Evaluation of reliability and the added value of satellite precipitation products in hydrological modelling calibration and forecasting in remote areas of northern Canada
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».