Techniques for Disaster Risk Management and Mitigation E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 871

Veröffentlichungsjahr: 2020

Beschreibung

A comprehensive guide to managing and mitigating natural disasters Recent years have seen a surge in the number, frequency, and severity of natural disasters, with further increases expected as the climate continues to change. However, advanced computational and geospatial technologies have enabled the development of sophisticated early warning systems and techniques to predict, manage, and mitigate disasters.Techniques for Disaster Risk Management and Mitigation explores different approaches to forecasting disasters and provides guidance on mitigation and adaptation strategies. Volume highlights include: * Review of current and emerging technologies for disaster prediction * Different approaches to risk management and mitigation * Strategies for implementing disaster plans and infrastructure improvements * Guidance on integrating artificial intelligence with GIS and earth observation data * Examination of the regional and global impacts of disasters under climate variability

Leseprobe

Techniques for Disaster Risk Management and Mitigation

Edited by

Prashant K. SrivastavaSudhir Kumar SinghU. C. MohantyTad Murty

This edition first published 2020© 2020 John Wiley & Sons, Inc.

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Finally, after several ups and downs, I am about to submit this book with help of coeditors. I had been writing the acknowledgement back and forth, wherein I expressed immense gratitude toward my parents, beloved wife, and kids. However, I felt that was not enough. I started this book with esteemed Professor Tad Murty who passed away in 2018. He was an inspiration, being humble, helpful, and persistent even in difficult times.

This book would be incomplete without mentioning his dedication and perseverance towards his work. It is noteworthy that Professor Murty was an Indian‐Canadian oceanographer and an expert on tsunamis. He was the former president of the Tsunami Society. He was an adjunct professor in the Department of Civil Engineering and Earth Sciences at the University of Ottawa. Professor Murty held a PhD degree in oceanography and meteorology from the University of Chicago. He was coeditor of Springer's journal, Natural Hazards, a renowned journal in the field.

He took part in a review of the 2007 Intergovernmental Panel on Climate Change. Professor Murty characterized himself as a global warming skeptic. In a 17 August 2006 interview, he stated, “I started with a firm belief about global warming, until I started working on it myself....I switched to the other side in the early 1990s when Fisheries and Oceans Canada asked me to prepare a position paper and I started to look into the problem seriously.”

He mentioned that “when natural disasters strike, there is more loss of life and more loss of materials in the developing world. Is it because there are more people here? Or, is it because the developing world is not as prepared as the developed world?”. Hence, advanced techniques are needed to combat natural disaster; then we planned and started this book.

With his unfortunate death, I really miss having scientific discussions with him; and even more now as the book is completed, and I wish he could have been here with us. May God rest his soul in peace; he will be forever in our hearts.

—Prashant K. Srivastava

CONTRIBUTORS

Craig AmosDepartment of Geography and Earth SciencesUniversity of AberystwythWales, United Kingdom

Prasad K. BhaskaranDepartment of Ocean Engineering and Naval ArchitectureIndian Institute of Technology KharagpurWest Bengal, India

R. BhatlaDepartment of GeophysicsBanaras Hindu UniversityVaranasi, Uttar Pradesh, India;DST‐Mahamana Centre of Excellence in Climate Change ResearchInstitute of Environment and Sustainable Development Banaras Hindu UniversityVaranasi, Uttar Pradesh, India

Sagarika ChandraIndian Institute of Tropical MeteorologyPune, India

Prabhjot Singh ChawlaGautam Buddha UniversityGreater Noida, India

Nicolas R. DaleziosUniversity of ThessalyDepartment of Civil EngineeringPedion AreosVolos, Greece

D. M. DenisDepartment of Irrigation and Drainage EngineeringSam Higginbottom University of Agriculture,Technology and SciencesUttar Pradesh, India

DikshaDepartment of GeoinformaticsCentral University of JharkhandRanchi, India

Devajyoti DuttaNational Centre for Medium Range Weather Forecasting (NCMRWF)Ministry of Earth SciencesUttar Pradesh, India

Dipanwita DuttaDepartment of Remote Sensing and GISVidyasagar UniversityWest Bengal, India

