Application of Civil Engineering Softwares for Downscaling GCM Results

Transcription

1 International Invention Journal of Engineering Science and Technology Vol. 2(1) pp. 1-9, February, 2015 Available online Copyright 2015 International Invention Journals Full Length Research Paper Application of Civil Engineering Softwares for Downscaling GCM Results I. Hassan 1, A.R. Ghumman 2 1 Assistant Professor Department of Civil Engineering Muhammad Ali Jinnah University (M.A.J.U.) Islamabad Campus Pakistan. 2 Professor, Department of Civil Engineering UET Taxila, Pakistan. Abstract Global Climate Models simulate hydrologic quantities for future planning and development. These models produce results in grid form which cannot be applied directly and, hence, require downscaling. In this study Eagle point surface modeling (EP SM) and AutoCAD2000i QuickSurf have been applied to downscale the results of precipitation simulated by global climate model named EdGCM. Both these softwares take into account three-dimensional (3-D) point data of any regular or irregular spacing and model out the area within these points in 3-D. These methods create isohyets of user-defined close interval, thus give visual output. Comparative study of EP SM and AutoCAD2000i QuickSurf with k-nn has been made. It is found that the two softwares i.e. EP SM and AutoCAD2000i QuickSurf are equally good as compared to k-nn approach. Their easy and user-friendly methodology with visual output gives an edge over the statistical methods. Keywords: EdGCM, downscaling, Eagle point, Surface modeling, AutoCAD2000i, Quick Surf, k-nn. INTRODUCTION Surface water has always been considered as an important factor for the settlement of human and other species because it plays pivotal role in in all parameters of life like agriculture, power generation, fisheries, drinking etc. (Rouder et al., 2014). The surface water experiences changes in its flow due to many factors one of which is the global warming, being the hot topic of present day. This global warming has been found continuously affecting the intensity, duration and spatial/temporal distribution of hydrologic parameters of surface water such as temperature and precipitation. This has serious impact on life in various regions of the world because the global warming is causing droughts in some areas and floods in others. Simulations of such changes are important for hydrologists and planners for future planning and management (Kang and Jorge, 2007). The researchers use climate (GCMs and RCMs) and hydrological models (Simon et al., 2014; Forsythe et al., 2014; Boe et al., 2007; Chen et al., 2007; Guo et al., *Corresponding Author eishtiaq@yahoo.com; Tel: ; Xu et al., 2005) for studies related to impact of climate change on component(s) of hydrologic cycle. But, there do exist key challenges in the application of GCMs/RCMs and hydrological models (Fowler et al., 2007). The two key issues are: (a) the spatial scales of climate models and hydrological models are inconsistent, so output of climate models cannot be applied directly as input to hydrological models, and (b) GCMs cannot meet the requirements of hydrological simulation especially in case of precipitation (Hua et al., 2012). Global Climate Models (GCMs) are the powerful tools to simulate the trends and patterns of hydrologic parameters for future. GCMs represent the atmosphere and its processes at a lesser scale. A GCM uses an analytical unit called a cell to reproduce mass and energy fluxes and storages that are related to the atmosphere (Julian and Andy, 2010). The cells of a GCM have three-dimensional object within which a number of equations are applied taking into account the effects of all neighboring cells. The size of each cell of a GCM cannot be reduced unlimitedly because of complex processing of a number of equations (Julian and Andy, 2010). Thus, GCMs produce results in the form of cells

