Analysis of deterministic and geostatistical interpolation techniques for mapping meteorological variables at large watershed scales

• Autorzy: Amini Mohammad Amin , Torkan Ghazale , Eslamian Saeid , Zareian Mohammad Javad , Adamowski Jan Franklin

Identyfikatory

DOI

10.1007/s11600-018-0226-y

• Języki publikacji: EN

Abstrakty

EN

The widely scattered pattern of meteorological stations in large watersheds and remote locations, along with a need to estimate meteorological data for point sites or areas where little or no data have been recorded, has encouraged the development and implementation of spatial interpolation techniques. The various interpolation techniques featured in GIS software allow for the extraction of this new information from spatially distinct point data. Since no one interpolation method can be accurate in all regions, each method must be evaluated prior to each geographically distinct application. Many methods have been used for interpolating minimum temperature ( Tmin ), maximum temperature ( Tmax ) and precipitation data; however, only a few methods have been used in the Zayandeh-Rud River basin, Iran, and no comparison of methods has ever been carried out in the area. The accuracies of six spatial interpolation methods [Inverse Distance Weighting, Natural Neighbor (NN), Regularized Spline, Tension Spline, Ordinary Kriging, Universal Kriging] were compared in this study simultaneously, and the best method for mapping monthly precipitation and temperature extremes was determined in a large semi-arid watershed with high temperature and rainfall variation. A cross-validation technique and long-term (1970–2014) average monthly Tmin , Tmax and precipitation data from meteorological stations within the basin were used to identify the best interpolation method for each variable dataset. For Tmin , Kriging (Gaussian) proved to be the most accurate interpolation method (MAE = 1.827 °C), whereas, for Tmax and precipitation the NN method performed best (MAE = 1.178 °C and 0.5241 mm, respectively). Accordingly, these variable-optimized interpolation methods were used to define spatial patterns of newly generated climatic maps.

Słowa kluczowe

EN

precipitation; sensitivity analysis; spatial interpolation; temperature; Zayandeh-Rud River basin

PL

opad atmosferyczny; analiza wrażliwości; interpolacja przestrzenna; temperatura

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2019
• Tom: Vol. 67, no. 1
• Strony: 191--203
• Opis fizyczny: Bibliogr. 42 poz.

Twórcy

autor

Amini Mohammad Amin

• Department of Water Resources Management, College of Agriculture, Tarbiat Modares University, Tehran, Iran

autor

Torkan Ghazale

• Department of Water Resources Management, College of Agriculture, Tehran University, Tehran, Iran

autor

Eslamian Saeid

• Department of Water Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, Iran

autor

Zareian Mohammad Javad

• Department of Water Resources Research, Water Research Institute (WRI), Ministry of Energy, Tehran, Iran

autor

Adamowski Jan Franklin

• Department of Bioresource Engineering, McGill University, Sainte‑Anne‑de‑Bellevue, QC, Canada

