A methodological approach of climatological modelling of air temperature and precipitation through GIS techniques (original) (raw)
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Climatological modeling of monthly air temperature and precipitation in Egypt through GIS techniques
Climate Research, 2010
This paper shows the results of modeling and mapping monthly maximum and minimum temperature, and total precipitation in Egypt with the purpose of obtaining accurate climate maps. A multivariate linear regression model enhanced by spline interpolation was undertaken. Climate variables were obtained from 40 quality controlled and homogenized series for the period 1957 to 2006. The predictors, including geographical variables (e.g. latitude, longitude, altitude and distance to water bodies) and the Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing indices, were integrated as raster layers in a Geographical Information System (GIS) environment. Inclusion of meaningful remote sensing indices (e.g. the Normalized Difference Vegetation Index and the Normalized Difference Temperature Index) generally improved accuracy of the predictions. The model integrating geographical and remote sensing indices explained an average of 76.5 and 51.7% of the spatial variability of maximum and minimum temperatures, respectively. For precipitation, the model explained an average of 60.2% of the spatial variability during the whole year and 70.7% during the wet season (September to April). The accuracy of the models was assessed through cross-validation between predicted and observed values using a set of statistics including the coefficient of determination (R 2), Mean Absolute Error (MAE) and Willmott's D. The cross-validation results were satisfactory for maximum temperature (average MAE = 1.03°C) and total precipitation (average MAE = 2.73 mm). A poorer fit of the model was obtained for minimum temperature (average MAE = 1.72°C). For each climatic variable, digital maps were finally obtained at a spatial resolution of 1 km. Considering the favourable results obtained using only a small number of observatories, such digital maps have significant potential for the study of climate change and climate impact assessment.
Theoretical and Applied Climatology, 2007
In this study, spatial interpolation techniques have been applied to develop an objective climatic cartography of precipitation in the Iberian Peninsula (583,551 km 2). The resulting maps have a 200m spatial resolution and a monthly temporal resolution. Multiple regression, combined with a residual correction method, has been used to interpolate the observed data collected from the meteorological stations. This method is attractive as it takes into account geographic information (independent variables) to interpolate the climatic data (dependent variable). Several models have been developed using different independent variables, applying several interpolation techniques and grouping the observed data into different subsets (drainage basin models) or into a single set (global model). Each map is provided with its associated accuracy, which is obtained through a simple regression between independent observed data and predicted values. This validation has shown that the most accurate results are obtained when using the global model with multiple regression mixed with the splines interpolation of the residuals. In this optimum case, the average R 2 (mean of all the months) is 0.85. The entire process has been implemented in a GIS (Geographic Information System) which has greatly facilitated the filtering, querying, mapping and distributing of the final cartography.
Objective air temperature mapping for the Iberian Peninsula using spatial interpolation and GIS
International Journal of Climatology, 2007
This study presents an objective mapping of monthly mean air temperature over the Iberian Peninsula using the spatial interpolation of climatological data. The research focuses on an interpolation method (multiple regression with residual correction) that combines statistical global analysis with a local interpolation (splines and inverse distance weighting). Geographical information (the independent variables) is used to predict air temperature (the dependent variable) through the regression relationship and to test several approaches. A comprehensive GIS implementation makes it possible to address many cartographic issues and to produce mean minimum, mean and mean maximum air temperature surfaces for the entire Iberian Peninsula. The spatial resolution of the maps is 200 m and their temporal resolution is monthly and annual. An associated error level obtained through validation tests with independent data is computed for each map. These validation tests show that the best results have an average R 2 (mean of all months) of 0.91 (mean temperature), 0.87 (mean maximum temperature) and 0.83 (mean minimum temperature). When the objective is to map a large area, best results are obtained when the model uses all stations of the Iberian Peninsula together (general peninsular model) and not when using different local subsets (hydrographical basins regional models). However, when the objective is mapping a regional area, basin models produce better outcomes than the general model being applied to these regional areas.
The use of GIS to evaluate and map extreme maximum and minimum temperatures in Spain
Meteorological Applications, 2006
Spanish building legislation has recently changed and now requires an updated and restructured Technical Building Code which is in accordance with European directives. The norm contained in this Code is based on studies of extreme values for climatic elements such as temperature, precipitation and wind. Revised maps of extreme values for climatic elements with a 50-year recurrence interval are required. Here, extreme maximum and minimum temperature maps for Spain are evaluated and mapped by means of geographical information technology. The data are extracted from the historical database held by the Spanish Meteorological Institute. Daily extreme temperatures from 1,181 stations with records going back more than 30 years have been used. The maximum and minimum temperatures are determined as 50-year mean recurrence interval values. To obtain these values, a Gumbel distribution is fitted to the extreme annual values extracted from the database. Spatial interpolation in a regular 5 km×5 km grid of the annual maximum temperature is made by ordinary kriging. Meanwhile, for the annual minimum temperature a residual kriging has been applied due to its strong dependence on altitude. Copyright © 2006 John Wiley & Sons, Ltd.
