Thu, 21 Aug 2008 07:20:19 BST CiteULike: xjlai flood http://www.citeulike.org/user/xjlai/tag/flood CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/xjlai/article/970492 <i>Journal of Hydrology, Vol. 328, No. 1-2. (30 August 2006), pp. 258-266.</i><br /><br />SummaryIn this paper, the potential for identifying discharge and/or flood hydrograph from remotely sensed data is explored. The parameter identification process is based on the minimization of the difference between the solution of the model equations and the observed system response which consists in maximum inundation extent. The river geometry is supposed to be known, effect of the accuracy of these data on the estimation has been tested. Sensitivity of the model to individual parameters is then assessed using an extension of the generalized sensitivity analysis. Synthetic data have been used to test the methodology. Results show that the Nash criterion of the estimated flood hydrograph is higher than 0.9 for all the tested cases. Use of parameter optimization to estimate a flood wave: Potential applications to remote sensing of rivers Helene Roux Denis Dartus doi:10.1016/j.jhydrol.2005.12.025 Journal of Hydrology, Vol. 328, No. 1-2. (30 August 2006), pp. 258-266. 2006-12-01T14:55:47-00:00 2006 Journal of Hydrology 328 1-2 258 266 1d assimilation flood rs http://www.citeulike.org/user/xjlai/article/968945 <i>Remote Sensing of Environment, Vol. 98, No. 2-3. (15 October 2005), pp. 344-355.</i><br /><br />Flooding over the Mackenzie River Basin, which is situated in northwestern Canada, is a complex and rapid process. This process is mainly controlled by the occurrence of ice jams. Flood forecasting is of very important in mitigating social and economic damage. This study investigates the potential of a rating curve model for flood forecasting. The proposed approach is based on the use of a Water Surface Fraction derived from SSM/I passive microwave images and discharge observations. The rating curve model is based on an existing correlation between flooded areas and measured discharge. However, a time lag can be observed between these two variables. Thus, the rating curve model has been modified by the introduction of a lag term that could vary depending on the flooding intensity and the features of the basin. Hence, the lag term is computed dynamically using a cross-correlation function between Water Surface Fraction values which are derived from SSM/I observations and the discharge vectors. The rating curve model is based on two empirical parameters that depend on the site features, which vary in both space and time. To overcome this dependency, the rating curve model was linked to a Kalman filter in order to dynamically estimate the empirical parameters according to the forecasting errors encountered at each time step. With the Kalman filter, the dynamic rating curve model continuously readjusts its parameters to satisfy the non-stationary behavior of hydrological processes. The model is thus sufficiently flexible and adapted to various conditions. Simulations were carried out over the Mackenzie River Basin (1.8 million km2) during the summers of 1998 and 1999. NOAA-AVHRR images were used to validate the forecast WSF values. The predicted flooded areas agree well with those derived from the NOAA-AVHRR images. Further simulations were carried out from 1992 to 2000 using the rating curve model to predict discharge at a downstream location. Even though an interannual variability of the water surface fractions was observed over the PAD area, the modified model was sufficiently flexible to be readjusted and to reproduce satisfactory results. This implies that a combination of passive microwave data and discharge observations presents an interesting potential in flood and discharge prediction. Flood monitoring over the Mackenzie River Basin using passive microwave data Marouane Temimi Robert Leconte Francois Brissette Naira Chaouch doi:10.1016/j.rse.2005.06.010 Remote Sensing of Environment, Vol. 98, No. 2-3. (15 October 2005), pp. 344-355. 2006-11-30T16:44:41-00:00 2005 Remote Sensing of Environment 98 2-3 344 355 flood monitoring rs http://www.citeulike.org/user/xjlai/article/965098 <i>Journal of Hydrology, Vol. 278, No. 1-4. (25 July 2003), pp. 17-38.</i><br /><br />Numerous studies have demonstrated the potential usefulness of river hydraulic data obtained from satellites in developing general approaches to tracking floods and changes in river discharge from space. Few studies, however, have attempted to estimate the magnitude of discharge in rivers entirely from remotely obtained information. The present study uses multiple-regression analyses of hydraulic data from more than 1000 discharge measurements, ranging in magnitude from over 200,000 to less than 1 m3/s, to develop multi-variate river discharge estimating equations that use various combinations of potentially observable variables to estimate river discharge. Uncertainty analysis indicates that existing satellite-based sensors can measure water-surface width (or surface area), water-surface elevation, and potentially the surface velocity of rivers with accuracies sufficient to provide estimates of discharge with average uncertainty of less than 20%. Development and validation of multi-variate rating equations that are applicable to the full range of rivers that can be observed from satellite sensors, development of techniques to accurately estimate the average depth in rivers from stage measurements, and development of techniques to accurately estimate the average velocity in rivers from surface-velocity measurements will be key to successful prediction of discharge from satellite observations. Evaluating the potential for measuring river discharge from space David Bjerklie Lawrence Charles Vorosmarty Carl Bolster Russell Congalton doi:10.1016/S0022-1694(03)00129-X Journal of Hydrology, Vol. 278, No. 1-4. (25 July 2003), pp. 17-38. 