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Hydro-Ecological Modeling

ISBN: 9783038422396 9783038422129 Year: Pages: XIV, 322 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Added to DOAB on : 2016-08-02 16:08:50
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Abstract

Water is not only an interesting object to be studied on its own, it also is an important component driving almost all ecological processes occurring in our landscapes. Plant growth depends on soil water content, as well is nutrient turnover by microbes. Water shapes the environment by erosion and sedimentation. Species occur or are lost depending on hydrological conditions, and many infectious diseases are water-borne.Modeling the complex interactions of water and ecosystem processes requires the prediction of hydrological fluxes and stages on the one side and the coupling of the ecosystem process model on the other. While much effort has been given to the development of the hydrological model theory in recent decades, we have just begun to explore the difficulties that occur when coupled model applications are being set up.

Sustainability Indicators in Practice

Authors: ---
ISBN: 9783110450507 9783110450675 Year: Pages: 271 DOI: 10.1515/9783110450507 Language: English
Publisher: De Gruyter
Subject: Environmental Engineering --- Sociology --- Ecology --- Biology
Added to DOAB on : 2016-01-06 16:24:03
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The goal of sustainable development is to meet the socio-economic and environmental objectives without comprising the needs of future generations. Since the Rio Summit of 1992, the concept of sustainability has captured our imaginations and aspirations and efforts to develop its indicators have increased. A range of sustainability indicators have been developed within various socio-economic, environmental and cultural contexts- including biodiversity, economy, energy, water, land use and transport. Sustainability indicators are widespread in international development arena. They have become popularized among governments, non-governmental organizations, private sector and the wider public.Based on multiple cases across the world, this book explores opportunities and challenges associated with the practical application of sustainability indicators. The book reflects diversity of professionals of inter-disciplinary backgrounds covering contemporary issues within different socio-economic and environmental contexts. Each chapter presents practical examples of the merits and challenges of using sustainability indicators and draws conclusions and lessons learned. The book targets a range of audience from students, academics to development practitioners and policy-makers.The two editors of this book: Dr. Agnieszka Ewa Latawiec and Dr. Dorice Agol are inter-disciplinary scientists who both have experience in research at the environmental conservation and development nexus

Application of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) in East Asia

Authors: ---
ISBN: 9783039212354 / 9783039212361 Year: Pages: 384 DOI: 10.3390/books978-3-03921-236-1 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Geography
Added to DOAB on : 2019-08-28 11:21:27
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To promote scientific understanding of surface processes in East Asia, we have published details of the CMADS dataset in the journal, Water, and expect that users around the world will learn about CMADS datasets while promoting the development of hydrometeorological disciplines in East Asia. We hope and firmly believe that scientific development in East Asia and our understanding of this typical region will be further advanced.

Keywords

East Asia --- CMADS --- meteorological input uncertainty --- hydrological modelling --- SWAT --- non-point source pollution models --- CMADS --- Qinghai-Tibet Plateau (TP) --- SWAT --- CFSR --- TRMM --- PERSIANN --- PERSIANN-CDR --- CMADS --- satellite-derived rainfall --- streamflow simulation --- SWAT --- Han River --- GLUE --- hydrological model --- ParaSol --- SUFI2 --- uncertainty analysis --- SWAT model --- CMADS --- Lijiang River --- runoff --- uncertainty analysis --- hydrological elements --- statistical analysis --- SWAT --- CMADS --- climate variability --- land use change --- streamflow --- potential evapotranspiration --- Penman-Monteith --- CMADS --- China --- CMADS dataset --- parameter sensitivity --- SUFI-2 --- Yellow River --- reanalysis products --- satellite-based products --- hydrological model --- bayesian model averaging --- Xiang River basin --- total nitrogen --- accumulation --- SWAT model --- CMADS --- Biliuhe reservoir --- CMADS --- SWAT --- East Asia --- meteorological --- hydrological --- precipitation --- TMPA-3B42V7 --- CMADS --- hydrologic model --- uncertainty --- reservoirs --- operation rule --- Noah LSM-HMS --- capacity distribution --- aggregated reservoir --- CMADS --- CMADS --- IMERG --- statistical analysis --- SWAT hydrological simulation --- Jinsha River Basin --- blue and green water flows --- climate variability --- sensitivity analysis --- Erhai Lake Basin --- CMADS --- SWAT --- JBR --- soil moisture --- hydrological processes --- spatio-temporal --- sloping black soil farmland --- soil moisture content --- freeze–thaw period --- soil temperature --- CMADS-ST --- reservoir parameters --- runoff --- CMADS --- SWAT --- Yalong River --- CMADS --- impact --- hydrological modeling --- SWAT --- runoff --- sediment yield --- land-use change --- SWAT --- CMADS

Progress in Water Footprint Assessment

Authors: --- ---
ISBN: 9783039210381 / 9783039210398 Year: Pages: 202 DOI: 10.3390/books978-3-03921-039-8 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Environmental Engineering
Added to DOAB on : 2019-06-26 08:44:06
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Water Footprint Assessment is a young research field that considers how freshwater use, scarcity, and pollution relate to consumption, production, and trade patterns. This book presents a wide range of studies within this new field. It is argued that collective and coordinated action - at different scale levels and along all stages of commodity supply chains - is necessary to bring about more sustainable, efficient, and equitable water use. The presented studies range from farm to catchment and country level, and show how different actors along the supply chain of final commodities can contribute to more sustainable water use in the chain.

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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