Impact Assessment of Future Climate Change on Streamflows Upstream

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Article Impact Assessment of Future Climate Change on Streamﬂows Upstream of Khanpur Dam, Pakistan using Soil and Water Assessment Tool

Saima Nauman *, Zed Zulkaﬂi , Abdul Halim Bin Ghazali and Badronnisa Yusuf Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Selangor Darul Ehsan 43400, Malaysia; [email protected] (Z.Z.); [email protected] (A.H.B.G.); [email protected] (B.Y.) * Correspondence: [email protected]

Received: 6 February 2019; Accepted: 9 April 2019; Published: 24 May 2019

Abstract: The study aims to evaluate the long-term changes in meteorological parameters and to quantify their impacts on water resources of the Haro River watershed located on the upstream side of Khanpur Dam in Pakistan. The climate data was obtained from the NASA Earth Exchange Global Daily Downscaled Projection (NEX-GDDP) for MIROC-ESM model under two Representative Concentration Pathway (RCP) scenarios. The model data was bias corrected and the performance of the bias correction was assessed statistically. Soil and Water Assessment Tool was used for the hydrological simulation of watershed followed by model calibration using Sequential Uncertainty Fitting version-2. The study is useful for devising strategies for future management of Khanpur Dam. The study indicated that in the future, at Murree station (P-1), the maximum temperature, minimum temperature and precipitation were anticipated to increase from 3.1 ◦C (RCP 4.5) to 4.0 ◦C (RCP 8.5), 3.2 ◦C (RCP 4.5) to 4.3 ◦C (RCP 8.5) and 8.6% to 13.5% respectively, in comparison to the baseline period. Similarly, at Islamabad station (P-2), the maximum temperature, minimum temperature and precipitation were projected to increase from 3.3 ◦C (RCP 4.5) to 4.1 ◦C (RCP 8.5), 3.3 ◦C (RCP 4.5) to 4.2 ◦C (RCP 8.5) and 14.0% to 21.2% respectively compared to baseline period. The streamﬂows at Haro River basin were expected to rise from 8.7 m3/s to 9.3 m3/s.

Keywords: climate change impact; water resources; Haro River watershed; Soil and Water Assessment Tool

1. Introduction The temperature of the Earth has increased significantly in recent decades with the decade of 2000s being the warmest [1]. The high emission of greenhouse gases (GHGs), particularly carbon dioxide that traps heat, poses a crucial threat to the environment [2]. It hinders the radiating efficiency of the surface of the Earth thereby affecting the overall temperature of the Earth [3]. The continued emission of these gases will not only increase the temperature but will also instigate long-term changes in the climate [4]. Global warming will disturb the global water cycle while altering the hydrological regimes that will affect management of fisheries, ecology, industrial and municipal water supply, human health and water energy balance [5]. The hydrological systems are under great threat due to changing climatic conditions in terms of variation in the availability of water as well as increased rate of precipitation [6]. Long-term changes in climate may alter the hydrological cycle to an extent that it may affect the intensity and frequency of floods as well as droughts. According to the United Nations report [7], climate change will increase the probability of natural disasters particularly related to water resources. These disasters pose negative social impacts and largely risk the economy of the region. Climate change impacts vary from region to region [8].

Water 2019, 11, 1090; doi:10.3390/w11051090 www.mdpi.com/journal/water Water 2019, 11, 1090 2 of 18

Global climate change processes have drastic effects on the climate of Pakistan in social, economic and environmental aspects [9]. Germanwatch, in its “Global Climate Risk Index”, has ranked Pakistan in third place after Haiti and Philippines, as a country most impacted by climate change effects [10]. Environmental dependency for fulfilling basic living requirements, increased rate of population growth, poverty and lesser resources to adjust to the negative effects of climate changes are also contributing to changes in the climate of the country [9]. Climate change is causing a significant rise in the average mean sea level with increase in global average temperature and variation in precipitation patterns regionally [11]. In Pakistan, during the past century, the average annual temperature has been observed to increase by 0.6 ◦C[12]. However, the rate of change varies throughout the country. In Northern Pakistan, the rate of increase in temperature is higher as compared to the Southern region [9]. The agrarian land of Pakistan receives most of the rainfall during the months of June till September. A twenty-five percent increase in precipitation observed in the previous century has been attributed to the impacts of climate change [12]. Despite numerous research activities, it is vital to quantify the effect of change in temperature, precipitation and atmospheric concentration of carbon dioxide on the water resources. At the watershed level, this information is useful for proper planning and decision-making [3]. In the past, Pakistan has already suffered from the extreme impact of climate change in the form of drought of 1998–2001 and flood of 2010 [9]. The river flows are affected by changes in climatic conditions [13]. Akhtar et al. [14] studied climate change impact on the water resources of Hindukush-Karakorum-Himalaya in Pakistan under three different glacial coverages. They predicted an increase in temperature, precipitation and discharge with increased risk of flooding. Azim et al. [15] also predicted an increase in discharge and sediment yield for Naran watershed in Pakistan. Kunhar River basin is likely to experience increased flooding as well as droughts; low flows are projected to decrease and high flows are expected to increase in the future [6]. Meanwhile, the flows in Chitral watershed in Pakistan are expected to decreases in future by up to 4.2% [13]. To assess impact of climate change on the water resources, climate model data is integrated into hydrologic models. The hydrological processes are simulated by linking the water yield and the climate changes. Based on the selected hydrological model and the climate scenario, the results of the study may greatly vary [16]. The Soil and Water Assessment Tool (SWAT) is a semi-distributed, continuous-time, physical model facilitating long-term hydrologic simulations. Although SWAT is not a three-dimensionally distributed model, it is comprised of spatially separated parts forming sub-basins and hydrologic response units (HRUs) [17]. The effects of changes in carbon dioxide gas, temperature, precipitation and humidity can be related to the plant growth and its effect on the evapotranspiration, snow and runoff can be evaluated [16]. Recently, SWAT is used in many studies for the simulation of watershed’s hydrological processes with focus on climate change impact assessment. SWAT was used by Abbaspour et al. [18] to study climate change impact on Iran’s water resources. Garee et al. [19] studied climate change impact on the streamflows of Hunza watershed in Pakistan. According to their study, the overall temperature is anticipated to rise form 1.39 ◦C to 6.58 ◦C with 31% increase in precipitation causing 5–10% increase in streamflows by the end of the century. The current study aimed to quantify the probable effects of climate change on the water resources of Haro River watershed in Pakistan by using SWAT. Haro River, flowing partly through Punjab and Khyber Pakhtunkhwa provinces in Pakistan, is located on the upstream side of Khanpur Dam. Khanpur dam is a major source of supplying drinking water to the twin cities of Islamabad and Rawalpindi. It also fulfils the irrigation demand for Punjab and Khyber Pakhtunkhwa. It is important to study the effects of future climate projection on the streamflows of Haro River watershed, as it will affect the flow at Khanpur Dam. The variation in future streamflows will aid in devising future policies and management measures for Khanpur Dam. To achieve the study objectives, bias corrected climate projections for the baseline period (1976–2005) and future period (2006–2095) from NEX-GDDP model MIROC-ESM, under two emission scenarios i.e., RCP 4.5 and RCP 8.5, were integrated in a calibrated and validated SWAT model. The study area and data description are discussed in Section2. Water 2019, 11, 1090 3 of 18

Water 2019, 11, x 3 of 20 The methodology adopted to achieve the study goals is presented in Section3. Section4 includes includesthe results the and results discussion and on discussion the results. on The the conclusion, results. limitationsThe conclusion, and the recommendationslimitations and the are recommendationspresented under Section are presented5. under Section 5.

