Athira P
@iitpkd.ac.in/people
Assistant Professor, Department of Civil Engineering
Indian Institute of Technology Palakkad
RESEARCH INTERESTS
Hydrological modelling and its uncertainty analysis, Watershed management, Predictions in ungauged basin, Climate Change Impact Assessment, Landuse Change Modelling, Forest Hydrology and Water Security Assessment
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Jose George and P. Athira
Springer Science and Business Media LLC
Jose George and Athira P.
Springer Science and Business Media LLC
Anju Elizbath Peter, Monish Raj, Praveena Gangadharan, Athira P., and S. M. Shiva Nagendra
Springer Science and Business Media LLC
Jose George and P. Athira
Springer Science and Business Media LLC
Rajat and P. Athira
Elsevier BV
Rajat Choudhary and P. Athira
Springer Science and Business Media LLC
P. Athira and K. P. Sudheer
Springer Science and Business Media LLC
P.K. Athira, S.P. Atul Narayan, J. Murali Krishnan, and Pankaj Kumar Jain
Elsevier BV
Saju Xavier, Athira P, Abel C. Mathew, Hathim Basheer, Rahana K, Muhammed Hashik PK, and Dilip Chandrasekhar
Elsevier BV
P. Athira, C. Nanda, and K. P. Sudheer
Springer Science and Business Media LLC
P. Athira, K. P. Sudheer, R. Cibin, and I. Chaubey
Springer Science and Business Media LLC
You Huang, Zhaohui Liu, Xudong Wang, and Sheng Li
CRC Press
P. Athira and K. P. Sudheer
Springer Science and Business Media LLC
R. Cibin, P. Athira, K. P. Sudheer, and I. Chaubey
Wiley
Stream flow predictions in ungauged basins are one of the most challenging tasks in surface water hydrology because of nonavailability of data and system heterogeneity. This study proposes a method to quantify stream flow predictive uncertainty of distributed hydrologic models for ungauged basins. The method is based on the concepts of deriving probability distribution of model's sensitive parameters by using measured data from a gauged basin and transferring the distribution to hydrologically similar ungauged basins for stream flow predictions. A Monte Carlo simulation of the hydrologic model using sampled parameter sets with assumed probability distribution is conducted. The posterior probability distributions of the sensitive parameters are then computed using a Bayesian approach. In addition, preselected threshold values of likelihood measure of simulations are employed for sizing the parameter range, which helps reduce the predictive uncertainty. The proposed method is illustrated through two case studies using two hydrologically independent sub‐basins in the Cedar Creek watershed located in Texas, USA, using the Soil and Water Assessment Tool (SWAT) model. The probability distribution of the SWAT parameters is derived from the data from one of the sub‐basins and is applied for simulation in the other sub‐basin considered as pseudo‐ungauged. In order to assess the robustness of the method, the numerical exercise is repeated by reversing the gauged and pseudo‐ungauged basins. The results are subsequently compared with the measured stream flow from the sub‐basins. It is observed that the measured stream flow in the pseudo‐ungauged basin lies well within the estimated confidence band of predicted stream flow. Copyright © 2013 John Wiley & Sons, Ltd.
P. ATHIRA, K. P. SUDHEER, CIBIN RAJ, and I. CHAUBEY
World Scientific Publishing Co. Pte. Ltd.
proposed amethod to derive the probability distribution function (PDF) of the sensitiveparameters using a single likelihood (a global measure over the entire rangesof flow) and use them for PUB. Instead of considering a single criterion forderiving the PDF, a multi-criteria approach that can account variation insensitivityof parametersand modelperformanceindifferent flowranges maybea better approach for identifying the parameter characteristics in terms of theirPDF. The study proposes a method to minimize the predictive uncertainty ofdistributed models by deriving the PDF of sensitive parameters based on theBayesian approach. The method employsMonte Carlosimulationsofparametersets generated by ‘Latin Hypercube Sampling.’ Within the Monte Carlosimulations, those parameter sets that produced reasonably good performancein all ranges of flow are used for estimating multi-criteria index and updatingwill continue till both (prior and posterior) PDFs converge in successive cycles.These PDFs, which are derived using gauged basin data, are then transferred tohydrologically similar ungauged basins for generating ensembles of simulations.The proposed methodology is illustrated through a case study of a watershedin USA. The Soil and Water Assessment Tool model was considered for theapplication. The study also discusses a comparison of PUB using a single-criterion approach and a multi-criteria approach. It is observed that confidenceband for predictions by proposed approach is narrow and the number of cyclesrequired for deriving the PDF is less as compared with the former.75