Author(s): Manuel Cobos; Pedro Magana; Pedro Otinar; Asuncion Baquerizo

Linked Author(s): Manuel Cobos, Pedro Magaña, Pedro Otiñar

Keywords: Coastal adaptation; Wave climate; Stochastic simulation

Abstract: The UN’s Intergovernmental Panel on Climate Change (IPCC) indicated that an annual investment of $2.4 trillion is needed to limit temperature rise to below 1.5 °C from pre-industrial levels. However, even if emissions are reduced and efforts on reforestation are done, it will be necessary to adapt to rising temperatures [1]. In coastal zones, the design of management strategies requires the statistical characterization of maritime climate from available projections. These consist of an ensemble of multivariate time series obtained from a wave generation and propagation model forced by atmospheric phenomena from different combinations of Global Climate Models (GCMs) and Regional Climate Models (RCM) under a given RCP scenario. The number of realizations of these models is still low what makes it difficult to assess the uncertainty associated to the future wave climate [2]. The present study is based on Marinetools.temporal, an open-source software aimed at providing users with a friendly and general code to statistically analyze a vector random process (VRP) to obtain new realizations of it [3]. It uses nonstationary mixed distributions to characterize the marginal variables and a vectorial autoregressive model to infer the dependence of each component at a given time with previous values of the vector. The model is applied along the Andalusian coast to a set of several GCM-RCM combinations following the procedure developed in [2]. The results are presented in a web app where managers can visualize the position along the Andalusian coast where the analysis is done using GIS technology. The values of the parameters used for the non-stationary characterization can be downloaded from the tool. These parameters can be used for generating new timeseries of future wave climate for their use in coastal engineering studies under a climate change scenario. During the presentation, the methodology applied to characterize and simulate new VRPs, the visualization and download tool of the web application, as well as the steps to generate new statistically equivalent stochastic realizations of the VRPs using the downloaded data and the tool, will be briefly presented. References [1] Where climate cash is flowing and why it’s not enough. Nature 573, 328-331 (2019). doi: https://doi.org/10.1038/d41586-019-02712-3 [2] Loarca, A. L., Cobos, M., Besio, G., & Baquerizo, A. (2021). Projected wave climate temporal variability due to climate change. Stochastic Environmental Research and Risk Assessment, 1-17. [3] Cobos, Manuel and Otíñar, P and Magaña, P and Lira-Loarca, A and Baquerizo, A (2021) MarineTools.temporal: A Python package to simulate Earth and environmental time series. Under review in Environmental Modelling and Software.

DOI: https://doi.org/10.3850/IAHR-39WC2521711920221614

Year: 2022