Abstract. Deep learning is becoming an increasingly important way to produce accurate hydrological predictions across a wide range of spatial and temporal scales. Uncertainty estimations are critical for actionable prediction, while standardized community benchmarks part model development research, similar tools benchmarking uncertainty estimation lacking. This contribution demonstrates that ca...

abstract prediction of input flow into water resources is regarded as one of the most important issues in optimum planning and management in producing electro-water energy and optimum allocation of water into different consumption sources. different parameters affect on input discharge into dams. climate variables including temperature and rainfall have the most effect on input runoff rate to w...

due to the importance of the role of land use changes on runoff changes, several studies have taken place to develop models with simulating land use changes. in this study, the l-thia model was used for estimating the runoff made by land use changes in chalousrud watershed. results of runoff modeling using l-thia model showed that runoff depth increased from 422.98mm to 809.168mm during 1984 to...

The calculation of loss is vital for design flood estimation models and in order to estimate continuing loss (CL), proportional loss (PL) and volumetric runoff coefficient, the surface runoff has to be separated from the total given in a stream flow hydrograph. To obtain the volume of surface runoff from the streamflow hydrograph, baseflow separation becomes necessary and in this paper a few ba...

Estimation of stormwater pollutants is a pre-requisite for the protection and improvement of the aquatic environment and for appropriate management options. The usual practice for the stormwater quality prediction is performed through water quality modeling. However, the accuracy of the prediction by the models depends on the proper estimation of model parameters. This paper presents the estima...