LEADER 05287nam 2201285z- 450 001 9910557114203321 005 20210501 035 $a(CKB)5400000000040904 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/69117 035 $a(oapen)doab69117 035 $a(EXLCZ)995400000000040904 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aRemote Sensing of Flow Velocity, Channel Bathymetry, and River Discharge 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 online resource (286 p.) 311 08$a3-03936-900-8 311 08$a3-03936-901-6 330 $aRiver discharge is a fundamental hydrologic quantity that summarizes how a watershed transforms the input of precipitation into output as channelized streamflow. Accurate discharge measurements are critical for a range of applications including water supply, navigation, recreation, management of in-stream habitat, and the prediction and monitoring of floods and droughts. However, the traditional stream gage networks that provide such data are sparse and declining. Remote sensing represents an appealing alternative for obtaining streamflow information. Potential advantages include greater efficiency, expanded coverage, increased measurement frequency, lower cost and reduced risk to field personnel. In addition, remote sensing provides opportunities to examine long river segments with continuous coverage and high spatial resolution. To realize these benefits, research must focus on the remote measurement of flow velocity, channel geometry and their product: river discharge. This Special Issue fostered the development of novel methods for retrieving discharge and its components, and thus stimulated progress toward an operational capacity for streamflow monitoring. The papers herein address all aspects of the remote measurement of streamflow-estimation of flow velocity, bathymetry (water depth), and discharge-from various types of remotely sensed data acquired from a range of platforms: manned and unmanned aircraft, satellites, and ground-based non-contact sensors. 606 $aResearch & information: general$2bicssc 610 $aacoustic Doppler current profiler (ADCP) 610 $aaerial photography 610 $aairborne laser bathymetry 610 $aAlaska 610 $abathymetry 610 $achange detection 610 $achannel bathymetry 610 $acold-water refuge 610 $adam 610 $adischarge 610 $aDoppler radar 610 $adrone 610 $aestuary 610 $aflooding 610 $aflow frequency 610 $aflow regime 610 $aflow velocity 610 $afluvial 610 $afull waveform processing 610 $ageomorphology 610 $ahigh resolution hydro-mapping 610 $ahigh-water marks (HWMs) 610 $ahydraulic modeling 610 $ahydrology 610 $ainundation 610 $aLandsat 610 $alarge-scale particle image velocimetry 610 $alidar bathymetry 610 $aLSPIV 610 $amachine learning 610 $amodelling 610 $amorphology 610 $aparticle image velocimetry 610 $aPend Oreille River 610 $aperformance assessment 610 $aphotogrammetry 610 $aPIV 610 $aprobability concept 610 $apulsed radar 610 $arapid assessment 610 $arefraction correction 610 $aremote sensing 610 $aremotely piloted aircraft system 610 $aremotely-sensed imagery 610 $ariver 610 $ariver discharge 610 $ariver flow 610 $arivers 610 $asalinity 610 $asalmonids 610 $asatellite revisit time 610 $asmall unmanned aerial system (sUAS) 610 $asmall unmanned aircraft systems (sUAS) 610 $astreamflow 610 $astructure-from-motion photogrammetry 610 $asurface velocity 610 $asurveying 610 $athermal infrared (TIR) 610 $athermal infrared imagery 610 $atool 610 $atopographic error 610 $aUAV LiDAR 610 $aungauged basins 610 $awater surface elevation 610 $awater temperature 615 7$aResearch & information: general 700 $aLegleiter$b Carl$4edt$01313315 702 $aPavelsky$b Tamlin$4edt 702 $aDurand$b Michael$4edt 702 $aAllen$b George$4edt 702 $aTarpanelli$b Angelica$4edt 702 $aFrasson$b Renato$4edt 702 $aGuneralp$b Inci$4edt 702 $aWoodget$b Amy$4edt 702 $aLegleiter$b Carl$4oth 702 $aPavelsky$b Tamlin$4oth 702 $aDurand$b Michael$4oth 702 $aAllen$b George$4oth 702 $aTarpanelli$b Angelica$4oth 702 $aFrasson$b Renato$4oth 702 $aGuneralp$b Inci$4oth 702 $aWoodget$b Amy$4oth 906 $aBOOK 912 $a9910557114203321 996 $aRemote Sensing of Flow Velocity, Channel Bathymetry, and River Discharge$93031277 997 $aUNINA LEADER 04841nam 2200661 a 450 001 9910955113703321 005 20251116191957.0 010 $a1-281-18659-7 010 $a9786611186593 010 $a0-08-055549-7 035 $a(CKB)1000000000383585 035 $a(EBL)331921 035 $a(OCoLC)162130416 035 $a(SSID)ssj0000127639 035 $a(PQKBManifestationID)11132157 035 $a(PQKBTitleCode)TC0000127639 035 $a(PQKBWorkID)10051737 035 $a(PQKB)10233192 035 $a(Au-PeEL)EBL331921 035 $a(CaPaEBR)ebr10216817 035 $a(CaONFJC)MIL118659 035 $a(CaSebORM)9780123694683 035 $a(MiAaPQ)EBC331921 035 $a(PPN)178933031 035 $a(EXLCZ)991000000000383585 100 $a20070201d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aComputational materials engineering $ean introduction to microstructure evolution /$feditors Koenraad G. F. Janssens ... [et al.] 205 $a1st edition 210 $aAmsterdam ;$aBoston $cElsevier / Academic Press$dc2007 215 $a1 online resource (359 p.) 300 $aDescription based upon print version of record. 311 08$a0-12-369468-X 320 $aIncludes bibliographical references and index. 327 $aFront Cover; Computational Materials Engineering: An Introduction to Microstructure Evolution; Copyright Page; Table of Contents; Preface; Chapter 1. Introduction; 1.1 Microstructures Defined; 1.2 Microstructure Evolution; 1.3 Why Simulate Microstructure Evolution?; 1.4 Further Reading; Chapter 2. Thermodynamic Basis of Phase Transformations; 2.1 Reversible and Irreversible Thermodynamics; 2.2 Solution Thermodynamics; Chapter 3. Monte Carlo Potts Model; 3.1 Introduction; 3.2 Two-State Potts Model (Ising Model); 3.3 Q-State Potts Model; 3.4 Speed-Up Algorithms 327 $a3.5 Applications of the Potts Model3.6 Summary; 3.7 Final Remarks; 3.8 Acknowledgments; Chapter 4. Cellular Automata; 4.1 A Definition; 4.2 A One-Dimensional Introduction; 4.3 +2D CA Modeling of Recrystallization; 4.4 +2D CA Modeling of Grain Growth; 4.5 A Mathematical Formulation of Cellular Automata; 4.6 Irregular and Shapeless Cellular Automata; 4.7 Hybrid Cellular Automata Modeling; 4.8 Lattice Gas Cellular Automata; 4.9 Network Cellular Automata-A Development for the Future?; 4.10 Further Reading; Chapter 5. Modeling Solid-State Diffusion; 5.1 Diffusion Mechanisms in Crystalline Solids 327 $a5.2 Microscopic Diffusion5.3 Macroscopic Diffusion; 5.4 Numerical Solution of the Diffusion Equation; Chapter 6. Modeling Precipitation as a Sharp-Interface Phase Transformation; 6.1 Statistical Theory of Phase Transformation; 6.2 Solid-State Nucleation; 6.3 Diffusion-Controlled Precipitate Growth; 6.4 Multiparticle Precipitation Kinetics; 6.5 Comparing the Growth Kinetics of Different Models; Chapter 7. Phase-Field Modeling; 7.1 A Short Overview; 7.2 Phase-Field Model for Pure Substances; 7.3 Study Case; 7.4 Model for Multiple Components and Phases; 7.5 Acknowledgments 327 $aChapter 8. Introduction to Discrete Dislocations Statics and Dynamics8.1 Basics of Discrete Plasticity Models; 8.2 Linear Elasticity Theory for Plasticity; 8.3 Dislocation Statics; 8.4 Dislocation Dynamics; 8.5 Kinematics of Discrete Dislocation Dynamics; 8.6 Dislocation Reactions and Annihilation; Chapter 9. Finite Elements for Mierostructure Evolution; 9.1 Fundamentals of Differential Equations; 9.2 Introduction to the Finite Element Method; 9.3 Finite Element Methods at the Meso- and Macroscale; Index 330 $aComputational Materials Engineering is an advanced introduction to the computer-aided modeling of essential material properties and behavior, including the physical, thermal and chemical parameters, as well as the mathematical tools used to perform simulations. Its emphasis will be on crystalline materials, which includes all metals. The basis of Computational Materials Engineering allows scientists and engineers to create virtual simulations of material behavior and properties, to better understand how a particular material works and performs and then use that knowledge to design improvements 606 $aCrystals$xMathematical models 606 $aMicrostructure$xMathematical models 606 $aPolycrystals$xMathematical models 615 0$aCrystals$xMathematical models. 615 0$aMicrostructure$xMathematical models. 615 0$aPolycrystals$xMathematical models. 676 $a548/.7 701 $aJanssens$b Koenraad G. F.$f1968-$01856520 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910955113703321 996 $aComputational materials engineering$94456050 997 $aUNINA