A multiple linear regression GIS module using spatial variables to model orographic rainfall

Naoum, Sherif; Tsanis, Ioannis K.

doi:10.2166/hydro.2004.0004

Cited by 29 publications

(14 citation statements)

References 19 publications

(16 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Such data are rarely available from operational meteorological stations, and therefore can only be considered and evaluated within local or regional-scaled studies, in which either expert-knowledge or a high density rainfall station network allows the identification of variables relevant to precipitation interpolation (e.g. wind circulation, identification of lee-and wind-ward sides, possible barriers that block wind movements) (Daly et al, 2002;Diodato, 2005;Gomez et al, 2008;Naoum and Tsanis, 2004;Oettli and Camberlin, 2005).…”

Section: Discussionmentioning

confidence: 99%

“…It has been shown that linear and multiple regression can sufficiently summarise local variance of total precipitation values (Daly et al, 1994;Daly et al, 2002;Johansson and Chen, 2003;Lull and Ellison, 1950;Naoum and Tsanis, 2004;Stathis, 1998) and in many cases regression analysis provides adequate results, even when compared with more complex geostatistical methods (Goovaerts, 2000). In addition, these regression models are simple to use and interpret (Ninyerola et al, 2000), and this is an important parameter when disseminating operationally oriented works, especially in cases where the use of GIS and relevant geostatistical methods is limited.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

The relationship between altitude of meteorological stations and average monthly and annual precipitation

2009

View full text Add to dashboard Cite

The aim of this study was to prove that altitudinal variability of average monthly and annual precipitation is better summarised when the altitude observed within a radius of several kilometres around a meteorological station is taken into consideration, instead of the altitude of the station itself. The use of the variable Z', which combines the altitude of the closest mountain with its distance from the station, is compared against the use of altitude alone in simple linear and multiple quadratic regression equations for the altitudinal interpolation of precipitation over Greece. The data-set comprised precipitation observations from 516 meteorological stations. The comparison between the two variables is discussed on the basis of the resulting determination coefficients (R 2 ) and standard errors of estimate (S). For all seasons, except summer, it was found that the variable Z′ improves the predictive ability of the regression equations, thus showing its potential for further use in interpolation procedures.K e y w o r d s :

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

The relationship between altitude of meteorological stations and average monthly and annual precipitation

2009

View full text Add to dashboard Cite

show abstract

“…GWR considers the spatially varying relationship with better accuracy by incorporating the local variations (Páez et al ., , ). Recent studies (Brunsdon et al ., , ; Naoum and Tsanis, ; Lloyd, ; Al‐Ahmadi and Al‐Ahmadi, ) investigated the spatial variability of the rainfall‐altitude relation in a mountainous region using OLS and GWR. Kumari et al .…”

Section: Introductionmentioning

confidence: 99%

“…GWR considers the spatially varying relationship with better accuracy by incorporating the local variations (Páez et al, 2002a(Páez et al, , 2002b. Recent studies (Brunsdon et al, 1996(Brunsdon et al, , 2001Naoum and Tsanis, 2004;Lloyd, 2005; Al-Ahmadi and Al-Ahmadi, 2013) investigated the spatial variability of the rainfall-altitude relation in a mountainous region using OLS and GWR. Kumari et al (2016) quantified the relationship between the rainfall and elevation in Central Himalayas of India using geographically weighted regression (GWR).…”

Section: Introductionmentioning

confidence: 99%

Non‐stationary modelling framework for rainfall interpolation in complex terrain

Kumari

Singh

Basistha

et al. 2017

Intl Journal of Climatology

View full text Add to dashboard Cite

The knowledge of rainfall‐elevation relationship forms an important input for modelling the orographic rainfall in mountainous areas. The conventional regression and geostatistical techniques assume the rainfall and elevation relationship to be constant; however, in complex terrain these exhibit non‐stationarity in their relationship. This study proposes a novel spatial rainfall modelling framework, stratified geographically weighted regression‐residual kriging (s‐GWRK) that integrates the benefit of spatially clustered data as an input, local‐scale model and incorporation of spatial correlation structure of residuals. The application of the framework is demonstrated in Indian Himalayas of Uttarakhand region with two spatial clusters of normal annual rainfall data, lowland and upland, based on natural clustering technique. The performance of the proposed model is compared with ordinary co‐kriging (OCK), ordinary least square regression (OLS), geographically weighted regression (GWR) and geographically weighted regression kriging (GWRK) that incorporates elevation as predictor variable. Model evaluation shows that s‐GWRK performed best with root mean square error (RMSE) of 153.98 and index of agreement (d) of 0.92. OLS performed least with RMSE of 461.2 and d value of 0.32. OCK using elevation as auxiliary variable performed better than OLS with RMSE of 454.7 and d value of 0.53. The predictive capability of s‐GWRK was validated using the data collected from rain gauge network in the mountainous terrain of Ashburton in New Zealand and Bhutan. An improvement of 30% and 55% in RMSE was observed compared to OCK for Ashburton and Bhutan respectively. For Uttarakhand, Ashburton and Bhutan, the positive rainfall lapse rate derived from GWR model of rainfall‐elevation relation, ranged from 0.01–1.3, 0.2–1.43 and 0.09–0.4 mm m−1.

show abstract

“…These methods use ancillary geographical information as independent variables to improve the estimation of the spatial distribution of the considered climate variables. Several studies present GIS-based spatial interpolation algorithms in order to derive tools for the analysis and the characterization of the spatial structure of precipitation for different applications [15]. Claps et al [16] applied regression analysis to annual and monthly average temperatures to characterize the temperature regime in Italy.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Spatial Interpolation Methods in the Mediterranean Area: Application to Temperature in Sicily

Piazza

Conti

Viola

et al. 2015

Water

View full text Add to dashboard Cite

An exhaustive comparison among different spatial interpolation algorithms was carried out in order to derive annual and monthly air temperature maps for Sicily (Italy). Deterministic, data-driven and geostatistics algorithms were used, in some cases adding the elevation information and other physiographic variables to improve the performance of interpolation techniques and the reconstruction of the air temperature field. The dataset is given by air temperature data coming from 84 stations spread around the island of Sicily. The interpolation algorithms were optimized by using a subset of the available dataset, while the remaining subset was used to validate the results in terms of the accuracy and bias of the estimates. Validation results indicate that univariate methods, which neglect the information from physiographic variables, significantly entail the largest errors, while performances improve when such parameters are taken into account. The best results at the annual scale have been obtained using the the ordinary kriging of residuals from linear regression and from the artificial neural network algorithm, while, at the monthly scale, a Fourier-series Water 2015, 7 1867 algorithm has been used to downscale mean annual temperature to reproduce monthly values in the annual cycle.

show abstract

A multiple linear regression GIS module using spatial variables to model orographic rainfall

Cited by 29 publications

References 19 publications

The relationship between altitude of meteorological stations and average monthly and annual precipitation

The relationship between altitude of meteorological stations and average monthly and annual precipitation

Non‐stationary modelling framework for rainfall interpolation in complex terrain

Comparative Analysis of Spatial Interpolation Methods in the Mediterranean Area: Application to Temperature in Sicily

Contact Info

Product

Resources

About