An evaluation
of Spatial Interpolation Methods for
Estimating Rainfall and Air Temperature
in Egypt
Dr. Mohamed Mohamed Abdelaal Ibrahim
Lecture of Physical Geography, Social Studies Department, Faculty of Education, Mansoura University, Egypt
THE ARABIAN JOURNAL OF GEOGRAPHICAL INFORMATION SYSTEMS,VOL. (9), NO. (2), DhuAlHijjah 1437 A.H./ October 2016 A.D.
Abstract:
GIS layers for climatic data, which were generated based on irregularly distributed points, are very important for many disciplines such as climatology, agriculture and hydrology. This study aims to evaluate eight spatial interpolation techniques such as IDW, spline, and kriging, and to compare their performance in generating optimal spatial distributions of monthly mean maximum and minimum temperature and total rainfall from meteorological stations network located across Egypt. Interpolation techniques were applied to monthly climatic normals over 40-year period for more than 40 spatially- representative observatories in Egypt covering different micro-climate conditions. Different statistical accuracy measurements such as Willmott Statistical methods and index of Nash-Sutcliffe efficiency criterion were used to determine the best methods. Results demonstrate that optimal temperature mapping was obtained using Kok, Kr, SWT interpolation methods. The performance of LPI and Kok interpolation methods was better than that of other methods as they produced better outcomes of surfaces prediction for rainfall.
Key Words: Geostatistical techniques, spatial interpolation, rainfall, temperature, cross-validation, Egypt
1. Introduction:
The need for developing spatial interpolation methods of climatic data from spare networks weather station has grown dramatically over the past decade, especially with the progress of computer technology and it has been the focus of many climate studies (Thiessen, 1911; Shepard, 1968; Hughes, 1982; Hutchinson & Bischof, 1983; Tabios, et al, 1985; Phillips et al, 1992; Daly, 1994; Holdaway, 1996; Price, et al, 2000; Vicente-Serrano, et al, 2003; Guler, et al, 2007; de Amorim et al, 2013). Geoestatistics is a branch of science that applies statistical methods to describe and evaluate the spatial variability of climatic events, allowing their mapping, quantification and modeling through the interpolation of sampled measurement points in space (Correa, et al, 2014; Wanderley et al, 2013)
It is a difficult task for a climatologist or a meteorologist to find a dense spatial climatic data covering wide and extended areas, and to determine where they are most needed in places where no climate observations have been made. That is because the climate data is recorded at too much permanent disperse ground-weather stations, and therefore many statistical and geostatistical interpolation methods have been developed to predict climatic values in areas without ground-weather stations (Vicente-Serrano, et al, 2003; Dobesch et al, 2007). Additionally, the networks of surface weather stations are usually concentrated on lower altitudes and populated areas (Tveito, et al, 2008). GIS capabilities and applications allow users of spatiotemporal (space-time) interpolation methods to estimate unknown values at unsampled times and locations with a satisfying level of accuracy (Li & Revesz, 2004). Climate data collected on a sparse table network of monitoring points are interpolated to a regular grid of points using GIS (Boer et al, 2001). It is argued that climate effects are felt locally, and that they are region-specific. Hence, it is crucial to use spatial interpolation techniques for climate forecasts at any place for any time (Bhowmik, 2012).
Spatially continuous data (or GIS layers), which were generated based on irregularly distributed points, are very important for many disciplines, particularly climatology. Furthermore, there is a surprising diversity of methods which perform such task. Each method has its own advantages and drawbacks with reference to the characteristics of the set of point data. Therefore, it is important to select the appropriate method which reproduces the actual surface as closely as possible (Caruso & Quarta, 1998). Li and Heap (2011) noted that there are several factors affecting the predictive performance of spatial interpolation methods. These methods include sampling density, sample spatial distribution, sample clustering, surface type, data variance, data normality, quality of secondary information, stratification, grid size or resolution, and interactions which may exist between these different factors.
Spatial interpolation methods have been applied to various disciplines. The applications of geostatistics are found in a wide range of fields; the top fields are (1) geosciences, (2) water resources, (3) environmental sciences, 4) agriculture or soil sciences, (5) mathematics, (6) statistics and probability, (7) ecology, (8) civil engineering, (9) petroleum engineering, and (10) limnology (Li & Heap, 2014). Rainfall data is very important input for hydrological models and plant growth models; however, it is characterized by spatial and temporal variations, very irregular or unpredictable, poorly distributed and fickle due to strongly influenced by local or regional factors such as topographic features and dominant direction for wind at the time of rainfall. Accordingly, it is important to transform rain gauge data into surface/polygon data (Mutua, F. & Kuria, D., 2012; Sumner, G., 1998; Nalder & Wein, 1998).
The main objectives of this paper are to evaluate eight of the most common GIS-based interpolation techniques such as IDW, spline, and kriging, and to compare their performance in generating optimal spatial distributions of monthly mean maximum and minimum temperature and total rainfall for meteorological stations in Egypt. Additionally, it seeks to obtain the most accurate spatially interpolated surfaces of mean maximum and minimum temperature and total rainfall in Egypt at different time scales (monthly, seasonally, annually) and to reach a finer resolution with the best interpolation method for hydrological and plant growth models. In addition to producing accurate climatic surfaces, this evaluation allows us to explore the relationship between climatic elements (in our case, rainfall and air temperature as the dependent variable) and climatic factors (altitude, latitude and longitude as the independent variables.
