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Emily Goldman of UC Berkeley's Global Policy Lab introduces new research from an international team co-led by CEGA affiliated professor Solomon Hsiang that extends the record of high-resolution earth observation images backwards in time, enabling the investigation of long-term relationships between society and the environment.Visualization of timeline of existing remote sensing data and the Directorate of Overseas Surveys archive. We plot how the resolution (y-axis) evolved over time (x-axis) as new remote sensing technologies launched. The aerial photographs contained in the DOS archive extend the instrumental record back to the 1940s and have sub-meter resolution comparable to state-of-the-art 21st century satellites. | Emily GoldmanA mission to fill in development blind spotsSatellite imagery is a powerful tool for understanding Earth and humanity's role in transforming it. Policymakers increasingly turn to satellite images to detect and make decisions about crucial social issues, including migration, poverty, crop forecasts, mining, methane emissions, and even aid targeting. Analyzing satellite data gives policymakers a birds-eye view of societal changes that are otherwise difficult to detect.Satellite imagery can also help us understand the past, but this approach only works for phenomena that occurred during or after the 1970s. The primary satellite imagery record began in 1972, which limits our collective understanding of how the global relationship between environment and society has changed over time. This problem is exacerbated in the world's poorest regions, where historical data is often insufficient to establish long-term baselines prior to the large-scale anthropogenic changes of the 20th century, including climate change. The decades of absent data constrain policymakers' ability to illustrate and analyze the severity of the changes experienced in recent years.With this challenge in mind, the team at the Global Policy Lab (GPL), in collaboration with the the custodians of the archive, the National Collection of Aerial Photography in the United Kingdom, and researchers at Stockholm University, Caltech, and MIT, set out to build a window back in time — a machine learning pipeline that translates historical aerial imagery into analysis-ready imagery. Much more than a theoretical exercise, the project provides the first geospatial longitudinal dataset constructed from aerial imagery in the 60+ surveyed countries and the first insight into the effects of climate on long-run capital accumulation and population changes throughout Africa, the Caribbean, and several other regions.The archived aerial photos used for this project are currently held by the National Collection of Aerial Photography | Historic Environment Scotland, reproduced with permission.Securing the imagesBefore satellites, the only aerial images available were taken by photographers in airplanes throughout much of the 20th century. In an effort that began as part of the process of mapping the then-British Empire, the British Directorate of Overseas Surveys collected more than 1.6 million high-resolution aerial photos, spanning more than 60 countries, from the 1940s to the 1990s. The research team secured access to these high-resolution historical aerial photos, worked with partners to digitize them, and set out to transform the photos into continuous basemaps resembling those constructed from modern satellite imagery.Example images from a survey of Jamaica, mosaicked together manually by the DOS mapmakers by arranging printed aerial photographs on the floor and photographing them from above. The team's work automates this process digitally. | Historic Environment Scotland, reproduced with permission.From Aerial Photo to Pseudo-Satellite Image: Two Big HurdlesTransforming aerial photos into pseudo-satellite imagery was an enormously challenging task. The research team faced two primary technical challenges: one, the photos had limited georeferencing information(e.g. latitude, longitude); and two, the aerial photos had different image characteristics than satellite photos. To address these challenges, the team developed an algorithm to stitch together and georeference large quantities of the historical aerial imagery. The algorithm uses common features between adjacent images to solve the puzzle of which individual aerial images went together, and conducted image preprocessing to transform the aerial photographs into images comparable to satellite photographs.Assembled "satellite-like" image of Barbados in 1951 created by the stitched mosaic process described. | Emily GoldmanSetting up the machine learning pipelineThe team needed a way to comparably label both sets of imagery and extract variables like forest cover or population density. To solve this analytical challenge, they turned to convolutional neural networks and MOSAIKS, a tool developed by researchers affiliated with GPL, CEGA, and other institutions. The team used a combination of these methods to extract structured information, like forest cover and population density, from the historical aerial images and modern satellite imagery.The team's assembled "satellite-like" images, like that of Barbados in 1951, enable comparisons between modern day and historical imagery. | Emily GoldmanThe team's first environmental analysis project using this approach examined the effects of hurricanes on eight Caribbean islands over time. By simulating each location's exposure to historical hurricanes, we estimated the long-term effects these events had on natural and human systems.Implications for global policy researchTimeline of the project's scope | Emily GoldmanThe team is only beginning to explore the various new analytical and policy frontiers that this technological innovation opens. During the next stage of analysis, we intend to use this new dataset to examine long-term patterns in land use change, infrastructure development, population growth, and wealth accumulation. Geotagged results from this analysis have numerous real-world applications, such as identifying hotspots where policy intervention is most valuable, providing spatially-explicit risk assessments that can inform infrastructure investment, and informing global climate policy design. It is our hope that this novel dataset will open new doors for researchers and policymakers to develop a more nuanced understanding of which policies are likely to have long-term, positive effects on the relationship between society and the environment.Note: This project is a collaboration between the Global Policy Lab, the National Collection of Aerial Photography in the United Kingdom, and researchers at Caltech (Hannah Druckenmiller), MIT (Sherrie Wang), and Stockholm University (Andreas Madestam, Anna Tompsett).[1] MOSAIKS is a GIS tool that centralizes satellite imagery processing, making it easier for users without large computational resources or deep learning experience to extract useful insights from this data. CEGA supported the development of an open-source API for MOSAIKS through our USAID-funded Development Impact Lab (DIL).Building a Satellite Imagery "Time Machine" was originally published in CEGA on Medium, where people are continuing the conversation by highlighting and responding to this story.

Chapter1. Effect of Lime And Brick Ash Inclusion on Engineering Behaviour of Expansive Soil -- Chapter2. Know your Daily Rainfall in any Location in India- A Web-based Approach Developed in Google Earth Engine -- Chapter3. IoT- Based Innovative Technological Solutions for Smart Cities and Villages -- Chapter4. A Review on Utilization of E-Waste in Construction -- Chapter5. Water Sensitive Urban Design (WSUD) for Treatment of Storm water Runoff -- Chapter6. Textile Industry Wastewater Treatment using Eco-friendly Techniques -- Chapter7. Sustainable Treatment of Metal-Contaminated Soil by Electrokinetic Remediation -- Chapter8. Eco-Restoration of lakes and water sustainability in urban areas -- Chapter9. Microplastics: Environmental Issues and their Management -- Chapter10. Elucidating the Effect of Cement Dust on Selective Soil Parameters around J&K Cements Limited, Khrew -- Chapter11. Development of Correlation between Ultrasonic Pulse Velocity and Rebound Hammer Test Results for Condition Assessment of Concrete Structures for Sustainable Infrastructure Development. Chapter12. Alternative Fine Aggregates to Produce Sustainable Self Compacting Concrete: A Review -- Chapter13. Structural Behavior of Reinforced Concrete Column Using Diamond Tie Configuration under Elevated Temperatures for Sustainable Performance: A Review -- Chapter14. Reusable and Recyclable Industrial Waste in Geopolymer Concrete -- Chapter15. Infrared Thermography Parameter Optimization for Damage Detection of Concrete Structures Using Finite Element Simulations -- Chapter16. Eco-friendly Concrete Admixture from Black Liquor Generated in Pulp and Paper Industry -- Chapter17. Behavioural study on concrete with organic materials for CO2 absorption -- Chapter18. An Efficient Design and Development of IoT based Real-Time Water Pollution Monitoring and Quality Management System -- Chapter19. Numerical Study of Composite Wrapped Reinforced Concrete Columns Subjected to Close-in Blast -- Chapter20. Evaluation of conventional red bricks with compressed stabilized earth blocks as alternate sustainable building materials in Indian context -- Chapter21. Experimental Study on Alternative Building Material using Cement and Stone Dust as Stabilizers in Stabilized Mud Block -- Chapter22. Utilizing the Potential of Textile Effluent Treatment Sludge in Construction Industry: Current Status, Opportunities, Challenges, and Solutions -- Chapter23. Identification of Suitable Solid Waste Disposal Sites for the Arba Minch Town, Ethiopia, Using Geospatial Technology and AHP Method -- Chapter24. Framing Conceptual Design of Adopting Interlocking Bricks Technology in Construction -- Chapter25. Arriving Factors in the Conceptual Design Framework of 3D Printing Techniques for Building construction -- Chapter26. Scenic Evaluation of the Hills for Tourism Development - A Study on the Hills Of Tamilnadu, India -- Chapter27. Influence of Groundnut Shell Ash and Waste Plaster of Paris on Clayey Soil for Sustainable Construction -- Chapter28. Influence of Metakaolin and Steel Fiber on Strength of Concrete - A Critical Review -- Chapter29. Decadal monitoring of Coastline shifts and recommendation of Non-structural Protection measures along the coast of Rameshwaram, Tamilnadu, India -- Chapter30. Development of sustainable concrete using slag and calcined clay -- Chapter31. Assessment of the impact of bacillus cereus bacteria on strength and water absorption capacity of sustainable concrete -- Chapter32. Design and Development of Corona-19 Pandemic Situation-based Remote Voting System -- Chapter33. Waste Pozzolanic Material as a substitute of Geopolymer Mortar -- Chapter34. Study of the carbon emissions from construction of a house in plain region using standard construction material and eco-friendly/ alternative materials -- Chapter35. Experimental investigation of the impacts of partial substitution of cement with rice husk ash (RHA) on the characteristics of cement mortar -- Chapter36. A Mini review on Current Advancement in Application of Bacterial Cellulose in Pulp and Paper Industry -- Chapter37. Effect of agro-waste as a partial replacement in cement for sustainable concrete production -- Chapter38. Analysis and Evaluation of Geopolymer Concrete from Mechanical standpoint -- Chapter39. Municipal Waste Management in India: A Critical Review of Disposal System and Model Implementation -- Chapter40. Experimental Study on Light Weight Geopolymer Concrete Using Expanded Clay Aggregate -- Chapter41. Seismic Response of Composite Bridges: A Review -- Chapter42. Assessing and Correlating the Flow Duration Curve and Drought Index for the Environmental Flow Requirements -- Chapter43. Effect on Rheological and Hardened properties of Fly ash-GGBS based High Strength Self Compacting Concrete with inclusion of Micro and Nano Silica -- Chapter44. Mechanical Property study on Glass fibre concrete with partial replacement of fine aggregate with steel slag -- Chapter45. Mechanical Properties of Geopolymer Concrete Partial Replacement of Fine Aggregate with Waste Crushed Glass -- Chapter46. A Performance Study on Lithium based admixture in the properties of concrete -- Chapter47. Self-Curing Concrete Made By Using Hemp: A Review -- Chapter48. Research Progress of India in Waste Management at Global Level: A Bibliometric Evaluation -- Chapter49. Performance Evaluation of Acrylic Based Coating on Carbonation Depth on Different Grades of Concrete -- Chapter50. Cost Benefit Analysis of Retrofitting for Existing Building as Net Zero Energy Building: A Case Study in Composite Climate Zone -- Chapter51. Advances in Building Materials Industry by Annexation of Nano Materials -- Chapter52. Experimental Investigations on Utilization of Electroplating Waste Sludge in Manufacturing of Polymer Based Checkered Tiles -- Chapter53. Alccofine as a partial substitute of cement with scrap iron slag as a coarser material in high strength non-conventional concrete as an experimentational representation. Chapter54. Water Pollution: "Dal Lake a case study" -- Chapter55. Durability Properties of Admixture of Fly ash, Bottom Ash And GBFS -- Chapter56. Comparative Studies of Compressive Strength on Different Brick Masonry Prisms -- Chapter57. Monitoring and Management of Construction Sites Using Drone -- Chapter58. Experimental Investigation on Buckling Behaviour of Transmission Tower using Cold Formed and Hot Rolled Steel -- Chapter59. Assessment of indoor air quality of buildings made of bricks developed from paper pulp waste -- Chapter60. Review on Shear Strengthened RC Rectangular beams with FRP Composites -- Chapter61. Machine Learning Based Quality Prediction of Reuse Water in Sewage Treatment Plant -- Chapter62. "Prediction, Impact and Mitigation of Ambient Air Quality Pollutant Concentrations in Chandigarh" A Review -- Chapter63. A Review of Environmental Flow Evaluation Methodologies – Limitations and Validations -- Chapter64. Sustainable development of Scheduled caste and Scheduled tribes' population in select villages of Himachal Pradesh, India: A Cross Sectional Study.

Dynamic population estimation – counting people in a special event (e.g. rally, sport events, evacuation) has been challenging because a large crowd is difficult to acquire an accurate count manually as people can join and leave the crowd at any time and place. In the context of a rally where people are moving on the streets over an extended period, it is often difficult to answer some questions with regards to confine the phenomenon, including but not limited to: When and where does the rally start/end precisely? Who are the attendees (e.g. activists, spectators, organizers, police, opposing crowd(s), etc.)? How does the confining environment affect stochastic human behaviours, interactions and movements over time and space? What is the count (e.g. peak attendance, total count)? Nevertheless, estimating the attendance of these dynamic events could be emotionally and politically charged. Due to these practical uncertainties of dynamic population estimation, the attendance of annual July 1st Rally in Hong Kong reported by the police and organizers could be very different as it often became a bluffing game to promote hidden political agenda. For example, the police estimated a peak attendance of 9,800 in the 2017 July 1st Rally, whereas the organizers reported five times the attendance at about 50,000. Independent researchers from the University of Hong Kong estimated the total attendance of 14,170 (Yip, 2017) and 29,000 (HKUPOP, 2017) respectively. With limited field observations, it is hard to examine the accuracy and confident level of these reported counts. This research aims to estimate the total attendance of 2017 July 1st Rally in Hong Kong and examine the counts reported by various stakeholders. Based on many stories reported by the public and social media, the timeline of the rally event was reconstructed to trace the observed rally time of the head and tail crowds participated in the rally event. Important geospatial features, including the street network, approved protest areas and entry/exit gateways along the main rally route, were reconstructed in the Geographic Information System (GIS). This study adopted a crowdsourcing-geocomputation approach to simulate how a dynamic crowd would have navigated in such as rally event (Chow, 2019). Using a mobile application that tracks individual trajectory, volunteers were recruited to contribute valuable in-situ data of dynamic human movements and behaviours attending the rally event. These data were used to formulate and calibrate the parameters of a computational cartographic model where each rally attendees were represented as a moving agent (x, y, t) confined in a micro spatial environment where the protesters marched from Victoria Park to the Government Headquarter in Hong Kong. Hence, individual GPS (Global Positioning System) trajectory during the rally was collected and converted into GIS data format for further analysis. More details about the data collection and processing can be found on the project website (https://chowte.wixsite.com/dynamicpop). By leveraging a large number of observations volunteered by crowdsourcing, this study attempted to answer the research question: What might be the simulated crowd size that reasonable range of model parameters can be converged? Using this crowdsoucing-geocomputation model, a sensitivity analysis was conducted to simulate varying model parameters, including maximum walking speed, maximum crowd density, early departure and late entry rates. Based on the reported count reported by the police, organizers and independent researchers, various crowd sizes were simulated to be compared against the observed rally time of 209 minutes (i.e. about 3.5 hours) from start to finish. Using the crowdsourced data for calibration, most rally models simulated an arrival time of head crowd between 106–108 minutes, which was very close to the observed rally time of head crowd of 107 minutes. In this study, crowd sizes were considered to be acceptable based on a 95% confidence interval of arrival time of tail crowd (i.e. 174–192 minutes) and total rally time (i.e. 199–219 minutes). For example, a crowd size of 9,800 people was simulated matching the total rally time to examine the credibility of calibrated model parameters (Figure 1). Within the tested range of calibrated model parameters, the results indicated that it was possible to tweak the model parameters of varying crowd size to match the observed rally time (Table 1). Despite the simulated rally time of some accepted models were within ±5% of the observed rally time, the parameters used to simulate such a model were not necessary reasonable in reality. The simulated count of 9,800, for example, would require a cap of maximum walking speed of 0.5 m/s, which seemed to be unreasonably slow under normal circumstance and incompatible with crowdsourced data (Figure 1). Given the observed rally time, it was found that the crowd sizes of 14,000–29,000 could be simulated with reasonable model parameters, whereas the crowd sizes of 9,800 and 50,000 would yield unreasonable model parameters. Taking the median within the range of 14,000–29,000, this study also found that a crowd size of 21,000 could yield eight matching simulations with varying reasonable model parameters that may be better simulate the actual rally attendance. This paper provided empirical evidences to examined the credibility of various crowd sizes of the 2017 July 1st Rally in Hong Kong reported by the stakeholders. The research also presents a transparent, repeatable and verifiable approach to explore, quantify and simulate human movements in a rally event, such as the early departure and late arrival, to better understand dynamic crowd behaviours and interactions.

