Explaining the magnitude of shift in the rainfall-runoff relationship

Transcription

1 Explaining the magnitude of shift in the rainfall-runoff relationship Saft, M., M. C. Peel, A. W. Western, and L. Zhang (2016), Predicting shifts in rainfall-runoff partitioning during multiyear drought: Roles of dry period and catchment characteristics, Water Resour. Res., 52, doi: /2016wr

2 Multiyear droughts Introduction Methods Normal part of climate variability -> fairly common. Recent examples: Northern China Canadian prairies Australia: Millennium Drought ~ Ongoing problem Example: California (2011 -?) Results Conclusions Photos: California Department of Water Resources South-Eastern Australia had three major droughts in ~20 th century Claims of disproportional streamflow reduction Bigger reductions in runoff than expected based on rainfall changes over the Millennium drought

3 Background: what previous research indicated Conclusions Results Methods Introduction Multiyear changes in climate can alter the annual rainfall-runoff relationship from the historical state Multiyear drought results in less runoff for a given rainfall than shorter / less sustained droughts Not all catchments were equally susceptible Number of explanatory mechanisms and factors were suggested (e.g. disproportional autumn rainfall reductions)

4 What we want to predict? Introduction Methods Conclusions Results Magnitude of shift in the rainfall-runoff relationship Drought runoff anomaly = historic rainfall elasticity of runoff + additional reduction (shift) Explain the variability in catchment response to prolonged and severe drought

5 Potential predictors Conclusions Results Methods Introduction Catchment pre-drought hydroclimatic characteristics (20) Climate Average annual rainfall Aridity index (rainfall/pet) Average maximum daily temperature Average minimum daily temperature C v of annual rainfall C v of monthly rainfall Average annual PET C v of annual PET Streamflow Average annual runoff Runoff ratio Coefficient of variation (C v ) of annual runoff C v of annual runoff ratio C v of monthly runoff Groundwater proxies C v of annual minimal 7-day flows Average annual minimal 7-day flow Range of annual minimal 7-day flows Annual minimal 7-day flow divided by mean 7-day flow Baseflow index (BFI) C v of annual BFI % cease to flow Catchment physical properties (9) Catchment area Mean elevation Elevation range Stream density Mean solum thickness Percentage of woody cover Mean plant available water capacity Mean slope Stream length Dry period climate characteristics (8) Drought rainfall anomaly Drought length (>7 years) Drought anomaly of winter rainfall Drought anomaly of spring rainfall Drought anomaly of summer rainfall Drought average maximum daily temperature anomaly Drought average minimum daily temperature anomaly Drought anomaly of autumn rainfall

6 Multimodel inference using information criteria Conclusions Results Methods Introduction Step 1. Akaike criterion AIC = -2 * llf + 2 * k [Akaike, 1973] llf is log-likelihood function k is dimension of the model n is number of data points (catchments) Step 2. Akaike differences Δ i = AIC i - AIC min where AIC min is the AIC of the best model Step 3. Akaike weights W i = exp(-0.5* Δ i ) / (-0.5* Δ all ) Step 4. Proportion of evidence = W i for all models containing predictor Akaike, H. (1973) Information theory and an extension of the maximum likelihood principle. Paper presented at Second International Symposium on Information Theory, Akademinai Kiado.

7 Conclusions Results Methods Introduction Proportion of evidence per predictor

8 Conclusions Results Methods Introduction Proportion of evidence per predictor

9 Conclusions Results Methods Introduction Subsample testing results

10 Introduction Exploring the better models Methods Results Conclusions Predictor 4-predictor model 5-predictor model Regression coefficient Significa nce (p) Regression coefficient Pre-drought climate aridity index (rainfall/pet) Pre-drought coefficient of variation of annual minimal 7-day flow Pre-drought coefficient of variation of monthly rainfall Drought anomaly of spring rainfall Mean solum thickness Significan ce (p)

11 Conclusions Conclusions Results Methods Introduction We can explain about 2/3 of variability in the magnitude of shift in the rainfall-runoff relationship related to the recent drought Shift was mostly influenced by catchment biophysical characteristics related to: historical climate Aridity index Cv of monthly rainfall (~seasonality) storage dynamics Cv of annual minimal 7-day flow Mean solum thickness Shift was also influenced by seasonal changes in the rainfall during the drought Spring rainfall deficits

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