TREND ANALYSIS, MODELING AND INTERPOLATION OF REFERENCE EVAPOTRANSPIRATION TIME SERIES IN THE SEMI ARID REGION OF DUHOK GOVERNORATE

MARWAN BASHEER ISMAIL  GOVAY; TARIQ HAMA  KARIM

doi:10.26682/ajuod.2023.26.2.13

MARWAN BASHEER ISMAIL GOVAY Dept. of Soil and Water., College of Agricultural Engineering Sciences, University of Duhok, Kurdistan Region-Iraq
TARIQ HAMA KARIM Dept. of Survey and Geomatics Engineering, Faculty of Engineering, Tishk International University-Erbil, Kurdistan Region-Iraq

DOI: https://doi.org/10.26682/ajuod.2023.26.2.13

Keywords: Reference evapotranspiration, forecasting, trend analysis, interpolation methods

Abstract

Reference Evapotranspiration (ET_o) is one of the most important components of the hydrologic cycle. Its assessment, forecasting, spatial interpolation besides climate change effects on this variable are supportive in applying management techniques to water resources and in determining appropriate adaptation strategies. Hence, this study was initiated to forecast, detect changes and generate surface maps for ET_o time series at different time scales. The datasets for this investigation encompassed the input climatic parameters from 12 stations within Duhok governorate for a time span varied from 18 to 20 years for estimating reference evapotranspiration according to penman-Monteith formula. The results indicated that about 17% of stations exhibited increasing trend in annual ET, while the remaining station exhibited significant and non-significant decreasing trends. Spearman rank test Sen’s slope and linear regression analysis offered slopes of similar signs but of different magnitudes. Seasonal ARIMA model denoted as (1, 1, 1)(0, 1, 1)₁₂ was the most appropriate model for predicting monthly ET_o time series at more than 90% of study stations. The indicated model predicted the monthly ET_o with a reasonable accuracy for the coming 36 months. Further, the results indicated that in general, the IDW and spline schemes method exhibited highest and lowest accuracy in most cases. Judging from several performance indicators, it can be inferred IDW, OK and UK methods yielded comparable results.

Downloads

Download data is not yet available.

