Increasing the accuracy of geostatistical assessments involving extrapolation and zonal classification techniques: case study of Karun basin daily rainfall, IRAN

Document Type : Research Paper

Authors

1 Associate Professor, Water Engineering Department, University of Zabol, Zabol, Iran

2 Professor, Department of Irrigation and Reclamation Engineering, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

3 Associate Professor, Soil Conservation and Watershed Management Research, Tehran, Iran

Abstract

In this paper with analyzing various geostatistical methods in three different scenarios, the influence of zonal classification and rainfall extrapolation on increasing accuracy of the proposed methods for daily rainfall in Karun basin has been analyzed. In the first scenario, only various geostatistical methods including weighting moving average, Kriging, Co-Kriging and TPSS were analyzed. In the second scenario, the study area was divided into homogeneous regions based on cluster method and the accuracy of the selected models were analyzed within each region. In the third scenario, some hypothetical stations were considered in elevation points without real observations, and then, the accuracy of geostatistical method with considering extrapolation technique was analyzed. Analyzing the related Variogram well demonstrated a geometric anisotropy, while this problem was solved up when an all-round variogram was applied. In the first scenario, the mean absolute errors and the mean bias errors were varied in the range of 14.7-36 mm and -1.5-4.5 mm, respectively. In this scenario, the Co-kriging method had the lowest estimation error due to the positive effect of the considered auxiliary variable on increasing variogram "range of influence". Although, the interpolation of daily rainfall data, after zonal classification, had no positive impact on decreasing of estimation error, but the weighting moving average and TPSS methods with the powers of 3-5 were the Exceptions. In addition, the estimation accuracy increased up to 16%, and the estimation bias reduced up to 10%, when precipitation was extrapolated in elevations with no rain gauges. Under this scenario, the maximum threshold of error variance reduced by 45% (from 7.8 mm to 4.8 mm). Based on the results, integrating extrapolation into the Co-Kriging method with considering elevation as an auxiliary variable results the best estimation when assessing the spatial variation of daily rainfall.

