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Transportation industry accounts for the majority of air pollutant emission, which is one of the leading factors of climate change. Many vehicle emission models have been proposed focusing on emission factors to study the pollutant emission issue. However, little research has been done in China on emission models, though China is a major air pollutant-emitting country. This paper first introduces two emission models- MOBILE model and MOVES model, then proposes model selection methods including AIC (Akaike information criterion) and BIC (Bayesian information criterion) to compare and select the better model which could be applied in China. Experimental results show that the value of AIC and BIC for MOVES model is significantly lower than MOBILE model, which implies that MOVES model has better performance on real data fitness and prediction. Our experimental findings may be useful for future research on air pollutant emission modelling in China. [ABSTRACT FROM AUTHOR]

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]

The Tarim River, the largest inland river in China, sits in the Tarim River Basin (TRB), which is an arid area with the ecosystem primarily sustained by water from melting snow and glaciers in the headstream area. To evaluate the pressures of natural disasters in this climate‐change‐sensitive basin, this study projected flash droughts in the headstream area of the TRB. We used the variable infiltration capacity (VIC) model to describe the hydrological processes of the study area, Markov chain Monte Carlo to quantify the parameter uncertainty of the VIC model. Ten downscaled general circulation models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were used to drive the VIC model, and the standardized evaporative stress ratio was applied to identify flash droughts. The results demonstrated that the VIC model after Bayesian parameters uncertainty analysis can efficiently describe the hydrological processes of the study area. In the future (2021–2100), compared with the plain region, the alpine region has higher flash drought frequency and intensity. Compared with the historical period (1961–2014), the frequency, duration, and intensity of flash droughts tend to increase throughout the study area, especially for the alpine area. Moreover, based on variance decomposition, CMIP6 model is the most important uncertainty source for flash drought projection, followed by the shared socioeconomic pathway of climate change scenario and VIC model parameters. Plain Language Summary: The Tarim River is the largest inland river in China, which is an arid area with the ecosystem primarily sustained by water from melting snow and glaciers in the headstream area. Compared with long‐duration drought, flash drought is more difficult to forecast and identify. We used the variable infiltration capacity model to build a hydrological model of the headstream area of the Tarim River Basin, and the model output (ET data) was used to identify flash droughts. Based on 10 representative Coupled Model Intercomparison Project Phase 6 (CMIP6) models, this study projected the future flash droughts in the study area under climate change. The findings indicated that the alpine region is likely to experience a greater frequency and intensity of flash droughts in the future (2021–2100) compared to the plain region. Compared with the historical period (1961–2014), the frequency, duration, and intensity of flash droughts tend to increase throughout the study area, especially for the alpine area. In addition, the result of uncertainty decomposition revealed that the CMIP6 model is the most important uncertainty source for flash drought projection. Key Points: The hydrological processes of study area are well described by the variable infiltration capacity model and Bayesian uncertainty analysisCompared with the plain region, the alpine region has higher flash drought frequency and intensity in the futureThe Coupled Model Intercomparison Project Phase 6 model is the most important uncertainty source for flash drought projection [ABSTRACT FROM AUTHOR]

Abstract: Crop models often simulate drought impacts with full and no irrigation scenarios, while planners are more interested in whether the current available irrigation water can cope with the future more serious droughts. This paper addresses a key constraint common to modeling studies: the limited representation of actual irrigation water supply. We present a data-driven approach to identify a benchmark for agronomic drought risk levels as defined by water availability thresholds at the baseline climate (1980–2008) using reported crop yields, climate and irrigation statistics. Then, holding the current irrigation supplies, we adopted Bayesian formula to estimate magnitude of the future water availability and the associated probability of crops yields being decreased to rainfall-deficiency under climate conditions in 2030s (2020–2040) based on the RegCM3 climate model output driven by IPCC SRES A1B scenario. Results reveal that future drought stress would overwhelm the irrigation capacity of current supplies in northern and western China, while drought remains at baseline climate levels in the central, eastern and southern regions. The largest increases in the probability of projected drought risk were in northeast and southwest, ranging from 14% to 28% above baseline climate. Regional drought impacts for grain self sufficiency are discussed. [Copyright &y& Elsevier]