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

Das Subhadarsini1, *N.V. Umamahesh2

1 Research Scholar, National Institute of Technology , , 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]

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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 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 flow data by SWAT Model.  Calibration (1966-1996) and validation (1997-2005) of SWAT Model for streamflow.  Simulation of recent past years (2006-2013) streamflow using calibrated SWAT model.  Assessment of GCM and Scenario uncertainty using possibilistic approach (assigning weightage).  Simulation of streamflow for three time slices (2020-2040,2041-2070,2071-2099).  To find out the possibilistic mean CDF for three time slices (2020-2040, 2041-2070, 2071-2099).  Finally, the flow conditions for future period can be determined by comparing the streamflow of 2006-2013. The possibility assigned to a scenario means the possibility that the scenario forces the climate best to indicate the change in the hydrological variable.

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2 STUDY AREA & METHODOLOGY The Indravati sub-basin of Godavari basin lies between longitude of 800 16’ 19’’ E to 830 07’ 10” E and latitude of 180 43’ 25’’ N to 190 26’ 46” N. By including all its tributaries, the total drainage area of is 41,665 km2. Mean annual rainfall over the basin is 1.588 m. After flowing through 531 km from its source, it joins the Godavari River at an elevation of about 82 m. 2.1 Data The Arc SWAT 2012 version has been used for simulations in the present study. The required spatial input data layers are Digital Elevation Model (DEM), Land Use Land Cover (LULC) map, Soil map and weather data. From Shuttle Radar Topography Mission (SRTM) of USGS, a DEM (90 m×90 m) is used to delineate and analyse the drainage patterns of the terrain and the boundary of the watershed. Weather data with a resolution of (0.50×0.50) are collected from all GCMs (ACCESS, CCSM4, CNRM,GFDL, MPI, NORESM) with scenarios RCP 4.5 and RCP 8.5 for both historical(1970-2005) and future (2006 -2099) from IITM, Pune for present study. The observed stream flow data of Indravati sub-basin at Pathegudam outlet point is collected from Water Resource Information System (WRIS). From IMD, observed temperature and precipitation (0.50×0.50) data are collected from 1960-2013. Software used : ArcGIS, Arc-SWAT, SWAT-CUP, MATLAB, RStudio, Microsoft Excel 2.2 Model Formulation The aim of the study is to give possibilities as a weightage to each GCM and its scenarios based on their capability of predicting the stream flow for the recent past years (2006–2013), when there are signals of climate forcing and to predict the future stream flow by hydrological model SWAT with the use of future hydrologic variables. The detailed procedure and estimation of the predicted value are presented in the form of flow chart as shown in Fig. 1. The steps illustrating the complete details of the methodology are as follows: Step-1: Set up SWAT model by using prepared temperature and precipitation data and GIS data (DEM, LULC map and Soil map) Step-2: Set up calibrated Model after calibration and validation of the Model by using SWAT- CUP (SUFI2 Algorithm). Step-3: Bias-correct all the GCM Model and its scenario (RCP4.5 and RCP8.5) data. Step-4: By taking bias-corrected data into SWAT with calibrated model parameters, runoff is predicted for recent past years (2006-2013) when there are indications of climate forcing and possibilities as a weightage are given to each GCM and its Scenarios. Step-5: Considering the possibilities for all GCM and its Scenario, future (2020-2040, 2041- 2070,2071-2099) stream flows are predicted. 2.3 Model Setup For running the SWAT model, the necessary spatial data layers i.e. DEM, LULC map and Soil map have been prepared. Hydrologic Response Units (HRU) are generally created to account for diversity. It is based on unique land use and soil type within each of the sub basin. To ignore minor land uses, slope and soil types in each of the sub-basin, HRUs are created by considering 10 % threshold value of land use, 5% threshold value for both slope and soil area. Because of the complexity in the prediction exact future land use characteristics, the present land use characteristics has been expected to be unchanged for the simulations of future periods. The curve number (CN) method

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Preparation of Data GCM Data (Scenario RCP4.5 Precipitation Data and GIS Data(DEM, LULC Map, Soil Map) and RCP8.5)

Temperature Data

Set up SWAT Model Bias-Correction of Input Data

Calibration and validation of model using SWAT-CUP Input Bias-Corrected Data into (SUFI2 Algorithm) SWAT precipitation and Temperature No Is calibration criteria satisfied ? Yes Calibrated Model Stop

