Purpose: This paper reviews the past and future trends of climate change in Malaysia, the major contributors of greenhouse gases and the impacts of climate change to Malaysia. It also reviews the mitigation and adaptations undertaken, and future strategies to manage the impacts of regional climate change. Methodology: The review encompasses historical climate data comprising mean daily temperature, precipitation, mean sea level and occurrences of extreme weather events. Future climate projections have also been reviewed in addition to scholarly papers and news articles related to impacts, contributors, mitigation and adaptations in relation to climate change. Findings: The review shows that annual mean temperature, occurrences of extreme weather events and mean sea level are rising while rainfall shows variability. Future projections point to continuous rise of temperature and mean sea level till the end of the 21st century, highly variable rainfall and increased frequency of extreme weather events. Climate change impacts particularly on agriculture, forestry, biodiversity, water resources, coastal and marine resources, public health and energy. The energy and waste management sectors are the major contributors to climate change. Mitigation of and adaptations to climate change in Malaysia revolve around policy setting, enactment of laws, formulation and implementation of plans and programmes, as well as global and regional collaborations, particularly for energy, water resources, agriculture and biodiversity. There are apparent shortcomings in continuous improvement and monitoring of the programmes as well as enforcement of the relevant laws. Originality/value: This paper presents a comprehensive review of the major themes of climate change in Malaysia and recommends pertinent ways forward to fill the gaps of mitigation and adaptations already implemented. (C) 2018 Elsevier B.V. All rights reserved.

Because of their dependence on water, natural and human systems are highly sensitive to changes in the hydrologic cycle. The authors introduce a new measure of hydroclimatic intensity (HY-INT), which integrates metrics of precipitation intensity and dry spell length, viewing the response of these two metrics to global warming as deeply interconnected. Using a suite of global and regional climate model experiments, it is found that increasing HY-INT is a consistent and ubiquitous signature of twenty-first-century, greenhouse gas–induced global warming. Depending on the region, the increase in HY-INT is due to an increase in precipitation intensity, dry spell length, or both. Late twentieth-century observations also exhibit dominant positive HY-INT trends, providing a hydroclimatic signature of late twentieth-century warming. The authors find that increasing HY-INT is physically consistent with the response of both precipitation intensity and dry spell length to global warming. Precipitation intensity increases because of increased atmospheric water holding capacity. However, increases in mean precipitation are tied to increases in surface evaporation rates, which are lower than for atmospheric moisture. This leads to a reduction in the number of wet days and an increase in dry spell length. This analysis identifies increasing hydroclimatic intensity as a robust integrated response to global warming, implying increasing risks for systems that are sensitive to wet and dry extremes and providing a potential target for detection and attribution of hydroclimatic changes.

极端降水在全球范围内呈现广泛增强的趋势,对农业生态系统的影响不容忽视。水稻作为重要的粮食作物,其产量的年际波动受到极端降水的影响,然而其响应机理和时空敏感性尚未厘清。论文总结了极端降水在水稻主产区的时空格局及对产量的影响程度,梳理了极端降水对水稻产量的生理、化学和物理过程的影响机制,对比分析了多个主流方法(统计模型和作物过程模型)的输入数据和应用上的优缺点。结果表明,极端降水增加1%导致水稻减产0.02%~0.5%,主要通过增加养分流失和造成淹水胁迫等途径。然而当前研究仍难以明确水稻产量如何响应于极端降水的不同特征值(强度、频次、持续时间等)及其敏感性的时空差异,尚未完善极端降水对水稻各产量组成的影响机理,同时缺乏作物模型与统计模型等相结合的研究方法,造成水稻产量预测的不确定性。建议未来相关研究应加强田间观测、控制性实验与模型改进,定量解析极端降水对产量的影响机理,促进模型—数据融合,提高数据精度以更好地模拟极端降水事件下的水稻产量,为优化当前稻作系统和建立气候智能型农业奠定理论基础。

