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Shiyu Mou
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China

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Journal article
Published: 10 March 2020 in Atmospheric Research
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The objective of this paper is to investigate the projected regional responses of univariate and bivariate behaviors of extreme precipitation to climate change over the upper-middle Huaihe River Basin. Based on twelve GCM outputs under historical, RCP4.5 and the observations at 32 rainfall stations, the equidistant cumulative distribution function matching method (EDCDFm) was utilized to bias correct daily precipitation during the historical (1961–2005) and future (2021–2080) periods. Four precipitation indices combinations were introduced based on eight precipitation indices to characterize the regional-scale changes of precipitation events, which designate the duration, intensity and amount of heavy and weak precipitation in a year. Their dependence structures were captured by Copulas. Kendall return period (KRP) were applied to discuss hazard scenarios and we quantified the spatial variability of KRPs under different marginal values. The results indicated that projected precipitation characteristics including the average intensity, the amount of annual precipitation, the intensity and amount of extreme precipitation together with annual extremes displayed increasing trends, while the changes of consecutive wet and dry days did not present pronounced trends. Decreased KRPs in the vast majority of the territory manifested that the frequency of simultaneous floods and droughts in a year as well as that of extreme heavy precipitation events would augment. Obvious spatial heterogeneity of the changes of KRP was partly attributed to the topography difference, especially the coastal areas along the main stream of the Huaihe River. Consequently, there will be a higher risk of water resources-related issues in this region for upcoming decades.

ACS Style

Shiyu Mou; Peng Shi; Simin Qu; Ying Feng; Chen Chen; Fengcheng Dong. Projected regional responses of precipitation extremes and their joint probabilistic behaviors to climate change in the upper and middle reaches of Huaihe River Basin, China. Atmospheric Research 2020, 240, 104942 .

AMA Style

Shiyu Mou, Peng Shi, Simin Qu, Ying Feng, Chen Chen, Fengcheng Dong. Projected regional responses of precipitation extremes and their joint probabilistic behaviors to climate change in the upper and middle reaches of Huaihe River Basin, China. Atmospheric Research. 2020; 240 ():104942.

Chicago/Turabian Style

Shiyu Mou; Peng Shi; Simin Qu; Ying Feng; Chen Chen; Fengcheng Dong. 2020. "Projected regional responses of precipitation extremes and their joint probabilistic behaviors to climate change in the upper and middle reaches of Huaihe River Basin, China." Atmospheric Research 240, no. : 104942.

Journal article
Published: 13 April 2019 in Water
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The geomorphologic instantaneous unit hydrograph (GIUH) is an applicable approach that simulates the runoff for the ungauged basins. The nash model is an efficient tool to derive the unit hydrograph (UH), which only requires two items, including the indices n and k. Theoretically, the GIUH method describes the process of a droplet flowing from which it falls on to the basin outlet, only covering the flow concentration process. The traditional technique for flood estimation using GIUH method always uses the effective rainfall, which is empirically obtained and scant of accuracy, and then calculates the convolution of the effective rainfall and GIUH. To improve the predictive capability of the GIUH model, the Xin’anjiang (XAJ) model, which is a conceptual model with clear physical meaning, is applied to simulate the runoff yielding and the slope flow concentration, integrating with the GIUH derived based on Nash model to compute the river network flow convergence, forming a modified GIUH model for flood simulation. The average flow velocity is the key to obtain the indices k, and two methods to calculate the flow velocity were compared in this study. 10 flood events in three catchments in Fujian, China are selected to calibrate the model, and six for validation. Four criteria, including the time-to-peak error, the relative peak flow error, the relative runoff depth error, and the Nash–Sutcliff efficiency coefficient are computed for the model performance evaluation. The observed runoff value and simulated series in validation stage is also presented in the scatter plots to analyze the fitting degree. The analysis results show the modified model with a convenient calculation and a high fitting and illustrates that the model is reliable for the flood estimation and has potential for practical flood forecasting.

ACS Style

Yingbing Chen; Peng Shi; Simin Qu; Xiaomin Ji; Lanlan Zhao; Jianfeng Gou; Shiyu Mou. Integrating XAJ Model with GIUH Based on Nash Model for Rainfall-Runoff Modelling. Water 2019, 11, 772 .

AMA Style

Yingbing Chen, Peng Shi, Simin Qu, Xiaomin Ji, Lanlan Zhao, Jianfeng Gou, Shiyu Mou. Integrating XAJ Model with GIUH Based on Nash Model for Rainfall-Runoff Modelling. Water. 2019; 11 (4):772.

Chicago/Turabian Style

Yingbing Chen; Peng Shi; Simin Qu; Xiaomin Ji; Lanlan Zhao; Jianfeng Gou; Shiyu Mou. 2019. "Integrating XAJ Model with GIUH Based on Nash Model for Rainfall-Runoff Modelling." Water 11, no. 4: 772.

Journal article
Published: 17 December 2018 in Water
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The issue of regional design flood composition should be considered when it comes to the analysis of multiple sections. However, the uncertainty accompanied in the process of regional design flood composition point identification is often overlooked in the literature. The purpose of this paper, therefore, is to uncover the sensibility of marginal distribution selection and the impact of sampling uncertainty caused by the limited records on two copula-based conditional regional design flood composition methods, i.e., the conditional expectation regional design flood composition (CEC) method and the conditional most likely regional design flood composition (CMLC) method, which are developed to derive the combinations of maximum 30-day flood volumes at the two sub-basins above Bengbu hydrological station for given univariate return periods. An experiment combing different marginal distributions was conducted to explore the former uncertainty source, while a conditional copula-based parametric bootstrapping (CC-PB) procedure together with five metrics (i.e., horizontal standard deviation, vertical standard deviation, area of 25%, 50%, 75% BCIs (bivariate confidence intervals)) were designed and employed subsequently to evaluate the latter uncertainty source. The results indicated that the CEC and CMLC point identification was closely bound up with the different combinations of univariate distributions in spite of the comparatively tiny difference of the fitting performances of seven candidate univariate distributions, and was greatly affected by the sampling uncertainty due to the limited observations, which should arouse critical attention. Both of the analyzed sources of uncertainty increased with the growing T (univariate return period). As for the comparison of the two proposed methods, it seemed that the uncertainty due to the marginal selection had a slight larger impact on the CEC scheme than the CMLC scheme; but in terms of sampling uncertainty, the CMLC method performed slightly stable for large floods, while when considering moderate and small floods, the CEC method performed better.

ACS Style

Shiyu Mou; Peng Shi; Simin Qu; Xiaomin Ji; Lanlan Zhao; Ying Feng; Chen Chen; Fengcheng Dong. Uncertainty Analysis of Two Copula-Based Conditional Regional Design Flood Composition Methods: A Case Study of Huai River, China. Water 2018, 10, 1872 .

AMA Style

Shiyu Mou, Peng Shi, Simin Qu, Xiaomin Ji, Lanlan Zhao, Ying Feng, Chen Chen, Fengcheng Dong. Uncertainty Analysis of Two Copula-Based Conditional Regional Design Flood Composition Methods: A Case Study of Huai River, China. Water. 2018; 10 (12):1872.

Chicago/Turabian Style

Shiyu Mou; Peng Shi; Simin Qu; Xiaomin Ji; Lanlan Zhao; Ying Feng; Chen Chen; Fengcheng Dong. 2018. "Uncertainty Analysis of Two Copula-Based Conditional Regional Design Flood Composition Methods: A Case Study of Huai River, China." Water 10, no. 12: 1872.