Estimation of Time of Concentration for Maryland Streams

2000 ◽  
Vol 1720 (1) ◽  
pp. 95-99 ◽  
Author(s):  
Wilbert O. Thomas ◽  
Michele C. Monde ◽  
Stanley R. Davis
Keyword(s):  
Lag Time ◽  
Channel Length ◽  
Urban Watersheds ◽  
Regression Equations ◽  
Hydrologic Models ◽  
Watershed Characteristics ◽  
Time Of Concentration ◽  
Station Data ◽  
Rural And Urban ◽  
Gauging Station

The time of concentration (TC) is an important input to most hydrologic models and is usually estimated by travel-time computations or by using rainfall-runoff data. Average TCs were determined for 78 rural and urban watersheds in Maryland and related to watershed characteristics using regression analysis. The regression equation is based on the channel length and slope; the percentage of the watershed covered with forests, lakes, and ponds; and the percentage of the watershed with impervious areas. The equation is applicable for estimating TCs for rural and urban watersheds in Maryland with watershed characteristics similar to the gauging station data. TC values computed at Maryland gauging stations were compared with estimates from an equation developed by Kirpich and the Soil Conservation Service (SCS) lag equation, and with basin lag times determined by the U.S. Geological Survey (USGS). The average TC values computed in this analysis were about 5 percent higher than the basin lag-time estimates, which is consistent with the USGS’s definition of lag time. TC estimates from the Kirpich or SCS equations were consistently lower than the values computed from gauging station data. The tendency to underestimate TCs is a major reason why hydrologic models often provide conservative estimates of design discharges compared with regional regression equations and gauging station data.

Simplified estimation of forage degradability in the rumen assuming zero-order degradation kinetics

2008 ◽  
Vol 147 (3) ◽  
pp. 225-240 ◽  
Author(s):  
M. S. DHANOA ◽  
S. LÓPEZ ◽  
R. SANDERSON ◽  
J. FRANCE
Keyword(s):  
Reference Values ◽  
Incubation Time ◽  
Lag Time ◽  
Zero Order ◽  
Regression Equations ◽  
Order Model ◽  
First Order ◽  
Simplified Procedure ◽  
The Impact ◽  
Time Point

SUMMARYIn the present paper, a simplified procedure using few in situ data points is derived and then evaluated (using a large database) against reference values estimated with the standard nylon bag first-order kinetics model. The procedure proposed involved a two-stage mathematical process, with a statistical prediction of some degradation parameters (such as lag time) and then a kinetic model derived by assuming degradation follows zero-order kinetics to determine effective degradability in the rumen (E). In addition to the estimation of washout fraction and discrete lag, which is common to both procedures, the simplified procedure requires measurement of dry matter losses at one incubation time point only. Thus, interference of the animal rumen will be much reduced, which will lead to increased capacity for feed evaluation. Calibration of the zero-order model against the first-order model showed that suitable estimates of E can be obtained with disappearance at 24, 48 or 72 h as the single incubation end time point. The strength of the calibration is such that an end incubation time point as low as 24 h may be sufficient, which may reduce substantially the total incubation time required and thus the impact on the experimental animal. Relevant regression equations to predict reference values of parameters such as lag time or E are also developed and validated.

Influence of bank vegetation on channel morphology in rural and urban watersheds

Geology ◽  
2003 ◽  
Vol 31 (2) ◽  
pp. 147 ◽  
Author(s):  
W.C. Hession ◽  
J.E. Pizzuto ◽  
T.E. Johnson ◽  
R.J. Horwitz
Keyword(s):  
Channel Morphology ◽  
Urban Watersheds ◽  
Rural And Urban

scholarly journals SWAT use of gridded observations for simulating runoff – a Vietnam river basin study

2011 ◽  
Vol 8 (6) ◽  
pp. 10679-10705 ◽  
Author(s):  
M. T. Vu ◽  
S. V. Raghavan ◽  
S. Y. Liong
Keyword(s):  
Swat Model ◽  
Model Performance ◽  
Tropical Rainfall Measuring Mission ◽  
Global Precipitation Climatology Project ◽  
Data Availability ◽  
Coefficient Of Determination ◽  
Reanalysis Dataset ◽  
Station Data ◽  
Rural And Urban ◽  
Grid Points

