Hydraulic Model for Flood Forecasting of Tajabad Khwar in Hayatabad Phase III Peshawar, Pakistan: A Case Study

Among all other natural disasters occurring throughout the world, floods are considered to be the worst and most devastating catastrophe as it causes loss of billions of lives. Flooding is caused due to inundation of water over the areas which are in close proximity of river or natural waterways resulting in severe damages to commercial and residential areas in the surrounding. Thus, an efficient flood forecasting system through the development of a combined hydrological and hydraulic model for the prediction of future flood events through marking the potential high-risk zone is required to minimize the damages. Due to large number of encroachments made in the waterway of Tajabad khwar located near Deans Residential Apartment of Hayatabad Phase III, a hydraulic model is developed for its flood forecasting as the floods in this khwar may cause severe damages to the inhabitants of the adjacent areas. In this research work, Flood zone maps are developed for 10 years, 20 years, 50 years, and 100 years flood return periods in order for deterring extent of the inundation as a result of these encroachments and to identify the areas at potential risk. Flood discharge for each return period was estimated using HEC-HMS software and was found to be 772, 1036, 1392 and 1666 m^3/sec for 10 years, 20 years, 50 years, and 100 years flood return periods respectively. The corresponding water surface elevation determine using HEC-RAS and was found to be 196 m, 197m, 201m, 202m. This model provides a basic idea for developing flood zone maps of a given period of return for the assessment of areas that can get adversely pretentious by floods.

A fish keeper’s life lessons: Implications for resilience promotion

A self-care crisis follows a predictable, four-step pattern: Tension-building, flood zone, recovery, equilibrium. When we learn to identify our tension-building signals and intervene quickly by using tailored coping strategies, we often are able to alleviate emotional distress and regain equilibrium. This article identifies three self-reflective exercises that could be used to increase insight, identify early signals of self-care tension-building, and encourage intentional selection of coping strategies.

TRANSFORMATION OF FLOODPLAIN LANDSCAPES UNDER THE INFLUENCE OF NATURAL AND ANTHROPOGENIC CHANGES IN THE ENVIRONMENT (ON THE EXAMPLE OF THE ZAKHARKOVO FLOODPLAIN OF THE MOSCOW RIVER)

The Zakharkovskaya floodplain of the Moscow River is located directly above the Moscow Ring Road. It is an example of a natural structure that is spontaneously restored after long-term economic use (hayfields, pastures, long-term plowing, summer cottege settlements, drainage and irrigation channels, silt sites, etc.) under conditions of cessation of intensive anthropogenic impact. The article deals with the history of the formation and development of the Zakharkovskaya floodplain in prehistoric times (late Holocene), in the early historical period (beginning-middle of the second millennium AD), and in the late historical period-XIX-XXI centuries. The regulation of water flow, which has occurred in recent decades, is accompanied by a relative stabilization of the Moscow riverbed, turning it into a canal, reducing the intensity of channel deformations, leveling the bottom relief with its general deepening. As in other floodplain plots, the territory of floodplain leaves the flood zone; depressions are filled in, lakes are degraded, and man-made terrain is formed. At the same time, the current restrictions on access to the territory of the Zakharkovskaya floodplain for 10-15 years have created conditions for the restoration of natural structures, in particular, forest and shrub tracts, and increased the landscape and ecological attractiveness of floodplain lands.

On the Cluster Validity Test (s) in Unsupervised Machine Learning TDA Approach for Atmospheric River Patterns on Flood Detection in Nigeria

TDA (i.e., Topological Data Analysis) has recently been a reliable and current research area in Statistics for extracting shape (information) from data. In this study, the researchers proposed an automated method that uses TDA & ML in identifying floods (ARs) in big data. Our process gives vital details on time series trends, which help mitigate the negative effect of ARs, such as flooding. The spatial data (between 1970 - 2018) from Nigeria Hydrological Services Agency (NIHSA) on four weather parameters were used. The daily datasets were converted to monthly datasets before the proposed method was applied. Python Software is used to develop code in the implementation of our process. Mostly, the outcome facts studied will drastically reduce disasters due to extreme events like floods and achieve some SDG goals related to the flood. The second objective is to identify potential flooding and no flooding in each zone. The work successfully used a real dataset and four variables that other studies have not used to fill a gap. After our model's training process, we obtained the best group at k = 2, where we have the highest Silhouette coefficient in each of the seven states. We have found a reasonable structure in the study considering the total average range (0.3 - 0.8). That gives an efficiency outcome of approximately 80%. Summary of clustered feature pattern shows the potential flood zone and no flood zone. We conducted cluster validity of our results using R software codes and, the test validated the best group at the same cluster k = 2. The Gap statistic shows efficiency ranging between 65% to 80% in the seven states. We found from figure 11 that only the Silhouette plot obtained optimal values at exactly k = 2; The researchers got the extent of the spread from the centroid using Excel software.

