19th Annual Meeting of the Asia Oceania Geosciences Society

The following sections are included:

• PREFACE
• EDITORIAL BOARD
• LIST OF REVIEWERS
• TABLE OF CONTENTS

This study addresses abrupt global warming and a slowdown thereafter that happened in recent decades. It separated the role of anthropogenic CO2 led linear trend to that from natural factors (volcano and the sun). It segregates a period 1976–1996 where two explosive volcanic eruptions occurred in active phases of strong solar cycles and also the period covers two whole solar cycles. That same period coincided with abrupt global warming. This study suggests that domination of a particular type of ENSO, the Central Pacific (CP) type ENSO and related feedback from water vapour played a crucial role. A plausible mechanism was proposed that could be triggered by explosive volcanos via a preferential North Atlantic Oscillation (NAO) phase. It modulates the CP ENSO via extratropical Rossby wave and affects the Aleutian Low. From that angle, it is possible to explain the disruption of ENSO and Indian Summer Monsoon teleconnection during the abrupt warming period and how it recovered subsequently afterward. Interestingly, individual models and also the CMIP5 model ensemble fails to agree with the observation. This study further explores important contributions due to natural drivers those are missed by models.

Cities are one of the major contributors to climate change and suffer potentially high health and environmental risks and great damages by natural disasters under a changing climate. Extreme rainfall events, often causing flash floods in urban areas, are one of the costliest natural disasters. In particular, rapid urbanization under local climate change often exerts strong impacts on extreme precipitation. Thus, understanding the characteristic changes in various aspects of extreme precipitation due to the urbanization processes is of utmost importance to properly plan and manage potential urban disasters. In numerical models, an urban region is usually represented by a land cover with low reflectivity, low heat capacity and water availability, and a large roughness length, whose characteristics can change according to the degree of urbanization. This study aims to quantify the effect of urbanization on various aspects of extreme precipitation, including its formation, development, location and amount, by conducting sensitivity experiments in terms of urban land cover/use for an extreme rainfall case in the Seoul metropolitan area, Korea, using the Weather Research and Forecast (WRF) model coupled with Urban Canopy Model (UCM) --- the WRF-UCM. Our results provide a basis for better quantitative precipitation forecasting and a reference for urban planning and design.

On 16 October 2021, localized torrential rainfall (> 70 mm h^-1) occurred in the slope area of northern Taiwan during an approaching surface front. Flash floods downstream of the catchment area resulted in a severe drowning accident without warning. In this observational study, the newly-established C-band disaster prevention rainfall radar in northern Taiwan is used for analysis and comparison. At 15-18 local time (LST), extreme values of the specific differential phase K_DP (> 2° km^-1) can be used to identify the heavy rainfall region’s location, intensity, and movement. For flash floods downstream of the catchment area, potentially, there is a priori warning capability. In this event, tracking intense and concentrated K_DP on complex terrain has an advantage over radar reflectivity. The direct application of K_DP on quantitative precipitation estimates can effectively complement the transmission-delay problem of ground rain gauges.

This paper reviews recent literature to synthesize the impact of rapid urbanization and the Urban Heat Island (UHI) effect on vulnerable populations in the US. Rapid urbanization characterized by expanding urban boundaries, replacement of vegetated surfaces with impervious surfaces, low tree canopy cover, and infrastructure development such as freeways complemented by former development-zoning policies have been well documented as a derivative of resource and energy inequality (or energy justice) in low-income communities who are underprepared and under-resourced while living in an aging and dilapidated housing stock. However, literature on UHI is primarily driven by energy and mitigation studies whereas vulnerability studies are driven by demographic data; thus, largely remain isolated due to the lack of an integrated interdisciplinary framework to capture the impact of UHI in vulnerable populations. Through a systematic literature review, we are enquiring, what are UHI impacts on the energy needs and overheating of the population living in aging and dilapidated housing. What resiliency frameworks exist and how does it relate to the UHI, overheating, and housing? We use ScienceDirect, Web-of-Science, Google-Scholar, and Scopus databases and limit our search to the first 200 literature written in English to do a systematic bibliometric analysis to synthesize and compare evidence from literature by using keywords “Urban Heat Island”, “Energy-Burden”, “Energy-Justice”, “Overheating”, “Vulnerability”, “Resilience”. Our study suggests that UHI disproportionately impacts the energy need and indoor overheating of the low-income population, and the discriminatory spatial pattern of intra-urban heat exposure strongly correlates with the built environment characteristics and historical housing policies. The impact further extends to heat-related mortality and cardiovascular diseases. The outcome of this study suggests that future investigation into UHI and its relation to the energy consumption needs to consider demographic data to synthesize the energy burden.

Recently, Volatile organic compounds (VOCs) pollution has become one of the main problems of global air pollution. HCHO is the main intermediate product of VOCs photolysis, so it is beneficial for us to better understand VOCs by mastering the distribution characteristics of HCHO and the influencing factors of its generation and attenuation. With the development of space remote sensing technology, monitoring atmospheric HCHO by satellite remote sensing has become an important detection method. Therefore, based on sentinel-5P HCHO product, this paper analyzed the spatial and temporal distribution characteristics of tropospheric HCHO concentration and its influencing factors in Shandong Province, China. The results show that the average HCHO concentration in Shandong province in 2019 and 2020 is 10.98×10¹⁵molec/cm². In terms of time, the average HCHO concentration from large to small is as follows: Summer> Autumn> Spring> Winter, the maximum and minimum values appeared in June and December, respectively. In space, it shows a decreasing trend from west to east, and the concentration in the eastern coastal area is lower than that in the western inland area. It is found that natural sources such as temperature, and anthropogenic sources such as industrial production all affect the change of tropospheric HCHO concentration. In terms of temperature, the higher the temperature, the higher the concentration of HCHO. VOCs released by vegetation in the growth stage will cause the increase of atmospheric HCHO concentration. In terms of industrial production, incomplete combustion of biomass such as coal, and industrial waste gas released after processing and utilization of HCHO as raw materials also affect the change of tropospheric HCHO concentration to a certain extent. COVID-19 had a certain impact on HCHO concentration, and the shutdown during COVID-19 led to a downward trend in HCHO concentration in industrial intensive areas.

This study attempted to examine spatio-temporal features of the present and future climate of Hissar-Allay Mountainous Region, Tajikistan. In addition, the characteristics of droughts were evaluated. For this purpose, historical data (1960-2018) from seven stations and bias-corrected future (2018-2100) projected precipitation and temperature data from RegCM4-3 based on two climate scenarios (RCP4.5 and RCP8.5) were used. Various statistical and geospatial tools, and indices were used to analyze changes, variability, and trends in precipitation and temperature. The Mann-Kendall (M-K) test and Sen’s slope estimator were applied to analyze the direction and magnitude of trends. We also used the Standard Precipitation Index (SPI), to characterize drought in the basin. The results suggest a marked climate variability (moderate to high), strongly forced by the topographic feature of the area. Time-series analyses over the past 59 years show that temperatures have constantly increased at an average rate of 0.2°C-0.25°C/decade. However, only weak signals of past changes were detected for precipitation. For the future, the two climate scenarios (RCP4.5 and RCP8.5) show precipitation increasing annually, both increasing and decreasing seasonally, and temperature increasing consistently at all stations. Moreover, droughts have been intensified and frequently occurred in most parts of the region.

This study investigates the impacts of climate change on streamflow of the Ribb River watershed using the SWAT model and a Regional Climate Model (RCM). Regional climate scenario projected by the CORDEX RCM (RCA4) with the horizontal grid spacings of ∼50km was used to estimate historical (1979-2015) and future (2020-2100) climate under RCP4.5 and RCP8.5 scenarios. The projected climate variables were bias-corrected using a linear-scaling approach. Trends of the projected climate variables were checked by the M-K trend analysis. The variables showed an increasing trend for monthly maximum and minimum temperatures compared to the reference period for both RCP scenarios. However, there was no systematic increasing and decreasing trend of precipitation over the century. Annual precipitation, long rainy and dry seasons showed an increasing trend, while short rainy seasons showed a decreasing trend under both RCPs. The SWAT model was applied to evaluate the impacts of future climate change on hydrology and water resources. The performance of the model was evaluated through calibration and validation process using observed streamflow data over the period (1979-2012). The SWAT model performed well with reasonable accuracy (R2>0.7). The projected streamflow showed an increasing trend except for the 2080s in the RCP8.5 scenario, and its magnitude varied up to 58-66% in dry and 21-55% in rainy season for each RCP scenario compared to the reference period. Increases in seasonal and annual streamflow were mainly associated with increases in the projected precipitation.

