نوع المادة: مجلة

الموضوعات

Utah, Periodicals., Snow surveys

Utah, Precipitation (Meteorology), Periodicals.

Periodicals., Water-supply, Utah

Stream measurements, Utah, Periodicals.

التصنيف:

العلوم الطبيعية و الرياضيات

علوم الأرض

الجيولوجيا

المصادر: University of Michigan

اللغة: الإنجليزية

ملف خارجي

نوع المادة: كتاب

الموضوعات

Evaporation (Meteorology), Saint Clair, Lake.

Runoff.

Storage, Water, Saint Clair, Lake.

Saint Clair, Lake., Lakes

Precipitation (Meteorology), Saint Clair, Lake.

المصادر: University of Michigan

اللغة: الإنجليزية

التاريخ: 1976

16 p. :

ill. ;

ملف خارجي

نوع المادة: نظرية

الموضوعات

Using spatial mixed-effect statistical models to gap-fill remote sensing measurements.

Representation error evaluation of remote sensing observations.

Quantification of the spatial variability of regional atmospheric carbon dioxide concentrations.

المصادر: University of Michigan

اللغة: الإنجليزية

التاريخ: 2009

ملف خارجي

نوع المادة: نظرية

الموضوعات

Boundary management or boundary work.

Regional Integrated Sciences and Assessments (RISA).

Building water system resilience to climate variability and change.

Decision making and governance.

Climate information use by water managers.

Mixed method - qualitative and quantitative - research.

المصادر: University of Michigan

اللغة: الإنجليزية

التاريخ: 2010

ملف خارجي

نوع المادة: كتاب

الموضوعات

Great Lakes (North America), Precipitation (Meteorology), Observations.

Observations., Runoff, Great Lakes (North America)

Hydrology, Observations., Great Lakes (North America)

Water-supply, Great Lakes (North America), Observations.

Evaporation (Meteorology), Observations., Great Lakes (North America)

المصادر: University of Michigan

اللغة: الإنجليزية

13 p. :

digital, PDF file.

ملف خارجي

نوع المادة: كتاب

الموضوعات

Water-supply, Great Lakes (North America), Observations.

Observations., Great Lakes (North America), Evaporation (Meteorology)

Observations., Great Lakes (North America), Runoff

Observations., Precipitation (Meteorology), Great Lakes (North America)

Hydrology, Great Lakes (North America), Observations.

المصادر: University of Michigan

اللغة: الإنجليزية

التاريخ: 1994

ii, 83 p. :

1 map ;

ملف خارجي

نوع المادة: كتاب

الموضوعات

complexity; streamflow; water level; composite multiscale sample entropy; trend; Poyang Lake basin; four-parameter exponential gamma distribution; principle of maximum entropy; precipitation frequency analysis; methods of moments; maximum likelihood estimation; flood frequency analysis; generalized gamma (GG) distribution; principle of maximum entropy (POME); entropy theory; principle of maximum entropy (POME); GB2 distribution; flood frequency analysis; non-point source pollution; ANN; entropy weighting method; data-scarce; multi-events; spatio-temporal variability; soil water content; entropy; arid region; joint entropy; NDVI; temperature; precipitation; groundwater depth; Hei River basin; turbulent flow; canopy flow; randomness; coherent structures; Shannon entropy; Kolmogorov complexity; entropy; information transfer; optimization; radar; rainfall network; water resource carrying capacity; forewarning model; entropy of information; fuzzy analytic hierarchy process; projection pursuit; accelerating genetic algorithm; entropy production; conditional entropy production; stochastic processes; scaling; climacogram; turbulence; water resources vulnerability; connection entropy; changing environment; set pair analysis; Anhui Province; cross-entropy minimization; land suitability evaluation; spatial optimization; monthly streamflow forecasting; Burg entropy; configurational entropy; entropy spectral analysis time series analysis; entropy; water monitoring; network design; hydrometric network; information theory; entropy applications; hydrological risk analysis; maximum entropy-copula method; uncertainty; Loess Plateau; entropy; water engineering; Tsallis entropy; principle of maximum entropy; Lagrangian function; probability distribution function; flux concentration relation; uncertainty; information; informational entropy; variation of information; continuous probability distribution functions; confidence intervals; precipitation; variability; marginal entropy; crop yield; Hexi corridor; flow duration curve; Shannon entropy; entropy parameter; modeling; spatial and dynamics characteristic; hydrology; tropical rainfall; statistical scaling; Tsallis entropy; multiplicative cascades; Beta-Lognormal model; rainfall forecast; cross entropy; ant colony fuzzy clustering; combined forecast; information entropy; mutual information; kernel density estimation; ENSO; nonlinear relation; scaling laws; power laws; water distribution networks; robustness; flow entropy; entropy theory; frequency analysis; hydrometeorological extremes; Bayesian technique; rainfall; entropy ensemble filter; ensemble model simulation criterion; EEF method; bootstrap aggregating; bagging; bootstrap neural networks; El Niño; ENSO; neural network forecast; sea surface temperature; tropical Pacific; entropy; cross elasticity; mean annual runoff; water resources; resilience; quaternary catchment; complement; substitute; entropy theory; complex systems; hydraulics; hydrology; water engineering; environmental engineering

