Evaluation of reliability and the added value of satellite precipitation products in hydrological modelling calibration and forecasting in remote areas of northern Canada
Bibliographic record
Abstract
Precipitation is a key component of the water cycle and an important forcing data for hydrological simulations and forecasts and other applications. Having high quality data of precipitation at the watershed scale is challenging. Many methods are used to estimate precipitation such as rain gauges, remote sensing, and reanalysis. Among these, rain gauge provides the most accurate estimate of precipitation, but its scarcely available in remote areas. This in turn badly affects hydrological studies and operational applications. However, the advent of remote sensing offers an opportunity to estimate precipitation in remote areas. The main objective of this study is to evaluate the reliability and the usefulness satellite precipitation products for hydrological modelling and forecasting. The study was carried out on 7 contrasting catchments located in Eastern Canada. Five gridded daily satellite precipitation products (SPP) including CMORPH, PERSIANN-CDR, CHIRPS, TMPA and GPM were first compared against ERA-5 daily precipitation product used as reference over the 2001-2015 period. Each precipitation product was then used to calibrate a lumped and a semi-distributed version of the GR4J model. Temperature data required by the hydrological models was from ERA-5. Calibration covered a 10-year period (2001-2010), while validation was on a 5-year period (2011-2015). Four scenarios were considered. First, both GR4J models were calibrated using ERA5 and satellites products separately as inputs. Second, SPP were used during the summer period and ERA5 precipitation was used for the remaining seasons separately as input to calibrate the lumped model. Third, the lumped GR4J model was calibrated only during summer seasons using precipitation of each SPP as forcing data. Lastly, the mean of SPP products was used as forcing data to calibrate lumped GR4J model for the first scenario. Evaluation of the reliability of the SPP demonstrate that the GPM product shows highest correlation for daily precipitation compared to reference data (ERA5) with a correlation coefficient of 0.73 for Androscoggin watershed for duration of 2001 to 2015.Moreover, the results depict that all SPP tend to underestimate daily precipitation compared to reference data. Preliminary results also show that the lumped and the semi-distributed two versions of GR4J give comparable results for the first scenario, with NSE values ranging between 0.480 and 0.86 for calibration and 0.357 and 0.86 for validation, respectively. This is followed by the last (0.586 < NSE < 0.809), second (0.0.470 < NSE < 0.85) and the third scenario(0.249
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".