Observation Stations Research Articles

Evaluation of Multisource Datasets in Characterizing Spatiotemporal Characteristics of Extreme Precipitation from 2001 to 2019 in China

Abstract The spatiotemporal characteristics of extreme precipitation intensity are crucial for hydroclimatic studies. This study delineates the spatiotemporal distribution features of extreme precipitation intensity across China from 2001 to 2019 using the gridded daily precipitation dataset CN05.1, constructed from an observation network of over 2400 stations. Furthermore, we evaluate the reliability of 12 widely used precipitation datasets (including gauge-based, satellite retrieval, reanalysis, and fusion products) in monitoring extreme precipitation events. Our findings indicate the following: 1) CN05.1 reveals a consistent spatial distribution characterized by a decline in extreme precipitation intensity from the southeastern coastal regions toward the northwestern inland areas of China. From 2001 to 2019, more pronounced declining intensity trends are discernible in the northern and southwestern regions of China, whereas marked increasing trends manifest in the northeastern and the Yangtze River plain regions. National mean extreme precipitation indices consistently exhibit significant increasing trends throughout China. 2) Datasets based on station observations generally exhibit superior applicability concerning spatiotemporal distribution. 3) Multisource weighted precipitation fusion products effectively capture the temporal variability of extreme precipitation indices. 4) Satellite retrieval datasets exhibit notable performance disparities in representing various intensity indices. Most products tend to overestimate the increasing trends of national mean intensity indices. 5) Reanalysis datasets tend to overestimate extreme precipitation indices, and inadequately capture the trends. ERA5 and JRA-55 underestimate trends, while CFSR and MERRA-2 significantly overestimate the trends. These findings serve as a basis for selecting reliable precipitation datasets for extreme precipitation and hydrological simulation research in China. Significance Statement Extreme precipitation events have increasingly become more widespread, posing significant threats to human lives and property. Accurately understanding the spatiotemporal patterns of these events is imperative for effective mitigation. Despite the proliferation of precipitation products, their capacity to faithfully represent extreme events remains inadequately validated. In this study, we utilize a gauge-based dataset derived from over 2400 gauge stations across China to investigate the spatiotemporal changes in extreme precipitation events from 2001 to 2019. Subsequently, we conduct a rigorous evaluation of 12 widely used precipitation datasets to assess their efficacy in depicting extreme events. The results of this research offer valuable insights into the strengths and weaknesses of various precipitation products in depicting extreme events.

The weakening effect of urbanization on tropical cyclone surface winds : An observational study for Shanghai

The observation-reanalysis difference method (OMR) and wind profile fitting method were used to analyze the influence urbanization has on the near-surface wind speed in Shanghai during tropical cyclone events. The data used include daily wind speed data from the Shanghai Meteorological Observatory Station collected from 1991 to 2020, hourly wind speed data from 100 m high wind towers from 2017 to 2019, and reanalysis data that covered the same period. The results indicated that during tropical cyclone occurrence, the wind speed of the OMR in the central urban region was significantly lower than that in the suburban area, and the OMR declined more obviously over the year, down to −8 m/s in 2020. Urbanization leads to the increase of average wind weakening rate to be higher than the maximum wind weakening rate, causing the maximum weakening rate of the central urban region to the average wind over 80%, while maximum wind speed was less than 80%. The weakening rate of both the average and maximum wind speeds during tropical cyclone impacts is greater than the rate that the annual average wind speed was reduced. A logarithmic variation was visible in the wind profile of the island station during tropical cyclone occurrence, with an average friction velocity U∗ of 0.9389 m/s and an average rough length Z0 of 0.4915 m. The wind speed during tropical cyclone events was higher than the three-year average wind speed within each layer. The suburban stations show a linear variation, and every hundred meters of height results in an increase of 5–6 m/s in the wind speed change rate. At 100 m of altitude, the wind speed in the suburban region is reduced by approximately 40%.

Susurluk Havzası Akımlarının Trend Analiz Yöntemleri Kullanılarak Değerlendirilmesi

Trend analysis of annual average flow data is very important for better water resources management, planning and operation. In this study, annual average flow data from M. Kemal Paşa, Orhaneli and Simav flow observation stations located in Susurluk Basin were tested with Mann-Kendall, Spearman Rho and Şen's Innovative trend methods and the trends of the stations were tried to be determined and the performances of these methods were evaluated. In addition, annual average flow data were submitted to regression analysis, yielding equation sets. For each station, the annual and 100-year changes in m3/s were determined. Analyzing the results, only Şen's Innovative Trend Method analysis found a general decreasing trend for all 3 stations. In the analyses of Mann-Kendall and Spearman’s Rho methods, no trend was detected in all 3 stations. In addition, 100-year percentage change values of the data of 3 stations were determined. According to these values, it is concluded that there will be a significant decrease in the flow values of the stations in the future.