المصادر: قاعدة بيانات الكتب المجانية

اللغة: الإنجليزية

التاريخ: 2019

ردمك: 9783038972228

ملف خارجي

نوع المادة: كتاب

الموضوعات

evapotranspiration; Northeast China; MS–PT algorithm; spatial-temporal variations; controlling factors; potential evapotranspiration; vegetation remote sensing; reflectance model; spectra; leaf; copper; PROSPECT; leaf area density; terrestrial LiDAR; tree canopy; vertical structure; voxel; spatial representativeness; heterogeneity; validation; land-surface temperature products (LSTs); observations; HiWATER; remote sensing; spatiotemporal representative; cost-efficient, sampling design; heterogeneity; validation; FY-3C/MERSI; GLASS; Land surface temperature; Land surface emissivity; GPP; SIF; MuSyQ-GPP algorithm; BEPS; vegetation phenology; Tibetan Plateau; MODIS; NDVI; start of growing season (SOS); end of growing season (EOS); GLASS LAI time series; forest disturbance; disturbance index; latent heat; machine learning algorithms; plant functional type; high-resolution freeze/thaw; AMSR2; MODIS; LAI; ZY-3 MUX; GF-1 WFV; HJ-1 CCD; maize; PROSPECT-5B+SAILH (PROSAIL) model; spatial heterogeneity; variability; evapotranspiration; land surface variables; probability density function; HiWATER; spectral; albedometer; interference filter; photoelectric detector; validation; land surface albedo; multi-scale validation; rugged terrain; MRT-based model; MCD43A3 C6; precipitation; statistics methods; China; Tibetan Plateau; South China’s; drought; SPI; TMI data; crop-growing regions; downward shortwave radiation; machine learning; gradient boosting regression tree; AVHRR; CMA; BRDF; aerosol; MODIS; sunphotometer; arid/semiarid; solar-induced chlorophyll fluorescence; fluorescence quantum efficiency in dark-adapted conditions (FQE); SCOPE; Fraunhofer Line Discrimination (FLD); gross primary productivity (GPP); longwave upwelling radiation (LWUP); Visible Infrared Imaging Radiometer Suite (VIIRS); surface radiation budget; hybrid method; remote sensing; leaf age; leaf spectral properties; leaf area index; Cunninghamia; Chinese fir; canopy reflectance; NIR; EVI2; geometric optical radiative transfer (GORT) model; land surface albedo; snow-free albedo; rugged terrain; topographic effects; black-sky albedo (BSA); GPP; NPP; MODIS; validation; phenology; RADARSAT-2; rice; Synthetic Aperture Radar (SAR); decision tree; forest canopy height; aboveground biomass; ICESat GLAS; Landsat; random forest model; anisotropic reflectance; BRDF; rugged terrain; solo slope; composite slope; surface solar irradiance; geostationary satellite; polar orbiting satellite; LUT method; SURFRAD; downward shortwave radiation; daily average value; Antarctica; sinusoidal method; cloud fraction; interpolation; boreal forest; GPP; spatiotemporal distribution and variation; meteorological factors; phenological parameters; multisource data fusion; aerosol retrieval; urban scale; vegetation dust-retention; multiple ecological factors; geographical detector model; snow cover; passive microwave; FY-3C/MWRI; algorithmic assessment; China; land surface temperature; satellite observations; flux measurements; latitudinal pattern; land cover change; fractional vegetation cover (FVC); multi-data set; northern China; spatio-temporal; inter-annual variation; uncertainty; standard error of the mean; downscaling; GPP; spatial heterogeneity; remote sensing; subpixel information; LiDAR; point cloud; leaf; gap fraction; 3D reconstruction; biodiversity; remote sensing; species richness; metric comparison; metric integration; leaf area index; MODIS products; Landsat; high resolution; homogeneous and pure pixel filter; pixel unmixing; vertical vegetation stratification; gross primary production (GPP); light use efficiency; dense forest; MODIS; VPM; temperature profiles; humidity profiles; n/a; geometric-optical model; thermal radiation directionality; quantitative remote sensing inversion; scale effects; comprehensive field experiment

