Spatio-temporal analysis of extreme hydrological events in a joint framework and its relationship with land-use land-cover change

Extreme hydrological events have had several economic, ecologic, and social impacts on many regions around the world. Although the impacts of floods and droughts are equally important and severe, they are typically treated as separate phenomena due to their hydrological differences. However, in order to address and mitigate their impacts, it is important to analyse and model the interactions between the spatial and temporal characteristics of the events, not only separately but also in a joint framework. Moreover, understanding and simulating the effects of land-use land-cover changes on extreme events dynamics is crucial to improve the development of land use policies and risk management plans. The Central America Dry Corridor (CADC) is one of the regions in the world with the highest vulnerability to floods and droughts due to its marked precipitation seasonality and climate variability. Land use change is also an important variable in this mainly rural area, in which forest cover has declined rapidly during the last decades, modifying basin runoff and affecting extreme events generation. Therefore, this study proposes a methodology to analyse and represent in a joint framework the spatio-temporal characteristics of CADC’s floods and droughts, and identify their relationship with land-use land-cover change patterns. To achieve this, a hybrid modelling framework that integrates Machine Learning (ML) techniques with a spatially distributed hydrological model is presented. It is expected that the integration of ML techniques increases hydrological model capabilities to accurately simulate the effects of land-use land-cover change on floods and droughts propagation. It is also expected that the hybrid model can be used as a tool to assess the effectiveness of different risk management measures and land use policies in floods and droughts mitigation.