Bibliografia

• 1. Agnew MD, Palutikof JP (2000) GIS-based construction of baseline climatologies for the Mediterranean using terrain variables. Clim Res 14(2):115–127CrossRefGoogle Scholar
• 2. Antonić O, Križan J, Marki A, Bukovec D (2001) Spatio-temporal interpolation of climatic variables over large region of complex terrain using neural networks. Ecol Model 138(1–3):255–263CrossRefGoogle Scholar
• 3. Attorre F, Alfo M, De Sanctis M, Francesconi F, Bruno F (2007) Comparison of interpolation methods for mapping climatic and bioclimatic variables at regional scale. Int J Climatol 27(13):1825–1843CrossRefGoogle Scholar
• 4. Bolstad PV, Swift L, Collins F, Régnière J (1998) Measured and predicted air temperatures at basin to regional scales in the southern Appalachian Mountains. Agric For Meteorol 91(3–4):161–176CrossRefGoogle Scholar
• 5. Burrough PA, McDonnell RA (1998) Creating continuous surfaces from point data. In: Burrough PA, Goodchild MF, McDonnell RA, Witzer P, Worboys M (eds) Principles of geographic information systems. Oxford University Press, OxfordGoogle Scholar
• 6. Burrough PA, McDonnell RA (2000) Principles of geographical information systems. Oxford University Press, New YorkGoogle Scholar
• 7. Courault D, Monestiez P (1999) Spatial interpolation of air temperature according to atmospheric circulation patterns in southeast France. Int J Climatol J R Meteorol Soc 19(4):365–378CrossRefGoogle Scholar
• 8. Das M, Hazra A, Sarkar A, Bhattacharya S, Banik P (2017) Comparison of spatial interpolation methods for estimation of weekly rainfall in West Bengal, India. MAUSAM 68(1):41–50Google Scholar
• 9. Davis BM (1987) Uses and abuses of cross-validation in geostatistics. Math Geol 19:241–248CrossRefGoogle Scholar
• 10. De Amorim Borges P, Franke J, Da Anunciação YMT, Weiss H, Bernhofer C (2016) Comparison of spatial interpolation methods for the estimation of precipitation distribution in Distrito Federal, Brazil. Theor Appl Climatol 123(1–2):335–348Google Scholar
• 11. Delbari M, Afrasiab P, Jahani S (2013) Spatial interpolation of monthly and annual rainfall in northeast of Iran. Meteorol Atmos Phys 122:103–113CrossRefGoogle Scholar
• 12. Dodson R, Marks D (1997) Daily air temperature interpolated at high spatial resolution over a large mountainous region. Clim Res 8(1):1–20CrossRefGoogle Scholar
• 13. Erdoğan S (2009) A comparison of interpolation methods for producing digital elevation models at the field scale. Earth Surf Process Landf 34(3):366–376CrossRefGoogle Scholar
• 14. Eslamian S, Safavi HR, Gohari A, Sajjadi M, Raghibi V, Zareian MJ (2017) Climate change impacts on some hydrological variables in the Zayandeh-Rud River Basin, Iran. Reviving the dying giant. Springer, Cham, pp 201–217CrossRefGoogle Scholar
• 15. Fadavi G, Bazarafshan J (2016) Comparative study of regional estimation methods for daily maximum temperature (a case study of the Isfahan province). J Soil Water 29(2):504–516Google Scholar
• 16. Fadavi G, Bazarafshan J, Ghahreman N (2016) Comparison of different regional estimation methods for daily minimum temperature (a case study of Isfahan province). J Agric Meteorol 3(2):14–23Google Scholar
• 17. Franke R (1982) Smooth interpolation of scattered data by local thin plate splines. Comput Math Appl 8(4):237–281CrossRefGoogle Scholar
• 18. Garen DC, Marks D (2005) Spatially distributed energy balance snowmelt modelling in a mountainous river basin: estimation of meteorological inputs and verification of model results. J Hydrol 315(1–4):126–153CrossRefGoogle Scholar
• 19. Goovaerts P (1999) Geostatistics in soil science: state-of-the-art and perspectives. Geoderma 89:1–45CrossRefGoogle Scholar
• 20. Hasenauer H, Merganicova K, Petritsch R, Pietsch SA, Thornton PE (2003) Validating daily climate interpolations over complex terrain in Austria. Agric For Meteorol 119(1–2):87–107CrossRefGoogle Scholar
• 21. Jarvis CH, Stuart N (2001) A comparison among strategies for interpolating maximum and minimum daily air temperatures. Part II: the interaction between number of guiding variables and the type of interpolation method. J Appl Meteorol 40(6):1075–1084CrossRefGoogle Scholar
