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A Convergent Satellite and Machine Learning Framework for Discharge Estimation in Ungauged Wetland Basins

Domaine:

environment and energygeospatial

Type de record:

datasetmodel
Créateur:
Mah
Éditeur:
Zenodo
Hôte:avatar

Research Data and Model Outputs: A Convergent Satellite and Machine Learning Framework for Discharge Estimation in the Ungauged Sobat–Baro–Akobo–Pibor (SBAP) Basin, Nile River (2000–2024).

This dataset accompanies the manuscript "A Convergent Satellite and Machine Learning Framework for Discharge Estimation in Ungauged Wetland Basins" (submitted to Applied Water Science, Springer), and contains all processed datasets and calibrated model outputs generated during the study. The research develops a seven-phase convergent modelling framework to reconstruct river discharge at the Hillet Doleib gauging station, located at the outlet of the Sobat–Baro–Akobo–Pibor (SBAP) Basin — a major tributary of the White Nile covering approximately 204,000 km² across Ethiopia and South Sudan — for which systematic monitoring ceased in 1983.

Visit

doi.org

Languages

Ndasa

Tags

Hydrology

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcodeGNU General Public License v3.0 or laterhttps://www.gnu.org/licenses/gpl-3.0-standalone.html

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BfN-Skripten (1.1998 - 631.2022); 613 [e]