Monthly precipitation mapping of the Iberian Peninsula using spatial interpolation tools implemented in a Geographic Information System

Ninyerola, Miquel; Pons, Xavier; Roure, Joan Maria

doi:10.1007/s00704-006-0264-2

Cited by 148 publications

(110 citation statements)

References 14 publications

(8 reference statements)

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“…To the best of our knowledge, this is the first approach in the scientific literature to objectively map this climatic variable. We demonstrated that the local and spatially complex nature of fogs makes them more difficult to map than other climatic variables such as precipitation and temperature for which it is common to obtain high R 2 values for regression models, usually higher than 0.7 (Ninyerola et al, 2007a(Ninyerola et al, , 2007b. R 2 values higher than 0.7 have been obtained previously for the Aragón region in monthly regression models of precipitation and temperature (Vicente-Serrano et al, 2003;Vicente-Serrano et al, 2007;López-Moreno et al, 2007).…”

Section: Discussionmentioning

confidence: 53%

“…The inclusion of these variables in a geographic information system (GIS) facilitates their calculation and helps to reveal their interrelation with climatic data. The most commonly used variables include those that affect the spatial distribution of climate, including elevation, longitude, latitude, distance to the ocean, and areas of topographic shadow (Ninyerola et al, 2000(Ninyerola et al, , 2007aVicente-Serrano et al, 2003. These variables are usually sufficient in mapping climate variables that show gradual spatial variations; however, for variables that show very large spatial variability and notable local features, such as the frequency of fogs, other spatially varying predictors that record local geographic and topographic characteristics are potentially useful in modelling.…”

Section: Ordinary Least Squares Regressionmentioning

confidence: 99%

“…We used a relatively high value of the tension parameter (400) in order to obtain interpolated residuals close to the values observed at the stations. This approach was employed because it has proved to be more suitable than other local techniques such as inverse-distance weighting or geostatistics (Nalder and Wein, 1998;Vicente-Serrano et al, 2003;Ninyerola et al, 2007a;). The sum of residual interpolation and predicted regression maps modified the initial results of the model, and real values were obtained at the locations of the weather stations:…”

Section: Local Correctionsmentioning

confidence: 99%

“…Many previous studies have mapped climate parameters such as precipitation (Ninyerola et al, 2000(Ninyerola et al, , 2007aBrown and Comrie, 2002), temperature (Vicente-Serrano et al, 2003;Ninyerola et al, 2007b), evapotranspiration (Martínez-Cob, 1996;Vicente-Serrano et al, 2007), and snow depth (López- Moreno and Nogués-Bravo, 2005). The climate variables that have received the greatest attention are precipitation and temperature, as maps of these variables are of great interest for numerous applications.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparison of regression techniques for mapping fog frequency: application to the Aragón region (northeast Spain)

Vicente‐Serrano

López‐Moreno

Vega-Rodriguez

et al. 2009

Intl Journal of Climatology

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ABSTRACT:We compare different spatial interpolation techniques in mapping the monthly frequency of fogs in the Aragón region (northeast Spain). The local and spatially complex nature of the fogs makes them more difficult to map than other climatic variables such as precipitation and temperature. We found clear seasonal differences in the quality of the obtained maps. The localized nature of spring and summer fogs, mainly restricted to valley bottoms in mountainous areas, gives rise to several limitations. The modelling of fog frequency is more complex than that of other climate variables; to improve the model predictions, it is necessary to consider topographic variables that simulate the terrain structure. Moreover, the highly complex nature of the relationship between fog frequency and terrain means that simple linear models perform poorly in accounting for the role of geographic and topographic variables in determining the spatial distribution of fog frequency. The inclusion of non-linear relationships between fog frequency and terrain variables in the models following a general additive model (GAM) procedure leads to an improvement in model performance because the flexibility of GAMs enables the inclusion of non-linear relationships and the generation of response-curve shapes that detail the exact relationship between the dependent variable and predictors throughout the entire range of the variable.

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Section: Discussionmentioning

confidence: 53%

Section: Ordinary Least Squares Regressionmentioning

confidence: 99%

Section: Local Correctionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of regression techniques for mapping fog frequency: application to the Aragón region (northeast Spain)

Vicente‐Serrano

López‐Moreno

Vega-Rodriguez

et al. 2009

Intl Journal of Climatology

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“…Local increases in rainfall with elevation often approximate a linear or curved distribution [38] in many regions. Under some conditions climate variables can best be estimated by non-linear regression models [39].…”

Section: Spatial Interpolation Of Rainfallmentioning

confidence: 99%

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

Mmbando

Kleyer

2018

Climate

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Abstract:It is still a challenge to provide spatially explicit predictions of climate parameters in African regions of complex relief, where meteorological information is scarce. Here we predict rainfall, temperature, and reference evapotranspiration (ET o ) for the southern Mkomazi River Basin in Northeastern Tanzania, East Africa, by means of regression-based, digital elevation models (DEM) at 90 m spatial-resolution and geographic information systems (GIS) techniques. We mapped rainfall for the period 1964-2010. The models accounted for orographic factors which strongly influenced the spatial variability of rainfall in the region. According to orography, the area was divided into three zones for modelling rainfall: windward, leeward, and transition zone. Rainfall indicates high spatial and temporal variability dominated by equatorial East-African climate circulation systems. Maximum and minimum temperatures were modelled for the period 1989-1994, the models accounted only for the altitude gradient. Mean temperature was calculated by arithmetic mean of maximum and minimum temperatures maps in ArcGIS. ET o was estimated in ArcGIS following the method described by Hargreaves and Samani. The maps were made on a monthly basis for rainfall, ET o , and mean, maximum, and minimum temperatures. The obtained maps are useful for the purpose of agriculture, ecological, and water resources management.

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Development and application of topographic descriptors for conditional analysis of rainfall

Ferranti

Whyatt

Timmis

2009

Atmospheric Science Letters

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Upland rainfall is changing but regional climate models (RCMs) are poor at simulating observed precipitation in such areas of complex topography. This paper presents a method of examining observed rainfall patterns and processes under different conditions of synoptic meteorology and local topography. Objective topographic descriptors are defined and used to distinguish the modification of rainfall by local topography from that due to different synoptic conditions and climate change. A case study examining winter rainfall in Cumbria is presented. The conditional analysis method can be used to test RCM outputs so that model parameterisation can be improved.

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Monthly precipitation mapping of the Iberian Peninsula using spatial interpolation tools implemented in a Geographic Information System

Cited by 148 publications

References 14 publications

Comparison of regression techniques for mapping fog frequency: application to the Aragón region (northeast Spain)

Comparison of regression techniques for mapping fog frequency: application to the Aragón region (northeast Spain)

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

Development and application of topographic descriptors for conditional analysis of rainfall

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