Ex Post Analysis of Water Supply Demand in an Agricultural Basin by Multi-Source Data Integration

Abstract

In this work, we present a new methodology integrating data from multiple sources, such as observations from the Landsat-8 (L8) and Sentinel-2 (S2) satellites, with information gathered in field campaigns and information derived from different public databases, in order to characterize the water demand of crops (potential and estimated) in a spatially and temporally distributed manner. This methodology is applied to a case study corresponding to the basin of the Longaví River, located in south-central Chile. Potential and estimated demands, aggregated at different spatio-temporal scales, are compared to the streamflow of the Longaví River, as well as extractions from the groundwater system. The results obtained allow us to conclude that the availability of spatio-temporal information on the water availability and demand pairing allows us to close the water gap—i.e., the difference between supply and demand—allowing for better management of water resources in a watershed.

Full chain integration algorithm of water sensing equipment operation data based on Internet of Things and transfer learning

“…Water resources are the basis of human survival and social and economic development, and water sensing equipment refers to the equipment that collects and transmits water parameters data in real time through sensors and communication technology. It provides key support for water resources management and environmental protection through real-time monitoring and collection of important indicator information such as water quality, water quantity and water level [1]. However, with the widespread application of sensing devices, the scale and complexity of the data generated are increasing, and the heterogeneity, uncertainty and distribution of the data also pose challenges.…”