Dynamic population estimation – counting people in a special event (e.g. rally, sport events, evacuation) has been challenging because a large crowd is difficult to acquire an accurate count manually as people can join and leave the crowd at any time and place. In the context of a rally where people are moving on the streets over an extended period, it is often difficult to answer some questions with regards to confine the phenomenon, including but not limited to: When and where does the rally start/end precisely? Who are the attendees (e.g. activists, spectators, organizers, police, opposing crowd(s), etc.)? How does the confining environment affect stochastic human behaviours, interactions and movements over time and space? What is the count (e.g. peak attendance, total count)? Nevertheless, estimating the attendance of these dynamic events could be emotionally and politically charged. Due to these practical uncertainties of dynamic population estimation, the attendance of annual July 1st Rally in Hong Kong reported by the police and organizers could be very different as it often became a bluffing game to promote hidden political agenda. For example, the police estimated a peak attendance of 9,800 in the 2017 July 1st Rally, whereas the organizers reported five times the attendance at about 50,000. Independent researchers from the University of Hong Kong estimated the total attendance of 14,170 (Yip, 2017) and 29,000 (HKUPOP, 2017) respectively. With limited field observations, it is hard to examine the accuracy and confident level of these reported counts. This research aims to estimate the total attendance of 2017 July 1st Rally in Hong Kong and examine the counts reported by various stakeholders. Based on many stories reported by the public and social media, the timeline of the rally event was reconstructed to trace the observed rally time of the head and tail crowds participated in the rally event. Important geospatial features, including the street network, approved protest areas and entry/exit gateways along the main rally route, were reconstructed in the Geographic Information System (GIS). This study adopted a crowdsourcing-geocomputation approach to simulate how a dynamic crowd would have navigated in such as rally event (Chow, 2019). Using a mobile application that tracks individual trajectory, volunteers were recruited to contribute valuable in-situ data of dynamic human movements and behaviours attending the rally event. These data were used to formulate and calibrate the parameters of a computational cartographic model where each rally attendees were represented as a moving agent (x, y, t) confined in a micro spatial environment where the protesters marched from Victoria Park to the Government Headquarter in Hong Kong. Hence, individual GPS (Global Positioning System) trajectory during the rally was collected and converted into GIS data format for further analysis. More details about the data collection and processing can be found on the project website (https://chowte.wixsite.com/dynamicpop). By leveraging a large number of observations volunteered by crowdsourcing, this study attempted to answer the research question: What might be the simulated crowd size that reasonable range of model parameters can be converged? Using this crowdsoucing-geocomputation model, a sensitivity analysis was conducted to simulate varying model parameters, including maximum walking speed, maximum crowd density, early departure and late entry rates. Based on the reported count reported by the police, organizers and independent researchers, various crowd sizes were simulated to be compared against the observed rally time of 209 minutes (i.e. about 3.5 hours) from start to finish. Using the crowdsourced data for calibration, most rally models simulated an arrival time of head crowd between 106–108 minutes, which was very close to the observed rally time of head crowd of 107 minutes. In this study, crowd sizes were considered to be acceptable based on a 95% confidence interval of arrival time of tail crowd (i.e. 174–192 minutes) and total rally time (i.e. 199–219 minutes). For example, a crowd size of 9,800 people was simulated matching the total rally time to examine the credibility of calibrated model parameters (Figure 1). Within the tested range of calibrated model parameters, the results indicated that it was possible to tweak the model parameters of varying crowd size to match the observed rally time (Table 1). Despite the simulated rally time of some accepted models were within ±5% of the observed rally time, the parameters used to simulate such a model were not necessary reasonable in reality. The simulated count of 9,800, for example, would require a cap of maximum walking speed of 0.5 m/s, which seemed to be unreasonably slow under normal circumstance and incompatible with crowdsourced data (Figure 1). Given the observed rally time, it was found that the crowd sizes of 14,000–29,000 could be simulated with reasonable model parameters, whereas the crowd sizes of 9,800 and 50,000 would yield unreasonable model parameters. Taking the median within the range of 14,000–29,000, this study also found that a crowd size of 21,000 could yield eight matching simulations with varying reasonable model parameters that may be better simulate the actual rally attendance. This paper provided empirical evidences to examined the credibility of various crowd sizes of the 2017 July 1st Rally in Hong Kong reported by the stakeholders. The research also presents a transparent, repeatable and verifiable approach to explore, quantify and simulate human movements in a rally event, such as the early departure and late arrival, to better understand dynamic crowd behaviours and interactions.

The research is motivated by an interest in evaluating the special Chilean water management framework, relating to the 1981 Water Code legislation, introduced by the military government. This law mainly strengthened private property rights and increased private autonomy in water use. In particular, it is of interest to assess the impacts of this legislation in the context of the current highly stressed water availability situation in central and northern Chile, combined with intensive and increasing agricultural demands. The reason to look at this region first is to test a catchment with a more or less vivid water market. The purpose of this research is to investigate the influence of water rights on water management practices under the present situation as well as changing situations. Here changing situation refer on one hand to improvement and extension of infrastructure, on the other hand to different use of the water in magnitude and further time and space. The latter one mainly based on the water market. The main objective of this study is to investigate if the proposed WRAP Modelling System (Water Rights Analysis Package) which is used in the whole state of Texas, is able to model the consequences of the allocation scheme in the present, as well changing situations, incorporating the Chilean legal framework, here especially the allocation according to water rights. The main changes are subject to i. new legislation to incorporate in the allocation of water resources ii. further development, like new reservoirs in the upstream sub-catchments, iii. water right transfers as well as iv. different operation policies WRAP was chosen to investigate the impacts of the water management practices. It combines detailed information describing water resource development, management, allocation and use with natural river system hydrology represented by naturalized streamflows, assuming that the hydrological pattern of a catchment stays the same in the future (Wurbs, 2011). Beside the development of the spatial configuration of the system, which has been defined as a set of control points (CP) that represent pertinent sites in the river basin, geospatial data, time series data, census data, operational data sheets of the organisations as well as information and data about the water rights of each stakeholder have been statistically and spatially pre-processed in order to be able to estimate agricultural water demand, understand the legal system in general and of the basin under study in particulary. Further information of the Food and Agricultural Organisation (FAO), monitored data of the National Water Authority (DGA), elaborated data of the National Centre of Natural Resources (CIREN) and historical and actual regional as well as local studies were consulted to elaborate all the needed information to model the system. With this information and preprocessed data the WRAP modelling system was implemented, to quantify the impacts of decision making and its consequences on the whole system. Model results include water supply reliabilities (including reliability indices) as well as flow and storage frequency statistics developed from the simulation results representing long-term probabilities or percent-of-time estimates. Furthermore shortage metrics have been developed by the model and evaluated for each scenario. The model includes the following frequency statistics for concisely summarizing modelling results: (a) volume and period reliability tables for water supply diversion, (b) frequency tables for naturalized, regulated and unappropriated flows, reservoir storage volumes, as well as instream flow shortages and (c) reservoir storage-reliability tables. After all the different scenario simulations and analysis of the results it can be stated that the WRAP modelling system is applicable for the questions under study based on the legal Chilean water management framework. Flexibility is provided for adaption of a broad range of modelling approaches. A huge variety of management records can be combined in many different ways to be able to model any application. Ingenuity is required from the modeller to achieve the incorporation of sometimes quite complex allocation rules, apply different target options, demands, administrate a variety of users and include new developments within a multiple and multipurpose reservoir-river management system. Although some simplification of the independent sub-catchments was necessary, the achieved results show that the consequences of allocation decisions, including water transfer and future development are simulated in a satisfactory manner and can therefore be much better understood. The model system is adequate to serve as a basis for decision making within the chilean legal framework. ; Die Motivation des Forschungsthemas basiert auf den speziellen chilenischen Rahmenbedingungen der Wasserwirtschaft, deren rechtliche Grundlage das Wassergesetz von 1981 (Codigo de agua) bildet. Es wurde von der Militärregierung eingeführt und stärkt private Eigentumsrechte sowie die private Autonomie in der Wassernutzung; jeder Nutzer benötigt Wasserrechte. Von besonderem Interesse ist die Untersuchung der Rahmenbedingungen im Kontext mit dem hohen Druck auf die Wasserverfügbarkeit im zentralen und nördlichen Chile, kombiniert mit intensiver Landwirtschaft. In der Studie wird untersucht, wie integrierte Wasserwirtschaft unter den vorliegenden Rahmenbedingungen von den Akteuren verstanden und umgesetzt wird, sowie deren Auswirkungen auf das betrachtete Untersuchungsgebiet. Da die gesamte Wasserverteilung auf Wasserrechten beruht und es in Chile noch kein Model gibt, dass die Wassernutzung auf der Grundlage der Wasserrechte und deren Prioritäten modelliert, wird in der vorliegenden Studie das Modelsystem WRAP (Water Rights Analysis Package), welches für die legalen Rahmenbedigung von Texas entwickelt wurde unter chilenischen Bedingungen getestet. Als Studienregion wurde ein Einzugsgebiet im semi-ariden Norden gewählt, welches sich durch eine starke Regulierung und viele Stakeholder auszeichnet. Folgende Fragestellung wird untersucht: Die Folgen der aktuellen Wasserverteilung im gegenwärtigen System und unter veränderten Bedingungen auf das gesamte System. Hier werden allgemein gültige Veränderungen in einem chilenischen Einzugsgebiet, welches hohem Druck ausgesetzt ist berücksichtigt: i. Neue legale Rahmenbedingungen, die für die Wasserverteilung berücksichtigt werden müssen ii. weiterer Stauseen in den oberen noch unregulierten Teileinzugsgebieten, iii. Wasserrechtübertragungen und iv. verschiedener Bewirtschaftungsregeln Zu diesem Zweck wurde daher ein Modell für das Einzugsgebiet mit dem WRAP Modellierungsystem entwickelt, um die Konsequenzen der Wassermanagement-Praxis zu untersuchen. Es kombiniert Informationen über Wasserressourcenentwicklung, Management, Verteilung und Nutzen mit der natürlichen Flusssystemhydrologie, repräsentiert durch wiederhergestellte natürliche Abflüsse. Dabei wird vorausgesetzt, dass die historischen hydrologischen Zeitreihen als Grundlage für die Zukunft gelten können (Wurbs, 2011). Mit dem Modell wird somit das primäre Ziel verfolgt, hydrologische und institutionelle Wasserverfügbarkeit und Versorgungszuverlässigkeit innerhalb eines auf Prioritäten basierten Wasserrechtsystems in einem Einzugsgebiet zu bewerten. Im vorliegenden Fall wird dies auf das Oberflächenwassermanagement beschränkt, da dieses die Hauptressource darstellt. Zunächst wurde die räumliche Struktur des wasserwirtschaftlichen Gesamtsystems erarbeitet und durch Kontrollpunkte, die die wichtigsten Bauwerke, wie Talsperren und Entnahmepunkte sowie andere für die Modellierung relevante Standorte darstellen, definiert. Es wurden Geodaten, Zeitreihen und Zensus Daten ausgewertet und bearbeitet, sowie verschiedene Datenbanken konsultiert, sowie Informationen einzelner Nutzerorganisationen erhoben (Landwirtschaftlicher Zensus, FAO, hydrologische Daten der DGA, Daten vom nationalen Zentrum für natürliche Ressourcen, CIREN sowie historische und aktuelle regionale und lokale Studien um z.B. die detaillierte Verteilung der Wasserrechte und den landwirtschaftliche Wasserbedarf aller Untereinzugsgebiete zu analysieren und zu berechnen, sowie die natürlichen Abflüsse zu modellieren. Die wesentlichen Ergebnisse können wie folgt zusammen gefasst werden: Für die am Cogoti Fluss geplante Talsperre konnten die günstigste Lage und das günstigste Szenario bezüglich der Verteilung der Wasserrechte und des maximalen jährlichen Volumens mit dem Modell herausgearbeitet werden. Dafür wurden auch verschiedene Bewirtschaftungsstrategien getestet. Das Modell zeigt die Möglichkeiten und Konsequenzen einer Nicht-Umverteilung. Genauso können zukünftig verschiedene Umverteilungen leicht simuliert werden und somit als transparente Diskussionsbasis dienen. Der Druck auf die sehr knappen Wasserressourcen steigt in dem Einzugsgebiet und wird für alle Akteure grösser, was bedeutet, dass es an der Zeit ist, ein Umdenken voranzutreiben. Obwohl in der vorliegenden Studie bei der Umsetzung einige Vereinfachungen des Systems angenommen wurden, können mit dem entwickelten Modell die beschriebenen Situationen zuverlässig simuliert und bewertet werden. Der Einsatz des Modellsystems kann für die Zukunft als Entscheidungsunterstützung uneingeschränkt empfohlen werden.

The celebration of the outstanding personalities of academia is always an occasion to exchange ideas, establish the state of art of a scientific area, and highlight the hallmarks and new paradigms. This was the case of the Anniversary Conference "Structural Components of Forest Ecosystems: ecology, conservation and management" held in honour of Prof. Nicolae Doniță, under the generous auspices of the Banat University of Agricultural Sciences and Veterinary Medicine "King Michael I of Romania" in Timișoara. The celebrated scientist, Professor Nicolae Doniță (also member of the Romanian Academy of Agricultural and Forest Sciences "Gheorghe-Ionescu Șișești" and, Doctor Honoris Causa of the Agricultural and Veterinary Sciences University of Banat "King Mihai I of Romania" and of the University Ștefan cel Mare of Suceava) marked in that occasion his 90th anniversary, surrounded by his fellow scientists, former students and collaborators. Fifty-three participants in the conference honoured the personality and lifetime achievements of one of the most prominent figures among the Romanian forest ecologists, who reshaped the fundamentals of the Romanian silviculture based on the ecosystemic approach. The most consistent section of the conference was dedicated to the presentation of oral communications and posters circumscribed to the conference's central theme. The systemic, holistic paradigm adopted in forest ecology was brought forward in the opening lecture given by Prof. Doniță: "On the formation of the forest ecosystem". The rest of presentations focused on more specific topics or case studies, of which some are briefly mentioned hereinafter. The old-growth forests in Romania were presented from the perspective of significance for the conservation efforts and various hindrances, with special reference to the integration in the larger European concern raised by climatic change and anthropogenic pressures. The forest dynamics in terms of species composition was employed as a tool for the evaluation of forest naturalness in Bosco Quarto (Gargano, Italy). The overview of tropical monospecific forest plantations brought a close insight into an old and controversial problem related to the balance between economic benefits and biodiversity loss. The study of the complex interactions between herbs and tree saplings in southern Appalachian forests revealed the mediator effects of soil fertility and stand evergreenness. The current status of forest habitats in Romania was summarized by means of the second national report to the European Commission (article 17 in the Habitats Directive). The importance of dead wood preserved in situ was demonstrated by the high diversity of saproxylic beetles within the natural reserve Voievodeasa Forest (North-Eastern Romania). The analysis of data contained in management plans was shown to provide useful information for mitigating the consequences of climatic change by improving the ecological status of forests and enhancing their environmental services. The synthesis on the old-growth and virgin beech forests from Carpathians and other European regions, included in the UNESCO World Heritage List, was presented as an important contribution to the forest conservation effort at European level. The role of soil physical and chemical properties, as important determinants for the distinction of forest ecosystem types, was highlighted in the Subsidiary Timiș Forest. The old-growth beech forests included in the nature reserve Izvoarele Nerei (South-Western Romania), famous for its big trees and pristine status, were shown to harbour a high biodiversity encompassing species from different groups. The comparison of two estimation methods of the foliar area index in a beechfir old-growth forest provided interesting insights on the influence of environmental factors. The speakers referred to the scientific stature of Prof. Doniță, but also to his friendly human nature, his talent in gathering people around a common theme, his generous approach to science and scientists, being a leader but not misusing his authority, while always remaining a kind and patient guide. His achievements, spanning across six decades, consist of important international projects, seminal books and a vast array of scientific papers. Several outstanding books he coordinated or co-authored should be mentioned in this context: Forest Ecosystem Types of Romania (1990), Habitats of Romania (2005-2006), Forest Ecology (1978), The Vegetation of Romania (1992), Silviculture on Ecosystemic Bases (1997), The Virgin Forests of Romania (2001), Population, Species, Biocoenosis - An integrating Vision (2019). One of the milestones of Nicolae Doniță's activity was his participation in the project "Map of the Natural Vegetation of Europe, Scale 1:2500000, with Explanatory Text", which extended over 20 years of intensive collaboration between experts from across all Europe. Another notable achievement consisted in the elaboration (along with several co-workers) of the Forest Geospatial Database of Romania according to ecosystemic units and the corresponding digital map. In conclusion, the conference gave the rare opportunity of gathering around a hallmark personality of forest ecology and a series of valuable scientific contributions, while celebrating the lifetime achievements of Prof. Doniță in the context of the contemporary forest science. A selection of papers, either presented at the conference or submitted later on, are included in the first section of the current journal issue.

