Flood Conditions Research Articles

Aggravation of Cd availability in the plastisphere of paddy soil

Soil plastisphere has attracted many concerns, however, its influence on cadmium (Cd) availability in paddy soil was still unclear. This study carried out batch microcosmic and bagging experiments to explore the influence of microplastic (MPs) on Cd availability in paddy soil under flooding conditions in the view of plastisphere. Results showed that the presence of MPs could act as plastisphere micro-environment. The bacterial community composition changed dramatically around the plastisphere compared with MPs-contaminated bulk soil and control soil. The relative abundance of Symbiobacteraceae, Rhodocyclaceae and Bryobacteraceae was improved in the plastisphere which contributed to the enhanced the reduction of Fe(III) and sulfate in flooding paddy soil. The higher content of Fe(II) and S content contributed to the enrichment of Cd in the plastisphere which aggravated Cd availability in paddy soil under flooding conditions. The partial least squares structure equation modeling results confirmed the presence of MPs in paddy soil could act as plastisphere which could change the bacterial community composition and improve the content Fe and S that was conductive to gather Cd in plastisphere. This study shed lights on the understanding of the role of plastisphere on Cd availability in paddy field ecosystem under flooding conditions.

Analysis of Flood Resistance of Masonry Arch Bridges Combining Numerical Simulation and Probabilistic Analysis

ABSTRACT In the context of global climate change, extreme weather events such as heavy rainfall and floods are becoming more frequent. The safety of masonry arch bridges is facing unprecedented challenges. This study aims to explore the collapse mechanism of bridges in flood environments by combining numerical simulation and stochastic analysis. Taking the collapse of the Original Zhenhai Bridge caused by heavy rainfall in July 2020 as a specific case, a three-dimensional numerical model is established to analyze the mechanical response of the bridge under the combined action of flood scour and water flow loads. Additionally, predictions of scour depth are made using both theoretical formulas and stochastic methods, comparing the impact of stochastic analysis on the mechanical performance of arch bridges under the same flood conditions. The study reveals that compared to pure scour, the structural characteristics of the bridge undergo significant changes under the coupled effects of scour and water flow loads. In extreme flood events, stochastic analysis has a significant impact on the structural safety under high flow conditions. The research results provide case references and scientific methods for the safety analysis of masonry arch bridges in flood environments.

Mitigating arsenic accumulation in rice plant in paddy soil: influence of persulphate and ferrous application

ABSTRACT Rice cultivation under flooded conditions usually leads to a high accumulation of arsenic (As) in grains. Sulphur and iron played vital roles in affecting the bioavailability of As in the soil-rice system. Herein, using pot experiments, we investigated the effects of persulphate (PS) and ferrous (Fe2+) on the transfer and accumulation of As in the soil-rice system under flooded conditions. The concentration of As and Fe in soil porewater declined with continuous flooding. Persulphate/ferrous addition significantly inhibited the formation of iron plaque and the transfer of As to the aboveground tissues of rice. The total As, dimethylarsinicacid (DMA), As (III), and As (V) in grains significantly decreased by 49∼75%, 60∼89%, 20∼24%, and 35∼36%, respectively, by persulphate/ferrous application. Furthermore, a decrease of As in husk, leaf, and, stem was also found in persulphate and ferrous treatment. To some degree, the Fe2+ can facilitate the decreased efficiency of As accumulation and translocation in rice tissue. The present study’s results demonstrated that applying persulphate/Fe2+ could effectively alleviate the excessive accumulation of As in rice grains in the soil-rice system under flooding conditions.

