Meteorological Disasters Research Articles

Global trend and epidemiological profiles of climate-related disasters from 2000 to 2021.

The objective of this study is to analyse the epidemiological profile of global climate-related disasters in terms of morbidity and mortality, as well as to examine their temporal trends. This cross-sectional study analysed climate-related global disasters from 2000 to 2021, utilising definitions and criteria from the United Nations Strategy for Disaster Reduction and the Centre for Research on the Epidemiology of Disasters. Data were sourced from the EM-DAT database. The study assessed trends over the entire period and compared them with previous years (1978-2000). A total of 7398 climate-related disasters were recorded, with hydrological disasters being the most frequent, followed by meteorological and climatological disasters. Statistically significant differences were noted in the average rates of affected individuals and injuries per million inhabitants. No significant trends were found in mortality rates, but the frequency trends for the entire period (1978-2021) and the subperiod (1978-2000) were increasing and statistically significant. However, the trend from 2000 onwards showed a non-significant decrease, potentially reflecting better disaster preparedness and response strategies under the Hyogo and Sendai Framework. The study highlights hydrological disasters as the most frequent and deadliest climate-related events, with climatological disasters affecting and injuring the most people. The lack of standardised criteria for disaster inclusion in databases presents a significant challenge in comparing results and analysing trends. Establishing uniform inclusion criteria is crucial for effective data analysis and disaster management.

Training on creating learning media for handling of disaster mitigation for elementary school teachers

Background: Indonesia is known as a disaster-prone country, the total amount of damage due to disasters is said to be the fourth largest in the world. The impact of global warming results in very high rainfall and large-scale damage. Apart from weather disasters, a number of damages also occur due to geological disasters. To stop disaster damage in the long term, it is important to create sustainable communities. It is important not to separate the environment and disaster prevention, but to learn about the nature that sometimes threatens us just as we learn about the nature around us. Under these conditions, until now Indonesia has not included practical disaster education in the learning curriculum, because it is considered important to include disaster management training in school communities. Because of this, disaster management methods were tested in Japan, tested in elementary schools in the city of Mataram, Indonesia. This research aims to evaluate whether adapting traditional Japanese educational methods such as "exploring the environment (Machitanken)" and "picture story performances (Kamishibai)", and presentations (hapyoukai) are adapted to local conditions. existing policies in Indonesia, can be applied in Indonesia. Methods: This research was conducted in the city of Mataram, carried out for one year via Zoom, involving 7 elementary school teachers, one middle school teacher, three lecturers. Findings: The participants were given knowledge about how to provide disaster education. Conclusion: To further strengthen teachers' abilities and skills in teaching about disasters, teachers were given training for 6 months via Zoom and for one week in Kitakyushu City to make teaching materials. Novelty/Originality of this Study: The novelty of this study lies in its implementation of disaster mitigation education for elementary school teachers in Mataram using traditional Japanese methods such as Machitanken and Kamishibai. This approach is unique as it integrates cultural techniques from Japan into the Indonesian educational context, aiming to enhance teachers' ability to educate students on disaster preparedness in a practical and engaging manner.

Monthly climate prediction using deep convolutional neural network and long short-term memory

Climate change affects plant growth, food production, ecosystems, sustainable socio-economic development, and human health. The different artificial intelligence models are proposed to simulate climate parameters of Jinan city in China, include artificial neural network (ANN), recurrent NN (RNN), long short-term memory neural network (LSTM), deep convolutional NN (CNN), and CNN-LSTM. These models are used to forecast six climatic factors on a monthly ahead. The climate data for 72 years (1 January 1951–31 December 2022) used in this study include monthly average atmospheric temperature, extreme minimum atmospheric temperature, extreme maximum atmospheric temperature, precipitation, average relative humidity, and sunlight hours. The time series of 12 month delayed data are used as input signals to the models. The efficiency of the proposed models are examined utilizing diverse evaluation criteria namely mean absolute error, root mean square error (RMSE), and correlation coefficient (R). The modeling result inherits that the proposed hybrid CNN-LSTM model achieves a greater accuracy than other compared models. The hybrid CNN-LSTM model significantly reduces the forecasting error compared to the models for the one month time step ahead. For instance, the RMSE values of the ANN, RNN, LSTM, CNN, and CNN-LSTM models for monthly average atmospheric temperature in the forecasting stage are 2.0669, 1.4416, 1.3482, 0.8015 and 0.6292 °C, respectively. The findings of climate simulations shows the potential of CNN-LSTM models to improve climate forecasting. Climate prediction will contribute to meteorological disaster prevention and reduction, as well as flood control and drought resistance.

