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The runoff curve number (also called a curve number or simply CN) is an empirical parameter used in hydrology for predicting direct runoff or infiltration from rainfall excess. [1] The curve number method was developed by the USDA Natural Resources Conservation Service , which was formerly called the Soil Conservation Service or SCS — the ...
[8] The runoff coefficients for different surface types on a site can be multiplied with the area for each surface along with the annual precipitation to generate a rough runoff footprint. If the runoff coefficient and areas of proposed stormwater green solutions like rain gardens and bioswales for the site are known, the reduction in overall ...
A runoff models or rainfall-runoff model describes how rainfall is converted into runoff in a drainage basin (catchment area or watershed). More precisely, it produces a surface runoff hydrograph in response to a rainfall event, represented by and input as a hyetograph. Rainfall-runoff models need to be calibrated before they can be used.
Typically one would convert a Runoff and Transport file to SWMM 5 or a Runoff and Extran File to SWMM 5. If there is a combination of a SWMM 4 Runoff, Transport and Extran network then it will have to be converted in pieces and the two data sets will have to be copied and pasted together to make one SWMM 5 data set.
The Kling–Gupta efficiency (KGE) is a goodness-of-fit indicator widely used in the hydrologic sciences for comparing simulations to observations. It was created by hydrologic scientists Harald Kling and Hoshin Vijai Gupta. [1]
SELDM provides input statistics for precipitation, prestorm flow, runoff coefficients, and concentrations of selected water-quality constituents from National datasets. Input statistics may be selected on the basis of the latitude, longitude, and physical characteristics of the site of interest and the upstream basin.
The linear-reservoir model (or Nash model) is widely used for rainfall-runoff analysis. The model uses a cascade of linear reservoirs along with a constant first-order storage coefficient, K, to predict the outflow from each reservoir (which is then used as the input to the next in the series).
The Nash–Sutcliffe coefficient masks important behaviors that if re-cast can aid in the interpretation of the different sources of model behavior in terms of bias, random, and other components. [11] The alternate Kling–Gupta efficiency is intended to improve upon NSE by incorporating bias and variance terms. [12]