Flooding Time Research Articles

The Timing of Global Floods and Its Association With Climate and Topography

AbstractUntil recently, the development of a global geography of floods was challenged by the fragmentation and heterogeneity of in situ data and the high costs of processing large amounts of remote sensing data. Such geography would facilitate the exploration of large‐scale drivers of flood extent and timing including wide latitudinal, climate, and topographic effects. Here we used a monthly data set spanning 30 years (Global Surface Water) to develop a worldwide geographical characterization of slow floods (1‐degree grid), weighting the relative contribution of seasonal, interannual, and long‐term fluctuations on overall variability, and quantified precipitation‐flooding delays where seasonality dominated. We explored the dominance of different flooding timings across five main Köppen‐Geiger climates and seven topography classes derived from modeled water table depths (i.e., hydro‐topography) to contribute top‐down insight about the most salient, cross‐regional flooding patterns and their likely large‐scale drivers. Our results showed that, globally, the mean extent of floods averaged 0.48% of the global land area, predominantly associated with hydro‐topography (>2× more extensive in flatter landscapes). Climate drove flood timings, with predictable, seasonally dominated fluctuations in cold regions, interannual, and mixed patterns in temperate climates, and more irregular (higher variability) and unpredictable (less seasonal) patterns in arid regions. Net gains of flooded area dominated temporal variability in 9% of the cells including boreal clusters likely affected by warming trends. We propose that this new geographical perspective of floods can aid different avenues of hydrological research in the upscaling and extrapolation of field studies and the parsimonious representation of floods in hydroclimatic models.

MATHEMATICAL MODELING OF WATER MOVEMENT DURING A DAM BREAK USING THE VOF METHOD

River valleys in mountainous areas are often subject to heavy rains and melting glaciers, resulting in the risk of mudflows and the destruction of hydraulic protective structures. In order to minimize the potential risk and negative outcomes of a disaster, both on an individual and environmental scale, it is crucial to possess essential information. This includes understanding the timing, location, and extent of flooding, as well as comprehending the force of water flow impact on protective structures. In the research, the numerical process of the movement of the water flow caused by the breakthrough of the dam is investigated. A two-dimensional numerical model of water flow during a dam break was constructed using the VOF method to describe the described process. With the help of the VOF method, the movement of the water surface is captured, while maintaining the law of conservation of mass. The mathematical model consists of Reynolds-averaged incompressible Navier-Stokes equations and includes the interphase equation. The turbulent k-e model was used to close the system of equations. The numerical algorithm used is PISO (Pressure-Implicit with Splitting of Operators). The obtained numerical results agree with the experimental data, indicating the developed algorithm’s reliability and accuracy. The results are presented as comparative graphs and images showing the contour of the free surface movement along the experimental reservoir. A numerical model that has been tested in this way can provide significant support in preventing the devastating consequences of a dam break and providing timely assistance during the evacuation of the population.

Intelligent flood forecasting and warning: a survey

Accurately predicting the magnitude and timing of floods is an extremely challenging problem for watershed management, as it aims to provide early warning and save lives. Artificial intelligence for forecasting has become an emerging research field over the past two decades, as computer technology and related areas have been developed in depth. In this paper, three typical machine learning algorithms for flood forecasting are reviewed: supervised learning, unsupervised learning, and semi-supervised learning. Special attention is given to deep learning approaches due to their better performance in various prediction tasks. Deep learning networks can represent flood behavior as powerful and beneficial tools. In addition, a detailed comparison and analysis of the multidimensional performance of different prediction models for flood prediction are presented. Deep learning has extensively promoted the development of real-time accurate flood forecasting techniques for early warning systems. Furthermore, the paper discusses the current challenges and future prospects for intelligent flood forecasting.

Projection and Analysis of Floods in the Upper Heihe River Basin under Climate Change

