Quantifying uncertainty sources in extreme flow projections for three watersheds with different climate features in China

Zhang, Limin; Yuan, Fei; Wang, Bing; Ren, Liliang; Zhao, Chenxiao; Shi, Jiayong; Liu, Yi; Jiang, Shanhu; Yang, Xiaoli; Chen, Tao; Liu, Shuya

doi:10.1016/j.atmosres.2020.105331

Cited by 11 publications

(5 citation statements)

References 65 publications

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“…These studies generally reach a similar conclusion that for most regions, CMs act as the leading source of uncertainty for the wet season or high flow projections (e.g., Aryal et al 2019;Zhang et al 2021), whereas HMs may contribute more to the uncertainty of dry season or low flow projections (e.g., Vetter et al 2017;Lee et al 2021). Also, the interactions among the uncertainty factors are non-negligible and may even play a larger role than the individual sources (Bosshard et al 2013;Vetter et al 2015;Zhang et al 2021). For example, the impact of BC on runoff will increase with the bias of CMs (Muerth et al 2013), because the correcting coefficient is scaled by the deviation of historical climate simulations from the observation.…”

Section: Introductionsupporting

confidence: 62%

“…The uncertainty sources of hydrological projections have been quantified in the previous studies for different regions around the world. These studies generally reach a similar conclusion that for most regions, CMs act as the leading source of uncertainty for the wet season or high flow projections (e.g., Aryal et al 2019;Zhang et al 2021), whereas HMs may contribute more to the uncertainty of dry season or low flow projections (e.g., Vetter et al 2017;Lee et al 2021). Also, the interactions among the uncertainty factors are non-negligible and may even play a larger role than the individual sources (Bosshard et al 2013;Vetter et al 2015;Zhang et al 2021).…”

Section: Introductionsupporting

confidence: 54%

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Reduction of the uncertainties in the hydrological projections in Korean river basins using dynamically downscaled climate projections

Qiu

Kim

et al. 2022

Clim Dyn

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How the added value of dynamically downscaled climate variables can be transferable to the hydrological impact assessment has been a long standing issue. This study investigates the potential benefit of highresolution climate data locally tailored over South Korea in terms of the reduction of uncertainties in hydrological projections. For this purpose, a large ensemble consisting of three Global Climate Model (GCM) projections and their downscaling products with different resolutions (i.e., 20 and 5 km), and four bias correction (BC) methods is fed into a semi-distributed hydrological model (HM) customized over Korean river basins. The in-depth comparison among the 45-members hydrological simulations proves that the added value of RCM would not be erased by the application of BC. While this study acknowledges the necessity of BC to remove the systematic bias in climate simulations, it is found that the high-resolution dynamical downscaling can significantly narrow the spread brought with different BC methods, thus reducing the uncertainty in the projected hydrological change. The projected streamflow changes for both the mean of wet season and the high flows indicate that there will be an intensified runoff, especially for the extremes, over South Korea under the warming. Altogether, this study provides a valuable exploration of uncertainty reduction in hydrological projections from the perspective of resolution effect of dynamical downscaling, which is meaningful for hydroclimate studies and climate change impact assessment.

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Section: Introductionsupporting

confidence: 62%

Section: Introductionsupporting

confidence: 54%

Reduction of the uncertainties in the hydrological projections in Korean river basins using dynamically downscaled climate projections

Qiu

Kim

et al. 2022

Clim Dyn

View full text Add to dashboard Cite

show abstract

“…Meanwhile, the runoff projections are significantly impacted by the choices of GCMs; this point has also been found in many studies [34]. For instance, Zhang, et al [55] found that the disparity between different GCMs may mainly impact on climate change research, and that the increased sample size of GCMs may conduct a complete uncertainty assessment. As an important tool for runoff simulation and prediction, the hydrological model is a non-negligible uncertainty contributor of overall uncertainty; among the uncertainty derived from the hydrological model, the model parameters obtained more attention [26,35] Moreover, the contribution and interaction effects are relatively small compared with the other uncertainty sources; these findings are consistent with some previous studies [31,33].…”

Section: Estimating the Source Of Uncertaintiesmentioning

confidence: 77%

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

et al. 2022

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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.

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“…(2) Multistep post-processing dynamics of our developed approach generated uncertainty in data, parameters, and structures of the developed Manning formula, GR6J model, and machine learning methods. Quantifying uncertainty contribution of each model and dataset is fundamental for our better understanding of hydrological forecasting mechanism, which will be further investigated and fully evaluated in the near future using uncertainty analysis tools such as analysis of variance [93]. (3) Due to the relatively short operation life of the Jason-2/3 satellite from 2009 up to present day, and the GRACE mission between April 2002 and June 2017, the length of the discharge series in this study is limited.…”

Section: Discussionmentioning

confidence: 99%

Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River

2021

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Remotely sensing data have advantages in filling spatiotemporal gaps of in situ observation networks, showing potential application for monitoring floods in data-sparse regions. By using the water level retrievals of Jason-2/3 altimetry satellites, this study estimates discharge at a 10-day timescale for the virtual station (VS) 012 and 077 across the midstream Yangtze River Basin during 2009–2016 based on the developed Manning formula. Moreover, we calibrate a hybrid model combined with Gravity Recovery and Climate Experiment (GRACE) data, by coupling the GR6J hydrological model with a machine learning model to simulate discharge. To physically capture the flood processes, the random forest (RF) model is employed to downscale the 10-day discharge into a daily scale. The results show that: (1) discharge estimates from the developed Manning formula show good accuracy for the VS012 and VS077 based on the improved Multi-subwaveform Multi-weight Threshold Retracker; (2) the combination of the GR6J and the LSTM models substantially improves the performance of the discharge estimates solely from either the GR6J or LSTM models; (3) RF-downscaled daily discharge demonstrates a general consistency with in situ data, where NSE/KGE between them are as high as 0.69/0.83. Our approach, based on multi-source remotely sensing data and machine learning techniques, may benefit flood monitoring in poorly gauged areas.

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Quantifying uncertainty sources in extreme flow projections for three watersheds with different climate features in China

Cited by 11 publications

References 65 publications

Reduction of the uncertainties in the hydrological projections in Korean river basins using dynamically downscaled climate projections

Reduction of the uncertainties in the hydrological projections in Korean river basins using dynamically downscaled climate projections

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

Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River

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