In this paper, we analyzed the streamflow droughts based on the Percent of Normal Index (PNI) and clustering approaches in the Kurdistan Province, Iran, over the 1981-2010. The Kolmogorov-Smirnov (K-S) test was considered for streamflow time series and the results of K-S test indicated that streamflow time series did follow the normal distribution at the 0.05 significance level. Generally, the ...

[1] We explore a strategy for long-range hydrologic forecasting that uses ensemble climate model forecasts as input to a macroscale hydrologic model to produce runoff and streamflow forecasts at spatial and temporal scales appropriate for water management. Monthly ensemble climate model forecasts produced by the National Centers for Environmental Prediction/Climate Prediction Center global spec...

We present an experiment on fifty multilayer perceptrons trained for streamflow forecasting on three watersheds using bootstrapped input series. This type of neural network is common in hydrology and using multiple training repetitions (ensembling) is a popular practice: the information issued by the ensemble is then aggregated and considered to be the final output. Some authors proposed that t...

We explore a strategy for long-range hydrologic forecasting that uses ensemble climate model forecasts as input to a macroscale hydrologic model to produce runoff and streamflow forecasts at spatial and temporal scales appropriate for water management. Coarse-scale monthly ensemble climate model forecasts produced by the NCEP/CPC Global Spectral Model (GSM) are bias corrected, downscaled to 1/8...

Current predictions indicate that the climate in Australia will become more variable and extreme. This will increase pressure on fragile natural resources and sound water resource management will become increasingly important. Forecasting is therefore essential in both agriculture and natural resource management. Is the current hydrological science up to this task and what tools are available f...