Local Flood Research Articles

The assessment of Kunovat sanctuary natural complexes in the area of the Siberian white crane (<i>Leucogeranus leucogeranus</i>) reintroduction

The paper deals with the research of soils, vegetation, bird and mammal population composition and structure in the vicinity of the white crane field research station in the Kunovat sanctuary (Kunovat Zakaznik), northern taiga subzone, Shuryshkarsky District, Yamalo-Nenets Autonomous Okrug, Russia. The aim of this investigation is to assess the present state of natural complexes in the area of the critically endangered Leucogeranus leucogeranus Ob population reintroduction. It has been established that various components of the natural complexes under study are generally consistent with the zone-and-province landscape, climate and biotope peculiarities of the area, which is a sign of their good health and relative stability. At the same time, in small mammal communities (an indicator group) the resistant sustainability index has been higher than the resilient sustainability index for two years in a row, which is characteristic of disturbed habitats or of early ecological succession stages. Such imbalance may be accounted for by local wild fires and floods in the natural complex under study. Botaurus stellaris nests have been registered in different parts of the quaking (white crane) bog, whose breeding grounds used to lie further to the south. This is evidence that the species range moves to the high latitudes and that the global climate change influences the natural complexes in the historic breeding area of the West-Siberian white crane population.

Reviewing Challenges of Flood Risk Management in Malaysia

This study reviewed some of the challenges faced by local authorities in disaster management, especially flood disasters that occurred in Malaysia. Flood disasters are the most frequent disasters in Malaysia, especially during the monsoon seasons. The hard structure developed by Malaysia’s National Security Council (MKN) under ‘Directive 20’ is used to manage disasters in the country. Although Malaysia has become more skillful in managing flood disasters, the frequent climate changes along with weakness in implementing flood risk management plans resulted in much losses and damages throughout the country. Therefore, this study explored the gaps and weaknesses in flood risk management (FRM) in Malaysia by reviewing the available literature to recommend better flood management. This study revealed four main issues which are weaknesses in terms of (i) coordination and communication, (ii) manpower and assets, (iii) public awareness, and (iv) power and authority among local authorities to implement flood management plans. The capacity of local authorities and individuals in charge of disaster management is inadequate, especially for flood risk preparedness and management. Hence, responsible individuals are also in a vulnerable situation to implement management plans or rescue operations when flood disasters occur since they are also flood victims. Thus, the National Disaster Management Agency (NADMA) which acts as the main coordinator of disaster management in Malaysia should re-examine the flood management plan to ensure that it can be implemented efficiently and effectively, especially at the local level as they are the first respondents on the scene when the disaster occurs. The combination of both structural and non-structural measures might require in many cases the management of flood disasters; however, the disaster risk preparedness and management of individuals via customized training is a must to prevent flood disasters as well as minimize their impact. The flood management plan should also incorporate natural-based approaches at the whole-river-basin level for the long-term solution and sustainable development, not only focusing to manage the localized flood problem at the specific area.

Motivations for urban front gardening: A quantitative analysis

Private gardens in urban settings offer multiple benefits for the environment and society. In addition to benefits to people’s health and well-being, planting in front gardens in particular can mitigate local flooding and urban heat islands. To encourage more front garden planting, greater understanding of householders’ motivations for front gardening is needed. Addressing research gaps on gardening for reasons other than food production and on motivations for gardening in front gardens, a large-scale online survey (n = 1,000) was conducted with urban/suburban dwellers in England. Exploratory factor analysis identified three factors of motivation: enjoyment, meaning and benefit (intrinsic), creating something beautiful (aesthetic) and functional outcomes (utilitarian). A multiple regression model incorporating the three factors and sociodemographic variables explained 11% of variance of time spent front gardening, with intrinsic motivations the strongest predictor. Intrinsic motivations were stronger for women than for men. The study provides a quantitative categorisation of motivational factors as a basis for comparative research and design of interventions and policy to increase front gardening.

