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Flooding under changing climate in Vellar river basin using global circulation models

Flooding is one of the major natural disasters from a storm event that is prevalent in many countries and greatly affects river morphology, modifying the flora and fauna of a given river environment. As a consequence of climate change, the probability of frequent floods and drought is acute in the near future, posing serious challenges to the water management sector. This paper analyses the impact of climate change on the likelihood of floods using MIKE HYDRO river 2016 model. The model is calibrated and validated using the past flood events occurred in the years 2005, 2008, 2010 and 2011. The downscaling of weather parameters of Canadian Global Circulation Model (GCM) to the station scale is performed by Statistical Down-Scaling Model (SDSM). In the hydrological model, daily rainfall, evapotranspiration (ET0) and the atmospheric variables statistically downscaled from climate change scenarios Representative Concentration Pathways (RCP) 4.5 and RCP 8.5 are provided as input and modifications in flood discharge are presented. It is found that there will be an increase in peak rainfall and peak discharge under the RCP 4.5 and RCP 8.5 scenarios for the future years 2050 and 2080. The changes in meteorological parameters would have a significant effect on the flow of floods since minor changes in weather pattern will greatly affect the hydrological cycle.


Introduction
The purpose of the present study is to assess the climate change impact on floods for a river basin hydrology which helps to deal with the effects of climate change. The present study can substantially aid farmers in understanding and creating environmental adaptation strategies. It can also serve as a reference to hydraulic engineers and water resource managers in designing structures to manage the increase in discharge due to climate change and constructing storage structures in order to reduce flood volume. In turn, the flood water can be used for irrigation when monsoons fail. Environmentalists could draw inferences from the study to preserve downstream river water quality by maintaining the minimum river flow. The study can also serve policy makers in framing adaptation measures for climatic problems.
The Vellar river basin experiences seasonal flooding between November and December every year. The people who live along the downstream banks of the Vellar river basin are highly vulnerable to flood damages during monsoon. The steep course of the river causes flash floods, inundating vast areas of the Cuddalore and Chidambaram districts of Tamil Nadu. Climate change has significantly accelerated flooding in the areas that have been studied. In 2009, massive flooding was observed in the Manimuktha river with a high discharge of 207000 cusecs near the Kudalaiyathur village due to which most of the agricultural lands submerged (Needhidasan et al., 2013).
Previous research on the subject suggests that the frequency, depth and extent of flooding in river basins will increase in the future due to changes in climatic conditions (Mujumdar et al., 2012). The timing, frequency and magnitude of environmental flow discharges play a major role in maintaining a healthy river system (Acreman et al., 2004). Notable factors are the flow in the rivers due to climate change.
MIKE11NAM parameters estimated using the auto-calibration and trial and error method for the given flood events provide reliable flood flow simulation (Giang et al., 2010). To evaluate the flood simulations at Sethiothope anaicut using MIKE HYDRO river module, NAM rainfall runoff model is utilized. MIKE11NAM simulates flow of the Vellar basin based on rainfall and ET 0 as input data (Lafdani et al., 2013). The discharge in the river is obtained as an output through the routing process. Rainfall and ET 0 data from meteorological stations in the river basin is used for calibration and verification. The NASH and R 2 were used to check the efficiency of the model (Suman et al., 2014). The flow at stream gauge station of the basin is simulated under two climate change scenarios -RCP 4.5 and RCP 8.5. The study utilizes second generation Canadian Earth System Model (CanESM2) which is developed by Canadian Centre for Climate Modeling and Analysis (CCCma) of Environment Canada. The Statistical Downscaling Model (SDSM) enables downscaling the daily meteorological data for the required station location and is useful in climate change impact studies.

