Multi-model driven by diverse precipitation datasets increases confidence in identifying dominant factors for runoff change in a subbasin of the Qaidam Basin of China.

Lv, Aifeng; Qi, Shanshan; Wang, Gangsheng

Lv, Aifeng; Qi, Shanshan; Wang, Gangsheng.

Afiliación

Lv A; Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China. Electronic address: lvaf@163.com.
Qi S; Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Wang G; State Key Laboratory of Water Resources and Hydropower Engineering Sciences, Wuhan University, Wuhan 430072, China. Electronic address: wang.gangsheng@gmail.com.

Sci Total Environ ; 802: 149831, 2022 Jan 01.

Article en En | MEDLINE | ID: mdl-34454152

Asunto(s)

Hidrología; Ríos; China; Actividades Humanas; Humanos; Movimientos del Agua

Palabras clave

Climatic variation; Human activities; Inland basin; Precipitation products; Predictions in ungauged basins (PUBs); SWAT

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Hidrología / Ríos Tipo de estudio: Prognostic_studies Límite: Humans País/Región como asunto: Asia Idioma: En Revista: Sci Total Environ Año: 2022 Tipo del documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google