In air quality prediction, it is essential to find the upstream areas from which a small initial error can grow into large forecast errors in the region of interest. The conditional nonlinear optimal perturbation for initial conditions (CNOP-I) is a suitable tool for targeted (adaptive) observations. To calculate the CNOP-I several variables are included; those are used from the multiple energy equations (eg. kinetic energy, dry energy, moist energy, etc). In particular, the previous studies on improving the Asian dust storms (ADSs) prediction mainly considered the kinetic energy calculation, which included only two variables (u-wind and v-wind). However, variables affecting the actual ADS’s development and movement also include the other variables. This study aims to identify the main variables and classify the synoptic patterns for given ADS cases using the principal component analysis (PCA). We focus on the ADS events that occurred during the last 32 years (1990—2021) over South Korea. Through the PCA, we identified the top five variables — temperature, specific humidity, divergence, ozone mass mixing ratio, and eastward wind — which affect origination and translocation of the ADSs. Furthermore, the ADSs are significantly affected by vertical velocity, divergence, relative humidity, eastward wind, and potential vorticity; for example, strong downdraft and divergence make ADSs finally land on the Korean Peninsula. For a further study, we plan to identify the sensitive areas in targeted observations for air quality prediction via CNOP-I. We expect to improve air quality forecasts by classifying the synoptic situations that bring about severe Asian dust storm outbreaks in South Korea and by identifying the upstream areas for targeted observations to which we can enhance observations, potentially through international collaborations.

A statistical post-processing (SPP) system called Bayesian Processor of Ensemble (BPE) is demonstrated in this study for the generation of extended-range probabilistic extreme temperature forecasts at selected weather stations in Taiwan. BPE is based on the Bayes’ Theorem, and comprises three main components: (1) the estimation of the prior, the climatic distribution of the predictand; (2) the generation of the likelihood distribution, capturing the relationship between the predictors and predictand, and (3) the fusion of the prior and likelihood distributions for the generation of the predictive (or posterior) distribution, given the latest operational ensemble forecasts. The Bayesian use of the prior distribution allows BPE to optimally calibrate, with a maximum level of informativeness, the predictive distribution, even under operational constraints such as limitations in the size of the reforecast data sample, and low skill in raw extended-range ensemble forecasts.

Rain was studied using 236 videosondes launched from 14 locations across tropical and subtropical Northeast Asia, Southeast Asia, and Oceania in 1988-2009. Particle distribution and sounding/radar data were used to determine precipitation mechanism. Over the tropical ocean, the major mechanism of raindrop growth was found to be the Freezing Process (based on frozen drops) and not the Cool Rain Process (based on graupel). Among heavy rain clouds, frozen drop and graupel particle concentrations were very high in some, fully accounting for their rainfall, but only moderate in others, too low to account for their very heavy rainfall. New analysis suggests the following mechanism for Mixed Process heavy rain formation: Falling graupel particles collect supercooled drops and become frozen drops which accumulate near the melting level, where they grow further by collecting drizzle drops from a merging cell. Melted drops grow further by coalescence. We also extend our analysis of mesoscale system, summer rain clouds at Ubon Ratchathani, observed with videosondes and C-band radar. We report that graupel-dominant clouds produced longer duration rainfall than frozen-drop-dominant clouds. Finally, we review our data showing that lightning frequency correlates with both graupel particle concentration and ice crystal concentration, consistent with the riming electrification mechanism of thunderstorm electrification. Our 2010-2012 Hokuriku winter cloud observations provide evidence in naturally-occurring clouds that space charge increases with the product of graupel particle and ice crystal peak concentrations. Graupel/ice crystal paucity explains the low lightning activity observed over the tropical ocean. All of these observations involve the videosonde, which we developed to observe precipitation particles in clouds.

Two statistical post-processing methods, ensemble Model Output Statistics (EMOS) and Ensemble Kernel Density MOS (EKDMOS), are applied in 20-year reforecasts of the National Centers for Environmental Prediction (NCEP) global ensemble forecast system version 12 (GEFS v12) to produce calibrated and downscaled 1-14-day probabilistic forecasts of cold extremes at specific stations over Taiwan. To generate an EMOS forecast, the MOS equation is built using the ensemble mean, and applied to each ensemble member. The EKDMOS uses a kernel density estimation (KDE) to create a probability density function (PDF) from the EMOS forecasts.

Calibration is performed using a leave-one-out cross-validation procedure, where one winter is used for validation, and the remaining 19 winters are used for training. Forecast evaluation shows that the EMOS is under-dispersive, just like the raw ensemble (RawEns) forecasts, with some bias removed. In contrast, the EKDMOS well represents the forecast uncertainty with most of the bias removed. Compared to the RawEns or EMOS, the EKDMOS obviously improves the reliability and discrimination of probabilistic forecasts. The EKDMOS increases the Brier skill score (BrSS) of the RawEns and EMOS by decreasing its reliability, and increasing its resolution components. For any threshold and any lead time, users with a wider spectrum of cost/loss ratio can obtain more benefit from the EKDMOS as compared to the RawEns or EMOS. The EKDMOS distribution, as expected from a reliable forecast system, necessarily approaches the climatology of the training sample when forecast informativeness is lost beyond 10 days.

Demand for probabilistic forecasts of consecutive days without measurable rainfall has grown significantly by users in different sectors of society, especially in agriculture, livestock, and water resource management. The purpose of this study is to provide users with reliable and skillful forecasts, which help users obtain more economic benefits in decision making. In this study, Analog post-processing (AP) is applied in 20-year reforecasts of the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System version 12 (GEFS v12) to produce calibrated and downscaled probabilistic forecasts of consecutive days without measurable rainfall over Taiwan land area. Long-term forecast evaluation indicates that: (1) the problem of under-dispersion of the raw forecasts is effectively mitigated through the AP. (2) The probabilistic forecasts of consecutive days without measurable rainfall have good reliability and discrimination (potential usefulness) within next four weeks. (3) The calibrated forecasts provide higher economic benefits for users with a much wider spectrum of cost-to-loss ratio compared to the raw forecasts.

Reservoir inflow forecasts, which can be used to estimate the future storage of reservoirs, are essential for water resources management. This work develops a framework for reservoir inflow forecasting for the coming 2 weeks based on the rainfall forecasts from the National Centers for Environmental Prediction (NCEP) global ensemble forecast system version 12 (GEFS v12).

Analog Post-processing (AP) and Probability-Matched mean (PM) are applied to obtain a quantitative precipitation forecast (QPF) as the input of a hydrological model for reservoir inflow forecasting. AP searches for the best analogs to the current forecast in a historical set of predictions. The AP ensemble forecasts are derived from the observed high-resolution rainfall patterns corresponding to the historical forecast analogs that most resemble the current ensemble rainfall forecast. Then PM is applied on the AP ensemble mean to get a QPF with a more realistic range of rainfall amounts. Evaluation shows that the QPF not only corrects the rainfall pattern and amount of the raw forecasts, but also displays the fine scale details of rainfall. The calibrated and downscaled QPF is then ingested into the Hydrologiska Byråns Vattenbalansavdelning-based (HBV-based) hydrological model for reservoir inflow forecasting. Shih-Men reservoir in northern Taiwan is chosen as the study area, which is a multifunction reservoir for water supply, agriculture, hydropower generation, and flood control. The HBV-based hydrological model is optimally calibrated by using the historical rainfalls, temperature and runoffs in the reservoir catchment. By coupling the calibrated and downscaled QPF with the HBV-based hydrological model, the 1-14 days ahead reservoir inflow forecasting can be carried out and shows a satisfactory forecasting performance.

