Rainfall downscaling using stochastic generators and machine learning
- 1IHCantabria - Instituto de Hidráulica Ambiental, Universidad de Cantabria, Santander, Spain (manuel.deljesus@unican.es)
- 2Instituto de Física de Cantabria (IFCA), Universidad de Cantabria-CSIC, Santander, Spain
- 3Dept. of Applied Mathematics and Computer Science (MACC), Universidad de Cantabria, Santander, Spain
Daily rainfall records are the most common form of rainfall information. These records are the ones normally used to characterize the extremes of rainfall. However, in many situations, sub-daily information is required, normally to characterize the extreme response of small watersheds. Different methods exist to extrapolate the daily information to smaller time scales -normally, hourly time scales-, which tend to be based on a limited number of finer than daily observations.
In this work, we will deal with two common downscaling problems that the hydrologist faces: transforming daily rainfall observations into estimates of sub-daily rainfall statistics and incorporating climate change information into these estimates. Although generally, these two procedures are different, both conceptually and mechanically, we will combine stochastic generators and machine learning to create a unified framework where both problems are connected and solved in a similar manner.
We will use NEOPRENE (Diez-Sierra et al., 2023), a Python-based open source library that implement the Nyeman-Scott, or Cox and Isham, stochastic model of rainfall (Cox & Isham, 1988) to characterize the rainfall process, and random forests to relate daily and hourly rainfall statistics (del Jesus & Diez-Sierra, 2023). The model assumes a geometric description of the rainfall process, that allows to decompose observed time series and reproduce several statistics at different levels of aggregation.
We will also demonstrate how downscaling can be carried out to generate plausible hourly rainfall distributions from daily ones, and how this process serves to characterize the uncertainties of the estimates.
Cox, D. R. & Isham, V., 1988. A Simple Spatial-Temporal Model of Rainfall. Proceedings of the royal society a: Mathematical, physical and engineering sciences, 415 (1849), 317–328.
Diez-Sierra, J., Navas, S. & Jesus, M. del., 2023. NEOPRENE v1.0.1: A Python library for generating spatial rainfall based on the NeymanScott process. Geoscientific model development, 16 (17), 5035–5048.
Jesus, M. del & Diez-Sierra, J., 2023. Climate change effects on sub-daily precipitation in Spain. Hydrological sciences journal, 68 (8), 1065–1077.
How to cite: del Jesus, M., Diez-Sierra, J., and Navas, S.: Rainfall downscaling using stochastic generators and machine learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6450, https://doi.org/10.5194/egusphere-egu24-6450, 2024.