Study area description
The Vellar river basin is a semi-arid basin lying at the northern part of Tamil Nadu exposed to frequent floods, droughts and associated water problems. The river itself originates from the Chitheri hills, having a total length of 150 kms up to the draining point of Parangipettai near Bay of Bengal, which has been depicted in Fig. 1. The river basin has seven sub basins. During the northeast monsoon, most downstream areas receive the freshwater and other seasons were in dry condition. Because of this reason the average water salinity is increased to 35-45 ppm during dry seasons (Selvam, 2003). The preferred meteorological station Mangalapuram has 30 years of historical data and other stations have shorter chronological data in comparison. Twenty two raingauge stations and one meteorological station (Mangalapuram) were identified in the study area. Mangalapuram station is used to represent the entire river basin due to the amount of data available. The 32 years of daily rainfall data was collected from Public Works Department, Tamil Nadu and 30 years of daily meteorological data from Institute of Water Studies, Tamil Nadu. The LISS III of 2006 satellite land use/land cover data has been classified into seven categories in this present study as Agricultural land of 67.8%, Forest land of 16.44%, Waste lands of 8.48%, Water bodies of 4.52%, Wet lands of 0.25%, grass land of 0.12% and Built-up land of 2.3% is clearly shown in Fig. 2.

General Circulation Models (GCMs)
General Circulation Models (GCMs) have very high spatial resolution of 200 by 300 km at ground level. The downscaling procedure is necessary to represent the characteristics of the ground terrain up to the basin level.
The GCM adopted in the present study is the second generation Canadian Earth System Model (CanESM2). The CanESM2 output at station location is downloaded and can be used directly as an input to SDSM for downscaling. The AR5 IPCC (Fifth Assessment Report of the United Nations Intergovernmental Panel on Climate Change) has different climate change scenarios viz., RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5. The CanESM2 output for RCP 4.5 and RCP 8.5 is acquired for this study. Apart from these, National Center for Environmental Protection (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis project 1 provides large scale atmospheric variables is vital for creating statistical relationship with the predictand data of the station. The CCCma generates 26 predictors for both CanESM2 and NCEP/NCAR data from 1961 to 2005.

Statistical DownScaling Model (SDSM)
A statistical downscaling tool works with the multiple linear regression technique for climate change impact studies. Initially, the model computes the relationship between observed predictands and predictors to ascertain the parameters for weather generation. In the second stage, the model generates future series by utilizing the GCM predicted data and the parameter file developed in the earlier stage (Chen et al., 2012).

Evapo-transpiration (ET 0 )
The FAO ET 0 calculator is utilized to generate ET 0 for the observed data of Mangalapuram station. The original Hargreaves equation is considered to calculate the ET 0 for GCM data which is given by (Subburayan et al., 2011), where, ET 0 -Evapo-transpiration in mm/day; R a -Extraterrestrial radiation in (MJ m -2 /day); T max -Maximum Temperature (°C); T min -Minimum temperature (°C).
The exponent 0.5 mentioned in the equation (1), over predicts the ET 0 data (Subburayan et al., 2011). Hence, the The modified equation is of the form (Subburayan et al., 2011), where, Using the meteorological data for the calibration period from 1978 to 1994, the relationship between ET 0 computed by FAO ET 0 calculator (Penman Monteith Air temperature at 2 m method) as a dependent variable Y and the independent variable X is used to find the regression equation. The best fit regression equation is obtained from the Datafit 9 software among the 242 non-linear regression models has the exponent value (b) and A value as 0.574 and 1.100. The regression equation used to compute the ET 0 is of the form, The performance of regression equation and the FAO ET 0 calculator (Penman Monteith method) in estimating ET 0 is evaluated using the statistical criteria, namely Mean Absolute Error (MAE), Mean Absolute

MIKE HYDRO river model setup
MIKE HYDRO river is a physical, deterministic, semi-distributed model for the simulation of flood flow (Yu et al., 2015). This model incorporates a modern, mapbased and highly intuitive Geographic Information System (GIS) for river modeling. The map layer coordinate system is helpful to project the DEM and other layers. After pre-processing of DEM, catchments and river branches can be digitized in the model. The basin outlet points, reservoirs, anaicuts and chainage points of river can be represented on maps with ease. The Vellar river basin is digitized with sub-basins and the river network and storages are clearly shown in Fig. 3. The whole basin is considered to simulate runoff at the outlet point of Sethiothope anaicut.

