Abstract: This paper presents a comprehensive review of a fundamental and challenging issue in hydrology: the regionalization of streamflow and its advances over the last two decades, specifically 1990–2011. This includes a discussion ...

Abstract: Missing values in in situ monitoring data is a problem often encountered in hydrologic research and applications. Values in a data set may be missing because of sensor error or failure of data recording devices. Whereas ...

Abstract: This paper examines the potential of the support vector machine (SVM) in long-term prediction of lake water levels. Lake Erie mean monthly water levels from 1918 to 2001 are used to predict future water levels up to

Abstract: Hydrologic extremes such as severe storms, floods, and droughts are inherently seasonal in nature and remain the main concern in designing hydrometric networks. In general, hydrometric networks have been designed without ...

Abstract: An experiment on predicting multivariate water resource time series, specifically the prediction of hydropower reservoir inflow using temporal neural networks, is presented. This paper focuses on dynamic neural networks ...

Abstract: Regionalization, a process of transferring hydrological information [i.e., parameters of a conceptual rainfall-runoff model, namely, the McMaster University-Hydrologiska Byråns Vattenbalansavdelning (MAC-HBV)] from gauged ...

Abstract: Climate change impact studies have generally downscaled large-scale global climate model (GCM) output data; however, few studies have considered downscaling regional climate model (RCM) data. It is unclear whether further ...

Abstract: A key challenge in enhancing flood forecast relies in the difficulty of reducing predictive uncertainty. The precipitation-dependent hydrologic uncertainty processor (HUP) is a flexible model independent Bayesian processor ...