Joy Sanyal

Assistant Professor

Postdoc JSPS Fellow at Tokyo Institute of Technology (2018-20)

PhD  Geography (Durham University, UK, 2013)

Master by Research Geography (National University of Singapore, 2005).

M. A. Geography (Jawaharlal Nehru University, 2002)

B.Sc. (Hons) Geography (Presidency College, 2000) Minors: Mathematics and Economics.

My interest in Geography originates from my life-long interest in maps. I am an avid traveller and have been always intrigued by the beauties of remote mountains and rivers. These interests inspired me to undertake a career in Geography.

After completing my Geography (Hons) from Presidency College, I went to Jawaharlal Nehru University (JNU) where I was awarded an MA in Geography. While studying at JNU, I developed an interest in remote sensing and its application in fluvial environments, especially flood hazard estimation. Subsequently, I joined National University of Singapore (NUS) as a research scholar to pursue a research-based post-graduate degree.  At NUS, I focused on GIS and remote sensing applications on flood management and developed strong fundamentals in geospatial science.

Upon completion of my post-graduate degree at NUS, I worked for two years in Singapore and India in the geospatial industry, including Google India as a member of the Google Earth Team. Later, I decided to return to academia and joined West Bengal Education Service (WBES) as a Lecturer in Geography at Taki Government College.

After working as a lecturer for few  years, I went back to full-time research and joined the Department of Geography, Durham University (UK) as a Durham Doctoral Fellow to pursue a PhD degree. My doctoral research was based on hydrological modelling and scientific computing for flood prediction in data-sparse situations.

I have been recently awarded a Young Scientist Start-Up Research Grant (2014) by Department of Science and Technology (DST), Government of India to undertake a research project on morphodynamic modelling of channel bed evolution below some of the major dams in the Teesta River.

My recent research experience, which I gathered during my PhD (2009 - 2013) at Durham University, UK, has been centred upon flood inundation modelling and development of probabilistic flood hazard maps in data-sparse regions of the world. I have experience of working with a diverse range of hydrodynamic models from the simple storage cell LISFLOOD-FP to the complex finite element TELEMAC2D at reach lengths of 20 km to 120 km. My Doctoral research suggests that when our general goal is aimed at predicting dynamics of high magnitude streamflow in data sparse regions we should pay particular attention to the choice of the model in relation to the available data and hydraulic characteristics of the event in question. Adaptations are imperative to create inputs for the models that have been primarily designed for areas with better availability of data. Freely available geospatial information of moderate resolution can often meet the minimum data requirements of hydrological and hydrodynamic models if they are supplemented strategically with available surveyed information.

A major component of my doctoral research was also quantifying predictive uncertainty in hydrodynamic models based on Generalised Likelihood Uncertainty Estimation (GLUE) framework.  A GLUE-based uncertainty assessment methodology depends on large numbers of Monte Carlo realisations of model outputs. In order to deal with this large dataset I acquired the necessary skills for process automation, extraction of pertinent parameters from a large number of model outputs, computing required indices and visualization of results. In particular, my research involved extracting flood depths from model results in raster and unstructured finite element mesh formats, processing and visualising the same in both graphs and probabilistic flood hazard maps. This was achieved using a combination of Matlab codes and batch processing in ArcGIS. Due to the large computation cost of running 2D finite element hydrodynamic models I made use of a high performance computing (HPC) cluster based at Durham University. This provided me with valuable experience in operating within a parallel computing environment.

Academic Experience:

Assistant Professor in Geography at Taki Government College. (2007-09)

Grade 7 Postdoctoral Position at Department of Geography and Earth Sciences, Aberystwyth University, UK (2013) -- Not Availed

Academic Tutor (Year 1 Physical Geography) and Module Demonstrator (Year 2 GIS & Remote Sensing, Year 3 Environmental Remote Sensing) at Deptt. of Geography, Durham University (2009 – 2013)

Industry Experience:

Two years of experience in geospatial industry (2004-06) in Singapore and India including Google India as part of the Google Earth Team.

