Trends in the consecutive days of temperature and precipitation extremes in China during 1961-2015

Complex adaptive systems-based framework for modeling the health impacts of climate change

Introduction

Climate change is a global phenomenon with far-reaching consequences, and its impact on human health is a growing concern. The intricate interplay of various factors makes it challenging to accurately predict and understand the implications of climate change on human well-being. Conventional methodologies have limitations in comprehensively addressing the complexity and nonlinearity inherent in the relationships between climate change and health outcomes.

Objectives

The primary objective of this paper is to develop a robust theoretical framework that can effectively analyze and interpret the intricate web of variables influencing the human health impacts of climate change. By doing so, we aim to overcome the limitations of conventional approaches and provide a more nuanced understanding of the complex relationships involved. Furthermore, we seek to explore practical applications of this theoretical framework to enhance our ability to predict, mitigate, and adapt to the diverse health challenges posed by a changing climate.

Methods

Addressing the challenges outlined in the objectives, this study introduces the Complex Adaptive Systems (CAS) framework, acknowledging its significance in capturing the nuanced dynamics of health effects linked to climate change. The research utilizes a blend of field observations, expert interviews, key informant interviews, and an extensive literature review to shape the development of the CAS framework.

Results and discussion

The proposed CAS framework categorizes findings into six key sub-systems: ecological services, extreme weather, infectious diseases, food security, disaster risk management, and clinical public health. The study employs agent-based modeling, using causal loop diagrams (CLDs) tailored for each CAS sub-system. A set of identified variables is incorporated into predictive modeling to enhance the understanding of health outcomes within the CAS framework. Through a combination of theoretical development and practical application, this paper aspires to contribute valuable insights to the interdisciplinary field of climate change and health. Integrating agent-based modeling and CLDs enhances the predictive capabilities required for effective health outcome analysis in the context of climate change.

Conclusion

This paper serves as a valuable resource for policymakers, researchers, and public health professionals by employing a CAS framework to understand and assess the complex network of health impacts associated with climate change. It offers insights into effective strategies for safeguarding human health amidst current and future climate challenges.

A systematic review and meta-analysis of cold exposure and cardiovascular disease outcomes

Background

Cold exposure has been considered an essential risk factor for the global disease burden, while its role in cardiovascular diseases is still underappreciated. The increase in frequency and duration of extreme cold weather events like cold spells makes it an urgent task to evaluate the effects of ambient cold on different types of cardiovascular disease and to understand the factors contributing to the population's vulnerability.

Methods

In the present systematic review and meta-analysis, we searched PubMed, Scopus, and Cochrane. We included original research that explored the association between cold exposure (low temperature and cold spell) and cardiovascular disease outcomes (mortality and morbidity). We did a random-effects meta-analysis to pool the relative risk (RR) of the association between a 1°C decrease in temperature or cold spells and cardiovascular disease outcomes.

Results

In total, we included 159 studies in the meta-analysis. As a result, every 1°C decrease in temperature increased cardiovascular disease-related mortality by 1.6% (RR 1.016; [95% CI 1.015–1.018]) and morbidity by 1.2% (RR 1.012; [95% CI 1.010–1.014]). The most pronounced effects of low temperatures were observed in the mortality of coronary heart disease (RR 1.015; [95% CI 1.011–1.019]) and the morbidity of aortic aneurysm and dissection (RR 1.026; [95% CI 1.021–1.031]), while the effects were not significant in hypertensive disease outcomes. Notably, we identified climate zone, country income level and age as crucial influential factors in the impact of ambient cold exposure on cardiovascular disease. Moreover, the impact of cold spells on cardiovascular disease outcomes is significant, which increased mortality by 32.4% (RR 1.324; [95% CI 1.2341.421]) and morbidity by 13.8% (RR 1.138; [95% CI 1.015–1.276]).

Conclusion

Cold exposure could be a critical risk factor for cardiovascular diseases, and the cold effect varies between disease types and climate zones.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO, identifier: CRD42022347247.