The increasing trend of extreme precipitation has become stronger globally, and is expected to have detrimental impact on agricultural ecosystems. Rice is one of the staple foods, and the inter-annual fluctuation of rice yield is highly affected by extreme precipitation. However, the mechanisms and spatiotemporal sensitivity of rice yield to extreme precipitation have not been clarified. This review summarized the temporal and spatial patterns of extreme precipitation in the main rice-producing regions of the world and its impact on rice yield, and explored the mechanism of extreme precipitation impact on rice growth and yield from the perspective of physiological, chemical, and physical processes. The input data and advantages and disadvantages in application of the main research methods, including statistical model and crop model, were evaluated and compared. The results indicate that an increase of 1% in extreme precipitation led to a decrease in rice yield by 0.02%-0.5%, mainly through increased nutrient loss and flooding. Yet, large uncertainties still exist in rice yield prediction of current studies, because it is difficult to clarify how rice yield responds to different characteristics (intensity, frequency, and duration) of extreme precipitation and its spatiotemporal sensitivity, and the mechanisms of extreme precipitation affecting rice yield components are not well understood. In addition, lacking the integration of crop models and statistical models also introduces uncertainties. We recommend to promote the integration of multi-methods, especially field observation, controlled experiment, and model improvement, to quantitatively analyze the mechanism of extreme precipitation impact on yield components, and to improve data accuracy to better simulate rice yields under extreme precipitation events in the future. Achieving these progresses will lay a foundation for optimizing the current rice cropping system and agricultural management to mitigate the impact of extreme precipitation.

沿海地区是城市化水平较高的区域,在全球气候变化背景下,该地区极端天气气候事件频发,灾害风险日益加大,已严重制约了该地区社会经济的可持续发展。论文立足气候变化背景,探讨沿海地区灾害风险变化的新特点,认为：当前极端天气气候灾害的发生仍具较大的不确实性,气候与灾害在时空尺度上呈现多样性变化特征,各气象灾害风险存在较大差异性,灾害系统构成要素也更加复杂。鉴于气候变化给沿海地区灾害风险研究带来新的挑战,文章提出在气候变化背景下,沿海地区灾害风险系统的构建应关注灾害的时空尺度变化、多灾种灾害的协同效应、城市化与气候变化,并构建了基于气候变化背景下的灾害风险系统框架。最后,为提升气候变化背景下我国沿海地区灾害风险应对能力建设,提出以下建议：注重灾害防御系统的多样化、注重灾害防御系统的稳健性、实现多灾种灾害信息的共享、强化承灾主体的自恢复能力、加强灾害风险分担与转移能力建设、逐步适应气候变化背景下的极端天气与自然灾害。

In the context of global climate change, extreme weather events and the concomitant natural disasters are becoming a primary risk restricting the socioeconomic development in coastal areas that are often highly urbanized. This study sets out to explore the new characteristics of natural hazards and disasters in the coastal areas of China against the backdrop of a changing climate. Major conclusions are as follows. First, the occurrences of extreme weather and climatic events are considerably uncertain. The climate and disasters are different across various temporal and spatial scales, and the components of the disaster risk system become more complicated. Second, in view of the new challenges brought by climate change, we suggest paying close attention to three issues in the future, including the change of temporal and spatial scales that are used to depict the disasters, the synergism of multi-disasters, and the effects of climate change and urbanization. Based on these, a new framework of disaster risk system is established to reflect the qualities of disasters in complex spatiotemporal contexts. Lastly, to develop more effective coping strategies for the climate and natural hazards and disasters in coastal areas, it is necessary to promote diversification of the disaster prevention system and enhance its robustness, and to strengthen the self-recovery capability of the social, economic, and ecological environments. Meanwhile, information sharing in multi-disaster management and risk sharing and transfer policies are also important for accommodating the negative impacts of extreme events and disasters coming with climate change.

