58 citations as recorded by crossref.

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5. Tool-Driven Revolutions in Archaeological Science S. Schmidt & B. Marwick https://doi.org/10.5334/jcaa.29
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10. An interactive graphical interface tool for parameter calibration, sensitivity analysis, uncertainty analysis, and visualization for the Soil and Water Assessment Tool T. Nguyen et al. https://doi.org/10.1016/j.envsoft.2022.105497
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12. Open-Source Data Alternatives and Models for Flood Risk Management in Nepal S. Thakuri et al. https://doi.org/10.3390/rs14225660
13. An innovative approach for predicting groundwater TDS using optimized ensemble machine learning algorithms at two levels of modeling strategy H. Elzain et al. https://doi.org/10.1016/j.jenvman.2023.119896
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22. A Near Real-Time Hydrological Information System for the Upper Danube Basin T. Pulka et al. https://doi.org/10.3390/hydrology8040144
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24. anem: A Simple Web‐Based Platform to Build Stakeholder Understanding of Groundwater Behavior G. Penny et al. https://doi.org/10.1111/gwat.13043
25. Evaluating the ability of numerical models to capture important shifts in environmental time series: A fuzzy change point approach M. Hollaway et al. https://doi.org/10.1016/j.envsoft.2021.104993
26. Technical note: Hydrology modelling R packages – a unified analysis of models and practicalities from a user perspective P. Astagneau et al. https://doi.org/10.5194/hess-25-3937-2021
27. The importance of open science for biological assessment of aquatic environments M. Beck et al. https://doi.org/10.7717/peerj.9539
28. An open‐source automated workflow to delineate storm events and evaluate concentration‐discharge relationships D. Millar et al. https://doi.org/10.1002/hyp.14456
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35. Remotely sensed rivers in the Anthropocene: state of the art and prospects H. Piégay et al. https://doi.org/10.1002/esp.4787
36. Global BROOK90 R Package: An Automatic Framework to Simulate the Water Balance at Any Location I. Vorobevskii et al. https://doi.org/10.3390/w12072037
37. On constraining a lumped hydrological model with both piezometry and streamflow: results of a large sample evaluation A. Pelletier & V. Andréassian https://doi.org/10.5194/hess-26-2733-2022
38. Assessment of flooding in future periods using the flow of the watershed (Case study: west and south of the Urmia watershed) M. Jahangir et al. https://doi.org/10.1016/j.nhres.2023.04.004
39. Simulating the Runoff of Watersheds in Dry Areas on A Monthly Scale using the SWAT Model (Case Study: Lar Watershed) M. Kiyani Majd et al. https://doi.org/10.61186/jwmr.14.27.135
40. evapoRe: An R-based application for exploratory data analysis of evapotranspiration A. Ziveh et al. https://doi.org/10.1016/j.envsoft.2026.106884
41. An R package of maximum entropy production model to estimate 41 years of global evapotranspiration Y. Yang et al. https://doi.org/10.1016/j.jhydrol.2022.128639
42. Correlations between water quality and precipitation in areas with different levels of human occupation L. Rodrigues et al. https://doi.org/10.1007/s42108-020-00097-y
43. riversCentralAsia: An R package to support data pre- and postprocessing for hydrological modelling with RS MINERVE B. Marti & T. Siegfried https://doi.org/10.21105/joss.04805
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45. Can-GLWS: Canadian Great Lakes Weather Service for the Soil and Water Assessment Tool (SWAT) modelling N. Shrestha et al. https://doi.org/10.1016/j.jglr.2020.10.009
46. Rainfall-runoff modeling using airGR and airGRteaching: application to a catchment in Northeast Algeria S. Yahiaoui et al. https://doi.org/10.1007/s40808-022-01444-0
47. Hydrostreamer v1.0 – improved streamflow predictions for local applications from an ensemble of downscaled global runoff products M. Kallio et al. https://doi.org/10.5194/gmd-14-5155-2021
48. From permits to policy: insights from an analysis of Ontario’s open water permit data (1960–2022) S. Saadi & C. Johns https://doi.org/10.1080/07011784.2025.2611760
49. Hard and soft clustering methods employed in groundwater research: A systematic review A. Rahbar et al. https://doi.org/10.1016/j.jhydrol.2025.134841
50. Challenges in modeling and predicting floods and droughts: A review M. Brunner et al. https://doi.org/10.1002/wat2.1520
51. Farm simulation platforms increase undergraduate skills and confidence in decision‐support technologies A. Basche et al. https://doi.org/10.1002/nse2.20058
52. Can continuous simulation be used as an alternative for flood regionalisation? A large sample example from Chile P. Acuña & A. Pizarro https://doi.org/10.1016/j.jhydrol.2023.130118
53. EvalHyd v0.1.2: a polyglot tool for the evaluation of deterministic and probabilistic streamflow predictions T. Hallouin et al. https://doi.org/10.5194/gmd-17-4561-2024
54. Introduction to Reproducible Geospatial Analysis and Figures in R: A Tutorial Article P. Maesen & E. Salingros https://doi.org/10.3390/data9040058
55. Towards an improved language for river data analysis: Demonstration for the highly-regulated Ohio River basin G. Atreya et al. https://doi.org/10.1016/j.envsoft.2026.106866
56. Convergence of mechanistic modeling and artificial intelligence in hydrologic science and engineering R. Muñoz-Carpena et al. https://doi.org/10.1371/journal.pwat.0000059
57. RavenR v2.1.4: an open-source R package to support flexible hydrologic modelling R. Chlumsky et al. https://doi.org/10.5194/gmd-15-7017-2022
58. Revisiting the tension water storage capacity distribution in conceptual rainfall-runoff modeling: A large-sample approach Y. Zhou et al. https://doi.org/10.1016/j.jhydrol.2024.131834