Future Climate Projections using the LARS-WG6 downscaling model over Upper Indus Basin, Pakistan

doi:10.21203/rs.3.rs-2469886/v1

This study investigates the projections of precipitation and temperature at local-scale in the Upper Indus Basin (UIB) in Pakistan using six Regional Climate Models (RCMs) from CORDEX, under two Representative Concentration Pathways (RCP 4.5 and RCP 8.5). For twenty-four stations spread across the study area, the daily data from the six different RCMs for maximum temperature (T_max), minimum temperature (T_min) and precipitation(pr) at a spatial resolution of 0.44^o was downscaled using Long Ashton Research Station Weather Generator, version six (LARS-WG6). Investigations were made to predict changes in mean annual values of T_max, T_min and precipitation during two future periods, i.e., the mid-century (2041–2070) and end-century (2071–2100). Model results from statistical and graphical comparison validated that the LARS-WG6 is capable of simulating both the temperature and the precipitation in the UIB. Each of the six RCMs and their ensemble revealed a continuous increased temperature projections in the basin; nevertheless, there is variation in projected magnitude across RCMs and between RCPs. In general, the rise in average T_max and T_min was found to be greater under RCP 8.5 than RCP 4.5 possibly due to unmitigated greenhouse gas emissions (GHGs). The precipitation projections follow nonuniform trend. i.e., not all RCMs agree whether the precipitation will increase or decrease in the basin and no orderly variations were detected during any of the future time periods under any RCP. However. an overall increase in precipitation is projected by the ensemble of RCMS.

Precipitation

Temperature

CORDEX

RCPs

UIB

LARS-WG6

The natural systems are under considerable threat because of rise in temperatures and consequent global shifts in precipitation patterns(IPCC, 2014) .This threat will get more worse and socio-economic systems will be severely affected by these changes by the end of this century (Ding et al., 2016). A global rise in temperature between 0.3^oC to 4.8 ^oC is predicted by the end of this century in 5th Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), due to the emissions of greenhouse gases (GHG)(IPCC, 2013) .

High altitude areas are more fragile to climate change(Buytaert et al., 2010) and are easily affected by climatic changes. These changes affect the services provided by these regions including water supply for hydropower and crop production etc. Increased warming trends over high altitudes have been predicted by Mountain Research Initiative(MRI, 2015) and (Gerlitz et al., 2016). These climatic changes endanger the overall hydrological equilibrium upstream and aggravate the issues associated with water management downstream. It is therefore of the utmost importance to investigate the likely impacts of climate change on the future water availability from the mountainous watersheds.

The upper Indus basin (UIB—Figure 1), contributes approximately half (Hasson et al., 2017) of the surface water availability in Pakistan, thereby ensuring the continued existence of the immediate downstream Tarbela reservoir, which is the major water storage structure in the country.

The future water availability is strongly dependent upon the future climate projections under different greenhouse gas (GHG) emissions. Since the agricultural sector of Pakistan's economy is highly dependent on erratic water supplies from the Hindukush-Karakoram-Himalayan (HKH) watersheds of the Indus basin, it is of the utmost importance to conduct a realistic evaluation of the future water availability from these watersheds in order to ensure the socioeconomic development of the country in a manner that is sustainable.

An awareness of the likely variations in precipitation and temperature that may occur in the future is important to explore the impact on water resources and livelihoods. It will be helpful in the process of creating and putting into action appropriate adaptation strategies on a river basin scale.