Performance measures of frequency ratio, weight of evidence and AHP models for flood risk assessment of Panchganga river basin (PRB), Kolhapur (India)

Flood events are the most common and frequent natural disasters causing enormous loss to property and life worldwide. In this study, the Panchganga River Basin (PRB) was selected for flood susceptibility mapping using the frequency ratio (FR), weight of evidence (WoE) and Analytical Hierarchy Process (AHP) model with remote sensing and GIS based approach. For this, a flood inventory with 224 historic flood locations was prepared using Google Earth Engine. About 75% of flood locations were randomly selected for training and 25% for testing purposes. The flood susceptibility map prepared using FR, WoE and AHP model was classified into five zones such as very high, high, moderate, low risk and safe zone using ArcGIS software. The receiver operating characteristic (ROC) curves were plotted with area under curve (AUC) of 89.27%, 87.34% and 80.60% for FR, WoE and AHP models respectively. The present study will help people near Panchganga river to move to a safer place during flooding. The flood risk zone maps are extremely helpful in infrastructural developments, town planning and civil protection.

Assessment of the drainage systems performance in response to future scenarios and flood mitigation measures using stormwater management model

Urban Flooding represents a significant problem in developing urban areas caused by inadequate stormwater drainage systems, increased impervious surface areas, and climate change. The performance of drainage systems can be evaluated using simulation models such as the stormwater management simulation model (SWMM). In this study, the potential impacts of future design rainfall on the performance of stormwater management systems were assessed by altering a hyetograph derived from rainfall intensity duration curves from three climate models. In addition, the effects of continued urbanization on the systems have been simulated by changing the percentage of imperviousness from current land use conditions. According to the findings, existing drainage systems cannot manage the expected flooding risks caused by a slight change in future rainfall intensity under each climate model. As a result of urban development, increasing the imperviousness ratio from 10% to 70% has increased peak runoff from 51.3 to 82.4 m3/s, flooding volume amplified from 24,320.5 x103 to 33,647.4 x103 m3 (representing 38.4% of the increase), and flooded nodes risen from 64 to 196 (representing 67.12% of raise). Overall, flooding locations and magnitude were identified, while drainage systems failed to safely convey surface runoff at baseline conditions, implying that future flooding will be more intense. As a result, selected mitigation strategies should be considered to alleviate the flooding risks that disrupt the socio-economic environment and the resulting significant property and life losses in Dodola, Ethiopia.

Optimal Scheduling of Rainwater Collection Vehicles: Mixed Integer Programming and Genetic Algorithms

Due to climate change, some areas in the world witnessed higher levels of heavy rain than the capacity of the wastewater system of the streets. Therefore, water tankers are used for the dewatering process to take the extra rainwater from the streets to keep a smooth flow of vehicles and to use the water in agriculture and industry. Water is taken to a water treatment plant. Performing the dewatering process as fast as possible, especially in crowded streets, was ignored by researchers. In this study, at first, the problem was solved using two mixed integer programming (MIP) models. A new variant of identical parallel machine scheduling with job splitting is proposed for the first time, where one or at most two tankers can work at the same flood location at the same time. This is performed in the second model. However, the first model considers dividing the dewatering processes into two phases, where the first one, which is more urgent, is to reduce the amount of floodwater. The second one is for dewatering the rest of the water. Then two genetic algorithms (GAs) were used to solve faster the two MIP models, which are NP-hard problems. At first, the MIP and GA models were applied to small-sized problems. Then GA was used for large practical data sets. Results showed that for small problems, MIP and GA gave optimal solutions in a reasonable number of iterations, while for larger problems, good solutions were obtained in a reasonable number of iterations.