MIKE11NAM
MIKE11NAM is a lumped conceptual model that simulates the rainfall-runoff processes occurring in single or multiple sub-basins in a river (Amir et al., 2013). The general structure of the model has four different but interrelated storages and has corresponding flows simulates flood flows precisely (Hafezparast et al., 2013). MIKE11NAM requires ET 0 data as input to the model. The data from the Mangalapuram meteorological station is utilized for ET 0 determination. The parameters such as maximum temperature, minimum temperature, maximum relative humidity, minimum relative humidity and wind speed were provided as an input to the FAO ET 0 calculator for the computation of ET 0 . The ET 0 and weighted time series of rainfall for the storm events were generated. Nash-Sutcliff index is used to evaluate the simulation results (Tran et al., 2011). The model performance is evaluated during calibration and verification of storm events with Correlation Coefficient R 2 and Efficiency Index (Nash-Sutcliff, 1970) values were given by the equation (8),

Downscaling by SDSM
The statistical relationship between the large scale atmospheric variables and the observed minimum temperature, maximum temperature and rainfall is acquired using SDSM. The GCM data is collected at Mangalapuram station which is located at 11°33'49.7" N (Lat.) and 78°22'34.72" E (Long.). The downloaded file is denoted by the grid box number 29X and 37Y and which contains files like such as NCEP-NCAR_1961-2005, CanESM2_historical_1961_2005, CanESM2_rcp26_ 2006_2100, CanESM2_rcp45_2006_2100 and CanESM2_rcp85_2006_2100. The files comprises of mentioned years of 26 daily predictor data, obtained from respective scenarios and the data is normalized with respect to the specified periods. The list of GCM predictor variables and their description is provided in Table 1. The observed NCEP-NCAR data consists of 366 days allowing for 29 days in February due to the leap year. The CanESM2 model has 365 days and hence the value to be changed to 365 days as default value. During the calibration step, the event threshold values to be specified to account trace values. The event threshold value is set zero for temperature and 0.1 mm/day for precipitation. The model modifies the missing data into -999. The quality control step identifies the errors in the data. Fourth root model transformation (conditional model) is applied to predictands of daily precipitation, since the predictands has shown skewed distribution. The predictands of daily temperature variables are normally distributed and hence no model transformation is employed. The default value of variance rise is given as 12 for daily temperature and 20 for daily precipitation. The downscaling model under conditional process can compute higher or lower than the mean value, it is rectified by bias correction. The bias correction value is fixed by trial and error method which continues till the observed and simulated variables matches well. After several trial and error, the bias correction value is found to be 0.98. The temperature data from Mangalapuram meteorological station and rainfall data from 22 rain gauge stations of Vellar basin were utilized. The Thiessen weightages are computed for all the rain gauge stations is provided in Table 2 and the resulting Thiessen polygon map is shown in Fig. 4. The precipitation process can undergo changes based upon local weather conditions. Hence the precipitation downscaling to local scale was performed by Thiessen The explained variance of various predictors was estimated from NCEP/NCAR reanalysis data in order to select the most suitable predictors. The high explained variance possessing predictors are chosen for correlation analysis to analyze the relationship among predictors and predictands. The correlation coefficient (r) and significance level (P) is used to decide the set of predictor variables selection. The set of predictor variables selected for downscaling based on 'r' and 'P' is clearly depicted in Table 3. In this study, the significance level was considered as p<0.05 (default value). The NCEP/NCAR predictors were selected based on the highly explained variance and correlation coefficient. The reason for the selection of Thiessen weighted average rainfall of all the stations is that it has shown good correlation relationship with predictors compared to other individual rainguage stations. The chosen predictors are utilized to establish transfer function. Then the new synthetic series was developed by this parameter file (.PAR file). In this study, the inter-annual variation is specified by selecting conditional processes for precipitation and unconditional process for temperature data (Shimola et al., 2014). The model calibration was performed by multiple linear regression equations using predict and predictor variables. The ordinary least squares optimization was selected for the entire station variables. The daily time step was adopted in the downscaling process. The initial 15 years of all the stations data was used for calibration which is from 1976 to 1990 for temperature and 1980 to 1994 for rainfall. In this SDSM procedure, the standard error (S.E.), R 2 and explained variance (E) in percentage are generated which is represented in Table 4. The calibrated model was validated by the weather generator. The weather generator produces synthetic daily weather records for the given period based upon the parameter file or regression weights and large scale atmospheric predictor variables from NCEP/NCAR dataset. Twenty ensembles of synthetic daily weather records were generated for station data and variables. The summary statistics step is used to compare the observed with the simulated data and if any adjustment is needed then the variance inflation and bias correction on data is performed. The validation period is from 1991 to 2005 for temperature and 1995 to 2005 for rainfall. The performance of SDSM simulated rainfall data during validation stage is evaluated with maximum precipitation and peaks over threshold (>90%) as given in Fig. 5. The performance of SDSM simulated temperature data during validation stage is evaluated with mean monthly temperature as depicted in Fig. 6. These values are used for the development of synthetic weather record.
The scenario generation step is based on the assumption that the predictor-predictand relationship is valid in present as well as in the future climate conditions. The synthetic weather data series is created by scenario generation option in the same way as weather generator. At this time the CanESM2 model output was provided as an alternative to NCEP/NCAR reanalysis data. Two CanESM2 scenarios, RCP 4.5 and RCP 8.5 are considered in this study. Twenty ensembles of synthetic daily weather records were generated for each scenario with the period of 2006 to 2100 to all the variables and station data. The average of twenty ensembles was utilized as a daily weather data for the required period. Large number of ensembles does not improve or much more prone to higher deviation compared to the mean of twenty ensembles, which gives satisfactory results. The future years considered for the downscaling of maximum temperature, minimum temperature and rainfall are 2050 and 2080. Under the present study, the future rainfall, maximum temperature and minimum temperature for the two different climate change scenarios were established.