Three-decade assessment of dry and wet spells change across Iran, a fingerprint of climate change

Extended periods of hydro-climate extremes with excessive or scarce rainfall associated with high or low temperatures have resulted in an imbalanced water cycle and inefficient socio-economic systems in several regions of Iran. However, there is a lack of comprehensive investigations on short-term to long-term variations in timing, duration, and temperature of wet/dry spells. This study bridges the current gap through a comprehensive statistical analysis of historical climatic data (1959-2018). Results indicated that the negative tendency of the accumulated rainfall (- 0.16/ - 0.35 mm/year during the past 60/30 years) in 2- to 6-day wet spells had made significant contributions to the ongoing downward trend in annual rainfall (- 0.5/ - 1.5 mm/year during the past 60/30 years) owing to a warmer climate condition. Warmer wet spells are likely responsible for precipitation patterns changes in snow-dominated stations since their wet spells temperature has more than threefold growth with increasing distance to coasts. The most detected trends in climatic patterns have started in the last two decades and become more severe from 2009 to 2018. Our results confirm the alteration of precipitation features across Iran due to anthropogenic climatic change, and suggest expected increase in air temperature would likely result in further dry and warm conditions over the coming decades.

Prediction of monthly dry days with machine learning algorithms: a case study in Northern Bangladesh

Dry days at varied scale are an important topic in climate discussions. Prolonged dry days define a dry period. Dry days with a specific rainfall threshold may visualize a climate scenario of a locality. The variation of monthly dry days from station to station could be correlated with several climatic factors. This study suggests a novel approach for predicting monthly dry days (MDD) of six target stations using different machine learning (ML) algorithms in Bangladesh. Several rainfall thresholds were used to prepare the datasets of monthly dry days (MDD) and monthly wet days (MWD). A group of ML algorithms, like Bagged Trees (BT), Exponential Gaussian Process Regression (EGPR), Matern Gaussian Process Regression (MGPR), Linear Support Vector Machine (LSVM), Fine Trees (FT) and Linear Regression (LR) were evaluated on building a competitive prediction model of MDD. In validation of the study, EGPR-based models were able to better capture the monthly dry days (MDD) over Bangladesh compared to those by MGPR, LSVM, BT, LR and FT-based models. When MDD were the predictors for all six target stations, EGPR produced highest mean R² of 0.91 (min. 0.89 and max. 0.92) with a least mean RMSE of 2.14 (min. 1.78 and max. 2.69) compared to other models. An explicit evaluation of the ML algorithms using one-year lead time approach demonstrated that BT and EGPR were the most result-oriented algorithms (R² = 0.78 for both models). However, having a least RMSE, EGPR was chosen as the best model in one year lead time. The dataset of monthly dry-wet days was the best predictor in the lead-time approach. In addition, sensitivity analysis demonstrated sensitivity of each station on the prediction of MDD of target stations. Monte Carlo simulation was introduced to assess the robustness of the developed models. EGPR model declared its robustness up to certain limit of randomness on the testing data. The output of this study can be referred to the agricultural sector to mitigate the impacts of dry spells on agriculture.

Changes in soil properties and CO2 emissions after biochar addition: Role of pyrolysis temperature and aging

Biochar has been regarded as an effective amendment for soil carbon sequestration and soil quality improvement. However, it remains unclear how pyrolysis temperature and biochar aging impact the responses of soil properties and CO₂ emissions to biochar addition. Here, we investigated the effect of biochar on soil properties and CO₂ emissions in a laboratory incubation using soils amended with/without fresh biochar produced at 300 (BC300), 450 (BC450), and 600 °C (BC600) and their corresponding naturally aged samples (aged in soil for 360 days). The results showed that biochar significantly increased soil total nitrogen (by 8-36%), available phosphorus (by 19-69%) and available potassium (by 1.5-4.2-fold) throughout the incubation. Both fresh and aged biochar promoted the formation of soil macroaggregate at the end of the incubation. Moreover, fresh and aged BC300 increased the soil dissolved organic matter (DOM) content, whereas for BC450 and BC600, at the beginning, the content of soil DOM was reduced, but the effects finally became insignificant. Generally, fresh biochar had no significant effect on soil enzyme activities and soil bacterial richness and diversity, but an inhibitory effect occurred in the aged samples. Both fresh and aged BC300 increased soil CO₂ emissions, which was due to the biochar-induced increase in soil DOM content and enrichment of copiotrophic bacteria (Proteobacteria) as well as the decline of oligotrophic bacteria (Acidobacteriota). A significant decrease in soil CO₂ emissions was observed after fresh BC450 and BC600 addition, owing to the biochar-induced decline in soil DOM content, while an opposite trend was found in aged samples, which could be attributed to the shift of the dominant soil phylum from Acidobacteriota to Proteobacteria. These findings enhance our understanding of biochar's potential to improve soil quality and sequester soil carbon.