2.2. Study Study Area Area and and Data Data Description Description

2.1.2.1. Study Study Area Area HaroHaro River River flows flows partly partly through through the the provinces provinces of of Punjab Punjab and and Khyber Khyber Pakhtunkhwa. Pakhtunkhwa. It It originates originates fromfrom Changla Changla Gali Gali and and Nathia Nathia Gali Gali and and is is drained drained by by Lora Lora Haro Haro and Stora Haro. The The Lora Lora Haro Haro drainsdrains through MurreeMurree hillshills whereaswhereas Stora Stora Haro Haro drains drains from from the the hills hills of Nathiaof Nathia Gali. Gali. At Jabri,At Jabri, the Lorathe LoraHaro Haro merges merges into Stora into Haro.Stora HaroHaro. River Haro flows Rive throughr flows narrowthrough and narrow deep and gorges deep and gorges is dammed and is at dammedKhanpur at [20 Khanpur] (Figure1 ).[20] The (Figure Dam was 1). builtThe inDam 1983 was with built the primaryin 1983 purposewith the of primary supplying purpose drinking of supplyingwater to Islamabad drinking andwater Rawalpindi to Islamabad as welland asRawalpindi to the industrial as well units as to at the Taxila. industrial Throughout units at the Taxila. year, Throughoutthe springs andthe year, streams the springs fulfil the and demand streams of fulfil the domesticthe demand water of the supply. domestic At thewater time supply. of droughts, At the timethe stream of droughts, discharge the isstream significantly discharge reduced is signif andicantly the seepage reduced through and the the seepage spring retainsthrough the the perennial spring retainsflow [21 the]. Theperennial major flow source [21]. of streamflowsThe major source in the of region streamflows is precipitation. in the region is precipitation.

(a) (b)

FigureFigure 1. 1. (a(a) )Location Location of of Haro Haro River River watershed watershed in in Pakistan; Pakistan; (b (b) )main main features features of of Haro Haro River River watershed. watershed.

2.2. Data Description 2.2. Data Description 2.2.1. Observed Climate Data 2.2.1. Observed Climate Data The daily data for maximum and minimum temperature and precipitation for the two nearby The daily data for maximum and minimum temperature and precipitation for the two nearby stations at Murree (P-1) and Islamabad (P-2) from 1987–2005, was obtained from Pakistan Meteorological stations at Murree (P-1) and Islamabad (P-2) from 1987–2005, was obtained from Pakistan Department (PMD). The wind speed, relative humidity and solar radiation data was obtained from Meteorological Department (PMD). The wind speed, relative humidity and solar radiation data was National Centre for Environmental Prediction’s (NCEP) Climate Forecast System Reanalysis (CFSR) obtained from National Centre for Environmental Prediction’s (NCEP) Climate Forecast System database available online under Global weather data for SWAT [22]. Reanalysis (CFSR) database available online under Global weather data for SWAT [22]. 2.2.2. Climate Model Data 2.2.2. Climate Model Data Climate projections, including precipitation and minimum and maximum temperature were obtainedClimate for theprojections, MIROC-ESM including model precipitation under NEX-GDDP. and minimum The projections and maximum were divided temperature into baseline were obtainedperiod (1976–2005) for the MIROC-ESM and three future model periods: under NEX-GDDP. early century The or 2020sprojections (2006–2035), were divided mid-century into baseline or 2050s period(2036–2065) (1976–2005) and late and century three future or 2080s periods: (2066–2095). early century or 2020s (2006–2035), mid-century or 2050s (2036–2065) and late century or 2080s (2066–2095).

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2.2.3. Discharge Data For Dhartian gauging station, the discharge data was acquired from the Surface Water Hydrology Project (SWHP) of the Water and Power Development Authority (WAPDA) from the year 1989 until 1991 and from 1996 until September 1998. The former period was used for the calibration of SWAT whereas the later period was used for the validation of SWAT.

2.2.4. Spatial Data The spatial data, including digital elevation model (DEM), landuse data, and soil data, was incorporated in the SWAT. All of the spatial dataset was projected in similar coordinate system i.e., WGS_1984_UTM_Zone_43N. The DEM of the study region was used by SWAT to develop the stream network and to delineate the watershed. It was acquired online from the database of CGIAR-Consortium for Spatial Information v4.1 [23]. It included DEM from Shuttle Radar-Topography Mission (SRTM) at the resolution of 90 m. Haro River lies mostly in the mountainous terrain with elevation ranging from 2786 m to 627 m above mean sea level. Landuse data was obtained from the Water Resource Engineering Division of National Engineering Services Pakistan (NESPAK). The landuse was classified into six categories including forest, hills, grasses, open/barren land, agricultural land and water. The soil classification of the region was obtained from Harmonized World Soil Database (HWSD) version 1.2 available online [24]. The soil was classified as Eutric Cambisol with clay texture having soil name Be72-3c-3672. Based on the DEM, three slope classes were defined in SWAT model i.e., gentle slope, medium slope and steep slope.

3. Methodology The methodology adopted to obtain the research objectives included the following steps:

1. Selection of a climate model. 2. Bias correction of baseline MIROC-ESM model data using linear scaling. 3. Performance evaluation of bias correction method using mean, standard deviation, 90th percentile and 10th percentile after dividing the bias corrected baseline model data into calibration and validation periods. 4. Bias correction of future model data using correction factors obtained in step 2. 5. Comparison of baseline and future climate projections on monthly time step. 6. Hydrological simulation of Haro River watershed in SWAT model using observed climate data obtained from PMD. 7. Calibration, validation, sensitivity analysis and uncertainty analysis for the SWAT model using Sequential Uncertainty Fitting version-2 (SUFI-2) in SWAT Calibration and Uncertainty Program (SWAT-CUP). 8. Hydrological simulation using calibrated SWAT model for baseline and future climate projections by incorporating the eﬀect of change in the level of carbon dioxide under both emissions scenarios; RCP 4.5 and RCP 8.5.

3.1. Selection of a Climate Model Five General Circulation Models (GCMs), (ACCESS 1.0, MPI-ESM-LR, CCSM4, CNRM-CM5 and MIROC-ESM) under NEX-GDDP were analyzed based upon the value of coeﬃcient of determination (R2) of the climate model data against meteorological station data. A single model was selected for climate change impact assessment at Haro River watershed based upon highest R2 value obtained for both stations, at quarterly scale. Water 2019, 11, 1090 5 of 18

3.2. Bias Correction of Climate Model Data Linear scaling was used to correct the historical and future model data for any systematic bias. The model data was corrected based upon the observed meteorological data for precipitation, and minimum and maximum temperature at P-1 and P-2 from 1987–2005. In bias correction, the correction factors were computed based upon the diﬀerence between mean monthly observed values and model simulated historical data. The correction factors were then applied to the model simulated data [25]. Precipitation was corrected using multiplicative factors whereas additive factor was used to correct mean monthly values of minimum and maximum temperature [26]. The bias correction of precipitation and temperature time series was based upon Equations (1) and (2).

P = P (P /P ) (1) sim(bc) sim· obs his T = T + (T T ) (2) sim(bc) sim obs − his where; Psim(bc) and Tsim(bc) are bias corrected monthly precipitation and temperature respectively. Psim and Tsim are NEX-GDDP values for precipitation and temperature respectively. Pobs and Tobs are observed values for precipitation and temperature respectively. Phis and This are NEX-GDDP simulated historical values for precipitation and temperature respectively. Steps for bias correction: 1. The period of observation (1987–2005) was divided into two periods: calibration period for bias correction (1987–1996) and validation period for bias correction (1997–2005). 2. Historical model data and observed data was used to compute the correction factors for precipitation and temperature for the calibration period. 3. Performance evaluation for the linear scaling method for the calibration period was evaluated based upon comparison of mean, standard deviation, 90th percentile and 10th percentile between the observed data and the model data before and after bias correction. 4. The bias corrected factors from the calibration period were applied to the validation period and the performance was evaluated for the validation period. 5. The monthly correction factors were applied to the future model data from 2006–2095.

