1. Introduction
Climate change is becoming a leading issue for the 21st century due to its devastating environmental and socioeconomic impacts. In the last few decades, the frequency and magnitude of extreme climatic events increased subsequently in response to the anthropogenic activities [
1]. Anthropogenic activities, primarily socioeconomic (fossil fuels burning and land use/land cover changes), have influenced the amount of greenhouse gases which trigger climate change and extreme climate events. The occurrences of extreme events are not uniform across the globe; some regions are more susceptible to climate change. Particularly, Pakistan, has faced frequent heatwaves and floods in the last few years [
2,
3,
4]. To cope with these extreme climatic events, the timely and effective monitoring of climate change is required to make policies for adaptation and mitigation. The impacts of climate change on water resources and the hydrological cycle are of extreme importance because all socioeconomic and natural systems ultimately depend on water resources. The climatic changes can directly impact the availability and changing patterns of water resources such as flooding and droughts [
3,
5], and some indirect impacts on food, agriculture and energy production [
6]. These climate change impacts may be worse for the transboundary river’s basins such as Jhelum where management of the basin depends on different economic, political, and social interest of the countries. Jhelum River basin is an integral part of the Himalayas region where an increasing trend of temperature has been observed that increases glacial melting and precipitation, and affects the availability of water resources [
7,
8]. Climatic change studies in the Jhelum River basin is still at its infancy due to a lack of significant weather station data. To understand the impacts of climate change on the transboundary Jhelum River basin of the Himalayas region, GCMs have been used to assess the present and future climatic changes.
The GCMs provide projections of climate at a global scale for policymakers to adapt better strategies to cope with climate change. GCMs represent significant outputs at the global, hemispherical and continental scales by incorporating the complexity of the global system, however, these global dynamics cannot be represented at the local sub-grid level [
9]. The efficiency of GCMs to project the future climate has been debated due to their uncertainties during the validation processes at the regional scale. Despite improvements in the GCMs to represent climate processes in better ways, these uncertainties cater to produce better climate projection but still remain a subject of ample concern at the regional/local level [
10]. GCMs are widely used tools to assess the climate change impacts but their coarse spatial resolution restricts direct use for the sustainable management at the regional or local scale [
11]. The downscaling techniques are essential to transform GCMs’ spatial resolution from coarse to fine to allow their direct use at the local/regional scale [
12]. The two widely used downscaling methods, statistical downscaling and dynamical downscaling applied to relate the GCMs’ coarse resolution and local climatic variables [
13]. Dynamic downscaling of GCMs are employed as a Regional Climate Model (RCM) at finer spatial resolution (10–50 km) to simulate regional climate by incorporating local features such as topography. Dynamic downscaling is an emerging and advanced method, but the advanced computational requirement and heavy data storage limit their use at regional scale [
2]. Statistical downscaling is both a flexible and computationally efficient approach to downscale GCMs and to use fine resolution data for a climate impact assessment at the local/regional level [
13]. Statistical downscaling has directly built a relationship between local observation, climatic variables and GCMs’ output without requiring the physical knowledge of the local region [
14]. Therefore, statistical downscaling methods have been extensively applied by the researchers to simulate climate projections at the local/regional scale for the climate impact studies [
15].
The main theme of statistical downscaling is to develop the relationship among predictors (GCMs variables) and predictands (local scale variables) through statistical and mathematical techniques such as linear and non-linear regression models, and weather generators [
13]. Among linear regression models, the Statistical Downscaling Method (SDSM) is a renowned statistical model developed by [
16] that is frequently used by research to downscale GCMs [
17]. SDSM is a hybrid model that employed the weather generators and regression models to downscale climatic variables. It facilitates the downscaling of long-term, low-cost, and rapid development of multiple daily weather parameters. The weather generator’s technique, Long Ashton Research Station Weather Generator (LARS-WG), is a well-known stochastic weather generator technique used to simulate the weather data for a single weather station in the form of time series data for both present and future climatic conditions. The long-term time series data of a climate variables group e.g., T
max, T
min and precipitation are simulated for the single weather station using the LARS-WS method [
6].
