Physical verification of the effect of land features and antecedent moisture on runoff curve number

Lal, Mohan; Mishra, Surendra Kumar; Pandey, Ashish

doi:10.1016/j.catena.2015.06.001

Cited by 36 publications

(16 citation statements)

References 51 publications

(76 reference statements)

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“…The CN method avoids the problems inherent to parameterizing and running more complex models due to its simplicity and relatively low data input requirements, and has been implemented in a variety of hydrologic, erosion, and water-quality models 7–9 . CN selection is derived from the hydrologic response of various combinations of soil types and land cover classes 2 , 10 . Particularly relevant to the subject of this analysis, and the data product we make available, is the classification and development of soil parameters for CN-based runoff modeling.…”

Section: Background and Summarymentioning

confidence: 99%

HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling

et al. 2018

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Hydrologic soil groups (HSGs) are a fundamental component of the USDA curve-number (CN) method for estimation of rainfall runoff; yet these data are not readily available in a format or spatial-resolution suitable for regional- and global-scale modeling applications. We developed a globally consistent, gridded dataset defining HSGs from soil texture, bedrock depth, and groundwater. The resulting data product—HYSOGs250m—represents runoff potential at 250 m spatial resolution. Our analysis indicates that the global distribution of soil is dominated by moderately high runoff potential, followed by moderately low, high, and low runoff potential. Low runoff potential, sandy soils are found primarily in parts of the Sahara and Arabian Deserts. High runoff potential soils occur predominantly within tropical and sub-tropical regions. No clear pattern could be discerned for moderately low runoff potential soils, as they occur in arid and humid environments and at both high and low elevations. Potential applications of this data include CN-based runoff modeling, flood risk assessment, and as a covariate for biogeographical analysis of vegetation distributions.

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Section: Background and Summarymentioning

confidence: 99%

HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling

et al. 2018

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“…The model function is as follows:{Q=(P−0.2S)2P+0.8S, P>0.2SQ=0, P≤0.2 where Q is the surface runoff volume (mm), P represents the rainfall volume (mm), and S denotes the potential maximum soil-water capacity, whose function is as follows:S=25400CN−254 where CN is the runoff curve coefficient. The CN is an important parameter for surface runoff simulation of SCS-CN because it is related to many attributes of land, including land use, soil type, and antecedent soil moisture [78]. The CN value is determined by the attached table (Tr-55) in the National Engineering Handbook, Section 4 under certain Antecedent Moisture Conditions (AMC II) [79].…”

Section: Methodsmentioning

confidence: 99%

Optimization of Impervious Surface Space Layout for Prevention of Urban Rainstorm Waterlogging: A Case Study of Guangzhou, China

Zhao

2019

IJERPH

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With the rapid expansion of impervious surfaces, urban waterlogging has become a typical “urban disease” in China, seriously hindering the sustainable development of cities. Therefore, reducing the impact of impervious surfaces on surface runoff is an effective approach to alleviate urban waterlogging. Presently, the development mode of many cities in China has shifted from an increase in urban scale to the improvement of urban quality through urban renewal, which is the current and future development path for most cities. Optimizing the design of impervious surfaces in urban renewal planning to reduce its impact on surface runoff is an important way to prevent and control urban waterlogging. The aim of this research is to construct an optimization model of impervious surface space layout under the framework of a geographic simulation technology-integrated ant colony optimization (ACO) and Soil Conservation Service curve number (SCS-CN) model (ACO-SCS) in a case study of Guangzhou in China. Urban runoff plots in the study area are divided according to the area of the urban planning unit. With the goal of minimizing the runoff coefficient, the optimal space layout of the impervious surfaces is obtained, which provides a technical method and reference for urban waterlogging prevention and control through urban renewal planning. The results reveal that the optimization of impervious surface space layout through ACO-SCS achieves a satisfactory effect with an average optimization rate of 9.52%, and a maximum optimization rate of 33.16%. The research also shows that the initial impervious surface layout is the key influencing factor in ACO-SCS. In the urban renewal planning stage, the space layout of the impervious surfaces with a high–low–high density discontinuous connection can be constructed by transforming medium-density impervious surfaces into low-density impervious surfaces to achieve the flat and long-type agglomeration of the low-density and high-density impervious surfaces, which can effectively reduce the influence of urban development on surface runoff. There is spatial heterogeneity of the optimal results in different urban runoff plots. Therefore, the policy of urban renewal planning for urban waterlogging prevention and control should be different. The optimized results of impervious surface space layout provide useful reference information for urban renewal planning.

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“…The generation of DSR is dependent on variables associated with the rainfall event, such as its intensity and duration, as well as characteristics of the watershed, e.g. soil classes, land uses, and topography (Elhakeem and Papanicolaou, 2009;Lal et al, 2016). Thus, according to Araújo Neto et al (2012) and Soulis and Valiantzas (2013), the use of hydrological models to estimate DSR in watersheds is necessary.…”

Section: Introductionmentioning

confidence: 99%

Adequacy of methodologies for determining SCS / CN in a watershed with characteristics of the Pampa biome

et al. 2021

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The Soil Conservation Service Curve Number Model is a conceptual model intended for estimating effective rainfall (ER). This model is grounded in a parameter – referred to as Curve Number (CN), which is determined from information on the characteristics of the watershed. The Standard Method (M1) for determining the CN is based on soil and land-use tables; however, some authors have proposed alternative methodologies for defining the CN value from monitored rainfall-runoff events, such as those described by Hawkins (1993) (M2), Soulis and Valiantzas (2012) (M3), and Soulis and Valiantzas (2013) (M4). The objective of this study was to evaluate the impact of using these methods for determination of the CN parameter on the estimation of ER, taking as reference forty rainfall-runoff events monitored between 2015 and 2018 in the Cadeia River Watershed, which has characteristics of the Pampa biome. The different methods assessed for definition of the CN parameter resulted in contrasting performances with respect to the estimation of ER for CRW, as the following findings: i) M1 gave ER values with little reliability, mainly due to the classification of antecedent moisture content classes; ii) M3 provided the best results in determining ER, followed by M2; and iii) the ER values estimated according to M4 differed from those observed, mainly for events with lower rainfall depths.

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Physical verification of the effect of land features and antecedent moisture on runoff curve number

Cited by 36 publications

References 51 publications

HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling

HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling

Optimization of Impervious Surface Space Layout for Prevention of Urban Rainstorm Waterlogging: A Case Study of Guangzhou, China

Adequacy of methodologies for determining SCS / CN in a watershed with characteristics of the Pampa biome

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