Estimating evapotranspiration and drought dynamics of winter wheat under climate change: A case study in Huang-Huai-Hai region, China

Drought is one of the vital meteorological disasters that influence crop growth. Timely and accurately estimating the drought dynamics of crops is valuable for decision-maker to formulate scientific management measures of agricultural drought risk. In this study, the evapotranspiration and drought dynamics of winter wheat from 1981 to 2020 in the Huang-Huai-Hai (HHH) region of China were evaluated based on long-term multi-source observation data. Four key developmental stages of winter wheat were given attentions: growth before winter stage, overwintering stage, stage of greening-heading, and stage of filling-maturity. The crop water deficit index (CWDI) on a daily scale was established for quantitatively appraising the impacts of drought on winter wheat. Our results indicated that interannual variation in reference crop evapotranspiration (ET0) during the growth season of winter wheat from 1981 to 2020 in the HHH region showed a slight increase trend, with an average of 602.4 mm and obvious spatial differences of decreasing from the Northeast to the Southwest. Over the past forty years, the winter wheat in the HHH region was most severely affected by severe drought, followed by moderate drought, and finally mild drought. In addition, the impacts of drought on winter wheat at different critical growth stages varied greatly. For the growth before winter stage, the winter wheat was mainly threatened by mild, moderate, and severe droughts. For the overwintering stage, the winter wheat was mainly threatened by moderate, severe, and extreme droughts. For the greening-heading stage, the winter wheat was mainly threatened by mild, moderate, severe, and extreme droughts. For the filling-maturity stage, the winter wheat was mainly threatened by mild and moderate droughts. Finally, the impacts of drought on winter wheat during 1981–2020 in the HHH region were revealed to differ extraordinarily in space. In particular, the areas of winter wheat affected by severe drought significantly decreased. However, the areas of winter wheat affected by moderate drought clearly expanded. Our findings provide new insights for further improving climate change impact studies and agricultural drought defense capabilities adapting to continuous environmental change.

Improving Nowcasting of Intense Convective Precipitation by Incorporating Dual-Polarization Radar Variables into Generative Adversarial Networks.

The nowcasting of strong convective precipitation is highly demanded and presents significant challenges, as it offers meteorological services to diverse socio-economic sectors to prevent catastrophic weather events accompanied by strong convective precipitation from causing substantial economic losses and human casualties. With the accumulation of dual-polarization radar data, deep learning models based on data have been widely applied in the nowcasting of precipitation. Deep learning models exhibit certain limitations in the nowcasting approach: The evolutionary method is prone to accumulate errors throughout the iterative process (where multiple autoregressive models generate future motion fields and intensity residuals and then implicitly iterate to yield predictions), and the "regression to average" issue of autoregressive model leads to the "blurring" phenomenon. The evolution method's generator is a two-stage model: In the initial stage, the generator employs the evolution method to generate the provisional forecasted data; in the subsequent stage, the generator reprocesses the provisional forecasted data. Although the evolution method's generator is a generative adversarial network, the adversarial strategy adopted by this model ignores the significance of temporary prediction data. Therefore, this study proposes an Adversarial Autoregressive Network (AANet): Firstly, the forecasted data are generated via the two-stage generators (where FURENet directly produces the provisional forecasted data, and the Semantic Synthesis Model reprocesses the provisional forecasted data); Subsequently, structural similarity loss (SSIM loss) is utilized to mitigate the influence of the "regression to average" issue; Finally, the two-stage adversarial (Tadv) strategy is adopted to assist the two-stage generators to generate more realistic and highly similar generated data. It has been experimentally verified that AANet outperforms NowcastNet in the nowcasting of the next 1 h, with a reduction of 0.0763 in normalized error (NE), 0.377 in root mean square error (RMSE), and 4.2% in false alarm rate (FAR), as well as an enhancement of 1.45 in peak signal-to-noise ratio (PSNR), 0.0208 in SSIM, 5.78% in critical success index (CSI), 6.25% in probability of detection (POD), and 5.7% in F1.

