Journal of Hydrologic Engineering 

About: Journal of Hydrologic Engineering is an academic journal published by American Society of Civil Engineers. The journal publishes majorly in the area(s): Surface runoff & Hydrological modelling. It has an ISSN identifier of 1084-0699. Over the lifetime, 3071 publications have been published receiving 79493 citations. The journal is also known as: ASCE journal of hydrologic engineering & Hydrologic engineering.

Status of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration

Abstract: The usefulness of a hydrologic model depends on how well the model is calibrated. Therefore, the calibration procedure must be conducted carefully to maximize the reliability of the model. In general, manual procedures for calibration can be extremely time-consuming and frustrating, and this has been a major factor inhibiting the widespread use of the more sophisticated and complex hydrologic models. A global optimization algorithm entitled shuffled complex evolution recently was developed that has proved to be consistent, effective, and efficient in locating the globally optimal model parameters of a hydrologic model. In this paper, the capability of the shuffled complex evolution automatic procedure is compared with the interactive multilevel calibration multistage semiautomated method developed for calibration of the Sacramento soil moisture accounting streamflow forecasting model of the U.S. National Weather Service. The results suggest that the state of the art in automatic calibration now can be exp...

Everything You Always Wanted to Know about Copula Modeling but Were Afraid to Ask

Abstract: This paper presents an introduction to inference for copula models, based on rank methods. By working out in detail a small, fictitious numerical example, the writers exhibit the various steps involved in investigating the dependence between two random variables and in modeling it using copulas. Simple graphical tools and numerical techniques are presented for selecting an appropriate model, estimating its parameters, and checking its goodness-of-fit. A larger, realistic application of the methodology to hydrological data is then presented.

Artificial Neural Networks in Hydrology. I: Preliminary Concepts

Abstract: In this two-part series, the writers investigate the role of artificial neural networks (ANNs) in hydrology. ANNs are gaining popularity, as is evidenced by the increasing number of papers on this ...

Artificial Neural Networks in Hydrology. II: Hydrologic Applications

Abstract: This paper forms the second part of the series on application of artificial neural networks (ANNs) in hydrology. The role of ANNs in various branches of hydrology has been examined here. It is foun...

Runoff Curve Number: Has It Reached Maturity?

Abstract: The conceptual and empirical foundations of the runoff curve number method are reviewed. The method is a conceptual model of hydrologic abstraction of storm rainfall. Its objective is to estimate direct runoff depth from storm rainfall depth, based on a parameter referred to as the “curve number.” The method does not take into account the spatial and temporal variability of infiltration and other abstractive losses; rather, it aggregates them into a calculation of the total depth loss for a given storm event and drainage area. The method describes average trends, which precludes it from being perfectly predictive. The observed variability in curve numbers, beyond that which can be attributed to soil type, land use/treatment, and surface condition, is embodied in the concept of antecedent condition. The method is widely used in the United States and other countries. Perceived advantages of the method are (1) its simplicity; (2) its predictability; (3) its stability; (4) its reliance on only one parameter; ...