The global climate is noticeably warming, and drought occurs frequently. Therefore, choosing a suitable index for drought monitoring is particularly important. The standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) are commonly used indicators in drought monitoring. The SPEI takes temperature into account, but the SPI does not. In the context of global warming, what are their differences and applicability in regional drought monitoring? In this study, after calculating the SPI and SPEI at 1-, 3-, 6-, and 12-month timescales at 102 meteorological stations in Inner Mongolia from 1981 to 2018, we compared and analyzed the performances of the SPI and SPEI in drought monitoring from temporal and spatial variations, and the consistency and applicability of the SPI and SPEI were also discussed. The results showed that (1) with increasing timescale, the temporal variations in the SPI and SPEI were increasingly consistent, but there were still slight differences in the fluctuation value and continuity; (2) due to the difference in time series, the drought characteristics identified by the SPI and SPEI were quite different in space at various timescales, and with the increase in timescale, the spatial distributions of the drought trends in Inner Mongolia were basically consistent, except in Alxa; (3) at the shortest timescale, the difference between the SPI and SPEI was the largest, and the drought reflected by the SPI and SPEI may be consistent at long timescales; and (4) compared with typical drought events and vegetation indexes, the SPEI may be more suitable than the SPI for drought monitoring in Inner Mongolia. It should be noted that the adaptability of the SPI and SPEI may be different in different periods and regions, which remains to be analyzed in the future.

Comparative Analysis of Drought Indicated by the SPI and SPEI at Various Timescales in Inner Mongolia, China

Since the 1980s, China has undergone rapid urbanization. Meanwhile, the climate has been warming substantially. In this paper, the urbanization effect on observed temperatures from 1980 to 2009 in China is estimated, based on analysis of urban land use from satellite observation. Urban land-use expansion (ΔU) during 1980–2005 is applied as an urbanization index. According to these ΔU values, stations are divided into three categories: (C1) intense urbanization around the stations; (C2) moderate urbanization around the stations; and (C3) minimal urbanization around the stations. Most C1 stations are in municipalities or provincial capitals, while C2 stations tend to be in prefecture-level cities. C3 stations are mostly in counties. The urban heat island (UHI) effect can be estimated if the urban effect on C3 is negligible. The warming of C1 or C2 relative to that of C3 represents their urbanization effects, assuming that the same larger-scale natural warming has affected each category. For C1, the local urbanization effect is 0.258°C/10 a over 1980–2009, accounting for 41% of the total warming; the trend at C2 is 0.099°C/10 a, or 21%. For all China, the urbanization effect is 0.09°C/10a, accounting for 20% of the total national warming. Winter urban warming is greater than in summer. The assumption of negligible urbanization effect on C3 is debatable, and so the true urbanization effect may equal or slightly exceed estimates. Further, the ΔU index may have some uncertainties, for it is only one of the urbanization indices. However, it provides a new and direct estimation of environmental change, in contrast to indirect indices.

Estimation of urbanization bias in observed surface temperature change in China from 1980 to 2009 using satellite land-use data

There are currently only two methods (the within-growing season method and the inter-growing season method) used to analyse the normalized difference vegetation index (NDVI)–climate relationship at the monthly time scale. What are the differences between the two methods, and why do they exist? Which method is more suitable for the analysis of the relationship between them? In this study, after obtaining NDVI values (GIMMS NDVI3g) near meteorological stations and meteorological data of Inner Mongolian grasslands from 1982 to 2015, we analysed temporal changes in NDVI and climate factors, and explored the difference in Pearson correlation coefficients (R) between them via the above two analysis methods and analysed the change in R between them at multiple time scales. The research results indicated that: (1) NDVI was affected by temperature and precipitation in the area, showing periodic changes, (2) NDVI had a high value of R with climate factors in the within-growing season, while the significant correlation between them was different in different months in the inter-growing season, (3) with the increase in time series, the value of R between NDVI and climate factors showed a trend of increase in the within-growing season, while the value of R between NDVI and precipitation decreased, but then tended toward stability in the inter-growing season, and (4) when exploring the NDVI–climate relationship, we should first analyse the types of climate in the region to avoid the impacts of rain and heat occurring during the same period, and the inter-growing season method is more suitable for the analysis of the relationship between them.

