Modelling seasonal flow regime and environmental flow in Punarbhaba river of India and Bangladesh

Abstract

Abstract Investigations of changing the flow regime and flow prediction are of vital societal and hydro-ecological importance in a transboundary river like Punarbhaba river between India and Bangladesh. The present paper investigated flow regime though advance periodicity models (Morlet’s wavelet transformation) at the seasonal scale. Flow prediction using advanced machine learning techniques like Support vector machine (SVM), Artificial Neural Network (ANN), Hybrid wavelet ANN (W-ANN), Random forest(RF) capturing the periodicity, duration, cyclic or semi-cyclic nature of flow wave or wavelet from the historical time series data (1978–2017) is very crucial for environmental flow management and estimating present and future states of environmental flow. Flow alteration modeling (using heat map) and estimation of environmental flow (using duration curve shifting and RVA methods) are another vital objectives of this work to know the present and future hydro-ecological state. The result of periodicity clearly identified two distinct flow regimes before and after 1992-93 triggered by damming over there in 1992. All the prediction models identified the declining trend of flow in all the seasons, however hybrid wavelet ANN model could be treated as the best suited because of its very high-performance level. The degree of hydrological alteration is identified very high in the post-hydrological alteration (PHA) (post-1992) period and it is likely to be intensified predicted period. The estimated environmental flow state in PHA falls under moderate to critically modified states but if alteration goes on in this way ecological deficit will be the obvious result. For the survival of the river estimated environmental flow could be released primarily.