A Framework for Assessing Modeling Performance and Effects of Rainfall-Catchment-Drainage Characteristics on Nutrient Urban Runoff in Poorly Gauged Watersheds

Gorgoglione, Angela; Gioia, Andrea; Iacobellis, Vito

doi:10.3390/su11184933

Cited by 40 publications

(18 citation statements)

References 18 publications

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“…It is a difficult and complicated process to know the water quality status of the whole river basin, determine the influencing factors of water quality, and improve the water environment quality of the river basin [26]. To provide a holistic vision of all the variables involved in the system, PCA was used on the Xin'anjiang River [27,28].…”

Section: Principal Component Analysismentioning

confidence: 99%

Using Principal Components Analysis and IDW Interpolation to Determine Spatial and Temporal Changes of Surface Water Quality of Xin’anjiang River in Huangshan, China

Yang

Zhao

Wang

et al. 2020

IJERPH

202

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This study was aimed at assessing the spatial and temporal distribution of surface water quality variables of the Xin’anjiang River (Huangshan). For this purpose, 960 water samples were collected monthly along the Xin’anjiang River from 2008 to 2017. Twenty-four water quality indicators, according to the environmental quality standards for surface water (GB 3838-2002), were detected to evaluate the water quality of the Xin’anjiang River over the past 10 years. Principal component analysis (PCA) was used to comprehensively evaluate the water quality across eight monitoring stations and analyze the sources of water pollution. The results showed that all samples could be analyzed by three main components, which accounted for 87.24% of the total variance. PCA technology identified important water quality parameters and revealed that nutrient pollution and organic pollution are major latent factors which influence the water quality of Xin’anjiang River. It also showed that agricultural activities, erosion, domestic, and industrial discharges are fundamental causes of water pollution in the study area. It is of great significance for water quality safety management and pollution control of the Xin’anjiang River. Meanwhile, the inverse distance weighted (IDW) method was used to interpolate the PCA comprehensive score. Based on this, the temporal and spatial structure and changing characteristics of water quality in the Xin’anjiang River were analyzed. We found that the overall water quality of Xin’anjiang River (Huangshan) was stable from 2008 to 2017, but the pollution of the Pukou sampling point was of great concern. The results of IDW helped us to identify key areas requiring control in the Xin’anjiang River, which pointed the way for further delicacy management of the river. This study proved that the combination of PCA and IDW interpolation is an effective tool for determining surface water quality. It was of great significance for the control of water pollution in Xin’anjiang River and the reduction of eutrophication pressure in Thousand Island Lake.

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Section: Principal Component Analysismentioning

confidence: 99%

Using Principal Components Analysis and IDW Interpolation to Determine Spatial and Temporal Changes of Surface Water Quality of Xin’anjiang River in Huangshan, China

Yang

Zhao

Wang

et al. 2020

IJERPH

202

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“…Despite continuous expensive research and efforts to gather hydrologic data, there are still some areas of the world with scarce hydrometric gauging stations [2,3]. These areas have been representing a motivating and challenging topic for researchers and decision-makers.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Physical Similarity Approach to Predict Runoff in Ungauged Watersheds in Sub-Tropical Regions

Narbondo

Gorgoglione

Crisci

et al. 2020

Water

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Regionalization techniques have been comprehensively discussed as the solution for runoff predictions in ungauged basins (PUB). Several types of regionalization approach have been proposed during the years. Among these, the physical similarity one was demonstrated to be one of the most robust. However, this method cannot be applied in large regions characterized by highly variable climatic conditions, such as sub-tropical areas. Therefore, this study aims to develop a new regionalization approach based on an enhanced concept of physical similarity to improve the runoff prediction of ungauged basins at country scale, under highly variable-weather conditions. A clustering method assured that watersheds with different hydrologic and physical characteristics were considered. The novelty of the proposed approach is based on the relationships found between rainfall-runoff model parameters and watershed-physiographic factors. These relationships were successively exported and validated at the ungauged basins. From the overall results, it can be concluded that the runoff prediction in the ungauged basins was very satisfactory. Therefore, the proposed approach can be adopted as an alternative method for runoff prediction in ungauged basins characterized by highly variable-climatic conditions.

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“…The distance measure was the Squared Euclidean distance. A distance criterion between two variables defined how closely they were associated within the group, and the smaller the clustering distance, the higher the similarity of the data points [9,10,40]. HCA by means of hierarchical dendrograms was performed by using SPSS 24.0 (IBA SPSS Statistics, Armonk, NY, USA) applied to the runoff data for different land use types.…”

Section: Statistical Analysesmentioning

confidence: 99%

Effect of Urban Stormwater Road Runoff of Different Land Use Types on an Urban River in Shenzhen, China

Liu

Wang²,

Yang

et al. 2019

Water

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Urban storm runoff is a major source of pollutants in receiving water bodies. To assess the impact of urban stormwater runoff on an urban river, the runoff process of total suspended solids (TSS), chemical oxygen demand (COD), ammonium (NH4), and total phosphorus (TP) were investigated on road surfaces classified as arterial road (AR), residential area (RA), and industrial area (IA) in the Pingshan River (PSR) watershed in Shenzhen, China. Event mean concentration (EMC) was calculated to analyze the water quality of road runoff, and the dimensionless M(V) cumulative curves were used to estimate the course of decreasing concentration of runoff pollutants during each rainfall event. Multicriteria decision making methods (PROMETHEE-GAIA) were used to identify the linkage between runoff pollutants, land use types, and rainfall intensity. The EMCs of COD and TP in runoff exceeded the class IV level of the water quality standard for surface water (China). RA was a major potential source for NH4, COD, and TP in the river. Controlling the first flush is critical to decrease the effect of road runoff on receiving water bodies, as most runoff pollutants in AR, RA, and IA had a first flush effect during heavy rainfall. The specific management measure for runoff pollution varied with land use type. Reducing road TSS concentrations was effective for controlling runoff pollution in AR and RA because NH4, TP, and COD attached to particulate matter. In IA, the collection and reuse of stormwater in the initial rainfall period were effective for reducing the effect of soluble pollutants in runoff on receiving water bodies. This study provides new information for managing urban road stormwater runoff in different land use types.

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A Framework for Assessing Modeling Performance and Effects of Rainfall-Catchment-Drainage Characteristics on Nutrient Urban Runoff in Poorly Gauged Watersheds

Cited by 40 publications

References 18 publications

Using Principal Components Analysis and IDW Interpolation to Determine Spatial and Temporal Changes of Surface Water Quality of Xin’anjiang River in Huangshan, China

Using Principal Components Analysis and IDW Interpolation to Determine Spatial and Temporal Changes of Surface Water Quality of Xin’anjiang River in Huangshan, China

Enhancing Physical Similarity Approach to Predict Runoff in Ungauged Watersheds in Sub-Tropical Regions

Effect of Urban Stormwater Road Runoff of Different Land Use Types on an Urban River in Shenzhen, China

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