Institut für Wasser- und Umweltsystemmodellierung
Lehrstuhl für Hydrologie und Geohydrologie
Prof. Dr. rer. nat. Dr.-Ing. András Bárdossy
Pfaffenwaldring 61, 70569 Stuttgart, Deutschland www.iws.uni-stuttgart.de
Universität Stuttgart
Simultaneous calibration of hydrological models to capture non-stationary conditions
András Bárdossy & Yingchun Huang
2 Introduction
• Non stationary conditions:– Input: Weather – Climate– Properties: Land use
• How to cope:– Temporal (limited in extent)– Spatial (limited in similarity)
• Similarity vs. Self similarity
Modelling
• Model calibration – parameter estimation– Known input and output
• Select a model• Select performance criteria (NS, GK,
Multiobjective)• Optimization principle
– Search a single optimum– Search a set of optima (equifinality)
• Model „Validation“– Known input and output
• Test calibration parameters on a different case
Goals
• To capture the essential features – Transferable to different conditions
– Modelling is not repeating what was observed
The tools
• Models– HBV– Hymod– Xianjiang
• Performance– NS– KG– NS+LogNS
• Calibration method– ROPE – depth based calibration (Half space depth)
with a set of optima (represented by 10000 pars.)
ROPE
• MC step• Selection step (Best 15%)
• Monte Carlo of the deep in the selected
• Select best 15% ...
Location of the study area15 selected catchments (300-1800 km2)
• Weather is not stationary (1950-2000)• 10 year mean values
– 0.5C difference between the mean values– Up to 40 % difference in precipitation
• Investigate transfer from one time period to the other– 10 years intervals starting 1950
• Model performance – Strongly dependent of the application period– Weakly dependent on the calibration period
Bad news – we can only modify calibration
For 8 catchments all calibrations transfer well for all periods good guys
For 3 catchments many transfers are problematic bad guys
• Optimal performance – For the given „distribution“ of weather
• Same weather different frequencies different parameters
– Parameter estimation „for any weather distribution“
• Distribution from other time periods (Time)• Distribution from other catchments (Space)
Method 1 -Time
• Adjust weather of the calibration period– Emphasize years with weather similar to
target• Known for observed periods• „Given“ for climate change
– Reshuffling not possible due to discharge observations
– Weighting
• Weighted objective function
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• Simple trick • Little but positive effect
– Better transfer of the model parameters• Mean – most cases• Minimum nearly always
– Catchment 4 1960 applied for 1970s NS 0.508 0.526
Method 2 - Space
• There are other catchments which experienced different (target) weather (German weather will be like Italian)– Take a similar catchment and use it
– What is similar?– How to use it?
• Similar – if common parameters work well in the calibration period
• Common parameters obtained via common calibration
• Parameters which are good for all catchments
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• Pairwise application C=2– All good for all bad – Similarity over the calibration periods
– If common calibration does not deteriorate calibration quality then similar
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Transfer Calibration 1970-1979 application 1950-1959
Gain in the average for catchment 4
Gain in the average for catchment 9
Gain in the minimum for catchment 4
Gain in the minimum for catchment 11
Method 2 results
• Common calibration improves transfer quality if similar catchments are used
• Similarity can be recognized• Minimum performance is strongly improved
– low risk of failure
Other possibilities
• Common calibration with C>2• Filtering observation errors
– Bias– Random errors
• Common calibration for land use change– Using implicit assumptions– Assigning parameter(s) to land use and
calibrate individually
Summary
• A good model should work under all conditions transferability
• Transferability is mainly receiver dependent• Transferability can be improved
– Using a weather mix (weights)– Using other catchments via common
calibration
Does this matter at all?
10 years + 1 Co scenario
Thank you!
