85 citations as recorded by crossref.

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6. Modeling Hydrological Response to Climate Change in a Data-Scarce Glacierized High Mountain Astore Basin Using a Fully Distributed TOPKAPI Model I. Atif et al. https://doi.org/10.3390/cli7110127
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11. The simulation of phenological development in dynamic crop model: The Bayesian comparison of different methods A. Ceglar et al. https://doi.org/10.1016/j.agrformet.2010.09.007
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21. A Comparison of Bayesian Methods for Uncertainty Analysis in Hydraulic and Hydrodynamic Modeling R. Camacho et al. https://doi.org/10.1111/1752-1688.12319
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24. Flood frequency analysis with PyTOPKAPI model-simulated stream flows from Aweitu river in Jimma town, Ethiopia Z. Rabba https://doi.org/10.1007/s40899-023-00825-2
25. Regional climate change projections and hydrological impact analysis for a Mediterranean basin in Southern Italy A. Senatore et al. https://doi.org/10.1016/j.jhydrol.2010.12.035
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34. The value of glacier mass balance, satellite snow cover images, and hourly discharge for improving the performance of a physically based distributed hydrological model D. Finger et al. https://doi.org/10.1029/2010WR009824
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37. Modeling distributed metal pollution transport in a mine impacted catchment: Short and long-term effects C. Sui et al. https://doi.org/10.1016/j.scitotenv.2021.151473
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41. Application of a developed distributed hydrological model based on the mixed runoff generation model and 2D kinematic wave flow routing model for better flood forecasting H. Bao et al. https://doi.org/10.1002/asl.754
42. Multimodel assessment of climate change-induced hydrologic impacts for a Mediterranean catchment E. Perra et al. https://doi.org/10.5194/hess-22-4125-2018
43. Comment on: ‘On undermining the science?’ by Keith Beven E. Todini & P. Mantovan https://doi.org/10.1002/hyp.6670
44. A hybrid runoff generation modelling framework based on spatial combination of three runoff generation schemes for semi-humid and semi-arid watersheds Y. Liu et al. https://doi.org/10.1016/j.jhydrol.2020.125440
45. CRML: A Convolution Regression Model With Machine Learning for Hydrology Forecasting C. Chen et al. https://doi.org/10.1109/ACCESS.2019.2941234
46. Streamflow modeling using the PyTOPKAPI model and remotely sensed rainfall: a case study of the Gilgel Ghibe catchment in Ethiopia Z. Rabba https://doi.org/10.2166/nh.2025.089
47. Preserving the dominant physical processes in a lumped hydrological model M. Martina et al. https://doi.org/10.1016/j.jhydrol.2010.12.039
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49. The comparison of sensitivity analysis of hydrological uncertainty estimates by GLUE and Bayesian method under the impact of precipitation errors L. Li & C. Xu https://doi.org/10.1007/s00477-013-0767-1
50. Enhanced LSTM Model for Daily Runoff Prediction in the Upper Huai River Basin, China Y. Man et al. https://doi.org/10.1016/j.eng.2021.12.022
51. Sensitivity analysis, calibration, and testing of a distributed hydrological model using error‐based weighting and one objective function L. Foglia et al. https://doi.org/10.1029/2008WR007255
52. Comparison between dynamic and static sensitivity analysis approaches for impact assessment of different potential evapotranspiration methods on hydrological models performance R. Nonki et al. https://doi.org/10.1175/JHM-D-20-0192.1
53. Scaling Point‐Scale (Pedo)transfer Functions to Seamless Large‐Domain Parameter Estimates for High‐Resolution Distributed Hydrologic Modeling: An Example for the Rhine River R. Imhoff et al. https://doi.org/10.1029/2019WR026807
54. Uncertainty assessment via Bayesian revision of ensemble streamflow predictions in the operational river Rhine forecasting system P. Reggiani et al. https://doi.org/10.1029/2007WR006758
