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Desirable Properties for a Drought Index, or

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Drought Index Evaluation and Implementation in a Geospatial Framework ... D. A. Wood, and P. A. Kay, Eds, Lincoln, NE, University of Nebraska, Lincoln, 29 33. ... – PowerPoint PPT presentation

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Title: Desirable Properties for a Drought Index, or


1
Desirable Properties for a Drought Index,
or Alices Adventures in SWSI-Land Kelly T.
Redmond Western Regional Climate Center Desert
Research Institute Reno Nevada Drought Index
Evaluation and Implementation in a Geospatial
Framework Linked to Hydrologic Data Web Services
Planning Workshop ESRL, Boulder CO, August
18-19, 2009
2
Some Drought Background Drought fundamentally
involves the concept of a water budget Supply
minus Demand Drought as accumulated Supply minus
Demand Need status of components of the water
balance Supply components Demand
components Ideally, everywhere in space, at the
necessary resolution Past, present, future
Drought is defined by its impacts A Working
Definition of Drought (very hard to avoid this
approach) Insufficient water to meet needs
3
Subjective / Objective Issue What does
objective mean? An objective process is one
that brings all relevant information to bear
- RSP discussion There are many ground truths
at once There are many droughts
simultaneously This approach is more
complicated, but more useful What is the purpose
of the Drought Monitor? Drought as a human
construct (is there natural drought?) Reinforce
ment of this notion in presentations at 2009
Climate Diagnostics and Prediction Workshop,
Lincoln NE, by Dave Stooksbury, Tom Pagano,
Andrea Ray None of the foregoing decreases the
need for quantitative measures of water inputs,
outputs, storage (human and natural)
4
DROUGHTS and RAINBOWs share one common
property Every person experiences their own
RAINBOW. Every person experiences their own
drought.
5
In general, the most consequential droughts
occur in the wettest portion of the year though
not always. Temperature seasonality is nearly
the same everywhere
6
Monthly USA Precipitation Climatologies Jan-Dec
www.wrcc.dri.edu/summary/sodusa.html
7
Madison Valley Seasonality Comparison Area.
8
Adapted from Phil Farnes, Western
Snow Conference, 1995.
9
Oct-Mar Apr-May-June Fraction of
Annual Total Precipitation, by Season July-Aug
WRCC / OSU
10
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11
K. Redmond, 2003. p 29-48, Water and Climate in
the Western United States. U Colorado Press.
12
From PRISM, courtesy of Chris Daly
13
March 10, 2004
70 / 1800 mm
55 / 1400 mm
12 / 300 mm
7.5 / 170 mm
14
Assorted Points about Indices The why question
What is the purpose? Who is the
audience? What do we want an index to tell
them? What do they want an index to tell
them? What are we expecting that people will do
with the information? Indices are human
constructions How do we want the index to
behave? In extreme or unprecedented cases
? In unusual cases (with other factors present)
? In known past episodes ?
15
Assorted Points about Indices continued A
fascination with numerics Diagnosis / evaluation
/ testing. How to ground truth indexes. Which
reality / realities do we wish to
describe? Judging good indices on the basis of
bad data. SPI (transparent) vs. Palmer
(obscure) Use promotes use Homogeneity of input
records
16
A useful series of articles about drought. August
2002 Bulletin of AMS
17
  • Desirable Properties of Climate Indices
  • Detailed understanding of caveats, limitations,
    assumptions should not be critical to proper
    interpretation for indices in wide public use.
    Indices should not be too complex.
  • 2) Indices should not be overly simplified
    (e.g., Colorado statewide precipitation lumps
    too many things together).
  • 3) Indices should offer improved information
    over raw data values.
  • 4) For routine practical usage, historical time
    series of data must be readily available, recent
    values must be quickly computable, and both must
    be compatible (homogeneous record).
  • 5) It is helpful if social and economic impacts
    are proportional to the value of the indices.

18
  • 6) Indices values should be open-ended.
    Unprecedented behavior yields unprecedented
    values.
  • Normalization to background climate, in
    non-dimensional units, greatly facilitates
    spatial comparisons across very different
    settings.
  • 8) Statistical properties and sensitivities
    should be thoroughly evaluated before operational
    usage.
  • 9) Subindices, component indices, or other
    spin-offs help debug or explain unusual,
    alarming, or otherwise interesting behavior.
  • Measures of placement within the historical
    context are invaluable and frequently requested,
    typically as percentiles. The goal should be a
    50100-year perspective.
  • From The Depiction of Drought, Kelly Redmond,
    Bulletin of AMS, August 2002, 83, 1143-1147.
  • Adapted from Redmond, K. T., 1991 Climate
    monitoring and indices, Proceedings of a
    Symposium on Drought Management and Planning, D.
    A. Wilhite, D. A. Wood, and P. A. Kay, Eds,
    Lincoln, NE, University of Nebraska, Lincoln,
    2933.

