Decision Support Model for Ecological Operation of Reservoirs Based on Dynamic Bayesian Network

Abstract

In this study, a model was proposed based on the sustainable boundary approach, to provide decision support for reservoir ecological operation with the dynamic Bayesian network. The proposed model was developed in four steps: (1) calculating and verifying the sustainable boundaries in combination with the ecological objectives of the study area, (2) generating the learning samples by establishing an optimal operation model and a Monte Carlo simulation model, (3) establishing and training a dynamic Bayesian network by learning the examples and (4) calculating the probability of the economic and ecological targets exceeding the set threshold from time to time with the trained dynamic Bayesian network model. Using the proposed model, the water drawing of the reservoir can be adjusted dynamically according to the probability of the economic and ecological targets exceeding the set threshold during reservoir operation. In this study, the proposed model was applied to the middle reaches of Heihe River, the effect of water supply proportion on the probability of the economic target exceeding the set threshold was analyzed, and the response of the reservoir water storage in each period to the probability of the target exceeding the set threshold was calculated. The results show that the risks can be analyzed with the proposed model. Compared with the existing studies, the proposed model provides guidance for the ecological operation of the reservoir from time to time and technical support for the formulation of reservoir operation chart. Compared with the operation model based on the designed guaranteed rate, the reservoir operation model based on uncertainty reduces the variation range of ecological flow shortage or the overflow rate and the economic loss rate by 5% and 6%, respectively. Thus, it can be seen that the decision support model based on the dynamic Bayesian network can effectively reduce the influence of water inflow and rainfall uncertainties on reservoir operation.

Assessing Flood Risk Dynamics in Data-Scarce Environments—Experiences From Combining Impact Chains With Bayesian Network Analysis in the Lower Mono River Basin, Benin

“…Well-suited for decision-support: ability to reveal hidden dependencies and indirect impacts (Marcot et al, 2006;Chen and Pollino, 2012;Rachid et al, 2021;Sahlin et al, 2021;Zhou et al, 2021) What-if scenario for the dependence on flood-sensitive crops was illustrated based on exemplary BN. The example suggested that alterations in the dependence on flood-sensitive crops (high vs. low) lead to changes of risk drivers pertaining, vulnerability and the final risk, including drivers with considerable causal distance to the observed driver (dependence on flood-sensitive crops).…”

“…Integration of spatial and temporal dynamics are not a core strength (Kragt, 2009;Terzi et al, 2019;Zhou et al, 2021) Given the already large number of parameters, this study has not explicitly integrated temporal and spatial dynamics by using GIS or time-step approaches. The integration of such approaches would have exceeded the feasibility and scope of this BN.…”