drought refers to a temporary condition characterised by a ‘severe’ reduction of water availability compared to the normal values extending along a significant period of time and over a large region Photo credit: http://www.tripadvisor.com/LocationPhotos-g1085994-Namib_Desert.html#24885310

  7. Drought Types

  8. Meteorological drought • A meteorological drought can be defined based on the degree of dryness (i.e., in comparison to some “normal” or long-term average amount of precipitation) and the duration of the dry period Source: http://www.fao.org/docrep/008

  9. Hydrological drought • Hydrological droughts are associated with the effects of periods of precipitation shortfalls on surface or subsurface water supply (i.e., reduced streamflow, reservoir, ground water levels) • The frequency and severity of a hydrological drought are often characterised on a watershed or river basin scale

  10. Agricultural drought • An agricultural drought occurs when soil moisture fails to meet the water requirements of a crop • It links the impacts of meteorological and hydrological droughts to agricultural impacts, focusing on precipitation shortages, differences between actual and potential ET, soil water deficits, reduced reservoir and ground water levels

  11. Socioeconomic drought • Socioeconomic drought- occurs when physical water shortage starts to affect people, individually and collectively; it associates the supply and demand of an economic good such as food with elements of meteorological, hydrological, and agricultural drought

  12. SADC THEMA Drought Service Drought Monitoring based on Precipitation Indices M. Masocha (Ph.D), I. Gwitira, A. Murwira (PhD), K. S. Murwira (PhD), M. D. Shekede Department of Geography and Environmental science University of Zimbabwe

  13. Drought Monitoring: Introduction • Monitoring drought is important for national agricultural and environmental planning • There are several indices that are used to monitor drought. • We differentiate two classes: • Traditional Precipitation based indices such as the Standardized precipitation Index (SPI) • Remote sensing based indices, in particular satellite-based indices, such as Vegetation condition index (VCI), among others.

  14. SADC THEMA Drought Service Precipitation based Drought Monitoring Indices M. Masocha (Ph.D), I. Gwitira, A. Murwira (PhD), K. S. Murwira (PhD), M. D. Shekede Department of Geography and Environmental science University of Zimbabwe

  15. Standardised Precipitation Index (SPI) • SPI is used to quantify the precipitation deficit, based on the probability of precipitation for multiple time scales (McKee et al., 1995) • Calculation of the SPI requires a long-term monthly precipitation database with 30 year or more of data • This long-term record is fitted to a probability distribution, which is then transformed to into a normal distribution so that the mean SPI for the location and desired period is zero

  16. Computing SPI • SPI is calculated by taking the difference of the precipitation from the mean for a particular time scale and then dividing it by the standard deviation i.e.,: where xi the precipitation of the selected period during the ithyear; and and are the mean and the standard deviation of the selected period, respectively

  17. SPI Interpretation • A drought event occurs any time the SPI is continuously negative and reaches an intensity where the SPI is –2.0 or less; a drought event ends when the SPI becomes positive

  18. SPI: Its Merits and Pitfalls • SPI has many advantages over other indices in that it is simple and temporally flexible, thus allowing observation of water deficits at different time scales • However, SPI is calculated on site and based on precipitation only, hence it does not allow scientists to quantify the spatial extent of drought or vegetation response to water deficit • Consequently, SPI is best used in conjunction with other indices

  19. SADC THEMA Drought Service Drought Monitoring Physical Basis of Remotely Sensed Drought Monitoring M. Masocha (Ph.D), A. Murwira (PhD), K. S. Murwira (PhD) I. Gwitira and M. D. Shekede Department of Geography and Environmental science University of Zimbabwe

  20. Satellite-Based Remote Sensing Source: Wardlow, 2009

  21. Monitoring Drought From Space: The Remote Sensing System radiation image sensor atmosphere atmosphere target reflectance reflectance transmission

  22. Electromagnetic Spectrum

  23. Optical Properties of Vegetation

  24. Spectral Differentiation of Features on Earth

  25. Basis of Vegetation monitoring NIR RED

  26. Chivero Landsat TM NIR and R Scatter: Water NIR RED

  27. Chivero Landsat TM, NIR and R Scatter: Water weed NIR RED

  28. Chivero Landsat TM, NIR and R Scatter: Bare NIR RED

  29. Drought Monitoring Indices: Normalized Difference Vegetation Index (NDVI) • Drought indices are important for detecting, monitoring and evaluating the magnitude of droughts • NDVI is a general measure of the state and health of vegetation (Tucker, 1979) and is one of the most widely used indices for monitoring droughts from landscape to global scales • Does NDVI increase or decrease during a drought event? Give reasons

