Model development of a participatory Bayesian network for coupling ecosystem services into integrated water resources management

Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

“…A high proportion of these studies have sought to understand climatic and/or non-climatic drivers and their impacts on water-related systems and to evaluate the performance of management options under these changing conditions. Effects of numerous climatic drivers have been considered, including sea-level rise on water supply management (e.g., [2]), and on water quality management (e.g., [49]); precipitation and temperature on groundwater management (e.g., [63,64,67]), on reservoir management (e.g., [135]), on water supply management (e.g., [3,132]), on water quality management (e.g., [27,43,45] and on nutrient management (e.g., [119,126]) and precipitation on water supply and demand management (e.g., [18,127,130,131]). Similarly, the non-climatic drivers that have been considered, have included effects of population growth on water supply and demand management (e.g., [128]), and on water quality management (e.g., [41]); crop production changes in irrigation system management (e.g., [96,102]); population growth and agricultural production on water supply and demand management (e.g., [128]); agricultural production on irrigation water management (e.g., [93][94][95]100]), on water supply management (e.g., [85]), and on groundwater management (e.g., [67]); changes in domestic use and in agricultural and industrial production on water supplies and demand management (e.g., [127]).…”

“…For example, expert knowledge was used to identify the environmental factors (e.g., land use, rainfall, storage level, agricultural areas) affecting the three key water quality parameters (turbidity, colour and crypto) in the reservoirs of New South Wales, Australia, and then quantify the relationships between environmental factors and these water quality variables through populating the CPTs for the model [47]. In addition, meetings with stakeholders, water managers, water experts and researchers were organized to determine the influential variables (e.g., water for people and animals, industrial water demand, groundwater exaction) and their relationships on a water supply and demand system, and then elicit the CPTs based on average values from all participants for the system in Qira Oasis area, Northwest China [3]. The ability of BNs to combine these different types of data is particularly useful for informing decisions under high levels of uncertainty, where the state of a variable or decision outcome is uncertain.…”

“…Institutional and social measures were found to be the most frequently assessed category of management measure. This may be because this category includes a wide range of measures, such as water pricing (e.g., [18,64,102,130]), compensation payments (e.g., [69]), discharge permits (e.g., [3,28]), educational programs (e.g., [2,67]) and improvements in management practices (e.g., [29,65,100,137]).…”

A Mixed Integer Linear Programming Method for Optimizing Layout of Irrigated Pumping Well in Oasis

“…Aside from the other water uses that account for approximately 2.3% of the total water consumption, the other 97.7% is used for agriculture irrigation. Approximately 82.1% of the agricultural irrigation water is diverted from the Cele River, with the remaining 17.9% extracted from oasis area groundwater [34]. If the area has a 70% annual groundwater recharge, then the average annual allowable groundwater exploitation amount would be approximately 0.9242 × 10 8 m 3 /a.…”

Assessing water distribution and agricultural expansion in the Cele Oasis, China

“…However, it is possible to obtain a relative measure of oasis sustainment through a mass balance allocation approach (Ling et al 2013;Xue et al 2017).…”