Many researchers have used Global Climate Models (GCMs) and Representative Concentration Pathways (RCPs) to forecast future climate change. However, because of the relatively coarse computational cells that they use, GCMs are not suitable for direct application to sub-grid or local scale impact studies (Fowler et al., 2007). So, these GCMs need to be downscaled either using statistical downscaling (SD) or dynamical downscaling (DD). DD can be achieved by using the regional climate model (RCM) embedded in a larger GCM. RCMs use the boundary conditions of the GCMs and dynamically downscale them to the limited area of interest with more detailed output(Abiodun & Adedoyin, 2016). Because of their ability to capture weather phenomena at various spatial and temporal scale, RCMs are now widely used to simulate the future climate along with assessing the impacts of climate on water resources and predicting extreme hydrological events(Kim et al., 2020; Lee & Cha, 2020). Coordinated Regional Downscaling Experiment (CORDEX) consists of a range of RCMs which have been used worldwide by many researchers. Bukovsky et.al(2020) have used multi model ensemble of NA-CORDEX RCMs to project temperature and precipitation. Luhunga et.al.(2018) have used CORDEX RCMs to project climate changes for Tanzania. Fotso-Nguemo et. al(2019) have used multi model ensemble of CORDEX to predict the impact of climate change on extreme precipitation indices over Central Africa. Chapagain et.al (2021)have used CORDEX-SA for future projection of climate indices under low and high emission scenarios for Nepal. EURO-CORDEX and MED-CORDEX has been used by(Baronetti et al., 2022) to predict drought events in northern Italy.

RCMs can be further downscaled to the point data of gauging station using SD techniques. The SD technique establishes statistical links between large scale weather and local scale weather. Extensive research on downscaling of GCMs have been carried out. However, there are only a few studies on downscaling of RCMs.

The SD techniques are regarded to be among the most popular since they have the advantages of being computationally affordable and being able to provide comprehensive station-specific data (Trzaska & Schnarr, 2014). Karam et.al (2022)have used sch technique to downscale CORDEX RCM to assess hydrological extremes in Congo basin. One example of SD technique is the Long Ashton Research Station Weather Generator (LARS-WG6) (Semenov et al., 2002). It has been adopted by various researchers and studies have shown that it is able to simulate future climate with reasonable skills (Hashmi et al., 2011) and (Bayatvarkeshi et al., 2020). It has been used successfully in numerous studies to downscale climate variables such as in the and Koshi River Basin in Nepal (Agarwal et al., 2014), Gujrat, India (Sarkar et al., 2015), Nile River Basin (Fenta Mekonnen & Disse, 2018) and Northeastern China(Sha et al., 2019).

Many studies have been conducted on the use of downscaled GCMs based on various RCP scenarios for evaluating potential climate changes and their impact on UIB hydrology (Lutz et al., 2016; Ougahi et al., 2022; Pomee & Hertig, 2021). Few researchers have used RCMS for Upper Indus Basin including (Khan et al., 2015; Hassan et al., 2019 and Shah et al., 2020). Among these, some has either used only one RCM, or have used set of RCMs for end century and only for one RCP scenario. Keeping in view the significance of the UIB for Pakistan, it is absolutely necessary to conduct a more in-depth study that investigates concerns regarding an uncertain future by taking into account climate predictions from a greater number of RCMs, additional RCPs, and more than one future time period in the twenty-first century. In this perspective, the goals of the current research are as follows: (a) to examine the capability of LARS-WG6 for downscaling RCMs in the UIB; and (b) to project the climate changes for two selected future time periods[2041–2070 (2050s)] and [2071–2100 (2080s)] under RCP4.5 and RCP8.5 scenarios. These findings will be valuable in environmental planning and risk reduction initiatives on a local scale, particularly those pertaining to water resources and extreme hydrological events.

2.1 Study Area Description

River Indus is one of the largest rivers of the world. It is a transboundary river which is spread across Pakistan, India, China and Afghanistan. The part of Indus up to Tarbela dam is considered as Upper Indus Basin which has been used as the study area. It is a mountainous watershed with elevation ranging from 270m to 8465 m above sea level. Most of the part is covered with perennial snow. It is a major source of fresh water for Pakistan. Since the watershed is mountainous, so the gauging stations are sparse as they are difficult to manage. Figure 1 shows various sub basins of UIB along with the gauge location. The importance of Upper Indus Basin in Pakistan is clear from the fact that it provides the largest amount of water to main industry of the country i.e., agriculture. Moreover, it is considered to be a climate change hot-spot by many researchers including (Nepal & Shrestha, 2015),(Kilroy, 2015) and(de Souza et al., 2015) with a tremendously uncertain future hydro-climatology.

