Assessing the adequacy of traditional hydrological models for climate change impact studies: A case for long-short-term memory (LSTM) neural networks
Bibliographic record
Abstract
Abstract. Climate change impact studies are essential for understanding the effects on water resources under changing climate conditions. This paper assesses the effectiveness of Long Short-Term Memory (LSTM) neural networks versus traditional hydrological models for these studies. Traditional hydrological models, which rely on historical climate data and simplified process parameterization, are scrutinized for their capability to accurately predict future hydrological streamflow in scenarios of significant warming. In contrast, LSTM models, known for their ability to learn from extensive sequences of data and capture temporal dependencies, present a viable alternative. This study utilizes a domain of 148 catchments to compare four traditional hydrological models, each calibrated on individual catchments, against two LSTM models. The first LSTM model is trained regionally across the study domain of 148 catchments, while the second incorporates an additional 1,000 catchments at the continental scale, many of which are in climate zones indicative of the future climate within the study domain. The climate sensitivity of all six hydrological models is evaluated using four straightforward climate scenarios (+3 °C, +6 °C, -20 %, and +20 % mean annual precipitation), as well as using an ensemble of 22 CMIP6 GCMs under the SSP5-8.5 scenario. Results indicate that LSTM-based models exhibit a different climate sensitivity compared to traditional hydrological models. Furthermore, analyses of precipitation elasticity to streamflow and multiple streamflow simulations on analogue catchments suggest that the continental LSTM model is most suited for climate change impact studies, a conclusion that is also supported by theoretical arguments.
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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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".