The paper presents the open source tool climdex-kit which includes utilities to compute, analyze and visualize climate indices based on the input data, target domain and temporal extent defined by the user. It is intended to support researchers as well as practitioners and decision makers to derive, handle and interpret meaningful information for climate change studies and sectoral applications. It currently includes the computation of 30 indices based on temperature and precipitation, describing both mean and extreme climate conditions, and it is designed to work with climate model projections. The tool is written in Python and integrates utilities from the well-established Climate Data Operators (CDO) and NetCDF Operators (NCO) libraries. The specific utilities for selecting, aggregating and visualizing data are thought to help users to produce tailored results improving understanding and communication of future climate change. In order to show the functionalities of the package as well as its potential integration in regional climate services, climdex-kit was applied to an ensemble of downscaled climate model projections up to 2100 for the region Trentino – South Tyrol (north-eastern Italian Alps). The projections for a selection of indices accounting for extreme temperature and precipitation conditions were derived and different visualization choices discussed. The package climdex-kit is developed in a way that allows users to implement additional routines for calculating other indices as well as for easily adapting the routines to handle with different data types and spatio-temporal targets defined by the specific application. Effective climate services can in fact be developed only if flexible tools and customizable climate information are integrated with a clear understanding of data features and limitations.

This work estimates yield losses due to drought events in mountain grasslands in north-eastern Italy, laying the groundwork for index-based insurance. Given the high correlation between Leaf Area Index (LAI) and grassland yield, we exploit LAI as a proxy for yield. We estimate LAI by the Sentinel-2 biophysical processor and we compare different gap-filling methods, including time-series interpolation and fusion with Sentinel-1 SAR data. We derive a Forage Production Index (FPI) as the growing season cumulate of the daily product between LAI and a meteorological water stress coefficient. Finally, we calculate the drought index as the anomaly of FPI. The validation of Sentinel-2 LAI with ground measurements showed RMSE of 0.92 [m2 m-2] and R2 of 0.81, on average over all the measurement sites. The comparison between FPI and yield showed R2 of 0.56 at the pixel scale and R2 of 0.74 at the parcel scale. The developed prototype FPI index was used at the end of the growing season of the year 2022 for calculating the payments of an experimental insurance scheme that was proposed to a group of farmers in Trentino-South Tyrol.