Historical Flood Research Articles

The Trend in the Risk of Flash Flood Hazards with Regional Development in the Guanshan River Basin, China

The economic development of China’s mountainous areas has been rapid, but the impact of flash floods is often ignored during regional development planning. Therefore, a method needed to be developed to simulate flash flood disasters and analyze the trends in flash flood disasters with regional development. Taking Guanshan River Basin as the research area, we combined the digital elevation model (DEM) with the inflow hydrograph, using the topography-based hydrological model (TOPMODEL). Verified by historical flash flood disasters, the watershed model was built based on the finite volume conservation flood routing model (FLO-2D). Then, we simulated the hazard distribution of flash flood disasters, developed a risk status assessment factor (RSAF) for flash flood disasters and calculated the increased ratio from 2003 to 2018. The results show that in 2003, the total hazard area of flash flood disasters affecting human settlements was 61,121 m2, which increased to 118,404 m2 in 2018. Since 2003, with the development of the regional economy, more residents moved to risk and extreme risk zones; thus, the risk of flash flood disasters also increased. Due to the significant damage caused by the flash flood on 5 August 2012, some of the local residents moved away from the high risk and extreme risk zones, indicating that the risk trend of flash flood hazard was consistent with the regional development of Guanshan River. We provide suggestions for regional economic development planning; the risk assessment for flash flood disasters must be considered to ensure the stable development of the regional economy, and using the RSAF would be efficient.

The Role Of Relative Slope Length In Flood Hazard Mapping Using Ahp And Gis (Case Study: Lam River Basin, Vietnam)

In addition to the five main factors affecting the formation of floods including slope, rainfall, drainage density, soil, and land cover, the relative slope length factor has also been considered to be one of the fundamental causes that contribute to flood hazard. The paper analyzes the theoretical basis for choosing the relative slope length criterion when zoning flood hazard in Lam river basin. The important role of this factor was evaluated by the results of the flood risk zoning map established by the method of integrating AHP and GIS technology in two cases: using 5 flood influence criteria and using 6 flood influence criteria. Flood hazard zoning maps for 2 cases were tested with 3 historic floods occurring on Oct 2010, Oct 2013 and Oct 2016. The results showed that the map established with six influence factors is more detailed and accurate than the one created with five factors affecting flood hazard because of the similarity with the reality of that map. The results of the study are applicable to other river basins which their geographical features are similar to characteristics the Lam river basin.

GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: A case of Topľa basin, Slovakia

Abstract Flood is a devastating natural hazard that may cause damage to the environment infrastructure, and society. Hence, identifying the susceptible areas to flood is an important task for every country to prevent such dangerous consequences. The present study developed a framework for identifying flood-prone areas of the Topľa river basin, Slovakia using geographic information system (GIS), multi-criteria decision making approach (MCDMA), bivariate statistics (Frequency Ratio (FR), Statistical Index (SI)) and machine learning (Naive Bayes Tree (NBT), Logistic Regression (LR)). To reach such a goal, different physical-geographical factors (criteria) were integrated and mapped. To access the relationship and interdependences among the criteria, decision-making trial and evaluation laboratory (DEMATEL) and analytic network process (ANP) were used. Based on the experts’ decisions, the DEMATEL-ANP model was used to compute the relative weights of different criteria and a GIS-based linear combination was performed to derive the susceptibility index. Separately, the flood susceptibility index computation through NBT-FR and NBT-SI hybrid models assumed, in the first stage, the estimation of the weight of each class/category of conditioning factor through SI and FR and the integration of these values in NBT algorithm. The application of LR stand-alone required the calculation of the weights of conditioning factors by analysing their spatial relation with the location of the historical flood events. The study revealed that very high and high flood susceptibility classes covered between 20% and 47% of the study area, respectively. The validation of results, using the past flood points, highlighted that the hybrid DEMATEL-ANP model was the most performant with an Area Under ROC curve higher than 0.97, an accuracy of 0.922 and a value of HSS of 0.844. The presented methodological approach used for the identification of flood susceptible areas can serve as an alternative for the updating of preliminary flood risk assessment based on the EU Floods Directive.

