Evaluating maximum likelihood estimation methods to determine the Hurst coeficient

Monitoring drought dynamics in China using Optimized Meteorological Drought Index (OMDI) based on remote sensing data sets

Timely and accurate monitoring of the spatiotemporal changes in drought is very important for the reduction in the social losses caused by drought. The Optimized Meteorological Drought Index (OMDI), originally established in southwestern China, showed great potential for drought monitoring over large regions on a large scale. However, the applicability of the index requires further evaluation, especially when used throughout China, which has a different agricultural divisions, variable climatic conditions, complex terrain and diverse land cover. In addition, the OMDI model relies on training data to construct local parameters for the model. On a large scale, it is of great significance to use multisource remote sensing data sets to construct OMDI model parameters. In this paper, the constrained optimization method was used to establish weights for the MODIS-derived Vegetation Conditional Index (VCI), TRMM-derived Precipitation Condition Index (PCI), and GLDAS-derived Soil Moisture Condition Index (SMCI) and calculate the OMDI based on the Standard Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and weather stations. The accuracy of the OMDI model was evaluated by using the correlation coefficient. Moreover, the spatiotemporal changes in drought were also analyzed through trend analysis, Mann-Kendall (MK) statistics and the Hurst index on the monthly and annual scales. The results showed that (1) the highest positive correlation between the OMDI and the SPI was SPI-1, which was higher than that for any other month interval, such as 3 months, 6 months, 9 months and 12 months of the SPI. The results indicated that the OMDI was suitable to monitor meteorological drought. (2) In the nine agricultural subareas in China, the degree of drought in the Yangtze River (DYR) area had the most severe evolution and change frequency. This region was very sensitive to drought in the past two decades. (3) The area with OMDI variation coefficient less than 0.1 accounted for 94%, indicating that the degree of drought fluctuates little; The linear tendency rate is 0.0004, and the area greater than 0 reaches 66.44%, indicating that the drought is developing in a lightning trend. (4) The Hurst index value is mostly higher than 0.5 (the area ratio is 56.31%), and the area of "Positive-Consistent" and "Negative- Opposite" accounted for 54.02%, indicating that more than half of China's area drought changes will show a trend of mitigation in the future.

Identifying vegetation restoration effectiveness and driving factors on different micro-topographic types of hilly Loess Plateau: From the perspective of ecological resilience

Vegetation restoration is an important way to improve the sustainability of the ecosystem in the hilly Loess Plateau. The variation of vegetation coverage, caused by the combined effects of meteorological factors and human activities, reflects the succession trend of regional ecosystems. Given the complexity and the diversity of landform in the hilly Loess Plateau, vegetation restoration is more affected by topographic factors. Nevertheless, few studies have considered the characteristics and trends of vegetation restoration under different micro-topographic types in the long-time series. From the perspective of ecological resilience based on the fractional vegetation cover (FVC), the trend, the hurst exponent, and the geographical spatial research were used to analyze the variation and future sustainability of vegetation restoration on different micro-topographic types for 20 years. Besides, the spatial autocorrelation, principal component analysis (PCA) and geographically weighted regression (GWR) were applied to identify the driving factors of vegetation restoration. The results showed: (1) the average of the overall regional vegetation coverage was 61.32%, and only 0.95% of the regional vegetation was degraded in the past 20 years. However, in the future, 69.87% of the area would be degraded from improvement, and 0.52% would be significantly decreased; (2) the vegetation coverage in descending order was as follows: ridge area with shady and steep slope, gully area with shady and steep slope, ridge area with sunny and steep slope, gully area with sunny and steep slope, gully area with shady and gentle slope, ridge area with shady and gentle slope, ridge area with sunny and gentle slope, gully area with sunny and gentle slope, valley area; (3) the difference of vegetation degradation among micro-topography was remarkable, and the valley area and gully area with sunny and steep slope have the greatest decrease; (4) the primary factors affecting vegetation restoration in the hilly Loess Plateau were temperature, moisture, soil quality, and social economical condition, and the dominant factors were various under different micro-topographic types and villages. Therefore, it is necessary to adjust ecological engineering measures by comprehensively considering the regional differences among dominant factors of vegetation restoration.

Spatial and Temporal Characteristics of Precipitation and Potential Influencing Factors in the Loess Plateau before and after the Implementation of the Grain for Green Project

Since the implementation of the Grain for Green Project (GFGP) in the 1990s, the warming and wetting trend in the Loess Plateau is becoming statistically significant in the context of climate change. However, the correlation between precipitation increase and the regional vegetation restoration is still controversial. To explore the main factors influencing the regional precipitation change, this study selected five potential influencing factors including potential evapotranspiration (PET), normalized difference vegetation index (NDVI), precipitable water (PW), surface temperature (ST), and water vapor transport (WVT). We used the statistical methods to analyze the spatial-temporal distribution of precipitation before and after the GFGP and to quantify the relative influence degree of different factors to precipitation change. The results show that: (1) The precipitation increased significantly (95% confidence level) after the GFGP, with an increase rate of 4.96 mm a−1; (2) from the perspective of spatial-temporal distribution, the precipitation in the southern part of the Loess plateau was significantly increasing with an increase rate of 20–50 mm in the period of 2000–2014; (3) the relative influence degree of NDVI to precipitation increased after the GFGP, and the annual precipitation (PREA) and summer precipitation (PRES) was more influenced by NDVI (relative influence degree of 30.18% and 31.37%, respectively) compared with winter precipitation. In winter, the PW and the PET are the main influencing factors for the precipitation change with relative influence degrees of 30.13% and 27.64%, respectively. Based on this study, we speculate that the warming and wetting trend of the Loess Plateau in recent years is not only closely related to global climate change, but also significantly affected by local climate change brought by vegetation restoration. The above conclusions are important for future ecological restoration and water resources management in the water-scarce Loess Plateau.

