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Record W4353101328 · doi:10.1360/tb-2022-1275

Identifying potential hotspots for atmospheric water resource management and source-sink analysis

2023· article· en· W4353101328 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueChinese Science Bulletin (Chinese Version) · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceSink (geography)Environmental resource managementGeographyCartography

Abstract

fetched live from OpenAlex

<p indent="0mm">Traditional water resource management is in the boundary of natural catchments. For catchment water balance, the precipitation is the amount of input, the runoff is the available water resource for society, and evaporation is considered a loss item. However, increasing evidence demonstrates that evaporation in the global terrestrial scale dominates the partition of precipitation, accounting for 60% of precipitation, which is much larger than the amount of runoff (40% of precipitation). In addition, the evaporated water vapor does not disappear, but is likely to fall as precipitation in other areas. The complex cycling and feedback between terrestrial evaporation and precipitation is called terrestrial moisture recycling. Atmospheric water resources can be defined as moisture that evaporates into the atmosphere and eventually falls as terrestrial precipitation. Theoretically, land cover change caused by human activities can directly affect land evaporation, which may affect the downwind precipitation through the terrestrial moisture cycle. However, the travel distance of atmospheric water vapor often exceeds the scale of most basins. Owing to the complexity and even the randomness of moisture recycling, the possibility of atmospheric water resource management is debated and largely untouched in traditional water management. Hence, it is important to identify the potential hotspots for atmospheric water resource management and clarify their complex source-sink relationship. Bridging this knowledge gap is beneficial to integrating atmospheric water resources into the traditional water resource management framework. In this study, based on the framework of precipitationshed, we developed the concept of “core precipitationshed”, which is the most central and influential moisture source region, contributing 40% of precipitation to the target area. The process of obtaining the core precipitation area is as follows: First, the moisture source contribution depth (mm/a) is sorted from largest to smallest, then the cumulative contribution based on this ranking is calculated, and finally the area with a cumulative contribution rate of 40% is classified as the core precipitation area. On a global scale, we used the UTrack moisture recycling dataset with a spatial resolution of 1° × 1° to calculate the core precipitationshed for each grid. Furthermore, we identified potential hotspots for atmospheric water resource management with a relatively small core precipitationshed area (less than 1.25 million km<sup>2</sup>) and dominant moisture source from the same national territory (more than 95%). The smaller the precipitationshed area, the less area required to manage land use, and the less difficult it is to manage atmospheric water resources. In the core precipitationshed, the higher the moisture contribution ratio from land in the same nation, the larger potential for land cover change, through the development and implementation of land policies. Results show that central China, southeastern Russia, central Democratic Republic of Congo, southern Brazil, western Peru, northwestern USA, and western Canada have larger potential for managing atmospheric water resources. For the potential hotspots, we further analyzed their source and sink characteristics, including national boundaries, land cover, and population. Notably, we identified that China has the largest potential hotspot area for atmospheric water resource management. China not only has the largest and most concentrated core precipitationshed area with the moisture source region from the same national territory but also the most intensive human activities that greatly influence land use and water cycle in both local and downwind regions. This study provides a new perspective to understand China’s water resources in the framework of the global water cycle and has great potential to benefit the conservation and optimization of China’s integrated water resource management.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.005
GPT teacher head0.223
Teacher spread0.219 · 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