3.3. Comparison of Future Climate Projection The climate change impact on mean monthly precipitation, minimum and maximum temperature was studied by evaluating the variation in future climate variables under two emissions scenarios: RCP 4.5 and RCP 8.5 with the historical/baseline period. The future period was divided into 30-year periods: early century (2006–2035), mid-century (2036–2065) and late century (2066–2095). RCPs provide the initiation point for climate change impact research [27]. RCP 8.5, the high-energy intensive scenario, represents highest range of predicted emissions in the order of 15–20 gigatonnes of carbon by end of the century [28]. The radiative forcing pathway of RCP 4.5 is an intermediate emission scenario, representing near-term climate projections [27]. In RCP 4.5, the radiative forcing level is stabilized at 2 4.5 W/m after 2100, with equivalent carbon dioxide concentration (CO2-eq) approximately equal to 650 ppm. In contrast, under RCP 8.5, the CO2-eq was approximated to be more than 1370 ppm with radiative forcing level more than 8.5 W/m2 in 2100 [28].

3.4. Hydrological Simulation Using SWAT The hydrological modelling of Haro River watershed was carried out using ArcSWAT 2012. In SWAT, the water balance of the river basin is considered as the major driving force behind all processes occurring in the watershed. The land phase in the model deals with surface runoff, evapotranspiration, lateral flow, tributary channel, groundwater, ponds and return flow whereas the routing phase or the in-stream phase deals with the movement of water, sediments, nutrients, organic chemicals within and through the channel [29]. Water 2019, 11, 1090 6 of 18

SWAT requires the climate dataset as well as the spatial data including DEM, landuse information, soil information and slope classification. All the spatial dataset needs to be projected in the same coordinate system. The climate data at station P-1 and P-2 from 1987–2005 was used to run the SWAT model with the outlet of the watershed defined at Dhartian gauging station. The DEM was used to delineate the watershed into nine subbasins whereas the spatial information of landuse, soil and slope was used by the model to create 96 lumped areas or HRUs. The model output was analyzed at a monthly time step. Surface runoff in SWAT was computed using SCS Curve Number method whereas the potential evapotranspiration was computed using the Penman-Monteith method. Variable storage method was used for the routing of water through main channel.

3.5. Calibration and Validation of SWAT SWAT was calibrated using SUFI-2 optimization algorithm. The model was calibrated for discharge at Dhartian station from 1989–1991. The calibration was done in two iterations each having 500 simulations. The model calibration was conducted until satisfactory values for Nash Sutcliffe efficiency (NSE) and R2 were obtained. The validation of the model was carried out from 1996 until 1998 using the parameter ranges obtained in the calibration period. A single iteration for the validation period was carried out by having a similar number of simulations as the last iteration of the calibration period. SUFI-2 makes use of inverse modelling technique to take into account uncertainty in parameter and carry out calibration [30]. It provides parameter ranges that ensure best results of simulation by satisfying two criteria denoted by “P-factor” and “R-factor”. P-factor ensures that the 95 percent prediction uncertainty (95PPU) computed at the 2.5 percent and 97.5 percent cumulative distribution levels of the output variables encloses maximum observed data. The thickness of the 95 PPU band is denoted by the R-factor [31]. The strength of the calibration and validation is indicated by the P-factor and R-factor. The best calibrated ranges are finalized when a balance is reached in both the factors. P-factor value ranges from 0–1 but a value greater than 0.7 is regarded as acceptable for the calibration of discharge whereas an R-factor having values lesser than 1.5 is considered adequate [32]. NSE defines the fit between the observed and simulated data on 1:1 line [33]. The NSE value ranges between 1 and [34]. According to Moraisi et al. [35], NSE value lesser or equal to 0.5 −∞ reflects unsatisfactory performance of model whereas value greater than 0.75 shows very good model performance. Coefficient of determination or R2 evaluates the strength of a linear relation between observed and simulated data [33]. Its value varies from 0–1 where R2 = 0 shows that no correlation exists between observed and simulated data. A perfect correlation is indicated by a value of 1 [34]. R2 value greater than 0.5 is considered acceptable [35].

3.6. Incorporating Climate Change Impacts in SWAT

The climate change impact was considered by changing the level of CO2 and the projected temperature and precipitation under each emission scenarios for three time periods: early century, mid-century and late century. The concentration of CO2 level for each period is given in Table1.

Table 1. Projected concentration of CO2 in ppm.

Period RCP 4.5 RCP 8.5 Early century 424 436 Mid-century 487 541 Late century 523 804 Water 2019, 11, 1090 7 of 18

The values for early and late century were obtained from a study by Li et al. [36], whereas the mid-century values were obtained from Meinshausen et al. [37].

4. Results and Discussion

4.1. Selection of Climate Model The results of the R2 for precipitation for ﬁve GCMs: ACCESS 1.0, MPI-ESM-LR, CCSM4, CNRM-CM5 and MIROC-ESM were 33.3%, 32.9%, 10.5%, 43.4% and 40.5% respectively at station P-1 and 52.5%, 0.2%, 7.5%, 31.5% and 54.7% respectively at station P-2. Hence, due to better performance of MIROC-ESM compared to the other models, it was chosen for climate change impact assessment at Haro River watershed.

4.2. Bias Correction Figure2 shows the variation in statistical indicators including mean, standard deviation, 90th percentile and 10th percentile for observed, non-bias corrected and bias corrected model data for precipitation, maximum and minimum temperature at stations P-1 and P-2. The correction factors found during the calibration phase (1987–1996) were applied to the validation period (1997–2005) and the performance during both phases was evaluated. At both stations, the bias corrected parameters during calibration period indicated more improvement as compared to the validation period. For precipitation, maximum and minimum temperatures, the non-bias corrected indicators showed underestimated values specifically for mean, standard deviation and 90th percentile at P-1 and P-2. The bias corrected mean precipitation at both stations became equal to the observed mean precipitation during the calibration period whereas non- bias corrected values showed a percentage difference of 44.3% and − 25.2% at P-1 and P-2 respectively in comparison to the observed values. − The indicators for maximum temperature showed overestimated values for non-bias corrected model data at station P-1 for both calibration and validation periods, whereas for station P-2, the observed, non-bias corrected and bias corrected statistical parameter values demonstrated little variation. The standard deviation values at P-1 for non-bias corrected and bias corrected cases during calibration as well as validation periods were close to the observed values. At P-1, the mean values for the maximum temperature were improved with percentage difference of +27.2% (non-bias corrected) to 0.4% (bias corrected) during the calibration period and from +24.4% to 2.2% during the validation − − period with respect to the observed data. The 90th percentile at P-1was improved from a difference of +22.9% (non-bias corrected) to 1.5% (bias corrected) during the calibration period and with a − difference of +18.2% (non-bias corrected) to 5.5% (bias corrected) with respect to observed data during − the validation period. The 10th percentile was improved with a difference of +35% to +3.26% during calibration period and +34.4% to +2.5% during the validation period, for station P-1. For minimum temperature, the non-bias corrected values for three indicators including mean, 90th percentile and 10th percentile were overestimated during calibration and validation period at both stations. At P-1, during the calibration period, the 90th percentile for non- bias corrected minimum temperature was almost equal to the observed value with a difference of +0.8% but bias correction increased the percentage difference to 6.0%. Overall, it was observed that the application of linear − scaling improved the statistical indicators significantly at both stations. This indicated that the linear scaling technique used for the improvement of the selected NEX-GDDP model output is suitable to be used for the study region. Water 2019, 11, 1090 8 of 18 Water 2019, 11, x 8 of 20