Geostationary Interferometric Infrared Sounder (GIIRS) onboard Fengyun-4A (FY-4A) satellite provides high-spectral-resolution infrared observations with high temporal resolution of the targeted observing areas. It provides useful information of the atmospheric thermodynamics like temperature and humidity profiles. Due to uncertainties in modeling cloudy radiances, only clear radiances of GIIRS are assimilated in the operational numerical weather prediction (NWP) model. Advanced Geostationary Radiation Imager (AGRI) onboard the FY-4A provides a variety of cloud products with high spatial resolution. Synergistic use of GIIRS and AGRI not only provides sub-pixel cloud information of GIIRS single field-of-view (FOV), but also helps retrieve the cloud-cleared radiances (CCRs) in some cloud regions. The inter-comparisons between GIIRS and AGRI radiance measurements show good agreements; the mean biases of (AGRI - GIIRS) are less than 0.2 K for B12 (10.8 um) and B13 (12.0 um) for clear sky only. The convolved GIIRS CCRs are compared to the mean radiances of AGRI clear pixels (AGRI CLRs); and the GIIRS CCRs are reasonably close to the AGRI CLRs. Furthermore, preliminary results show that 32% more of CCRs than clear sky are added for radiance assimilation in NWP model without consideration of cloud impact for Typhoon Maria (2018) case.

Atmospheric motion vectors (AMVs) have produced positive impacts on global weather forecasts, but few studies have evaluated the impacts of AMVs data from Fengyun (FY) geostationary satellite series, especially from FY-2G and FY-4A, on typhoon forecasts in a regional model. In this study, the AMVs data of FY-2G and FY-4A were compared and evaluated by pre-processing methods such as height assignment, quality control, channel combination and thinning. Typhoon Haishen (No.10 super typhoon in 2020) was taken as an example. The AMVs data of the two satellites were assimilated by using 3DVAR provided by WRFDA and simulated by the WRF model to evaluate the forecast results of the two satellites, respectively. The results show that the AMV data from FY-4A are better overall than those from FY-2G, with smaller RMSEs and biases for full wind speeds. On the other hand, assimilation of AMVs data improves the forecasts of environmental fields, resulting in the simulated track closer to the best track. Another experiment shows that the assimilation of AMVs data has a good impact on precipitation prediction In general, the assimilation of FY-2G and FY-4A AMV data has a relatively positive impact on typhoon prediction, and the AMVs data combined with multiple channels can provide better prediction.

Soil moisture is essential in numerical weather prediction using a coupled atmosphere-land surface model because it affects the latent and sensible fluxes, emission from the land surface, and eventually the atmospheric variables, especially temperature, water vapor mixing ratio, and precipitation. Therefore, soil moisture observations in a coupled atmosphere-land surface data assimilation system can provide useful information for both land surface and atmosphere. The National Aeronautics and Space Administration’s Soil Moisture Active Passive (SMAP) mission provides space-borne observations of soil moisture and freeze/thaw state: the L-band microwave radiometer aboard SMAP observes soil moisture at the top 5 cm of the land surface, having nearly global coverage every 2-3 days with a 1000 km swath. In this study, we employ the Maximum Likelihood Ensemble Filter (MLEF) to assimilate the SMAP 9-km enhanced soil moisture retrievals into the Noah land surface model (Noah LSM or simply Noah) coupled with the Weather Research and Forecasting (WRF) model. As a strongly coupled atmosphere-land data assimilation system, MLEF simultaneously corrects atmospheric and land surface variables. For the soil moisture assimilation, the observation processing includes quality control, thinning, statistical bias correction, and horizontal and vertical covariance localization. To investigate the soil moisture impacts on the coupled data assimilation, we assimilate both soil moisture and atmospheric observations — the SMAP soil moisture retrievals and the National Centre for Environmental Prediction (NCEP) Prepared Binary Universal Form for the Representation of meteorological data (PrepBUFR), respectively. Our results indicate that the WRF-Noah-MLEF system generates analysis increments of soil moisture that provide additional information to atmospheric variables, especially in the lower atmospheric layers, through cross-covariance between land and atmosphere.

This study aims to improve the air quality forecasting skill in East Asia by employing the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and by selecting its optimal set of physical parameterization schemes, especially in the planetary boundary layer (PBL) and land surface (LS) processes, via the micro-genetic algorithm (μGA). Among a bunch of available options (8 PBL schemes and 4 LS schemes), the selected optimal schemes are the Asymmetric Convective Model, version 2 (ACM2) for PBL and the Noah land surface model with multiple parameterization options (Noah-MP) for LS, respectively. For the given Asian dust storms cases, the WRF-Chem, using the optimized schemes, resulted in higher correlation coefficients with observations in all variables, including aerosol optical depth, PBL height, temperature and relative humidity at 2 m, and wind speed and direction at 10 m, than using the other sets of parameterization schemes.

In Hong Kong (as in much of SE Asia), rainfall surpasses 2200 millimetres per year. In addition, local granite-derived soils are poorly retentive of nutrients, creating oligotrophic conditions with < 0.47% nitrogen in surface soil levels and negligible concentrations below. Despite such challenges, numerous non-leguminous (and often lithophytic) plants, such as Ficus microcarpa and Glochidion hongkongense, are not only abundant but also grow rapidly to great size. These very high levels of carbon sequestration imply correspondingly large rates of nitrogen uptake that can only be explained by microbial nitrogen fixation. In this project, samples of aerial roots were recovered from a number of common Hong Kong plant species. nitrogen free (NF) media was used to identify potentially nitrogen fixing bacteria, which were then streaked to purity before DNA extraction and sequencing. Complete genomes of three isolates, 9ba2, Kosakonia radicincitans JS2a2 and Gluconobacter thailandicus ISBL3, were generated by hybrid assembly, using both Illumina MiSeq and Oxford Nanopore MinION platforms. Genomic analysis revealed numerous highly-conserved nitrogenase related (nif) genes across the different genera. Mineral transport systems, particularly for iron and molybdate, were also strongly represented. For example, in K. variicola 9bα-2, a 166 kbp plasmid not only encodes the fec(RABCDE) operon for ferric citrate uptake, but also the molybdate-responsive ModE transcription factor. The probiotic use of nitrogen fixing bacteria such as these has been proposed as a more sustainable measure for plant growth promotion that could reduce requirements for chemical fertilisers. Significantly, the extent of microbial nitrogen fixation that we estimate in Hong Kong suggests that the contribution to the nitrogen cycle of non-legume-associated microbes may be greatly underestimated.

Plastic waste pollution is an environmentally threatening issue that especially harms the marine population. Target 14.1 of the UN Sustainability Goals seeks to “prevent and significantly reduce marine pollution of all kinds” by 2025. Plastics constitute up to 80% of marine debris but, of this, microplastics may be the most damaging to marine ecosystems. In their recent study of microplastics recovered from the South China Sea, Yu et al. (2020) found that over 80% were microfibres, with the greatest proportion (30.8%) composed of the polyester, polyethylene terephthalate (PET) (Yu et al. 2020). The purpose of this investigation is to discover microbes with PET-degrading activity that might then be used in water/waste treatment to reduce polyester microfibre pollution in marine environments. Working with bacteria recovered from samples of polyester fabric that had been buried in soil/leaf litter for three months before recovery, we have used a progressive series of aromatic substrates (toluene, sodium benzoate, benzyl benzoate and finally a suspension of PET microfibres) as a screen, since the repeat unit of PET includes benzene carboxylate ester bonds.

