Modeling rainfall-runoff processes is a very challenging task due to data collection, time, money, and technology constraints. Artificial neural networks (ANNs) are modeling tools that can quickly adapt and learn input-output relationships for many different engineering problems. An Elman-type recurrent ANN was trained to simulate observed streamflow for Fountain Creek at Pueblo, CO, using vary...

This paper presents the combination of different precipitation data sets and the distributed hydrological model, in order to examine the flood runoff reproductivity of scattered observation catchments. The precipitation data sets were obtained from observation using rain-gages, satellite based estimate (TRMM), and numerical weather prediction model (NWP), then were coupled with the super tank m...

Rainfall-runoff models are used for various applications, ranging from the estimation of catchment water yield to the estimation of land use and climate change impacts on runoff characteristics. Most rainfall-runoff models can be calibrated successfully to reproduce the recorded runoff, however it is difficult to determine appropriate parameter values to use for modelling runoff in an ungauged ...

Rainfall is a driving force for the transport of environmental contaminants from agricultural soils to surficial water bodies via surface runoff. The objective of this study was to characterize the effects of antecedent soil moisture content on the fate and transport of surface applied commercial urea, a common form of nitrogen (N) fertilizer, following a rainfall event that occurs within 24 hr...

Using long-term data on rainfall and annual runoff, an investigation was made of the spatial and temporal variability of rainfall and runoff in the Casamance River basin located in southern Senegal, West Africa. A 5-year moving average was employed to identify trends in the data. Monthly and annual rainfall tends to have been decreasing, and the annual maximum temperature rising from around the...

Two important applications of rainfall-runoff models are forecasting and simulation. At present, rainfall-runoff models based on artificial intelligence methods are built basically for short-term forecasting purposes and these models are not very effective for simulation purposes. This study explores the applicability and effectiveness of adaptive neuro-fuzzy-system-based rainfall-runoff models...

Soil and water conservation measures can impact hydrological cycle, but quantitative analysis of this impact is still difficult in a watershed scale. To assess the effect quantitatively, a three-dimensional finite-difference groundwater flow model (MODFLOW) with a surface runoff model-the Soil Conservation Service (SCS) were calibrated and applied based on the artificial rainfall experiments. T...

The characteristics of rainfall-runoff are important aspects of hydrological processes. In this study, rainfall-runoff processes and soil moisture dynamics at different soil depths and slope positions of grassland with two different row spacings (5 cm and 10 cm, respectively, referred to as R5 and R10) were analyzed, by means of a solution of rainfall simulation experiments. Bare land was also ...

This paper found rainfall and runoff was in a state of chaos in Poincare section and developed a method for constructing a relationship model of rainfall-runoff based on time series analysis technology. It aimed at estimating and discussing the relationship of rainfall-runoff time series by Euclidean distance (n>2). A linear model was well built to illustrate time-correlation characteristics wi...