A global dynamic runoff application and dataset based on the assimilation of GPM, SMAP, and GCN250 curve number datasets

Lara H. Sujud; Hadi H. Jaafar

doi:10.1038/s41597-022-01834-0

Scientific Data (Nov 2022)

A global dynamic runoff application and dataset based on the assimilation of GPM, SMAP, and GCN250 curve number datasets

Lara H. Sujud,
Hadi H. Jaafar

Affiliations

Lara H. Sujud: Department of Agriculture, American University of Beirut
Hadi H. Jaafar: Department of Agriculture, American University of Beirut

DOI: https://doi.org/10.1038/s41597-022-01834-0
Journal volume & issue: Vol. 9, no. 1
pp. 1 – 11

Abstract

Read online

Measurement(s) runoff Technology Type(s) Natural Resources Conservation Service Curve Number Method Factor Type(s) Curve Number • Soil Moisture • Rainfall Sample Characteristic - Environment land

Published in Scientific Data

ISSN: 2052-4463 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/sdata/

About the journal