Mapping dynamic changes in hydrological time series using the average directional index

ABSTRACT The current trend detection methods have mostly concerned with testing the monotonic change in the mean of a hydrologic variable concerned (hypotheses testing) but limited in detecting overtime change in time series. To the best of the authors' knowledge, this study made the first use of the average directional index (ADX), a trading index, and its components (positive and negative directional indices, DI±) in mapping the dynamic change in the streamflow, specifically the strength of a trend. The calculation of ADX depends on three parameters, i.e. differences between high and minimum streamflows relative to the range in a given time interval. Monthly datasets of 17 worldwide streamflow were collected and analyzed (1900–2018). The results were verified against two classical trend methods (the Mann-Kendall, MK, and the Partial Trend Index, PTI). While, MK and PTI indicated that all rivers progressed towards non-stationary conditions (presence of a trend); however, based on the ADX, the trend's strength at 35% of the tested rivers was unclear due to the cyclic nature of streamflow and the remaining unveiled unequivocally decreasing and increasing signals at 30% and 35% of the rivers, respectively. The ADX also was more sensitive in detecting shifts in the regime compared to the Regime_Shift method based on the annual mean of the streamflow. The ADX components (DI±) have also managed to detect the historical flood and drought events at 77–100% and 36–100% of the cases respectively, with a magnitude of error of ±1-year. Individually, the Zambezi is found to be the most sensitive worldwide river to change in high and minimum streamflows while Fraser and Mekong rivers are the least sensitive rivers worldwide.

Debris flooding magnitude estimation based on relation between dendrogeomorphological and meteorological records

Debris floods are mass movement events which are usually triggered by intense short duration rainfall events. They often occur on alluvial fans in an alpine environment. Due to their sever geohazard potential they pose a serious threat to infrastructure and human life. To minimize their threat understanding of their past magnitude occurrence is crucial. Dendrogeomorphology has proven to be a highly useful method in studies of past slope mass movements. However, establishing magnitudes of past events has so far been based on indirect indicators, such as: spatial distribution of affected trees, characteristics of tree injures and sedimentological records. In this study we present a method that directly estimates the magnitudes of past debris flood events on an alluvial fan using dendrogeomorphological and meteorological data sets. The studied dendrogeomorphological data set is based on tree-ring series from 105 sampled trees (Picea abies, Abies alba and Larix decidua) growing on an active alluvial fan in a typical alpine environment of the Julian Alps in NW Slovenia. Based on sudden growth suppression thirteen debris flood events since 1903 were dated. Meteorological data from a nearby meteorological station was used to determine the exact triggering meteorological event for ten events. Comparing the It index of affected trees and calculated return period of an individual triggering meteorological event established the magnitude of debris flooding. We showed that more trees are affected at high return period/intensity of the triggering meteorological event and therefore higher magnitudes of debris floods. This research presents the first combined use of dendrogeomorphological and meteorological data sets for magnitude estimation of historic debris flood events which could be successfully applied in similar environments.

Hydrological changes during the Roman Climatic Optimum in northern Tuscany (Central Italy) as evidenced by speleothem records and archaeological data

ABSTRACTStudy of the climate in the Mediterranean basin during different historical periods has taken on a particular importance, particularly regarding its role (together with other factors) in the evolution of human settlement patterns. Although the Roman age is traditionally considered a period with a favourable climate, recent studies have revealed considerable complexity in terms of regional climate variations. In this paper, we compare the hydrological change from speleothem proxy records with flood reconstructions from archaeological sites for Northern Tuscany (central Italy). We identify a period of oscillating climatic conditions culminating in a multidecadal dry event during the 1st century bc, followed by a century of increased precipitation at the beginning of the Roman Empire and subsequently a return to drier conditions in the 2nd century ad. The period of rainfall increase documented by the speleothems agrees with both the archaeological flood record as well as historical flood data available for the Tiber River, ca. 300 km to the south. These data also suggest a return to wetter conditions following the 3nd and 4rd centuries ad.

