O. Antonic et al., Spatio-temporal interpolation of climatic variables over large region of complex terrain using neural networks, ECOL MODEL, 138(1-3), 2001, pp. 255-263
Empirical models for seven climatic variables (monthly mean air temperature
, monthly mean daily minimum and maximum air temperature, monthly mean rela
tive humidity, monthly precipitation, monthly mean global solar irradiation
and monthly potential evapotranspiration) were built using neural networks
. Climatic data from 127 weather stations were used, comprising more than 3
0000 cases for each variable. Independent estimators were elevation, latitu
de, longitude, month and time series of respective climatic variable observ
ed at two weather stations (coastal and inland), which have long time-serie
s of climatic variables (from mid last century). Goodness of fit by model w
as very high for all climatic variables (R > 0.98), except for monthly mean
relative humidity and monthly precipitation, for which it was somewhat low
er (R = 0.84 and R = 0.80, respectively). Differences in residuals around m
odel were insignificant between months, but significant between weather sta
tions, both for all climatic variables. This was the reason for calculation
of mean residuals for all stations, which were spatially interpolated by k
riging and used as a model correction. Similarly interpolated standard devi
ation and standard error of residuals are estimators of the model precision
and model error, respectively. Goodness of fit after the averaging of mont
hly values between years was very high for all climatic variables, which en
ables construction of spatial distributions of average climate (climatic at
las) for a given period. Presented interpolation models provide reliable, b
oth spatial and temporal estimations of climatic variables, especially usef
ul for dendroecological analysis. (C) 2001 Elsevier Science B.V. All rights
reserved.