55. Real‐Time Flood Forecasting Based on a High‐Performance 2‐D Hydrodynamic Model and Numerical Weather Predictions X. Ming et al. https://doi.org/10.1029/2019WR025583
56. Investigating Parameter Transferability across Models and Events for a Semiarid Mediterranean Catchment E. Perra et al. https://doi.org/10.3390/w11112261
57. Flash flood hydrology in karstic terrain: Flumineddu Canyon, central-east Sardinia J. De Waele et al. https://doi.org/10.1016/j.geomorph.2010.03.021
58. Exploiting remote sensing land surface temperature in distributed hydrological modelling: the example of the Continuum model F. Silvestro et al. https://doi.org/10.5194/hess-17-39-2013
59. Uncertainty assessment of a stochastic groundwater flow model using GLUE analysis A. Hassan et al. https://doi.org/10.1016/j.jhydrol.2008.08.017
60. Large spatial variations in the distributions of and factors affecting forest water retention capacity in China X. Wu et al. https://doi.org/10.1016/j.ecolind.2020.106152
61. Impacts of Data Quantity and Quality on Model Calibration: Implications for Model Parameterization in Data-Scarce Catchments Y. Huang & A. Bardossy https://doi.org/10.3390/w12092352
62. Coupling the Xinanjiang model to a kinematic flow model based on digital drainage networks for flood forecasting J. Liu et al. https://doi.org/10.1002/hyp.7255
63. Potential and limitations of using soil mapping information to understand landscape hydrology F. Terribile et al. https://doi.org/10.5194/hess-15-3895-2011
64. Estimation of Critical Rainfall for Flood Disasters in the Qinghai-Tibet Plateau M. Weidong et al. https://doi.org/10.5814/j.issn.1674-764x.2021.05.003
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66. Development and comparison in uncertainty assessment based Bayesian modularization method in hydrological modeling L. Li et al. https://doi.org/10.1016/j.jhydrol.2013.02.002
67. Evaluating model structure adequacy: The case of the Maggia Valley groundwater system, southern Switzerland L. Foglia et al. https://doi.org/10.1029/2011WR011779
68. Assessment of TOPKAPI-X Applicability for Flood Events Simulation in Two Small Catchments in Saxony F. Janabi et al. https://doi.org/10.3390/hydrology8030109
69. An edge intelligence empowered flooding process prediction using Internet of things in smart city C. Chen et al. https://doi.org/10.1016/j.jpdc.2022.03.010
70. Study on a Hybrid Hydrological Forecasting Model SCE-GUH by Coupling SCE-UA Optimization Algorithm and General Unit Hydrograph Y. Xu et al. https://doi.org/10.3390/w15152783
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72. Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa T. Vischel et al. https://doi.org/10.5194/hess-12-751-2008
73. Challenges and Uncertainties in Hydrological Modeling of Remote Hindu Kush–Karakoram–Himalayan (HKH) Basins: Suggestions for Calibration Strategies F. Pellicciotti et al. https://doi.org/10.1659/MRD-JOURNAL-D-11-00092.1
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75. Practical Use of Computationally Frugal Model Analysis Methods M. Hill et al. https://doi.org/10.1111/gwat.12330
76. Numerical Solution to Reservoir Flood Routing X. Li et al. https://doi.org/10.1061/(ASCE)1084-0699(2009)14:2(197)
77. Implications of Ensemble Quantitative Precipitation Forecast Errors on Distributed Streamflow Forecasting G. Mascaro et al. https://doi.org/10.1175/2009JHM1144.1
78. Verification of short-term runoff forecasts for a small Philippine basin (Marikina) D. Kneis et al. https://doi.org/10.1080/02626667.2016.1183773
79. Application of a Simple Raster-Based Hydrological Model for Streamflow Prediction in a Humid Catchment with Polder Systems G. Zhao et al. https://doi.org/10.1007/s11269-010-9719-4
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81. Early warning index of flash flood disaster: a case study of Shuyuan watershed in Qufu City X. Teng et al. https://doi.org/10.2166/wst.2023.016
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85. Simulation and Evaluation of Runoff in Tributary of Weihe River Basin in Western China Y. Liu et al. https://doi.org/10.3390/w16020221