Recent Past year (2006-2013) runoff Prediction

Possibility distribution of GCM

and its Scenario

Weights

Future (2020-2040, 2041- 2070, 2071-2099) Stream flow Prediction

Fig. 1 Flow chart for estimation of future Predicted value

5 is generally used for generating surface runoff volume from rainfall data. The first (1960-1964) years of simulation have been taken as a warm up period for stabilization of the model. 2.4 Calibration and Uncertainty Analysis Here the model uses SUFI-2 for calibration and uncertainty analysis. 16 sensitive parameters are taken based on the study area for calibration and validation. Calibration has been done by taking monthly simulated streamflows. The model has been simulated for a period of 46 years (1960–2005) by considering the first 4 years as warm up and then next 31 years (1966–1996) for calibration and the last 9 years (1997–2005) used for validation. By using SWAT-CUP, the model sensitivity, calibration and uncertainty analysis have been accepted. In the calibrated model, the strength of calibration and uncertainty have been measured on the basis of p-factor and r-factor, (R2) and (NSE). The p-factor denotes the percentage of measured data within the band of 95 Percent Prediction Uncertainty (95PPU). The 95PPU is determined at the 2.5 % and 97.5 % levels of the cumulative distribution. Ratio of the average thickness of the 95PPU band w.r.t the standard deviation of the observed data represents the r-factor which indicates the strength of calibration. 2.5 Bias Correction The GCMs output data i.e. temperature and precipitation already consists of different kind of biases. These bias may be due to scenarios of forthcoming socio-economic development, GHG emission, Regional Climate Models (RCMs), or statistical downscaling methods etc. So it is necessary to do bias-correction for GCMs data to remove bias up to some extent. After bias- correction, the data are used for prediction of stream flow. Here, in this study, Quantile- mapping bias correction method is used. The Quantile-mapping is based on non- parameterization. 2.6 SWAT Model Simulation The calibrated SWAT model is used to predict stream flow of Indravati sub-basin for Recent past year (2006-2013) and for 2020–2040, 2041-2070 and 2071–2099 periods. For the climate change detection, the predicted stream flow obtained from the recent past period (2006-2013) of all the GCMs data outputs have been used . 2.7 Possibility Distribution With the historical data in the baseline period 1970-2005, when there is no evidence of the signs of climate change, all GCMs and scenarios can be interpreted to have equal possibilities, all equal to 1. Only the limits of CDF will be considered in such cases. The CDFs generated from the GCMs with different scenarios will be within the interval of the boundaries. Over time, taking into account the growing evidence of climate change signals, it should be relevant to assign an approximate weightage to all the GCMs associated with scenarios based on their performance for the period of time when the influence of climate change is visible. Performance measures can be checked normally by a function of the deviation of predicted data of the model from the observed data for a certain time for all climate models. In this study, the Nash-Sutcliffe coefficient is formulated as a system performance benchmark. The co-efficient (C) is considered as possibility (Po), given by equation number (1).

2 ∑푖(푄표푏푠−푄푝푟푒)푖 (1) C = 1 – 2 ∑푖(푄표푏푠,푖−푄푚푒푎푛)

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where, Qobs and Qpre are the observed and predicted stream flow (by a GCM under a th scenario) respectively and Qmean is the mean observed stream flow and i is the i no of observation. For computing C, predicted stream flow are taken from the recent past year (2006-2013). C can vary similarly as the Nash-Sutcliffe coefficient. It varies from 0 (by considering the linearity and unbiasedness of model) to 1. C value 0 indicates that the GCM can’t capture the variability of the model and 1 indicates a perfect model. Coefficient C implies possibility value as performance measure, by considering the performance of how well a particular scenario associated with GCM predicts the observed values during recent past years. C value of 1 is nearly impossible. The property of possibility distribution that there should be at least one scenario simulated by any of the GCMs with a possibility value 1. So the results obtained from the equation of ‘C’ can’t be used directly. To complete the property, the results which are derived from the equation of ‘C’ for all the six GCM with its associated scenarios are normalized first. Each value of C is divided with the maximum value of C. Thus the obtained value is used as the corresponding possibility value. 3 RESULTS After using all GIS data (LULC map, Soil map,Slope map, DEM ) , total 27 sub basins are created in the watershed of Indravati sub-basin. Based on unique land use, slope and soil type, the sub basins have been further divided into 293 no. of HRUs to account for diversity within each of the sub basin. After many iteration of calibration and validation, the sensitive parameters are acquired new minimum, maximum and best fitted values. These best fitted values are used for setting calibrated model. The values of p-factor and r-factor which has been used to evaluate the strength of calibration and the values of R2 and NSE which measures uncertainty are taken into consideration for fixing calibrated model. These values in Table 1 confirm a approximately good performance of the model during the calibration and validation period.

