Comparing Discharge Estimates Made via the BAM Algorithm in High-Order Arctic Rivers Derived Solely From Optical CubeSat, Landsat, and Sentinel-2 Data

Conventional satellite platforms are limited in their ability to monitor rivers at fine spatial and temporal scales: suffering from unavoidable trade-offs between spatial and temporal resolutions. CubeSat constellations, however, can provide global data at high spatial and temporal resolutions, albeit with reduced spectral information. This study provides a first assessment of using CubeSat data for river discharge estimation in both gauged and ungauged settings. Discharge was estimated for 11 Arctic rivers with sizes ranging from 16 to >1,000 m wide using the Bayesian at-many-stations hydraulic geometry-Manning algorithm (BAM). BAM-at-many-stations hydraulic geometry solves for hydraulic geometry parameters to estimate flow and requires only river widths as input. Widths were retrieved from Landsat 8 and Sentinel-2 data sets and a CubeSat (the Planet company) data set, as well as their fusions. Results show satellite data fusion improves discharge estimation for both large (>100 m wide) and medium (40–100 m wide) rivers by increasing the number of days with a discharge estimation by a factor of 2–6 without reducing accuracy. Narrow rivers (<40 m wide) are too small for Landsat and Sentinel-2 data sets, and their discharge is also not well estimated using CubeSat data alone, likely because the four-band sensor cannot resolve water surfaces accurately enough. BAM technique outperforms space-based rating curves when gauge data are available, and its accuracy is acceptable when no gauge data are present (instead relying on global reanalysis for discharge priors). Ultimately, we conclude that the data fusion presented here is a viable approach toward improving discharge estimates in the Arctic, even in ungauged basins

A Comparative Study of Machine Learning Regression Methods on LiDAR Data: A Case Study

Light Detection and Ranging (LiDAR) is a remote sensor able to extract vertical information from sensed objects. LiDAR-derived information is nowadays used to develop environmental models for describing fire behaviour or quantifying biomass stocks in forest areas. A multiple linear regression (MLR) with previous stepwise feature selection is the most common method in the literature to develop LiDAR-derived models. MLR defines the relation between the set of field measurements and the statistics extracted from a LiDAR flight. Machine learning has recently been paid an increasing attention to improve classic MLR results. Unfortunately, few studies have been proposed to compare the quality of the multiple machine learning approaches. This paper presents a comparison between the classic MLR-based methodology and common regression techniques in machine learning (neural networks, regression trees, support vector machines, nearest neighbour, and ensembles such as random forests). The selected techniques are applied to real LiDAR data from two areas in the province of Lugo (Galizia, Spain). The results show that support vector regression statistically outperforms the rest of techniques when feature selection is applied. However, its performance cannot be said statistically different from that of Random Forests when previous feature selection is skipped

Combining Optical Remote Sensing, McFLI Discharge Estimation, Global Hydrologic Modeling, and Data Assimilation to Improve Daily Discharge Estimates Across an Entire Large Watershed

Remote sensing has gained attention as a novel source of primary information for estimating river discharge, and the Mass-conserved Flow Law Inversion (McFLI) approach has successfully estimated river discharge in ungauged basins solely from optical satellite data. However, McFLI currently suffers from two major drawbacks: (1) existing optical satellites lead to temporally and spatially sparse discharge estimates and (2) because of the assumptions required, McFLI cannot guarantee downstream flow continuity. Hydrological modeling has neither drawback, yet model accuracy is frequently limited by a lack of discharge observations. We therefore combine McFLI and models in a data assimilation framework applicable globally. We establish a daily “ungauged” baseline model for 28,998 reaches of the Missouri river basin forced by recently published global runoff data, which we do not calibrate. We estimate discharge via McFLI using ∼1 million width measurements made from 12,000 Landsat scenes and assimilate McFLI into the model before validating at 403 USGS gauges. Results show that assimilated discharges did not impair already accurate baseline flows and achieved median improvements of 28% normalized root mean square error, 0.50 Nash–Sutcliffe efficiency (NSE), and 0.23 Kling–Gupta efficiency where baseline performance was poor (defined as baseline negative NSE, 225/403 reaches). We ultimately improved flows at 92% of these originally poorly modeled gauges, even though Landsat images only provide McFLI discharges at 1.5% of reaches and 26% of simulated days. Our results suggest that the combination of McFLI and state-of-the-art hydrology models can improve flow estimations in ungauged basins globally

Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches

Spatiotemporally continuous global river discharge estimates across the full spectrum of stream orders are vital to a range of hydrologic applications, yet they remain poorly constrained. Here we present a carefully designed modeling effort (Variable Infiltration Capacity land surface model and Routing Application for Parallel computatIon of Discharge river routing model) to estimate global river discharge at very high resolutions. The precipitation forcing is from a recently published 0.1° global product that optimally merged gauge-, reanalysis-, and satellite-based data. To constrain runoff simulations, we use a set of machine learning-derived, global runoff characteristics maps (i.e., runoff at various exceedance probability percentiles) for grid-by-grid model calibration and bias correction. To support spaceborne discharge studies, the river flowlines are defined at their true geometry and location as much as possible—approximately 2.94 million vector flowlines (median length 6.8 km) and unit catchments are derived from a high-accuracy global digital elevation model at 3-arcsec resolution (~90 m), which serves as the underlying hydrography for river routing. Our 35-year daily and monthly model simulations are evaluated against over 14,000 gauges globally. Among them, 35% (64%) have a percentage bias within ±20% (±50%), and 29% (62%) have a monthly Kling-Gupta Efficiency ≥0.6 (0.2), showing data robustness at the scale the model is assessed. This reconstructed discharge record can be used as a priori information for the Surface Water and Ocean Topography satellite mission's discharge product, thus named “Global Reach-level A priori Discharge Estimates for Surface Water and Ocean Topography”. It can also be used in other hydrologic applications requiring spatially explicit estimates of global river flows

A high-resolution airborne color-infrared camera water mask for the NASA ABoVE campaign

The airborne AirSWOT instrument suite, consisting of an interferometric Ka-band synthetic aperture radar and color-infrared (CIR) camera, was deployed to northern North America in July and August 2017 as part of the NASA Arctic-Boreal Vulnerability Experiment (ABoVE).We present validated, open (i.e., vegetation-free) surface water masks produced from high-resolution (1 m), co-registered AirSWOT CIR imagery using a semi-automated, object-based water classification. The imagery and resulting high-resolution water masks are available as open-access datasets and support interpretation of AirSWOT radar and other coincident ABoVE image products, including LVIS, UAVSAR, AIRMOSS, AVIRIS-NG, and CFIS. These synergies offer promising potential for multi-sensor analysis of Arctic-Boreal surface water bodies. In total, 3167 km2 of open surface water were mapped from 23,380 km2 of flight lines spanning 23 degrees of latitude and broad environmental gradients. Detected water body sizes range from 0.00004 km2 (40 m2) to 15 km2. Power-law extrapolations are commonly used to estimate the abundance of small lakes from coarser resolution imagery, and our mapped water bodies followed power-law distributions, but only for water bodies greater than 0.34 (±0.13) km2 in area. For water bodies exceeding this size threshold, the coefficients of power-law fits vary for different Arctic-Boreal physiographic terrains (wetland, prairie pothole, lowland river valley, thermokarst, and Canadian Shield). Thus, direct mapping using high-resolution imagery remains the most accurate way to estimate the abundance of small surface water bodies. We conclude that empirical scaling relationships, useful for estimating total trace gas exchange and aquatic habitats on Arctic-Boreal landscapes, are uniquely enabled by high-resolution AirSWOT-like mappings and automated detection methods such as those developed here

Quantum Mechanics from Focusing and Symmetry

A foundation of quantum mechanics based on the concepts of focusing and symmetry is proposed. Focusing is connected to c-variables - inaccessible conceptually derived variables; several examples of such variables are given. The focus is then on a maximal accessible parameter, a function of the common c-variable. Symmetry is introduced via a group acting on the c-variable. From this, the Hilbert space is constructed and state vectors and operators are given a clear interpretation. The Born formula is proved from weak assumptions, and from this the usual rules of quantum mechanics are derived. Several paradoxes and other issues of quantum theory are discussed.Comment: 26 page

Canada’s Contributions to the SWOT Mission–Terrestrial Hydrology(SWOT-C TH)

