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This book was inspired by the Hydrology–H030 Session of the 2019 AGU (America Geophysical Union) Fall Meeting. In recent years, simulating potential future vulnerability and sustainability of water resources due to climate change are mainly focused on global and regional scale watersheds by using climate change scenarios. These scenarios may have low resolution and may not be accurate for local watersheds. This book addresses the impacts of climate change upon water quantity and quality at small scale watersheds. Emphases are on climate-induced water resource vulnerabilities (e.g., flood, drought, groundwater depletion, evapotranspiration, and water pollution) and methodologies (e.g., computer modeling, field measurement, and management practice) employed to mitigation and adapt climate change impacts on water resources. Application implications to local water resource management are also discussed in this book.

Research & information: general --- Environmental economics --- GCM --- bias correction methods --- hydrological simulation --- climate change --- IRES --- OM --- DOC --- POC --- ephemeral stream --- event sampling --- headwaters --- tailwater recovery ditch --- AnnAGNPS --- BMP --- asymmetric warming --- normalized difference vegetation index --- second-order partial correlation analysis --- day and nighttime warming --- diurnal temperature range --- cottonwood --- sap flux --- STELLA --- vapor pressure deficit --- water year type --- hydrological drought --- adaptive strategies --- Central Valley --- streamflow --- SWAT model --- CNRM-CM5 --- CESM1-BGC.1 --- HADGEM2-AO.1 --- Alabama River Basin --- GRACE --- GGDI --- drought --- wavelet coherence --- teleconnections --- water model --- energy model --- climate scenario --- Nile River Basin --- perception --- adaptation --- irrigation water quality --- agriculture --- smallholder farmers --- Ethiopia Rift Valley --- vulnerability assessment --- Indian Himalayas --- springs --- springshed management --- water security --- n/a

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This book was inspired by the Hydrology–H030 Session of the 2019 AGU (America Geophysical Union) Fall Meeting. In recent years, simulating potential future vulnerability and sustainability of water resources due to climate change are mainly focused on global and regional scale watersheds by using climate change scenarios. These scenarios may have low resolution and may not be accurate for local watersheds. This book addresses the impacts of climate change upon water quantity and quality at small scale watersheds. Emphases are on climate-induced water resource vulnerabilities (e.g., flood, drought, groundwater depletion, evapotranspiration, and water pollution) and methodologies (e.g., computer modeling, field measurement, and management practice) employed to mitigation and adapt climate change impacts on water resources. Application implications to local water resource management are also discussed in this book.

GCM --- bias correction methods --- hydrological simulation --- climate change --- IRES --- OM --- DOC --- POC --- ephemeral stream --- event sampling --- headwaters --- tailwater recovery ditch --- AnnAGNPS --- BMP --- asymmetric warming --- normalized difference vegetation index --- second-order partial correlation analysis --- day and nighttime warming --- diurnal temperature range --- cottonwood --- sap flux --- STELLA --- vapor pressure deficit --- water year type --- hydrological drought --- adaptive strategies --- Central Valley --- streamflow --- SWAT model --- CNRM-CM5 --- CESM1-BGC.1 --- HADGEM2-AO.1 --- Alabama River Basin --- GRACE --- GGDI --- drought --- wavelet coherence --- teleconnections --- water model --- energy model --- climate scenario --- Nile River Basin --- perception --- adaptation --- irrigation water quality --- agriculture --- smallholder farmers --- Ethiopia Rift Valley --- vulnerability assessment --- Indian Himalayas --- springs --- springshed management --- water security --- n/a

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Although air pollution is usually linked with human activities, natural processes may also lead to major concentrations of hazardous substances in the low atmosphere. Pollutant levels may be reduced when emissions can be controlled. However, the impact of meteorological variables on the concentrations measured may be noticeable, and these variables cannot be controlled. This book is devoted to the influence of meteorological processes on the pollutant concentrations recorded in the low atmosphere. Measurements, cycles, statistical procedures, as well as specific variables such as the synoptic pattern, temperature inversion, or the calculation of back-trajectories, are considered in the studies included in this book to highlight the relationship between air pollution and meteorological variables. In addition, the state of the art of this subject following meteorological scales, from micro to macro-scale, is presented. Consequently, this book focuses on applied science and seeks to further current knowledge of what contribution meteorological processes make to the concentrations measured in order to achieve greater control over air pollution.

Research & information: general --- air pollution --- synoptic situation pattern --- meteorological variables --- threshold values --- air quality forecast --- modelling --- pollution episodes --- national holiday --- COVID-19 --- particulate matter --- natural ventilation --- indoor air quality --- regional variation --- nitrogen dioxide --- in situ urban concentrations --- meteorological measurements --- NO2 variation --- partial correlation --- gross alpha activity --- northern Iberian Peninsula --- radon --- airflow patterns --- surface winds --- atmospheric boundary layer --- weather types --- Gaussian plume model --- low-level jet --- recirculation --- microscale --- macroscale --- mesoscale --- source apportionment --- cluster analysis --- health risks --- residential wood burning --- sustainable urban development --- urban haze --- temperature inversion --- Obukhov length --- HYSPLIT --- biomass burning --- cold surge, emission --- BaP --- HPLC --- carcinogenic --- diagnostic ratio --- air pollution --- synoptic situation pattern --- meteorological variables --- threshold values --- air quality forecast --- modelling --- pollution episodes --- national holiday --- COVID-19 --- particulate matter --- natural ventilation --- indoor air quality --- regional variation --- nitrogen dioxide --- in situ urban concentrations --- meteorological measurements --- NO2 variation --- partial correlation --- gross alpha activity --- northern Iberian Peninsula --- radon --- airflow patterns --- surface winds --- atmospheric boundary layer --- weather types --- Gaussian plume model --- low-level jet --- recirculation --- microscale --- macroscale --- mesoscale --- source apportionment --- cluster analysis --- health risks --- residential wood burning --- sustainable urban development --- urban haze --- temperature inversion --- Obukhov length --- HYSPLIT --- biomass burning --- cold surge, emission --- BaP --- HPLC --- carcinogenic --- diagnostic ratio