Index Calibration (monthly) Validation (monthly)

Correlation coefficient 0.65 0.58

Nash and Sutcliffe coefficient 0.64 0.57

P-factor 0.44 0.26

r-factor 0.21 0.21

PBIAS -0.4 2.9

Table 1 Statistical criteria for examining the accuracy of Calibration (1966–1996) and Validation (1997–2005)

The results show that most of the observations with different parameters are fixed by 95PPU with NS value from (0.5 to 0.7) indicating that SUFI-2 can capture the model behavior. So the

7 final setting is the best solution for Indravati sub-basin and the SWAT simulation results expect a satisfactory outcome forecast.

1 1 access4.5 0.8 0.8 access8.5 obs cnrm4.5 0.6 ccsm4.5 0.6 cnrm8.5 ccsm8.5

0.4 0.4 gfdl4.5 CDF CDF mpi4.5 mpi8.5 0.2 noresm4.5 0.2 obs 0 0 0 2000 4000 6000 8000 0 2000 4000 6000 8000 Stream flow(m^3/s) Stream Flow (m^3/s)

(a) (b)

Fig. 3 ( a ) CDF of six GCMs projected Stream flow for recent past years(2006-2013) ( b ) CDF of four GCMs projected Stream flow for recent past years(2006-2013)

GCM RCP C

ACCESS 4.5 0.609

ACCESS 8.5 0.774

CCSM4 4.5 -0.08

CCSM4 8.5 -0.20

CNRM 4.5 0.673

CNRM 8.5 0.567

GFDL 4.5 0.732

MPI 4.5 0.347

MPI 8.5 0.721

(a) (b) Table 2 : Performance Measure C for the Fig. 2 Possibility distribution of GCMs six GCMs associated with RCP4.5 and with RCP4.5 and RCP8.5 RCP8.5

3.1 Predicted Streamflow using GCM data The CDFs of stream flow predicted from GCMs (ACCESS4.5, ACCESS8.5, GFDL4.5, CNRM4.5, CNRM8.5, MPI8.5) for recent past years (2006-2013) are relatively nearer to the CDF of observed stream flow for that period and the CDFs of stream flow predicted from GCMs (CCSM4 4.5, CCSM4 8.5, MPI4.5, NORESM4.5) are deviated from the CDF of

8 observed stream flow. For 2041-2071, the CNRM with RCP8.5 data are not available and for 2071-2099, the GFDL with RCP4.5 data are not available. The CDFs of stream flow predicted from the available GCMs for 2020–2040, 2041-2070 and 2071–2099 periods are calculated. 3.2 Possibilistic modelling results The CDF of stream flow predicted from one GCM is entirely different from that of another and also that dissimilarity exists among two scenarios of any particular GCM. The un- normalised value of C (Table 2 ) indicates that CCSM4 associated with RCP4.5 and RCP 8.5 have –ve C values which means this model fails to predict streamflow whereas MPI 4.5 and NORESM4.5 GCMs predict very poor results for Indravati sub-basin. So, when possibility distribution (Fig 2) is calculated with normalised C value, GCMs with associated RCP4.5 and RCP8.5, CCSM4, MPI with RCP4.5 and NORESM with RCP4.5 scenario are ignored for prediction of streamflow. The GCMs other than the failure model are considered for the prediction of streamflow for future periods. By giving possibility to each models after normalization, it is considered that model with possibility 1 is better one compared to other models. The CDFs of streamflow for the time slices 2020-2040, 2041-2070, 2071-2099 by using the considered GCMs are presented in Fig. 3(a,b,c). The possibility values obtained for each GCM and scenario are used as weights to compute the possibilistic mean CDF (Fpm or Po mean) for the time slices 2020-2040, 2041-2070, 2071- 2099.

푛 ∑푖=1(푃표 ×퐹푖) (2) 퐹푝푚= 푛 ∑푖=1 푃표

th Where i means i no of GCM model and ‘n’ is the no of GCM models. ‘Po’ and Fi are the the possibility and CDF of GCM associated with RCP4.5/RCP8.5. The possibilistic mean CDFs for years 2020-2040, 2041-2070, 2071-2099 comparing with the CDF of observed stream flow of 2006-2013 are presented in Fig. 3 (d,e,f).

