A framework for short-term probabilistic forecasting of watershed flood stage conditions using a distributed rainfall-runoff model is proposed. A stochastic rainfall pattern simulation model capable of generating input for a distributed rainfall-runoff model is developed. Generation of rainfall patterns over a 6-hour period is achieved using a translation vector rainfall forecasting process mod...

in this study, several data-driven techniques including system identification, adaptive neuro-fuzzy inference system (anfis), artificial neural network (ann) and wavelet-artificial neural network (wavelet-ann) models were applied to model rainfall-runoff (rr) relationship. for this purpose, the daily stream flow time series of hydrometric station of hajighoshan on gorgan river and the daily rai...

Determination of maximum rivers flow resulted from surface runoff in watersheds without adequate hydrometric records is based on rainfall-runoff models. The short duration rainfalls are one of the basic elements to apply these models. The access to quantitative and intrinsic worth storm data is restricted around the word and in Iran too. There are several empirical and statistical methods to es...

i-Tree Streets/Design and Eco estimate annual rainfall interception by urban forest based on different physics-based water balance models. Streets/Design is based on Xiao et al. (1998), while Eco is based on Wang et al. (2008) and improved by Hirabayashi (2013). In Xiao et al. (1998), annual rainfall interception percentage against the gross precipitation in Sacramento County, CA was estimated ...

this study aims to evaluate effects of two downscaling methods; change-factor and statistical downscaling on therunoff of the azam-harat river located at yazd province (with an arid climate) of iran, under the a2 emission scenariofor the period of 2010-2039. for this purpose, cgcm3-ar4 model; a rainfall-runoff conceptual model, ihacres; twodownscaling models, change factor and lars-wg were appl...

rainfall-runoff relationship is very important in many fields of hydrology such as water supply and water resourcemanagement and there are many models in this field. among these models, the artificial neural network (ann) wasfound suitable for processing rainfall-runoff and opened various approaches in hydrological modeling. in addition,anns are quick and flexible approaches which provide very ...

Catchments that do not behave the way the hydrologist expects, expose the frailties of hydrological science, particularly its unduly simplistic treatment of input and model uncertainty. A conceptual rainfall– runoff model represents a highly simplified hypothesis of the transformation of rainfall into runoff. Sub-grid variability and mis-specification of processes introduce an irreducible model...

Run off resulted from rainfall is the main way of receiving water in most parts of the World. Therefore, prediction of runoff volume resulted from rainfall is getting more and more important in control, harvesting and management of surface water. In this research a number of machine learning and data mining methods including support vector machines, regression trees (CART algorithm), model tree...

The transformation of rainfall into runoff is one of the most important processes in hydrology. In the past few decades, a wide variety of automated or computer-based approaches have been applied to model this process. However, many such approaches have an important limitation in that they treat the rainfall-runoff process as a realization of only a few parameters of linear relationships rather...