The projection of future hydrological processes can provide insights into the risks associated with potential hydrological events in a changing environment and help develop strategies to cope with and prevent them. The Heihe River basin in Northwest China is crucial for providing water resources to water-scarce regions. Thus, understanding the future runoff trends in the context of climate change can optimize water allocation, alleviate water shortages, and mitigate flood risks in the region. In this study, we use meteorological data from 10 general circulation models under two future scenarios to drive the Soil and Water Assessment Tool (SWAT) model and project hydrological processes in the upper Heihe River basin from 2026 to 2100. After examining the future changes in total runoff in the basin, we assess the magnitude, frequency, and timing of daily flood events in the future. The results of the multi-model ensemble averaging (MMEA) method show that the change in the multi-year average annual runoff is −4.5% (2026–2050), −1.8% (2051–2075), and +2.0% (2076–2100) under the SSP245 scenario and −1.0% (2026–2050), +0.4% (2051–2075), and +0.2% (2076–2100) under the SSP585 scenario compared to the historical period. The analysis of flood magnitudes indicates that the basin will experience higher-magnitude floods in the future, with the largest increase rates of 61.9% and 66.4% for the 1-day maximum flows under the SSP245 and SSP585 scenarios, respectively. The flood return period is projected to be shorter in the future, and the 1-day maximum flows of a 100-year flood are expected to increase by 44.7% and 63.7% under the SSP245 and SSP585 scenarios, respectively. Furthermore, a significant shift in the flood timing is expected, with the highest frequency moving from July to August, representing a one-month lag compared to the historical period. Our findings suggest that the hydrological characteristics of the upper Heihe River basin may be significantly altered in the future due to the effects of climate change, resulting in floods with higher magnitudes and frequencies and different timings. Therefore, it is imperative to consider these changes carefully when developing risk prevention measures.

Numerical simulation of the deadliest flood event of Portugal: Unravelling the causes of the disaster

The flood event of November 25 and 26, 1967 corresponds to the deadliest storm affecting Portugal in recent centuries being responsible for >500 fatalities. The main trigger was the heavy rain that fell in just a few hours, provoking a rapid increase in river flows, although other concurrent circumstances had to occur to reach the dramatic water levels estimated in some affected places. However, even today, several important uncertainties related to water levels achieved and timing of floods remain. Here we aim to clarify some of the pending issues by applying suitable high performance numerical tools to elucidate the main conditioning factors that played a key role in the intensification of this dramatic flood. In particular, the analysis has been focused on Quintas village, the location most affected, where >100 fatalities were recorded, close to 2/3 of its total population at the time. The main conclusion provided by the numerical simulations was that a plugging of water flow downstream of Quintas village, favoured by a poor terrain maintenance coupled with the bottleneck created by topographic features, caused the critical over-elevation of water levels. Simulations also corroborate the rapid increase in water levels in Quintas village, with an estimated rise of >2 m in just two hours, as well as the occurrence of the flood during the night, preventing many people to be aware of the extreme danger they were facing and safeguarding themselves.

An assessment of the present hydroclimatic regime of the Madeira River basin using climate and hydrological models

ABSTRACT Southwestern Amazonia has low spatial coverage of hydro-meteorological data, making climate and hydrological models essential tools. Thus, it is necessary to evaluate these models to verify their suitability for future climate change assessments. We evaluate the ability of a regional climate model (Eta) forced with three Earth system models and a large-scale hydrological model (MGB) to represent hydroclimatic variables for the current climate in the Madeira River basin. Different hydro-meteorological databases are used to evaluate the systematic errors of the coupled model system. The simulation results show that the Eta model underestimates (overestimates) precipitation over the basin during the rainy (dry) season. The hydrological processes simulated by the MGB are considered satisfactory, although the model has some limitations with respect to the timing of floods and their removal in different discharge regimes.

Review on Effect of Nutrition during Flood on Children

Purpose: Nepal is a highly disaster-prone country, experiencing earthquakes, floods, landslides, drought, hot and cold waves, and vector-borne diseases on a regular basis. Monsoonal floods and landslides affect the most vulnerable communities in the hills and plains every year. The paper is aimed to review the effects of nutrition during floods on children. Design/Methodology/Approach: It is a review-based study. In this review, the researcher attempts to recommend a pragmatic solution for the time being through the systematic literature. The researcher uses deductive logic reasoning at large during the review. In-depth archival analysis followed by an intensive review would be strategies adopted during the scientific review. Findings/Result: The floods affected children, mothers’ and elders in Nepal. June to September is the main monsoon month in Nepal. Floods damage houses, foods, agricultural crops, livestock, poultry, and cloth. Floods affect the Terai area of Nepal. There is found floods affected 3-5 years children in Bhutan, Bangladesh, Bhutan, India, and Nepal. At the flooding time floods damage houses, food, crops, livestock, and cloths. In Nepal all the family members’ victimised time more vulnerable found mothers, children and elders. June to July summer time floods affect more in the Terai districts children nutrition. It is needed to do research study in seven provinces of Nepal. Central, Province, and Local government allocate special funds for flooding victim children, mother, and elders. Originality/Value: The paper provides pragmatic solutions to the effect of the disaster on Children's nutrition status which might be the foundation for a healthy society overcoming the issue of flood in Nepal. Paper Type: Review paper