全球气候变化背景下,海平面上升以及高潮位和风暴潮引起的极值水位导致的海岸洪水对沿海社会经济和自然环境造成巨大影响,已是国内外关注的重点。论文梳理了广义和狭义海岸洪水的定义和要素,重点阐述了狭义海岸洪水的组成部分,从致灾因子、孕灾环境和承灾体以及风险评估方法与模型3个方面,系统总结了相关研究方法与研究成果的主要进展,以及存在的主要问题,并透视了未来拟加强的研究方向。建议加强沿海地区应对全球气候变化风险的研究,包括全球气候变化下多致灾因子耦合危险性和不确定性研究,沿海关键地区和关键暴露(关键基础设施)的风险评估研究,全球气候变化风险适应与减缓性措施的成本效益评价研究,提高沿海地区应对全球气候变化风险的韧性研究,以及建立多学科间的基础数据共享机制,采用交叉学科手段以便更综合、系统、动态研究海岸带问题,保障沿海地区开展全球气候变化下风险评估的需要。

The sea level rise under global climate change and coastal floods caused by extreme sea levels due to the high tide levels and storm surges have huge impacts on coastal society, economy, and natural environment. It has drawn great attention from global scientific researchers. This study examines the definitions and elements of coastal flooding in the general and narrow senses, and mainly focuses on the components of coastal flooding in the narrow sense. Based on the natural disaster system theory, the review systematically summarizes the progress of coastal flood research in China, then discusses existing problems in present studies and future research directions with regard to this issue. It is proposed that future studies need to strengthen research on adapting to climate change in coastal areas, including studies on the risk of multi-hazards and uncertainties of hazard impacts under climate change, risk assessment of key exposure (critical infrastructure) in coastal hotspots, and cost-benefit analysis of adaptation and mitigation measures in coastal areas. Efforts to improve the resilience of coastal areas under climate change should be given more attention. The research community also should establish the mechanism of data sharing among disciplines to meet the needs of future risk assessments, so that coastal issues can be more comprehensively, systematically, and dynamically studied.

High-temperature risk disaster, a common meteorological disaster, seriously affects people’s productivity, life, and health. However, insufficient attention has been paid to this disaster in urban communities. To assess the risk of high-temperature disasters, this study, using remote sensing data and geographic information data, analyzes 973 communities in downtown Wuhan with the geography-weighted regression method. First, the study evaluates the distribution characteristics of high temperatures in communities and explores the spatial differences of risks. Second, a metrics and weight system is constructed, from which the main factors are determined. Third, a risk assessment model of high-temperature disasters is established from disaster-causing danger, disaster-generating sensitivity, and disaster-bearing vulnerability. The results show that: (a) the significance of the impact of the built environment on high-temperature disasters is obviously different from its coefficient space differentiation; (b) the risk in the old city is high, whereas that in the area around the river is low; and (c) different risk areas should design built environment optimization strategies aimed specifically at the area. The significance of this study is that it develops a high-temperature disaster assessment framework for risk identification, impact differentiation, and difference optimization, and provides theoretical support for urban high-temperature disaster prevention and mitigation.

This paper presents an analysis of projected precipitation extremes over the East African region. The study employs six indices defined by the Expert Team on Climate Change Detection Indices to evaluate extreme precipitation. Observed datasets and Coupled Model Intercomparison Project Phase six (CMIP6) simulations are employed to assess the changes during the two main rainfall seasons: March to May (MAM) and October to December (OND). The results show an increase in consecutive dry days (CDD) and decrease in consecutive wet days (CWD) towards the end of the 21st century (2081–2100) relative to the baseline period (1995–2014) in both seasons. Moreover, simple daily intensity (SDII), very wet days (R95 p), very heavy precipitation >20 mm (R20 mm), and total wet-day precipitation (PRCPTOT) demonstrate significant changes during OND compared to the MAM season. The spatial variation for extreme incidences shows likely intensification over Uganda and most parts of Kenya, while a reduction is observed over the Tanzania region. The increase in projected extremes may pose a serious threat to the sustainability of societal infrastructure and ecosystem wellbeing. The results from these analyses present an opportunity to understand the emergence of extreme events and the capability of model outputs from CMIP6 in estimating the projected changes. More studies are recommended to examine the underlying physical features modulating the occurrence of extreme incidences projected for relevant policies.

