Abstract:The estimation of the ozone (O 3 ) stomatal dose absorbed by a forest is a crucial step for O 3 risk assessment. For this purpose, data on O 3 concentrations at the forest top-canopy are needed. However, O 3 is barely measured at that height, while more often it is measured at a lower height above a different surface, typically a grassland near to the forest edge. The DO3SE model for O 3 stomatal flux calculation estimates the top-canopy O 3 concentration in near neutral stability conditions. However, near-neutrality is quite rare in the field, particularly in southern Europe. In this work, we present a modification of the DO3SE gradient calculation scheme to include the atmospheric stability. The performance of the new calculation scheme was tested against the direct measurements above a mature forest. Different gradient estimation options were also tested and evaluated. These options include simplified gradient calculation schemes and the techniques of the tabulated gradients described in the UN/ECE Mapping Manual for O 3 risk assessment. The results highlight that the inclusion of the atmospheric stability in the DO3SE model greatly improves the accuracy of the stomatal dose estimation. However, the simpler technique of the tabulated gradients had the best performance on a whole-season time frame.

Abstract. Weather index insurance is an innovative tool in risk transfer for disasters induced by natural hazards. This paper proposes a methodology that uses machine learning algorithms for the identification of extreme flood and drought events aimed at reducing the basis risk connected to this kind of insurance mechanism. The model types selected for this study were the neural network and the support vector machine, vastly adopted for classification problems, which were built exploring thousands of possible configurations based on the combination of different model parameters. The models were developed and tested in the Dominican Republic context, based on data from multiple sources covering a time period between 2000 and 2019. Using rainfall and soil moisture data, the machine learning algorithms provided a strong improvement when compared to logistic regression models, used as a baseline for both hazards. Furthermore, increasing the amount of information provided during the training of the models proved to be beneficial to the performances, increasing their classification accuracy and confirming the ability of these algorithms to exploit big data and their potential for application within index insurance products.

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Abstract. Since drought is a multifaceted phenomenon, more than one variable should be considered for a proper understanding of such an extreme event in order to implement adequate risk mitigation strategies such as weather or agricultural indices insurance programmes or disaster risk financing tools. This paper proposes a new composite drought index that accounts for both meteorological and agricultural drought conditions by combining in a probabilistic framework two consolidated drought indices: the standardized precipitation index (SPI) and the vegetation health index (VHI). The new index, called the probabilistic precipitation vegetation index (PPVI), is scalable, transferable all over the globe and can be updated in near real time. Furthermore, it is a remote-sensing product, since precipitation is retrieved from satellite data and the VHI is a remote-sensing index. In addition, a set of rules to objectively identify drought events is developed and implemented. Both the index and the set of rules have been applied to Haiti. The performance of the PPVI has been evaluated by means of a receiver operating characteristic curve and compared to that of the SPI and VHI considered separately. The new index outperformed SPI and VHI both in drought identification and characterization, thus revealing potential for an effective implementation within drought early-warning systems.