Subject Training

Context

Managing rainwater is a major challenge in cities, especially with climate change and the increase in impermeable surfaces such as roads and buildings. Green roofs (GRs) have become an important part of sustainable urban development [1]. They can help reduce stormwater runoff by slowing it down and decreasing the total volume of water flowing into drainage systems.

To understand and predict how much water a green roof can retain, researchers use models based on water flow in porous materials like soil [2]. These models include soil parameters (related to the physical properties of the substrate) and vegetation parameters (related to plant characteristics). However, these parameters are often uncertain because they are difficult to measure accurately or vary naturally.

These uncertainties can affect the model's predictions of water content. Therefore, the model must be calibrated ⬔ that is, adjusted so that its outputs match real measurements. Calibration involves changing the model parameters step by step and comparing the simulated results to observed data until the model performs well according to a chosen objective function.

What You Will Do

The first goal of this work is to review the different calibration methods found in the literature, with a particular focus on the Bayesian Generalized Likelihood Uncertainty Estimation (GLUE) approach [3]. Unlike traditional methods that provide a single "optimal" parameter set, GLUE yields a range of parameter sets that all produce acceptable model simulations. In addition, a global sensitivity analysis [4] helps identify the parameters that most strongly influence the model's ability to reproduce the experimental data, as well as those with negligible impact, which can be excluded from the calibration process and fixed at a nominal value. The aim of the work is to explore this type of approach.

The second goal is to apply the selected method to the calibration of a hydrological green-roof model. Real monitoring data collected between 2020 and 2024 will be used for this application.

References

[1] D. Perivoliotis, I. Arvanitis, A. Tzavali, V. Papakostas, S. Kappou, G. Andreakos, A. Fotiadi, J.A. Paravantis, M. Souliotis, G. Mihalakakou. Sustainable Urban Environment through Green Roofs: A Literature Review with Case Studies. Sustainability} 2023, 15(15976). https://doi.org/10.3390/su152215976.

[2] Y. Li and Jr. RW Babcock. Green roof hydrologic performance and modeling: a review. Water Science and Technology, 2014, 69(4):727-38. doi:10.2166/wst.2013.770.

[3] M. Mirzaei, Y. F. Huang, A. El-Shafie and A. Shatirah. Application of the generalized likelihood uncertainty estimation (GLUE) approach for assessing uncertainty in hydrological models: A review. Stochastic Environmental Research and Risk Assessment, 2015, DOI: 10.1007/s00477-014-1000-6.

[4] E. Borgonovo and E. Plischke. Sensitivity analysis: A review of recent advances. European Journal of Operational Research 248 (2016)869-887, 2016, http://dx.doi.org/10.1016/j.ejor.2015.06.032