The Intergovernmental Panel on Climate Change (IPCC) and other researchers have reported compelling concerns revolving around the impact of climate change on our societies [1
]. Climate change is a global phenomenon exhibited by three prominent signals, that is: (1) global average temperatures are gradually increasing; (2) changes in global rainfall patterns; and (3) rising of sea levels. One of the major impacts of this phenomenon is on local water resource availability, whose impact will be felt by many sectors, including agriculture. According to the recent Fifth Assessment Report by IPCC, the increase in temperature and warm trends will continue across most of the Southeast Asia region in the current century and that water is expected to be a major challenge because of the corresponding demand for it and lack of adaptive capacity to the impacts [4
]. In Malaysia, climate change-related policies are still underway, and most of the climate knowledge is largely inferred from the Southeast Asia region due to limited local studies [5
]. Effective adaptation measures require good understanding of local changes taking into account the projections provided by global climate models. Nowadays, basin-wide hydrologic investigations are becoming a basis for the implementation of robust adaptation strategies [7
]. This contribution assesses the effects of climate change on the hydrological flows of the Upper Bernam River Basin in Malaysia.
Rice is an important staple food for a large section of the people, and yet, its water demand is relatively high, accounting for over 80% of the country’s water resources [9
]. The country’s rice self-sufficiency policy of 85% is sustained through practicing the double-cropping method, which requires constant supply of paddy water in the command areas at all times. Most of the rice farming systems are run-of-the-river systems and depend largely on direct river flows. Due to insufficient flows in certain periods, cultivation of paddy is usually staggered in phases to ensure constant supply of water on rice fields. However, it is a well-known fact that in many run-of-the-river irrigation schemes, water demand seldom matches the unreliable streamflows of rivers. In the past, this has led to difficulties in allocating irrigation water to paddy farmers. The Bernam River Basin is one important basin that supports paddy irrigation. The future outlook of climate change may thus pose serious implications on water resources for future paddy production in this basin. A previous study assessed only the impact of future land use changes on the flow of this river [11
]. Assessment that also accounts for climate change is therefore critical for planning and management of future water allocations.
To do this, information derived from Global Climate Models (GCMs) is currently the most suitable in assessing both past and future likely changes in climate scenarios [12
]. This climate information is then used as input to drive hydrologic and water demand models. Long-term locally-observed climate data are also required to validate climate model outputs to capture local conditions. However, direct application of GCM outputs to any model for subsequent assessment of impact is discouraged in climate studies because of resolution issues. GCMs run their simulations on large scales to account for various grids across the globe. Yet, the hydrologic system or cropping system of interest is affected by local climatic conditions [2
]. The Bernam River Basin, for instance, is covered within an area of 1.2° × 1.0° longitude and latitude, whereas a GCM typically takes about 2.8° × 2.8° longitude and latitude. To overcome this issue, downscaling is adopted, a process of bringing down the climate information from GCM to hydrologic scales to produce outputs of higher resolution, which are more realistic with the local scale before assessing the risks associated with the future hydrologic scenarios.
Several downscaling techniques have advanced over the past two decades, evolving from two major schemes; dynamic downscaling and statistical downscaling techniques. The dynamic technique is often viewed as a mini-GCM because it attempts to simulate local climate variables by reducing the horizontal area covered (typically around 25 by 25 km or even smaller) using the same boundary conditions as the driving GCM. Although they produce high resolution climate data, they have not been widely adopted because of the costs and complexities involved in running this type of technique [14
] in order to capture local- or regional-scale climate variables. Statistical downscaling techniques, involving methods such as, weather typing procedures, transfer functions and stochastic weather generators, are the most popular methods used in climate change studies today [2
]. They produce future climate scenarios based on a statistical relationship between climate variables at one or more GCM grid points with the variable of interest at a particular station. They are adopted because they are relatively inexpensive to apply and also give point climate data at a specific site of interest [16
]. A more inclusive review of these approaches as relates to hydrologic impact assessment is covered by [7
]. Each method has its merits and demerits, and the choice will depend largely on the purpose of the study, the availability of observed data, the scale of required data (daily or monthly, etc.) and the size of the study area. In areas where there are no readily-available regional climate model outputs, such as in Malaysia, the change factor or delta method is widely used [7
] on the GCM outputs for hydrological and crop modeling studies [2
Carbon emission scenarios are the main forcings in driving climate models. Scenarios are pictures or images of how the world is likely to evolve in the future in terms of greenhouse gas [20
]. In the present study, we use the latest scenarios, called Representative Concentration Pathways (RCPs), which have rarely been applied in Malaysia. RCPs offer a better understanding in terms of the concentration of future greenhouse gases for running climate models than previous scenarios [21
]. The downscaled simulation outputs are then applied in a separate hydrologic model to develop future hydrologic scenarios for a particular river basin. The Soil and Water Assessment Tool (SWAT) is a widely-applied model in assessing hydrologic impacts. Global SWAT model applications are summarized in the work of [23
The main hydrologic impact of interest in this study is the monthly flows of the Bernam River Basin as a main source of water for the large irrigation scheme in the basin. However, generating future climate scenarios is still a challenge, as it involves several steps before outputs can be employed in impact assessments. In this study, a simple graphical user interface was developed that integrates all of the procedures of assessing climate change impacts, which includes GCMs and scenarios’ selection, downscaling procedures, climate projections. These procedures are linked in one executable module to generate future climate sequences that can be used as inputs to various models including hydrological and crop models. The core objective of the study, therefore, is to investigate the potential effects of climate change on the flows of the Bernam River Basin using the projections of the 5th Assessment Report of the IPCC. This involves: (1) evaluating the performance of the SWAT model for the simulation of flows in the Bernam River Basin; (2) developing an interface for the rapid simulation of climate variables (e.g., rainfall, temperature, etc.) at the local scale from large-scale climate models; (3) evaluating future changes in rainfall and temperature patterns in the basin; and (4) investigating future changes in the flow scenarios of the basin. The results will be used as input in a decision support system for modeling water allocation and planning for a run-of-the-river irrigation scheme under climate change impacts in Malaysia.
