Discrete principal‐monotonicity inference for hydro‐system analysis under irregular nonlinearities, data uncertainties, and multivariate dependencies. Part I: methodology development

Cheng, Guanhui; Dong, Chang Qing; Huang, Guohe; Baetz, Brian W.; Han, Jing‐Cheng

doi:10.1002/hyp.10909

Cited by 26 publications

(22 citation statements)

References 69 publications

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“…A review of many related publications (e.g. Mandal et al, 2016;Sarhadi et al, 2016;Borges et al, 2016;Mizukami et al, 2016;Singh et al, 2016;Liu et al, 2016;Dong et al, 2016, Dong et al, 2015, Dong et al, 2014a, 2014b, 2014c, Dong et al, 2013, Dong et al, 2012, Dong et al, 2011Cheng et al, 2016aCheng et al, , 2016b indicates that some of them are of interests for various impact studies regarding continental river basins such as the ARB. The selected climate variables are listed in Table IV in the Appendix.…”

Section: Data Collectionmentioning

confidence: 99%

An Evaluation of CMIP5 GCM Simulations over the Athabasca River Basin, Canada

Cheng

Huang

Dong

et al. 2017

River Research & Apps

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Long‐term hydrological forecasting, water resources management and other climate change impacts or adaptation analysis studies on large continental river basins, for example, the Athabasca River Basin (ARB) in Canada, desire a reliable climatic projection. This usually relies on general circulation models (GCMs) in the fifth phase of the Coupled Model Intercomparison Project (CMIP5). However, there is a lack of a systematic evaluation of CMIP5 GCM performances over the ARB that vary with multiple factors, for example, statistical metrics, temporal scales and spatial locations, challenging the reliability of water‐related or other studies over the ARB. For this gap to be filled, six CMIP5 GCMs, namely, IPSL‐CM5A‐LR, IPSL‐CM5A‐MR, MIROC‐ESM‐CHEM, MIROC5, GFDL‐ESM2G and GFDL‐ESM2M, and their ensemble mean are selected according to data availabilities of representative climate variables: Tmin, Tmax and Prec (TTP). Accuracies of the selected CMIP5 GCMs in reproducing TTP over the ARB are evaluated comprehensively. The ensemble mean cannot outperform any GCM in all cases in the ARB, although its overall accuracy seems to be higher in consideration of all cases. These accuracies vary with TTP, locations, metrics and scales. For instance, ESM2G shows the highest accuracies in reproducing monthly/seasonal variability and magnitudes of grid‐averaged TTP and inter‐annual variability of grid‐averaged annual means of Tmax; CM5A‐LR in multi‐year‐averaged spatial variability of TTP and magnitudes of spatially distributed multi‐year‐averaged Tmax; while the ensemble mean only in some aspects, for example, intraseasonal variability and magnitudes of TTP and inter‐annual variability and magnitudes of grid‐averaged annual means of TTP. GCMs should be systematically integrated according to accuracy variations. Multiple statistical metrics are recommended in GCM evaluations. These findings facilitate water resources systems analyses and other related studies in the ARB. Copyright © 2017 John Wiley & Sons, Ltd.

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Section: Data Collectionmentioning

confidence: 99%

An Evaluation of CMIP5 GCM Simulations over the Athabasca River Basin, Canada

Cheng

Huang

Dong

et al. 2017

River Research & Apps

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“…In the first module of the FIGGRA approach, the grouping is enabled through a systematic analysis of the spatial heterogeneity and homogeneity among these grids based on advanced statistical inferences. [Shapiro and Wilk, 1965;Royston, 1995]; DDT: the discrete distribution transformation approach [Cheng et al, 2016a[Cheng et al, , 2016b; MNV test: the modified Nel and van der Merwe test [Krishnamoorthy and Yu, 2004]; α: statistical significance level; Nmin: minimum row number; ReDSICC: recursive dissimilarity and similarity inferential climate classification [Cheng et al, 2017].…”

Section: Recursive Inferential Grid Groupingmentioning

confidence: 99%

Factorial inferential grid grouping and representativeness analysis for a systematic selection of representative grids