Changes in Vegetation Dynamics and Relations with Extreme Climate on Multiple Time Scales in Guangxi, China

Understanding the responses of vegetation to climate extremes is important for revealing vegetation growth and guiding environmental management. Guangxi was selected as a case region in this study. This study investigated the spatial-temporal variations of the Normalized Difference Vegetation Index (NDVI), and quantitatively explored effects of climate extremes on vegetation on multiple time scales during 1982–2015 by applying the Pearson correlation and time-lag analyses. The annual NDVI significantly increased in most areas with a regional average rate of 0.00144 year−1, and the highest greening rate appeared in spring. On an annual scale, the strengthened vegetation activity was positively correlated with the increased temperature indices, whereas on a seasonal or monthly scale, this was the case only in spring and summer. The influence of precipitation extremes mainly occurred on a monthly scale. The vegetation was negatively correlated with both the decreased precipitation in February and the increased precipitation in summer months. Generally, the vegetation significantly responded to temperature extremes with a time lag of at least one month, whereas it responded to precipitation extremes with a time lag of two months. This study highlights the importance of accounting for vegetation-climate interactions.

Spatiotemporal Variation and Circulation Characteristics of Extreme Maximum Temperature Events in East China (1961–2020)

This study analyzed the spatiotemporal variation characteristics of extreme maximum temperature events (EMTEs) in East China in the last 60 years and investigated the relationship between EMTEs and atmospheric circulation. The arithmetic mean, linear trend, and the Mann–Kendall test were applied to daily maximum temperature (DMT) data (1961–2020) from 345 meteorological observation stations with complete observation records in East China to compile four characteristic indexes of EMTEs: intensity, consecutive days, first days, and last days. The analysis of these indexes revealed the following: (1) The annual number of days with a DMT ≥ 35 °C increased at the rate of 1.45 d/decade (p ≤ 0.05); the mutation occurred in 2009 with a growth rate before and after the mutation of 0.4 and 2.8 d/10a, respectively. Most of the region showed an increasing trend, with the most significant increase to the east of the Yangtze River Delta, in coastal areas of Zhejiang and Fujian, and south of Jiangxi. (2) The EMTE intensity rose at the rate of 0.15 °C/decade (p ≤ 0.05). Most areas showed a significant upward trend, and the historical extreme values of EMTEs mostly appeared in the 21st century. (3) The annual mean growth rate of consecutive EMTE days was 0.24 d/10a, which increased significantly after 2003. In comparison with 1961–2002, consecutive EMTE days increased by 35% during 2003–2020. The rate of increase was significant (p ≤ 0.05) for most areas east of the Yangtze River Delta, coastal areas of Zhejiang and Fujian, and areas south of Jiangxi. The mean mutation time was 2003, and the growth rate before and after the mutation was 0.4 and 1.4 d/10a, respectively. (4) The mean first EMTE day advanced and the mean last EMTE day became delayed, especially in the 21st century. Over the study period, the mean first EMTE day advanced by 12 days and the mean last EMTE day became delayed by 7 days. (5) The analysis of National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis data indicated that an increasing number of EMTEs have occurred in East China. The reason is that this region experiences atmospheric subsidence resulting from the intensification and westward extension of the subtropical high coupled with the weakening and northward displacement of the mid-latitude westerly trough.

Analysis of the Temporal and Spatial Distribution of Extreme Climate Indices in Central China

Using the daily precipitation and temperature data of 101 meteorological stations in four provinces of central China (Henan, Hubei, Hunan, Jiangxi) from 1988 to 2017, we analyzed the temporal and spatial dynamics and periodicity of nine extreme climate indices in central China, using the predefined methods for analyzing extreme climate events, such as a M-K test, a linear trend analysis, and a wavelet analysis. The extreme climate characteristics and changes in central China in the past 30 years were revealed. The results showed that the CSDI was significantly reduced linearly at a rate of −0.19 d/10a, and the WSDI and TXx increased significantly at rates of 0.25 d/10a and 0.30℃/10a, respectively. The CDD decreased significantly at a rate of −1.67 d/10a. The duration of extreme low-temperature and drought events in central China showed a gradual shortening, while the duration of extreme high-temperature events and the high-temperature values increased. The results of the abrupt climate change test showed that some extreme climate indices in central China had significant abrupt climate changes after 2000. Analyzing the cyclicality of each index, it was determined that the extreme climate index in central China had a significant cyclical change every 2–4 years, and the change was more notable after 2000. Analyzing the spatial distribution of the extreme climate indices, it was determined that Jiangxi had the longest duration of all high-temperature events, and was the largest and longest of events of extreme precipitation. It was also determined that the Jiangxi region was at greater risk of extreme climate events in central China. The results of this study can provide a scientific basis for climate change trends, local disaster prevention, and mitigation management in central China.

