Runoff estimation using Landsat Thematic Mapper data and the SCS model

Sharma, Kapil; Singh, Surendra

doi:10.1080/02626669209492560

Cited by 26 publications

(14 citation statements)

References 2 publications

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“…Land cover has a strong influence on key hydrological variables such as infiltration, interception and evaporation and typically is the dominant variable determining the remotely sensed response of a site. Consequently, land cover maps derived from remotely sensed data have often been used to estimate a range of hydrological variables for the parameterisation of hydrological models (Hoshi et al, 1989;Sharma and Singh, 1992;Storck et al, 1998;Su, 2000). These models may be used to predict the response of a site to a precipitation event.…”

Section: Introductionmentioning

confidence: 99%

Predicting locations sensitive to flash flooding in an arid environment

Foody

Ghoneim

Arnell

2004

Journal of Hydrology

142

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Section: Introductionmentioning

confidence: 99%

Predicting locations sensitive to flash flooding in an arid environment

Foody

Ghoneim

Arnell

2004

Journal of Hydrology

142

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“…This is more convenient because other hydrologic models may require several input parameters, which are sometimes difficult to obtain, Two approaches in estimating CN values using remotely sensed data have been proposed (Sharma and Singh, 1992). One approach is to use remotely sensed data to classify land cover and land use, and then comPare them to those listed in the standard CN table to obtain CN values (Ragan and Jackson, 1980;Slack and Welch, 1980;Bondelid ef a/.. 1980;Still and Shih, 1985;Tiwari et at., 1991;and Sharma and Singh, 1992 any pixel classified as forest will have the CN value of 55. In reality, CN values are not precise coefficients, rather they represent the 'best fit' to the small watershed rainfall-runoff data upon which the model is based (Ragan and Jackson' 1980).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Runoff Index Using Landsat (Tm) Data and an Artificial Neural Network System

Emaruchi¹,

Jin²,

Sauchyn³

et al. 1997

Canadian Water Resources Journal

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Water resources managers require information on the amount and spatial distribution of sudace runoff. This information can be estimated by using a hydrologic model such as the one developed by the U.S. Soil Conservation Service. Most inputs to the model can be defined as runoff indices or runoff curve numbers (CN values) which were derived by using recorded data from a number of experimental watersheds. For a large area, CN values can be estimated from Landsat (TM) data. In conventional methods, two steps are required. First, brightness values of Landsat data are transformed to represent land cover classes by using 'supervised' classification methods.Next, with the classified land cover classes, cN values can be read direcily from a standard CN table. However, CN values can also be directly estimated from Landsat data using an Artificial Neural Network System. By using the Artificial Neural Network System, the accuracy of the estimation can be improved. Results of this investigation show that the Arlificial Neural Network System gives more accurate results than those obtained by using the minimum distance and maximum likelihood approaches. R6sum6Pour diriger les ressources hydrographiques il faut connaitre la quantite et la distribution spatial de l' 6coulement de surface. ll est possible d' estimer ces donn6es en utilisant un moddle hydrologique tel que le moddle d6velopp6 par le "U.S. Soil Conservation Service." Les resultats auxquels on arrive en utilisant ce moddle peuvent donner un indice de l'6coulement ou ceux-ci peuvent servir de chiffres de courbe d6riv6s de donn6es obtenues d' exp6riences sur la ligne de partage des eaux.On propose une m6thode de d6tection a distance et un systdme neural artificiel pour estimer la valeur CN. On peut estimer la distribution de l' 6coulement sur la ligne de parlage d'eau une fois que les valeurs cN et les donnees de la pr6cipitation sont connus. Les r6sultats de cette investigation indiquent que la m6thode propos6e donnerait des r6sultats plus exacts que ceux obtenus par des m6thodes conventionnelles. IntroductionThe spatial distribution of surface runoff water detention structures, and flood has important implications for water reprevention measures. This information sources management as, for example, in can be obtained by using a hydrologic locating land use developments, storm model.

