MONOMOY GOSWAMI
@cit.ac.in
Professor of Civil Engineering
Central Institute of Technology Kokrajhar, Assam, INDIA
Dr. Monomoy Goswami is a Professor of Civil Engineering in Central Institute of Technology Kokrajhar, a deemed to be University under Govt. of India. His specializations include Engineering hydrology; Water resources engineering, development & management with extensive coverage of all aspects of hydropower projects; sustainability & impact studies; mathematical modelling; and Techno-economic assessment of projects.
Dr. Goswami did his PhD and Masters from the National University of Ireland Galway in Ireland having obtained a Bachelor degree in Civil Engineering from Regional Engineering College, now, NIT, Silchar in India.
His career spans more than 35 years as on November 2022 with spells of 25 years in industry, and the rest in academia.
Dr. Goswami has research publications in highly acclaimed international journals, and in proceedings of international conferences, besides chapters in edited books.
EDUCATION
PhD in Engineering Hydrology (National University of Ireland, Galway, IRELAND)
M.Sc in Hydrology (National University of Ireland, IRELAND)
Bachelor of Civil Engineering (Regional Engineering College, now, National Institute of Technology, Silchar, INDIA)
RESEARCH INTERESTS
Engineering hydrology; Water resources development, engineering & management; Hydropower project planning, design & engineering; Conceptual and data-driven modelling; Techno-economic evaluation of projects. Techno-economic assessment of projects.
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Ishika Goswami and Monomoy Goswami
Springer International Publishing
Monomoy Goswami
Informa UK Limited
Monomoy Goswami and Kieran M. O'Connor
Informa UK Limited
Abstract In earlier studies involving simulation of the Fergus River flows in Ireland, the conceptual Soil Moisture Accounting and Routing (SMAR) model had been found to consistently outperform a number of black-box models. Subsequently, in investigating any loss of flow through this catchment's subsurface karstic features, it was verified from the overall long-term water balance that such losses were substantial. This raised the awkward question of why the volume-conservative SMAR model had performed so well on this considerably non-conservative catchment. Further analyses revealed that, to compensate for the excess volume of total runoff generated by the model's conservative water balance component, the memory length of the surface runoff response function had been unrealistically curtailed in the optimization process, effectively truncating that function and thereby violating the conservation property of the routing process. This embarrassing revelation called for reconsideration of the model structure to account more sensibly for actual losses, while still achieving high model efficiency. This paper highlights not only the discovery of the karstic Fergus catchment as a “hydrological monster”, in the context of the SMAR model, but also why conservative models perform poorly in such cases. In an attempt to “tame the monster”, better simulation of the observed flows was achieved by conceptually adapting the SMAR model, in a pragmatic empirical manner, by simply modifying its water balance component. Citation Goswami, M. & O'Connor, K. M. (2010) A “monster” that made the SMAR conceptual model “right for the wrong reasons”. Hydrol. Sci. J. 55(6), 913–927.
J Vaze, F H S Chiew, J M Perraud, N Viney, D Post, J Teng, B Wang, J Lerat, and M Goswami
Informa UK Limited
Abstract This study describes a daily rainfall, potential evaporation and streamflow data set compiled for the important water resources region of southeast Australia, and the application of six commonly used lumped conceptual rainfall-runoff models to estimate daily runoff across the region. The daily climate data set and the daily modelled runoff are available from 1895 to 2008 at 0.05° grid resolution across the region. The modelling exercise indicates that the rainfall-runoff models can generally be calibrated to reproduce the daily observed streamflow (for 232 catchments in the high runoff generation areas), and the regionalisation results indicate that the use of optimised parameter values from a gauged catchment nearby can model runoff reasonably well in the ungauged areas. There are differences between the six models, but they are relatively small when used to describe aggregated results across large regions.
MONOMOY GOSWAMI and KIERAN MICHAEL O'CONNOR
Informa UK Limited
Abstract In this application-based study, six automated strategies of parameter optimization are used for calibration of the conceptual soil moisture accounting and routing (SMAR) model for rainfall—runoff simulation in two catchments, one small and the other large. The methods used are: the genetic algorithm, particle swarm optimization, Rosenbrock's technique, shuffled complex evolution of the University of Arizona, simplex search, and simulated annealing. A comparative assessment is made using the Nash-Sutcliffe model efficiency index and the mean relative error (MRE) to evaluate the performance of each optimization method. It is found that the degree of variation of the values of the water balance parameters is generally less for the small catchment than for the large one. In the case of both catchments, the probabilistic global population-based optimization method of simulated annealing is considered best in terms of having the least variability of parameter values in successive tests, thereby alleviating the phenomenon of equifinality in parameter optimization, and also in producing the lowest MRE in verification.
Monomoy Goswami and Kieran M. O’Connor
Elsevier BV
M. Goswami, K.M. O’Connor, and K.P. Bhattarai
Elsevier BV
M. Goswami, K. M. O'Connor, K. P. Bhattarai, and A. Y. Shamseldin
Copernicus GmbH
Abstract. The flow forecasting performance of eight updating models, incorporated in the Galway River Flow Modelling and Forecasting System (GFMFS), was assessed using daily data (rainfall, evaporation and discharge) of the Irish Brosna catchment (1207 km2), considering their one to six days lead-time discharge forecasts. The Perfect Forecast of Input over the Forecast Lead-time scenario was adopted, where required, in place of actual rainfall forecasts. The eight updating models were: (i) the standard linear Auto-Regressive (AR) model, applied to the forecast errors (residuals) of a simulation (non-updating) rainfall-runoff model; (ii) the Neural Network Updating (NNU) model, also using such residuals as input; (iii) the Linear Transfer Function (LTF) model, applied to the simulated and the recently observed discharges; (iv) the Non-linear Auto-Regressive eXogenous-Input Model (NARXM), also a neural network-type structure, but having wide options of using recently observed values of one or more of the three data series, together with non-updated simulated outflows, as inputs; (v) the Parametric Simple Linear Model (PSLM), of LTF-type, using recent rainfall and observed discharge data; (vi) the Parametric Linear perturbation Model (PLPM), also of LTF-type, using recent rainfall and observed discharge data, (vii) n-AR, an AR model applied to the observed discharge series only, as a naïve updating model; and (viii) n-NARXM, a naive form of the NARXM, using only the observed discharge data, excluding exogenous inputs. The five GFMFS simulation (non-updating) models used were the non-parametric and parametric forms of the Simple Linear Model and of the Linear Perturbation Model, the Linearly-Varying Gain Factor Model, the Artificial Neural Network Model, and the conceptual Soil Moisture Accounting and Routing (SMAR) model. As the SMAR model performance was found to be the best among these models, in terms of the Nash-Sutcliffe R2 value, both in calibration and in verification, the simulated outflows of this model only were selected for the subsequent exercise of producing updated discharge forecasts. All the eight forms of updating models for producing lead-time discharge forecasts were found to be capable of producing relatively good lead-1 (1-day ahead) forecasts, with R2 values almost 90% or above. However, for higher lead time forecasts, only three updating models, viz., NARXM, LTF, and NNU, were found to be suitable, with lead-6 values of R2 about 90% or higher. Graphical comparisons were made of the lead-time forecasts for the two largest floods, one in the calibration period and the other in the verification period.