David M. Bjerklie

Evaluating the potential for measuring river discharge from space

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.

An intercomparison of remote sensing river discharge estimation algorithms from measurements of river height, width, and slope

The Surface Water and Ocean Topography (SWOT) satellite mission planned for launch in 2020 will map river elevations and inundated area globally for rivers >100 m wide. In advance of this launch, we here evaluated the possibility of estimating discharge in ungauged rivers using synthetic, daily ‘‘remote sensing’’ measurements derived from hydraulic models corrupted with minimal observational errors. Five discharge algorithms were evaluated, as well as the median of the five, for 19 rivers spanning a range of hydraulic and geomorphic conditions. Reliance upon a priori information, and thus applicability to truly ungauged reaches, varied among algorithms: one algorithm employed only global limits on velocity and depth, while the other algorithms relied on globally available prior estimates of discharge. We found at least one algorithm able to estimate instantaneous discharge to within 35% relative root-mean-squared error (RRMSE) on 14/16 nonbraided rivers despite out-of-bank flows, multichannel planforms, and backwater effects. Moreover, we found RRMSE was often dominated by bias; the median standard deviation of relative residuals across the 16 nonbraided rivers was only 12.5%. SWOT discharge algorithm progress is therefore encouraging, yet future efforts should consider incorporating ancillary data or multialgorithm synergy to improve results.

Extreme rainfall, vulnerability and risk: a continental-scale assessment for South America

Extreme weather continues to preoccupy society as a formidable public safety concern bearing huge economic costs. While attention has focused on global climate change and how it could intensify key elements of the water cycle such as precipitation and river discharge, it is the conjunction of geophysical and socioeconomic forces that shapes human sensitivity and risks to weather extremes. We demonstrate here the use of high-resolution geophysical and population datasets together with documentary reports of rainfall-induced damage across South America over a multi-decadal, retrospective time domain (1960–2000). We define and map extreme precipitation hazard, exposure, affectedpopulations, vulnerability and risk, and use these variables to analyse the impact of floods as a water security issue. Geospatial experiments uncover major sources of risk from natural climate variability and population growth, with change in climate extremes bearing a minor role. While rural populations display greatest relative sensitivity to extreme rainfall, urban settings show the highest rates of increasing risk. In the coming decades, rapid urbanization will make South American cities the focal point of future climate threats but also an opportunity for reducing vulnerability, protecting lives and sustaining economic development through both traditional and ecosystem-based disaster risk management systems.

Benchmarking wide swath altimetry‐based river discharge estimation algorithms for the Ganges river system

The objective of this study is to compare the effectiveness of three algorithms that estimate discharge from remotely sensed observables (river width, water surface height, and water surface slope) in anticipation of the forthcoming NASA/CNES Surface Water and Ocean Topography (SWOT) mission. SWOT promises to provide these measurements simultaneously, and the river discharge algorithms included here are designed to work with these data. Two algorithms were built around Mannings equation, the Metropolis Manning (MetroMan) method, and the Mean Flow and Geomorphology (MFG) method, and one approach uses hydraulic geometry to estimate discharge, the at-many-stations hydraulic geometry (AMHG) method. A well-calibrated and ground-truthed hydrodynamic model of the Ganges river system (HEC-RAS) was used as reference for three rivers from the Ganges River Delta: the main stem of Ganges, the Arial-Khan, and the Mohananda Rivers. The high seasonal variability of these rivers due to the Monsoon presented a unique opportunity to thoroughly assess the discharge algorithms in light of typical monsoon regime rivers. It was found that the MFG method provides the most accurate discharge estimations in most cases, with an average relative root-mean-squared error (RRMSE) across all three reaches of 35.5%. It is followed closely by the Metropolis Manning algorithm, with an average RRMSE of 51.5%. However, the MFG methods reliance on knowledge of prior river discharge limits its application on ungauged rivers. In terms of input data requirement at ungauged regions with no prior records, the Metropolis Manning algorithm provides a more practical alternative over a region that is lacking in historical observations as the algorithm requires less ancillary data. The AMHG algorithm, while requiring the least prior river data, provided the least accurate discharge measurements with an average wet and dry season RRMSE of 79.8% and 119.1%, respectively, across all rivers studied. This poor performance is directly traced to poor estimation of AMHG via a remotely sensed proxy, and results improve commensurate with MFG and MetroMan when prior AMHG information is given to the method. Therefore, we cannot recommend use of AMHG without inclusion of this prior information, at least for the studied rivers. The dry season discharge (within-bank flow) was captured well by all methods, while the wet season (floodplain flow) appeared more challenging. The picture that emerges from this study is that a multialgorithm approach may be appropriate during flood inundation periods in Ganges Delta.