Use of global climate model outputs to fill missing temperature and precipitation data (Case study: Khanmizza Basin Watersheds)

Authors

10.22052/deej.2018.7.18.59

Abstract

Introduction: Due to importance of data quality, issues relating to filling the missing data has found a great deal of interest. Regeneration methods for missing data can be classified into two kinds of classical and modern categories. Application of statistical methods such as relationship with nearby stations and approaches on the base of hydrological, climatological or physiographical similarity are among the common techniques for gap filling. For such cases, correlation with elevation is one of the common techniques. The modern methods make use of approaches such as data mining, ANN, fuzzy arithmetic and so on to improve the completeness of data. Typically, the output of climate models is used for future predictions/assessment. After development of climate model like general circulation model (GCM) several researchers have reported a good correlation between global atmosphere or ocean and local station data. This correlation has a potential usage for estimation of missing data at local scale.
 
Materials and methods: Two types of data, including meteorological data and climatic data outputs, were used in this study. Reconstruction of two key variables at monthly scale, temperature and precipitation, were investigated over a period of 20 years (1995-2016). Climate data for these two climatic parameters downloaded from Canadian website site (GCM / RCM), were used as an auxiliary variable for data reconstruction. The least squares error method was applied to determine the scenario with the most realistic site-based conditions. The coefficient of determination was used as a measure to assess how well scenarios was able predicts observed data. For this purpose, four scenarios that generally showed either a high linear or non-linear correlation were selected. Then, by means of cross validation and the evaluating relationship between these two series of data and observation were tested. The outcome at this stage was 4 out of climate scenarios which show a high correlation with local station. Cross validation was employed the final scenario. Here the correlation between climate data and station data was utilized as an auxiliary variable for reconstruction of missing values. The outputs of new method were compared with elevation-based method.
 
Results: The established relationship between observations and climatic data (climate model output) by the least squares error method showed a high correlation for the selected models, thus ECHO-G and NCARCCSM3 models were selected for rainfall and temperature respectively. The performance of these predictive models was tested by evaluating them after assuming each row data as missing data. The observational data was re-organized into monthly, seasonal and annual time interval. The obtained coefficients were successfully verified against observed data by eliminating the data for each month and establishing a new relationship. Four scenarios including ECHO-G (SR-A1B), ECHO-G (SR-A2), NCARCCSM3 (SR-A2) and NCARCCSM3 (SR-B1), showed a high correlation between climatic and calculated data. Although the correlation coefficient between rainfall and the climatic model output was low but a good correlation between the combination factor of rainfall and temperature with observed data was found in the study area (r=0.98). The established relationships for local station even can be used for future (up to 2050) predictions. A comparison of the developed method with the elevation-based method showed a better performance at monthly scale.
 
Discussion and conclusion: Awareness of climatic conditions is a helpful strategy to reduce the damage caused by natural disasters. This most often requires an extensive and continuous data sets. Traditionally, correlation with elevation was used as an auxiliary variable to fill the gaps in rainfall and temperature data. Previous studies have reported a strong correlation between the local data of stations and climatic data. This study was conducted to investigate the possibility of using downloaded data from Canadian climate site (GCM / RCM for filling temperature and precipitation meteorological data. The results of the new method were more consistent with observed data. Cross validation indicates the applicability of using climate model as an auxiliary variable for regenerating missing rainfall and temperature data or predicting these variables in the area of interest. In addition to fill the missing data, the common advantage the use of climate model output is that, it could be used as future predictor. On the other hand, the comparison of the above mentioned method with the elevation-based method with observational data indicated that the new approach provide a solution especially for the case that there exist no or limited number of neighboring stations.