Ioannis N. FaraslisUniversity of ThessalyDepartment of Planning and Regional DevelopmentPedion AreosVolos, Greece

Konstantinos P. FerentinosHellenic Agricultural Organization “Demeter” Soil & Water Resources InstituteDepartment of Agricultural EngineeringAthens, Greece

Dawei HanDepartment of Civil EngineeringUniversity of BristolBristol, UK

Tanvir IslamNASA Jet Propulsion LaboratoryPasadena, California, USA

Shilpi KalraGautam Buddha UniversityGreater Noida, India

M. L. KhanDepartment of BotanyDr. Harisingh Gour Vishwavidyalaya SagarMadhya Pradesh, India

Vinod Prasad KhanduriDepartment of ForestryUttarakhand University of Horticulture and ForestryRanichauri, Uttarakhand, India

Anna KozlovaScientific Centre for Aerospace Research of the EarthNational Academy of Sciences of UkraineKiev, Ukraine

Amit KumarDepartment of GeoinformaticsCentral University of JharkhandRanchi, India

Awadhesh KumarDepartment of HAMPMizoram UniversityMizoram, India

Kewat Sanjay KumarDepartment of ForestryMizoram UniversityMizoram, India

Sushil KumarGautam Buddha UniversityGreater Noida, India

Arnab KunduDST‐Mahamana Centre of Excellence in Climate Change ResearchBanaras Hindu UniversityVaranasi, Uttar Pradesh, India;Institute of Environment and Sustainable DevelopmentBanaras Hindu UniversityVaranasi, Uttar Pradesh, India

Shona MackieDepartment of Earth SciencesUniversity of BristolBristol, United Kingdom

R. K. MallDST‐Mahamana Centre of Excellence in Climate Change ResearchBanaras Hindu UniversityVaranasi, Uttar Pradesh, India;Institute of Environment and Sustainable DevelopmentBanaras Hindu UniversityVaranasi, Uttar Pradesh, India

Anoop Kumar MishraCenter for Remote Sensing and GeoinformaticsSathyabama UniversityChennai, Tamil Nadu, India

Tad MurtyDepartment of Civil EngineeringUniversity of OttawaOntario, Canada

I. NistorDepartment of Civil EngineeringUniversity of OttawaOntario, Canada

A. C. PandeyDepartment of GeoinformaticsCentral University of JharkhandRanchi, India

H. K. PandeyDepartment of Civil EngineeringMotilal Nehru National Institute of TechnologyAllahabad, India

Varsha PandeyInstitute of Environment and SustainableDevelopment and DST‐Mahamana Center for Excellence in Climate Change ResearchBanaras Hindu UniversityUttar Pradesh, India

Dhruvesh P. PatelDepartment of Civil EngineeringSchool of TechnologyPDPU, Gujarat, India

N. R. PatelDepartment of Agriculture and SoilIndian Institute of Remote Sensing (ISRO)Uttarakhand, India

Prajakta PatilDepartment of Earth SciencesUniversity of BristolBristol, United Kingdom

George P. PetropoulosSchool of Mineral & Resources Engineering Technical University of Crete Kounoupidiana Campus Crete, Greece;Department of Soil & Water Resources Institute of Industrial & Forage Crops Hellenic Agricultural Organization “Demeter” (former NAGREF)Directorate General of Agricultural Research, Larisa, Greece

S. PichéDepartment of Civil EngineeringUniversity of OttawaOntario, Canada

Iryna PiestovaScientific Centre for Aerospace Research of the EarthNational Academy of Sciences of UkraineKiev, Ukraine

Cristina PrietoEnvironmental Hydraulics Institute Universidad de CantabriaParque Científico y Tecnológico de Cantabria Santander, Spain

Praveen Kumar RaiAmity Institute of Geo‐Informatics and Remote SensingAmity UniversityNoida, India;Department of GeographyInstitute of ScienceBanaras Hindu UniversityUttar Pradesh, India

Raveena RajEnvironmental ScienceDepartment of BotanyBanaras Hindu UniversityVaranasi, Uttar Pradesh, India

