Assessing the relative importance of parameter and forcing uncertainty and their interactions in conceptual hydrological model simulations

Predictions of river flow dynamics provide vital information for many aspects of water management including water resource planning, climate adaptation, and flood and drought assessments. Many of the subjective choices that modellers make including model and criteria selection can have a significant impact on the magnitude and distribution of the output uncertainty. Hydrological modellers are tasked with understanding and minimising the uncertainty surrounding streamflow predictions before communicating the overall uncertainty to decision makers. Parameter uncertainty in conceptual rainfall-runoff models has been widely investigated, and model structural uncertainty and forcing data have been receiving increasing attention. This study aimed to assess uncertainties in streamflow predictions due to forcing data and the identification of behavioural parameter sets in 31 Irish catchments. By combining stochastic rainfall ensembles and multiple parameter sets for three conceptual rainfall-runoff models, an analysis of variance model was used to decompose the total uncertainty in streamflow simulations into contributions from (i) forcing data, (ii) identification of model parameters and (iii) interactions between the two. The analysis illustrates that, for our subjective choices, hydrological model selection had a greater contribution to overall uncertainty, while performance criteria selection influenced the relative intra-annual uncertainties in streamflow predictions. Uncertainties in streamflow predictions due to the method of determining parameters were relatively lower for wetter catchments, and more evenly distributed throughout the year when the Nash-Sutcliffe Efficiency of logarithmic values of flow (lnNSE) was the evaluation criterion

Unravelling the unusually curved X-ray spectrum of RGB J0710 + 591 using AstroSat observations

International audienceWe report the analysis of simultaneous multiwavelength data of the high-energy-peaked blazar RGB J0710 + 591 from the Large Area X-ray Proportional Counters, Soft X-ray focusing Telescope, and Ultraviolet Imaging Telescope (UVIT) instruments onboard AstroSat. The wide band X-ray spectrum (0.35–30 keV) is modelled as synchrotron emission from a non-thermal distribution of high-energy electrons. The spectrum is unusually curved, with a curvature parameter β_p ∼ 6.4 for a log parabola particle distribution, or a high-energy spectral index p_2 > 4.5 for a broken power-law distribution. The spectrum shows more curvature than an earlier quasi-simultaneous analysis of Swift–XRT/NuSTAR data where the parameters were β_p ∼ 2.2 or p_2 ∼ 4. It has long been known that a power-law electron distribution can be produced from a region where particles are accelerated under Fermi process and the radiative losses in acceleration site decide the maximum attainable Lorentz factor, γ_max. Consequently, this quantity decides the energy at which the spectrum curves steeply. We show that such a distribution provides a more natural explanation for the AstroSat data as well as the earlier XRT/NuSTAR observation, making this as the first well-constrained determination of the photon energy corresponding to γ_max. This in turn provides an estimate of the acceleration time-scale as a function of magnetic field and Doppler factor. The UVIT observations are consistent with earlier optical/UV measurements and reconfirm that they plausibly correspond to a different radiative component than the one responsible for the X-ray emission