Calculation and program realization of coal pillar setting parameters in Huainan mining area.

Coal pillar retention plays a crucial role in ensuring safety and minimizing ground deformation in coal mining operations. However, accurately and efficiently determining the optimal size of protective pillars, reducing coal pillar pressure, and addressing challenges such as limited access to retention parameters, lengthy observation times, and high labor costs are challenges that must be addressed. In this paper, we presented a methodology using Huainan mine as a case study to address these challenges. The solution involves deriving the formula for coal pillar retention parameters based on the Three Regulations definition and requirements. The total least squares algorithm was integrated with surface observation station data and the MATLAB software platform to automate the coal pillar retention solution. Furthermore, a linear regression model of coal pillar retention-related parameters was established using the geological mining condition data. The proposed ELM neural network model was optimized using a genetic algorithm and combined with the linear regression model to establish a predictive model. The results demonstrated that the proposed machine learning algorithm attains the requisite level of accuracy for industrial production.

Spatiotemporal investigation of near-surface CH4 and factors influencing CH4 over South, East, and Southeast Asia

Methane (CH4) is the second most abundant greenhouse gas after CO2, which plays the most important role in global and regional climate change. To explore the long-term spatiotemporal variations of near-surface CH4, datasets were extracted from Greenhouse gases Observing SATellite (GOSAT), and the Copernicus Atmospheric Monitoring Service (CAMS) reanalyzed datasets from June 2009 to September 2020 over South, East, and Southeast Asia. The accuracy of near-surface CH4 from GOSAT and CAMS was verified against surface observatory stations available in the study region to confirm both dataset applicability and results showed significant correlations. Temporal plots revealed continuous inflation in the near-surface CH4 with a significant seasonal and monthly variation in the study region. To explore the factors affecting near-surface CH4 distribution, near-surface CH4 relationship with anthropogenic emission, NDVI data, wind speed, temperature, precipitation, soil moisture, and relative humidity were investigated. The results showed a significant contribution of anthropogenic emissions with near-surface CH4. Regression and correlation analysis showed a significant positive correlation between NDVI data and near-surface CH4 from GOSAT and CAMS, while a significant negative correlation was found between wind and near-surface CH4. In the case of temperature, soil moisture, and near-surface CH4 from GOSAT and CAMS over high CH4 regions of the study area showed a significant positive correlation. However significant negative correlations were found between precipitation and relative humidity with GOSAT and CAMS datasets over high CH4 regions in South, East, and Southeast Asia. Moreover, these climatic factors showed no significant correlation within the low near-surface CH4 areas in our study region. Our study results showed that anthropogenic emissions, NDVI data, wind speed, temperature, precipitation, soil moisture, and humidity could significantly affect the near-surface CH4 over South, East, and Southeast Asia.

Low-wind climatology (1979–2018) over Europe from ERA5 reanalysis

Research on wind speed characteristics is of interest for many disciplines from renewable energy to ecology. Whereas mean values and trends are commonly studied, less attentions is paid to the evaluation of other features such as low-wind conditions. However, there is no clear definition of “low-wind” on the literature. Here we propose the Beaufort scale to characterize low-wind values over Europe through a fixed threshold of 3.3 m/s (“light breeze" category). Climatological (1979–2018) assessment is performed using ERA5 reanalysis hourly data. The limited amount of observational stations indicate a 40-year averaged amount of around 3500 low-wind hours/year, comparable to the corresponding ERA5 reanalysis cells, which shows severe limitations over mountainous areas. The European domain features a strong north–south low-wind hours gradient. Remarkable patterns are obtained over coasts and complex orography regions. Seasonal low-wind variability range around 20–25% for most of the regions, and interannual coefficient of variability from 0.05 to 0.17. Oceanic regions present smaller low-wind values than land areas, with Atlantic and Mediterranean regions behaving differently. The largest annual spells (consecutive) hourly low-wind episodes are within the range from 5 to 10 days, (from 120 to 240 h) over many land areas. Annual mean hourly wind spells typically extend from 15 to 25 h, with more than 200 episodes.