Assembly-based flood repair cost and time models for industrial buildings in Turkey

Flooding is one of the most frequent hazard events significantly affecting the industry in Turkey, leading to severe economic losses and business disruptions. To predict the direct flood losses and business downtimes on the industry, this study proposes probabilistic cost and time models of repairing direct physical flood damage to industrial buildings in Turkey. Using field notes and literature reviews, a typical industrial building was first disassembled into a list of building components vulnerable to flooding. With a focus on façade and interior building components, the flood damage potential of each component was assessed at different flood depths and velocities. Damage state relationships were established for the building components to connect their damage levels under flood actions to their individual repair work. Using the assembly-based approach, costs and times of repairing each flood-damaged component and their associated variabilities were assembled in a probabilistic approach to develop total flood repair cost and time models. The results show that typical repair costs and times for facilities subjected to significant flood depths and velocities might reach substantial levels, up to 28% of building replacement costs and 165 working days on average. The proposed models systematically incorporate existing important uncertainties, therefore, provide reliable estimates.

From Staple Food to Scarce Resource: The Population Status of an Endangered Striped Catfish Pangasianodon hypothalamus in the Mekong River, Cambodia

Striped catfish Pangasianodon hypopthalmus (Sauvage, 1878) is a flagship catfish species of the Mekong River region, a commercially valuable food fish that is important in freshwater fisheries, and a popular aquaculture species in many Asian countries. The species was assessed as “Endangered” by the International Union for Conservation of Nature (IUCN) due to range contraction and declining abundance, though the status of the species’ wild population in Cambodia, a critical habitat for the species, is not well understood. Here, we assess the population status of the striped catfish in Cambodia using multiple sources, including time-series catch data and length frequency distribution data from a commercial fishery (stationary trawl bagnet or dai) operated in the Tonle Sap River from 1998/99 to 2017/18 and larval drift data monitored in the Mekong River in Phnom Penh from 2004 to 2018. We found that there was a significant decline (R2 = 0.54, p = 0.0002) in the catch (metric tonnes) of the striped catfish from the commercial dai fishery over the last two decades. Similarly, length-based indicator analysis indicates that striped catfish mean length and abundance have both declined over the study period, raising concerns about the sustainability of river catfish fisheries. Moreover, long-term larval drift monitoring in Mekong River shows that there was a marginally significant decline in the quantity of striped catfish larvae/juvenile drifting downstream to the lower floodplain over the last decade. Changes in flood index (extent and duration of flood) in the Tonle Sap floodplain affected by the Mekong’s flow are likely key factors driving the decline of the wild populations of the striped catfish. Both larval fish abundance and floodplain fish harvests have a significant positive relationship with Mekong flow and flood extent. Indiscriminate fishing exacerbates pressures on striped catfish stocks. Therefore, actions such as maintaining natural seasonal flows (flood timing, extent, and duration) to the Tonle Sap floodplain and protecting migratory fish stocks from overharvest and habitat fragmentation are essential to the persistence of stocks of striped catfish and other large-bodied migratory fishes that utilize both the Cambodian Mekong and Tonle Sap floodplains.

Ship Flooding Time Prediction Based on Composite Neural Network

When a ship sailing on the sea encounters flooding events, quickly predicting the flooding time of the compartments in the damaged area is beneficial to making evacuation decisions and reducing losses. At present, decision-makers obtain flooding data through various sensors arranged on board to predict the time of compartment flooding. These data help with the calculation of the flooding time in emergency situations. This paper proposes a new approach to obtaining the compartment flooding time. Specifically in damage scenarios, based on Convolutional Neural Network and Recurrent Neural Network (CNN-RNN), using a composite neural network framework estimates the time when the compartment’s flooding water reaches the target height. The input of the neural network is the flooding images of the damaged compartment. Transfer learning is utilized in the paper. The ResNet18 model in Pytorch is used to extract the spatial information from the flooding images. The Long Short-Term Memory (LSTM) model is then applied to predict when the compartment flooding water reaches the target height. Experimental results show that, for the damaged compartment, the flooding time predicted by the neural network is 85% accurate while the others’ accuracy is more than 91%. Intuitively, when it comes to the actual flooding event, the composite neural network’s average prediction error for compartment flooding time is approximately 1 min. To summarize, these results suggest that the composite neural network proposed above can provide flooding information to assist decision-makers in emergency situations.

