Towards a Sustainable and Adaptive Groundwater Management: Lessons from the Benalup Aquifer (Southern Spain)

Precipitation Variability and Drought Assessment Using the SPI: Application to Long-Term Series in the Strait of Gibraltar Area

The standardized precipitation index (SPI) provides reliable estimations about the intensity, magnitude and spatial extent of droughts in a variety of time scales based on long-term precipitation series. In this work, we assess the evolution of monthly precipitation in the Barbate River basin (S. Iberian Peninsula) between 1910/11 and 2017/18 through the generation of a representative precipitation series for the 108-year period and the subsequent application of the SPI. This extensive series was obtained after processing all the precipitation data (67 stations) available within and nearby the basin and subsequent complex gap-filling stages. The SPI identified 26 periods of drought, 12 of them severe and 6 extreme, with return periods of 9 and 18 years, respectively. Complementary analysis evidenced changes in precipitation cyclicity, with periodicities of 5 and 7–8 years during the first and second half of the study period, respectively. Additionally, the amplitude of pluviometric oscillations increased during the second half of the period, and extreme events were more frequent. While the decade 1940–1950 was very dry, with precipitation 11% below the basin’s average, 1960–1970 was very humid, with precipitation 23% above average. Contrary to the results of climate change projections specific to this area, a clear downward trend in precipitation is not detected.

Modeling River Runoff Temporal Behavior through a Hybrid Causal–Hydrological (HCH) Method

The uncertainty in traditional hydrological modeling is a challenge that has not yet been overcome. This research aimed to provide a new method called the hybrid causal–hydrological (HCH) method, which consists of the combination of traditional rainfall–runoff models with novel hydrological approaches based on artificial intelligence, called Bayesian causal modeling (BCM). This was implemented by building nine causal models for three sub-basins of the Barbate River Basin (SW Spain). The models were populated by gauging (observing) short runoff series and from long and short hydrological runoff series obtained from the Témez rainfall–runoff model (T-RRM). To enrich the data, all series were synthetically replicated using an ARMA model. Regarding the results, on the one hand differences in the dependence intensities between the long and short series were displayed in the dependence mitigation graphs (DMGs), which were attributable to the insufficient amount of data available from the hydrological records and to climate change processes. The similarities in the temporal dependence propagation (basin memory) and in the symmetry of DMGs validate the reliability of the hybrid methodology, as well as the results generated in this study. Consequently, water planning and management can be substantially improved with this approach.