چکیده:
Drought monitoring is a fundamental component of drought risk management. It is normally performed using various drought indices that are effectively continuous functions of rainfall and other hydrometeorological variables. In many instances, drought indices are used for monitoring purposes. Geostatistical methods allow the interpolation of spatially referenced data and the prediction of values for arbitrary points in the area of interest. In this research, several interpolation methods, including ordinary kriging (OK), indicator kriging (IK), residual kriging (RK), probability kriging (PK), simple kriging (SK), universal kriging (UK), and inverse distance weighted (IDW) techniques were assessed for the derivation of maps of drought indices at 19 climatic stations in Zayandehroud River Basin of Iran. Monthly rainfall data of period 1989 to 2013 were taken from 19 meteorological stations. The results showed that based on the used error criteria, kriging methods were chosen as the best method for spatial analysis of the drought indices and also, the lowest error (RMSE) and R2 is related to the kriging method. The results showed that SK and OK were more suitable for the spatial analysis of the Z-Score Index (ZSI) and the Standard Precipitation Index (SPI) index. The mean errors (RMSE) of kriging methods for ZSI and SPI indices were 0.40 and 0.19 respectively.
خلاصه ماشینی:
"ir Desert 21-2 (2016) 165-172 Spatio-Temporal Analysis of Drought Severity Using Drought Indices and Deterministic and Geostatistical Methods (Case Study: Zayandehroud River Basin) A.
In this research, several interpolation methods, including ordinary kriging (OK), indicator kriging (IK), residual kriging (RK), probability kriging (PK), simple kriging (SK), universal kriging (UK), and inverse distance weighted (IDW) techniques were assessed for the derivation of maps of drought indices at 19 climatic stations in Zayandehroud River Basin of Iran.
The precipitation spatio-temporal variability is significant in arid and semi-arid regions of the world, such as Iran, (Naserzadeh and Ahmadi, scheme of the study area via IDW and kriging methods.
The most appropriate technique for drought severity interpolation was determined based on the estimated values of the correlation coefficient (R2) and Root Mean Squared Error (RMSE) indices.
Conclusion Current research was carried out to determine the best technique (lowest cross validation error) for spatial interpolation of SPI and ZSI drought indices using of kriging and Inverse Distance Weighted (IDW methods in the Zayandehroud River Basin of Iran for 1989 to 2013.
The results showed that based on R2 and RMSE values, among kriging methods, SK identified as the appropriate technique for spatial analysis and interpolation of SPI index.
Spatial analysis and interpolation techniques provide climate- dependent variables prediction for catchment assessment and management, soil–plant–water interaction researches and crop growth modeling; however temporal and spatial measurements of meteorological variables are not yet completely suitable in the some parts of the study area."