44 citations as recorded by crossref.

1. Is arctic greening consistent with the ecology of tundra? Lessons from an ecologically informed mass balance model A. Rocha et al. https://doi.org/10.1088/1748-9326/aaeb50
2. On the role of soil moisture in daytime evolution of temperatures N. Lockart et al. https://doi.org/10.1002/hyp.9525
3. Rainfall‐runoff modelling of a humid tropical catchment: the TOPMODEL approach P. Campling et al. https://doi.org/10.1002/hyp.341
4. Parameter sensitivity in simulations of snowmelt R. Essery & P. Etchevers https://doi.org/10.1029/2004JD005036
5. Bayesian analysis for uncertainty estimation of a canopy transpiration model S. Samanta et al. https://doi.org/10.1029/2006WR005028
6. Sensitivity, uncertainty and time dependence of parameters in a complex land surface model L. Prihodko et al. https://doi.org/10.1016/j.agrformet.2007.08.006
7. Weighted objective function selector algorithm for parameter estimation of SVAT models with remote sensing data J. Pollacco et al. https://doi.org/10.1002/wrcr.20554
8. Modelling heat, water and carbon fluxes in mown grassland under multi-objective and multi-criteria constraints N. Senapati et al. https://doi.org/10.1016/j.envsoft.2016.02.025
9. Using model‐data fusion to interpret past trends, and quantify uncertainties in future projections, of terrestrial ecosystem carbon cycling T. Keenan et al. https://doi.org/10.1111/j.1365-2486.2012.02684.x
10. Responses of hydrological model equifinality, uncertainty, and performance to multi-objective parameter calibration Y. Her & C. Seong https://doi.org/10.2166/hydro.2018.108
11. Verifying model performance using validation of Pareto solutions N. Harvey et al. https://doi.org/10.1016/j.jhydrol.2023.129594
12. TOPMODEL for Streamflow Simulation of a Tropical Catchment Using Different Resolutions of ASTER DEM: Optimization Through Response Surface Methodology A. Suliman et al. https://doi.org/10.1007/s11269-016-1338-2
13. Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology K. Beven & J. Freer https://doi.org/10.1016/S0022-1694(01)00421-8
14. One decade of multi-objective calibration approaches in hydrological modelling: a review A. Efstratiadis & D. Koutsoyiannis https://doi.org/10.1080/02626660903526292
15. Sequential Assimilation ofERS-1SAR Data into a Coupled Land Surface–Hydrological Model Using an Extended Kalman Filter C. Francois et al. https://doi.org/10.1175/1525-7541(2003)4<473:SAOESD>2.0.CO;2
16. Estimating parameters of a forest ecosystem C model with measurements of stocks and fluxes as joint constraints A. Richardson et al. https://doi.org/10.1007/s00442-010-1628-y
17. Toward an estimation of global land surface heat fluxes from multisatellite observations C. Jiménez et al. https://doi.org/10.1029/2008JD011392
18. Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States E. Melaas et al. https://doi.org/10.1111/gcb.13122
19. On the uncertainty of phenological responses to climate change, and implications for a terrestrial biosphere model M. Migliavacca et al. https://doi.org/10.5194/bg-9-2063-2012
20. Constraining Land Surface and Atmospheric Parameters of a Locally Coupled Model Using Observational Data Y. Liu et al. https://doi.org/10.1175/JHM407.1
21. Investigating the Uncertainty in Predicting Responses to Atmospheric Deposition using the Model of Acidification of Groundwater in Catchments (MAGIC) within a Generalised Likelihood Uncertainty Estimation (GLUE) Framework T. Page et al. https://doi.org/10.1023/A:1022011520036
22. The model–data fusion pitfall: assuming certainty in an uncertain world T. Keenan et al. https://doi.org/10.1007/s00442-011-2106-x
23. Incorporation of conceptual and parametric uncertainty into radionuclide flux estimates from a fractured granite rock mass D. Reeves et al. https://doi.org/10.1007/s00477-010-0385-0
24. A Bayesian approach to estimate sensible and latent heat over vegetated land surface C. van der Tol et al. https://doi.org/10.5194/hess-13-749-2009
25. Calibration of a land surface model using multiple data sets M. McCabe et al. https://doi.org/10.1016/j.jhydrol.2004.07.002
26. Enhancing gradient-based parameter estimation with an evolutionary approach E. Agyei & K. Hatfield https://doi.org/10.1016/j.jhydrol.2005.05.010
27. A test of functional convergence in carbon fluxes from coupled C and N cycles in Arctic tundra K. Wright & A. Rocha https://doi.org/10.1016/j.ecolmodel.2018.05.017
28. Sensitivity and predictive uncertainty of the ACASA model at a spruce forest site K. Staudt et al. https://doi.org/10.5194/bg-7-3685-2010
29. Toward a new generation of satellite surface products? F. Aires & C. Prigent https://doi.org/10.1029/2006JD007362
30. Development of the Biome-BGC model for simulation of managed herbaceous ecosystems D. Hidy et al. https://doi.org/10.1016/j.ecolmodel.2011.11.008
31. Quantifying uncertainties from additional nitrogen data and processes in a terrestrial ecosystem model with Bayesian probabilistic inversion Z. Du et al. https://doi.org/10.1002/2016MS000687
32. Calibration of a distributed hydrology and land surface model using energy flux measurements M. Larsen et al. https://doi.org/10.1016/j.agrformet.2015.11.012
33. Complementarity of flux- and biometric-based data to constrain parameters in a terrestrial carbon model Z. Du et al. https://doi.org/10.3402/tellusb.v67.24102
34. Stochastic capture zone analysis of an arsenic‐contaminated well using the generalized likelihood uncertainty estimator (GLUE) methodology B. Morse et al. https://doi.org/10.1029/2002WR001470
35. Calibrating a Basin‐Scale Groundwater Model to Remotely Sensed Estimates of Groundwater Evapotranspiration R. Carroll et al. https://doi.org/10.1111/jawr.12285
36. Insights into the tussock growth form with model–data fusion S. Curasi et al. https://doi.org/10.1111/nph.18751
37. Comparison of two evapotranspiration schemes on a sub-urban site E. Berthier et al. https://doi.org/10.1016/j.jhydrol.2006.01.007
38. Rate my data: quantifying the value of ecological data for the development of models of the terrestrial carbon cycle T. Keenan et al. https://doi.org/10.1890/12-0747.1
39. Modelling carbon and water cycles in a beech forest E. Dufrêne et al. https://doi.org/10.1016/j.ecolmodel.2005.01.004
40. Predictive Capability in Estimating Changes in Water Quality: Long-Term Responses to Atmospheric Deposition T. Page et al. https://doi.org/10.1023/B:WATE.0000009893.66091.ec
41. Model-based analysis of the impact of diffuse radiation on CO2 exchange in a temperate deciduous forest M. Lee et al. https://doi.org/10.1016/j.agrformet.2017.11.016
42. Flexible automated parameterization of hydrologic models using fuzzy logic S. Samanta & D. Mackay https://doi.org/10.1029/2002WR001349
43. Comparison of spectral and time domain calibration methods for precipitation‐discharge processes J. Quets et al. https://doi.org/10.1002/hyp.7546
44. Parameter transferability under changing climate: case study with a land surface model in the Durance watershed, France C. Magand et al. https://doi.org/10.1080/02626667.2014.993643