Spatio-temporal characterisation of drought: data analytics, modelling, tracking, impact and prediction
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Studies of drought have increased in light of new data availability and advances in spatio-temporal analysis. However, the following gaps still need to be filled: 1) methods to characterise drought that explicitly consider its spatio-temporal features, such as spatial extent (area) and pathway; 2) methods to monitor and predict drought that include the above-mentioned characteristics and 3) approaches for visualising and analysing drought characteristics to facilitate interpretation of its variation. This research aims to explore, analyse and propose improvements to the spatio-temporal characterisation of drought. Outcomes provide new perspectives towards better prediction.
The following objectives were proposed. 1) Improve the methodology for characterising drought based on the phenomenon’s spatial features. 2) Develop a visual approach to analysing drought variations. 3) Develop a methodology for spatial drought tracking. 4) Explore machine learning (ML) techniques to predict crop-yield responses to drought. The four objectives were addressed and results are presented.
Finally, a scope was formulated for integrating ML and the spatio-temporal analysis of drought. Proposed scope opens a new area of potential for drought prediction (i.e. predicting spatial drought tracks and areas). It is expected that the drought tracking and prediction method will help populations cope with drought and its severe impacts.
Table of Contents
Introduction, Literature review, Methodological framework, Case studies and data, Spatio-temporal drought characterisation, Comparison of drought indicators, Machine-learning approach to crop yield prediction, Visual approaches to drought analysis, Spatial drought tracking development, Conclusions and recommendations
Vitali Díaz Mercado is a civil engineer, passionate programmer, data analyst, modeler, and remote-sensing-based approaches developer to overcome water challenges. Vitali is originally from Mexico. He holds a BSc in Civil Engineering and an MSc in Water Science from the Faculty of Engineering at the Autonomous University of Mexico State. He received his PhD from IHE Delft and the Delft University of Technology. His BSc thesis, MSc and PhD studies were financed and supported by the National Council for Science and Technology of Mexico. He has collaborated on various projects with case studies in Mexico, Colombia, Ecuador, Dominican Republic, El Salvador, Honduras, Costa Rica, Mauritania, Senegal, Mali, Côte d'Ivoire, Burkina Faso, Tanzania, Greece, India and Vietnam. The Albert II of Monaco Foundation supported the last stage of his PhD through the project "Uncertainty-aware intervention design for Mediterranean aquifer recharge". His research interests include extreme hydrological events (drought and flood), machine learning, data visualization, hydrological modeling, integration of models and remote sensing data, development of GIS-based applications and water accounting. These lines of research have arisen during different stages of Vitali's academic and professional journey.Vitali's PhD research aimed to increase understanding of the mechanisms by which drought develops in space and time, i.e., drought dynamics. Drought was conceptualized as an event whose spatial extent changes over time. Learn more about drought dynamics can enhance its characterization, i.e., higher accuracy in calculating its onset, duration, intensity and spatial extent and trajectory. Data analysis and machine learning techniques were explored to unravel those mechanisms. Expected outcomes of his PhD research will help to better monitor and predict drought.