Vegetation Dynamics and Diverse Responses to Extreme Climate Events in Different Vegetation Types of Inner Mongolia

As the global climate has changed, studies on the relationship between vegetation and climate have become crucial. We analyzed the long-term vegetation dynamics and diverse responses to extreme climate changes in Inner Mongolia, based on long-term Global Inventory Monitoring and Modelling Studies (GIMMS) NDVI3g datasets, as well as the eight extreme precipitation indices and six extreme temperature indices that are highly correlated with the occurrence of droughts or floods, heat or cold temperature disasters, and vegetation growth in Inner Mongolia. These datasets were analyzed using linear regression analysis, the Hurst exponent index, residual analysis, and the Pearson correlation analysis. The results showed the following: (1) The vegetation dynamical changes exhibited trends of improvement during 1982 to 2015, and 68% of the vegetation growth changes in Inner Mongolia can be explained by climate changes. (2) The extreme precipitation indices exhibited a slight change, except for the annual total wet–day precipitation (PRCPTOT). The occurrence of extreme cold temperatures showed a significant decline, while the occurrence of extreme warm temperatures showed an upward trend in Inner Mongolia. (3) The typical steppe, desert steppe, and forest steppe regions are more sensitive to extreme large precipitation, and the forest regions are more sensitive to extreme warm temperatures. (4) Extreme precipitation exhibits a one-month lagged effect on vegetation that is larger than the same-month effects on the grassland system. Extreme temperature exhibits same-month effects on vegetation, which are larger than the one-month lagged effects on the forest system. Therefore, studies of the relationship between extreme climate indices and vegetation are important for performing risk assessments of droughts, floods, and other related natural disasters.

Spatiotemporal drought variability on the Mongolian Plateau from 1980-2014 based on the SPEI-PM, intensity analysis and Hurst exponent

Knowledge about variations of drought can provide a scientific basis for water resource planning and drought mitigation. In this study, the variations and patterns of drought identified by the Standardized Precipitation Evapotranspiration Index (SPEI) were investigated on the Mongolian Plateau for the period 1980-2014, based on intensity analysis, linear regression, the Mann-Kendall test, wavelet analysis, and Hurst exponent. The results show that: 1) the annual SPEI decreased at a rate of -0.0133/yr over the past 35years, and a major abrupt change occurred in 1999; 2) drought on the Mongolian Plateau intensified from 1980 to 2014, and the drought in Mongolia has been more serious than in Inner Mongolia since the beginning of the 21st century; 3) the rate of drought/wet changes in 1980s-1990s and 1990s-2000s were faster than in 2000s-2010s. In 1980s-1990s, the different drought levels were transformed into various wet levels. In 1990s-2000s, the wet levels were transformed into drought, and in 2000s-2010s, the losses of drought levels were larger than the gains in wet levels; 4) the Hurst exponent is a reliable way to predict drought tendency, with a predictive accuracy as high as 91.7%; 5) the mean H value of the SPEI time series during 1980-2014 was 0.533, indicating that the future drought trend is generally consistent with the current state. In the future, the proportion of area with increasingly severe drought (72.2%) will be larger than that with increasingly wetter conditions (27.8%) on the Mongolian Plateau.

Nonlinear time series analysis of ground-level ozone dynamics in Southern Taiwan

The ozone pollution at ground level in rural and urban areas has been a long-standing problem in the world. This paper focuses on estimating self-affined nature of nonlinearity of ground-level peak ozone time series, which is analyzed by two nonlinear fractal statistical methods, including R/S analysis and BDS test. To explore the underlying structure of ozone observations at ground level and improve the forecasting capacity in urban region, practical implementation was assessed by a case study via collecting and analyzing the monitoring data at Chaojhou and Zenwu in the Kaohsiung metropolitan region, Taiwan. Based on R/S analysis, the time series can be identified as persistent and long-memory processes with Hurst exponents of both about 0.75. In addition, the V statistics specifies possible fluctuation cycle lengths of 32, 170, and 420 day simultaneously. Such results are consistent with the regional meteorological conditions leading to help characterize the regional scale ozone behavioral trend. Furthermore, the BDS test results confirm a strong nonlinearity of both time series associated with these two cities. Yet in both cases, nonlinearity implies chaos. The R/S analysis and BDS test provide strong evidence for nonlinearity and fractality of ozone time series due to noisy chaos, and we could not rule out the possibility of deterministic chaos in tropospheric ozone system.