Transfer learning model for estimating site amplification factors from limited microtremor H/V spectral ratios

Summary Site amplification factors (SAFs) of seismic ground motions are essential in evaluating and estimating seismic hazards. In our previous study, the authors proposed a simple and cost-effective method to estimate a SAF based on a deep neural network (DNN) model and microtremor horizontal-to-vertical spectral ratio (MHVR). Since the previous DNN model was based on the observed SAFs and MHVRs within a limited district in Japan, the applicability of the previous model to non-source regions with different site conditions was limited. This study explored the application of a transfer learning (TL) technique to adapt an existing (pre-trained) DNN model to new regions and a different database. The SAFs obtained through generalized spectral inversion technique (GIT) at the seismic observation stations (K-NET and KiK-net) in Japan were collated as the ground truth for site effects. MHVRs recorded at the stations in several districts of Japan were collected to construct a dataset for the development of the TL model. Subsequently, a TL model was constructed, leveraging the neural network layers and their weights from the pre-trained model while incorporating additional neural network layers to enhance the performance. During the training process, a total data set of 112 sites was divided into training set, validation set, and external test set by 1:1:5. Utilizing a cross-validation strategy, the residuals between pSAFs (pseudo-SAFs) estimated by the TL model and the observed SAFs were analyzed for the external test set containing 80 sites. The results showed that the TL model outperformed the pre-trained DNN model. The cross-validation results demonstrated that almost consistent prediction results were obtained regardless of any combination of 16 sites selected as the training set. Furthermore, by contrasting the influence of varying training set sizes on the performance of the TL model and comparing the TL model to a DNN model with an extended training set, the effectiveness of constructing the model with the limited number of data (16 sites) was ascertained. Finally, the effectiveness and limitations of the TL model were evaluated using MHVRs with peak frequencies falling outside the training set's range.

Towards Improved Satellite Data Utilization in China: Insights from an Integrated Evaluation of GSMaP-GNRT6 in Rainfall Patterns

To improve the utilization of satellite-based data and promote their development, this analysis comprehensively evaluates the performance of GSMaP Near-real-time Gauge-adjusted Rainfall Product version 6 (GSMaP_GNRT6) data in depicting precipitation over China from 2001 to 2020 by comparing four precipitation indices—accumulated precipitation, number of rainy days and rainstorm days, and precipitation maxima—with daily precipitation data from 2419 stations across China on monthly and annual time scales. The results show that the GSMaP-GNRT6 data effectively capture the overall spatial pattern of the four precipitation indices, but biases in the spatial distribution of the number of rainy days from July to September and the precipitation maxima during the wintertime are evident. A general underestimation of GSMaP-GNRT6 data is observed in the average for China. The annual precipitation amount, the number of rainy days and rainstorm days, and the precipitation maxima based on the GSMaP-GNRT6 data are 17.6%, 35.5%, 31.6% and 11.8% below the station observations, respectively. The GSMaP-GNRT6 data better depict the precipitation in eastern China, with the errors almost halved. And obvious overestimation of the number of rainstorm days and precipitation maxima occurs in western China, reaching up to 60%. Regarding the accumulated precipitation, the number of rainstorm days and the precipitation maxima, the GSMaP-GNRT6 data show an almost consistent interannual variation and increasing trends that are consistent with the station observations. However, the magnitude of the increasing trend based on the GSMaP-GNRT6 data is larger, especially at the beginning of the 21st century. Conversely, a considerable discrepancy in the annual variation and an almost opposite trend can be observed in the number of rainy days between the GSMaP-GNRT6 data and the station observations.

Real-time high-precision baseline measurement of satellite formation flying based on GNSS

