Development of an inexact-variance hydrological modeling system for analyzing interactive effects of multiple uncertain parameters

Wang, C.X.; Li, Y. P.; Zhang, J.L.; Huang, Guohe

doi:10.1016/j.jhydrol.2015.06.004

Cited by 15 publications

(4 citation statements)

References 43 publications

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“…Several types of hydrological, topographical, geological and anthropogenic variables can have different influences on coastal environments and local urban infrastructures, and thus the frequency and severity of flooding can vary significantly between distinct areas. The interactions among multiple type of variable should not be neglected or underestimated (Wang et al 2015). With the objective of determine the grouping variable, Pearson (r) parametric correlation methods and Kendall's tau (τ) and Spearman's Rho (ρ) rank non-parametric correlations were applied.…”

Section: Cluster Analysismentioning

confidence: 99%

“…All statistics analysis were performed using the STATISTIC® 8.0 software, and the spatial constraints and models were developed in the ArcGIS® 10.1 software. More detailed information about correlation methods, cluster analysis (CA) and ANOVA can be obtained from (Haaf and Barthel 2018;Sehgal et al 2018;Tosunoğlu and Onof 2017;Wang et al 2015;Xie et al 2018).…”

Section: Cluster Analysismentioning

confidence: 99%

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Geostatistical strategy to build spatial coastal-flooding models

Caprario

Azevedo

Santana

et al. 2021

Urban Water Journal

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The intensification of flooding events in urban coastal areas and the management of the extremely vulnerable ecosystems has become a global issue and has been the subject of several studies in recent decades. The combination of statistical modelling techniques and geospatial analysis has gained prominence in integrated management of natural hazards. Combined approach represents a promising strategy to reduce complexity, providing a holistic representation of the system. However, for applying planning and risk management alternatives in urban space of coastal areas, the causal relationships between environmental and anthropogenic factors distribution and the flooding sites have to be well clarified. This article aimed to propose a new strategy to build Spatial Coastal-Flooding Models based on records of flooding points occurred and environmental and anthropogenic characteristics of the area. The strategy was designed to combine simple but robust statistical techniques and relate geospatially data obtained from free and easily accessible online databases. The geostatistical strategy was applied in a coastal city of Brazil, where high environmental vulnerability and constantly flooding are observed. The strategy finds that the relationships between environmental and anthropogenic variables and flooding events are not homogeneous over space. The results indicate that the strategy can be easily replicable and refined through the inclusion of other factors of influence, obtaining good precision in the representation and explanation of the flooding events in coastal areas. In general, the strategy provides information that can support decision-making by government agencies in relation to integrated urban planning and mitigation of flooding risk coastal areas.

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Section: Cluster Analysismentioning

confidence: 99%

Section: Cluster Analysismentioning

confidence: 99%

Geostatistical strategy to build spatial coastal-flooding models

Caprario

Azevedo

Santana

et al. 2021

Urban Water Journal

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“…However, due to the complexity of hydrological system under specified temporal and spatial variations, i.e., the interaction of evaporation, infiltration, transpiration and groundwater recharge, it is difficulty to identify the characteristics of runoff process. As a result, hydrological models, such as the Soil and Water Assessment Tool (SWAT) and the semi-distributed Topographic hydrologic model (TOPMODEL) have been widely used in watershed management for prediction of streamflow, as well as investigation of the effects of climate change [1]. However, due to the simplification of the model structure, the gross accuracy of the input data and the temporal and spatial difference of parameters, model representations of the process from rainfall to streamflow are inadequate and need to be improved.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Stepwise Cluster Analysis and Multiple Linear Regression for Hydrological Simulation

Wang

Sun

et al. 2022

J. Phys.: Conf. Ser.

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Investigating the dynamic characteristic of hydrological processes is of vital significance for environmental protection. In this study, the stepwise cluster analysis (SCA) method was used for monthly streamflow simulation in a hypothetical case. According to SCA, a cluster tree was formulated through training the data of monthly temperature, precipitation and streamflow from 2004 to 2010. Then, the generated tree was used to reproduce monthly streamflow in calibration period (i.e., 2004-2010) and validation period (i.e., 2011-2013). A comparison of SCA and multiple linear regression (MLR) was conducted to reflect the complex relationship of meteorological parameters (e.g., precipitation) and hydrological parameters. Model performance was assessed using Nash-Sutcliffe efficiencies (NSE), the determination coefficient (R 2), the root-mean-square error (RMSE) and the mean absolute error (MAE). NSE and R2 obtained from SCA are higher than that obtained from MLR. RMSE and MAE obtained from SCA are smaller than that obtained from MLR, indicating a better coincidence between simulated streamflow and the observed values in SCA. Results indicated that SCA has advantage in revealing the nonlinear relationship among precipitation, temperature and streamflow.

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“…Due to the mismatch between the fine‐scale nature of local hydrological processes and the coarse resolution of large‐scale climate projections, the downscaling approach is essential for generating climate change scenarios within a watershed system. Unfortunately, there is still much uncertainty in obtaining high‐resolution climate projections which are needed to predict runoff in responses to flood and/or drought occurring risk (Wang et al , ). The difficulty lies in the fact that general circulation models (GCMs) which are used to project global climate change cannot adequately resolve factors that might influence regional climates of watershed systems (Cai et al , ).…”

Section: Introductionmentioning

confidence: 99%

Evaluating climate change impacts on the hydrology of watershed in northwestern China using a stepwise‐clustered downscaling approach

Zhuang

Huang

et al. 2016

Intl Journal of Climatology

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In this study, a stepwise-clustered downscaling model (SCDM) is advanced for transferring atmospheric simulation outputs to acquire high-resolution climate projections at a large-scale watershed system. SCDM can operate different temporal resolutions of atmospheric variables with continuous and discrete complexities. SCDM coupling with hydrological model is used for evaluating climate change impacts on hydrology of the Kaidu watershed in northwestern China. The daily and monthly series of large-scale atmospheric simulation outputs for the Kaidu watershed are extracted from the ensemble of GCMs during past, recent and future periods. Results reveal that (1) SCDM is capable of downscaling climate projections for different stations, and can help understand the spatial heterogeneity of climate change, (2) the performance of SCDM is more acceptable for temperature than precipitation, (3) increase trends of T min and T max (minimum and maximum temperatures) from recent to future are projected. Besides, results from multiple downscaled climate change projections are used for driving a daily climate-streamflow hydrological model. Results disclose that the streamflow would increase because temperature change will cause more glacier melt in future.

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Development of an inexact-variance hydrological modeling system for analyzing interactive effects of multiple uncertain parameters

Cited by 15 publications

References 43 publications

Geostatistical strategy to build spatial coastal-flooding models

Geostatistical strategy to build spatial coastal-flooding models

A Comparison of Stepwise Cluster Analysis and Multiple Linear Regression for Hydrological Simulation

Evaluating climate change impacts on the hydrology of watershed in northwestern China using a stepwise‐clustered downscaling approach

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