Seasonal contamination of well-water in flood-prone colonias and other unincorporated U.S. communities

Many of the six million residents of unincorporated communities in the United States depend on well-water to meet their needs. One group of unincorporated communities is the colonias, located primarily in several southwestern U.S. states. Texas is home to the largest number of these self-built communities, of mostly low-income families, lacking basic infrastructure. While some states have regulations that mandate minimum infrastructure for these communities, water and sewage systems are still lacking for many of their residents. Unprotected wells and self-built septic/cesspool systems serve as the primary infrastructure for many such colonias. This research was designed to probe how wells and septic/cesspool systems are influenced by heavy rainfall events. Such events are hypothesized to impact water quality with regard to human health. Inorganic and microbiological water quality of the wells in nine colonias located in Nueces County, Texas, were evaluated during dry and wet periods. Nueces County was selected as an example based on its flooding history and the fact that many colonias there depend entirely on well-water and septic/cesspool systems. The results demonstrate that well-water quality in these communities varies seasonally with respect to arsenic (up to 35 μg/L) and bacterial contamination (Escherichia coli), dependent on the amount of rainfall, which leaves this population vulnerable to health risks during both wet and dry periods. Microbial community analyses were also conducted on selected samples. To explore similar seasonal contamination of well-water, an analysis of unincorporated communities, flooding frequency, and arsenic contamination in wells was conducted by county throughout the United States. This nationwide analysis indicates that unincorporated communities elsewhere in the United States are likely experiencing comparable challenges for potable water access because of a confluence of socioeconomic, infrastructural, and policy realities.

Effectof Unit Hydrographs and Rainfall Hyetographs on Critical Rainfall Estimates of Flash Flood

To obtain critical rainfall (CR) estimates similar to the rainfall value that causes minor basin outlet flooding, and to reduce the flash flood warning missed/false alarm rate, the effect of unit hydrographs (UHs) and rainfall hyetographs on computed threshold rainfall (TR) values was investigated. The Tanjia River basin which is a headwater subbasin of the Greater Huai River basin in China was selected as study basin. Xin’anjiang Model, with subbasins as computation units, was constructed, and time-variant distributed unit hydrographs (TVUHs) were used to route the channel network concentration. Calibrated Xin’anjiang Model was employed to derive the TVUHs and to obtain the maximum critical rainfall duration (Dmax) of the study basin. Initial soil moisture condition was represented by the antecedent precipitation index (Pa). Rainfall hyetographs characterized by linearly increasing, linearly decreasing, and uniform hyetographs were used. Different combinations of the three hyetographs and UHs including TVUHs and time-invariant unit hydrographs (TIVUHs) were utilized as input to the calibrated Xin’anjiang Model to compute the relationships between TR and Pa (TR-Pa curves) by using trial and error methodology. The computed TR-Pa curves reveal that, for given Pa and UH, the TR corresponding to linearly increasing hyetograph is the minimum one. So, the linearly increasing hyetograph is the optimum hyetograph type for estimating CR. In the linearly increasing hyetograph context, a comparison was performed between TR-Pa curves computed from different UHs. The results show that TR values for different TIVUHs are significantly different and the TR-Pa curve gradient of TVUHs is lower than that of TIVUHs. It is observed that CR corresponds to the combination of linearly increasing hyetograph and TVUHs. The relationship between CR and Pa (CR-Pa curves) and that between CR and duration (D) (CR-D curves) were computed. Warnings for 12 historical flood events were performed. Warning results show that the success rate was 91.67% and that the critical success index (CSI) was 0.91. It is concluded that the combination of linearly increasing hyetograph and TVUHs can provide the CR estimate similar to the minimum rainfall value necessary to cause flash flooding.

Migrants into Humanitarians: Ethnic Solidarity and Private Aid-Giving during Romania’s Historic Flood of 1970

This article studies the involvement of Western diaspora communities of Transylvanian Saxons in the humanitarian campaign for Romania during the flood disaster in 1970. It shows how the Saxons’ private relief campaigns established new forms of connectivity and exchange across the Iron Curtain through a transnational regime of care, ethnic solidarity, and charitable work. The article argues that the contingent and precarious situation of such spatially divided ethnic communities refashioned a generation of World War II refugees and migrants from “speechless” objects of humanitarian intervention into passionate advocates, effective mediators, and surprising champions of private aid giving in postwar Europe.

