Drought projection based on a hybrid drought index using Artificial Neural Networks

Hydrological Processes

Volumn 29, Issue 11, 2015, Pages 2635-2648

  Yang, Tao ^a,b   Zhou, Xudong ^a   Yu, Zhongbo ^a   Krysanova, Valentina ^c   Wang, Bo ^a  

^a HOHAI UNIVERSITY   (China)

^b XINJIANG INSTITUTE OF ECOLOGY AND GEOGRAPHY   (China)

^c POTSDAM INSTITUTE FOR CLIMATE IMPACT RESEARCH   (Germany)

Author keywords

Artificial Neural Network; Climate change; Drought projection; Endorheic arid basin; Hybrid drought index

Indexed keywords

AGRICULTURAL ROBOTS; ARID REGIONS; BUDGET CONTROL; CATCHMENTS; CLIMATE CHANGE; DROUGHT; SNOW; WATER RESOURCES; WATERSHEDS;

AGRICULTURE DEVELOPMENT; ARTIFICIAL NEURAL NETWORK APPROACH; DROUGHT CHARACTERISTICS; DROUGHT INDICES; ENDORHEIC ARID BASIN; HYDROLOGICAL CONDITION; STANDARDIZED PRECIPITATION INDEX; WATER RESOURCES MANAGEMENT;

NEURAL NETWORKS;

ARID ENVIRONMENT; ARTIFICIAL NEURAL NETWORK; CLIMATE CHANGE; CLIMATE MODELING; CLIMATE PREDICTION; DRAINAGE BASIN; DROUGHT; GENERAL CIRCULATION MODEL; PRECIPITATION ASSESSMENT; WATER BUDGET;

CENTRAL ASIA; CHINA; TARIM RIVER; XINJIANG UYGUR;

EID: 84929501552     PISSN: 08856087     EISSN: 10991085     Source Type: Journal    
DOI: 10.1002/hyp.10394     Document Type: Article

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