Bayesian Network Modeling to Improve Water Pricing Practices in Northwest China

Abstract

Water pricing is regarded as the most important and simplest economic instrument to encourage more efficient use of irrigation water in crop production. In the extremely water-scarce Tarim River basin in northwest China, improving water use efficiency has high relevance for research and policy. A Bayesian network modeling approach was applied, which is especially suitable under data-scarce conditions and the complex geo-hydrological, socioeconomic, and institutional settings of the study region, as it allows the integration of data from various types of sources. The transdisciplinary approach aimed at understanding the actual water pricing practices, the shortcomings of the current system, and possible ways of improvement. In an iterative procedure of expert interviews and group workshops, the key factors related to water pricing and water use efficiency were identified. The interactions among specific factors were defined by the respective experts, generating a causal network, which describes all relevant aspects of the investigated system. This network was finally populated with probabilistic relationships through a second round of expert interviews and group discussions. The Bayesian modeling exercise was then conducted using Netica software. The modeling results show that the mere increase of water price does not lead to significant increases in water use efficiency in crop production. Additionally, the model suggests a shift to volumetric water pricing, subsidization of water saving irrigation technology, and advancing agricultural extension to enable the farmer to efficiently react to increased costs for water. The applied OPEN ACCESSWater 2015, 7 5618 participatory modeling approach helped to stimulate communication among relevant stakeholders from different domains in the region, which is necessary to create mutual understanding and joint targeted action. Finally, the challenges related to the applied transdisciplinary Bayesian modeling approach are discussed in the Chinese context.

Spatiotemporal Patterns of Crop Irrigation Water Requirements in the Heihe River Basin, China

“…Thus, to meet escalating water demands for crop irrigation, agricultural producers have utilized increasing volumes of runoff and groundwater which leads to ecosystem degradation including the seasonal cutoff of rivers, groundwater overdraft, and decreasing ecological land area [1,40,41]. To mitigate these issues, the Chinese government has implemented a range of water management measures concerning water yield, water rights, and water prices and has also promoted the application of new technologies including drip and sprinkler irrigation [28,42,43]. However, managers and producers are still unable to effectively allocate irrigation water according to actual demand because of the absence of high-precision, fine-scale spatiotemporal IWR c data.…”

Innovative Analysis of Runoff Temporal Behavior through Bayesian Networks

“…BNs have been frequently and successfully applied to environmental studies [37][38][39], and to deal with problems within the water resources management field [40][41][42][43][44].…”

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

“…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]).…”