Cascading Failure and Resilience of Urban Rail Transit Stations under Flood Conditions: A Case Study of Shanghai Metro

Cascading Failure and Resilience of Urban Rail Transit Stations under Flood Conditions: A Case Study of Shanghai Metro

Geospatial Modelling of Flood Susceptibility of the Calabar City, Cross River State, Nigeria

Flooding remains a major environmental problem in many parts of the world including Nigeria, causing untold losses. This study modelled the flood susceptibility of Calabar Metropolis using frequency ratio and geographical information system (GIS). Direct field observation and key informant approaches were used to obtain 127 previous and current flood locations in the study area from which a database of historical flood occurrence was developed. Using the geostatistical analyst tool within a geographical information environment the flood locations were split into two parts in the ratio of 70:30 percent for the training and testing processes, respectively. Eight flood conditioning factors (elevation, slope, aspect, curvature topographic position index, topographic wetness index, land cover and normalized difference vegetation index) were extracted from SRTM-30m DEM and Landsat 8 data accordingly and used in the geospatial analysis of flood occurrence within the GIS. The frequency ratio model was then developed using the training dataset. The final product was the flood susceptibility map of the study area. The results revealed that 9.3 percent of the study area was considered to have very high flood susceptibility, 19.97 percent was classified as high flood susceptibility while 31.79 percent was under moderate risk area. About 39 percent of the study area was classified at least under low flood susceptibility. The order of contribution of the conditioning factors to the model were topographic position index (TPI), elevation, normalized difference vegetation index (NDVI), land cover, curvature, aspect, slope, and topographic wetness index (TWI). The validation of the model using the receiver operating characteristics (ROC) curve returned an AUC value of 74.81 percent. Hence, the result was acceptable for the prediction of flood locations in the study area. The findings would assist urban planners, environmental managers, risk reduction and management agencies, land developers, policy makers, and indeed the general public.

A Systematic Scoping Review of the Collaborative Governance of Environmental and Cultural Flows

This study systematically reviews English-language papers about the collaborative governance of environmental and cultural flows. With mixed-methods analyses, we illustrate that the determination of environmental flow needs is common, with authors in 42 countries across 112 watersheds describing their management. In contrast, cultural flows (characterized by attention to both ecological and non-ecological needs, decision-making authority of Indigenous Nations, and Indigenous rights) were reported only in papers by authors in commonwealth, colonial countries: Australia, Canada, and New Zealand. Evaluated against the Organisation for Economic Co-operation and Development’s (OECD) Water Governance Principles, we found that the literature reported efforts that considered appropriate local and regional scales, information and data, regulatory frameworks, and capacity building of communities and authorities engaged in environmental and cultural flow initiatives. However, there was limited consideration of the roles of communities in policymaking, which was more common in jurisdictions with decentralized governance. In jurisdictions with democratic community-based initiatives, environmental and cultural flows have not been approached in merely technical processes to communicate hydro-social-ecological information to decision-makers. Instead, the initiatives have created the context for evaluating new developments at the watershed level in light of communities’ social and ecological water goals, collaborating during unique drought and flooding conditions, working to rebalance power in decision-making through water justice, creating ecological and Indigenous reserved water rights, granting legal personhood for rivers, and protecting water for the environment and dependent people in water markets. Going forward, we identify a need for greater attention to community roles in environmental and cultural flows protection in water governance including policy creation and evaluation, regulatory initiatives, strategic planning, and impact assessments.

Optimization of phosphorus and potassium use efficiency of rice (Oryza sativa L) under different flood depths within the Everglades Agricultural Area

Rice cultivation in South Florida, especially within crop rotation systems, presents a promising avenue for mitigating phosphorus (P) levels in drainage water by optimizing nutrient absorption across varying flooding conditions. This study aimed to assess the impacts of different flooding depths on rice growth and nutrient efficiency while exploring the interrelationships among these factors. Conducted over a two-year period, the field study involved planting a single rice variety (Diamond: 95 kg ha−1, long-grain, short-season) at four distinct flooding depths (5, 10, 15, and 20 cm) without fertilizer application. Results revealed minimal effects of flooding depths on root and shoot length or dry matter accumulation. Additionally, concentrations of total P and potassium (K) in both shoot and root tissues remained consistent across the different flooding depths. Despite the absence of P and K fertilizer inputs, no significant changes were observed in P use efficiency (PUE), K use efficiency (KUE), or water use efficiency (WUE). Cultivating rice under optimal flood depths of 5 cm within the Everglades Agricultural Area (EAA) demonstrated potential for conserving water and essential nutrients such as P and K. These findings highlight the feasibility of rice cultivation in fertile soils as a means to decrease nutrient (P and K) concentrations in drainage water, thereby promoting sustainable agricultural practices in the region.