4. Methodological Limitations
The methodologies developed in this study, which have linked gridded meteorological data, climate models’ outputs and hydrological modeling, have several limitations. The SWAT model evaluation for the simulation of future streamflow scenarios of the Bernam River watershed was based on gridded hydro-meteorological data (rainfall, temperature: minimum and maximum) developed for the whole of Peninsular Malaysia. However, this may not truly represent the actual data for the watershed as reflected in the model evaluation results obtained. Another source of uncertainty could be the lack of daily data for the other climate variables, including wind speed, relative humidity and solar radiation. These variables are usually generated using the internal SWAT stochastic weather generator based on their monthly records. Although gridded data are widely used for generating new data from GCM outputs [52
], a more detailed assessment would need to consider all of the climate variables and realistic station data.
Linked to this is the technique of downscaling. A popular approach using change factors (or the delta method) was used in this study to modify daily temperature and rainfall time series in order to remove the bias between observed station data and future data from GCMs over the watershed. However, this approach represents another source of uncertainty. This approach assumes that the temporal rainfall and temperature pattern are identical in the future. It does not adjust future rainfall occurrence and distribution. Future studies should consider employing regression-based and stochastic weather generators, which would allow more detailed analysis of climate change impacts.
Land use/cover is another source of uncertainty on the future streamflow of this basin. In this study, the impact of climate change on future streamflow was based on the hypothesis that land use is stationary in the future. However, land use change is generally considered one of the main factors affecting the rainfall-runoff relationship. A previous study by [11
] was performed using five scenarios to identify the effect of mixed land use change on the streamflow of the Bernam River basin. The results showed that land use changes are responsible for the increase in the annual flow depth between 8% and 39% during high flow months and decreases between 3% and 32% during low flow months, thus the need for best management practices as mitigative measures on future changes in land use on flow quantity. Table 1 and Figure 3 in [11
] also show that the land use in this basin is changing based on data from 1984 to 2009. To-date, for instance, land use for oil palm has increase from 18.4% to 21.7% and rubber from 12.8% to 18.1%. Therefore, in our future studies, multiple GCMs and scenarios (including RCP2.6) and other downscaling methods would be employed to assess water shortage and allocation taking into account land use change scenarios within the basin. It is believed that the change of land use in the future will have a corresponding impact on streamflow.
The impact of climate change on the flows of the Upper Bernam River Basin in Malaysia was studied using the SWAT hydrological model. A graphical user interface was developed that integrates all of the common procedures of assessing climate change impacts, based on the delta method, to quickly simulate climate variables (e.g., rainfall, temperature, etc.) at the local scale from large-scale climate models. The interface is run with ten GCMs under three climate forcings. The tool was modified to generate future daily sequences by perturbing the baseline observed series using change factors derived from mean projected changes for that particular variable from climate models.
Climate variables influential for streamflow (mainly rainfall and temperature) were generated from the interface on the basis of the ten GCMs under RCP4.5, RCP6.0 and RCP8.5 and used as inputs to the SWAT hydrological model. This modeling study was carried out for the periods 2010–2039, 2050–2069 and 2070–2099 relative to the baseline period (1976–2005). The analysis was done on the basis of the rice farming seasons; dry (January–June) and wet (July–December). The evaluation results indicated that the hydrological model performed acceptably well in the study watershed with monthly R2, NSE and PBIAS values of 0.67, 0.62 and −9.4 and 0.62, 0.61 and −4.2 for the calibration and validation periods, respectively.
The multi-model projections indicated that the Upper Bernam River basin is likely to become warmer and wetter during some months in the future. Temperature is projected to increase in all respective scenarios. The largest monthly mean changes are observed during the dry season months (February–June) in all scenarios and are more significant under the most severe scenario, RCP8.5. Changes in rainfall showed variation between the two agricultural seasons (dry and wet) from the ensemble projections of the 10 GCMs. The dry season indicated a shortage in future rainfall with average changes of −2.4%, −3.2% and −3.7% under RCP4.5, 6.0 and 8.5, respectively. The wet season, on the other hand, is predicted to be wetter with average changes of about 1.0%, 0.8% and 2.4% for RCP4.5, 6.0 and 8.5, respectively. Overall annual future rainfall is likely to increase slightly with no significant changes from the baseline, although the monthly changes might affect rice water availability during the dry season and flooding in the wet season.
Streamflow projections followed that of rainfall to a great extent with a distinct change between the dry and wet season. Generally, all future periods show a decreasing trend in streamflow during this season and an increasing pattern in the wet season. The significant decrease (−6.6%) occurs under the RCP8.5 scenario in the late century (2080s), while a significant increase of 11.4% occurs in the same future period under the same scenario. On the basis of these results, it can be inferred that the water resource of the Bernam River Basin may be sufficient up to the end of the century, since the projected decreases are counteracted by the increases. However, the basin may experience tremendous pressure due to low flows during the dry season and flooding during the wet season. Thus, it necessitates integrated water resources solutions to ensure water availability for rice production at all times and mitigative measures against flooding risk. It can be concluded that the interface is user friendly and can, in principle, be customized for application to other geographical areas or catchments by incorporating baseline data for those particular stations of the new catchment area and their corresponding GCMs’ outputs, for the quick generation of climate variables for hydrological assessment and/or as inputs to crop models.