Meteorological Disasters Research Articles

Underwriting Decision Modeling Based on Regional Hazard Assessment and Urban Resilience

In recent years, frequent extreme weather disasters have posed serious challenges to the economic social, and post-disaster recovery capabilities of places such as the United States, and original insurance models and payout strategies are difficult to adapt. Scientific assessment and response to natural disaster risks have become a key topic. This paper presents the Urban Resilience Index (URI) derived from 18 indicators. The ability of the city itself is rationally assessed for disaster resilience, which better helps insurance companies predict the loss compensation caused by disasters. This study starts by analyzing the risk of disasters in various regions of the United States, takes the intensity and frequency of disasters and the toughness of the city's resistance to disasters into account, and uses nonlinear multi-objective optimization to derive the threshold value of the payout strategy in a certain region to be around 0.21 and 0.94, respectively, and at the same time, after the control of the scalar function of the number of iterations is between 10 and 15 times, the final determination of the insurance model that responds to the different regions and verifies. The insurance model to cope with other areas is finally determined and its feasibility is verified.

Multi-Global Navigation Satellite System (GNSS) real-time tropospheric delay retrieval based on state-space representation (SSR) products from different analysis centers

Abstract. The troposphere plays an important role in a range of weather and various climate changes. With the development of the Global Navigation Satellite System (GNSS), the zenith tropospheric delay (ZTD) retrieval using GNSS technology has become a popular method. Research on ZTD accuracies of state-space representation (SSR) corrections from different analysis centers derived from real-time precise point positioning (RT-PPP) is important for Earth observation correction, meteorological disaster forecasting, and warning with the increasing abundance of state-space representation (SSR) products obtained by the International GNSS Service (IGS) analysis center. Therefore, accuracies and availability of real-time orbits and clock errors obtained by the Chinese Academy of Sciences (CAS), GMV Aerospace and Defense (GMV), Centre National d'Etudes Spatiales (CNE), and Wuhan University (WHU) are evaluated, and the RT positioning performance and ZTD accuracies are analyzed for Global Positioning System (GPS), Galileo (GAL), and BeiDou Navigation Satellite System-3 (BDS3) satellites. The results indicate that CAS has the higher satellite availability, providing SSR corrections for 82 GPS, Galileo, and BDS3 satellites. The accuracies of GPS, Galileo, and BDS3 orbits are best at WHU, CAS, and WHU with values of 5.57, 5.91, and 11.77 cm, respectively; the standard deviations (SDs) of clock error are all better than 0.22, 0.19, and 0.55 ns, and the root mean square errors (RMSEs) are better than 0.54, 0.32, and 1.46 ns. CAS has the best signal-in-space ranging errors (SISREs) followed by WHU, while CNE and GMV are worse. In the RT-PPP test, convergence times for CAS and WHU are 14.9 and 14.4 min, respectively, with 3D positioning accuracy for both of around 3.3 cm, which is better than for CNE and GMV. Among them, WHU SSR has the higher accuracy of RT-PPP-derived ZTD, with an RMSE of 6.06 mm and desirable availability with a completeness rate of 89 %.

Disaster Management Strategy Through Social Capital Development

Indonesia is one of the countries with the highest level of natural disaster vulnerability in the world due to extreme geographical and climate conditions. In facing this risk, the government has implemented a disaster management policy based on community involvement. One important aspect in this approach is social capital, which includes social participation, interpersonal trust, and community networks, as a crucial component to strengthen community resilience. This study explores the role of the Meteorology, Climatology, and Geophysics Agency (BMKG) in Lampung Province in developing weather literacy and disaster mitigation programs through digital media to build social capital in the community. The results of the study show that although BMKG has succeeded in increasing community participation through digital channels, community involvement is currently still passive and limited. To achieve comprehensive disaster preparedness, it is necessary to increase active community participation, strengthen trust through two-way communication, and expand community networks. With a social capital-based approach, BMKG is expected to be able to create a more responsive and adaptive mitigation program, making the community an active partner in facing the threat of disasters in the future.

