Abstract
Precipitation prediction is one of the most effective management aspects for rehabilitating dried water resources such as Urmia Lake, Iran. This study was conducted to investigate the efficiency of the first-order multi-site autoregressive [MSAR (1)] model in the spatiotemporal simulation of annual precipitation in the Urmia Lake basin. To determine the model parameters, data from the period of 47 years (1961–2007) were used. These parameters were obtained by computing the lag-zero (lag 0) and lag-one (lag1) correlation among the annual precipitation time series of stations. A 12-year period (2008–2019) was used to evaluate the model. The region's precipitation in a year (t) was estimated based on its precipitation in the previous year (t − 1). The mean absolute error percentage (MAPE) for the test data was 16.7%. Also, the statistical characteristics of the generated and historical data were similar and their differences were not significant. Therefore, considering the appropriate efficiency of the MSAR (1) model in forecasting and generating annual precipitation, its application is recommended to help better manage water resources in this area.
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Data availability
All data used for this study are available from public institutions (Meteorological Organization of Iran).
Code availability
The software programs used in this research were free.
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Acknowledgements
The authors would like to thank the Editorial Board and anonymous reviewers for their helpful comments. The authors also appreciate the support of the Iran Meteorological Organization (IMO) for providing climatic data.
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The authors contributed to the study's conception and design. Material preparation, data collection, and analysis were performed by HF. The first draft of the manuscript was written by HF and JB commented on previous versions of the manuscript. The authors read and approved the final manuscript.
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Faghih, H., Behmanesh, J. Spatiotemporal simulation of annual precipitation in the Urmia Lake basin. Stoch Environ Res Risk Assess 37, 4215–4227 (2023). https://doi.org/10.1007/s00477-023-02503-3
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DOI: https://doi.org/10.1007/s00477-023-02503-3