Flood risk awareness and transportation mode choice in communities with varying flooding frequencies: The cases of Bozkurt, Kastamonu, Turkey and Malanday, Marikina, Philippines

This study investigates the post-flood transportation mode choices (TMC) of communities exposed to floods of varying frequencies, focusing on Bozkurt, Turkey, recently afflicted by a major flood, and Malanday, Philippines, which endures an average of 10 floods annually. The study considers 17 factors affecting TMC, including age, occupation, income, housing type, flood experience, education level, changes in travel time, primary transportation mode, duration of residence, gender, marital status, relocation status, personal losses, disruption of daily activities, time to return to routine, satisfaction with road maintenance, and satisfaction with flood measures. Data were collected through surveys and analysed using regression analysis.The findings indicate that personal and cultural characteristics, and flood frequency, have limited effects on TMC. The most impactful factors on post-flood TMC are changes in travel time (42.11 %), personal losses (23.75 %), and satisfaction with road maintenance (11.30 %). The study demonstrates that flood risk awareness is directly related to education level and flood experience. In Malanday, where 84 % of the population are university or high school graduates, floods are regarded as routine events. In contrast, in Bozkurt, where only 37 % have similar education levels, the community experienced panic under flood conditions, adversely affecting decision-making processes.The research provides insights into how communities exposed to floods adjust their TMC, offering essential information for mitigating flood risks and increasing flood awareness. Moreover, the findings deliver suggestive information for local governments and planners in developing more effective flood management and transportation planning strategies.

Karakteristik Hidraulik Banjir Bendung Karet Sungai Juwana Kabupaten Pati

Pati Regency, Central Java Province is an area that frequently experiences floods, including tidal floods and flash floods during the rainy season. The Pemali-Juana River Basin Center has planned the construction of a rubber weir which will be built in 2022-2024 to overcome this problem. It is hoped that this rubber weir will be a solution for controlling floods and as a water storage system during the dry season. This research aims to model the hydraulic characteristics of the flow in Karet Juwana Dam under flood conditions (Q10, 20, 25, 50, and 100 years). Modeling of flow hydraulic characteristics using HEC-RAS 5.0.3 software. The modeling results show that when the rubber weir expands, maximum backwater occurs for 2.26 km upstream of the weir. The water level above the rubber weir is 1.0 meters with flow speeds upstream and downstream of the weir threshold of 0.40 m/s and 0.66 m/s respectively. Controlling flood levels against overtopping can be optimal if the water level is 0.60 meters above the crest. Hydraulically, the simulation results show that a deflation speed of 0.2 meters/minute can control overtopping optimally.

Optimized ensemble-based flood hazard mapping in low altitude subtropical riverine terrane

Among various natural hazards, those induced by hydrological and meteorological factors have emerged as the most frequent and damaging. India is at the second from the top, after Bangladesh, of flood hazard-affected countries of the world, causing immense damage to life and property every year. The Middle Ganga Plain in the country is one of the most severely affected regions due to the convergence of Himalayan rivers in the Ganga (Ganges) plain. This study focuses on optimizing novel ensemble models to enhance the mapping of flood hazard susceptibility in specific topoclimatic setting, characterized by a low-altitude-range, sub-tropical monsoonal climate, and a riverine floodplain environment of the tectonically active Middle Ganga Plain. This region forms part of the Ganga Foreland Basin prone to frequent floods. We employ a comprehensive flood inventory and twelve selected flood conditioning factors and is the first study that has developed these ensemble models in low relief riverine floodplain environmental setting with high resolution geomorphology as one of the conditioning factors. Four artificial intelligence ensemble models were employed in this work: Logistic Regression-Evidential Belief Function (LR-EBF), Logistic Regression-Frequency Ratio (LR-FR), Multi-Layer Perceptron-Evidential Belief Function (MLP-EBF), and Multi-Layer Perceptron-Frequency Ratio (MLP-FR). Results showed that LR-based ensembles outperformed MLP-based ensembles in the studied topoclimatic setting, with LR-EBF model achieving 87.2% and 84.7% success and prediction rates respectively. Our results reveal consistent performance differences between LR-based ensembles with FR and EBF models across different environmental settings. MLP-based ensembles, particularly with Frequency Ratio, displayed more significant performance variations. The findings underscore the importance of considering specific topoclimatic and altitudinal range environments when applying flood hazard susceptibility models. Our study also provides valuable insights for planning and policymaking in flood management practices in similar topoclimatic settings worldwide.

