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heavily Environmental mathematical models represent one of the key aids for scientists to forecast, create, and evaluate complex scenarios. These models rely on the data collected by direct field observations. However, assembly of a functional and comprehensive dataset for any environmental variable is difficult, mainly because of i) the high cost of the monitoring campaigns and ii) the low reliability of measurements (e.g., due to occurrences of equipment malfunctions and/or issues related to equipment location). The lack of a sufficient amount of Earth science data may induce an inadequate representation of the response’s complexity in any environmental system to any type of input/change, both natural and human-induced. In such a case, before undertaking expensive studies to gather and analyze additional data, it is reasonable to first understand what enhancement in estimates of system performance would result if all the available data could be well exploited. Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. Different approaches are available to deal with missing data. Traditional statistical data completion methods are used in different domains to deal with single and multiple imputation problems. More recently, machine learning techniques, such as clustering and classification, have been proposed to complete missing data. This book showcases the body of knowledge that is aimed at improving the capacity to exploit the available data to better represent, understand, predict, and manage the behavior of environmental systems at all practical scales.

geophysical monitoring --- data scarcity --- missing data --- climate extreme indices (CEIs) --- rule extraction --- Dataset Licensedatabase --- data assimilation --- data imputation --- support vector machines --- environmental observations --- multi-class classification --- earth-science data --- remote sensing --- magnetotelluric monitoring --- soil texture calculator --- machine learning --- ClimPACT --- invasive species --- species distribution modeling --- 3D-Var --- ensemble learning --- data quality --- water quality --- microhabitat --- k-Nearest Neighbors --- Expert Team on Climate Change Detection and Indices (ETCCDI) --- decision trees --- processing --- attribute reduction --- Expert Team on Sector-specific Climate Indices (ET-SCI) --- core attribute --- rough set theory --- GLDAS --- arthropod vector --- environmental modeling --- statistical methods

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Over the past few decades, global warming and climate change have impacted the hydrologic cycle. Many models have been developed to simulate hydrologic processes. Obtaining accurate climatic data on local/meso, and global scales is essential for the realistic simulation of hydrologic processes. However, the limited availability of climatic data often poses a challenge to hydrologic modeling efforts. Hydrologic science is currently undergoing a revolution in which the field is being transformed by the multitude of newly available data streams. Historically, hydrologic models that have been developed to answer basic questions about the rainfall–runoff relationship, surface water, and groundwater storage/fluxes, land–atmosphere interactions, have been optimized for previously data-limited conditions. With the advent of remote sensing technologies and increased computational resources, the environment for water cycle researchers has fundamentally changed to one where there is now a flood of spatially distributed and time-dependent data. The bias in the climatic data is propagated through models and can yield estimation errors. Therefore, the bias in climatic data should be removed before their use in hydrologic models. Climatic data have been a core component of the science of hydrology. Their intrinsic role in understanding and managing water resources and developing sound water policies dictates their vital importance. This book aims to present recent advances concerning climatic data and their applications in hydrologic models.

statistical weather generator --- stochastic process --- Diyala River basin --- Wilks’ technique --- hydrological models --- rainfall --- surface runoff --- linear regression models --- curve number --- SCS.CN model --- mulching --- wildfire --- prescribed fire --- n/a --- CHIRPS --- GPM-IMERG --- rainfall data scarcity --- agro-hydrology --- Rift Valley Lake Basin --- hydrological research basin --- precipitation --- temperature --- long-term trends --- climate change --- evapotranspiration --- groundwater recharge --- thresholds --- seasonality --- spatiotemporal variations --- regional-scale --- long-term --- HydroBudget model --- cold and humid climates --- Quebec (Canada) --- tank cascade system --- dry zone --- water governance --- flood control --- traditional knowledge --- community participation --- Sri Lanka --- Wilks' technique

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Political scientists often ask themselves what might have been if history had unfolded differently: if Stalin had been ousted as General Party Secretary or if the United States had not dropped the bomb on Japan. Although scholars sometimes scoff at applying hypothetical reasoning to world politics, the contributors to this volume--including James Fearon, Richard Lebow, Margaret Levi, Bruce Russett, and Barry Weingast--find such counterfactual conjectures not only useful, but necessary for drawing causal inferences from historical data. Given the importance of counterfactuals, it is perhaps surprising that we lack standards for evaluating them. To fill this gap, Philip Tetlock and Aaron Belkin propose a set of criteria for distinguishing plausible from implausible counterfactual conjectures across a wide range of applications. The contributors to this volume make use of these and other criteria to evaluate counterfactuals that emerge in diverse methodological contexts including comparative case studies, game theory, and statistical analysis. Taken together, these essays go a long way toward establishing a more nuanced and rigorous framework for assessing counterfactual arguments about world politics in particular and about the social sciences more broadly.

International relations. Foreign policy --- Methods in social research (general) --- History as a science --- World politics. --- History --- Counterfactuals (Logic) --- Thought experiments. --- Philosophy. --- World politics --- -Counterfactuals (Logic) --- Thought experiments --- #SBIB:327H03 --- #SBIB:327.1H10 --- Experiments, Thought --- Methodology --- Contrary-to-fact conditional --- Counterfactual conditionals --- Conditionals (Logic) --- Logic --- Annals --- Auxiliary sciences of history --- Colonialism --- Global politics --- International politics --- Political history --- Political science --- World history --- Eastern question --- Geopolitics --- International organization --- International relations --- Philosophy --- Internationale betrekkingen: onderwijs en onderzoek --- Internationale betrekkingen: theorieën --- Counterfactuals (Logic). --- History, Modern --- Gedankenexperiment --- Internationale Politik --- Weltpolitik --- Azerbaijan crisis. --- Bay of Pigs. --- Goldstone, Jack. --- Halifax, Lord. --- Iranian revolution. --- Jowitt, Ken. --- Khrushchev, N. --- Leninism. --- Napoleon. --- Nash equilibrium. --- Qavam. --- Riser, E. --- Russian revolution. --- Stalinism. --- Thucydides. --- data scarcity. --- experimental method. --- hindsight. --- legitimacy issues. --- moral catastrophes. --- nomothetic counterfactuals. --- optimality constraints. --- perfect equilibrium. --- policy makers. --- proximity criterion. --- reification. --- structuralism. --- tripolar world. --- Politique mondiale --- Histoire --- Contrefactuel (logique) --- Pensée --- philosophie --- expériences --- Weltordnungspolitik --- Politik --- Internationale Beziehungen --- Politische Beziehungen --- Zwischenstaatliche Beziehungen --- Außenpolitik --- Internationales politisches System --- Experiment --- Pensée --- expériences