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A Comparison of Three Statistical Downscaling Techniques Int.J.Curr.Microbiol.App.Sci (2020) 9(1): 1676-1688 International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 9 Number 1 (2020) Journal homepage: http://www.ijcmas.com Original Research Article https://doi.org/10.20546/ijcmas.2020.901.185 Downscaling daily precipitation over the Upper Shivnath basin: A comparison of three statistical downscaling techniques Vijay Kumar Singh* and Devendra Kumar Department of Soil and Water Conservation Engineering, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand – 263145, India *Corresponding author ABSTRACT In this study, three different statistical downscale method namely delta method K e yw or ds (DM), quantile mapping method (QMM) and empirical quantile mapping method (EQMM) are compare to downscale precipitation for Upper Shivnath basin. Model Delta method, for Interdisciplinary Research on Climate 5 (MIRCO5) simulation model is used quantile mapping for future Representative Concentration Pathways (RCP) scenarios (RCP4.5) to method and empirical quantile project decadal changes in precipitationacross eighteen selected stations of Upper mapping method, Shivnath basin. Three decades are selected for future prediction viz. 2020–2039, downscale 2040–2059, 2060–2079 and 2080-2099 and are compared decadal variabilities with period 1990–2013. Delta method is accurately downscale precipitation from Article Info Model for Interdisciplinary Research on Climate (MIRCO5) model for future Representative Concentration Pathways (RCP) scenarios (RCP4.5) to project Accepted: 15 December 2019 decadal changes in precipitationacross eighteen selected stations of Upper Available Online: Shivnath basin as compere to quantile mapping and empirical quantile mapping 20 January 2020 methods. The downscale precipitation of Upper Shivnath basin from Interdisciplinary Research on Climate (MIRCO5) model for 2020-2039, 2040- 2059, 2060-2079 and 2080-2099 scenarios are observed decrease trend in future. Introduction climate change, it is then essential to develop tools to study the regional climate and its Chhatishgarh state has a great variety of possible evolution. Global climate models climates, mainly given its vast latitudinal (GCM) have been designed to describe large- extent, its complex orography. These features scale climate characteristics and the potential in complex combination with remote forcing evolution of climate under future emission contribute to define the climates of Shivnath scenarios (Meehl et al., 2007). However, basin and their variability. With such a varied GCM still show major deficiencies when it range of climate regions and in the context of comes to representing small-scale processes 1676 Int.J.Curr.Microbiol.App.Sci (2020) 9(1): 1676-1688 that may condition the regional climate Materials and Methods (Bettolli & Penalba, 2014; Maenza, AgostaScarel, & Bettolli, 2017; Silvestri & The Mahanadi river is one of the major inter- Vera, 2008). Therefore, in order to generate state east flowing perennial rivers in climate information at a regional or local peninsular India. Number of tributaries viz. scale, it is necessary to apply downscaling Shivnath, Jonk, Hamp, Pairi and Kharun joins techniques to relate large-scale information Mahanadi river. The river basin is sub divided provided by GCM to the regional-scale into three sub-basins viz. upper, middle and climate information needed to assess impacts lower Mahanadi basins. The upper basin and and decision-making. To obtain high- part of middle Mahanadi basin falls in resolution projections, two approaches are Chhattisgarh state while the lower basin falls commonly used. Dynamical downscaling under Odisha state. The total catchment area techniques are based on regional climate of the Upper Mahanadi Basin is 29,759 Km2 models (RCMs) that simulate regional climate and it covers the districts of Bilaspur, processes with a greater spatial resolution, Kawardha, Raipur, Bemetara, Dhamtari, using GCM fields as boundary conditions Kabirdham, Rajnanadgaon and Durg. (Giorgi & Mearns, 1991; Rummukainen, 2010). On the other hand, empirical statistical On scrutiny of all the watersheds of sub-basin downscaling (ESD) methods generate climate of upper Mahanadi Basin, the Upper Shivnath information at local scale or with a greater river basin is selected for the current study. resolution than that achieved by GCMs by The Upper Shivnath river originates near means of empirical/statistical relationships village Panabaras in the Rajnandgaon District. between large-scale atmospheric variables The Upper Shivnath basin is located between and local climate (Wilby et al., 2004). latitude 200 16' N to 220 41' N and Longitude Comparative studies between RCM and ESD 800 25' E to 82035' E. The