The origins of the Surface Water and Ocean Topography (SWOT) mission date back to the mid-1970s with the launch of GOES-3 and SEASAT. These missions were then followed in 1992 by the Topex-Poseidon satellite, then by Jason-1 (2001), OSTM/Jason-2 (2008), and Jason 3 (2016), a series of joint satellite missions between NASA and CNES with a goal to monitor global ocean circulation. The proposed new SWOT mission will provide 120-km-wide swath interferometric coverage with a 20-km-wide gap at the nadir. The SWOT measurements will consist of water surface elevations and water surface slopes covering nearly all of the earth’s land surface at least once every 21 days. In 2010, NASA invited the Canadian Space Agency to contribute, and Canadian scientists welcomed the invitation to join the SWOT Science Definition Team and contribute to the experiments. The Canadian segment of the mission is known as the “SWOT-C” project. The SWOT satellite mission will provide unique opportunities in the Canadian context for water managers in both the public domain and in the private sector. This paper provides an overview of recent scientific progress by the SWOT-C Terrestrial Hydrology team, outlining current plans and progress towards applications and calibration post-launch

Discharge Estimation From Dense Arrays of Pressure Transducers

In situ river discharge estimation is a critical component of studying rivers. A dominant method for establishing discharge monitoring in situ is a temporary gauge, which uses a rating curve to relate stage to discharge. However, this approach is constrained by cost and the time to develop the stage-discharge rating curve, as rating curves rely on numerous flow measurements at high and low stages. Here, we offer a novel alternative approach to traditional temporary gauges: estimating Discharge via Arrays of Pressure Transducers (DAPT). DAPT uses a Bayesian discharge algorithm developed for the upcoming Surface Water Ocean Topography satellite (SWOT) to estimate in situ discharge from automated water surface elevation measurements. We conducted sensitivity tests over 4,954 model runs on five gauged rivers and conclude that the DAPT method can robustly reproduce discharge with an average Nash-Sutcliffe Efficiency (NSE) of 0.79 and Kling-Gupta Efficiency of 0.78. Further, we find that the DAPT method estimates discharge similarly to an idealized temporary gauge created from the same input data (NSE differences of less than 0.1), and that results improve significantly with accurate priors. Finally, we test the DAPT method in nine poorly gauged rivers in a realistic and complex field setting in the Peace-Athabasca Delta, and show that the DAPT method largely outperforms a temporary gauge in this time and budget constrained setting. We therefore recommend DAPT as an effective tool for in situ discharge estimation in cases where there is not enough time or resources to develop a temporary gauge

Advancing Field-Based GNSS Surveying for Validation of Remotely Sensed Water Surface Elevation Products

To advance monitoring of surface water resources, new remote sensing technologies including the forthcoming Surface Water and Ocean Topography (SWOT) satellite (expected launch 2022) and its experimental airborne prototype AirSWOT are being developed to repeatedly map water surface elevation (WSE) and slope (WSS) of the world’s rivers, lakes, and reservoirs. However, the vertical accuracies of these novel technologies are largely unverified; thus, standard and repeatable field procedures to validate remotely sensed WSE and WSS are needed. To that end, we designed, engineered, and operationalized a Water Surface Profiler (WaSP) system that efficiently and accurately surveys WSE and WSS in a variety of surface water environments using Global Navigation Satellite Systems (GNSS) time-averaged measurements with Precise Point Positioning corrections. Here, we present WaSP construction, deployment, and a data processing workflow. We demonstrate WaSP data collections from repeat field deployments in the North Saskatchewan River and three prairie pothole lakes near Saskatoon, Saskatchewan, Canada. We find that WaSP reproducibly measures WSE and WSS with vertical accuracies similar to standard field survey methods [WSE root mean squared difference (RMSD) ∼8 cm, WSS RMSD ∼1.3 cm/km] and that repeat WaSP deployments accurately quantify water level changes (RMSD ∼3 cm). Collectively, these results suggest that WaSP is an easily deployed, self-contained system with sufficient accuracy for validating the decimeter-level expected accuracies of SWOT and AirSWOT. We conclude by discussing the utility of WaSP for validating airborne and spaceborne WSE mappings, present 63 WaSP in situ lake WSE measurements collected in support of NASA’s Arctic-Boreal and Vulnerability Experiment, highlight routine deployment in support of the Lake Observation by Citizen Scientists and Satellites project, and explore WaSP utility for validating a novel GNSS interferometric reflectometry LArge Wave Warning System