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Research interests

My research activities cover most areas of water resources planning and management, and can be grouped under the following sub-headings:

Water supply reservoir planning and performance evaluation: Activities here have focused on the development of new techniques that lend themselves to application in data-sparse situations for determining the planning characteristics of reservoirs, including the reservoir capacity, yield, reliability, vulnerability, resilience and sustainability. A highlight of this is the development of the modified sequent peak algorithm (mSPA) that has been demonstrated to not mis-behave (as the widely used, traditional reservoir behaviour simulation approach), thereby giving a unique solution to the reservoir planning problem. Despite this attractiveness of the mSPA, it still requires that time series data are available for planning analyses, which may not be the case in some regions of the world. To address this limitation, the mSPA has been applied as the basis for the development of more rapid reservoir planning techniques that are also parsimonious in that they only use few summary statistics of runoff- e.g. the mean, the coefficient of variation (CV)- which are obtainable either from time series data (if available) or indirectly from catchment characteristics when runoff data are unavailable. Classical regression and artificial neural networks have been used to relate reservoir capacity obtained using the mSPA to these summary statistics with encouraging results.

Water supply reservoir operation: The focus here is the development of reservoir release rules that incorporate hedging targets and by so doing improve the operational performance of reservoirs during droughts.  The development of these enhanced rule curves has also used the mSPA, which has been possible because, as part of the enhanced development of the mSPA (see the above Section), it can now handle water-shortage (or vulnerability) targets explicitly during planning analyses. Extensive testing of the hedging-integrated rule curves has demonstrated that their use significantly improves the overall performance of water supply reservoirs in terms of reliability, resilience and sustainability.

Artificial Intelligence modelling of environmental systems: The attractiveness of AI modelling over traditional mechanistic approaches is that the former is usually data-driven and hence does not require the explicit formulation of the mathematical description of the system being modelled. This is of significance for environmental systems in which, because of their complex nature, identifying the correct model structure of their behaviour in closed mathematical form is fraught with immense difficulties. Activities in this area have focused on water quality applications (modelling of operational performance of wastewater treatment plants; real-time prediction of water quality characteristics, notably the BOD5); water supply reservoir capacity estimation; flood prediction and forecasting; and the estimation of reference evapotranspiration.  The work has utilised supervised artificial neural networks (ANNs), unsupervised ANNs (typified by the self organising map, SOM), hybrids of these, and Fuzzy inference systems.

Climate change impacts on water resources: Activities here have focused on how predicted changes in climate will affect the availability of water resources for water supply, irrigation and ecosystems. The highlight of my approach to impacts assessment is the use of ensembles of baseline and climate-change perturbed hydro-climate within a Monte Carlo simulation framework leading to the quantification of the uncertainties in the assessed impacts. The approach has been applied to major UK water resources systems in the recent past and was recently deployed on a changing water cycle (CWC) South Asia project in India. “Mitigating climate change impact on India Agriculture through improved irrigation water management (MICCI)” is one of five on-going scientific efforts being sponsored jointly by the UK-NERC and India-MOES to further the understanding of the climate-change-water problem and proffer solutions that are robust and effective for Indian irrigators. The project involves international collaboration of UK scientists (Heriot-Watt University and Cranfield University) and 3 Indian institutions led by the Indian Institute of Technology, Roorkee. Further information about MICCI and the wider CWC/LWEC is available by clicking this link: http://web.sbe.hw.ac.uk/sites/micci/.

Groundwater evaluation, modelling and management: On a global context, accessible groundwater resources exceed surface water by more than 70 times; groundwater is therefore extremely important and in some situations, especially in arid and semi-arid regions where excessive evaporation makes surface water development unsustainable, is the only available option for meeting water needs. Our activities here have focused on the mathematical modelling of groundwater with a view to assessing the long-term impacts of large scale abstraction for irrigation. Work was recently completed on the non-renewable Murzuq aquifer in southwest Libya which serves several large scale irrigation projects in addition to contributing water to the great manmade river project. Another recent work completed investigated the feasibility of optimising the blending of groundwater and seawater desalination for meeting increasing domestic and agricultural water demands at Ash-Sharqiy region of the Sultanate of Oman.

Other activities: These include economic value of hydro-meteorological data; economic design of hydro-meteorological data collection networks; statistical analysis of floods and low flows; rainfall-runoff modelling; and general hydrological studies.


Finally, why not watch me on YouTube discussing contemporaneous water security challenges that face humankind at:






Happy viewing!


Professor Adebayo (Bayo) Adeloye graduated with BSc (First Class, First Position) degree from the University of Ife (now Obafemi Awolowo University, Ile-Ife), Nigeria in 1977, specialising in irrigation and soil and water engineering. After a stint in consulting practice, he went on to obtain the MSc and PhD degrees at the University of Newcastle upon Tyne, UK, specialising in Water Resources engineering and management. In 1987, Dr Adeloye won the prestigious and highly competitive Fellowship of the Royal Commission for the Exhibition of 1851, a post-doctoral fellowship tenable at the Imperial College of Science, Technology and Medicine, London. Professor Adeloye joined Heriot-Watt University as a lecturer in 1992. He has published his research widely including being the co-author of “Water Resources Yield”, a postgraduate reference textbook first published in 2005 by Water Resources Publications, Colorado, USA. In addition to being a chartered engineer, and a chartered water and environmental manager, Professor Adeloye is also currently a Fellow of the Higher Education Academy.

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Co Author Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 1989 2019

Open Access
freshwater ecosystem
ecosystem service
river basin

Community Perceptions of Local Knowledge for Community-Based Flood Risk Management: The Case of ‘Zamakolo’ in Malawi

Sakic Trogrlic, R., Wright, G., Adeloye, A., Duncan, M. J. & Mwale, F. D., 17 Jul 2018

Research output: Contribution to conferencePaper

traditional knowledge
risk management
local participation
Open Access
climate change
genetic algorithm

Optimization of irrigation scheduling for spring wheat based on simulation-optimization model under uncertainty

Li, J., Song, J., Li, M., Shang, S., Mao, X., Yang, J. & Adeloye, A. J., 30 Sep 2018, In : Agricultural Water Management. 208, p. 245-260 16 p.

Research output: Contribution to journalArticle

irrigation scheduling
spring wheat
crop prices

Prediction of Consumptive Use Under Different Soil Moisture Content and Soil Salinity Conditions Using Artificial Neural Network Models

Qi, Y., Huo, Z., Feng, S., Adeloye, A. J. & Dai, X., Oct 2018, In : Irrigation and Drainage. 67, 4, p. 615-624 10 p.

Research output: Contribution to journalArticle

soil salinity
artificial neural network
neural networks
soil water content