Abstract
Abstract. A hydrological impact analysis concerns the study of the consequences of certain scenarios on one or more variables or fluxes in the hydrological cycle. In such an exercise, discharge is often considered, as floods originating from extremely high discharges often cause damage. Investigating the impact of extreme discharges generally requires long time series of precipitation and evapotranspiration to be used to force a rainfall-runoff model. However, such kinds of data may not be available and one should resort to stochastically generated time series, even though the impact of using such data on the overall discharge, and especially on the extreme discharge events, is not well studied. In this paper, stochastically generated rainfall and corresponding evapotranspiration time series, generated by means of vine copulas, are used to force a simple conceptual hydrological model. The results obtained are comparable to the modelled discharge using observed forcing data. Yet, uncertainties in the modelled discharge increase with an increasing number of stochastically generated time series used. Notwithstanding this finding, it can be concluded that using a coupled stochastic rainfall–evapotranspiration model has great potential for hydrological impact analysis.
Highlights
Precipitation is the most important variable in the terrestrial hydrological cycle that determines soil moisture and discharge from a watershed
The results shown in Sect. 2.3.2 and the work of Pham et al (2016) reflect that the C-vine copula VT P EpE performs well and its simulations lie very close to the values of the observed evapotranspiration
Discharge is a very important variable which can be simulated via a rainfall-runoff model using recorded precipitation and evapotranspiration data
Summary
Precipitation is the most important variable in the terrestrial hydrological cycle that determines soil moisture and discharge from a watershed. As such, it impacts water management where generally the occurrences of extreme events, e.g. storms or droughts which have very low frequencies, are of concern. Stochastic multi-scale models describe the spatial evolution of the rainfall process regardless of scale factors. These models involve an assumption of temporal invariance of rainfall over a range of scales (Bernardara et al, 2007).
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