|Al-Qurashi, A. - IMPERIAL COLLEGE LONDON|
|Mcintyre, N. - IMPERIAL COLLEGE LONDON|
|Wheater, H. - IMPERIAL COLLEGE LONDON|
Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 14, 2008
Publication Date: May 28, 2008
Citation: Al-Qurashi, A., Mcintyre, N., Wheater, H., Unkrich, C.L. 2008. Application of the Kineros2 rainfall-runoff model to an arid catchment in Oman. Journal of Hydrology. 355:91-105. Interpretive Summary: It is difficult to predict the amount and rate of runoff resulting from rain storms in areas with arid climates. When using computer models to predict runoff, the type of input data available is a primary consideration when choosing which model to use. This study evaluated the performance of a relatively complex model, Kineros2, by comparing its runoff predictions against measured runoff from 27 storms in Oman. It was found that the model’s performance was generally poor, mainly because there were not enough rain gauges to adequately describe the spatial variability of rainfall during the storms.
Technical Abstract: The difficulty of predicting rainfall-runoff responses in arid catchments using typically available data sets is well-known, hence the need to carefully evaluate the suitability of alternative modelling approaches for a given problem and data set; and to identify causes of uncertainty in order to prioritise research and data. In this paper, we evaluate the distributed model, Kineros2, in application to an arid catchment in Oman, using rainfall-runoff data from 27 storm events. The analysis looks at model sensitivities, uncertainty and performance, based on uniform random sampling of the model parameter space and predictions of features of the observed hydrograph at the catchment outlet. A series of three experiments used different calibration strategies (an 11-parameter calibration, a 5-parameter calibration, and a 4-parameter calibration including some spatial variability of the saturated hydraulic conductivity). The parameters most significantly affecting flow peak and volume performance are those controlling infiltration rates on hillslopes. The model output was also generally sensitive to a parameter within the rainfall interpolation model. Relatively little sensitivity to initial catchment wetness was observed. Prediction performance was generally poor, for all events and for all the tested calibration and prediction strategies; and the uncertainty, estimated using model ensembles, was very high. A previously applied 2-parameter regression model was found to perform better for predicting flow peaks. Literature review shows our results are consistent with experience of other modellers of arid climate hydrology. In order to realise the potential value of distributed, physically based models, for application to arid zones, significant data collection and further research is required, in particular regarding spatial rainfall observation and modelling.