A. D. RaoCentre for Atmospheric SciencesIndian Institute of Technology DelhiNew Delhi, India

Kishan Singh RawatCenter for Remote Sensing and Geoinformatics Sathyabama UniversityChennai, Tamil Nadu, India

Ashish RoutrayNational Centre for Medium Range Weather Forecasting (NCMRWF)Ministry of Earth SciencesUttar Pradesh, India

Sourabh SakhareIndian Institute of Surveying & Mapping Survey of India Training InstituteHyderabad, India

Ankit SharmaAmity Institute of Geo‐Informatics and Remote SensingAmity UniversityNoida, India

ShivaniEnvironmental ScienceDepartment of BotanyBanaras Hindu UniversityVaranasi, Uttar Pradesh, India

Devendraa SiinghIndian Institute of Tropical MeteorologyPune, India

Prafull SinghAmity Institute of Geo‐Informatics and Remote Sensing Amity UniversityNoida, India

Sudhir Kumar SinghK. Banerjee Centre of Atmospheric and Ocean StudiesUniversity of AllahabadAllahabad, India

Prashant K. SrivastavaInstitute of Environment and Sustainable Development and DST‐Mahamana Center for Excellence in Climate Change ResearchBanaras Hindu UniversityUttar Pradesh, India

Sergey StankevichScientific Centre for Aerospace Research of the EarthNational Academy of Sciences of UkraineKiev, Ukraine

Sawyer Reid StippaDepartment of Geography and Earth SciencesUniversity of AberystwythWales, United Kingdom

Ujjwal SurAmity Institute of Geo‐Informatics and Remote SensingAmity UniversityNoida, India

Olga TitarenkoScientific Centre for Aerospace Research of the EarthNational Academy of Sciences of UkraineKiev, Ukraine

N. Jeni VictorIndian Institute of Tropical MeteorologyPune, India

I. M. WatsonDepartment of Earth SciencesUniversity of BristolBristol, United Kingdom

PREFACE

We are often told our Universe began with a Big Bang, a disaster that made the stars and galaxies we see today. And, ever since, life on earth has evolved and flourished, surviving a series of unexpected bangs and calamities of one or another type. Nowadays, every place on earth is vulnerable to some kind of disaster, whether natural or human induced. Not only are developing and third world countries with less infrastructure and facilities in danger, but leading developed economies too face severe negative impacts due to disasters in terms of human and capital losses, mostly due to our lack of understanding of the processes involved in contributing to the severity of a disaster. With the most stated reason for the increase in the number and frequency of these disasters being the recent episodes of climate change and variability in earth’s history, many researchers have directed their studies towards developing more advanced and sophisticated early warning systems and techniques for precise prediction and forecasting of disaster.

In this context, this book highlights state‐of‐the‐art new approaches, various modelling aspects, the role of field observations and management strategies, and efficient use of infrastructure in combating disasters. It addresses the interests of a wide spectrum of readers with a common interest in geospatial science, geology, water resource management, database management, planning and policy making, and resource management. The chapters in book focus mostly on emphasizing the investigation and identification of disasters through advanced computational techniques in conjunction with Geographic Information Systems (GIS) and Earth observation data sets for better management, adaptation, and mitigation of natural disasters.

The book is divided into four sections. Section I focuses on a general introduction to the disaster management and mitigation, with an overview on the different types of disaster and the importance of the existing traditional technologies mostly widely used for natural disasters, emphasizing the relevance of indigenous approaches in disaster management. The section also underlines the importance of community‐based techniques in disaster management, postdisaster management, and developing mitigation plans. Section II contains chapters presenting detailed studies on atmospheric hazards and disasters, with some studies focusing on extreme weather events such clouds burst and tropical cyclones. To highlight the advancement in modern technologies for disaster management on land surfaces, Section III presents the role of modern technologies for disaster management and mitigation in cases such as drought and landslides. The section contains articles focusing on the role of earth observing techniques, database management through cloud management, and emergency preparedness using Global Positioning Systems (GPS). The next section, Section IV, illustrates the application and capability of satellite and mesoscale modelling for better understanding and management of oceanic disasters disasters and hazards.

