Journal of Hydrologic Engineering

Papers
(The H4-Index of Journal of Hydrologic Engineering is 14. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2021-09-01 to 2025-09-01.)
ArticleCitations
Closure to “ANFIS Modeling with ICA, BBO, TLBO, and IWO Optimization Algorithms and Sensitivity Analysis for Predicting Daily Reference Evapotranspiration” by Maryam Zeinolabedini Rezaabad, Sadegh Gha124
Statistical Modeling of Spatial Extremes through Max-Stable Process Models: Application to Extreme Rainfall Events in South Africa97
Geographic Dependency of the Curve Number Method’s Initial Abstraction Ratio24
Discussion of “Application of a Hybrid Model Based on Secondary Decomposition and ELM Neural Network in Water Level Prediction”23
Discussion of “Nonoverlapping Block Stratified Random Sampling Approach for Assessment of Stationarity” by Ramesh S. V. Teegavarapu and Priyank J. Sharma21
Regional Trends and Spatiotemporal Analysis of Rainfall and Groundwater in the West Coast Basins of India20
Analytically Supported Numerical Modeling of Horizontal and Radial Collector Wells18
Dual-Phase Calibration for Surface–Subsurface Hydrologic Models with Diverse Hydrologic Conditions18
Improving Rainfall Fields in Data-Scarce Basins: Influence of the Kernel Bandwidth Value of Merging on Hydrometeorological Modeling17
Analysis of Extreme Precipitation under Nonstationary Conditions in the Yangtze River Basin16
NRCS Curve Number Method: Comparison of Methods for Estimating the Curve Number from Rainfall-Runoff Data14
Impact of Progressive Reservoir Construction on Nonstationary Sediment Load Response to Streamflow in the Upper Yangtze River, China14
Investigating the Spatiotemporal Complexity of Rainfall from a Chaotic Perspective: Case Study in the Jinsha River Basin, China14
Long-Term Streamflow Prediction Using Hybrid SVR-ANN Based on Bayesian Model Averaging14
Discussion of “Runoff Predictions in a Semiarid Watershed by Convolutional Neural Networks Improved with Metaheuristic Algorithms and Forced with Reanalysis and Climate Data”14
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