Dr Raj Mehrotra

Curriculum vitae for Raj Mehrotra

DR RAJ MEHROTRA

Water Research Centre, School of Civil and Environmental Engineering, University of New South Wales, Sydney, Australia

Tel: +612 9385 5140;          Fax +612 9385 6139;          email: raj.mehrotra@unsw.edu.au

Expertise

Rajeshwar Mehrotra (Raj) has been working at the Water Research Centre at UNSW, ranked 1^st in Australia and 7^th globally as per the Shanghai AWRU rankings. Raj continues to work in a very active research group focussing on statistical applications in hydrology and hydro-climatology with special focus on development and application of downscaling and bias correction approaches. Raj has developed many approaches and software for correction of systematic biases in climate model simulations, stochastic downscaling and generation of precipitation and evaporation sequences and has applied them in many projects in Australia and abroad.

Research contributions

• Raj is widely recognized as one of the leading researchers on multivariate-multisite bias correction and multisite stochastic generation and downscaling, having published a majority of his journal papers in this area. He has found innovative to process climate data for use in hydrology and climate change related applications.
• Raj has developed parametric and nonparametric (as well as their combinations) innovative approaches for stochastic generation and downscaling of daily rainfall at multiple locations and extend existing methods to represent variability across multiple temporal scales.
• Raj is the lead developer of the Multivariate Bias Correction (MBC) and the Multisite Rainfall Downscaling (MRD) software, available for download and use from http://www.hydrology.unsw.edu.au/download.
• Raj’s research has received support from many national and international funding agencies/research councils,
• Visiting Academic, Kasetsart University, Bangkok, Thailand.

Projects and grants

• Multivariate bias correction of regional climate simulations administered by the NSW Department of Industry, Planning and Environment (DPIE). This project undertakes a multivariate bias correction of, for example, temperature, precipitation, specific humidity, evaporation, surface pressure, and (u, v) wind speed over the 10km NARCliM domain. The correction uses observationally-based datasets (e.g., gridded data or reanalysis products). The multivariate bias correction considers the relationships between corrected variables.
• A Bias correction research project funded by WaterNSW. The projected aimed at providing bias corrected rainfall and temperature simulations over the NSW valley catchments using CMIP5 GCMs.
• ARC Linkage project on stochastic downscaling, partly funded by the Sydney Catchment Authority. The project involves stochastically downscaled sequences of rainfall, evaporation and temperature used as an input to a rainfall-runoff model to ass the water resources availability over the Sydney region.
• Australian Rainfall and Runoff (ARR) Project 4: “Development, testing and validation of procedures for generating continuous rainfall sequences for all of Australia.” A new method for generating continuous (sub-daily) rainfall sequences at any location was developed and tested. The method is suitable for both gauged and ungauged locations, and therefore can be useful in supplementing historical data in data-sparse regions.
• Research project funded jointly by the NSW Cabinet Office, Department of Environment and Climate Change, Sydney Water Corporation and Sydney Catchment Authority is about to finish. The project aims at evaluating the climate change and its impacts on supply and demand in Sydney. The study provides important insights into the potential impacts of climate change on Sydney’s predominantly rain- fed water supply system, and on Sydney’s future demand for urban water.
• Research project with partners as the Indian Institute of Science, Bangalore, India, as part of the Australia-India Strategic Research Funds initiated by the previous federal government. The aim of this study is to assess and manage impacts of climate change on soil moisture, agricultural productivity and water management policies in an agricultural catchment in India.
• An ARC funded research project on assessment of climate change, climate input errors and land-use change on soil-moisture and carbon-balance in a catchment simulation framework. This is an ambitious Linkage project that seeks to (a) develop a computationally efficient physically based catchment modeling system that can be used for simulation in ungauged catchments, (b) develop a procedure for specifying this modeling system to take account of errors in rainfall and evaporation inputs, and (c) use the system to simulate catchment soil moisture and carbon balance in a future climatic setting.
• An ARC funded research project on Ensemble Modelling Framework for Prediction in Ungauged Catchments. This project seeks to develop methodologies for prediction in ungauged catchments that allow for a nonstationary rainfall runoff modeling system referred to as the Hierarchical Mixtures of Experts or HME. This modeling structure is being developed in a Bayesian framework using Markov Chain Monte Carlo (MCMC) techniques, using extensive daily rainfall, evaporation and flow data from catchments across Australia.
• An ARC funded research project on Stochastic Rainfall Generation for design flow simulation. This project sought to develop methodologies for stochastic generation of continuous rainfall sequences for use in design flood estimation studies, thereby removing deficiencies in traditional design flood estimation methods where catchment antecedent conditions are assumed to remain stationary.
• Bureau of Meteorology, Australia funded research project on CMIP5 decadal prediction for rainfall. The project aimed at investigating the predictive capability of CMIP5 decadal predictions for rainfall and other climate variables. 
• Australia-India strategic research fund (AISRF) funded research project, ‘projecting environmental change in a warming world for semi-arid landscapes. The project seeks to use the decadal climate and the groundwater water projections as the basis of assessing the viability of meeting the current and the projected groundwater needs.
• Development of a Multivariate Iterative Nested Bias Correction (MINBC) package for use by the Bureau of Meteorology, Australia. The aim of the project is to develop a multivariate nested bias correction approach to post process the GCM outputs for use in statistical downscaling. The aim here is to bias correct current and future climates time series of GCM simulated multiple variables in a way that the resulting post-processed series reproduces the important temporal as well as cross-dependence attributes as seen in the observed series.
• Visiting researchers at Kasetsart University, Thailand to help and guide the research students in the development and application of multisite rainfall simulation model for use in the estimation of floods and droughts over different parts of the country.