2 2 Int. Inv. J. Eng. Sci Tech. (also called grids) with grid spacing of about 100 to 700 kms. Although efforts are made by more than twelve GCM centers all over the world, yet GCMs resolution is too coarse to assess impacts of climate change on biodiversity, agriculture, ecosystems, species distribution, planning etc. (Tabor and Williams, 2010; Zhang, 2006; Fowler et al., 2007; Salathe et al., 2007; Kremen et al., 2008; Jones and Thornton, 2003; Jarvis et al., 2008; Seiller and Anctil, 2014). This limitation of GCMs is overcome by using downscaling techniques and methods. The downscaling techniques help researchers to predict regional climate changes starting from smoothing and interpolation of GCM anomalies (Julian and Andy, 2010; Tabor and Williams, 2010) to neural networks and regional climate modelling (RCM). RCMs have resolutions ranging from 20 to 50 km and are thus, applicable to a limited number of GCMs. Dynamical and statistical downscaling methods are applied to overcome the limitations of GCMs coarser resolution for determination of a hydrological parameter. These downscaling methods provide high resolution data (Kang et al., 2009; Vasiliades et al., 2009; Ahn et al., 2012; Suchul et al., 2014). The simplest of all methods, known as change factor method, for obtaining higher resolution scenarios involves the application of coarse-scale climate change projections to a high resolution observed climate baseline (Wilby et al., 2004). Statistical techniques are being mostly applied by the researchers for downscaling GCM data. These techniques are applied affectively to predict the stationbased climate (Suchul et al., 2014). These techniques include firstly Random-Cascade and secondly Kernelbased (k-nn) nonparametric models. These models are developed for stream-flow simulation (Sharma et. al., 1997), stream-flow desegregations (Tarboton, et al., 1998) and multivariate weather generation (Rajagopalan et. al., 1997). Third fairly simple, flexible and computationally economical statistical technique involves use of stochastic weather models. In this study, two softwares have been introduced for the purpose of downscaling. These two softwares include Eagle Point with a feature called Surface Modeling (EP SM) (Ishtiaq et al., 2009; 2011) and AutoCAD2000i QuickSurf. The comparisons of findings of these two softwares have been made with the k-nn statistical output. The purpose of this paper is to provide downscaling techniques which are easy to use by the civil engineers who want to perform their effective role in climate impact studies. The methods introduced in this paper produce a smoothed (interpolated) surface for the parameter under consideration for study of climate change. These methods produce graphical output that can be readily used to observe the pattern of change across the study area. Study Area This study focuses twenty five (25) cities (stations) of Pakistan detailed in Table 1. Figure 1 shows that these stations are located in the range of latitudes x longitudes. Map of Pakistan has been superimposed on latitude-longitude grids compatible with the 8 x10 resolution (grid spacing) of EdGCM (The Basic Guide to EdGCM, ). Hence, sixteen EdGCM grid values have been used to study precipitation downscaling for 2CO 2 experiment only (Table 2). The study has been carried out for the annual averages. As this study is made to define applicability of EP SM and AutoCAD2000i, therefore, it is not of much importance to use a coarser resolution of EdGCM. METHODOLOGY The details of statistical downscaling technique (k-nn) have been explained by Abdul et al and the same has been used for calculating station values. Eagle Point (Ishtiaq et al., 2009; 2011) is the most widely used software for a variety of civil engineering design works. It has twenty one (21) modules including surface modeling, watershed modeling and water surface modeling. The surface modeling module has been applied in this research. Surface Modeling is a program package designed to generate a Triangulated Integrated Network (TIN). Surface Modeling (SM) uses an advanced algorithm to develop a three dimensional (3-D) plan on the basis of 3-D point data. Each 3-D point is defined by its two horizontal (x and y) and one vertical (z) value. Surface modeling (SM) feature of Eagle Point is applied for this purpose. Eagle Point Surface Modeling works on the principles of triangulation and contouring. The Triangulation routine of surface modeling is based on the Delaunay Triangulation approach, in which a circle drawn through the vertices of any triangle contains no other point. The model accuracy can be ensured by providing additional controls to the triangulation routine e.g. defining boundary (limits of SM) and voids (internal areas/regions with no data) etc. In this study, no such limitations have been encountered. In triangulation each 3-D point is joined with every other neighboring 3-D point by a line. As a result a TIN (Triangulated Integrated Network) of non-intersecting lines is framed. The software interpolates third (called z, i.e. precipitation in this case) value along each line taking into effect the values of end 3-D points. Creation of Isohyets is also termed as contouring in the language of civil engineering. Isohyets are the lines joining paths of same z values. SM has flexibility to create these isohyets (also called contours) of any desired interval defined by the user. In this study, this user-defined interval has been reduced from 0.75 to 0.25 mm/day for the underconsideration parameter of precipitation. Development of

3 Hassan and Ghumman 3 Table 1. Twenty-five (25) stations of Pakistan grouped in to 04 Areas (Provinces). AREA STATION STATION NAME ABREVATION ELEVATION (M) LATITUDE LONGITUDE PUNJAB (Pb) 1 ISLAMABAD ID LAHORE LE FAISALABAD FD BAHAWALPUR BR JHELUM JM MULTAN MN SARGODHA SA SIALKOT ST SIND (Sd) 1 BADIN BN KARACHI KI HYDERABAD HD KHANPUR (PBO) KP PADIDAN PN JACCOBABAD JD NWFP (Np) 1 PESHAWAR PR D.I. KHAN DN GILGIT GT KAKUL KL DROSH DH CHERAT CT BALUCHISTAN 1 QUETTA QA (Bn) 2 KHUZDAR KR PANJGUR PG BARKHAN BN ZHOB ZB Figure Stations and 04 Areas (provinces) super-imposed on of 8 x10 part-plan of EdGCM.

