Enhancing the In-situ Environmental Observations across Under-sampled Deserts

Desert ecosystems are predicted to be one of the most vulnerable ecosystems to global climate change; rising temperature, decreasing rainfall, and increasing atmospheric CO_2, are expected to strongly affect the structure and function of desert ecosystems. Desert-adapted species are vulnerable to climate change and among them, endemic plant species are particularly susceptible to the loss of suitable habitat. The negative effect of climate change on desert plants has been demonstrated worldwide. However, desert ecosystems and local meteorological conditions can differ so much that over the last 15 years, some deserts have taken on a greener appearance. Increasing temperatures cause more evaporation over the sea, which causes condensation to fall onto dry land. For example, in the middle Sahara, rainfall is greater, especially in the summer. According to climate change specialists, such enormous increases will eventually reshape desert ecosystems. The rapid development of plants is likely to disrupt finely balanced desert ecosystems, altering the nutrition cycle, fire cycle, and water distribution. Furthermore, climate change effects in desert areas affect also, in great extent, neighbouring areas, where dust storms can travel thousands of kilometres across countries and continents carrying along other pollutants on the way and depositing particles far away from their origin. Desert dust outbreaks from north Africa are a common occurrence in the Mediterranean Basin. The prevalence of this sort of particulate matter in combination with others (most notably, anthropogenic pollution from intensive human activity) makes air quality in this region a serious concern. Taking into account that remote places, such as semi-arid and desert regions, generally lack ground-based observational networks, satellite-derived data is used to estimate the great majority of surface and near-surface meteorological parameters. These datasets, however, are known to have large biases, making it difficult to test and calibrate the performance of a numerical models. Surface fields, such as temperature and relative humidity, can be more accurately estimated using ground-based measurements and can also be combined with Artificial Intelligence trained models to enhance the capabilities of existing satellite data. Moreover, they can be used to evaluate and calibrate numerical models, as well as to review satellite-derived data and optimise retrieval methods, as well as to gain insight into local atmospheric dynamics.

CiROCCO’s vision is to establish an end-to-end sensing system, composed of a distributed network of cost-effective sensing nodes coupled with state-of-the-art data fusion remote sensing and assimilation modelling techniques. The defined network of sensors will enhance the current lack of ground observation in desert areas offering an operational and in parallel easy-to-maintain and -expand solution. Commercialisation services will ensure the installation’s sustainability and simultaneously the findability, accessibility, interoperability, and reusability (FAIR) management of data will support Cross-COPERNICUS ecosystem integration and assimilation services.