Study on the pathways of thermochemical sulfate reduction between sulfates and alkanes at low temperatures

To reduce the energy consumption of heavy oil thermal recovery, low-temperature hot water flooding is becoming popular. The occurrence of thermochemical sulfate reduction (TSR) reactions during the hot water flooding process generates H2S, which can corrode equipment and jeopardize personnel safety. Preventing and managing H2S requires studying TSR reactions. The study investigated the reactivity of SO42-, HSO4-, and [MgSO4]CIP in the TSR reaction during heavy oil low-temperature hot water flooding. The results indicated that during low-temperature hot water flooding, no significant thermal cracking of heavy oil occurs, only a minor aquathermolysis takes place, while the TSR reaction proceeds vigorously. The TSR reaction leads to increased yields of H2S, CH4, and CO2, as well as a higher S content in the heavy oil. Based on experimental results, numerical simulations of the reaction process between SO42- and CH4 were conducted using density functional theory and transition state theory. The results indicated that the reduction of SO42- to produce SO32- is the rate-limiting step in the entire reaction pathway between SO42- and CH4. Further analysis of the reaction barriers and chemical reaction rates of the three sulfates (SO42-, HSO4-, and [MgSO4]CIP) and C1-C5 alkanes was conducted based on the limiting step. Through macro and micro analysis, this study not only confirmed previous findings regarding the order of alkanes participating in TSR reactions from the perspective of microscopic molecular but also clarified the oxidation order of sulfate species under heavy oil low-temperature thermal water flooding conditions, with the sequence being HSO4- > [MgSO4]CIP>SO42-. This study provides a foundational understanding of TSR reactions during heavy oil low-temperature hot water flooding and is of significance for the prediction and management of H2S.

GSM Based Canal Gate and Flood Monitoring and Control System

Technology based modern Irrigation systems are the recent requirement in every part of world today. The irrigation water is being used for agricultural, industrial and domestic purposes. Owing to mismanagement and inequitable distribution of water, it is necessary to have a fool-proof system where water is supplied to the end-users judiciously. Due to the variable atmospheric circumstances these conditions sometimes may vary from place to place, which makes very difficult to operate the canal gate manually and instantly. Therefore, we proposed. The “GSM based Canal gate and Flood monitoring and control system’’ canal gates are monitored and controlled after sensing the water level and flow speed. In our project the water level and flow will be calculated automatically and send towards the operator through SMS and the Canal Gate is operated according to the collected data, this system also Announce flood in case of high level of water and speed of water in River or canals by the help of Alarming as well as lightning tower with green, yellow and red lights indication of normal, intermediate and flood conditions respectively.

