Assessing current tank storage state from multi-mission satellite observations to support water management in southern India
Notice bibliographique
Résumé
Small reservoirs represent a critical water supply to farmers across semi-arid regions. Managing these water resources is challenging because hydrological forecasting suffers from sparse rainfall measurements that do not capture highly localised rainfall accumulations. Small reservoir tank structures across South India form part of a complex ancient traditional water distribution system that has historically supplied irrigation to cropped fields during the dry-season. Despite their historical significance and the critical need for water storage in an agrarian dominated country with unpredictable rainfall, thousands of tanks have fallen into a state of disrepair with the introduction of groundwater wells and cheap electrification in the 1960s. Our current understanding of these systems lacks knowledge about the functional state of these ancient traditional water systems. This is especially critical information that is needed to rehabilitate tank structures and support water management. Previous studies suggest that functioning tanks have the potential to increase both the current water supply and support groundwater recharge. But there is little quantitative evidence to support this assertion. To understand tank functionality, spatially explicit and temporally dynamic frequent high-resolution surface water (SW) estimates developed in a synoptic and detailed way are needed. The increased availability of high-resolution satellite imagery provides a substantial opportunity to fulfill this need through the monitoring of small inland water bodies. Monitoring tank SW from earth observation (EO) sources is constrained by their small size (5-50 ha) and rapid water drainage. To support tank monitoring during cloud-covered monsoon seasons, synthetic aperture radar (SAR) observations used in synergy with high temporal resolution visible infra-red observations is desirable. Building from an existing surface water monitoring approach (Vanthof and Kelly, 2019), the primary aim here is to assess large-scale dynamics of tank water storage state at a basin scale. This is achieved by using multi-date and multi-sensor satellite images (Landsat-8, Sentinel-1, Sentinel-2, PlanetScope) for three years covering the northeast monsoon (Sept. – Dec.). SW observations from optical-infrared and radar observations are used to estimate tank SW areas for three monsoon seasons and converted to volumes using empirical rating curves developed for the region from Vanthof and Kelly (2019). Annually tanks were categorized by ‘tanks with water’ or ‘tanks without water’. For the ‘tanks with water’ category, an analysis was performed annually to identify spatial and temporal patterns in two indicators: temporal period of water storage and the rate of storage loss. Results show that hundreds of tanks are not able to store water despite precipitation inputs to the system. For tanks with water, further analysis reveals great variability among tanks for both indicators. As shown, this decade of EO offers exciting opportunities to apply data-driven approaches to complement more traditional physically-based hydrological understanding. Vanthof, V., & Kelly, R. (2019). Water storage estimation in ungauged small reservoirs with the TanDEM-X DEM and multi-source satellite observations. Remote Sens. of Environ., 235, 111437.
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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 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 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 ».