المصادر: قاعدة بيانات الكتب المجانية

اللغة: الإنجليزية

التاريخ: 2019

ردمك: 9783038972709

ملف خارجي

نوع المادة: كتاب

الموضوعات

evapotranspiration; Northeast China; MS–PT algorithm; spatial-temporal variations; controlling factors; potential evapotranspiration; vegetation remote sensing; reflectance model; spectra; leaf; copper; PROSPECT; leaf area density; terrestrial LiDAR; tree canopy; vertical structure; voxel; spatial representativeness; heterogeneity; validation; land-surface temperature products (LSTs); observations; HiWATER; remote sensing; spatiotemporal representative; cost-efficient, sampling design; heterogeneity; validation; FY-3C/MERSI; GLASS; Land surface temperature; Land surface emissivity; GPP; SIF; MuSyQ-GPP algorithm; BEPS; vegetation phenology; Tibetan Plateau; MODIS; NDVI; start of growing season (SOS); end of growing season (EOS); GLASS LAI time series; forest disturbance; disturbance index; latent heat; machine learning algorithms; plant functional type; high-resolution freeze/thaw; AMSR2; MODIS; LAI; ZY-3 MUX; GF-1 WFV; HJ-1 CCD; maize; PROSPECT-5B+SAILH (PROSAIL) model; spatial heterogeneity; variability; evapotranspiration; land surface variables; probability density function; HiWATER; spectral; albedometer; interference filter; photoelectric detector; validation; land surface albedo; multi-scale validation; rugged terrain; MRT-based model; MCD43A3 C6; precipitation; statistics methods; China; Tibetan Plateau; South China’s; drought; SPI; TMI data; crop-growing regions; downward shortwave radiation; machine learning; gradient boosting regression tree; AVHRR; CMA; BRDF; aerosol; MODIS; sunphotometer; arid/semiarid; solar-induced chlorophyll fluorescence; fluorescence quantum efficiency in dark-adapted conditions (FQE); SCOPE; Fraunhofer Line Discrimination (FLD); gross primary productivity (GPP); longwave upwelling radiation (LWUP); Visible Infrared Imaging Radiometer Suite (VIIRS); surface radiation budget; hybrid method; remote sensing; leaf age; leaf spectral properties; leaf area index; Cunninghamia; Chinese fir; canopy reflectance; NIR; EVI2; geometric optical radiative transfer (GORT) model; land surface albedo; snow-free albedo; rugged terrain; topographic effects; black-sky albedo (BSA); GPP; NPP; MODIS; validation; phenology; RADARSAT-2; rice; Synthetic Aperture Radar (SAR); decision tree; forest canopy height; aboveground biomass; ICESat GLAS; Landsat; random forest model; anisotropic reflectance; BRDF; rugged terrain; solo slope; composite slope; surface solar irradiance; geostationary satellite; polar orbiting satellite; LUT method; SURFRAD; downward shortwave radiation; daily average value; Antarctica; sinusoidal method; cloud fraction; interpolation; boreal forest; GPP; spatiotemporal distribution and variation; meteorological factors; phenological parameters; multisource data fusion; aerosol retrieval; urban scale; vegetation dust-retention; multiple ecological factors; geographical detector model; snow cover; passive microwave; FY-3C/MWRI; algorithmic assessment; China; land surface temperature; satellite observations; flux measurements; latitudinal pattern; land cover change; fractional vegetation cover (FVC); multi-data set; northern China; spatio-temporal; inter-annual variation; uncertainty; standard error of the mean; downscaling; GPP; spatial heterogeneity; remote sensing; subpixel information; LiDAR; point cloud; leaf; gap fraction; 3D reconstruction; biodiversity; remote sensing; species richness; metric comparison; metric integration; leaf area index; MODIS products; Landsat; high resolution; homogeneous and pure pixel filter; pixel unmixing; vertical vegetation stratification; gross primary production (GPP); light use efficiency; dense forest; MODIS; VPM; temperature profiles; humidity profiles; n/a; geometric-optical model; thermal radiation directionality; quantitative remote sensing inversion; scale effects; comprehensive field experiment

