dr.ir. SMT (Syed) Mustafa

I graduated from the Interuniversity Master of Water Resources Engineering combinedly from KU Leuven and Vrije Universiteit Brussel in Belgium with Great Distinction. I obtained a PhD in Hydrogeology with the greatest distinction from the Vrije Universiteit Brussel (VUB) in Belgium.

I work as a Lecturer and researcher in Hydrogeology at the Hydrology and Quantitative Water Management group at WUR. Before this, I was a postdoctoral research fellow, respectively, at the University of Oulu in Finland, the University of Aberdeen in the UK and the Vrije Universiteit Brussel in Belgium. I also worked as a part-time guest professor at the Vrije Universiteit Brussel in Belgium. I have also been working as a principal investigator (PI) of a UK-based GCRF and Scottish Funding Council-funded research project on groundwater security in the world's largest refugee camp, Cox’s Bazar. I am also involved in the EU H2020 WATERAGRI project and UKRI-funded Connect4 Water Resilience project. 

I have been involved in teaching activities at Vrije Universiteit Brussel in Belgium, the University of Aberdeen in the UK, Bangladesh Agricultural University in Bangladesh and the University of Oulu in Finland.

My research and interests focus on (i) numerical modelling & uncertainty analysis of groundwater flow and solute transport processes, (ii) sustainable groundwater resource development, management, and impact analysis, (iii) development of numerical model-based decision support systems for improved assessment and management of available natural resources and adaptation, (iv) hydrological extremes and their interaction with human activities, (v) integrated hydrological modelling and data assimilation for water retention and optimal crop water management, (vi) groundwater-surface water interactions (vii) crop water modelling and management, and (viii) interdisciplinary research on climate resilience & water security. 

I developed a novel Integrated Bayesian Multi-model Uncertainty Estimation Framework (IBMUEF) to quantify input, parameter and conceptual model structure uncertainty in groundwater modelling. 

In this fully Bayesian framework, the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm with a novel likelihood function is combined with the Bayesian Combined Model Averaging (BCMA) to simultaneously quantify the uncertainty arising from the conceptual model structural, input and parameter of a fully distributed groundwater flow model. Groundwater recharge and groundwater abstraction multipliers are introduced to quantify the uncertainty of the spatially distributed input data of the groundwater model in addition to parameter uncertainty. Additionally, the proposed approach is applicable for all types of residual errors i. e. both for homoscedastic and heteroscedastic errors.