P-1 P-2 16 16 14 14 12 12 10 10 8 8 6 6 4 4 Precipitation (mm) Precipitation Precipitation (mm) Precipitation 2 2 0 0 Mean Standard 90th 10th Mean Standard 90th 10th Deviation Percentile Percentile Deviation Percentile Percentile

(a) (b) P-1 P-2 35 35 °C)

30 °C) 30

25 25

20 20

15 15

10 10

5 5

Maximum Temperature ( Temperature Maximum 0 0 Mean Standard 90th 10th ( Temperature Maximum Mean Standard 90th 10th Deviation Percentile Percentile Deviation Percentile Percentile (c) (d) P-1 P-2 18 18 °C)

16 °C) 16 14 14 12 12 10 10 8 8 6 6 4 4 2 2 0 0 Minimum Temperature ( Minimum Temperature

Mean Standard 90th 10th ( Minimum Temperature Mean Standard 90th 10th Deviation Percentile Percentile Deviation Percentile Percentile (e) (f)

FigureFigure 2. 2.Line Line up up of variationof variation in observedin observed (blue), (blue), non-bias non-bias corrected corrected (green) (green) and biasand correctedbias corrected (orange) statistical(orange) indicators statistical duringindicators calibration during (ﬁlledcalibration markers) (filled and markers) validation and(hollow validation markers) (hollow periods markers) for (a) precipitationperiods for at(a P-1;) precipitation (b) precipitation at P-1; at ( P-2;b) precipitation (c) maximum at temperature P-2; (c) maximum at P-1; (temperatured) maximum at temperature P-1; (d) atmaximum P-2; (e) minimum temperature temperature at P-2; (e) at minimum P-1; (f) minimum temperature temperature at P-1; (f) minimum at P-2. temperature at P-2.

4.3.4.3. Comparison Comparison of of Mean Mean Monthly Monthly FutureFuture Meteorological Parameters Parameters at at P-1 P-1 and and P-2 P-2 TheThe mean mean monthly monthly amountsamounts ofof precipitation,precipitation, maximum maximum and and minimum minimum temperature temperature forfor the the baselinebaseline and and the the future future periods: periods: earlyearly century (2020s (2020s),), mid-century mid-century (2050s) (2050s) and and late late century century (2080s), (2080s), underunder RCP RCP 4.5 4.5 and and RCP RCP 8.5 8.5 for for stationstation P-1P-1 are shown in in Figure Figure 3 below. Mean monthly precipitation was expected to be the highest during the month of July followed by August for baseline and future periods. The maximum difference in precipitation, for both scenarios during the month of July was expected in the mid-century from 20.4 mm/day under RCP

Water 2019, 11, 1090 9 of 18

Mean monthly precipitation was expected to be the highest during the month of July followed by

WaterAugust 2019 for, 11, baselinex and future periods. The maximum diﬀerence in precipitation, for both scenarios9 of 20 during the month of July was expected in the mid-century from 20.4 mm/day under RCP 4.5 to 4.525.0 to mm 25.0/day mm/day under under RCP 8.5. RCP Under 8.5. Under both scenarios, both scenarios, in July, noin July, precipitation no precipitation diﬀerence difference was expected was expectedduring the during early the century early butcentury during but the during late century,the late century, the precipitation the precipitation was expected was expected to increase to increaseby 1mm by/day 1mm/day under RCPunder 8.5 RCP compared 8.5 compared to that to under that under RCP RCP 4.5. 4.5. Overall, Overall, the the maximum maximum increase increase in inprecipitation precipitation up up to to 90.6% 90.6% was was expected expected under under RCP RCP 8.5 8.5 inin latelate centurycentury during the month of of October October comparedcompared to to the the baseline baseline precipitation precipitation of of 3.2 3.2 mm/day. mm/day. TheThe maximum temperaturetemperature was was expected expected to beto highestbe highest during during the month the month of June. of TheJune. late The century late centurytemperatures temperatures were expected were expected to be highest to be in highest future butin future the increment but the underincrement RCP 8.5under was RCP substantially 8.5 was substantiallyhigher than underhigher RCP than 4.5. under The RCP highest 4.5. maximum The highest temperature maximum undertemperat RCPure 8.5 under was expected RCP 8.5 towas be expected34 ◦C approximately, to be 34 °C approximately, 29.2% higher than 29.2% the high baselineer than temperature the baseline of temperature 26.3 ◦C. of 26.3 °C. UnderUnder RCP RCP 4.5, 4.5, the the highest highest minimum minimum temperature temperature was was expected expected in in the the late late century century June June with with thethe increment increment of of 26.0% 26.0% compared compared to to the the baseline baseline minimum minimum temperature temperature of of 16.9 16.9 °C.◦C. Under Under RCP RCP 8.5, 8.5, thethe minimum minimum temperature temperature early early in in the the century century was was ex expectedpected to to be be lower lower than than that that under under RCP RCP 4.5 4.5 but but thethe late late century century temperatures temperatures were were anticipated anticipated to to be be higher. higher. The The highest highest minimum minimum temperature temperature was was expectedexpected during during the the late late century century June June with with temperat temperatureure expected expected to to be be 42.6% 42.6% higher higher than than the the baseline baseline temperaturetemperature of of 16.9 16.9 °C.◦C.

24 35 28 40 30 35 20 24 30 25 20 16 25 20 16 20 12 15 12 15 10 8 10 Precipitation Temperature Temperature Precipitation 8 5 5 4 0 4 0 0 -5 0 -5 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

(a) (b)

FigureFigure 3. 3. MeanMean monthly monthly precipitation, precipitation, maximum maximum and and minimum minimum temperature temperature for for baseline baseline and and future future periodperiod at at P-1 P-1 under under ( (aa)) RCP RCP 4.5; 4.5; ( (bb)) RCP RCP 8.5. 8.5. (Note: (Note: The The bars bars represent represent the the precipitation precipitation in in mm/day, mm/day,

dasheddashed lines lines represent represent maximum maximum temperature temperature in in °C◦C and and the the solid solid lines lines represent represent the the minimum minimum temperaturetemperature inin ◦°C.C. YellowYellow color color represents represents the the baseline baseline period, period, green green color representscolor represents the early the century, early century,orangecolor orange represents color represents the mid-century the mid-century and the blueand the color blue represents color represents the late century). the late century).

TheThe mean mean monthly monthly amounts amounts of of precipitation, precipitation, maximum maximum and and minimum minimum temperature temperature for for the the baselinebaseline and and the the future future periods periods under under RCP RCP 4.5 4.5 and and RCP RCP 8.5 8.5for forstation station P-2 are P-2 shown are shown in Figure in Figure 4. The4. precipitation,The precipitation, at station at station P-2, was P-2, wasalso alsoexpected expected to be to highest be highest during during the month the month of July of July followed followed by August.by August. Under Under RCP RCP 4.5, the 4.5, early the early century century was wasantici anticipatedpated to receive to receive maximum maximum precipitation precipitation of 20.2 of mm/day,20.2 mm /approximatelyday, approximately 20.9% 20.9% higher higher than thanthe baseline the baseline precipitation precipitation of 16.7 of 16.7mm/day mm/ daywhereas whereas the latethe latecentury century and andmid-century mid-century were were expected expected to toreceive receive 18% 18% and and 14% 14% increased increased precipitation precipitation as as comparedcompared to to baseline baseline precipitation precipitation respectively. respectively. Under Under RCP RCP 8.5, 8.5, the the early early century century precipitation precipitation was was projectedprojected to to be be lower lower than than that that under under RCP RCP 4.5 4.5 but but about about 19% 19% higher higher than than the the baseline baseline precipitation. precipitation. Mid-centuryMid-century was was likely likely to to receive receive most most amount amount of of precipitation, precipitation, about about 38% 38% higher higher than than the the baseline baseline precipitationprecipitation followed followed by by late late century. century. Similar to the case at station P-1, the maximum temperature at P-2 was also expected to be highest during the month of June. Under RCP 4.5, the maximum temperature late in the century was expected to reach as high as 43.6 °C, almost 4.7 °C higher than the baseline temperature of 38.9 °C. The early century temperatures under RCP 8.5 were anticipated to be lesser than that under RCP 4.5 whereas the mid-century temperatures under both scenarios were anticipated to be similar. Under RCP 8.5, the late century temperature was anticipated to reach up to 47 °C, approximately 8.1 °C higher than the baseline temperature. The minimum temperature was expected to be highest during July at station P-2. In July, during the early century, under RCP 4.5, the minimum temperature was 1.1 °C higher than the baseline

