OBSERVING THE TERRESTRIAL WATER CYCLE FROM SPACE: ERA OF BIG DATA TO SOLVE GLOBAL WATER PROBLEMS

2019 ◽  
Author(s):  
Venkataraman Lakshmi ◽  
Keyword(s):  
Big Data ◽  
Water Cycle ◽  
Terrestrial Water ◽  
Water Problems ◽  
Global Water

GRACE/GRACE-FO to constrain regional to global Evapotranspiration

2020 ◽  
Author(s):  
John Reager ◽  
Madeleine Pascolini-Campbell
Keyword(s):  
Large Scale ◽  
Water Cycle ◽  
Water Flux ◽  
Mass Conservation ◽  
Mass Change ◽  
Global Water Cycle ◽  
Basin Scale ◽  
Global Land ◽  
Terrestrial Water ◽  
Global Water

<p>A frontier in hydrology lies in understanding the potential impacts of a warming planet on water cycle variability from regional to global scales.  The fluxes that constitute the terrestrial water cycle present various complexity in observability, with Evapotranspiration (ET) being generally the most challenging variable to quantify directly.  Because of the ability to apply mass conservation and to "close" a water flux budget across scales, mass change measurements present the best opportunity to quantify evapotranspiration and changes in evapotranspiration at larger scales, ranging from basins to global. Here we present work on: (1) using GRACE/GFO observations to estimate basin-scale ET in the continental United States as a target for validation and error analysis of up-scaled ET products from other sources, and (2) using GRACE/GFO observations to estimate ET globally over the full joint record (2003-2020) in order to quantify observed changes in the global water cycle.  We find that because of the way that errors in mass change measurements inherently change in scale (i.e. decreasing with larger study domains), GRACE/GFO measurements offer a very clear and robust uncertainty quantification approach for large scale ET monitoring.  We also find that there is a clear and statistically significant signal in global land ET over the record length that indicates changes in the global water cycle consistent with our understanding of climate change.  These methods and results will be presented and discussed.</p>

scholarly journals Global water cycle and remote sensing big data: overview, challenge, and opportunities

2018 ◽  
Vol 2 (3) ◽  
pp. 282-297 ◽  
Author(s):  
Yaokui Cui ◽  
Xi Chen ◽  
Jinyu Gao ◽  
Binyan Yan ◽  
Guoqiang Tang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Water Cycle ◽  
Global Water Cycle ◽  
Global Water

scholarly journals HydroSat: a repository of global water cycle products from spaceborne geodetic sensors

2021 ◽  
Author(s):  
Mohammad J. Tourian ◽  
Omid Elmi ◽  
Yasin Shafaghi ◽  
Sajedeh Behnia ◽  
Peyman Saemian ◽  
...  
Keyword(s):  
Satellite Altimetry ◽  
Water Cycle ◽  
Water Storage ◽  
Reservoir Water ◽  
Global Database ◽  
In Situ Data ◽  
Global Water Cycle ◽  
Terrestrial Water ◽  
Global Water

Abstract. Against the backdrop of global change, both in terms of climate and demography, there is a pressing need for monitoring the global water cycle. The publicly available global database is very limited in its spatial and temporal coverage worldwide. Moreover, the acquisition of in situ data and their delivery to the database are in decline since the late 1970s, be it for economical or political reasons. Given the insufficient monitoring from in situ gauge networks, and with no outlook for improvement, spaceborne approaches have been under investigation for some years now. Satellite-based Earth observation with its global coverage and homogeneous accuracy has been demonstrated to be a potential alternative to in situ measurements. This paper presents HydroSat as a repository of global water cycle products from spaceborne geodetic sensors. HydroSat provides time series and their uncertainty of: water level from satellite altimetry, surface water extent from satellite imagery, terrestrial water storage anomaly from satellite gravimetry, lake and reservoir water storage anomaly from a combination of satellite altimetry and imagery, and river discharge from either satellite altimetry or imagery. These products can contribute to understanding the global water cycle within the Earth system in several ways. They can act as inputs to hydrological models, they can play a complementary role to current and future spaceborne observations, and they can define indicators of the past and future state of the global freshwater system. The repository is publicly available through http://hydrosat.gis.uni-stuttgart.de.

