Drought forecasting methods Blaz Kurnik DESERT Action JRC
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1 Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 1 Drought forecasting methods Blaz Kurnik DESERT Action JRC
2 Motivations for drought forecasting Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 2 Transport and energy Crop production Water resources management Health and diseases Tourism and pleasure
3 Methods of drought forecasting Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 3 STATISTICAL Based on observed historical correlations between global variables and regional weather patterns NUMERICAL Based on the use of WPMs easy to calculate (no need of the complicated physical models) Physical equations used for describing the connections between variables Probabilistic approach using number of model simulations historical correlation between global variables and regional climate is stationary (Climate changing!!??) Chaotic behaviour of the climate systems produce uncertainty for long range forecasts
4 Time scale for drought forecasting Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 4 Creeping phenomena long range forecast needed!! Starting 06/2003 Ending 09/2003 Starting 04/2007 Ending 09/2007
5 Probabilistic or deterministic approach Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 5 Drought indicator probabilistic Time Deterministic
6 Hydrological prediction system for drought forecasting Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 6 Meteorological forecasts Deterministic forecasts Ensemble forecasts Meteorological observations Hydrological model Land cover, soil properties, topography, Hydrological model Meteorological observations Forecasted soil moisture Probability of exceeding SM threshold Probability of exceeding index threshold up to 10 days 5 km resolution monthly forecasts km resolution
7 Probabilistic or deterministic approach Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 7 Deterministic weather forecasts are rarely used for drought forecasting because of the additional unpredictable uncertainty beyond few days. No added value for better decision making Using ensemble predictions allows (some) quantification of the uncertainty
8 Model or Index Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 8 Production of soil moisture information: the LISFLOOD model Standardized Precipitation Index - SPI calculations A physically based distributed rainfall-runoff model programmed in a dynamic GIS-language The SPI is an index based on the probability of recording a given amount of precipitation, and the probabilities are standardized so that an index of zero indicates the median precipitation amount McKee et al Calculation of the soil moisture important drought indicator Complex model which needs a lot of validation and calibration for different regions Easy to compute (historical and operational precipitation data) Drought is calculated using only precipitations
9 SPI-3 forecasting using ensemble ECMWF monthly forecasts Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring ERA-40 2 months precipitations over Europe 50 ensemble precipitation monthly ECMWF forecasts over Europe SPI calculation using historical ( ) ERA-40 time series.. between 2005 and
10 Going from ensembles to probability Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring possible states of the forecast Probabilistic approach (calculating the probability for exceeding thresholds )
11 Probabilistic approach for SPI-3 forecasts Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 11 Probability that SPI-3 for the next month is severe dry or worse (SPI < -1.5) 1-month probabilistic forecast of SPI-3 (based on ERA-40 and monthly ensemble ECMWF forecasts)
12 Data availability Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 12 ERA-40 data Directly from ECMWF ecaccess server using retrieve scripts Special account needed Indirectly from JRC-FOODSEC page (free) ECMWF monthly forecasts Directly from ECMWF ecaccess server using retrieve scripts Special account needed GPCC precipitation data Directly from DWD server (free)
13 Forecasted probability vs. observed SPI-3 Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 13 Forecasts (probability for SPI<-1.5) based on the ERA-40 and monthly ensemble forecasts Observations (SPI< severe&extreme dry) based on the GPCC precipitation gridded measurements
14 Climatological probability vs. observed SPI-3 Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 14 Forecasts (probability for SPI<-1.5) based on the GPCC precipitation database historical probability Observations of SPI-3 based on the GPCC precipitation database
15 Validation of the probabilistic forecasts Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 15 Direct validation of probability forecasts Conversion to the categorical forecasts Probability threshold For N Y Obs Y N a c b d Contingency table Brier score (BS) Brier skill score (BSS) Proportion correct (PC) Hit rate (HR) False alarm rate (FAR) Pierce s skill score (PSS) Relative Operation Characteristic - ROC
16 Indirect validation conversion to the CT Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 16 If (prob of SPI-3 >= 60 % ) then FORECAST = 1 Obs 1 0 If (prob of SPI-3 < 60 % ) then FORECAST = 0 If (SPI-3 <= -1.5 ) then OBSERVATION = 1 For 0 1 a c b d Contingency table If (SPI-3 > -1.5 ) then OBSERVATION = 0 counting over all grid cells Proportion correct (PC) Hit rate (Probability of detection) False alarm rate PC = a + d n HR = a a + c FAR = b b + d
17 Indirect validation conversion to the CT Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 17 If (prob of SPI-3 >= 60 % ) then FORECAST = 1 Obs 1 0 If (prob of SPI-3 < 60 % ) then FORECAST = 0 If (SPI-3 <= -1.5 ) then OBSERVATION = 1 For 0 1 a c b d Contingency table If (SPI-3 > -1.5 ) then OBSERVATION = 0 counting over all grid cells Relative Operation Characteristic - ROC plotting HR against FAR Pierce s skill score or Kuipers skill score PSS = HR FAR hr decision thresholds far PSS = ad bc ( a + c)( b + d)
18 Direct validation of probability forecasts Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 18 Brier score (BS) : quadratic scoring measure BS = 1 n n j= 1 ( p j x j ) 2 p j = probability (SPI-3 <= -1.5) if (SPI-3 <= -1.5) ; 1 X j = if (SPI-3 > -1.5) ; 0 BS = 0 : perfect forecast BS = 1 : systematically erroneous forecast
19 Direct validation of probability forecasts Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 19 Brier skill score (BSS) BSS = 1 BS BS ref if ( BS < BS ref ); BSS > 0 If ( BS >= BS ref ); BSS <=0 BSS = 1 : perfect skilled forecast BSS < 0 : poorer than reference system BSS - inf
20 What is a reference system? Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 20 Reference system can be ensemble of climatological forecasts, where probability is base rate (climatological) probability of occurrence of the event. Climatological probabilistic forecasts are often assumed as a low skill forecasts.
21 How climatological SPI-3 probabilistic forecasts are produced? Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring months precipitations observation over Europe 33 years of precipitations (33 years of data between ) SPI calculation using historical ( ) time series.. between 2005 and
22 Going from historical events to probability Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring possible states of the forecast Probabilistic approach (calculating the probability for exceeding thresholds, based on historical probability )
23 Results: Contingency table measures Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring Proportion correct: EPS Proportion correct: CLI 2005/ / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / 12
24 Results: Contingency table measures Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 24 Hit Rate: EPS Hit Rate: CLI 2005/ / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / False Alarm Rate: EPS False Alarm Rate: CLI
25 Results: ROC scatter plot Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring Forecasts yjex Climatology Low system TH 0.7 Medium system TH Hit Rate High system TH FAR > HR : no skill False Alarm Rate
26 Results : Brier Score Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 26 Seasonality??? Extremely low BS and BS ref
27 Results : Brier Skill Score Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 27 Brier Skill Score 2005/ / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /12
28 Results: BS and BS ref maps Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 28 Average BS and BS ref maps in the period between
29 Results : BSS map Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 29 Average BSS map over the period
30 Conclusions Ljubljana on 24 September st DMCSEE JRC Workshop on Drought Monitoring 30 for drought forecasting long range meteorological forecasts are needed (more than 2 weeks); drought forecasting should be done with probabilistic approach; both statistical and numerical forecasting have to be considered; low or no skill detected over the whole European domain. However spatial and temporal variation is high; integrated approach for drought forecasting is necessary - both hydrological indicators (soil moisture, low flows, ) and meteorological indicators (SPI, PDSI, ) should be forecasted; validation of the forecast has to be improved, using longer timeseries and regional specifications.
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