The accurate methods for the forecasting of hydrological characteristics are significantly important water resource management and environmental aspects. In this study, a novel approach daily streamflow discharge data is proposed. Streamflow discharge, temperature, precipitation were used feature extraction which systematically employed studies. While correlation-based selection (CFS) was selec...

Uncertainty due to resolution of current satellite-based rainfall products is believed to be an important source of error in applications of hydrologic modeling and forecasting systems. A method to account for the input’s resolution and to accurately evaluate the hydrologic utility of satellite rainfall estimates is devised and analyzed herein. A radar-based Multisensor Precipitation Estimator ...

[1] Annual minimum, median, and maximum daily streamflow for 400 sites in the conterminous United States (U.S.), measured during 1941 – 1999, were examined to identify the temporal and spatial character of changes in streamflow statistics. Results indicate a noticeable increase in annual minimum and median daily streamflow around 1970, and a less significant mixed pattern of increases and decre...

Hydrological phenomena are characterized by the formation of a non-linear dynamic system, and streamflows not unrelated to this premise. Data assimilation offers an alternative for flow forecasting using Ensemble Kalman Filter, given its relative ease implementation lower computational effort in comparison with other techniques. The hourly streamflow Chapalagana station was forecasted based on ...

Abstract. This study proposes a comprehensive benchmark dataset for streamflow forecasting, WaterBench-Iowa, that follows FAIR (findability, accessibility, interoperability, and reuse) data principles is prepared with focus on convenience utilizing in data-driven machine learning studies, provides performance state of art deep architectures the comparative analysis. By aggregating datasets stre...

In this work, we suggest that the poorer results obtained with particle swarm optimization (PSO) in some previous studies should be attributed to the cross-validation scheme commonly employed to improve generalization of PSO-trained neural network river forecasting (NNRF) models. Crossvalidation entails splitting the training dataset into two, and accepting particle position updates only if fit...

[1] Given the importance of Upper Colorado River Basin (UCRB) snowpack as the primary driver of streamflow (water supply) for the southwestern United States, the identification of Pacific Ocean climatic drivers (e.g., sea surface temperature (SST) variability) may prove valuable in long‐lead‐time forecasting of snowpack in this critical region. Previous research efforts have identified El Niño–...

There is increasing consensus in the hydrologic literature that an appropriate framework for streamflow forecasting and simulation should include explicit recognition of forcing, parameter and model structural error. This paper presents a novel Markov Chain Monte Carlo (MCMC) sam-pler, entitled DiffeRential Evolution Adaptive Metropolis (DREAM), that is especially designed to efficiently estima...

Waterborne gastrointestinal (GI) illnesses demonstrate seasonal increases associated with water quality and meteorological characteristics. However, few studies have been conducted on the association of hydrological parameters, such as streamflow, and seasonality of GI illnesses. Streamflow is correlated with biological contamination and can be used as proxy for drinking water contamination. We...