Qualification: 
Ph.D, M.E, BE
b_soundharajan@cb.amrita.edu

Dr. B. Soundharajan currently serves as Assistant Professor (SG) at the department of Civil Engineering, School of Engineering, Coimbatore. He has done his PhD in Water Resources Engineering at Indian Institute of Technology Madras.

Experience

Month/Year Affiliation
August 2016-January 2017 Assistant Professor, Arba Minch University, Ethiopia
April 2012-June 2016 Post-Doctoral Researcher, Heriot-Watt University, UK
Sep-Oct 2014 Adjunct Faculty, Washington State University, USA
Sep 2011 – March 2012 Scientist, International Water Management Institute, India

Sponsored Projects

Year Title
2017 Can Irrigated agriculture using Lake Abaya be sustainable?, Arba Minch University, Ethiopia.
2017 Assessment of soil erosion risk and uncertainties in the soil loss predictions using different resolutions to topographic data in sub-catchment of Rift valley basin, Ethiopia, Arba Minch University, Ethiopia
2015 Mitigation of climate change impacts on Indian Agriculture through improved irrigation water management, Natural Environment Research Council (UK) – Top up Funding
2011 Local response to extreme events due to climate change - Asian trans-boundary river basin: a case study, Brown University, USA
2010 Mitigation of Green House Gas (GHG) emission from rice fields - through water management. Climate Food and Farming Research Network (CGIAR Challenge Programme on Climate Change, Agriculture and Food Security).

Awards/Recognition

Year Award/Recognition
2014 Post-Doctoral and early carrier researcher exchange award from Edinburgh Research Partnership in Engineering and Mathematics (ERPem), UK
2011 Climate Change 2011 Award by Brown International Advanced Research Institute, Brown University, USA
2011 German-Indian STAR Scholarship by DAAD, Germany (University visited: Christian-Albrechts-University of Kiel, Germany)

Publications

Publication Type: Journal Article

Year of Publication Publication Type Title

2018

Journal Article

B. Soundharajan and Adeloye, A. J., “Effect of reservoir zones and hedging factor dynamism on reservoir adaptive capacity for climate change impacts”, Proc. IAHS , 2018.

2018

Journal Article

C. Chiamsathit, Adeloye, A. J., and Soundharajan, B., “Inflow forecasting using Artificial Neural Networks for reservoir operation”, Institute for Infrastructure and Environment, Heriot-Watt University, Edinburgh, EH14 4AS, UK, 2018.[Abstract]


In this study, multi-layer perceptron (MLP) artificial neural networks have been applied to forecast one-month-ahead inflow for the Ubonratana reservoir, Thailand. To assess how well the forecast inflows have performed in the operation of the reservoir, simulations were carried out guided by the systems rule curves. As basis of comparison, four inflow situations were considered: (1) inflow known and assumed to be the historic (Type A); (2) inflow known and assumed to be the forecast (Type F); (3) inflow known and assumed to be the historic mean for month (Type M); and (4) inflow is unknown with release decision only conditioned on the starting reservoir storage (Type N). Reservoir performance was summarised in terms of reliability, resilience, vulnerability and sustainability. It was found that Type F inflow situation produced the best performance while Type N was the worst performing. This clearly demonstrates the importance of good inflow information for effective reservoir operation. More »»

2017

Journal Article

B. Soundharajan, Mohammed, S. A., and Adeloye, A. J., “Harmonisation of reliability performance indices for planning and operational evaluation of water supply reservoirs. Water Resources Management”, Water Resources Management, vol. 7, 2017.

