A new approach to regionalisation for prediction of flow characteristics in ungauged catchments is presented. If flows in ungauged catchments are to be predicted using calibrated rainfall-runoff models, regional relationships between the parameters of such models and catchment attributes must be determined. This is only possible with parsimonious models (fewer than about 7 parameters), and even...

Rainfall-Runoff and Signal Separation Problems: The process of converting rainfall into runoff is a highly nonlinear problem due to the soil-water interaction that starts when rainfall reaches the ground. Additional variables to consider are evaporation, transpiration, losses due to vegetation and land use, and the different flow processes that take place in a watershed. For instance, baseflow ...

Computer models are a rapid, inexpensive way to identify agricultural areas with a high potential for P loss, but most models poorly simulate dissolved P release from surface-applied manures to runoff. We developed a simple approach to predict dissolved P release from manures based on observed trends in laboratory extraction of P in dairy, poultry, and swine manures with water over different wa...

climate change is one of the most important factors that will affect different parts of human life on earth and will be adverse effects on environmental resources, economic, social and especially water resources, with changes in rainfall and timing of runoff amount.one of the strategies to mitigate the effects of climate change is to assess its effects on precipitation, temperature and runoff i...

Watershed models such as SWAT (Soil and Water Assessment Tool) simulate water quality impacts of land and water resource management alternatives. To simulate these impacts, long-term daily rainfall data are necessary. In the absence of measured rainfall data, watershed models use weather generators to simulate rainfall events. The objective of this study is to examine several daily precipitatio...

Runoff forecasting in mountainous regions with processed based models is often difficult and inaccurate due to the complexity of the rainfall-runoff relationships and difficulties involved in obtaining the required data. Machine learning models offer an alternative for runoff forecasting in these regions. This paper explores and compares two machine learning methods, support vector regression (...

This paper presents a method for appropriate coupling of deterministic and statistical models. In the decision-support system for the Elbe river, a conceptual rainfall-runoff model is used to obtain the discharge statistics and corresponding average number of flood days, which is a key input variable for a rule-based model for floodplain vegetation. The required quality of the discharge time se...

runoff simulation is a vital issue in water resource planning and management. various models with different levels of accuracy and precision are developed for this purpose considering various prediction time scales. in this paper, two models of ihacres (identification of unit hydrographs and component flows from rainfall, evaporation and streamflow data) and ann (artificial neural network) mode...