Climate state of the Three Gorges Region in the Yangtze River basin in 2022–2023

Based on daily observation data of the Three Gorges Region (TGR) of the Yangtze River basin and global reanalysis data, the climate characteristics, climate events, and meteorological disasters of the TGR in 2022 and 2023 were analyzed. For the TGR, the average annual temperature for 2022 and 2023 was 0.8 °C and 0.4 °C higher than normal, respectively, making them the two warmest years in the past decade. In 2022, the TGR experienced its warmest summer on record. The average air temperature was 2.4 °C higher than the average, and there were 24.8 days of above-average high temperature days during summer. Rainfall in the TGR varied significantly between 2022 and 2023. Annual rainfall was 18.4 % below normal and drier than normal in most parts of the region. In contrast, the precipitation in 2023 was considerably higher than the long-term average, and above normal for almost the entire year. The average wind speed exhibited minimal variation between the two years. However, the number of foggy days and relative humidity increased in 2023 compared to 2022. In 2022–2023, the TGR mainly experienced meteorological disasters such as extreme high temperatures, regional heavy rain and flooding, overcast rain, and inverted spring chill. Analysis indicates that the abnormal western Pacific subtropical high and the abnormal persistence of the eastward-shifted South Asian high were the two important drivers of the durative enhancement of record-breaking high temperature in the summer of 2022.摘要基于长江三峡地区观测资料和全球再分析资料, 分析了该地区 2022–2023年气候特征, 酸雨状况以及主要天气气候事件. 2022 年和2023年三峡地区平均气温分别较常年偏高0.8 °C和0.4 °C, 是近十年来最暖的两年, 特别是2022年夏季出现破记录极端高温; 2022年三峡地区降水量较常年偏少18.4 %, 2023年降水量转为偏多15.3 %. 在这两年中该地区主要出现了极端高温, 区域性暴雨洪涝, 连阴雨和倒春寒等气候事件. 分析表明, 西太平洋副热带高压和南亚高压协同异常是2022年夏季极端高温维持的两个重要因素.

Power supply disruptions deter electric vehicle adoption in cities in China

Electrification plays a crucial role in deep decarbonization. However, electrification and power infrastructure can cause mutual challenges. We use nationwide power outage and electric vehicle adoption data in China to provide empirical evidence on how power infrastructure failures can deter electrification. We find that when the number of power outages per district increases by 1 in a given month, the number of new electric vehicles adopted per month decreases by 0.99%. A doubling of power outages in one year on average across the nation can create a depressed adoption rate for up to a decade, implying a decline of more than $ 31.3 million per year in carbon reduction benefits from electric vehicle adoptions. This paper adds to the policy discussion of the costs of increased power outages due to extreme weather and natural disasters, and the urgency for policy to address this issue to facilitate wide adoption of electrification.