2.2 Observed Data

The gauge data of three climatic parameters i.e., precipitation(pr), maximum temperature (T_max) and minimum temperature (T_min) at daily time step for all the twenty-four stations of the study area has been collected from Pakistan Meteorological Department (PMD) and Water and Power Development Authority (WAPDA). Detail of gauging stations has been listed in Table 1.

Table 1

Detail of Gauging Stations in UIB
Sr.No.	Meteorological Station	Longitude	Latitude	Elevation (m)	Data Period	Data Providing Agency
1	Astore	74.90	35.37	2168	1960–2019	PMD
2	Bunji	74.63	35.67	1372	1961–2019	PMD
3	Chillas	74.10	35.42	1250	1961–2019	PMD
4	Chitral	71.80	35.90	1498	1981–2019	PMD
5	Dir	71.90	35.20	1369	1968–2019	PMD
6	Drosh	71.70	35.40	1464	1961–2019	PMD
7	Gilgit	74.33	35.92	1460	1960–2019	PMD
8	Gupis	73.40	36.17	2156	1961–2019	PMD
9	Skardu	75.68	35.30	2210	1961–2019	PMD
10	Burzil	75.90	34.92	4310	1995–2019	WAPDA
11	Dainyor	74.37	35.93	1550	1997–2019	WAPDA
12	Deosai	75.54	35.09	4240	1995–2019	WAPDA
13	Hushe	76.37	35.42	3245	1995–2019	WAPDA
14	Khot Pass	72.58	36.52	3455	1995–2019	WAPDA
15	Khunjrab	75.42	36.84	4730	1995–2019	WAPDA
16	Naltar	74.18	36.17	3075	1995–2019	WAPDA
17	Ramma	74.81	35.36	3344	1995–2019	WAPDA
18	Rattu	74.80	35.15	2920	1995–2019	WAPDA
19	Shendoor	72.55	36.09	3560	1995–2019	WAPDA
20	Shigar	75.53	35.63	2560	1996–2019	WAPDA
21	Ushkor	73.39	36.05	2970	1995–2019	WAPDA
22	Yasin	73.50	36.40	3150	1995–2019	WAPDA
23	Zani	72.17	36.33	3000	1995–2013	WAPDA
24	Ziarat	74.46	36.77	3669	1995–2019	WAPDA

The location of meteorological stations is shown in Fig. 1

Quality control of gauge data has been done using RClimdex (Zhang & Yang, 2004) and inhomogeneities were removed using Climatol package in R, developed by Guijarro(2017).

2.3 Regional Climate Model Data

Regional Climate Model (RCM) data has been collected from CORDEX-SA experiment for six different models at daily time step from the website https://esgf-data.dkrz.de/search/esgf-dkrz/. The CORDEX (Coordinated Regional Downscaling Experiment) project was initiated by the WCRP (World Climate Research Program) to provide reliable predictions of future climate change at regional scale. The climatic factors selected for this study are maximum temperature, minimum temperature, average temperature and precipitation. Detail has been provided in Table 2. The data was downloaded using the WGET script in Linux and data for area of interest was extracted using coding in R software.

Table 2

Detail of RCMs used in the study
Sr.No.	RCM	Driving GCM	Short Name (adopted in this study)	Historical simulation period	Future Simulation Period	Scenario	Horizontal Resolution
1	REMO 2009	MPI-M-MPI-ESM-LR	REMO	1961–2005	2006–2100	RCP4.5, RCP8.5	0.44^o
2	RCA4	ICHEC-EC-EARTH	ICHEC	1951–2005	2006–2100	RCP4.5, RCP8.5	0.44^o
3	RCA4	IPSL-IPSL-CM5A-MR	IPSL	1951–2005	2006–2100	RCP4.5, RCP8.5	0.44^o
4	RCA4	MPI-M-MPI-ESM-LR	MPI	1951–2005	2006–2100	RCP4.5, RCP8.5	0.44^o
5	RCA4	NCC-NorESM1-M	NOR	1951–2005	2006–2100	RCP4.5, RCP8.5	0.44^o
6	RCA4	NOAA-GFDL-GFDL-ESM2M	NOA	1951–2005	2006–2100	RCP4.5, RCP8.5	0.44^o