Water 2019, 11, 1090 10 of 18

Similar to the case at station P-1, the maximum temperature at P-2 was also expected to be highest during the month of June. Under RCP 4.5, the maximum temperature late in the century was expected to reach as high as 43.6 ◦C, almost 4.7 ◦C higher than the baseline temperature of 38.9 ◦C. The early century temperatures under RCP 8.5 were anticipated to be lesser than that under RCP 4.5 whereas the mid-century temperatures under both scenarios were anticipated to be similar. Under RCP 8.5, the late century temperature was anticipated to reach up to 47 ◦C, approximately 8.1 ◦C higher than the baseline temperature. The minimum temperature was expected to be highest during July at station P-2. In July, during Water 2019, 11, x 10 of 20 the early century, under RCP 4.5, the minimum temperature was 1.1 ◦C higher than the baseline temperature of 23.6 C but 0.6 C higher than that under RCP 8.5. During mid-century, in July, temperature of 23.6 ◦°C but 0.6 °C◦ higher than that under RCP 8.5. During mid-century, in July, the the minimum temperature was anticipated to rise under both emissions scenarios but the temperature minimum temperature was anticipated to rise under both emissions scenarios but the temperature underunder RCP RCP 8.5 8.5 waswas still expected to to be be 0.2 0.2 °C◦ Clower lower than than that that under under RCP RCP4.5. In 4.5. the Inlate the century, late century, the theminimum minimum temperature temperature under under RCP RCP 4.5 4.5was was expected expected to reach to reach 27.6 27.6°C whereas◦C whereas it was it anticipated was anticipated to be to be30.1 30.1 °C◦C under under RCP RCP 8.5; 8.5; approximately approximately 6.5 6.5°C higher◦C higher than than the baseline the baseline minimum minimum temperature. temperature.

24 50 30 50

40 18 24 40

30 18 30 12 20 12 20 Precipitation Temperature Precipitation 6 Temperature 10 6 10

0 0 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

(a) (b)

FigureFigure 4. 4.Mean Mean monthly monthly precipitation,precipitation, maximum and and minimum minimum temperature temperature for for baseline baseline and and future future periodperiod at at P-2 P-2 under under ( a(a)) RCP RCP 4.5;4.5; ((bb)) RCPRCP 8.5. (Note: (Note: The The bars bars represent represent the the precipitation precipitation in mm/day, in mm/day,

dasheddashed lines lines representrepresent maximummaximum temperature in in °C◦C and and the the solid solid lines lines represent represent the the minimum minimum temperature in °C. Yellow color represents the baseline period, green color represents the early temperature in ◦C. Yellow color represents the baseline period, green color represents the early century, orangecentury, color orange represents color represents the mid-century the mid-century and the blueand the color blue represents color represents the late the century). late century).

TableTable2 summarizes2 summarizes average average annual annual climateclimate parameters for for stations stations P-1 P-1 and and P-2 P-2 for for the the baseline baseline asas well well as as under under bothboth emissions scenarios. scenarios. In Inthe the future, future, at P-1, at P-1,the precipitation the precipitation was projected was projected to rise to risewith with respect respect to tobaseline baseline precipitation precipitation of 5.6 of 5.6mm/da mm/y.day. At AtP-2, P-2, the the precipitation precipitation was was anticipated anticipated to to increaseincrease more more under under RCP RCP 8.5 8.5 thanthan under RCP RCP 4.5 4.5 with with respect respect to to baseline baseline precipitation precipitation of 3.9 of mm/day. 3.9 mm/day. Overall,Overall, under under RCPRCP 4.5, the the precipitation precipitation varied varied from from 0.5 0.5mm/day mm/day to 0.6 to mm/day, 0.6 mm/ day,at P-1 at and P-1 P- and 2 respectively whereas under RCP 8.5, the precipitation varied from 0.8 mm/day to 0.9 mm/day, at P- P-2 respectively whereas under RCP 8.5, the precipitation varied from 0.8 mm/day to 0.9 mm/day, 1 and P-2 respectively. The minimum and maximum temperatures were expected to increase in the at P-1 and P-2 respectively. The minimum and maximum temperatures were expected to increase future under both scenarios with more rise expected under RCP 8.5 as compared to RCP 4.5. The in the future under both scenarios with more rise expected under RCP 8.5 as compared to RCP 4.5. temperature varied from 3.1 °C to 3.3 °C under RCP 4.5 and 4.0 °C to 4.3 °C under RCP 8.5. The temperature varied from 3.1 C to 3.3 C under RCP 4.5 and 4.0 C to 4.3 C under RCP 8.5. In a study by Ikram et al. [38],◦ the future◦ increase in precipitation◦ in Pakistan◦ was predicted to be In2–3 a studymm/day by Ikramunder etRCP al. [4.538], and the future3–4 mm/day increase under in precipitation RCP 8.5 whereas in Pakistan the temperature was predicted was to be 2–3projected mm/day to underincrease RCP from 4.5 3–4 and °C 3–4 under mm RCP/day 4.5 under and 3–8 RCP °C 8.5 under whereas RCP 8.5. the The temperature results of wasthe present projected tostudy increase indicated from 3–4the ◦temperatureC under RCP extremes 4.5 and to 3–8 be well◦C under within RCP the projected 8.5. The resultslimit by of Ikram the present et al. [38], study indicatedbut the precipitation the temperature was extremesunderestimated. to be well This within could thebe projecteddue to the limit use of by different Ikram et GCM al. [38 for], but the the precipitationclimate projections was underestimated. as use of different This climate could models be due tofor thethe usestudy of diareasfferent may GCM suggest for thedifferent climate projectionsresults. as use of different climate models for the study areas may suggest different results.

Water 2019, 11, 1090 11 of 18

Table 2. Comparison of climate variables at station P-1 and P-2 under RCP 4.5 and RCP 8.5.

Time Increase with Respect Station ID Climate Variable Values Period to Baseline Baseline 5.6 P-1 RCP 4.5 6.1 8.9 RCP 8.5 6.4 14.2 Precipitation Baseline 3.9 P-2 RCP 4.5 4.5 15.4 RCP 8.5 4.8 23.1 Baseline 17.7 P-1 RCP 4.5 20.8 3.1 RCP 8.5 21.7 4.0 Maximum Temperature Baseline 28.6 P-2 RCP 4.5 31.9 3.3 RCP 8.5 32.7 4.1 Baseline 9.4 P-1 RCP 4.5 12.6 3.2 RCP 8.5 13.7 4.3 Minimum Temperature Baseline 13.6 P-2 RCP 4.5 16.9 3.3 RCP 8.5 17.9 4.2

Note: Precipitation is given in mm/day, temperature in ◦C. Precipitation change is given as percentage (%).

