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(Un)certainty in hydrological modeling: Analysis tools

16 important questions on (Un)certainty in hydrological modeling: Analysis tools

Assume that we could know all states and all fluxes of everything, would then be able to exactly predict what would happen in the next time fraction? Is the world deterministic or probabilistic?

Probabilistic: even if we know everything, there's still multiple things that could potentially happen

Deterministic: if everything is known, we can predict everything (min 5)

Currently it seems like the world is probabilistic (Lieke), as we do not know everything (and never will) and multiple things seem to be able to happen from the same state. So we cannot model everything.

What is Heisenbergs uncertainty principle?

You can never know, at the same time, both the location and the velocity of a particle (because for velocity measurements you need two locations, if you measure the velocity, what is then the location of that particle at a certain velocity/what is the velocity of that particle at that location).

Name 3 main sources of uncertainty in hydrological sciences?

Data uncertainty

Parameter uncertainty
Model structural uncertainty

Parameter uncertainty: Name several important examples/definitions occuring

Equifinality
Generalized Likelihood Uncertainty Estimation (GLUE)

Measurement device issues, scaling issues, no physical representation

What is equifinality, part of parameter uncertainty?

Equifinality is the principle that in open systems a given end state can be reached by many potential means.

Model parameters: several parameters combis can lead to the same model performance (and then how do you know which combi is good); overparameterization, non-uniqueness, ill-defined problem.

Equifinality also applies to model structures, but it more often used in parameter uncertainty within hydrology.

What does overparameterization/non-uniqueness/ill-defined problems mean? (it's the same thing but different terms in different fields)

Having too many parameters to describe a system that only has information for a limited number of parameters

How problematic is equifinality

VIC model > 30 parameters
Usually, around 5 parameters are highly sensitive that you calibrate
For 5000 grid cells: 25.000 parameters (5 calibrated parameters x 5000 grid cells)

What are the 3 ways of dealing with equifinality?

Pareto-optimization or multiple objective functions
Add multiple observations to calibrate/validate
Use transfer functions (for distributed modelling)

Explain how you could use multiple observations to calibrate/validate, to deal with equifinality?

Adding more data sources could help further constrain your model.

First calibrate on Q, you have limited parameter sets and you see how well they do on eg ET/soil moisture/tracers. So by adding more info/requirements you can further determine which parameter sets make most sense and hereby constrain your model.

Problem: model performance could decrease quite a bit by adding more info. Because models are made to calculate Q, adding more data on SM, it colud show the internal consistency of your model (so the processes it simulates) may not make sense at all.

How to read the GLUE parameter uncertainty graph and what are the 4 steps taken to get to a prob dist?

Dotted line: uniform distribution from which we sample
NSE: calculated for each run
> 0.5: only values with result of NSE > 0.5 are considered. All values below are irrelevant or non-behaviourial
Probability distribution of values with KGE > 0.5 is made

Multiplying y-axis with x-axis should give a 1, since it is a probability distribution.

Highest point in the most probable value and we also have an idea of uncertainty.

I think NSE/KGE etc are called objective functions.

What are the two main sources of model structural uncertainty?

Assumptions and simplifications
Process representation

How to deal with model structural uncertainty?

Model ensembles: can come from different parameter values and from different models (where you represent diff model structures).

Ensemble mean generally outperforms individual ensemble members. Why is not entirely clear, but hypothesis is that all models have some things they do(nt) account for or some bugs, idea is that these cancel each other out in the ensemble. In this way, errors can cancel each other out.

Con: very time consuming to make model ensemble from different models

How you select these ensembles you should be very carfeul about!

Explain the modular modelling frameworks?

Hydro modles > break them into their individual hypothesis on e.g. ET/snow/infiltration > reconstruct models based on individual hypotheses

Then we can test which part of the model structure causes the difference (instead of working with all these off the shelf models and then you don't know which hypotheses you are testing). New problems such as do you need to calibrate again, arise.

You can add new building blocks in a new framework more easily than in an already existing model.

What are the main sources of uncertainty?

Within a model

Model structure uncertainty
Parameter uncertainty
Input data uncertainty
Observation uncertainty

Also as modellers we make a lot of decisions on forcing data, resolution, calibration period, etc, etc. These influence the sources of uncertainty within the model.

Question: predict a flood-event ....

See lside!

It is really hard to make the 'right' choice!

What are the main four conclusions on these model uncertainty lecture series?

Models are never complete and always prone to uncertainty
Estimating uncertainty puts results into perspective
Models are not 'value-free' objective tools, but social constructs
Models are prone to deep uncertainty: unknown unknowns

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Catchment and Climate Hydrology

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(Un)certainty in hydrological modeling: Analysis tools

16 important questions on (Un)certainty in hydrological modeling: Analysis tools

Assume that we could know all states and all fluxes of everything, would then be able to exactly predict what would happen in the next time fraction? Is the world deterministic or probabilistic?

What is Heisenbergs uncertainty principle?

Name 3 main sources of uncertainty in hydrological sciences?

Parameter uncertainty: Name several important examples/definitions occuring

What is equifinality, part of parameter uncertainty?

What does overparameterization/non-uniqueness/ill-defined problems mean? (it's the same thing but different terms in different fields)

How problematic is equifinality

What are the 3 ways of dealing with equifinality?

Explain how you could use multiple observations to calibrate/validate, to deal with equifinality?

How to read the GLUE parameter uncertainty graph and what are the 4 steps taken to get to a prob dist?

What are the two main sources of model structural uncertainty?

How to deal with model structural uncertainty?

Explain the modular modelling frameworks?

What are the main sources of uncertainty?

Question: predict a flood-event ....

What are the main four conclusions on these model uncertainty lecture series?

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