The data from 1976–2005 (1990s) has been selected as base period and the data for future simulation has been divided into two periods. The first future period for mid-century is taken from 2041 to 2070 (considered as 2050s later in this study) and second future period for end-century is considered from 2071 to 2100 (considered as 2080s later in this study). Both the future periods are taken for two emission scenarios which are Representative Concentration Pathways 4.5 (also known as RCP4.5) and Representative Concentration Pathways 8.5 (also known as RCP8.5). RCP 4.5 is a stabilizing scenario in which the use of technologies to reduce the greenhouse gases stabilizes the radiative forcing to 4.5 Watt/m² at the end of this century while RCP 8.5 is considered as a high-risk scenario where the radiative forcing level is considered to be 8.5 Watt/m² by the end of this century because of absence of any mitigation policies against greenhouse gas emissions.

2.4 Downscaling using LARS-WG6

The RCM data for all six models was further downscaled statistically with the help of sixth version of Long Ashton Research Station Weather Generator (LARS-WG6). It is an inexpensive stochastic weather generator tool implemented by(Semenov et al., 2002) for statistical downscaling of global or regional climate data to local scale for impact assessment study of future climate at daily timestep. The software can be accessed without any cost from https://sites.google.com/view/LARS-WG6/. The capability of LARS-WG6 for downscaling climatic parameters including temperature and precipitation has been ascertained in diverse climates around the world.

The first step of model calibration involves analysis of observed data using “Site Analysis” option. The second stage of model validation involves comparison of statistical properties of synthetic data with observed data. Statistical tests include comparison of probability distribution, variance and means with the help of Kolmogorov-Smirnov (KS) test, F‐test and t-test respectively. The last step involves creation of a scenario file using the RCMs data and hence generating future output at local scale. The model can use only one year of data to generate synthetic climate data. However, it is recommended to use at least 20–30 years of data for better representation of the climate for the site under consideration(Semenov et al., 2002). The use of long time series aid in capturing less frequent or extreme climate events.

To generate future climate time series for a station, scenario files were created by calculating change factors. These change factors were computed on a monthly scale by calculating the differences between the data of control period and future periods of six different RCMs mentioned in the data section for the two different emission scenarios. These change factors were then applied to LARS-WG6 parameters for the baseline precipitation of each station and hence local scale future simulation was generated for required number of years at daily time step. Figure 2 represents flow chart of LARS-WG6 working.

3.1 Calibration and Validation of LARS-WG6

The capability of LARS-WG6 was evaluated by computing the statistical properties of observed time series and then comparing these with the synthetic time series. This calibration and validation process was executed for each of the UIB's twenty-four meteorological stations to assess the performance of LARS-WG6 for downscaling RCMS.

The Kolmogorov–Smirnov (KS) test is executed to check goodness of fit of the seasonal distributions of dry and wet (DW) series, distributions of daily precipitation (D.pr), as well as distributions of daily maximum (D.T_max) and daily minimum (D.T_min) temperature. The second test is F-test which is conducted to check monthly variances of precipitation (MV.pr) while the third test i.e., the t- test is carried out for validating likeness of monthly average precipitation (MM.pr), monthly average of daily maximum temperature (DM.T_max), and monthly average of daily minimum temperature (DM.T_min). All of the above-mentioned tests calculate a p-value. The p-value is used to accept or reject the hypotheses that the observed and generated data may belong to the same distribution at the 5% significance level.

