Use of high-resolution satellite data, GIS and NRCS-CN technique for the estimation of rainfall-induced run-off in small catchment of Jharkhand India

Tirkey, Anamika Shalini; Pandey, Arvind Chandra; Nathawat, Mahendra Singh

doi:10.1080/10106049.2013.841773

Cited by 21 publications

(8 citation statements)

References 10 publications

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“…The correlation coefficient was found to be 0.91 (due to small study area) (Fig. 9), Tirkey et al [62] also find good coefficient of determination (0.891) for large area therefore SCS-CN model can handle properly small as well as large study area.…”

Section: Rainfall Runoff Correlation Analysismentioning

confidence: 99%

“…Aldoma and Mohamed [3]; simulated rainfall runoff process for Khartoum state (Sudan) using SCS-CN method and EO data as input of model and found SCS-CN method is capable for predicting runoff. Tirkey et al [62], estimated runoff (for Jharkhand, India) using SCS-CN method and compared with observed runoff and found a strong correlation between rainfall and runoff as well as between observed runoff and estimated runoff with high accuracy of runoff estimation by SCS-CN method. Fan et al [13] have developed a simulation model based on the SCS-CN method to analyze the rainfall-runoff relationship in Guangzhou (in southern China).…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Surface Runoff from Semi-arid Ungauged Agricultural Watershed Using SCS-CN Method and Earth Observation Data Sets

Rawat

Singh

2017

Water Conserv Sci Eng

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In the present study, Soil Conservation ServiceCurve Number (SCS-CN) method, Earth Observation (EO) data sets and Geographic Information System (GIS) have been used in order to estimate the surface runoff from Jhagrabaria an agricultural watershed of Allahabad district, Uttar Pradesh (India). LANDSAT-7ETM+, NOAA data and hydrologic soil groups have been used to prepare land use/land cover, rainfall and soil map. The traditional SCS-CN method for calculating the composite CN is very tedious and time demanding process of the hydrologic modeling. Therefore, GIS is now being used in combination with the SCS-CN method. The outcome of work showed 79.35 (CN II ) of normal condition, of dry condition 61.76 (CN I ) and of wet condition 89.84 (CN III ), respectively. This investigation outline that ungauged watershed exhibits an annual average of 14 years runoff volume as 3.58 × 10 6 m 3 from an average annual rainfall of 14 years 110.77 cm and the average annual surface runoff of 14 years was 23.83 cm and annual average runoff coefficient of 14 years was 0.22. The correlation analysis suggests that the strong correlation as R 2 (0.91) was observed between satellite drive rainfall and runoff from SCS-CN method. The developed rainfall-runoff model for the region will be useful to understand the watershed and its runoff flow characteristics.

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Section: Rainfall Runoff Correlation Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Surface Runoff from Semi-arid Ungauged Agricultural Watershed Using SCS-CN Method and Earth Observation Data Sets

Rawat

Singh

2017

Water Conserv Sci Eng

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“…The SCS-N technique presents adequate consequences without the use of complicated statistics. 10,11 Supporting this statement, 12 employed the SCS-CN approach for simulating the once-a-year depth of runoff over an ungauged catchment of Vindhyachal region. They revealed that the SCS-CN approach can be used efficiently to estimate the depth of runoff when there's no good enough hydrological data.…”

Section: Introductionmentioning

confidence: 99%

Rainfall runoff estimation using GIS and SCS-CN method for awash river basin, Ethiopia

Sishah¹

2021

IJH

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Understanding hydrological behavior is an important part of effective watershed management and planning. Runoff resulted from rainfall is a component of hydrological behavior that is needed for efficient water resource planning. In this paper, GIS based SCS-CN runoff simulation model was applied to estimate rainfall runoff in Awash river basin. Global Curve Number (GCN250), Maximum Soil Water Retention (S) and Rainfall was used as an input for SCS-CN runoff simulation model. The final surface runoff values for the Awash river basin were generated on the basis of total annual rainfall and maximum soil water retention potential (S) of the year 2020. Accordingly, a runoff variation that range from 83.95 mm/year to a maximum of 1,416.75 mm/year were observed in the study region. Conversely, recently developed Global Curve Number (GCN250) data was tested with Pearson correlation coefficient to be used as an input for SCS-CN runoff simulation model. In doing so, predicted runoff generated in SCS-CN using GCN250 as a model input was validated with observed runoff obtained from station gauges in the study region. The results of validation show that, predicted runoff was well correlated with observed runoff with correlation coefficient of 0.9253. From this stand point, it is observed that the new GCN250 data can be used as an input for SCS-CN model to estimate rainfall runoff at basin level. Furthermore, correlation analysis was performed to explain the relationship between mean annual rainfall and surface runoff. The relationship between these two variables indicates a strong linear relationship with correlation coefficient of 0.9873.

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“…The hydrological modelling community is progressively benefiting from the incorporation of spatial soil moisture measurements, with a varied degree of success (Brocca et al 2017). Remote sensing has been used for indirect estimation of the CN value by obtaining land use information from satellite images (Tirkey et al 2014), but also for the adjustment of loss parameters (Silvestro et al 2015). Rajib et al (2016) explored the usage of spatially distributed remotely sensed soil moisture in the calibration of a hydrological model.…”

Section: Introductionmentioning

confidence: 99%

NRCS-CN Estimation from Onsite and Remote Sensing Data for Management of a Reservoir in the Eastern Pyrenees

et al. 2020

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On-site and Earth observation (EO) data are used for the calibration of the Natural Resources Conservation Service-Curve Number (NRCS-CN) value in a hydrological simulation model. The model was developed for La Muga catchment (Eastern Pyrenees) highly vulnerable to flood and drought episodes. It is an integral part of a regional reservoir management tool, which aims at minimizing the flood risk, while maximizing the preservation of water storage. The CN values were optimized for five recorded events for the model to match the observed hydrographs at the reservoir, when supported with the measured rainfall intensities. This study also investigates the possibilities of using antecedent moisture conditions (AMC) retrieved from satellite data to inform the selection of the NRCS-CN losses parameter. A good correlation was found between the calibrated CN values and the AMC obtained from satellite data. This correlation highlights the interest in using EO data to update NRCS-CN estimates. This advances in hydrologic-hydraulic coupled modelling combined with new remote sensing datasets present valuable opportunities and potential benefits for flood risk management and water resources preservation.

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Use of high-resolution satellite data, GIS and NRCS-CN technique for the estimation of rainfall-induced run-off in small catchment of Jharkhand India

Cited by 21 publications

References 10 publications

Estimation of Surface Runoff from Semi-arid Ungauged Agricultural Watershed Using SCS-CN Method and Earth Observation Data Sets

Estimation of Surface Runoff from Semi-arid Ungauged Agricultural Watershed Using SCS-CN Method and Earth Observation Data Sets

Rainfall runoff estimation using GIS and SCS-CN method for awash river basin, Ethiopia

NRCS-CN Estimation from Onsite and Remote Sensing Data for Management of a Reservoir in the Eastern Pyrenees

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