2016

Journal Article

A. J. Adeloye and Soundharajan, B., “Effect of dynamically varying zone hedging policies on surface water reservoir operational performance during climate change”, Rain, Rivers & Reservoirs Conference, 2016.[Abstract]


Projected Climate Change (CC) will influence Temperature, Rainfall & ET with implications for  Irrigation Water Supply/Demand  River’s Discharge & Reservoir’s Inflow  Performance of Water Infrastructures e.g. Reservoirs  Where reservoirs are involved Hedging, (or deliberate water rationing during normal operation) can stem performance deterioration More »»

2016

Journal Article

B. Soundharajan, Adeloye, A. J., and Remesan, R., “Evaluating the variability in surface water reservoir planning characteristics during climate change impacts assessment”, Journal of Hydrology, vol. 538, pp. 625 - 639, 2016.[Abstract]


Summary This study employed a Monte-Carlo simulation approach to characterise the uncertainties in climate change induced variations in storage requirements and performance (reliability (time- and volume-based), resilience, vulnerability and sustainability) of surface water reservoirs. Using a calibrated rainfall–runoff (R–R) model, the baseline runoff scenario was first simulated. The R–R inputs (rainfall and temperature) were then perturbed using plausible delta-changes to produce simulated climate change runoff scenarios. Stochastic models of the runoff were developed and used to generate ensembles of both the current and climate-change-perturbed future runoff scenarios. The resulting runoff ensembles were used to force simulation models of the behaviour of the reservoir to produce ‘populations’ of required reservoir storage capacity to meet demands, and the performance. Comparing these parameters between the current and the perturbed provided the population of climate change effects which was then analysed to determine the variability in the impacts. The methodology was applied to the Pong reservoir on the Beas River in northern India. The reservoir serves irrigation and hydropower needs and the hydrology of the catchment is highly influenced by Himalayan seasonal snow and glaciers, and Monsoon rainfall, both of which are predicted to change due to climate change. The results show that required reservoir capacity is highly variable with a coefficient of variation (CV) as high as 0.3 as the future climate becomes drier. Of the performance indices, the vulnerability recorded the highest variability (CV up to 0.5) while the volume-based reliability was the least variable. Such variabilities or uncertainties will, no doubt, complicate the development of climate change adaptation measures; however, knowledge of their sheer magnitudes as obtained in this study will help in the formulation of appropriate policy and technical interventions for sustaining and possibly enhancing water security for irrigation and other uses served by Pong reservoir. More »»

2016

Journal Article

A. J. Adeloye, Soundharajan, B., Ojha, C. S. P., and Remesan, R., “Effect of Hedging-Integrated Rule Curves on the Performance of the Pong Reservoir (India) During Scenario-Neutral Climate Change Perturbations”, Water Resources Management, vol. 30, pp. 445–470, 2016.[Abstract]


This study has evaluated the effects of improved, hedging-integrated reservoir rule curves on the current and climate-change-perturbed future performances of the Pong reservoir, India. The Pong reservoir was formed by impounding the snow- and glacial-dominated Beas River in Himachal Pradesh. Simulated historic and climate-change runoff series by the HYSIM rainfall-runoff model formed the basis of the analysis. The climate perturbations used delta changes in temperature (from 0° to +2 °C) and rainfall (from −10 to +10 {%} of annual rainfall). Reservoir simulations were then carried out, forced with the simulated runoff scenarios, guided by rule curves derived by a coupled sequent peak algorithm and genetic algorithms optimiser. Reservoir performance was summarised in terms of reliability, resilience, vulnerability and sustainability. The results show that the historic vulnerability reduced from 61 {%} (no hedging) to 20 {%} (with hedging), i.e., better than the 25 {%} vulnerability often assumed tolerable for most water consumers. Climate change perturbations in the rainfall produced the expected outcomes for the runoff, with higher rainfall resulting in more runoff inflow and vice-versa. Reduced runoff caused the vulnerability to worsen to 66 {%} without hedging; this was improved to 26 {%} with hedging. The fact that improved operational practices involving hedging can effectively eliminate the impacts of water shortage caused by climate change is a significant outcome of this study. More »»

