Non-stationary rainfall and natural flows modeling at the watershed scale

In areas in which natural water resources are variable over time, tools that determine the probability distribution of hydrological variables are required to evaluate various management alternatives. In this article, a stochastic simulation framework of hydrological variables through atmospheric pressure modeling is proposed. This methodology employs the mean value of the atmospheric pressure in the winter to differentiate the wet, medium and dry years in terms of rainfall and flow at different temporal scales. Monthly mean and daily maximum rainfall and flow data series are stochastically replicated. To achieve this replication, a non-stationary parametric mixture distribution model that combines a Weibull and a Normal distribution is fitted to the univariate distribution of the atmospheric pressure. This model includes interannual variability through two covariables: extraterrestrial solar radiation and the NAO index. This model is applied to the Guadalete River Basin in southern Spain, in which the river flow regime is influenced by the highly seasonal precipitation regime typically found in the Mediterranean area. The non-stationary parametric mixture distribution model with the two covariables showed a good fit to the observed sea level pressure, displaying an important reduction on the BIC. A good correlation was obtained between the average sea level pressure in winter and the accumulated precipitation and flow (r = −0.8 for monthly values and −0.6 for maximum daily values). The statistical similarity indicated that the synthetic series of precipitation and flow preserved the distribution trends in the observed data. The identical methodology can be applied in other watersheds once the direct relationship between the mean atmospheric pressure and the hydrology of the area is known.