Rainfall–runoff estimation using SCS–CN and GIS approach in the Pappiredipatti watershed of the Vaniyar sub basin, South India

Satheeshkumar, S.; Venkateswaran, S.; Kannan, R.

doi:10.1007/s40808-017-0301-4

Cited by 142 publications

(53 citation statements)

References 6 publications

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“…The SCS-CN was developed in the early 1950s by the United States Department of Agriculture (USDA) and the Natural Resource Conservation Society. This method can also be called the CN method and it was established to estimate rainfall runoff (Satheeshkumar et al, 2017;Rallison, 1980;Tailor and Shrimali, 2016). The SCS-CN is grounded on the water balance equation and the hypotheses which are outlined as follows, (a) ratio of the actual direct runoff to the potential runoff is equal to the ratio of the actual infiltration to the potential infiltration, and (b) the quantity of the initial abstraction is some fraction of the potential infiltration (Vandersypen et al, 1972).…”

Section: Scs Curve Number Modelmentioning

confidence: 99%

“…CN is the curve number, a dimensionless number that range from 0-100, which is drawn from a table provided by the SCS handbook of Hydrology depending on the HSG, landuse/landcover , and AMC (Songara et al, 2015;Satheeshkumar et al, 2017;Amutha and Porchelvan, 2009). HSG is expressed in four groups which are A, B, C, and D, with A signifying the lowest rate of runoff potential and highest rate of infiltration, B and C moderate, and D signifies the highest runoff potential and lowest rate of infiltration.…”

Section: Scs Curve Number Modelmentioning

confidence: 99%

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Rainfall-runoff estimation of Bojiang lake watershed using SCS-CN model coupled with GIS for watershed management

Matomela

Morahanye

et al. 2019

jaar

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A proper understanding of watershed spatio-temporal hydrological characteristics is critical to the management of a watershed and its natural resources such as water and vegetation. Rainfall runoff estimation plays an important role as an integral part of watershed management. Runoff volume and distribution data provides valuable information for water management strategies such as selection of artificial water abstraction sites, water storage facilities, and soil erosion control strategies. In the present study Bojiang lake watershed was used to indicate the application of Soil Conservation Service Curve Number method (SCS-CN) coupled with Geographic Information System (GIS) and Remote Sensing (RS) techniques. The watershed falls within Erdos Larus Relictus National Nature Reserve (ELRNNR) which was listed under the wetlands of international importance in 2002. Rainfall runoff is influenced by a variety of factors within a watershed such as soil and land use/cover types, soil moisture content, rainfall, drainage density, and shape and size of the watershed. The SCS Curve number is the most popular and widely applied method for runoff estimation. GIS and Remote Sensing play an important role in estimating surface runoff by SCS-CN method. ArcGIS 10.2 software was used to overlay different thematic layers and develop an attribute table and calculate a weighted curve number. The weighted curve number was applied to the SCS-CN equations to estimate daily, monthly, and yearly runoff. Correlation coefficient (r) was used to test for the relationship between rainfall and runoff, and verify the computation of the method. The results show an average runoff of 17.78 mm which is about 7.18% of the annual average rainfall for the years 2001-2016. The derived output maps can assist in identifying suitable areas for water recharge/abstraction. The study demonstrates that SCS-CN in conjunction with GIS and RS can be used to calculate runoff for ungagged watersheds and assist in watershed management strategies.

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Section: Scs Curve Number Modelmentioning

confidence: 99%

Section: Scs Curve Number Modelmentioning

confidence: 99%

Rainfall-runoff estimation of Bojiang lake watershed using SCS-CN model coupled with GIS for watershed management

Matomela

Morahanye

et al. 2019

jaar

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“…22,27,[29][30][31] However, other values of λ were reported for some regions. [21][22][23][24][25][26][27][28] Therefore, in this study, the λ parameter will be determined for the study area through calibration and validation.…”