Flood risk decomposed: Optimized machine learning hazard mapping and multi-criteria vulnerability analysis in the city of Zaio, Morocco

Urban flood risk mapping has become crucial for effective mitigation and urban planning. This study assesses and maps flood risk in the city of Zaio, Morocco, using machine learning and Multi-Criteria Decision Analysis (MCDA) techniques to overcome data scarcity challenges. We employed the Random Forest (RF) model with nine flood conditioning factors for flood hazard and the Analytical Hierarchy Process (AHP) for vulnerability assessment. To enhance RF model performance, we compared three hyperparameter tuning techniques: Bayesian Optimization (RF-BO), Genetic Algorithm (RF-GA), and Grid Search (RF-GS). RF-BO demonstrated superior accuracy in flood hazard modelling. Flood vulnerability was assessed using AHP, incorporating social and physical factors. The final flood risk map was produced by combining the RF-BO hazard model with the AHP vulnerability assessment. Results indicate that flood hazard in Zaio is significantly affected by factors such as topography and distance to rivers. Moreover, areas with high population density closer to rivers, especially in the south-western residential area, were found to be more exposed to flood risk. The findings highlight the utility of ML models, MCDA, and hyperparameter optimization in urban flood risk mapping, enabling the identification of high-risk urban areas that should be prioritized for flood protection efforts. This approach proves especially valuable in ungauged regions with limited hydrological data.

CO2-Induced In-Situ Targeted Precipitation of Asphaltene in Heavy Oil Reservoirs: Balancing Formation Gas Channeling Regulation and Wellbore Asphaltene Blockage Prevention

Summary To investigate the mechanisms of the asphaltene precipitation in oil caused by CO2, the sandstone core oil displacement experiments and asphaltene structure observation experiments are designed in this work. The oil displacement experiments create CO2 flooding conditions under different pressures in heavy oil reservoirs and analyze the produced oil components and precipitated asphaltene proportions. Meanwhile, nuclear magnetic resonance (NMR) analysis is conducted on the sandstone cores to discuss the precipitation characteristics of asphaltene in the reservoir pores. The observation experiments analyze the microstructure of precipitated asphaltene after interactions between oil and CO2. The results show that the increasing pressure promotes the precipitation of asphaltene from oil by enhancing the dissolution and component extraction of CO2 in oil, which reduces oil viscosity and promotes reservoir development efficiency. This process also leads to an increase in CO2 sequestration in the reservoir. However, the precipitated asphaltene reduces reservoir permeability, hindering the optimization of the oil recovery rate. During the process of increasing pressure, the rate of increase in oil recovery decreases. In reservoirs containing oil with high asphaltene proportion, the oil recovery rate even decreases under high pressure. Additionally, in-situ targeted precipitation and retention of asphaltenes in large pores can reduce the distribution differences of pores with different sizes in the reservoir, weakening the above negative effects and enhancing oil recovery by regulating gas channeling. Moreover, the ratio of resin in oil affects the asphaltene precipitation form, and CO2 can promote the association of asphaltenes by weakening the steric stabilization effect of resin on asphaltene in oil, which makes the microstructure of precipitated asphaltenes dense and regular and promotes asphaltene precipitation and oil recovery increasing. This work aims to verify the advantages of CO2-induced asphaltene precipitation in improving the efficient and environmentally friendly development of heavy oil reservoirs, while exploring the significance of CO2 flooding in promoting carbon sequestration.

Flood sediments as a source of replenishment of alluvial soils with plant nutrition elements

Flood sedimentation in conditions of regular floodplain flooding is a key factor in the formation of alluvial soil. The nature of the passage of hollow waters and their composition affect the quantitative and qualitative characteristics of flood sediments, while having a significant impact on the agroecological state of the floodplain agricultural landscape. The aim of the research was to study the role of flood sediments in enriching the underlying alluvial soil with nutrients. The experiment was conducted on reclaimed arable lands of the Ryazan region in 2023. Determination of the sediment load level and sampling on the surface were carried out using plastic sedimentation samplers fixed on the soil surface, located at a distance of 200 m from each other. The installation was carried out on March 23, the duration of sediment collection was 42 days before the descent of the hollow waters. The average sediment load level of 15.4 t/ha was established, the agrochemical properties of flood sediments and underlying soil were studied, the structure of organic matter, macro– and microelements with flood sediments and their removal from the corn harvest to silage was analyzed. Together with sediments the soil received (kg/ha): total nitrogen – 107.80, total phosphorus – 43.10, total potassium – 104.70, organic matter – 2464,0, mobile phosphorus 15.20, exchangeable potassium 17.80; trace elements (g/ha): boron – 11.86, molybdenum – 1.54, zinc – 122.74, manganese – 1215.06, copper – 200.20, cobalt – 52.05. With a corn harvest for silage of 36.1 kg/ha, the takeaway was: N – 105 kg/ha, P2O5 – 33 kg/ha, K2O – 105 kg/ha. The conducted studies indicate the undoubted agronomic value of flood sediments, they can partially meet the need for fertilizers, therefore, it is necessary to take into account the mass of the substances used to carry out appropriate adjustments to the fertilizer application system. In 2023, the mass of incoming substances with flood sediments completely covered the need for nitrogen; for mobile phosphorus the need decreased to 17.8 kg/ha, for exchangeable potassium the need decreased to 87.2 kg/ha.