With the establishment of the Chinese space station, a series of space science missions will be conducted in its vicinity. One of the extended tasks for the in-orbit service of space station is the formation flying of small satellites centered around the space station. Satellite formations can perform simultaneous three-dimensional observations of the space station, generating three-dimensional images and monitoring and assessing the status of critical components of the space station. The real-time and accurate relative position measurement is one of the key issues in formation flying. It is a fundamental prerequisite for maintaining and reconfiguring formation configurations, collision avoidance and safety assurance. GNSS-based relative positioning is a low-cost and high-precision measurement method, which is not constrained by light, weather, or relative attitude of satellites. This study introduces a novel approach to determine the relative position of satellites based on single-epoch Global Navigation Satellite System (GNSS) carrier phase difference measurements. Unlike traditional methods, the proposed technique eliminates the need for precise ephemeris and clock bias files and does not require historical observation data. The algorithm avoids the complexity associated with full cycle ambiguity jumps, facilitating real-time, high-precision calculations of relative position baseline vectors. Special attention is given to the influence of Geostationary Earth Orbit (GEO) satellites in the BeiDou Navigation Satellite System (BDS), and measures are taken to mitigate their impact on measurement accuracy by adjusting their observation weights. Our newly developed GNSS-based real-time measurement module, designed for high-quality GNSS signal reception and data communication, was tested on terrestrial and simulated low-orbit platforms. Remarkably, over baselines ranging from 10 m to 9.3 km, while traditional pseudorange differential measurements showed errors up to 1.5571 m, our proposed method consistently held errors under 5 cm and ensured computational speeds within 0.1 s per epoch. High-dynamic zero-baseline tests further validated its potential, with errors less than 1 cm. The innovations encapsulated in this work, resonating with the evolving trends of miniaturized and scalable satellites, present a cost-efficient and real-time solution for inter-satellite relative position measurement. This promises a new horizon in satellite formation technology, with future endeavors focusing on on-orbit experiments.

Detection of extreme hydrological droughts in the poyang lake basin during 2021–2022 using GNSS-derived daily terrestrial water storage anomalies

Poyang Lake is the largest freshwater lake in China, serving as a natural reservoir and playing a paramount role in climate regulation, ecological environment, and water resource management. However, in recent years, Poyang Lake has approached desiccation multiple times, with severe droughts becoming increasingly common. Consequently, precise quantification and analysis of the terrestrial water storage anomalies (TWSA) and drought characteristics of the Poyang Lake basin (PLB) are of profound scientific and practical significance. This paper, for the first time, utilizes data for the period 2021–2022 from 77 newly-established GNSS observation stations in the PLB to precisely determine its vertical crustal displacement, invert daily and monthly TWSA, and investigate extreme hydrological drought. The results reveal the following: 1) The annual amplitude range of vertical surface displacements at GNSS stations in the Poyang Lake basin is from 7 to 14 mm, with the most substantial seasonal vertical displacements occurring during the months of June and July; 2) monthly GNSS-TWSA maintains a commendable consistency with TWSA data obtained from the Gravity Recovery and Climate Experiment (GRACE), the Global Land Data Assimilation System (GLDAS), and precipitation, with correlation coefficients of 0.67, 0.55, and 0.62, respectively; 3) at daily scale, the GNSS-derived Drought Severity Index (GNSS-DSI) accurately recorded the severity and intensity of eight drought events in the PLB during 2021–2022, in particular the period of extensive drought between October 2021 and February 2022, when drought intensity reaching a notable 1.03, which is classified as an extreme and prolonged drought event. Additionally, at local temporal scales, daily GNSS-DSI exhibits heightened sensitivity to drought signals. This study provides novel technological tools and datasets for multi-source satellite-based drought monitoring in the PLB.

GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications

Abstract. Ensemble geophysical datasets are foundational for research to understand the Earth system in an uncertainty-aware context and to drive applications that require quantification of uncertainties, such as probabilistic hydro-meteorological estimation or prediction. Yet ensemble estimation is more challenging than single-value spatial interpolation, and open-access routines and tools are limited in this area, hindering the generation and application of ensemble geophysical datasets. A notable exception in the last decade has been the Gridded Meteorological Ensemble Tool (GMET), which is implemented in FORTRAN and has typically been configured for ensemble estimation of precipitation, mean air temperature, and daily temperature range, based on station observations. GMET has been used to generate a variety of local, regional, national, and global meteorological datasets, which in turn have driven multiple retrospective and real-time hydrological applications. Motivated by an interest in expanding GMET flexibility, application scope, and range of methods, we have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP) that offers GMET functionality along with additional methodological and usability improvements, including variable independence and flexibility, an efficient alternative cross-validation strategy, internal parallelization, and the availability of the scikit-learn machine learning library for both local and global regression. This paper describes GPEP and illustrates some of its capabilities using several demonstration experiments, including the estimation of precipitation, temperature, and snow water equivalent ensemble analyses on various scales.