Abstract. Many research studies that focus on basin hydrology have used the SWAT model to simulate runoff. One common practice in calibrating the SWAT model is the application of station data rainfall to simulate runoff. But over regions lacking robust station data, there is a problem of applying the model to study the hydrological responses. For some countries and remote areas, the rainfall data availability might be a constraint due to many different reasons such as lacking of technology, war time and financial limitation that lead to difficulty in constructing the runoff data. To overcome such a limitation, this research study uses some of the available globally gridded high resolution precipitation datasets to simulate runoff. Five popular gridded observation precipitation datasets: (1) Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE), (2) Tropical Rainfall Measuring Mission (TRMM), (3) Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN), (4) Global Precipitation Climatology Project (GPCP), (5) modified Global Historical Climatology Network version 2 (GHCN2) and one reanalysis dataset National Centers for Environment Prediction/National Center for Atmospheric Research (NCEP/NCAR) are used to simulate runoff over the Dakbla River (a small tributary of the Mekong River) in Vietnam. Wherever possible, available station data are also used for comparison. Bilinear interpolation of these gridded datasets is used to input the precipitation data at the closest grid points to the station locations. Sensitivity Analysis and Auto-calibration are performed for the SWAT model. The Nash-Sutcliffe Efficiency (NSE) and Coefficient of Determination (R2) indices are used to benchmark the model performance. This entails a good understanding of the response of the hydrological model to different datasets and a quantification of the uncertainties in these datasets. Such a methodology is also useful for planning on Rainfall-runoff and even reservoir/river management both at rural and urban scales.

scholarly journals A Combination between Synthetic Unit Hydrograph (SCS) and Rational Method in a similar Conditions Water Shed

2021 ◽  
Vol 9 (3) ◽  
Author(s):  
Mona Fathi ◽  
Neveen B. Abelmageed ◽  
M. Hassan
Keyword(s):  
Rational Method ◽  
Rainfall Data ◽  
Return Periods ◽  
Unit Hydrograph ◽  
Engineering Applications ◽  
Watershed Characteristics ◽  
Time Of Concentration ◽  
Excess Water ◽  
The Difference ◽  
Peak Runoff

Studying watershed characteristics and choosing the most applicable methods to determine the amount of access rainfall that ran off is very important in many engineering applications, especially hydrology applications. That is to know the more suitable methods for protection against floods and to maximize benefits from the excess water. This study aims to establish a relation between the rational method and the SCS method. A subbasin in Wadi Dahab in Sinai, Egypt is investigated as a study area. To achieve the study aims, HEC-WMS software is chosen, which can analyze a watershed by using DEM and delineating basin. It calculates also important watershed parameters like area, runoff distances, and slope. The rainfall data is compiled and arranged. A statical analysis is executed to obtain the IDF curves. Hyfran-plus software is employed to locate the maximum depths for different return periods. Various values for the time of concentration are studied. It is concluded that the difference between the rational and SCS methods is great for the time of concentration till 2 hours, then it decreases obviously from 2 till 6 hours. Also, it is concluded that the difference between the two methods is bigger for the small return periods of 2 and 5 years for all values of the time of concentration. Employing the obtained equations, the peak runoff for one of the two methods (the rational and SCS methods) can be calculated knowing the time of concentration and the peak runoff for the second method.  

scholarly journals Estimating longitudinal profiles of ordinary water level using gauging station data - a case study: Shonai River, Japan.

2018 ◽  
Vol 21 (1) ◽  
pp. 53-60
Author(s):  
Shigenari MIYAWAKI ◽  
Shigeya NAGAYAMA ◽  
Yasumitsu KATO ◽  
Hanae ITO ◽  
Yuichi KAYABA
Keyword(s):  
Water Level ◽  
Ordinary Water ◽  
Station Data ◽  
Longitudinal Profiles ◽  
Gauging Station

scholarly journals Calculation of time of concentration for hydrologic design and analysis using geographic information system vector objects

2002 ◽  
Vol 4 (2) ◽  
pp. 75-81 ◽  
Author(s):  
Jonathan I. Green ◽  
E. James Nelson
Keyword(s):  
Lag Time ◽  
Flow Path ◽  
Specific Flow ◽  
Analysis And Design ◽  
Time Of Concentration ◽  
Concentrated Flow ◽  
Hydrologic Analysis ◽  
Two Parameters ◽  
Unit Hydrographs ◽  
Hydrologic Design

Synthetic unit hydrographs are commonly used to estimate runoff from rainfall events in a hydrologic analysis. A key parameter required as part of any hydrologic analysis using a synthetic unit hydrograph is time of concentration or lag time. Generally, equations used to compute time of concentration or lag time are empirically derived from basin parameters such as area, slope, and a specific flow path length. A more realistic method for determination of flow path travel time is to divide the flow path according to different hydraulic conditions such as sheet flow, shallow concentrated flow and open channel flow as specified in the NRCS method using TR55. Such equations are all based on flow length and the slope of the flow path, two parameters that are easily calculated from GIS vector objects. A method is presented that uses GIS vector objects with equations assigned for the calculation of time of concentration or lag time for use in hydrologic analysis and design.

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