Exploring climate shifts in the Ganga-Brahmaputra basin based on rainfall and temperature variability.

Climate change has a significant impact on the Ganga-Brahmaputra (GB) basin, the major food belt of India, which frequently experiences flooding and varied incidences of drought. The current study examines the changing trend of rainfall and temperature in the GB basin over a period of 30years to identify areas at risk with an emphasis on the Paris Agreement's mandate to keep increasing temperatures below 2°C. The maximum temperature anomaly in the middle Ganga plains recorded an increase of more than 1.5°Cyear-1 in 1999, 2005, and 2009. Some extreme events were observed in the Brahmaputra basin during 1999, 2009, and 2010, where a prominent temperature increase of 1.5°Cyear-1 was observed. The minimum temperature revealed an increasing trend for the G-B basin with an anomalous increase of 0.04 to 0.06°Cyear-1. The rainfall variability across the Ganga basin shows a rising tendency over the lower Ganga region while the Brahmaputra basin showed a downward trend. To identify the statistical relation between the Global climatic oscillations and regional climate, Standardized Precipitation Index (SPI) and Niño 3.4 were used. The wet and dry period estimation shows a rise in flood conditions in the Ganga basin whereas, in the Brahmaputra basin, an increase in drought frequency was observed. The correlation based on Niño 3.4 and SPI3 presents a negative relation for the monsoon season in the G-B basin revealing a situation of drought occurrence (SPI3 below 0) with increased Nino 3.4 values (El Niño above + 0.4C).

Deep Learning-Based Flood Detection for Bridge Monitoring Using Accelerometer Data

Flooding and consequential scouring are the primary causes of bridge failures, making the detection of such events crucial for structural safety. This study investigates the characteristics of accelerometer data from bridge pier vibrations and proposes a flood detection method with deep learning-based models based on ResNet18 and 1D Convolution architectures. These models were comprehensively evaluated for (1) detecting vehicles passing on bridges and (2) detecting flood events based on axis-specific accelerometer data under various traffic conditions. Continuous Wavelet Transform (CWT) was employed to convert the accelerometer data into richer time-frequency representations, enhancing the detection of passing vehicles. Notably, when vehicles are passing over bridges, the vertical direction exhibits a magnified and more sustained energy distribution across a wider frequency range. Additionally, under flooding conditions, time-frequency representations from the bridge direction reveal a significant increase in energy intensity and continuity compared with non-flooding conditions. For detection of vehicles passing, ResNet18 outperformed the 1D Convolution model, achieving an accuracy of 97.2% compared with 91.4%. For flood detection without vehicles passing, the two models performed similarly well, with accuracies of 97.3% and 98.3%, respectively. However, in scenarios with vehicles passing, the 1D Convolution model excelled, achieving an accuracy of 98.6%, significantly higher than that of ResNet18 (81.6%). This suggests that high-frequency signals, such as vertical vibrations induced by passing vehicles, are better captured by more complex representations (CWT) and models (e.g., ResNet18), while relatively low-frequency signals, such as longitudinal vibrations caused by flooding, can be effectively captured by simpler 1D Convolution over the original signals. Consequentially, the two model types are deployed in a pipeline where the ResNet18 model is used for classifying whether vehicles are passing the bridge, followed by two 1D Convolution models: one trained for detecting flood events under vehicles-passing conditions and the other trained for detecting flood events under no-vehicles-passing conditions. This hierarchical approach provides a robust framework for real-time monitoring of bridge response to vehicle passing and timely warning of flood events, enhancing the potential to reduce bridge collapses and improve public safety.

