Effects of precipitation uncertainty on discharge calculations for main river basins

This study quantifies the uncertainty in discharge calculations caused by uncertainty in precipitation input for 294 river basins worldwide. Seven global gridded precipitation datasets are compared at river basin scale in terms of mean annual and seasonal precipitation. The representation of seasonality is similar in all datasets, but the uncertainty in mean annual precipitation is large, especially in mountainous, arctic, and small basins. The average precipitation uncertainty in a basin is 30%, but there are strong differences between basins. The effect of this precipitation uncertainty on mean annual and seasonal discharge was assessed using the uncalibrated dynamic global vegetation and hydrology model Lund-Potsdam-Jena managed land (LPJmL), yielding even larger uncertainties in discharge (average 90%). For 95 basins (out of 213 basins for which measurements were available) calibration of model parameters is problematic because the observed discharge falls within the uncertainty of the simulated discharge. A method is presented to account for precipitation uncertainty in discharge simulations

Optimal Stochastic Enhancement of Photoionization

The effect of noise on the nonlinear photoionization of an atom due to a femtosecond pulse is investigated in the framework of the stochastic Schr\"odinger equation. A modest amount of white noise results in an enhancement of the net ionization yield by several orders of magnitude, giving rise to a form of quantum stochastic resonance. We demonstrate that this effect is preserved if the white noise is replaced by broadband chaotic light.Comment: 4 pages, 4 figure