Estimates of future flood risk rely on projections from climate models. The relatively few climate models used to analyze future flood risk cannot easily quantify of their associated uncertainties. In this study, we demonstrated that the projected fluvial flood changes estimated by a new generation of climate models, the collectively known as Coupled Model Intercomparison Project Phase 6 (CMIP6), are similar to those estimated by CMIP5. The spatial patterns of the multi-model median signs of change (+ or -) were also very consistent, implying greater confidence in the projections. The model spread changed little over the course of model development, suggesting irreducibility of the model spread due to internal climate variability, and the consistent projections of models from the same institute suggest the potential to reduce uncertainties caused by model differences. Potential global exposure to flooding is projected to be proportional to the degree of warming, and a greater threat is anticipated as populations increase, demonstrating the need for immediate decisions.

洪涝灾害对产业系统造成的经济影响既有直接经济损失也有间接经济损失,而间接经济损失远远高于直接经济损失,研究间接经济损失对灾害风险控制具有重要意义。论文基于投入产出模型,利用2016年湖北省洪涝灾害直接经济损失数据,从产业关联视角计算不同洪涝淹没水深下的产业部门间接经济损失。研究结果表明：① 随着淹没水深增加,不同产业部门受洪涝灾害冲击引起的直接经济损失与间接经济损失相应增加。② 大部分产业部门由于产业关联关系造成的间接经济损失高于直接经济损失;当淹没水深较小时,一些资产较密集的产业部门受洪涝灾害冲击较大,造成的直接经济损失高于间接经济损失。③ 全社会总直接经济损失与间接经济损失呈非线性关系,且间接经济损失随着直接经济损失增加而增加;当淹没水深大于2.093 m时,间接经济损失约是直接经济损失的1.15倍。

Flood disasters cause both direct and indirect economic losses to industrial systems, and indirect economic losses may be much higher than direct economic losses. Studying indirect economic losses is of great significance for disaster risk reduction. Based on the input-output model, this study used the direct economic loss data of flood disasters in Hubei Province in 2016 to evaluate indirect economic losses under different flood water depth, from the perspective of sectorial interconnectedness. The results show that: 1) With the increase of flood water depth, direct economic losses and indirect economic losses increase accordingly. 2) For most industrial sectors, indirect economic losses caused by industrial interconnectedness are higher than direct economic losses. However, when flood water depth is shallow, some industrial sectors with more intensive fixed assets are more affected by flood disasters, resulting in higher direct economic losses. 3) The total indirect economic losses show a nonlinear relationship with the total direct economic losses, but with the same trend. When the flood water depth is greater than 2.093 m, indirect economic losses are about 1.15 times of direct economic losses.

The scientific gaps identified in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) that guided the experiment for its next phase, CMIP6, are identified.

. By coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1) a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations (1850–near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2) common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3) an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions: – How does the Earth system respond to forcing? – What are the origins and consequences of systematic model biases? – How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs.\n

Earth system models are complex and represent a large number of processes, resulting in a persistent spread across climate projections for a given future scenario. Owing to different model performances against observations and the lack of independence among models, there is now evidence that giving equal weight to each available model projection is suboptimal. This Perspective discusses newly developed tools that facilitate a more rapid and comprehensive evaluation of model simulations with observations, process-based emergent constraints that are a promising way to focus evaluation on the observations most relevant to climate projections, and advanced methods for model weighting. These approaches are needed to distil the most credible information on regional climate changes, impacts, and risks for stakeholders and policy-makers.

使用1961—2019年全国2 510个气象站点日值降水数据和2030—2100年第六次耦合模式比较计划的12个耦合模式的SSP2-4.5未来情景的降水模拟数据，基于核密度函数分别计算4个重现期（五年一遇、十年一遇、二十年一遇和五十年一遇）历史和未来情景中3个年暴雨要素（年暴雨日数、年暴雨量和年暴雨强度）的数值，在此基础上评估了中国未来暴雨危险性的变化。得到的主要结论如下：①从全国来看，在未来年暴雨日数和雨量呈增加趋势，而年暴雨强度在不同重现期下表现不一样。4个重现期下全国的年暴雨日数变化均值分别为0.36、0.57、0.73和0.92 d；年暴雨量变化均值分别为22.30、36.24、46.92和60.12 mm；年暴雨强度变化均值分别为2.43、0.27、-1.95和 -4.86 mm/d。②从不同气候区来看，年暴雨量和雨日在青藏高原、东部干旱区、东北地区、华北地区和西南地区呈增加趋势，在西部干旱（半干旱）区、华中地区和华南地区呈减少趋势。4个重现期下年暴雨强度在青藏高原均呈增加趋势，而其他地区以减少为主要趋势。③热点分析结果显示青藏高原东南部和南部是暴雨危险性增加最显著的区域；年暴雨日数和雨量减少的区域集中分布在西南地区的中部、华中和华南地区的中部和南部，年暴雨强度减少的区域集中在东部干旱区的中部、西南地区西部及华北地区北部和南部。