2015

Journal Article

A. J. Adeloye, Soundharajan, B., Musto, J. N., and Chiamsathit, C., “Stochastic assessment of Phien generalized reservoir storage–yield–probability models using global runoff data records”, Journal of Hydrology, vol. 529, pp. 1433 - 1441, 2015.[Abstract]


Summary This study has carried out an assessment of Phien generalised storage–yield–probability (S–Y–P) models using recorded runoff data of six global rivers that were carefully selected such that they satisfy the criteria specified for the models. Using stochastic hydrology, 2000 replicates of the historic records were generated and used to drive the sequent peak algorithm (SPA) for estimating capacity of hypothetical reservoirs at the respective sites. The resulting ensembles of reservoir capacity estimates were then analysed to determine the mean, standard deviation and quantiles, which were then compared with corresponding estimates produced by the Phien models. The results showed that Phien models produced a mix of significant under- and over-predictions of the mean and standard deviation of capacity, with the under-prediction situations occurring as the level of development reduces. On the other hand, consistent over-prediction was obtained for full regulation for all the rivers analysed. The biases in the reservoir capacity quantiles were equally high, implying that the limitations of the Phien models affect the entire distribution function of reservoir capacity. Due to very high values of these errors, it is recommended that the Phien relationships should be avoided for reservoir planning.

More »»

2015

Journal Article

B. Soundharajan, Adeloye, A., and Remesan, R., “Quantifying the uncertainties of climate change effects on the storage-yield and performance characteristics of the Pong multi-purpose reservoir, India”, Proceedings of the International Association of Hydrological Sciences (PIAHS), vol. 371, pp. 49-57, 2015.[Abstract]


Climate change is predicted to affect water resources infrastructure due to its effect on rainfall, temperature and evapotranspiration. However, there are huge uncertainties on both the magnitude and direction of these effects. The Pong reservoir on the Beas River in northern India serves irrigation and hydropower needs. The hydrology of the catchment is highly influenced by Himalayan seasonal snow and glaciers, and Monsoon rainfall; the changing pattern of the latter and the predicted disappearance of the former will have profound effects on the performance of the reservoir. This study employed a Monte-Carlo simulation approach to characterise the uncertainties in the future storage requirements and performance of the reservoir. Using a calibrated rainfall-runoff (R-R) model, the baseline runoff scenario was first simulated. The R-R inputs (rainfall and temperature) were then perturbed using plausible delta-changes to produce simulated climate change runoff scenarios. Stochastic models of the runoff were developed and used to generate ensembles of both the current and climatechange perturbed future scenarios. The resulting runoff ensembles were used to simulate the behaviour of the reservoir and determine “populations” of reservoir storage capacity and performance characteristics. Comparing these parameters between the current and the perturbed provided the population of climate change effects which was then analysed to determine the uncertainties. The results show that contrary to the usual practice of using single records, there is wide variability in the assessed impacts. This variability or uncertainty will, no doubt, complicate the development of climate change adaptation measures; however, knowledge of its sheer magnitude as demonstrated in this study will help in the formulation of appropriate policy and technical interventions for sustaining and possibly enhancing water security for irrigation and other uses served by Pong reservoir. More »»

2014

Journal Article

C. Chiamsathit, Soundharajan, B., and Adeloye, A., “Assessing Competing Policies at Ubonratana Reservoir, Thailand”, Proceedings of the ICE - Water Management, vol. 167, pp. 551-560, 2014.

2013

Journal Article

B. Soundharajan and Sudheer, K. P., “Sensitivity analysis and auto-calibration of ORYZA2000 using simulation-optimization framework”, Paddy and Water Environment, vol. 11, pp. 59–71, 2013.[Abstract]