Table 3 shows the results for assessing the performance of LARS-WG6 for each of the twenty-four stations in UIB. The values in this table show that how many times the results differ significantly at 5% significance level. The total number of tests performed are 8 each for dry and wet series and 12 for the remaining. The performance of LARS-WG6 is considered to be excellent if the values in the table approach zero. It is clear from the table that the Wet Series, Dry Series, D.pr, D.T_min and D.T_max do not differ significantly at all twenty-four stations; for MM.pr, MV.pr, DM.T_max and DM.T_min, the average results were found to be 0.04, 3.96, 0.17 and 0.21, respectively. It is therefore concluded that LARS-WG6 is capable of modeling the distributions as well as means of all the three climatic variables of this study for all the months as compared to the monthly variances of precipitation (MV.pr).

Table 3

Results of LARS-WG6 performance evaluation
Variable	D.pr	MM.pr	MV.pr	D.T_min	DM.T_min	D.T_max	DM.T_max	Wet Series	Dry series
Station /Test	KS test	T-test	F-test	KS-test	T-test	KS-test	T-test	KS-test	KS-test
ASTORE	0	1	3	0	0	0	0	0	0
BUNJI	0	0	3	0	0	0	0	0	0
BURZIL	0	0	3	0	1	0	0	0	0
CHILLAS	0	0	2	0	1	0	0	0	0
CHITRAL	0	0	2	0	0	0	0	0	0
DAINYOR	0	0	4	0	0	0	1	0	0
DEOSAI	0	0	4	0	0	0	0	0	0
DIR	0	0	3	0	0	0	0	0	0
DROSH	0	0	1	0	0	0	0	0	0
GILGIT	0	0	4	0	0	0	0	0	0
GUPIS	0	0	5	0	0	0	0	0	0
HUSHE	0	0	5	0	1	0	0	0	0
KHOTPAS	0	0	4	0	1	0	0	0	0
KHUNJRAB	0	0	4	0	1	0	0	0	0
NALTAR	0	0	4	0	0	0	0	0	0
RAMA	0	0	4	0	0	0	0	0	0
RATTU	0	0	3	0	0	0	2	0	0
SHENDUR	0	0	4	0	0	0	0	0	0
SHIGAR	0	0	3	0	0	0	1	0	0
SKARDU	0	0	4	0	0	0	0	0	0
USHKOR	0	0	4	0	0	0	0	0	0
YASIN	0	0	4	0	0	0	0	0	0
ZANI	0	0	4	0	0	0	0	0	0
ZIARAT	0	0	4	0	0	0	0	0	0
AVERAGE	0	0.04	3.54	0	0.21	0	0.17	0	0
TOTAL NO. OF TESTS	12	12	12	12	12	12	12	8	8

Figure 3 shows the comparison of observed mean monthly precipitation(pr) along with the maximum and minimum temperature (T_max and T_min) with the simulated time series for each of the twenty-four gauging stations. The graphical results for precipitation show satisfactory results where the simulated mean monthly precipitation matched well with the observed data for all stations. Similarly, in case of maximum and minimum temperature, the simulated values perfectly matched the observed values for all the stations. Therefore, based on these statistical and graphical results, it is inferred that the LARS-WG6 is a trustable tool for future projections of temperature and precipitation predictions in UIB. The correlation between mean monthly values of observed and synthetic weather data varies from 0.86–0.99.

3.2 Down scaling with LARS-WG6

As LARS-WG6 model has been found suitable during performance evaluation phase, so the climate scenarios can be downscaled from six selected RCMs under RCP 4.5 and RCP 8.5 for the two defined future time periods.