Using the daily precipitation data of 2 510 meteorological stations in China from 1961 to 2019 and the precipitation simulation data of 12 coupling models of CMIP6 from 2030 to 2100 under the SSP2-4.5 future scenario， the values of three annual rainstorm elements （annual rainstorm days， annual rainstorm rainfall， and annual rainstorm intensity） in the historical and future scenarios were calculated based on the kernel density function under four return periods （5，10，20，50 years）. Based on this， the future hazard change of rainstorms in China was assessed. The main conclusions are as follows： ① From a national perspective， the number of rainstorm days and rainfall amount are estimated to increase in the future， and the annual rainstorm intensity is estimated to change differently in different return periods. Under the four return periods， the mean change of annual rainstorm days in China is expected to be 0.36， 0.57， 0.73 and 0.92 days； the mean change of annual rainstorm rainfall is expected to be 22.30， 36.24， 46.92 and 60.12 mm； and the mean change of annual rainstorm intensity is expected to be 2.43， 0.27， -1.95 and -4.86 mm/d. ② From the perspective of different climatic zones， the annual rainstorm rainfall and rainstorm days showed an increasing trend in the Qinghai-Tibet Plateau， Eastern arid zone， Northeast China， North China， and Southwest China， and a decreasing trend in the western arid （semi-arid） zone， Central China， and South China. The annual rainstorm intensity is expected to increase in the Qinghai-Tibet Plateau and decrease in most other regions under the four return periods. ③ The hot spot analysis shows that the southeastern and southern Qinghai-Tibet Plateau are expected to be the areas with the most significant increases in rainstorm hazards. The areas with reduced annual rainstorm days and annual rainstorm rainfall are expected to be concentrated in the middle of Southwest China， the middle and south of Central China and South China. Finally， the areas with reduced annual rainstorm intensities are expected to be concentrated in the middle of the eastern arid zone， the west of Southwest China， and the north and south of North China.

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.© 2018 John Wiley & Sons Ltd.

空间分异是自然和社会经济过程的空间表现,也是自亚里士多德以来人类认识自然的重要途径。地理探测器是探测空间分异性,以及揭示其背后驱动因子的一种新的统计学方法,此方法无线性假设,具有优雅的形式和明确的物理含义。基本思想是：假设研究区分为若干子区域,如果子区域的方差之和小于区域总方差,则存在空间分异性;如果两变量的空间分布趋于一致,则两者存在统计关联性。地理探测器q统计量,可用以度量空间分异性、探测解释因子、分析变量之间交互关系,已经在自然和社会科学多领域应用。本文阐述地理探测器的原理,并对其特点及应用进行了归纳总结,以利于读者方便灵活地使用地理探测器来认识、挖掘和利用空间分异性。

Spatial stratified heterogeneity is the spatial expression of natural and socio-economic process, which is an important approach for human to recognize nature since Aristotle. Geodetector is a new statistical method to detect spatial stratified heterogeneity and reveal the driving factors behind it. This method with no linear hypothesis has elegant form and definite physical meaning. Here is the basic idea behind Geodetector: assuming that the study area is divided into several subareas. The study area is characterized by spatial stratified heterogeneity if the sum of the variance of subareas is less than the regional total variance; and if the spatial distribution of the two variables tends to be consistent, there is statistical correlation between them. Q-statistic in Geodetector has already been applied in many fields of natural and social sciences which can be used to measure spatial stratified heterogeneity, detect explanatory factors and analyze the interactive relationship between variables. In this paper, the authors will illustrate the principle of Geodetector and summarize the characteristics and applications in order to facilitate the using of Geodetector and help readers to recognize, mine and utilize spatial stratified heterogeneity.