There has been an increasing interest to employ crop growth simulation models for taking decision on irrigation water management. The effectiveness of such decisions mainly lies on the efficiency of the model in simulating the crop growth and the yield, which are influenced by the value of the parameters of the model. Therefore, calibration of such models is necessary before it can be employed for any application. This study proposes an auto-calibration procedure for ORYZA2000, a rice crop growth simulation model, for its application in South India. The data employed for calibration is taken from a field experiment conducted for 2 years in an experimental farm in South India. The ORYZA2000 model was integrated within Genetic Algorithm optimizer, which calls the simulator during each generation to evaluate the objective function. The auto-calibrated model was tested for its performance using a validation data set from the same experimental data. The results showed that the calibrated ORYZA2000 model is capable of simulating the full irrigation and water stress condition of rice crop effectively, and can be used to develop deficit irrigation management schedules. More »»

2013

Journal Article

B. Soundharajan, Adeloye, A., and Remesan, R., “Assessing climate change impacts on operation and planning characteristics of Pong Reservoir, Beas (India)”, Considering Hydrological Change in Reservoir Planning and Management, vol. 362, pp. 207-212, 2013.[Abstract]


In India, there is a considerable change in both spatial and temporal patterns of the monsoon rainfall, resulting in reduced crop yields and increasing uncertainty in the agriculture-based livelihoods of the rural population. Changes in rainfall, temperature and evapotranspiration are affecting water resources availability and demands, and hence the performance of irrigation water supply facilities such as reservoirs and canal diversions. In order to accommodate these changes in the water resources situation, there must be substantial improvement in water use and management efficiency but this can only be meaningfully done if the impact of climate change and variability is quantified. Consequently, this work has investigated the effects of climate change and variability on irrigation water security in Beas River basin in India by characterising the yield and performance (reliability, resilience and vulnerability) of the associated Pong Reservoir for current (baseline) and climate-change perturbed future horizons. Climate change perturbations based on CGCM3.1 (third generation coupled GCM) for the A1B and B2A IPCC SRES socio-economic scenarios, appropriately downscaled to basin scale were used. The whole analysis was conducted within a Monte Carlo simulation framework, thus enabling the variability and uncertainty associated with each of these variables to also be quantified. The results show that future inflow series to the Pong will exhibit higher inter-annual variability than the baseline, necessitating increased reservoir capacity to meet existing irrigation water demands. In terms of overall performance, while the reliability (both volume- and time-based) was largely unaffected by climate change, the resilience significantly deteriorated especially for the A1B scenario. There were also noticeable changes in the rule curves as a result of climate change. Assessing climate change impacts on operation and planning characteristics of Pong Reservoir, Beas (India) (PDF Download Available). More »»

2011

Journal Article

B. Soundharajan and Sudheer, K., “Irrigation water allocation in a simulation - Optimization framework”, Water and Energy International, vol. 68, pp. 40-47, 2011.

2011

Journal Article

B. Soundharajan and Sudheer, K. P., “Parameter estimation of the AquaCrop model for Blackgram(Vigna mungo)”, UNW-DPC Publication series No.7, United Nations University, Bonn., 2011.

2009

Journal Article

B. Soundharajan and Sudheer, K. P., “Deficit irrigation management for rice using crop growth simulation model in an optimization framework”, Paddy and Water Environment, vol. 7, pp. 135–149, 2009.[Abstract]


Optimization of irrigation water is an important issue in agricultural production for maximizing the return from the limited water availability. The current study proposes a simulation–optimization framework for developing optimal irrigation schedules for rice crop (Oryza sativa) under water deficit conditions. The framework utilizes a rice crop growth simulation model to identify the critical periods of growth that are highly sensitive to the reduction in final crop yield, and a genetic algorithm based optimizer develops the optimal water allocations during the crop growing period. The model ORYZA2000, which is employed as the crop growth simulation model, is calibrated and validated using field experimental data prior to incorporating in the proposed framework. The proposed framework was applied to a real world case study of a command area in southern India, and it was found that significant improvement in total yield can be achieved by the model compared to other water saving irrigation methods. The results of the study were highly encouraging and suggest that by employing a calibrated crop growth model combined with an optimization algorithm can lead to achieve maximum water use efficiency. More »»
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