Co-occurrence of pluvial and fluvial floods exacerbates inundation and economic losses: evidence from a scenario-based analysis in Longyan, China

Hydrodynamics and socio-economic impacts of compound floods as the result of co-occurring pluvial and fluvial floods, have not been well studied, which challenges conventional urban flood risk management that treats different types of floods separately. This study generates a high-resolution land surface digital surface model based on images from unmanned aerial vehicles, and incorporates it into a combined flooding model that integrates the 1D river hydrodynamic model, 1D urban drainage hydrodynamic model and 2D overland flow model, in order to simulate the inundation characteristics of extreme floods when local pluvial floods coincide with upstream fluvial floods, and in a further step to quantify the direct economic losses on urban physical systems. As demonstrated by an empirical study of Longyan, China, the combination of pluvial and fluvial floods leads to substantial exacerbation in inundation extent and depth. Consequently, the joining of pluvial and fluvial floods greatly amplifies economic losses to urban physical systems, indicating that ignoring co-occurrence of different types of floods will potentially lead to underestimation of flood risk and insufficient adaptation. Our study emphasizes the need to account for the co-occurrence of multiple types of floods in flood risk assessment and management, to avoid the shortcomings of fragmented responses.

Predicting land use effects on flood susceptibility using machine learning and remote sensing in coastal Vietnam

Abstract Flood damage is becoming increasingly severe in the context of climate change and changes in land use. Assessing the effects of these changes on floods is important, to help decision-makers and local authorities understand the causes of worsening floods and propose appropriate measures. The objective of this study was to evaluate the effects of climate and land use change on flood susceptibility in Thua Thien Hue province, Vietnam, using machine learning techniques (support vector machine (SVM) and random forest (RF)) and remote sensing. The machine learning models used a flood inventory including 1,864 flood locations and 11 conditional factors in 2017 and 2021, as the input data. The predictive capacity of the proposed models was assessed using the area under the curve (AUC), the root mean square error (RMSE), and the mean absolute error (MAE). Both proposed models were successful, with AUC values exceeding 0.95 in predicting the effects of climate and land use change on flood susceptibility. The RF model, with AUC = 0.98, outperformed the SVM model (AUC = 0.97). The areas most susceptible to flooding increased between 2017 and 2021 due to increased built-up area.

Assessment of the Feasibility of Implementing a Flash Flood Early Warning System in a Small Catchment Area

The occurrence of flash floods is an increasingly common phenomenon. In many parts of the world, it is associated with an increase in the intensity of rainfall. Reducing the financial and social losses caused by the occurrence of local urban floods is possible through the use of hydrodynamic modeling and real-time flood forecasting. The purpose of this study is to assess the ability of the modeling technique to simulate the flow in a small catchment area and to determine the time remaining to reach the set warning and danger levels. SWMM 5.2 and QuantumGIS software were used in the study. The analysis showed that for the considered catchment area with a short length of the main stream (1612 m), the time possible for implementing countermeasures and evacuating the population is 70 to 120 min. The study also confirmed that short-term rainfall requires less depth to reach high stormwater elevations than long-term rainfall. In addition, a relationship was noted between the preceding rainfall and the height of stormwater and the forecast time. There was an unfavorable reduction in forecasting time as the depth of rainfall increased and its duration shortened. In the case of the analyzed catchment, the maximum elevation of stormwater (Esw,max) is generated by rainfall that is characterized by the highest intensity in the final phase of their duration. Similarly, the longest forecast time (tf) for the maximum stormwater elevation is caused by rainfall, which is characterized by the highest intensity in its final phase. The results of the study can significantly assist local governments when developing a catchment management plan and when trying to implement practices to minimize the negative effects of flash floods.

