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. In the past decades, catastrophic natural disasters such as storms and hurricanes have impacted millions of people around the world: around 33 million of people are affected per year between 2007 and 2016 (Bellow and Wallemacq, 2018). For instance, hurricane Katrina (USA, 2015) caused catastrophic damage from central Florida to Eastern Texas, at least 1836 people died and total property damage was estimated at 125 billion dollars. Furthermore, climate change is likely to increase frequency of most intense categories of hurricanes in some parts of the world and is expected to increase sea levels, leading to more destructive storm surges when hurricanes occur (IPCC, 2013). In the same time, growing populations and rising incomes are expected to place more people and property in harm’s way. Depending on their nature and severity, disasters can create large volumes of debris and waste (Brown et al., 2011). In some cases, debris volume from a single event were the equivalent of 5–15 times the annual waste generation rates of the affected community (Reinhart and McCreanor, 1999). Often, the debris removal process takes months or even years to treated (Pramudita et al., 2014). In France, storm Xynthia (2010) waste generation was equivalent to about 12 normal years (CEPRI, 2012). Thus, waste can overwhelm existing solid management facilities and the presence of disaster waste impacts almost every aspect of an emergency response and recovery effort (Brown et al., 2011). For instance, in the immediate response, disaster debris can cause road blockages. Moreover, if poorly managed, the waste can have significant environmental and public health impacts (Brown et al., 2011), such as water and air pollution. In this paper we present the Depos data modelling (named Depos-model) to improve the management of debris and waste after hurricanes. In the Depos project, funded by the French research agency (ANR), we intend to understand the process of waste management thanks to testimonies on previous hurricanes and to propose a digital modelling of waste management. In the following we do not distinguish debris and waste. The case study is the French west Indies (composed of different territories including the Guadeloupe and Martinique) and the very first island to study is the Island of Saint Martin in the Caribbean Sea where Irma hurricane (2017) had huge impact. This hurricane was the most powerful ever recorded hurricane in Atlantic with sustainable winds of nearly 300 km/h, with a gust to 360 km/h. The research work began with a study from the association Robin des Bois to describe and estimate waste after Irma on Saint Martin. These experts were questioned to identify the key entities and actors involved in hurricane waste management. We set a first data modelling that will be questioned and improved during the next 2 years. The depos-model should be able 1- to replay what occurred during a previous hurricane, 2- to simulate different situations on fictive hurricanes and hopefully 3- to be used after a future hurricane to improve waste management. In this paper we do not include the actors although they are key elements in the modelling. To introduce the data model, we decomposed it in different part. We first present the waste (figure 1) before presenting the transportation of waste to waste treatment areas (figure 3).

Spatial Data modelling to study and improve waste management after hurricanes. The case of French West Indies