Historical Flood Research Articles

Flood Hazard Vulnerability Assessment by using Geo Spatial Techniques: Krishna and Guntur Districts, Andhra Pradesh

The study on flood hazard vulnerability assessment of Guntur and Krishna district in Andhra Pradesh. The present study has attempted by using the historical flood inundation data (2000-2020) and obtain flood hazard zones using multi temporal data sets through remote sensing and GIS. The Krishna floods of 2020 have been used as a case study for ground-truthing verification. Flood Hazard Zonation (FHZ)mapwas generated after getting flood hazard layers and integrating them in the GIS environment using a multi-criteria overlay analysis approach.The flood hazard zones has been classified into low, moderate and highbased on frequency of floods, their spatial extent and number of flooding events occurred in the study area during last 21 years. About414 villages are identified as low hazard category, 58 villages fall in Moderate and 35 villages are in High flood hazard category in the study area. Minimizing Disaster Risk Reduction, the Government particularly the disaster management authorities shall integrate long-term mitigation strategies based on risk assessment and geospatial technologies for sustainable development and planning.

Assessment of the effects of natural and anthropogenic drivers on extreme flood events in coastal regions

With climate change leading to sea level rise and frequent occurrence of extreme weather events, extreme flood disasters occur frequently in coastal cities, especially under the combined influence of heavy rainfall, fluvial floods, and storm surges. Compound floods from multiple sources are prone to superimposed effects, greatly aggravating flooding. In addition, the influence of anthropogenic activities such as man-made flood control projects in coastal regions also need to be explored. Using the Ling River Basin on the east coast of China as a case study, this study investigated the impacts of natural and human drivers on the spatiotemporal distribution of extreme floods based on a 1D–2D coupled hydrodynamic model. The hydrodynamic model was calibrated and validated based on historical flood events, and the validated model was further used to reproduce the devastating flood caused by Typhoon Lekima in 2019. The results show that the model has great capability and accuracy in simulating both the flood occurrence process and its inundation extent under natural and anthropogenic influences. The results also indicate that the high amount of discharge from the upper reaches of the Ling River was the main driver that caused the catastrophic consequences, and 83% of the inundation is caused by this factor. Comparatively, the failure of flood protection measures, which was a human driver, aggravated the influence of floods in local regions. Assessing the individual and superimposed effects of natural and human drivers on flooding may help enhance the resilience of flooded coastal areas.

Reconstruction of historical suspended particulate matter contributions of Rhône River tributaries to the Mediterranean Sea

Total metal concentrations are usually used as geochemical tracers to identify sources of suspended particulate matter (SPM) and sediment deposits, but their non-conservative behaviour can lead to large uncertainties. This non-conservatism may be a significant issue when total metal concentrations of contemporary SPM are used to determine historical sediment sources, as diagenesis processes and/or past anthropogenic inputs modify metal concentrations in sediment core profiles. For the Rhône River, which is the main supplier of SPM to the Mediterranean Sea, there is no information on historical SPM source contributions from its main tributaries during major flooding events. The objective of this study was to highlight the relevance of using tracers in the non-reactive fraction to estimate the historical relative SPM contributions of the main tributaries of the Rhône River. These historical SPM contributions (1981–2017) were reconstructed based on a sediment core sampled at the outlet of the river basin and on sampling of contemporary SPM (2011–2019, Rhône Sediment Observatory monitoring network) from each main tributary. Fine stratigraphic resolution enabled us, using this original fingerprinting method, to reconstruct historical tributary contributions to sediment deposits at the scale of past flooding events over the last 37 years. When using tracers in the total fraction of SPM/sediments, the Isère and Ardèche rivers were the main contributors of SPM (32 % and 22 % on average, respectively) during the 1981–2017 period, whereas when using tracers in the non-reactive fraction, the main SPM source was the Durance River (52 %). The results obtained by this fingerprinting approach using the non-reactive fraction are fully consistent with literature data estimated using other methods (e.g. mean interannual SPM contribution of 53 % for the Durance River), and with documented historical flood events. This study showed that using tracers in the non-reactive fraction is more reliable than using tracers in the total fraction, especially for the deepest layers of the sediment core that are influenced by historical anthropogenic inputs.