19
The Quantification of Drought An Evaluation of
Drought Indexes. Keyantash and Dracup, BAMS,
2002. Robustness Usefulness over a wide range of
physical conditions. Tractability Practical
computability (high level numerics, sparse data,
short or incomplete records, etc). Transparency Cl
arity of the objective, rationale behind the
index. Sophistication Conceptual soundness. May
oppose trnasparency. e.g., relativity theory is
not transparent or tractable, but nonetheless is
a superior description. Extendability Degree to
which an index may be extended across time to
alternate sequences and histories. Dimensionality
Composed of fundamental physical units, or, of
normalized, dimensionless, probabilistic forms.
20
Surface Water Supply Index Considerations Purpose
Need for application specificity, to be of
use What is the index supposed to correlate
with? Which quantitative impact or economic
measures? Are these available? And for a
sufficient time? Components are as important as
the whole thing Precipitation Snowpack Streamfl
ow Reservoir storage Groundwater and soil
moisture status relevant to surface water
21
Surface Water Supply Index Issues Manipulated
water systems have non-stationarity and/or
non-normal statistics Changes through time to
system infrastructure Dams raised Dams
added Reservoirs kept low for repairs or other
reasons Changes through time in system
measurement points Gages added Gages
removed Gages moved Changes through time in
operational policy and practices -
reservoirs Operations guidance changes
emphasis Project operated for different or
additional purposes Reservoir levels bounded by
full and empty Preference toward keeping
reservoirs full (power, recreation)
22
Lake Powell Storage Through July 23, 2009
As of 23 July 2009 67 full (capacity 24.17
MAF) Minimum 33 full on April 8, 2005
23
Lake Powell Elevation Through July 23, 2009
Water level on July 23, 2009 was 3641.76 ft, -
58 ft below full. Minimum level on April 8,
2005 was 3555 ft, -145 ft below full.
Source www.usbr.gov/uc/water/index.htl
24
Surface Water Supply Index Issues Different
periods of record for input data yield different
statistics Different SWSI components have
different periods of record (POR) ex
Snow vs reservoirs vs streamgages vs
precipitation For a given component (eg,
snowpack) not all sites have same POR Not all
past droughts may have been sampled by all
components or sites Inclusion (or not) of
1950s or 1930s drought can make a
difference Tails of the distributions are most
affected, and of greatest interest Intercorrelati
on among the components (how independent is the
input info?) There is always at least some
correlation, and sometimes a lot
Intercorrelations among components vary through
the seasonal cycle For some components,
significant correlation with adjoining months
25
Surface Water Supply Index Issuess Elephant
versus mouse issue Applies to each component,
but especially to reservoirs How to account for
large and small components in a basin Large
differences within many basins See upcoming
examples When should large and small systems be
normalized ? Relative versus absolute
water Percentages vs percentiles Which
reservoirs to include or exclude ? All
reservoirs matter to somebody, otherwise why
build them ? Statistics of aggregated water vs
Aggregated statistics of water When should we
do one, or the other ? In the general case
these are different. Sometimes very different.
26
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29
0.011
Capacity Total 31,058,490 MAF
0.121
0.004
0.027
0.055
0.783
30
Capacity Total 4,175,850 MAF
0.009
0.083
0.898
0.003
0.007
31
Capacity Total 1,182,954 MAF
0.090
0.014
0.015
0.701
0.099
0.070
0.011
32
0.169
0.018
0.056
0.004
0.753
Capacity Total 2,252,243 MAF
33
Surface Water Supply Index Issues How to
calculate the monthly or seasonal coefficients
(weights) ? What to optimize against (what
criteria ?) Multiple possibilities, but nothing
seems to stand out as best Monthly or seasonal
shocks with step changes in coefficients Creat
e continuously varying coefficients (Fourier
series, etc) ? Use coefficients as mechanism to
include/exclude components/sites Should work in
all climates in the US In non-snowy climates,
snow coefficients go to zero Inter-correlation
issue comes up here as well as other
places Predictive vs diagnostic
applications Some diagnostic information is also
(highly) prognostic This varies (substantially)
by season
34
Surface Water Supply Index Issues Is there
quantifiable impact or criteria information
? Much impact information does not have
stationary properties Changes through time in
quantification procedures Most impact
information records are not very long Impact
information often very short records (1-20
years) Most impact information records differ in
length from physical records Water rights,
allocated water Not all available water is
available for every purpose Much water is