5. Conclusions:
This study compared eight spatial interpolation methods (Global polynomial interpolation (GPI), local polynomial interpolation(LPI), Inverse distance weighted Interpolation(IDW), Completely regularized spline (CRS), spline with tension (SWT), Ordinary Kriging (Kr), CoKriging (Kok) and Linear Regression (Reg) using three topographical factors: elevation, longitude ,and latitude) to over 40-year monthly climate data recorded for (mean maximum and minimum temperatures and total rainfall ) during 1960-2006 fom a network of over 40 weather stations located across in Egypt. To decide the best spatial interpolation method for estimating temperature and rainfall in Egypt, The performances of the eight interpolation methods used in our study were assessed and compared using different types of measures for errors: Univariate statistical measures, Willmott Statistical method, index of agreement (d) and Nash – Sutcliffe efficiency criterion.
For temperature, the results indicated that Kok, Kr, SWT and Reg methods have the lowest values of RMSE and highest values for the Nash – Sutcliffe efficiency criterion (NSC), so were more reliable than other interpolation methods used in our study for monthly and seasonally mean maximum temperature. For monthly, it was not clear a single specific pattern for all or mostly months .for seasonally, The RMSE ranged from 0.6 to 1.5 °C for Kr interpolation method, the highest values for the NSC index was (from 0.82 to 0.94 for Kr) Also the results have shown that Kok and IDW were the most suitable interpolation methods for monthly and seasonally mean minimum temperature. For monthly , The RMSE range between 0.80 and 1.5 °C for IDW, from 0.95 to 1.5 °C for Kok, the highest values for the NSC index were (from 0.74 , 0.81 ) With the application of Kok and IDW. For seasonally, the RMSE range between 0.8 and 1.2 °C for IDW, from 1 to 1.2 °C for Kok, the highest values for the NSC index were ( 0.63 , 0.59) with the application of Kok and IDW respectively. For rainfall, the performance of LPI and Kok were better than other methods used in this study for monthly rainfall. On the other hand, LPI method has the lowest RMSE of (35.73, 27.24, 3.46, 6.26) mm and has highest values of Nash – Sutcliffe efficiency criterion (NSC) (0.66, 0.63, 0.75, 0.71) respectively for (annual, winter, spring, and fall). This indicates that LPI method is a bit better and more reliable than other interpolation methods used in the present study for seasonal and annual rainfall in Egypt.
The simple and multiple regression with geostatistical model were not the better interpolation models for temperature and rainfall estimation in Egypt for most months and seasons. This can be explained because the representative of low-density for weather station network using in our study, and these Network have an irregular spatial distribution, mostly located in populated areas and lower altitudes. Our results conform to similar results of previous studies; still, some other results are markedly different with reference to in Mediterranean area, the Middle East and Africa. The obtained results are reasonable, logical and suitable with climatic conditions in Egypt and its effecting factors. Obtaining accurate and reliable climatic maps is an important issue to conduct and complete several studies in disciplines such as environment, agriculture, bioclimatology and hydrology.
تقييم طرق الاستيفاء المكاني لتقدير الأمطار ودرجة حرارة
الهواء في مصر
د. محمد محمد عبد العال إبراهيم
مدرس الجغرافيا الطبيعية بقسم المواد الاجتماعية
كلية التربية، جامعة المنصورة، مصر
الملخص:
يُعد إنشاء طبقات نظم املعلومات الجغرافية للبيانات المناخية باستخدام نقاط موزعة بشكل غير منتظم ؛ أمر ذو أهمية للعديد من المجالات مثل المناخ والزراعة والهيدرولوجيا . لقد هدفت هذه الدراسة إلى تقييم ثمانية طرق للاستيفاء المكاني Interpolation منها kriging and, spline, IDW ومقارنة نتائجها للحصول على التوزيع المكاني الأمثل لمتوسط درجات الحرارة العظمى والصغرى و كمية الأمطار من خلال شبكة من المحطات المناخية داخل مصر. وقد اعتُمد خلال الدراسة على بيانات معدلات مناخية تم استخراجها من سلسلة متوسطات شهرية لفترة تزيد عن 40 سنة لأكثر من 40 محطة مناخية موزعة مكانياً قدر المتاح لتمثل أغلب الظروف المناخية الدقيقة داخل الأراضي المصرية . وقد استخدمت عدة قياسات للدقة الإحصائية مثل and methods Statistical Willmott efficiency Sutcliffe-Nash of index لتحديد أفضل هذه الطرق . وقد بينت النتائج أن الخرائط المثلى لتوزيع درجات الحرارة تم الحصول عليها باستخدام طرق ,Kr, Kok SWT . وبالنسبة للمطر كان أداء الاستيفاء لطرق Kok and LPI أفضل من الطرق الأخرى لأنها تنتج أفضل سطوح التنبؤ المكاني للمطر في مصر .
الكلمات المفتاحية: التقنيات الجيوإحصائية، الاستيفاء المكاني، الأمطار، درجة الحرارة، التحقق من الصحة، مصر.
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