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• The input population and output population are considered. • 108 scenarios are listed from the input population and quarantine strategies. • The second outbreak of disease is obtained. In this study, an epidemic model was developed to simulate and predict the disease variations of Guangdong province which was focused on the period from Jan 27 to Feb 20, 2020. To explore the impacts of the input population and quarantine strategies on the disease variations at different scenarios, four time points were assumed as Feb 6, Feb 16, Feb 24 and Mar 5 2020. The major results suggest that our model can well capture the disease variations with high accuracy. The simulated peak value of the confirmed cases is 1002 at Feb 10, 2020 which is mostly close to the reported number of 1007 at Feb 9, 2020. The disease will become extinction with peak value of 1397 at May 11, 2020. Moreover, the increased numbers of the input population can mainly shorten the disease extinction days and the increased percentages of the exposed individuals of the input population increase the number of cumulative confirmed cases at a small percentage. Increasing the input population and decreasing the quarantine strategy together around the time point of the peak value of the confirmed cases, may lead to the second outbreak. [ABSTRACT FROM AUTHOR]

The Coronavirus Disease 2019 (COVID-19) firstly announced in Wuhan of Hubei province, China is rapidly spreading to all the other 31 provinces of China and to more than 140 countries. Quarantine strategies play the key role on the disease controlling and public health in the world with this pandemic of the COVID-19 defined by the World Health Organization. In this study, a SEIRQ epidemic model was developed to explore the dynamic changes of COVID-19 in Wuhan and mainland China, from January 27, 2020 to March 5, 2020. Moreover, to investigate the effects of the quarantine strategies, two perspectives are employed from the different quarantine magnitudes and quarantine time points. The major results suggest that the COVID-19 variations are well captured by the epidemic model with very high accuracy in the cumulative confirmed cases, confirmed cases, cumulative recovered cases and cumulative death cases. The quarantine magnitudes in the susceptible individuals play larger roles on the disease control than the impacts of the quarantines of the exposed individuals and infectious individuals. For the quarantine time points, it shows that the early quarantine strategy is significantly important for the disease controlling. The time delayed quarantining will seriously increase the COVID-19 disease patients and prolongs the days of the disease extinction. Our model can simulate and predict the COVID-19 variations and the quarantine strategies are important for the disease controlling, especially at the early period of the disease outbreak. These conclusions provide important scientific information for the government policymaker in the disease control strategies. [ABSTRACT FROM AUTHOR]

[Display omitted] • Proposed: DISO-based CESAI for comprehensive ESs assessment in arid regions. • Wind erosion's significant impact mandates its inclusion in arid ESs assessments. • From 1992 to 2020, Uzbekistan's ESs declined due to drops in HQ and SF functionality. • Natural factors affect WY, and SR, while human activities impact SF, CS, and HQ. Wind erosion, a crucial disruptor of ecosystem health and stability, especially in arid regions, remains unincorporated in extensively utilized ecosystem services (ESs) quantification models such as InVEST. This neglect results in a gap: the profound influence of wind erosion on ESs remains largely underexplored and unquantified. Moreover, current models predominantly concentrate on individual ESs, thus bypassing the necessary comprehensive evaluation of various ESs' developmental status. To address this gap, we propose a systematic framework based on the Distance between Indices of Simulation and Observation (DISO), which integrates the Revised Wind Erosion Equation (RWEQ) and key modules of the InVEST model to derive a Comprehensive Ecosystem Services Assessment Index (CESAI) for arid regions. We evaluated five ESs, including water yield, soil retention, sand fixation (measured by wind erosion), carbon storage, and habitat quality. To further investigate the influence of each ES on the comprehensive assessment, we employed a suite of statistical methods, including independent samples t-tests and a generalized additive model (GAM), to elucidate the complex relationships and significant effects of wind erosion on the CESAI. The results of the t-tests and GAM highlighted the significant influence of wind erosion, which demonstrated the highest edf-value (2.000) and the highest F-value (1039.6, p < 0.001). Spatiotemporal dynamics show that ES functionality in Uzbekistan has deteriorated from 1992 to 2020, a trend starkly exemplified by the severe harm inflicted by the Aral Sea crisis. Drawing upon the DISO concept, we have innovatively designed a framework that steers the CESAI index, a comprehensive instrument for evaluating ESs. Beyond providing a holistic perspective on ESs assessments, this framework also deciphers the multifaceted interplay within ecosystems through statistical dissection of each service's influence. Collectively, our findings deepen the understanding of ESs dynamics, shedding light on the crucial role of wind erosion within arid ecosystem service assessments. [ABSTRACT FROM AUTHOR]

The resolution mismatch between GCMs and in-situ gauge observations is an issue that has to be addressed for assessments and projections of precipitation extremes. The impacts of using different strategies to address this issue on GCM assessments and projections are evaluated in this study. The differences of precipitation extremes derived from GCMs at the original gridded resolutions and site-scale observations can be mostly explained by resolution mismatch. As the spatial and temporal "discontinuous" nature of precipitation, consecutive dry days (precipitation intensity) estimated from GCM data over a grid are likely to be shorter (smaller) than in-situ observations. By interpolating GCMs and observations to a common resolution, areal differences are moderately reduced, but spatial correlations between GCMs and observations may not be necessarily improved. By statistically downscaling the GCM-derived precipitation extremes, the indices agree better with the in-situ observations substantially. Using interpolation or downscaling to resolve resolution mismatch in GCMs may result in contradictory projected changes in extremes. Downscaled precipitation extremes generally change in greater magnitude than interpolated extremes in the projections. • Resolution mismatch explains the differences in precipitation extremes between the gridded GCMs and site-scale observations. • Interpolation partially reduces the differences, and downscaling largely improves the performance of GCM-based extremes. • Using interpolation or downscaling to resolve resolution mismatch may result in contradictory projected changes in extremes. [ABSTRACT FROM AUTHOR]