Combined influence of future land use change and spatially distributed seasonal climatic variations on surface hydrology
Bibliographic record
Abstract
This study evaluated the isolated and combined impacts of land use change and climate change on built runoff and streamflow variation using future climatic projections under RCP4.5 and RCP8.5 scenarios. Seasonal and annual downscaled climate data were integrated into the Sacramento rainfall-runoff model within eWater Source to adjust daily rainfall inputs and to assess the influence of the temporal resolution of future rainfall data. The results indicated a significant increase in built runoff and streamflow due to land use change. Streamflow predictions under seasonal projections showed increase of 62.7 % (RCP4.5) and 42.8 % (RCP8.5), while annual projections yielded lower increase of 40.2 % (RCP4.5) and 30.3 % (RCP8.5) combined with land use change. These results suggest that annual projections underestimated streamflow and built runoff compared to seasonal data. Streamflow patterns are strongly influenced by the integration of seasonal rainfall projections exhibiting a significant bias (> ±20 %) under the RCP4.5 scenario, particularly when combined with land use change. Furthermore, predicted winter streamflow variation showed a substantial bias, either with or without the land use change (−45.5 % and −44.7 % respectively). This is attributed to the higher variability of rainfall patterns in these seasons, which is smoothed when using rainfall data derived from annual projections. Overall results indicate that climate change predominantly alters streamflow patterns, while land use change primarily impacts its volume. This study highlighted the dominant role of land use change and emphasizes the importance of incorporating different temporal resolution climatic data in hydrological modelling. • Combined impacts of land use and climate change on surface hydrology. • Seasonal projections resulted in higher built runoff and streamflow than annual projections. • Seasonal projections captured extreme wet and dry periods more effectively. • Intra annual fluctuations in streamflow and runoff are more sensitive to climate change. • Higher mitigation of climate change impacts at coarser temporal resolution of rainfall data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".