Member of the International Advisory Board of ISI Journal Singapore Journal of Tropical Geography (Wiley)

GoogleScholar Profile 

Sanyal, J (2024) Evaluating the continued significance of dam induced vigorous downstream channel erosion in the context of projected climate change: a case study from Peninsular India. International Journal of River Basin Management, DOI:
10.1080/15715124.2024.2423697 (Taylor & Francis) Download

Sanyal, J (2023) Assessing the influence of upstream basin and climatic characteristics on post-dam downstream streamflow changes: empirical insights from Peninsular India. Water Resources, 50(6), 842-856. (Pleiades-Springer)  Access

Sanyal, J. and Chowdhury, A (2023) Reassessment of reservoir sedimentation rates under a monsoon climate with combined optical and microwave remote sensing: a case study of three reservoirs in the Upper Godavari Basin, India. Journal of Hydrologic Engineering, 28(11), 05023022, DOI:10.1061/JHYEFF.HEENG-5995 (American Society of Civil Engineers)

Sanyal, J. (2023). Flood inundation modelling in data-sparse flatlands: challenges and prospects. In: , et al. Floods in the Ganga–Brahmaputra–Meghna Delta. Springer Geography. Springer, Cham. https://doi.org/10.1007/978-3-031-21086-0_2

Sanyal, J., Lauer, J. W., & Kanae, S (2021) Examining the downstream geomorphic impact of a large dam under climate change. Catena, 196, 104850. (Elsevier) 

Sanyal, J., Dasgupta, P., & Kanae, S. (2020). Predicting dam-related downstream geomorphic response with widely available stream gauge data: A case study of the Godavari River Basin, India. Singapore Journal of Tropical Geography, 41(2), 284-298. (Wiey)

Bhattacharyya, S. and Sanyal, J. (2019) Impact of different types of meteorological data inputs on predicted hydrological and erosive responses to projected land use changes. Journal of Earth System Science, 128:60. (Springer) 

Sanyal, J. (2017) Uncertainty in levee heights and its effect on the spatial pattern of flood hazard in a floodplain. Hydrological Sciences Journal, 62(9), 1483-1498.(Taylor and Francis) [Selected as the featured article of the issue by the editorial board]

Sanyal, J. (2017) Predicting possible effects of dams on downstream river bed changes of a Himalayan river with morphodynamic modelling. Quaternary International, 453, 48-62. (Elsevier) 

Sanyal, J. and Roychowdhury, K. (2016) Tracking the relationship between changing skyline and population growth of an Indian megacity using earth observation technology. Geocarto International, 32(12), 1421-1435 (Taylor and Francis) 

Sanyal, J., Densmore, A. L., Carbonneau, P. (2014) 2D finite element inundation modelling in anabranching channels with sparse data - examination of uncertainties. Water Resources Management, 28(8), 2351-2366 (Springer).

Sanyal, J., Densmore, A. L., Carbonneau, P. (2014) Analysing the effect of land use/cover changes at sub-catchment levels on the downstream flood peak: a semi-distributed modelling approach with sparse data. Catena, 118, 28-40 (Elsevier)

Sanyal, J., Carbonneau, P., Densmore, A. L. (2014) Low-cost flood inundation modelling at a reach scale with sparse data in the Damodar river basin, India. Hydrological Sciences Journal, 59(12), 2086-2102 (Taylor and Francis)

Sanyal, J., Carbonneau, P., Densmore, A. L. (2013) Hydraulic routing of extreme floods in a large ungauged river and the estimation of associated uncertainties: A case study of the Damodar River, India. Natural Hazards, 66(2), 1153-1177 (Springer).

Sanyal, J. and Lu, X. X. (2009) Ideal location for flood shelter: A geographic information system approach, Journal of Flood Risk Management. 2(4), 262-271 (Wiley-Blackwell).

Sanyal, J. and Lu, X. X. (2006) GIS-based flood hazard mapping at different administrative scales: A case study in Gangetic West Bengal, India. Singapore Journal of Tropical Geography, 27, 207-220 (Wiley-Blackwell)

Sanyal, J. and Lu, X. X. (2005) Remote sensing and GIS-based flood vulnerability assessment of human settlements: a case study of Gangetic West Bengal, India. Hydrological Processes, 19 (18), 3699-3717 (Wiley)

Sanyal, J. and Lu, X. X. (2004) Application of remote sensing in flood management with special reference to monsoon Asia: A review. Natural Hazards. 33, 283-301 (Springer)