Estimation models for precipitation in mountainous regions: the use of GIS and multivariate analysis

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

“…For example, although due to complexities of the precipitation process there are many exceptions to simple rules (Andersson, 1980), precipitation usually increases with elevation and several authors have introduced this into geostatistical interpolation methods (Goovaerts, 2000;Ninyerola et al, 2000;Brunsdon et al, 2001;Johansson and Chen, 2003;Marquínez et al, 2003;Vicente-Serrano et al, 2003;Diodato and Ceccarelli, 2005). Other variables having an effect on precipitation are the longitude and latitude (having positional effects in our case), relief, slope, aspect, and distance to the sea.…”

“…Nevertheless, a major period has been used in order to have a greater number of data when calculating mean values. Other authors have used longer or shorter series with satisfactory results (Hevesi et al, 1992;Goodale et al, 1998;Ninyerola et al, 2000;Marquínez et al, 2003;Vicente-Serrano et al, 2003). Data analysis shows several temporal and spatial lags, showing greater density at more populated areas, mainly corresponding to low areas close to the coast or to flat elevated areas at the interior.…”

“…Some authors, as Marquínez et al (2003), have used the sub-basins as influence areas. Other works, like the ones developed for the PRISM model (Gibson et al, 1997;Daly et al, 2002), have also used the units of hillslopes as influence areas to evaluate the repercussion of the exposure to some humid flows differentiating the areas of windward and leeward, though they have followed a substantially different methodology (Gibson et al, 1997;Sotillo et al, 2003).…”

“…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.…”

Mapping Precipitation, Temperature, and Evapotranspiration in the Mkomazi River Basin, Tanzania

“…Under some conditions climate variables can best be estimated by non-linear regression models [39]. However, the linear form is easy to use where there are precipitation-elevation relationships and appears to be an acceptable approximation in most situations [13].…”