‘The Land is in Good Heart’: Flood Mitigation and the Drainage Boards in Cumbria, 1844–1985

Abstract While much research has been done to utilise historic flood data, much more work is required to understand richly nuanced historic human relationships with water qualitatively. This article combines an in-depth oral history interview with a retired Cumbrian Land Drainage and Flood Risk Management engineer, whose career spanned from 1978 to 2011, with the documentary archives of the largely overlooked local Drainage Boards (DBs) and their successors after the Land Drainage Act (1930), Internal Drainage Boards (IDBs). These boards were established across Cumbria and the rest of England from the early nineteenth century to organise the collection of communal drainage rates charged by hectare of land to fund the installation and maintenance of flood prevention infrastructure. The records of these locally-specific, flexible and relatively small drainage boards demonstrate loudly and clearly the benefits of decentralised flood management, able to respond directly to the particularities of their own catchment’s environment, residents, economy, infrastructure, topography and climatic challenges. It is vitally important to listen to the voices contained in the minute books of IDBs because they counterbalance historiographically-dominant narratives of top-down, large-scale infrastructural installations, inflexible centralisation of water governance and the powerlessness and gradual demise of many similarly small-scale, locally rooted and bottom-up organisations. The article argues that these local collectives, while far from being environmentalist, were nevertheless deeply in touch with the landscapes and waterscapes they managed and with intergenerational understanding of and respect for the watery environments within their boundaries. DBs and IDBs developed strong, deep and dynamic relationships with water as it coursed through the Cumbrian landscape. These boards also forged long-term relationships with central government and the Ministry of Agriculture. Those who served on Drainage Boards were regulators and stewards of the English landscape and their archival voices can tell us a great deal about how and why human relationships with water changed over the nineteenth and twentieth centuries.

A Hydrometeorological Assessment of the Historic 2019 Flood of Nebraska, Iowa, and South Dakota

AbstractDuring early 2019, a series of events set the stage for devastating floods in eastern Nebraska, western Iowa, and southeastern South Dakota. When the floodwaters hit, dams and levees failed, cutting off towns while destroying roads, bridges, and rail lines, further exacerbating the crisis. Lives were lost and thousands of cattle were stranded. Estimates indicate that the cost of the flooding has topped $3 billion as of August 2019, with this number expected to rise. After a warm and wet start to winter, eastern Nebraska, western Iowa, and southeastern South Dakota endured anomalously low temperatures and record-breaking snowfall. By March 2019, rivers were frozen, frost depths were 60–90 cm, and the water equivalent of the snowpack was 30–100 mm. With these conditions in place, a record-breaking surface cyclone rapidly developed in Colorado and moved eastward, producing heavy rain toward the east and blizzard conditions toward the west. In areas of eastern Nebraska, western Iowa, and southeastern South Dakota, rapid melting of the snowpack due to this rain-on-snow event quickly led to excessive runoff that overwhelmed rivers and streams. These conditions brought the region to a standstill. In this paper, we provide an analysis of the antecedent conditions in eastern Nebraska, western Iowa, and southeastern South Dakota and the development of the surface cyclone that triggered the historic flooding, along with a look into the forecast and communication of flood impacts prior to the flood. The study used multiple datasets, including in situ observations and reanalysis data. Understanding the events that led to the flooding could aid in future forecasting efforts.

An integrated approach to modeling the impact of floods on emergency services: A case study of Calgary, Alberta

Floods are becoming more frequent and the magnitude of direct consequences, relating to destruction of critical infrastructure and loss of life, has highlighted the importance of flood management. This study proposes a methodology for quantifying the impact of predicted and historic flood events on emergency services. The approach moves beyond simple flood inundation mapping by accounting for the relationship between flood depth and vehicular speed. A case study is presented for Calgary, Alberta, where the depths of a predicted 100-year flood and a historic 2013 flood event are modeled. The methodology applies geographic information systems to flood depth mapping, utilizing digital elevation models, flood extents, and hydrological data. Flood depths are then assigned to links comprising the road network, where the maximum vehicle speed is calculated as a function of the standing depth of water on a link. The flooded network is used to derive service areas for several types of emergency services (emergency medical services (EMS), fire, and police), following targeted response times. The results locate and quantify the residential and work populations that no longer meet the targeted response times. During both flood scenarios, EMS is found to have the greatest reduction in accessibility, with 23% and 47% of residents and workers, respectively, not served. Fire services are seen to be more resilient with only 3% and 9% of residents and workers, respectively, not served. The results for police services are similar to fire services. However, the former have a greater range of response times, meaning these areas represent those that are completely isolated during both flood events. Overall, the integrated methodology quantifies vulnerable populations on a partially degraded network, the results of which can be used to develop evacuation plans and emergency response strategies.

