Hydrological change from space geodetic data and correlation with climate indices in Sudan
Bibliographic record
Abstract
<!--!introduction!--> Hydrological data in Sudan are generally sparse, often difficult to access, and frequently dominated by long periods of missing data. They must be considered as heterogeneous datasets with a limited quantity and quality which prevents reliable mentoring of hydrological change in Sudan. Thus, alternative data sources such as satellite observations are essential for monitoring hydrological changes in such areas. In addition, space geodetic observations have the potential to fill observation gaps in the in situ datasets. Hydrological change phenomena, e.g. floods and droughts, can be associated with climate patterns that cause extreme weather conditions such as the El Niño–Southern Oscillation or the Indian monsoon phenomenon. The main aim of this research is to investigate the use of space geodetic data to study the hydrological variables over the area of Sudan and their correlation with climate indices. Temporal variations of equivalent water thickness (ΔEWT) over Sudan were determined from hydrological models and from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On data using the IGiK-TVGMF (Instytut Geodezji i Kartografii–Temporal Variations of Gravity/Mass Functionals) software. Temporal surface water variations ΔSWV over the area of interest were obtained from satellite altimetry data. The correlations between these hydrological mass changes (i.e. ΔEWT and ΔSWV) and climate indices were investigated. The results obtained were analyzed. The use of space geodetic data for monitoring hydrological changes in Sudan is discussed in terms of their potential to fill observational gaps and their correlation with relevant climate indices. Keywords: Hydrological change, climate indices, space geodetic data, altimetry
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".