Section I, opens with an introductory chapter (Dalezios et al.) on concepts and methodologies of environmental hazards and disasters, providing the basics concepts of disasters in different fields. Chapter 2 (Kumar et al.), on indigenous knowledge for disasters solution in hilly states, discusses the role of local or indigenous people’s knowledge towards understanding and developing mitigation plans for disasters in hilly areas. Chapter 3 (Diksha et al.) presents an overview of the risk of disasters in urban areas and the relationship with climate change. The chapter provides a perspective from those cities on the Indian subcontinent with more than million inhabitants. The last chapter of this section (Pandey et al.), on the role of earth observing techniques in disaster prediction, management, and mitigation, provides a brief description of remote sensing and GIS techniques in disaster monitoring.

Section II of the book focuses on atmospheric hazards and related disasters. Chapter 5 (Bhatla and team) provides detailed accounts of tropical cyclones over the North Indian Ocean in changing climate, while Chapter 6 (Kumar and team) provides a detailed analysis of the simulation of the intensity and track of tropical cyclones over the Arabian Sea using the WRF modelling system. In Chapter 7 (Dutta et al.) a soft computing model developed using reanalyzed atmospheric data to detect severe weather conditions is described. Chapter 8 (Victor et al.) covers lightning, the global electric circuit, and the relationship with the climate. Chapter 9 (Stippa et al.) provides an exploration of the Panther Mountain crater impact using spatial data and GIS spatial correlation analysis techniques.

Section III of the book focuses on land hazards and disasters; it highlights disasters on land such as drought, landslides, volcanic eruption, and forest fires. The section starts with Chapter 10 (Stankevich and team) exploring satellite radar interferometry processing and elevation change analysis for geo‐environmental hazard assessment and continues with Chapter 11 (Amos et al.) documenting the use of Sentinel‐2 in burnt area cartography and the findings from a case study in Spain. Chapter 12 (Patil and team) provides an assessment of the Name‐III dispersion model after assimilating the SEVIRI satellite observation for volcanic ash forecast. Chapter 13 (Kundu et al.) describes geo‐information technology for drought assessment using satellite and geospatial techniques. Chapter 14 (Pandey et al.) provides an introduction to the causes and control of landslides, while Chapter 15 (Sharma and team) reviews probabilistic landslide hazard assessment using Statistical Information Value (SIV) and GIS techniques. Chapter 16 (Patel et al.) introduces 1D hydrodynamic modelling for flood risk assessment, to simulate and understand flooding risk in coastal areas.

The last section of the book contains chapters discussing oceanic hazards and disasters, with Chapter 17 (Bhaskaran et al.) covering tropical cyclone induced storm surges and wind‐waves in the Bay of Bengal, Chapter 18 (Mishra and Rawat) discussing space‐based measurement of rainfall over India and nearby oceans using remote sensing applications, and Chapter 19 (Piche et al.) detailing the modelling of tsunami attenuation and the impact on coastal communities.

We believe that this book will be beneficial for people with a common interest in disaster management and mitigation. The variety of techniques outlined in this book, such as geospatial techniques, remote sensing and applications, emergency preparedness, policy making, and other diverse topics, in the earth, environmental, and hydrological sciences fields will provide readers with updated knowledge. We hope that this book will be beneficial for academics, scientists, environmentalists, meteorologists, environmental consultants, and computing experts working in the area of disaster risk management and mitigation.

Prashant K. Srivastava

Sudhir Kumar Singh

U. C. Mohanty

Tad Murty

Section IIntroduction

1Concepts and Methodologies of Environmental Hazards and Disasters

Nicolas R. Dalezios1, George P. Petropoulos2, and Ioannis N. Faraslis3

1 University of Thessaly, Department of Civil Engineering, Pedion Areos, Volos, Greece

2 School of Mineral & Resources Engineering, Technical University of Crete, Kounoupidiana Campus, Crete, Greece; Department of Soil & Water Resources, Institute of Industrial & Forage Crops, Hellenic Agricultural Organization “Demeter” (former NAGREF), Directorate General of Agricultural Research, Larissa, Greece

