Vitali Diaz Mercado

PhD fellow

Biography

Vitali Diaz is pursuing a PhD at IHE Delft and Delft University of Technology (TU Delft). His research interests include extreme hydrological events, hydrological modelling, integration of models and remote sensing data, development of GIS-based applications, water accounting, data visualisation, and machine learning. These lines of research have arisen during different stages of Vitali's professional journey.

He holds a BSc degree in Civil Engineering, and an MSc degree on Water Sciences from the Faculty of Engineering at Autonomous University of the State of Mexico (UAEMex), Toluca, Mexico. His BSc Thesis and MSc research were financed and supported by the Mexican National Council for Science and Technology (CONACYT). His MSc Thesis is entitled “GIS-based design of the CEQUEAU hydrological model and its implementation in Idrisi”.

Topic

Spatio-temporal analysis of hydrological drought: integration of data-driven and process-based models

Research Summary

Vitali’s PhD research aims to increase understanding of the mechanisms by which drought moves in space and time. Learn more about drought dynamics can enhance its characterisation, i.e., higher accuracy in the calculation of its onset, duration, intensity and area. Data analysis and machine learning techniques are being explored to unravel those mechanisms. Expected outcomes of this PhD research will help to better monitor drought. Although the investigation is focused on Latin America which faces droughts severely and more frequently, the developed methodology is also being tested in other parts of the world, including India, where drought causes agricultural severe damage. More details on the development of this research can be found at  (Spatio-Temporal ANalysis of Drought) STAND project

Publications

A complete listing via ResearchGate

Papers

Diaz, V., Corzo Perez, G. A., Van Lanen, H. A. J., Solomatine, D., and Varouchakis, E. A. (2020). An approach to characterise spatio-temporal drought dynamics. Advances in Water Resources, 137, 103512. https://doi.org/10.1016/j.advwatres.2020.103512 link

Diaz, V., Corzo Perez, G. A., Van Lanen, H. A. J., Solomatine, D., and Varouchakis, E. A. (2019). Characterisation of the dynamics of past droughts. Science of The Total Environment, 134588. https://doi.org/10.1016/j.scitotenv.2019.134588 link

Book chapters

Diaz, V., Corzo, G., and Perez, J. R. (2019). 3 - Large-scale exploratory analysis of the spatiotemporal distribution of climate projections: applying the STRIVIng toolbox. In G. Corzo and E. A. Varouchakis (Eds.), Spatiotemporal Analysis of Extreme Hydrological Events (pp. 59–76). Elsevier. https://doi.org/10.1016/B978-0-12-811689-0.00003-3 link

Diaz, V., Corzo, G., Lanen, H. A. J. Van, and Solomatine, D. P. (2019). 4 - Spatiotemporal drought analysis at country scale through the application of the STAND toolbox. In G. Corzo and E. A. Varouchakis (Eds.), Spatiotemporal Analysis of Extreme Hydrological Events (pp. 77–93). Elsevier. https://doi.org/10.1016/B978-0-12-811689-0.00004-5 link

Le, H. M., Corzo, G., Medina, V., Diaz, V., Nguyen, B. L., and Solomatine, D. P. (2019). 7 - A comparison of spatial–temporal scale between multiscalar drought indices in the South Central Region of Vietnam. In G. Corzo and E. A. Varouchakis (Eds.), Spatiotemporal Analysis of Extreme Hydrological Events (pp. 143–169). Elsevier. https://doi.org/10.1016/B978-0-12-811689-0.00007-0 link

Conference papers

Diaz, V., Corzo Perez, G. A., Van Lanen, H. A. J., and Solomatine, D. (2018). Intelligent drought tracking for its use in Machine Learning: implementation and first results. (G. La Loggia, G. Freni, V. Puleo, and M. De Marchis, Eds.), HIC 2018. 13th International Conference on Hydroinformatics (Vol. 3). Palermo: EasyChair. https://doi.org/10.29007/klgg link

Diaz Mercado, V., Corzo Perez, G., Solomatine, D., and Van Lanen, H. A. J. (2016). Spatio-temporal analysis of hydrological drought at catchment scale using a spatially-distributed hydrological model. Procedia Engineering, 154, 738–744. https://doi.org/10.1016/j.proeng.2016.07.577 link 

Conference abstracts 

Diaz, V., Corzo Perez, G. A., Van Lanen, H. A. J., and Solomatine, D. (2018). Comparative analysis of two evaporation-based drought indicators for large-scale drought monitoring. Geophysical Research Abstracts Vol. 20, EGU2018-18728. EGU General Assembly. Vienna. link

Osman, A., Diaz, V., Corzo Perez, G. A., Varouchakis, E., Solomatine, D. (2018). Finding negative response of crop yield to drought: a spatiotemporal approach over East India. International Conference on Water, Environment, Energy and Society (ICWEES), Tunisia. Based on Ahmed's MSc Thesis

Diaz, V., Corzo G., Van Lanen H.A.J., Solomatine D. (2017). On the visualization of water-related big data: extracting insights from drought proxies’ datasets. Geophysical Research Abstracts Vol. 19, EGU2017-10718-1. EGU General Assembly, Vienna link

Diaz, V., Corzo Perez G., Van Lanen H.A.J., Solomatine D. (2016). Spatio-temporal analysis of large-scale meteorological drought: helping to achieve the SDGs 6.A and 11.5. 12th Kovacs Colloquium, Paris, France. DOI: 10.13140/RG.2.1.2595.2888 link

Other information

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Co-supervisor of MSc research

Spatiotemporal analysis and prediction of crop yield using data-driven models and drought areas. Case study of India. Ahmed Abdelmoneim Ahmed Osman. MSc Thesis. WSE-HERBD.18-17, IHE-Delft, March 2018. Delft, Netherlands link

Integrated spatial precipitation drought index by combining remotely sensed information and local stations. Case study Guerrero State, Mexico. Yousra Omer Elfaroug Mohammed Khair. MSc Thesis. WSE-HI. 16-03, IHE-Delft, April 2016. Delft, Netherlands

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