Estimating the Effect of Climate Internal Variability and Source of Uncertainty in Climate-Hydrological Projections in a Representative Watershed of Northeastern China

Abstract

The decomposition and quantification of uncertainty sources in ensembles of climate-hydrological simulation chains is a key issue in climate impact researches. The mainly objectives of this study partitioning climate internal variability (CIV) and uncertainty sources in the climate-hydrological projections simulation process, the climate simulation process formed by six downscaled GCMs under two emission scenarios called GCMs-ES simulation chain, the hydrological simulation process add one calibrate Soil and Water Assessment Tool (SWAT) model called GCMs-ES-HM simulation chain. The CIV and external forcing of climate projections are investigated in each GCMs-ES simulation chain. The CIV of precipitation and ET are large in rainy season, and the single-to-noise ratio (SNR) are also relatively high in rainy season. Furthermore, the uncertainty decomposed frameworks based on analysis of variance (ANOVA) are established. The CIV and GCMs are the dominate contributors of runoff in rainy season. It worth noting the CIV can propagate from precipitation and ET to runoff projections. In additional, the hydrological model parameters are the third uncertainty contributor of runoff, which embody the hydrological model simulate process play important role in hydrological projections in future. The findings of this study advised that the uncertainty is complex in hydrological, hence, it is meaning and necessary to estimate the uncertainty in climate simulation process, the uncertainty analysis results can provide effectively efforts to reduce uncertainty and then give some positive suggestions to stakeholders for adaption countermeasure under climate change.

Understanding Contributions of Paleo-Informed Natural Variability and Climate Changes on Hydroclimate Extremes in the Central Valley Region of California

“…found that flood frequency and wintertime runoff in Europe are mostly influenced by choice of GCM, although they quantified natural climate variability using a limited number of GCM integrations with different initial conditions. found that natural variability highly influences low flows in snow-dominated catchments in the French Alps, and Cai et al (2021) found that natural variability is a dominant driver of rainy season runoff in Northeastern China. quantified natural variability using a block bootstrap on the historical record and found it to have the largest impact on the variance of large floods, as compared to GCM structure, emission scenario, land use change scenario, and hydrologic model parameter uncertainty.…”

“…These studies primarily attribute variability in projected global and regional temperature and precipitation to climate change scenario uncertainty, global climate change model (GCM) uncertainty, and natural variability.Lehner et al (2020) shows that scenario and model uncertainty are the dominant drivers of global decadal mean annual temperature and precipitation, but that natural variability dominates projections of regional temperatures (in Southern Europe) and precipitation (in the U.S. Pacific Northwest and Sahel region), particularly at shorter (and more decision-relevant) time scales. Fewer studies have explicitly considered the role of natural climate variability when partitioning variance in projections of hydrologic and water systems variablesCai et al, 2021). found that flood frequency and wintertime runoff in Europe are mostly influenced by choice of GCM, although they quantified natural climate variability using a limited number of GCM integrations with different initial conditions.…”

Understanding the Contributions of Paleo‐Informed Natural Variability and Climate Changes to Hydroclimate Extremes in the San Joaquin Valley of California

“…Lehner and Deser (2023) similarly demonstrates how natural variability becomes the increasingly dominant driver of variability in winter mean air temperature projections at smaller spatial scales. Fewer studies have explicitly considered the role of natural climate variability when partitioning variance in projections of hydrologic and water systems variables (Cai et al., 2021; I.‐W. Jung et al., 2011; Kay et al., 2009; Schlef et al., 2018; Vidal et al., 2015; Whateley & Brown, 2016).…”

“…Lehner and Deser (2023) similarly demonstrates how natural variability becomes the increasingly dominant driver of variability in winter mean air temperature projections at smaller spatial scales. Fewer studies have explicitly considered the role of natural climate variability when partitioning variance in projections of hydrologic and water systems variables (Cai et al, 2021;I.-W. Jung et al, 2011;Kay et al, 2009;Schlef et al, 2018;Vidal et al, 2015;Whateley & Brown, 2016). Kay et al (2009) found that flood frequency and wintertime runoff in Europe are mostly influenced by choice of GCM, although they quantified natural climate variability using a limited number of GCM integrations with different initial conditions.…”

“…Kay et al (2009) found that flood frequency and wintertime runoff in Europe are mostly influenced by choice of GCM, although they quantified natural climate variability using a limited number of GCM integrations with different initial conditions. Vidal et al (2015) found that natural variability highly influences low flows in snow-dominated catchments in the French Alps, and Cai et al (2021) found that natural variability is a dominant driver of rainy season runoff in Northeastern China. I.-W. Jung et al (2011) quantified natural variability using a block bootstrap on the historical record and found it to have the largest impact on the variance of large floods, as compared to GCM structure, emission scenario, land use change scenario, and hydrologic model parameter uncertainty.…”