Analysis of Spatiotemporal Variability in Extreme Climate and Potential Driving Factors on the Yunnan Plateau (Southwest China) during 1960–2019

Global warming is increasing the frequency and intensity of extreme weather events around the world. The extreme climate in plateau and mountainous areas is sensitive and fragile. Based on the software Rclimdex 1.0, the spatio-temporal variation characteristics of 27 extreme climate indices at 120 meteorological stations were calculated in Yunnan from 1960 to 2019. The results show that the extreme temperature is rising, and the warming rate at night is higher than that in the daytime. It showed a trend of warming and drying, and precipitation was concentrated into more intense bursts. Extreme temperature cold indices (TX10p, TN10p, FD0, ID0, and CSDI) were negatively correlated with extreme precipitation indices (R × 5 day, PRCPTOT, R10 mm, R20 mm, and R25 mm). Extreme temperature warmth indices (TX90p and TN90p) were positively correlated with extreme precipitation indices (R × 5 day, CWD, PRCPTOT, R10 mm, R20 mm, and R25 mm). The change rate of extreme temperature does not increase linearly with altitude. The increase in middle-altitude and high-altitude areas is higher than that in low-altitude areas. Compared with ENSO and AO, NAO is a vital circulation pattern affecting the extreme climate in Yunnan. The influence of NAO on Yunnan’s extreme climate indices is most significant in the current month and the second month that follows. NAO was negatively correlated with extreme temperature warm indices (TN90p, TX90p, SU25, and TR20). NAO positively correlates with the extreme cold temperature indices (TN10p and TX10p). Except that ENSO has a significant effect on CDD, the effect of the general circulation patterns on the extreme temperature indices was more significant than that on the extreme precipitation indices in Yunnan. The results of this study are helpful to further understand and predict the characteristics of extreme climatic events and the factors affecting their geographical locations and atmospheric circulation patterns in Yunnan.

Evaluation and Future Projection of Extreme Climate Events in the Yellow River Basin and Yangtze River Basin in China Using Ensembled CMIP5 Models Data

The Yellow River Basin (YLRB) and Yangtze River Basin (YZRB) are heavily populated, important grain-producing areas in China, and they are sensitive to climate change. In order to study the temporal and spatial distribution of extreme climate events in the two river basins, seven extreme temperature indices and seven extreme precipitation indices were projected for the periods of 2010-2039, 2040-2069, and 2070-2099 using data from 16 Coupled Model Intercomparison Project Phase 5 (CMIP5) models, and the delta change and reliability ensemble averaging (REA) methods were applied to obtain more robust ensemble values. First, the present evaluation indicated that the simulations satisfactorily reproduced the spatial distribution of temperature extremes, and the spatial distribution of precipitation extremes was generally suitably captured. Next, the REA values were adopted to conduct projections under different representative concentration pathway (RCP) scenarios (i.e., RCP4.5, and RCP8.5) in the 21st century. Warming extremes were projected to increase while cold events were projected to decrease, particularly on the eastern Tibetan Plateau, the Loess Plateau, and the lower reaches of the YZRB. In addition, the number of wet days (CWD) was projected to decrease in most regions of the two basins, but the highest five-day precipitation (Rx5day) and precipitation intensity (SDII) index values were projected to increase in the YZRB. The number of consecutive dry days (CDD) was projected to decrease in the northern and western regions of the two basins. Specifically, the warming trends in the two basins were correlated with altitude and atmospheric circulation patterns, and the wetting trends were related to the atmospheric water vapor content increases in summer and the strength of external radiative forcing. Notably, the magnitude of the changes in the extreme climate events was projected to increase with increasing warming targets, especially under the RCP8.5 scenario.

