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| Titel |
Long-term precipitation forecast for drought relief using atmospheric circulation factors: a study on the Maharloo Basin in Iran |
| VerfasserIn |
S. K. Sigaroodi, Q. Chen, S. Ebrahimi, A. Nazari, B. Choobin |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 18, no. 5 ; Nr. 18, no. 5 (2014-05-27), S.1995-2006 |
| Datensatznummer |
250120370
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| Publikation (Nr.) |
 copernicus.org/hess-18-1995-2014.pdf |
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| Zusammenfassung |
Long-term precipitation forecasts can help to reduce
drought risk through proper management of water resources. This study took
the saline Maharloo Lake, which is located in the north of Persian Gulf,
southern Iran, and is continuously suffering from drought disaster, as a
case to investigate the relationships between climatic indices and
precipitation. Cross-correlation in combination with stepwise regression
technique was used to determine the best variables among 40 indices and
identify the proper time lag between dependent and independent variables for
each month. The monthly precipitation was predicted using an artificial neural
network (ANN) and multi-regression stepwise methods, and results were
compared with observed rainfall data. Initial findings indicated that
climate indices such as NAO (North Atlantic Oscillation), PNA (Pacific North
America) and El Niño are the main indices to forecast drought in the study
area. According to R2, root mean square error (RMSE) and Nash–Sutcliffe
efficiency, the ANN model performed better than the multi-regression model,
which was also confirmed by classification results. Moreover, the model
accuracy to forecast the rare rainfall events in dry months (June to
October) was higher than the other months.
From the findings it can be concluded that there is a relationship between
monthly precipitation anomalies and climatic indices in the previous 10 months in
Maharloo Basin. The highest and lowest accuracy of the ANN model were in
September and March, respectively. However, these results are subject to
some uncertainty due to a coarse data set and high system complexity.
Therefore, more research is necessary to further elucidate the relationship between
climatic indices and precipitation for drought relief. In this regard,
consideration of other climatic and physiographic factors (e.g., wind and
physiography) can be helpful. |
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