Gerald Corzo Perez

Associate Professor of Hydroinformatics


Dr Gerald A. Corzo is a senior researcher with extensive knowledge on ICT and its applications in water resources problems. He has completed civil engineering with studies in teleinformatics, and he has a Master and PhD in hydroinformatics applications using machine learning models. Gerald has postdoctoral experience using Big Data analytics for Climate Change extremes at Wageningen University.

His primary areas of expertise are in hybrid machine learning and process-based modelling. He has worked with spatiotemporal analysis of extreme events under climate change scenarios, and contributed to the area of pattern recognition and tracking of extremes.

Gerald produced more than 40 high impact journal publications, various book chapters, journal special issues and book editorials. In 2012 he won the Tison award from the International Association for Hydrological Science (IAHS). He participated in various EU and international projects and developed a trajectory in spatial statistics. He coordinated the statistics of the Climate change inventory of adaptation and mitigation actions for Latin-America, presented at the World Water Forum in 2012. He also wrote the report 43 on future extremes from the WATCH EU forcing data set and a big data analysis of the Climate model scenarios.

In 2020 he coordinated the analysis of climate change for the national atlas of climate change in the Dominican Republic. Currently Gerald is focussing on AI and spatiotemporal analysis of socio technological and water resources problems.

One of his recent projects focuses on Natural processing language for understanding and finding causation of decision making from cloud data. Part of the development in the area of data collection use of citizens science and mobile teechnologies. As part of the concept of citizens involvement an “intelligent” serious game was developed to collect data and explore knowledge and behaviour in citizens. As project leader, lecturer and research supervisor he has the opportunity to cooperate with institutes around the world, building a large network for science and development cooperation.


Varouchakis, E. A., Hristopulos, D. T., Karatzas, G. P., Corzo Perez, G. A., & Diaz, V. (2021). Spatiotemporal geostatistical analysis of precipitation combining ground and satellite observations. Hydrology Research.

Diaz, V., Corzo Perez, G. A., Van Lanen, H. A. J., Solomatine, D., & Varouchakis, E. A. (2020). Characterisation of the dynamics of past droughts. Science of the Total Environment, 718.

Ritter, J., Corzo, G., Solomatine, D. P., & Angarita, H. (2020). Multiobjective Direct Policy Search Using Physically Based Operating Rules in Multireservoir Systems. Journal of Water Resources Planning and Management.

Valles, J., Corzo, G., & Solomatine, D. (2020). Impact of the Mean Areal Rainfall Calculation on a Modular Rainfall-Runoff Model. Journal of Marine Science and Engineering, 8(12), 980.

Amaranto, A., Pianosi, F., Solomatine, D., Corzo, G., & Munoz-Arriola, F. (2020). Sensitivity analysis of data-driven groundwater forecasts to hydroclimatic controls in irrigated croplands. Journal of Hydrology, 587, 124957.

Laverde-Barajas, M., Corzo, G. A., Poortinga, A., Chishtie, F., Meechaiya, C., Jayasinghe, S., ... & Solomatine, D. P. (2020). St-corabico: A spatiotemporal object-based bias correction method for storm prediction detected by satellite. Remote Sensing, 12(21), 3538.

Diaz, V., Perez, G. A. C., Van Lanen, H. A., Solomatine, D., & Varouchakis, E. A. (2020). An approach to characterise spatio-temporal drought dynamics. Advances in Water Resources, 137, 103512.  

Uribe, N., Srinivasan, R., Corzo, G., Arango, D., & Solomatine, D. (2020). Spatio-temporal critical source area patterns of runoff pollution from agricultural practices in the Colombian Andes. Ecological Engineering, 149, 105810.

On the spatio-temporal analysis of hydrological droughts from global hydrological models, Hydrology and Earth System Sciences, July 2011

This paper presents two methodologies that extend the tools to analyze spatio-temporal drought development and characteristics using large scale gridded time series of hydrometeorological data. The methodologies are distinguished and defined as non-contiguous and contiguous drought area analyses (i.e. NCDA and CDA). The NCDA presents time series of percentages of areas in drought at the global

Authors: Gerald Corzo Perez, Henny van Lanen, M. H. J. van Huijgevoort,F. Voss

Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - part 1Hydrology and Earth System Sciences, April 2010

A comprehensive data driven modeling experiment is presented in a two-part paper. In this first part, an extensive data-driven modeling experiment is proposed. The most important concerns regarding the way data driven modeling (DDM) techniques and data were handled, compared, and evaluated, and the basis on which findings and conclusions were drawn are discussed. A concise review of key articles

Authors: Gerald Corzo Perez, Dimitri Solomatine, A. Elshorbagy, S. Srinivasulu.

Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: ApplicationHydrology and Earth System Sciences, 2010

Inputs for the five case studies (half-hourly actual evapotranspiration, daily peat soil moisture, daily till soil moisture, and two daily rainfall-runoff datasets) are identified, either based on previous studies or using the mutual information content. Twelve groups (realizations) were randomly generated from each dataset by randomly sampling without replacement from the original dataset. Neural

Authors: Gerald Corzo Perez, Dimitri Solomatine, A. Elshorbagy, S. Srinivasulu.

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