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!