3.2.1 Downscaling of Maximum Temperature (T_max)

Figure 4 presents the projections of annual average T_max by the six RCMs under two emission scenarios in two future periods. Results represent increase in T_max for all RCMs and both RCPs during both the mid-century and end-century time period for all the 24 stations. It is also apparent that the extent of variation in T_max is higher in end-century than T_max in mid-century for all RCMs under both emission scenarios. Moreover, the increase in T_max is the lowest under RCP 4.5 during the mid-century (0.87 ^oC for Hushe − 1.97 ^oC for Chillas) predicted by REMO and largest under RCP 8.5 during the end-century (7.51 ^oC for Ziarat– 10.14 ^oC for Gupis) predicted by IPSL respectively. Among all RCMs and stations, the IPSL simulation scenarios consistently provided the highest rise in future predictions under both RCPs and for both time periods. Considering the average ensemble values, the increase in T_max varies from 1.58 ^oC for Rattu to 3.08 ^oC for Bunji under RCP4.5 2050, 2.35^oC for Rattu- 3.49 ^oC for Drosh under RCP 4.5 2080, 2.38 ^oC for Rattu – 3.85 ^oC for Drosh under RCP 8.5 2050 and 4.82 ^oC for Shigar – 6.45 ^oC for Drosh under RCP 8.5 2080. This indicates that UIB is expected to get warmer in future.

The projected increase in maximum temperature in the UIB require extreme attention as it may cause water stress particularly if there is reduction in precipitation in the basin.

Figure 4 Changes in average annual T_max, T_min and pr for six RCMS for 24 stations during the two future periods and two emission scenarios

3.2.2 Downscaling of Minimum Temperature (T_min)

Figure 4 depicts the variations in annual average T_min projected by six RCMs under RCP 4.5 and RCP 8.5 in two future periods. Results for each of the 24 sites exhibit the same trend toward higher values as T_max. It was also noticed that for all the four scenarios, projections under RCP 4.5 in the mid-century consistently showed the smallest increase in T_min (0.81 ^oC for Shigar predicted by REMO − 2.3 ^oC for Skardu predicted by ICHEC), while those under RCP8.5 in the end-century consistently showed the largest increase (6.33 ^oC for Rama – 10.65 ^oC for Skardu) predicted by IPSL. Similar to T_max, among all the RCMs, the IPSL simulations always depict the largest increment in future projections under both RCPs. The average ensemble of multiple RCMs shows an increase of T_min from 1.53 ^oC for Burzil – 2.95 ^oC for Bunji during RCP 4.5 2050s and 4.54 ^oC for Dainyor – 7.04 ^oC for Skardu during RCP 8.5 2080s.

T_max and T_min results indicate that the Upper Indus Basin's future climate will most likely be warmer with the passage of time. However, the RCMs disagree on the magnitude of variation over the two future time periods. The findings of this study are in agreement with the IPCC 5th assessment report and confirm that RCP8.5 projections are higher than RCP4.5, which may be attributed to the former's limited mitigation and adaptation policies. The temperature trends expected by different RCMs are also consistent with previous research findings by (Stott & Kettleborough, 2002), (Fenta Mekonnen & Disse, 2018), and (Kavwenje et al., 2022) where they all noticed that increment in temperature was more sensitive to emission scenario in 2080s as compared to 2050s. Akhtar et.al (2008) have used PRECIS Regional Climate Model and predicted that temperature shows increasing trends towards the end of this century. Ismail et al. (2020) have used an ensemble of four GCMs to predict future climate over UIB. The ensemble projects an increase of 6^oC in temperature under RCP 8.5 scenario by the end of this century. These studies are in agreement with our findings where an average increase of 5.47 ^oC in T_min and 5.58^oC in T_max is projected over UIB by the end of century under RCP 8.5.

In general, based on the projected temperature trends under various climate change scenarios, it is clear that throughout this century, the UIB will experience pronounced warming trends which could have a direct impact on the availability of water resources.