Calibration and parameter optimization by MIKE11NAM
The initial condition of the catchment is specified for each storm event until the simulated flow matches with the observed flow at the beginning of hydrograph. Among the four flood events, two events (2005 and 2008) were chosen for calibration to find the best parameters of MIKE11NAM model, the 2010 and 2011 events are used for testing the calibrated parameters. The auto-calibration procedure based upon shuffle complex evaluation algorithm and trial and error method are employed to find the best set of parameters. Some of the NAM parameters associated to ground water (GW) flow such as C area (change ratio of GW area to catchment area), S y (specific yield), GWLBF0 (GW depth for base flow threshold), GWLBF1 (capillary flux) and C qlow (lower base flow recharge to reservoirs) were not considered in calibration. The water balance error and peak runoff values were analyzed to assess the effectiveness of the model in simulation of runoff. The U max value 14.64 mm shows that the amount of water reserved with the interception storage, depression storage and top most part of the ground surface. The L max value 179.2 mm represents the amount of maximum water available in the root zone for crop transpiration. A slightly higher CQOF value of 0.784 was acquired due to the existence of rocky terrain and low permeable soil in the upper part of the basin. The U/U max value 0.4 denotes that there is a low surface storage due to the presence of thin forest cover in the upper region. The L/L max value 0.6 signifies that there is a presence of major crop land having prominent root zone storage. The overland flow is occurring during wet periods is indicated by the higher value of L/L max in comparison with TOF. The QOF and QIF values are given as zero for the initial condition.
The sensitivity analysis is carried out by changing the NAM parameters with ±20% and ±10% of the optimal value and then it is investigated with model output is given in Table 5. The variations of model parameters with respect to model outputs like water balance and peak runoff were compared to assess the sensitivity. The level of sensitivity has been classified as high for model output variation higher than 10%, moderate for variation between 5-10% and less for variation less than 5%. The U max value with respect to the calibrated value shows that moderate percentage variations exist between -5.93% to 5% due to the influence of thin forest cover. There is a significant percentage variation between 12.54% to 10% in comparison with calibrated value depicts that the effect has been made by the major crop land and major root zone storage. The CQOF value with respect to the water balance error and peak runoff shows that there is a higher percentage variations exist between -5.42% to 6% and 15.42% to 13.94%. The other NAM parameters such as CKIF, CK12, TOF, TIF, TG and CKBF has less significance on runoff volume except CK12 which has evident effect on peak runoff is clearly given in Table 5. The selection of values for the prominent NAM parameters like U max , L max , CQOF and CK12 which has major effect on peak runoff and water balance is a challenging task in developing a rainfall-runoff model.
The observed discharge data matches well with the simulated discharge data of the two storm events during the calibration period are clearly shown in Figs. 7&8 and also the scatter plots are represented in Figs. 11&12. The effects of changing the MIKE11NAM parameters will vary the simulated discharge was analyzed (Shamsudin and Hashim, 2002). The optimal parameters are represented in the Table 6. During calibration of two storm events, R 2 and EI values depicts that there is a good correlation between observed and simulated flood flows mentioned in Table 7.