Second-generation biofuels derive energy from lignocellulosic waste, a considerably more sustainable feedstock than food commodities. Lignocellulose, however, requires pre-treatment to release fermentable sugars, a process that may be bacterial. In this work, Kluyvera sp. CRP, isolated from faeces of a Chinese red panda (Ailurus fulgens), demonstrated significant cellulolytic activity in vitro. Its complete genomic sequence (5,157,963 bp, 54.80% GC content), which was established through hybrid assembly, found similarity to an endophytic strain of Kluyvera and genomic analysis revealed an extensive carbohydrate metabolism with multiple isoforms of degradative enzymes. Hence, Kluyvera sp. CRP, or its isolated enzymes, may provide a useful tool for the valorisation of agricultural waste.

Flooding has been detrimental to economies, globally causing severe damage to human lives and property. Developing countries, including Sri Lanka, are highly vulnerable to heavy floods mainly due to the lack of infrastructure and expertise on efficient flood risk management. Gin River basin in Sri Lanka, having a catchment area of 932 km², is one such river basin that has been affected by heavy floods in the past. Therefore, bias-corrected General Circulation Model (GCM) outputs from Coupled Model Inter-comparison Project Phase 5 (CMIP5) were used as inputs for the Rainfall-Runoff Inundation (RRI) model to simulate the impact of climate change on flood peak discharge, maximum inundation depths of the Gin River basin. Future climate projections of five GCMs that performed well in the target area, corresponding to the RCP4.5 emission scenario, were used to simulate the future flood flow using the validated RRI model. GCM averaged projections indicated a 10% increase in southwest monsoon rainfall from May to September compared with the past (1980-2000). Projected changes in the precipitation have declined the mean annual discharge by 34% while aggravating the flow exceeding 5% of the time by 5% on average at the Baddegama river gauging station (downstream) of the basin. Furthermore, the RRI model simulations corresponding to the RCP4.5 emission scenario for 2040-2060 revealed critical information, including expansion of peak inundation extent, which will aid efficient flood risk planning and management in the basin.

The present paper provides an overview of some recent advances and shortcomings of downscaling methods for assessing the potential impacts of climate change on hydrological regime. In general, three broad categories of these downscaling procedures currently exist: (i) dynamical downscaling (DD) methods based on Regional Climate Models; (ii) statistical downscaling (SD) procedures that relied on the empirical relationships between large-scale atmospheric variables (predictors) and surface hydrologic parameters (predictands); and downscaling approaches based on machine learning (ML) methods. More specifically, it has been widely known that DD methods could provide reasonable description of the large regional climate conditions, but they could not accurately capture observed characteristics of hydrologic processes at a local or station scale. SD procedures could describe accurately the observed properties of various local hydrologic processes and could be adapted to the local climatic conditions for a given site. ML-based downscaling methods were found to be more efficient and more robust for describing the linkages between large-scale climate predictors and local hydrologic variables, but they could not provide significant improvements over SD procedures. However, since DD, SD, and ML methods have different associated skills, it is recommended that the best approach to developing climate change scenario for impact and adaptation studies should be based on the combination of these three procedures.

The present study investigated the physicochemical and metal concentrations in water samples collected from Pasur River, Bangladesh. Mongla seaport stands on the bank of this river. Many industries and other commercial sectors situated in this port area are discharging their wastes into the river without proper treatment. The concentration range of TSS, chloride, iron (Fe), and manganese (Mn) were from 363.2 to 1482.7, 108.2 to 708.93, 1.13 to 2.75, and 0.19 to 1.41 mg/L, significantly exceeding the health-based guideline of WHO and Bangladesh (DoE) standards. The average pH value was 8.73, higher than the WHO and DoE standard limit. The water quality evaluation indices such as Metal Index (MI), Comprehensive Pollution Index (CPI), and Water Quality Index (WQI) were used to determine the pollution levels of the Pasur River. WQI (ranging from 391.3 to 1336.1), CPI (6.71 to 23.13), and MI (7.23 to 23.27) were very high and greatly exceeded standard limits indicating that the Pasur River water is highly polluted. The results of Pearson correlation analysis, principal component analysis (PCA), and cluster analysis (CA) indicated that the sources of pollutants were both geogenic and anthropogenic. The spatial distribution of quality indices and cluster groups indicates that the studied river’s urban and seaport areas were more contaminated. The primary anthropogenic sources are municipal wastewater, industrial effluents, runoff from an agricultural area, local bazar, car garage wastes, highway, and stormwater runoff.

Among natural disasters in Pakistan, flooding is the most frequent and devastating. In Punjab, the flood-prone areas suffer heavy damage during the heaving rainfall of the monsoon. The population density of Punjab region is increasing every year. Conversely, the dependence of population on rivers and urbanization on agricultural land is increasing every year. Many researchers tried to investigate flood mechanism and predict flood disasters. Researchers have mainly focused on the upstream of the Indus River due to flash flood disaster risk. At the downstream of Indus river, the high population density area after the confluence of the Indus and Chenab rivers is also important due to the riverine. In this region, severe inundation by flooding occurred in 2015. In this study, Rainfall-Runoff-Inundation Model (RRI) and River model (iRIC) were used to evaluate the inundation. We aimed to comprehensively evaluate the inundation risks (inundation depth, peak inundation discharge, and inundation area) by considering extreme rainfall events over densely populated areas located downstream of the confluence point of the Indus and Chenab Rivers. In addition, sensitivity experiments for land cover were conducted to reveal the impact of land-cover change (urbanization and afforestation) on inundation. The Nash-Sutcliffe model efficiency coefficient for river discharge calculation at the Tounsa barrage and Trimmu headworks (on just before the confluence point of Indus and Chenab) was 0.83 and 0.67 respectively. Furthermore, the flooding at the confluence point of the Indus River was reproduced by iRIC with high accuracy. The results showed that planting and afforestation will mitigate flooding scale, but urbanization increases the risk of flooding especially after the confluence point of two rivers at the high population dense area. Planting between the Indus River and Chenab River could mitigate flood disasters downstream of the confluence point.

Complete information on hydro climatology is necessary for developing water resource-based sectors. Rainfall forecast is one of the critical issues for the hydrologic community, which indirectly affects human beings, socio-economic status, climate change, and global well-being. Recent advances in science and technology have evolved and improved the rainfall forecast skill. Despite the advances, the challenge is identifying appropriate accurate techniques to forecast rainfall. Although there are many forecasting techniques, it is noted that considerable improvement is still needed for rainfall forecast at a smaller time scale. The difficulty in forecasting increases with the smaller time step of the rainfall series. In the present study, we have attempted rainfall forecast on a daily scale. The singular spectrum analysis (SSA) is found to be an efficient method that can handle the non-linear, non-stationary time series data. In the present study, SSA is applied to forecast the daily rainfall for the next one year based on daily rainfall data from NASA power. The observations from this study show that SSA successfully interprets the trend, periodicity, cyclic component, and noise of the time series. The method successfully displays the repetition pattern of rainfall, rise and fall in rainfall magnitude. Overall, rainfall at the daily time step for the whole year was forecasted with reasonable accuracy.

The Lian river catchment hold one of the largest e-waste recycling centers in the world between 1995 and 2015. Valuable elements were extracted and much of the residues were discarded into the Lian watershed. These practices may have released toxic trace elements to water bodies nearby, as it has been reported for flame retardants in the area before. Therefore, our aim was to evaluate the distribution, potential risks and sources of 7 trace metals (Cr, Ni, Cu, Zn, As, Cd and Pb) known to be linked to these effluents in the Lian river. Sediments cores from 5 sites from the upper to the lower reaches of the river were collected. The total concentrations of trace metals were determined by means of a digestion with HNO₃-HF followed by measurement with an ICP-MS. The results showed that the sediments of the river were significantly polluted by Cr (18.32–83.73 mg/kg), Ni (6.45–50.6 mg/kg), Cu (9.36–531.5 mg/kg), Zn (38.57–441.9 mg/kg), Cd (0.209–1.135 mg/kg) and Pb (54.02–492.8 mg/kg). The pollution load index showed values higher than 4 in the middle section and between 2 and 3 in the lower sections of the study area. Source identification based on principal components analysis showed strong positive correlations among al metals except of Cd, that showed a different behavior, suggesting industrial pollution for the formers and agriculture pollution for the latter.