Methodology for Determining the Nearest Destinations for the Evacuation of People and Equipment from a Disaster Area to a Safe Area

Floods are the most frequent natural disasters in the world. In the system of warning and flood protection of areas at risk of flooding in the event of its occurrence, it seems advisable to initially work out the possibility of evacuating the population, animals, equipment, material values, etc. In this article, a methodology for determining destinations (points of destination) for the evacuation of people and equipment from a predicted flood zone (of a natural disaster) to a safe area is proposed based upon the criterion of the shortest possible distance. In the paper, a scenario is considered that involves the contours of the flood zone boundaries for several variants of the intensity of the probable development of future events (with the aid of geoinformation technologies), and the coordinates of the objects to evacuate are permanent and known in advance. With the known coordinates of the objects and the closest points of the boundary of the predicted flood zone, the shortest distances can be calculated. Based on these calculations, the appropriate destinations for evacuation are determined. The proposed methodology can be used for flood forecasting and flood zone modeling to assess the economic and social risks of their aftereffects and to allow the public, local governments, and other organizations to better understand the potential risks of floods and to identify the measures needed to save lives and avoid damage to and loss of property and equipment. This methodology, in contrast to known approaches, allows the determination of the nearest locations for the evacuation of people and equipment from a flood zone (of a natural disaster) to safe areas, to be determined for several variants, depending on the possible development of future events. The methodology is algorithm-driven and presented in the form of a flowchart and is suitable for use in the appropriate software. The proposed methodology is an introduction to the next stages of research related to the determination of safe places for evacuation of people and their property (equipment) to safe places. This is especially important in case of sudden weather events (flash floods).

Household Evacuation Planning and Preparation for Future Hurricanes: Role of Utility Service Disruptions

We analyzed data from a survey administered to 1,212 respondents living in superstorm Hurricane Sandy-affected areas. We estimated the effect of having experienced hurricane-induced disruptions to utility services, such as electricity, water, gas, phone service, and public transportation, on having an evacuation plan. Around 39% of respondents reported having an evacuation plan in case a hurricane affects their neighborhood this year. Respondents who had experienced disruptions to electricity supply had an approximately 11 percentage-point higher likelihood of having an evacuation plan than those who had experienced no such disruptions. Respondents who had experienced monetary losses from Hurricane Sandy had around a five percentage-point higher likelihood of having an evacuation plan compared with those who had not. Among control variables, prior evacuation, distance to the coastline, residence in a flood zone, concern about the impacts of future natural disaster events, had window protection, and household members being disabled, each had an association with residents’ future evacuation planning and hurricane preparedness. In light of these findings, we discuss the policy implications of our findings for improving disaster management in hurricane-prone areas.

FEATURES OF TEXURE OF SOILS OF THE KHANKAISKIY NATURE RESERVE

Изучение гранулометрического состава почв Ханкайского заповедника показало, что профили почв представляют собой многослойные спектры различного литологического сложения. Сложность почвенных профилей по гранулометрическому составу определяется степенью проявления поемного и аллювиального процессов, литологическими особенностями почвообразующего материала. По типу сложения выделено несколько литологических групп. Темно-гумусовые глеевые, аллювиальные луговые глеевые почвы и буроземы глееватые отличаются резкой дифференциацией профиля по гранулометрическому составу на верхнюю легкую и нижнюю глинистую толщу. Для этих почв отмечено наибольшее содержание фракций физической глины и ила по всему почвенному профилю. Буроземы типичные и аллювиальные луговые глееватые, вышедшие из зоны затопления, характеризуются литологически однородным легким составом. В этих почвах выявлено высокое содержание фракций мелкого песка. The soil profiles the Khankaiskiy Nature Reserve represent multilayer spectra of various lithological addition. The complexity of soil profiles in terms of particle-size distribution is determined by the degree of manifestation of soil and alluvial processes, lithological features of soil-forming material. Several lithological groups are distinguished by the type of texture. Dark humus gley, alluvial meadow gley soils and burozem gleyic shrouds are distinguished by a sharp differentiation of the profile by granulometric composition into an upper light and lower clay thickness. The largest content of fractions of physical clay and silt was noted throughout the profiles for these soils. Burozem typical and alluvial meadow gleyic soils that have emerged from the flood zone characterize this with a lithologically homogeneous light composition. A high content of fine sand fractions was revealed in these soils.

Publicly Available Geographic Information Sources and Common Analysis Tools

Geographic information and geospatial data are vital in many practical fields, including precision agriculture, natural resources management, flood zone mapping and management, and environmental assessment. This 6-page publication introduces publicly available geospatial data, including elevation, land use, soil, satellite imagery, and other thematic maps and GIS software commonly used in spatial analysis. Written by Satbyeol Shin and Young Gu Her, and published by the UF/IFAS Department of Agricultural and Biological Engineering, January 2021.

Hemodialysis Clinics in Flood Zones: A Case Study of Hurricane Harvey

Introduction: Hurricane Harvey (2017) forced the closure of hemodialysis centers across Harris County, Texas (USA) disrupting the provision of dialysis services. This study aims to estimate the percentage of hemodialysis clinics flooded after Harvey, to identify the proportion of such clinics located in high-risk flood zones, and to assess the sensitivity of the Federal Emergency Management Agency (FEMA) Flood Insurance Rate Maps (FIRMs) for estimation of flood risk. Methods: Data on 124 hemodialysis clinics in Harris County were extracted from Medicare.gov and geocoded using ArcGIS Online. The FIRMs were overlaid to identify the flood zone designation of each hemodialysis clinic. Results: Twenty-one percent (26 of 124) of hemodialysis clinics in Harris County flooded after Harvey. Of the flooded clinics, 57.7% were in a high-risk flood zone, 30.8% were within 1km of a high-risk flood zone, and 11.5% were not in or near a high-risk flood zone. The FIRMs had a sensitivity of 58%, misidentifying 42% (11 of 26) of the clinics flooded. Conclusion: Hurricanes are associated with severe disruptions of medical services, including hemodialysis. With one-quarter of Harris County in the 100-year floodplain, projected increases in the frequency and severity of disasters, and inadequate updates of flood zone designation maps, the implementation of new regulations that address the development of hemodialysis facilities in high-risk flood areas should be considered.