The global water cycle and continental erosion during Phanerozoic time (570 my)

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

scholarly journals Transferability Intercomparison: An Opportunity for New Insight on the Global Water Cycle and Energy Budget

2007 ◽  
Vol 88 (3) ◽  
pp. 375-384 ◽  
Author(s):  
E. S. Takle ◽  
J. Roads ◽  
B. Rockel ◽  
W. J. Gutowski ◽  
R. W. Arritt ◽  
...  
Keyword(s):  
Energy Budget ◽  
Climate Models ◽  
Climate Model ◽  
Water Cycle ◽  
Regional Climate ◽  
Climate Conditions ◽  
Continental Scale ◽  
Global Water Cycle ◽  
Wide Range ◽  
Global Water

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.

Self-validating deep learning of continental hydrology through satellite gravimetry and altimetry

2021 ◽  
Author(s):  
Christopher Irrgang ◽  
Jan Saynisch-Wagner ◽  
Robert Dill ◽  
Eva Boergens ◽  
Maik Thomas
Keyword(s):  
Neural Network ◽  
Satellite Altimetry ◽  
Water Cycle ◽  
Global Water Cycle ◽  
Network Training ◽  
The Neural Network ◽  
Continental Hydrology ◽  
Terrestrial Water ◽  
Combine Machine ◽  
Groundwater Basins

<p>Space-borne observations of terrestrial water storage (TWS) are an essential ingredient for understanding the Earth's global water cycle, its susceptibility to climate change, and for risk assessments of ecosystems, agriculture, and water management. However, the complex distribution of water masses in rivers, lakes, or groundwater basins remains elusive in coarse-resolution gravimetry observations. We combine machine learning, numerical modeling, and satellite altimetry to build and train a downscaling neural network that recovers simulated TWS from synthetic space-borne gravity observations. The neural network is designed to adapt and validate its training progress by considering independent satellite altimetry records. We show that the neural network can accurately derive TWS anomalies in 2019 after being trained over the years 2003 to 2018. Specifically for validated regions in the Amazonas, we highlight that the neural network can outperform the numerical hydrology model used in the network training.</p><p>https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL089258</p>

Plants, vital players in the terrestrial water cycle

2021 ◽  
Author(s):  
Marie-Claire ten Veldhuis ◽  
Tom van den Berg ◽  
Martine van der Ploeg ◽  
Elias Kaiser ◽  
Satadal Dutta ◽  
...  
Keyword(s):  
Water Transport ◽  
Water Cycle ◽  
Turgor Pressure ◽  
Water Dynamics ◽  
Plant Water ◽  
Flow Monitoring ◽  
Vital Functions ◽  
Main Challenge ◽  
Macro Scale ◽  
Terrestrial Water

<p>Plant transpiration accounts for about half of all terrestrial evaporation (Jasechko et al., 2013). Plants need water for many vital functions including nutrient uptake, growth, maintenance of cell turgor pressure and leaf cooling. Due to the regulation of water transport by stomata in the leaves, plants lose 97% of the water they take via their roots, to the atmosphere. They can be viewed as transpiration-powered pumps on the interface between the soil and atmosphere.</p><p>Measuring plant-water dynamics is essential to gain better insight into their role in the terrestrial water cycle and plant productivity. It can be measured at different levels of integration, from the single cell micro-scale to the ecosystem macro-scale, on time scales from minutes to months. In this contribution, we give an overview of state-of-the-art techniques for transpiration measurement and highlight several promising innovations for monitoring plant-water relations. Some of the techniques we will cover include stomata imaging by microscopy, gas exchange for stomatal conductance and transpiration monitoring, thermometry for water stress detection, sap flow monitoring, hyperspectral imaging, ultrasound spectroscopy, accelerometry, scintillometry and satellite-remote sensing.</p><p>Outlook: To fully assess water transport within the soil-plant-atmosphere continuum, a variety of techniques is required to monitor environmental variables in combination with biological responses at different scales. Yet this is not sufficient: to truly solve for spatial heterogeneity as well as temporal variability, dense network sampling is needed.</p><p>In PLANTENNA (https://www.4tu.nl/plantenna/en/) a team of electronics, precision and microsystems engineers together with plant and environmental scientists develop and implement innovative (3D-)sensor networks that measure plant and environmental parameters at high resolution and low cost. Our main challenge for in-situ sensor autonomy (“plug and forget”) is energy: we want the sensor nodes to be hyper-efficient and rely fully on (miniaturised) energy-harvesting.</p><p><strong>REFERENCES: </strong></p><p>Jasechko, S., Sharp, Z. D., Gibson, J. J., Birks, S. J., Yi, Y., & Fawcett, P. J. (2013). Terrestrial water fluxes dominated by transpiration. Nature, 496(7445), 347-350.<br>Plantenna: "Internet of Plants". (n.d.). https://www.4tu.nl/plantenna/en/</p><p> </p>

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