4 4 Int. Inv. J. Eng. Sci Tech. Table 2. EdGCM Annual precipitation grid values for Pakistan for 2CO 2 experiment GRIDS Annual LONGITUDE (DEGREES) LATITUDE(DEGREES) 2CO 2 GRID VALUES Figure 2(a). Global precipitation pattern - map for annual run generated by performing EdGCM simulation using double CO 2 experiment. isohyets makes it easy for the user to determine precipitation value at any location inside the available grid-point data of GCM output. AutoCAD2000i is also a powerful software being used for drafting two dimensional (2-D) and threedimensional (3-D) planes. QuickSurf is a tool in AutoCAD2000i that is capable of modeling out a surface in 3-D. It reads the data listed in three column (x=longitude, y=latitude, z=precipitation) of MS Excel directly. It models out the surface in a similar fashion as done in Eagle Point Surface Modeling. The results produced in it are more colorful as compared to Eagle Point Surface Modeling. Both softwares are being widely used by the civil engineers and hence are user-friendly. RESULTS AND DISCUSSIONS (i). Pattern of Isohyets: Figures 2(b) and figure 3 show pattern of precipitation isohyets. Comparison of figure 2(b) with figure 3 indicates that the pattern (shape and layout) of isohyets is identical. In figures 2(b) and 3(a and b), three values of isohyets are comparable which have the similar pattern as mentioned hereunder:

5 Hassan and Ghumman 5 Figure 2 (b). Blow up of figure 2(a) highlighting isohyets pattern around Pakistan. Figure 3 (a and b). 2CO 2 Precipitation Isohyets developed for a contour interval of 0.25mm/day by applying (a) EP SM and (b) AutoCAD2000i Quick Surf methods for EdGCM grid values given in table 2. an isohyet of value in figure 2(b) is comparable with an isohyet of 1.75 in figure 3(a and b): In these figures, the isohyet starts from west, moves up slightly in north-east direction, bends down towards south-east direction while passing through middle of the country (i.e. through south of Khyber PakhtoonKhawa (KPK) and through middle of Punjab) and finally moves out in east; an isohyet of value 2.47 in figure 2(b) is comparable with an isohyet of 2.50 in figures 3(a and b): In these figures, the isohyet starts from west, moves up slightly in north-east direction, bends down towards south-east direction while passing through middle of the country (i.e. through northern of Baluchistan and through the border of Punjab and Sind) and finally moves out in east; an isohyet of value 3.26 in figure 2(b) is comparable with an isohyet of 3.25 in figures 3(a and b): In these figures, the isohyet starts from south, moves up in north, in east, in south, in north-east and finally moves out in

6 6 Int. Inv. J. Eng. Sci Tech. Figure 4 (a and b). 2CO 2 Precipitation Isohyets developed for a contour interval of 0.50mm/day by applying (a) EP SM and (b) AutoCAD2000i Quick Surf methods for EdGCM grid values given in table 2. Figure 5(a and b). 2CO2 Precipitation Isohyets developed for a contour interval of 0.75mm/day by applying (a) EP SM and (b) AutoCAD2000i Quick Surf methods for EdGCM grid values given in table 2. the east this isohyet is passing through Baluchistan and Sind. Figure 2(b) shows only three above-mentioned isohyets passing through Pakistan while figures 3(a) and 3(b) show additional isohyets meandering through the entire country to earmark the pictorial pattern of precipitation change that can be utilized to make estimates of station values and trend of change across the country. Thus, it is very easy to read directly an isohyet value at station of interest throughout the country. (ii). Accuracy of Results: The accuracy of results has been verified by defining contour/isohyet intervals of 0.75 and 0.50 in addition to 0.25 mm/day for both Eagle Point Surface Modeling (EP SM) and AutoCAD2000i QuickSurf, as shown in figures 3, 4 and 5. The station values estimated from these figures are then compared with average annual value obtained by k-nn statistical method used as a reference/validation check. It is important to mention here that although the reading of station value from the EP SM and AutoCAD2000i QuickSurf visual output (isohyets map) depends on the readers experience and depth of understanding the trend of isohyets, yet the results of EP SM and AutoCAD2000i QuickSurf have been found closer to the statistical values as the contour interval is narrowed i.e. interval is set for 0.75, 0.50 and 0.25 (refer figure 6). Figure 7 shows station-wise comparison of the study