A big data-driven dynamic estimation model of relief supplies demand in urban flood disaster

The dynamic relief supplies estimation for urban flood disaster still remains an important and challenging topic in emergency response. Traditional relief supplies estimation mainly depends on static census data rather than dynamic spatio-temporal population distribution, which may easily lead to a serious imbalance between supply and demand for relief resources. The emergence of big data originating from web mapping service, social media, crowdsourcing system and other methods provides alternative data sources to understand the dynamic distribution of urban population, which can help accurately estimating the relief supplies demand when urban flood occurs. This paper proposes a big data-driven dynamic estimation model of relief supplies demand, which merges the dynamic population distribution from Baidu big data and Multilayer Perceptron (MLP) neural network so as to improve the accuracy and timeliness. Taking Wuhan as an example, a specific day in summer flooding period was chosen to dynamically predict three kinds of relief materials. The results indicate that the proposed model is more feasible to estimate the relief supplies because it is integrated with MLP neural network trained by national-wide historical flood disaster cases and Baidu big data. The utility of Baidu big data for flood-affected population statistics has been shown to be more comprehensive than that of the traditional demographic methods. Thorough analysis exhibits the superiority of our proposed model with respect to accuracy and dynamics. This allows our proposed model to be more widely applied in pre- and post-disaster operations activities carried out by government and humanitarian aid organizations.

Real Time Flow Forecasting in a Mountain River Catchment Using Conceptual Models with Simple Error Correction Scheme

Methods in operational hydrology for real-time flash-flood forecasting need to be simple enough to match requirements of real-time system management. For this reason, hydrologic routing methods are widely used in river engineering. Among them, the popular Muskingum method is the most extended one, due to its simplicity and parsimonious formulation involving only two parameters. In the present application, two simple conceptual models with an error correction scheme were used. They were applied in practice to a mountain catchment located in the central Pyrenees (North of Spain), where occasional flash flooding events take place. Several relevant historical flood events have been selected for calibration and validation purposes. The models were designed to produce real-time predictions at the downstream gauge station, with variable lead times during a flood event. They generated accurate estimates of forecasted discharges at the downstream end of the river reach. For the validation data set and 2 h lead time, the estimated Nash-Sutcliffe coefficient was 0.970 for both models tested. The quality of the results, together with the simplicity of the formulations proposed, suggests an interesting potential for the practical use of these schemes for operational hydrology purposes.

A sub-centennial-scale optically stimulated luminescence chronostratigraphy and late Holocene flood history from a temperate river confluence

AbstractRiver confluences can be metastable and contain valuable geological records of catchment response to decadal- to millennial-scale environmental change. However, in alluvial reaches, flood stratigraphies are particularly hard to date using 14C. In this paper, we use a novel combination of optically stimulated luminescence and multiproxy sedimentological analyses to provide a flood record for the confluence of the Rivers Severn and Teme (United Kingdom) over the past two millennia, which we compare with independent European climate records. The results show that by ca. 2000 yr B.P., the Severn-Teme confluence had stabilized and overbank alluviation had commenced. Initially, this occurred from moderately high flood magnitudes between ca. 2000 and 1800 yr B.P. (50 BCE–150 CE), but was followed from 1800 to 1600 yr B.P. (150–350 CE) by fine alluvial deposition and decreased flood intensity. From 1600 to 1400 yr B.P. (350–550 CE), the accumulation rate increased, with evidence of large flood events associated with the climatic deterioration of the Dark Age Cold Period. Following a period of reduced flood activity after ca. 1400 yr B.P. (ca. 550 CE), larger flood events and increase in accumulation rate once again became more prevalent from ca. 850 yr B.P. (ca. 1100 CE), coincident with the start of the Medieval Climate Anomaly, a period associated with warmer, wetter conditions and increased land-use intensity. This state persisted until ca. 450 yr B.P. (ca. 1500 CE), after which increased flood magnitudes can be associated with climatic variations during the Little Ice Age. We demonstrate that from the combination of high-resolution dating techniques and multiple analytical parameters, distinctive phases of relative flood magnitude versus flood duration can be determined to a detailed chronological precision beyond that possible from 14C dating. This permits the identification of the regional factors behind floodplain sedimentation, which we correlate with the intensification of land-use and climatic drivers over the last two millennia.

Occurrence of Microplastic Pollution at Oyster Reefs and Other Coastal Sites in the Mississippi Sound, USA: Impacts of Freshwater Inflows from Flooding.