References

Agha, O. M. A. M., Bağçacı, S. Ç., & Şarlak, N. (2017). Homogeneity analysis of precipitation series in North Iraq. IOSR Journal of Applied Geology and Geophysics, 5(03), 57-63.
Aghelpour, P., Varshavian, V., & Hamedi, Z. (2021). Comparing The Models SARIMA, ANFIS And ANFIS-DE In Forecasting Monthly Evapotranspiration Rates Under Heterogeneous Climatic Conditions.
Akaike, H. (1974). A new look at the statistical model identification. IEEE transactions on automatic control, 19(6), 716-723.
Alhassoun, S., Sendil, U., Al-Othman, A. A., & Negm, A. M. (1997). Stochastic generation of annual and monthly evaporation in Saudi Arabia. Canadian water resources journal, 22(2), 141-154.
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300(9), D05109.
Asadi, A., Vahdat, S. F., & Sarraf, A. (2013). The forecasting of potential evapotranspiration using time series analysis in humid and semi humid regions. American Journal of Engineering Research, 2(12), 296-302.
Capozzi, V., Cotroneo, Y., Castagno, P., De Vivo, C., & Budillon, G. (2020). Rescue and quality control of sub-daily meteorological data collected at Montevergine Observatory (Southern Apennines), 1884–1963. Earth System Science Data Discussions, 2020, 1-34.
Shenbin, C., Yunfeng, L., & Thomas, A. (2006). Climatic change on the Tibetan Plateau: potential evapotranspiration trends from 1961–2000. Climatic change, 76(3-4), 291-319.
Chiles, J. P., & Delfiner, P. (2012). Geostatistics: modeling spatial uncertainty (Vol. 713). John Wiley & Sons.
Gautam, R., & Sinha, A. K. (2016). Time series analysis of reference crop evapotranspiration for Bokaro District, Jharkhand, India. Journal of Water and Land Development, 30(1), 51.
Gong, L., Xu, C. Y., & Chen, D. L. (2005). Spatial interpolation and analyses of reference evapotranspiration and its temporal trends in Changjiang (Yangtze River) Catchment, China.
Goroshi, S., Pradhan, R., Singh, R. P., Singh, K. K., & Parihar, J. S. (2017). Trend analysis of evapotranspiration over India: Observed from long-term satellite measurements. Journal of Earth System Science, 126, 1-21.
Govay, M. B. I. and Tariq H.K., (2023). Comparative Analysis of Different Techniques for Spatial Interpolation of Rainfall Datasets in Duhok Governorate. The Seybold report. Vol.18, No.01.
Hodam, S., Sarkar, S., Marak, A. G., Bandyopadhyay, A., & Bhadra, A. (2017). Spatial interpolation of reference evapotranspiration in India: Comparison of IDW and Kriging methods. Journal of the Institution of Engineers (india): Series A, 98, 511-524.
Hurtado, S. I., Zaninelli, P. G., Agosta, E. A., & Ricetti, L. (2021). Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina. Atmospheric Research, 254, 105482.
Mardikis, M. G., Kalivas, D. P., & Kollias, V. J. (2005). Comparison of interpolation methods for the prediction of reference evapotranspiration—an application in Greece. Water Resources Management, 19, 251-278.
Marino, M. A., Tracy, J. C., & Taghavi, S. A. (1993). Forecasting of reference crop evapotranspiration. Agricultural water management, 24(3), 163-187.
Mitas, L., & Mitasova, H. (1999). Spatial interpolation. Geographical information systems: principles, techniques, management and applications, 1(2).
Mohan, S., & Arumugam, N. (1995). Forecasting weekly reference crop evapotranspiration series. Hydrological sciences journal, 40(6), 689-702.
Nalder, I. A., & Wein, R. W. (1998). Spatial interpolation of climatic normals: test of a new method in the Canadian boreal forest. Agricultural and forest meteorology, 92(4), 211-225.
Roy, S., & Chakravarty, N. (2021). Rainfall Analysis by Using Mann-Kendall Trend, Sen’s Slope and Variability at Six Stations of Andaman & Nicobar Islands.
Shadmani, M., Marofi, S., & Roknian, M. (2012). Trend analysis in reference evapotranspiration using Mann-Kendall and Spearman’s Rho tests in arid regions of Iran. Water resources management, 26, 211-224.
Sonali, P., & Kumar, D. N. (2013). Review of trend detection methods and their application to detect temperature changes in India. Journal of Hydrology, 476, 212-227.
Swain, S., Nandi, S., & Patel, P. (2018). Development of an ARIMA model for monthly rainfall forecasting over Khordha district, Odisha, India. In Recent findings in intelligent computing techniques (pp. 325-331). Springer, Singapore.
Tabari, H., Grismer, M. E., & Trajkovic, S. (2013). Comparative analysis of 31 reference evapotranspiration methods under humid conditions. Irrigation Science, 31, 107-117.
Tomas-Burguera, M., Beguería, S., Vicente-Serrano, S., & Maneta, M. (2018). Optimal Interpolation scheme to generate reference crop evapotranspiration. Journal of Hydrology, 560, 202-219.
Valipour, M., Banihabib, M. E., & Behbahani, S. M. R. (2012). Parameters estimate of autoregressive moving average and autoregressive integrated moving average models and compare their ability for inflow forecasting. J Math Stat, 8(3), 330-338.
Valipour, M., Bateni, S. M., & Jun, C. (2021). Global surface temperature: a new insight. Climate, 9(5), 81.
Vogt, J. V., Viau, A. A., & Paquet, F. (1997). Mapping regional air temperature fields using satellite‐derived surface skin temperatures. International Journal of Climatology: A Journal of the Royal Meteorological Society, 17(14), 1559-1579.
Wang, W. C., Chau, K. W., Cheng, C. T., & Qiu, L. (2009). A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of hydrology, 374(3-4), 294-306.
Wijngaard, J. B., Klein Tank, A. M. G., & Können, G. P. (2003). Homogeneity of 20th century European daily temperature and precipitation series. International Journal of Climatology: A Journal of the Royal Meteorological Society, 23(6), 679-692.
Xu, C. Y., Gong, L., Jiang, T., Chen, D., & Singh, V. P. (2006). Analysis of spatial distribution and temporal trend of reference evapotranspiration and pan evaporation in Changjiang (Yangtze River) catchment. Journal of hydrology, 327(1-2), 81-93.
Zhang, Q., Wang, B. D., He, B., Peng, Y., & Ren, M. L. (2011). Singular spectrum analysis and ARIMA hybrid model for annual runoff forecasting. Water resources management, 25, 2683-2703.
Zhou, Y., & Michalak, A. M. (2009). Characterizing attribute distributions in water sediments by geostatistical downscaling. Environmental science & technology, 43(24), 9267-9273.