7 Hassan and Ghumman 7 Figure 6. Effect of Isohyet Interval on results for EP SM and AutoCAD2000i QuickSurf, a comparison with k-nn. Figure 7. Average Change for in Annual Precipitation for all stations Comparison of results obtained from k-nn statistical and Eagle Point Surface Modeling for EdGCM. area. In this figure, closeness of EP SM, AutoCAD2000i and k-nn for each station explains the accuracy of new downscaling techniques. The results are found almost same because these three methods are based on mathematical equations taking into account cause and effects of four neighboring points with known z value to interpolate the value of an encircled point (station) with unknown z value. K-NN is based on distance weightage equations (Abdul et al., 2013) and triangulation in the other two methods is based on Delaunay Triangulation algorithms. CONCLUSIONS This study introduces two new civil engineering

8 8 Int. Inv. J. Eng. Sci Tech. softwares for downscaling the results of GCMs. Both of these softwares are easy to understand and easy to apply in climate research studies by the civil engineers and others. Visual output of these two softwares has an edge over the statistical methods. All downscaling techniques perform estimation by applying interpolation procedures. Eagle Point Surface Modeling and AutoCAD2000i QuickSurf are novel softwares that can be applied to model out the surface based on the 3-D point data available at any grid spacing. The widely spaced isohyets (contours), created by a GCM, are not of any use for direct estimation of a hydrologic value (e.g. precipitation) at a station. The results produced with these approaches are identical to the contour pattern of GCM map but close interval of contouring in this method facilitates estimation easier and helpful. The results produced by Eagle Point Surface Modeling and AutoCAD2000i QuickSurf have been found as good as those produced by the k-nn statistical downscaling method with an additive advantage of showing the results in a manner that it can be realized as what is happening physically over the study area. The easier application of these two methods makes the downscaling procedure faster and reduces the errors which are otherwise expected in statistical calculations because in statistical calculations one has to define four nearest grid points data (longitude, latitude, P value) out of a list of many grid points (e.g. 72 points required to cover Pakistan in ECHAM5-OM output) for each query point and a wrong entry may lead to error in worked out station values. On the other hand, in Eagle Point Surface Modeling and AutoCAD2000i QuickSurf applications, the data list is imported directly from ASCII format into an.xls, a.csv and a text document format from which the isohyets are generated through built-in linear interpolation equations within the software. Graphical super-imposition of study area map with stations can be used to read the station values. These softwares would be very much helpful for the researchers all over the world. The ease, quickness and visual output in application of Eagle Point Surface Modeling gives this method an edge over the statistical methods. Hence, both Eagle Point Surface Modeling and AutoCAD2000i QuickSurf approaches work as satisfactorily as k-nn statistical method with an additive advantage that the researcher can see the produced pattern graphically and can make visual comparison with respective GCM-produced global map. These methods can be applied for downscaling the grid results of any GCM data. ACKNOWLEDGEMENTS We acknowledge the efforts made by the EdGCM cooperative at Columbia University who made EdGCM software available for environmental and climatic studies. This paper is based on the results reproduced by running EdGCM model software for the years REFERENCES Abdul RG, Ishtiaq H, Qaiser UZK, Mumtaz AK (2013). Investigation of impact of environmental changes on precipitation pattern of Pakistan. Environ Monitoring Assessment, Vol. 185, PP Ahn JB, Lee J, Im ES (2012). The reproducibility of surface air temperature over South Korea using dynamical downscaling and statistical correction. J. Meteorol. Soc. Jpn. 90(4), , DOI: /jmsj Boe J, Terray L, Habets F, Martin E (2007). Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies. Int. J. Climatol 27 (12), Chen H, Guo SL, Xu CY, Singh VP (2007). Historical temporal trends of hydroclimatic variables and runoff response to climate variability and their relevance in water resource management in the Hanjiang basin. J. of Hydrol. 344, Forsythe N, Fowler HJ, Blenkinsop S, Burton A, Kilsby CG, Archer DR, Harpham C, Hashmi MZ (2014). Application of a stochastic weather generator to assess climate change impacts in a semi-arid climate. The upper Indus bsin. Journal of Hydrology doi.org/ /j.jhydol Fowler HJ, Blenkinsop S, Tebaldi C (2007). Linking climate change modeling to impact studies: recent advances in downscaling techniques of hydrological modeling. International Journal of Climatology 27: Guo SL, Wang JX, Xiong LH, Ying AW, Li DF (2002). A macro-scale and semi-distributed monthly water balance model to predict climate change impacts in China. J. Hydrol. 268 (1 4), Hua C, Chong-YU, Shenglian G (2012). Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff. Journal of Hydrology, , p Ishtiaq H, Abdul RG, Hshim NH (2011). Impact of Environmental Changes and Global Warming on Temperature in Pakistan. Mehran University Research Journal of Engineering and Technology, Volume 30, No. 1 Pages Ishtiaq H, Abdul RG, Hshim NH, Abdul SS (2009). Investigation of the impact of global warming on precipitation pattern of Saudi Arabia. Civil Engineering and Environmental Systems, DOI: / Jarvis A, Lane A, Hijmans RJ (2008). The effect of climate change on crop wild relatives. Agriculture, Ecosystems and Environment p 126: Jones PG, Thornton PK (2003). The potential impacts of climate change on maize production in Africa and Latin America in Global Environmental Change 13: Julian RV, Andy J (2010). Downscaling Global Circulation Model Outputs: The Delta Method Decision and Policy Analysis. Working Paper No. 1. CIAT. Kang B, Jorge AR (2007). Response of Stream flow to weather variability under climate change in the Colorado Rockies / (ASCE) (2007) 12:1(63). Kang H, Park CK, Hameed SN, Ashok K (2009). Statistical downscaling of precipitation in Korea using multimodel output variables as predictors. Mon. Weather Rev. 137: , DOI: /2008MWR Kremen C, Cameron A, Moilanen A, Phillips SJ, Thomas CD, Beentje H, Dransfield J, Fisher BL, Glaw F, Good TC, Harper GJ, Hijmans RJ, Lees DC, Louis Jr E, Nussbaum RA, Raxworthy CJ, Razafimpahanana A, Schatz GE, Vences M, Vieites DR, Wright PC, Zjhra ML (2008). Aligning conservation priorities across taxa in Madagascar with high resolution planning tools. Science 320: Masaaki S (1987). Methods of numerical integration of oscillatory functions by the DE-formula with the Richardson extrapolation.

