CAVA: a user-driven climate service for the assessment of risks in the agriculture sector

 

Climate change impacts agricultural production globally, affecting food security and economic development at all scales. The Climate and Agriculture risk Visualization and Assessment (CAVA) framework has been co-designed by the University of Cantabria, Predictia Intelligent Data Solutions and the Food and Agriculture Organization (FAO) of the United Nations in response to the need for evidence-based climate information in formulating climate change adaptation projects (e.g. Green Climate Fund) and investment plans in the agriculture sector. 

Within this framework, CAVA Platform has been designed as a climate service which provides users with an easy access to state-of-the-art climate information through a web portal, with the aim to facilitate the assessment of risks in the agricultural sector at regional, national, and sub-national scales. In particular, this is done based on global gridded observations, reanalysis, and the ensemble of CORDEX-CORE simulations covering the period up to 2100. The tool provides immediate access to essential climate variables (temperatures, precipitation, wind, humidity, radiation), and a series of pre-computed climate-derived indices relevant to agriculture (e.g., number of days below/above temperature thresholds, number and length of dry/wet spells, frequency and intensity of heat waves, etc.), allowing the user to select his/her region, period and season of interest. Moreover, users are also allowed to conduct more sophisticated analyses on demand; e.g. by modifying the thresholds that define the aforementioned indicators, focusing on specific crops, etc. In addition, all this information can be downloaded via automatic reports. 

Concurrently to the CAVA Platform, CAVA Analytics is a cloud-based service that allows users with basic programming skills to access, process, and visualize most of the data CAVA Platform builds on. This computing environment, which is available via a web browser, relies on a Jupyter hub with a pre-installed version of the R package CAVAanalytics (https://github.com/Risk-Team/CAVAanalytics), which internally builds on the climate4R (https://github.com/SantanderMetGroup/climate4R) suite.