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The Potential of Hydrogeodesy to Address Water-related Problems and Sustainability Challenges

2023· preprint· en· W4390311728 on OpenAlexaff
Fernando Jaramillo, Saeid Aminjafari, Pascal Castellazzi, Ayan Santos Fleischmann, Etienne Fluet‐Chouinard, Hossein Hashemi, Clara Hübinger, Hilary R. Martens, Fabrice Papa, Tilo Schöne, Angelica Tarpanelli, Vili Virkki, Lan Wang‐Erlandsson, Rodrigo Abarca-del-Río, A. A. Borsa, Georgia Destouni, Giuliano Di Baldassarre, Michele‐Lee Moore, José Andrés Posada-Marín, Shimon Wdowinski, George H. Allen, Donald F. Argus, Omid Elmi, Luciana Fenoglio-Marc, Frédéric Frappart, Xander Huggins, Zahra Kalantari, Simon Munier, Sebastián Palomino- Ángel, Abigail Robinson, Kristian Rubiano, Gabriela Siles, Marc Simard, Chunqiao Song, Christopher Spence, Mohammad J. Tourian, Yoshihide Wada, Chao Wang, Jida Wang, Fangfang Yao, Wouter R. Berghuijs, Jean‐François Crétaux, Alice César Fassoni‐Andrade, Jessica V. Fayne, Félix Girard, Matti Kummu, Kristine M. Larson, Martin Marañon, Daniel M. Moreira, Karina Nielsen, Tamlin M. Pavelsky, Francisco J. Peña, J. T. Reager, Maria Cristina Rulli, Juan F. Salazar

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsEnvironment and Climate Change CanadaUniversité LavalUniversity of Victoria
FundersMarcus och Amalia Wallenbergs minnesfondJapan Aerospace Exploration AgencyCentre National d’Etudes SpatialesVetenskapsrådetSwedish National Space AgencyEuropean CommissionHORIZON EUROPE Framework ProgrammeSvenska Forskningsrådet FormasNational Aeronautics and Space Administration
KeywordsSustainabilityMultidisciplinary approachRemote sensingEnvironmental scienceWater resourcesEnvironmental resource managementInterferometric synthetic aperture radarWater cycleEarth system scienceEarth observationSynthetic aperture radarSatelliteGeographyGeologyOceanographyEcologyEngineering

Abstract

fetched live from OpenAlex

Increasing climatic and human pressures are changing the world's water resources and hydrological processes at unprecedented rates.These changes require monitoring water resources from ground and space at different temporal and spatial scales.This monitoring can be achieved with Hydrogeodesy, the science that measures the Earth's solid and aquatic surfaces, gravity field, and their changes over time.Hydrogeodesy encompasses geodetic technologies such as Altimetry, Interferometric Synthetic Aperture Radar (InSAR), Mass gravimetry, and Global Navigation Satellite Systems (GNSS).During the last thirty years, these technologies have contributed to quantifying changes in surface and groundwater resources locally, regionally, and globally.Yet, to our knowledge, the evolution and combination of these technologies and their role within current hydrological, sustainability science, and management frameworks remain unaddressed.Here, we first perform a meta-analysis of over 3,000 articles to understand the range, trends, and applications of hydrogeodetic technologies.Second, we discuss the potential of Hydrogeodesy to significantly advance hydrology, water-related sustainability, and water management.For this, we focus on the 23 Unsolved Questions of the International Association of Hydrological Sciences and the Planetary Boundaries framework (meant as guidance towards a safe operating space for humanity).We find a growing body of literature relating to the advancements in methods, accuracy, precision, and measurements of these technologies and support of hydrological modeling.Hydrogeodesy is also largely published in multidisciplinary and remote sensing journals, which points to considerable potential for integration with water-related sciences, especially regarding terrestrial water features such as wetlands, permafrost, lakes, and rivers.We call for a coordinated way forward for hydrogeodesists to increase interdisciplinary collaboration and broader and deeper application of Hydrogeodesy for understanding and managing water resources and to provide guidance for a safe operating space for humans regarding water resources.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.014
Science and technology studies0.0010.003
Scholarly communication0.0040.009
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.251
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2023
Admission routes1
Has abstractyes

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