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Alhamed A, Lakshmivarahan S and Stensrud DJ (2002). Cluster analysis of multimodel ensemble from SAMEX. Mon. Weather Rev. 130: 226–256.
Basistha A, Arya DS and Goel NK (2008) Spatial Distribution of Rainfall in Indian Himalayas – A case study of Uttarakhand Region, Water Resour. Manag. 22: 1325–1346.
Beek EG (1992) Spatial variability and interpolation of daily precipitation amount, Stochastic Hydrol. Hydraul. 6: 304–320.
Borga M, and Vizzaccaro A (1997). On the interpolation of hydrologic variables: Formal equivalence of multiquadratic surface fitting and kriging. Journal of Hydrology. 195(1–4): 160–171.
Buytaert W, Celleri R, Willems P, Bi`evre DB, and Wyseure G (2006). Spatial and temporal rainfall variability in mountainous areas: A case study from the south Ecuadorian Andes. Journal of Hydrol. 329: 413–421.
Carrera-Hern´andez JJ and Gaskin SJ (2007) Spatio temporal analysis of daily precipitation and temperature in the Basin of Mexico, Journal of Hydrology. 336, 231–249.
Darzi-Naftchali, A., Karandish, F., Asgari, A. 2017. Diagnosing drainage problems in coastal areas using machine-learning and geostatistical models. Irrigation and Drainage. 10.1002/ird.2107
Driks KN, Hay JE, Stow CD, Harris D (1998). High resolution studies of rainfall on Norfolk Island. Part II: Interpolation of rainfall data. Journal of Hydrology, Amesterdam. 208 (3-4): 187-193.
Ganjalikhani, M, Zounemat-Kermani, M, Rezapour, M and Rahnama, M (2016), Evaluation of Copula Performance in Groundwater Quality Zoning (Case Study: Kerman and Ravar regions), Iranian Journal of Soil and Water Research, 48 (1):177-186 (In Farsi)
Goovaerts P (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology. 228,113-129.
Hargrove WW (2001). Interpolation of Rainfall in Switzerd Using Regularized Splines with Tension, Geographic Information and Spatial Technologies Group Oak Ridge National Laboratory, http://www.geobabble.org/~hnw/sic97.
Hevesi JA, Flint AL and Istok JD (1992). Precipitation estima tion in mountainous trerrain using multivariate geostatistics. Part I: Structural analysis. J. Appl. Meteorol. 31: 661–676
Hohn ME (1999). Geostatistics and petroleum geology. Kluwer Academic, The Netherlands.
Issaks E, and Srivastava RM (1989). Applied geostatistics, Oxford University Press, New York.
Johnson GL and Hanson CL (1995) Topographic and atmospheric influences on precipitation variability over a mountains watershed, J. Appl. Meteor. 34: 68–87.
Karandish F., Mousavi, S.S., Tabari, H. Climate change impact on precipitation and cardinal temperatures in different climatic zones in Iran: analysing the probable effects on cereal water-use efficiency. Stoch Environ Res Risk Assess, 2016, doi 10.1007/s00477-016-1355-y.
Karandish, F., Darzi-Naftchali, A., Asgari, A. 2017. Application of machine-learning models for diagnosing health hazard of nitrate toxicity in shallow aquifers. Paddy Water Environ, 15(1), 201-215.
Karandish, F., Ebrahimi, K., Porhemmat, J. 2013. Investigating the flood intensity of Karuns’ sub-basins and effective parameters on it for lumped and distributed simulation of flood. Iranian Journal of Water and Irrigation Management, 3(2), 1-12.
Karandish, F., Shahnazari, A. 2015. Analyzing the Geostatistical Methods Accuracy in Preparing Air Temperature Spatial Distribution in Mountainous Regions (Case Study: Karoun Basin). Journal of Watershed Management Research, 6(11), 36-46.
Kavian, A, Ahmadi, R, Habibnejad, M. and Jafarian, Z (2017), Evaluation of Spatial changes in Soil infiltration Using Experimental and Geostatistical Methods in coastal plain of Behshahr-Galugah, Iranian Journal of Soil and Water Research, 47 (3):551-560. (In Farsi)
Leander R, Buishand TA, van den Hurk BJJM and de Wit MJM (2008). Estimated changes in flood quantiles of the river Meuse from resampling of regional climate model output, Journal of Hydrology. 351, 331–343.
Lloyd CD (2005). Assessing the effect of integrating elevation data into the estimation of monthly precipitation in Great Britain, Journal of Hydrology. 308: 128–150.
Ly, S., Degre, A. 2011. Geostatistical interpolation of daily rainfall at catchment scale: The use of several variogram models in the Ourthe and Ambleve catchments, Belgium. Hydrological Earth System Sciences. 15, 2259-2274.
Mahdavi M, HosheniChegini A, Mahdian M and Rahemi Bandabadi S (2004). Geostatistics Comparison Methods in Estimation of Annual Rainfall Spatial Distribution in Semi- Arid and Arid Region of SE Iran, Journal of Natural Resources of Iran. 2, 211-234.
Mirmousavi SH, Mazidi A and Khosravi Y (2010). The Determination of Optimum Geostatistics Method for Estimating Precipitation Distribution Using GIS (Case Study of Esfahan Province). Geographic Space. 10, 105-120.
Moulin L, Gaume E and Obled C (2009). Uncertainties on mean areal precipitation: assessment and impact on streamflow simulations, Hydrol. Earth Syst. Sci. 13, 99–114.
Saghafian B and Rahimi Bondarabadi S (2008). Validity of Regional Rainfall Spatial Distribution Methods in Mountainous Areas. Journal of engineering hydrology. 13(7): 531-540.
Sarmadin F. and Taghizadeh Mehrjerdi R. (2010), A Comparison of Interpolation Methods for Preparing Soil Quality Maps: Case study: (Agricultural Faculty Experimental Field), Iranian Journal of Soil and Water Research, 40 (2):157-165.(in farsi)
Solaimani K, Habibnejad M, Abkar A and Bani-Asadi M (2006). Analysis of Depth-Area- Duration Curves of Rainfall in Semi-Arid and Arid Region Using Geostatistical Methods (Case Study: Sirjan), Journal of Desert. 1, 31-42.
Tobies GQ and Salas JD (1985). A Comparative Analysis of Techniques for spatial Analysis Precipitation, Water Resources Bulletin. 21, 365-380.
Verworn A and Haberlandt U (2011). Spatial interpolation of hourly rainfall effect of additional information, variogram inference and storm properties, Hydrol. Earth Syst. Sci. 15: 569–584.
Vicent-Serrano SM, Saz-Sanchez MA and Cuadrat JM (2003). Comparative analysis of interpolation methods in the middle Ebro Valley (Spain): application to annual precipitation and temperature, Clim. Res. 24, 161–180.