Time-series generative adversarial networks for flood forecasting

Reliable flood forecasting serves as one of the fundamental tasks in flood management. However, the forecast performance of modern data-driven models depends heavily on the quantity and quality of training data. The lack of field data of flood events highly underpins the development of machine learning (ML) flood forecasting techniques. With consideration of the rareness of flood events and the high dimensionality of flood time series, two latest variants of generative adversarial networks (GANs), Time-series Generative Adversarial Network (TimeGAN) and Real-world Time Series Generative Adversarial Network (RTSGAN), were applied to generate synthetic flood timeseries. In the example analysis of the Xijiang River Basin in southern China, results show that time-series GANs can accurately and efficiently mimic the spatiotemporal correlations between flood series from multiple sites, and RTSGAN outperforms TimeGAN especially when the lengths of time series are long. Additionally, gradient boosting regression tree (GBRT), long-short term memory (LSTM) and quantile regression method integrated LSTM networks (QRLSTM) flood forecasting models were trained with real sequences and an extra amount of synthetic sequences. It shows that the expansion of training datasets in this way is promising in reducing prediction errors of classical machine learning models. In the 3-day-ahead flood forecast, the introduction of synthetic training datasets has little positive effect on both GBRT and LSTM. In 24-hour-ahead flood forecast, as the lead period increases, GBRT-RTSGAN outperforms GBRT evidenced by a 6.7% increase in Nash-Sutcliffe efficiency (NSE) metric, a 56.5% reduction in average absolute relative error (AARE) metric and a 33.1% reduction in root-mean square error (RMSE) metric. For interval forecast, the use of synthetic training datasets helps QRLSTM obtain lower prediction interval normalized averaged width (PINAW), however, at the cost of prediction interval coverage probability (PICP).

Mechanism of CO2 enhanced oil recovery in kerogen pores and CO2 sequestration in shale: A molecular dynamics simulation study

The strong interactions between kerogen and CO2 give an opportunity to enhance shale oil recovery (EOR) by CO2 injection, and also reduce greenhouse gas emissions through CO2 capture and geological storage. Understanding the mechanism of CO2 enhanced shale oil recovery is important significance for achieving optimum shale oil exploration and development. In this work, the oil storage behavior and mechanism of CO2 enhanced shale oil recovery in kerogen pores are studied by using molecular dynamics (MD) simulations. For oil storage, the density curves are calculated and the results show that it can be found that there are two adsorption layers near the wall, and the slight fluctuations density near the two opposite wall presents different trends due to the roughness of the walls surface. For flooding behavior, CO2 molecules are easily dissolved into the oil phase and drive out most of the oil within the kerogen slit pores after 3 ns with differential pressure of 10 MPa for our model. The higher differential pressure corresponds to earlier CO2 breakthrough and smaller value of oil recovery. Oil molecules located in smaller pores require a longer flooding time to be displaced. The displacement is the main mechanism of oil recovery in nanoscale kerogen pores for CO2 flooding. The diffusion coefficients of CO2/oil and interaction energy are calculated and analyzed. Further, the CO2 storage capacity in shale formations are computed and its value is 466 kg/m3. This work reveals oil storage behavior and the mechanism of CO2 flooding in shale reservoirs, and the results are significant for the CO2 enhancement of oil recovery, and for CO2 capture and storage in kerogen pores.

A deep-learning-technique-based data-driven model for accurate and rapid flood predictions in temporal and spatial dimensions

Abstract. An accurate and rapid urban flood prediction model is essential to support decision-making for flood management. This study developed a deep-learning-technique-based data-driven model for flood predictions in both temporal and spatial dimensions, based on an integration of long short-term memory (LSTM) network, Bayesian optimization, and transfer learning techniques. A case study in northern China was applied to test the model performance, and the results clearly showed that the model can accurately predict the maximum water depths and flood time series for various hyetograph inputs, with substantial improvements in the computation time. The model predicted flood maps 19 585 times faster than the physically based hydrodynamic model and achieved a mean relative error of 9.5 %. For retrieving the spatial patterns of water depths, the degree of similarity of the flood maps was very high. In a best case scenario, the difference between the ground truth and model prediction was only 0.76 %, and the spatial distributions of inundated paths and areas were almost identical. With the adoption of transfer learning, the proposed model was well applied to a new case study and showed robust compatibility and generalization ability. Our model was further compared with two baseline prediction algorithms (artificial neural network and convolutional neural network) to validate the model superiority. The proposed model can potentially replace and/or complement the conventional hydrodynamic model for urban flood assessment and management, particularly in applications of real-time control, optimization, and emergency design and planning.