Development of an integrated multivariate trend-frequency analysis method: Spatial-temporal characteristics of climate extremes under global warming for Central Asia

Temperature and precipitation are the two most critical climate variables and their extreme states have more severe impacts than average states on both human society and natural ecosystem. In this study, an integrated multivariate trend-frequency analysis (IMTFA) approach is developed for the risk assessment of climate extremes under the global warming. Through incorporating multiple time series analysis techniques (i.e., M-K test, Sen's slope estimator and Pettitt test) and copula function into a general framework, IMTFA is capable not only of analyzing the temporal trends and change points of extreme temperatures and precipitations, but also of quantifying their univariate and multivariate risks. IMTFA is applied to the Central Asia with considering a long-term (1881-2018) observation data. Our findings are: (i) significant wetting and warming trends were occurred in the Central Asia over past one hundred years, where 42.5%, 59.4% and 79.2% stations have change points for extreme precipitations, maximum and minimum temperatures, respectively; (ii) the occurrences of extreme climate events show obviously spatial heterogeneity, where the highest risks of meteorological drought, flood and frost events are occurred in the southwest, southeast and northeast regions, respectively; (iii) global warming significantly affects the intensities and frequencies of extreme precipitations and temperatures, and their univariate and multivariate risks are intensified in the most regions of Central Asia. The above findings can provide more valuable information for risk assessment and disaster adaptation of climate extremes in Central Asia.

Vegetation Change and Its Response to Climate Extremes in the Arid Region of Northwest China

Changes in climate extremes have a profound impact on vegetation growth. In this study, we employed the Moderate Resolution Imaging Spectroradiometer (MODIS) and a recently published climate extremes dataset (HadEX3) to study the temporal and spatial evolution of vegetation cover, and its responses to climate extremes in the arid region of northwest China (ARNC). Mann-Kendall test, Anomaly analysis, Pearson correlation analysis, Time lag cross-correlation method, and Least absolute shrinkage and selection operator logistic regression (Lasso) were conducted to quantitatively analyze the response characteristics between Normalized Difference Vegetation Index (NDVI) and climate extremes from 2000 to 2018. The results showed that: (1) The vegetation in the ARNC had a fluctuating upward trend, with vegetation significantly increasing in Xinjiang Tianshan, Altai Mountain, and Tarim Basin, and decreasing in the central inland desert. (2) Temperature extremes showed an increasing trend, with extremely high-temperature events increasing and extremely low-temperature events decreasing. Precipitation extremes events also exhibited a slightly increasing trend. (3) NDVI was overall positively correlated with the climate extremes indices (CEIs), although both positive and negative correlations spatially coexisted. (4) The responses of NDVI and climate extremes showed time lag effects and spatial differences in the growing period. (5) Precipitation extremes were closely related to NDVI than temperature extremes according to Lasso modeling results. This study provides a reference for understanding vegetation variations and their response to climate extremes in arid regions.

Impact of Climate Change on Precipitation Extremes over Ho Chi Minh City, Vietnam

In the context of climate change, the impact of hydro-meteorological extremes, such as floods and droughts, has become one of the most severe issues for the governors of mega-cities. The main purpose of this study is to assess the spatiotemporal changes in extreme precipitation indices over Ho Chi Minh City, Vietnam, between the near (2021–2050) and intermediate (2051–2080) future periods with respect to the baseline period (1980–2009). The historical extreme indices were calculated through observed daily rainfall data at 11 selected meteorological stations across the study area. The future extreme indices were projected based on a stochastic weather generator, the Long Ashton Research Station Weather Generator (LARS-WG), which incorporates climate projections from the Coupled Model Intercomparison Project 5 (CMIP5) ensemble. Eight extreme precipitation indices, such as the consecutive dry days (CDDs), consecutive wet days (CWDs), number of very heavy precipitation days (R20mm), number of extremely heavy precipitation days (R25mm), maximum 1 d precipitation amount (RX1day), maximum 5 d precipitation amount (RX5day), very wet days (R95p), and simple daily intensity index (SDII) were selected to evaluate the multi-model ensemble mean changes of extreme indices in terms of intensity, duration, and frequency. The statistical significance, stability, and averaged magnitude of trends in these changes, thereby, were computed by the Mann-Kendall statistical techniques and Sen’s estimator, and applied to each extreme index. The results indicated a general increasing trend in most extreme indices for the future periods. In comparison with the near future period (2021–2050), the extreme intensity and frequency indices in the intermediate future period (2051–2080) present more statistically significant trends and higher growing rates. Furthermore, an increase in most extreme indices mainly occurs in some parts of the central and southern regions, while a decrease in those indices is often projected in the north of the study area.

