One of the principal issues related to hydrologic models for prediction of runoff is the estimation of extreme values (floods). It is well understood that unless the models capture the dynamics of rainfall-runoff process, the improvement in prediction of such extremes is far from reality. In this paper, it is proposed to develop a dual (combined and paralleled) artificial neural network (D-ANN)...

[1] Predictive uncertainty analysis in hydrologic modeling has become an active area of research, the goal being to generate meaningful error bounds on model predictions. State-space filtering methods, such as the ensemble Kalman filter (EnKF), have shown the most flexibility to integrate all sources of uncertainty. However, predictive uncertainty analyses are typically carried out using a sing...

This study provides a comprehensive assessment of state-of-the-art evolutionary multiobjective optimization (EMO) tools’ relative effectiveness in calibrating hydrologic models. The relative computational efficiency, accuracy, and ease-of-use of the following EMO algorithms are tested: Epsilon Dominance Nondominated Sorted Genetic AlgorithmII (ε-NSGAII), the Multiobjective Shuffled Complex Evol...

[1] Despite significant recent developments in computational power and distributed hydrologic modeling, the issue of how to adequately address the uncertainty associated with hydrological predictions remains a critical and challenging one. This issue needs to be properly addressed for hydrological modeling to realize its maximum practical potential in environmental decision-making processes. Ar...

[1] Hydrologic processes control much of the export of organic matter and nutrients from the land surface. It is the variability of these hydrologic processes that produces variable patterns of nutrient transport in both space and time. In this paper, we explore how hydrologic ‘‘connectivity’’ potentially affects nutrient transport. Hydrologic connectivity is defined as the condition by which d...

[1] It is known that GPS time series contain a seasonal variation that is not due to tectonic motions, and it has recently been shown that crustal seismic velocities may also vary seasonally. In order to explain these changes, a number of hypotheses have been given, among which thermoelastic and hydrology‐induced stresses and strains are leading candidates. Unfortunately, though, since a genera...

Model selection is an extremely important aspect of many hydrologic modeling studies because of the complexity, variability, and uncertainty that surrounds the current understanding of watershed-scale systems. However, development and implementation of a complete precipitation-runoff modeling framework, from model selection to calibration and uncertainty analysis, are rarely confronted. This pa...

The Muskingum model is the most widely used method for flood routing in hydrologic engineering. However, the application of the model still suffers from a lack of an efficient method for parameter estimation. Particle swarm optimization PSO is applied to the parameter estimation for the nonlinear Muskingum model. PSO does not need any initial guess of each parameter and thus avoids the subjecti...

In this paper, the issue of nonlinear sensitivity analysis for dimensionality reduction in hydrologic model calibration is discussed, and a novel method to quantify the sensitivity of each parameter that considers the nonlinear relationship in the model is presented. The method is based on computing the absolute variation of the nonlinear function represented by the model in its parameter space...