Thermal stress information as a tourism-oriented climate product: Performance analysis for selected urban destinations in Romania and Italy

The study addresses the characteristics of a climate service targeting tourists and discusses the evaluation of its products with a particular focus on the thermal stress information. Furthermore, an assessment of the impact of input data on the accuracy and relevance of the thermal stress product is presented. The thermal stress is expressed through UTCI (Universal Thermal Climate Index) and it is computed from UERRA regional reanalysis and E-OBS gridded dataset, for summer season during 2011-2018. The analysis targets 10 cities with different characteristics located in Romania and Italy. It focuses on the impact of three temperature-related input data (instantaneous temperature at 12:00 UTC, daily maximum and daily mean temperature) on the thermal stress intensity. The results show that differences up to 4 days in the pronounced thermal stress category may appear when employing daily maximum temperature compared to the use 12:00 UTC instantaneous temperature, while the use of daily mean temperature leads to strong underestimation of thermal stress in this category. The findings are of interest in defining the technical choices of products to be incorporated in a climate service for tourism in order to assure a good user uptake.

Clisagri: An R package for agro-climate services

Crop yields are affected by unfavourable/extreme weather and climate events occurring during sensitive growth stages. Understanding the risks associated with these events is essential to adapt agro-management decisions and reduce losses. For this purpose, we propose a targeted climate service integrating a dynamic crop phenology model with an approach based on dedicated agro-climate risk indicators. The initial set of these indicators has been developed in a co-design approach with agronomists and durum wheat farmers participating as end-users in the H2020-MedGOLD project. Four groups of indicators characterize drought events, excessive wetness, cold stress and heat stress during sensitive growth stages. The proposed approach has been fully implemented as an R-package named Clisagri. The package is complemented with a set of optimization functions, which target optimal variety selection in terms of crop cycle duration.