Spatial modeling of seasonal precipitation–elevation in Iran based on aphrodite database

被引：13

作者：

Ahmadi M. ^{[1
]}

Kashki A.R. ^{[2
]}

Dadashi Roudbari A.A. ^{[3
]}

机构：

[1] Faculty of Earth Sciences, Shahid Beheshti University, Tehran

[2] Climatology Department, Hakim Sabzevari University, Sabzevar

[3] Urban Climatology, Shahid Beheshti University, Tehran

来源：

Modeling Earth Systems and Environment | 2018年 / 4卷 / 2期

关键词：

GWR model; Iran; OLS model; Precipitation–elevation; Spatial autocorrelation;

D O I：

10.1007/s40808-018-0444-y

中图分类号：

学科分类号：

摘要：

The precipitation–elevation relationship plays a significant role in rainfall-runoff, watershed management, and environmental management. Therefore, providing an appropriate rainfall-runoff model for areas lacking enough stations, such as mountainous regions, can play a significant role in environmental forecasting and planning. This study was designed with the aim of seasonal precipitation–elevation modeling in Iran. For this purpose, the Aphrodite precipitation Database for the period of 1951–2007 was obtained and the modeling was conducted by OLS and GWR methods by using 30-m DEM of Iran. The results showed that GWR presents the results with high precision in modeling so that this method has explained the spatial variation of seasonal precipitation of 94% for winter, 96% for spring, 86% for summer, and 93% for autumn in Iran. Precipitation- elevation modeling has shown that Iran rainy centers follow the pattern of areas facing the wind; after the final limit of elevation and the areas back to the wind. At the final limit of elevation or peak of the mountain, regardless of whether the elevation increases or decreases, the precipitation decreases and in the northern coasts there is no maximum precipitation in high elevation areas, but as we go from the mountain to the sea, the precipitation is rising. © 2018, Springer International Publishing AG, part of Springer Nature.

引用

页码：619 / 633

页数：14

共 49 条

[31]

Li S., Zhao Z., Miaomiao X., Wang Y., Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression, Environ Model Softw, 25, 12, pp. 1789-1800, (2010)

[32]

Lloyd C.D., Assessing the effect of integrating elevation data into the estimation of monthly precipitation in Great Britain, J Hydrol (Amst), 308, 1, pp. 128-150, (2005)

[33]

Lloyd C.D., Multivariate interpolation of monthly precipitation amount in the United Kingdom, geoENV VII–geostatistics for environmental applications, pp. 27-39, (2010)

[34]

Lundquist J.D., Minder J.R., Neiman P.J., Sukovich E., Relationships between barrier jet heights, orographic precipitation gradients, and streamflow in the northern Sierra Nevada, J Hydrometeorol, 11, 5, pp. 1141-1156, (2010)

[35]

Lundquist J.D., Hughes M., Henn B., Gutmann E.D., Livneh B., Dozier J., Neiman P., High-elevation precipitation patterns: using snow measurements to assess daily gridded datasets across the Sierra Nevada, California J Hydrometeorol, 16, 4, pp. 1773-1792, (2015)

[36]

Manzano-Agugliaro F., Zapata-Sierra A., Fernandez-Castaneda C., Garcia-Cruz A., Hernandez-Escobedo Q., Extreme rainfall relationship in Mexico, J Maps, 11, 3, pp. 405-414, (2015)

[37]

Molnar P., Burlando P., Variability in the scale properties of high-resolution precipitation data in the Alpine climate of Switzerland, Water Resour Res, (2008)

[38]

Mondal B., Das D.N., Dolui G., Modeling spatial variation of explanatory factors of urban expansion of Kolkata: a geographically weighted regression approach, Model Earth Syst Environ, 1, 4, (2015)

[39]

Moran P.A., Notes on continuous stochastic phenomena, Biometrika, 37, 1-2, pp. 17-23, (1950)

[40]

Nosrati K., Application of multivariate statistical analysis to incorporate physico-chemical surface water quality in low and high flow hydrology, Model Earth Syst Environ, 1, 3, (2015)

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