Influence of mesh structure on 2D full shallow water equations and SCS Curve Number simulation of rainfall/runoff events

2012 ◽  
Vol 448-449 ◽  
pp. 39-59 ◽  
Author(s):  
Daniel Caviedes-Voullième ◽  
Pilar García-Navarro ◽  
Javier Murillo
Keyword(s):  
Shallow Water ◽  
Shallow Water Equations ◽  
Curve Number ◽  
Rainfall Runoff ◽  
Mesh Structure ◽  
Scs Curve Number ◽  
Runoff Events

GPU implementation of the 2D shallow water equations for the simulation of rainfall/runoff events

2015 ◽  
Vol 74 (11) ◽  
pp. 7295-7305 ◽  
Author(s):  
Asier Lacasta ◽  
Mario Morales-Hernández ◽  
Javier Murillo ◽  
Pilar García-Navarro
Keyword(s):  
Shallow Water ◽  
Shallow Water Equations ◽  
Rainfall Runoff ◽  
Gpu Implementation ◽  
Runoff Events ◽  
2D Shallow Water Equations

scholarly journals Initial abstraction ratio and Curve Number estimation using rainfall and runoff data from a tropical watershed

RBRH ◽  
2019 ◽  
Vol 24 ◽  
Author(s):  
Luiz Claudio Galvão do Valle Junior ◽  
Dulce Buchala Bicca Rodrigues ◽  
Paulo Tarso Sanches de Oliveira
Keyword(s):  
Conversion Factor ◽  
Poor Performance ◽  
Curve Number ◽  
Storm Events ◽  
Rainfall Runoff ◽  
Initial Abstraction ◽  
Rainfall Depth ◽  
Standard Value ◽  
Tropical Watershed ◽  
Runoff Events

ABSTRACT The Curve Number (CN) method is extensively used for predict surface runoff from storm events. However, remain some uncertainties in the method, such as in the use of an initial abstraction (λ) standard value of 0.2 and on the choice of the most suitable CN values. Here, we compute λ and CN values using rainfall and runoff data to a rural basin located in Midwestern Brazil. We used 30 observed rainfall-runoff events with rainfall depth greater than 25 mm to derive associated CN values using five statistical methods. We noted λ values ranging from 0.005 to 0.455, with a median of 0.045, suggesting the use of λ = 0.05 instead of 0.2. We found a S0.2 to S0.05 conversion factor of 2.865. We also found negative values of Nash-Sutcliffe Efficiency (to the estimated and observed runoff). Therefore, our findings indicated that the CN method was not suitable to estimate runoff in the studied basin. This poor performance suggests that the runoff mechanisms in the studied area are dominated by subsurface stormflow.

scholarly journals Determination of Curve Number for snowmelt-runoff floods in a small catchment

2015 ◽  
Vol 370 ◽  
pp. 167-170 ◽  
Author(s):  
L. Hejduk ◽  
A. Hejduk ◽  
K. Banasik
Keyword(s):  
Soil Conservation ◽  
Curve Number ◽  
Rainfall Runoff ◽  
Snowmelt Runoff ◽  
Ungauged Catchments ◽  
Department Of Agriculture ◽  
Small Catchment ◽  
Rainfall Depth ◽  
Runoff Events

Abstract. One of the widely used methods for predicting flood runoff depth from ungauged catchments is the curve number (CN) method, developed by Soil Conservation Service (SCS) of US Department of Agriculture. The CN parameter can be computed directly from recorded rainfall depths and direct runoff volumes in case of existing data. In presented investigations, the CN parameter has been computed for snowmelt-runoff events based on snowmelt and rainfall measurements. All required data has been gathered for a small agricultural catchment (A = 23.4 km2) of Zagożdżonka river, located in Central Poland. The CN number received from 28 snowmelt-runoff events has been compared with CN computed from rainfall-runoff events for the same catchment. The CN parameter, estimated empirically varies from 64.0 to 94.8. The relation between CN and snowmelt depth was investigated in a similar procedure to relation between CN and rainfall depth.

scholarly journals Simulation of Rainfall Runoff Process Using HEC-HMS Model for Upper Godavari Basin Maharashtra, India

2019 ◽  
Vol 4 (4) ◽  
pp. 102-107 ◽  
Author(s):  
Vaishnavi Kiran Patil ◽  
Vidya R. Saraf ◽  
Omkesh V. Karad ◽  
Swapnil B. Ghodke ◽  
Dnyanesvar Gore ◽  
...  
Keyword(s):  
Management Practices ◽  
Hydrological Modeling ◽  
Curve Number ◽  
Rainfall Runoff ◽  
Watershed Model ◽  
Scs Curve Number ◽  
Muskingum Routing ◽  
Routing Methods ◽  
Peak Runoff ◽  
Godavari Basin

