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Handbook of Operations Research in Natural Resources will be the first systematic handbook treatment of quantitative modeling natural resource problems, their allocated efficient use, and societal and economic impact. Andres Weintraub is the very top person in Natural Resource research. Moreover, he has an international reputation in OR and a former president of the International Federation of Operational Research Societies (IFORS). He has selected co-editors who are at the top of the sub-fields in natural resources: agriculture, fisheries, forestry, and mining. The book will cover these areas in terms with contributions from researchers on modeling natural research problems, quantifying data, developing algorithms, and discussing the benefits of research implementations. The handbook will include tutorial contributions when necessary. Throughout the book, technological advances and algorithmic developments that have been driven by natural resource problems will be called out and discussed.

Operations Research Management Science approaches have helped people for the last 40 years to understand the complex functioning of the systems based upon natural resources, as well as to manage natural resources in the most efficient manner. The areas usually viewed within the natural resources field are: agriculture, fisheries, forestry, and mining and water resources. All of these areas share the common problem of optimally allocating scarcity over a period of time. The scale of time or length of the planning horizon differs from one area to another. We have almost a continuous renewal in the case of the fisheries, periodic cycles in the case of agriculture and forestry and enormous periods of time much beyond the human perception in the case of mining resources. But in all the cases, the critical issue is to obtain an efficient use of the resource along its planned time horizon. Another element of connection among the different natural resources is due to the interaction between the use of the resource and the environmental impact caused by its extraction or harvest. This type of interaction implies additional complexities in the underlying decision-making process, making the use of OR/MS tools especially relevant. HANDBOOK OF OPERATIONS RESEARCH IN NATURAL RESOURCES will be the first systematic handbook treatment of quantitative modeling natural resource problems, their allocated efficient use, and societal and economic impact. Andrés Weintraub is the very top person in Natural Resource research. Moreover, he has an international reputation in OR and a former president of the International Federation of Operational Research Societies (IFORS). He has selected co-editors who are at the top of the sub-fields in natural resources: agriculture, fisheries, forestry, and mining. The book will cover these areas in terms with contributions from

Sustainable model for fisheries One approach to more sustainable fisheries is that of co-management, in which fishers and managers take joint responsibility for regulation. The evidence that this works is largely anecdotal, so Nicolás Gutiérrez and colleagues systematically examined 130 co-managed fisheries to find which attributes of co-management are required for success. Leadership, social cohesion, clear incentives and conservation efforts topped the list. On their evidence, the authors suggest, the co-management model could solve many of the problems facing commercial fisheries around the world. One approach to sustainable fisheries is that of co-management, in which fishers and managers take joint responsibility for regulation. The evidence that this works is largely anecdotal, so this study systematically examined 130 co-managed fisheries. Several attributes of co-management were required for success, with leadership being the most important. A total of 8 attributes of co-management were required for a successful fishery, and above this number there was a linear relationship between the extent of co-management and success. One billion people depend on seafood as their primary source of protein and 25% of the world’s total animal protein comes from fisheries 1 . Yet a third of fish stocks worldwide are overexploited or depleted 1 , 2 . Using individual case studies, many have argued that community-based co-management 3 should prevent the tragedy of the commons 4 because cooperative management by fishers, managers and scientists often results in sustainable fisheries 3 , 5 , 6 . However, general and multidisciplinary evaluations of co-management regimes and the conditions for social, economic and ecological success within such regimes are lacking. Here we examine 130 co-managed fisheries in a wide range of countries with different degrees of development, ecosystems, fishing sectors and type of resources. We identified strong leadership as the most important attribute contributing to success, followed by individual or community quotas, social cohesion and protected areas. Less important conditions included enforcement mechanisms, long-term management policies and life history of the resources. Fisheries were most successful when at least eight co-management attributes were present, showing a strong positive relationship between the number of these attributes and success, owing to redundancy in management regulations. Our results demonstrate the critical importance of prominent community leaders and robust social capital 7 , combined with clear incentives through catch shares and conservation benefits derived from protected areas, for successfully managing aquatic resources and securing the livelihoods of communities depending on them. Our study offers hope that co-management, the only realistic solution for the majority of the world’s fisheries, can solve many of the problems facing global fisheries.

