Streamflow forecasting in a climate change perspective using E-FUSE

Abstract The present work aims to identify the best hydrological model structure suitable for Lower Godavari River Basin, India, that forecasts streamflows. An extended version of Framework Understanding Structural Errors (FUSE), termed E-FUSE, is developed this purpose. It consists 1248 structures. K means cluster analysis (KCA), and Davies Bouldin Cluster Validation Index (DBCVI) are used identifying optimal clusters, whereas Technique Order Preference by Similarity Ideal Solution (TOPSIS) employed structure. Correlation coefficient (r), normalized root mean square error (NRMSE), bias (MBE) as evaluation criteria. obtained exhibits r, NRMSE MBE 0.734, 0.74 -0.09 respectively during calibration 0.69, 0.802 -0.28 validation. then forecast discharges a global climate model, EC-Earth3, four Shared Socioeconomic Pathways, SSP126, SSP245, SSP370, SSP585 scenarios. Analysis was made three time horizons, namely, near-future scenario (2021–2046), mid-future (2047–2072), far future (2073–2099). observed July–September months contribute greatly total runoff SSPs horizons.