More frequent and widespread persistent compound drought and heat event observed in China

Compound drought and heat event (CDHE) causes severe impacts on agriculture, ecosystem, and human health. Based on daily maximum surface air temperature and meteorological drought composite index data in China, changing features of CDHEs in warm season from 1961 to 2018 is explored at a daily time scale based on a strict and objective definition in this study. Results reveal that CDHEs have occurred more frequently and widely in China, especially since the late 1990s. Notably, such changes are more obvious in Southwest China, eastern Northwest China, northern North China, and the coastal area of southeastern China. A prominent feature is that persistent CDHEs on a daily scale have increased significantly. To better understand climate change of compound extreme events, further studies on the physical mechanism, especially attribution analyses at a regional scale, are urgently needed.

Applicability and Hydrologic Substitutability of TMPA Satellite Precipitation Product in the Feilaixia Catchment, China

The assessment of various precipitation products’ performances in extreme climatic conditions has become a topic of interest. However, little attention has been paid to the hydrological substitutability of these products. The objective of this study is to explore the performance of the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) product in the Feilaixia catchment, China. To assess its applicability in extreme consecutive climates, several statistical indices are adopted to evaluate the TMPA performance both qualitatively and quantitatively. The Cox–Stuart test is used to investigate extreme climate trends. The Soil and Water Assessment Tool (SWAT) model is used to test the TMPA hydrological substitutability via three scenarios of runoff simulation. The results demonstrate that the overall TMPA performance is acceptable, except at high-latitudes and locations where the terrain changes greatly. Moreover, the accuracy of the SWAT model is high both in the semi-substitution and full-substitution scenarios. Based on the results, the TMPA product is a useful substitute for the gauged precipitation in obtaining acceptable hydrologic process information in areas where gauged sites are sparse or non-existent. The TMPA product is satisfactory in predicting the runoff process. Overall, it must be used with caution, especially at high latitudes and altitudes.

Spatial and temporal distribution and trend in flood and drought disasters in East China

Frequent floods and droughts have brought serious impact on economy, society and living environment in East China. Based on the disaster census data of rainstorm-induced flood (including landslide and mud-rock flow) and drought disasters in 637 counties (districts) in East China, the distribution and change of flood and drought disasters were analyzed. The results indicate that the number of records of flood disaster increased at a rate of 77.4 times per decade from 1984 to 2010, while that of drought disaster had no significant trend in East China as a whole. Population affected by floods and droughts increased at rates of 8.7 million and 3.8 million persons per decade, respectively, and the direct economic losses increased at rates of 12.6 billion and 1.9 billion Chinese Yuan per decade respectively, whereas the affected area and the total failure area of crops caused by floods and droughts showed no clear trends. Spatially, the number of records of floods in the southern parts of East China was higher than that in the northern parts, and Anhui, Jiangxi, Zhejiang and Fujian had more records of affected population, deaths, affected crops, total crop failures and direct economic losses, as well as more affected population and deaths. The affected crop area and total crop failure area by floods were also larger in Anhui, Jiangxi and Jiangsu. Drought disaster had higher number of records of affected population, affected crops, total crop failures and direct economic losses in Anhui, Jiangxi, Zhejiang and Shandong, and also affected more people and larger area of crops, leading to larger area of total crop failures and higher direct economic losses in Anhui, Jiangxi and Shandong. The results can provide reference for disaster risk regionalization and environmental risk assessment in East China.