暴雨洪涝灾害给中国造成了巨大人口和经济损失。本文通过对中国气象灾情普查数据的分析,结合小时降水数据、统计年鉴等资料,研究了1984—2007年间中国极端降水和暴雨洪涝灾情时空分异特征。在此基础上,采用地理探测器研究了中国暴雨洪涝灾情时空格局的影响因素。结果表明,研究时段内全国极端降雨指标没有一致的变化趋势。长江、珠江及东南沿海等流域暴雨洪涝频次显著增加,但因灾人口死亡率下降,表明设防能力的提升对人口损失的缓减影响明显;西北地区因灾死亡人数和人口受灾率、经济损失等均增加,表明设防能力薄弱;因暴雨洪涝受灾人口贡献率最高的因子是气象致灾因素,又因暴雨洪涝死亡人口贡献率最高的是地理等孕灾环境和社会经济等承灾体因素,故暴雨洪涝直接经济损失贡献率最高的是地理等孕灾环境因素。该研究可为洪水灾情的影响因素定量化分析提供理论参考。

Understanding the influencing factors and controls of rainstorm-induced floods, which have caused tremendous losses of human lives and national economy, is a pressing need for flood risk management in China. Based on the meteorological disaster census data of counties in China, hourly precipitation data at 2420 stations, statistical yearbook, terrain data and other data, the authors (1) investigated the spatiotemporal pattern of flood impacts in China over the period from 1984 to 2007 using trend analysis techniques and (2) explored the driving factors of the spatiotemporal pattern by adopting the geospatial statistical analysis tool (Geodetector). This study considered the spatiotemporal patterns and their interplays among county-level flood impacts (i.e., flood-induced mortality rate, proportion of population affected, and economic loss in percentage), disaster-formative environmental factors (i.e., population density, urban population percentages, average elevation, river density, average slope, and average distance to the seashore), and extreme precipitation characteristics (i.e., annual average volume and duration of extreme rainfall). The results show that: (1) there were no consistent temporal trends of extreme rainfall characteristics over the study period across China. (2) The frequencies of flood disasters in the Yangtze and Pearl rivers and southeast coastal areas increased significantly, but the casualties over these regions decreased. (3) Flood-induced casualties, proportion of population affected and economic loss in percentage increased in Northwest China; and meteorological factors, disaster-formative environment factors such as geographical conditions and social economy, and geographical conditions contribute mostly to the proportion of population affected, flood-induced death and economic loss in percentage. These results indicate that more attention should be paid to improving the flood control capacity of small or medium-sized cities in the inland river basins, especially in Northwest China, and we should recognize the important roles that disaster-formative environment plays in triggering flood losses.

Accurate identification of maize plantation distribution and timely examination of key spatial-temporal drivers is a practice that can support agricultural production estimates and development decisions. Previous studies have rarely used efficient cloud processing methods to extract crop distribution, and meteorological and socioeconomic factors were often considered independently in driving force analysis. In this paper, we extract the spatial distribution of maize using classification and regression tree (CART) and random forest (RF) algorithms based on the Google Earth Engine (GEE) platform. Combining remote sensing, meteorological and statistical data, the spatio-temporal variation characteristics of maize plantation proportion (MPP) at the county scale were analyzed using trend analysis, kernel density estimation, and standard deviation ellipse analysis, and the driving forces of MPP spatio-temporal variation were explored using partial correlation analysis and geodetectors. Our empirical results in Heilongjiang province, China showed that (1) the CART algorithm achieved higher classification accuracy than the RF algorithm; (2) MPP showed an upward trend in more than 75% of counties, especially in high-latitude regions; (3) the main climatic factor affecting the inter-annual fluctuation of MPP was relative humidity; (4) the impact of socioeconomic factors on MPP spatial distribution was significantly larger than meteorological factors, the temperature was the most important meteorological factor, and the number of rural households was the most important socioeconomic factor affecting MPP spatial distribution. The interaction between different factors was greater than a single factor alone; (5) the correlation between meteorological factors and MPP differed across different latitudinal regions and landforms. This research provides a key reference for the optimal adjustment of crop cultivation distribution and agricultural development planning and policy.