Much of the seafood that humans consume comes from estuaries and coastal areas where microplastics (MPs) accumulate, due in part to continual input and degradation of plastic litter from rivers and runoff. As filter feeders, oysters (Crassostrea virginica) are especially vulnerable to MP pollution. In this study, we assessed MP pollution in water at oyster reefs along the Mississippi Gulf Coast when: (1) historic flooding of the Mississippi River caused the Bonnet Carré Spillway to remain open for a record period of time causing major freshwater intrusion to the area and deleterious impacts on the species and (2) the spillway was closed, and normal salinity conditions resumed. Microplastics (~25 µm–5 mm) were isolated using a single-pot method, preparing samples in the same vessel (Mason jars) used for their collection right up until the MPs were transferred onto filters for analyses. The MPs were quantified using Nile Red fluorescence detection and identified using laser direct infrared (LDIR) analysis. Concentrations ranged from ~12 to 381 particles/L and tended to decrease at sites impacted by major freshwater intrusion. With the spillway open, average MP concentrations were positively correlated with salinity (r = 0.87, p = 0.05) for sites with three or more samples examined. However, the dilution effect on MP abundances was temporary, and oyster yields suffered from the extended periods of lower salinity. There were no significant changes in the relative distribution of MPs during freshwater intrusions; most of the MPs (>50%) were in the lower size fraction (~25–90 µm) and consisted mostly of fragments (~84%), followed by fibers (~11%) and beads (~5%). The most prevalent plastic was polyester, followed by acrylates/polyurethanes, polyamide, polypropylene, polyethylene, and polyacetal. Overall, this work provides much-needed empirical data on the abundances, morphologies, and types of MPs that oysters are exposed to in the Mississippi Sound, although how much of these MPs are ingested and their impacts on the organisms deserves further scrutiny. This paper is believed to be the first major application of LDIR to the analysis of MPs in natural waters.

Historical floods within the Selenga river basin: chronology and extreme events

Large floods are among the most hazardous natural phenomena, which in many cases cause enormous losses to the economy and lead to human casualties. Along with the use of modern instrumental data, the analysis of historical information on large past floods is widely practiced in the world. This allows obtaining qualitative and quantitative characteristics of historical floods and significantly expanding the observation series. The Selenga River is one of the largest rivers of Central Asia with catchment area equal to 447,060 km2, and also it is rather flood-prone river. The hydrological regime of the Selenga River is quite well studied in the twentieth century on the basis of gauging stations data, but there is still a lack of knowledge about past floods. In this paper, we present a list of 26 known floods within the Selenga River basin from 1730 to 1900, compiled from available historical documents (newspapers, scientific reports, diaries, memoirs, etc.). We estimated peak water levels for three catastrophic floods (1830, 1869 and 1897), the historical maximum of which was 850 cm. The reliability of our estimates is confirmed by a comparative analysis of the large 1971 flood. It was revealed that the largest floods can cause a rise of the Lake Baikal water level up to 200 cm. The inflow to Lake Baikal resulting from the largest floods in the Selenga River basin is comparable to the average annual inflow of water into the lake. We can conclude that the use of historical data for the analysis of floods in Eastern Siberia is quite acceptable, but some limitations must be taken into account.

Crop residue carbon-to-nitrogen ratio regulates denitrifier N2O production post flooding

The response of nitrifier and denitrifier populations and associated N2O emissions to different carbon-to-nitrogen (C/N = 17 or 45) straw amendments was monitored under flooding-drying and non-flooding conditions. A 10-week laboratory mesocosm study was conducted in two soils, (i) a paddy soil with a long history of managed flooding-drying (CN), and (ii) a wheat cropping soil with no previous history of flooding (UK). We measured N2O fluxes and the abundances of ammonia-oxidizing archaea (AOA) and bacteria (AOB), nitrite reductase (nirK and nirS) genes, and nitrous oxide reductase (nosZI and nosZII) genes during flooding (4 weeks) and post-flooding (6 weeks). Straw addition enhanced N2O emissions, with higher fluxes apparent after incorporation of narrow C/N residues. Moreover, the impact of crop amendment on N2O emission was exacerbated when soil was under flooding-drying conditions. The abundances of nirS and nosZI genes in CN soil and AOA gene in UK soil were increased by straw amendment, with highest in narrow C/N straw amendments. Structural equation modeling showed that the impact of denitrifier gene abundance on the N2O flux was stronger than that of nitrifier gene abundance in the two soils, and significant correlations were observed between N2O fluxes and the consumption of DOC and NO3−, indicating that denitrification was the dominant N2O production pathway during the drying phase. The ratio of (nirS + nirK)/(nosZI + nosZII) in the narrow C/N amendment was greater than in the wide C/N treatment after flooding, suggesting that the straw C/N ratio had an effect on the capacity for N2O production via denitrification. We conclude that crop amendments with an appropriate C/N ratio could minimize N2O fluxes through regulating the denitrification process when soils are subjected to regular flooding and drying and also experiencing greater frequencies of flooding.