Glaciers distinct from the Greenland and Antarctic ice sheets are shrinking rapidly, altering regional hydrology 1 , raising global sea level 2 and elevating natural hazards 3 . Yet, owing to the scarcity of constrained mass loss observations, glacier evolution during the satellite era is known only partially, as a geographic and temporal patchwork 4 , 5 . Here we reveal the accelerated, albeit contrasting, patterns of glacier mass loss during the early twenty-first century. Using largely untapped satellite archives, we chart surface elevation changes at a high spatiotemporal resolution over all of Earth’s glaciers. We extensively validate our estimates against independent, high-precision measurements and present a globally complete and consistent estimate of glacier mass change. We show that during 2000–2019, glaciers lost a mass of 267 ± 16 gigatonnes per year, equivalent to 21 ± 3 per cent of the observed sea-level rise 6 . We identify a mass loss acceleration of 48 ± 16 gigatonnes per year per decade, explaining 6 to 19 per cent of the observed acceleration of sea-level rise. Particularly, thinning rates of glaciers outside ice sheet peripheries doubled over the past two decades. Glaciers currently lose more mass, and at similar or larger acceleration rates, than the Greenland or Antarctic ice sheets taken separately 7 – 9 . By uncovering the patterns of mass change in many regions, we find contrasting glacier fluctuations that agree with the decadal variability in precipitation and temperature. These include a North Atlantic anomaly of decelerated mass loss, a strongly accelerated loss from northwestern American glaciers, and the apparent end of the Karakoram anomaly of mass gain 10 . We anticipate our highly resolved estimates to advance the understanding of drivers that govern the distribution of glacier change, and to extend our capabilities of predicting these changes at all scales. Predictions robustly benchmarked against observations are critically needed to design adaptive policies for the local- and regional-scale management of water resources and cryospheric risks, as well as for the global-scale mitigation of sea-level rise. Analysis of satellite stereo imagery uncovers two decades of mass change for all of Earth’s glaciers, revealing accelerated glacier shrinkage and regionally contrasting changes consistent with decadal climate variability.

Mangroves shelter coastlines during hazardous storm events with coastal communities experiencing mangrove deforestation are increasingly vulnerable to economic damages resulting from cyclones. To date, the benefits of mangroves in terms of protecting coastal areas have been estimated only through individual case studies of specific regions or countries. Using spatially referenced data and statistical methods, we track from 2000 to 2012 the impact of cyclones on economic activity in coastal regions inhabited by nearly 2,000 tropical and subtropical communities across 23 major mangrove-holding countries. We use nighttime luminosity to represent temporal trends in coastal economic activity and find that direct cyclone exposure typically results in permanent loss of 5.4–6.7 mo for a community with an average mangrove extent (6.3 m per meter of coastline); whereas, a community with more extensive mangroves (25.6 m per meter of coastline) experiences a loss equivalent to 2.6–5.5 mo. These results suggest that mangrove restoration efforts for protective benefits may bemore cost effective, and mangrove deforestation more damaging, than previously thought.

Groundwater is one of the most valuable natural resources in the world (Jha et al., 2007). However, it is not an unlimited resource; therefore understanding groundwater potential is crucial to ensure its sustainable use. The aim of the current study is to propose and verify new artificial intelligence methods for the spatial prediction of groundwater spring potential mapping at the Koohdasht–Nourabad plain, Lorestan province, Iran. These methods are new hybrids of an adaptive neuro-fuzzy inference system (ANFIS) and five metaheuristic algorithms, namely invasive weed optimization (IWO), differential evolution (DE), firefly algorithm (FA), particle swarm optimization (PSO), and the bees algorithm (BA). A total of 2463 spring locations were identified and collected, and then divided randomly into two subsets: 70 % (1725 locations) were used for training models and the remaining 30 % (738 spring locations) were utilized for evaluating the models. A total of 13 groundwater conditioning factors were prepared for modeling, namely the slope degree, slope aspect, altitude, plan curvature, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), distance from fault, distance from river, land use/land cover, rainfall, soil order, and lithology. In the next step, the step-wise assessment ratio analysis (SWARA) method was applied to quantify the degree of relevance of these groundwater conditioning factors. The global performance of these derived models was assessed using the area under the curve (AUC). In addition, the Friedman and Wilcoxon signed-rank tests were carried out to check and confirm the best model to use in this study. The result showed that all models have a high prediction performance; however, the ANFIS–DE model has the highest prediction capability (AUC = 0.875), followed by the ANFIS–IWO model, the ANFIS–FA model (0.873), the ANFIS–PSO model (0.865), and the ANFIS–BA model (0.839). The results of this research can be useful for decision makers responsible for the sustainable management of groundwater resources.

