p roblems in detecting trend in hydrometeorological series for climate change studies jasna...
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PROBLEMS IN DETECTING PROBLEMS IN DETECTING TREND IN TREND IN
HYDROMETEOROLOGICAL HYDROMETEOROLOGICAL SERIES FOR CLIMATE SERIES FOR CLIMATE
CHANGE STUDIESCHANGE STUDIES
Jasna Plavšić1 and Zoran Obušković2
1University of Belgrade – Faculty of Civil Engineering2Energoproject – Hydroengineering
16. naučno savetovanje SDHI/SDH, 22-23. oktobar 2012, Donji Milanovac
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Climate changeClimate change
• Global warming and increased concentrations of greenhouse gases
Hansen et al, Proc. Natl. Acad. Sci., (2006)
Copenhagen Diagnosis (2009)
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Climate change – we knowClimate change – we know
Radionica - Klimatske Promene - 2010 www.slobodansimonovic.com
Copenhagen Diagnosis (2009)
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Climate change – we knowClimate change – we know
Radionica - Klimatske Promene - 2010 www.slobodansimonovic.com
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Climate change – we knowClimate change – we know
Radionica - Klimatske Promene - 2010 www.slobodansimonovic.com
Church and White , Geophysical Research Letters, (2006)Cazenave et al, Global and Planetary Change, (2009)
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Climate change impactsClimate change impacts
• Questions:– Change projections?– Impact on water
resources?
IPCC (2007)
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Impact of climate change on water Impact of climate change on water resourcesresources
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?
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Trend detectionTrend 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 non-
stationarities 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
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Practical aspects of trend analysis – Practical aspects of trend analysis – choice of variableschoice 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.
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Practical aspects of trend analysis – Practical aspects of trend analysis – choice of stationschoice 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.)
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Tests for trendTests for trend
• Linear regression: X = a + bt
slope significance?
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Tests for trendTests 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
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Other test for detecting changes in Other test for detecting changes in time seriestime 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
Mann-Whitney, Kolmogorov-Smirnov
Tests for randomness Run test
Tests for serial correlation Bartlett’s test
Tests for trend Mann-Kendall, Spearman rho, linear regression slope
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ExampleExample
• Runoff, precipitation and temperatures in the Drina Basin – Brodarevo/Lim– Drina/Radalj
Energoprojekt- Hidroinženjering
2011, 2012
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ExampleExample• Precipitation and runoff cycles– cumulative standardized deviation from the
mean
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ExampleExample
• Runoff– no
significant trend
MEAN ANNUAL FLOWS
ANNUAL MAXIMUM FLOODS
LOW FLOWS (annual minimum monthly flows)
Radalj
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ExampleExample
• Runoff– Significant
decreasing trend in mean annual flow
MEAN ANNUAL FLOWS
ANNUAL MAXIMUM FLOODS
LOW FLOWS (annual minimum monthly flows)
Brodarevo
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ExampleExample
• Temperatures– 8 met.
stations
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Results of trend Results of trend analysisanalysis• Temperatures– change in 2035
– in accordance with other studies
BeraneKolašin
Bijelo Polje
Brodarevo
Prijepolje
Bajina Bašta
Loznica
Radalj
Zlatibor
Pljevlja
Žabljak
1.5
1.5
1.3
1.1
1.0
1.01.3
1.5
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ExampleExample
• Precipitation– 10 stations
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Results of trend Results of trend analysisanalysis• 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 Berane
Kolašin
Bijelo Polje
Brodarevo
Prijepolje
Bajina Bašta
Loznica
Radalj
Zlatibor
Pljevlja
Žabljak
14.7%
-16.5%
22.7%
-0.6%
5.9%0.4%
15.2%
9.9%5.0%
6.5%
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ConclusionsConclusions
• 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
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ConclusionsConclusions
• 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
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THANKS FOR THANKS FOR ATTENTIONATTENTION