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Adaptive Catchment Management and Reservoir Operation

Authors: --- ---
ISBN: 9783038977384 / 9783038977391 Year: Pages: 498 DOI: 10.3390/books978-3-03897-739-1 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-08-28 11:21:27
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Abstract

River catchments and reservoirs play a central role in water security, food supply, flood risk management, hydropower generation, and ecosystem services; however, they are now under increasing pressure from population growth, economic activities, and changing climate means and extremes in many parts of the world. Adaptive management of river catchments and reservoirs requires an in-depth understanding of the impacts of future uncertainties and thus the development of robust, sustainable solutions to meet the needs of various stakeholders and the environment. To tackle the huge challenges in moving towards adaptive catchment management, this book presents the latest developments in cutting-edge knowledge, novel methodologies, innovative management strategies, and case studies, focusing on the following themes: reservoir dynamics and impact analysis of dam construction, optimal reservoir operation, climate change impacts on hydrological processes and water management, and integrated catchment management.

Keywords

Siemianówka --- hydrology --- Narew River --- dam --- reservoir --- discharge --- flow regime --- reservoir flushing --- numerical simulation --- flushing efficiency --- Kurobe River --- two-dimensional bed evolution model --- sediment flushing of empty storage --- shaft spillway pipe --- sediment flushing efficiency --- sediment regime --- suspended sediment concentration --- vertical profiles of concentration --- Jingjiang River Reach --- Yangtze River --- CO2 --- reservoirs --- general regression neural network --- back propagation neural network --- climate change --- CMIP3 --- CMIP5 --- downscaling --- runoff response --- SWAT model --- stochastic linear programming --- Markov chain --- reliability --- vulnerability --- reservoir operation --- stochastic dynamic programming --- protection zone --- nutrient uptake --- NPP --- South-to-North Water Transfer Project --- Miyun Reservoir --- reservoir operation --- optimization --- SWAT --- HEC-ResPRM --- climate change --- CORDEX-Africa --- Tekeze basin --- long distance water diversion --- inverted siphon --- sensitivity analysis --- integrated supply system modeling --- sediment regime --- suspended sediment concentration --- vertical profiles of concentration --- the Jingjiang River Reach --- the Yangtze River --- reservoir operation --- multi-stage stochastic optimization --- TB-MPC --- flood control --- real-time control --- energy --- hydropower stations --- differential evolution algorithm --- optimal scheduling --- ?-constrained method --- drinking water resources --- water environmental capacity (WEC) --- Environmental Fluid Dynamics Code (EFDC) model --- the Huangshi Reservoir --- seasonal rainfall --- upper Chao Phraya River Basin --- El Niño/Southern Oscillation --- Indian Monsoon --- sea surface temperatures --- reverse regulation --- coupling model --- aftereffect --- accompanying progressive optimality algorithm --- Dokan Dam --- runoff --- sediment load --- SWAT --- natural flow regime --- multi-objective model --- uncertainty --- genetic algorithm --- land and water resources --- system dynamics --- modeling --- scenario analysis --- Heilongjiang --- tropical reservoir --- heating impact --- Langcang-Mekong River --- Kappa distribution --- parameter relation --- partial gauged basin --- power function --- ratio curve --- ungauged basin --- reservoir operation --- integrated surface water-groundwater model --- Heihe River Basin --- environmental flow --- irrigation --- design and operation of the multipurpose reservoir --- water deficit --- reservoir simulation model --- climate change --- multi-objective optimization NSGA II --- resilience and robustness --- costs and benefits --- water energy --- multi-agent of river basin --- game theory --- water resources allocation --- optimal flood control operation --- cascade reservoirs --- dynamic programming with progressive optimality algorithm (DP-POA) --- the upper Yangtze River Basin --- parameterization --- simulation --- optimization --- direct policy search --- hedging policy --- shortage ratio: Vulnerability --- NSGA-II --- lentic habitats --- bitterling --- mussel --- floodplain vertical shape index --- sediment management --- adaptive management --- catchment modelling --- integrated management --- reservoir operation

Google Earth Engine Applications

Authors: ---
ISBN: 9783038978848 9783038978855 Year: Pages: 420 DOI: 10.3390/books978-3-03897-885-5 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- Environmental Technology
Added to DOAB on : 2019-04-25 16:37:17
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In a rapidly changing world, there is an ever-increasing need to monitor the Earth’s resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth’s surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales.

Keywords

Google Earth Engine --- NDVI --- vegetation index --- Landsat --- remote sensing --- phenology --- surface reflectance --- cropland mapping --- cropland areas --- 30-m --- Landsat-8 --- Sentinel-2 --- Random Forest --- Support Vector Machines --- segmentation --- RHSeg --- Google Earth Engine --- Africa --- remote sensing --- semi-arid --- ecosystem assessment --- land use change --- image classification --- seasonal vegetation --- carbon cycle --- Google Earth Engine --- crop yield --- gross primary productivity (GPP) --- data fusion --- Landsat --- MODIS --- MODIS --- Random Forest --- pasture mapping --- Brazilian pasturelands dynamics --- Google Earth Engine --- crop classification --- multi-classifier --- cloud computing --- time series --- high spatial resolution --- BACI --- Enhanced Vegetation Index --- Google Earth Engine --- cloud-based geo-processing --- satellite-derived bathymetry --- image composition --- pseudo-invariant features --- sun glint correction --- empirical --- spatial error --- Google Earth Engine --- low cost in situ --- Sentinel-2 --- Mediterranean --- burn severity --- change detection --- Landsat --- dNBR --- RdNBR --- RBR --- composite burn index (CBI) --- MTBS --- lower mekong basin --- landsat collection --- suspended sediment concentration --- online application --- google earth engine --- Landsat --- Google Earth Engine --- protected area --- forest and land use mapping --- machine learning classification --- China --- temporal compositing --- image time series --- multitemporal analysis --- change detection --- cloud masking --- Landsat-8 --- Google Earth Engine (GEE) --- Google Earth Engine --- LAI --- FVC --- FAPAR --- CWC --- plant traits --- random forests --- PROSAIL --- small-scale mining --- industrial mining --- google engine --- image classification --- land-use cover change --- seagrass --- habitat mapping --- image composition --- machine learning --- support vector machines --- Google Earth Engine --- Sentinel-2 --- Aegean --- Ionian --- global scale --- soil moisture --- Soil Moisture Ocean Salinity --- Soil Moisture Active Passive --- Google Earth Engine --- drought --- cloud computing --- remote sensing --- snow hydrology --- water resources --- Google Earth Engine --- user assessment --- MODIS --- snow cover --- flood --- disaster prevention --- emergency response --- decision making --- Google Earth Engine --- land cover --- deforestation --- Brazilian Amazon --- Bayesian statistics --- BULC-U --- Mato Grosso --- spatial resolution --- Landsat --- GlobCover --- SDG --- surface urban heat island --- Geo Big Data --- Google Earth Engine --- global monitoring service --- Google Earth Engine --- web portal --- satellite imagery --- trends --- earth observation --- wetland --- Google Earth Engine --- Sentinel-1 --- Sentinel-2 --- random forest --- cloud computing --- geo-big data --- cloud computing --- big data analytics --- long term monitoring --- data archival --- early warning systems

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MDPI - Multidisciplinary Digital Publishing Institute (2)


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CC by-nc-nd (2)


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eng (2)


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2019 (2)