The assessment of local and regional impacts of climate change often requires downscaling of general circulation model (GCM) projections from coarser GCM-scale to finer localor catchmentscale spatial resolution. This paper provides an assessment of two downscaling approaches for simulation of daily rainfall over Sydney, Australia. The two downscaling alternatives compared include a multivariate...

The simulations of rainfall from historical data were created in this study by using statistical downscaling. Statistical downscaling techniques are based on a relationship between the variables that are solved by the General Circulation Models (GCMs) and the observed predictions. The Modified Constructed Analog Method (MCAM) is a technique in downscaling estimation, suitable for rainfall simul...

Rainfall is very important parameter in hydrological model. Many techniques and models have been developed for rainfall time series prediction. In this study an artificial neural network (ANN) based model was developed for rainfall time series forecasting. Proposed model used Multilayer perceptron (MLP) network with back propagation algorithm for training. Discharge and rainfall data are took a...

Between the 11 and 13 October 2005 several flash floods were produced along the coast of Catalonia (NE Spain) due to a significant heavy rainfall event. Maximum rainfall achieved values up to 250 mm in 24 h. The total amount recorded during the event in some places was close to 350 mm. Barcelona city was also in the affected area where high rainfall intensities were registered, but just a few s...

Using results from a factor analysis regionalization of nontropical storm convective rainfall over the island of Puerto Rico, a statistical methodology is investigated for its potential to forecast rain events over limited areas. Island regionalization is performed on a 15-yr dataset, while the predictive model is derived from 3 yr of surface and rainfall data. The work is an initial attempt at...

portation. This method of forecasting is not a substitute for present forecasting techniques or thorough analyses of the physical processes of the atmosphere, but if used in conjunction with these it can be a valuable forecasting aid. These results shoulcl be used cautiously unt8il more cases have been studied in order to find those variables which will produce the highest degree of forecasting...

The overall objective is to use diagnostic and modeling methods to decipher and understand physical processes/causal links that connect tropical and North Atlantic sea surface temperature (SST) variations associated with the Atlantic Multidecadal Oscillation (AMO) to changes in atmospheric circulation and warm season precipitation regimes for North America. The ultimate goal is to include repre...

General circulation models (GCMs), used to predict rainfall at a seasonal lead-time, tend to simulate too many rainfall events of too low intensity relative to individual stations within a GCM grid cell. Even if bias in total rainfall is corrected relative to a target location, this distortion of frequency and intensity is expected to adversely affect simulations of crop growth and yield. We pr...

In flow forecasting, additionally to the need of long time series of historic discharges for model setup and calibration, hydrological models also need real-time discharge data for the updating of the initial conditions at the time of the forecasts. The need of data challenges operational flow forecasting at ungauged or poorly gauged sites. This study evaluates the performance of different choi...

artificial neural networks (anns) have become one of the most promising tools for rainfall simulation since a few years ago. however, most of the researchers have focused on rainfall intensity records as well as on watersheds, which generally are utilized as input records of other hydro-meteorological variables. the present study was conducted in kechik station, golestan province (northern iran...