Florida mangrove dieback on a decadal and centennial timescales

Hurricanes are considered some of the most devastating weather phenomena, causing deaths and destruction along the coast. Mangroves are perceived as natural defenses against coastal hazards. However, mangrove gaps, known as mangrove dieback, have occurred in southwestern Florida. This study implemented a spatial-temporal analysis based on satellite, lidar and unmanned aerial vehicle (UAV) images collected along the southwestern Florida coast between Tampa and Naples from 1994 to 2020 CE, and multi-proxy analyses on sediment cores sampled from two mangrove dieback sites to determine the cause(s) of mangrove death and the implications for the sustainability of these forests. The data indicated that out of the 86 identified dieback, 78 % arose between 2004 and 2017 CE, when one tropical storm and three hurricanes made landfall on the studied coast, suggesting hurricanes as the main driver for this mangrove degradation. Multi-proxy analysis indicated mangrove expansion since ∼600 cal yr BP due to relative sea-level (RSL) rise. However, hurricanes caused losses in the mangrove area between 1930 and 1950 CE, and during the last two decades in the studied sites. Ground topographic data and aerophotogrammetry revealed circular dieback with permanent ponds (∼30 cm depth) inhibiting mangrove recovery. The mangrove death occurred in <3 years, with a gradual dieback expansion due to positive feedback involving physicochemical processes in the sediments. In addition, recent hurricanes increased pre-existing dieback areas. Therefore, dieback sites are vulnerability hotspots for mangroves in the face of projected RSL rise and higher frequency of intense hurricane strikes. Mangrove dieback should be monitored on a temporal (seasonal - annual) and spatial (cm-m) resolution only possible through the combination of satellite and UAV data. Integrating remote sensing and stratigraphic analysis enabled us to identify mangrove diebacks on a decadal to centennial timescale, improving our comprehension of how catastrophic weather events affect these forests.

Construction of Freezing Injury Grade Index for Nanfeng Tangerine Plants Based on Physiological and Biochemical Parameters.

Low-temperature freezing stress constitutes the most significant meteorological disaster during the overwintering period in the Nanfeng Tangerine (NT) production area, severely impacting the normal growth and development of the plants. Currently, the accuracy of meteorological disaster warnings and forecasts for NT orchards remains suboptimal, primarily due to the absence of quantitative meteorological indicators for low-temperature freezing stress. Therefore, this study employed NT plants as experimental subjects and conducted controlled treatment experiments under varying intensities of low-temperature freezing stress (0 °C, -2 °C, -5 °C, -7 °C, and -9 °C) and durations (1 h, 4 h, and 7 h). Subsequently, physiological and biochemical parameters were measured, including photosynthetic parameters, chlorophyll fluorescence parameters, reactive oxygen species, osmoregulatory substances, and antioxidant enzyme activities in NT plants. The results demonstrated that low-temperature freezing stress adversely affected the photosynthetic system of NT plants, disrupted the dynamic equilibrium of the antioxidant system, and compromised cellular stability. The severity of freezing damage increased with decreasing temperature and prolonged exposure. Chlorophyll (a/b) ratio (Chl (a/b)), maximum quantum yield of photosystem II (Fv/Fm), soluble sugar, and malondialdehyde (MDA) were identified as key indicators for assessing physiological and biochemical changes in NT plants. Utilizing these four parameters, a comprehensive score (CS) model of freezing damage was developed to quantitatively evaluate the growth status of NT plants across varying low-temperature freezing damage gradients and durations. Subsequently, the freezing damage grade index for NT plants during the overwintering period was established. Specifically, Level 1 for CS ≤ -0.50, Level 2 for -0.5 < CS ≤ 0, Level 3 for 0 < CS ≤ 0.5, and Level 4 for 0.5 < CS. The research results provide valuable data for agricultural meteorological departments to carry out disaster monitoring, early warning, and prevention and control.

Impact of 2022 flood on socio-economic and health status of people residing in flood-stricken areas of Pakistan: a cross-sectional survey.

