Modelling hydrological responses under climate change using machine learning algorithms – semi-arid river basin of peninsular India

G Sireesha Naidu,M Pratik,S Rehana

doi:10.2166/h2oj.2020.034

G Sireesha Naidu, M Pratik + Show 1 more

Open Access

https://doi.org/10.2166/h2oj.2020.034

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Abstract

AbstractCatchment scale conceptual hydrological models apply calibration parameters entirely based on observed historical data in the climate change impact assessment. The study used the most advanced machine learning algorithms based on Ensemble Regression and Random Forest models to develop dynamically calibrated factors which can form as a basis for the analysis of hydrological responses under climate change. The Random Forest algorithm was identified as a robust method to model the calibration factors with limited data for training and testing with precipitation, evapotranspiration and uncalibrated runoff based on various performance measures. The developed model was further used to study the runoff response under climate change variability of precipitation and temperatures. A statistical downscaling model based on K-means clustering, Classification and Regression Trees and Support Vector Regression was used to develop the precipitation and temperature projections based on MIROC GCM outputs with the RCP 4.5 scenario. The proposed modelling framework has been demonstrated on a semi-arid river basin of peninsular India, Krishna River Basin (KRB). The basin outlet runoff was predicted to decrease (13.26%) for future scenarios under climate change due to an increase in temperature (0.6 °C), compared to a precipitation increase (13.12%), resulting in an overall reduction in water availability over KRB.

Highlights

Hydrological models are promising tools to study various water resources engineering problems such as flood prediction and design, drought assessment, water quantity and quality assessment and hydrological responses under climate variability (Sood & Smakhtin 2015)
The model parameters estimated based on the closure of water balance by comparing the observed and uncalibrated runoff will be considered as the predictand
The present study proposed a modelling framework to study the hydrological response under climate change over the Krishna River Basin (KRB)

Summary

Introduction

Hydrological models are promising tools to study various water resources engineering problems such as flood prediction and design, drought assessment, water quantity and quality assessment and hydrological responses under climate variability (Sood & Smakhtin 2015). The most important classification of hydrological models is empirical (data-driven), conceptual and physically based. Conceptual models developed by including such calibration factors on various processes have been used to study the hydrological responses under climate, land use and water changes as a function of various hydroclimatic variables and observed historical streamflows (e.g. Asokan et al 2010)

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