SDSM and LARS-WG have been widely used techniques by the researchers to downscale the GCMs’ data for local/regional basins [
6,
9]. These techniques have been used for three GCMs (BCC-CSM1-1, CanESM2 and MICROC5) and future projections depicted that mean annual temperature and precipitation for the future are on the rise [
18]. These studies demonstrated statistical downscaling methods as a vigorous tool to analyze the futuristic climatic changes for the regional/local basin level. SDSM and LARS-WG have been used for the better assessment of climate changes in the Jhelum River basin using different GCMs. The study area is of key importance as it is part of the Indus basin and greater Himalayas that have permafrost mountain tops. The climate changes ultimately trigger the melting of snow/glacier at mountain tops. The recent study focused on examining the efficiency of these statistical downscaling techniques SDSM as a regression model and LARS-WG as weather generators for downscaling the T
max, T
min and precipitation data for the Jhelum River basin. The basin is the transboundary and conflicted region located at the greater Himalayas, therefore, future projections of T
max, T
min and precipitation will help to study the dynamics of hydrometeorological changes in the basin. The study designed a methodology to incorporate multiple GCMs for the basin based on local conditions. The selection of GCMs helped to downscale the long-term time series climatic data for the 21st century by using two different statistical downscaling techniques (SDSM and LARS-WG). The meteorological station’s data of Jhelum River basin was applied to evaluate the accuracy of SDSM and LARS-WG. After evaluation of the statistical downscaling techniques, climate change projections were simulated for RCP 4.5 and RCP 8.5 using six GCMs for the 21st century.
4. Conclusions and Remarks
This study focused on the climatic changes for the transboundary mountainous region by using CMIP5 GCMs and climate scenarios. The Jhelum River basin is the part of the Himalayas mountains which are affluent in glaciers after polar regions and climate changes can induce tremendous, unexpected variabilities in the basin. GCMs are the most reliable mathematical tools to project climatic changes as it is a global phenomenon and linked through permanent circulations such as atmospheric winds and oceanic currents. The GCM projections for the local/regional basin may cause uncertainties due to the influence of the micro/regional climate. To overcome the uncertainties, five GCMs from CMIP5 project were selected for the study and two GCMs, CCSM4 and HadCM3, were chosen based on the relationship with the reference observed data. The large-scale GCM predictors were downscaled by using two statistical downscaling methods (SDSM and LARS-WG) that can relate large-scale predictors with the local climatic variables (Tmax, Tmin and precipitation).
SDSM was proved to be a more effective and efficient method than LARS-WG in downscaling the climatic variables for the validation period, except for a bit in improved performance of LARS-WG for precipitation due to its wet and dry spells. Both climatic indicators of temperature and precipitation projected a rising trend for the 21st century, but the change was more pronounced for RCP-8.5 than RCP-4.5, which depicted the storyline of the climatic scenario. The seasonal changes depicted that the winter season was more threatened and became warmer and wetter with time which may disturb the existing snow cover. After winter, the pre-monsoon season predicted a rising trend for temperature that may introduce early deglaciation by disturbing the balance of the cryosphere and hydrosphere of the basin.
The study area is a transboundary conflicted region and has a limited number of weather stations that affect the accuracy of the climatic projections. Multiple GCMs can be used to check the most suitable location for the study area but it can make the research more intensive as the focus is on the multiple task of first selecting the GCMs then downscaling by using two comparable methods. The dynamic downscaling can also be used but it is a very intensive and computer-oriented downscaling method difficult for the individual researcher. Two climatic scenarios: one medium stabilization scenario RCP 4.5 and the other, a high emission scenario RCP 8.5, were selected to project climatic changes. The other two RCP-2.5 and RCP-6.0 can be used for future projections.