Проаналізовано проблеми створення галузевих геоінформаційних систем на базі існуючих універсальних геоінформаційних систем та проблеми підвищення ефективності підтримки прийняття рішень в галузевих геоінформаційних системах за рахунок створення відповідних моделей, методів та інформаційних технологій. В дослідженні визначено необхідність розв'язання таких задач: аналіз проблем створення галузевих геоінформаційних систем, а саме, адміністративно-територіальному та муніципальному управлінні, у соціальних проектах при дослідженні туристичного потенціалу територій та проведенні геомаркетингу, у військових додатках, системах екологічної та техногенної безпеки і показано, що при створенні інформаційної технології підтримки прийняття рішень необхідно враховувати багатоцільову специфіку предметної галузі. Для уніфікації представлення геоданих, забезпечення необхідної точності отримання просторово-розподіленних даних та повноти атрибутивних даних про об'єкти галузевих геоінформаційних систем розроблено інформаційну модель геознань. Для забезпечення цілісності геоданих запропоновано зв'язувати їх просторові та атрибутивні складові за допомогою ідентифікаторів об'єктів. Отримала подальший розвиток концептуальна модель підтримки прийняття рішень в галузевій геоінформаційній системі, яка враховує інформаційну модель геоданих, ієрархію задач галузевої геоінформаційної системи, вхідні оперативні дані, функцію переваг та критерій вибору особи, що приймає рішення, що дозволило розробити метод підтримки прийняття рішень для знаходження раціонального рішення задач другого рівня. Вперше розроблено метод підтримки прийняття рішень при побудові галузевої геоінформаційної системи, який враховує запропоновану концептуальну модель, що дозволило автоматизувати процес створення ефективних геопросторових розподілених систем за галузевим призначенням. ; The problems of creating GIS industry based on existing GIS and universal problem of increasing the efficiency of decision support systems, GIS industry by the alignment liability these models, methods and information technologies. The study identified the need to solve the following problems: analysis of problems creation of GIS industry, namely, administrative, territorial and municipal government, and in social projects for the study of tourism potential areas and conducting geomarketing, military applications, systems and environmental technogenic safety and it is shown that when creating information technology support decision-making it is necessary to consider the multi-purpose specifics of the subject field. Regarding the frequency of changes in incoming data, it is proposed to classify and catalog sectoral geographic information systems not only by territorial coverage and purpose, but also by the dynamics of the change in input data. At the same time we consider stationary (administrative-territorial geoinformation systems), quasi-stationary (tourist geoinformatics and systems) and dynamic (military geoinformation systems) sectoral geographic information systems. To unify the presentation location, with the required accuracy to obtain space-distribution data and attribute data completeness of industrial facilities heoinformation systems developed information model heoknowledge. To ensure the integrity of the proposed location bind them just attribute components using the asset ID. Further developed a conceptual model of decision support in the industry geographic information system that takes into account the information model location, hierarchy problems branch of geographic information system, incoming operational data, function, advantages and selection criteria person decides that allowed method of decision support for finding a rational solution to the problems of the second level. For the first time the support method decisions in the construction sector geographic information system that takes into account the proposed conceptual model that allow pouring automate the process of creating effective systems for distributed geospatial by industry purpose. ; Проанализированы проблемы создания отраслевых геоинформационных систем на базе существующих универсальных геоинформационных систем и проблемы повышения эффективности поддержки принятия решений в отраслевых геоинформационных системах за счет создания ответственных моделей, методов и информационных технологий. В исследовании определена необходимость решения следующих задач: анализ проблем создания отраслевых геоинформационных систем, а именно, административнотерриториальном и муниципальном управлении, в социальных проектах при исследовании туристического потенциала территорий и проведении геомаркетинга, в военных приложениях, системах экологической и техногенной безопасности и показано, что при создании информационной технологии поддержки принятия решений необходимо учитывать многоцелевую специфику предметной области. Относительно периодичности изменения входных данных предложено классифицировать отраслевые геоинформационные системы не только по территориальному охвату и целевому назначению, но и по динамике изменения входных данных. При этом рассматриваем стационарные (административно-территориальные геоинформационные системы), квазистационарные (туристические геоинформационные системы) и динамические (военные геоинформационные системы) отраслевые геоинформационные системы. Для унификации представления геоданных, обеспечения требуемой точности получения пространственно-распределенных данных и полноты атрибутивных данных об объектах отраслевых геоинформационных систем разработана информационная модель геознаний. Для обеспечения целостности геоданных предложено связывать их пространственные и атрибутивные составляющие с помощью идентификаторов объектов. Получила дальнейшее развитие концептуальная модель поддержки принятия решений в отраслевой геоинформационной системе, которая учитывает информационную модель геоданных, иерархию задач отраслевой геоинформационной системы, входные оперативные данные, функцию преимуществ и критерий выбора лица, принимающего решение, позволившее разработать метод поддержки принятия решений для нахождения рационального решения задач второго уровня. Впервые разработан метод поддержки принятия решений при построении отраслевой геоинформационной системы, учитывающий предложенную концептуальную модель, что позволило автоматизировать процесс создания эффективных геопространственных распределенных систем по отраслевому назначению.

Nel settore dell'Informazione Geografica (GI), esiste in generale un gap a livello europeo tra l'istruzione e la preparazione offerte dalle università e le competenze e capacità richieste sul mercato da aziende ed enti pubblici. Per far fronte alle nuove sfide poste dai continui sviluppi tecnologici in atto (basti pensare a temi quali Geo Big Data, posizionamento indoor, Internet of Things, ecc.) è perciò necessario introdurre nuove forme di collaborazione università-impresa. giCASES – Creation of a University-Enterprise Alliance for a Spatially Enabled Society (http://www.gicases.eu) è un'Alleanza per le Conoscenze, co-finanziata dal Programma UE Erasmus+, che affronta questo problema con l'obiettivo di sviluppare approcci innovativi e multidisciplinari all'insegnamento e all'apprendimento nel settore GI facilitando lo scambio, il flusso e la co-creazione di conoscenza. L'approccio consiste nello sviluppo collaborativo e condiviso, tra università ed imprese, di nuovi materiali didattici e processi di apprendimento basati su casi reali (case-based learning). In maniera innovativa rispetto alle tradizionali forme di collaborazione tra università ed imprese (spesso limitate a semplici tirocini e allo sviluppo di tesi presso le imprese), la collaborazione con le università finalizzata alla co-creazione di conoscenza si esplica sin dalla fase iniziale di progettazione del corso accademico e del processo di apprendimento. Il progetto, iniziato nel 2016 e formato da un consorzio di 14 partner da 8 diversi Paesi europei (con una componente bilanciata di università e imprese), ha visto una prima fase volta alla definizione della metodologia di case-based learning e alla progettazione dei casi di studio. Un questionario inizialmente distribuito tra università, imprese ed enti pubblici europei nel settore GI ha evidenziato che: a) la collaborazione tra il settore accademico e quello industriale è spesso organizzata in modo tradizionale; b) le competenze richieste dal mercato del lavoro (specialmente programmazione, analisi e modellazione spaziale) spesso non sono in linea con l'istruzione universitaria; c) l'apprendimento basato su casi reali è uno dei metodi suggeriti per rendere la preparazione degli studenti conforme alle richieste del mondo del lavoro. Utilizzando lo standard BPMN (Business Process Model and Notation), è stata quindi sviluppata una metodologia rigorosa per la modellazione degli schemi di co-creazione di conoscenza tra università ed imprese (ad esempio per lo sviluppo collaborativo del materiale didattico e la formazione congiunta degli studenti). Questi schemi descrivono analiticamente i processi di collaborazione utilizzati, i relativi attori e le loro reciproche interazioni. Un insieme di linee guida, modelli e bozze di convenzioni/accordi è stato prodotto per facilitare l'implementazione pratica degli schemi sviluppati. Parallelamente, è stata definita e condivisa una serie di strumenti software per garantire funzionalità collaborative (sviluppo congiunto di codice, produzione congiunta di materiale didattico, ecc.), facilitando così lo sviluppo pratico di conoscenze condivise e la messa in opera dei casi di studio. Il materiale didattico e la metodologia di apprendimento sviluppati sono infine sottoposti ad un'accurata fase di test e validazione (al momento in corso) tramite l'implementazione in 6 casi di studio (CS), ognuno dei quali prevede la partecipazione congiunta di un partner accademico ed uno industriale e si concentra su un tema attuale nel panorama GI: CS1 "Use of indoor GIS in healthcare"; CS2 "Environmental analysis using cloud service system"; CS3 "From INSPIRE to e-Government"; CS4 "Integrated management of the underground"; CS5 "Harmonizing data flows in Energy saving EU policies"; CS6 "GIS applications in Forestry". Per ognuno di essi è stato definito un dettagliato piano di lavoro comprendente la descrizione degli attori coinvolti (studenti, professori, tutor accademici, tutor industriali, ecc.), il contesto applicativo (ovvero la modalità con cui il CS è incluso nel corso/programma accademico), le tempistiche di svolgimento e i risultati attesi. Questi ultimi, che includono il materiale didattico e i processi collaborativi applicati, saranno resi disponibili con licenza aperta sulla piattaforma del progetto, in modo da massimizzarne l'adozione da parte di altre comunità e portatori di interesse. I risultati parziali ottenuti dall'implementazione dei CS stanno dimostrando, a giudizio non solo degli studenti ma di tutti gli attori coinvolti, elementi positivi in termini di innovazione ed efficacia. Benché l'uso di software open source (e in particolare FOSS4G) non fosse formalmente richiesto dalle specifiche del progetto, è interessante notare come in tutti i 6 CS sia largamente prevista l'adozione di tali tecnologie, in particolare di QGIS, GRASS GIS, PostGIS, GeoServer, OpenLayers, Leaflet e Geomajas. Ciò testimonia come le soluzioni open source siano già fortemente utilizzate tanto come strumenti didattici presso le università quanto, da parte delle imprese, come mezzi per realizzare prodotti da immettere sul mercato. Si può anzi affermare che il software open source (che, oltre all'ecosistema FOSS4G, comprende in questo contesto strumenti di condivisione e documentazione di codice, piattaforme di e-Learning e strumenti di Project Management) sia un elemento essenziale per il case-based learning. Non solo: le comunità ed i progetti open source sono per natura un habitat ideale per la co-creazione di conoscenza, fondandosi essi stessi sull'idea della creazione collaborativa del software per il bene comune. Il progetto giCASES, tra i cui partner figurano membri attivi della Open Source Geospatial Foundation (OSGeo), intende fare tesoro di queste esperienze per assicurare il successo dei propri risultati.