Basin area of river simulations suggest that both approaches up to confluence with the Mahanadi River is show comparable skills to represent regional 16786 Km2. The river traverses a length 380 climate characteristics (Huth et al., 2015; Km. The main tributaries of Upper Shivnath Menéndez et al., 2009), adding value to GCM river are Tandula, Kharun, Arpa, Hamp, Agar simulations. and Maniyari Rivers. The Upper Shivnath river is mainstream of the Shivnath river and Therefore, efforts to develop and validate its outlet is at Sigma. The Shivnath river is different regional climate modelling situated at the upper most boundary of the techniques are of great importance not only to Mahanadi basin and most of the upland area better understand the climate system, but also with steep slopes causes losses of soil, to generate detailed and tailored climate providing essential reasoning for the selection information. Therefore, the objective of this of the watershed for this study.The northern study is to calibrate and validate the analogue and western parts are surrounded by Maikal method as a technique to downscale daily mountain ranges of Satpura. Here the highest precipitation of Upper Shivnath basin. This peak is Kesmarda in Maikal Mountain, which region in particular was selected mainly is 978 m high, while the lowest elevation of because it extends over the most productive the watershed is 237 m. The central east and agricultural area, whose predominant southern part of the Shivnath basin is plain, activities are extensive rainfed agriculture and whereas the northern and western part is livestock farming. mountainous. The climate of upper Shivnath is temperate, where the maximum 1677 Int.J.Curr.Microbiol.App.Sci (2020) 9(1): 1676-1688 temperature in summer is 420C and the bridged the gap between coarse and fine scale minimum temperature in winter goes to the climatic data. Downscaling can be performed lowest of 150C. The overall climate of the on spatial and temporal aspects of climate area can be classified as sub-humid tropical. projections. Spatial downscaling methods are Major crops grown in the area are paddy used to extract finer-resolution spatial climate (rice), maize, soybean in Kharif (monsoon information from coarser-resolution GCM season) and chick pea and sugarcane in Rabi output. (winter season). Downscaling concept is classified into two Data acquisition types downscaling namely dynamical downscaling (DD) and statistical downscaling Meteorological data (SD). The dynamical downscaling is very computationally intensive, very complex, The daily rainfall data for 24 years (1990 - requires substantial computational resources, 2013) is acquired from Hydrometeorology making its use in impact studies limited and Division of Central Water Commission essentially impossible for multi-decade (CWC) and Meteorological Centre of Indian simulations. Therefore, statistical Meteorological Department, Raipur, downscaling is used in present study. In Chhattisgarh. Due to unavailability of general, the statistical methods can be divided location specific data of other meteorological into three categories: regression (transfer observations, the global and high resolution function) method, stochastic weather meteorological data such as precipitation generator and weather pattern schemes. There (mm) is downloaded from Climate Forecast are a numerous number of statistical System Reanalysis (CFSR) site downscaling methods. One of the most (https://rda.ucar.edu/pub/cfsr.html). Long popular and common of them is bias term data based on availability were also correction (BC) that has been applied collected from the Hydrology Data Center, extensively for impact assessment and Department of Water Resources, Govt. of employed in climate change studies in all over Chhattisgarh for two rainfall gauging stations the world. These methods include many namely Amdi and Singdai, which lies within statistical techniques that is run with various Kharun and Upper Shivnath basin. The application in every part of the world, but elevation, location and list of meteorological almost many of these applications with stations, which are acquired from different CMIP5 outputs are difficult to run by sources. respectively are given in Table 1. researchers. In the present studied, three methods of statistical downscaling namely Downscaling concept delta method (DM), quantile mapping method (QMM) and empirical quantile mapping Raw outputs from General Circulation Model method(EQMM) are used. All of them are (GCM) simulations are inadequate for explained in the following paragraphs. As assessing the impact of hydrological, presented in Eq. 3.16 the precipitation of agricultural, and other studies. Due to GCM data are downscaled. inadequate and too coarse
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