Distribution Characteristics of Near Surface Ozone Volume Fraction in Shanxi Province Based on Atmospheric Composition Observation Network

Based on the continuous data of O3， NO， NO2， and NOx and the meteorological data from March 2019 to February 2020 at six atmospheric composition observation stations in Shanxi Province， the characteristics and influence factors of O3 volume fractions were studied using statistical analysis and backward trajectory analysis. The results showed that O3 volume fractions were generally higher from April to September and lower from October to the following March. During the study period， O3 pollution represented by φ（MDA8O3）， i.e.， the maximum daily 8-h average of O3 volume fractions， was the most serious at the Jincheng and Linfen stations in the south of Shanxi， followed by that in the Wutaishan， Shuozhou， and Datong stations in the north， with the least pollution occurring at the Taiyuan station in the middle. There were differences between the urban and alpine stations， although their seasonal O3 volume fractions were both summer > spring > autumn > winter. O3 volume fractions at the urban station were usually lower than those at the alpine station； O3 at the urban station might have been influenced by photochemical reactions with precursor NOx； however， this was not the main source of high O3 at the alpine station. The peak and valley values appeared at 15：00 and 06：00， respectively， at the urban station， whereas they appeared at 20：00 and 10：00， respectively， at the alpine station， representing diametrically opposite diurnal variation patterns. Further， the daily amplitude of O3 at the urban station was much larger than that at the alpine station. For urban stations specifically， temperature was the most important meteorological factor affecting O3 volume fraction， compared with sunlight hours， precipitation， and total cloud cover. The NO2 volume fraction in the daytime affected the daily amplitude of O3； although the photochemical generation potential of O3 at the Taiyuan station was good， the O3 volume fractions were the lowest among urban stations due to strong NO titration. The higher O3 corresponded to lower NOx in which NO2 was dominant， and the higher NOx was largely composed of NO， under which conditions O3 would be depleted completely. The surface wind that affected O3 volume fractions of all stations primarily came from the southeast， south， and southwest， and specific wind speed led to the increase in O3 volume fraction. The geographical situation of the station would cause the difference in the transport of atmospheric pollutants， whereas the horizontal transmissions of high O3 from the North China Plain and Fenwei Plain were likely to be the common reason for the increase in O3 volume fraction in Shanxi.

Pneumonia in children before and during the COVID-19 pandemic: an observation from a climate change and environmental perspective in Indonesia

Background. Pneumonia is the second leading cause of child mortality in Indonesia. The COVID-19 pandemic in Indonesia is increasing a challenging environment for children’s respiratory health. 
 This study aims to examine pneumonia in children using the perspective of climate change and the changing environment in Indonesia before and during COVID-19 pandemic. 
 Materials and methods. This research is a retrospective longitudinal study conducted during the period 2017–2020. The study area of the study is in Indonesia. All the province and city in Indonesia are included in this study. The number of children surveyed each year is 30.73 million. Outdoor air quality, temperature and rainfall observations were carried out at 185 observation stations spread throughout Indonesia
 Results. In Indonesia, child pneumonia cases declined by 39.42% from 2017 (511,434 cases) to 2020 (309,838 cases) during the COVID-19 pandemic, with a 47.06% decrease in mortality. Outdoor air quality worsened by 51.64% pre-pandemic (2017–2019), improving notably in 2020. Conversely, temperature and rainfall, which decreased before 2017–2019, sharply increased in 2020, with rainfall at 524 mm and temperatures rising by 0.7°C compared to 2019.
 Limitations. A detailed analysis and cautious interpretation are necessary due to the complex, suggestive links between air quality, climate, behavior, and respiratory health highlighted in this study, especially during the pandemic.
 Conclusion. The pandemic triggered unexpected shifts. Reduced cases correlated with better air quality due to lockdowns and behavior changes, while increased temperature and rainfall may have hindered pathogen transmission.

Biodiversity of Mangrove Brachyuran Crabs of Family Ocypodidae and Sesarmidae in Koto XI Tarusan District, West Sumatera, Indonesia

