Libraries for hydrologic model (GR4J and Sacramento) parameters which are robust under various climate conditions

The data consists of rainfall runoff model parameter sets that produce expected rainfall runoff coefficients (ratio of runoff to rainfall) for a range of environmental conditions. Parameter sets were collected for two popular rainfall runoff models, GR4J and Sacramento. The parameter libraries are designed to be suitable for climate change studies. \nLineage: Using historic SILO climate and streamflow data across Australia, observed rainfall runoff coefficients were computed for headwater catchments across the continent. Individual parameter sets of conceptual rainfall runoff models were evaluated in their ability to simulate all the rainfall runoff coefficients of these headwater catchments. Furthermore, the difference between the simulated and observed rainfall runoff coefficients over all the catchments was characterised statistically, allowing uncertainty estimation of the rainfall runoff parameters under a Bayesian framework. The result was the generation of a parameter library that would include parameter sets that would produce appropriate rainfall runoff coefficients over a range of conditions. Uncertainty analyses were performed using the Gibbs sampler which is a popular and efficient Markov Chain Monte Carlo algorithm (Gelman et al., 2013). Further details of how the data was generated can be found in Hughes and Kim (under review).\n\nGelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis. Bayesian Data Analysis. https://doi.org/10.1201/B16018\nHughes, J. D., & Kim, S. S. H. (under review). Climate-proofing rainfall runoff models: development and evaluation of parameter libraries that produce dependable predictions across diverse conditions. Water Resources Research.