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Issues in flow and oxygenation dependent contrast (FLOOD) imaging of tumours: NMR in Biomedicine, Vol. 14, No. 7-8. (2001), pp. 497-506.The sensitivity of blood oxygenation level dependent (BOLD) contrast techniques to changes to tumour deoxyhaemoglob in concentration is of relevance to many strategies in cancer treatments. In the context of tumour studies, which frequently involve the use of agents to modify blood flow, there are underlying physiological changes different to those of BOLD in the brain. Hence we use the term, flow and oxygenation dependent (FLOOD) contrast, to emphasize this difference and the importance of flow effects. We have measured the R2* changes in a prolactinoma tumour model for a variety of vasoactive challenges [carbogen, 100% oxygen and 100% nitrogen as different breathing gases, and administration of tumour blood flow modifiers such as calcitonin gene related peptide (CGRP), hydralazine and nicotinamide]. In addition we have measured other relevant physiological parameters, such as bioenergetic status from 31P MRS, and blood pH and glucose, that may change during a vasoactive challenge. Here we discuss how they relate to our understanding of FLOOD contrast in tumours. We frequently observe R2* changes that match the expected action of the vascular stimulus: R2* decreases with agents expected to improve tumour oxygenation and blood flow, and increases with agents designed to increase tumour hypoxia. Unlike most normal tissues, tumours have a chaotic and poorly regulated blood supply, and a mix of glycolytic and oxidative metabolism; thus the response to a vasoactive challenge is not predictable. Changes in blood volume can counteract the effect of blood oxygenation changes, and changes in blood pH and glucose levels can alter oxygen extraction. This can lead to R2* changes that are smaller or the reverse of those expected. To properly interpret FLOOD contrast changes these effects must be accounted for. Copyright © 2001 John Wiley & Sons, Ltd.FA Howe, SP Robinson, DJO Mcintyre, M Stubbs, JR Griffiths

Source: NMR in Biomedicine, Vol. 14, No. 7-8. (2001), pp. 497-506.
Data assimilation (4D-VAR) to forecast flood in shallow-waters with sediment erosion: Journal of Hydrology, Vol. 300, No. 1-4. (10 January 2005), pp. 114-125.In this paper, the four-dimension al 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.Eric Belanger, Alain Vincent

Source: Journal of Hydrology, Vol. 300, No. 1-4. (10 January 2005), pp. 114-125.
Use of parameter optimization to estimate a flood wave: Potential applications to remote sensing of rivers: Journal of Hydrology, Vol. 328, No. 1-2. (30 August 2006), pp. 258-266.Summar yIn 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.Helene Roux, Denis Dartus

Source: Journal of Hydrology, Vol. 328, No. 1-2. (30 August 2006), pp. 258-266.
Satellite remote sensing of river inundation area, stage, and discharge: a review: Hydrological Processes, Vol. 11, No. 10. (1997), pp. 1427-1439.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-resolutio n visible/infrar ed 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-polariz ation, fixed-frequenc y SARs are not sufficient for mapping inundation area in all riverine environments. In the absence of a space-borne multi-paramete r SAR, a synergistic approach using single-frequen cy, fixed-polariza tion SAR and visible/infrar ed data will provide the best results over densely vegetated river floodplains. © 1997 John Wiley & Sons, Ltd.Laurence Smith

Source: Hydrological Processes, Vol. 11, No. 10. (1997), pp. 1427-1439.
Evaluating the potential for measuring river discharge from space: Journal of Hydrology, Vol. 278, No. 1-4. (25 July 2003), pp. 17-38.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-regre ssion 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-base d 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-veloci ty measurements will be key to successful prediction of discharge from satellite observations.D avid Bjerklie, Lawrence, Charles Vorosmarty, Carl Bolster, Russell Congalton

Source: Journal of Hydrology, Vol. 278, No. 1-4. (25 July 2003), pp. 17-38.
Towards Formulation of a Space-borne System for Early Warning of Floods: Can Cost-Effective ness Outweigh Prediction Uncertainty?: Natural Hazards, Vol. 37, No. 3. (March 2006), pp. 263-276.Faisal Hossain

Source: Natural Hazards, Vol. 37, No. 3. (March 2006), pp. 263-276.
Flood monitoring over the Mackenzie River Basin using passive microwave data: Remote Sensing of Environment, Vol. 98, No. 2-3. (15 October 2005), pp. 344-355.Floodi ng 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-correlat ion 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.Mar ouane Temimi, Robert Leconte, Francois Brissette, Naira Chaouch

Source: Remote Sensing of Environment, Vol. 98, No. 2-3. (15 October 2005), pp. 344-355.
Monitoring Flood Extent and Forecasting Excess Runoff Risk with RADARSAT-1 Data: Natural Hazards, Vol. 35, No. 3. (July 2005), pp. 377-393.Ferdin and Bonn, Roy Dixon

Source: Natural Hazards, Vol. 35, No. 3. (July 2005), pp. 377-393.
Data assimilation and adaptive forecasting of water levels in the river Severn catchment, United Kingdom: Water Resources Research, Vol. 42 (14 June 2006), W06407.This paper describes data assimilation (DA) and adaptive forecasting techniques for flood forecasting and their application to forecasting water levels at various locations along a 120 km reach of the river Severn, United Kingdom. The methodology exploits the top-down, data-based mechanistic (DBM) approach to the modeling of environmental processes, concentrating on the identification and estimation of those ?dominant modes? of dynamic behavior that are most important for flood prediction. In particular, hydrological processes active in the catchment are modeled using the state-dependen t parameter (SDP) method of estimating a nonlinear, effective rainfall transformation together with a linear stochastic transfer function (STF) method for characterizing both the effective rainfall?river level behavior and the river level routing processes. The complete model consists of these lumped parameter, linear and nonlinear stochastic, dynamic elements connected in a quasi-distribu ted manner that represents the physical structure of the catchment. The adaptive forecasting system then utilizes a state-space form of the complete catchment model, including allowance for heteroscedasti city in the errors, as the basis for data assimilation and forecasting using a Kalman filter forecasting engine. Here the predicted model states (water levels) and adaptive parameters are updated recursively in response to input data received in real time from sensors in the catchment. Direct water level forecasting is considered, rather than flow, because this removes the need to transform the level measurement through the rating curve and tends to decrease the forecasting errors.Renata Romanowicz, Peter Young, Keith Beven

Source: Water Resources Research, Vol. 42 (14 June 2006), W06407.
The Buffalo Creek Disaster : How the survivors of one of the worst disasters in coal-mining history brought suit against the coal company--and won (Vintage): (12 February 1977)Gerald Stern

Source: (12 February 1977)

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