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This book deals with sustainable agriculture at a time of climate change. It seeks to identify a number of solutions to deal with the agricultural stresses caused by climate change. These range from the identification and cultivation of appropriate crop varieties and the adoption of climate adaptive agricultural practices. Significant sustainable agricultural innovation is required to deal with these challenges. Intellectual property rights (IPRs) may be of crucial importance for modern agriculture. They serve to make R&D in agriculture attractive, by encouraging investment in new technologies and generating tradeable assets. A number of the chapters of this book refer to the principal IPRs relevant to agricultural innovation, namely: (i) patents, which protect inventions; (ii) plant variety rights, which protect the breeding of new and distinct plant varieties; and (iii) trademarks and geographical indications, which facilitate the marketing of products by providing protection for the symbols of their manufacturing or geographic origin. The United Nations Climate Change Panel has urged the consideration of the agricultural practices of traditional communities and some of these practices particularly involving rice, banana, and brassica cultivation are explored in the book. This book is essential reading for officials of governments and international organizations concerned with sustainability, as well as scholars and students concerned with these subjects

Research. --- Biology. --- Technology. --- Engineering. --- Agriculture. --- alternative energy source --- Ethiopian mustard --- sustainability --- agricultural green development --- entropy weight method --- spatial heterogeneity --- spatial spillover effect --- China --- rice yields --- climate change --- phenology --- relative contribution --- partial correlation --- seed security --- banana tissue culture planting material --- uptake --- banana farmers --- central Uganda --- traditional rice economics --- institutional --- socio-demographic factors --- multinomial logit model --- constraints --- China-Africa cooperation --- agricultural program --- agricultural training --- technology adoption --- dams --- agriculture --- livelihoods --- health --- schistosomiasis --- restoration --- sustainable development --- climate adaptation --- rice–wheat cropping system --- South Asia --- water requirements --- nitrogen --- direct seeding --- agricultural science, technology and innovation --- Innovation efficiency --- DEA --- G20 --- productivity --- efficiency --- food security --- digital agriculture --- smart farming --- digitalization --- digital technologies --- Middle East and North Africa --- decision making --- instrumental variable --- neighbourhood effects --- rice farmers --- risk attitudes --- spatial dependence --- agricultural innovation --- sustainable agriculture --- plant breeding --- cereals --- intellectual property --- agricultural law --- plant variety rights --- seed marketing --- European Union --- fertilizer --- knowledge --- attitude --- ease of use --- motivation --- work performance --- n/a --- rice-wheat cropping system

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Econometric Modeling provides a new and stimulating introduction to econometrics, focusing on modeling. The key issue confronting empirical economics is to establish sustainable relationships that are both supported by data and interpretable from economic theory. The unified likelihood-based approach of this book gives students the required statistical foundations of estimation and inference, and leads to a thorough understanding of econometric techniques. David Hendry and Bent Nielsen introduce modeling for a range of situations, including binary data sets, multiple regression, and cointe.

Econometric models. --- Econometrics. --- Accuracy and precision. --- Asymptotic distribution. --- Autocorrelation. --- Autoregressive conditional heteroskedasticity. --- Autoregressive model. --- Bayesian statistics. --- Bayesian. --- Bernoulli distribution. --- Bias of an estimator. --- Calculation. --- Central limit theorem. --- Chow test. --- Cointegration. --- Conditional expectation. --- Conditional probability distribution. --- Confidence interval. --- Confidence region. --- Correlation and dependence. --- Correlogram. --- Count data. --- Cross-sectional data. --- Cross-sectional regression. --- Distribution function. --- Dummy variable (statistics). --- Econometric model. --- Empirical distribution function. --- Equation. --- Error term. --- Estimation. --- Estimator. --- Exogeny. --- Exploratory data analysis. --- F-distribution. --- F-test. --- Fair coin. --- Forecast error. --- Forecasting. --- Granger causality. --- Heteroscedasticity. --- Inference. --- Instrumental variable. --- Joint probability distribution. --- Law of large numbers. --- Least absolute deviations. --- Least squares. --- Likelihood function. --- Likelihood-ratio test. --- Linear regression. --- Logistic regression. --- Lucas critique. --- Marginal distribution. --- Markov process. --- Mathematical optimization. --- Maximum likelihood estimation. --- Model selection. --- Monte Carlo method. --- Moving-average model. --- Multiple correlation. --- Multivariate normal distribution. --- Nonparametric regression. --- Normal distribution. --- Normality test. --- One-Tailed Test. --- Opportunity cost. --- Orthogonalization. --- P-value. --- Parameter. --- Partial correlation. --- Poisson regression. --- Probability. --- Probit model. --- Quantile. --- Quantity. --- Quasi-likelihood. --- Random variable. --- Regression analysis. --- Residual sum of squares. --- Round-off error. --- Seemingly unrelated regressions. --- Selection bias. --- Simple linear regression. --- Skewness. --- Standard deviation. --- Standard error. --- Stationary process. --- Statistic. --- Student's t-test. --- Sufficient statistic. --- Summary statistics. --- T-statistic. --- Test statistic. --- Time series. --- Type I and type II errors. --- Unit root test. --- Unit root. --- Utility. --- Variable (mathematics). --- Variance. --- Vector autoregression. --- White test.