Estimating Discharge in Low-Order Rivers With High-Resolution Aerial Imagery

Remote sensing of river discharge promises to augment in situ gauging stations, but the majority of research in this field focuses on large rivers (\u3e50 m wide). We present a method for estimating volumetric river discharge in low-order (wide) rivers from remotely sensed data by coupling high-resolution imagery with one-dimensional hydraulic modeling at so-called virtual gauging stations. These locations were identified as locations where the river contracted under low flows, exposing a substantial portion of the river bed. Topography of the exposed river bed was photogrammetrically extracted from high-resolution aerial imagery while the geometry of the remaining inundated portion of the channel was approximated based on adjacent bank topography and maximum depth assumptions. Full channel bathymetry was used to create hydraulic models that encompassed virtual gauging stations. Discharge for each aerial survey was estimated with the hydraulic model by matching modeled and remotely sensed wetted widths. Based on these results, synthetic width-discharge rating curves were produced for each virtual gauging station. In situ observations were used to determine the accuracy of wetted widths extracted from imagery (mean error 0.36 m), extracted bathymetry (mean vertical RMSE 0.23 m), and discharge (mean percent error 7% with a standard deviation of 6%). Sensitivity analyses were conducted to determine the influence of inundated channel bathymetry and roughness parameters on estimated discharge. Comparison of synthetic rating curves produced through sensitivity analyses show that reasonable ranges of parameter values result in mean percent errors in predicted discharges of 12%–27%

Estimating Discharge in Low-Order Rivers With High-Resolution Aerial Imagery

Remote sensing of river discharge promises to augment in situ gauging stations, but the majority of research in this field focuses on large rivers (\u3e50 m wide). We present a method for estimating volumetric river discharge in low-order (wide) rivers from remotely sensed data by coupling high-resolution imagery with one-dimensional hydraulic modeling at so-called virtual gauging stations. These locations were identified as locations where the river contracted under low flows, exposing a substantial portion of the river bed. Topography of the exposed river bed was photogrammetrically extracted from high-resolution aerial imagery while the geometry of the remaining inundated portion of the channel was approximated based on adjacent bank topography and maximum depth assumptions. Full channel bathymetry was used to create hydraulic models that encompassed virtual gauging stations. Discharge for each aerial survey was estimated with the hydraulic model by matching modeled and remotely sensed wetted widths. Based on these results, synthetic width-discharge rating curves were produced for each virtual gauging station. In situ observations were used to determine the accuracy of wetted widths extracted from imagery (mean error 0.36 m), extracted bathymetry (mean vertical RMSE 0.23 m), and discharge (mean percent error 7% with a standard deviation of 6%). Sensitivity analyses were conducted to determine the influence of inundated channel bathymetry and roughness parameters on estimated discharge. Comparison of synthetic rating curves produced through sensitivity analyses show that reasonable ranges of parameter values result in mean percent errors in predicted discharges of 12%–27%

Application of CryoSat-2 altimetry data for river analysis and modelling

Availability of in situ river monitoring data, especially of data shared across boundaries, is decreasing, despite growing challenges for water resource management across the entire globe. This is especially valid for the case study of this work, the Brahmaputra Basin in South Asia. Commonly, satellite altimeters are used in various ways to provide information about such river basins. Most missions provide virtual station time series of water levels at locations where their repeat orbits cross rivers. CryoSat-2 is equipped with a new type of altimeter, providing estimates of the actual ground location seen in the reflected signal. It also uses a drifting orbit, challenging conventional ways of processing altimetry data to river water levels and their incorporation in hydrologic–hydrodynamic models. However, CryoSat-2 altimetry data provides an unprecedentedly high spatial resolution. This paper suggests a procedure to (i) filter CryoSat-2 observations over rivers to extract water-level profiles along the river, and (ii) use this information in combination with a hydrologic–hydrodynamic model to fit the simulated water levels with an accuracy that cannot be reached using information from globally available digital elevation models (DEMs) such as from the Shuttle Radar Topography Mission (SRTM) only. The filtering was done based on dynamic river masks extracted from Landsat imagery, providing spatial and temporal resolutions high enough to map the braided river channels and their dynamic morphology. This allowed extraction of river water levels over previously unmonitored narrow stretches of the river. In the Assam Valley section of the Brahmaputra River, CryoSat-2 data and Envisat virtual station data were combined to calibrate cross sections in a 1-D hydrodynamic model of the river. The hydrologic–hydrodynamic model setup and calibration are almost exclusively based on openly available remote sensing data and other global data sources, ensuring transferability of the developed methods. They provide an opportunity to achieve forecasts of both discharge and water levels in a poorly gauged river system

