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

Abstract

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.

Inter-Annual and Seasonal Variability of Flows: Delivering Climate-Smart Environmental Flow Reference Values

“…Alternatively, and perhaps more appropriately, the frequency of occurrence approach with the new reference values per streamflow type could be examined within existing frameworks for assessing people's and ecosystems' resilience based on rivers' discharge stochastic models [19,20,47]. Unlike deterministic models, stochastic models account for spatial-temporal variability of extreme events with successful outcomes towards reducing uncertainty in river basins with the influence of the Mediterranean climate [48][49][50].…”

The application of spreadsheets for teaching hydrological modeling and climate change impacts on streamflow

“…The hydrological Témez model [69] is a lumped, rainfallrunoff model widely used in Spain for water resource management [11,12,18,19,21,24,32,33,41,46,49,53,54,83], as well as in other countries [52,56,75]. Although a monthly interval is usually considered, the model can be applied to other intervals.…”

“…The principal activity was evaluating the long-term impact of climate change (2071-2100) on streamflow in a watershed (different for each student) in Peninsular Spain. The lumped hydrological Témez model [69] was used due to the low amount of necessary hydrological data [33], its ease of implementation into an Excel spreadsheet [49], and its long history of application in Spanish water resources assessment [41,49,54,83]. Furthermore, during the exercise, students accessed meteorological, streamflow, and climate change databases, calibrated and validated the model, and analyzed the sensitivity of the model parameters.…”

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

“…In the study area, precipitation shows a strong interannual irregularity and mainly concentrates on autumn and winter months, with practically no precipitation between June and September. Rainfall shows average annual values of 835 mm, which can range between 365 mm during dry years and 1606 mm during wet years [41]. The average annual temperature is 18.2 • C, with milder values in mountainous areas.…”