The Future of Earth Observation in Hydrology

Abstract

Published by Copernicus Publications on behalf of the European Geosciences Union. Hydrol. Earth Syst. Sci., 21, 3879-3914, 2017 www.hydrol-earth-syst-sci.net/21/3879/2017/ M. F. McCabe et al.: The future of Earth observation in hydrology 3881 4000+ Satellites orbiting the earth 1459 Active satellites 52 % Global communication 7 % Global navigation 26 % Earth observation 11 % Technology demonstration 4 % Space science 6 % Commercial 57 % Government 26 % Military 6 % Civilian OPERATION OF EO SYSTEMS

Scaling, similarity, and the fourth paradigm for hydrology

“…Beginning with the challenge of Dooge (1986), we posit that roadblocks in the search for universal laws of hydrology are hindered by our third-paradigm approach, and assert that it is time for hydrology to embrace a fourth paradigm of data-intensive science. Building on other synthesis papers in this issue McCabe et al, 2017), advances in data-intensive hydrologic science (e.g., Nearing and Gupta, 2015) have laid the foundation for a datadriven hypothesis testing framework for scaling and similarity. To achieve this goal, we have (1) summarized important scaling and similarity concepts (hypotheses) that require testing; (2) described a mutual information framework for testing these hypotheses; (3) described boundary condition, state flux, and parameter data requirements across scales to support testing these hypotheses; and (4) discussed some challenges to overcome while pursuing the fourth hydrological paradigm.…”

“…As discussed by McCabe et al (2017), there has been a dramatic increase in the type and density of hydrologic information that is becoming available on multiple scales, from point-to mesoscale and regional to global. For example, the number of remote sensing missions dedicated to observing the water cycle allows further development of (large scale) hydrological models and data assimilation frameworks for more accurate soil moisture, evaporation, and streamflow prediction.…”

“…As discussed by Clark et al (2017), computational science approaches to modeling hydrologic systems have been discussed for decades. With the advent of high-resolution Earth observing systems (McCabe et al, 2017), proximal sensing (Robinson et al, 2008), sensor networks , and advances in data-intensive hydrologic science (e.g., Nearing and Gupta, 2015), there is now an opportunity to recast the hydrologic scaling problem into a data-driven hypothesis testing framework (e.g., Rakovec et al, 2016a). By embracing such a framework, hydrologic analysis can become explicitly "scale-aware" by testing specific parameterizations on a given model scale.…”

Capitalizing on Cellular Technology—Opportunities and Challenges for Near Ground Weather Monitoring

“…While the conditions for CML technology becoming useful seems to be right, and we may see it soon in products as well as in public-good services, it also serves as a pioneering example of the new trend of capitalizing on existing technology by utilizing it for non-intended, opportunistic use [86][87][88]. Opportunistic sensing is believed to be the future of environmental monitoring, being a sustainable source of (big) environmental data.…”

A Basin Approach to a Hydrological Service Delivery System in the Amur River Basin

“…The developed subsystem (the permanent operation site is the Roshydromet Main Computing Center) is Satellite data and products are used in the system for monitoring the current hydrological conditions, obtaining the areal characteristics of the zones of flooding, and tracking the spatial-temporal changes. The effectiveness of satellite data for the solution of these tasks is well known and has been shown in several papers [22][23][24][25].…”