The prediction of spatial and temporal distribution of precipitation regime in Iran: the case of Fars province

The prediction of spatial and temporal distribution of precipitation regime in Iran: the case of... Precipitation regime and its spatial and temporal distribution are essential requirements of environmental planning. Precipitation Concentration Index (PCI) and geographical weight regression method are two practical methods for the prediction of the temporal and spatial distribution of precipitation variables. For this purpose, the precipitation regime data for 13 synoptic stations in Fars province in Iran were used since their foundation in 2014. Then, the data were interpolated using Inverse Distance Weighting (IDW) method and precipitation pixels were prepared corresponding to the Digital Elevation Model (DEM), slope and aspect. The results of the study showed that the precipitation regime of the northern, northwest, and western parts of Fars province with PCI values of 13–20 with a maximum rainfall in winter was different from that of the southern and eastern parts of the studied area. In addition, the PCI values higher than 22 (PCI > 22) in the southwestern and eastern parts of the region suggested significant changes of rainfall throughout the year. Summer is the dry season of the area, and except for a very small part of southern parts of the province, the other areas were without rainfall. The results of Geographically Weighted Regression (GWR) also indicated that the prediction of precipitation regime with topographic elements shows much better results than the Ordinary Least Squares (OLS) regression, in a way that the relationship between altitude and rainfall in the western, northwestern, and central parts of the province were positive and significant, and the areas of eastern parts of Fars province showed a negative relationship in terms of topographic elements (aspect and altitude). Finally, the results of the prediction of GWR model showed that the mentioned model was able to assess more than 80% of the existing relationship between observe and predict the monthly precipitation amount based on the studied spatial factors successfully. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Modeling Earth Systems and Environment Springer Journals

The prediction of spatial and temporal distribution of precipitation regime in Iran: the case of Fars province

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Publisher
Springer International Publishing
Copyright
Copyright © 2018 by Springer International Publishing AG, part of Springer Nature
Subject
Earth Sciences; Earth System Sciences; Math. Appl. in Environmental Science; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Mathematical Applications in the Physical Sciences; Ecosystems; Environment, general
ISSN
2363-6203
eISSN
2363-6211
D.O.I.
10.1007/s40808-018-0451-z
Publisher site
See Article on Publisher Site

Abstract

Precipitation regime and its spatial and temporal distribution are essential requirements of environmental planning. Precipitation Concentration Index (PCI) and geographical weight regression method are two practical methods for the prediction of the temporal and spatial distribution of precipitation variables. For this purpose, the precipitation regime data for 13 synoptic stations in Fars province in Iran were used since their foundation in 2014. Then, the data were interpolated using Inverse Distance Weighting (IDW) method and precipitation pixels were prepared corresponding to the Digital Elevation Model (DEM), slope and aspect. The results of the study showed that the precipitation regime of the northern, northwest, and western parts of Fars province with PCI values of 13–20 with a maximum rainfall in winter was different from that of the southern and eastern parts of the studied area. In addition, the PCI values higher than 22 (PCI > 22) in the southwestern and eastern parts of the region suggested significant changes of rainfall throughout the year. Summer is the dry season of the area, and except for a very small part of southern parts of the province, the other areas were without rainfall. The results of Geographically Weighted Regression (GWR) also indicated that the prediction of precipitation regime with topographic elements shows much better results than the Ordinary Least Squares (OLS) regression, in a way that the relationship between altitude and rainfall in the western, northwestern, and central parts of the province were positive and significant, and the areas of eastern parts of Fars province showed a negative relationship in terms of topographic elements (aspect and altitude). Finally, the results of the prediction of GWR model showed that the mentioned model was able to assess more than 80% of the existing relationship between observe and predict the monthly precipitation amount based on the studied spatial factors successfully.

Journal

Modeling Earth Systems and EnvironmentSpringer Journals

Published: Mar 31, 2018

References

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