Rainfall and runoff trends and their relation – a case study in Lower Saxony U. Haberlandt, A. Belli, J. Dietrich Institute for Water Resources Management, Hydrology and Agricultural Hydraulic Engineering, Leibniz University of Hannover, Germany HydroPredict 2010, Prague
Outline 1. Motivation/ Objectives 2. Methods 3. Study region & data 4. Results 5. Conclusions 2/12
1. Motivation & Objectives Analysis of past developments is usually the first step for prediction in climate impact studies. 1. Trend analysis for precipitation (P) and runoff (Q) focussing on extremes 2. Analysis of correspondence between P trends and Q trends 3. Discrimination between climate impacts and other anthropogenic impacts 3/12
2. Methods Mann-Kendall trend test significance Slope of regression line magnitude pq90 – 90%-quantile from daily P (P>1mm/d) px5d – maximum 5-day P sum P indices pnl90 – no. of events > long term 90%-quantil pxcdd – max. no. of consecutive dry days (P<0.1) HQ - peak flows nQ75 – no. of events > 75%-quantile of HQ Q indices MQ - mean daily flow NM7Q – lowest mean daily flow over 7 days 4/12
3. Study region & data Lower Saxony ~48000 km 2 263 daily P-stations (period 1951-2005) 88 Q-gauges (daily & peaks) (1966-2005) 28 catchments for comparisons areal P and Q trends 4 Seasons: winter (DJF), spring (MAM), summer (JJA), autumn (SON) 5/12
4.1 Results Precipitation 300 250 winter spring 250 200 200 150 150 100 . 100 50 50 0 0 -50 -50 pq90 px5d pnl90 pxcdd pq90 px5d pnl90 pxcdd -100 -100 300 300 200 autumn 200 100 100 0 0 v pq90 px5d pnl90 pxcdd pq90 px5d pnl90 pxcdd -100 -100 -200 -200 summer -300 -300 No. of stations with trends (blue indicates significant trends at = 0.05 6/12
Maximum 5 day precipitation sum (Px5d) winter summer Legend 30% < x < 60% x < -30 % 60% < x < 90% -30% < x < 0% 90% < x 0% < x < 30% 7/12
4.2 Results Runoff 90 90 winter spring 60 60 30 30 . 0 0 HQ nQ75 MQ MN7Q HQ nQ75 MQ MN7Q -30 -30 -60 -60 -90 -90 90 90 60 summer autumn 60 30 30 0 0 v HQ nQ75 MQ MN7Q HQ nQ75 MQ MN7Q -30 -30 -60 -60 -90 -90 No. of gauges with trends (blue indicate significant trends at = 0.05 8/12
Peak runoff (HQ) winter summer Legende 0% < x < 30% 0% > x > -30% -30% > x > 0% 30% < x < 60% 0 -60% > x > -90% 60% < x < 90% -90% > x 90% < x 9/12
4.3 Comparison for 28 catchments 28 28 P Q P&Q P Q P&Q 24 No. of catchments No. of catchments 24 20 20 16 16 12 12 8 8 4 4 0 0 rising trend falling trend no trend rising trend falling trend no trend Significant trends in winter (DJF): Significant trends in summer (JJA): 90% quantile of precipitation (P) vs. Max dry duration (P) *1 vs. mean low peak flow (Q) flow over 7 days (Q) ( = 0.10) ( = 0.10) *1 Max. dry duration trend is rising means 10/12 precipitation trend is falling
Why differences in P – Q trends? 1. Data issues Hypotheses 2. Catchments attenuate the P signal 3. Human interventions Hydrological modelling Assumptions: a) Data issues solved, Intended b) Model can simulate trends procedure Modelling with time invariant conditions (par’s, land use) should help to discriminate between 2. and 3. 11/12
5. Conclusions 1. Significant trends for rainfall have shown wetter conditions in winter and longer dry periods in summer 2. Significant trends for runoff occur mainly in summer and are decreasing for all indices 3. Correspondence of runoff trends to rainfall trends is about 55% for decreasing low flows in summer and only 20% for increasing flood flows in winter 4. Further research is necessary to find reasons for non- correspondence and to discriminate for trend causes 12/12
Thank you for your attention! Acknowledgement: Research is funded by the Ministry of Science and Culture as well as by the Ministry of Environment and Climate Protection, Lower Saxony
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