Surface velocimetry has been steadily increasing interest and research on its applicability to stationary discharge measurement due to its measurement efficiency and economy. However, it has not yet been widely used due to some uncertainties in the measurement of surface velocity. The first is the uncertainty of the relation between the surface and mean velocity, and the others is for wind effect on surface velocity.

In the case of measurement of water surface velocity, the direction and height of the wave on water surface can be changed by wind, which may cause an error in the surface velocity value. For higher velocity in flood flow, the influence of the wind speed is negligible on surface velocity, but as the velocity decreases, the influence of the wind effect on the surface velocity increases relatively.

In this study, in order to analyze the effect of wind speed and direction on the surface velocity under constant flow conditions when measuring discharge using surface velocity meter, wind direction anemometer(150WX of AIRMAR Co.) was installed with radar surface velocimetry(RQ-30 of SOMMER co.) in an test river.

From the results, speed and direction of wind was found to affect the measured surface velocity in low flow condition. Wind speeds of 2.5 m/s or more caused fluctuations of 20.0~71.4 % of the surface velocity depending on the wind direction, while the effect was not significant as 2.9~8.6 % at less than 1 m/s of wind speed. Therefore, for calculating the discharge by measuring the surface velocity, the effect of wind speed and direction should be considered, especially in low flow conditions where the influence of wind is relatively large.

Water resources in equatorial atolls are amongst the most vulnerable globally, partly because of extreme variability of annual precipitation (P) due to frequent ENSO events. IPCC projections for the central and western tropical Pacific indicate mean annual rainfall will increase as sea surface temperatures (SSTs) rise. Projections of the intensity and frequency of ENSO events and hydrological droughts are of low confidence. Here, relationships between 12-month May to April precipitation (P_M-A) in two equatorial atolls, Tarawa and Kiritimati in Kiribati, and 12-month SST_M-A in the Nino regions surrounding the atolls are examined between 1950 and 2022. Only the Nino4 region has as significant temporal trend in SST_M-A. There are no significant trends in P_M-A in either atoll due to large ENSO-related interannual variability. In both atolls, strong, highly significant correlations are found between P_M-A and SST_M-A in the Nino regions eastward of the atolls. These show ∂P_M-A/∂SST_M-A for both atolls over 1,000 mm/°C. The relationship for Kiritimati appears nonlinear. Comparison of ranked very much above normal (VMAN, percentile > 0.9) and very much below normal (VMBN, percentile < 0.1) SST_M-A with ranked VMAN and VMBN P_M-A revealed poor correspondence for both atolls, suggesting that extreme SST_M-A are not a sole determinant of extreme P_M-A. The nonlinearity of the relation is shown by ∂P_M-A/∂SST_M-A for below normal P_M-A being an order of magnitude smaller than trends for above normal P_M-A.

The information sharing capability is the highest priority feature for the disaster information system. Even if the system has excellent functions, without information sharing capability, it may not work in a serious disaster situation. However, by only information sharing, it is not enough to invoke effective disaster response activities, thus the information products to support the decision makers on the front-line of disaster response is needed. According to this concept, we have been developing the “Dynamic Decision-support System for Disaster Response (DDS4D)” since 2019. DDS4D has capabilities that are implementation of three technological elements; captures and stores dynamic disaster data in real time, analyzes disaster dynamics according to analysis scenarios defined by users and visualize results of analysis in real time. By having these capabilities, DDS4D can demonstrate the decision support for disaster response in the “real” disaster situation. It can be said that these features are fundamental functions of the “Disaster Digital Twin.” In this paper, we present the overview of DDS4D and the results of feasibility demonstration.

Air pollution is a severe environmental problem in China. Meteorological factors that directly affect the dispersion of air pollutants are closely related to the air quality index(AQI). Extensive literature describes the non-stationarities between meteorological factors and air quality by employing the geographically weighted regression (GWR) or geographically and temporally weighted regression (GTWR). However, most previous studies have ignored the case where the rate of numerical variations has spatial heterogeneity. To better reveal the potential spatiotemporal patterns of the impacts of some selected meteorological factors, such as ground pressure, relative humidity, temperature, wind speed, on air quality in China from 2013 to 2018, we utilized the spatiotemporal weighted regression (STWR) to explore the spatio-temporal non-stationary. Results show that the R-squared (R²) of the STWR model are higher than that of GWR and OLS especially when the rate of change of AQI is fast. Some spatial coefficient surfaces corresponding to different variables show obvious seasonal variations. Their spatial distribution also varies significantly over time. Some interesting findings are as follows: 1) The impacts of humidity on AQI are more obvious in northwestern China from April to June and July to September. 2) Before 2016, humidity had a positive effect on the AQI in most of the northwest, but since then there has been a negative effect. 3) In most southern China areas, the negative correlation between temperature and AQI tends to weaken, which may be caused by the gradual weakening of the temperature inversion phenomenon under climate warming.

We constructed a low-cost and easy-to-handle local-area RTK-GNSS positioning system which can automatically perform RTK positioning every second. The positioning was conducted at two reference stations to evaluate the accuracy and reliability of the real-time data: (1) at the university campus (ground fixed point) with obstacles such as buildings and trees, hindering a GNSS satellite signal reception and (2) at the open-sky rooftop of a 11-story school building (rooftop fixed point), where the reference station had been set up with only few obstacles to GNSS satellite signal reception. At the ground station, the network connection status, temperature, and relative humidity were also simultaneously measured. Consequently, 600 data were acquired in 10 min, and the data with the highest ratio were further selected for analysis. For the ground-based fixed points, the standard errors of all the data were >10 times more accurate than those of D-GNSS; however, such result is unsatisfactory, as previously anticipated for RTK positioning. We further filtered the data with less than 15 satellites and less than 9 ratio values and found that the data quality and reliability were improved to a satisfactory level. For the rooftop fixed point, the standard error of all the data was approximately 1 mm in both horizontal and vertical directions, thereby indicating higher accuracy than the data from the ground fixed point. Consequently, we obtained accurate and reliable data with less scatter. Overall, it is possible to obtain accurate and reliable RTK positioning data in real time using statistical processing to eliminate outliers.

A single geophysical method is limited in providing adequate model accuracy and resolution for inversion of subsurface structure. Using multi-geophysical techniques with different sensitivity to subsurface structures can complement each other and obtain more relevant results. We numerically perform joint inversion of electrical resistivity tomography (ERT) and seismic refraction tomography (SRT). The joint inversion approach is used to detect horizontally layered sedimentary units. The result shows unclear and considerably merged layer interfaces for separate inversion of the ERT and SRT. However, jointly inverted results exhibit relatively improved layer structures, reducing model ambiguity. In addition, the numerical experiment shows the adequacy of regularization selection on model resolution.

The Regional Advisory Committee (RAC) has been established in 2018 to promote the attendance and participation in the AOGS annual meetings by researchers and students from the ASEAN countries and India that constitute the largest population of the AOGS geographic area. In addition, RAC is interested in building research networks on scientific issues of common interest and importance in the Asian geoscience community. In spite of the COVID 19 pandemic, we have gradually introduced a number of projects that will form the building blocks of the RAC program in the coming years. These include the launch of new publications, the organization of working groups on the coastal zone risk mitigation and management and on the low-latitudinal ionospheric research, respectively. Furthermore, it is planned that a series of satellite meetings on topical areas of interest to the local scientific communities will be organized in coordination with the Scientific Sections in complement to the AOGS annual meetings.

Influenza virus has the characteristics of easy mutation, easy transmission and high morbidity, and has caused many disease outbreaks around the world. Influenza outbreaks are closely related to the environment and meteorology with obvious spatiotemporal heterogeneity. Little research has been done on the main drivers of influenza outbreaks and the extent of their effects in both spatial and temporal dimension. If the temporal characteristics of influenza data are ignored, this not only leads to insufficient extraction of spatiotemporal information, but also may lead to misinterpretation of the spread and prevalence of infectious diseases. Therefore, we employed the spatiotemporal weighted regression (STWR) model to explore the response mechanism of influenza on a certain spatiotemporal scale, and analyzed the relationships between the numbers of influenza at county level in Fuzhou and major meteorological factors such as temperature and humidity, social factors, and NDBI from 2013 to 2019. Results show that the main factor affecting the flu in Fuzhou is the large temperature difference between winter and spring caused by the subtropical monsoon climate. The high population and dense buildings in urban areas were also an important incentive for the rapid spread of influenza in Fuzhou.