3 University of Thessaly, Department of Planning and Regional Development, Pedion Areos, Volos, Greece

ABSTRACT

Natural disasters have significant impact on several sectors of the economy, including agriculture. Moreover, under climate uncertainty, the role of several sectors of the economy, such as agriculture, as a provider of environmental and ecosystem services, is expected to further gain importance. Indeed, increasing climate variability and climate change lead to increases in climate extremes. The objective of this review is to present concepts and methodologies of environmental hazards and extremes that affect agriculture and agroecosystems, based on remote sensing data and methods, since this is a field gaining in potential and reliability. In this chapter, the relationship between environmental hazards and agriculture is presented; this is followed by concepts and quantitative methodologies of environmental hazards affecting agriculture, namely hydrometeorological hazards (floods and excess rain, droughts, hail, desertification) and biophysical hazards (frost, heat waves, wild fires). The emphasis is on concepts and the three stages of hazard development: forecasting‐nowcasting (before), monitoring (during), and assessment (after). Examples and case studies are presented using recorded data sets, model simulations, and innovative methodologies.

1.1. INTRODUCTION

Agriculture faces several current and future challenges, such as international competition and further liberalization of trade policy. Additionally, environmental hazards play a major role in agriculture; this has resulted in a gradual and significant increase of the economic cost associated with all environmental hazards. Needless to say, agricultural production is highly dependent on climate, and is adversely affected by anthropogenic climate change and increasing climate variability, which have led to increases in climate extremes. During the 21st century, scientific projections, among others, point to changes in climate extremes, such as heat waves, heavy rainfall, and droughts, in many semiarid and arid regions around the world. Specifically, southern Europe and the entire Mediterranean basin are characterized as vulnerable regions due to the combined effect of temperature increases and reduced precipitation in areas already coping with water scarcity (Dalezios et al., 2018a; Srivastava et al., 2019). Agricultural production risks could become an issue in areas such as the entire Mediterranean basin, as mainly droughts and heat waves are likely to increase the incidence of crop failure. As yield variability increases, food supply is at increasing risk.

Under a changing climate, the role of agriculture as a provider of environmental and ecosystem services is expected to gain further importance. Improvement of water use efficiency in dry regions is an example of agricultural management. Vulnerability of agriculture can be reduced through adaptation measures and tools to increase climate variability (EU, 2007). Some farming systems may adapt more readily to climate pressures due to an inherent resilience. Other systems may need interventions for adaptation. However, besides traditional knowledge and technologies, more sophisticated technologies seem to be required due to increasing climate variability and change. Seasonal to interannual climate forecasting is a new branch of climate science that promises to reduce vulnerability in agriculture. Improved seasonal forecasts are now being linked to decision making for cropping. The application of climate knowledge to improving risk management is expected to increase the resilience of farming systems.

Environmental degradation is one of the major factors contributing to the vulnerability of agriculture because it directly magnifies the risk of environmental disasters. In order to ensure sustainability in agricultural production, a better understanding of the environmental hazards and disasters that impact agriculture is essential. A comprehensive assessment of impacts of natural disasters on agriculture requires a multidisciplinary, multisectoral, and integral approach involving several components and factors. Priority should be given to supporting applied research, since research is necessary to understand the physical and biological factors contributing to disasters. Community‐wide awareness and capacity building on environmental hazards and disasters, mainly for farmers and stakeholders, should also be included in any research effort. Programs for improving prediction and early warning methods, as well as dissemination of warnings, should be expanded and intensified. Moreover, efforts are required to determine the impact of disasters on natural resources.

