Dimensional analysis offers an ideal playground to tackle complex hydrological problems. The powerful dimension reduction, in terms of governing dimensionless groups, afforded by PI-theorem and related self-similarity arguments is especially fruitful in case of nonlinear models and complex datasets. After briefly reviewing these main concepts, in this lecture I will present several applications ranging from hydrologic partitioning (Budyko’s curve) and stochastic ecohydrology, to global weathering rates and soil formation, as well as landscape evolution and channelization. Since Copernicus-dot-org asks me to add at least 25 words to the abstract, I would like to thank the colleagues who supported my nomination and my many collaborators.