List of FiguresList of TablesPreface AcknowledgementPart 1: Living with Floods⁰́₄Case Studies in Flood Risk Management1. Assessing Vulnerability, Coping Mechanism & Adaptive Capacity of Community During Urban Floods:A Case of Kerala Floods 2018B. Sneha Singh and Anil Kumar Roy2. Flooding Problems in Periyar River Basin, Kerala⁰́₄The Effects of Land Use Land Cover ChangesKashish Sadhwani, T. I. Eldho and Subhankar Karmakar3. Attitudinal Capacity and Flood Risk Management of the People of Kerala after the Flood DisasterM. V. Bindu4. Kosi Floods in Bihar: A Study from Anthropological PerspectiveRahul Kumar Yaduka5. Operation Research on Flash Flood in River Dhauli Ganga On 7th February, 2021 at Tapovan⁰́₄Raini Village, Joshimath, ChamoliAditya Pratap Singh6. Multi-Stakeholder Planning and Coordination for Flood Risk Reduction in BiharVivek Kumar Singh, Geetanjali Kumari, Amrita Dhiman and Amritanjali Kumari7. Urban Floods in the Time of Pandemic⁰́₄Hyderabad 2020Vijaya Kumari Nunna8. Himalayan Flash Floods, the Complexities and Challenges in Mitigation: Case Study of Chamoli DisasterAgraj Upadhyay, S. K. Joshi and P. K. SatyawaliPart 2: Planning and Managing Risks of Floods and River Erosion9. Cloudburst Induced Flood Assessment in the North-Western Himalayan Region⁰́₄A Case Study of Upper Beas BasinSachchidanand Singh and Mitthan Lal Kansal10. PARA-FM: Graphical User Interface for Emergency Flood Planning and ManagementUtsav Rai, Durgakant Pushp, Aditya Choudhary, Amit Kumar, Chaitanya B., Rolif Lima, Abhinay N. S.,Rudrashis Majumder, Shuvrangshu Jana, Kaushik Das and Debasish Ghose11. Flood Response to Geomorphic Setup and Coastal Land Use Patterns: A Case Study inKrishna River Delta, Andhra Pradesh, IndiaM. V. Ramana Murty, K. Mruthyunjaya Reddy and K. V. Swamy12. Flood Hazard Mapping for Disaster Management: Multisectoral Approach for a Riverine RegionDeeksha Biswas and Anurag Bagade13. Benefit-Cost Analysis of Flood Management, A Case Study of Jammu and KashmirKowser Ali Jan and R. Balaji14. Tackling River Erosion: A Post-Disaster Transformative FrameworkSiji Chacko and Anil Kumar15. Flood Inundation Mapping Using C-band Synthetic-aperture Radar and Random Forest Algorithm:A Methodological BasisSwapnil Singh Parihar and Shafique Matin16. Public-Private-People Partnership (4P) Framework for Managing Floods in Raigad [MH]A. N. Chavan, S. M. Pore and S. R. Bhagat17. Role of Kerala Fire & Rescue Services Department in Handling Kerala Floods 2018B. Sandhya IPS, Nousad M. and Abdul Rasheed K.Part 3: Cyclone Risk Mitigation and Management18. Impact and Management of Super Cyclone Amphan in OdishaD. Panda and M. Devi19. At the Cross-roads in Cyclone-prone Odisha: Titli⁰́₄The Butterfly⁰́₉s Duel with DevelopmentGeeta Vaidyanathan and Ramani Sankaranarayanan20. Resilient Settlements Amid Cyclonic Storm⁰́₄Empirical Evidence on Factors at Play fromSouthwest Coastal BangladeshSumaiyah Binte Mamun and Khandaker Shabbir Ahmed21. Climate Change Induced Cyclones in Arabian Sea and Mitigating the Emerging Risks for SmallScale Fishermen CommunityVaishnavi and Katyayini Sood22. Vulnerability Assessment of a Cyclone Affected Community Kapateswar, Puri SadarChestha Khurana and Meghna Chatterji23. Evolution of Vulnerability to Cyclones: A Study of Storm Surge Vulnerability in theCoastal Blocks of South Twenty-four Parganas, West Bengal During 1997⁰́₃2019Nandita Singh and Neeti Neeti24. Study and Impact of Cyclones Over Coastal Regions of IndiaAakanksha Darge and S. R. BhagatPart 4: Managing Risks of Drought25. Machine Learning Tools and Techniques for Prediction of DroughtsRashmi Nitwane, Vaishali D. Bhagile and R. R. Deshmukh26. Assessment of Rainfall Variability and Drought Features in Bundelkhand Region ofMadhya Pradesh, IndiaR. V. Galkate, R. P. Pandey and Shalini Yadava27. Drought Crisis In Rajasthan and Their ManagementHarish Samaria28. Floods as Prequels to Droughts and Lessons for Drought ResilienceI. Kavila, B. V. Hari and Amrutha Sasidharan29. Reliability, Resilience, and Vulnerability Risk Assessment of Extreme Hydrological DisastersUsing Gravity Recovery and Climate Experiment (GRACE) of IndiaSachin Bhere and Manne Janga Reddy30. A Survey of Drought Impact and Mitigation in the State of TelanganaVijaya Kumari NunnaPart 5: Climate Change and Disasters⁰́₄New Dimensions31. Role of Local Sea-level Rise on Disaster Exposure in Coastal IslandVinay S., Aishwarya N. and Bharath H. A.32. Spatial Variability of Climate Extreme Indices Over Mahanadi Basin (India)R. K. Jaiswal, A. K. Lohani, R. V. Galkate and S. Jain33. Climate Change⁰́₄Induced Natural Disaster: A Case Study of 2013 Kedarnath Disaster,UttrakhandNeha Gupta, Josodhir Das and James Xavier Paul34. Framework for Evaluating the Physical Infrastructure Vulnerability Due to Relative Sea-LevelRise in Coastal Districts of Kerala⁰́₄Case of AlappuzhaR. S. Vishnu and Anurup K.35. Increasing Risk of Glacial Lake Outburst Floods (GLOFs) as a Consequence of Climate Changein the Himalayan RegionAhmed Faraz Khan36. Glacial Lake Outburst Flood ModellingA. K. Lohani, Sanjay K. Jain and R. K. Jaiswal37. Comparative Analysis of Developed, Developing and Underdeveloped Countries in Regards toClimate Change and Global PoliciesAanchal Pundir38. The Most Credible 05 Elements to Tackle Displacement in Nearest 10 Years of AsiaMahendra Jagath Kuragodage39. Climate Change and its Interconnectedness with Natural Disasters: A Global PerspectiveAreeba Naaz and Parvez Hayat IPS40. Climate-Induced Displacement and Climate Disaster Law: Challenges and OpportunitiesSunil Kumar Chaudhary41. Climate Hazards and the Role of Local Authorities: Exploring the Indian Legal FrameworkChandrika MehtaPart 6: Technological Disasters42. An Incident of Styrene Monomer Gas Poisoning at VisakhapatnamChandrasekhar Krishnamurti, M. D., Saurabh Dalal and Mounika Jonnavittula House Surgeon43. Chemical Disaster: Leakage of Styrene Gas at L. G. Polymers, Vishakhapatnam (Andhra Pradesh)Anupam Srivastava44. Managing a Blackout⁰́₄The Consequences of an Oil Spill at SeaMalini S. Shankar and K. M. Sivakholundu45. Baghjan Fire: A Case Study of the ⁰́₈2020 Assam Gas and Oil Leak⁰́₉ at Baghjan Oil Field,Tinsukia, AssamSwapnali Gogoi, Bubu Baruah and Annekha Chetia46. Assessing the Risk of Fire Hazard: A Case Study of DelhiKanika Bhatia and Aakash Upadhyay47. CBRN Management in IndiaPankaj Kumar48. Covid-19 Necessitates Review of Existing Framework for Management of CBRNDisasters/Emergencies in IndiaBrigadier Kamal Singh ChauhanPart 7: Other Disasters49. Are Strong Aftershocks Always Triggered by a Positive Coulomb Stress Change?:A Case Study of the 2018 Indonesia (Sulawesi) MW 7.5 Tsunamigenic EarthquakeNazeel Sabah and Daya Shanker50. Sedimentation Modeling of 2004 Indian Ocean Tsunami: Towards Early Warning StrategyAbdullah Ansari, Javed N Malik and Shivam Tripathi51. Forest Fire Risk Zone Mapping in Parambikulam Tiger Reserve, Western Ghats, Kerala, IndiaUsing Geospatial Techniques and Fire Risk Index (FRI) MethodShalu George, Kripa K., Anbazhagi S. and Muthukumar Muthuchamy52. Forest Fire Early Detection System using Wireless Beacon Network and UAV based Object DetectionAmal Sujith, Sagar Sajeev and Vishnu O. V.53. Patterns and Consequences: Australian BushfiresRitu Bir and Meenakshi Singh54. Global trend of Forest Fire and its ManagementRajesh Thakur55. Building Disaster Resilience to Cloud Burst Events in Uttrakhand, IndiaAnjali Saraswat and Satish Pipralia56. Heat Wave and Outbreak of Encephalitis⁰́₄A Case Study of BiharSunil Kumar Chaudhary

The urgency of the research. At present, new aviation rules concerning the provision of air navigation information have been introduced in Ukraine. The rules take into account the legislation of the European Union, Eurocontrol documents, ICAO Standards on the accuracy, processing and use of aeronautical data, which include data on terrain and obstacles in the areas of airports. Target setting. One of the factors influencing the safety of civil aviation is the consideration of obstacles on the routes in the form of elevations and high-ltitude objects. This is evidenced by accidents and catastrophes of aircraft caused by collisions with high-altitude obstacles. Therefore, it is necessary to monitor the ground space in the areas of an airport for the timely detection of obstacles to the updating of the electronic database on terrain and obstacles. Actual scientific researches and issues analysis. Recent open access publications on existing methods of collecting geospatial data to determine terrain and obstacles in areas of the airport were reviewed. Uninvestigated parts of general matters defining. The analysis of the given sources allows to draw a conclusion that concerning area 1 of the airport which is the territory of the state, the contradictory information on use of this or that method of definition of a relief of district and obstacles is resulted. The research objective. The main purpose of the article is to analyze the methods of monitoring spatial information on terrain and obstacles in area 1 of the airport using space-based radar systems that would meet the requirements of ICAO in this area for the maintenance of electronic databases of terrain and obstacles. The statement of basic materials. The quantitative requirements of ICAO for data on terrain and obstacles in area 1 of the airport are given. The errors of the results of the satellite radar topographic survey (SRTM), which allowed to obtain a digital model of the Earth's topography, were resolution in the vertical plane 1 m, and in the horizontal plane - 30 m. The method of Permanent Scatterer SAR Interferometry PSInSAR - interferometry of stable reflectors using radars with synthesized space-based aperture allows to obtain the accuracy of stable reflectors (natural and man-made objects) in the vertical plane of about 1 m and the error in determining the heights of other objects is 14 m. Currently, the PSInSAR method is used to monitor the subsidence of the earth's surface in cities, which allows to determine the deformation of the earth's surface to the nearest millimeter. The grouping of remote sensing satellites TerraSAR-X and TanDEM-X provided global coverage of the earth's surface with a digital terrain model with an accuracy of at least 2 m in height. The spatial resolution was about 1 m. In 2020, a satellite with a synthesized aperture of the Capella-2 radar survey was launched into Earth orbit. This unique micro satellite weighs 107 kg. Its camera equipment currently has an ultra-high spatial resolution of 50 x 50 cm. Conclusions. Modern methods of satellite radar can determine the planned and altitude position of objects in the area of airport 1 (territory of the state) with accuracy and resolution that meets the requirements of ICAO for the maintenance of an electronic database of terrain and obstacles. It is proposed to perform the first phase of monitoring objects in the country with the help of satellite radar, and in the second phase of monitoring to involve a ground survey - to clarify the attributive information about the objects detected in the first phase. ; Актуальність теми дослідження. На сьогодні в Україні введено нові авіаційні правила, які стосуються обслуговування аеронавігаційною інформацією. Правила враховують законодавство Європейського Союзу, документи Євроконтролю, Стандарти ІСАО стосовно точності, опрацювання та використання аеронавігаційних даних, складовими яких є дані про місцевість і перешкоди в районах аеропортів. Постановка проблеми. Одним із чинників, що впливають на безпеку польотів цивільної авіації, є врахування перешкод на трасах перельотів у вигляді підвищень рельєфу та висотних об'єктів. Про це свідчать аварії та катастрофи повітряних суден, спричинені зіткненням із висотними перешкодами. Тому необхідно проводити моніторинг наземного простору в районах аеропорту для своєчасного виявлення перешкод для актуалізації електронної бази даних щодо місцевості та перешкод. Аналіз останніх досліджень і публікацій. Були розглянуті останні публікації у відкритому доступі, які присвячені існуючим методам збирання геопросторових даних щодо визначення рельєфу місцевості та перешкод у районах аеропорту. Виділення недосліджених частин загальної проблеми. Аналіз наведених джерел дозволяє зробити висновок, що стосовно району 1 аеропорту, яким є територія держави, наведена суперечлива інформація щодо використання того чи іншого методу визначення рельєфу місцевості та перешкод. Мета статті. Головною метою статті є аналіз методів моніторингу просторової інформації щодо місцевості та перешкод у районі 1 аеропорту з використанням радіолокаційних систем космічного базування, які б задовольняли вимоги ІКАО в цьому районі щодо ведення електронної баз даних про місцевість та перешкоди. Виклад основного матеріалу. Наведено кількісні вимоги ІКАО щодо даних про місцевість та перешкоди в районі 1 аеропорту. Розглянуто похибки результатів супутникового радіолокаційного топографічного знімання (Shuttle radar topographic mission (SRTM), яке дозволили отримати цифрову модель рельєфу Землі. Похибки для території Євразії – де знаходиться Україна, складали 8,8 м у плані та 6,2 по висоті та мали роздільну здатність у вертикальній площині 1 м, а у горизонтальній площині – 30 м. Метод Permanent Scatterer SAR Interferometry PSInSAR) – інтерферометрії стійких відбивачів із використанням радіолокаторів із синтезованою апертурою космічного базування дозволяє отримати точність стійких відбивачів (природних та техногенних об'єктів) у вертикальній площині близько 1 м та похибка визначення висот інших об'єктів місцевості складає 14 м. Нині методику PSInSAR застосовують для моніторингу осідання земної поверхні в містах, що дозволяє визначати деформації земної поверхні з точністю до міліметрів. Угрупування супутників ДЗЗ: TerraSAR-X і TanDEM-X забезпечило глобальне покриття земної поверхні цифровою моделлю рельєфу з точністю за висотою не менше ніж 2 м. Просторове розрізнення становило близько 1 м. У 2020 році було виведено на навколоземну орбіту супутник із синтезованою апертурою радіолокаційного знімання Capella-2. Цей унікальний мікро супутник має масу 107 кг. Його знімальна апаратура має наразі надвисоку просторову роздільну здатність 50×50 см. Висновки відповідно до статті. Сучасні методи супутникового радіолокаційного знімання дозволяють визначити планове та висотне положення об'єктів місцевості в районі аеропорту 1 (територія держави) з точністю та роздільною здатністю, яка відповідає вимогам ІКАО щодо ведення електронної бази даних про місцевість та перешкоди. Пропонується виконувати першу фазу моніторингу об'єктів місцевості на території держави за допомогою супутникового радіолокаційного знімання, а на другій фазі моніторингу залучати наземне обстеження – для уточнення атрибутивної інформації щодо об'єктів, виявлених на першій фазі.

The urgency of the research. At present, new aviation rules concerning the provision of air navigation information have been introduced in Ukraine. The rules take into account the legislation of the European Union, Eurocontrol documents, ICAO Standards on the accuracy, processing and use of aeronautical data, which include data on terrain and obstacles in the areas of airports. Target setting. One of the factors influencing the safety of civil aviation is the consideration of obstacles on the routes in the form of elevations and high-ltitude objects. This is evidenced by accidents and catastrophes of aircraft caused by collisions with high-altitude obstacles. Therefore, it is necessary to monitor the ground space in the areas of an airport for the timely detection of obstacles to the updating of the electronic database on terrain and obstacles. Actual scientific researches and issues analysis. Recent open access publications on existing methods of collecting geospatial data to determine terrain and obstacles in areas of the airport were reviewed. Uninvestigated parts of general matters defining. The analysis of the given sources allows to draw a conclusion that concerning area 1 of the airport which is the territory of the state, the contradictory information on use of this or that method of definition of a relief of district and obstacles is resulted. The research objective. The main purpose of the article is to analyze the methods of monitoring spatial information on terrain and obstacles in area 1 of the airport using space-based radar systems that would meet the requirements of ICAO in this area for the maintenance of electronic databases of terrain and obstacles. The statement of basic materials. The quantitative requirements of ICAO for data on terrain and obstacles in area 1 of the airport are given. The errors of the results of the satellite radar topographic survey (SRTM), which allowed to obtain a digital model of the Earth's topography, were resolution in the vertical plane 1 m, and in the horizontal plane - 30 m. The method of Permanent Scatterer SAR Interferometry PSInSAR - interferometry of stable reflectors using radars with synthesized space-based aperture allows to obtain the accuracy of stable reflectors (natural and man-made objects) in the vertical plane of about 1 m and the error in determining the heights of other objects is 14 m. Currently, the PSInSAR method is used to monitor the subsidence of the earth's surface in cities, which allows to determine the deformation of the earth's surface to the nearest millimeter. The grouping of remote sensing satellites TerraSAR-X and TanDEM-X provided global coverage of the earth's surface with a digital terrain model with an accuracy of at least 2 m in height. The spatial resolution was about 1 m. In 2020, a satellite with a synthesized aperture of the Capella-2 radar survey was launched into Earth orbit. This unique micro satellite weighs 107 kg. Its camera equipment currently has an ultra-high spatial resolution of 50 x 50 cm. Conclusions. Modern methods of satellite radar can determine the planned and altitude position of objects in the area of airport 1 (territory of the state) with accuracy and resolution that meets the requirements of ICAO for the maintenance of an electronic database of terrain and obstacles. It is proposed to perform the first phase of monitoring objects in the country with the help of satellite radar, and in the second phase of monitoring to involve a ground survey - to clarify the attributive information about the objects detected in the first phase. ; Актуальність теми дослідження. На сьогодні в Україні введено нові авіаційні правила, які стосуються обслуговування аеронавігаційною інформацією. Правила враховують законодавство Європейського Союзу, документи Євроконтролю, Стандарти ІСАО стосовно точності, опрацювання та використання аеронавігаційних даних, складовими яких є дані про місцевість і перешкоди в районах аеропортів. Постановка проблеми. Одним із чинників, що впливають на безпеку польотів цивільної авіації, є врахування перешкод на трасах перельотів у вигляді підвищень рельєфу та висотних об'єктів. Про це свідчать аварії та катастрофи повітряних суден, спричинені зіткненням із висотними перешкодами. Тому необхідно проводити моніторинг наземного простору в районах аеропорту для своєчасного виявлення перешкод для актуалізації електронної бази даних щодо місцевості та перешкод. Аналіз останніх досліджень і публікацій. Були розглянуті останні публікації у відкритому доступі, які присвячені існуючим методам збирання геопросторових даних щодо визначення рельєфу місцевості та перешкод у районах аеропорту. Виділення недосліджених частин загальної проблеми. Аналіз наведених джерел дозволяє зробити висновок, що стосовно району 1 аеропорту, яким є територія держави, наведена суперечлива інформація щодо використання того чи іншого методу визначення рельєфу місцевості та перешкод. Мета статті. Головною метою статті є аналіз методів моніторингу просторової інформації щодо місцевості та перешкод у районі 1 аеропорту з використанням радіолокаційних систем космічного базування, які б задовольняли вимоги ІКАО в цьому районі щодо ведення електронної баз даних про місцевість та перешкоди. Виклад основного матеріалу. Наведено кількісні вимоги ІКАО щодо даних про місцевість та перешкоди в районі 1 аеропорту. Розглянуто похибки результатів супутникового радіолокаційного топографічного знімання (Shuttle radar topographic mission (SRTM), яке дозволили отримати цифрову модель рельєфу Землі. Похибки для території Євразії – де знаходиться Україна, складали 8,8 м у плані та 6,2 по висоті та мали роздільну здатність у вертикальній площині 1 м, а у горизонтальній площині – 30 м. Метод Permanent Scatterer SAR Interferometry PSInSAR) – інтерферометрії стійких відбивачів із використанням радіолокаторів із синтезованою апертурою космічного базування дозволяє отримати точність стійких відбивачів (природних та техногенних об'єктів) у вертикальній площині близько 1 м та похибка визначення висот інших об'єктів місцевості складає 14 м. Нині методику PSInSAR застосовують для моніторингу осідання земної поверхні в містах, що дозволяє визначати деформації земної поверхні з точністю до міліметрів. Угрупування супутників ДЗЗ: TerraSAR-X і TanDEM-X забезпечило глобальне покриття земної поверхні цифровою моделлю рельєфу з точністю за висотою не менше ніж 2 м. Просторове розрізнення становило близько 1 м. У 2020 році було виведено на навколоземну орбіту супутник із синтезованою апертурою радіолокаційного знімання Capella-2. Цей унікальний мікро супутник має масу 107 кг. Його знімальна апаратура має наразі надвисоку просторову роздільну здатність 50×50 см. Висновки відповідно до статті. Сучасні методи супутникового радіолокаційного знімання дозволяють визначити планове та висотне положення об'єктів місцевості в районі аеропорту 1 (територія держави) з точністю та роздільною здатністю, яка відповідає вимогам ІКАО щодо ведення електронної бази даних про місцевість та перешкоди. Пропонується виконувати першу фазу моніторингу об'єктів місцевості на території держави за допомогою супутникового радіолокаційного знімання, а на другій фазі моніторингу залучати наземне обстеження – для уточнення атрибутивної інформації щодо об'єктів, виявлених на першій фазі.

