ABSTRACT:

This resource provides the necessary code to run all exercises of the CIROH_HydroLearn Course "Model structure uncertainty with MARRMoT".
The course can be found here: https://edx.hydrolearn.org/courses/course-v1:CIROH_HydroLearn+TUD_MHYD03_MARRMoT+2025/about

Please note that this resource includes only a subset of MARRMoT model structures and four workflow examples on how to use MARRMoT.
It is not a full version of the modelling toolbox.

ABSTRACT:

This resource provides the necessary code to run all exercises of the CIROH_HydroLearn Course "Model structure uncertainty with MARRMoT".
The course can be found here: https://edx.hydrolearn.org/courses/course-v1:CIROH_HydroLearn+TUD_MHYD03_MARRMoT+2025/about

Please note that this resource includes only a subset of MARRMoT model structures and four workflow examples on how to use MARRMoT.
It is not a full version of the modelling toolbox.

ABSTRACT:

This folder contains output files from Spieler & Schütze (2024), Investigating the Model Hypothesis Space: Benchmarking Automatic Model Structure Identification with a Large Model Ensemble, Water Resources Research (in review)

Output files three modelling experiments conducted in the paper are presented.

The data for the Automatic Model Structure Identification (AMSI) experiment contains:
- 1 table of all 100 AMSI models for each of the 12 catchments. Included is the KGE in calibration and validation, the rank in calibration and validation as well as the identified structural choices for every model
- 1 table of all 100 AMSI models for each of the 12 signatures. Included are 79 hydrological signatures for each of the 100 models. They are ranked after their calibration performance.

The data for the Brute-Force-Modelling (BFM) experiment contains:
- 1 table of all 7488 BFM models for each of the 12 catchments. Included is the KGE in calibration and validation, the rank in calibration and validation as well as the corresponding structural choices of every model
- 1 table of all 7488 BFM models for each of the 12 signatures. Included are 79 hydrological signatures for each of the models. They are ranked after their calibration performance.

The data for the MARRMoT (MRMT) experiment contains:
- 1 table of all 45 MRMT models for each of the 12 catchments. Included is the KGE in calibration and validation, the rank in calibration and validation as well as the corresponding model ID of every model
- 1 table of all 45 MRMT models for each of the 12 signatures. Included are 79 hydrological signatures for each of the models. They are ranked after their calibration performance.

Study abstract:
Selecting an appropriate model for a catchment is challenging, and choosing an inappropriate model can yield unreliable results. The Automatic Model Structure Identification (AMSI) method simultaneously calibrates model structural choices and model parameters, which reduces the workload of comparing different models. We benchmark AMSI’s capabilities in two ways, using 12 hydro-climatically diverse MOPEX catchments. First, we calibrate parameter values for 7488 different model structures and test AMSI’s ability to find the best-performing models in this set. Second, we compare the performance of these 7488 models and AMSI’s selection to the performance of 45 commonly used, structurally more diverse, conceptual models. In both cases, we quantify model accuracy (through the Kling-Gupta Efficiency) and model adequacy (through various hydrologic signatures). AMSI effectively identifies high-accuracy models among the 7488 options, with KGE scores comparable to the best among the 45 models. However, model adequacy remains poor for the accurate models, regardless of the selection method. In nine of the tested catchments, none of the most accurate models replicate observed signatures with less than 50% errors; in the remaining three catchments, only a handful of models do so. This paper thus provides strong empirical evidence that relying on aggregated efficiency metrics is unlikely to result in hydrologically adequate models, no matter how the models themselves are selected. Nevertheless, AMSI has been shown to effectively search the model hypothesis space it was given. Combined with an improved calibration approach it can therefore offer new ways to address the challenges of model structure selection.

ABSTRACT:

This folder contains output files from Spieler et al. (2020), Automatic Model Structure Identification for Conceptual Hydrologic Models, Water Resources Research, doi: https://doi.org/10.1029/2019WR027009

Output files for a synthetic and real world experiment conducted in the paper are presented.

The data for the synthetic experiment contains:
- 100 identified AMSI models for 20 synthetic experiments for five different calibration budgets
- the objective function values (NSE) of all 100 models for all experiments & all different budgets (100*20*5)

The data for the real world experiment contains:
- 100 identified AMSI models for 12 MOPEX catchments
- the objective function values (NSE) of all 100 models for all catchments in calibration and validation
- dicharge timeseries for 100 identified AMSI models for all catchments in validation

Study abstract:
Choosing (an) adequate model structure(s) for a given purpose, catchment, and data situation is a critical task in the modelling chain. However, despite model intercomparison studies, hypothesis testing approaches with modular modelling frameworks, and continuous efforts in model development and improvement, there are still no clear guidelines for identifying a preferred model structure. By introducing a framework for Automatic Model Structure Identification (AMSI), we support the process of identifying (a) suitable model structure(s) for a given task. The proposed AMSI-framework employs a combination of the modular hydrological model RAVEN and the heuristic global optimization algorithm dynamically dimensioned search (DDS). It is the first demonstration of a mixed-integer optimization algorithm applied to simultaneously optimize model structure choices (integer decision variables) and parameter values (continuous decision variables) in hydrological modelling. The AMSI-framework is thus able to sift through a vast number of model structure and parameter choices for identifying the most adequate model structure(s) for representing the rainfall-runoff behavior of a catchment. We demonstrate the feasibility of the approach by re-identifying given model structures that produced a specific hydrograph and show the limits of the current setup via a real-world application of AMSI on twelve MOPEX catchments. Results show that the AMSI-framework is capable of inferring feasible model structures reproducing the rainfall-runoff behaviour of a given catchment. However, it is a complex optimization problem to identify model structure and parameters simultaneously. The variance in the identified structures is high due to near equivalent diagnostic measures for multiple model structures, reflecting substantial model equifinality. Future work with AMSI should consider the use of hydrologic signatures, case studies with multiple types of observation data, and the use of mixed-integer multi-objective optimization algorithms.