GIS-based machine learning algorithm for flood susceptibility analysis in the Pagla river basin, Eastern India

The unique characteristics of drainage conditions in the Pagla river basin cause flooding and harm the socioeconomic environment. The main purpose of this study is to investigate the comparative utility of six machine learning algorithms to improve flood susceptibility and ensemble techniques' capability to elucidate the underlying patterns of floods and make a more accurate prediction of flood susceptibilities in the Pagla river basin. In the present scenario, the frequency of flood conditions in this study area becomes high with heavy and sudden rainfall, so it is essential to study flood mitigation and measure. At first, a spatial flood database was built with 200 flood locations and sixteen flood influencing factors, and its process with the help of the Geographic Information System (GIS) environment and build up different models applying the machine learning techniques. It has found different flood susceptibility zone using machine learning-based Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), Reduced Error Pruning Tree (REPTree), Logistic Regression (LR), and Bagging helping GIS environment and the model validation using the Receiver Operating Characteristic (ROC) Curve. Afterward, ensemble all the models to gate comparative accuracy of the flood zone. The calculated areas are under the very high flood susceptibility zone 8.69%, 14.92%, 14.17%, 12.98%, 14.65%, 13.24% and 13.41% for ANN, SVM, RF, REPTree, LR and Bagging, respectively. Finally, ROC curve, the Standard Error (SE), and the Confidence Interval (CI) at 95 per cent were used to assess and compare the performance of the models. The obtained results indicate that all models are highly accepted Area Under Curve (AUC) of ROC between 0.889 (LR) to 0.926 (Ensemble). From the estimation of the accuracy of the applied methods using ROC, it is found that the Ensemble model has the higher capability compared to the other applied models in projecting flood susceptibility in ​the ​study ​area. It has the highest area under the ROC curve the AUC values are 0.918 and 0.926, the SE (0.023, 034), and the narrowest CI (95 per cent) (0.873–0.962, 0.859–0.993) whereas highest area under Bagging (the ROC) curve (AUC) value (0.914, 0.919), for both the training and validation datasets. After ensembling, the result shows that the result is a highly flood susceptible area located at the lower part of the study area. In this area, the very high flood susceptibility zone values lie between 4.46 and 6.00 in the ensemble result. The areas comprise the low height and belong to Murarai I, Murarai II, Suti I and Suti II C.D. block of West Bengal. The current study will help the policymakers and the researcher determine the flood conditioning problems for prospects.

Dampak Sosial Ekonomi Bencana Banjir dan Pemetaannya Berbasis Sistem Informasi Geografis (SIG)

The purpose of this study was to determine the distribution of flood locations in the Rokan Hulu regional administrative area and to determine the socio-economic impact of flooded households by modeling the distribution of flood locations based on Geographic Information Systems (GIS). The population is residential residents of Rokan Hulu Regency and floodplain sampling points located in the floodplains. The possibility of a flood disaster in Rokan Hulu Regency is very vulnerable and is divided into 16 districts. The research data was evaluated using point analysis method. There are two types of spatial mapping used, namely spatial mapping and spatial overlay. Spatial mapping is a description of conditions on a map, while spatial overlay is an analysis of several maps to create a new map. This mapping produces a flood hazard map, as well as a combination of point analysis and coverage analysis.

Simultaneous measurement of local current and temperature distributions in proton exchange membrane fuel cells with dead-ended anode

The dead-ended anode (DEA) operation could cause local flooding and local hydrogen starvation in proton exchange membrane fuel cells (PEMFCs), resulting in drastic changes in fuel cell performance. In order to study the detailed local dynamic characteristics of DEA operation, simultaneous measurements of the dynamic variation of local current and local temperature in a PEMFC with DEA have been performed using a local current measurement gasket and micro thermocouples. The results show that the local fuel cell performance distribution gradually becomes uneven during DEA operation and the evolutions of local current and temperature strongly depend on the locations along the flow channel. The highest local current and local temperature are observed near the anode inlet, posing serious threats for the local fuel cell. Furthermore, with anode purging, the current distribution becomes uniform quickly due to the fast mass transfer capacity of hydrogen, but the local temperatures take much longer time to become uniform and stable because of the thermal capacity of fuel cell components. The anode purging also causes local temperature undershoots and the amplitude is over 4℃ in this study. Finally, the effects of operating conditions are discussed. The local current and temperature distributions become more uneven as overall operating current increases and operating temperature decreases. The local temperatures are all reduced as anode inlet pressure and air stoichiometry increase due to the enhanced convection cooling effect. It is demonstrated that measuring local current and temperature is effective in investigating local dynamic performance of PEMFCs with DEA.

