Estimating the Role of Climate Internal Variability and Sources of Uncertainties in Hydrological Climate-Impact Projections

Abstract

Hydrological climate-impact projections in the future are limited by large uncertainties from various sources. Therefore, this study aimed to explore and estimate the sources of uncertainties involved in climate change-impacted assessment, in a representative watershed of Northeastern China. Moreover, recent studies have indicated that the climate internal variability (CIV) plays an important role in various hydrological climate-impact projections. Six downscaled global climate models (GCMs) under two emission scenarios, and a calibrated Soil and Water Assessment Tool (SWAT) model were used to obtain hydrological projections in future periods. The CIV and signal-to-noise ratio (SNR) are investigated to analyze the role of internal variability in hydrological projections. The results shows that the internal variability shows a considerable influence on hydrological projections, which need to be particularly partitioned and quantified. Moreover, it is worth noting the CIV can propagate from precipitation and ET to runoff projections through the hydrological simulation process. In order to partition the CIV and the sources of uncertainties, the uncertainty decomposed frameworks based on analysis of variance (ANOVA) are established. The results demonstrate that the CIV and GCMs are the dominant contributors of runoff in the rainy season. In contrast, the CIV and SWAT model parameter sets provided obvious uncertainty to the runoff in January to May, and October to December. The findings of this study advised that the uncertainty is complex in the hydrological simulation process; hence, it is meaningful and necessary to estimate the uncertainty in the climate simulation process. The uncertainty analysis results can effectively provide efforts for reducing uncertainty, and then give some positive suggestions to stakeholders for adaption countermeasures under climate change.