Most of the world's population lives in areas facing a severe water crisis. Climatology researchers need precipitation information, pattern analysis, modeling of spatial relationships, and more to cope with these conditions. Therefore, in this paper, a comprehensive approach is developed for describing geographic phenomenon using various geostatistical techniques. Two main methods of interpolation (Inverse Distance Weighting and Kriging) are used and their results are compared. The Urmia Lake Basin in Iran was selected as a case-study area that has faced critical conditions in recent years. Precipitation was initially modeled using both conventional, non-statistical approaches and advanced geo-statistical methods. The result of the comparison shows that ordinary Kriging is the best interpolation method for precipitation, with an RMS of 4.15, and Local Polynomial Interpolation with the exponential kernel function is the worst method, with an RMS of 5.02. Finally, a general regression analysis was conducted on precipitation data to examine its relationship with other variables. The results show that the latitude variable was identified as the dependent variable with the most influence on precipitation, with an impact factor of 81%, and that the slope has the lowest impact on precipitation, at nearly zero percent. The influence of latitude on precipitation appears to be localized, suggesting that it may not be a significant variable for predicting global environmental threats.

Hossein Aghamohammadi

Saeed Behzadi

Spatial Statistics Analysis of Precipitation in the Urmia Lake Basin

ivySCI AI Smartly Parses PDF, Answers Researchers' Questions, and Helps You Understand Papers in Seconds

Journal Info