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Assessment of GCM and Scenario Uncertainty to Project Streamflow Under Climate Change

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Assessment of GCM and Scenario Uncertainty to Project Streamflow Under Climate Change Assessment of GCM and Scenario Uncertainty to Project Streamflow Under Climate Change 1 2 Das Subhadarsini , *N.V. Umamahesh 1 Research Scholar, National Institute of Technology Warangal, Telangana, India 2 Professor, National Institute of Technology Warangal, Telangana, India ABSTRACT: The ongoing process of global heating and climate change controlled by different driving forces such as greenhouse gases, aerosol and others, relying on regional and local spatial scales are highly uncertain. In India, climate projections indicated the increments in temperature and rainfall are likely to be around 30 C and 10 to 20% respectively over central India by the end of this century (IPCC 2007 a,b,c). Therefore, it is necessary to assess the importance of climate change. A single trajectory derived from a single GCM with single climate change scenario cannot represent a future hydrologic scenario. Thus for this study, the purpose is to understand the importance of the climate change by applying a weightage to different GCMs and scenarios according to their capability to model climate change for flow data by SWAT(Soil and Water Assessment tool) Model. Arc SWAT 2012 version is used for simulations of streamflow over Indravati Sub-basin of Godavari basin, India and SWAT-CUP (SUFI2 Algorithm) is used for calibration and validation of SWAT model. We have used Quantile Mapping bias-correction method for GCM Models (ACCESS, CCSM4, CNRM, GFDL, MPI, NORESM) and its scenario (RCP4.5 and RCP8.5) data to avoid some data uncertainty. By taking this bias- corrected data into SWAT with calibrated model parameters, runoff has been predicted for recent past years (2006-2013) when the climate indicates its fluctuations. Then, possibility as a weightage factor (i.e. performance measure) (C) same as Nash–Sutcliff Efficiency (NSE) are given to each GCM and its Scenarios. The results which are derived from the equation of ‘C’ for all the six GCM and its scenarios are normalized first as there should be at least one GCM with its associated scenario with a possibility value 1. Then, by considering the possibilities for all GCM and its Scenario, future (2020-2040, 2041-2070, 2071-2099) streamflow has been predicted. The possibilistic mean CDF (Fpm) is computed by using the possibility values and the CDFs of streamflow, obtained for each GCM with scenario for the time slices 2020-2040, 2041-2070, 2071-2099.The possibilistic mean CDFs for years 2020-2040, 2041-2070, 2071- 2099 when compared with the CDF of observed stream flow of 2006-2013 indicates that the value of streamflow at which the possibilistic mean CDF reaches the value of 1 for years (2020- 2040), (2041-2070), (2071-2099) are lower than that of baseline period 2006-2013. The streamflow reduces for time slices (2020-2040) and (2041-2070) and then increases slightly for (2071-2099), but is less than the value of maximum streamflow for baseline period. Keywords: GCM,CDF, Possibilistic mean, Streamflow *Corresponding Author: N.V. Umamahesh, Professor, National Institute of Technology Warangal, Telangana, India E-mail: [email protected] 1 1 INTRODUCTION Climate change is a global event, relying on regional or local spatial scales that are highly uncertain and limiting multiple sources. India is a fast developing country with 2/3rd of its population directly dependent on climate-sensitive areas such as agriculture, fisheries etc. The climate areas of India are controlled by the climatic conditions of the monsoon. The ongoing process of global heating and changes in sea surface temperature are the reasons behind the disturbances in temperature and precipitation. Therefore, it is vital to assess the importance of climate change at river basin level. Downscaling is a method of formulating hydrological variables at a regional level, which is based on Global Climate Model (GCM)'s large-scale results. But the downscaled GCM data also contains different heights of uncertainty such as uncertainty of GCM, internal model fluctuations, uncertainty of scenarios or inconsistencies between scenarios. So a single climate model with its single scenario cannot represent a forthcoming hydrological scenario and will never be useful to evaluate the hydrological impact as a result of climate change. GCMs are used for weather forecast prediction. The Special Report on Emission Scenarios (SRES), identifies the greenhouse gas emissions scenarios to make predictions of possible forthcoming changes in climate, was published in 2000 by the IPCC. SRES was followed by representative concentration paths (RCPs) in 2014. The four RCPs, RCP2.6, RCP4.5, RCP6 and RCP8.5 are named subsequently on a potential range