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• W4367592166 abstract "Interception refers to the quantity of rainfall prevented by vegetation from reaching the soil surface, which is one of the significant and integral parts of the hydrological cycle. Most hydrological models depend on the water balance components, where the rainfall intercepted by vegetation is considered a loss. It is an essential and controlling parameter in hydrological modeling studies and flood forecasting analysis, thus establishing that its impacts at local, regional, and global scales are imminent. Remote sensing is one of the advanced techniques that helps generate a spatiotemporal variation of interception by vegetation canopy. The present study aimed to generate spatiotemporal variation interception maps for an agricultural watershed covering 53.75 km2 of the area near Tadepalligudem, West Godavari district, Andhra Pradesh. The study area is covered with vegetation cover that constitutes about 40–55% of the total catchment; thus, interception is a critical component in hydrological modeling studies in this watershed. Landsat 8 datasets acquired from USGS EarthExplorer during different months of 2020 are used in this study. Leaf area index (LAI) and canopy storage capacity (Smax) are the influential parameters in estimating canopy rainfall interception. The interception maps at varying spatial and temporal scales are generated using MATLAB programming platform. The result obtained gives a better understanding of the spatiotemporal variation of interception and its importance at a regional scale. The canopy rainfall interception model derived can be applied to various agriculture watersheds. Further, results obtained from the analysis can be used in rainfall–runoff modeling and water resource management studies." @default.
• W4367592166 created "2023-05-02" @default.
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• W4367592166 date "2023-01-01" @default.
• W4367592166 modified "2023-10-18" @default.
• W4367592166 title "Spatiotemporal Variation of Interception in an Agriculture Watershed—Tadepalligudem, West Godavari, India" @default.
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• W4367592166 doi "https://doi.org/10.1007/978-981-19-9147-9_33" @default.
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