In the shadow of the dam – Hydrology of the Little Conemaugh river and its South Fork, with insights about past and future flooding

The Little Conemaugh River watershed and its South Fork sub-basin figure prominently in historical flooding of Johnstown, Pennsylvania and nearby communities with catastrophic flooding in 1889, 1936, and 1977 (reviewed herein). Historical stream gage data and data from a new gage on the South Fork (established via a novel, portable cableway system) are used with Nexrad rainfall data to assess watershed response and provide novel analysis of flood hydrology in the Little Conemaugh basin and the sub-basin. Using unit hydrograph estimates for longer duration storms (>8 h) and different baseflow conditions, we probe possible effects of several design storms, including those stemming from a hurricane remnant scenario (Agnes in 1972) and 50-, 100-, and 500-year 12-hour precipitation depths. The unit hydrographs provided peak discharge (Qpeak) estimates for 1977 (the only flood event with available hourly rainfall data) that are in good agreement with empirical peak discharges.Significant channel improvements completed in 1943 were designed to carry the largest known natural flow on record at that time (1936 Qpeak). Preliminary results from design storm scenarios indicate the need for a careful evaluation of extreme discharges and their return periods (including snowmelt-related contributions), as future flood levels in Johnstown may occur more frequently than originally thought. The 1977 flood, which triggered 7 dam failures and eclipsed 1936 Qpeak, resulted from less than 40% the estimated probable maximum precipitation (PMP) for a 12-hour storm. Peak discharges of similar magnitude would have ensued in 1972 had remnants of Hurricane Agnes tracked slightly westward. Flooding and infrastructure problems could be compounded for storms of 24-hour or longer durations, similar to record flooding seen in central Pennsylvania and New York in 1972. Flood recurrence, emergency procedures, and dam safety (particularly, spillway capacity in the Little Conemaugh basin and surrounding region) should likely be reassessed and protective early-warning measures (ineffective in 1977) implemented for the people of Johnstown.

Optimality of flood influencing factors for flood hazard mapping: An evaluation of two multi-criteria decision-making methods

• Two Multi-criteria Decision-Making (MCDM) methods were evaluated by applying two groups of flood influencing factors. • Compared the suitability of the two MCDM methods in the identification of flood hazard areas. • Evaluated the influence of each group of flood influencing factors on the flood hazard maps. • Validated the resulting flood hazard maps with historical flood record to ascertain the accuracy of the two MCDM methods. Flooding is a serious and recurring natural hazard. Flood hazard mapping requires synthesizing relevant factors that affect flood occurrence to allow for an accurate geographical assessment of flood characteristics for flood management activities. Two Multi-criteria Decision-Making Methods consisting of the Analytical Hierarchy Process (AHP) and Fuzzy-AHP (F-AHP) methods were applied in identifying flood hazard areas by using two groups of flood influencing factors (FIFs) to decrease the number of pairwise comparisons required for AHP to enable the use of an optimum set of factors. For the AHP and F-AHP methods, fewer pairwise comparisons can reduce uncertainty caused by inconsistency in the decision-making process of a larger number of factors. Thirteen non-correlating FIFs were selected and divided into two groups namely the flood susceptibility factors group (FSFG) and flood vulnerability factors group (FVFG). FSFG consisted of drainage density, elevation, land use, slope, rainfall, infiltration, topographic wetness index, time of concentration, and flood depth) and FVFG consisted of distance from inundated locations, distance from rivers, population, and distance from roads & buildings. The methodology was evaluated using three test scenarios; a) FSFG group only; b) FVFG group only; c) flood susceptibility and vulnerability factors group (FSVFG). The AHP and the F-AHP methods were applied in evaluating the influence of each group of factors on the flood hazard maps. The result of the evaluation indicated that the FVFG had the highest influence based on the spatial extent of the region by each varying flood hazard class (low to very high) on the generated flood hazard maps. The FVFG had (40.15% and 42.09%) influence in comparison with FSFG (25.85% and 25.26%), and FSVFG (34% and 32.65%) for the AHP and F-AHP methods respectively. The findings from the sensitivity analysis showed similar findings with the AHP and F-AHP methods confirming the accuracy of the methods. The validation of the AHP and F-AHP methods showed excellent compatibility with the historical flood record and affirmed the relevance of applying FSVFG. The methodology applied in this study can be a dependable support tool in flood risk analysis and management for efficient decision-making to achieve a resilient city.