Incompleteness of natural disaster data and its implications on the interpretation of trends

ABSTRACT We use data from a leading global database, primarily the reported numbers of fatal ‘hydrological’, ‘meteorological’ and ‘geophysical’ disasters and associated fatalities, to elucidate long-term trends in natural disaster impact. There are very strong upward trends in the number of reported disasters. However, we show that these trends are strongly biased by progressively improving reporting. We apply well-established methods based on analysing evolving differences in the patterns of large (many fatalities) to small (fewer fatalities) events to assess and quantify this bias. For all disaster types investigated, the long-term (multi-decade) true number of disasters appears to be unexpectedly stable over time. There was, however, a clear temporary increase between 1980 and 1990 for hydrological and meteorological disasters. Importantly, there has been a large long-term decline in total fatalities reported for weather-related disasters, attributable to very effective mitigation for larger disasters. Probably due to improved mitigation rather than reduced hazard, data for the most recent decades shows a systematic decrease in the number of larger (more fatality) weather-related disasters compared to smaller ones. In contrast, for the geophysical disasters, predominantly earthquakes, the relative numbers of larger versus smaller disasters, are fairly constant, as are total fatalities.

Unveiling the Nexus: Climate change, green innovation, and the pendulum of energy consumption and carbon emissions

This study delves into the complex interplay of climate change, natural disasters, energy consumption, and carbon emissions across 111 countries from 1990 to 2019. A structural shift in 2004 signifies altered global dynamics in CO2 emissions, closely linked to the escalation of meteorological and hydrological disasters driven by climate change. Globally, we illuminate the significant impact of climate-induced natural disasters, especially storms and extreme temperatures, on energy consumption and carbon emissions, albeit with variations. Regionally, we establish a notable positive association between extreme temperature-related disasters and both energy consumption and carbon emissions in Europe. Examining the aftermath of catastrophic events reveals an intensified influence of these disasters on carbon emissions and energy consumption in Africa, Latin America, and Europe. Developed economies experience significant impacts on carbon emissions and energy consumption from storms and extreme temperatures, while flood severity predominantly affects carbon emissions in developing countries. Additionally, we explore the potential of green patents in mitigating energy consumption and emissions triggered by disasters. While not conclusively proven, the statistically significant impact of green patents on energy conservation holds profound policy implications for advancing climate understanding, transforming energy landscapes, and addressing future sustainability concerns.

Current status and prospects of research on safety situation awareness of high speed railway operation environment

PurposeThe safety of high-speed rail operation environments is an important guarantee for the safe operation of high-speed rail. The operating environment of the high-speed rail is complex, and the main factors affecting the safety of high-speed rail operating environment include meteorological disasters, perimeter intrusion and external environmental hazards. The purpose of the paper is to elaborate on the current research status and team research progress on the perception of safety situation in high-speed rail operation environment and to propose directions for further research in the future.Design/methodology/approachIn terms of the mechanism and spatio-temporal evolution law of the main influencing factors on the safety of high-speed rail operation environments, the research status is elaborated, and the latest research progress and achievements of the team are introduced. This paper elaborates on the research status and introduces the latest research progress and achievements of the team in terms of meteorological, perimeter and external environmental situation perception methods for high-speed rail operation.FindingsBased on the technical route of “situational awareness evaluation warning active control,” a technical system for monitoring the safety of high-speed train operation environments has been formed. Relevant theoretical and technical research and application have been carried out around the impact of meteorological disasters, perimeter intrusion and the external environment on high-speed rail safety. These works strongly support the improvement of China’s railway environmental safety guarantee technology.Originality/valueWith the operation of CR450 high-speed trains with a speed of 400 km per hour and the application of high-speed train autonomous driving technology in the future, new and higher requirements have been put forward for the safety of high-speed rail operation environments. The following five aspects of work are urgently needed: (1) Research the single factor disaster mechanism of wind, rain, snow, lightning, etc. for high-speed railways with a speed of 400 kms per hour, and based on this, study the evolution characteristics of multiple safety factors and the correlation between the high-speed driving safety environment, revealing the coupling disaster mechanism of multiple influencing factors; (2) Research covers multi-source data fusion methods and associated features such as disaster monitoring data, meteorological information, route characteristics and terrain and landforms, studying the spatio-temporal evolution laws of meteorological disasters, perimeter intrusions and external environmental hazards; (3) In terms of meteorological disaster situation awareness, research high-precision prediction methods for meteorological information time series along high-speed rail lines and study the realization of small-scale real-time dynamic and accurate prediction of meteorological disasters along high-speed rail lines; (4) In terms of perimeter intrusion, research a multi-modal fusion perception method for typical scenarios of high-speed rail operation in all time, all weather and all coverage and combine artificial intelligence technology to achieve comprehensive and accurate perception of perimeter security risks along the high-speed rail line and (5) In terms of external environment, based on the existing general network framework for change detection, we will carry out research on change detection and algorithms in the surrounding environment of high-speed rail.