• 22. Karamouz M, Torabi S, Araghinejad S (2007) Case study of monthly regional rainfall evaluation by spatiotemporal geostatistical method. J Hydrol Eng 12(1):97–108CrossRefGoogle Scholar
• 23. Kisaka M, Monicah Mucheru-Muna O, Ngetich FK, Mugwe J, Mugendi D, Mairura F, Shisanya C, Makokha GL (2016) Potential of deterministic and geostatistical rainfall interpolation under high rainfall variability and dry spells: case of Kenya’s Central Highlands. Theor Appl Climatol 124(1–2):349-364CrossRefGoogle Scholar
• 24. Krivoruchko K, Gotway CA (2004) Creating exposure maps using kriging. Public Health GIS News Info 56:11–16Google Scholar
• 25. Madani K, Marino MA (2009) System dynamics analysis for managing Iran’s Zayandeh-Rud River Basin. Water Resour Manage 23:2163–2187CrossRefGoogle Scholar
• 26. Merwade VM, Maidment DR, Goff JA (2006) Anisotropic considerations while interpolating river channel bathymetry. J Hydrol 331(3–4):731–741CrossRefGoogle Scholar
• 27. Mitas L, Mitasova H (1988) General variational approach to the interpolation problem. Comput Math Appl 16(12):983–992CrossRefGoogle Scholar
• 28. Mutua F, Kuria D (2012) A comparison of spatial rainfall estimation techniques: a case study of Nyando River Basin Kenya. J Agric Sci Technol 14(2):149–165Google Scholar
• 29. Ninyerola M, Pons X, Roure JM (2000) A methodological approach of climatological modelling of air temperature and precipitation through GIS techniques. Int J Climatol 20(14):1823–1841CrossRefGoogle Scholar
• 30. Shen SS, Dzikowski P, Li G, Griffith D (2001) Interpolation of 1961–97 daily temperature and precipitation data onto Alberta polygons of ecodistrict and soil landscapes of Canada. J Appl Meteorol 40(12):2162–2177CrossRefGoogle Scholar
• 31. Sibson R (1981) A brief description of natural neighbor interpolation. In: Barnet V (ed) Interpreting multivariate data. Wiley, Chicester, pp 21–36Google Scholar
• 32. Skirvin SM, Marsh SE, McClaran MP, Meko DM (2003) Climate spatial variability and data resolution in a semi-arid watershed, south-eastern Arizona. J Arid Environ 54(4):667–686CrossRefGoogle Scholar
• 33. Tatalovich Z, John Wilson P, Cockburn M (2006) A comparison of Thiessen polygon, kriging, and spline models of potential UV exposure. Cartogr Geogr Inf Sci 33(3):217–231CrossRefGoogle Scholar
• 34. Thornton PE, Running SW, White MA (1997) Generating surfaces of daily meteorological variables over large regions of complex terrain. J Hydrol 190(3–4):214–251CrossRefGoogle Scholar
• 35. Ver Hoef JM (1993) Universal kriging for ecological data. In: Goodchild MF, Parks BO, Steyaert LT (eds) Environmental modelling with GIS. Oxford University Press, New York, pp 447–453Google Scholar
• 36. Voltz M, Webster R (1990) A comparison of kriging, cubic splines and classification for predicting soil properties from sample information. J Soil Sci 41(3):473–490CrossRefGoogle Scholar
• 37. Wamwling A (2003) Accuracy of geostatistical prediction of yearly precipitation in Lower Saxony. J Environmetr 14(7):699–709CrossRefGoogle Scholar
• 38. Watson DF, Phillip GM (1987) Neighborhood based interpolation. Geobyte 2(2):12–16Google Scholar
• 39. Xia Y, Fabian P, Winterhalter M, Zhao M (2001) Forest climatology: estimation and use of daily climatological data for Bavaria, Germany. Agric For Meteorol 106(2):87–103CrossRefGoogle Scholar
• 40. Xiao Y, Gu X, Yin S, Shao J, Cui Y, Zhang Q, Niu Y (2016) Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China. SpringerPlus 5(1):425CrossRefGoogle Scholar
• 41. Xu C, Wang J, Li Q (2018) A new method for temperature spatial interpolation based on sparse historical stations. J Climate 31(5):1757–1770CrossRefGoogle Scholar
• 42. Zareian MJ, Eslamian S, Safavi HR (2015) A modified regionalization weighting approach for climate change impact assessment at watershed scale. Theor Appl Climatol 122:497–516

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).