Applying New Drought Decision Support Tools Mark Svoboda National Drought Mitigation Center...

Applying New Drought Decision Support Tools

Mark SvobodaNational Drought Mitigation Center

International Drought Information CenterUniversity of Nebraska-Lincoln

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Historical reference (probability of recurrence)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

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)

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

How the SPI works

Need 30 years of continuous monthly precipitation dataSPI time scale intervals longer than 24 months may be unreliableIs spatially invariant in its interpretationProbability based (probability of observed precipitation transformed into an index) nature makes it well suited for risk management

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 medianNormal distribution allows for estimating both dry and wet periodsAccumulated values can be used to analyze drought severity

Hilo 6-Month SPI (1950-1998)

-3

-2

-1

0

1

2

3

Jan

-50

Jan

-55

Jan

-60

Jan

-65

Jan

-70

Jan

-75

Jan

-80

Jan

-85

Jan

-90

Jan

-95

Time

SP

I

Hilo 12-Month SPI (1950-1998)

-3

-2

-1

0

1

2

3

Jan

-50

Jan

-55

Jan

-60

Jan

-65

Jan

-70

Jan

-75

Jan

-80

Jan

-85

Jan

-90

Jan

-95

Time

SP

I

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/

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

1-m onth SPI through the end of April 2001

C opyright © 2001 N ational D rought M itigation C enter

+ 2.0 and above (extremely wet)

+ 1.50 to + 1.99 (very wet)

+ 1.0 to + 1.49 (moderately wet)

-0.99 to + 0.99 (near normal)

-1.00 to -1.49 (m oderately dry)

-1.50 to -1.99 (severely dry)

-2.0 and less (extremely dry)




+ 1.50 to + 1.99 (very wet)









+ 1.50 to + 1.99 (very wet)









+ 1.50 to + 1.99 (very wet)






Results

Increased spatial and temporalresolution of SPI/PDSI improves:

• Drought monitoring

• Vulnerability mapping

• Decision support

-exposure analyses

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

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: [email protected]

The weekly SPI code can be found at:http://nadss.unl.edu/

How to get the SPI Programs

http://nadss.unl.edu

A Case Study:A Case Study:

Monitoring Drought in Hungary with Monitoring Drought in Hungary with the Standardized Precipitation Indexthe Standardized Precipitation Index

A. Bussay1, M. Hayes2, Cs. Szinell1, and M. Svoboda2

1. Hungarian Meteorological Service2. National Drought Mitigation Center, University of Nebraska-Lincoln

Goals of the Study

One of the first applications of the SPI in Hungary and in EuropeTo identify the relationship of the SPI with streamflow, groundwater levels and

soil moisture valuesTo assess and compare the monitoring capabilities between the SPI and the Palmer Drought Severity Index

The Study AreaThe Study Area

Table 1. Classification scale for SPI values (after Edwards and McKee 1996)

SPI Values Category Time in Category (%)

+2.00 Extremely Wet 2.3 +1.50 to +1.99 Very Wet 4.4

+1.00 to +1.49 Moderately Wet 9.2

-0.99 to +0.99 Near Normal 68.2

-1.00 to -1.49 Moderately Dry 9.2

-1.50 to -1.99 Severely Dry 4.4 -2.00 Extremely Dry 2.3

Classification Scale for SPI Values

-3-2.5

-2-1.5

-1-0.5

00.5

11.5

2

1981 1982 1983 1984 1985

Years

SP

I

-6-5-4-3-2-101234

PD

SI

3-month SPI 18-month SPI PDSI

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

0

50

100

150

200

250

300

1981 1982 1983 1984 1985Years

Wat

er ta

ble

dept

h (c

m)

-2.5-2-1.5-1-0.500.511.5

SPI

Water Table 6-month SPI 12-month SPI

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

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

http://nadss.unl.edu

Applying New Drought Decision Support Tools Mark Svoboda National Drought Mitigation Center...

Documents

Transcript of Applying New Drought Decision Support Tools Mark Svoboda National Drought Mitigation Center...