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Adele Young

PhD candidate

Biography

Adele is from the twin island of Trinidad and Tobago. She is now a fulltime PhD fellow in the Netherlands at IHE Delft with the Flood Resilience Group and at the  Delft Technical University (TUDelft) in the Civil Engineering and Geosciences faculty.  Adele holds an MSc. in Water Science and Engineering with a specialisation in Hydraulic Engineering and River Basin Development from IHE Delft and an MSc. in Civil and Environmental Engineering from the University of the West Indies, St Augustine. Prior to returning to further her studies, she practised for over eight years as a civil engineer on several flood hydrological studies and drainage infrastructure design and construction management projects in Trinidad and Tobago. Her past work experience combined with her current research has made her interested in alternative solutions to address flood risk management challenges.

Adele is also an active member of the Water Youth Network's Disaster Risk Reduction thematic group and a coordinator of the Early Warning systems Young Professionals group; connecting young professionals across disciplines in EWS. As a volunteer, she has hosted and convened young professional networking sessions at Europan GeoScience Union (EGU) Conference 2019 and early warning system focused sessions at Multi-Hazard Early Warnings System International Conference (MHEWS-IC), Understanding Risk and EGU. 

As of 2021, Adele holds the position of  Chair in IHE Delft's PhD Association Board (PAB).

Research Summary

With the risk of pluvial flooding on the rise as more cities are challenged by a changing climate and local drivers: increased urbanisation and inadequate sewer systems. Sustainable flood risk management requires a hybrid of structural and non-structural measures to ensure water hazard resilient cities. In this regard, flood forecasting and early warning systems have been proposed as a “low regret” measure to reduce flood risk and increase preparedness through forecast-based actions. Nevertheless, many cities do not have the capabilities (data-scarce regions) to produce high-quality rainfall forecast and well-calibrated flood forecast (timing, water levels, extent and impact). As a result, there is a cascading effect on the ability to make and provide good reliable decisions given the uncertainty in the forecast or inaccuracy in the input data. For example, decisions in anticipatory flood management (to start pumping or not) becomes problematic given its dependence on the knowledge generated from uncertain data and the consequences of an incorrect prediction and/or action. 
 
Probabilistic forecast “ knowledge” is beneficial in quantifying uncertainty and have been hailed as a means to support decision making but there is no one consensus on the most suitable and effective way to incorporate them into the decision-making framework.  In weather warnings and flood forecast, cost loss ratio approaches and Bayesian decision theory have been used to define the so-called “optimal decision rule” under uncertainty. Bayesian decision theory formulates a Bayes rule consisting of an expected utility (loss function) and a posterior probability for establishing criteria for selecting an alternative. However, to what extent inherent spatiotemporal inaccuracies influence this posterior probability and thus the resultant decision has not been considered especially in data scare regions. In this regard, the proposed research will focus on providing understanding on how the influence of the varying degrees of input data, particularly forecast rainfall spatial and temporal distributions will ultimately affect the ability to make decisions.
 
The objective of this research is not to make forecast more accurate but rather to highlight the interdependences of the flood forecast and decision-making chain in order to address what decision can be made given the quality of forecast. The success of such an approach will support robust anticipatory forecast-based decision making in data-scarce cities given limitations in high-resolution data availability while increasing preparedness and strengthening resilience against future extreme events.

Publications

Young, A., Bhattacharya, B., Daniels, E., and Zevenbergen, C.: Evaluation of a WRF model in forecasting extreme rainfall in the urban data-scarce coastal city of Alexandria, Egypt, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9241, https://doi.org/10.5194/egusphere-egu21-9241, 2021.

Young, A., Bhattacharya, B. and Zevenbergen, C. (2021) ‘A rainfall threshold-based approach to early warnings in urban data-scarce regions: A case study of pluvial flooding in Alexandria, Egypt’, Journal of Flood Risk Management, (February), pp. 1–16. doi: 10.1111/jfr3.12702.

Young, A., Bhattacharya, B. and Zevenbergen, C. (2020) ‘Pluvial flood forecasting in urban data-scarce regions: Influence of rainfall spatio-temporal data (in)accuracy on decision-making’, in EGU General Assembly Conference Abstracts. (EGU General Assembly Conference Abstracts), p. 9492.  https://doi.org/10.5194/egusphere-egu2020-9492   

Young, Adele; Bhattacharya, Biswa; Wu, Ziyi; Huang, Hung-Hsiang; Radhakrishnan, Mohanasundar; Zevenbergen, Chris; Khalil, Mohamed Hasan (2020) 'Forecasting extreme floods in arid regions: A case study on Alexandria'. Geophysical Research Abstracts .2019, Vol. 21, p1-1. 1p. 

Bhattacharya, B., Zevenbergen, C., Young, A. and Radhakrishnan, M. (2018) ‘Extreme Flooding in Alexandria: Can Anticipatory Flood Management be a Solution?’, HIC 2018. 13th International Conference on Hydroinformatics. EasyChair (EPiC Series in Engineering), pp. 252–257. doi: 10.29007/wvth.

 

Other information

  • Living with floods challenge - Nosso mural de cheias. Collaborate with HKV on an idea to raise awareness of flood risk and preparedness in vulnerable peri-urban communities in Mozambique. The idea called "Nosso mural de cheias" means "Our Floor Mural' in Portuguese. More information at https://www.facebook.com/nossomuraldecheias
  • Crowdsourcing for improving pluvial flood forecast and decision making. Presented at the Caribbean Water and Wastewater (CWWA) Virtual Conference 2020.  My presentation proposed the use of crowdsourced data and machine learning methods to improve pluvial flood forecasting in the city of Port of Spain, Trinidad and Tobago. Video can be viewed here.
  • Webinar: Managing flood risk in semi-arid data scarce regions, 2018. Webinar can be viewed here.

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