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

Obstructions in waste stabilization pond use in Uganda

Obstructions in waste stabilization pond use in Ugand

Statistical downscaling of precipitation in the Upper Nile: use of generalized linear models (GLMs) for the Kyoga Basin

General circulation model (GCM) climate projections cannot be relied on to provide information at scales finer than the GCM model-grid resolutions; hence, fine-scale information can be achieved by the use of high spatial resolution in dynamical models or empirical statistical downscaling. This study briefly reviews methods of downscaling climate projections with particular emphasis on rainfall simulation and the results of a first attempt to apply generalized linear models (GLMs) for statistical downscaling in the Upper Nile (a challenging equatorial climate of East and Central Africa)