An accurate water balance projection is essential to a successful water resources planning for an uncertain future, especially in a nonstationary world [1
]. Water balance projections simply calculate the difference between the total water demand and the total supply capability for a basin. When the demand exceeds the supply, water shortage occurs. To be systematic, short- and medium-range water resource planning alternatives, which can reduce potential water shortages, should be established under a predefined long-range water resource plan encompassing at least 20 years. For such a long-range time scale, the impacts of climate change cannot be ignored.
This study began with a broad review of studies that focused on water balance projection methodologies reflecting climate change impacts. Most studies utilized multiple general circulation model (GCM) climate change scenarios, and the average number of GCM scenarios used in these water balance projection studies ranges from four to five. For instance, some studies relied on only a single GCM scenario [2
], whereas Islam et al
] applied the most number of GCM scenarios, which is 12 in total. In terms of water shortage calculation, water balance analysis models have been commonly used with GCM-driven streamflow scenarios. Thus, most studies that project future water balance have been using GCM-driven runoff series as water supply scenarios. They also used multiple GCM scenarios to overcome the heterogeneity that arises from GCMs. Moreover, various water balance analysis models have been utilized, which include CalSim II [6
], WEAP [7
], CALVIN [3
], WRAP [9
], Mospa [10
], Sibuc [11
], IQQM [12
], Riverware [13
], and WSM [14
]. In Korea, the K-WEAP and the K-MODSIM models have been popularly employed for water balance analysis, and there have been several efforts to incorporate GCM scenarios into water balance analysis to reflect future climate change impact [15
]. Thus, most studies made use of different models for their own purposes.
The assessment of future water resources using GCM-driven streamflow, which is the general analysis framework of most previous studies, would provide an evaluation of possible climate change impacts. However, there are two possible problems to be considered when GCM-driven streamflow scenarios are used for water balance analysis. First, Korea, for example, has established its own national water resource plan based on historical data. Although it is imperative that the impacts of climate change on long-range water resources be considered, an inconsistency in methodologies may occur if historical runoff data are suddenly substituted with GCM-driven runoff scenarios. Furthermore, it would be risky to establish water resource plans based on the results of GCM-driven scenarios when they are directly inputted into the water balance analysis models because of the heterogeneity and uncertainty that arise from GCMs.
This paper describes a new alternative approach that uses GCM climate information by assigning weights to historical runoff scenarios rather than directly inputting GCM-driven runoff into water balance models. By maintaining most of the existing water balance analysis framework, the proposed methodology is able to mitigate the risk created by the uncertainty of GCM-driven values.
The remainder of this paper is organized in the following manner. The first section demonstrates the previous methodology for water shortage projection. The next section describes the new water shortage projection methodology, which incorporates climate change impacts and compares it to the previous methodology. The application of this approach is described in the following section. The application results are analyzed by comparing them with the results of the previous methodology, and future water shortage projection results in each future period are presented. This paper concludes with a summary of the results and a discussion of possible avenues for further studies.
This study proposed a new methodology for water shortage projection under climate change impacts, which is essential to national water resources planning, to overcome the limitations of the GCM value input method that inputs GCM-driven flow directly. The new methodology uses GCM-driven flow scenarios to assign different weights to each observation-driven flow scenario rather than directly inputting GCM-driven flow scenarios into a water balance analysis model.
The average water shortage projections for 2020s in the Han River basin (unit: 106 m3).
The average water shortage projections for 2020s in the Han River basin (unit: 106 m3).
|Water Vision 2020 a (Target Year: 2020)||Future Projection (Target Period: 2020s)|
|GCM Models||Proposed Methodology||Previous Methodology b|
|(increase ratio from the current)||(15% ↑)||(144% ↑)|
The proposed methodology was applied to project future water shortages in the Han River basin in Korea. The K-nn scheme selected seven observation-driven flows, which were most similar to the GCM-driven flows in the low-flow season. By calculating the weighted average values of the simulation results of the selected seven observation-driven flows, the water shortages of each target period were projected.
Compared to most previous studies, which assess climate change impacts on water resources by directly using GCM-driven flows, we have focused on how to provide reliable projection results for decision-makers in the water resources management field. As mentioned in the introduction, a radical change of methodology may confuse people with uncertain projection results. The comparison between the proposed method and the GCM value input method also showed a considerable difference in the projections. The results of the GCM value input method predicted a dramatic increase of 144% in the 2020s, which seems unrealistic. On the other hand, the proposed methodology used GCM-driven flows to assign different weights to each historical scenario rather than directly inputting them into a model so that we could reflect a more conservative climate change impact in water shortage projections, a 15% increase in the 2020s. Thus, this study successfully utilized observation-driven flow series as water supply scenarios and also reflected climate change impacts by assigning different weights to those observation-driven flows in accordance with the given GCM. The proposed methodology projected that the water shortage in the next 90 years in the Han River basin would gradually increase by 15%, 41%, and 57% in the 2020s, 2050s, and 2080s, respectively.
It is difficult to argue that the implementation of this conservative approach is the best way to assess climate change impacts. However, the blind use of GCM-driven flows should be reevaluated. Because the proposed methodology successfully reflected climate change impacts on water resources using GCM-driven flows, it may become one of the appropriate alternatives to be considered when addressing the impact of climate change. Nonetheless, as we proposed a rather conservative approach given that each annual water shortage value is projected within the limits of historical data, efforts to project extreme values beyond the observed record will be pursued in future studies.
In the near future, the current results of this study should be updated with the AR5 RCP scenarios that have been presented to the public in 2014 and are, thus, consequently being tested in Korea. The primary reason that we used the A2 emission scenario from AR4 was because of a consistent comparison, that is, because both Water Vision 2020 and the Strategic Report utilized the A2 emission scenario. Moreover, the QM technique was used to correct systematic bias on GCM datasets in this study. Other up-to-date bias correction schemes [32
], however, can also be considered for future studies.
The assumption of a constant water demand until 2100 made in this study because of a consistency with Water Vision 2020. Note that the choice of the demand scenario used in Water Vision 2020 was a very valuable product that results from a long negotiation process between the Korean government and non-governmental organizations. However, the suitability of this future demand scenario should be re-studied and updated to be more realistic as a future study. For instance, Koutroulis et al
] developed a simple future water demand storyline based on future irrigation extension plans, population trends, etc
. Above all, however, it needs to be reiterated that the main purpose of this study is to alert decision makers of the risk of the direct use of GCM-driven data without thorough analysis.
The proposed methodology is likely to be more useful for regions where the rainfall regime is highly variable and thus hard to find a consensus between GCMs. Korea, affected by the monsoon climate, belongs to this category. The proposed methodology may perform differently for other rainfall regimes such as semiarid regions.
To conclude, this approach could provide decision makers a reliable and realistic alternative for future water resources management plans. In addition, various approaches that take into consideration multiple variables, such as weighting factors, and project extreme water shortage values should be pursued in future studies.