At present continental to global scale flood forecasting predicts at a point discharge, with little attention to detail and accuracy of local scale inundation predictions. Yet, inundation variables ...are of interest and all flood impacts are inherently local in nature. This paper proposes a large‐scale flood inundation ensemble forecasting model that uses best available data and modeling approaches in data scarce areas. The model was built for the Lower Zambezi River to demonstrate current flood inundation forecasting capabilities in large data‐scarce regions. ECMWF ensemble forecast (ENS) data were used to force the VIC (Variable Infiltration Capacity) hydrologic model, which simulated and routed daily flows to the input boundary locations of a 2‐D hydrodynamic model. Efficient hydrodynamic modeling over large areas still requires model grid resolutions that are typically larger than the width of channels that play a key role in flood wave propagation. We therefore employed a novel subgrid channel scheme to describe the river network in detail while representing the floodplain at an appropriate scale. The modeling system was calibrated using channel water levels from satellite laser altimetry and then applied to predict the February 2007 Mozambique floods. Model evaluation showed that simulated flood edge cells were within a distance of between one and two model resolutions compared to an observed flood edge and inundation area agreement was on average 86%. Our study highlights that physically plausible parameter values and satisfactory performance can be achieved at spatial scales ranging from tens to several hundreds of thousands of km2 and at model grid resolutions up to several km2.
Key Points
A first large scale, yet detailed flood inundation forecasting model
Although large scale, the proposed model agrees with high accuracy observations
The model was fully tested in a large data‐scarce, flood‐prone river basin
Hydraulic and hydrologic modeling has been moving to larger spatial scales with increased spatial resolution, and such models require a global database of river widths and depths to facilitate ...accurate river flow routing. Hydraulic geometry relationships have a long history in estimating river channel characteristics as a function of discharge. A simple near‐global database of bankfull widths and depths (along with confidence intervals) was developed based on hydraulic geometry equations and the HydroSHEDS hydrography data set. The bankfull width estimates were evaluated with widths derived from Landsat imagery for reaches of nine major rivers, showing errors ranging from 8 to 62% (correlation of 0.88), although it was difficult to verify whether the satellite observations corresponded to bankfull conditions. Bankfull depth estimates were compared with in situ measurements at sites in the Ohio and Willamette rivers, producing a mean error of 24%. The uncertainties in the derivation approach and a number of caveats are identified, and ways to improve the database in the future are discussed. Despite these limitations, this is the first global database that can be used directly in hydraulic models or as a set of constraints in model calibration.
Key Points
Developed a global river database
Used hydraulic geometry equations
Leveraged existing hydrologic datasets
Very high resolution synthetic aperture radar (SAR) sensors represent an alternative to aerial photography for delineating floods in built-up environments where flood risk is highest. However, even ...with currently available SAR image resolutions of 3 m and higher, signal returns from man-made structures hamper the accurate mapping of flooded areas. Enhanced image processing algorithms and a better exploitation of image archives are required to facilitate the use of microwave remote-sensing data for monitoring flood dynamics in urban areas. In this paper, a hybrid methodology combining backscatter thresholding, region growing, and change detection (CD) is introduced as an approach enabling the automated, objective, and reliable flood extent extraction from very high resolution urban SAR images. The method is based on the calibration of a statistical distribution of "open water" backscatter values from images of floods. Images acquired during dry conditions enable the identification of areas that are not "visible" to the sensor (i.e., regions affected by "shadow") and that systematically behave as specular reflectors (e.g., smooth tarmac, permanent water bodies). CD with respect to a reference image thereby reduces overdetection of inundated areas. A case study of the July 2007 Severn River flood (UK) observed by airborne photography and the very high resolution SAR sensor on board TerraSAR-X highlights advantages and limitations of the method. Even though the proposed fully automated SAR-based flood-mapping technique overcomes some limitations of previous methods, further technological and methodological improvements are necessary for SAR-based flood detection in urban areas to match the mapping capability of high-quality aerial photography.
Flood modeling at the regional to global scale is a key requirement for equitable emergency and land management. Coupled hydrological‐hydraulic models are at the core of flood forecasting and risk ...assessment models. Nevertheless, each model is subject to uncertainties from different sources (e.g., model structure, parameters, and inputs). Understanding how uncertainties propagate through the modeling cascade is essential to invest in data collection, increase flood modeling accuracy, and comprehensively communicate modeling results to end users. This study used a numerical experiment to quantify the propagation of errors when coupling hydrological and hydraulic models for multiyear flood event modeling in a large basin, with large morphological and hydrological variability. A coupled modeling chain consisting of the hydrological model Hydrologiska Byråns Vattenbalansavdelning and the hydraulic model LISFLOOD‐FP was used for the prediction of floodplain inundation in the Murray Darling Basin (Australia), from 2006 to 2012. The impacts of discrepancies between simulated and measured flow hydrographs on the predicted inundation patterns were analyzed by moving from small upstream catchments to large lowland catchments. The numerical experiment was able to identify areas requiring tailored modeling solutions or data collection. Moreover, this study highlighted the high sensitivity of inundation volume and extent prediction to uncertainties in flood peak values and explored challenges in time‐continuous modeling. Accurate flood peak predictions, knowledge of critical morphological features, and an event‐based modeling approach were outlined as pragmatic solutions for more accurate prediction of large‐scale spatiotemporal patterns of flood dynamics, particularly in the presence of low‐accuracy elevation data.