4.4. Water Balance in SWAT The average annual water balance for the baseline period simulated in the SWAT hydrological model is represented in Table3. The incoming flow in the watershed was almost equal to the outflows except a minor difference, which was due to the losses occurring in the watershed. Almost 61% of the incoming flow was discharged as water yield, which showed that the major portion of the precipitation was contributing to streamflow. In comparison, the water yield for the Simly Dam watershed in Pakistan was also estimated to be as high as 59% of the total inflows in the watershed [39]. Evapotranspiration accounted for almost 38% of the total precipitation in the watershed.

Table 3. Average annual water balance for Haro River watershed (1976–2005).

Water Balance Flow Symbology Computed Values (mm) Total Flow(mm) Components Inﬂow Precipitation PRECIP 1873.8 1873.8 Water Yield WYLD 1149.82 Outﬂow Deep Aquifer Recharge DA_RCHG 8.57 Evapotranspiration ET 713.8 1872.19 Losses 1.61

Figure5 shows the variation in the water balance components expressed as percentages, computed with respect to precipitation amount in the baseline and three future periods under RCP 4.5 and RCP 8.5. Water 2019, 11, x 12 of 20

Water 2019, 11, 1090 12 of 18

38% 61% 1%

WYLD DA_RCHG ET

(a)

35% 37% 35% 62% 1% 64% 1% 1% 64%

WYLD DA_RCHG ET WYLD DA_RCHG ET WYLD DA_RCHG ET

(b) (c) (d)

35% 34% 34% 65% 66% 66% 0% 0% 0%

WYLD DA_RCHG ET WYLD DA_RCHG ET WYLD DA_RCHG ET

(e) (f) (g)

FigureFigure 5. 5.Relative Relative percentages percentages of water water balance balance compon componentsents under under baseline baseline and future and future periods periods (a) (a) baselinebaseline period; period; (b ()b early) early century century RCP 4.5; 4.5; (c ()c mid-century) mid-century RCP RCP 4.5; 4.5;(d) late (d) century late century RCP 4.5; RCP (e) 4.5; early (e ) early centurycentury RCP RCP 8.5; 8.5; (f )(f mid-century) mid-century RCP 8.5; 8.5; (g (g) )late late century century RCP RCP 8.5. 8.5. (Note: (Note: WYLDWYLD refers refersto water to yield, water yield, DA_RCHG refers to deep aquifer recharge and ET refers to evapotranspiration). DA_RCHG refers to deep aquifer recharge and ET refers to evapotranspiration). 4.5.4.5. Calibration Calibration and and Validation Validation ofof SWATSWAT Using SUFI-2 SUFI-2 in in SWAT-CUP. SWAT-CUP TableTable4 summarizes 4 summarizes thethe results of of calibration calibration and andvalidation validation of the ofSWAT the within SWAT SUFI-2. within The SUFI-2. calibration and validation results were based on monthly observed and simulated streamflows for The calibration and validation results were based on monthly observed and simulated streamﬂows for the calibration period (1989–1991) and validation period (1996–September 1998). The model thecalibration results period also (1989–1991) fulfilled the and criteria validation for P periodand R factors. (1996–September The results 1998).of the SUFI-2 The model indicated calibration resultsthat also83% fulﬁlledof the observed the criteria data forduring P and calibration R factors. and The 79% results of the ofdata the during SUFI-2 validation indicated period that 83%was of the observedbracketed data by during the 95 calibrationPPU band. andThe 79%criterion of the for data the duringR-factor validation was also periodachieved. was According bracketed to by the 95 PPUAbbaspour band. et The al. [30], criterion a P factor for thevalue R-factor of 0.80–1.00 was indicates also achieved. the least According number of conceptual to Abbaspour errors et in al. [30], a Pthe factor model value as well of 0.80–1.00 as a higher indicates quality of the input least data. number NSE value of conceptual of 0.80 and errors0.77 and in R the2 of model0.82 and as 0.77 well as a higherfor calibration quality of and input validation data. NSErespectively valueof indicated 0.80 and good 0.77 model and Rperformance.2 of 0.82 and 0.77 for calibration and validation respectively indicated good model performance.

Table 4. Calibration Results for SWAT model.

Performance Indicator P-Factor R-Factor NSE R2 Calibration 0.83 0.88 0.80 0.82 Validation 0.79 0.84 0.77 0.77

Figure6 shows the comparison of observed and simulated flows for Haro River watershed during the calibration and validation period. The simulated discharges during the calibration period were higher compared to the observed flows from July until September 1989 whereas the simulated flows were lower in April 1991 as well as from July until September 1991. Similarly, during the validation Water 2019, 11, x 13 of 20

Table 4. Calibration Results for SWAT model.

Performance Indicator P-Factor R-Factor NSE R2 Calibration 0.83 0.88 0.80 0.82 Validation 0.79 0.84 0.77 0.77

Figure 6 shows the comparison of observed and simulated flows for Haro River watershed Water 2019, 11, 1090 13 of 18 during the calibration and validation period. The simulated discharges during the calibration period were higher compared to the observed flows from July until September 1989 whereas the simulated period,flows the were simulated lower in flows April were 1991 much as well lower as from than July the observeduntil September flows from1991. AprilSimilarly, 1997 during untilJune the 1997 validation period, the simulated flows were much lower than the observed flows from April 1997 and in August 1997. This flow difference was due to the difference in observed precipitation and the until June 1997 and in August 1997. This flow difference was due to the difference in observed climateprecipitation model precipitation and the climate at bothmodel stations precipit P-1ation and at both P-2. stations P-1 and P-2.

60 100 90 Observed Observed 50 80 Simulated Simulated 70 40 60 /sec) /sec) 3 3 30 50 40 20 Flow(m

Flow (m 30

10 20 10 0 0 Jan-89 Jun-89 Nov-89Apr-90 Sep-90 Feb-91 Jul-91 Dec-91 Jan-96 Jul-96 Dec-96 May-97 Oct-97 Mar-98 Aug-98

(a) (b)

FigureFigure 6. Observed 6. Observed and and simulated simulated discharge discharge at Haroat Haro River River (a )( duringa) during the the calibration calibration period; period; (b ()b during) validationduring period.validation period.

TheThe initial initial and and final final ranges ranges of of the the parameters parameters considered for for the the calibration calibration of discharge of discharge in SUFI- in SUFI-2 are given2 are given in Table in 5Table. The 5. absolute The absolute values values of the of parametersthe parameters identified identified in in SUFI-2 SUFI-2 were were constrained constrained with eachwith iteration each whileiteration computing while computing the objective the objectiv function.e function. The final The ranges final of rang thees parameters of the parameters indicated the indicated the uncertainty ranges that gave the best value for the objective function i.e., NSE and R2. uncertainty ranges that gave the best value for the objective function i.e., NSE and R2.

Table 5. Description of parameters with the initial and ﬁnal parameter in SUFI-2.