The Development of a Hailstone Disdrometer and Its Preliminary Observation in Aksu, Xinjiang

Hailfall is a severe local weather event that can cause great economic losses as well as the loss of people’s property; however, it is still difficult for domestic meteorological stations to comprehensively observe hail, and domestic independently developed hail observation instruments are still scarce. To help enable better automatic hail observations, a new independently developed hailstone disdrometer based on the acoustic principle, which can be used to measure the hailstone number and particle size and to calculate the corresponding equivalent liquid precipitation of hailstones, is proposed in this paper. The characteristics of hailstones were preliminarily analyzed using observation data from two hailstone disdrometers installed in Aksu, Xinjiang, where three hail events were observed via the hailstone disdrometer in the summer of 2023. By analyzing the development of deep convection clouds using the Fengyun 4A satellite-based cloud-top brightness temperature, and synoptic conditions based on the fifth-generation global climate reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (the ECMWF ERA5 dataset), the hail formation mechanism was investigated in detail for one hailfall event. Accurate hail observations are an important basis for understanding spatiotemporal hail variation. The hailstone disdrometer proposed in this study offers a useful approach for domestic hail observation to provide first-hand hail information for the inspection of weather modification effects and disaster prevention and reduction.

Sudden weather disasters as triggers for ethnic protest in autocracies?

Under what conditions do disastrous storms and floods provoke anti-government protests in autocracies? While grievances, as they often occur after disastrous weather events, are necessary preconditions for protests, they are by no means sufficient. This is particularly true in undemocratic states, where protesters typically fear repressive sanctions. Therefore, this essay hypothesizes that disastrous storms and floods primarily result in anti-government mobilization in autocracies when they occur in the homelands of marginalized ethnic groups. In such scenarios, the immediate hardships caused by the disaster come on top of underlying more structural grievances. Moreover, groups with shared ethnic identities are more likely to mobilize for collective action. This argument is tested using quantitative cross-national protest-event data from the Mass Mobilization in Autocracies Database (MMAD). The results of numerous regression analyses on a sample pre-processed with Coarsened Exact Matching to address potential concerns of endogeneity yield robust support for the outlined hypothesis.

A study of impact of climate change on the U.S. stock market as exemplified by the NASDAQ 100 index constituents

This paper employs an innovative event study methodology to demonstrate the impact of climate change on the NASDAQ index from the unique perspective of extreme weather events. This is achieved through the application of the event study methodology to a total of 526 biological, climatic, geological, hydrological, and meteorological disasters of climate change occurring in the U.S. during the period of 2000–2019. The results of the study demonstrate that: ① it can be generally observed that the five dimensions of climate change have a significant impact on stock returns. ② Empirical evidence indicates that the impact of different climate change dimensions on the return rate of stocks from NASDAQ stocks varies. In contrast, the biological and hydrological dimensions have a significantly negative impact on the return rate of stocks from the NASDAQ index, while the climate dimension has a significantly positive impact on the return rate of stocks from the NASDAQ index. ③ From the perspective of time, the impact of the five dimensions of climate change on the stock yield exhibits certain non-linear characteristics. This can be observed in the phenomenon of shock reversal, which occurs before and after the event.

