Remote Sensing of River Discharge: A Review and a Framing for the Discipline

Abstract

Remote sensing of river discharge (RSQ) is a burgeoning field rife with innovation. This innovation has resulted in a highly non-cohesive subfield of hydrology advancing at a rapid pace, and as a result misconceptions, mis-citations, and confusion are apparent among authors, readers, editors, and reviewers. While the intellectually diverse subfield of RSQ practitioners can parse this confusion, the broader hydrology community views RSQ as a monolith and such confusion can be damaging. RSQ has not been comprehensively summarized over the past decade, and we believe that a summary of the recent literature has a potential to provide clarity to practitioners and general hydrologists alike. Therefore, we here summarize a broad swath of the literature, and find after our reading that the most appropriate way to summarize this literature is first by application area (into methods appropriate for gauged, semi-gauged, regionally gauged, politically ungauged, and totally ungauged basins) and next by methodology. We do not find categorizing by sensor useful, and everything from un-crewed aerial vehicles (UAVs) to satellites are considered here. Perhaps the most cogent theme to emerge from our reading is the need for context. All RSQ is employed in the service of furthering hydrologic understanding, and we argue that nearly all RSQ is useful in this pursuit provided it is properly contextualized. We argue that if authors place each new work into the correct application context, much confusion can be avoided, and we suggest a framework for such context here. Specifically, we define which RSQ techniques are and are not appropriate for ungauged basins, and further define what it means to be ‘ungauged’ in the context of RSQ. We also include political and economic realities of RSQ, as the objective of the field is sometimes to provide data purposefully cloistered by specific political decisions. This framing can enable RSQ to respond to hydrology at large with confidence and cohesion even in the face of methodological and application diversity evident within the literature. Finally, we embrace the intellectual diversity of RSQ and suggest the field is best served by a continuation of methodological proliferation rather than by a move toward orthodoxy and standardization.

Constraining Remote River Discharge Estimation Using Reach‐Scale Geomorphology

“…At the global scale, RS of rivers is changing current perceptions of rivers and their role in the Earth system: Globally modeled hydrography at fine-spatial scales (Lehner et al, 2008;Yamazaki et al, 2019), daily runoff routed through almost 3 million river reaches over 30 years (Lin et al, 2019), assessments of rivers and climate (Yang et al, 2020), water quality (Ross et al, 2019), surface area (Allen & Pavelsky, 2018), and hydrological connectivity (Grill et al, 2019) have all debuted recently. These manuscripts extend a continuation of RS for hydrology going back several decades (see Lettenmaier et al, 2015, andDurand, 2020, for thorough reviews). These examples, along with similar recent work quantifying global fluvial geomorphic patterns (e.g., Chen et al, 2019;Frasson, Pavelsky, et al, 2019;Lin et al, 2020), suggest that RS is coming of age in its ability to provide global-scale data that honors local differences in rivers.…”

“…In ungauged settings however, there are no gauge records to extend. Ground‐based knowledge would improve RSQ accuracy in these scenarios, but in lieu of such information, these methods must produce reasonably accurate results without relying on in situ knowledge (Gleason & Durand, 2020). In ungauged settings, standard practice is again to introduce RS data into hydrologic models (e.g., Emery et al, 2018; Sun et al, 2015) or hydraulic models (e.g., Andreadis et al, 2007; Biancamaria et al, 2011; Durand et al, 2008; Yoon et al, 2012).…”

“…In ungauged settings, standard practice is again to introduce RS data into hydrologic models (e.g., Emery et al, 2018; Sun et al, 2015) or hydraulic models (e.g., Andreadis et al, 2007; Biancamaria et al, 2011; Durand et al, 2008; Yoon et al, 2012). The most recent and sophisticated methods for assimilating RS into hydraulic models (e.g., Oubanas, Gejadze, Malaterre, Durand, et al, 2018; Oubanas, Gejadze, Malaterre, & Mercier, 2018) are highly accurate in ungauged settings but computationally burdensome for global application (Gleason & Durand, 2020).…”

“…A particular subset of this literature is showing that accurate global RS of river discharge (RSQ) is presently possible with some gauging information in hand and should be globally possible in ungauged basins in the near future (Gleason & Durand, 2020). In basins with stream gauges or extensive field measurements, RSQ approaches frequently calibrate RS to local channel hydraulics (e.g., Brakenridge et al, 2007; LeFavour & Alsdorf, 2005; Pavelsky, 2014; Pavelsky & Smith, 2009; Tarpanelli et al, 2013) or introduce RS data into hydrologic or hydraulic models (e.g., Bjerklie et al, 2005; Chandanpurkar et al, 2017; King et al, 2018; Lin et al, 2019; Neal et al, 2009; Silvestro et al, 2015; Siqueira et al, 2018; Zhang et al, 2016).…”

Integrating Lateral Inflows Into a SWOT Mission River Discharge Algorithm

“…Finally, estimated discharge is compared against synthetic true discharge. Using the mean annual discharge as prior information and the McFLI method (Gleason & Durand, 2020; Gleason et al, 2017), MetroMan estimates the same discharge event over a series of trials without accounting for the lateral inflows in the synthetic truth measurements. MetroMan is then modified to ingest an estimate of lateral inflows.…”