The influence of topographic co-variables on the spatial variability of precipitation over small regions of complex terrain

Abstract

Precipitation variability results from atmospheric circulation and complex site-specific bio-geoclimatic characteristics; therefore, climatic variables are expected to be correlated in a scale-dependent way. This paper studies the influence of topographic co-variables on the spatial variability of precipitation over small regions of complex terrain. For this purpose, the mutual benefits of an integrated geographic information system (GIS) and a geostatistics approach was used for spatial precipitation interpolation from rainfall observations measured at 51 climatic stations in a mountainous region of southern Italy (Benevento province). As no single method is optimal for all regions, it is important to compare the results obtained using alternative methods applied to the same data set. Therefore, besides ordinary kriging examination, two auxiliary variables were added for ordinary co-kriging of annual and seasonal precipitation: terrain elevation data and a topographic index. Cross-validation indicated that the ordinary kriging yielded the largest prediction errors. The smallest prediction errors were produced by a multivariate geostatistical method. However, the results favour the ordinary co-kriging with inclusion of information on the topographic index. The application of co-kriging is particularly justified in areas where there are nearby stations and where landform is very complex. We conclude that ordinary co-kriging is a very flexible and robust interpolation method because it may take into account several properties (soft and hard data) of the landscape.

Production of climatic and bioclimatic maps by Universal Kriging with external drift: theory and examples for Italy

“…Altri metodi geostatistici universalmente riconosciu ti come buoni predittori sono l'interpolazione poli nomiale (Tabios III & Salas 1985, Lennon & Turner 1995, la regressione multipla e polinomiale (Gooda le et al 1998, Kurtzman & Kadmon 1999, Agnew & Palutikof 2000, Ninyerola et al 2000, Blasi et al 2007, il kriging e le sue possibili modulazioni (ordi nario, universale), co-kriging (Tabios III & Salas 1985, Phillips et al 1992, Hudson & Wackernagel 1994, Holdaway 1996, Nalder & Wein 1998, Diodato 2005, lo splines (Hulme et al 1995, Lennon & Turner 1995, Kurtzman & Kadmon 1999 e le reti neurali (Antonić et al 2001).…”

Seasonal precipitation interpolation at the Valencia region with multivariate methods using geographic and topographic information

“…For example, Brunsdon et al (2001) show that in Great Britain the elevation factor presents a major influence in the NW part than in the SE, due to the fact that the majority of the rainfalls are associated to the zonal flows of the W and NW. Even in areas with little extension, in cases where the rains come from flows of contrasted winds, the elevation stops are the most significant variables, as can be observed in Diodato (2005).…”

“…of Catalunya (Spain); Wilk and Andersson (2000) compared a Thiessen based method with a regression based method for a mountainous river basin with monsoon climate (southern India), obtaining significant differences in seasonal rainfall analyses; Wotling et al (2000) used stepwise regression -that included some topographical relief features -applied to a regular grid that was later interpolated with spline functions; Lin and Chen (2003) proposed a radial basis function network incorporating a semivariogram model to estimate rainfall distribution at the Tanshui river basin (Taiwan); Johansson and Chen (2003) found a regression model that included the wind variable for Sweden; Marquínez et al (2003), used a regression model performed with topographic variables for Cantabria (Spain); Vicente-Serrano et al (2003) concluded that the best results were obtained by geostatistical methods and a multiple regression (MR) model using four geographic and topographic variables for the central part of the Ebro Valley (Spain); Diodato and Ceccarelli (2005) compared the inverse squared distance method with linear regression and ordinary cokriging (OCK) for the Sannio Mountains (southern Italy), obtaining the best results for the cokriging that included the elevation as secondary information; finally, Diodato (2005) centers his study on Benevento's province (Italy), where the elevation factor is low correlated with the rainfall. He compares the methods of the ordinary kriging (OK) and cokriging, using as secondary information a function that relates the level of greenery of the existing vegetation with the elevation, and concludes that the better results are for the cokriging approach.…”

Χωροχρονική Ανάλυση Προσομοίωση Και Πρόγνωση Ξηρασίας Στην Υδρολογική Λεκάνη Πηνειού Ποταμού Θεσσαλίας

“…Η ακρίβεια όμως των μεθόδων αυτών σε ορεινές περιοχές όπου οι μετρήσεις είναι χωρικά πιο αραιές, είναι περιορισμένη [Basist et al, 1994;Daly et al, 1994;Marquinez et al, 2003;Alijani et al, 2008]. Η ενσωμάτωση, όμως, στη μέθοδο της παλινδρόμησης την πληροφορία της χωρικής συσχέτισης της βροχόπτωσης μπορεί να δώσει ικανοποιητικά αποτελέσματα [Goovaerts, 2000;Diodato, 2005;Guan et al, 2005;Moral, 2010] Οι παραπάνω μελέτες έδειξαν ότι οι χωρικοί χάρτες υπολογισμένοι με διαφορετικές μεθοδολογίες παρέχουν παρόμοια επιφανειακή βροχόπτωση αλλά διαφορετικές τιμές μέγιστης και ελάχιστης βροχόπτωσης και διαφορετικό συντελεστή μεταβλητότητας (coefficient of variation). Τα αποτελέσματα των παραπάνω μελετών έδειξαν ότι η ακρίβεια των μεθόδων χωρικής παρεμβολής σημειώνουν σημαντικές διαφορές και ότι οι προχωρημένες γεωστατιστικές τεχνικές έχουν καλύτερη χωρική απόδοση από τις απλές τεχνικές χωρικής παρεμβολής (π.χ., IDW και Thiessen Polygons) που χρησιμοποιούνται ευρέως σε υδρολογικές εφαρμογές και σε αδρομερή, ημικατανεμημένα και κατανεμημένα υδρολογικά μοντέλα και συστήματα.…”

“…Τα αποτελέσματα των παραπάνω μελετών έδειξαν ότι η ακρίβεια των μεθόδων χωρικής παρεμβολής σημειώνουν σημαντικές διαφορές και ότι οι προχωρημένες γεωστατιστικές τεχνικές έχουν καλύτερη χωρική απόδοση από τις απλές τεχνικές χωρικής παρεμβολής (π.χ., IDW και Thiessen Polygons) που χρησιμοποιούνται ευρέως σε υδρολογικές εφαρμογές και σε αδρομερή, ημικατανεμημένα και κατανεμημένα υδρολογικά μοντέλα και συστήματα. Τα αποτελέσματα αυτά έδειξαν επίσης ότι η χωρική παρεμβολή της βροχόπτωσης βελτιώνεται με τη χρήση του υψομέτρου του γεωγραφικού ανάγλυφου ως δευτερογενής μεταβλητή ακόμα και όταν η μεταβλητή αυτή (υψόμετρο) παρουσιάζει μικρή συσχέτιση με τη βροχόπτωση [Diodato, 2005;Guan et al, 2005;Schuurmans and Bierkens, 2007;Li and Heap, 2008;Vasiliades and Loukas, 2009b;Zhang and Srinivasan, 2009;Moral, 2010].…”