Establishing resilience-targeted prediction models of rainfall for transportation infrastructures for three demonstration regions in China

Rainstorm is one of the global meteorological disasters that threaten the safety of transportation infrastructure and the connectivity of transportation system. Aiming to support the resilience assessment of transportation infrastructure in three representative regions: Sichuan–Chongqing, Yangtze River Delta, and Beijing-Tianjin-Hebei-Shandong, rainfall data over 40 years in the three regions are collected, and the temporal distribution of rainfall are analyzed. Prediction equations of rainfall are established. For the purpose of this, the probabilistic density function (PDF) is assigned to the rainfall by fitting the frequency distribution histogram. Using the assigned PDF, the rainfall data are transformed into standard normal space where regression of prediction equations is performed and the prediction accuracy is tested. The results show that: (1) The frequency of rainfall in the three regions follows a lognormal distribution based on which the prediction equations of rainfall can be established in standard normal space. The error of regression shows no remarkable dependence on self-variables, and the significance analysis indicates that the equations proposed in this paper are plausible for predicting rainfalls for the three regions. (2) The Yangtze River Delta region has a higher risk of rainstorm disaster compared to the other two regions according to the frequency of rainfall and the return period of precipitation concentration. (3) Over the period of 1980–2021, the Sichuan–Chongqing region witnessed an increase in yearly rainfall but a decrease in rainstorm disasters, whereas the other two regions experienced a consistent rise in both metrics.

Application of the Mask R‐CNN model to cold front identification in Eurasia

AbstractCold fronts often bring catastrophic weather events, which are exacerbated under global warming. Thus, the automatic and objective identification of cold fronts will be helpful for accurate forecasting and comprehensive analysis of cold fronts. Recently, machine learning methods have been applied to meteorological study. In this study, a cold front identification method based on the deep learning model Mask R‐CNN is proposed to automatically identify cold fronts from massive data. The Mask R‐CNN method shows high accuracy after the comparison with traditional methods and is effective for identifying the cold fronts in both continuous time and extreme precipitation events. Based on the obtained cold‐front samples, we conduct some statistical analysis. The results show that the frequency of cold front is unevenly distributed over Eurasia, with the highest in the Daxing'anling region and the mid‐latitude storm axis, especially in winter. The method and results presented in this study may have some implications for the application of deep learning models in weather system identification.

SARIMA Model-Based Monte Carlo Simulation of Option Contract Design for Maize Seasonal Heavy Precipitation in Shenyang

Agricultural production is highly dependent on weather conditions such as temperature, light, water and heat. The huge loss caused by extreme disaster weather makes the global insurance and reinsurance market overwhelmed, and seriously affects the enthusiasm of agricultural investment and the development of agricultural economy. With unique topography and vast territory, China is vulnerable to complex and diverse climate disasters. In recent years, the annual average economic loss caused by extreme weather disasters has reached about 200-300 billion yuan, among which floods caused by extreme heavy rainfall are the main agricultural disasters in China. In order to enhance the "farmer - insurance company - government" interest association to resist the risk of flood disaster caused by extreme heavy rainfall, financial hedging derivatives of weather disaster risk have gradually become a new hedging tool besides traditional insurance and reinsurance. This paper takes Shenyang, the main grain producing area in northeast China, as the sample of the study area, and uses the SARIMA model to obtain the distribution characteristics of seasonal rainfall time series. Different seasonal rainfall index option products (call option and put option contracts) are designed respectively, and the final option pricing is obtained by Monte Carlo stochastic simulation. The seasonal rainfall call option and put option contracts have opened up a new hedging model of agricultural extreme weather catastrophe outside the insurance and reinsurance market, which has enriched the varieties of weather financial derivatives market in China, reduced the severe impact of extreme weather disasters on agriculture, and enhanced the ability of agricultural stakeholders to resist the risk of extreme weather disasters.

