Hydrologic model calibration using MODIS-ET data: The impact on predictions at gauged and ungauged locations
Bibliographic record
Abstract
Increasing availability of satellite remote sensing data triggered the use of hydrologically relevant satellite-based fluxes and variables towards improved modelling. Importance of innovative and satellite data sources for a better understanding of hydrologic processes has been highlighted in the IAHS scientific assembly’s 23 unsolved problems in hydrology. In this context, the present study investigates the use of satellite ET dataset form MODIS in the physically based semi-distributed model Soil and Water Assessment Tool (SWAT). The study area is Nith river watershed, located in Southern Ontario, Canada. We compare the potential of MODIS ET in improving the performance of SWAT at gauged and ungauged locations of Nith River watershed. Streamflow calibrated SWAT model is used as a benchmark model. The benchmark model results are compared with the MODIS ET only calibrated model results to understand the importance of satellite data in hydrologic model calibration. It is found that the calibration of SWAT model only using MODIS ET data resulted better or similar results to that of streamflow-based calibration. The results show that SWAT model improvement is highly dependent on the input data quality such as the precipitation data and land use data used in the initial model set up. The SWAT model calibrated using MODIS ET improved the soil moisture accounting and crop yield estimation. From our results we conclude that the satellite datasets can be a potential solution to parameter estimation in ungauged basins across the world.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".