Using Standardized Precipitation Index for Monitoring
Transkript
Using Standardized Precipitation Index for Monitoring
1st Joint DMCSEE-JRC Workshop On Drought Monitoring 21.-25. September 2009 Ljubljana, Slovenia Using Standardized Precipitation Index for Monitoring and Analyzing Drought presented by Ertan TURGU* eturgu@meteor.gov.tr Research Department *Turkish State Meteorological Service, Ankara, Turkey 1st Joint DMCSEE-JRC Workshop On Drought Monitoring 21.-25. September 2009 Ljubljana, Slovenia 1 TSMS Drought Assessment Tools • • • • • • • • Percent of Normal Rainfall Deciles Palmer Drought Severity Index (PDSI) Crop Moisture Index (CMI) Surface Water Supply Index (SWSI) Standardized Precipitation Index (SPI) NDVI based indices U.S. Drought Monitor Key Indicators for Drought Monitoring • • • • • • • • Climate data(Precipitation,Temperature) Soil moisture Snowpack Vegetation stress, Stream flow levels Ground water levels Reservoir and lake levels Short, medium and long-range forecasts 1st Joint DMCSEE-JRC Workshop On Drought Monitoring 21.-25. September 2009 Ljubljana, Slovenia 2 TSMS Standardized Precipitation Index(SPI) Strengths • minimal data requirements (only monthly precipitation data) • simple and quick • can help assess drought severity • can answer such questions as; when, how long, and how severe a drought is. • can be computed for different time scales • can be used for comparison between locations While Palmer's indices are water balance indices that consider water supply (precipitation), demand(evapotranspiration) and loss (runoff), the Standardized Precipitation Index (SPI) is a probability index which is negative for drought, and positive for wet conditions. As the dry or wet conditions become more severe, the index becomes more negative or positive. Weaknesses • Requires transformation to normal distribution • Requires long rainfall record (>30 years) • Ignores water demand and other losses 1st Joint DMCSEE-JRC Workshop On Drought Monitoring 21.-25. September 2009 Ljubljana, Slovenia 3 TSMS SPI software and SPI Classification: •Intensity (severity) •Frequency •Duration (onset - end) •Spatial Extent (area affected by) Charts of SPI Values at 3 and 6 months Time Scales: 1st Joint DMCSEE-JRC Workshop On Drought Monitoring 21.-25. September 2009 Ljubljana, Slovenia 4 TSMS Chart of Equiprobability Transformation from Fitted Gamma to Standard Normal Distributions we can determine minimum amount of rainfall that is required to avoid from a drought formation at different severity categories and varying time scales. 1st Joint DMCSEE-JRC Workshop On Drought Monitoring 21.-25. September 2009 Ljubljana, Slovenia 5 TSMS DROUGHT OCCURENCES AND SPATIAL ANALYSIS: 28° 32° 36 ° 4 0° 4 4° BULGARIA KIR KLA RE LI GR EEC E E DÝRN E KAS TAMON U G IR ESU N KAR S CA NK IR I A MAS YA C O RU M ARMENIA G U MU SH AN E BAY BU RT TO KA T BI LE CI K I GD I R ANKARA ER ZUR U M KIR IK KA LE ESK IS EH IR B ALIK ESI R AR DA HA N RI ZE TRA BZO N O RD U B O LU Y ALO VA BU RS A Ç AN A KKA LE AR TVIN SA MSU N K AR AB UK ÝSTANBUL ÝZMÝT A DA PAZ AR I 40 ° GEORGIA SEA B AR TIN ZON GU LD AK TEK IR DA G YOZG AT 40 ° A G RI ER ZIN C AN S IV AS KU TAH YA KI R SEH IR MA NI SA TU NC EL I B IN GO L A FYO N U SA K NE VS EH IR ÝZ MÝR BU RD U R V AN EL AZI G BI TL IS MAL ATY A A YD IN DE NI ZL I IRAN M US K AYS ER I A KSA R AY N IGD E K ONY A DI YAR BA KI R S IIR T B ATMA N A DI YA MAN IS PAR TA H AKK AR I S IR NA K K AH RA MAN MA RA S MU G L A MAR DI N K AR AMA N OSM AN IYE AN TAL YA ME RS IN SA NL I UR FA G AZI AN TEP IRAQ AD AN A K IL IS A NTA KYA 36 ° 36° 3 - MONTH MODERATE DROUGHT OCCURRENCES (%) MEDITERRANEAN SEA 5 km 28 ° 32° 28 ° 7 36 ° 32° 9 11 13 40 ° 36° 44 ° 4 0° DMÝ 250 200 100 0 15 0 SYRIA 50 ▲ SPI index has been applied to long-term precipitation data at 101 stations for 1951-2001 period. ▲ Here, our aim is to identify some areas vulnerable to drought at comparable time steps based on their occurence frequencies. S IN O P BLACK 44° BULGARIA KIR KL A RE LI S IN O P BLACK E DÝRN E B AR TIN GR EE CE ZO N GU L D AK TEK IR DA G KAS TAMO N U O R DU TRA BZO N KA RS CA NK IR I A MAS YA C O RU M ARMENIA GU MU SH AN E BAY BU RT TO KA T BI LE CI K I GD I R ANKARA ER ZUR U M KIR IK KA LE ES KIS EH IR B AL IK ESI R AR DA HA N RI ZE G IR ESU N B OL U Y AL OVA BU RS A ÇA NA KKA LE AR TVIN SA MSU N K AR AB UK ÝSTANBUL ÝZMÝT A DA PAZ AR I 40° GEORGIA SEA YOZG AT 40 ° A GRI ER ZIN C AN S IV AS KU TAH YA K IR SEH IR MA NI SA U S AK TU NC EL I B IN GO L A FYO N NE VS EH IR ÝZMÝR DE NI ZL I BU RD U R BI TL IS MAL ATY A N IGD E KO N YA VA N EL AZI G A KS AR AY AYD IN IRAN M US K AYS ER I DI YAR BA KI R S IIR T B ATMA N A DI YA MAN IS PAR TA S IR NA K H AKK AR I K AH RA MAN MA RA S MU GL A MAR DI N K AR AMA N OSM AN IYE AN TALYA ME RS IN G AZ IAN TEP SA NL I UR FA IRAQ AD AN A K IL IS A NTA KYA 36° 36° 6 - MONTH MODERATE DROUGHT OCCURRENCES (%) MEDITERRANEAN SEA 28° 5 32° 36 ° 7 9 11 40 ° 1st Joint DMCSEE-JRC Workshop On Drought Monitoring 21.-25. September 2009 Ljubljana, Slovenia 13 44° DMÝ 25 0 2 00 15 0 1 00 0 50 SYRIA km 6 TSMS 1. 2. 3. CONCLUSION In this study, frequency and severity of meteorological droughts in Turkey have been investigated from a hazard concept and a detailed analysis of geographical variations in terms of the drought vulnerability using the Standardized Precipitation Index (SPI) is presented. Frequency of drought events at different severity categories and critical (threshold) rainfall data are computed at different time scales to identify drought vulnerability. Monitoring drought requires multiple indicators or indices. New approahes such as numerical hydrological models and numerical weather prediction models can be used for monitoring drought together with drought indexes. 1st Joint DMCSEE-JRC Workshop On Drought Monitoring 21.-25. September 2009 Ljubljana, Slovenia 7 TSMS Thank you for your attention… eturgu@meteor.gov.tr 1st Joint DMCSEE-JRC Workshop On Drought Monitoring 21.-25. September 2009 Ljubljana, Slovenia 8 TSMS