Constraints on Marine Isotope Stage 3 and 5 Sea Level From the Flooding History of the Karimata Strait in Indonesia

AbstractGlobal mean sea level (GMSL) during intermediate interglacial Marine Isotope Stage 3 (MIS 3) (60–26 ka) has proven difficult to constrain. Paleo‐sea level estimates based on ice margin, modeling, and paleo‐shoreline reconstructions indicate that MIS 3 GMSL was substantially higher than reconstructed from deep‐ocean benthic foraminifera oxygen isotope (δ18O) and coral records, implying much smaller ice sheets during MIS 3. Here, we use the δ18O and Mg/Ca chemistry of surface and thermocline dwelling foraminifera in the Sulu Sea in the western Pacific margin to estimate relative changes of the influx of South China Sea surface flow through the Sulu Sea over the last 140 ka. We show that this South China Sea throughflow is controlled in part by changes in GMSL modulating the depth of the 36 m deep Karimata Strait at the southern end of the South China Sea. We constrain maximum allowable GMSL at the beginning and end of MIS 3 to −22 ± 6 and −29 ± 5 m, respectively, and minimum allowable GMSL during interglacial stages MIS 5c and 5a (117–72 ka) to range from −3 ± 8 to −8 ± 8 m and −11 ± 7 to −12 ± 7 m, respectively. Our results constrain MIS 3 GMSL, but do not rule out higher MIS 3 ice margin, modeling, and paleo‐shoreline‐based MIS 3 GMSL estimates or lower coral and seawater δ18O‐based estimates. Our results favor the highest MIS 5a and 5c GMSL estimates and confirm that the Sunda Shelf served as a land‐bridge for human and megafauna migration during MIS 3 when humans first arrived in Borneo.

Resilience to Climate Challenges: Avoid Rebuilding in High-Impact Areas of Postfire Debris Flow

Debris flow is a deadly disaster occurring within the landforms of the debris catchment, the flow track, and the impact area. During the 2013 Colorado historic flood, highly impacted areas often involved postfire debris flows. The catastrophes in high-impact zones demonstrate site selection failures. Lack of knowledge also weakens the public’s awareness of potentially reoccurring debris flow. Current insurance policy requests to rebuild the house ‘like for like” the original one at the same high-impact spot could risk re￾destruction in future debris flow attacks. By combining field investigations with geomorphic analyses, this paper discusses the dangerous nature of postfire debris flows and their inevitability in specific landforms during heavy rainstorms. Thus, residents should avoid rebuilding in high-impact areas, and relocating to safer places is the most effective strategy to enhance mountain community resilience to current extreme weather.

Future growth and decline of high mountain Asia's ice-dammed lakes and associated risk

Glaciers around the world are shrinking rapidly and will continue to do so in the next decades. Anticipating the consequences resulting from such glacier changes is key to design and implement adequate mitigation measures. Here, we focus on the future evolution of potential ice-dammed and supraglacial lakes in High Mountain Asia, as such lakes are responsible for the majority of glacier lake outburst floods in the region. We identify 11,129 potential lakes at present, with a total maximum volume of 2070 million m3. We find a strong correlation between large modelled lakes and historical outburst floods. By accounting for the evolution of glaciers under different climate change mitigation measures, we project that the number of potential ice-dammed lakes could increase by between 15 and 18% until 2080, with a concomitant 45–55% increase in their volume. Our findings thus suggest that a temporary increase of glacier lake outburst floods is to be expected in the coming decades.