Geoinformatics Perspective of Landslide and Catastrophic Flash Floods in Dhauliganga, Uttarakhand, India

On 07 February 2021, around 10:30 hrs local time catastrophic flash flood occurred in the Dhauliganga River (a tributary of the Ganga River) near Rini village at 2000 m above MSL (mean sea level) (Chamoli District), which killed 79 people and about 125 people were missing. Part of the area belongs to Nanda Devi Biosphere Reserve, which is completely protected from human interventions. Further, on the Dhauliganga River, two run-of-river hydroelectric power Rishiganga Small Hydro (13.2 MW) at 1975 m above MSL and Tapovan Vishnugad (520 MW) at 1795 m above MSL projects were also severely damaged due to the devastating flash flood. More than 150 workers were also trapped in the under-construction power tunnel of the Tapovan Vishnugad project. Initial assessment on the day of the event suggested that there was a glacial burst. Later, it was evaluated through time series of high spatial resolution remote sensing images of various satellites that a large part of a north-facing triangular-shaped slope at 5540 m above MSL had failed, which was also supporting a small hanging glacier. This landslide and, consequently, massive debris flow into the Raunthi Gadhera initially blocked the flow of Dhauliganga near Rini village [at 2000 m above MSL (mean sea level)], which later failed around 10:30 hrs on 07 February 2021 and brought a catastrophic flash flood in the Dhauliganga river. Further, remote sensing images acquired around 10:33 hrs of 07 February 2021 revealed a large dust cloud which clearly unravels the sequence of events from a high-altitude landslide, collapse of a small hanging glacier, and snow avalanche to catastrophic flooding. Even after the catastrophic flash flood of 07 February 2021, an elongated lake was created due to the blocking of the flow of the Rishi Ganga River. For detailed analysis, the calculation of dimension, area and volume of the failed slope was done using the high-resolution satellite images and digital elevation model using the RAMMS modelling technique. The north-facing triangular shape had a base of about 660 m and 1100 m height and the estimated total volume calculated was 20 million cubic meters, including rocks, snow, and ice. The debris flow runout simulation of the event was performed using the RAMMS debris flow model to calculate flow depth, flow velocity and maximum pressure. Also, from high-resolution satellite images, the dimensions of the artificial Rini Lake were estimated to have a length of about 800 m, a width at the front of about 100 m and a depth of about 46m, including freshly deposited debris and silt of about 10 m. To calculate the volume of the lake, simulation of lake was done in ArcView software using digital elevation model, and it came out to be ~5 million cubic meters. The paper also emphasizes monitoring of such vulnerable areas based on high-resolution time series satellite images, which are available on a regular basis to avoid the loss of human lives in the future.

River's Edge Construction: Third-Grade Students Engineer Solutions to Prevent Local Flooding

River's Edge Construction: Third-Grade Students Engineer Solutions to Prevent Local Flooding

The Impact of Flooding on Snail Spread: The Case of Endemic Schistosomiasis Areas in Jiangxi Province, China.

