Analysis of spatio-temporal dependence of inflow time series through Bayesian causal modelling

This paper aims to assess fully the spatio-temporal dependence dimensions of inflow across two adjacent and parallel basins and among different time steps through Causality. This is addressed from the perspective of Causal Reasoning, supported by Bayesian modelling, under a novel framework named Bayesian Causal Modelling (BCM). This is applied, through a concept-proof, to the Jucar River Basin (the second largest basin of Eastern Spain, characterized by long and severe drought conditions). In this concept-proof a double goal is evaluated; first dedicated to a lumped analysis of dependence and second a specific one over dry periods focused on time-horizon of the Jucar basin typical drought (3 years). These challenges comprise the development of two fully connected Bayesian Networks (BNs), one for each challenge populated/trained from historical-inflow records. BNs were designed at a season-scale and consequently, time was upscaled and grouped into Irrigation and Non-Irrigation periods, according to Jucar River Basin Authority operational practices. Results achieved showed that BCM framework satisfactorily captured the spatio-temporal dependencies of systems. Furthermore, BCM is able to answer to some key questions over interdependencies between adjacent and parallel subbasins. Those questions may comprise, the amount of spatial dependences among time series, the temporarily conditionality among subbasins and the spatio-temporal dependence among basins. This provides a relevant insight on the intrinsic spatio-temporal dependence structure of inflow time series in complex basins systems. This approach could be very valuable for water resources planning and management, due to its application power for predicting extreme events (e.g. droughts) as well as improving and optimizing the reservoirs operation rules.

Automated summary

This paper evaluates the spatio-temporal dependence dimensions of inflow across two adjacent and parallel basins through the Bayesian Causal Modelling framework, showing satisfactory capturing of system dependencies. Additionally, it can answer key questions about interdependencies between adjacent and parallel subbasins.