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
<p>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<NSE< 0.809) during calibration for the lumped model. Similarly, the NSE values ranging from 0.50 to 0.77 ,0.59 to 0.81 and 0.293 to 0.68 for the last, second and the third scenario for validation respectively. In addition, the third scenario illustrates that CMORPH product performs well in the summer period whereas all the other SPP outperform CMORPH during the spring and winter seasons. In conclusion, merging the 3 SPP contribute to the improvement of the performance of GR4J lumped model. The next step will be to implement short-term forecasting experiments for a subset of the catchments that were already calibrated and validated with the five SPP.</p>
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| 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 tête enseignante, 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 ».