The Mangrove ecosystem is an ecosystem that is around the edge of the coast and many organisms live in this ecosystem, one of them is crabs. The crabs that are often found are deposit crabs from the Ocypodidae and Sesarmidae families that act as balancers in the mangrove ecosystem. This study aimed to analyze species, diversity, evenness, and dominance in the mangrove vegetation area of Koto XI Tarusan District, West Sumatera, Indonesia. The method used is the descriptive method. The biodiversity of brachyuran crabs’ families, Ocypodidae and Sesarmidae, was analyzed with the Shannon-Wiener index (H’). Four indices were calculated for crabs Ocypodidae and Sesarmidae: species identification, Shannon-wiener (H’) diversity, evenness, and dominance indices. The results showed that there were six species of deposited crabs from the Ocypodidae and Sesarmidae families, namely Uca bellator, U. rosea, Perisesarma eumolpe, P. plicatum, Sarmatium germaimi, and Sesarma curoense. The most abundant species (Di) came from the family Sesarmidae. However, for all stations, diversity (H') has a value that is in the medium category, and evenness (E) is in the high category at each station. The dominance (D) obtained a value categorized as low at each observation station. The condition of environmental parameters in the mangrove vegetation area is included in the category of a good environment. It is still within normal limits for the crabs of the Ocypodidae and Sesarmidae families to live. Two substrate types are found in each observation path: muddy and sandy mud.

Water Quality of Ranowangko River That Crosses Tomohon City and Minahasa Regency, North Sulawesi Province

The Ranowangko River passes through Tomohon City and Minahasa Regency, ending in the Sulawesi Sea. Tombariri District, is the Bunaken National Park area. Ranowangko River Water Monitoring in 2022 by the Regional Environmental Service of North Sulawesi Province shows that Fecal Coli and Total Coli parameters in the Ranowangko River at Station 1 do not meet the requirements according to Government Regulation Number 22 of 2021 concerning Class 2 National Water Quality Standards (BMA). Research to assess water quality from biological, physical and chemical aspects as well as determine water quality status. Monitoring was carried out 4 times in 2 consecutive years at 3 observation stations. Quantitative data and qualitative data include water quality analysis through determination of 14 parameters compared according to BMA Class 2. The water quality data was analyzed using the Storet Method to determine water quality status in accordance with Minister of Environment Decree Number 115 of 2003. The results showed Fecal Coli and Total Coli (Biological Aspects), TSS (Physical Aspects), Total Phosphate (Chemical Aspects) exceeded the BMA. Score calculation results using the Storet method at Station 1: -68, Station 2: -72 and Station 3: -76. Based on the value system According to Minister of Environment Decree 115 of 2003, the score above shows that the Water Quality Status of the Ranowangko River is heavily polluted. Recommendations to the regional governments of Tomohon City and Minahasa Regency to carry out further monitoring of the water quality of the Ranowangko River.

Applied the Cokriging interpolation method to survey Air Quality Index (AQI) for dust TSP in Da Nang city

Mapping to forecast the air pollution concentration in Da Nang city is an urgent issue for management agencies and researchers of environmental pollution. Although the simulation of spatial location has become popular, it uses the classical interpolation methods with low reliability. Based on the distribution of air quality monitoring stations located in industrial parks, residential areas, transport axes... and sources of air pollution, the application of geostatistical theories, this study presents the results of the Cokriging's interpolation selection which provides forecast results of air pollution distribution in Da Nang city with high reliability. In this article, we use the recorded TSP concentrations (one of major air pollution causes at large metropolis) at several observational stations in Da Nang city, employ the Cokriging interpolation method to find suitable models, then predict TSP dust concentrations at some unmeasured stations in the city. Our key contribution is finding good statistical models by several criteria, then fitting those models with high precision.

TYPES AND ABUNDANCE OF MARINE DEBRIS IN THE CORAL REEF ECOSYSTEM OF PASUMPAHAN ISLAND, WEST SUMATRA

Debris has long been a worrying problem, especially in the marine environment. Human activities and marine tourism activities cause coral reef ecosystem damage. The research was carried out in June - September 2022 to determine the type and abundance of marine debris, lifeforms, and condition of coral cover, as well as the relationship between the abundance of marine debris and coral cover on Pasumpahan Island, West Sumatra. The research method used was a survey, and the data obtained was analyzed descriptively. The research location was divided into three observation stations by placing three transects at each station. Based on the research results, six types of marine waste were found, with abundance ranging from 0.01 to 0.085 units/m2. The coral lifeforms found are Acropora branching, Acropora encrusting, Acropora submassive, Coral encrusting, Coral foliose, Coral millepora, Coral mushroom, and Coral massive, with a percentage of live coral cover ranging from 1.00-36.48%. A strong negative relationship exists between the abundance of marine debris and the percentage of coral cover in Pasumpahan Island, West Sumatra