Career-best and most relevant publications

Summary: H-Index 35 with 150 publications since PhD.

A few key papers

• Hu H, Yang K, Sharma A, Mehrotra R. Assessment of water and energy scarcity, security and sustainability into the future for the Three Gorges Reservoir using an ensemble of RCMs Journal of Hydrology 586 (2020)
• Kim S, Kim S, Mehrotra R, Sharma A. Predicting cyanobacteria occurrence using climatological and environmental controls Water Research 175 (2020)
• Han X, Ouarda TBMJ, Rahman A, Haddad K, Mehrotra R, Sharma A. A Network Approach for Delineating Homogeneous Regions in Regional Flood Frequency Analysis Water Resources Research 56(3) (2020)
• Nguyen, H., Mehrotra, R. & Sharma, A. Assessment of climate change impacts on reservoir storage reliability, resilience and vulnerability using a Multivariate Frequency Bias Correction approach. Water Resources Research, doi:10.1029/2019WR026022 (2020).
• Mehrotra R, Sharma A. A Resampling Approach for Correcting Systematic Spatiotemporal Biases for Multiple Variables in a Changing Climate. Water Resources Research 55(1):754-770 (2019).
• Choudhury, D., Mehrotra, R., Sharma, A., Sen Gupta, A., & Sivakumar, B. Effectiveness of CMIP5 decadal experiments for interannual rainfall prediction over Australia. Water Resources Research, 55, 7400– 7418. (2019).
• Nguyen, H., Mehrotra, R. & Sharma, A. Correcting systematic biases across multiple atmospheric variables in the frequency domain. Climate Dynamics 52, 1283-1298, doi:10.1007/s00382-018-4191-6 (2019).
• Wasko C, Lu WT, Mehrotra R. Relationship of extreme precipitation, dry-bulb temperature, and dew point temperature across Australia Environmental Research Letters 13(7) (2018).
• Reshmidevi, T. V., Nagesh Kumar, D., Mehrotra, R. & Sharma, A. Estimation of the climate change impact on a catchment water balance using an ensemble of GCMs. Journal of Hydrology, doi:10.1016/j.jhydrol.2017.02.016 (2018).
• Mehrotra, R., Johnson, F. & Sharma, A. A software toolkit for correcting systematic biases in climate model simulations. Environmental Modelling and Software 104, 130-152, doi:10.1016/j.envsoft.2018.02.010 (2018).
• Nguyen, H., Mehrotra, R. & Sharma, A. Can the variability in precipitation simulations across GCMs be reduced through sensible bias correction? Climate Dynamics, 1-19, doi:10.1007/s00382-016-3510-z (2017).
• Moalafhi, D. B., Sharma, A., Evans, J. P., Mehrotra, R. & Rocheta, E. Impact of bias‐corrected reanalysis‐derived lateral boundary conditions on WRF simulations. Journal of Advances in Modeling Earth Systems (2017).
• Mehrotra R, Sharma A. A multivariate quantile-matching bias correction approach with auto- and cross-dependence across multiple time scales: implications for downscaling Journal of Climate 29(10):3519-3539 (2016).
• Woldemeskel, F. M., Sharma, A., Sivakumar, B. & Mehrotra, R. Quantification of precipitation and temperature uncertainties simulated by CMIP3 and CMIP5 models. Journal of Geophysical Research-Atmospheres 121, 3-17, doi:10.1002/2015JD023719 (2016).
• Sharma, A., Mehrotra, R., Li, J. & Jha, S. A programming tool for nonparametric system prediction using Partial Informational Correlation and Partial Weights. Environmental Modelling and Software 83, 271-275, doi:10.1016/j.envsoft.2016.05.021 (2016).
• Mehrotra, R. & Sharma, A. A Multivariate Quantile-Matching Bias Correction Approach with Auto- and Cross-Dependence across Multiple Time Scales: Implications for Downscaling. Journal of Climate 29, 3519-3539 (2016).