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, oktobar 2012, Donji Milanovac

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 Copenhagen Diagnosis (2009)

Climate change – we know Radionica - Klimatske Promene

Climate change – we know Radionica - Klimatske Promene Church and White, Geophysical Research Letters, (2006) Cazenave et al, Global and Planetary Change, (2009)

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 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

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 ) 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 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 – H 0 : no monotonic decreasing or increasing trend – H 0 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 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

Example Runoff, precipitation and temperatures in the Drina Basin – Brodarevo/Lim – Drina/Radalj Energoprojekt- Hidroinženjering 2011, 2012

Example Precipitation and runoff cycles – cumulative standardized deviation from the mean

Example Runoff – no significant trend MEAN ANNUAL FLOWS ANNUAL MAXIMUM FLOODS LOW FLOWS (annual minimum monthly flows) Radalj

Example Runoff – Significant decreasing trend in mean annual flow MEAN ANNUAL FLOWS ANNUAL MAXIMUM FLOODS LOW FLOWS (annual minimum monthly flows) Brodarevo

Example Temperatures – 8 met. stations

Results of trend analysis Temperatures – change in 2035 – in accordance with other studies

Example Precipitation – 10 stations

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

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

T HANKS FOR ATTENTION