1 Spatial Modelling of the Terrestrial Environment - Georeferencial

274 Index
snow depth measurement networks 37–8, 38
U.S. COOP station network 38, 38, 40, 40, 41
FSU network 38, 38, 40, 41
WMO GTS network 37, 38, 38, 40, 41
snow depth/SWE 11
estimation using pysically based models 42–3,
50
improving estimates of at all scales
combining models and observations 49–52
validation frameworks 53
methodological approaches to 50, 51
modelling spatial variations of using in situ snow
measurements 36–42
remote sensing estimates
recent approaches and limits to accuracy 43–7
spatial representivity of SSM/I snow depth
estimates 47–9
retrieval schemes based on empirical formulations
46–7
spatial dependency of 37
spatial variability investigated using variograms
39–42
use of terrain and meteorological variables in
spatial modelling 42
snow hydrology models 42–3, 53
combined with microwave emission models 53
snow maps, uses of 37
snow pack energy balance models 49
snow packs
layering 46
interaction with vegetation cover 266
snow variables and land data assimilation 254
snow volume
global, retrieval of 4
satellite passive microwave estimates 36
snow water equivalent (SWE)
general estimation approach 53–4
changes at hemispheric level 35
regionally calibrated approaches 36
SNTHERM model, coupled with dense media
radiative transfer (DMRT) model 50–1,
54
soil erosion 109
required parameters 111
models of erosion by water 109–10
regional scale, near real-time modelling of
157–73
Sediment flux 168, 172
source areas of erosion 168
soil erosion model, applied to Lake Tanganyika 158,
159–63
overland flow estimated using SCS model with
FEWS data 161–3
soil erodibility computed from soil properties maps
161, 162
vegetation cover estimated using LAC AVHRR
159–61
relationship between vegetation cover and NDVI
160–1, 160
scaling problem, overcome by use of Polya
function 161
soil moisture, improving accuracy of in models 59
soil moisture estimates 11
downscaling
four-dimensional assimilation algorithm 71
modified fractal interpolation technique 71
extending estimates from deeper within the profile
69–70
use of statistical methods 69–70
using assimilation techniques 70
from ground-based and aircraft radiometer systems
4–5
remotely sensed observations using L-band passive
microwave radiometer 60
subpixel heterogeneity 70–2
Soil Moisture Ocean Salinity (SMOS) mission see
SMOS mission
soil moisture retrieval algorithms
effects of vegetation in 66–9
ancillary information to estimate optical depth
66–7
use of NDVI 67
quantifying errors due to assumptions about the
vegetation 68–9
space syntax theory 204
spatial autocorrelation 223
of snow depth 39
spatial data
incorporation into environmental models 102–3
integration with hydraulic models 92–100, 101
automatic mesh generation 96
high resolution topographic data 92–4
model calibration and validation studies 96–8
spatially distributed friction data 94–5
uncertainty estimation using spatial data and
distributed mapping 98–100, 101
spatial models/modelling 2–4
the future 267
in hydrology 9–12
importance of 264
of the terrestrial environment, key research issues
264–7
DEMs: improved accuracy and error
characterization 265
spatial resolution: scales of variation and size of
support 266–7
vegetation cover: improved characterization
265–6
spatial reclassification techniques 203
spatial resolution 267