The Yangtze River Delta is the most densely populated and economically developed region in China, where the interaction mechanism between the river and the sea has been a hotspot in recent years. As a key physical phenomenon of the Yangtze River Estuary (YRE), the salinity front has an important influence on salinity distribution and sediment transport. There are three major projects around the mouth in the YRE, the North Passage Deepwater Channel Project (NPDCP), the Hengsha East Shoal Reclamation Project (HESRP) and the Nanhui East Shoal Reclamation Project (NESRP), which have different impacts on salinity front due to the different locations and scales. In this study, a two dimensional hydrodynamic and salinity transport model of the YRE was established by MIKE21 to study the influence of the responses of the three major projects on the horizontal salinity front in the YRE. The model has been well validated by the measured data of tidal level, current speed and direction and salinity. The numerical results show that: (1) the salinity front in the North Channel (NC) is akin to a single-front pattern, however, the salinity fronts in the North Passage (NP), the South Passage (SP) and the North Branch (NB) almost have a double-front pattern. (2) the three joint projects can make the main salinity front of the NP and NB move downstream, and decrease main salinity front intensity of the NB, NP and SP.

In order to simulate the storm surge in the Bay of Bengal (BoB), the Wind-Wave-Water level coupling model is introduced in this paper. In this study, an Atmosphere-Wave-Hydrodynamic real-time coupled model (WRF + SWAN + FVCOM) is established using the Model Coupling Toolkit (MCT) coupler, and by this coupled model, the storm surge during tropical storms considering the wave radiation stress was calculated. The cyclone path, wave height, wind speed and water level of this coupled model was verified using the typhoon path of JTWC, the wave height and wind data of Jason satellite data and the site-measured water level data of several nearshore stations. Using this model, the storm surge caused by 38 Cyclones affecting the BoB from 1987 to 2018 was calculated, and the distribution of BoB extreme storm-surge of various return years was analyzed according to the calculation results. This paper also discussed the influence of wave radiation stress on storm surge for some severe cyclones. This study can guide the disaster prevention and engineering design of nearshore projects.

Fine scale eddies and circulation features in the ocean govern many small and large-scale processes, which get modulated due to small-scale horizontal variations in density. Some of these phenomena are responsible for nutrient upwelling, while some have implications on acoustic propagation. Three-dimensional observations at such a fine scale (∼ < 1 km) are hard to obtain in continuity from in-situ or satellite measurements. Numerical models of the ocean provide an opportunity to simulate the small-scale features of the oceanic circulation. In this study, we present a very high-resolution model for the ocean surrounding Vishakhapatnam (Vizag) region at the East Coast of India. The objective is to test and evaluate the configuration that has been prepared for this domain and then to nest/couple it within a larger/coarser domain model. We have made use of the Massachusetts Institute of Technology general circulation model (MITgcm) for this study. Open boundary conditions are prescribed from a regional ocean model for the Indian Ocean (IO) Region. The model is forced with ERA-interim winds and fluxes for a period of around 40 days. Ocean surface currents simulated from the model are compared with the Nucleus for European Modelling of the Ocean (NEMO) observations as well as from the outputs of the larger domain IO model

The upper ocean mixing in the Bay of Bengal (BoB) gets substantial influence from the local winds, huge fresh water influx and tidal forcing that makes the mixing more complex and dynamic. This study aims to understand the role of tidal forcing on upper ocean mixing in the BoB and how this mixing process changes in presence of river fresh water. Moreover, it is also seen that how this tidal forcing modulates the air-sea interactions in the region. The analysis includes three high resolution (1/12°) Regional Ocean Modelling System (ROMS) simulations, a control run, a tide run and a river-tide run. In the tide forcing simulations, the tidal components included are M2, S2, N2, K2, K1, O1, P1, Q1, Mf and Mm. Again, the river-tide simulation additionally has the discharge from the major rivers (Ganges, Brahmaputra, Irrawaddy, Godavari, Krishna, Mahanadi and Subarnarekha) in the region. The analyses show that the tidal buoyancy deepens the mixed layer increasing the integrated upper ocean temperature, while the surface temperature reduces. The upper ocean temperature profile further reduces in the tidal vertical mixing process in presence of river fresh water. The tidal mixing increases the surface salinity, whereas the addition of low saline river discharge largely reduces the surface and subsurface salinity. The warmer upper ocean layers in the tidal forcing simulation radiates more surface longwave, sensible and latent surface heat fluxes over the region. Therefore, the study shows that the tidal forcing has major influence in the upper ocean mixing as well as the air-sea fluxes over the BoB.

Probabilistic tsunami hazard and risk assessment (PTHA/PTRA) are vital tools for understanding tsunami risk and planning measures to mitigate impacts. At large-scales their use and scope are currently limited by the computational costs of numerically intensive simulations which are not always feasible without large computational resources like HPCs and may require reductions in resolution, number of scenarios modelled or use of simpler approximation schemes. To conduct the PTHA/PTRA for large proportions of a coast, we need therefore to develop concepts and algorithms for reducing the number of events simulated and more rapidly approximating the needed simulation results. This case study for a coastal region of Tohoku, Japan utilizes a limited number of tsunami simulations from submarine earthquakes along the subduction interface to generate a wave propagation and inundation database at different depths and fits these simulation results to a machine learning (ML) based variational autoencoder model to predict the intensity measure (water depth, velocity, etc.) of the tsunami at the location of interest. Such a hybrid ML-physical model can be further extended to compute the inundation for probabilistic tsunami hazard and risk onshore.

Non-point source microbial pollution damages the overall health of marine ecosystems. Beyond their direct effects, contaminating microbes can introduce antimicrobial resistance and virulence genes into resident species by horizontal gene transfer, a process that may be enhanced by co-pollutants such as heavy metals. In this work, a number of heavy metal- and antimicrobial-resistant (AMR) strains of E. coli were isolated from water and sediment samples collected in the Causeway Bay, Hong Kong (22.2833 N, 114.1847 E) and their complete genomes obtained by hybrid assembly using both Illumina MiSeq and Oxford Nanopore MinION sequencing platforms. Genomic analysis showed an abundance of plasmids and mobile elements bearing heavy metal- and AMR genes, such as the mer operon and tetR-tetA efflux system, with these features frequently co-located. In addition, NCBIBLAST–using isolates collected from in-land soil, freshwater, and animal faeces in Hong Kong, as well as the NCBI database – showed that these genes, while common amongst human and animal-derived Enterobacteriaceae, could also be shown present in a number of strains of obligate halophilic Vibrio spp.. Previous water chemistry analysis at this location showed high levels of heavy metal contamination (98 ppm Pb, 0.12 ppm Hg, and 0.4 ppm Cd), which has been shown to promote AMR transfer by selecting for co- resistance. Further isolation and characterisation of marine bacterial species from Hong Kong coastal waters will help to determine the extent of this process.

The effects of hydrodynamic and mixing conditions on the variation of phytoplankton composition and their habitats were investigated in the tide-dominated macrotidal Chikugo River estuary during a neap-spring tidal cycle in 2021. The estuary changed from stratified to well-mixed conditions during a neap-spring transition. The river discharge was < 60 m³s^-1 during the study period. Seawater intruded towards 17 km (upstream) during neap tide and until 16 km during spring tide. Surface suspended sediment concentration (SSC) was low during neap tide and maximum (∼400 mg/L) during spring tide corresponding with changes in mixing and an estuarine turbidity maximum (ETM) was developed between 8-12 km during spring tide. Marine habitats diatoms were the major group with 20-89% of total phytoplankton. During a neap-spring tidal cycle, the maximum abundance of freshwater green algae was found during neap tide while diatoms (both marine and freshwater habitats) was in two days after neap tide and freshwater blue-green algae was in intermediate tide in response to changes in mixing conditions. The distribution of marine habitat diatoms and freshwater blue-green algae were positively correlated with salinity whereas freshwater green algae and freshwater habitat diatoms were negatively correlated with salinity. This study concludes that saltwater intrusion and mixing conditions driven by tidal forcing mainly controlled the species composition and their habitats in the Chikugo River estuary.