Recent research findings suggest that variability of climate, if encompassing more intense and frequent extremes, such as major large‐scale hazards like droughts, heat waves, or floods, results in the occurrence of natural disasters that are beyond our socioeconomic planning levels. It is estimated that about 65% of the global damage from natural disasters has a meteorological origin. Also, meteorological factors contribute to 87% of people affected by natural disasters and 85% of relevant deaths (UN/ISDR, 2015; EM‐DAT, 2009; WMO, 2004). This is expected to stretch regional response capabilities beyond their capacity and will require new adaptation and preparedness strategies (Salinger et al., 2005). Disaster prevention and preparedness should become a priority, and rapid response capacities to climate change need to be accompanied by a strategy for disaster prevention. Nevertheless, each type of extreme event has its own particular climatic, cultural, and environmental setting, and mitigation activities must use these settings as a foundation of proactive management. There is significant complexity involved in homogenizing and issuing global or regional statistics for disasters affecting agriculture, since this depends on the specific climatic zone and environment where the agricultural activity takes place, as well as the type, areal extent, and microclimatic and agronomic characteristics of the crops in that zone, including agroclimatic features. Nevertheless, international organizations, such as the Food and Agriculture Organization (FAO), the World Meteorological Organization (WMO), or the United Nations International Strategy for Disaster Reduction (UN/ISDR), issue statistics periodically that refer to environmental hazards and disasters that affect agriculture and agroecosystems. There is an urgent need to assess the forecasting skills for environmental hazards affecting agriculture in order to determine those where greater research is required. It is well known that lack of good forecast skill is a constraint to improve management, mitigation, and adaptation.

A holistic and integrated approach to environmental risks has gradually explored the use of common methodologies, such as risk analysis, including risk assessment and management. Indeed, through risk analysis, there are efforts to develop preventive measures and hazard processes before the crisis. It should be stated that current natural disaster management is crisis driven. It is thus realized that there is an urgent research need for a more risk‐based management approach to natural disaster planning in agriculture, which would include a timely and user‐oriented early warning system (Dalezios, 2017). Agricultural risk zoning is also an essential component of natural disaster mitigation and preparedness strategies. GIS and remote sensing and, in general, geoinformatics are increasingly employed due to the complex nature of databases to facilitate strategic and tactical applications at the farm and policy levels. Therefore, additional research is required to incorporate GIS, remote sensing, global positioning systems (GPS), simulation models, and other computational techniques into an integrated multihazard risk management framework for sustainable agriculture that includes early warnings of natural disasters (Sivakumar et al., 2005). There should also be more research attention to the potential impact of the increasing frequency and severity of extreme events associated with global change and appropriate mitigation strategies.

In general, risk assessment methodologies include three stages, or sectors, such as forecasting and early warnings before the phenomenon occurs, monitoring during a natural disaster, and estimating damage after the end of a disaster. In addition, risk identification involves quantifying, monitoring, and event risk, statistical inference, and database development, which should include records and historical information on disasters and their impacts. Risk assessment also entails reviewing the risk of these events, that is, the probability of occurrence, as well as the magnitude–duration–frequency and area‐to‐risk ratio. Finally, the risk assessment includes an environmental impact assessment and cost–benefit analysis of the adaptation options for the development of countermeasures (Dalezios & Eslamian, 2016).

The current scientific and technological capabilities of remote sensing cover all three areas of risk management. Remote sensing has gradually become an important tool for the quantification and detection of the spatial and temporal distribution and variability of several environmental variables at different scales. At the present time, the growing number and effectiveness of pertinent observation satellite systems present a wide range of new capabilities in assessing and monitoring several features of environmental variables. Moreover, remote sensing methods have also reached a significant level of accuracy and reliability over the last 40 years. Specifically, remotely sensed models are currently considered suitable for crop water use estimation at field as well as regional scales (Dalezios, 2014). Thus, remotely sensed forecasting‐nowcasting, monitoring, and assessment of environmental hazards are becoming attractive, since these systems provide consistently available data with high resolution covering large areas.

In this chapter, the major environmental hazards affecting agriculture are considered under increasing climate variability, namely hydrometeorological hazards (floods and excess rain, droughts, hail, desertification) and biophysical hazards (frost, heat waves, wild fires). The emphasis is placed on environmental hazards concepts and methodologies on the three stages of hazard development, namely forecasting‐nowcasting (before), monitoring (during), and assessment (after). Examples and case studies are presented using recorded data sets, model simulations, and innovative methodologies. in selected agricultural regions in southern Europe.