Initial Parameter Ranges Final Parameter Ranges Description Sr. No. Parameters Minimum Maximum Minimum Maximum Value Value Value Value 1 CN2 SCS Runoff Curve Number 0.2 0.2 0.2 0.02 − − 2 ALPHA_BF Base flow alpha Factor 0 1 0.32 0.98 3 GW_DELAY Groundwater delay 30 450 30 278 Threshold depth of water in the 4 GWQMN shallow aquifer required for return 0 2 0 1.1 flow to occur 5 GW_REVAP Groundwater revap coefficient 0.02 0.2 0.02 0.19 6 ESCO Soil evaporation compensation factor 0 1 0 0.58 Manning’s “n” value for the main 7 CH_N2 0 0.15 0.07 0.15 channel Effective hydraulic conductivity in 8 CH_K2 0 130 0 66 main channel alluvium Available water capacity of the soil 9 SOL_AWC 0 1 0.3 0.9 layer 10 SOL_K Saturated hydraulic conductivity 1 1 1 0.17 − − 11 SOL_BD Moist bulk density 1 1 0.3 1 − − 12 SFTMP Snowfall temperature 5 5 0.25 5 − − Maximum melt rate for snow during 13 SMFMX 1.5 7.5 4.5 7.5 year Minimum melt rate for snow during 14 SMFMN 1.5 7.5 3.9 7.5 the year 15 SMTMP Snow melt base temperature. 5 5 0.35 5 − − 16 TIMP Snow pack temperature lag factor 0 1 0.24 0.75 Water 2019, 11, 1090 14 of 18 Water 2019, 11, x 15 of 20

The results of the global sensitivity analysis in SUFI-2 are shown in Figure 7. Based upon least The results of the global sensitivity analysis in SUFI-2 are shown in Figure7. Based upon least p-valueWater 2019 and, 11 the, x largest absolute t-stat, GW_DELAY followed by CN2, SOL_K and SOL_BD15 of were20 p-value and the largest absolute t-stat, GW_DELAY followed by CN2, SOL_K and SOL_BD were ranked ranked as the most sensitive parameters. This indicated that parameters related to groundwater The results of the global sensitivity analysis in SUFI-2 are shown in Figure 7. Based upon least asprocesses, the most sensitivesurface runoff parameters. and soil processes This indicated have th thate highest parameters impact relatedon the results to groundwater of the SWAT processes, model p-value and the largest absolute t-stat, GW_DELAY followed by CN2, SOL_K and SOL_BD were surfacefor this runo particularff and soil watershed. processes Ahave higher the value highest of CN2 impact indicated on the resultsits key ofrole the in SWAT the generation model for of this ranked as the most sensitive parameters. This indicated that parameters related to groundwater particularstreamflow watershed. for Haro ARiver higher watershed value of as CN2 CN indicatedis used by its SWAT key role to divide in the precipitation generation of into streamflow overland for processes, surface runoff and soil processes have the highest impact on the results of the SWAT model Haroflow River or infiltration. watershed For as CN each is usedHRU,by surface SWAT runoff to divide was precipitation generated within into overlandSWAT by flow using or infiltration.SCS-CN for this particular watershed. A higher value of CN2 indicated its key role in the generation of For each HRU, surface runoff was generated within SWAT by using SCS-CN method [40]. methodstreamflow [40]. for Haro River watershed as CN is used by SWAT to divide precipitation into overland flow or infiltration. For each HRU, surface runoff was generated within SWAT by using SCS-CN method [40].

FigureFigure 7. 7.Results Results ofof GlobalGlobal Sensitivity Analysis Analysis for for Haro Haro River River watershed. watershed. Figure 7. Results of Global Sensitivity Analysis for Haro River watershed. 4.6.4.6. Variation Variation in in Streamflows Streamflows 4.6.TheThe Variation monthly monthly in variation Streamflows variation in in streamflows streamflows under under RCP RCP 4.5 4.5 and and RCP RCP 8.5 for8.5 thefor baselinethe baseline and and three three future 3 3 periodsfuture isTheperiods shown monthly is in shown Figurevariation in8. Figure Overall,in streamflows 8. comparedOverall, under compared toRCP the 4.5 baseline toand the RCP baseline discharge 8.5 for dischargethe of baseline 7.7 m of and /s7.7 at threem Dhartian/s at gaugingDhartianfuture station, periodsgauging in is thestation, shown future, inin theFigure the future, streamflows 8. Overall, the stream compared wereflows expected were to the expected tobaseline increase to discharge increase by 13.0% ofby 7.7 under13.0% m3/s underRCP at 4.5 andRCP 20.8%Dhartian 4.5 underand gauging 20.8% RCP station, under 8.5. Under inRCP the both8.5.future, Under scenarios, the stream both noflowsscenarios, shift were in thenoexpected peakshift in dischargesto increasethe peak by wasdischarges 13.0% anticipated under was in future.anticipatedRCP The 4.5 trend andin future. 20.8% in the The under streamflows trend RCP in the8.5. was streamflowsUnder directly both related wasscenarios, directly to theno related trendshift in into the meanthe peaktrend monthly discharges in mean precipitation monthly was atprecipitation bothanticipated stations inat P-1 future.both and stations The P-2. trend On P-1 thein andthe other streamflowsP-2. hand, On the temperature was other directly hand, didrelated temperature not to show the trend adid major in not mean influenceshow monthly a major on the peakinfluenceprecipitation flows on in the at peak Haro both flows Riverstations in watershed the P-1 Haro and RiverP-2. in future.On wa thetershed other The in maximumhand, future. temperature The streamflows maximum did not streamflows during show a the major during baseline andthe theinfluence baseline future on and scenarios the the peak future flows were scenariosin expected the Haro were duringRiver expected wa thetershed month during in future. of July.the Themonth Under maximum of RCP July. streamflows 4.5, Under the maximumRCP during 4.5, the flow the baseline and the future scenarios were expected during the month of July. Under RCP 4.5, the wasmaximum expected flow during was theexpected early centuryduring the followed early century by the followed late century by the and late mid-century century and whereasmid-century under whereasmaximum under flow RCP was 8.5, expected the flow during was theexpected early century to be maximum followed by during the late the century mid-century and mid-century followed by RCP 8.5, the flow was expected to be maximum during the mid-century followed by the early and late thewhereas early and under late RCP century. 8.5, the This flow was was found expected to be to similar be maximum to the precipitation during the mid-century trend of Murree. followed by century.the early This and was late found century. to be This similar was found to the to precipitation be similar to the trend precipitation of Murree. trend of Murree.

30 35 30 Baseline 35 Baseline 25 2020Baseline 30 Baseline2020 30 25 20502020 2020 25 2050 20 20802050 2050 20 25 2080 2080 20 2080 15 20 15 15 15 10 10 10 10 Streamflow (cumecs) Streamflow (cumec) Streamflow (cumecs) 5 Streamflow (cumec) 5 55

0 0 00 Jul Jul Jan Jul Jun Oct Jan Feb Jul Sep Jan Jun Dec Oct Jun Feb Sep Oct Apr Jan Feb Sep Mar Dec Jun Dec Oct Apr Aug Nov Feb Sep Apr Mar May Mar Dec Aug Nov Apr Aug Nov May Mar May Aug Nov May (a(a) ) (b()b )

FigureFigureFigure 8. 8.Monthly 8.Monthly Monthly variation variation variation inin in streamﬂows streamflstreamflows of of HaroHaro Haro River River River under under under (a ()a (RCP)a )RCP RCP 4.5; 4.5; 4.5;(b ()b RCP ()b RCP) RCP 8.5. 8.5. 8.5.

Figure9 shows the box and whisker plot for the mean annual streamflows during the baseline and future periods under RCP 4.5 and RCP 8.5. Under RCP 4.5, during the late century the increase in Water 2019, 11, x 16 of 20 Water 2019, 11, 1090 15 of 18 Figure 9 shows the box and whisker plot for the mean annual streamflows during the baseline and future periods under RCP 4.5 and RCP 8.5. Under RCP 4.5, during the late century the increase averagein average annual annual streamflow streamflow was highestwas highest among among all future all future periods periods compared compared to the to baseline the baseline flow. flow. Under RCPUnder 8.5, theRCP maximum 8.5, the maximum increment increment in streamflows in stream wasflows expected was expected duringearly during century early withcentury respect with to therespect baseline to the period. baseline The period. largest The variation largest invariation streamflows in streamflows was expected was expected to be during to be during the late the century late undercentury RCP under 4.5. RCP 4.5.

18 16 14 12 10 8 6 4

2 Baseline 2020s 2050s 2080s

Average Annual Streamflow (cumecs) Streamflow Annual Average 0 Baseline RCP 4.5 RCP 8.5 RCP 4.5 RCP 8.5 RCP 4.5 RCP 8.5

FigureFigure 9. 9.Box Box and and whisker whisker plot plot forfor averageaverage annual streamflows streamﬂows under under RCP RCP 4.5 4.5 and and RCP RCP 8.5. 8.5.

