Merging rainfall data of ground and satellite measurements in order to correct and improve the performance of data at the catchment area (Case study: Mond Basin)

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

Estimation of precipitation as one of the most important factors affecting human life and activities is one of the most important issues of interest among decision makers such as water resource managers, farmers, industry owners, and in general, water and climate researchers, especially in arid and semi-arid regions of the world.  However, so far, access to real rainfall in the basins, especially the mountainous basins, is a vague and complicated issue. Precise measurements of precipitation in not usually possible in these basins due to environmental conditions and relatively moderate spatial changes in rainfall.
At present, there are various methods and tools for measuring rainfall or estimating it (Barrett, 1970, Rabiei et al., 2013), which includes: 1) Ground rain-gauge stations 2) Ground radars 3) Satellite estimates. But the most common method is to measure rainfall are meteorological radar Cremonini et al, (2015) and ground rain gauge stations (Acquaotta et al., 2016). The ground rain-gauge stations are usually used by sensors or rain measuring devices. They represent direct measurements of rainfall over the ground (precipitation depth), but they are not able to transmit the spatial pattern of precipitation (Huff, 1970). Meteorological radars and satellite data, on the other hand, are capable of recording and estimating rainfall data with high spatial and temporal resolution. However, considering the variable rainfall uncertainties in this type of data, they are not able to accurately estimate rainfall (Jordan et al. 2000). To solve this inherent problem, there is a need for a methodology that uses all methods of rainfall measurement in the best way. The most commonly used method for reconstructing the precipitation rate with higher accuracy is based on comparison of satellite observations or meteorological radars with ground measurements recorded by rain gauge stations that are spatially dispersed appropriately. In this research, in order to combine and correct the rainfall data, appropriate methods for the integration and correction of rainfall data using rain gauge stations and satellite images at the basin level have been used.

Keywords

Main Subjects


Materials and methods

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 method

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)

 

Bias field conditional merging method

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.

 

Results and discussion

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.

 

 

Conclusion

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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Volume 48, Issue 1
April 2025
Pages 1-19
  • Receive Date: 17 July 2019
  • Revise Date: 14 September 2019
  • Accept Date: 16 September 2019
  • Publish Date: 21 March 2025