Navigating urban challenges: Quantifying emergency service accessibility and robustness amid meteorological disasters

The frequent occurrence of meteorological disasters presents a significant challenge to the effective functioning of urban public services. This paper seeks to assess the response capacity of urban emergency services during such disasters by integrating perspectives on accessibility and robustness. Through a case study on emergency medical services (EMS), we develop a framework for evaluating the EMS system in downtown Shanghai under the scenario of typhoon In-Fa. This framework encompasses the calculation of both the accessible area of ambulance vehicles and the accessibility to each resident address. Our findings indicate that under normal conditions, the majority of residential areas in downtown Shanghai can access nearby medical services within 10 min. However, accessibility decreases by 28.85 % and 43.83 % under moderate and severe flooding conditions, respectively. Furthermore, by analyzing changes in the robustness of the service supply network, we identify warning and critical points, offering valuable insights for the formulation of disaster warning and response strategies. This study integrates system-level robustness and address-level accessibility to establish a comprehensive framework for evaluating urban emergency service performance under meteorological disaster scenarios. The proposed approach holds promise for evaluating other types of public services by adapting the framework to account for specific supply and demand dynamics.

Environmental Politics and Policy in Aotearoa New Zealand

With an ensemble of experts, Maria Bargh and Julie L. MacArthur have done a timely service to the communities of academic research, teaching-and-learning, and public policymaking by editing the book Environmental Politics and Policy in Aotearoa New Zealand. It is timely as we witness another year of record-breaking dramatic weather and climate-related disasters worldwide. More than ever, all sectors of society at large are compelled to learn more about the environment we live in, the histories of the lands, waters, and forests around us, interconnections across complex geological, ecological, and socio-economical systems, and, ultimately, the relationship between individuals and the future of Aotearoa and beyond.

Enhancing Climate Change Resilience: Assessing Adaptation Needs, and Significance of Monitoring and Evaluation Systems

Losses and damages from climate change related extreme weather events and disasters require the development of adaptation measures to increase resilience to the adverse impacts of climate change. In line with the United Nations Framework Convention on Climate Change (UNFCCC) and the Paris Agreement's Global Goal on Adaptation, Parties have developed strategies that include adaptation actions, but there are significant gaps in identification of adaptation needs and the monitoring and evaluation (M&E) of actions to address them. Adaptation M&E systems are critical for measuring the success of adaptation actions, providing feedback from the implementation process, and identifying new actions. There is no global methodology for adaptation M&E. At international climate change negotiations in 2023, it was agreed that countries should operationalize their national adaptation M&E systems by 2030. The aim of this study is to evaluate adaptation M&E methodologies developed by countries at different development levels and to present future policy recommendations for the adaptation M&E system planned to be established in Turkey. The study reveals the necessity of up-to-date socio-economic data as well as climate data in determining adaptation needs and adaptation M&E systems. In Turkey, which is vulnerable to the impacts of climate change, for the success of adaptation actions, besides the rapid operationalization of adaptation M&E system, establishment of the system with an approach that includes all stakeholders in the process and considers adaptation actions as integrated with disaster risk management actions is an important requirement in the context of Turkey's international commitments, national security, and development.

An Efficient Lossless Compression Algorithm for Maritime Safety Information Using Byte Encoding Network

The short message function of the BeiDou satellite system, due to its strong concurrent processing capabilities, can quickly and accurately send information to the target location in emergency situations. However, because of data redundancy and limitations on message length, a single piece of information often requires multiple transmissions through BeiDou short messages to be completed, resulting in limited transmission capacity. To improve the transmission capacity of information, it is necessary to compress the information during transmission using BeiDou’s short message communication function for maritime safety information. This paper proposes a Byte Encoding-enhanced Prediction by Partial Matching, variant D (BPPMd) algorithm that is particularly suitable for transmitting maritime safety information. Combined with a maritime safety information encoding algorithm (ME), it further improves compression efficiency, optimizes byte space, reduces information redundancy, and ensures the accuracy of the information. In this study, we constructed a maritime safety information dataset that includes three categories of information: meteorological warnings, navigation warnings, and disaster warnings. Experimental results show that the proposed algorithm is particularly suitable for compressing the maritime safety information dataset and outperforms other benchmark algorithms. Therefore, this study indicates that the proposed lossless compression method can be a feasible and effective solution for BeiDou short message communication.