9 Hassan and Ghumman 9 Journal of Computational and Applied Mathematics Volume 17, Issues 1-2, Pages Rajagopalan B, Lall U, Tarboton DG, Bowels DS (1997). Multivariate non-parametric re-sampling scheme for simulation of daily weather variables. Journal of Stochastic hydrology and hydraulics 11(1), p Roudier P, Ducharne A, Feyen L (2014). Climate change impacts on runoff in West Africa: a review. Hydrol. Earth Syst. Sci., 18, , doi: /hess , Salathé PJr, Mote PW, Wiley MW (2007). Review of scenario selection and downscaling methods for the assessment of climate change impacts on hydrology in the United States pacific northwest. International Journal of Climatology, 27: Seiller G, Anctil F (2014). Climate change impacts on the hydrologic regime of a Canadian river: comparing uncertainties arising from climate natural variability and lumped hydrological model structures. Hydrol. Earth Syst. Sci., 18, , doi: /hess Sharma A, Tarboton DG, Lall U (1997). Streamflow Simulation: a nonparametric approach. Water Resource. Res. 33(2), p Simon B, Mamunur R, Rezaul KC (2014). Statistical downscaling of multi-site daily rainfall in a South Australian catchment using a Generalized Linear Model. Int. J. of Climatology 34: doi: /joc StatSoft Inc ( ). k-nearest Neighbors. STATISTICA is a trademark of StatSoft, Inc. Suchul K, Jina H, Joong-Bae A (2014). Statistical downscaling methods based on APCC multi-model ensemble for seasonal prediction over South Korea. Int. J. of Climatology, DOI: /joc Tabor K, JW Williams (2010). Globally downscaled climate projections for assessing the conservation impacts of climate change. Ecological Applications, 20(2): Tarboton DG, Sharma A, Lall U (1998). Disaggregation Procedures for stochastic verification and validation. Water Resource., Res. 34(1), p The Basic Guide to EdGCM, Version 3.2, Columbia University, USA, ( ). Vasiliades L, Loukas A, Patsonas G (2009). Evaluation of a statistical downscaling procedure for the estimation of climate change impacts on droughts Nat. Hazards. Earth Syst. Sci. 9: , DOI: /nhess Xu CY, Widen E, Halldin S (2005). Modelling hydrological consequences of climate change progress and challenges. Adv. Atmos. Sci. 22 (6), Zhang XC (2006). Spatial downscaling of global climate model output for site-specific assessment of crop production and soil erosion. Agricultural and Forest Meteorology, 135: How to cite this article: Hassan I, Ghumman AR (2015). Application of Civil Engineering Softwares for Downscaling GCM Results. Int. Inv. J. Eng. Sci. Tech. 2(1):1-9