(Un)certainty in hydrological modeling: the perceptual model
11 important questions on (Un)certainty in hydrological modeling: the perceptual model
What is the definition of a model?
A hypothesis/theory of how a system works, codified in quantitative terms.
Explain what the different model types contain: perceptual, conceptual, procedural, calibration/sensitivity analysis, evaluation
Conceptual model: deciding on the equations
Procedural model: get code running on a computer
Calibration / sensitivity analysis: identify parameters, estimate uncertainty
evaluation: compare output with observersations
What is the soil topographic index, developed by the creators of TOPmodel?
High index values will saturate first and therefore indicating potential (sub)surface contributing areas. So areas can be variable in contributing to flow > account spatial distribution in catchment response for various altitudes. (min 18)
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Why is calibration of models usually done?
2. To make the model more accurate (Hollander et al)
What devastating results did Hollander et al find at Chicken Creek (artificial catchment with homogeneous slope, K, etc)
What is the main result from Hollander et al at Chicken Creek?
Check quote slide 22
What drives model selection: adequacy or legacy?
Variable of interest, spatial scale, landscape, temporal scale do not explain why model was used. We do not choose model based on research question, but based on legacy. We take the models that we know.
Why is model selection based on a legacy a system that works and continues to work?
- Experience: very useful asset, e.g. You recognize weird behaviour and bugs and set-up and interpretation of results become more easy.
- Modelling ecosystem: unis or research institutes have entire infrastructure set up to work with a certain model (instead of taking days-weeks to prepare good input data). It can overcome mistakes and make work faster.
- Conventional approach bias: phenomenom in science: conventional approaches are generally more appreciated - easier to publish if it generally accepted.
Do we miss opportunities because of the prevalence of legacy in model selection?
- Most adequate for research questions: this remains unknown. Only way to know if your model is adequate, is comparing
- Parallel development: each research institute has their own model and invests in e.g. A snow parameterisation
Model frameworks - do not build one big model, but just building blocks of models (lego's)
What are main conclusions from the perceptual model lecture?
- Deciding on the processes or selecting an off-the-shelf model is the first step in modelling
- Defining the perceptual model/selecting a model is a subjective procedure
- But important, becuase it determines the outcome fo the study
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