Estuaries in developing countries have a strong impact on the livelihood of local people and the economic growth of the country through small-scale fisheries and farming. However, these estuaries are currently facing with a major problem of decline in fish stock due to the over-exploitation of fish resources. Additionally, the saltwater intrusion in the estuary will affect the freshwater supply and agriculture. A large amount of saltwater intrusion also causes tidal flooding and resulting in the coastal population homeless. The recent trend of changes in rainfall patterns associated with climate change can exacerbate the above issues. A deep understanding of the hydrodynamic processes in these estuaries is required for the better planning and management of their aquatic environment. Hence, the seasonal and tidal (neap-spring) variation of salinity intrusion and mixing conditions in the Tanintharyi River estuary (TRE), Myanmar was studied from 2017-2019. The study area was influenced by the monsoon-generated high river discharge from the Tanintharyi river (90% during the wet season) and high tidal ranges from the Andaman Sea. The results reveal that the salinity intrusion in the TRE was maximum during dry periods (>35km) and minimum (0.6-16km) during peak monsoon. The salinity intrusion during pre-monsoon and post-monsoon exhibits 17.4 km and 28.4 km, respectively. From a neap-spring tidal perspective, the mixing conditions in the TRE varied from partially mixed to well-mixed conditions in dry periods and from stratified to partially mixed conditions in peak monsoon. Therefore, the seasonal changes in rainfall patterns are a major influencing factor of salinity intrusion, and the combined effects of rainfall and tidal forces were responsible for mixing in the TRE. It was concluded that the narrower duration of monsoon periods with increased rainfall intensity patterns and strong tidal forces are responsible for the hydrodynamic changes in estuaries of Southeast Asian regions.

This study aims to predict the long-term morphological changes of the Chikugo River using river cross-sectional surveyed data from 1953-2020. The study area was divided into the estuary (0-23 km), middle stream (23-50 km) and upstream (50-64 km). The mean bed elevation of every cross-section in each surveyed year was calculated to analyze the long-term longitudinal elevation changes. Spatial (0-64km) and temporal (1953-2020) changes in channel shapes were found. In addition, three different morphological trends were found during the study period of 67 years. From 1953 to 1993, dredging, sand & gravel mining, and dam construction decreased the bed elevation of Chikugo River; 1-4 m for estuary, 2-4 m for middle stream and 1-3 m for upstream. From 1993-2009, elevation changes became less compared to the first period and elevation increased uniformly (about 0.5 m). From 2009-2020, non-uniform morphological changes were observed due to changes in river flow and sediment supply by climate change disasters. The disaster events not only increase the river flow but also carries more sediments from the watershed. Most of these sediments were deposited in the middle stream (50-64 km) increasing the bed elevation by about 1-1.5 m. The upstream (50-64 km) showed a quick and significant response to the type of disasters although elevation change was almost stable due to the net result of deposition by landslide disaster and erosion by the flood. However, the elevation of the estuary (8-23 km) decreased due to net erosion by high river flow and insufficient sediment supply from upstream while the elevation of 0-8 km increased slightly due to the deposition of eroded sediments from 8-23 km. Therefore, climate change disasters affect the morpho dynamic equilibrium of Chikugo River non-uniformly although elevation changes were less compared to that of human activities.

The interaction between saltwater and freshwater related to complex topography at the confluence of Arakawa and Sumida River estuaries were studied using a 3D hydrodynamic model Fantom Refined and analyzed the flow characteristics using particle tracking. The analysis results showed that the saltwater distribution in the Arakawa River was well-mixed during spring tide and partially mixed during neap tide. The results of the particle tracking revealed that most of the river water flowing from the upstream to downstream of Arakawa River flows into the mainstream Arakawa River itself instead of splitting into Sumida River. In other words, it flows into a wide river with wide and straight channel. When the particles are released from the Sumida River at downstream of the confluence during the flood tide, all the particles in the surface layer pass through the Iwabuchi gate and flow into the Arakawa, while about 80% of the particles in the bottom layer flow into the upstream of the Sumida River (known as Shingashi River). In addition, the surface layer is more affected by freshwater inflow in spring tide. Furthermore, the impact of freshwater inflow at neap tide was greater than at spring tide.

The productivity of an aquatic system is often measured in terms of Chlorophyll-a, which is supplemented by the nutrients like nitrogen and phosphorous. An excess amount of Chlorophyll-a is often caused as a consequence of nutrient enrichment, termed as eutrophication. However, the measurement of Chlorophyll-a is not included in the monitoring programs of the Pollution Control Board (Central and State) and hence assessing the eutrophication status of an estuary is difficult. This necessitates the prediction of Chlorophyll-a from its influential parameters that are monitored regularly. In this study, the possible influence of salinity on Chlorophyll-a was studied and an Artificial Neural Network (ANN) model for prediction of Chlorophyll-a using parameters like Total Phosphorous (TP), Turbidity and Salinity was developed based on data from Pollution Control Board (PCB). The proposed model was tested using the field data from Ashtamudi estuary and a hydrodynamically and geographically different estuarine system, the Chikugo estuary Japan. From the study, it was found that salinity has an influence on Chl-a. Also, the prediction model needs to be modified according to the unique characteristics of estuaries for better prediction. The developed model is envisaged to support monitoring agencies, planning agencies and decision makers in the assessment of eutrophication status of estuarine systems.

During the Cassini Mission, when the spacecraft crossed the D ring, it detected a magnetic perturbation of 15-25 nT. This doesn’t arise intrinsically but induced by external currents. Such an unexpected current system can be explained in terms of the dynamo mechanism originated from the difference in the zonal wind speed at the footpoints of the planetary magnetic field lines. In Khurana et al., 2018, the total current is accordingly estimated to be as large as ∼7 MA. In this study, we apply the same pattern of the zonal wind-driven currents at the other three outer planets, and find out that the maxima of Jupiter, Uranus and Neptune are 0.20 MA, 0.44 MA and 0.63 MA.

It is known that small-scale structures in the transit light curves of exoplanets can be used to infer the sizes and temperatures of dark spots or bright faculae of their host stars. Among many factors, the precision depends on the observational statistics, the ratio of the size of the exoplanet to that of the dark (or bright) spot and the cadence used in the light curve measurements. We have developed a numerical code allowing us to explore the effects and limitations of such a method for stars of different spectral types.

The non-thermal escape via the production of hot oxygen atoms from the electron recombination dissociation of the O₂⁺ molecules is known to be the most important atmospheric loss mechanism of Mars. The numerical modelling of such a dynamical process usually used to be done by assuming a static Martian ionosphere. In this study, we examine the effect caused by the flow field in the lower Martian ionosphere by coupling the standard collisional dynamics of the hot oxygen atoms to the MHD model of the Martian ionosphere according to Ma et al. 2004. We will compare hot oxygen escape rate with and without ionospheric flows.

At present, several meteor monitoring networks have been established worldwide. However, in China, the research on fireball monitoring is still unsystematic, and the monitoring network is in its initial stage. Jiangsu Regional All-sky Fireball Network (JRAFN), covering the entire region of Jiangsu Province and surrounding areas, consists of 10 stations with the baseline being 50∼110km. The video camera used is QHY5III485C with a 2.5mm/f1.6 fisheye lens to achieve a local all-sky field of view, detecting limiting magnitude -1 for meteors, and +3 for stellar, at 20 frames per second. All observation data are uploaded to the central server at the Purple Mountain Observatory (PMO). Since September 2021, hundreds of meteors and several fireball events were recorded, including a super-bright fireball that occurred in Henan Province. The main purpose of this monitoring network is to perform long-term meteor and fireball surveillance, and to obtain the radiation, flux and size distribution of fireballs. We also aim to determine the fall point of the meteorites and recover them by calculating the trajectories and orbits of the fireballs, from single-station system extended to a future nationwide fireball monitoring network of China.