River systems, as a major component of the water environment, have the most interactions with human beings in many ways, especially along with social development and population growth, as well as the intense utilization of the river systems by humans, a series of major ecological environmental problems have arisen in recent years. Therefore, river system regulation and management has become a crucial focus point of research. Although the integrated river management is the preferred and most commonly accepted method that is applied in water resources management, a significant problem is that there is a shortage of integrated river ecological research which focuses specifically on urban rivers. The current problems governing urban river regulation and ecological status rehabilitation can be summarised into the following points: The definitions of urban and rural rivers are still not clarified; Interactions between urban and rural rivers are neglected; Need for an applicable mapping method for driving the urban river health index; Limited availability of input data sources, especially in the developing world for assessment and management of environmental problems. This research aims to study on the above addressed issues, to clarify the characteristics of urban rivers and to find an applicable method of assessing their health status. In this PhD research, the identification of urban and rural river reaches is studied as the first main purpose, a generic conceptual model namely the Urban-Rural River Continuum Identification System (URRCI) is developed. Reference of this system is to review the classification of urbanization, in order to distinguish whether the surveyed river section belongs to urban river or rural river, and further to solve the problem of the unclear definition of urban rivers. The second important issue has been addressed in this PhD research is to focus on urban river regulation, aiming to set up an advanced urban river ecological health assessment system which can be applied easily. The case study area of Shenzhen River in China has been selected to build up the database in order to establish and prove this new improved river health assessment system, and simulate effective rehabilitation measurements scenarios. The fuzzy logic approach is integrated into the fish habitat model CASiMiR, which is developed at the University of Stuttgart, Institute for Modelling Hydraulic and Environmental Systems, and is receiving a continuously growing acceptance in Europe and worldwide. The customized tool took advantage of an existing fuzzy inference calculator, which is integrated within the ArcGIS, thereby allowing direct data integration from various geospatial sources as input parameters and the presentation of output in the form of spatial and temporal maps. Indeed, the system of fuzzy rules (rulebase) can contain more than one input and one output parameter. The interaction between the urban river reach and the rural river reach is also identified as the third addressed objective. The integrated health status index is generated for both the urban and rural reaches base on the ArcGis based simulation. One expression is developed to calculate the health index for each of the urban and rural river stretches which are identified by the urban-rural river continuum identification system. The study on the Urban/Rural river interaction helps to find out what the thresholds of these main parameters in the urban river reach are, so that the urban river will not bring any stress onto the whole river system, which will affect the habitat suitability, and the most important is to lead a direction on the specific influenced plot and effective rehabilitation measures. As the last main objective of this PhD research, several mitigation scenarios are planned for the simulation of river rehabilitation. From the assessment of the urban/rural river ecological health status, bank/bed protection and riparian vegetation are the two parameters which have the most influence. Removal of bank protection and the enhancement of riparian vegetation have been carried out as the river rehabilitation measures individually and combined as well, from a comparison of the enhanced integrated river health index for each urban/rural stretch with different fuzzy membership functions, we can see that when the integrated fish suitability index has more weight in the river's ecological health assessment system, the subsequent river ecological rehabilitation measures have less impact. When the integrated fish suitability index, bank protection and riparian vegetation have equal importance in the river's ecological health assessment system, river enhancement is more effective. On the other hand, parameters related to the water body itself refer more to the stands of hydraulics and hydrology, which are not easy to change. So in this research they are not considered for improvement. The implementation in Shenzhen River shows that the advanced urban/rural River Ecological Health Assessment System was successfully established. It is easy to use and interpret since it adopts the standard governing parameters of river health that are widely accepted all over the globe. This approach allows rapid scenario analysis for large regions and has the potential to be used as a practical tool for the assessment of urban river ecological health by policy-makers and scientists. ; Fließgewässersysteme, als ein wesentlicher Bestandteil der Wasserwirtschaft zeichnen sich durch vielseitige Interaktion mit der Menschheit aus. Insbesondere die intensive Nutzung der Fließgewässersysteme sowie die gesellschaftliche Entwicklung und das Bevölkerungswachstum führten in den vergangenen Jahren zu erheblichen ökologischen Beeinträchtigungen. Daher stellt das Fließgewässermanagement ein bedeutendes und zentrales Forschungsthema dar. Obwohl im Rahmen des Wasserressourcen-Management meistens das integrative Flussgebietsmanagement angewendet wird, finden dort ökologische Aspekte häufig zu wenig Beachtung. Dies trifft insbesondere im Bereich der urbanen Fließgewässer zu. Die aktuellen Schwierigkeiten im urbanen Fließgewässermanagements und der entsprechenden ökologische Bewertung können wie folgt zusammengefasst werden: Eine exakte Unterscheidung von urbanen und ländlichen Fließgewässertypen ist bisher nicht explizit definiert, Interaktionen zwischen urbanen und ländlichen Fließgewässertypen werden vernachlässigt, Fehlen einer anwendbaren Kartier-Methode die den ökologischen Zustand vor urbanen Fließgewässern erfasst; begrenzte Verfügbarkeit von Datenquellen in Entwicklungsländern um Umweltprobleme bewerten zu können. Die vorliegende Arbeit befasst sich daher mit oben genannten Aspekten um einerseits urbane Fließgewässer charakterisieren und um andererseits deren ökologischen Zustand bewerten zu können. Erste Zielstellung dieser Doktorarbeit ist die Entwicklung eines Modellkonzepts zur Unterscheidung von urbanen und ländlichen Fließgewässerabschnitten basierend auf verschiedenen Urbanisierungsklassen (Urban-River Continuum Identification System, URRCI), in dem eine klare Definition für urbane Fließgewässertypen enthalten ist. Der zweite Schwerpunkt dieser Promotionsarbeit ist die Entwicklung eines urbanen ökologischen Bewertungssystems. Hierfür wird der Shenzhen River in China als Fallstudie ausgewählt, um dort alle notwendigen Daten zu erheben, das neu entwickelte urbane ökologische Bewertungssystem zu testen und zu verifizieren und um potentielle Renaturierungsszenarien zu simulieren. Ein wesentlicher Bestandteil des ökologischen Bewertungssystems ist das Fischhabitatsimulationsmodell CASiMiR, welches an dem Institut für Wasser- und Umweltsystemmodellierung der Universität Stuttgart entwickelt wurde. CASiMiR wird weltweit eingesetzt und basiert auf einen fuzzy-logischen Ansatz um abiotische Variablen mit Habitatansprüchen von Indikatorarten zu verknüpfen. Eine Neuentwicklung von CASiMiR ist eine ArcGIS-Version, die eine direkte Verarbeitung von georeferenzierten Daten erlaubt und die die Ergebnisse in zeitlicher und räumlicher Variabilität darstellt. Ein dritter Schwerpunkt dieser Arbeit befasst sich mit der Wechselwirkung zwischen urbanen und ländlichen Fließgewässerabschnitten, um Grenzwerte hinsichtlich beeinträchtigender Parameter in urbanen Fließgewässerabschnitten formulieren zu können, die das gesamte Fließgewässersystem beeinträchtigen könnten, sowie Entscheidungsprozesse bezüglich potentieller Renaturierungsmaßnahmen zu unterstützen. Als letztes Hauptziel dieser Promotionsarbeit werden verschiedene Renaturierungsmaßnahmen geplant und simuliert. Bezugnehmend auf die Ergebnisse der entwickelten ökologischen Bewertungsmethode haben die Uferbeschaffenheiten und die Auenvegetation den größten Einfluss auf den ökologischen Zustand des Shenzhen Rivers. Daher eignen sich der Rückbau von Ufersicherungen und eine Revitalisierung der Auengebiete als potentielle Renaturierungsmaßnahmen, die sowohl einzeln als auch kombiniert simuliert und bewertet werden. Zusätzlich werden die fuzzy-logischen Zugehörigkeitsfunktionen der Eingangsparameter im Rahmen einer Sensitivitätsanalyse variiert mit dem Ergebnis, dass bei einer höheren Gewichtung des integrierten Fischhabitat-Indexes (ISFI), die ökologische Renaturierungsmaßnahme geringere Auswirkungen aufzeigt. Für eine identische Gewichtung des ISFI-Werts, der Ufersicherung und der Auenvegetation zeigen die simulierten Renaturierungsmaßnahmen einen größeren Effekt. Allerdings gilt zu beachten, dass Parameter, die das Fließgewässer selbst betreffen (z. B. Hydaulik und Hydrology) im Rahmen dieser Untersuchung nicht für die Renaturierung verwendet wurden. Die Anwendung am Beispiel des Shenzen Flusses zeigt, dass das entwickelte ökologische Bewertungssystem für urbane und ländliche Fließgewässerabschnitte (URRHI) erfolgreich eingesetzt werden konnte. Durch die Verwendung von Standardvariablen, die weltweit Akzeptanz finden, ist das Bewertungssystem einfach anzuwenden und zu interpretieren. Mithilfe dieses Ansatzes können für großskalige Fließgewässersysteme rasch Szenarienanalysen durchgeführt werden und zusätzlich kann der Ansatz als ein nützliches Werkzeug für die ökologische Bewertung von Fließgewässern mit urbanen Fließgewässerabschnitten sowohl von Politikern als auch von Wissenschaftlern eingesetzt werden.