2006-11-28T10:27:01-00:00 2003 Journal of Hydrology 278 1-4 17 38 discharge flood rs http://www.citeulike.org/user/xjlai/article/505089 <i>Natural Hazards, Vol. 37, No. 3. (March 2006), pp. 263-276.</i> Towards Formulation of a Space-borne System for Early Warning of Floods: Can Cost-Effectiveness Outweigh Prediction Uncertainty? Faisal Hossain doi:10.1007/s11069-005-4645-0 Natural Hazards, Vol. 37, No. 3. (March 2006), pp. 263-276. 2006-02-14T12:46:10-00:00 2006 Natural Hazards 0921-030X 37 3 263 276 Springer flood rs uncertainty http://www.citeulike.org/user/xjlai/article/965055 <i>Hydrological Processes, Vol. 11, No. 10. (1997), pp. 1427-1439.</i><br /><br />The growing availability of multi-temporal satellite data has increased opportunities for monitoring large rivers from space. A variety of passive and active sensors operating in the visible and microwave range are currently operating, or planned, which can estimate inundation area and delineate flood boundaries. Radar altimeters show great promise for directly measuring stage variation in large rivers. It also appears to be possible to obtain estimates of river discharge from space, using ground measurements and satellite data to construct empirical curves that relate water surface area to discharge. Extrapolation of these curves to ungauged sites may be possible for the special case of braided rivers.Where clouds, trees and floating vegetation do not obscure the water surface, high-resolution visible/infrared sensors provide good delineation of inundated areas. Synthetic aperture radar (SAR) sensors can penetrate clouds and can also detect standing water through emergent aquatic plants and forest canopies. However, multiple frequencies and polarizations are required for optimal discrimination of various inundated vegetation cover types. Existing single-polarization, fixed-frequency SARs are not sufficient for mapping inundation area in all riverine environments. In the absence of a space-borne multi-parameter SAR, a synergistic approach using single-frequency, fixed-polarization SAR and visible/infrared data will provide the best results over densely vegetated river floodplains. © 1997 John Wiley & Sons, Ltd. Satellite remote sensing of river inundation area, stage, and discharge: a review Laurence Smith doi:10.1002/(SICI)1099-1085(199708)11:10<1427::AID-HYP473>3.0.CO;2-S Hydrological Processes, Vol. 11, No. 10. (1997), pp. 1427-1439. 2006-11-28T08:56:31-00:00 1997 Hydrological Processes 11 10 1427 1439 discharge flood inundation rs http://www.citeulike.org/user/xjlai/article/963247 <i>Journal of Hydrology, Vol. 300, No. 1-4. (10 January 2005), pp. 114-125.</i><br /><br />In this paper, the four-dimensional variational data assimilation technique (4D-VAR) is presented as a tool to forecast floods. Our study is limited to purely hydrological flows and supposes that the weather, here a big rain, has been already forecasted by meteorological services. The technique consists in minimizing, in the sense of Lagrange, the cost function: a measure of the difference between calculated data and available observations, here the water level. This is done under constraints that are the equations of the physical model. In our case, we modified the shallow-water equations to include a simplified sediment transport model. The steepest descent algorithm is then used to find the minimum. This is made possible because we can compute analytically the gradient of the cost function by using the adjoint equations of the model. As an application of the 4D-VAR technique, the overflowing of the Chicoutimi River at the Chute-Garneau dam, during the 1996 flood, is investigated. It is found that the 4D-VAR method reduces the error in the water height forecast even when the erosion model is not activated. In terms of Lyapunov exponents, we estimate the predictability horizon of such an event to be about half-an-hour after a big rain. However, this limit of predictability can be increased by using more observations or by using a finer computational grid. Data assimilation (4D-VAR) to forecast flood in shallow-waters with sediment erosion Eric Belanger Alain Vincent doi:10.1016/j.jhydrol.2004.06.009 Journal of Hydrology, Vol. 300, No. 1-4. (10 January 2005), pp. 114-125. 2006-11-27T13:13:11-00:00 2005 Journal of Hydrology 300 1-4 114 125 assimilation flood modeling http://www.citeulike.org/user/xjlai/article/206625 <i>Natural Hazards, Vol. 35, No. 3. (July 2005), pp. 377-393.</i> Monitoring Flood Extent and Forecasting Excess Runoff Risk with RADARSAT-1 Data Ferdinand Bonn Roy Dixon doi:10.1007/s11069-004-1798-1 Natural Hazards, Vol. 35, No. 3. (July 2005), pp. 377-393. 2005-05-21T08:16:59-00:00 2005 Natural Hazards 0921-030X 35 3 377 393 Kluwer Academic Publishers extent flood monitoring