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Journal of Hydrologic Engineering is a peer-reviewed journal published by ASCE, covering Hydrology and Watershed Management Studies, Flood Risk Assessment and Management, Hydrology and Drought Analysis. Over its lifetime it has published 3,589 papers, which have received 99,291 citations.
| Publisher | ASCE |
|---|---|
| Reference style | Author–year (ASCE) Author–year — (Smith, 2023) in the text Smith, A., B. Jones, and C. Lee. (2023). "A representative article title." Journal of Hydrologic Engineering, 12(3), 45–58.
Formats any DOI in Journal of Hydrologic Engineering style. No sign-up. |
| Publishes research in | Hydrology and Watershed Management Studies Flood Risk Assessment and Management Hydrology and Drought Analysis Hydrological Forecasting Using AI Groundwater flow and contamination studies |
| ISSN | 1084-0699 |
| Citation impact (2-yr) | 2.42 |
| h-index | 123 |
| i10-index | 1,898 |
| Top institutions publishing here | Texas A&M University |
| Journal website | scitation.aip.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
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…
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…
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 topic appearing in hydrology journals, especially over the last decade. In terms of hydrologic applications, this modeling tool is still in its nascent stages.…
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 found that ANNs are robust tools for modeling many of the nonlinear hydrologic processes such as rainfall-runoff, stream flow, ground-water management, water…
Hydrometeorological time series include recent trends, especially over the past 30 years, as a result of climate change impact according to the Intergovernmental Panel on Climate Change (IPCC). Although there are commonly used trend identification techniques, such as Mann-Kendall (MK) and Spearman’s rho (SR) tests, their validity is possible under a set of restrictive assumptions,…
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