[1] Effective control of nonpoint source contaminants in runoff from urbanized watersheds requires knowledge about the locations of runoff source areas under a variety of conditions. Physical monitoring of spatially variant processes, such as runoff production form variable source areas, is time-consuming and expensive. Thus modeling the processes may provide a valuable cost-effective alternati...

Understanding the variations of streamflow of rivers is an important prerequisite for designing hydraulic structures as well as managing surface water resources in basins. An overview of the impact of climate change on the streamflow in the Hablehroud River, the main river of a semi-arid basin in north-central Iran, is provided. Using the LARS-WG statistical downscaling model, the outputs of Ha...

Streamflow forecasting is very important for water resources management and flood defence. In this paper two forecasting methods are compared: ARIMA versus a multilayer perceptron neural network. This comparison is done by forecasting a streamflow of a Mexican river. Surprising results showed that in a monthly basis, ARIMA has lower prediction errors than this Neural Network. Key-Words: Auto re...

in this research prediction methods of artificial neural network and chaos theory are employed to predict daily, weekly and monthly runoff. for this, runoff series data observed at pole-kohneh located in the qareh-soo river. the nonlinear predictions of chaos are found to be in close agreement with the observed runoff, with high correlation coefficient for daily and weekly time scales. predicte...

The Hydrological Recursive Model (HRM), a conceptual rainfall-runoff model, was applied for local and regional simulation of hourly discharges in the transnational Alzette River basin (Luxembourg-France-Belgium). The model was calibrated for a range of various sub-basins with a view to analysing its ability to reproduce the variability of basin responses during flood generation. The regionaliza...

It is essential to examine the sensitivity of hydrologic responses to climate and landuse change across different physiographic regions in order to formulate sound water management policies for local response to projected global change. This study used the U.S. Geological Survey's Precipitation Runoff Modeling System (PRMS) model to examine the potential impacts of climate and landuse changes o...

In this paper, we analyze dynamics of runoff data from actual hydrologic station on the basis of the chaos theory. Through analyzing Characteristics of power spectrum, geometric structure of attractors and calculating Lyapunov index, it confirmed that actual runoff has chaos dynamics behavior. Local prediction model based on chaotic runoff data is inferior for heavy computation load and poor ac...

Hydrology helps determine the character of wetlands; wetlands, in turn, regulate water flow, which influences regional hydrology. To understand these dynamics, we studied the Naoli basin where, from 1954 to 2005, intensive marshland cultivation took place, and the watershed's wetland area declined from 94.4 × 10(4)ha to 17.8 × 10(4)ha. More than 80% of the wetland area loss was due to conversio...

Abstract Rainfall–runoff models are valuable tools for flood forecasting, management of water resources, and drought warning. With the advancement in space technology, a plethora satellite precipitation products (SPPs) available publicly. However, application data data-driven rainfall–runoff model is emerging requires careful investigation. In this work, two rainfall sets, namely Global Precipi...