Cheng

Huang

Dong

et al. 2017

Earth and Space Science

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A factorial inferential grid grouping and representativeness analysis (FIGGRA) approach is developed to achieve a systematic selection of representative grids in large‐scale climate change impact assessment and adaptation (LSCCIAA) studies and other fields of Earth and space sciences. FIGGRA is applied to representative‐grid selection for temperature (Tas) and precipitation (Pr) over the Loess Plateau (LP) to verify methodological effectiveness. FIGGRA is effective at and outperforms existing grid‐selection approaches (e.g., self‐organizing maps) in multiple aspects such as clustering similar grids, differentiating dissimilar grids, and identifying representative grids for both Tas and Pr over LP. In comparison with Pr, the lower spatial heterogeneity and higher spatial discontinuity of Tas over LP lead to higher within‐group similarity, lower between‐group dissimilarity, lower grid grouping effectiveness, and higher grid representativeness; the lower interannual variability of the spatial distributions of Tas results in lower impacts of the interannual variability on the effectiveness of FIGGRA. For LP, the spatial climatic heterogeneity is the highest in January for Pr and in October for Tas; it decreases from spring, autumn, summer to winter for Tas and from summer, spring, autumn to winter for Pr. Two parameters, i.e., the statistical significance level (α) and the minimum number of grids in every climate zone (Nmin), and their joint effects are significant for the effectiveness of FIGGRA; normalization of a nonnormal climate‐variable distribution is helpful for the effectiveness only for Pr. For FIGGRA‐based LSCCIAA studies, a low value of Nmin is recommended for both Pr and Tas, and a high and medium value of α for Pr and Tas, respectively.

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“…Nevertheless, besides its calculation complexity, the performance of the SCA is sensitive to its inputs and internal parameters; the difference within leaf clusters of a SCA tree is usually not well described. The SCA has been applied to various water resources and environmental management problems, such as urban air quality prediction (Huang, 1992), lung cancer diagnosis (Ren et al, 1997), waste treatment process simulation (Sun et al, 2009;Sun et al, 2011), groundwater bioremediation optimization (Huang et al, 2006;Qin et al, 2007;He et al, 2008b;Wang et al, 2012;Zhao et al, 2017), open water forecasting (Fan et al, 2015;Li et al, 2015;Han et al, 2016;Zhuang et al, 2016b;Cheng et al, 2016;Fan et al, 2017;) and climate model downscaling (Wang et al, 2013;Zhuang et al, 2016a;Zhai et al, 2019). However, few applications of SCA to river ice forecasting are reported.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning Enhanced Stepwise Cluster Analysis for River Ice Breakup Date Forecasting

Sun¹,

Shi²,

Huang³

et al. 2019

J ENVIRON INFORM LET

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Frequently occurring ice jams often cause concern in northern regions. Breakup timing is directly related to emergency responses preparation and thus its early accurate forecasting is beneficial to ice-related flooding management. The stepwise cluster analysis (SCA) is a non-parameter regression method, which generates a classification tree in the sense of probability through cutting or merging operations according to certain statistic criteria. To enhance SCA's predictive performance, a SCA ensemble (SCAE) method is developed and applied to forecasting of annual river ice breakup dates (BDs). In detail, the SCA is employed as a base model at the lower level while the simple average method is selected as combining models at the upper level. The SCA base models are selected according to different performance selection criteria and searched for further combination. A site on a representative river prone to river ice flooding in Alberta, Canada is selected to demonstrate the effectiveness of the proposed SCAE. The results mainly show that: the SCA base models with multiple combinations of inputs and internal parameters are able to predict the BDs with good performances (the highest average of correlation coefficients for training can be 0.958); the optimal SCA base model has three inputs, which indicates that the temperatures before breakup and just after freeze-up as well as the maximum of water flow in March are relatively important indicators of BD. The optimal SCAE, including base models from different performance selection criteria, has the lowest average of root mean squared error, which improves upon the optimal SCA base model by 25.3%. It indicates the different model selection criteria do improve the diversity and thus further help to improve the performance of ensemble models. This first application of the SCAE to river ice forecasting highlights the possibility of using the ensemble learning paradigm to enhance the SCA. The potential applications of the SCAE to other forecasting problems are expected.

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Discrete principal‐monotonicity inference for hydro‐system analysis under irregular nonlinearities, data uncertainties, and multivariate dependencies. Part I: methodology development

Cited by 26 publications

References 69 publications

An Evaluation of CMIP5 GCM Simulations over the Athabasca River Basin, Canada

An Evaluation of CMIP5 GCM Simulations over the Athabasca River Basin, Canada

Factorial inferential grid grouping and representativeness analysis for a systematic selection of representative grids

Ensemble Learning Enhanced Stepwise Cluster Analysis for River Ice Breakup Date Forecasting

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