Urban pluvial flood susceptibility mapping based on a novel explainable machine learning model with synchronous enhancement of fitting capability and explainability

Urban pluvial flood susceptibility mapping aims to identify flood-prone areas in cities. Machine learning methods are increasingly used, but typically face a trade-off between accurate identification of flood-susceptible areas and development of effective local mitigation strategies. To synchronously enhance fitting capability and explainability, this study introduced a novel machine learning model, Residual-Logistic Regression (RES-LR). RES-LR is developed by integrating Artificial Neural Network (ANN) and Logistic Regression (LR). The model adopts ANN to form its explainability module, which increases model complexity to ensuring the model’s fitting ability. Meanwhile, the explainability module was incorporated into the LR structure through residual connections to provide parametric expressions for local feature contributions, enabling the identification of key flood conditioning factor by site. The model was applied to the Beijing Metropolitan Area, achieving an accuracy rate exceeding 80% in identifying flooded sites. Based on its local explainability, the model unveiled the diverse flood formation mechanism in the study area and the complex impact of drainage-related factors. These results were informative for formulating site-specific flood mitigation measure. This study represents a significant advancement in the application of explainable machine learning for systematic flood management.

Transfer of antibiotic resistance genes from soil to rice in paddy field

The global spread and distribution of antibiotic resistance genes (ARGs) has received much attention whereas knowledge about the transmission of ARGs from one matrix to another is still insufficient. In this study, the paddy fields fertilized with chemical fertilizer, swine compost, and no fertilizer were investigated to assess the transfer of ARGs from soil to rice. Soil and plant samples were collected at day 0, 7, 30 and 79 representing various stages of paddy growth. High throughput qPCR was applied to quantify ARGs using a set of 144 primers. Gene copy number of ARGs measured in soil initially decreased and then increased in soil with no fertilizer and chemical fertilizer, indicating that crop planting and flooding conditions did influence the ARGs profiles in soil. Application of swine compost significantly enhanced the relative abundance and gene copy number of ARGs in paddy soil. Rice seedlings contained substantial amount of ARGs and their relative abundance continually decreased after transplant. Compared with initial stage, detection frequencies of ARGs increased in soil without swine compost at harvest time (day 79), indicating the transmission of ARGs from irrigation water to soil. Detection frequencies of ARGs increased in soil and rice root with swine compost at harvest time, indicating the transfer of ARGs from swine compost to soil and rice root. There was no significant difference in abundance and diversity of ARGs in rice grains with these three different fertilizations. The source of the ARGs in rice grain still needs further exploration.

Adaptation measures to alleviate degradation of urban mobility by urban flooding in Ubon Ratchathani, Thailand

Since floods caused by overflowing rivers are increasing in Southeast Asia cities, reducing urban mobility, adaptation measures are required to cope with this situation. One possible measure would be to develop transportation facilities to make them passable even during flooding conditions. Another measure is to encourage people to relocate from high-risk areas to low-risk areas. Implementing an appropriate combination of these two different adaptation measures is necessary. However, there is limited research to assess the impacts of adaptation measures on flood risk and urban activity. Therefore, this study developed an urban adaptation model that could assess mobility and land use for Ubon Ratchathani, Thailand, and analyzed to what extent the decrease in urban mobility can be alleviated by combining these adaptation measures. It showed that a combination of both adaptation measures could ensure mobility under flood conditions, with benefits from travel time reduction estimated at up to 230.44 million baht (6.36 million USD) over 20 years.