The events of extreme weather and climate-related disasters such as drought, flood, and heat waves are increasing worldwide. This paper highlights the impact of the 2022 flood in Pakistan on the socio-economic and health status of people residing in flood-stricken areas of Pakistan. A post-flood survey was conducted from three districts of Pakistan with a myriad of questions inquiring about the biopsychosocial aspects of the affected community. Data were collected through a validated questionnaire and was analyzed through SPSS software version 25. Forty percent of people became homeless; the number of individuals with a low income decreased by 9%; and the number of individuals with a moderate income decreased by 22%. Additionally, 48.7% of subjects lost their income due to flooding, 83.4% of subjects reported having some type of illness or disability since the flooding (previously 16.4%), and 92.8% believed that the floods had affected the health and sanitation of their area. Importantly, 22.6% of subjects expressed difficulty in accessing food after flooding compared to before and 59.9% of respondents noted they were unaware of proper pre-flood evacuation protocols in their area. The authors' study indicates that the floods had a significant impact on the socio-economic and health status of residents, particularly those with lower incomes depriving a large proportion of nutrition, health facilities, and property. Without access to necessities and healthcare, individuals are more vulnerable to illnesses, such as chronic respiratory disease, diabetes, and heart disease. The authors stress the importance of providing infrastructural, nutritional, and medical services support to ensure that a community such as Pakistan can recover and thrive post-environmental disaster.

Development and Application of a Tornado Database for the Chinese Mainland

This study develops a preliminary tornado database for the Chinese mainland using information provided by the Yearbook of Meteorological Disasters in China (YMDC) from 2003 to 2019 and other public media report data. This database includes tornado occurrence time, geographical location, intensity, and damage descriptions. A modified Enhanced Fujita (EF) scale criterion adapted to the description of the damage indicator (DI) and degree of damage (DOD) in the YMDC and media reports is presented to better estimate the tornado intensity. The spatial and temporal distribution characteristics of tornadoes in Chinese mainland are examined. A stochastic simulation algorithm is proposed to perform a risk assessment of tornado hazards. The occurrence of tornadoes is randomly sampled using negative binomial distribution. The kernel density estimation (KDE) method based on the Gaussian kernel is applied to estimate the probability density of geographically dependent tornado occurrence before randomly generating tornado occurrence locations. The probability and conditional probability of tornado occurrence with different intensity levels are calculated for different counties in Jiangsu and Guangdong Provinces using Bayes’ theorem. The database provides a forward step toward rational assessment of tornado-induced disasters in Chinese mainland. The database can be accessed at https://doi.org/10.4121/19801633.v1 (Zhang et al., 2022).

The Palamas Archaeological Project 2023. A preliminary report of the Greek–Swedish collaboration in the municipality of Palamas, region of Karditsa, Thessaly

The 2023 fieldwork in the area of Palamas, Thessaly, was severely hampered by catastrophic weather events affecting the area. In spite of this, the project managed to conduct excavations, surface and architectural surveys, as well as geophysical prospection at a number of sites in the region. The work continues to highlight the chronological breadth of archaeological remains in the area, as well as further details regarding little-known periods of Thessalian history.

Risk assessment of urban infrastructure vulnerability to meteorological disasters: A case study of Dongguan, China

Effective forecasting and response to meteorological hazards are crucial for safeguarding life, property, and supporting sustainable socioeconomic development. With the rising frequency and severity of meteorological hazards worldwide, this study proposes an enhanced risk assessment framework for urban infrastructure exposed to extreme weather events, with a focus on cascading impacts to critical services such as electricity, communication, and transportation networks (roads and subways). A disaster-loss model is developed to quantify infrastructure vulnerability at various spatial and temporal scales under heavy rainfall conditions, accounting for secondary effects. The model's performance is validated through empirical analysis of a 15-year rainfall event in Dongguan City, China, occurring from September 7–8, 2023. Results indicate the model's ability to predict real-event outcomes with approximately 70% accuracy. This model offers valuable insights for disaster prevention and mitigation strategies, aiding decision-makers in optimizing emergency resource allocation, enhancing disaster response efficiency, and issuing timely public risk warnings to minimize losses.