The β-meteoroids are high-speed submicron dust particles that come from the near-sun region. After breaking up from larger bodies as a result of the collisional process, or sublimation for smaller sizes, these small grains will be accelerated radially forward by solar radiation. Their detectability has been testified by measurements with instruments on multiple spacecraft. Among these spacecraft, Solar Orbiter (SolO) will be the first spacecraft to explore the high-altitude dust distribution with its high inclination orbits in the later phase of the mission. In this work, we examined the 3D structure of the near-solar dust zone with a simple model based on the Parker Solar Probe (PSP) observations and the theoretical dynamics of β-meteoroids governed by gravity-to-radiation ratio β = 0.7. The results show a distribution of β-meteoroid fluxes restricted to ± 36.7° out of the ecliptic plane because of the assumed orbital inclination of grains in the F-corona and demonstrate the impact rates of the first six PSP orbits qualitatively consistent with the observations. Despite further calibrations with the size distribution of the near-solar dust population and the collisional production rates of the sub-micron dust grains being needed, we expect future studies of the physical properties of grains will improve our model.

A sample set of stratigraphic structure model airborne transient electromagnetic responses is established, the sample label is attached by unsupervised learning clustering technology, and the multilayer perceptron deep learning network with supervised learning is used to complete multiclassification tasks. Then, the sample set is input into the network for training to establish the inversion from the input response data to the output formation model. Verification results show that the prediction results are consistent with the types of sample stratigraphic models, which proves that the inversion method designed in this paper is correct, and efficient inversion from the test data to the prediction model is realized.

In early 2000-s the amplitude of the Chandler wobble (CW) started to decrease and in 2017-2020 reached its historic minima, comparable only with the minima of ∼1928 yr. In 2021 the CW appeared again. We demonstrate that the phase of the CW is changing now, as during 1920-1930s. Since 2010 it has changed almost by 2 radians, and we expect it will finally reach π radians. This implies splitting of the CW spectra into two pikes. Since 2016 the length of day LOD is decreasing and Earth’s rotation velocity is growing. In 2022 it is the highest over the last 90 years. We hypothesize that the anomalies of the CW are interrelated with LOD decrease through some synchronization and/or energy transfer?

The seismic hazard function analysis around the off coast of Java Island is investigated based on the changes of the b-value using the shallow crustal earthquake catalog data from 1963 to 2016. The study areas took place around M7.8 in 1994 and M7.7 in 2006. The change of the b-value is estimated using the maximum likelihood method with a constant number. First, the b-value surrounding the center area of interest with a radius of about 150 km is calculated based on the earthquake catalog data from 1963 to 2016 (b50). Second, the b-value based on five years with a one-year moving window (b5) is estimated before M7.8 in 1994 and M7.7 in 2006. The b5 is calculated based on the constant number of events of 25, 50, 75, and 100, and we evaluate the mean b-value. Furthermore, the SHF of b50 and b5 are calculated, and then they are compared. The results showed that the Probability of Exceedance (PE) of SHF b5 increased by about five years before the two large earthquake events. Therefore, the results obtained in this study might be very beneficial for earthquake mitigation and modeling efforts for the possible potential of the earthquake hazard study and future analysis.

Tropical karst is highly sensitive to surface and subsurface changes, with natural and anthropogenic factors contributing to its potential degradation and overexploitation. Thus, the appropriate management and protection of karst environments are needed. This study aims to determine the anthropogenic-driven changes in the karst landscape and groundwater resources of El Nido, Palawan Province. Interferometric Synthetic Aperture Radar (IfSAR), LandSat 8, and Google Earth imagery were used for pre-field geomorphological and land cover delineation. Semi-detailed stratigraphic surveys and rock sampling were conducted. In-situ water quality testing and sampling were done to obtain physicochemical parameters such as pH, conductivity, and total dissolved solids. Focusing on preliminary findings from El Nido, petrographic characterization and microfossil age dating have shown that the limestone is composed of Middle Permian faunal assemblage. Geomorphological analysis shows that the area is dominated by karst towers, remnant valleys, sinkholes, and caves. Georesistivity surveys reveal that the water-saturated layer becomes thicker in the extensive floodplains of Villa Libertad, Dewil Valley, and Villa Paz. In contrast, the town center has a thin and permeable water-saturated layer that is approximately 2-3 m thick. Fecal coliform, nitrates, and sulfates are relatively higher in groundwater collected from karst areas in the urban and tourism center.

We compared Pn and Sn velocities of major cratons of the world to provide constraints on how much these cratons have been modified from their initial states. In particular, we employed pseudowave (PSn) velocity computed from Sn-Pn traveltimes to estimate Vp/Vs ratio. We sorted the Pn, Sn, and PSn traveltime data into two groups based on epicentral distance: (1) 2°-12° and (2) 2°-7°. Our results suggest that most cratonic keels show comparable seismic properties with high velocities and low Vp/Vs ratio, implying a highly depleted mantle lithosphere. One exception is the Eastern North China Craton (ENCC). The mantle lithosphere beneath the ENCC has the lowest P- and S-wave velocities, as well as distinctly high Vp/Vs ratios in both distance groups, suggesting a complete removal of the depleted Archean mantle lithosphere. The Wyoming Craton also has a high Vp/Vs ratio close to that of the ENCC but a high P-wave velocity comparable to that of a typical craton, suggesting that its mantle keel has been significantly modified by metasomatism, rather than been completely removed and replaced by a thermal lithosphere. The Ordos Block located in the western part of the North China Craton (WNCC) displays high velocities and low Vp/Vs ratio, similar to those of the typical cratons, suggesting that the Ordos Block maintained its Archean mantle lithosphere. The seismic properties of the Trans-North China Orogen (TNCO), located between the WNCC and ENCC, show transitional values between those of the ENCC and the Ordos Block, suggesting an intermediate mantle lithosphere state between that of the ENCC (fertile Early Cretaceous) and the Ordos Block (depleted Archean).

MHD (Magneto-Hydro-Dynamic) linear theory of magnetic reconnection process is studied for fourth-order differential magnetic diffusion effects which may be called hyper-resistivity. In general, the second-order magnetic diffusion, i.e., resistivity, is employed to drive the reconnection process. In this paper, the fourth-order diffusion is examined and compared with the second-order diffusion. In fact, the importance of such a higher-order diffusion is predicted from plasma kinetic particle simulations of reconnection process. Rather than the second-order diffusion, higher-order diffusion may be important to achieve the fast magnetic reconnection. In this paper, firstly, the equilibrium is numerically derived for the magnetic annihilation process in a 1D current sheet by an initial value problem (shooting) technique. It is shown that the equilibrium established by mixing those two types of the magnetic diffusion is simply dominated by the ratio of two Lundquist numbers defined for each magnetic diffusion. Second, on the basis of the equilibrium, the linear growth rate of the tearing instability is studied as an initial value problem technique. As the remarkable point, it is shown that, the linear growth rate is determined by two dimensionless parameters, i.e., the aspect ratio εof the magnetic diffusion region and the ratio of two Lundquist numbers, in addition to the wave length k and upstream boundary condition c.

Coronal mass ejections (CMEs) are large-scale magnetic flux rope that carries large amount of plasma and energy from the Sun to the heliosphere. They can significantly disturb the space weather and disrupt space activities. Therefore, it is important to be able to accurately predict their arrival time. Many prediction studies are based on the commonly believed assumption that the drag force is the dominant force acting on the CMEs and all other forces are negligible. However, there has been no observational evidence to support this assumption. In this study, we apply erupting flux rope (EFR) model, which is a model that includes all force components, to model observed CME trajectories. The results show that the drag force is often not the dominant force and that no force can be assumed negligible.

The following section is included:

• AUTHOR INDEX