Historical Development of the Global Water Cycle as a Science Framework

Author(s):  
Richard G. Lawford ◽  
Sushel Unninayar

The global water cycle concept has its roots in the ancient understanding of nature. Indeed, the Greeks and Hebrews documented some of the most some important hydrological processes. Furthermore, Africa, Sri Lanka, and China all have archaeological evidence to show the sophisticated nature of water management that took place thousands of years ago. During the 20th century, a broader perspective was taken and the hydrological cycle was used to describe the terrestrial and freshwater component of the global water cycle. Data analysis systems and modeling protocols were developed to provide the information needed to efficiently manage water resources. These advances were helpful in defining the water in the soil and the movement of water between stores of water over land surfaces. Atmospheric inputs to these balances were also monitored, but the measurements were much more reliable over countries with dense networks of precipitation gauges and radiosonde observations. By the 1960s, early satellites began to provide images that gave a new perception of Earth processes, including a more complete realization that water cycle components and processes were continuous in space and could not be fully understood through analyses partitioned by geopolitical or topographical boundaries. In the 1970s, satellites delivered quantitative radiometric measurements that allowed for the estimation of a number of variables such as precipitation and soil moisture. In the United States, by the late 1970s, plans were made to launch the Earth System Science program, led by the National Aeronautics and Space Agency (NASA). The water component of this program integrated terrestrial and atmospheric components and provided linkages with the oceanic component so that a truly global perspective of the water cycle could be developed. At the same time, the role of regional and local hydrological processes within the integrated “global water cycle” began to be understood. Benefits of this approach were immediate. The connections between the water and energy cycles gave rise to the Global Energy and Water Cycle Experiment (GEWEX)1 as part of the World Climate Research Programme (WCRP). This integrated approach has improved our understanding of the coupled global water/energy system, leading to improved prediction models and more accurate assessments of climate variability and change. The global water cycle has also provided incentives and a framework for further improvements in the measurement of variables such as soil moisture, evapotranspiration, and precipitation. In the past two decades, groundwater has been added to the suite of water cycle variables that can be measured from space. New studies are testing innovative space-based technologies for high-resolution surface water level measurements. While many benefits have followed from the application of the global water cycle concept, its potential is still being developed. Increasingly, the global water cycle is assisting in understanding broad linkages with other global biogeochemical cycles, such as the nitrogen and carbon cycles. Applications of this concept to emerging program priorities, including the Sustainable Development Goals (SDGs) and the Water-Energy-Food (W-E-F) Nexus, are also yielding societal benefits.

2021 ◽  
Author(s):  
Basil Kraft ◽  
Martin Jung ◽  
Marco Körner ◽  
Sujan Koirala ◽  
Markus Reichstein

Abstract. Progress in machine learning in conjunction with the increasing availability of relevant Earth observation data streams may help to overcome uncertainties of global hydrological models due to the complexity of the processes, diversity, and heterogeneity of the land surface and subsurface, as well as scale-dependency of processes and parameters. In this study, we exemplify a hybrid approach to global hydrological modeling that exploits the data-adaptiveness of machine learning for representing uncertain processes within a model structure based on physical principles like mass conservation. Our H2M model simulates the dynamics of snow, soil moisture, and groundwater pools globally at 1º spatial resolution and daily time step where simulated water fluxes depend on an embedded recurrent neural network. We trained the model simultaneously against observational products of terrestrial water storage variations (TWS), runoff, evapotranspiration, and snow water equivalent with a multi-task learning approach. We find that H2M is capable of reproducing the key patterns of global water cycle components with model performances being at least on par with four state-of-the-art global hydrological models. The neural network learned hydrological responses of evapotranspiration and runoff generation to antecedent soil moisture state that are qualitatively consistent with our understanding and theory. Simulated contributions of groundwater, soil moisture, and snowpack variability to TWS variations are plausible and within the large range of traditional GHMs. H2M indicates a somewhat stronger role of soil moisture for TWS variations in transitional and tropical regions compared to GHMs. Overall, we present a proof of concept for global hybrid hydrological modeling in providing a new, complementary, and data-driven perspective on global water cycle variations. With further increasing Earth observations, hybrid modeling has a large potential to advance our capability to monitor and understand the Earth system by facilitating a data-adaptive yet physically consistent, joint interpretation of heterogeneous data streams.


2020 ◽  
Author(s):  
yijian zeng ◽  

<p>In the past decades, space-based Earth Observations (EO) have been rapidly advancing in monitoring the global water cycle, in particular for the variables related to precipitation, evapotranspiration and soil moisture, often at (tens of) kilometre scales. Whilst these data are highly effective to characterise water cycle variation at regional to global scale, they are less suitable for sustainable management of water resource, which needs more detailed information at local and field scale due to inhomogeneous characteristics of the soil and vegetation. To effectively exploit existing knowledge at different scales we thus need to answer the following questions: How to downscale the global water cycle products to local scale using multiple sources/scales of EO data? How to explore and apply the downscaled information at the management level for understanding soil-water-vegetation-energy processes? And how to use such fine-scale information to improve the management of soil and water resources? An integrative information aqueduct (iAqueduct) is proposed to close the gaps between global satellite observation of water cycle and local needs of information for sustainable management of water resources. iAqueduct aims to accomplish its goals by combining Copernicus satellite data (with intermediate resolutions) with high resolution Unmanned Aerial System (UAS) and in-situ observations to develop scaling functions for soil properties and soil moisture and evapotranspiration at high spatial resolution scales.</p>


1989 ◽  
Vol 289 (4) ◽  
pp. 455-483 ◽  
Author(s):  
Y. Tardy ◽  
R. N'Kounkou ◽  
J.-L. Probst

2007 ◽  
Vol 88 (3) ◽  
pp. 375-384 ◽  
Author(s):  
E. S. Takle ◽  
J. Roads ◽  
B. Rockel ◽  
W. J. Gutowski ◽  
R. W. Arritt ◽  
...  

A new approach, called transferability intercomparisons, is described for advancing both understanding and modeling of the global water cycle and energy budget. Under this approach, individual regional climate models perform simulations with all modeling parameters and parameterizations held constant over a specific period on several prescribed domains representing different climatic regions. The transferability framework goes beyond previous regional climate model intercomparisons to provide a global method for testing and improving model parameterizations by constraining the simulations within analyzed boundaries for several domains. Transferability intercomparisons expose the limits of our current regional modeling capacity by examining model accuracy on a wide range of climate conditions and realizations. Intercomparison of these individual model experiments provides a means for evaluating strengths and weaknesses of models outside their “home domains” (domain of development and testing). Reference sites that are conducting coordinated measurements under the continental-scale experiments under the Global Energy and Water Cycle Experiment (GEWEX) Hydrometeorology Panel provide data for evaluation of model abilities to simulate specific features of the water and energy cycles. A systematic intercomparison across models and domains more clearly exposes collective biases in the modeling process. By isolating particular regions and processes, regional model transferability intercomparisons can more effectively explore the spatial and temporal heterogeneity of predictability. A general improvement of model ability to simulate diverse climates will provide more confidence that models used for future climate scenarios might be able to simulate conditions on a particular domain that are beyond the range of previously observed climates.


Science ◽  
2012 ◽  
Vol 336 (6080) ◽  
pp. 455-458 ◽  
Author(s):  
P. J. Durack ◽  
S. E. Wijffels ◽  
R. J. Matear

2001 ◽  
Vol 32 (1-2) ◽  
pp. 231-246 ◽  
Author(s):  
Siegfried Franck ◽  
Christine Bounama

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