Identifying community practices in marine benthic data usage in Florida

A significant proportion of Florida's population lives on the coast and is directly impacted by alterations to the coastal zone, weather disasters (e.g., hurricanes, erosion, flooding), or changes to ecosystem services. Data collected in Florida waters (including water quality, habitat health, bathymetry, and fisheries data) are important for the maintenance of coastal waters, communities, and ecosystems. Yet benthic data collected by a variety of stakeholders are often not shared or openly available, with little metadata to ease data reuse, and are often stored in incompatible formats. To assess the needs of government agencies, private companies, academic researchers, and data managers, we conducted a survey and organized an expert focus group to determine the current state of coastal and marine data usage and distribution in Florida. Through the survey, we asked participants to describe the types of data they use or collect, how they use that data, what limitations they encounter with data sharing, how and when they share their data, and what sorts of metadata standards they use in their work. We determined that many data producers and users are unaware of data standards and often do not follow best management practices for data collection and sharing. The sector of activity of the individual respondent (government, academic, non-profit) determined how data users were interacting with or collecting data and what standards they followed when sharing data. Our expert panel largely echoed our findings, with consistent, well-documented, and standardized datasets being the most important components for data integration in projects. To advance accessibility and reusability of benthic data, our project highlights the need for additional training of stakeholders on data standardization, collaboration and integration, which needs to be applied across institutions. A major need that was identified is tools that make data sharing and metadata creation easier and more efficient.

Analysis of the Response Relationship Between PWV and Meteorological Parameters Using Combined GNSS and ERA5 Data: A Case Study of Typhoon Lekima

Precipitable water vapor (PWV) is a crucial parameter of Earth’s atmosphere, with its spatial and temporal variations significantly impacting Earth’s energy balance and weather patterns. Particularly during meteorological disasters such as typhoons, PWV and other meteorological parameters exhibit dramatic changes. Studying the response relationship between PWV and typhoon events, alongside other meteorological parameters, is essential for meteorological and climate analysis and research. To this end, this paper proposes a method for analyzing the response relationship between PWV and meteorological parameters based on Wavelet Coherence (WTC). Specifically, PWV and relevant meteorological parameters were obtained using GNSS and ERA5 data, and the response relationships between PWV and different meteorological parameters before and after typhoon events were studied in time–frequency domain. Considering that many GNSS stations are not equipped with meteorological monitoring equipment, this study interpolated meteorological parameters based on ERA5 data for PWV retrieval. In the experimental section, the accuracy of ERA5 meteorological parameters and the accuracy of PWV retrieval based on ERA5 were first analyzed, verifying the feasibility and effectiveness of this approach. Subsequently, using typhoon Lekima as a case study, data from six GNSS stations affected by the typhoon were selected, and the corresponding PWV was retrieved using ERA5. The WTC method was then employed to analyze the response relationship between PWV and meteorological parameters before and after the typhoon’s arrival. The results show that the correlation characteristics between PWV and pressure can reveal different stages before and after the typhoon passes, while the local characteristics between PWV and temperature better reflect regional precipitation trends.

A Soybean Pyrroline-5-Carboxylate Dehydrogenase GmP5CDH1 Modulates Plant Growth and Proline Sensitivity

Soybean [Glycine max (L.) Merr.], as a globally commercialized crop, is an important source of protein and oil for both humans and livestock. With more frequent extreme weather disasters, abiotic stress has become one of the critical factors restricting soybean production. Proline (Pro) is a well-known substance in plants that responds to abiotic stress. To identify potential effector genes involved in soybean resistance to abiotic stress, we focused on the pyrroline-5-carboxylate dehydrogenase (P5CDH) which is a key enzyme in the degradation process of Pro. Through homologous sequence alignment, phylogenetic tree, and predicted expression, we chose GmP5CDH1 (Glyma.05G029200) for further research. Tissue-specific expression assay showed that GmP5CDH1 had higher expression levels in soybean seed and cotyledon development. Subcellular localization assay revealed that GmP5CDH1 was a nuclear-membrane-localized protein. As the result of the predicted cis-acting regulatory element indicates, the expression level of GmP5CDH1 was induced by low temperature, drought, salt stress, and ABA in soybean. Next, we constructed transgenic Arabidopsis overexpressing GmP5CDH1. The results showed that GmP5CDH1 also strongly responded to exogenous Pro, and overcame the toxicity of abiotic stress on plants by regulating the endogenous concentration of Pro. The interaction between GmP5CDH1 and GmSAM1 was validated through yeast two-hybrid, LUC fluorescence complementary, and BIFC. In conclusion, overexpression of a soybean pyrroline-5-carboxylate dehydrogenase GmP5CDH1 regulates the development of Arabidopsis thaliana by altering proline content dynamically under salt stress, especially improving the growth of plants under exogenous Pro.