In February 2022, many experts in academia and government predicted a quick Russian military victory in its expanded war in Ukraine. Instead, Russia quickly lost the initiative and its elite forces and regular army suffered heavy losses. The Armed Forces of Ukraine (AFU) has recaptured approximately half of the territory Russia held at the apex of its campaign. Russia has lost more than territory. As it stands now, it will likely take more than a decade for the Russian military to recover its elite special operations capabilities. As members in the House debate the future of US support for Ukraine, they should consider the decade of unequivocal success the United States and its allies achieved in preparing and supporting Ukraine.American and allied support combined with Ukrainian will to fight have proven a near-lethal combination for Russian troops over the past two years. The AFU was able to hold fast against the air and ground assault by Russia's most elite forces in the opening weeks of the war for one key reason: the capabilities of the AFU had dramatically increased since 2014 with US and NATO member assistance. Bipartisan Policy InitiativeFew foreign policy initiatives offer as clear a correlation between implementation and outcome as US support to AFU has. There is a clear before and after picture. In 2014, Russia used its special purpose forces to easily capture territory from Ukraine with minimal casualties in Crimea. They also successfully started and sustained separatist conflicts in two more Ukrainian regions in the east. But in 2022, Russia's "little green men" and regular army were decimated. The improved supply of weapons to the AFU was important, but it pales in comparison with the impact of eight years of training for dedicated Ukrainian troops.In 2014, the Ukrainian military was not capable of defending its sovereignty. It had a small, corrupt, poorly equipped military, according to its own leadership.[1] Russia's 2014 land grabs proved to be a critical juncture for both Ukraine and its NATO partners. Volunteers within Ukraine mobilized quickly to hold the line in the east. Longer term, the Obama administration authorized a new policy approach that provided training and equipment to the AFU. As early as 2015, US Special Operations and Army forces began training Ukrainian counterparts in core military and intelligence competencies. It was a training initiative that "without a doubt, improves the Armed Forces of Ukraine's capability, readiness, and lethality," according to a commander of the Joint Multinational Training Group-Ukraine (JMTG-U).[2] Both the Trump and Biden administrations continued providing lethal support to the AFU, bringing it ever closer to NATO standards. Training evolutions in Ukraine only stopped a few weeks prior to the full invasion. Since February 2022, the United States has provided approximately $47 billion in military aid to the AFU. To be clear, the vast majority of these funds went to US defense contractors and personnel tasked with delivering the equipment.[3] Rather than the newest American gear, Ukraine was provided weapons systems already in our inventory using Presidential Drawdown Authority. Congressionally allocated funds were mainly spent in the United States, to replace those older stocks transferred to Ukraine. Although the primary beneficiary of this sustained policy was Ukraine, the US military and intelligence services also directly benefitted from this policy. Supporting the JMTG-U provided the U.S. Army National Guard with a stable and consistent mission over those eight years.[4] As mission requirements in Afghanistan and Iraq decreased, the long-term training partnership in Ukraine was critical to US military readiness. Additionally, helping the AFU resist Russian aggression allowed the US Army and other agencies to better understand and counter Russian combat tactics.[5] Every branch of the US military, from the Army to Space Force, has benefitted from observing the efforts of the AFU against a shared adversary.[6] Artillery tactics, anti-drone tactics, and counter electronic warfare are just a few of the areas where this collaboration has improved US competency. With thousands of Russian personnel operating in the open in Ukraine, all Russian tactics are now open for study. It will make countering Russian aggression in Ukraine and further abroad easier for the AFU and its allies.Highlighting this external support does not minimize the Ukraine's resolve and sacrifice. Simply giving a partner force the tools to improve its effectiveness is a necessary but not sufficient step in achieving our policy objectives. In fact, having a dedicated partner with high resolve is often the most important factor to mission (or policy) success. Missions where this is lacking are often counterproductive in the long term. Success comes from the interaction between good policy and strong partner force will. This bears repeating, given the policy environment in the United States and Europe currently. Effective US and European policy paired with Ukrainian resolve resulted huge military losses for a declared U.S. adversary.[7] After a summer of difficult fighting in 2023, Ukraine was on the verge of making significant advances in October, especially in the south. The AFU broke through layers of Russian entrenched defenses in multiple locations.[8] Russia was staring a catastrophic military defeat in the face—until Ukraine's ammunition supply began to run dry.[9] The loss of predictability in material support has put the Ukrainian military at risk and prevented any attempted advance. The timing of the disruption was no accident. As the AFU was establishing military positions on the east side of the Dnipro in the autumn of 2023, Russian influence operations in the U.S. and Europe intensified. Russian propaganda about corruption and stalemates began dominating policy debates.[10] The result: additional funding for Ukraine was put on hold in both Washington and Brussels.This creates an unusual situation. After ten years of success helping the AFU develop into a force that can credibly defend itself against a "great power," continued support is now at risk. Ukrainian officials, from President Zelenskyy down, are understandably concerned. Their state is facing an existential threat. Subduing Ukraine was not the only goal of the Russian "special military operation." It was only phase one. Russia had standing plans to target another prospective EU state, Moldova, once Ukraine had been subdued. Therefore, it is important and timely to consider how, specifically, NATO member support to the AFU has degraded the Russian adversary and perhaps prevented additional conflict. Battlespace ImpactWestern media and policymakers may not fully comprehend the severity of the losses Russia has sustained. Consider Russia's elite or special purpose military forces: Naval Infantry (MP), Air Assault (VDV), and Army and Navy Spetsnaz brigades.[11] Doctrinally, these are the only forces that matter for Russia. With a regular army largely made up of conscripts, Russian doctrine relies on mass to overwhelm an enemy after special purpose forces have led a vanguard incursion to soften the target. Long term efforts to modernize Russia's military have only placed greater reliance on this strategy. Without battle-ready special purpose forces, Russia's ability to wage offensive war is greatly curtailed, as is Russia's ability to project military power. A snapshot of the Russian performance in the war thus far provides a clear picture of strategic failure. Official US estimates from December 2023 show that Russian conventional forces have suffered catastrophic losses. They have likely suffered more than 300,000 casualties to all ground forces. This means that about 90% of Russia's pre-war army has been killed or wounded in action. For Russian leaders, losing thousands of regular army conscripts is inconsequential. The results in Bakhmut and Avdiivka reinforce Russia's willingness to use human waves as their primary tactic. However, several elite brigades also suffered 90% combat losses and may not be recoverable.[12] Prior to 2022, Russian Army and Navy Spetsnaz brigades were considered near-peer to US special forces. Analysts in the United States also thought highly of the even more elite Spetsnaz units in the Russian Joint Special Operations Command (KSSO). The VDV and MP were considered less capable, but still formidable. Special operations forces in the United States and NATO were tasked with countering these Russian formations. Both Spetsnaz and VDV had some operational successes in Chechnya, Crimea, Donbas, Kazakhstan, Georgia, and Syria over the past 20 years, helping build Russian prestige. Yet all these missions were conducted against unprepared and far inferior adversaries. They found a far different opponent in the AFU in 2022. Ukraine, with Western assistance, had been preparing since 2014 for the next Russian assault. In early 2023, U.S. geospatial intelligence analysis concluded that four out of five Army Spetsnaz brigades that saw combat in Ukraine in 2022 returned to Russia in a non-mission capable (NMC) state.[13] A few examples of Spetsnaz losses may be helpful to put this in context. The elite 346th Spetsnaz Brigade from the KSSO returned to garrison in 2022 with only 125 of 900 personnel. It functionally ceased to exist. The 22nd Separate Guards Brigade and two others had an even higher attrition rate, with between 90 to 95% lost as casualties. There was no clear signature that these units even returned to garrison.[14]Both the VDV and MP brigades experienced similar losses. For example, the 331st Guards Parachute regiment was destroyed during fighting in Ukraine. After combat in 2022, the VDV has likely been reduced to 50% of its pre-war capacity. For the MP, there were similar results. It lost two of four brigades in Ukraine, meaning two Russian fleets no longer have a marine component for security. Instead of decapitating Ukrainian leadership, Russia has lost more than half of its elite forces. All losses on the battlefield are costly. Some are more costly, however, considering the training time and resources required to reconstitute a troop. For VDV and MP a conservative peacetime timeline to reconstitute a unit is between three and five years. To replace the Spetsnaz losses, it likely takes between six to ten years from entry into the armed forces to reconstitute a fully trained, fully mission capable (FMC) special purpose operator. For Spetsnaz personnel screened for KSSO, a further two years of training is normally necessary. But these are peacetime estimates and assumes all candidates screened for spetsnaz are second- or third-term contract personnel.Of course, neither the Ukrainian nor Russian forces are at peace, so reconstitution timelines become fuzzy. The case of the 155th MP provides a good illustration. This unit was rendered NMC in 2022 but reconstituted twice and sent back into Ukraine.[15] Replacement personnel probably had no specialized training, and the unit was repeatedly fielded as an inferior force. Further, if the 155th MP ceases to exist, the pool of potential new recruits for Naval Spetsnaz is reduced, hindering the ability of these more capable units to reconstitute personnel. Likewise, if the 22nd Separate Guards Brigade is functionally broken, there are no personnel to select or train Spetsnaz replacements. In short, all elite Russian forces are connected in a training and development cycle. Losses in Ukraine have broken this cycle, so reconstitution will take significantly longer than the process during peacetime. How much longer will depend on how long the war lasts. Currently, there is no path to returning to a peacetime scenario, so the Russian General Staff will likely try to reconstitute these forces on a much quicker timeline. This will put new recruits to elite units at higher risk and will limit training opportunities. It is very likely Russian elite units will not recover pre-war capabilities for six to 12 years after the war ends. Some brigades may not recover at all. The Russian military has lost most of its ground offensive capability for at least the next decade. While conscripts can backfill conscripts, the Spetsnaz and other elite forces cannot be replaced quickly. The quality of Russian troops will continue to decline as long as the war in Ukraine continues. Strategic ImpactFrom a strategic standpoint, training and equipping the AFU is probably the most successful US foreign policy initiative since World War II. Eight years of training and two years of weapons shipments have done what almost 50 years of Cold War military spending could not do: decimate the Russian military. Imagine what National Security Advisor Kissinger or Scowcroft would think about the value of spending about $50 billion to achieve such results. They would probably argue its results were worth ten times that amount—and they would certainly be in favor of continuing to build on that success with additional resources.If the policy of training and equipping the AFU is restored and maintained, Russia will lose the war in Ukraine. Even if Russia manages to retain some internationally recognized Ukrainian territory in a future armistice, it has already lost the war by several measures:Russia's elite forces have, conservatively, been reduced to below 50% of pre-war capacity. Some of these troops have been reconstituted, but the replacements are not as capable.Conventional ground forces have likely suffered a 90% degradation from pre-war levels. Some have been reconstituted and some have been replaced by "private" contract personnel. The once-feared Black Sea Fleet has been forced to flee Crimea and much of the Black Sea itself. The AFU has recaptured several gas and oil platforms near Crimea in order to limit Russia's ability to target the Ukrainian coast with precision. Russian Aerospace Forces have underperformed and suffered high losses.[16] Russia has lost demographically, through combat deaths and emigration, which makes recruiting replacements more difficult and more costly.[17] The losses inflicted by the AFU will limit Russia's offensive capability for the next 10 to 20 years. The joint Ukraine and NATO effort may have also insulated Belarus, Moldova, and Georgia from additional territory grabs by Russia. As Robert Litwak has argued, war in Ukraine has likely reduced Russia to being a one-dimensional nuclear power.[18] This is a positive, but only preliminary, outcome. Based on the outcomes of the war thus far, it is imperative for the United States to commit the additional $60 billion to help Ukraine functionally destroy the Russian Army and eject the Black Sea Fleet from the Black Sea. The Putin regime has engaged in gray zone warfare against the United States for more than a decade. Allowing the same government time to reconstitute its military and retake territory from a Ukrainian ally is unconscionable from a national security policy standpoint. Further, a loss in Ukraine could force the Putin regime out of power. Historically, authoritarian regimes that lose external conflicts or achieve minimal gain are at risk of internal collapse.[19] In October 2023, Russia's defensive lines in Ukraine began to buckle. With uninterrupted US weapons shipments, the AFU may have been able to reinforce the breech and expand it. Instead, Russian propaganda has helped delay critical congressional support for almost six months. Ukraine has time-sensitive military needs. Delays cost lives and will soon cost Ukraine more of its sovereign territory. As Russia continues to receive timely resupply from North Korea and Iran, the United States looks increasingly wobbly as an ally. Leaders in the People's Republic of China have likely taken note. [1] Valeriy Akimenko, "Ukraine's Toughest Fight: The Challenge of Military Reform," Carnegie Endowment for International Peace, February 22, 2018, https://carnegieendowment.org/2018/02/22/ukraine-s-toughest-fight-challenge-of-military-reform-pub-75609. [2] Matthew Baldwin, "Task Force Raven Takes Command of Joint Multinational Training Group-Ukraine," U.S. Army, April 17, 2021, https://www.army.mil/article/245354/task_force_raven_takes_command_of_joint_multinational_training_group_ukraine. [3] Elizabeth Hoffman, Jaehyun Han, and Shivani Vakharia, "The Past, Present, and Future of U.S. Assistance to Ukraine: A Deep Dive into the Data," Center for Strategic & International Studies, September 26, 2023, https://www.csis.org/analysis/past-present-and-future-us-assistance-ukraine-deep-dive-data. [4] Jared Saathoff, "Red Arrow Soldiers Deployed in Ukraine for Multinational Mission," Defense Visual Information Distribution Service, 2019, https://www.dvidshub.net/news/353722/red-arrow-soldiers-deployed-ukraine-multinational-mission; and Gleb Garanich, "Ukraine Holds Military Drills with US, Poland, Lithuania," Reuters, 2021.[5] Wesley Morgan, "US Army Unprepared to Deal with Russia in Europe," Politico, 2017, https://www.politico.eu/article/us-army-unprepared-to-deal-with-russia-in-europe/. [6] Caleigh Kelly, "Russia-Ukraine War Holds Key Lessons for US Space Command: Top Official," The Hill, 2023, https://thehill.com/policy/defense/4089559-russia-ukraine-war-holds-key-lessons-for-us-space-command-top-official/; and Jen Judson, "Change of Plans: US Army Embraces Lessons Learned from War in Ukraine," Defense News, 2023, https://www.defensenews.com/land/2023/10/09/change-of-plans-us-army-embraces-lessons-learned-from-war-in-ukraine/. [7] Russia has engaged in political and cyber warfare against NATO member states in both the near abroad and further. [8] Mstyslav Chernov and Lori Hinnant, "How Ukrainian Special Forces Secured a Critical Dnipro River Crossing," Associated Press, 2023, https://www.voanews.com/a/how-ukrainian-special-forces-secured-a-critical-dnipro-river-crossing/7413472.html. [9] In addition to Russian ground force losses, the AFU has also significantly degraded the Russian air component and forced the Black Sea Fleet to flee Crimea.[10] Katie Bo Lillis, "Newly Declassified US Intel Claims Russia Is Laundering Propaganda through Unwitting Westerners," CNN, 2023, https://www.cnn.com/2023/08/25/politics/us-intel-russia-propaganda/index.html; and Hoffman, Han, and Vakharia.[11] See Galeotti (2022) for a reasonable description of each of the elite forces and their mission and training profiles. [12] Alex Horton, "Russia's Commando Units Gutted by Ukraine War, U.S. Leak Shows," Washington Post, 2023; and Warren P. Strobel and Matthew Luxmoore, "Russia Has Lost Almost 90% of Its Prewar Army, U.S. Intelligence Says; The Declassified Estimate Says 315,000 Personnel Have Been Killed or Injured in Ukraine," Wall Street Journal, December 12, 2023, https://www.wsj.com/articles/russian-has-lost-almost-90-of-its-prewar-army-u-s-intelligence-says-2e0372ab. [13] If a unit falls below 50% combat readiness, it is considered non-mission capable. [14] Alex Horton, "Russia's Commando Units Gutted by Ukraine War, U.S. Leak Shows," Washington Post, 2023.[15] Ellie Cook, "Russia's 'Elite' Units Might Not Be So Elite," Newsweek, 2023; and Ellie Cook, "Russian General's Leak of Elite Casualties 'Endorses' 50% Loss Figure: U.K.," Newsweek, 2023.[16] International Institute for Strategic Studies, The Military Balance 2024 (London, UK: Routledge, 2024), https://doi.org/10.4324/9781003485834. [17] Russia had among the lowest birth rates in the world prior to 2022 and a high death rate among military aged males, according to the CDC International Database. See United States Census Bureau, "International Database: World Population Estimates and Projections," https://www.census.gov/programs-surveys/international-programs/about/idb.html; and Jennifer Dabbs Sciubba, The Future Faces of War: Population and National Security (Santa Barbara, CA: Praeger, 2011): 40, 134-137.[18] Robert S. Litwak, Tripolar Instability: Nuclear Competition Among the United States, Russia, and China (Washington, DC: Wilson Center, 2023).[19] Alyssa K. Prorok, "Leader Incentives and Civil War Outcomes," American Journal of Political Science 60, no. 1 (2016): 70; and Mark Peceny and Christopher K. Butler, "The Conflict Behavior of Authoritarian Regimes," International Politics 41 (2004): 565–81.

