Regionalization of a Distributed Hydrology Model Using Random Forest
Bibliographic record
Abstract
Distributed hydrology models are suitable tools for understanding the hydrological processes, which take place on heterogeneous media under ever-changing internal (e.g. land use change) and boundary conditions (e.g. climate change). The generally accepted practice for applying such models is to calibrate their parameters using observed data. Still in many locations, even in developed countries, observed data is lacking or unreliable. Regionalization is a way around this problem. In this research, we built a Random Forest (RF) model to regionalize the parameters of a distributed hydrology models (Hydrotel), which is the operational model at Quebec government. Using the RF model, the following three hypotheses were tested regarding the efficiency and spatio-temporal variability of the proposed regionalization technique: (1) A finer time-step adds more information to the calibrated parameters and therefore improves the efficiency of the regionalization method; (2) The parameters approximated by RF are spatially consistent and therefore transferrable across spatial scales (i.e. from lumped to sub-catchment to hydrological response units); (3) Using more spatially representative predictors (i.e. by refining the spatial resolution of CDs) to reflect heterogeneity of the catchment will improve the performance of regionalization at internal ungauged locations. All these hypotheses were tested on three groups of nested catchments at 3- and 24-hour time-steps. The results show that for simulations at sub-daily time-steps, the calculated loss of regionalization efficiency (with respect to calibration) is less than that of the 24-hour time-step (12% improvement). Approximating the parameters at different levels of spatial discretization demonstrates that the parameters are spatially consistent as the distribution of parameters and catchment descriptors are spatially correlated. Finally, we found a consistent improvement of simulations when we replace lumped with fully distributed parameters, for simulations with a 24-hour time step. This improvement in the efficiency is higher for catchments with a higher degree of spatial heterogeneity (up to 12%). However, no significant improvement in the efficiency of simulation from lumped to distributed parameters has been observed when the time-step of the simulation was reduced to 3-hour.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".