1.2. HYDROMETEOROLOGICAL HAZARDS IN AGRICULTURE

In this section, hydrometeorological hazards affecting agriculture are considered, namely floods and excess rain, droughts, hail, and desertification. For each hazard, some concepts are presented, along with methodologies on the three stages of hazard development: forecasting‐nowcasting (before), monitoring (during), and assessing (after).

1.2.1. Floods and Excess Rain

Floods can be devastating disasters that can affect anyone at almost any time (Ireland et al., 2015). Flooding has been one of the most costly disasters in terms of both human casualties and property throughout the last centuries. Hazards associated with flooding can be divided into primary hazards that occur due to contact with water, secondary effects that occur because of the flooding, such as disruption of services and health impacts, for example famine and disease, and tertiary effects, such as changes in the position of river channels. The term hazard (or cause), which in this case is flood, may be defined as the potential threat to humans and their welfare (Smith, 2013). Hazards can include latent conditions that may represent future threats and can have different origins, such as natural hazards or those induced by human processes (UN/ISDR, 2005).

1.2.1.1. Flood Forecasting

The prediction of flood events is of hydrological importance. As a prognosis, it is not only the estimation of the frequency of a hydrologic episode of a certain size, but also the forecast of the size and time of a flood peak. In order to reduce the risk due to flooding, three steps are considered for flood prediction. First, determination is conducted of the probability and frequency of high discharges of streams that cause flooding. Second, floods can be modeled and maps can be produced to determine the extent of possible flooding that may occur in the future. Third, since the main causes of flooding are abnormal amounts of rainfall and sudden melting of snow or ice, storms and snow levels can be monitored to provide short‐term flood prediction. Determining the timing and magnitude of floods is necessary for design flood purposes. In most cases, it is also necessary to classify the flood flows according to the flood‐producing mechanisms, for example in flood‐frequency studies. The classification of flood flows in various physical types should provide a better and reliable estimate of the magnitude of design floods, which, in turn, is necessary for the design of hydrotechnical projects. Flood‐frequency analysis is used to predict design floods for sites along a river. The frequency of occurrence of floods of different magnitude can be estimated by a variety of methods depending on the availability of hydrometric data (Loukas et al., 2002). Under normal conditions, observed annual peak flow discharge data are used to calculate statistical information, which then constitute the basis to construct frequency distributions, which delineate the likelihood of various discharges as a function of recurrence interval or exceedance probability. The choice of a design flood magnitude with its assessed return period depends both on the expected life of the scheme and on the degree of protection required. However, in ungauged watersheds, the flood flow is estimated by various methods, which require the estimation of rainfall of particular critical duration and return period. This leads to the design storm concept, which is still the dominant design method in hydrological engineering.

1.2.1.2. Flood Monitoring

Flood monitoring can be achieved through hydrological simulation. A hydrological model is an approximation of the real hydrological system. The input data and the outputs are measurable hydrological parameters and the structure of the models is a set of equations, which relate the inputs to the outputs. Modeling efforts are considered in three levels of temporal and spatial scaling (Schultz & Engman, 2000).

Design of water supply systems. This type of modeling requires long‐term time series data records of hydrological variables with the minimum time step being the month. For instance, the conventional data source could be observed or generated runoff data. The employed hydrological model could be a transfer function model in convolution integral, which is a stochastic black‐box model based on the theory of linear systems.

Design of flood protection measures. This type of modeling requires data from numerous cases of short‐term extreme hydrological events with a time step of days, hours, or even 10 min, for example, in the case of urban systems. The data source could be observed or extrapolated runoff data. Hydrological modeling could include a rainfall model in association with a rainfall‐runoff model of a distributed or lumped system type, for example, unit hydrograph.

Operation of water resource systems. This type of modeling requires short‐term or even real‐time data with a time step of 10 minutes, hours or even a day. The data source should be rainfall observed in real time, forecast of rainfall, as well as observed runoff. Additional data sources should be ground‐based weather radar and IR data from geostationary meteorological satellites. The hydrological modeling system to be used should include a rainfall model and a rainfall‐runoff model, preferably of distributed system type, in order to conduct now‐casting of extreme events in real time or semi‐real time.

1.2.1.3. Assessment and Causes of Floods