The Hydrologic Engineering Centers Hydrologic Modeling System (HEC-HMS) is a popularly used watershed model to simulate rainfall- runoff process. Hydrological modeling is a commonly used tool to estimate the basin’s hydrological response due to precipitation. It allows to predict the hydrologic response to various watershed management practices and to have a better understanding of the impacts of these practices. It is evident from the extensive review of the literature that the studies on comparative assessment of watershed models for hydrologic simulations are very much limited in developing countries including India. In this study, modified SCS Curve Number method is applied to determine loss model as a major component in rainfall-runoff modeling. The study of HEC-HMS model is used to simulate rainfallrunoff process in Nashik region (Upper Godavari basin), Maharashtra. To compute runoff volume, peak runoff rate, and flow routing methods SCS curve number, SCS unit hydrograph, Exponential recession and Muskingum routing methods are chosen, respectively. The results of the present study indicate that HEC-HMS tool applied to watershed proved to be useful in achieving the various objectives. The study confirmed a significant increase in runoff as a result of urbanization. It is a powerful tool for flood forecasting  Index

scholarly journals Curve Number Estimation for a Small Urban Catchment from Recorded Rainfall-Runoff Events

2014 ◽  
Vol 40 (3) ◽  
pp. 75-86 ◽  
Author(s):  
Kazimierz Banasik ◽  
Adam Krajewski ◽  
Anna Sikorska ◽  
Leszek Hejduk
Keyword(s):  
Urban Areas ◽  
Land Use Changes ◽  
Low Impact Development ◽  
Curve Number ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Department Of Agriculture ◽  
Urban Catchment ◽  
Urban Catchments ◽  
Runoff Events

Abstract Runoff estimation is a key component in various hydrological considerations. Estimation of storm runoff is especially important for the effective design of hydraulic and road structures, for the flood flow management, as well as for the analysis of land use changes, i.e. urbanization or low impact development of urban areas. The curve number (CN) method, developed by Soil Conservation Service (SCS) of the U.S. Department of Agriculture for predicting the flood runoff depth from ungauged catchments, has been in continuous use for ca. 60 years. This method has not been extensively tested in Poland, especially in small urban catchments, because of lack of data. In this study, 39 rainfall-runoff events, collected during four years (2009–2012) in a small (A=28.7 km2), urban catchment of Służew Creek in southwest part of Warsaw were used, with the aim of determining the CNs and to check its applicability to ungauged urban areas. The parameters CN, estimated empirically, vary from 65.1 to 95.0, decreasing with rainfall size and, when sorted rainfall and runoff separately, reaching the value from 67 to 74 for large rainfall events.

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

2021 ◽  
Vol 16 (4) ◽  
pp. 1
Author(s):  
Zandra Almeida da Cunha ◽  
Samuel Beskow ◽  
Maíra Martim de Moura ◽  
Tamara Leitzke Caldeira Beskow ◽  
Carlos Rogério de Mello
Keyword(s):  
Curve Number ◽  
Rainfall Runoff ◽  
Antecedent Moisture ◽  
Pampa Biome ◽  
Definition Of ◽  
Service Curve ◽  
The Impact ◽  
Runoff Events

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.

CN-China: Revised runoff curve number by using rainfall-runoff events data in China

2020 ◽  
Vol 177 ◽  
pp. 115767 ◽  
Author(s):  
Huishu Lian ◽  
Haw Yen ◽  
Jr-Chuan Huang ◽  
Qingyu Feng ◽  
Lihuan Qin ◽  
...  
Keyword(s):  
Curve Number ◽  
Rainfall Runoff ◽  
Runoff Curve Number ◽  
Events Data ◽  
Runoff Events

MAPPING TIDAL CURRENTS AND RESIDUAL CURRENTS BY USE OF COASTAL ACOUSTIC TOMOGRAPHY

2017 ◽  
Author(s):  
Xiao-Hua Zhu ◽  
Xiao-Hua Zhu ◽  
Ze-Nan Zhu ◽  
Ze-Nan Zhu ◽  
Xinyu Guo ◽  
...  
Keyword(s):  
Shallow Water ◽  
Shallow Water Equations ◽  
Mean Amplitude ◽  
Momentum Equation ◽  
Tidal Currents ◽  
Acoustic Tomography ◽  
Mean Values ◽  
Residual Currents ◽  
Water Depths ◽  
Deep Area

A coastal acoustic tomography (CAT) experiment for mapping the tidal currents in the Zhitouyang Bay was successfully carried out with seven acoustic stations during July 12 to 13, 2009. The horizontal distributions of tidal current in the tomography domain are calculated by the inverse analysis in which the travel time differences for sound traveling reciprocally are used as data. Spatial mean amplitude ratios M2 : M4 : M6 are 1.00 : 0.15 : 0.11. The shallow-water equations are used to analyze the generation mechanisms of M4 and M6. In the deep area, velocity amplitudes of M4 measured by CAT agree well with those of M4 predicted by the advection terms in the shallow water equations, indicating that M4 in the deep area where water depths are larger than 60 m is predominantly generated by the advection terms. M6 measured by CAT and M6 predicted by the nonlinear quadratic bottom friction terms agree well in the area where water depths are less than 20 m, indicating that friction mechanisms are predominant for generating M6 in the shallow area. Dynamic analysis of the residual currents using the tidally averaged momentum equation shows that spatial mean values of the horizontal pressure gradient due to residual sea level and of the advection of residual currents together contribute about 75% of the spatial mean values of the advection by the tidal currents, indicating that residual currents in this bay are induced mainly by the nonlinear effects of tidal currents.

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