Natural gas hydrate (NGH) has been widely considered as an alternative to conventional oil and gas resources in the future energy resource supply since Trofimuk’s first resource assessment in 1973. At least 29 global estimates have been published from various studies so far, among which 24 estimates are greater than the total conventional gas resources. If drawn in chronological order, the 29 historical resource estimates show a clear downward trend, reflecting the changes in our perception with respect to its resource potential with increasing our knowledge on the NGH with time. A time series of the 29 estimates was used to establish a statistical model for predict the future trend. The model produces an expected resource value of 41.46 × 10 12 m 3 at the year of 2050. The statistical trend projected future gas hydrate resource is only about 10% of total natural gas resource in conventional reservoir, consistent with estimates of global technically recoverable resources (TRR) in gas hydrate from Monte Carlo technique based on volumetric and material balance approaches. Considering the technical challenges and high cost in commercial production and the lack of competitive advantages compared with rapid growing unconventional and renewable resources, only those on the very top of the gas hydrate resource pyramid will be added to future energy supply. It is unlikely that the NGH will be the major energy source in the future.

In the context of climate change and urbanization, flood becomes one of the most important threats to human life, health, and property. Coastal areas gathering large numbers of population, capital, and industries are vulnerable to suffering from the compound floods caused by hydrological and oceanic processes. The disaster mechanisms of compound floods are more complex, and the consequences are even more serious. Based on the existing research results, this article sorts out the main disaster mechanisms of compound floods in coastal areas and explains the main methods, including using statistical models to study the dependence between flood drivers or joint probability and numerical models to simulate compound flood inundation, and presents the characteristics of different methods. We also discuss the advantages and disadvantages of different models and analyze their uncertainties. Current research seldom considers the rainfall-runoff-storm surge compound flood and the effect of climate change. In addition, there are only a few kinds of literature that integrate statistical models and numerical models to investigate compound flood hazard. Uncertainties in compound flood study methods are also less considered. Future investigation should focus on the characteristics and uncertainties of different models and consider the impact of climate change on compound floods. These will help to fully understand compound floods, research models, and provide effective opinions for flood management in coastal areas.

The prediction of groundwater level is a viable strategy for attaining sustainable water resource management. Recently, machine learning techniques have gained popularity as an alternative to numerical and statistical time series models grounded in physical principles. These methods excel at spotting complex trends and non-linear relationships. This study applies and compares seven machine learning techniques to predict the 20-year monthly groundwater level over the Mashhad plain aquifer in Iran. A novel idea based on the Thiessen polygon is proposed to provide a compressive dataset for presenting groundwater level of whole aquifer where the machine learning models can be trained and tested. This is because there are several piezometric wells, rainfall gauges, and hydrometric stations in the entire aquifer. Extensive simulations and modelling are conducted to select the appropriate input combinations for each technique, to identify the best model, and to carry out sensitivity analysis using a novel criterion known as the Global Performance Index, which integrates several regression performance criteria. The results show that the well-known Long Short-Term Memory performs significantly better than its competitors. Its superiority is about 13% over the next technique. To have a more accurate Long Short-Term Memory model, the sensitivity analysis was performed to reach the optimal parameters are as 120, 0.17, and 100 for the number of neurons, dropout rate, and batch size, respectively. Furthermore, an uncertainty analysis using Moving Block Bootstrap is performed to ensure that all uncertain effects are eliminated.