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“…In practice, the rainfall-runoff process is most commonly modelled using either the Rational Equation (Mulvaney 1851, Turazza 1880, Kuichling 1889 or the US Department of Agriculture (USDA) Soil Conservation Service (SCS; presently Natural Resources Conservation Service or NRCS) curve number (CN) method (USDA-NRCS 2004), because these two methods are simple, easy to understand, and applicable for gauged as well as ungauged watersheds (Wigham 1970, Sharma and Singh 1992, Kurothe et al 2001, Sahu et al 2007. The Rational Equation is often recommended for estimating design peak runoff from impervious areas (Guo 2001) and small (<40 ha) rural watersheds (Hewlett andHibbert 1967, Hayes andYoung, 2005), and is rarely used to predict direct runoff depth or volume (Debo and Reese 2003).…”

Section: Introductionmentioning

confidence: 99%

Development of a robust runoff-prediction model by fusing the Rational Equation and a modified SCS-CN method

Wang

Liu

Yang

2012

Hydrological Sciences Journal

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The objective of this study is to develop a Modified Rational Equation (MoRE) that combines the advantages of the Rational Equation (e.g. simplicity and global acceptance) and those of the standard US Department of Agriculture (USDA) Soil Conservation Service (SCS) curve number (CN) method (e.g. easy parameterization and extensive verification across the world). Herein, the hypothesis is that the MoRE is more accurate, consistent and robust than the SCS-CN method and its improved versions in predicting runoff in watersheds with limited data. The MoRE was designed to have a simple structure that is described by four intrinsic parameters: CN, permanent wilting point, field capacity and saturation soil moisture, and does not include initial abstraction as a variable. An evaluation of 77 USDA small agricultural watersheds indicated that CN of the MoRE has different physical meanings from CN of the SCS-CN method. The MoRE (mean Nash-Sutcliffe coefficient, E > 0.73) performed better than the SCS-CN (mean E < 0.32) and the four improved models (mean E < 0.56) in reproducing the runoff of the study watersheds. Performance of all six models varied greatly between watersheds, as well as between events, but was independent of watershed drainage area. However, the model performances tend to be better for watersheds and/or events with a runoff-to-rainfall ratio of between 0.1 and 0.3 than for those with a ratio outside this range. The MoRE has the most consistent and robust performance. Elaboration d'une modèle robuste d'estimation du ruissellement fusionnant la méthode rationnelle et une méthode du SCS-CN modifiéeRésumé L'objectif de cette étude est de développer une méthode rationnelle modifiée (MoRE), qui combine les avantages de la méthode rationnelle (par exemple la simplicité et l'acceptation mondiale) et ceux de la méthode standard du "curve number" (CN) du Soil Conservation Service (SCS) du Département américain de l'agriculture (USDA) (par exemple le paramétrage aisé et une vérification approfondie à travers le monde). Ici, l'hypothése est que la méthode MoRE est plus précise, cohérente et robuste que la méthode SCS-CN et que ses versions améliorées pour prévoir le ruissellement dans les bassins versants avec des données limitées. La méthode MoRE a été conçue pour avoir une structure simple qui est décrite par quatre paramètres intrinsèques: CN, le point de flétrissement permanent, la capacité au champ et l'humidité du sol à saturation, et qui ne comprend pas comme variable le prélèvement initial. Une évaluation sur 77 petits bassins versants agricoles de l'USDA a indiqué que le paramètre CN de la méthode MoRE a des significations physiques différentes du CN de la méthode SCS-CN. La méthode MoRE (avec un critère de Nash-Sutcliffe moyen, E, supérieur à 0,73) obtient de meilleurs résultats que la méthode SCS-CN (moyenne E inférieure 0,32) et que les quatre modèles améliorés (moyenne E inférieure à 0,56) pour la reproduction de l'écoulement des bassins versants d'étude. La performance des six modèles varie grandement en...

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Runoff estimation using Landsat Thematic Mapper data and the SCS model

Cited by 26 publications

References 2 publications

Predicting locations sensitive to flash flooding in an arid environment

Predicting locations sensitive to flash flooding in an arid environment

Estimation of Runoff Index Using Landsat (Tm) Data and an Artificial Neural Network System

Development of a robust runoff-prediction model by fusing the Rational Equation and a modified SCS-CN method

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