Can 7000 Years of flood history inform actual flood risk management? A case study on Lake Mondsee, Austria

Flood risk models typically rely on discharge records of time series no longer than a few hundred years, which leads to high uncertainty in the estimates for major flood events. In this paper, we investigate the capability of geological flood records retrieved from lake sediment cores to prolong available data sets, lower the uncertainty of flood risk analysis, and detect changes in flood patterns by releasing the common assumption of stationarity. Paleoflood records of Lake Mondsee, Austria, covering 7000 years are linked to flood damage data of the considered catchment area to calculate (1) the average annual damage, i.e. the fair premium and (2) the damage at 99.5% Value at Risk, i.e. the Solvency II capital requirement. To illustrate our results, the performance of a hypothetical catastrophe fund for these 7000 years is studied at current vulnerability levels. Our findings show that high resolution paleoflood records with robust chronologies and flood information have significant impacts on flood model outcomes for disaster risk management and that damage potentials derived merely from recent damage data may overestimate actual risks.

3D modeling of the impact of ancient Yellow River floods on urban landscapes: A new look at the late imperial landscape at the Shuntianmen Site of Kaifeng City, Henan Province, China

AbstractFloods had a massive impact on the development of ancient cities around the world and understanding this phenomenon constitutes an essential part of the history of long‐term and dynamic human–environment interactions. There remains, however, an enormous challenge in identifying records of ancient floods in urban environments due to various sedimentation and postdepositional processes that often remove, erase, and alter such environmental records. During archaeological excavations in the famous historic city of Kaifeng, we identified records of two historical floods at the Shuntianmen site. Related stratums were carefully studied, from which dating and sediment samples were collected. These excavated stratums were also scanned to obtain digital data for modeling. Combining these data, we then applied three‐dimensional modeling to reconstruct the evolution of the natural and cultural landscape of the site since the Northern Song Dynasty (960–1127 AD), which was not possible in previous studies due to the deep burial of archaeological strata. Our results indicate that the two floods occurred during the late Ming Dynasty (1368–1644 AD) and the Qing Dynasty (1636–1912 AD), respectively. During the Northern Song Dynasty, Shuntianmen was an important part of Kaifeng, which was, at that time, the capital of China and one of the largest cities in the world. Later, during the Yuan and Ming dynasties, Shuntianmen became a suburban settlement situated along various traffic routes. During the Qing Dynasty, the area became a small village. Subsequently, the area was completely abandoned and deserted until modern Kaifeng, which was built at the same place. The evolution of the regional landscape is the direct result of the interaction between the natural environment and human activities, among which the precarious alluvial processes of the Yellow River were playing an increasingly vital role. Over the centuries, the Yellow River floods, warfare, and wind‐blown sand accumulation reduced the Kaifeng region from a prosperous capital to a comparatively deserted area.

Augmenting a socio-hydrological flood risk model for companies with process-oriented loss estimation

ABSTRACT Socio-hydrological flood risk models describe the temporal co-evolution of coupled human–flood systems. However, most models oversimplify the flood loss processes and do not consider companies’ substantial contribution to total losses. This work presents a socio-hydrological flood risk model for companies that focuses on changes in vulnerability. In addition, we augment the socio-hydrological model with a process-oriented, sector-specific loss model in order to capture damage processes more realistically. In a case study, we simulate the historical flood risk dynamics of companies in the floodplain of Dresden, Germany, over the course of 120 years. Our analysis suggests that the companies in Dresden increase their exposure more cautiously than private households and decrease their vulnerability more actively through private precaution. The augmentation, consisting of informative predictors, a refined probabilistic model, and the incorporation of additional data, improves the accuracy and reliability of the flood loss estimates and reduces their uncertainty.