(a) (d)

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(b) (e)

( c ) ( f )

Fig. 3 CDFs of stream flow projected from GCMs for (a) 2020-2040 (b) 2041-2070 (c) 2071- 2099, Possibilistic mean CDF of years (d) 2020-2040 (e) 2041-2070 (f) 2071-2099 with CDF of observed streamflow (2006-2013)

4 SUMMARY AND CONCLUSION The predicted multi-model ensembles create complexity for decision making. So multi-models are difficult to apply in explicit stochastic optimization models whereas a single CDF is better to be considered as an input to the optimization model. Multiple CDFs derived with different GCMs and scenarios are therefore not useful in decision making. The possibilistic mean CDF is obtained from all the CDFs derived with different GCMs and scenarios with their assigned weights. This total work has mainly focused on the GCMs associated with scenario which can predict approximately good result by assigning weightage factor. By considering this, the following are the conclusions derived from the study. 4.1 Conclusion  After calibration and validation, the value of p-factor (0.44), r- factor (0.26), R2 (0.65) and NSE(0.64) presented approximately good calibration strength and good uncertainty analysis. Then the calibrated model is fixed with 16 sensitive parameters with their best fitted value.  After simulating the streamflow for recent past years (2006-2013) for all GCMs with their associated RCP, it is concluded that each GCMs are showing different values of streamflow compared to the observed value for recent past years.  By using possibilistic approach i.e. assigning weights to each GCMs with their associated RCP and then by getting suitable GCM models, the streamflow for three time slices (2020- 2040, 2041-2070,2071-2099) are predicted for Indravati sub-basin.  Then the possibistic mean CDF has been calculated for these time slices to know the flow condition.

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 And finally, it is concluded that the streamflow for Indravati sub-basin is reducing compared to the observed data for 2006-2013. But on the other side, where the lower streamflow is increasing, the higher streamflow is going to be reduced in future.

5 REFERENCES

Chen, D., Achberger, C., Räisänen, J., & Hellström, C. (2006). Using statistical downscaling to quantify the GCM-related uncertainty in regional climate change scenarios: A case study of Swedish precipitation. Advances in Atmospheric Sciences. https://doi.org/10.1007/s00376-006-0006-5 Das, J., & Umamahesh, N. V. (2015). Multisite Downscaling of Monsoon Precipitation over Godavari River Basin under the RCP 4.5 Scenario. World Environmental and Water Resources Congress 2015: Floods, Droughts, and Ecosystems. https://doi.org/10.1061/9780784479162.105 Ghosh, S., & Mujumdar, P. P. (2009). Climate change impact assessment: Uncertainty modeling with imprecise probability. Journal of Geophysical Research. https://doi.org/10.1029/2008JD011648 Jhajharia, D., Dinpashoh, Y., Kahya, E., Choudhary, R. R., & Singh, V. P. (2014). Trends in temperature over Godavari River basin in Southern Peninsular India. International Journal of Climatology. https://doi.org/10.1002/joc.3761 Kay, A. L., Davies, H. N., Bell, V. A., & Jones, R. G. (2009). Comparison of uncertainty sources for climate change impacts: Flood frequency in England. Climatic Change. https://doi.org/10.1007/s10584- 008-9471-4 Mujumdar, P. P., & Ghosh, S. (2008). Modeling GCM and scenario uncertainty using a possibilistic approach: Application to the River, India. Water Resources Research, 44(6), 1–15. https://doi.org/10.1029/2007WR006137 Pandey, B. K., Gosain, A. K., Paul, G., & Khare, D. (2017). Climate change impact assessment on hydrology of a small watershed using semi-distributed model. Applied Water Science, 7(4), 2029– 2041. https://doi.org/10.1007/s13201-016-0383-6 Prudhomme, C., & Davies, H. (2009). Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 1: Baseline climate. Climatic Change. https://doi.org/10.1007/s10584-008-9464-3 Thompson, J. R., Crawley, A., & Kingston, D. G. (2016). GCM-related uncertainty for river flows and inundation under climate change: the Inner Niger Delta. Hydrological Sciences Journal, 61(13), 2325–2347. https://doi.org/10.1080/02626667.2015.1117173 Yesuf, H. M., Melesse, A. M., Zeleke, G., & Alamirew, T. (2016). Streamflow prediction uncertainty analysis and verification of SWAT model in a tropical watershed. Environmental Earth Sciences. https://doi.org/10.1007/s12665-016-5636-z

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