Achieving Breakthroughs in Global Hydrologic Science by Unlocking the Power of Multisensor, Multidisciplinary Earth Observations

Abstract

Over the last half century, remote sensing has transformed hydrologic science. Whereas early efforts were devoted to observation of discrete variables, we now consider spaceborne missions dedicated to interlinked global hydrologic processes. Furthermore, cloud computing and computational techniques are accelerating analyses of these data. How will the hydrologic community use these new resources to better understand the world's water and related challenges facing society? In this commentary, we suggest that optimizing the benefits of remote sensing for advancing hydrologic research will happen by integrating multidisciplinary and multisensor data, leveraging commercial satellite measurements, and employing data assimilation, cloud computing, and machine learning. We provide several recommendations to these ends.

An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space

“…In addition to conventional space agency driven science missions, sensing platforms from commercial companies have also started to become more commonplace (Dash & Ogutu, 2016; Houborg & McCabe, 2016; Tollefson, 2017). There has been a growing recognition in the community that modeling systems that can effectively exploit such multi‐sensor data are key to answering fundamental questions about global and regional water, energy, and carbon cycle changes (Durand et al., 2021). In particular, it is imperative that DA systems be designed to take advantage of this presumably “golden age” of land surface remote sensing (Schimel et al., 2019; Sellars et al., 2013; Stavros et al., 2017) and be designed and positioned to take advantage of upcoming missions as soon as data become available, such as the surface water storage from the upcoming Surface Water Ocean Topography (SWOT; Biancamaria et al., 2016) mission, Surface Biology and Geology‐designate observable (Cawse‐Nicholson et al., 2021) or ESA's BIOMASS (Quegan et al., 2019).…”

Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication

“…Land DA developments have been reviewed earlier (Reichle, 2008;Lahoz and De Lannoy, 2014;Jin et al, 2018;Huang et al, 2019;Xia et al, 2019;Girotto et al, 2020;Baatz et al, 2021;Durand et al, 2021). In parallel to our paper, Kumar et al (2022, in review) review and identify current community-agreed gaps and priorities for the future of state estimation via land DA.…”

“…Most visions for future land DA include multisensor DA (Durand et al, 2021), multivariate DA (Kumar et al, 2022), and multi-scale DA with a push toward finer resolutions. Our priorities above should be viewed against the backdrop of these foreseen developments, and here we highlight some associated opportunities.…”

Snowpack Estimation in Key Mountainous Water Basins from Openly-Available, Multimodal Data Sources

“…While the underlying physics governing precipitation in mountainous regions is well understood, physically modeling this process in these regions is difficult due to their high variability in topography, surface characteristics, and complex basin and subbasin formations which lead to highly variable physical processes and dramatically different snowpack [5,11,15]. As a result, many hydrologists believe that making progress on this problem will require leveraging multimodal and multisource remote sensing data and machine learning [7].…”