Extracting historical flood locations from news media data by the named entity recognition (NER) model to assess urban flood susceptibility

Flood susceptibility assessment for identifying flood-prone areas plays a significant role in flood hazard mitigation. Machine learning is an optional assessment method because of its high objectivity and computational efficiency, but how to get enough and accurate information of historical flood locations to train the machine learning models has been a key problem. In recent years, news media data from both news websites and social media accounts has emerged as a promising source for natural science studies. However, the application of news media data in urban flood susceptibility assessment is still inadequate. This study proposed an approach to fill this gap. Firstly, flood locations were extracted from news media data based on a named entity recognition (NER) model. Then, a frequency or distance-based data quality control method was employed to improve the representativeness of the extracted flooded locations. Finally, flood conditioning factors with information of historical flood locations were input into a Support Vector Machine (SVM) model for flood susceptibility assessment. We took the central city of Dalian, China as a case study. The T-test results show that there was no significant difference between the distributions of most flood conditioning factors at the flood locations from the news media data and the official planning report. In the obtained flood susceptibility map, the high flood susceptibility areas got a recall of 90% compared with the high flood hazard areas in the planning report. Performing data quality control in the frequency-based method can improve the precision of the flood susceptibility map by up to 5%, while the distance-based method is ineffective. This study provides an example and offers the value of applying new data sources and modern deep learning techniques for urban flood management.

Extreme historical droughts and floods in the Hanjiang River Basin, China, since 1426

Abstract. The major droughts and floods in the Hanjiang River Basin, central China, have a significant impact on flood prevention and control in the middle reaches of the Yangtze River and water resources management in the areas of the South–North Water Diversion Middle Line Project. However, there is a lack of understanding of the multi-decadal to centennial-scale patterns of extreme droughts and floods in the area. Applying the yearly drought and flood records from historical documents and precipitation data in the period of instrumental measurements, this study constructs a time series of extreme droughts and floods in the Hanjiang River Basin from 1426–2017 and analyzes the temporal and spatial characteristics of the extreme drought and flood event variations. The results show that there were a total of 45 extreme droughts and 52 extreme floods in the basin over the past 592 years. Extreme droughts and floods were highly variable on a multi-decadal to centennial scale, and the frequencies were higher in the first and last 100 years or so of the study period and lower in between. Spatially, the frequencies of extreme droughts and floods were generally higher in the middle and lower reaches than in the upper reaches. It was also found that there is a good correlation of drought and flood frequencies between the upper Hanjiang River Basin and North China. These results are informative for the study of mechanisms and predictability of multi-decadal to centennial-scale variability of extreme hydroclimatic events in the river basin.

Developing a high-resolution gridded rainfall product for Bangladesh during 1901–2018

A high-resolution (1 km × 1 km) monthly gridded rainfall data product during 1901–2018, named Bangladesh Gridded Rainfall (BDGR), was developed in this study. In-situ rainfall observations retrieved from a number of sources, including national organizations and undigitized data from the colonial era, were used. Leave-one-out cross-validation was used to assess product’s ability to capture spatial and temporal variability. The results revealed spatial variability of the percentage bias (PBIAS) in the range of −2 to 2%, normalized root mean square error (NRMSE) <20%, and correlation coefficient (R2) >0.88 at most of the locations. The temporal variability in mean PBIAS for 1901–2018 was in the range of −4.5 to 4.3%, NRMSE between 9 and 19% and R2 in the range of 0.87 to 0.95. The BDGR also showed its capability in replicating temporal patterns and trends of observed rainfall with greater accuracy. The product can provide reliable insights regarding various hydrometeorological issues, including historical floods, droughts, and groundwater recharge for a well-recognized global climate hotspot, Bangladesh.

