The implementation of emission reductions during the 2014 Asia-Pacific Economic Cooperation (APEC) summit provides a valuable opportunity to study air pollution in Beijing. From 15 October to 30 November 2014, the height of the atmospheric mixing layer and the vertical attenuated backscattering coefficient profiles were observed online using a~lidar ceilometer. Compared with fine particulate matter (PM_2.5) and aerosol optical depth (AOD) data, the attenuated backscattering coefficients measured by the lidar ceilometer were strongly correlated with the PM_2.5 concentration and AOD (correlation coefficients of 0.89 and 0.86, respectively). This result demonstrated the reliability of the vertical distribution of particles measured by the lidar ceilometer. By classifying different degrees of air pollution based on visibility, we found that during the transition period of air pollution, which was affected by transport of southerly flows in the mixing layer, the attenuated backscattering coefficient from 0 to 1500 m was enhanced by approximately 1.4 Mm⁻¹ sr⁻¹ (140 %). During the polluted period, the attenuated backscattering coefficient from 0 to 300 m suddenly increased, and the coefficient near the surface peaked (approximately 14 Mm⁻¹ sr⁻¹); however, the attenuated backscattering coefficient from 300 to 900 m decreased gradually, and the average value from 0 to 1500 m decreased by 0.5 Mm⁻¹sr⁻¹ (20 %). The height of the mixing layer gradually decreased, and the ratio of CO / SO₂ gradually increased, which indicate that the polluted period was dominated by local contribution. Due to the emission reductions during APEC (DAPEC), the concentration of PM_2.5 decreased by 59.2 and 58.9 % and visibility improved by 70.2 and 56.0 % compared to before (BAPEC) and after APEC (AAPEC), respectively. The contribution of regional transport in DAPEC decreased by approximately 36 and 25 %, and the local contribution decreased by approximately 48 and 54 % compared to BAPEC and AAPEC, respectively. The most effective method of controlling air pollution in the Beijing area is to reduce regional emissions during the transition period and reduce local emissions during the polluted period.

Global water models (GWMs) simulate the terrestrial water cycle on the global scale and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modelling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how 16 state-of-the-art GWMs are designed. We analyse water storage compartments, water flows, and human water use sectors included in models that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to enhance model intercomparison, improvement, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Six models used six compartments, while four models (DBH, JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPI-HM) simulate only water for the irrigation sector. We conclude that, even though hydrological processes are often based on similar equations for various processes, in the end these equations have been adjusted or models have used different values for specific parameters or specific variables. The similarities and differences found among the models analysed in this study are expected to enable us to reduce the uncertainty in multi-model ensembles, improve existing hydrological processes, and integrate new processes.

Compound flooding is generated when two or more flood drivers occur simultaneously or in close succession. Multiple drivers can amplify each other and lead to greater impacts than when they occur in isolation. A better understanding of the interdependence between flood drivers would facilitate a more accurate assessment of compound flood risk in coastal regions. This study employed the D-Flow Flexible Mesh model to simulate the historical peak coastal water level, consisting of the storm surge, astronomical tide, and relative sea level rise (RSLR), in Shanghai over the period 1961–2018. It then applies a copula-based methodology to calculate the joint probability of peak water level and rainfall during historical tropical cyclones (TCs) and to calculate the marginal contribution of each driver. The results indicate that the astronomical tide is the leading driver of peak water level, followed by the contribution of the storm surge. In the longer term, the RSLR has significantly amplified the peak water level. This study investigates the dependency of compound flood events in Shanghai on multiple drivers, which helps us to better understand compound floods and provides scientific references for flood risk management and for further studies. The framework developed in this study could be applied to other coastal cities that face the same constraint of unavailable water level records.