Climate Change and Climate Justice in Rural Sindh: Evidences and Experiences from the Rural-Based Population of Khairpur District

The present research study was carried out to collect the evidences of the climate change impact on the local communities of district Khairpur and to understand the views of people how they experience the climate change impact on their lives. The current study was undertaken employing the qualitative research methodology with a small sample size of villages aging from 25 to 65 age from a single union council. Qualitative research cannot be generalized as qualitative findings are generalized where large sample size is selected to collect the data. The study was carried out on four major areas of the climate change stressors which are Changes in weather patterns, floods, heat waves and heavy rains. All four stressor have direct severed effect on the lives of rural-based population. The findings of the study reveal that climate change has severe impact on the lives of common people which bear lots of pain in terms of agriculture loss where their standing crops are vanished away , their homes are destroyed completely, livestock is dying off due to the diseases spread and lack of pastures in the vicinity where flooded water stand. Other than this, findings tell us that the role of government seems very pathetic towards the affected people of district Khairpur where score of people have no food items to eat, no shelter to live in and other temporary things at the time of heavy rains and floods. This study can be a torch bearer for the policy makers, governments to formulate the policies according to the ground realities. The vulnerabilities of people due to these climate change stressors are visible to governments. Hence, Climate disasters are affecting every corner of the Sindh, and its marginalized communities are left to face a disproportionate share of the impacts of climate change which is violation of UN Framework for climate change.

Alluvial fan and fan delta facies architecture recording initial marine flooding in the Mio‐Pliocene syn‐rift sequence of the Fish Creek‐Vallecito Basin, southern California

AbstractThe timing and character of the initial marine flooding of extensional basins has implications for their tectonic history. Yet, the recognition of such flooding is difficult along rift basin margins due to the dominance of coarse‐grained systems and the lack of marine fauna. This study conducts detailed facies and stratigraphic analysis of a Mio‐Pliocene alluvial fan and fan‐delta succession in the Fish Creek Vallecito Basin in southern California. Our goal is to characterize the marine flooding surface, determine the paleogeographic position of the shoreline and estimate the magnitude of relative base‐level rise that occurred during the marine incursion associated with the opening of the Gulf of California. Our results show that the flooding of the Elephant Trees alluvial fans is often marked by an abrupt lithologic and facies change from meter‐scale boulder‐rich subaerial debrites (proximal alluvial fan facies association) to centimetre‐scale granule‐rich subaqueous debrites, ripple‐laminated sandstones and mudstones (prodelta facies association). By delineating the zone of transition between the subaerial and subaqueous facies, we place the initial flooding paleo‐shoreline 4 km up the alluvial fan's paleo‐depositional slope. Considering alluvial fan slope gradients between 1° and 5°, this 4 km transgression would require an estimated 70–350 m of water depth during the initial marine incursion. Interfingering of fan‐delta deposits with subaqueous marine and planktonic‐rich evaporites suggests that the basin was below sea‐level after, and perhaps even before, the marine flooding. Subaerial subsea‐level basins exist in Death Valley and the Salton Trough today within similar extensional and transtentional tectonic regimes. This subaerial subsea‐level interpretation might explain the high magnitude and abrupt relative base‐level rise recorded by the facies transitions in the Fish Creek Vallecito Basin. These results suggest that the Fish Creek Vallecito Basin underwent significant extension during its early and nonmarine depositional phase, allowing it to reach subsea‐level elevations. The tectonic history of the Fish Creek Vallecito Basin maybe similar to other extensional basins where rapid subsidence allows the accumulation of nonmarine strata below sea‐level prior to the marine flooding, then restricted and deep marine strata immediately after flooding.

Study of flood mitigation system for improving the resilience of pluvial flood control of south Jakarta. (Case study: Ciledug raya, Cipulir.)