Flood hazard zone mapping incorporating geographic information system (GIS) and multi-criteria analysis (MCA) techniques

Flood is one of the most common natural disasters worldwide and has caused serious harm to humans, socio-economy and the environment. Identification and classification of flood hazard areas is therefore essential for flood management and risk decision-making which attempt to combat this challenge. This study selected eleven flood hazard conditioning factors as evaluation criteria, integrated geographic information system (GIS) with three multi-criteria analysis (MCA) techniques, viz. the analytical hierarchy process (AHP), technique ordered preference by similarity to the ideal solution (TOPSIS), and ordered weighted averaging (OWA), to form a GIS-MCA framework for analyzing the spatial distribution of flood-prone areas. Its application in the Dadu River basin, China derived a total of eleven flood hazard maps across the basin, and their analysis and comparison results demonstrated the spatial distributions of flood-prone areas based on various GIS-MCA approaches and their overlap situations, and indicated that hazard class distribution patterns of the AHP, TOPSIS, and OWA (α = 1) maps are similar, and Luding, Yuexi and their nearby areas are the highest hazard zones, where should be concerned on firstly under the condition of limited resources. To validate the three MCA methods, the AHP, TOPSIS, and OWA (α = 1) maps were compared with the historical flood disaster ranges. The verification results exhibited that the largest proportion of historical flood disaster ranges fell into the high and highest hazard levels in the three maps, and shown the resultant flood hazard maps are scientific, rational and consistent with the actual situation. The GIS-MCA approach is a powerful and feasible guidance tool for conducting flood disaster management studies, and a reference for future more efficient flood management and risk decision-making in the basin.

Winter wet–dry weather patterns driving atmospheric rivers and Santa Ana winds provide evidence for increasing wildfire hazard in California

Floods caused by atmospheric rivers and wildfires fanned by Santa Ana winds are common occurrences in California with devastating societal impacts. In this work, we show that winter weather variability in California, including the occurrence of extreme and impactful events, is linked to four atmospheric circulation regimes over the North Pacific Ocean previously named and identified as the “NP4 modes”. These modes come in and out of phase with each other during the season, resulting in distinct weather patterns that recur throughout the historical record. Some phase combinations favor atmospheric river landfalls and extreme daily or multi-day precipitation, while other phase combinations favor anomalously hot weather and drying Santa Ana wind conditions over Southern California. This historical perspective of atmospheric circulation and impacts over 70 years reveals that weather patterns are changing in a way that enhances wildfire hazard in California, while the frequency of weather patterns linked to historical floods is not diminishing. These changes highlight the rising hazards of cascading weather extremes in California’s present and future.

A comparison of performance measures of three machine learning algorithms for flood susceptibility mapping of river Silabati (tropical river, India)

Flood is the most common phenomenon causing extensive disruption to the environment, socio-economy, infrastructure and many other aspects of human life. Flood susceptibility mapping (FSM) is a crucial step for policymakers in disaster management. However, in the present study, we applied three ensemble machine learning models, namely, Random Forest (RF), Naive Bayes (NB), and Extreme Gradient Boosting (XGB) for FSM of Silabati river (tropical river, India). A total of 500 historical flood points and field observations with considering set of twelve flood influencing factors (rainfall, elevation, slope, curvature, stream power index (SPI), Sediment Transport Index (STI), Terrain ruggedness index (TRI), topographic wetness index (TWI), clay content in soil (SC), distance from the river (DFR), drainage density (DD), and land use and land cover (LULC) for generating the training and validation datasets. To investigate and perceive the flood vulnerability of the study basin, five factors, such as elevation, DD, rainfall, DFR and SC turn out to be the most dominating factors out of the adopted twelve factors considered for the present study in all models. It is found that an area of around 36.08% of the total basin comes under the very high to high FSM. The prediction ability and performance efficiency of three models were comparison and validation measures by statistical techniques such as multicollinearity diagnosis test, Kappa index, MAE, (Mean absolute error), RMSE (Root mean square error), Pearson's correlation coefficients and receiver operating characteristic (ROC). The highest ROC was achieved by the RF model (84.7%), followed by the XGB model (83.1%), and NB model (82.1%) respectively. The RF model performs better for FSM then the other models.