Composition and Diversity of Forestry Plant Species in Forest Areas Manado State University, North Sulawesi

Indonesia is an archipelagic country with a tropical climate so it has a large forest area. High rainfall and sufficient solar irradiation make the diversity of living things both flora and fauna very high. This study aims to identify and determine the level of diversity of forestry plants in the forest area of Manado State University, North Sulawesi. This research will be carried out in April - May 2023 in the forest area of Manado State University, North Sulawesi. Sampling in this study was carried out using the Purposive Sampling (Judgmental Sampling) technique with 9 plots in 3 observation stations. The results of the study found that in all sampling plots, there were 17 individuals of forestry plants with 9 species. The highest Important Value Index (IVI) belongs to the species Petrocarpus indicus with an IVI of 61%. The Shannon Winner diversity index H = 2.07 was found to be in the medium category.

Analyzing urban influence on extreme winter precipitation through observations and numerical simulation of two South China case studies

This study investigates the impact of urbanization on extreme winter rainfall in the South China Greater Bay Area (GBA) through the analysis of hourly station observations and simulations using the Weather Research and Forecasting Model with the Single Layer Urban Canopy Model (WRF-SLUCM). Data from 2008 to 2017 reveal that urban areas in the GBA experience lower 99th percentile hourly winter rainfall intensity compared to surrounding rural regions. However, urban locations exhibit higher annual maximum hourly rainfall (Rmax) and very extreme rainfall events (99.99th percentile) in winter, suggesting a positive influence of urbanization on extreme winter precipitation. A case study further underscores the role of the Urban Heat Island (UHI) effect in enhancing extreme rainfall intensity and probability in the GBA urban areas. Additionally, two extreme cases were dynamically downscaled using WRF-SLUCM, involving four parallel experiments: replacing urban land use with cropland (Nourban), using historical urban land use data from 1999 (99LS), projecting near-future urban land use for 2030 (30LS), and considering 2030 urban land use without anthropogenic heat (AH) (30LS-AH0). Synoptic analysis demonstrates that cold air intrusion suppresses the GBA UHI in Case 2013 but not in Case 2015. Reduced evaporation and humidity induced by urban surfaces significantly decrease urban precipitation in Case 2013. In contrast, the persistent UHI in Case 2015 enhances local convection and land–ocean circulation, leading to increased moisture flux convergence and amplified urban precipitation intensity and probability in 30LS compared to Nourban. This amplification is primarily attributed to AH, while the change in 99LS remains insignificant. These findings suggest that urban influences on extreme precipitation in the GBA persist during winter, particularly when the UHI effect is maintained.

Spatiotemporal variability and trends of droughts in the Mediterranean coastal region of Türkiye

AbstractDrought, defined as the period when water availability considerably remains below normal levels for a certain time period, has devastating social, economic and environmental impacts. It is significant to closely monitor the temporal and spatial variability of drought in order to implement policies to mitigate the drought risk and ensure the sustainable management of water resources. In this study, three different drought indices, standard precipitation index (SPI), standardized precipitation‐evapotranspiration index (SPEI) and reconnaissance drought index (RDI), are calculated for 3‐, 6‐ and 12‐month time scales from the precipitation and temperature data measured in 11 meteorological observation stations within the Mediterranean coastal region of Türkiye during 1972–2020. Initially, two absolute homogeneity tests were applied, namely the standard normal homogeneity test (SNHT) and the Pettitt test, to detect abrupt changes in drought index time series. Then, trend analysis was carried out with the innovative trend analysis (ITA) and improved visualization of ITA (IV‐ITA) methods to determine the temporal variation of the drought in the region. A graphical representation is developed for the drought classifications based on IV‐ITA. As a result of the SNHT and Pettitt test, significant change points were found at a 5% significance level in 91.92% and 85.86% of all indices, respectively. In both tests, breaks were detected at similar times (between 1972 and 1974) at Marmaris station, while at Alanya station, changes were found between 1972 and 1973 for the SNHT and in 2008 for the Pettitt test. The trend differences between extreme conditions (extremely dry and wet) decreased with an increase in time scale, distinctively. The ITA trends were almost identical for 3‐month time scales, and they started to differ with time scale increases. Particularly, the presence of negative differences in data points contributes to the augmentation of drought periods, despite a few instances of extreme cases and minor fluctuations that can also be observed in IV‐ITA. The findings obtained from the study are expected to provide valuable information about the recent shifts in drought in the Mediterranean basin.