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The effects of global climate change and human activities are anticipated to significantly impact ecosystem services (ESs), particularly in urban agglomerations of arid regions. This paper proposes a framework integrating the dynamic Bayesian network (DBN), system dynamics (SD) model, patch generation land use simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for predicting land use change and optimizing ESs spatial patterns that is built upon the SSP-RCP scenarios from CMIP6. This framework is applied to the oasis urban agglomeration on the northern slope of the Tianshan Mountains in Xinjiang (UANSTM), China. The findings indicate that both the SD model and PLUS model can accurately forecast the distribution of future land use. The SD model shows a relative error of less than 2.32%, while the PLUS model demonstrates a Kappa coefficient of 0.89. The land use pattern displays obvious spatial heterogeneity under different climate scenarios. The expansion of cultivated land and construction land is the main form of land use change in UANSTM in the future. The DBN model proficiently simulates the interactive relationships between ESs and diverse factors. The classification error rates for net primary productivity (NPP), habitat quality (HQ), water yield (WY), and soil retention (SR) are 20.04%, 3.47%, 4.45%, and 13.42%, respectively. The prediction and diagnosis of DBN determine the optimal ESs development scenario and the optimal ESs region in the study area. It is found that the majority of ESs in UANSTM are predominantly influenced by natural factors with the exception of HQ. The socio-economic development plays a minor role in such urban agglomerations. This study offers significant insights that can contribute to the fields of ecological protection and land use planning in arid urban agglomerations worldwide. • An integrated framework combining DBN, SD, PLUS and InVEST models was proposed. • Impact of natural and socioeconomic factors on ESs was determined under climate scenarios. • The spatial pattern of ESs was optimized and partitioned using DBNs. • ESs in arid oasis urban agglomerations are mainly affected by natural factors. [ABSTRACT FROM AUTHOR]

Abstract Past water resource developments in dryland regions rarely estimated the full suite of environmental and social impacts arising from damming and diverting water for food production. Nowadays there is a greater focus on sustainable resource development which considers the economic, social and environmental costs and benefits. A challenge is to apply tools and methods which can capture the often disparate knowledge and data describing many costs and benefits. This paper describes a proof of concept application of a large Bayesian Decision Net to estimate the total utility of water resource development according to criteria of social, economic and environmental sustainability. We focus on two water-scarce catchments in remote northern Australia which are under investigation for development. The study catchments contain a diverse set of ecosystem services and socio-cultural values, including important Indigenous values and high value freshwater ecosystems. The Bayesian Decision Net was shown to have many properties that made it useful for performing a social, economic and environmental sustainability assessment, in particular its ease of construction; its ability to handle quantitative and qualitative data types; its preservation of system knowledge and; its ease of use in aiding decision making. From the perspective of the sustainability assessment in our case study, the total utility of water resource development for new irrigation is negative in both the studied catchments. The overall utility of water resource development could be positive if irrigation development is highly sensitive to the environment and there are very low environmental impacts, and much higher net economic returns to irrigators eventuate, possibly through higher commodity prices, lower capital costs of irrigation development or some combination of both. Highlights • Development of water resources involves trade-offs between economy and environment. • Integrated social-environment-economy sustainability (SEES) assessments support trade-offs analyses. • Bayesian Decision Networks (BDNs) support SEES assessments with diverse data types. • BDN applied to irrigation development scenarios of two remote catchments in north Australia. • The economic and ecosystem service benefits of the case study irrigation developments are questionable. [ABSTRACT FROM AUTHOR]