A Study on the Pre-Survey and Plan for the Establishment of the Korean Typhoon Impact-Based Forecast

The intensity of typhoons affecting the Korean Peninsula has been rapidly increasing, resulting in significant damage. Notably, this intensification correlates with the rise in Sea Surface Temperature (SST) in the western Pacific Ocean and surrounding sea areas, where typhoons that impact the Korean Peninsula originate and develop. The SST in these regions is increasing at a faster rate than the global average. Typhoon-related meteorological disasters are not isolated events, such as strong winds, heavy rains, or storm surges, but rather multi-hazard occurrences that can affect different areas simultaneously. As a result, preparation and evaluation must address multi-hazard disasters, rather than focusing on individual weather phenomena. This study develops the Typhoon Ready System (TRS) to improve impact-based forecasting in Korea, in response to the growing threat of multi-hazard weather disasters. By providing region-specific pre-disaster information, the TRS enables local governments and individuals to better prepare for and mitigate the impacts of typhoons. The system will be continuously refined in collaboration with the U.S. Weather-Ready Nation (WRN), which possesses advanced impact forecasting capabilities. The findings of this study offer a crucial framework for enhancing Korea’s ability to forecast and respond to the escalating threats posed by typhoons. By utilizing the TRS, it will be possible to assess the risks of various multi-hazard weather disasters specific to each region during the typhoon forecast period, and the relevant data can be efficiently applied at both the individual and local government levels for typhoon prevention efforts. The system will be continuously improved through cooperation with the U.S. WRN, leveraging their advanced impact forecasting systems. It is expected that the TRS will enhance the accuracy of typhoon impact forecasts, which have been responsible for significant damage in Korea.

Economic Benefits and Uses of Indigenous Seasonal Weather Forecasts in Zimbabwe

This study presents a seminal contribution regarding the economic value of indigenous seasonal weather forecasts in Zimbabwe. Many farmers (58%) use indigenous seasonal weather forecasts to make maize farming decisions such as selecting suitable varieties. The main indicators used for indigenous seasonal weather forecasts are flowering and fruition of specific trees. Based on travel cost analysis, which incorporates a multi-purpose visit scenario, the study establishes the economic importance of indigenous seasonal weather forecasts with a consumer surplus of US$1,044 per year among the 290 farmers using the forecasts. There is therefore a need to integrate indigenous weather forecasts into national seasonal weather forecasting and disaster risk reduction systems to complement modern seasonal weather forecasts. Co-production of seasonal weather forecasts with farmers is proposed in this regard. This further calls for the need to digitally document, visualize, and disseminate indigenous seasonal weather forecast indicators to a wider audience to increase their use.

Real-time hazard assessment of maize based on the chilling injury process —— Using a standard curve to establish a daily cumulative assessment method

Cold damage caused by low temperatures is known as chilling injury (CI), and it has consistently been one of the primary meteorological disasters affecting maize. With ongoing global climate change, the issue of chilling injury is becoming more prominent, exhibiting new characteristics and presenting new challenges. Consequently, understanding the disaster process and conducting a more refined real-time chilling injury identification have become significant challenges. In this study, we divided maize planting areas into seven maturity types based on the accumulated temperature, constructed a standard curve of the daily accumulated temperature from 1991 to 2020, proposed real-time identification indicators based on the CI process, and developed a real-time CI hazard assessment model. The results indicated that the model can capture independent CI events and rapidly determine the location, intensity, duration and scope of CIs, thereby providing a basis for accurately understanding the impact of chilling injury and taking timely countermeasures. The combination of accumulated temperature standard curves for seven maturity types of maize and the CI curve was used to construct the CI daily scale identification indicator, ΔEAT. Judgment thresholds for the CI identification indicators at various maturity levels were obtained by correlating them with historical disaster data. The frequency and intensity of maize CI gradually increased from the extremely late-maturing zone to the extremely early-maturing zone, with the seeding and emergence periods being the peak periods for CI. The spatiotemporal evolution characteristics of the three different degrees of CI events in 1992, 2004, and 2017 were consistent with the historical disaster records. Northeastern Inner Mongolia and most of Heilongjiang were found to be high-hazard areas for maize CIs. The constructed daily CI identification indicators can accurately and rapidly identify maize CIs, providing practical and targeted guidance for combating these injuries.