https://www.mdpi.com/2071-1050/11/24/7243/pdf

The Relationship between NDVI and Climate Factors at Different Monthly Time Scales: A Case Study of Grasslands in Inner Mongolia, China (1982–2015)

Spatial-temporal variation in irrigation water requirement for the winter wheat-summer maize rotation system since the 1980s on the North China Plain

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2018JD029829

Modeling the Impacts of Urbanization on Summer Thermal Comfort: The Role of Urban Land Use and Anthropogenic Heat

Studies based on the 10th (90th) percentiles as thresholds have been presented to assess moderate extremes in China and globally. However, there has been notably little research on the occurrences of high extremes of warm days and hot days (TX95p and TX99p) and cold nights and very cold nights (TN05p and TN01p), based on the 95th and 99th (5th and 1st) percentiles of the daily maximum (minimum) temperature data at a certain station in the period 1971–2000, which have more direct impacts on society and the ecosystem. The trends analyses of cool nights or warm days are based upon the hypothesis that expects a linear trend and no abrupt change. However, abrupt changes in the climate, especially in extreme temperatures, have been pointed to as a major threat to ecosystem services. This study demonstrates that (1) the mean frequencies of TX95p and TX99p increased by 1.80 day/10 year and 0.62 day/10 year, respectively, and that those of TN05p and TN01p decreased by 3.18 day/10 year and 1.01 day/10 year, respectively, in mainland China. Additionally, the TX95p and TX99p increased significantly by 50.42% and 58.21%, respectively, while the TN05p and TN01p of all of the stations decreased significantly by 83.76% and 76.48%, respectively. Finally, (2) the TX95p and TX99p trends underwent abrupt changes in the 1990s or 2000s, but the trends of TN05p and TN01p experienced abrupt changes in the late 1970s and early 1980s. After the abrupt change points, the trend of warm and hot days increased more rapidly than before in most regions, but the trend of cold days and very cold days decreased more slowly than before in most regions, which indicates a greater risk of heat waves in the future.

https://www.mdpi.com/2073-4433/7/2/22/pdf

Changing Trends and Abrupt Features of Extreme Temperature in Mainland China from 1960 to 2010

Changes in temperature extremes can be linked to mean temperature changes and variability. This study aims to quantify observed trends in mean and extreme temperature values and to analyse the relationship between mean and extreme temperatures in mainland China, based on daily data from 1960 to 2015. This is the first analysis undertaken of the relationship between mean and extreme temperatures in mainland China. Based on the 95th and 99th percentiles of daily Tmax and the 5th and 1st percentiles of daily Tmin, warm days (TX95p), hot days (TX99p), cold nights (TN05p), and very cold nights (TN01p) were defined. The results showed the following: (1) large increasing tendencies of TX95p and TX99p nearly all occurred in locations where mean temperature had substantially increased, and large decreasing tendencies of TN05p were more probably at locations of warming in mainland China; (2) the rise of mean temperature significantly increased the frequency of TX95p and TX99p, and decreased the frequency of TN05p, which indicates a simple shift of the entire distribution towards a warmer climate and greater potential risk of heat waves in the future. The likelihood of occurrence of TX95p and TX99p increased by about 3 and 1 day, respectively, and the occurrence of TN05p was reduced by about 4 days with a mean temperature increase of 1 °C, but the occurrence of TN01p was hardly affected, indicating increased variability of Tmin temperatures; and (3) the mean and extreme temperatures increased with the urbanization rate in China, and advanced phenologies and unaffected frequency of very cold nights (TN01p) could pose more potential risk of frost and freeze injury to crops in China in the future.

Guojun Han

Papers

Changing Trends and Abrupt Features of Extreme Temperature in Mainland China from 1960 to 2010

Change in temperature extremes and its correlation with mean temperature in mainland China from 1960 to 2015