Influencing Factors and Mortality Prediction of Covid-19 Contact Infection Rate in China

Taking the COVID-19 data from 2020-1-23 to 3-21 days released by the China Health Protection Committee as the sample,the hospital remaining rate,mortality rate and cure rate are selected as covariates,and the contact infection rate is used as response variable to establish a high precision ADL model,results of return substitution show that the predicted value of contact infection rate almost coincides with the sample value,and shows three stages of change characteristics.After March 1,2020,the overall trend is downward,stable below 12%.Main influencing factors of contact infection rate are analyzed quantitatively.Based on this,the ARIMA(1,2,2)model is established to analyze and predict the mortality change trend.The results showed that the domestic COVID-19 mortality rate is stable near 4%after 2020-3-27.

Optimization and Empirical Analysis Based on Markov Forecasting Model

The optimization model based on Markov chain is established to optimize the prediction of industrial structure and provide reference for policy adjustment.The vectorization operator is used to transform the Markov prediction model into an optimization problem with constraints,which highlights the theoretical proof and computational rigor.Based on the data of three industrial structures in Yunnan Province from 1989 to 2019,this paper establishes Markov optimization model to predict the proportion of three industrial structures in Yunnan Province from 2020 to 2030.The maximum percentage average absolute error and hill inequality coefficient of the prediction are 1.2335%and 0.2,respectively.The order-degree of the three industrial structures is a stable series,which is stable around 1 after 1996.The sample data and the predicted values show four stages of change characteristics.After 2020,the three industrial structures are stable in the"three,two and one"structure.

Increasing probability of concurrent drought between the water intake and receiving regions of the Hanjiang to Weihe River Water Diversion Project,China

Water diversion projects are an effective measure to mitigate water shortages in water-limited areas.Understanding the risk of such projects increasing concurrent drought between the water intake and receiving regions is essential for sustainable water management.This study calculates concurrent drought probability between the water intake and receiving regions of the Hanjiang to Weihe River Water Diversion Project using Standardized Precipitation Index and Copula functions.Results showed an increasing trend in drought probability across both the water intake and receiving regions from 2.67%and 8.38%to 12.47%and 14.18%,respectively,during 1969-2018.The return period of concurrent drought decreased from 111.11 to 13.05 years,indicating larger risk of simultaneous drought between the two regions.Projections from CMIP6 suggested that under the SSP 2-4.5 and 5-8.5 scenarios,concurrent drought probability would increase by 2.40%and 7.72%in 2019-2050 compared to that in 1969-1990,respectively.Although increases in precipitation during 2019-2050 could potentially alleviate drought conditions relative to those during 1991-2018,high precipitation variability adds to the uncertainty about future concurrent drought.These findings provide a basis for better understanding concurrent drought and its impact on water diversion projects in a changing climate,and facilitate the establishment of adaptation countermeasures to ensure sustainable water availability.

Climate-driven changes in drought metrics across the Yellow River Basin based on Coupled Model Intercomparison Project Phase 6

Recent climate change has accelerated the global hydrological cycle, substantially affecting drought metrics such as drought duration and drought propagation;however, knowledge of drought patterns in these metrics remains limited. Here, we aimed to address the evolution and influencing factors of major drought metrics under past and future climate scenarios within the Yellow River Basin(YRB) based on Coupled Model Intercomparison Project Phase 6(CMIP6). Accordingly, we investigated the changes in drought duration for meteorological drought and agricultural drought across the YRB and identified the variability in drought propagation time from meteorological drought to agricultural drought by using a standardized precipitation/soil moisture index and run theory. Meteorological and agricultural drought duration, and propagation time, increased from 1850 to 2014, decreased significantly from 2015 to 2100 with change trends of –0.0027, –0.0197, and –0.002 month/year, respectively. Drought duration had a negative sensitivity to humidification, and agricultural drought was more sensitive than meteorological drought. Propagation time exhibited a greater sensitivity to meteorological humidification than agricultural humidification. The results also suggest that precipitation, evapotranspiration, and soil moisture are the main drivers of drought metric changes, with air temperature and crop cover exhibiting a strong indirect effect on drought metrics in the YRB. Decreased propagation time from meteorological to agricultural drought and decreased duration provide evidence for the accelerated occurrence and increased impact of drought, highlighting the importance of a more comprehensive understanding of drought metric changes under rapid climate change.