Evaluation of 1D and 2D numerical models for predicting river flood inundation

[1] This review surveys the basic theories, observational methods, satellite algorithms, and land surface models for terrestrial evapotranspiration, E (or λE, i.e., latent heat flux), including a long-term variability and trends perspective. The basic theories used to estimate E are the Monin-Obukhov similarity theory (MOST), the Bowen ratio method, and the Penman-Monteith equation. The latter two theoretical expressions combine MOST with surface energy balance. Estimates of E can differ substantially between these three approaches because of their use of different input data. Surface and satellite-based measurement systems can provide accurate estimates of diurnal, daily, and annual variability of E. But their estimation of longer time variability is largely not established. A reasonable estimate of E as a global mean can be obtained from a surface water budget method, but its regional distribution is still rather uncertain. Current land surface models provide widely different ratios of the transpiration by vegetation to total E. This source of uncertainty therefore limits the capability of models to provide the sensitivities of E to precipitation deficits and land cover change.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2011RG000373

A review of global terrestrial evapotranspiration: observation, modeling, climatology, and climatic variability

A Review of Global Terrestrial Evapotranspiration: Observation, Modeling, Climatology, and Climatic Variability

[1] Surface fresh water is essential for life, yet we have surprisingly poor knowledge of the spatial and temporal dynamics of surface freshwater discharge and changes in storage globally. For example, we are unable to answer such basic questions as “What is the spatial and temporal variability of water stored on and near the surface of all continents?” Furthermore, key societal issues, such as the susceptibility of life to flood hazards, cannot be answered with the current global, in situ networks designed to observe river discharge at points but not flood events. The measurements required to answer these hydrologic questions are surface water area, the elevation of the water surface (h), its slope (∂h/∂x), and temporal change (∂h/∂t). Advances in remote sensing hydrology, particularly over the past 10 years and even more recently, have demonstrated that these hydraulic variables can be measured reliably from orbiting platforms. Measurements of inundated area have been used to varying degrees of accuracy as proxies for discharge but are successful only when in situ data are available for calibration; they fail to indicate the dynamic topography of water surfaces. Radar altimeters have a rich, multidecadal history of successfully measuring elevations of the ocean surface and are now also accepted as capable tools for measuring h along orbital profiles crossing freshwater bodies. However, altimeters are profiling tools, which, because of their orbital spacings, miss too many freshwater bodies to be useful hydrologically. High spatial resolution images of ∂h/∂t have been observed with interferometric synthetic aperture radar, but the method requires emergent vegetation to scatter radar pulses back to the receiving antenna. Essentially, existing spaceborne methods have been used to measure components of surface water hydraulics, but none of the technologies can singularly supply the water volume and hydraulic measurements that are needed to accurately model the water cycle and to guide water management practices. Instead, a combined imaging and elevation-measuring approach is ideal as demonstrated by the Shuttle Radar Topography Mission (SRTM), which collected images of h at a high spatial resolution (∼90 m) thus permitting the calculation of ∂h/∂x. We suggest that a future satellite concept, the Water and Terrestrial Elevation Recovery mission, will improve upon the SRTM design to permit multitemporal mappings of h across the world's wetlands, floodplains, lakes, reservoirs, and rivers.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2006RG000197

Measuring surface water from space

http://www.catalytics.asia/wp-content/themes/catalytics/flood/Bates%20et%20al%202010%20new%20lisflood.pdf

A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling.

Uncertainty in the calibration of effective roughness parameters in HEC-RAS using inundation and downstream level observations

In this paper we extend the generalized likelihood uncertainty estimation (GLUE) technique to estimate spatially distributed uncertainty in models conditioned against binary pattern data contained in flood inundation maps. Untransformed binary pattern data already have been used within GLUE to estimate domain-averaged (zero-dimensional) likelihoods, yet the pattern information embedded within such sources has not been used to estimate distributed uncertainty. Where pattern information has been used to map distributed uncertainty it has been transformed into a continuous function prior to use, which may introduce additional errors. To solve this problem we use here ‘raw’ binary pattern data to define a zero-dimensional global performance measure for each simulation in a Monte Carlo ensemble. Thereafter, for each pixel of the distributed model we evaluate the probability that this pixel was inundated. This probability is then weighted by the measure of global model performance, thus taking into account how well a given parameter set performs overall. The result is a distributed uncertainty measure mapped over real space. The advantage of the approach is that it both captures distributed uncertainty and contains information on global likelihood that can be used to condition predictions of further events for which observed data are not available. The technique is applied to the problem of flood inundation prediction at two test sites representing different hydrodynamic conditions. In both cases, the method reveals the spatial structure in simulation uncertainty and simultaneously enables mapping of flood probability predicted by the model. Spatially distributed uncertainty analysis is shown to contain information over and above that available from global performance measures. Overall, the paper highlights the different types of information that may be obtained from mappings of model uncertainty over real and n-dimensional parameter spaces. Copyright © 2002 John Wiley & Sons, Ltd.

Matthew S. Horritt

Papers

Evaluation of 1D and 2D numerical models for predicting river flood inundation

A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling.

Uncertainty in the calibration of effective roughness parameters in HEC-RAS using inundation and downstream level observations

Assessing the uncertainty in distributed model predictions using observed binary pattern information within GLUE

Simple spatially-distributed models for predicting flood inundation: A review