Document Type : Research Paper
Authors
1 Graduated with a PhD in water resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2 Professor, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
3 professor, Department of Irrigation and Drainage, Facualty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
4 Professor, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran..
Abstract
Keywords
Main Subjects
A variety of methods have been developed for creating a relationship between rainfall measurement methods at the basin level (Goudenhoofdt and Delobbe, 2009). Currently, Kriging with the correction of satellite-based error or simple Kriging and the conditional merging method Ehret (2002), Sinclair and Pegram (2005) is the most appropriate method for due to the high quality of the results and because of its simplicity and computational efficiency. In this research, the conditional merging method and the methods based on it have been used. In this method, ground rainfall data and satellite data are merged, then information on the expansion of combined data at the basin level are obtained by using Kriging geostatistical methods.
Conditional merging (CM) using a satellite network is in order to estimate ordinary kriging error at the rain gauge and attempt to correct it. First, the satellite values (S) at each station (G) are used to provide kriging KS(g). Then, this field decreases from the initial satellite precipitation to get the error map. Finally, the error value is added to the interpolated values of the rain gauge station by the Kriging KG(g) method (Goudenhoof and Delobbe, 2009). The final formula is expressed by relation (1):
CM= S - Ks(g) + KG(g) (1)
This method is calculated by dividing the initial values of the satellite's precipitation, the interpolated satellite values using the Kriging method, and eventually multiplying it by the result of the Kriging interpolation in the ground station values, which is expressed in terms of relation (2):
BFCM = (S/KS(g)) K G (g) (2)
This method introduces some of the partial problems associated with division by zero or even indefinite forms, but the approach taken in these cases simply assumed that the final value is zero.
Mean conditional merging
The second method, called the mean conditional merging method, is expressed in terms of relation (3), which represents the average between (CM) and (BFCM).
(3)
Since all the methods presented here are based on spatial algorithms applying to a set of points, the presence of a compressed rain-gage network provides more accurate results.
The validation process is carried out through the methods used to merge and modify rainfall at the catchment area with the help of rain gauge stations and satellite rainfall values, by comparing the values of images and maps derived from methods based on the conditional merging with observational values of the ground stations using criterion and statistical analysis, the results of which have been presented in Table (1).
According to the results presented in Table (1) and by comparing statistical analyzes based on the results of each method with observational data, all three methods used for merging and correcting rainfall in the catchment area have close and acceptable results and there is a significant correlation with ground data, however, in order to select and propose a suitable method for merging and correcting rainfall with both spatial distribution characteristics and preserving the precipitation rate, it can be said that the conditional merging method has better and more acceptable results and was determined as the appropriate method.
The purpose of this study was to modify and improve the performance of rainfall data at the watershed area by using the combination of rainfall and satellite data using geostatistical analysis and merging methods. Accordingly, the statistical analysis criteria had been used for comparing the results of regional rainfall (distributed at the area) by the different merging methods used in the study with observational values of the network of ground stations at the catchment area. The results showed that the regional precipitation values estimated from the conditional merging method provided better results, so that the merged precipitation values retained the precipitation rates of the ground station and has improved the spatial distribution of rainfall on the watershed area relative to the satellite rainfall. In general, this study shows the validity of the conditional merging method for estimating and merging land and satellite data in order to modify regional rainfall in the Mond basin.
Acknowledgment
All the respected officials of regional water experts of Fars and Bushehr, thank you for your great cooperation in providing the required information and also Shahid Chamran University of Ahvaz.
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