1. Applying New Drought Decision Support Tools Mark Svoboda National Drought Mitigation Center International Drought Information Center University of Nebraska-Lincoln

2. Importance of Drought Indices Simplify complex relationships and provide a good communication tool for diverse audiences Quantitative assessment of anomalous climatic conditions: *Intensity *Duration *Spatial Extent Historical reference (probability of recurrence) *Planning and design applications

3. Sample of Drought Indices Percent of Normal Precipitation Deciles Aridity Index (AI) Palmer Drought Index (PDI) suite of indices Crop Moisture Index (CMI) Surface Water Supply Index (SWSI) Reclamation Drought Index (RDI) Standardized Precipitation Index (SPI)

4. What is the SPI? Developed in 1993 in the United States (McKee et al. 1993, CSU) Being studied or applied in over 50 countries Simple to use (precipitation only) Temporal flexibility allows the user to look at and monitor all water resources in a region

5. How the SPI works Need 30 years of continuous monthly precipitation data SPI time scale intervals longer than 24 months may be unreliable Is spatially invariant in its interpretation Probability based (probability of observed precipitation transformed into an index) nature makes it well suited for risk management

6. How the SPI works It is NOT simply the “difference of precipitation from the mean… divided by the standard deviation” Precipitation is normalized using a probability distribution so that values of SPI are actually seen as standard deviations from the median Normal distribution allows for estimating both dry and wet periods Accumulated values can be used to analyze drought severity

8. The SPI Program Monthly program w/ interface PC version is coded in C++ Easy to use interface compared to original command-line UNIX version Distributed to over 60 countries UNIX version also available (robust) Download PC code from the NDMC: http://www.drought.unl.edu/monitor/spi/ program/spi_program.htm Weekly code available at: http://nadss.unl.edu/us/download/

14. National Agricultural Decision Support System NSF 3-year project ($1M) Digital Gov. Allows for weekly table/map output of the SPI, PDSI and Newhall values 1948-present (base 61-90’ for distribution) Leap years and missing/estimated data HPRCC/UCAN SHEF real-time data from COOP and first order sites

30. Results Increased spatial and temporal resolution of SPI/PDSI improves: Drought monitoring Vulnerability mapping Decision support -exposure analyses

31. Next Steps Incorporate elevation factor (SNOTEL) Integrate TD3206 data for pre-1948 analysis Incorporate projected SPI maps based on.80/.50/.20 for decision makers Implement Pearson III in the code Extend out to 104 weeks (2 years) More exposure analysis research

32. The UNIX version of the SPI can be found at: http://ulysses.atmos.colostate.edu/SPI.html Additional copies of the PC version of the SPI can be obtained by emailing me at: msvoboda2@unl.edu The weekly SPI code can be found at: http://nadss.unl.edu/ How to get the SPI Programs

33. http://nadss.unl.edu

34. A Case Study: Monitoring Drought in Hungary with the Standardized Precipitation Index A. Bussay 1, M. Hayes 2, Cs. Szinell 1, and M. Svoboda 2 1. Hungarian Meteorological Service 2. National Drought Mitigation Center, University of Nebraska-Lincoln

35. Goals of the Study One of the first applications of the SPI in Hungary and in Europe To identify the relationship of the SPI with streamflow, groundwater levels and soil moisture values To assess and compare the monitoring capabilities between the SPI and the Palmer Drought Severity Index

36. The Study Area

37. Classification Scale for SPI Values

38. The course of 3-month SPI, 18-month SPI and PDSI during the 1983 drought in the southeast of Hungary

39. The course of the 6-month SPI, 12-month SPI and water table depth during the 1983 drought in the southeast of Hungary

40. Findings The SPI and PDSI showed the strongest relationship at around 6 or 7 months Shorter time scales worked best with soil moisture and streamflow (2 to 3 months) Longer time scales worked best with groundwater levels (12 to 24 months) In all cases, the SPI captured tendencies and characteristics of these variables

41. http://nadss.unl.edu

48. Next Steps Compute and map the indices at a regional/national levels Fully utilize ACIS Continue to work on weekly PDSI Implement cluster-computing to speed up processing Account for elevation Integrate TD3206 for pre-1948 analysis