Keywords

References

Abdollahi, Kh., 2005. "Provide a new model and algorithm for the reconstruction of lost hydrologic data", Third National Conference on Erosion and Sediment, Tehran, Soil Conservation and Water Conservation Research Center of Iran. 2. Ashraf, M., Loftis, J. C., Hubbard, K. G., 1997. Application of geostatistics to evaluate partial weather station networks. Agricultural and forest meteorology84(3-4), 255-271.‌ 3. Ahmadi, M., Lashkari, H., Keikhisravi, Gh., Azadi, M., 2015. Comparison of performance of LARS and REGCM4 models in simulation and post-processing of annual data of the temperature and precipitation of Khorasan. Geographical Information Geographic Quarterly. Volume 25, Issue 98. 4. Carrega, P., 1995. A method for the reconstruction of mountain air temperatures with automatic cartographic applications. Theoretical and applied climatology 52(1-2), 69-84.‌ 5. Change, I. C., 1996. The science of climate change. Second Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge.‌ 6. Chang, Y., Ayyub, B., 2001. Fuzzy regression methods-a comparative assessment. Fuzzy Sets and Systems 119, 187-203. 7. Di Piazza, A., Conti, F. L., Noto, L. V., Viola, F., La Loggia, G., 2011. Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy. International Journal of Applied Earth Observation and Geoinformation 13(3), 396-408. 8. Ghajarniya, N., Liyaghat, A., Daneshkar, A.P., 2014. Verification of rainfall data of unstable stations of Meteorological and Tectonic Organization in the watershed of Lake Urmia, Journal of Water and Soil Conservation. Journal of Soil and Water Resources Conservation 4(1), 91-109. 9. Hammer, G. L., Nicholls, N., 1996. Managing for climate variability—the role of seasonal climate forecasting in improving agricultural systems. In Proceedings of the 2nd Australian Conference on Agricultural Meteorology’. Brisbane pp. 19-27.‌ 10. Harmsen, E. W., Miller, N. L., Schlegel, N. J., Gonzalez, J. E. 2009. Seasonal climate change impacts on evapotranspiration, precipitation deficit and crop yield in Puerto Rico. Agricultural water management 96(7), 1085-1095. 11. IPPC, 1996. Climate change 1995, the science of climate change, Summary for Pllicymakers, contribution of working group1 to the second assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK: 86-91. 12. Khalil, M., Panu, U. S., & Lennox, W. C., 2001. Groups and neural networks based streamflow data infilling procedures. Journal of Hydrology 241(3), 153-176.‌ 13. Lookzadeh, S., 2004. Evaluation of some methods for reconstruction of rainfall data in Alborz region, MSc, thesis, Tehran University, P. 96. 14. Mileva-Boshkoska, B., Stankovski, M., 2007. Prediction of missing data for ozone concentrations using support vector machines and radial basis neural networks. Informatica 31(4).‌ 15. Mwale, F. D., Adeloye, A. J., Rustum, R. 2012. Infilling of missing rainfall and streamflow data in the Shire River basin. Malawi–A self organizing map approach. Physics and Chemistry of the Earth,Parts A/B/C, 50, 34-43.‌ 16. Macculloch, J.A.W., Booth, M., 1970. Estimation of basin precipitation by regression equation .Water Resources Research 16(6) 1753-1758. 17. Naghdi. R., Shayannejad. M., Sadatikehad, S.J., 2010. Comparison of Different Methods for Estimating Lost Data of the Monthly Discharge of Karun Big Watershed. Management Research Report of Watershed 1, 59-73. 18. Rahimi, J., khalil, A., Bazrafshan, J., 2017. Evaluation of different methods and the proposed new method of regeneration of statistical vacuum data of minimum daily temperature in elevators of Iran 48(2), 231-239. 19. Sadatinejad, S.J., Naghdi, R., Shayannejad, M., 2010. Application of Fuzzy Linear Regression Method to Estimate Fractal Data of Annual Discharge of Hydrometer Station and Comparison with Other Common Methods. Journal of Soil and Water Protection Research. Volume 17, Issue 4. 20. Samadi, S., 2006. The project provides seasonal forecasts with the GCM scale change method. Clinical Research Center. 21. Teegavarapu, R. S., Chandramouli, V., 2005. Improved weighting methods, deterministic and stochastic data-driven models for estimation of missing precipitation records. Journal of Hydrology312(1), 191-206.‌ 22. Technical Report Proposed Prohibition of Chivalrous Range, 2015. Water Resources Research Center. Chaharmahal & Bakhtiari Regional Water Company,pp. 11-12. 23. Wagner, P. D., Fiener, P., Wilken, F., Kumar, S., Schneider, K., 2012. Comparison and evaluation of spatial interpolation schemes for daily rainfall in data scarce regions. Journal of Hydrology 464, 388-400.‌ 24. Xia, Y., Fabian, P., Stohl, A., Winterhalter, M. 1999. Forest climatology: estimation of missing values for Bavaria, Germany. Agricultural and Forest Meteorology96(1), 131-144.‌ 25. You, J., Hubbard, K. G., Goddard, S., 2004. Comparison of estimates from spatial regression and inverse distance method. J. Atmos. Oceanic Tech. 26. Yozgatligil, C., Aslan, S., Iyigun, C., Batmaz, I. 2013. Comparison of missing value imputation methods in time series: the case of Turkish meteorological data. Theoretical and applied climatology 112(1-2), 143-167.‌
Desert Ecosystem Engineering
Volume 7, Issue 18
June 2018
Pages 63-79

Files

Share

How to cite

Statistics