11 citations as recorded by crossref.

1. A Bayesian alternative for multi-objective ecohydrological model specification Y. Tang et al. https://doi.org/10.1016/j.jhydrol.2017.07.040
2. Utilizing Satellite Surface Soil Moisture Data in Calibrating a Distributed Hydrological Model Applied in Humid Regions Through a Multi-Objective Bayesian Hierarchical Framework H. Yang et al. https://doi.org/10.3390/rs11111335
3. Uncertainty analysis of gross primary production partitioned from net ecosystem exchange measurements R. Raj et al. https://doi.org/10.5194/bg-13-1409-2016
4. Predicting the Future Distribution of Ara rubrogenys, an Endemic Endangered Bird Species of the Andes, Taking into Account Trophic Interactions A. Hambuckers et al. https://doi.org/10.3390/d13020094
5. Modelling precipitation uncertainties in a multi-objective Bayesian ecohydrological setting Y. Tang et al. https://doi.org/10.1016/j.advwatres.2018.10.015
6. A Bayesian inversion framework to evaluate parameter and predictive inference of a simple soil respiration model in a cool-temperate forest in western Japan M. Toda et al. https://doi.org/10.1016/j.ecolmodel.2019.108918
7. Inferring the cross-sectional area profile of an unseen solid in a draining tank from liquid level dynamics G. Fabusola & C. Simon https://doi.org/10.1016/j.ces.2025.121488
8. Biome classification across global vegetation models reveals consistent biome shifts under future climate change S. Scheiter et al. https://doi.org/10.5194/bg-23-6409-2026
9. Towards a More Realistic Simulation of Plant Species with a Dynamic Vegetation Model Using Field-Measured Traits: The Atlas Cedar, a Case Study A. Hambuckers et al. https://doi.org/10.3390/f13030446
10. A high-resolution monitoring approach of urban CO2 fluxes. Part 2 – surface flux optimisation using eddy covariance observations S. Stagakis et al. https://doi.org/10.1016/j.scitotenv.2023.166035
11. Refining Species Traits in a Dynamic Vegetation Model to Project the Impacts of Climate Change on Tropical Trees in Central Africa M. Dury et al. https://doi.org/10.3390/f9110722