Section: Mathematical Model-predicted Environmental Concentration In mentioning

confidence: 99%

“…4,[13][14][15][16][17][18][19][20] However, some authors reported that the default initial abstraction in the CN method was not appropriate for runoff simulation for soils in some countries. [21][22][23][24][25][26][27][28] The objectives of this study were (1) to develop a pollutant runoff model that simulates runoff water, sediment concentration and yield in runoff water, and pesticide concentrations in runoff water and sediment; and (2) to apply this model to validate the case study of plot-scale monitoring of pesticide runoff from bare soil under artificial rainfall conditions.…”

Section: Introductionmentioning

confidence: 99%

Modeling of runoff water and runoff pesticide concentrations in upland bare soil using improved SPEC model

Thinh

Yadav²,

Boulangé³

et al. 2019

J. Pestic. Sci.

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The SPEC model (Predicted Environmental Concentrations in agricultural Soils) was developed and improved for the simulation of pesticide runoff. The model was applied to the Sakaecho upland bare soil field (Tokyo, Japan) to predict runoff water, sediment concentration in runoff water, pesticide concentrations in runoff water, and runoff sediment (clothianidin and imidacloprid) under artificial rainfall conditions. The results showed that the simulated time to first runoff agreed very well with the observed data. The simulated cumulative runoff, sediment yield, and imidacloprid concentration in sediment agreed well with the observed data (Nash-Sutcliffe Efficiency (NSE)>0.75). The simulated runoff rate agreed reasonably well with the observed data (NSE >0.5). The predicted clothianidin concentrations in sediment and in runoff water had acceptable agreement with the observed data (NSE >0). The results implied the model's potential to predict runoff water, sediment yield, and pesticide runoff.

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“…Remote sensing data along with ground truth verification is widely utilized for mapping various resources in an economic and efficient way including soil mapping, land use mapping, and fertility mapping [26,27]. The applicability of SCS model was improved by accounting for spatial and temporal variability in soil and land use with remote sensing and geographic information system [28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Surface Runoff for Tank Watershed in Tamil Nadu Using Hydrologic Modeling

Abraham

Mathew

2018

International Journal of Geophysics

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Providing safe and wholesome water in sufficient quantity on a sustainable basis remains elusive for large population especially in semiarid regions and hence water balance estimation is vital to assess water availability in a watershed. The water balance study is formulated to assess the runoff that can be harvested for effective utilization. The study area is Urapakkam watershed with a chain of 3 tanks having an aerial extent of 4.576 km 2 with hard rock formation underneath and thus has limited scope for groundwater recharge. Hence surface water is the main water source in this area. Runoff computed for the watershed using USDA-NRCS model varied from 94.95 mm to 2324.34 mm and the corresponding rainfall varied from 575.7 mm to 3608.0 mm, respectively. A simple regression model was developed for the watershed to compute runoff from annual rainfall. Average annual runoff estimated for the watershed was around 37% of the rainfall for the study period from 2000-01 to 2013-14. Statistical analysis and test of significance for runoff obtained by NRCS model and regression model did not show any significant difference thus proving that regression model is efficient in runoff computation for ungauged basins. The volume of water accessible for fifty percent dependable flow year is obtained as 2.46 MCM and even if 50% of it can be effectively harnessed the water available in the watershed is 1.23 MCM. The water demand of the area is estimated as 0.148 MCM for domestic purpose and 0.171 MCM for irrigation purpose, which is much lower than the available runoff that can be harnessed from the watershed. Thus there is scope to harvest 1.23 MCM of water which is more than the demand of the watershed. The study reveals that it is feasible to harvest and manage water effectively if its availability and demand are computed accurately.

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Rainfall–runoff estimation using SCS–CN and GIS approach in the Pappiredipatti watershed of the Vaniyar sub basin, South India

Cited by 142 publications

References 6 publications

Rainfall-runoff estimation of Bojiang lake watershed using SCS-CN model coupled with GIS for watershed management

Rainfall-runoff estimation of Bojiang lake watershed using SCS-CN model coupled with GIS for watershed management

Modeling of runoff water and runoff pesticide concentrations in upland bare soil using improved SPEC model

Assessment of Surface Runoff for Tank Watershed in Tamil Nadu Using Hydrologic Modeling

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