Flooding is the main natural factor in snail diffusion, and it has a negative impact on schistosomiasis transmission. There are few studies on the spread and migration of snails following a flood; therefore, we aimed to investigate the influence of flooding on snail diffusion and explore the characteristics and laws of snail diffusion in Jiangxi Province. By using a retrospective survey and cross-sectional survey, the data on snail spreading in Jiangxi Province from 2017 to 2021 were collected. The distribution, nature, and area of snail spread were systematically analyzed in combination with the hydrological situation, types of region, and types of flood. From 2017 to 2021, a total of 120 snail-spread environments were found, including in 92 hilly areas and in 28 lake areas. The areas caused by flood and by other means numbered 6 and 114, respectively. The proportions of recurrence, expansion, and first-time occurrences were 43.42%, 38.16%, and 18.42%, respectively, and the 14 new snail environments were only distributed in the hilly areas. With the exception of 2018, the ratio of snail-spread areas in the hilly region was higher than that in lake region in other years. The average density of live snails was 0.0184-1.6617 no./0.1 m2 and 0.0028-0.2182 no./0.1 m2 in the hilly region. Among the 114 environments affected by floods, 86 consisted of hilly environments, including 66 spreading environments affected by rainstorm floods, and 20 rainstorm debris flow environments. There were 28 lake areas, of which 10 were in the Jiangxi section of Yangtze River and were affected by rainstorm floods. Snail spread following flooding has a certain 'lag effect,' and = simple annual changes in hydrological characteristics have little effect on the diffusion of snails or on their density = in the affected environment, but it is more closely related to local floods. The hilly environments are more susceptible to floods than the lake region, and the risk of snail spread is much higher in the hilly than in the lake region.

A planning framework to mitigate localized urban stormwater inlet flooding using distributed Green Stormwater Infrastructure at an urban scale: Case study of Dallas, Texas

Mitigation of localized pluvial flooding is one of the major resiliency goals in urban environments, and Green Stormwater Infrastructure (GSI) has the potential to deliver such an outcome. However, there is a lack of systematic approaches to prioritize investment in different candidate areas. This study provides a framework to identify vulnerable stormwater drainage inlets and their contributing areas, prioritize them, identify dominant factors in their selection, assess the potential of GSI in mitigating their overflows, and compare the impact and its cost to gray infrastructure upgrade alternatives. Using SWMM 5.1.013, decision trees, and a volumetric-based assessment of GSI overflow capture, we applied the framework to the City of Dallas, Texas, for three design storms with three GSI practices— bioretention cells, raingardens, and rainwater harvesting tanks. Results showed that there was a significant increase in the number of overflowing stormwater drainage inlets, referred to as hotspots, and their contributing subwatersheds, referred to as opportunity areas, with more intense storms especially in problematic watersheds. Also, prioritization results provided a series of maps to rank the opportunity areas based on overflow severity, recurrence of the overflows, and GSI availability. Moreover, classification results showed that inlet features, especially the inlet depth, were the dominant factors in the identification of the non-problematic inlets. Finally, the GSI impact assessment showed substantial overflow mitigation even at the “very high” severity levels when GSI is comprehensively deployed across opportunity areas. Despite gray infrastructure upgrades yielding higher reduction levels, their cost per cubic meter was higher than GSI. Therefore, a combination of GSI and gray results in maximum overflow reduction at a lower cost compared to common practices.

Environmental drivers of spatio-temporal dynamics in floodplain vegetation: grasslands as habitat for megafauna in Bardia National Park (Nepal)

Abstract. Disturbance-dependent grasslands, often associated with hydromorphological and fire dynamics, are threatened, especially in subtropical climates. In the Nepalese and Indian Terai Arc Landscape at the foot of the Himalayas, natural and cultural grasslands serve a viable role for greater one-horned rhinoceros (Rhinoceros unicornis) and for grazers that form prey of the Royal Bengal tiger (Panthera tigris). The grasslands are vulnerable to encroachment of forest. We aimed to establish the effects of environmental drivers, in particular river discharge, river channel dynamics, precipitation and forest fires, on the spatio-temporal dynamics of these grasslands. The study area is the floodplain of the eastern branch of the Karnali River and adjacent western part of Bardia National Park. We created annual time series (1993–2019) of land cover with the use of field data, remotely sensed LANDSAT imagery and a supervised classification model. Additionally, we analysed the pattern of grassland patches and aerial photographs of 1964. Between 1964 and 2019, grassland patches decreased in abundance and size due to encroachment of forest. Outside the floodplain, conversion of grassland to bare substrate coincides with extreme precipitation events. Within the floodplain, conversion of grassland to bare substrate correlates with the magnitude of the annual peak discharge of the bifurcated Karnali River. Since 2009, however, this correlation is absent due to a shift of the main discharge channel to the western branch of the Karnali River. Consequently, alluvial tall grasslands (Saccharum spontaneum dominant) have vastly expanded between 2009 and 2019. Because the hydromorphological processes in the floodplain have become more static, other sources of disturbances – local flooding of ephemeral streams, anthropogenic maintenance, grazing and fires – are more paramount to prevent encroachment of grasslands. Altogether, our findings underscore that a change in the environmental drivers impact the surface area and heterogeneity of grassland patches in the landscape, which can lead to cascading effects for the grassland-dependent megafauna.

