Water resources management (WRM) for sustainable development presents many challenges in areas with sparse in situ monitoring networks. The exponential growth of satellite based information over the past decade provides unprecedented opportunities to support and improve WRM. Furthermore, traditional barriers to the access and usage of satellite data are lowering as technological innovations provide opportunities to manage and deliver this wealth of information to a wider audience. We review data needs for WRM and the role that satellite remote sensing can play to fill gaps and enhance WRM, focusing on the Latin American and Caribbean as an example of a region with potential to further develop its resources and mitigate the impacts of hydrological hazards. We review the state-of-the-art for relevant variables, current satellite missions, and products, how they are being used currently by national agencies across the Latin American and Caribbean region, and the challenges to improving their utility. We discuss the potential of recently launched, upcoming, and proposed missions that are likely to further enhance and transform assessment and monitoring of water resources. Ongoing challenges of accuracy, sampling, and continuity still need to be addressed, and further challenges related to the massive amounts of new data need to be overcome to best leverage the utility of satellite based information for improving WRM.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2017WR022437

Satellite Remote Sensing for Water Resources Management: Potential for Supporting Sustainable Development in Data-Poor Regions

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.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2019WR025287

Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches

Recent deployments of CubeSat imagers by companies such as Planet may advance hydrological remote sensing by providing an unprecedented combination of high temporal and high spatial resolution imagery at the global scale. With approximately 170 CubeSats orbiting at full operational capacity, the Planet CubeSat constellation currently offers an average revisit time of <1 day for the Arctic and near-daily revisit time globally at 3 m spatial resolution. Such data have numerous potential applications for water resource monitoring, hydrologic modeling and hydrologic research. Here we evaluate Planet CubeSat imaging capabilities and potential scientific utility for surface water studies in the Yukon Flats, a large sub-Arctic wetland in north central Alaska. We find that surface water areas delineated from Planet imagery have a normalized root mean square error (NRMSE) of <11% and geolocation accuracy of <10 m as compared with manual delineations from high resolution (0.3–0.5 m) WorldView-2/3 panchromatic satellite imagery. For a 625 km2 subarea of the Yukon Flats, our time series analysis reveals that roughly one quarter of 268 lakes analyzed responded to changes in Yukon River discharge over the period 23 June–1 October 2016, one half steadily contracted, and one quarter remained unchanged. The spatial pattern of observed lake changes is heterogeneous. While connections to Yukon River control the hydrologically connected lakes, the behavior of other lakes is complex, likely driven by a combination of intricate flow paths, underlying geology and permafrost. Limitations of Planet CubeSat imagery include a lack of an automated cloud mask, geolocation inaccuracies, and inconsistent radiometric calibration across multiple platforms. Although these challenges must be addressed before Planet CubeSat imagery can achieve its full potential for large-scale hydrologic research, we conclude that CubeSat imagery offers a powerful new tool for the study and monitoring of dynamic surface water bodies.

Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery

https://par.nsf.gov/servlets/purl/10184223

A review of remote sensing applications for water security: Quantity, quality, and extremes

Discharge estimation in high-mountain regions with improved methods using multisource remote sensing: A case study of the Upper Brahmaputra River

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 125% SWOT discharge algorithm progress is therefore encouraging, yet future efforts should consider incorporating ancillary data or multialgorithm synergy to improve results

/pdf/an-intercomparison-of-remote-sensing-river-discharge-2sthezwabq.pdf

J. M. Fiset

Papers

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