4.7.4.7. Comparison Comparison in in Future Future Projections Projections under under RCPRCP 4.54.5 and RCP 8.5 8.5 Overall,Overall, the the results results of climate of climate projections projections under MIROC-ESMunder MIROC-ESM suggested suggested an increase an inincrease precipitation in asprecipitation well as maximum as well and as minimummaximum temperatures and minimum at temperatures both stations, at P-1 both and stations, P-2. The P-1 projected and P-2. increase The in futureprojected climate increase variables in future was climate higher undervariables RCP was 8.5 higher than under under RCP RCP 4.5, 8.5 compared than under to theRCP baseline 4.5, period.compared The precipitationto the baseline increase period. atThe both precipitation stations ranged increase from at both 4.5 mmstations/day ranged to 6.1 mm from/day 4.5 undermm/day RCP 4.5to and 6.14.8 mm/day mm/day under to 6.4 RCP mm 4.5/day and under 4.8 mm/day RCP 8.5. to Under 6.4 mm/day RCP 4.5, under the maximumRCP 8.5. Under temperature RCP 4.5, at the both maximum temperature at both stations was projected to increase by 20.8 °C and 31.9 °C while the stations was projected to increase by 20.8 ◦C and 31.9 ◦C while the increment in minimum temperature increment in minimum temperature was 12.6 °C and 16.9 °C at the end of 21st century. Similarly, was 12.6 ◦C and 16.9 ◦C at the end of 21st century. Similarly, under RCP 8.5, the maximum temperature under RCP 8.5, the maximum temperature at both stations was projected to increase by 21.7 °C and at both stations was projected to increase by 21.7 ◦C and 32.7 ◦C while the minimum temperature was 32.7 °C while the minimum temperature was projected to increase by 13.7 °C and 17.9 °C. The projected to increase by 13.7 ◦C and 17.9 ◦C. The streamflows in the Haro River watershed were also streamflows in the Haro River watershed were also projected to increase in future with higher projected to increase in future with higher streamflows expected under RCP 8.5 (9.3 m3/s) compared to streamflows expected under RCP 8.5 (9.3 m3/s) compared to RCP 4.5 (8.7 m3/s). The higher range of RCP 4.5 (8.7 m3/s). The higher range of climate and hydrologic changes under RCP 8.5 can be attributed climate and hydrologic changes under RCP 8.5 can be attributed to the highest radiative forcing level to the highest radiative forcing level and concentration level of gases in this particular scenario among and concentration level of gases in this particular scenario among all scenarios [37]. all scenarios [37]. 5. Conclusions, Limitations and Recommendations 5. Conclusions, Limitations and Recommendations 5.1. Conclusions 5.1. Conclusions The study concludes that the linear scaling technique can be successfully applied to remove bias The study concludes that the linear scaling technique can be successfully applied to remove bias in the temperature and precipitation time series. The statistical indicators, after bias correction, in the temperature and precipitation time series. The statistical indicators, after bias correction, showed showed considerable improvement compared to the observed time series. SWAT well simulated the considerablehydrology improvementof the data-scarce, compared high elevation to the observed region and time was series. a good SWAT tool in well evaluating simulated the components the hydrology ofof the the data-scarce, water balance. high elevation region and was a good tool in evaluating the components of the water balance.Precipitation was expected to increase in future under both emissions scenarios during early, midPrecipitation and late century. was expected Summer toseason increase was inanticipated future under to receive both maximum emissions precipitation scenarios during whereas early, midwinter and lateseason century. was expected Summer to season remain was relative anticipatedly dry. The to receiveminimum maximum and maximum precipitation temperatures whereas winterwere season also expected was expected to be highest to remain during relatively early summer dry. The season. minimum The increase and maximum in minimum temperatures temperature were also expected to be highest during early summer season. The increase in minimum temperature in future was more significant as compared to the increase in maximum temperature with respect to the baseline period. Water 2019, 11, 1090 16 of 18

The study indicated that in the future, the streamflows at Haro River watershed were expected to increase with maximum streamflows expected during the summer season. In the future, an increase in streamflows indicate that water scarcity at Khanpur Dam is less likely to be the major concern for policy makers. On the other hand, this increase may enhance erosion and sedimentation and may lead to a decrease in the storage capacity of the Khanpur Dam reservoir. The management strategies at Khanpur Dam may focus on adapting de-siltation plans as well as increasing water storage capacity of the reservoir. The stored water, during the high flow periods could be used to fulfill the demand during low flow period. The changing streamflows at Haro River basin would affect the discharge at Khanpur Dam, which is a major source of fulfilling the domestic water supply needs of large population. The coordination among the involved institutions, for example, Water and Sanitation Authority (WASA), Capital Development Authority (CDA) and Rawalpindi Cantonment Board (RCB), in conceiving prudent schemes for effective utilization of water supply, is essential to fulfil the demands of all sub-sectors utilizing water in all the periods of the year. Finally, the potential climate change impact on sedimentation should be studied and sedimentation plans should be proposed. The impact on the sediment yield can be analyzed in SWAT model using long-term suspended sediment data as well as discharge data in the historical period and use it to project future periods. This is an opportunity for continuing research for the Khanpur Dam. The integration of sedimentation plans, in future studies, may help to preserve the storage capacity of reservoir and encourage proper maintenance plans. An insight into devising watershed management plans focusing on delaying the siltation process in the reservoir may help to reduce the effect of climate change on the reservoir. The effect of changing landuse can also be integrated in the SWAT model to study its impact on discharge and sedimentation within the watershed.

5.2. Limitations and Recommendations The discharge data was available only for seven years in total: 1989–1991 and 1996–1998 which made the calibration and validation challenging. However, the results of the calibrated parameter ranges when applied to an independent validation time series showed good model performance. Steps should be taken by authorities to set up gauging stations in the watershed, as Dhartian gauging station is the only discharging measuring station in Haro River, which is non-functional since 1998. The daily discharge data must be recorded in order to understand the hydrological processes occurring in the watershed in a better way. The daily data for solar radiation, relative humidity and wind speed data was not available from local meteorological stations. Therefore, this data was extracted from the CFSR dataset for the similar time period at the stations corresponding to P-1 and P-2. In the study, landuse was assumed constant. In future, an insight into climate change due to variation in landuse and land cover is recommended to be considered. Research in future may also focus on studying the eﬀect of variation in climatic parameters on sedimentation. Finally, there is inherent uncertainty in the use of one GCM to propagate climate change projections into projections of future ﬂows. Future work should include a full ensemble of GCM model outputs to better capture the uncertainty of the future climate.

Author Contributions: The first three authors contributed in conceptualization and devising methodology for the study. S.N. prepared the input data, implemented SWAT model, carried out analysis of the results and prepared the original draft. Z.Z., A.H.B.G. and B.Y. supervised the study and contributed in reviewing and editing the final manuscript. Funding: The first author gratefully acknowledges the Ministry Of Higher Education Malaysia for providing financial funding under the Malaysian International Scholarship for carrying out this research study at Universiti Putra Malaysia. This research work was also funded by Geran Putra-Inisiatif Putra Muda from Universiti Putra Malaysia, grant number GP-IPM/2016/9498000 and the FUNDAMENTAL RESEARCH GRANT SCHEME from The Ministry of Education Malaysia, grant number KPT-FGRS/2017/5540017. Water 2019, 11, 1090 17 of 18

Acknowledgments: The authors acknowledge with gratitude the cooperation of the Pakistan Meteorological Department, Water and Power Development Authority and National Engineering Services Pakistan for the provision of data for the research work. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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