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Research article - Peer-reviewed, 2020

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

Su, Zhongbo; Zeng, Yijian; Romano, Nunzio; Manfreda, Salvatore; Francés, Félix; Ben Dor, Eyal; Szabó, Brigitta; Vico, Giulia; Nasta, Paolo; Zhuang, Ruodan; Francos, Nicolas; Mészáros, János; Fortunato Dal Sasso , Silvano; Bassiouni, Maoya; Zhang, Lijie; Tendayi Rwasoka, Donald; Retsios, Bas; Yu, Lianyu; Blatchford, Megan Leigh; Mannaerts, Chris;

Abstract

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.

Keywords

unmanned aerial system (UAS); soil moisture; pedotransfer function (PTF); soil spectroscopy; ecohydrological modelling; sustainable water resources management

Published in

Water

2020, volume: 12, number: 5, article number: 1495

Authors' information

Su, Zhongbo
University of Twente
Zeng, Yijian
University of Twente
Romano, Nunzio
University of Naples Federico II
Manfreda, Salvatore
University of Naples Federico II
Francés, Félix
Polytechnic University of Valencia
Ben Dor, Eyal
Tel Aviv University
Francés, Félix
Polytechnic University of Valencia
Szabó, Brigitta
Hungarian Academy of Sciences
Ben Dor, Eyal
Tel Aviv University
Swedish University of Agricultural Sciences, Department of Crop Production Ecology
Nasta, Paolo
University of Naples Federico II
Zhuang, Ruodan
University of Basilicata
Francos, Nicolas
Tel Aviv University
Mészáros, János
Hungarian Academy of Sciences
Fortunato Dal Sasso , Silvano
University of Basilicata
Bassiouni, Maoya
Swedish University of Agricultural Sciences, Department of Crop Production Ecology
Zhang, Lijie
University of Twente
Tendayi Rwasoka, Donald
University of Twente
Retsios, Bas
University of Twente
Yu, Lianyu
University of Twente

Sustainable Development Goals

SDG12 Ensure sustainable consumption and production patterns

UKÄ Subject classification

Oceanography, Hydrology, Water Resources

Publication Identifiers

DOI: https://doi.org/10.3390/W12051495

URI (permanent link to this page)

https://res.slu.se/id/publ/105930