We examined features of citizen science that influence data quality, inferential power, and usefulness in ecology. As background context for our examination, we considered topics such as ecological sampling (probability based, purposive, opportunistic), linkage between sampling technique and statistical inference (design based, model based), and scientific paradigms (confirmatory, exploratory). We distinguished several types of citizen science investigations, from intensive research with rigorous protocols targeting clearly articulated questions to mass-participation internet-based projects with opportunistic data collection lacking sampling design, and examined overarching objectives, design, analysis, volunteer training, and performance. We identified key features that influence data quality: project objectives, design and analysis, and volunteer training and performance. Projects with good designs, trained volunteers, and professional oversight can meet statistical criteria to produce high-quality data with strong inferential power and therefore are well suited for ecological research objectives. Projects with opportunistic data collection, little or no sampling design, and minimal volunteer training are better suited for general objectives related to public education or data exploration because reliable statistical estimation can be difficult or impossible. In some cases, statistically robust analytical methods, external data, or both may increase the inferential power of certain opportunistically collected data. Ecological management, especially by government agencies, frequently requires data suitable for reliable inference. With standardized protocols, state-of-the-art analytical methods, and well-supervised programs, citizen science can make valuable contributions to conservation by increasing the scope of species monitoring efforts. Data quality can be improved by adhering to basic principles of data collection and analysis, designing studies to provide the data quality required, and including suitable statistical expertise, thereby strengthening the science aspect of citizen science and enhancing acceptance by the scientific community and decision makers. Examinamos las características de la ciencia ciudadana que influyen sobre la calidad de datos, el poder inferencial, y la utilidad en la ecología. Consideramos temas como el muestreo ecológico (basado en probabilidad, deliberado, oportunista), la conexión entre la técnica de muestreo y la inferencia estadística (basada en diseño, basada en modelo) y los paradigmas científicos (confirmatorio, exploratorio) como trasfondo contextual para nuestra evaluación. Distinguimos varios tipos de investigación de ciencia ciudadana, desde investigación intensiva con protocolos rigurosos enfocados en preguntas claramente articuladas hasta proyectos de participación masiva en plataformas de internet con recolección de datos oportunistas carentes de un diseño de muestreo, y examinamos los objetivos generales, el diseño, el análisis, y la preparación de los voluntarios y el desempeño. Identificamos características clave que influyen sobre la calidad de los datos: los objetivos del proyecto, el diseño y el análisis, y la preparación y el desempeño de los voluntarios. Los proyectos con buenos diseños, voluntarios preparados, y supervisión profesional pueden cumplir con criterios estadísticos para producir datos de alta calidad con un fuerte poder inferencial, y por lo tanto son muy adecuados para los objetivos de investigación ecológica. Los proyectos con una recolección oportunista de datos, un diseño de muestreo ínfimo o nulo, y una preparación mínima de los voluntarios son más adecuados para los objetivos generales relacionados con la educación pública o la exploración de datos ya que la estimación estadística confiable puede ser complicada o imposible. En algunos casos los métodos analíticos estadísticamente sólidos, los datos externos, o ambos, pueden incrementar el poder inferencial de ciertos datos recolectados de manera oportunista. El manejo ecológico, en especial el que realizan las agencias gubernamentales, requiere frecuentemente de datos apropiados para una inferencia confiable. Con protocolos estandarizados, métodos analíticos modernos, y programas supervisados correctamente, la ciencia ciudadana puede contribuir de forma valiosa a la conservación al incrementar el alcance de los esfuerzos de monitoreo para una especie. La calidad de datos puede mejorarse si se adhiere a los principios básicos de la recolección y análisis de datos, se diseñan los estudios para que proporcionen la calidad requerida de datos, y si se incluye una pericia estadística adecuada, fortaleciendo así el aspecto científico de la ciencia ciudadana y aumentando su aceptación dentro de la comunidad científica y con quienes toman las decisiones. 本研究分析了生态学中影响数据质量.、推论统计效カ和有用性的公民科学的特征。检验的背景包括如生 态学抽样(基于概率的抽样、目的抽样、机会抽样X 抽样技术与统计推断(基于设计或基于模型) 的联系，以及 科学范式(验怔性或探索性) 等话题。我们区分出不同类型的公民科学调查，从有清晰明确的问题及严格实验规 范的深入研究，到缺少抽样设计、投机型数据收集的基于互联网的大规模参与项目；并研究了项目的总体目标、 设计、分析、志愿者培训和实现情况。本研究确定了影响数据质量的关键特征，包括项目目标、设计和分析、 志愿者培训和实现情況。拥有良好的设计、训练有素的志愿者和专业监督的项目通常符合统计学标准，能获得 推论统计效カ强的高质量数据，因此可以达到生态学研究目标。而投机型数据收集、很少或没有进行抽样设计 且志愿者培训非常有限的项目，更适合与公共教育或数据探索相关的一般性目标，因为它们很难或不可能进行可 靠的统计估计。在一些情况下，统计上強健的分析方法和 外部数据也会増加某些投机型数据的推论效力。 生态管理特别是来自政府机构的管理，常常需要那些适合进行可靠统计推论的数据。当采用标准化的实验规 范、最先进的分析方法和良好监督的程序时，公民科学可以扩大物种监测范围，为保护做出重要贡献。坚持数据 收集和分析的基本原则、设计研究方案来提供所需的高质量数据，并恰当运用统计学知识，能够增强数据质置 从而加强公民科学的科学性，提高科学界和决策者对公民科学的接受度。