Model verification
The observed discharge data matches well with the simulated discharge data of 2010 and 2011 storm events during the validation period are clearly mentioned in Figs. 9&10 and also the scatter plots are represented in Figs. 13&14. The best optimal sets of parameters generated by calibration step were utilized to run the 2010 and 2011 storm events in MIKE11NAM model. During verification of the two storm events, the R 2 and EI values proves that there is a very good correlation between observed and simulated flood flows and it is depicted in the Table 7. The best optimal parameters of MIKE11NAM are utilized to simulate flood flows under the RCP 4.5 and RCP 8.5 climate change scenarios.

Flood simulation under different climate change scenarios
The rainfall is downscaled by SDSM from the CanESM2 GCM model under the RCP 4.5 and RCP 8.5 scenarios for 2050 and 2080. The GCM results were compared with the major flood event in the year 2005, to analyze variability in peak rainfall and peak discharge. The CanESM2 predicts an increase in rainfall by 170.94% in 2050 and 285.24% in 2080 under RCP 4.5 and increase in rainfall by 232.79% in 2050 and 424.11% in 2080 under RCP 8.5 shown in Table 8. The increase in discharge computed as 56.9% and 108.06% in RCP 4.5 and 104.12% and 519.3% in RCP 8.5 in 2050 and 2080 years is denoted in the Table 8. According to the results obtained from the CanESM2, there will an increase in peak discharge under the RCP 4.5 and RCP 8.5 scenarios for 2050 and 2080.

Conclusions
The present research paper on climate change and its impact on flood flows in the Vellar river basin were analyzed. The possible variability of flood was assessed using the MIKE11NAM hydrological model. SDSM model gives better results in simulating rainfall under AR5 scenarios with the selected GCM. The ET 0 generated using observed data and GCM data is essentially provided as an input to the hydrological model. The GCM CanESM2 predicts an increase in peak rainfall and discharge compared to the 2005 flood event. According to the results obtained from the GCM CanESM2, there will be an increase in peak rainfall and peak discharge under the RCP 4.5 and RCP 8.5 scenarios for both future years 2050 and 2080. The flood risk will increase and produce flash floods in a quick response river basin. The excess release of water for few days and dry flow will be prevailing for most of the year is revealed through the analysis of all flood events. As major part of the land use is agricultural land of 67.8% of the whole area is under serious threat due to floods and climate change effect. Because of lesser flow the average water salinity is increased to 35-45 ppm at present and in future, this will cause a detrimental effect on river environment. Taking the high flood flow as per RCP 4.5 and 8.5 into account, structural control measures has to be made on the upstream side of the river basin to reduce the flood damage.