Streamlining configurations of process-based models through extensible and free workflows
Bibliographic record
Abstract
High-resolution and high-complexity process-based hydrological models play a pivotal role in advancing our understanding and prediction of water cycle dynamics, particularly in ungauged basins and under nonstationary climate conditions. However, the configuration, application, and evaluation of these models are often hindered by the intricate and inconsistent nature of a priori information available in various datasets, necessitating extensive preprocessing steps. These challenges can limit the reproducibility, applicability, and accessibility of such models for the broader scientific user community. To address these challenges, we introduce our generalized Model-Agnostic Framework (MAF), aimed at simplifying the configuration and application of data-intensive process-based hydrological models. Through a systematic investigation of commonly used models and their configuration procedures, we provide workflows designed to streamline the setup process for this category of hydrological models. Building on earlier efforts, this framework adheres to the principle of separating model-agnostic and model-specific tasks in the setup procedure of such models. The model-agnostic workflows focus on both dynamic datasets (e.g., meteorological data) and static datasets (e.g., land-use maps), while the model-specific components feed preprocessed, relevant data to the hydrological models of interest. Our initial prototypes of MAF includes recipes for various static and dynamic datasets and also tailored model-specific workflows for MESH, SUMMA, and HYPE process-based modelling frameworks. We demonstrate the effectiveness of these novel workflows in reducing configuration complexity and enhancing the reproducibility of process-based hydrological models through test applications in high-performance computing environments. The framework automates numerous manual tasks, significantly saving time, and enabling continuity in research efforts. Moreover, by minimizing human error and enhancing reproducibility, this research has fostered collaboration with several Canadian government entities, leveraging sophisticated process-based models to address complex environmental challenges.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".