Quantifying the influence of dominant factors on the long-term sandstorm weather - A case study in the Yellow River Basin during 2000–2021

Sandstorm is a disastrous weather phenomenon that often occurs in arid and semi-arid areas, endangering the ecological environment and affecting people's lives and property safety seriously. Since the 21st century, the sandstorm weather in the Yellow River Basin has ameliorated obviously. However, the causes of the long-term trends in sandstorms during 21st century were still unknown. In this study, fifteen influencing factors from five aspects: ecology, meteorology, hydrology, geography and man-made were selected to comprehensively analyze the driving mechanism of sandstorm activities in the Yellow River Basin since the 21st century, and the effect of each influencing factor on sandstorm weather was quantified. The results indicated that ecological, meteorological and geographical factors had dominant impacts on the spatio-temporal variation of sandstorms during 2000–2021, while hydrological and human factors played little role in the long-term variation of sandstorms. Sandstorms frequently occurred in semi-desert or grassland or non-high vegetation covered areas in spring. Vegetation coverage, precipitation, surface pressure, surface roughness, and soil moisture content were negatively correlated with sandstorms, while wind speed, friction velocity, evaporation, and soil temperature were positively correlated with sandstorms. Precipitation, runoff, evaporation, soil moisture content, soil temperature, and surface temperature indirectly acted on normalized brightness temperature dust index (NBTDI) and sandstorms by changing soil texture. Normalized Vegetation Index (NDVI) had direct negative effects on NBTDI, while wind speed 10 m (WS10m), slope of sub-gridscale orography (SOSGO), and forecast surface roughness (FSR) had direct positive effects on NBTDI. This study comprehensively revealed the dominant factors and their driving mechanism of sandstorm weather in the Yellow River Basin since the 21st century, which had practical application value for the prevention of sandstorms.

Design of a High-Power Nanosecond Electromagnetic Pulse Radiation System for Verifying Spaceborne Detectors

The Spaceborne Global Lightning Location Network (SGLLN) serves the purpose of identifying transient lightning events occurring beneath the ionosphere, playing a significant role in detecting and warning of disaster weather events. To ensure the effective functioning of the wideband electromagnetic pulse detector, which is a crucial component of the SGLLN, it must be tested and verified with specific signals. However, the inherent randomness and unpredictability of lightning occurrences pose challenges to this requirement. Consequently, a high-power electromagnetic pulse radiation system with a 20 m aperture reflector is designed. This system is capable of emitting nanosecond electromagnetic pulse signals under pre-set spatial and temporal conditions, providing a controlled environment for assessing the detection capabilities of SGLLN. In the design phase, an exponentially TEM feed antenna has been designed firstly based on the principle of high-gain radiation. The feed antenna adopts a pulser-integrated design to mitigate insulation risks, and it is equipped with an asymmetric protective loading to reduce reflected energy by 85.7%. Moreover, an innovative assessment method for gain loss, based on the principle of Love’s equivalence, is proposed to quantify the impact of feed antenna on the radiation field. During the experimental phase, a specialized E-field sensor is used in the far-field experiment at a distance of 400 m. The measurements indicate that at this distance, the signal has a peak field strength of 2.2 kV/m, a rise time of 1.9 ns, and a pulse half-width of 2.5 ns. Additionally, the beamwidth in the time domain is less than 10°. At an altitude of 500 km, the spaceborne detector records a signal with a peak field strength of approximately 10 mV/m. Particularly, this signal transformed into a nonlinear frequency-modulated signal in the microsecond range across its frequency spectrum, which is consistent with the law of radio wave propagation in the ionosphere. This study offers a stable and robust radiation source for verifying spaceborne detectors and establishes an empirical foundation for investigating the impact of the ionosphere on signal propagation characteristics.