In February 2022, many experts in academia and government predicted a quick Russian military victory in its expanded war in Ukraine. Instead, Russia quickly lost the initiative and its elite forces and regular army suffered heavy losses. The Armed Forces of Ukraine (AFU) has recaptured approximately half of the territory Russia held at the apex of its campaign. Russia has lost more than territory. As it stands now, it will likely take more than a decade for the Russian military to recover its elite special operations capabilities. As members in the House debate the future of US support for Ukraine, they should consider the decade of unequivocal success the United States and its allies achieved in preparing and supporting Ukraine.American and allied support combined with Ukrainian will to fight have proven a near-lethal combination for Russian troops over the past two years. The AFU was able to hold fast against the air and ground assault by Russia's most elite forces in the opening weeks of the war for one key reason: the capabilities of the AFU had dramatically increased since 2014 with US and NATO member assistance. Bipartisan Policy InitiativeFew foreign policy initiatives offer as clear a correlation between implementation and outcome as US support to AFU has. There is a clear before and after picture. In 2014, Russia used its special purpose forces to easily capture territory from Ukraine with minimal casualties in Crimea. They also successfully started and sustained separatist conflicts in two more Ukrainian regions in the east. But in 2022, Russia's "little green men" and regular army were decimated. The improved supply of weapons to the AFU was important, but it pales in comparison with the impact of eight years of training for dedicated Ukrainian troops.In 2014, the Ukrainian military was not capable of defending its sovereignty. It had a small, corrupt, poorly equipped military, according to its own leadership.[1] Russia's 2014 land grabs proved to be a critical juncture for both Ukraine and its NATO partners. Volunteers within Ukraine mobilized quickly to hold the line in the east. Longer term, the Obama administration authorized a new policy approach that provided training and equipment to the AFU. As early as 2015, US Special Operations and Army forces began training Ukrainian counterparts in core military and intelligence competencies. It was a training initiative that "without a doubt, improves the Armed Forces of Ukraine's capability, readiness, and lethality," according to a commander of the Joint Multinational Training Group-Ukraine (JMTG-U).[2] Both the Trump and Biden administrations continued providing lethal support to the AFU, bringing it ever closer to NATO standards. Training evolutions in Ukraine only stopped a few weeks prior to the full invasion. Since February 2022, the United States has provided approximately $47 billion in military aid to the AFU. To be clear, the vast majority of these funds went to US defense contractors and personnel tasked with delivering the equipment.[3] Rather than the newest American gear, Ukraine was provided weapons systems already in our inventory using Presidential Drawdown Authority. Congressionally allocated funds were mainly spent in the United States, to replace those older stocks transferred to Ukraine. Although the primary beneficiary of this sustained policy was Ukraine, the US military and intelligence services also directly benefitted from this policy. Supporting the JMTG-U provided the U.S. Army National Guard with a stable and consistent mission over those eight years.[4] As mission requirements in Afghanistan and Iraq decreased, the long-term training partnership in Ukraine was critical to US military readiness. Additionally, helping the AFU resist Russian aggression allowed the US Army and other agencies to better understand and counter Russian combat tactics.[5] Every branch of the US military, from the Army to Space Force, has benefitted from observing the efforts of the AFU against a shared adversary.[6] Artillery tactics, anti-drone tactics, and counter electronic warfare are just a few of the areas where this collaboration has improved US competency. With thousands of Russian personnel operating in the open in Ukraine, all Russian tactics are now open for study. It will make countering Russian aggression in Ukraine and further abroad easier for the AFU and its allies.Highlighting this external support does not minimize the Ukraine's resolve and sacrifice. Simply giving a partner force the tools to improve its effectiveness is a necessary but not sufficient step in achieving our policy objectives. In fact, having a dedicated partner with high resolve is often the most important factor to mission (or policy) success. Missions where this is lacking are often counterproductive in the long term. Success comes from the interaction between good policy and strong partner force will. This bears repeating, given the policy environment in the United States and Europe currently. Effective US and European policy paired with Ukrainian resolve resulted huge military losses for a declared U.S. adversary.[7] After a summer of difficult fighting in 2023, Ukraine was on the verge of making significant advances in October, especially in the south. The AFU broke through layers of Russian entrenched defenses in multiple locations.[8] Russia was staring a catastrophic military defeat in the face—until Ukraine's ammunition supply began to run dry.[9] The loss of predictability in material support has put the Ukrainian military at risk and prevented any attempted advance. The timing of the disruption was no accident. As the AFU was establishing military positions on the east side of the Dnipro in the autumn of 2023, Russian influence operations in the U.S. and Europe intensified. Russian propaganda about corruption and stalemates began dominating policy debates.[10] The result: additional funding for Ukraine was put on hold in both Washington and Brussels.This creates an unusual situation. After ten years of success helping the AFU develop into a force that can credibly defend itself against a "great power," continued support is now at risk. Ukrainian officials, from President Zelenskyy down, are understandably concerned. Their state is facing an existential threat. Subduing Ukraine was not the only goal of the Russian "special military operation." It was only phase one. Russia had standing plans to target another prospective EU state, Moldova, once Ukraine had been subdued. Therefore, it is important and timely to consider how, specifically, NATO member support to the AFU has degraded the Russian adversary and perhaps prevented additional conflict. Battlespace ImpactWestern media and policymakers may not fully comprehend the severity of the losses Russia has sustained. Consider Russia's elite or special purpose military forces: Naval Infantry (MP), Air Assault (VDV), and Army and Navy Spetsnaz brigades.[11] Doctrinally, these are the only forces that matter for Russia. With a regular army largely made up of conscripts, Russian doctrine relies on mass to overwhelm an enemy after special purpose forces have led a vanguard incursion to soften the target. Long term efforts to modernize Russia's military have only placed greater reliance on this strategy. Without battle-ready special purpose forces, Russia's ability to wage offensive war is greatly curtailed, as is Russia's ability to project military power. A snapshot of the Russian performance in the war thus far provides a clear picture of strategic failure. Official US estimates from December 2023 show that Russian conventional forces have suffered catastrophic losses. They have likely suffered more than 300,000 casualties to all ground forces. This means that about 90% of Russia's pre-war army has been killed or wounded in action. For Russian leaders, losing thousands of regular army conscripts is inconsequential. The results in Bakhmut and Avdiivka reinforce Russia's willingness to use human waves as their primary tactic. However, several elite brigades also suffered 90% combat losses and may not be recoverable.[12] Prior to 2022, Russian Army and Navy Spetsnaz brigades were considered near-peer to US special forces. Analysts in the United States also thought highly of the even more elite Spetsnaz units in the Russian Joint Special Operations Command (KSSO). The VDV and MP were considered less capable, but still formidable. Special operations forces in the United States and NATO were tasked with countering these Russian formations. Both Spetsnaz and VDV had some operational successes in Chechnya, Crimea, Donbas, Kazakhstan, Georgia, and Syria over the past 20 years, helping build Russian prestige. Yet all these missions were conducted against unprepared and far inferior adversaries. They found a far different opponent in the AFU in 2022. Ukraine, with Western assistance, had been preparing since 2014 for the next Russian assault. In early 2023, U.S. geospatial intelligence analysis concluded that four out of five Army Spetsnaz brigades that saw combat in Ukraine in 2022 returned to Russia in a non-mission capable (NMC) state.[13] A few examples of Spetsnaz losses may be helpful to put this in context. The elite 346th Spetsnaz Brigade from the KSSO returned to garrison in 2022 with only 125 of 900 personnel. It functionally ceased to exist. The 22nd Separate Guards Brigade and two others had an even higher attrition rate, with between 90 to 95% lost as casualties. There was no clear signature that these units even returned to garrison.[14]Both the VDV and MP brigades experienced similar losses. For example, the 331st Guards Parachute regiment was destroyed during fighting in Ukraine. After combat in 2022, the VDV has likely been reduced to 50% of its pre-war capacity. For the MP, there were similar results. It lost two of four brigades in Ukraine, meaning two Russian fleets no longer have a marine component for security. Instead of decapitating Ukrainian leadership, Russia has lost more than half of its elite forces. All losses on the battlefield are costly. Some are more costly, however, considering the training time and resources required to reconstitute a troop. For VDV and MP a conservative peacetime timeline to reconstitute a unit is between three and five years. To replace the Spetsnaz losses, it likely takes between six to ten years from entry into the armed forces to reconstitute a fully trained, fully mission capable (FMC) special purpose operator. For Spetsnaz personnel screened for KSSO, a further two years of training is normally necessary. But these are peacetime estimates and assumes all candidates screened for spetsnaz are second- or third-term contract personnel.Of course, neither the Ukrainian nor Russian forces are at peace, so reconstitution timelines become fuzzy. The case of the 155th MP provides a good illustration. This unit was rendered NMC in 2022 but reconstituted twice and sent back into Ukraine.[15] Replacement personnel probably had no specialized training, and the unit was repeatedly fielded as an inferior force. Further, if the 155th MP ceases to exist, the pool of potential new recruits for Naval Spetsnaz is reduced, hindering the ability of these more capable units to reconstitute personnel. Likewise, if the 22nd Separate Guards Brigade is functionally broken, there are no personnel to select or train Spetsnaz replacements. In short, all elite Russian forces are connected in a training and development cycle. Losses in Ukraine have broken this cycle, so reconstitution will take significantly longer than the process during peacetime. How much longer will depend on how long the war lasts. Currently, there is no path to returning to a peacetime scenario, so the Russian General Staff will likely try to reconstitute these forces on a much quicker timeline. This will put new recruits to elite units at higher risk and will limit training opportunities. It is very likely Russian elite units will not recover pre-war capabilities for six to 12 years after the war ends. Some brigades may not recover at all. The Russian military has lost most of its ground offensive capability for at least the next decade. While conscripts can backfill conscripts, the Spetsnaz and other elite forces cannot be replaced quickly. The quality of Russian troops will continue to decline as long as the war in Ukraine continues. Strategic ImpactFrom a strategic standpoint, training and equipping the AFU is probably the most successful US foreign policy initiative since World War II. Eight years of training and two years of weapons shipments have done what almost 50 years of Cold War military spending could not do: decimate the Russian military. Imagine what National Security Advisor Kissinger or Scowcroft would think about the value of spending about $50 billion to achieve such results. They would probably argue its results were worth ten times that amount—and they would certainly be in favor of continuing to build on that success with additional resources.If the policy of training and equipping the AFU is restored and maintained, Russia will lose the war in Ukraine. Even if Russia manages to retain some internationally recognized Ukrainian territory in a future armistice, it has already lost the war by several measures:Russia's elite forces have, conservatively, been reduced to below 50% of pre-war capacity. Some of these troops have been reconstituted, but the replacements are not as capable.Conventional ground forces have likely suffered a 90% degradation from pre-war levels. Some have been reconstituted and some have been replaced by "private" contract personnel. The once-feared Black Sea Fleet has been forced to flee Crimea and much of the Black Sea itself. The AFU has recaptured several gas and oil platforms near Crimea in order to limit Russia's ability to target the Ukrainian coast with precision. Russian Aerospace Forces have underperformed and suffered high losses.[16] Russia has lost demographically, through combat deaths and emigration, which makes recruiting replacements more difficult and more costly.[17] The losses inflicted by the AFU will limit Russia's offensive capability for the next 10 to 20 years. The joint Ukraine and NATO effort may have also insulated Belarus, Moldova, and Georgia from additional territory grabs by Russia. As Robert Litwak has argued, war in Ukraine has likely reduced Russia to being a one-dimensional nuclear power.[18] This is a positive, but only preliminary, outcome. Based on the outcomes of the war thus far, it is imperative for the United States to commit the additional $60 billion to help Ukraine functionally destroy the Russian Army and eject the Black Sea Fleet from the Black Sea. The Putin regime has engaged in gray zone warfare against the United States for more than a decade. Allowing the same government time to reconstitute its military and retake territory from a Ukrainian ally is unconscionable from a national security policy standpoint. Further, a loss in Ukraine could force the Putin regime out of power. Historically, authoritarian regimes that lose external conflicts or achieve minimal gain are at risk of internal collapse.[19] In October 2023, Russia's defensive lines in Ukraine began to buckle. With uninterrupted US weapons shipments, the AFU may have been able to reinforce the breech and expand it. Instead, Russian propaganda has helped delay critical congressional support for almost six months. Ukraine has time-sensitive military needs. Delays cost lives and will soon cost Ukraine more of its sovereign territory. As Russia continues to receive timely resupply from North Korea and Iran, the United States looks increasingly wobbly as an ally. Leaders in the People's Republic of China have likely taken note. [1] Valeriy Akimenko, "Ukraine's Toughest Fight: The Challenge of Military Reform," Carnegie Endowment for International Peace, February 22, 2018, https://carnegieendowment.org/2018/02/22/ukraine-s-toughest-fight-challenge-of-military-reform-pub-75609. [2] Matthew Baldwin, "Task Force Raven Takes Command of Joint Multinational Training Group-Ukraine," U.S. Army, April 17, 2021, https://www.army.mil/article/245354/task_force_raven_takes_command_of_joint_multinational_training_group_ukraine. [3] Elizabeth Hoffman, Jaehyun Han, and Shivani Vakharia, "The Past, Present, and Future of U.S. Assistance to Ukraine: A Deep Dive into the Data," Center for Strategic & International Studies, September 26, 2023, https://www.csis.org/analysis/past-present-and-future-us-assistance-ukraine-deep-dive-data. [4] Jared Saathoff, "Red Arrow Soldiers Deployed in Ukraine for Multinational Mission," Defense Visual Information Distribution Service, 2019, https://www.dvidshub.net/news/353722/red-arrow-soldiers-deployed-ukraine-multinational-mission; and Gleb Garanich, "Ukraine Holds Military Drills with US, Poland, Lithuania," Reuters, 2021.[5] Wesley Morgan, "US Army Unprepared to Deal with Russia in Europe," Politico, 2017, https://www.politico.eu/article/us-army-unprepared-to-deal-with-russia-in-europe/. [6] Caleigh Kelly, "Russia-Ukraine War Holds Key Lessons for US Space Command: Top Official," The Hill, 2023, https://thehill.com/policy/defense/4089559-russia-ukraine-war-holds-key-lessons-for-us-space-command-top-official/; and Jen Judson, "Change of Plans: US Army Embraces Lessons Learned from War in Ukraine," Defense News, 2023, https://www.defensenews.com/land/2023/10/09/change-of-plans-us-army-embraces-lessons-learned-from-war-in-ukraine/. [7] Russia has engaged in political and cyber warfare against NATO member states in both the near abroad and further. [8] Mstyslav Chernov and Lori Hinnant, "How Ukrainian Special Forces Secured a Critical Dnipro River Crossing," Associated Press, 2023, https://www.voanews.com/a/how-ukrainian-special-forces-secured-a-critical-dnipro-river-crossing/7413472.html. [9] In addition to Russian ground force losses, the AFU has also significantly degraded the Russian air component and forced the Black Sea Fleet to flee Crimea.[10] Katie Bo Lillis, "Newly Declassified US Intel Claims Russia Is Laundering Propaganda through Unwitting Westerners," CNN, 2023, https://www.cnn.com/2023/08/25/politics/us-intel-russia-propaganda/index.html; and Hoffman, Han, and Vakharia.[11] See Galeotti (2022) for a reasonable description of each of the elite forces and their mission and training profiles. [12] Alex Horton, "Russia's Commando Units Gutted by Ukraine War, U.S. Leak Shows," Washington Post, 2023; and Warren P. Strobel and Matthew Luxmoore, "Russia Has Lost Almost 90% of Its Prewar Army, U.S. Intelligence Says; The Declassified Estimate Says 315,000 Personnel Have Been Killed or Injured in Ukraine," Wall Street Journal, December 12, 2023, https://www.wsj.com/articles/russian-has-lost-almost-90-of-its-prewar-army-u-s-intelligence-says-2e0372ab. [13] If a unit falls below 50% combat readiness, it is considered non-mission capable. [14] Alex Horton, "Russia's Commando Units Gutted by Ukraine War, U.S. Leak Shows," Washington Post, 2023.[15] Ellie Cook, "Russia's 'Elite' Units Might Not Be So Elite," Newsweek, 2023; and Ellie Cook, "Russian General's Leak of Elite Casualties 'Endorses' 50% Loss Figure: U.K.," Newsweek, 2023.[16] International Institute for Strategic Studies, The Military Balance 2024 (London, UK: Routledge, 2024), https://doi.org/10.4324/9781003485834. [17] Russia had among the lowest birth rates in the world prior to 2022 and a high death rate among military aged males, according to the CDC International Database. See United States Census Bureau, "International Database: World Population Estimates and Projections," https://www.census.gov/programs-surveys/international-programs/about/idb.html; and Jennifer Dabbs Sciubba, The Future Faces of War: Population and National Security (Santa Barbara, CA: Praeger, 2011): 40, 134-137.[18] Robert S. Litwak, Tripolar Instability: Nuclear Competition Among the United States, Russia, and China (Washington, DC: Wilson Center, 2023).[19] Alyssa K. Prorok, "Leader Incentives and Civil War Outcomes," American Journal of Political Science 60, no. 1 (2016): 70; and Mark Peceny and Christopher K. Butler, "The Conflict Behavior of Authoritarian Regimes," International Politics 41 (2004): 565–81.

