Copernicus services and data to support a sustainable and adaptive water resource management

An effective, efficient, and sustainable allocation of resources has gained prominence on a global scale in recent years, taking into account far more than in the past the environmental needs of the ecosystems linked to them. Proper resource management and planning, as well as the detection of specific sustainable indicators, are required to respond to increasing demand while keeping in mind that its increase corresponds to a gradual reduction in the availability and usability of resources, which is also a result of climate change. This becomes even more important when the resource under consideration is water, which is essential for human survival and well-being as, after all, agricultural production. In comparison to other European countries, Italy has an abundance of meteoric inflows, albeit unevenly distributed across its entire territory. Because of this, the Italian country is particularly vulnerable to water crises, which are becoming more common, particularly in South Italy. The management of water resources, which is already complex, becomes even more so when dealing with scarcity situations, an area in which it is critical, to begin with the cognitive assumption of hydrological balance. Remote Sensing (RS) approaches are essential for investigating and assessing water bodies, meteoric inflows, and water balance parameters, allowing for effective surface water management support. RS is widely used for the aforementioned purposes due to the increasing availability of novel medium-high-resolution remote sensing big data, as well as Copernicus services and data related to water management (https://climate.copernicus.eu/water-management). Thus, the goal of this study is to take a "snapshot" of the current state of natural water resource availability in the Apulian region by extracting and estimating the main hydrological balance components introduced by the BIGBANG model (Braca et al., 2021) by exploiting Copernicus services and freely available medium-high resolution satellite data. Following the collection of all necessary input data, such as high-resolution Digital Elevation Model (DEM), Corine Land Cover maps, remote sensing-based soil sealing maps, mean monthly air temperature and rainfall, Google Earth Engine (GEE) environment, a free cloud platform recently released by Google to manage big geospatial data, were used to handle and estimate the main hydrological balance components. The proposed approach, based on the integration of Copernicus services and the BIGBANG model, appears as a useful and operational tool for supporting sustainable and adaptive resource management activities, particularly in water crisis situations. In fact, it allows extracting trustworthiness water balance components quickly.