3.2.3 Downscaling of Precipitation (pr)

The percent change in average annual precipitation envisaged by the six RCMs and their ensemble average, under the two emission scenarios for mid-century and end-century in the UIB is depicted in Fig. 4. It is clear from these graphs that change in precipitation has no discernible trend for the future time slices for all the six RCMs under both emission scenarios. Throughout the future time periods, both increase and decrease has been predicted in average annual precipitation. The non-uniform behavior of precipitation under various scenarios and time periods as investigated in this study is in agreement with the findings of (Agarwal et al., 2014) in Nepal, (Fenta Mekonnen & Disse, 2018) in Nile, (Kavwenje et al., 2022) in Malawi and (Alotaibi et al., 2018) in Saudi Arabia. Therefore, an evaluation of the impact of a broad range of precipitation projections during the twenty-first century may yield a range of reasonable variations in future runoff quantities in the basin. However, it is also observed in the current study that the majority of RCMs depict an increase rather than a decrease in the majority of the stations for all the future scenarios. Out of 24, twenty stations show increasing precipitation. Overall average increase in future precipitation is predicted to be 6.92% (6.4%) under RCP 4.5 2050 (2080) and 9.45% (12.2%) under RCP 8.5 2050 (2080) respectively. These are in agreement with the predictions made by (Ismail et al., 2020). According to them the average annual precipitation increases by 2%-6% under RCP 2.6 and 6%-15% under RCP 8.5. Similarly, Forsythe et.al (2014) have predicted an increase of 18% over UIB using a stochastic rainfall model.

Future increase in precipitation may present a favorable chance for farmers who practice rainfed agriculture to increase crop yields and alter their way of life.

In the present research, the model performance of LARS-WG6 to downscale daily time series of precipitation (pr), and maximum (T_max) and minimum (T_min) temperatures in Upper Indus Basin was evaluated. After getting satisfactory results from statistical and graphical comparison, LARS-WG6 was then used to project future changes for three climatic variables i.e., pr, T_min, and T_max for twenty-four stations in UIB from the outputs of six RCM and their ensemble under RCP 4.5 and RCP 8.5 scenario for the periods of 2041–2070 and 2071–2100. From this research, it is deduced that the LARS-WG6 tool is well capable to downscale daily precipitation as well as daily maximum and minimum temperatures for UIB. The downscaled precipitation projected by six RCMs and their ensemble under the two emission scenarios for two future time periods has inconsistent changing trends. However, overall, an increase in precipitation is projected by most of the stations. On the other hand, the downscaled projections of T_max and T_min from all the six RCMs and their ensemble exhibited a continuous and similar increasing trend in both the future time periods under both scenarios of RCP 4.5 and RCP 8.5 in whole UIB.

Acknowledgement

The authors like to thank Pakistan Meteorological Department (PMD) and Water and Power Development Authority (WAPDA) Pakistan for providing the meteorological data used in this research.

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Summera Fahmi Khan. The first draft of the manuscript was written by Summera Fahmi Khan. The draft was reviewed and edited by Dr. Usman Ali Naeem.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose

Data Availability

CORDEX data used in the study is available at https://esgf-node.llnl.gov/projects/esgf-llnl/
The data that support the findings of this study are available from Pakistan Meteorological Department (PMD) and Water and Power Development Authority (WAPDA) but restrictions apply to the availability of these data, which were used under permission for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of PMD and WAPDA.

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No competing interests reported.

Future Climate Projections using the LARS-WG6 downscaling model over Upper Indus Basin, Pakistan

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Materials And Methods

2.1 Study Area Description

2.2 Observed Data

2.3 Regional Climate Model Data

2.4 Downscaling using LARS-WG6

3 Results And Discussions

3.1 Calibration and Validation of LARS-WG6

3.2 Down scaling with LARS-WG6

3.2.1 Downscaling of Maximum Temperature (T_max)

3.2.2 Downscaling of Minimum Temperature (T_min)

3.2.3 Downscaling of Precipitation (pr)

4 Conclusion

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1

Future Climate Projections using the LARS-WG6 downscaling model over Upper Indus Basin, Pakistan

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Materials And Methods

2.1 Study Area Description

2.2 Observed Data

2.3 Regional Climate Model Data

2.4 Downscaling using LARS-WG6

3 Results And Discussions

3.1 Calibration and Validation of LARS-WG6

3.2 Down scaling with LARS-WG6

3.2.1 Downscaling of Maximum Temperature (Tmax)

3.2.2 Downscaling of Minimum Temperature (Tmin)

3.2.3 Downscaling of Precipitation (pr)

4 Conclusion

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1

3.2.1 Downscaling of Maximum Temperature (T_max)

3.2.2 Downscaling of Minimum Temperature (T_min)