A segmented grid model for vertical adjustment of precipitable water vapor in China

Precipitable Water Vapor (PWV) is vital in climate research, monitoring in troposphere stability and weather disasters. This study investigates the detailed vertical variation of PWV in China, utilizing data from the 5th generation European Centre for Medium-Range Weather Forecasts (ECMWF). A PWV segmented vertical adjustment grid model (CPWV-H) was developed based on a sliding window algorithm, with a spatial resolution of 0.5° × 0.5° and a temporal resolution of one day. The performance of the CPWV-H model was evaluated using multi-source PWV data and compared with the conventional empirical model (EPWV-H) and the recently released C-PWVC1 model. Results show that the CPWV-H model has superior accuracy and stability. Using ERA5 PWV profiles as a reference, the CPWV-H model has a mean Bias of −0.45 mm and a mean RMSE of 1.24 mm, improving by 21.52 % over the EPWV-H model and 31.11 % over the C-PWVC1 model. With radiosonde profiles as a reference, it has a mean Bias of −0.10 mm and a mean RMSE of 2.86 mm, improving by 7.14 % and 13.86 % over the EPWV-H and C-PWVC1 models, respectively. Consequently, the CPWV-H model exhibits enhanced stability and vertical adjustment accuracy in China, offering significant utility for the fusion of multi-source water vapor data, comparative analysis, and climate research.

Assessment of waterlogging hazard during maize growth stage in the Songliao plain based on daily scale SPEI and SMAI

Waterlogging is one of the major disasters affecting crop yield and food security. The Songliao Plain, located in the mid-latitude region and known as the "Golden Maize Belt," is severely impacted by waterlogging, which significantly affects maize yield. Therefore, it is essential to conduct a detailed assessment of the waterlogging hazard for maize in the Songliao Plain and to apply the results to agricultural meteorological disaster prevention and mitigation measures. In this study, a comprehensive waterlogging hazard assessment index was constructed by combining environmental factors conducive to disaster and disaster-causing factors. Environmental factors included terrain slope, distance from rivers, and soil clay content, while disaster-causing factors included daily SPEI and SMAI during the maize growing season in the Songliao Plain from 1982 to 2020. The results indicate that: (1) The spatial distribution of waterlogging hazard in the Songliao Plain ranges from extremely high to extremely low, showing a gradual decrease from west to east. The western and southern parts of the Songliao Plain, such as Baicheng, Songyuan, Changchun, and Fuxin, are more prone to waterlogging disasters. (2) During different maize growth stages, the spatial distribution of high and extremely high levels of waterlogging hazard exhibited significant heterogeneity. There were notable differences in the duration of waterlogging around the year 2000, with a reduction in the duration of extremely high and high levels of waterlogging after 2000. (3) A Pearson correlation analysis was conducted between the comprehensive waterlogging hazard index and SIF (Solar-Induced Fluorescence) data during different maize growth stages. The results showed a strong correlation between the comprehensive waterlogging hazard index and SIF data, with the highest correlation coefficient reaching −0.9 and a p-value less than 0.05. The comprehensive maize waterlogging hazard index can be used for precise and timely assessment of waterlogging hazard during different growth stages of maize, and it has a positive impact on improving the ability to prevent and mitigate waterlogging risks.

Exploring variations in flood events and casualties over Yamuna River basin, India

ABSTRACT The current study seeks to investigate the temporal and spatial distribution of flood events and casualties in the Yamuna River basin from 1978 to 2014. The data were compiled from the published reports on disastrous weather events and analyzed with simple quantitative methods (sums, averages, and percentages). The analysis revealed the occurrence of a total of 1886 flood events, which resulted in 5481 casualties (148 per year). The years 1995, 2003, 1993, and 1996 were observed as the most devastating years, contributing about 30 percent of the total flood events, while 1978, 1980, 1981, 1995, 2006, and 2013 were the deadliest years in terms of casualties (47 percent). The largest number of flood events and casualties were recorded during June–September months. Considering the event/casualty density and rates, the states of Himachal Pradesh and Uttarakhand experienced the highest event density (0.036 and 0.091) and casualty rates (2.1 and 1.9 persons). These findings will help in identifying and demarcating the worst flood-affected areas over the Yamuna River basin, which can subsequently help in reducing the flood risk and human casualties in these regions after initiating suitable flood mitigation measures.