CMIP5-Based Spatiotemporal Changes of Extreme Temperature Events during 2021–2100 in Mainland China

The increasing number of extreme climate events is having a great impact on the terrestrial ecosystem. In this study, we applied a Taylor diagram to evaluate the 7 extreme temperature indices (ETI) of 12 models and the multi-model ensemble (MME) mean from phase 5 of the Coupled Model Intercomparison Project (CMIP5) during 1961–2005, and found that the MME has the best simulation effect. Warm indices and warm duration indices increase slowly, rapidly, and extremely under the representative concentration pathway 2.6 (RCP2.6), RCP4.5, and RCP8.5 scenarios, respectively. In contrast, the decrease in cold indices and cold duration indices are slow, rapid and extreme, respectively. The ETI from 2021–2100 under the RCP2.6 and RCP4.5 scenarios have primary periods ranging from 1–16 years. Under the RCP2.6 and RCP4.5 scenarios, the changes of warm indices are relatively largest in the basin of the central, and southeastern, while, under the RCP8.5 scenario, the changes are relatively significant, except for basin of northeast. The cold indices have the most significant decreasing trend in the Tibetan Plateau and its surrounding areas, under the three RCP scenarios. The findings from this study can provide reference for the risk management and prevention of climate disasters in the context of climate change in mainland China.

How Is the Intensity of Rainfall Events Best Characterised? A Brief Critical Review and Proposed New Rainfall Intensity Index for Application in the Study of Landsurface Processes

In many studies of landsurface processes, the intensity of rainfall events is expressed with clock-period indexes such as I30, the wettest 30-minute interval within a rainfall event. Problematically, the value of I30 cannot be estimated for rainfall events shorter than 30 min, excluding many intense convective storms. Further, it represents a diminishing proportion of increasingly long rainfall events, declining to <2% of the duration of a 30-hour event but representing 25% of the duration of a two-hour event. Here, a new index termed EDf5 is proposed: It is the rainfall depth in the wettest 5% of the event duration. This can be derived for events of any duration. Exploratory determinations of EDf5 are presented for two Australian locations with contrasting rainfall climatologies—one arid and one wet tropical. The I30 index was similar at both sites (7.7 and 7.9 mm h−1) and was unable to differentiate between them. In contrast, EDf5 at the arid site was 7.4 mm h−1, whilst at the wet tropical site, it was 3.8 mm h−1. Thus, the EDf5 index indicated a greater concentration of rain at the arid site where convective storms occurred (i.e., the intensity sustained for 5% of event duration at that site is higher). The EDf5 index can be applied to short, intense events that can readily be included in the analysis of event-based rainfall intensity. I30 therefore appears to offer less discriminatory power and consequently may be of less value in the investigation of rainfall characteristics that drive many important landsurface processes.

A Study of Precipitation Patterns through Stochastic Ordering

The study of spatial and temporal changes in precipitation patterns is important to agriculture and natural ecosystems. These changes can be described by some climate change indices. Because these indices often have skewed probability distributions, some common statistical procedures become either inappropriate or less powerful when they are applied to the indices. A nonparametric approach based on stochastic ordering is proposed, which does not make any assumption on the shape of the distribution. This approach is applied to the average length of the period between two adjacent precipitation days, which is called the average number of consecutive dry days (ACDD). This approach is shown to be able to reveal some patterns in precipitation that other approaches do not. Using daily precipitations at 756 stations in China from 1960 to 2015, this work compares the ACDDs in three periods, 1960–1965, 1985–1990, and 2010–2015 for each province in China. The results show that ACDD increases stochastically from the period 1960–1965 to either the period 1985–1990 or the period 2010–2015, or from the period 1985–1990 to the period 2010–2015 in all but three provinces in China.

Multiscale Spatio-Temporal Changes of Precipitation Extremes in Beijing-Tianjin-Hebei Region, China during 1958–2017

In this study, based on daily precipitation records during 1958–2017 from 28 meteorological stations in the Beijing-Tianjin-Hebei (BTH) region, the spatio-temporal variations in precipitation extremes defined by twelve indices are analyzed by the methods of linear regression, Mann-Kendall test and continuous wavelet transform. The results showed that the spatial patterns of all the indices except for consecutive dry days (CDD) and consecutive wet days (CWD) were similar to that of annual total precipitation with the high values in the east and the low value in the west. Regionally averaged precipitation extremes were characterized by decreasing trends, of which five indices (i.e., very heavy precipitation days (R50), very wet precipitation (R95p), extreme wet precipitation (R99p), max one-day precipitation (R × 1day), and max five-day precipitation (R × 5day)) exhibited significantly decreasing trends at 5% level. From monthly and seasonal scale, almost all of the highest values in R × 1day and R × 5day occurred in summer, especially in July and August due to the impacts of East Asian monsoon climate on inter-annual uneven distribution of precipitation. The significant decreasing trends in annual R×1day and R×5day were mainly caused by the significant descend in summer. Besides, the possible associations between precipitation extremes and large-scale climate anomalies (e.g., ENSO (El Niño Southern Oscillation), NAO (North Atlantic Oscillation), IOD (Indian Ocean Dipole), and PDO (Pacific Decadal Oscillation)) were also investigated using the correlation analysis. The results showed that the precipitation extremes were significantly influenced by ENSO with one-year ahead, and the converse correlations between the precipitation extremes and climate indices with one-year ahead and 0-year ahead were observed. Moreover, all the indices show significant two- to four-year periodic oscillation during the entire period of 1958–2017, and most of indices show significant four- to eight-year periodic oscillation during certain periods. The influences of climate anomalies on precipitation extremes were composed by different periodic components, with most of higher correlations occurring in low-frequency components.