reserved for a specific purpose A full
reservoir is useless (to others) if one has no
water rights A nearly empty reservoir may be ok
if remainder is for the user Users are concerned
only with water that is available / useful to
them With SWSI, all water is the same color
35
Surface Water Supply Index Issues Index
behavior Sensitivity change in index value per
unit change in input values Middle percentiles
sensitive to small changes in water amounts Tail
percentiles not very sensitive to large changes
in amount Tails are where there is most
interest ( drought) Bounded values Percentiles
bounded at 0 and 100 Good and bad
implications SWSI is bounded at -4.17 and 4.17
-4.07 or -4.00 seem almost the same as -4.17
. not! Nothing intuitive about these values,
like 0 and 100 No unprecedented values
allowed Is this a desirable property ?
36
Surface Water Supply Index Issues Practical and
logistical issues All data must be available in a
timely manner Generalized access to water
information Has to be on a publicly accessible
system Water data often hard to get Especially
reservoir time series Double-especially private
reservoir time series Metadata often in poor
shape, scattered, not quality controlled Need the
actual data time series, not just the
statistics Infilling of missing segments Quality
control to produce a working copy operational
data base Automated ingest for nearly
everything What is the updating cycle? Some
data only available manually (weakest link
problems)
37
Surface Water Supply Index Issues Migrate from
monthly to daily ? A lot can change in a month,
and right after a new month starts Some data
readily available daily, others hard to obtain
even monthly Social Issues Neutral information
broker is needed Who calculates it ? Is this
even a relevant question ? All components
available for a user to create their own
SWSI SWSI versus BWI Why leave out
groundwater ? BWI Basin Water
Index Recommended by participants in 2002 NRCS
workshop SWSI comes from setting groundwater
coefficient to zero Can have many flavors of
indices by setting coefficients to zero
38
Surface Water Supply Index Issues Testing and
validation Drought trigger (SWSI or any other)
behavior in past droughts Case studies of past
drought episodes As earlier droughts evolved,
which information was superior ? Are there
certain situations where SWSI is the more useful
index ? Is one particular component driving a
low SWSI value ? Occams Razor Most drought
indicators are correlated Simpler approach often
almost as good (SPI versus Palmer example) Might
we be letting the perfect be the enemy of the
good ? Too simple vs Too complicated Presenta
tion Need creative ways to show all components
at once
39
The Quantification of Drought An Evaluation of
Drought Indexes. Keyantash and Dracup, BAMS,
2002. Robustness Usefulness over a wide range of
physical conditions. Tractability Practical
computability (high level numerics, sparse data,
short or incomplete records, etc). Transparency Cl
arity of the objective, rationale behind the
index. Sophistication Conceptual soundness. May
oppose trnasparency. e.g., relativity theory is
not transparent or tractable, but nonetheless is
a superior description. Extendability Degree to
which an index may be extended across time to
alternate sequences and histories. Dimensionality
Composed of fundamental physical units, or, of
normalized, dimensionless, probabilistic forms.
40
Keyantash and Dracup, BAMS, 2002
41
Thank You
42
DISCARDS
43
The Standardized Precipitation Index. McKee,
Doesken, Kleist, 1995. From Experience, Five
Commonly Asked Questions How much precipitation
have we had? Absolute Amount in units. How much
more or less than usual is this? Absolute
Departure in units. What percent of average is
this? Relative Departure in Percentage
units. How often does this happen? Historical
context. Is there some kind of description
comparable across space? Standard Plus We
can be in different situations in different time
scales.
44
Rationale for use of Standardized Precipitation
Index Straightforward interpretation Depends
only on precipitation Similar to Palmer Index in
some ways Correlates well and best at 8-12
month time scales But, disagreement is not
necessarily undesirable they measure
different things Several useful associated
quantities Basic time step is now monthly
Consideration of shorter, sub-monthly, time
scales Precipitation data easiest to get
45
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46

R. Seager, M.F. Ting, I.M. Held, Y. Kushnir, J.
Lu, G. Vecchi, H.-P. Huang, N. Harnik, A.
Leetmaa, N.-C. Lau, C. Li, J. Velez, N. Naik,
2007. Model Projections of an Imminent Transition
to a More Arid Climate in Southwestern North
America. Science, DOI 10.1126/science.1139601
47
Seager et al, 2007. Average of 19 climate
models. Figure by Naomi Naik. www.ldeo.columbia.
edu/res/div/ocp/drought/science.shtml
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