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P ROBLEMS IN DETECTING TREND IN HYDROMETEOROLOGICAL SERIES FOR CLIMATE CHANGE STUDIES Jasna Plavšić 1 and Zoran Obušković 2 1 University of Belgrade – Faculty of Civil Engineering 2 Energoproject – Hydroengineering 16. naučno savetovanje SDHI/SDH, 22-23. oktobar 2012, Donji Milanovac
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Climate 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 know Radionica - Klimatske Promene - 2010 www.slobodansimonovic.com Copenhagen Diagnosis (2009)
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Climate change – we know Radionica - Klimatske Promene - 2010 www.slobodansimonovic.com
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Climate 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 impacts Questions: – Change projections? – Impact on water resources? IPCC (2007)
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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?
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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 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 – 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.
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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.)
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Tests for trend Linear regression: X = a + bt slope significance?
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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 – H 0 : no monotonic decreasing or increasing trend – H 0 is rejected when S significantly departs from 0 – serial correlation decreases detection power
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Other test for detecting changes in time series Tests for change in the meanZ-test, t-test, Pettitte test Tests for change in varianceF-test Tests for change in distribution Mann-Whitney, Kolmogorov- Smirnov Tests for randomnessRun test Tests for serial correlationBartletts test Tests for trendMann-Kendall, Spearman rho, linear regression slope
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Example Runoff, precipitation and temperatures in the Drina Basin – Brodarevo/Lim – Drina/Radalj Energoprojekt- Hidroinženjering 2011, 2012
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Example Precipitation and runoff cycles – cumulative standardized deviation from the mean
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Example Runoff – no significant trend MEAN ANNUAL FLOWS ANNUAL MAXIMUM FLOODS LOW FLOWS (annual minimum monthly flows) Radalj
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Example 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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Example Temperatures – 8 met. stations
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Results of trend analysis Temperatures – change in 2035 – in accordance with other studies
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Example Precipitation – 10 stations
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Results of trend analysis 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
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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
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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
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T HANKS FOR ATTENTION
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