Plain Language Summary
Floods are among the most devastating natural hazards, affecting multiple regions and millions of people each year. Accurate inundation predictions are vital information for land and emergency management. This objective can be achieved through a cascade of numerical models. However, each model is subject to uncertainties from different sources (e.g., input data, model structure, and parameters), and an understanding of how these uncertainties are propagated through each step of the modeling cascade is pivotal to improving inundation prediction accuracy. This study investigated the impact of uncertainties in streamflow predictions on the accuracy of floodplain inundation predictions. For this purpose, the Murray Darling Basin (Australia), a large basin that is affected by destructive floods, was used as a case study. The analysis illustrated the high sensitivity of floodplain inundation predictions to predicted streamflow peak values. Moreover, when attempting to model a long time series of low‐ and high‐flow periods, uncertainties in the inundation patterns increased over time and from upstream to downstream areas of the basin. These results demonstrated the need for accurate predictions of streamflow peak values and suggested that focusing on the modeling of each large flood event separately is a more effective strategy for reliable inundation predictions.
Key Points
A numerical experiment was used to improve understanding of uncertainty propagation in coupled hydrologic‐hydraulic models
Discrepancies in measured and simulated flow peaks lead to large uncertainties in predicted floodplain inundation volumes and extent
The challenges of multiyear continuous modeling are highlighted with an event‐based approach recommended
A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was ...present, could be a useful tool for operational flood relief management. The paper describes an automatic algorithm using high-resolution synthetic aperture radar (SAR) satellite data that builds on existing approaches, including the use of image segmentation techniques prior to object classification to cope with the very large number of pixels in these scenes. Flood detection in urban areas is guided by the flood extent derived in adjacent rural areas. The algorithm assumes that high-resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, classifying 89% of flooded pixels correctly, with an associated false positive rate of 6%. Of the urban water pixels visible to TerraSAR-X, 75% were correctly detected, with a false positive rate of 24%. If all urban water pixels were considered, including those in shadow and layover regions, these figures fell to 57% and 18%, respectively.
Microwave remote sensing of flood inundation Schumann, Guy J.-P.; Moller, Delwyn K.
Physics and chemistry of the earth. Parts A/B/C,
2015, 2015-00-00, 20150101, Letnik:
83-84
Journal Article
Recenzirano
•SAR for flood applications has grown considerably over the past decade.•Multitemporal SAR can monitor flood dynamics.•High-resolution SAR can map flooding in urban areas.•Long radar wavelengths can ...detect flooding under dense vegetation.•New SAR sensors expected to have significant impacts on flood applications.
Flooding is one of the most costly natural disasters and thus mapping, modeling and forecasting flood events at various temporal and spatial scales is important for any flood risk mitigation plan, disaster relief services and the global (re-)insurance markets. Both computer models and observations (ground-based, airborne and Earth-orbiting) of flood processes and variables are of great value but the amount and quality of information available varies greatly with location, spatial scales and time. It is very well known that remote sensing of flooding, especially in the microwave region of the electromagnetic spectrum, can complement ground-based observations and be integrated with flood models to augment the amount of information available to end-users, decision-makers and scientists. This paper aims to provide a concise review of both the science and applications of microwave remote sensing of flood inundation, focusing mainly on synthetic aperture radar (SAR), in a variety of natural and man-made environments. Strengths and limitations are discussed and the paper will conclude with a brief account on perspectives and emerging technologies.
The middle reach of the Congo remains one of the most difficult places to access, with ongoing conflicts and a lack of infrastructure. This has resulted in the Congo being perhaps the least ...understood large river hydraulically, particularly compared to the Amazon, Nile, or Mississippi. Globally the Congo River is important; it is the largest river in Africa and the basin contains some of the largest areas of tropical forests and wetlands in the world, which are important to both the global carbon and methane cycles. This study produced the first detailed hydraulic characterization of the middle reach, utilizing mostly remotely sensed data sets. Using Landsat imagery, a 30 m resolution water‐mask was created for the middle reach, from which effective river widths and the number of channels and islands were determined. Water surface slopes were determined using ICESat observations for three different periods during the annual flood pulse, and while the overall slope calculated was similar to previous estimates, greater spatial variability was identified. We find that the water surface slope varies markedly in space but relatively little in time and that this appears to contrast with the Amazon where previous studies indicate that time and spatial variations are of equal magnitude. Five key hydraulic constraints were also identified, which play an important role in the overall dynamics of the Congo. Finally, backwater lengths were approximated for four of these constraints, with the results showing that at high water, over a third of the middle reach is affected by backwater effects.
Key Points
Previous methods of river widths fail to account for the spatial variability
Water surface slopes of the Congo are more varied spatially than temporally
Backwater affects over a third of the middle reach of the Congo at high water
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