Perspectives on factors that influence local communities’ vulnerability to coastal floods in Ketu South Municipality of Ghana

Vulnerability studies in relation to coastal floods in Ghana have generally focused on assessing the biophysical vulnerability and flood hazard modelling - rather than exploring the combination of both biophysical and socioeconomic factors that affect the vulnerability of coastal communities to floods. In response, this study aimed to explore the factors that influence the vulnerability of coastal communities in Ketu South Municipality, Ghana, to floods. These communities remain vulnerable to coastal flood events from storm surges, high tidal waves, lagoon overflow and heavy rainfall. However, the local conditions that make these communities vulnerable is poorly understood. The study used data from eight (8) in-depth interviews, nine (9) Focus Group Discussions and 354-household survey to explore local vulnerability factors in the study area. Results show that factors such as low land elevation, inadequate sea defence structures, high flood frequency, large flood extent, long flood duration, flood impacts, are among the exposure factors influencing vulnerability in the study area. It also shows that socioeconomic factors; such as weak demographic groups, low-income levels, low level of education, and some local adaptation measures plus external flood relief supports from National Disaster Management Organization (NADMO) influence the vulnerability of the communities. The result further revealed salt production activities, and relocating to flood prone areas, as well as rise in coastal water table and rapid shoreline erosion are the observable vulnerability factors in the study area whiles gender was a significant social factor influencing flood preparedness. These results can be useful for flood disaster management programs.

Analysing networks of networks

We consider data with multiple observations or reports on a network in the case when these networks themselves are connected through some form of network ties. We could take the example of a cognitive social structure where there is another type of tie connecting the actors that provide the reports; or the study of interpersonal spillover effects from one cultural domain to another facilitated by the social ties. Another example is when the individual semantic structures are represented as semantic networks of a group of actors and connected through these actors’ social ties to constitute knowledge of a social group. How to jointly represent the two types of networks is not trivial as the layers and not the nodes of the layers of the reported networks are coupled through a network on the reports. We propose to transform the different multiple networks using line graphs, where actors are affiliated with ties represented as nodes, and represent the totality of the different types of ties as a multilevel network. This affords studying the associations between the social network and the reports as well as the alignment of the reports to a criterion graph. We illustrate how the procedure can be applied to studying the social construction of knowledge in local flood management groups. Here we use multilevel exponential random graph models but the representation also lends itself to stochastic actor-oriented models, multilevel blockmodels, and any model capable of handling multilevel networks.

Dam Incidents and Failures – Cases in the Czech Republic

Abstract Dam incidents and failures mainly occur during extreme floods. In the Czech Republic (CR), large-scale regional floods were recorded in different regions in 1985, 1987, 1997, 2002, 2006, 2010, and 2013, while local floods especially occurred in 1991, 1996, 1997, 1998, 2002, 2004, 2005, 2009, and 2010. During these events, numerous incidents and total breaches of small dams were recorded, and a few large dams were also critically endangered, although they were not completely breached. This paper presents a comprehensive summary of incidents and failures affecting small dams in the CR since 1985. The most significant incidents concerning large dams are listed as well. The statistics show that 62 small dams were completely breached and that 350 were seriously damaged over 35 years in the period 1985–2020. The annual frequency amounts to 1.85 collapsed and 10 small damaged dams per year. The most common causes of the complete breaching of dams were overtopping (85 %) and internal erosion (15 %).