Hydrological assessment of the Tungabhadra River Basin based on CMIP6 GCMs and multiple hydrological models

Autor: G. K. Rudraswamy, Velpuri Manikanta, Nanduri Umamahesh
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Journal of Water and Climate Change, Vol 14, Iss 5, Pp 1371-1394 (2023)
Druh dokumentu: article
ISSN: 2040-2244
2408-9354
DOI: 10.2166/wcc.2023.272
Popis: Climate change significantly impacts the natural systems, accelerating the global water cycle, and impacting various ecosystem services. However, the expected effects of climate change on the frequency and severity of extreme events on hydrological systems vary significantly with location. The present study investigates the uncertainties engulfed in hydrological predictions for the Tungabhadra River Basin. The ensemble streamflow projections were generated using four hydrological models, five climate models, and four climate scenarios to illustrate the associated uncertainties. The uncertainty in hydrological components such as streamflow (QQ), water availability (WA), and potential evapotranspiration (PET) was analysed in the future period (2015–2100). The results suggest that, in the monsoon period, precipitation projections increase by about 10.43–222.5%, whereas QQ projections show an increment between 34.50 and 377.7%. The analysis of variance (ANOVA) technique is used to further quantify the contribution of different sources to the total uncertainty. Furthermore, the ensemble spread is optimized using quantile regression forests (QRF), and the post-processed flows are likely to decrease up to 7% in June and increase up to 70% in September. This study is envisaged to give insights into the quantification of uncertainties in the prediction of future streamflow for rational and sustainable policymaking. HIGHLIGHTS Hydrological assessment of the Tungabhadra Basin using CMIP6 GCMs and multiple hydrological models.; Diagnostic evaluation of performance of hydrological models were estimated.; Uncertainty in the ensemble flows decomposed using the analysis of variance (ANOVA) technique.; The ensemble spread is optimized using quantile regression forests (QRF), and the post-processed flows were generated using the QRF method.;
Databáze: Directory of Open Access Journals
Externí odkaz: