72 citations as recorded by crossref.

1. Use of recession-curve analysis for estimation of karstification degree and its application in assessing overflow/underflow conditions in closely spaced karstic springs P. Malík & S. Vojtková https://doi.org/10.1007/s12665-012-1596-0
2. Hydrological field data from a modeller's perspective: Part 1. Diagnostic tests for model structure H. McMillan et al. https://doi.org/10.1002/hyp.7841
3. Incorporating natural variability in master recession curves T. McMahon et al. https://doi.org/10.5194/hess-30-893-2026
4. Time‐Variability of Flow Recession Dynamics: Application of Machine Learning and Learning From the Machine M. Kim et al. https://doi.org/10.1029/2022WR032690
5. Promising new baseflow separation and recession analysis methods applied to streamflow at Glendhu Catchment, New Zealand M. Stewart https://doi.org/10.5194/hess-19-2587-2015
6. Prediction of Streamflow Recession Curves in Gauged and Ungauged Basins S. Singh & G. Griffiths https://doi.org/10.1029/2021WR030618
7. Towards a methodology for testing models as hypotheses in the inexact sciences K. Beven https://doi.org/10.1098/rspa.2018.0862
8. Dynamics of storm-driven suspended sediments in a headwater catchment described by multivariable modeling M. Onderka et al. https://doi.org/10.1007/s11368-012-0480-6
9. Applicability Assessment of Estimation Methods for Baseflow Recession Constants in Small Forest Catchments H. Yang et al. https://doi.org/10.3390/w10081074
10. Dam operation affects the evolution and propagation of hydrological extremes P. Dey et al. https://doi.org/10.1080/02626667.2024.2311755
11. Discharge recession patterns of karstic springs as observed in Triassic carbonate aquifers of Slovakia P. Malík et al. https://doi.org/10.1007/s10040-020-02276-x
12. The effect of forcing and landscape distribution on performance and consistency of model structures T. Euser et al. https://doi.org/10.1002/hyp.10445
13. Assessing the impact of mixing assumptions on the estimation of streamwater mean residence time F. Fenicia et al. https://doi.org/10.1002/hyp.7595
14. Elements of a flexible approach for conceptual hydrological modeling: 1. Motivation and theoretical development F. Fenicia et al. https://doi.org/10.1029/2010WR010174
15. What can flux tracking teach us about water age distribution patterns and their temporal dynamics? M. Hrachowitz et al. https://doi.org/10.5194/hess-17-533-2013
16. Calibration of a conceptual rainfall‐runoff model for flood frequency estimation by continuous simulation R. Lamb https://doi.org/10.1029/1999WR900119
17. Active rock glaciers as shallow groundwater reservoirs, Austrian Alps T. Wagner et al. https://doi.org/10.1007/s00767-020-00455-x
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20. On the calibration of hydrological models in ungauged basins: A framework for integrating hard and soft hydrological information H. Winsemius et al. https://doi.org/10.1029/2009WR007706
21. Water storage at the Panola Mountain Research Watershed, Georgia, USA N. Peters & B. Aulenbach https://doi.org/10.1002/hyp.8334
22. Consistency between hydrological models and field observations: linking processes at the hillslope scale to hydrological responses at the watershed scale M. Clark et al. https://doi.org/10.1002/hyp.7154
23. HydRun: A MATLAB toolbox for rainfall–runoff analysis W. Tang & S. Carey https://doi.org/10.1002/hyp.11185
24. Storage-Discharge Relationships under Forest Cover Change in Ethiopian Highlands S. Gebrehiwot et al. https://doi.org/10.3390/w13162310
25. Estimating catchment-scale groundwater dynamics from recession analysis – enhanced constraining of hydrological models T. Skaugen & Z. Mengistu https://doi.org/10.5194/hess-20-4963-2016
26. Technical note: Introduction of a superconducting gravimeter as novel hydrological sensor for the Alpine research catchment Zugspitze C. Voigt et al. https://doi.org/10.5194/hess-25-5047-2021
27. Effects of suburban development on runoff generation in the Croton River basin, New York, USA D. Burns et al. https://doi.org/10.1016/j.jhydrol.2005.01.022
28. The influence of the measurement errors on the Brutsaefrt and Nieber analysis of flow recession curves J. Kurnatowski et al. https://doi.org/10.1051/itmconf/20182300022
29. Time‐Varying Storage–Water Age Relationships in a Catchment With a Mediterranean Climate N. Rodriguez et al. https://doi.org/10.1029/2017WR021964
30. Topological controls on catchment‐scale sediment dynamics Y. Walley & A. Henshaw https://doi.org/10.1002/esp.5510
31. Inferring Changes in Dynamic Groundwater Storage from Recession Curve Analysis of Discharge Data U. Somorowska https://doi.org/10.2478/mgrsd-2004-0018
32. Changes in catchment runoff after harvesting and burning of a Pinus caribaea plantation in Viti Levu, Fiji M. Waterloo et al. https://doi.org/10.1016/j.foreco.2007.06.050
33. Linking hydrologic signatures to hydrologic processes: A review H. McMillan https://doi.org/10.1002/hyp.13632
34. Simulating typhoon-induced storm hydrographs in subtropical mountainous watershed: an integrated 3-layer TOPMODEL J. Huang et al. https://doi.org/10.5194/hess-13-27-2009
35. Master Recession Curve Parameterization Tool (MRCPtool): Different approaches to recession curve analysis T. Carlotto & P. Chaffe https://doi.org/10.1016/j.cageo.2019.06.016
36. A GPGPU-accelerated implementation of groundwater flow model in unconfined aquifers for heterogeneous and anisotropic media T. Carlotto et al. https://doi.org/10.1016/j.envsoft.2017.12.004
37. Dynamic TOPMODEL: A new implementation in R and its sensitivity to time and space steps P. Metcalfe et al. https://doi.org/10.1016/j.envsoft.2015.06.010
38. A decade of Predictions in Ungauged Basins (PUB)—a review M. Hrachowitz et al. https://doi.org/10.1080/02626667.2013.803183
39. Uncertainty quantification in watershed hydrology: Which method to use? A. Gupta & R. Govindaraju https://doi.org/10.1016/j.jhydrol.2022.128749
40. On the value of combined event runoff and tracer analysis to improve understanding of catchment functioning in a data-scarce semi-arid area M. Hrachowitz et al. https://doi.org/10.5194/hess-15-2007-2011
41. HESS Opinions: The complementary merits of competing modelling philosophies in hydrology M. Hrachowitz & M. Clark https://doi.org/10.5194/hess-21-3953-2017
42. Automatic procedure for selecting flood events and identifying flood characteristics from daily streamflow data Q. Zhang et al. https://doi.org/10.1016/j.envsoft.2021.105180
43. On constructing limits-of-acceptability in watershed hydrology using decision trees A. Gupta et al. https://doi.org/10.1016/j.advwatres.2023.104486
44. Event-scale power law recession analysis: quantifying methodological uncertainty D. Dralle et al. https://doi.org/10.5194/hess-21-65-2017
45. Concepts of Information Content and Likelihood in Parameter Calibration for Hydrological Simulation Models K. Beven & P. Smith https://doi.org/10.1061/(ASCE)HE.1943-5584.0000991
46. Can time domain and source area tracers reduce uncertainty in rainfall‐runoff models in larger heterogeneous catchments? R. Capell et al. https://doi.org/10.1029/2011WR011543
47. Spatio-temporal analysis of river flow master recession curve characteristics over Ardabil province rivers N. Parchami et al. https://doi.org/10.1016/j.pce.2024.103586
48. On constraining TOPMODEL hydrograph simulations using partial saturated area information S. Blazkova et al. https://doi.org/10.1002/hyp.331
49. Modelling catchment‐scale water storage dynamics: reconciling dynamic storage with tracer‐inferred passive storage C. Birkel et al. https://doi.org/10.1002/hyp.8201
50. A limits of acceptability approach to model evaluation and uncertainty estimation in flood frequency estimation by continuous simulation: Skalka catchment, Czech Republic S. Blazkova & K. Beven https://doi.org/10.1029/2007WR006726
51. The hydrological response of baseflow in fractured mountain areas A. Millares et al. https://doi.org/10.5194/hess-13-1261-2009
52. Construction of Master Recession Curve Using Genetic Algorithms M. Gregor & P. Malík https://doi.org/10.2478/v10098-012-0001-8
53. A dynamic TOPMODEL K. Beven & J. Freer https://doi.org/10.1002/hyp.252
54. Modelling the chloride signal at Plynlimon, Wales, using a modified dynamic TOPMODEL incorporating conservative chemical mixing (with uncertainty) T. Page et al. https://doi.org/10.1002/hyp.6186
55. Influence of calculation criteria on the values of low-flow recession constants in a non-homogenous catchment in Slovenia K. Sapač et al. https://doi.org/10.15292/acta.hydro.2019.01
56. New data-based analysis tool for functioning of natural flood management measures reveals multi-site time-variable effectiveness M. Roberts et al. https://doi.org/10.1016/j.jhydrol.2024.131164
57. Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data S. Gharari et al. https://doi.org/10.1029/2020WR027948
58. Shallow Landslide Modeling in Complex Hillslope by Using TOPMODEL and SINMAP Models F. Bahmani et al. https://doi.org/10.17491/jgsi/2024/174019
59. Catchments as simple dynamical systems: Catchment characterization, rainfall‐runoff modeling, and doing hydrology backward J. Kirchner https://doi.org/10.1029/2008WR006912
60. Evaluation of storage–discharge relationships and recession analysis-based distributed hourly runoff simulation in large-scale, mountainous and snow-influenced catchment T. Hailegeorgis et al. https://doi.org/10.1080/02626667.2016.1170939
61. Using internal catchment information to reduce the uncertainty of discharge and baseflow predictions F. Gallart et al. https://doi.org/10.1016/j.advwatres.2006.06.005
62. Development of a low-flow forecasting model using the M5 machine learning method L. ŠTRAVS & M. BRILLY https://doi.org/10.1623/hysj.52.3.466
63. Comparative analysis of baseflow characteristics of two Andean catchments, Ecuador P. Guzmán et al. https://doi.org/10.1002/hyp.10422
64. The Use of the Recession Index as an Indicator for Streamflow Recovery After a Multi-Year Drought S. Kienzle https://doi.org/10.1007/s11269-006-9019-1
65. Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures A. Gupta et al. https://doi.org/10.1016/j.jhydrol.2024.131774
66. Use of spatially distributed water table observations to constrain uncertainty in a rainfall–runoff model R. Lamb et al. https://doi.org/10.1016/S0309-1708(98)00020-7
67. PREDICTION OF MASTER RECESSION CURVES AND BASEFLOW RECESSIONS IN THE LUQUILLO MOUNTAINS OF PUERTO RICO1 H. Rivera‐Ramirez et al. https://doi.org/10.1111/j.1752-1688.2002.tb00990.x
68. Monthly and annual effective infiltration coefficients in Dinaric karst: example of the Gradole karst spring catchment O. BONACCI https://doi.org/10.1080/02626660109492822
69. On the colour and spin of epistemic error (and what we might do about it) K. Beven et al. https://doi.org/10.5194/hess-15-3123-2011
70. Impact of stochastic fluctuations in storage‐discharge relations on streamflow distributions S. Suweis et al. https://doi.org/10.1029/2009WR008038
71. Signature‐Domain Calibration of Hydrological Models Using Approximate Bayesian Computation: Theory and Comparison to Existing Applications D. Kavetski et al. https://doi.org/10.1002/2017WR020528
72. A history of TOPMODEL K. Beven et al. https://doi.org/10.5194/hess-25-527-2021