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

The future of Earth observation in hydrology

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smart-phones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the "internet of things" as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems

Innovative techniques for the hydraulic and hydrological variables assessment

This Thesis focues on two main research topics: (1) the use of innovative techniques for the evaluation of main hydraulic variables of natural rivers (e.g. river bathymetry, discharge, water level) and (2) the monitoring and hydrological modelling of Monate Lake (Varese, Italy)

Continental hydrosystem modelling: the concept of nested stream–aquifer interfaces

International audienceCoupled hydrological-hydrogeological models, emphasising the importance of the stream–aquifer interface, are more and more used in hydrological sciences for pluri-disciplinary studies aiming at investigating environmental is-sues. Based on an extensive literature review, stream–aquifer interfaces are described at five different scales: local [10 cm– ∼ 10 m], intermediate [∼ 10 m–∼ 1 km], watershed [10 km 2 – ∼ 1000 km 2 ], regional [10 000 km 2 –∼ 1 M km 2 ] and conti-nental scales [> 10 M km 2 ]. This led us to develop the con-cept of nested stream–aquifer interfaces, which extends the well-known vision of nested groundwater pathways towards the surface, where the mixing of low frequency processes and high frequency processes coupled with the complexity of geomorphological features and heterogeneities creates hy-drological spiralling. This conceptual framework allows the identification of a hierarchical order of the multi-scale con-trol factors of stream–aquifer hydrological exchanges, from the larger scale to the finer scale. The hyporheic corridor, which couples the river to its 3-D hyporheic zone, is then identified as the key component for scaling hydrological pro-cesses occurring at the interface. The identification of the hy-porheic corridor as the support of the hydrological processes scaling is an important step for the development of regional studies, which is one of the main concerns for water practi-tioners and resources managers. In a second part, the modelling of the stream–aquifer in-terface at various scales is investigated with the help of the conductance model. Although the usage of the temperature as a tracer of the flow is a robust method for the assess-ment of stream–aquifer exchanges at the local scale, there is a crucial need to develop innovative methodologies for as-sessing stream–aquifer exchanges at the regional scale. After formulating the conductance model at the regional and inter-mediate scales, we address this challenging issue with the de-velopment of an iterative modelling methodology, which en-sures the consistency of stream–aquifer exchanges between the intermediate and regional scales. Finally, practical recommendations are provided for the study of the interface using the innovative methodology MIM (Measurements–Interpolation–Modelling), which is graphi-cally developed, scaling in space the three pools of methods needed to fully understand stream–aquifer interfaces at vari-ous scales. In the MIM space, stream–aquifer interfaces that can be studied by a given approach are localised. The ef-ficiency of the method is demonstrated with two examples. The first one proposes an upscaling framework, structured around river reaches of ∼ 10–100 m, from the local to the wa-tershed scale. The second example highlights the usefulness of space borne data to improve the assessment of stream– aquifer exchanges at the regional and continental scales. We conclude that further developments in modelling and field measurements have to be undertaken at the regional scale to enable a proper modelling of stream–aquifer exchanges from the local to the continental scale

Low-cost, non-contact sensor networks for river stage monitoring and dynamic discharge estimation

River monitoring and discharge estimation are crucial to developing mitigation measures for weather and climate extremes. This study demonstrates the potential of non-contact, low-cost, bespoke lidar sensors for monitoring river levels and proposes a methodology for estimating discharge using river stage data from such sensor networks. Firstly, using different laboratory and field experiments, this study evaluates the sensor performance as a function of measurement distance, surface roughness, air temperature, water turbidity, and measurement angle to monitor river levels. To enable the computational experiments that underpin my scientific enquiry and part of discharge estimation methodology development, I developed a Python application to calibrate hydraulic models under homogenous and heterogenous Manning’s n assumptions, perform uncertainty and sensitivity analysis of unsteady flow parameters, and perform probabilistic flood inundation analysis in HEC-RAS. Then, using synchronous measurements of stage data from a network of sensors, a novel method for estimating the dynamic river discharge has been developed. This methodology has been tested on idealised rivers with varying channels and flow conditions, as well as on the Wandle River in the UK. After testing the developed discharge estimation method, two approaches for optimising a sensor network, that is the sensor position, number, and spacing, have been developed and assessed for various case studies. The laboratory experiments demonstrate that the sensors can take measurements under all tested conditions, up to an incidence angle of ∼ 40° and within a relative error of 0.1%. The test results show that the developed discharge estimation method can be successfully applied to both prismatic and natural channels with or without lateral flow. Moreover, unlike previous studies, this method does not require an initial discharge value. The optimisation results show that, compared to three sensors, using four sensors placed closer to the downstream boundary improves parameter calibration and discharge estimation.Open Acces