Using Standardized Precipitation Index for Monitoring and Analyzing Drought

Transcription

1 1st Joint DMCSEE-JRC Workshop On Drought Monitoring September 2009 Ljubljana, Slovenia Using Standardized Precipitation Index for Monitoring and Analyzing Drought presented by Ertan TURGU* Research Department *Turkish State Meteorological Service, Ankara, Turkey 1

2 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 Drought Assessment Tools 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 2

3 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 3

4 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: 4

5 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. 5

6 0 0 DROUGHT OCCURENCES AND SPATIAL ANALYSIS: SPI index has been applied to long-term precipitation data at 101 stations for period. Here, our aim is to identify some areas vulnerable to drought at comparable time steps based on their occurence frequencies. BULGARIA GREECE 50 KIR KLA RE LI E DÝRN E Ç ANA KKA LE 100 MA NISA ÝZ MÝR TEK IR DA G 150 A YDIN 28 B ALIK ESI R MUGLA 250 km ÝSTANBUL BU RS A DE NI ZLI Y ALOVA U SA K ÝZMÝT BI LE CI K KU TAH YA A DA PAZ AR I BURD U R ESK IS EHIR A FYON ANTALYA IS PAR TA MEDITERRANEAN SEA S INOP BLACK SEA ZON GU LD AK B OLU 32 B AR TIN K AR AB UK K AR AMA N KAS TAMON U CA NK IR I ANKARA KIRIK KA LE K ONY A KIR SEHIR A KSA R AY NE VS EH IR N IGD E ME RS IN C ORU M YOZGAT K AYS ER I AD AN A A MAS YA SA MSU N OSM ANIYE A NTA KYA TOKA T S IV AS K AHRA MAN MA RA S GAZI AN TEP K ILIS SYRIA ORDU GIRESU N MALATY A A DI YA MAN SA NLI UR FA ELAZI G TRA BZON GU MU SH AN E ER ZINC AN TU NCELI RI ZE BAY BU RT B IN GOL DI YAR BA KI R MARDI N ER ZUR U M AR TVIN M US B ATMA N BI TLIS S IIR T AR DA HA N S IR NA K GEORGIA KAR S A GRI V AN IRAQ H AKK ARI 3 - MONTH MODERATE DROUGHT OCCURRENCES (%) IGD I R ARMENIA 44 IRAN DMÝ BULGARIA KIRKLA RE LI E DÝRNE S INOP BLACK SEA B AR TIN GEORGIA GREECE TEK IR DA G ÇA NA KKA LE B ALIK ESI R ÝSTANBUL ÝZMÝT A DA PAZ AR I Y ALOVA BU RS A BI LE CI K ES KIS EH IR ZON GU LD AK KAS TAMONU K AR AB UK B OLU CA NK IR I ANKARA KIR IK KA LE C ORU M YOZGAT SA MSUN A MAS YA TOKA T S IV AS ORDU GIR ESU N RI ZE TRA BZON GU MUSH AN E BAY BURT ERZIN CAN ER ZUR UM AR TVIN ARDA HA N KA RS A GRI ARMENIA I GD I R KU TAH YA K IRSEH IR TUNC ELI MA NI SA ÝZMÝR U S AK A FYON A KS AR AY NE VS EH IR K AYS ER I MALATY A ELAZI G B IN GOL M US BITLIS VA N IRAN AYD IN DE NI ZLI BURD UR IS PAR TA KONYA N IGD E A DI YA MAN DI YARBA KI R B ATMA N S IIRT S IR NA K H AKK AR I K AHRA MAN MA RA S MU GLA MARDI N ANTALYA K ARAMA N OSM AN IYE GAZ IANTEP SA NLI URFA IRAQ ME RS IN AD AN A K ILIS A NTA KYA MEDITERRANEAN SEA 6 - MONTH MODERATE DROUGHT OCCURRENCES (%) SYRIA km DMÝ 6

7 CONCLUSION 1. 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. 2. Monitoring drought requires multiple indicators or indices. 3. New approahes such as numerical hydrological models and numerical weather prediction models can be used for monitoring drought together with drought indexes. 7

8 Thank you for your attention 8