Improvement of the integration of Soil Moisture Accounting into the NRCS-CN model

Durán‐Barroso, Pablo; González, Javier; Valdés, Juan B.

doi:10.1016/j.jhydrol.2016.09.053

Cited by 15 publications

(2 citation statements)

References 52 publications

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“…Alves (2016) comments that for CNI antecedent moisture conditions, initial abstraction values are higher because the soil is dry and water infi ltration is higher, which is not the case for the conditions CNII and CNIII. Durán-Barroso et al (2016) highlight that the SCS-CN method is very sensitive to the CN value, i.e., small variations of this parameter interfere considerably with maximum fl ows. This is because the CN value is directly related to factors that aff ect surface runoff , such as soil type, land use and occupation, and soil moisture.…”

Section: Maximum Observed and Estimated Flow For Diff Erent Return Periodsmentioning

confidence: 96%

Maximum Flow Study by the Hydrogram Method for a Watershed in the South of Santa Catarina, Brazil

Aguiar

Back²,

Recio

et al. 2021

R. Ci. Amb.

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CN) method is used. To estimate the maximum ow in the watershed, three background moisture scenarios (CNI, CNII and CNIII) and six empirical equations proposed for rural watersheds were adopted to calculate the concentration time. To evaluate the observed upstream ow, a 34-years historical series was used, measured at the São Martinho downstream uviometric station (code 84598002), located in the outlet section of the study watershed.  e results showed that the maximum ows estimated by the SCS-CN method for the conditions of CNII and CNIII had greater diff erences (-198% and -287%) compared to the observed ow.  e smallest diff erences were veri ed for the CNI condition for all the analyzed return periods (2, 5, 10, 15, 20, 25, 50 and 100). Also, it was observed that, the longer is the return period, smaller the diff erence among the maximum observed and estimated ows, and, the smallest was veri ed for the 50year period, indicating that this is the most appropriate for studies of extreme events in the study watershed.

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Section: Maximum Observed and Estimated Flow For Diff Erent Return Periodsmentioning

confidence: 96%

Maximum Flow Study by the Hydrogram Method for a Watershed in the South of Santa Catarina, Brazil

Aguiar

Back²,

Recio

et al. 2021

R. Ci. Amb.

View full text Add to dashboard Cite

show abstract

“…The evaluation proposed by Hawkins (1993) have been implemented in different parts of the world which had runoff observed data as USA (Hawkins, 1993;Durán-Barroso et al, 2016;Santikari & Murdoch, 2019), Argentina ), Mexico (Ibáñez-Castillo et al, 2014Pérez Nieto et al, 2015), Brasil (da Cunha et al, 2019Durán-Barroso et al, 2016) India (Gundalia & Dholakia, 2014;Lal et al, 2015Lal et al, , 2017Lal et al, , 2019 and South Korea (Ajmal et al, 2015); in all these studies a great difference between the CN of SCS-CN model and the CN derived from observed data was found, showing that SCS-CN method cannot calculate runoff properly.…”

Section: Introductionmentioning

confidence: 99%

Runoff Curve Number (CN model) Evaluation Under Tropical Conditions

Mosquera

Villada²,

Prieto³

2022

Earth sci. res. j.

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In tropical countries the Curve Number method (CN) of the Soil Conservation Service (SCS) is widely used in civil engineering (to calculate drainage infrastructure) as well as in agricultural and environmental studies. However, little is known about the accuracy of CN method in tropical watersheds. To reveal the CN method accuracy, this study compares the CN method results with field data measured in an Andean micro watershed. For this, CN values for the tropical micro watershed "La Vega" were identified using the tables proposed by the SCS and its respective moisture and slope corrections (CNAMC2S); subsequently CN values were derived from 55 events (CNQ) in which runoff and rainfall were measured. It was observed a different of 27% between CNQ and CNAMC2S (CNQ = 80, CNAMC2S 58.1). According to that, the data shows that in the tropical micro watershed evaluated in this study, the SCS method overestimated runoff. The CN model of the SCS is strongly influenced by the antecedent humidity and its impact becomes stronger after high rainfalls records; however, the antecedent humidity influence was not observed in measured runoff data. Despite the CN – SCS model did not present a relationship between CN and precipitation, this relationship was observed in measure data. This study shows that CN method has some inaccuracies and it requires further studies to know its applicability in tropical conditions.

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Rainstorm‐generated sediment yield model based on soil moisture proxies (SMP)

Gupta

Singh

Tyagi

et al. 2020

Hydrological Processes

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This study develops improved Soil Moisture Proxies (SMP) based suspended sediment yield (SMPSY) models corresponding to three antecedent moisture conditions (AMCs) (i.e., AMC‐I‐AMC‐III) by coupling the improved initial abstraction (Ia‐λ) model, the SMA procedure and the SMP concept for modelling the rainfall generated suspended sediment yield. The SMPSY models specifically incorporate a watershed storage index (S) model to accentuate the transformation from storm to storm and to avoid the sudden jumps in sediment yield computation. The workability of the SMPSY models is tested using a large dataset of rainfall and sediment yield (98 storm events) from twelve small watersheds and a comparison has been made with the existing MSY model. The goodness‐of‐fit (GOF) statistics is evaluated in terms of the Nash Sutcliffe efficiency (NSE), and error indices, i.e., root mean square error (RMSE), normalized root mean square error (nRMSE), standard error (SE), mean absolute error (MAE), and RMSE‐observations standard deviation ratio (RSR). The NSE values vary from 74.31% to 96.57% and from 75.21% to 91.78%, respectively for the SPMSY and MSY model. The NSE statistics indicate that the SMPSY model has lower uncertainty in simulating sediment yield as compared to the MSY model. The error indices are lower for the SMPSY model than the MSY model for most of the watersheds. These results show that the SMPSY model has less uncertainty and performs better than the MSY model. A sensitivity analysis of the SMPSY model shows that the parameter β is most sensitive followed by parameter S, α and A. Overall, the results show that the characterization of soil moisture variability in terms of SMPs and incorporation of improved delivery ratio and runoff coefficient relationship improves the simulation of the erosion and sediment yield generation process.

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Improvement of the integration of Soil Moisture Accounting into the NRCS-CN model

Cited by 15 publications

References 52 publications

Maximum Flow Study by the Hydrogram Method for a Watershed in the South of Santa Catarina, Brazil

Maximum Flow Study by the Hydrogram Method for a Watershed in the South of Santa Catarina, Brazil

Runoff Curve Number (CN model) Evaluation Under Tropical Conditions

Rainstorm‐generated sediment yield model based on soil moisture proxies (SMP)

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