Data-Driven Community Flood Resilience Prediction

Climate change and the development of urban centers within flood-prone areas have significantly increased flood-related disasters worldwide. However, most flood risk categorization and prediction efforts have been focused on the hydrologic features of flood hazards, often not considering subsequent long-term losses and recovery trajectories (i.e., community’s flood resilience). In this study, a two-stage Machine Learning (ML)-based framework is developed to accurately categorize and predict communities’ flood resilience and their response to future flood hazards. This framework is a step towards developing comprehensive, proactive flood disaster management planning to further ensure functioning urban centers and mitigate the risk of future catastrophic flood events. In this framework, resilience indices are synthesized considering resilience goals (i.e., robustness and rapidity) using unsupervised ML, coupled with climate information, to develop a supervised ML prediction algorithm. To showcase the utility of the framework, it was applied on historical flood disaster records collected by the US National Weather Services. These disaster records were subsequently used to develop the resilience indices, which were then coupled with the associated historical climate data, resulting in high-accuracy predictions and, thus, utility in flood resilience management studies. To further demonstrate the utilization of the framework, a spatial analysis was developed to quantify communities’ flood resilience and vulnerability across the selected spatial domain. The framework presented in this study is employable in climate studies and patio-temporal vulnerability identification. Such a framework can also empower decision makers to develop effective data-driven climate resilience strategies.

Melioidosis cases study in Central Vietnam after historical flood 2020

Introduction: Melioidosis (Whitmore) is a severe acute bacterial infection caused by Burkholderia pseudomallei. Melioidosis transmitted to humans from soil and water environments; especially when exposed to flood water, which can cause epidemics because flood currents carry bacteria to a large area. After the historical floods in the Central region an outbreak of Melioidosis occured. Objectives: 1) To investigate the relationship between the outbreak of melioidosis and the 2020 floods. 2) To describe some characteristics of melioidosis patients in the North Central provinces after the floods in 2020. Subjects and research methods: Patients in the central provinces where the flood has just occurred, were diagnosed melioidosis, treated at Hue Central Hospital from January 1, 2020 to December 31, 2020. Results and discussion: In 2020, there were 43 patients melioidosis, but only during the 3-month period of floods from October 1 to December 31, 2020 there were 33 patients (76.74%). The rate of diseases appearing in 3 months with floods was 3.1 times higher than the rate of 25% diseases appearing every 3 months, p &lt; 0.0001. The majority of patients (81.4%) are farmers and live in rural areas (76.7%). The mean age was 56.14 ± 14.85. Most of the patients had chronic diseases such as diabetes (34.9%), chronic kidney disease (9.3%), chronic lung disease (34.9%), 18.6% alcoholism. 72.1% of patients had sepsis, the most commonly damaged organs were the lungs (69.8%) and abscesses of the liver, spleen, and kidney (27.9%). There were 13 patients died (30.2%). Conclusion: The rate of melioidosis patients in 3 months with floods was 3.1 times higher than the average rate of disease appearing every 3 months (p &lt; 0.0001). Most patients had risk factors such as diabetes, chronic kidney disease, chronic lung disease, alcoholism. The most commonly injured organs are the lungs and abscesses of the liver, spleen, and kidneys. The mortality rate was 30.2%. Key words: Melioidosis, historical floods

HISTORICAL ANALYSIS AND MANAGEMENT ON FLOODS DURING PANDEMIC COVID-19 IN DKI JAKARTA INDONESIA

A flood is an event that occurs due to an accumulation of water that falls and cannot be accommodated by the ground. Thestudy aims to find out the history of floods in DKI Jakarta and the handling of floods amid the current Covid-19 pandemic. Natural events, such as floods, are not new in an urban area. The study discusses whyfloods that occur in urban areas, especially the DKI Jakarta area, are an annual cycle?.But there is a difference in 2020 and 2021 because of the flood, this time amid the Covid-19 pandemic, so extra handling is needed because if there is a crowd in the refugee camp it can spread the virus more widely. Thus, the government is doing everything it can to handle floods so that the community remains in a healthy body condition. The method used in the study is descriptive with a literature study approach, namely collecting data from reliable sources.Floods can be caused by two factors, namely natural causes and unnatural causes (from human activities).&#x0D;