Climate Research, 2003
This paper analyzes the validity of various precipitation and temperature maps obtained by means of diverse interpolation methods. The study was carried out in an area where geographic differences and spatial climatic diversity are significant (the middle Ebro Valley in the northeast of Spain). Two variables, annual precipitation and temperature, and several interpolation methods were used in the climate mapping: global interpolators (trend surfaces and regression models), local interpolators (Thiessen polygons, inverse distance weighting, splines), geostatistical methods (simple kriging, ordinary kriging, block kriging, directional kriging, universal kriging and co-kriging) and mixed methods (combined global, local and geostatistical methods). The validity of the maps was checked through independent test weather stations (30% of the original stations). Different statistical accuracy measurements determined the quality of the models. The results show that some interpolation methods are very similar. Nevertheless, in the case of precipitation maps, we obtained the best results using geostatistical methods and a regression model formed by 4 geographic and topographic variables. The best results for temperature mapping were obtained using the regression-based method. The accuracy measurements obtained by the different interpolation methods change significantly depending on the climatic variable mapped. The validity of interpolation methods in the creation of climatic maps, useful for agricultural and hydrologic management, is discussed.
Theoretical and Applied Climatology, 2013
This study presents a methodology for modeling and mapping the seasonal and annual air temperature and precipitation climate normals over Greece using several topographical and geographical parameters. Data series of air temperature and precipitation from 84 weather stations distributed evenly over Greece are used along with a set of topographical and geographical parameters extracted with Geographic Information System methods from a digital elevation model (DEM). Normalized difference vegetation index (NDVI) obtained from MODIS Aqua satellite data is also used as a geographical parameter. First, the relation of the two climate elements to the topographical and geographical parameters was investigated based on the Pearson's correlation coefficient to identify the parameters that mostly affect the spatial variability of air temperature and precipitation over Greece. Then a backward stepwise multiple regression was applied to add topographical and geographical parameters as independent variables into a regression equation and develop linear estimation models for both climate parameters. These models are subjected to residual correction using different local interpolation methods, in an attempt to refine the estimated values. The validity of these models is checked through cross-validation error statistics against an independent test subset of station data. The topographical and geographical parameters used as independent variables in the multiple regression models are mostly those found to be strongly correlated with both climatic variables. Models perform best for annual and spring temperatures and effectively for winter and autumn temperatures. Summer temperature spatial variability is rather poorly simulated by the multiple regression model. On the contrary, best performance is obtained for summer and autumn precipitation while the multiple regression model is not able to simulate effectively the spatial distribution of spring precipitation. Results revealed also a relatively weaker model performance for precipitation than that for air temperature probably due to the highly variable nature of precipitation compared to the relatively low spatial variability of air temperature field. The correction of the developed regression models using residuals improved though not significantly the interpolation accuracy.
Geostatistical modelling of air temperature in a mountainous region of Northern Spain
Air temperature is one of the most important factors affecting vegetation and controlling key ecological processes. Air temperature models were compared in a mountainous region (Asturias in the North of Spain) derived from five geostatistical and two regression models, using data for January (coolest month) and August (warmest month). The geostatistical models include the ordinary kriging (OK), developed in the XY plane and in the X, Yand Z-axis (OKxyz), with zonal anisotropy in the Z-axis (variogram fitting procedure developed in this study), and three techniques that introduce elevation as an explanatory variable: ordinary kriging with external drift (OKED) and universal kriging, using the ordinary least squares (OLS) residuals to estimate the variogram (UK1) or the generalised least squares (GLS) residuals (UK2). The OKED, UK1 and UK2 techniques were more satisfactory than OK in terms of standard prediction error and mean absolute error, which were inferior by 1 8C, but OKxyz improved the results obtained with those techniques. Moreover, OKxyz, OKED, UK1 and UK2 improved slightly the results of a regression model with UTM coordinates and elevation data as independent variables in terms of bias (R1); whereas a complex regression model, which includes altitude, latitude, distance to the sea and solar radiance as independent variables (R2), showed better results in terms of mean absolute error, under 0.16 8C for both months. A second validation carried out with stations discarded for the interpolation showed a greater similarity between the efficiency of R2 and the geostatistical techniques.
