DROUGHT FORECASTING IN CROATIA USING STANDARDIZED PRECIPITATION INDEX (SPI)

Transcription

1 Meteorological and Hydrological Service of Croatia DROUGHT FORECASTING IN CROATIA USING STANDARDIZED PRECIPITATION INDEX (SPI) K. Cindrićand L. Kalin 7 10h European Conference on Applicaions of Meteorlogy (ECAM) 12 th 16 th Sep 2011, Berlin, Germany

2 Outline 1. Motivation 2. Data and methods 3. Results 4. Conclusions and future work

3 1. Motivation in Croatia drought causes highest economic losses(39%) among all hydromet. events; most frequent hazard in last two decades the highest damages due to drought impacts were in 2000 (84%), 2003 (90%) and 2007 (80%) e.g. Eastern Cro: 2007 the highest monthly precipitation deficit in the last 110yrs current drought monitoring system in Croatian HM Service ( is based on SPI on different time scales (1, 3, 6, 12, 24, 48 months)

4 => an increasing interest in developing methods for drought warning system in Croatia Drought early warning system should: - drought monitor - provide an early warning of drought onset and it s intensity in timely manner - have drought prediction compoment (to protect crops, fire risk, water supply etc. )

5 Outline 1. Motivation 2. Data and methods 3. Results 4. Conclusions and future work

6 Standardized Precipitation Index (SPI) universal measure of meteorological drought accepted by WMO developed by McKee et al (1993) suitable tool for assesing drougth intensityand duration uses only the precipitation data at given location can be calculated for different time scales -separates different types of drought(meteorological, hydrological, agricultural)

7 Standardized Precipitation Index (SPI) fit the precipitation sums for a certain time scales to the gamma probability distribution a long base period precipitation data are needed for calculating parameters (calibration) cumulative prob. distribution is transformed to a standardized normal distribution SPI > 0 : precipitation > median SPI < 0 : precipitation < median utilizes incomplete gamma pdf due to comparisons around the world

8 daily and monthly precipitation records for 5 met. stations representing different climate regions in Croatia ECMWF precipitation forecast: -medium range ( 9 days) - monthly (28 days) - seasonal (1 month) calibration period

9 real ECMWF forecast 21 days 9 days SPI days SPI 28 1 month SPI 1 2 months 1 month SPI 3

10 Outline 1. Motivation 2. Data and methods 3. Results 4. Conclusions and future work

11 Verification methods 1) as for continuous variable Mean error -me Mean absolute error -mae Root mean square error -rmse

12 Verification methods (2) 2) as for categorical values define forecasted event based on SPI threshold based on forecasted/observed events a contingency table is calculated

13 2 2 contingency table observed Yes No forecast Yes No a HITS c MISS b FALSE ALARM d CORR. REJECT. Bias=(a+b)/(a+c) H= a/(a+c) hit rate F = b/(b+d) false alarm rate PC = (a+d)/n proportion correct HSS =2*(ad-bc)... Heidke skill score PSS=(ad bc)/(a+c)*(b+d) Pierce skill score CSI=a/(a+b+c) Critical success index

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16 thresh. a b c d n clim bias H F PC HSS PSS CSI ZGM OSI GOS RIJ SPL

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18 threshold a b c d n klim bias H F PC HSS PSS CSI ZGM NaN NaN NaN NaN NaN me=-.256 mae=1.042 rmse=1.185 OSI NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN me=-.363 mae=0.653 rmse=0.788 GOS NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN me=-.219 mae=0.882 rmse=1.119 RIJ NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN me=-.265 mae=0.854 rmse=1.049 SPL NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Infinity NaN NaN NaN me=0.438 mae=0.736 rmse=0.940

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20 threshold a b c d n clim bias H F PC HSS PSS CSI ZGM NaN NaN NaN NaN NaN me=0.263 mae=0.705 rmse=1.012 OSI me=0.283 mae=0.830 rmse=1.133 GOS me=0.259 mae=0.763 rmse=1.175 RIJ me=0.187 mae=0.911 rmse=1.258 SPL me=0.060 mae=0.740 rmse=1.014

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22 threshold a b c d n clim bias H F PC HSS PSS CSI ZGM Infinity NaN NaN NaN Infinity NaN NaN NaN NaN NaN NaN NaN NaN me=0.049 mae=0.435 rmse=0.537 OSI Infinity NaN NaN NaN me=0.021 mae=0.391 rmse=0.484 GOS Infinity NaN NaN NaN me=0.065 mae=0.492 rmse=0.620 RIJ Infinity NaN NaN NaN me=0.095 mae=0.418 rmse=0.527 SPL NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Infinity NaN NaN NaN me=0.038 mae=0.448 rmse=0.570

23 Outline 1. Motivation 2. Data and methods 3. Results 4. Conclusions and future work

24 4.1. Conclusions SPI30 (21 real + 9 forecasted) skillful slight overestimating of SPI (too wet) often still not catching extreme events Monthly and seasonal forecasts (SPI28, SPI1 and SPI3) significantly less skill signal too weak(no extreme forecasts) monthly fc. skill comparable to seasonal?

25 4.2. Future work establish operational SPI forecast(spi30) combining real and forecasted data apply probabilisticapproach (use ensemble members instead of ensemble median/mean) find best fitting PDF to calculate station SPI SPI6, SPI12...

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