In February 2022, many experts in academia and government predicted a quick Russian military victory in its expanded war in Ukraine. Instead, Russia quickly lost the initiative and its elite forces and regular army suffered heavy losses. The Armed Forces of Ukraine (AFU) has recaptured approximately half of the territory Russia held at the apex of its campaign. Russia has lost more than territory. As it stands now, it will likely take more than a decade for the Russian military to recover its elite special operations capabilities. As members in the House debate the future of US support for Ukraine, they should consider the decade of unequivocal success the United States and its allies achieved in preparing and supporting Ukraine.American and allied support combined with Ukrainian will to fight have proven a near-lethal combination for Russian troops over the past two years. The AFU was able to hold fast against the air and ground assault by Russia's most elite forces in the opening weeks of the war for one key reason: the capabilities of the AFU had dramatically increased since 2014 with US and NATO member assistance. Bipartisan Policy InitiativeFew foreign policy initiatives offer as clear a correlation between implementation and outcome as US support to AFU has. There is a clear before and after picture. In 2014, Russia used its special purpose forces to easily capture territory from Ukraine with minimal casualties in Crimea. They also successfully started and sustained separatist conflicts in two more Ukrainian regions in the east. But in 2022, Russia's "little green men" and regular army were decimated. The improved supply of weapons to the AFU was important, but it pales in comparison with the impact of eight years of training for dedicated Ukrainian troops.In 2014, the Ukrainian military was not capable of defending its sovereignty. It had a small, corrupt, poorly equipped military, according to its own leadership.[1] Russia's 2014 land grabs proved to be a critical juncture for both Ukraine and its NATO partners. Volunteers within Ukraine mobilized quickly to hold the line in the east. Longer term, the Obama administration authorized a new policy approach that provided training and equipment to the AFU. As early as 2015, US Special Operations and Army forces began training Ukrainian counterparts in core military and intelligence competencies. It was a training initiative that "without a doubt, improves the Armed Forces of Ukraine's capability, readiness, and lethality," according to a commander of the Joint Multinational Training Group-Ukraine (JMTG-U).[2] Both the Trump and Biden administrations continued providing lethal support to the AFU, bringing it ever closer to NATO standards. Training evolutions in Ukraine only stopped a few weeks prior to the full invasion. Since February 2022, the United States has provided approximately $47 billion in military aid to the AFU. To be clear, the vast majority of these funds went to US defense contractors and personnel tasked with delivering the equipment.[3] Rather than the newest American gear, Ukraine was provided weapons systems already in our inventory using Presidential Drawdown Authority. Congressionally allocated funds were mainly spent in the United States, to replace those older stocks transferred to Ukraine. Although the primary beneficiary of this sustained policy was Ukraine, the US military and intelligence services also directly benefitted from this policy. Supporting the JMTG-U provided the U.S. Army National Guard with a stable and consistent mission over those eight years.[4] As mission requirements in Afghanistan and Iraq decreased, the long-term training partnership in Ukraine was critical to US military readiness. Additionally, helping the AFU resist Russian aggression allowed the US Army and other agencies to better understand and counter Russian combat tactics.[5] Every branch of the US military, from the Army to Space Force, has benefitted from observing the efforts of the AFU against a shared adversary.[6] Artillery tactics, anti-drone tactics, and counter electronic warfare are just a few of the areas where this collaboration has improved US competency. With thousands of Russian personnel operating in the open in Ukraine, all Russian tactics are now open for study. It will make countering Russian aggression in Ukraine and further abroad easier for the AFU and its allies.Highlighting this external support does not minimize the Ukraine's resolve and sacrifice. Simply giving a partner force the tools to improve its effectiveness is a necessary but not sufficient step in achieving our policy objectives. In fact, having a dedicated partner with high resolve is often the most important factor to mission (or policy) success. Missions where this is lacking are often counterproductive in the long term. Success comes from the interaction between good policy and strong partner force will. This bears repeating, given the policy environment in the United States and Europe currently. Effective US and European policy paired with Ukrainian resolve resulted huge military losses for a declared U.S. adversary.[7] After a summer of difficult fighting in 2023, Ukraine was on the verge of making significant advances in October, especially in the south. The AFU broke through layers of Russian entrenched defenses in multiple locations.[8] Russia was staring a catastrophic military defeat in the face—until Ukraine's ammunition supply began to run dry.[9] The loss of predictability in material support has put the Ukrainian military at risk and prevented any attempted advance. The timing of the disruption was no accident. As the AFU was establishing military positions on the east side of the Dnipro in the autumn of 2023, Russian influence operations in the U.S. and Europe intensified. Russian propaganda about corruption and stalemates began dominating policy debates.[10] The result: additional funding for Ukraine was put on hold in both Washington and Brussels.This creates an unusual situation. After ten years of success helping the AFU develop into a force that can credibly defend itself against a "great power," continued support is now at risk. Ukrainian officials, from President Zelenskyy down, are understandably concerned. Their state is facing an existential threat. Subduing Ukraine was not the only goal of the Russian "special military operation." It was only phase one. Russia had standing plans to target another prospective EU state, Moldova, once Ukraine had been subdued. Therefore, it is important and timely to consider how, specifically, NATO member support to the AFU has degraded the Russian adversary and perhaps prevented additional conflict. Battlespace ImpactWestern media and policymakers may not fully comprehend the severity of the losses Russia has sustained. Consider Russia's elite or special purpose military forces: Naval Infantry (MP), Air Assault (VDV), and Army and Navy Spetsnaz brigades.[11] Doctrinally, these are the only forces that matter for Russia. With a regular army largely made up of conscripts, Russian doctrine relies on mass to overwhelm an enemy after special purpose forces have led a vanguard incursion to soften the target. Long term efforts to modernize Russia's military have only placed greater reliance on this strategy. Without battle-ready special purpose forces, Russia's ability to wage offensive war is greatly curtailed, as is Russia's ability to project military power. A snapshot of the Russian performance in the war thus far provides a clear picture of strategic failure. Official US estimates from December 2023 show that Russian conventional forces have suffered catastrophic losses. They have likely suffered more than 300,000 casualties to all ground forces. This means that about 90% of Russia's pre-war army has been killed or wounded in action. For Russian leaders, losing thousands of regular army conscripts is inconsequential. The results in Bakhmut and Avdiivka reinforce Russia's willingness to use human waves as their primary tactic. However, several elite brigades also suffered 90% combat losses and may not be recoverable.[12] Prior to 2022, Russian Army and Navy Spetsnaz brigades were considered near-peer to US special forces. Analysts in the United States also thought highly of the even more elite Spetsnaz units in the Russian Joint Special Operations Command (KSSO). The VDV and MP were considered less capable, but still formidable. Special operations forces in the United States and NATO were tasked with countering these Russian formations. Both Spetsnaz and VDV had some operational successes in Chechnya, Crimea, Donbas, Kazakhstan, Georgia, and Syria over the past 20 years, helping build Russian prestige. Yet all these missions were conducted against unprepared and far inferior adversaries. They found a far different opponent in the AFU in 2022. Ukraine, with Western assistance, had been preparing since 2014 for the next Russian assault. In early 2023, U.S. geospatial intelligence analysis concluded that four out of five Army Spetsnaz brigades that saw combat in Ukraine in 2022 returned to Russia in a non-mission capable (NMC) state.[13] A few examples of Spetsnaz losses may be helpful to put this in context. The elite 346th Spetsnaz Brigade from the KSSO returned to garrison in 2022 with only 125 of 900 personnel. It functionally ceased to exist. The 22nd Separate Guards Brigade and two others had an even higher attrition rate, with between 90 to 95% lost as casualties. There was no clear signature that these units even returned to garrison.[14]Both the VDV and MP brigades experienced similar losses. For example, the 331st Guards Parachute regiment was destroyed during fighting in Ukraine. After combat in 2022, the VDV has likely been reduced to 50% of its pre-war capacity. For the MP, there were similar results. It lost two of four brigades in Ukraine, meaning two Russian fleets no longer have a marine component for security. Instead of decapitating Ukrainian leadership, Russia has lost more than half of its elite forces. All losses on the battlefield are costly. Some are more costly, however, considering the training time and resources required to reconstitute a troop. For VDV and MP a conservative peacetime timeline to reconstitute a unit is between three and five years. To replace the Spetsnaz losses, it likely takes between six to ten years from entry into the armed forces to reconstitute a fully trained, fully mission capable (FMC) special purpose operator. For Spetsnaz personnel screened for KSSO, a further two years of training is normally necessary. But these are peacetime estimates and assumes all candidates screened for spetsnaz are second- or third-term contract personnel.Of course, neither the Ukrainian nor Russian forces are at peace, so reconstitution timelines become fuzzy. The case of the 155th MP provides a good illustration. This unit was rendered NMC in 2022 but reconstituted twice and sent back into Ukraine.[15] Replacement personnel probably had no specialized training, and the unit was repeatedly fielded as an inferior force. Further, if the 155th MP ceases to exist, the pool of potential new recruits for Naval Spetsnaz is reduced, hindering the ability of these more capable units to reconstitute personnel. Likewise, if the 22nd Separate Guards Brigade is functionally broken, there are no personnel to select or train Spetsnaz replacements. In short, all elite Russian forces are connected in a training and development cycle. Losses in Ukraine have broken this cycle, so reconstitution will take significantly longer than the process during peacetime. How much longer will depend on how long the war lasts. Currently, there is no path to returning to a peacetime scenario, so the Russian General Staff will likely try to reconstitute these forces on a much quicker timeline. This will put new recruits to elite units at higher risk and will limit training opportunities. It is very likely Russian elite units will not recover pre-war capabilities for six to 12 years after the war ends. Some brigades may not recover at all. The Russian military has lost most of its ground offensive capability for at least the next decade. While conscripts can backfill conscripts, the Spetsnaz and other elite forces cannot be replaced quickly. The quality of Russian troops will continue to decline as long as the war in Ukraine continues. Strategic ImpactFrom a strategic standpoint, training and equipping the AFU is probably the most successful US foreign policy initiative since World War II. Eight years of training and two years of weapons shipments have done what almost 50 years of Cold War military spending could not do: decimate the Russian military. Imagine what National Security Advisor Kissinger or Scowcroft would think about the value of spending about $50 billion to achieve such results. They would probably argue its results were worth ten times that amount—and they would certainly be in favor of continuing to build on that success with additional resources.If the policy of training and equipping the AFU is restored and maintained, Russia will lose the war in Ukraine. Even if Russia manages to retain some internationally recognized Ukrainian territory in a future armistice, it has already lost the war by several measures:Russia's elite forces have, conservatively, been reduced to below 50% of pre-war capacity. Some of these troops have been reconstituted, but the replacements are not as capable.Conventional ground forces have likely suffered a 90% degradation from pre-war levels. Some have been reconstituted and some have been replaced by "private" contract personnel. The once-feared Black Sea Fleet has been forced to flee Crimea and much of the Black Sea itself. The AFU has recaptured several gas and oil platforms near Crimea in order to limit Russia's ability to target the Ukrainian coast with precision. Russian Aerospace Forces have underperformed and suffered high losses.[16] Russia has lost demographically, through combat deaths and emigration, which makes recruiting replacements more difficult and more costly.[17] The losses inflicted by the AFU will limit Russia's offensive capability for the next 10 to 20 years. The joint Ukraine and NATO effort may have also insulated Belarus, Moldova, and Georgia from additional territory grabs by Russia. As Robert Litwak has argued, war in Ukraine has likely reduced Russia to being a one-dimensional nuclear power.[18] This is a positive, but only preliminary, outcome. Based on the outcomes of the war thus far, it is imperative for the United States to commit the additional $60 billion to help Ukraine functionally destroy the Russian Army and eject the Black Sea Fleet from the Black Sea. The Putin regime has engaged in gray zone warfare against the United States for more than a decade. Allowing the same government time to reconstitute its military and retake territory from a Ukrainian ally is unconscionable from a national security policy standpoint. Further, a loss in Ukraine could force the Putin regime out of power. Historically, authoritarian regimes that lose external conflicts or achieve minimal gain are at risk of internal collapse.[19] In October 2023, Russia's defensive lines in Ukraine began to buckle. With uninterrupted US weapons shipments, the AFU may have been able to reinforce the breech and expand it. Instead, Russian propaganda has helped delay critical congressional support for almost six months. Ukraine has time-sensitive military needs. Delays cost lives and will soon cost Ukraine more of its sovereign territory. As Russia continues to receive timely resupply from North Korea and Iran, the United States looks increasingly wobbly as an ally. Leaders in the People's Republic of China have likely taken note. [1] Valeriy Akimenko, "Ukraine's Toughest Fight: The Challenge of Military Reform," Carnegie Endowment for International Peace, February 22, 2018, https://carnegieendowment.org/2018/02/22/ukraine-s-toughest-fight-challenge-of-military-reform-pub-75609. [2] Matthew Baldwin, "Task Force Raven Takes Command of Joint Multinational Training Group-Ukraine," U.S. Army, April 17, 2021, https://www.army.mil/article/245354/task_force_raven_takes_command_of_joint_multinational_training_group_ukraine. [3] Elizabeth Hoffman, Jaehyun Han, and Shivani Vakharia, "The Past, Present, and Future of U.S. Assistance to Ukraine: A Deep Dive into the Data," Center for Strategic & International Studies, September 26, 2023, https://www.csis.org/analysis/past-present-and-future-us-assistance-ukraine-deep-dive-data. [4] Jared Saathoff, "Red Arrow Soldiers Deployed in Ukraine for Multinational Mission," Defense Visual Information Distribution Service, 2019, https://www.dvidshub.net/news/353722/red-arrow-soldiers-deployed-ukraine-multinational-mission; and Gleb Garanich, "Ukraine Holds Military Drills with US, Poland, Lithuania," Reuters, 2021.[5] Wesley Morgan, "US Army Unprepared to Deal with Russia in Europe," Politico, 2017, https://www.politico.eu/article/us-army-unprepared-to-deal-with-russia-in-europe/. [6] Caleigh Kelly, "Russia-Ukraine War Holds Key Lessons for US Space Command: Top Official," The Hill, 2023, https://thehill.com/policy/defense/4089559-russia-ukraine-war-holds-key-lessons-for-us-space-command-top-official/; and Jen Judson, "Change of Plans: US Army Embraces Lessons Learned from War in Ukraine," Defense News, 2023, https://www.defensenews.com/land/2023/10/09/change-of-plans-us-army-embraces-lessons-learned-from-war-in-ukraine/. [7] Russia has engaged in political and cyber warfare against NATO member states in both the near abroad and further. [8] Mstyslav Chernov and Lori Hinnant, "How Ukrainian Special Forces Secured a Critical Dnipro River Crossing," Associated Press, 2023, https://www.voanews.com/a/how-ukrainian-special-forces-secured-a-critical-dnipro-river-crossing/7413472.html. [9] In addition to Russian ground force losses, the AFU has also significantly degraded the Russian air component and forced the Black Sea Fleet to flee Crimea.[10] Katie Bo Lillis, "Newly Declassified US Intel Claims Russia Is Laundering Propaganda through Unwitting Westerners," CNN, 2023, https://www.cnn.com/2023/08/25/politics/us-intel-russia-propaganda/index.html; and Hoffman, Han, and Vakharia.[11] See Galeotti (2022) for a reasonable description of each of the elite forces and their mission and training profiles. [12] Alex Horton, "Russia's Commando Units Gutted by Ukraine War, U.S. Leak Shows," Washington Post, 2023; and Warren P. Strobel and Matthew Luxmoore, "Russia Has Lost Almost 90% of Its Prewar Army, U.S. Intelligence Says; The Declassified Estimate Says 315,000 Personnel Have Been Killed or Injured in Ukraine," Wall Street Journal, December 12, 2023, https://www.wsj.com/articles/russian-has-lost-almost-90-of-its-prewar-army-u-s-intelligence-says-2e0372ab. [13] If a unit falls below 50% combat readiness, it is considered non-mission capable. [14] Alex Horton, "Russia's Commando Units Gutted by Ukraine War, U.S. Leak Shows," Washington Post, 2023.[15] Ellie Cook, "Russia's 'Elite' Units Might Not Be So Elite," Newsweek, 2023; and Ellie Cook, "Russian General's Leak of Elite Casualties 'Endorses' 50% Loss Figure: U.K.," Newsweek, 2023.[16] International Institute for Strategic Studies, The Military Balance 2024 (London, UK: Routledge, 2024), https://doi.org/10.4324/9781003485834. [17] Russia had among the lowest birth rates in the world prior to 2022 and a high death rate among military aged males, according to the CDC International Database. See United States Census Bureau, "International Database: World Population Estimates and Projections," https://www.census.gov/programs-surveys/international-programs/about/idb.html; and Jennifer Dabbs Sciubba, The Future Faces of War: Population and National Security (Santa Barbara, CA: Praeger, 2011): 40, 134-137.[18] Robert S. Litwak, Tripolar Instability: Nuclear Competition Among the United States, Russia, and China (Washington, DC: Wilson Center, 2023).[19] Alyssa K. Prorok, "Leader Incentives and Civil War Outcomes," American Journal of Political Science 60, no. 1 (2016): 70; and Mark Peceny and Christopher K. Butler, "The Conflict Behavior of Authoritarian Regimes," International Politics 41 (2004): 565–81.