A GIS-based method for deriving high-resolution (in space) maps of mean temperature (Base Period; 1971 is developed for Turkey. Heights (one of terrain variables) and lapse rate value (changing rate of temperature with height) are used as predictors of temperatures on 1km resolution of grid points, using a lapse rate-based approach. In this study, mean annual temperature values measured at 228 meteorological stations of Turkish State Meteorological Service over Turkey are used for visualization and interpolation to reveal spatial distribution of mean annual temperature values. Mean annual temperatures have been obtained from period of 1971-2000 long term temperature data sets. Elevation data have been obtained from digital elevation models (DEM) with the help of GIS. There have been studied with temperature data of in and around Uludağ stations to determine value of lapse rate. Lapse rate have been found average 5°CKm -1 with regression coefficient (R 2 ) 0.97. Temperature data from 78 stations for first group and 103 stations for second group have been selected from 228 meteorological stations and used during the study. 150 stations for first group and 125 stations for second group were retained for validation. For observations and predicted temperature values of first group (150 stations); maximum, minimum and mean errors are respectively, 2.89, -3.20 and -0.14°C and root-mean-squareerror (RMSE) is 1,025 and regression coefficient (R 2 ) is 0.93. For observations and predicted temperature values of second group (125 stations); maximum, minimum and mean errors are respectively, 2.64, -3.17 and -0.18°C and root-mean-square-error (RMSE) is 0,868 and regression coefficient (R 2 ) is 0.94. In addition, the method was applied to ERA40 re-analysis data set of the European Center for Medium-Term Weather Forecasts (ECMWF) for method validation. For observations and extracted temperature values of stations; maximum, minimum and mean errors are respectively, 3.1, -3.8 and -0.3°C and root-mean-square-error (RMSE) is 1.114 and regression coefficient (R 2 ) is 0.94. Predicted temperature values in study, were compared with mean temperature data from the World Climate Data (WorldClim) which were produced by ANUSPLIN model for data verification. For predicted and WorldClim temperature values; maximum, minimum and mean errors are respectively, 2.5, -1.9 and 0.5°C and root-mean-square-error (RMSE) is 0.793 and regression coefficient (R 2 ) is 0.97.
International Journal of Climatology, 2009
The spatial pattern of precipitation is a complex variable that strongly depends on other geographic and topographic factors. As precipitation is usually known only at certain locations, interpolation procedures are needed in order to predict this variable in other regions. The use of multivariate interpolation methods is usually preferred, as secondary variables -generally derived using GIS tools -correlated with precipitation can be included. In this paper, a comparative study on different univariate and multivariate interpolation methodologies is presented. Our study area is centred in the region of Valencia, located to the eastern Spanish Mediterranean coast. The followed methodology can be divided in three steps. First, secondary variables having significant correlations with the precipitation were derived, where the hillsides were used as influence areas of certain variables. Secondly, precipitation was interpolated with different methodologies. Finally, the derived models were compared in terms of predicted errors. Models were achieved for seasonal scales, considering a total of 179 raingauges; data of another 45 raingauges were also used to predict errors. Results prove that there is no ideal method for all the cases but it will depend on one hand, on the number of geographical factors that influence the rainfall and, on the other hand, on the major or minor spatial correlation within the rainfall.
Meteorological Applications, 2007
An empirical methodology in modelling and mapping air temperatures (monthly minimum, mean and maximum), monthly relative humidities and cumulative precipitation using geographical information system (GIS) techniques was proposed. Linear regression analyses were developed between weather data and some of the geographical and climatical variables (altitude, monthly mean temperature and relative humidity) of the study area. Data were obtained from 11 different meteorological stations located in the study area, and elaborated from a 250 m resolution digital elevation model (DEM). Analyses of digital layers of each independent variable with basic GIS techniques were used, and the most suitable models obtained from regression analysis were used to create final maps. The coefficients of determination for monthly mean and minimum temperatures were 0.68 and 0.98, respectively. In the case of monthly relative humidity, r2 ranged between 0.80 and 0.98, while in the case of monthly cumulative precipitation, it ranged from 0.82 to 0.98, respectively. Maximum monthly temperature, particularly for the summer months had a low relationship with elevation, therefore determination coefficient ranged from 0.46 to 0.81. When spatial information is available, the proposed method could be used as an alternative to classical interpolation techniques. Copyright © 2007 Royal Meteorological Society