Single-Pot Method for the Collection and Preparation of Natural Water for Microplastic Analyses: Microplastics in the Mississippi River System during and after Historic Flooding.

We describe a simple single-pot method for collection and preparation of natural water for microplastic analyses. The method prepares samples in the same vessel (mason jars) that they are collected in right up until the microplastics are transferred onto filters or spectroscopic windows for analyses. The method minimized contamination, degradation, and losses, while increasing recoveries and throughput when compared with conventional sieving. We applied it to surface grab samples collected from the Mississippi River and its major tributaries during and after historic flooding in 2019. Microplastics (>~30 µm) were quantified using Nile red fluorescence detection, and a small subset of samples were identified by micro-Fourier transform infrared imaging spectroscopy. Concentrations were lower during the flooding, likely due to dilution. Concentrations ranged from approximately 14 microplastics/L in the Tennessee River during flooding to approximately 83 microplastics/L in the Ohio River during low-flow (summer) conditions. Loads of microplastics tended to increase downriver and ranged from approximately 87 to approximately 129 trillion microplastics/d near New Orleans. Most of the microplastics (>60%) were in the lower size fraction (~30-90 µm) and consisted primarily of fragments (~85%), followed by fibers (~8%) and beads (~7%), with polyester, polyethylene, polypropylene, and polyacrylate as the primary microplastic types. Overall, we demonstrate that the single-pot method is effective and versatile and, because it uses relatively inexpensive and easily assembled materials, can be adapted for microplastic surveys worldwide, especially those involving sample collection by volunteers from the community and schools. Environ Toxicol Chem 2020;39:986-995. © 2020 SETAC.

SMOTE-Boost-based sparse Bayesian model for flood prediction

With a significant development of big data analysis and cloud-fog-edge computing, human-centered computing (HCC) has been a hot research topic worldwide. Essentially, HCC is a cross-disciplinary research domain, in which the core idea is to build an efficient interaction among persons, cyber space, and real world. Inspired by the improvement of HCC on big data analysis, we intend to involve related core and technologies to help solve one of the most important issues in the real world, i.e., flood prediction. To minimize the negative impacts brought by floods, researchers pay special attention to improve the accuracy of flood forecasting with quantity of technologies including HCC. However, historical flood data is essentially imbalanced. Imbalanced data causes machine learning classifiers to be more biased towards patterns with majority samples, resulting in poor classification of pattern with minority samples. In this paper, we propose a novel Synthetic Minority Over-sampling Technique (SMOTE)-Boost-based sparse Bayesian model to perform flood prediction with both high accuracy and robustness. The proposed model consists of three modules, namely, SMOTE-based data enhancement, AdaBoost training strategy, and sparse Bayes model construction. In SMOTE-based data enhancement, we adopt a SMOTE algorithm to effectively cover diverse data modes and generate more samples for prediction pattern with minority samples, which greatly alleviates the problem of imbalanced data by involving experts’ analysis and users’ intentions. During AdaBoost training strategy, we propose a specifically designed AdaBoost training strategy for sparse Bayesian model, which not only adaptively and inclemently increases prediction ability of Bayesian model, but also prevents its over-fitting performance. Essentially, the design of AdaBoost strategy helps keep balance between prediction ability and model complexity, which offers different but effective models over diverse rivers and users. Finally, we construct a sparse Bayesian model based on AdaBoost training strategy, which could offer flood prediction results with high rationality and robustness. We demonstrate the accuracy and effectiveness of the proposed model for flood prediction by conducting experiments on a collected dataset with several comparative methods.