  30. NDVI-Rainfall Relationships: ETOSHA, Namibia (C. Sannier, Cranfield University, Silsoe,)

  31. SADC THEMA:Drought Service Thank you A. Murwira (PhD), K. S. Murwira (PhD), M. Masocha (PhD), I. Gwitira and M. D. Shekede Department of Geography and Environmental science University of Zimbabwe and Scientific and Industrial Research and Development Centre, Geo-information and Remote Sensing Institute (SIRDC-GRSI)

  32. SADC THEMA:Drought Service Remotely sensed Drought IndicesDAY III

  33. SADC THEMA Drought Service Drought Monitoring Remotely Sensed Drought Indices M. Masocha (Ph.D), I. Gwitira, A. Murwira (PhD), K. S. Murwira (PhD), M. D. Shekede Department of Geography and Environmental science University of Zimbabwe

  34. Introduction • NDVI itself does not reflect drought or non-drought conditions but its deviation from the ‘normal’ is a useful tool for detecting and monitoring droughts • However, a number of indices based on the NDVI time series data for assessing drought have been proposed

  35. Introduction • The severity of a drought may be defined as NDVI deviation from its long-term mean • A monthly NDVI time series for a drought year (1987) and a wet year (1993) compared to the NDVI long-term mean • (averaged for the study area (pixel)

  36. Vegetation Anomalies and Droughts Map source: http://earthobservatory.nasa.gov/NaturalHazards/view.php?id=18226 Jan-Mar 2007

  37. NDVI Difference • NDVIdiff =(NDVI – LT NDVI) • Where NDVIdiff is NDVI difference • LT NDVI is the long term average NDVI

  38. Standardized difference vegetation index (SDVI) • SDVI =(NDVI – LT NDVI)/(STDEVNDVI for that period over time) • Where SDVI is standardized difference vegetation index • NDVI is the observed NDVI at in a particular dekad • STDEVNDVI is the Long term standard deviation of NDVI over the period

  39. Vegetation Condition Index (VCI) • VCI shows how close the NDVI of the current time is to the minimum NDVI calculated from the long-term record for that given time (Kogan, 1995) and is calculated as: • where, NDVImax and NDVImin are calculated from the long-term record (e.g., 18 years) for that time (e.g., week, month) and j is the index of the current time

  40. VCI Interpretation • The condition of the vegetation presented by VCI is measured in percent • Different degrees of a drought severity are indicated by VCI values below 50% • A VCI threshold of 36% signifies an extreme drought condition (Liu and Kogan, 1996) but further research is needed to categorize the VCI by its severity in the range between 0% and 36%

  41. VCI for Southern Africa: Frequency of Drought in January • We analysed VCI based extreme drought using a 25 year data series in Southern Africa (1982-2006) and mapped areas extreme drought frequency

  42. Temperature Condition Index (TCI) • TCI is based on brightness temperature and represents the deviation of the current month’s (week’s) temperature from the recorded maximum (Kogan 1995, 1997) • It is computed as: where, BT is the brightness temperature (e.g., AVHRR band 4); BTmax and BTmin are the maximum and minimum values of respectively calculated from the long-term (e.g., 18 years) record of remote-sensing images for each calendar month or week j

  43. Vegetation Health Index (VHI) • VHI integrates both Vegetation Condition Index (VCI) and thermal-based Temperature Condition Index (TCI) indicators derived from satellite data e.g., Advanced Very High Resolution Radiometer (AVHRR) to depict drought stress as function of vegetation canopy greenness and temperature a and b are coefficients to quantify share of NDVI based VCI and TCI contribution in total vegetation health (Wardlow, 2009)

  44. Percent of Average Seasonal Greenness (PASG) • PASG is a phenology metric which is based on time-series NDVI and is used for drought monitoring • where SGPnYn refers to the seasonal greenness (SG) for a ten-day period (Pn) of a specific year (Yn) and xSGPn is the historical average for the same ten-day period

  45. Relationships between VCI, PASG and SPI The trends of time-series SPOT VGT-S NDVI for cropland and correlation coefficients between VCI, PASG and multi-scale SPIs in the Huang-Huai- Hai region, China (Zhou, L. et al., 2010)

  46. Discussion • As satellite data are now readily available, an operational drought-monitoring system based on remotely sensed drought indices has the potential to improve the efficiency of existing drought policies and management in Southern Africa • The satellite drought indices covered correlate well with key meteorological indicators of drought such as SPI

  47. SADC THEMA:Drought Service Thank you A. Murwira (PhD), K. S. Murwira (PhD), M. Masocha (PhD), I. Gwitira and M. D. Shekede Department of Geography and Environmental science University of Zimbabwe and Scientific and Industrial Research and Development Centre, Geo-information and Remote Sensing Institute (SIRDC-GRSI)

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