Flooding is the inability of the channel capacity to accept stormwater so that it overflows into the surrounding area. The main problem of flooding in Jakarta is not in the main channel but in micro drainage which is commonly known as pluvial flooding. This research was conducted to increase resilience (reduce the risk of flooding) by making a device that is able to handle floods that can reduce the height and time of flooding in a location where flooding occurs. Floods cause damage. The parameters that are very important in determining the damage are inundation height and inundation duration. The methodology used in this research is to combine early warning system and emergency response based on hydrology engineering, hydraulics engineering and data science. The approach taken is to use an emergency response using the help of an early warning system. The emergency response used in this study is based on a mobile pump and its equipment and power that can be moved at any time. The emergency response is to use an early warning by predicting the microphysical symptoms of clouds that occur in the atmosphere. Emergency response is needed to cooperate together with early warning system in pluvial flooding problem. Unifying early warning system and emergency response system that this research called flood device is able to reduce the depth and duration of inundation.

Application of GIS and SWMM model to assess urban flooding in Metro Residential Area of Ninh Kieu District

Ninh Kieu District is an urban district with a rapid urbanization rate and a complex dimension. This factor has made urban flooding in this district very serious. Metro Residential Area is one of the areas currently being strongly affected by urban flooding in Ninh Kieu District. This research is conducted to apply GIS and SWMM model to assess urban flooding in Metro Residential Area. GIS is used to digitize the drainage system, while SWMM model is used to simulate urban flooding in the study area. Research results have shown the flood level, flood time, flood volume, flow velocity and frequently flooded locations in Metro Residential Area. This is an important basis for the development of solutions to reduce flooding, thereby reducing the impact of urban flooding and improving the quality of life of the residential community living in the study area.

Probabilistic Conditioning and Recalibration of an Event-Based Flood Forecasting Model Using Real-Time Streamflow Observations

Flood warnings provide information about the timing and magnitude of impending floods, which can help mitigate the adverse impacts of flooding. Flood forecasts are highly influenced by uncertainty associated with rainfall forecasts as well as initial catchment wetness. Event-based models are simple and parsimonious and are widely favored by practitioners for flood estimation. However, these models require loss parameters to be manually specified for each simulated event, and this represents an additional source of uncertainty that needs to be considered along with errors in observations and rainfall forecasts. Little attention has been given to the coupling of updating techniques with event-based models to reduce the uncertainty associated with catchment wetness. To this end, we devised a sequential recalibration scheme to characterize uncertainty in ensemble forecasts derived using an event-based flood model. This scheme uses information on both observation and model errors to filter and update catchment loss estimates to improve the accuracy of the forecasts. Analysis of flood forecasts for 22 events showed that although initially, there was low skill in forecasts derived solely from external estimates of catchment wetness, the reliability and accuracy of the forecasts improved rapidly once the flood event commenced and the flood model was coupled with an updating scheme. Compared with forecasts made without any updating scheme, the conditioning and recalibration steps progressively improved the accuracy of the forecasts as measured by Nash-Sutcliffe efficiency from −0.14 to 0.88, bias was reduced by 78%, and root-mean square error reduced by 67%. The use of such schemes thus reinforces the advantages of using parsimonious models that have long been favored by practitioners for design and other purposes.

The Polish Heritage Library and Archives in Panna Maria, Texas

The Polish Heritage Library and Archives in Panna Maria, Texas

Flood-associated hydrobiological changes in the nearshore off Kochi, along the southwest coast of India

This study presents how flood water transforms the hydrobiology of the nearshore waters close to estuarine inlets along the southwest coast of India, which are susceptible to significant freshwater discharge during the Southwest Monsoon (SWM) and depression events during the rest of the year. In the current study, coastal hydrography off Kochi that was acquired just before and during the flood event in August 2018 was compared with normal SWM conditions that prevailed in August 2014 and 2015. The SWM hydrography of August 2014 and 2015 revealed upwelling waters in the subsurface and a low saline lens in the top layer with a high concentration of chlorophyll-a (>3 mg m−3). The impact of floodwater was found in August 2018, even at a distance of 7 km from the Kochi inlet, as the low saline plume that was 3 m thick before the flood, expanded to 8 m thick and occupied almost the entire water column during the flood. It was evident that the flood water decreased the total chlorophyll-a concentration and microplankton abundance in the study zone. During the typical SWM in August 2014 and 2015, the nano-size fraction of phytoplankton dominated the chlorophyll-a (>70%), but the micro-size fraction dominated (50%) during the August 2018 flood time. All these suggest that the dilution effect of the flood could noticeably decline the phytoplankton biomass and abundance even at 7 km out to sea from the Kochi inlets, although it is unknown how this affects the higher trophic levels.