An Integrative Information Aqueduct to Close the Gaps between Global Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources (iAqueduct)

2020 ◽  
Author(s):  
yijian zeng ◽  
Keyword(s):  
Soil Moisture ◽  
Water Resources ◽  
Sustainable Management ◽  
Water Cycle ◽  
Satellite Observation ◽  
Global Scale ◽  
Multiple Sources ◽  
Management Level ◽  
Global Water Cycle ◽  
Global Water

<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>

scholarly journals An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1495
Author(s):  
Zhongbo Su ◽  
Yijian Zeng ◽  
Nunzio Romano ◽  
Salvatore Manfreda ◽  
Félix Francés ◽  
...  
Keyword(s):  
Water Resources ◽  
Sustainable Management ◽  
Water Cycle ◽  
Big Data Analytics ◽  
Satellite Observation ◽  
Theoretical Framework ◽  
Machine Learning Algorithms ◽  
Multiple Sources ◽  
Local Water

The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m–1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results.

Global Terrestrial Network of Water Resources Observation Infrastructures

2021 ◽  
Author(s):  
Stephan Dietrich ◽  
Valentin Aich ◽  
Wouter Dorigo ◽  
Thomas Recknagel ◽  
Harald Koethe ◽  
...  
Keyword(s):  
Water Resources ◽  
Global Change ◽  
Water Cycle ◽  
Water Storage ◽  
Global Water Cycle ◽  
Terrestrial Network ◽  
Data Centres ◽  
Global Data ◽  
Global Water

<p>Life on earth is closely linked to the availability of water and its variability. However, global change means that the demands placed on water resources are constantly increasing. According to the conclusions of the IPCC's 5th Assessment Report, it is likely that human activities have influenced the global water cycle since 1960. Satellite-based remote sensing of water-related parameters and operational data-assimilation services are becoming increasingly important to assess changes of the global water cycle as part of the essential climate variables (gcos.wmo.int). However, particularly over land or in the deep ocean where space-borne monitoring is not possible, in-situ data provide long-term records of changes in the various components of the hydrological cycle.</p><p>Global data centres, often operating under the auspices of UN agencies, collect and harmonise water data worldwide and make the global data sets available to the public again. Most of these relevant Global Data Centres are members of the Global Terrestrial Network of Hydrology (GTN-H) that operates under auspices of WMO and the Terrestrial observation Panel of Climate (TOPC) of the Global Climate Observing System GCOS. GTN-H links existing networks and systems for integrated observations of the global water cycle. The network was established in 2001 as a „network of networks“ to support a range of climate and water resource objectives, building on existing networks and data centres, and producing value-added products through enhanced communications and shared development. Since 2017 the GTN-H coordination is held by the International Centre for Water Resources and Global change (ICWRGC, operating under auspices of the UNESCO) aiming for a data and knowledge transfer between data providers, scientists and decision makers as well as between the different institutional bodies on UN-level inter alia the WMO, UNESCO, FAO, UNEP or GCOS.</p><p>We will demonstrate the state-of-the art of the global in-situ terrestrial water resources monitoring and draw a picture of a global water observation architecture. <br>As a major outcome we will share the most recent evaluation of global water storage and water cycle fluxes. Here, we assess the relevant land, atmosphere, and ocean water storage and the fluxes between them, including anthropogenic water use. Based on the assessment, we discuss gaps in existing observation systems and formulate guidelines for future water cycle observation strategies.</p>

scholarly journals Towards hybrid modeling of the global hydrological cycle

2021 ◽  
Author(s):  
Basil Kraft ◽  
Martin Jung ◽  
Marco Körner ◽  
Sujan Koirala ◽  
Markus Reichstein
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Soil Moisture ◽  
Data Streams ◽  
Hydrological Modeling ◽  
Water Cycle ◽  
Hybrid Modeling ◽  
Hydrological Models ◽  
Global Water Cycle ◽  
Global Water

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.

Historical Development of the Global Water Cycle as a Science Framework

2017 ◽  
Author(s):  
Richard G. Lawford ◽  
Sushel Unninayar
Keyword(s):  
Soil Moisture ◽  
Prediction Models ◽  
Hydrological Cycle ◽  
Water Cycle ◽  
Energy System ◽  
The United States ◽  
Global Perspective ◽  
Hydrological Processes ◽  
Global Water Cycle ◽  
Global Water

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.

Multi-source quantification of precipitation in the global water cycle

2020 ◽  
Author(s):  
Mijael Rodrigo Vargas Godoy ◽  
Rajani Kumar Pradhan ◽  
Shailendra Pratap ◽  
Akif Rahim ◽  
Yannis Markonis
Keyword(s):  
Remote Sensing ◽  
Water Cycle ◽  
Remote Sensing Data ◽  
Time Frame ◽  
Global Scale ◽  
Global Water Cycle ◽  
Data Products ◽  
Surrounding Areas ◽  
Global Precipitation ◽  
Global Water

<p>The knowledge of global precipitation is of crucial importance to the study of climate dynamics and the global water cycle in general. Although global precipitation climatologies have existed for some time, and their understanding has improved dramatically due to the vast amount of different data sources, their information has not been comprehensive enough due to precipitation spatial-temporal variability. Thus, ground station reports are, in some cases, not representative of the surrounding areas. Remote sensing data and model simulations complemented the traditional surface measurements and offered unprecedented coverage on a global scale. It is important to note that satellite data records are now of sufficient time frame lengths and with methods “mature” enough to develop meaningful precipitation climatologies that are able to provide information on precipitation patterns and intensities on a global scale. While data (and in some cases exploration/visualization tools as well) are widely available, each dataset comes with different spatial resolution, temporal resolution, and biases.</p><p>Consequently, this unique opportunity to obtain a robust quantification of global precipitation has been hindered by the uncertainty, already revealed in the first attempts of the unification of different data products. Herein, we present a multi-source quantification of global precipitation, focusing on the description of the underlying uncertainties. Our approach combines station (CRU, GHCN-M, PRECL, UDEL, and CPC Global), remote sensing (PERSIANN, PERSIANN-CCS, PERSIANN-CDR, GPCP, GPCP_PEN_v2.2, CMAP, and CPC-Global) and reanalysis (NCEP1, NCEP2, and 20CRv2) data products, providing an updated overview of the role of precipitation in global water cycle.</p>

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
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