of radiative compelling values in the year of 2100, comparable to pre-industrial values (+2.6, +4.5, +6.0, and + 8.5 W / m2, respectively). A comparison was done with the uncertainty in flow simulation due to modelling of precipitation (using 3 GCMs and two scaling techniques) with the existing natural flow variability in three catchment areas in Scotland m (Prudhomme & Davies, 2009).The result showed that the natural variability could be large and vary from one catchment to another and the uncertainty as a result of GCMs was constantly greater than those of scaling techniques. (Mujumdar & Ghosh, 2008) modelled GCM and scenario uncertainty. They calculated the possibilistic average of the CDFs((Cumulative Distribution Functions) estimated for 3 standard time slots 2020s, 2050s and 2080s. Here, results indicate that the value of current flow at which the CDF reaches 1 decreases, confirming the reduction in probability of incidence of extremely high flow events in the future. Then again the uncertainty with inaccurate probability based on the impact of climate change was modelled(Ghosh & Mujumdar, 2009). He assigned weights to the GCMs convergence, which is estimated by the CDFs generated from GCM output and observed data. Previously,(Chen, Achberger, Räisänen, & Hellström, 2006) proposed a method where the distribution of the evaluations could be used as a measure of uncertainty associated with GCMs when tried to use standardized GCM simulations and found the differences in the climate models and the natural variability in the simulated climates were effected by difference in the downscaled variables. It has been shown that the projected uncertainty was almost independent. However, there was a periodic dependency. The impact of climate change was also investigated on the frequency of flood analysis by discussing about diverse sources of uncertainties (Kay, Davies, Bell, & Jones, 2009). GCM-related fading in climate change with 7 GCMs was investigated (Thompson, Crawley, & Kingston, 2016) and the decline in precipitation was found for some GCMs while PET increases for all scenarios. The flood area for some GCM increases while it is falling for other GCMs. In Godavari basin there are many research papers about the impact of climate change. A study was conducted about how global surface temperature and the global warming induced 2 by earthquake were likely to play a significant role in the organization of water resources of Godavari River Basin (Jhajharia, Dinpashoh, Kahya, Choudhary, & Singh, 2014). The trends in temperature across the Godavari basin were dissimilar at different stations for different months. Statistical downscaling technique was also used with monthly precipitation of Godavari River Basin, India(Das & Umamahesh, 2015). In this paper, the forth-coming scenario of monsoon precipitation on different India Meteorological Department (IMD) grids was estimated using the statistical scaling of simulations with the RCPs. They found that rainfall across the entire pool had an increasing tendency. Then (Pandey, Gosain, Paul, & Khare, 2017) gave the ideas how the climate change effect on the hydrologic conditions of Armur watershed located at Godavari River Basin, India. Finally, it was found in this study that variations in mean annual temperature (+3.25 ° C), evapotranspiration (28%), mean annual precipitation (+ 28%) and water yield (49%) increase for GHG scenarios with respect to baseline scenario by using HadRM3, a regional climate Model. SWAT (Soil and Water Assessment Tool) model can predict the quality and quantity of water by simulation and assess the characteristics of land use and human actions for sustainable water resource management. SWAT and Sequential uncertainty fitting 2 (SUFI-2) were used for estimating calibration, validation and uncertainty analysis of the model compared to historical monthly perceived flow data (Yesuf, Melesse, Zeleke, & Alamirew, 2016).The results showed that the model was usually acceptable as demonstrated for the fitness conditions by the calibration, validation and uncertainty analysis. Two types of sensitivity analysis (local and global) has to be done by determining the complex parameters for a given watershed. There are many statistical concepts that can be used to evaluate SWAT predictions that include the determination coefficient (r2), root mean square error (RMSE), Nash Sutcliffe Efficiency (NSE), t-test, objective functions, non-parametric tests, cross correlation and autocorrelation. 1.1 Objectives of Study Due to the problem of uncertainties in forthcoming hydrological scenario while considering only one trajectory alone, it is necessary to give each GCM and Scenario a weightage for flow under climate change to project. Thus, for this study, the purpose is to know the value of the climate change that comes from different GCMs and scenarios to model climate change for
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