المصادر: قاعدة بيانات الكتب المجانية

اللغة: الإنجليزية

التاريخ: 2019

ردمك: 9783038972761

ملف خارجي

نوع المادة: كتاب

الموضوعات

data scarce basins; runoff series; data forward prediction; ensemble empirical mode decomposition (EEMD); stopping criteria; method of tracking energy differences (MTED); deep learning; convolutional neural networks; superpixel; urban water bodies; high-resolution remote-sensing images; monthly streamflow forecasting; artificial neural network; ensemble technique; phase space reconstruction; empirical wavelet transform; hybrid neural network; flood forecasting; self-organizing map; bat algorithm; particle swarm optimization; flood routing; Muskingum model; machine learning methods; St. Venant equations; rating curve method; nonlinear Muskingum model; hydrograph predictions; flood routing; Muskingum model; hydrologic models; improved bat algorithm; Wilson flood; Karahan flood; flood susceptibility modeling; ANFIS; cultural algorithm; bees algorithm; invasive weed optimization; Haraz watershed; ANN-based models; flood inundation map; self-organizing map (SOM); recurrent nonlinear autoregressive with exogenous inputs (RNARX); ensemble technique; artificial neural networks; uncertainty; streamflow predictions; sensitivity; flood forecasting; extreme learning machine (ELM); backtracking search optimization algorithm (BSA); the upper Yangtze River; deep learning; LSTM network; water level forecast; the Three Gorges Dam; Dongting Lake; Muskingum model; wolf pack algorithm; parameters; optimization; flood routing; flash-flood; precipitation-runoff; forecasting; lag analysis; random forest; machine learning; flood prediction; flood forecasting; hydrologic model; rainfall–runoff, hybrid &; ensemble machine learning; artificial neural network; support vector machine; natural hazards &; disasters; adaptive neuro-fuzzy inference system (ANFIS); decision tree; survey; classification and regression trees (CART), data science; big data; artificial intelligence; soft computing; extreme event management; time series prediction; LSTM; rainfall-runoff; flood events; flood forecasting; data assimilation; particle filter algorithm; micro-model; Lower Yellow River; ANN; hydrometeorology; flood forecasting; real-time; postprocessing; machine learning; early flood warning systems; hydroinformatics; database; flood forecast; Google Maps

المصادر: قاعدة بيانات الكتب المجانية

اللغة: الإنجليزية

التاريخ: 2019

ردمك: 9783038975489

ملف خارجي