16. naučno savetovanje SDHI/SDH,
22-23. oktobar 2012, Donji Milanovac
PROBLEMS IN DETECTING TREND
IN HYDROMETEOROLOGICAL
SERIES FOR CLIMATE CHANGE
STUDIES
Jasna Plavšić1 and Zoran Obušković2
1University
of Belgrade – Faculty of Civil Engineering
2Energoproject – Hydroengineering
Climate change
• Global warming and increased
concentrations of greenhouse gases
Hansen et al, Proc. Natl. Acad. Sci., (2006)
Copenhagen Diagnosis (2009)
Climate change – we know
Radionica - Klimatske Promene - 2010
Copenhagen Diagnosis (2009)
www.slobodansimonovic.com
Climate change – we know
Radionica - Klimatske Promene - 2010
www.slobodansimonovic.com
Climate change – we know
Church and White , Geophysical Research Letters, (2006)
Cazenave et al, Global and Planetary Change, (2009)
Radionica - Klimatske Promene - 2010
www.slobodansimonovic.com
Climate change impacts
• Questions:
– Change projections?
– Impact on water
resources?
IPCC (2007)
Impact of climate change on water
resources
Estimation of climate change impacts
Future climate
scenarios + hydrologic
models
Statistical trends
fairly complicated approach;
propagation of uncertainty
simple calculations; but:
How to prove presence of a trend?
How to interpret the trend?
Trend detection
• Starting point: hydrometeorological series are
considered stationary
– stationarity is well defined and departures from
stationary indicate changes
• Trend detection vs. identification of non-stationarities
– trend in mean is just one type of non-stationarities
– false trend detection in time series where other nonstationarities are present
• slow changes (long memory) can look like trend when observed
in shorter periods
– significance of trends can decrease in series with long
memory and high serial correlation
Practical aspects of trend analysis –
choice of variables
• Runoff
– mean flows, floods, low
flows
– annual and monthly
values
– time of occurrence of
annual maximum flood
– ice start and end dates,
number of days with ice
• Precipitation
– annual and monthly
precipitation
– daily precipitation
annual maxima
– number of rainy days
– etc.
Practical aspects of trend analysis –
choice of stations
• Trend analysis is valid if performed on adequate
series
– time series should be long enough for reliable
statistical analysis
• WMO recommends 30-year statistics for describing climate
(eg. standard climatological period 1961-1990)
• series used for analysis of change in climate should be
much longer than 30 years
– series should reflect natural flow regime with no
human interventions within the basin
– data from a station should be checked for accuracy
and consistency (rating curves etc.)
Tests for trend
• Linear regression: X = a + bt
140
Lim/Brodarevo
120
Q (m3/s)
100
80
60
40
y = -0.1676x + 401.87
20
Qsrgod
0
1920
1930
1940
1950
1960
Linear (Qsrgod)
1970
1980
1990
2000
2010
slope significance?
Tests for trend
• Non-parametric tests
– data need not be drawn from a (normal) distribution
– some test assume data independence
• Most popular: Mann-Kendall test
– H0: no monotonic decreasing or increasing trend
– H0 is rejected when S significantly departs from 0
– serial correlation decreases detection power
Other test for detecting changes in
time series
Tests for change in the mean
Z-test, t-test, Pettitte test
Tests for change in variance
F-test
Tests for change in
distribution
Tests for randomness
Mann-Whitney, KolmogorovSmirnov
Run test
Tests for serial correlation
Bartlett’s test
Tests for trend
Mann-Kendall, Spearman rho,
linear regression slope
700
Example
Drina/Radalj
600
10
6
400
4
300
2
0
200
-2
100
0
1920
– Brodarevo/Lim
– Drina/Radalj
Qsrgod
LOWESS (0.3)
sred. vred.
Sum Z
-4
-6
1940
1960
1980
2000
140
18
Lim/Brodarevo
120
16
Q (m3/s)
12
10
80
8
60
6
4
40
2
20
0
1920
Qsrgod
LOWESS (0.3)
sred. vred.
Sum Z
0
-2
1940
1960
1980
2000
SUM (Q - Qsr)/SQ
14
100
EnergoprojektHidroinženjering
2011, 2012
SUM (Q - Qsr)/SQ
8
500
Q (m3/s)
• Runoff,
precipitation and
temperatures in
the Drina Basin
12
Example
• Precipitation and runoff cycles
– cumulative standardized deviation from the
mean
15
padavinske stanice
Σ(X - Xsr)/Sx
Qsrgod Radalj
0
-15
1950
1960
1970
1980
1990
2000
2010
Radalj
Example
• Runoff
– no
significant
trend
MEAN ANNUAL FLOWS
ANNUAL MAXIMUM FLOODS
LOW FLOWS (annual minimum monthly flows)
Brodarevo
Example
• Runoff
– Significant
decreasing
trend in
mean
annual
flow
MEAN ANNUAL FLOWS
ANNUAL MAXIMUM FLOODS
LOW FLOWS (annual minimum monthly flows)
Example
• Temperatures
– 8 met.
stations
Results of trend
analysis
1.5
Loznica
Radalj
• Temperatures
Bajina Bašta
1.5
– change in 2035
Zlatibor
1.3
– in accordance with
other studies
Prijepolje
Pljevlja
1.1
Žabljak
1.5
Brodarevo
Bijelo Polje
1.0
Berane
Kolašin 1.3
1.0
Example
• Precipitation
– 10 stations
1.4
1.3
Berane
Prijepolje
Pljevlja
Bijelo Polje
Kolašin
Zlatibor
Brodarevo
Žabljak
Loznica
Pgod / Psr
1.2
1.1
1
0.9
0.8
0.7
1940
1950
1960
1970
1980
1990
2000
2010
2020
Results of trend
analysis
14.7%
Loznica
Radalj
• Precipitation:
– % change in 2035
– other studies: absence of
trend or weak increasing
or decreasing trends
– change in seasonal
distribution of
precipitation, with
opposite tendencies for
summer and winter
seasons
Bajina Bašta
-16.5%
Zlatibor
22.7%
Pljevlja
5.9%
Žabljak
6.5%
Prijepolje
-0.6%
Brodarevo
0.4%
Bijelo Polje
15.2%
Berane
Kolašin 5.0%
9.9%
Conclusions
• Trend detection – problems:
– Series of different lengths can exhibit different,
even opposite, trends
– Spatial inconsistency of the stations are
considered separately
– Presence of non-stationarities makes trend
detection more difficult
– Opposite changes in different seasons result in
insignificant changes at annual level
Conclusions
• River basins with heavily modified flow
regime (such as reservoirs) require detailed
and careful analysis based on climate and
hydrologic modelling with consideration of
water management practices
THANKS FOR ATTENTION