Variations of compound precipitation and temperature extremes in China during 1961-2014

Concurrent precipitation and temperature extremes usually have significant impacts on the society, economy and ecosystem. Changes in precipitation or temperature extremes in China have been extensively studied; however, less attention has been paid to their concurrence (or compound extremes) to date. This study explores the historical variations of compound extremes including dry/warm, dry/cold, wet/warm, and wet/cold combinations based on monthly precipitation and temperature observations during summer and winter from 1961 to 2014 over China. Results show that there is a significant increase in the frequency of compound dry/warm and wet/warm extremes while a decrease in compound dry/cold and wet/cold extremes for the period 1988-2014 relative to 1961-1987. In addition, statistically significant increase in the spatial extent exists in compound dry/warm and wet/warm extremes, while the areas affected by the compound dry/cold and wet/cold extremes present a decrease across China, which is shown to be partly related to the large-scale circulation patterns. The results of this study could improve our understanding of changes of compound precipitation and temperature extremes from a multivariate perspective.

Trend Analysis of Temperature and Precipitation Extremes during Winter Wheat Growth Period in the Major Winter Wheat Planting Area of China

In this study, the major winter wheat planting area of China is selected as the study area, with the time scale of the growth period of winter wheat (a total of 56 growth periods during October 1961 to May 2016). The significance, stability, magnitude of the trend and the average trend of the study area with eight temperature indices and seven precipitation indices of 453 meteorological stations are tested by Mann–Kendall method and Sen’s nonparametric method. The following observation can be made: (1) the cold extreme indices show strong and stable downward trend in most of the stations in the study area, while the hot extreme indices show a strong and stable upward trend, especially in the northern winter wheat planting area and the north of the southern winter wheat planting area. (2) The trends of extreme precipitation indices in most of the sites in the study area are insignificant and unstable. Only in R20mm, a significant and stable decreasing trend is shown in some stations, which is mainly located in the northern winter wheat planting area and part of the central and western regions in the study area. The results in some ways could enrich the references for understanding the climate change in the growth period of winter wheat in the region and help to formulate a better agronomic management practice of winter wheat.

Sediment phosphorus release in response to flood event across different land covers in a restored wetland

The phosphorus (P) fraction and its release characteristics from sediment in response to flood events across different land covers (i.e., reclaimed land with dominant vegetation of Phragmites australis and/or Typha orientalis, grassland with dominant vegetation of annual and perennial forbs, and bare land) in the lakeshore of Chaohu Lake were investigated. The results indicated that the re-flooding of a restored wetland led to P release. IP (inorganic P) was the major P fraction in the soils pre-flood and post-flood. For all the soil samples, the rank order of P fractions was Ca-P (P associated with calcium) > OP (organic P) > Fe/Al-P (P bound to Al, Fe, and Mn oxides and hydroxides). During flooding, Fe/Al-P contributed the most as the P release source in the soils and to the P sources for the overlying water. In reclaimed land, Fe/Al-P release correlated significantly with soil pH. In grassland, Fe/Al-P release correlated significantly with soil pH and Al content. In bare land, Fe/Al-P release correlated significantly with Al and clay content. The max TP release rates were also significantly influenced by land cover, and the values in bare land, grassland, and reclaimed land were 9.91 mg P m^-2 day^-1, 8.10 mg P m^-2 day^-1, and 5.05 mg P m^-2 day^-1, respectively. The results showed that the P release processes might be regulated by different factors across different land covers, and that the re-introduction of vegetation during wetland restoration must be taken into account prior to flood events to avoid an undesirable degradation of water quality.

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.