1 Spatial Modelling of the Terrestrial Environment - Georeferencial

More documents

Recommendations

Info

100 Spatial Modelling of the Terrestrial Environment a single optimum set of parameters, which produces a maximum value of F 〈2〉 , we identify a broad region of the parameter space where values of F 〈2〉 are greater than 70%. The complexity of the parameter space also appears to be a function of model non-linearity. Thus, Horritt and Bates (2001a) showed that the parameter space for the weakly nonlinear LISFLOOD-FP model typically contains a single optimum region, whilst the same parameter space for the more highly non-linear TELEMAC-2D model contained multiple optima. Such parameter space mappings do not, however, give information on the distributed risk of inundation across the floodplain given model uncertainty. The problem here is to design a measure that both captures distributed uncertainty and, at the same time, contains information on global simulation likelihood that can be used to condition predictions of further events for which observed data are not available. Thus, to unearth the spatial uncertainty in model predictions for the particular flood being modelled, we take the flood state as predicted by the model for each pixel for each realization, and weight it according to the measure-of-fitF 〈2〉 to give a flood risk for each pixel i, P flood i : P flood i = ∑ j ∑ j f ij F (2) j where f ij takes a value of 1 for a flooded pixel and is zero otherwise and F 〈2〉 j is the global performance measure for simulation j. Here Pi flood will assume a value of 1 for pixels that are predicted as flooded in all simulations and 0 for pixels always predicted as dry. Model uncertainty (here defined by the interaction of the global performance measure and spatially distributed probabilities of the event being modelled) will manifest itself as a region of pixels with intermediate values, maximum uncertainty being indicated by pixels with Pi flood ≈ 0.5. When events different from the conditioning event are to be modelled, the same values of F 〈2〉 j can be used (these are associated with each parameterization), but the predicted risk map f ij will differ. Whilst the technique has been developed with the problem of flood inundation modelling in mind, it should equally well apply to any distributed model conditioned against binary pattern data. Plots of Pi flood over the real space of the Imera and Thames models are shown in Figures 5.7 and 5.8, respectively. These maps indicate the risk that a given pixel is predicted as wet in the simulations whilst retaining information on the global performance of the model determined by F 〈2〉 . When compared to the observed shoreline (solid line), this gives an indication of the degree of model under- or over-prediction. Figures 5.7 and 5.8 give much more information on the spatial structure in simulation uncertainty than can be obtained from a global measure and significant discrepancies between model predictions and the observed shoreline are indicated at the limits of the parameter space. Finally, Pi flood may be updated as more information becomes available and one is thus able to determine the extent to which additional data act to reduce predictive uncertainty. Such techniques are thus capable of telling us something about the value of data, and here further studies are thus required that examine combinations of data, both hydrometric and inundation extent, and calibration strategies that act to reduce uncertainty in model predictions. F (2) j (2)
0 m 1000 m 2000 m 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Figure 5.7 Probability map of predicted inundation, P f lood i , for the October 1991 event for the Imera River (from Aronica et al., 2002). c○ John Wiley & Sons, Ltd. 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 0 m 500 m 1000 m Figure 5.8 Probability map of predicted inundation, P f lood i , for the December 1992 event for the River Thames (from Aronica et al., 2002). c○ John Wiley & Sons, Ltd.
Page 2 and 3:
Spatial Modelling of the Terrestria
Page 4 and 5:
Copyright C○ 2004 John Wiley & So
Page 6 and 7:
Contents List of Contributors Prefa
Page 8 and 9:
Contributors Jonathan L. Bamber Sch
Page 10 and 11:
List of Contributors xi George Perr
Page 12 and 13:
xiv Preface in theory and applicati
Page 14 and 15:
2 Spatial Modelling of the Terrestr
Page 16 and 17:
Page 18 and 19:
Page 20 and 21:
Editorial: Spatial Modelling in Hyd
Page 22 and 23:
Editorial: Spatial Modelling in Hyd
Page 24 and 25:
2 Modelling Ice Sheet Dynamics with
Page 26 and 27:
Modelling Ice Sheet Dynamics by Sat
Page 28 and 29:
Page 30 and 31:
Page 32 and 33:
Page 34 and 35:
Page 36 and 37:
Page 38 and 39:
Page 40 and 41:
Page 42 and 43:
Page 44 and 45:
Page 46 and 47:
36 Spatial Modelling of the Terrest
Page 48 and 49:
Page 50 and 51:
Page 52 and 53:
Page 54 and 55:
Page 56 and 57:
Page 58 and 59: 48 Spatial Modelling of the Terrest
Page 68 and 69: 4 Using Coupled Land Surface and Mi
Page 70 and 71: Coupled Land Surface and Microwave
Page 110 and 111: 102 Spatial Modelling of the Terres
Page 116 and 117: Editorial: Terrestrial Sediment and
Page 118 and 119: Editorial: Terrestrial Sediment and
Page 120 and 121: 6 Remotely Sensed Topographic Data
Page 122 and 123: Remotely Sensed Topographic Data fo
Page 144 and 145: 7 Modelling Wind Erosion and Dust E
Page 146 and 147: Modelling Wind Erosion and Dust Emi
Page 158 and 159:
Modelling Wind Erosion and Dust Emi
Page 160 and 161:
Page 162 and 163:
Page 164 and 165:
8 Near Real-Time Modelling of Regio
Page 166 and 167:
Near Real-Time Modelling of Regiona
Page 168 and 169:
Page 170 and 171:
Page 172 and 173:
Page 174 and 175:
Page 176 and 177:
High Low TSM Concentration Figure 8
Page 178 and 179:
Page 180 and 181:
Page 182 and 183:
9 Estimation of Energy Emissions, F
Page 184 and 185:
Fireline Intensity and Biomass Cons
Page 186 and 187:
Page 188 and 189:
Page 190 and 191:
Page 192 and 193:
Page 194 and 195:
Figure 9.4 The upper row shows EOS
Page 196 and 197:
Page 198 and 199:
Page 200 and 201:
Page 202 and 203:
Page 204 and 205:
PART III SPATIAL MODELLING OF URBAN
Page 206 and 207:
200 Spatial Modelling of the Terres
Page 208 and 209:
Page 210 and 211:
Page 212 and 213:
Page 214 and 215:
Page 216 and 217:
Page 218 and 219:
Page 220 and 221:
Page 222 and 223:
Page 224 and 225:
Page 226 and 227:
Page 228 and 229:
Page 230 and 231:
Page 232 and 233:
11 Modelling the Impact of Traffic
Page 234 and 235:
Modelling the Impact of Traffic Emi
Page 236 and 237:
Page 238 and 239:
Page 240 and 241:
Page 242 and 243:
Page 244 and 245:
Page 246 and 247:
Page 248 and 249:
PART IV CURRENT CHALLENGES AND FUTU
Page 250 and 251:
Page 252 and 253:
Page 254 and 255:
Page 256 and 257:
Page 258 and 259:
Page 260 and 261:
Page 262 and 263:
Page 264 and 265:
Page 266 and 267:
Page 268 and 269:
Page 270 and 271:
Page 272 and 273:
268 Index Bi-spectral InfraRed Dete
Page 274 and 275:
270 Index floodplain maps, UK 92 fl
Page 276 and 277:
272 Index Long-Term Ecological Rese
Page 278 and 279:
274 Index snow depth measurement ne
Page 280:
276 Index urban land use in remotel
show all

1 Spatial Modelling of the Terrestrial Environment - Georeferencial

Create successful ePaper yourself

Delete template?

Save as template?