Spatiotemporal Identification and Characterization of Extreme Hydrometeorological Events Patterns in the Magdalena River Basin

Single extreme hydrometeorological events have been highly studied around the world, however, concerns related to the spatiotemporal variations have extended the studies to look for more insight into space and time dimensions. In this context, of increasing importance are the relations of extreme events properties over multiple spatial and temporal scales. Nevertheless, the study of these relations has not been widely developed. The interaction between events, like floods and droughts with different spatiotemporal characteristics, so far, has still yet to be further studied. Some studies show that there are complex relations linking between both extremes, since occurrences of both are observed in single catchment areas around the world. Furthermore, when analyzing time and space scales for concurrent or successive events, the complexity increase. Recent advances in the spatiotemporal analysis of droughts and floods include tracking approaches, data-driven probabilistic models and machine learning applications. Likewise, new studies have highlighted the usefulness of data mining techniques in extracting knowledge, identifying patterns and detecting anomalies from climate databases.

Therefore, the main objective of this research is to characterize and identify spatial and temporal patterns related to extreme hydrometeorological events generation and propagation using data mining techniques. The selected case study is the Magdalena River basin in Colombia. This basin produces most of Colombia’s Gross Domestic Production (GDP), which is highly dependent on the water resource. Because of this, extreme hydrological events such as floods or droughts have a large impact all over the basin.

ERA5-Land information (precipitation, temperature, surface pressure and wind U and V components) from 1980-2020 with a resolution of 0.1°x0.1° at multiple time scales (hourly and monthly) was collected for this study. This data was used to identify and characterize extreme hydrometeorological events for multiple time steps and indices thresholds. Temporal, spatial, climatic and geometrical properties of each extreme event region were calculated and stored in a hydrometeorological database. Unsupervised machine learning clustering algorithms (k-means, hierarchical clustering, DBSCAN and spectral) were applied on the database to cluster elements with similar property values. At last, a data mining association rules method (APRIORI) was applied to identify clear patterns between cluster elements of extreme hydrometeorological events. As a main result of this study, is expected an improved understanding of the extreme hydrometeorological events patterns and their associated hydro-climatic processes in the region. This knowledge can help to obtain more accurate and less uncertain estimations of extreme hydrological events, as these are major challenges of many water resources problems, such as monitoring and forecasting.