TUM project seminar
Uncertainty Quantification in Hydrology
How much can a river model be trusted?
- Role
- Group contributor; individual ownership of assignment results is not claimed
- Classification
- Group coursework
- Institution
- Technical University of Munich
Problem
A well-fitted model can hide large uncertainty.
How it was solved
Our group perturbed its inputs and measured the damage.
- Calibrate
- Perturb
- Sensitivity
Result
NSE 0.908
best fit in the group seminar
Built with
- Python
- NumPy
- SciPy
TUM project seminar / Hydrology
Disturb the rain and a calibrated model barely notices. Disturb the ruler the river was measured with and the calibration collapses.
Best seminar resultNSE 0.908
A calibrated model, and the two ways of disturbing it
This diagram in words
- HBV baseline source — A conceptual rainfall-runoff model over one high-flow event.
- Calibration process — Eighteen parameters fitted until the hydrograph matches. The fit is not the problem.SciPy (QMC sampling)
- Local sensitivity process — One parameter at a time, in the neighbourhood of the optimum.
- Global sensitivity process — Across the whole parameter space, which ranks them differently.NumPy
- Perturb the inputs gate — The rainfall going in, and the stage the discharge was inferred from.
- Two envelopes artifact — One barely opens. The other swallows the calibrated run entirely.Matplotlib
Read the full background
Problem
Why this work exists
A calibrated hydrological model can still hide large uncertainty from forcing data, rating curves, parameters, and model structure.
Contribution
What I can claim
Contributed within a three-person TUM seminar team to a progressive workflow covering differential-evolution calibration, sensitivity analysis, precipitation perturbation, and rating-curve uncertainty. Individual assignment ownership is not claimed.
Method
Where an interval comes from
An uncertainty band is not produced by widening a line.
Perturb the rain834 of 2,000 still beat the reference
Perturb the ruler0 of 2,000 beat the reference
Simulate
Several members are run from the same starting point under different assumptions.
Diverge
They agree near the start and separate through the middle of the horizon, which is where the modelled system is genuinely hardest.
Summarise
The spread is collapsed into a band.
What this does not show
An ensemble only explores the assumptions it was given. Agreement between members is not evidence of correctness - it can equally mean they share a blind spot.
How it was built
System
Workflow and decisions
- 1HBV baseline
- 2Parameter calibration
- 3Local sensitivity
- 4Global sensitivity
- 5Input and output uncertainty
- Python
- NumPy
- SciPy (QMC sampling)
- pandas
- Matplotlib
- LaTeX
Evidence
What is actually versioned
Versioned group-seminar result for the calibrated HBV baseline.
The seminar report finds rating-curve uncertainty more consequential than the tested precipitation noise.
How the work is checked
Quality controls
How the work is checked
- Versioned reports, figures, and result artifacts
- Explicit environment variables replace machine-specific data paths
- Repository integrity checker validates key seminar artifacts
- Team authorship and unavailable course inputs are documented
Limitations
Where the evidence stops
- Group coursework; individual ownership of each result is not established
- Course-provided forcing data and the hmg package are not redistributable
- Full scientific reruns require authorized course inputs
What this changed in my practice
What this changed in my practice
Calibration quality cannot stand in for uncertainty analysis; observation and transformation errors can dominate parameter uncertainty.