Comparative Study on the Application of Water Resources Index for a Highly-Regulated Urbanized Basin

doi:10.21203/rs.3.rs-3955718/v1

As water scarcity intensifies in the face of climate change, water resources decision-making process requires the selection of appropriate indices which are subjected to accurate interpretation. This study undertakes a comparative analysis of three water resources indices: the Falkenmark Indicator (FI), Water Stress Index (WSI) and Water Resources Index (WRI). These indices are useful to assess water resource availability but each using different methodology and thus giving different insights to the basin water resource condition. We consider Klang River basin in Malaysia, a highly-regulated and urbanized basin with escalating water demand and competition between sectorial users. The indicator/ indices are derived from water allocation modelling based on historical data between year 2016 to 2018. Results show that consideration of basin storage (WRI) and water abstraction (WSI) offers more realistic representation of the basin water availability. The WRI offers further advantage in terms of temporal resolution over the course of the calendar year. It is concluded that decision-makers and stakeholders stand to benefit from effective water resource management strategies derived based on reliable analysis using the most relevant index.

Falkenmark indicator

Klang river basin

water resources index (WRI)

water stress index (WSI)

water availability

Water is a vital natural resource that underpins life, ecosystems and human civilization. Water scarcity is influenced by water consumption, which is determined by demographic changes and socioeconomic advancements. Meanwhile, climate change alters hydrological patterns, exacerbates water scarcity and even threatens the quality of available water resources. In response to the complex challenges of water resource management, there is a growing need for comprehensive and robust water resource assessment methodologies in securing a resilient water future.

Over the years, various approaches for quantifying water scarcity (Liu et al., 2017) have been proposed by scholars to support policy development and decision making. These indices amalgamate various indicators and variables, providing a representation of the trends and status of the water systems. By encompassing multiple dimensions of water resources, from hydrology to socio-economic aspects and environmental health, water resources indices offer valuable insights into the interactions between natural processes and human activities (Xu & Wu, 2017). The aim is to provide actionable insights so that tailored management interventions can be devised in a timely manner to prevent overexploitation of water resources and ensure equitable access.

Over the past few decades, numerous attempts have been undertaken to create a general index to measure the link between water demand and water resources in a regional context. Although, water availability may be conceptually described as a function of relative supply and demand. However, it is surprisingly complex and difficult to develop a common accepted generic water availability indicator in practice. Based on a review paper from Xu & Wu in 2017, they categorised the water resources indicators into blue water and green water. Blue water is defined as rainwater that runs off or passes through into the deep aquifer, whereas green water is water that eventually evaporates to the atmosphere. In general application, blue water is used for multiple competing sectors, but green water is particularly for agricultural production. In the study, the blue water indicators are being focused on to compare which is more significant than the other.

Falkenmark Indicator (FI) developed in the 1980s laid an important foundation for assessing water security around the world. It measures per capita water availability. The FI is simple to use and quick to compute, but it oversimplifies regional variances by assuming that each nation or the world as a whole has an equal per capita water demand. A number of indices based on withdrawal-to-availability (WTA) or consumption-to-availability ratio (CTA) have also been developed to measure human water consumption and its relative water availability (Hoekstra et al., 2012). For instance, Water Stress Index (WSI) developed by Vörösmarty et al. considers WTA by including regional total water withdrawals and stream water flow. Another approach is streamflow-based surface water availability index, developed by Tidwell et al. (2012). More recently, a water resources index (WRI) is introduced as an alternative approach to the assessment of water availability in a highly regulated basin (Lim et al., 2023). This gives a better accuracy as it is a function of inflow, outflow and storage (if any) at a given intake. Many of the published indices did not include storage in their formulas.

The above indices require a wide range of data sources, of which ground-based hydro-meteorological data is the utmost important (Javadinejad et al., 2020). Additionally, data obtained from remote sensing (Khalid et al., 2021) and socio-economic databases (Frizzone et al., 2021) etc. are increasingly popular to be incorporated into the framework. Statistical analyses and numerical models are employed to interpret the data, identify patterns, and project future scenarios under different management strategies and climate change scenarios.

The primary aim of this paper is to compare the applicability of selected water indices. We have chosen the Falkenmark indicator, the water stress index (WSI) and the water resources index (WRI). The methods are applied to a highly-developed urbanized river basin in Malaysia. The merits and limitations of each method are discussed and the results interpretations compared and evaluated in terms of their suitability in fostering sustainable water management practices.

2.1 Falkenmark Indicator

The Falkenmark indicator (Falkenmark, 1989) assesses per capita water availability, using a straightforward approach that considers the annual surface runoff and the number of populations of the region. Falkenmark indicator is defined as:

$$\begin{array}{c}Falkenmark Indicator \left(FI\right)= \frac{Annual Runoff}{Population Size}\left(1\right)\end{array}$$

Falkenmark indicator is amenable to direct comparison with other regions or countries. It thus laid an important foundation for measuring water security throughout the world. The data required is easy to obtain and the outcome easy to interpret (Table 1).

Table 1

Classification of the Falkenmark Indicator
Category	Index (m³/cap/year)
No Stress Stress Scarcity Absolute Scarcity	> 1,700 1,000–1,700 500–1,000 < 500

Based on Table 1, there is no stress where annual runoff is in excess of over 1,700 m³/cap/year, but on the other hand, it is absolute scarcity where the annual runoff drops below 500 m³/cap/year. Note that the index does not explicitly quantify the different purposes of water need, sectorial water need, or total environmental water need. It is also insensitive to climate and cultural differences. It does not provide details of seasonal differences nor does it easily adaptable for a subregion.

2.2 Water Stress Index (WSI)

The Water Stress Index (WSI) described by Smakhtin et al. (2005) considers the surface water available for withdrawals while meeting environmental water requirements (i.e. the volume of water needed for the maintenance of freshwater ecosystem functions), otherwise known as local relative water use, is defined as:

$\begin{array}{c}Water Stress Index \left(WSI\right) = \frac{\text{w}\text{i}\text{t}\text{h}\text{d}\text{r}\text{a}\text{w}\text{a}\text{l}\text{s}}{MAR-EWR}\left(2\right)\end{array}$

This index also takes into account mean annual runoff (MAR) and environmental water requirement (EWR) that ranges from 0.2 to 0.4 globally. the withdrawal-to-availability resource ratio (WTA ratio) by considering regional total water withdrawals as well as stream water flow, where D, I and A are the withdrawals of the domestic, industrial and agricultural sectors, respective, and Q is the streamflow. This approach splits a research region into regular grids and computes WSI for each grid.

This index takes into account the withdrawal-to-availability resource ratio (WTA ratio) by considering regional total water withdrawals as well as stream water flow. It is a function of withdrawals, where it considers domestic, industrial and agricultural use. The denominator is calculated based on the mean annual runoff (MAR) and the ecological water requirement (EWR). EWR ranges from 0.2 to 0.4 globally.

The interpretation of the withdrawals to discharge ratio was inspired from the original Water Stress Index, developed by Vörösmarty et al. (2005) (Table 2). Water stress begins when the withdrawals exceed 0.3 of its runoff, and may be a limiting factor for economic growth if the WSI exceeds 0.6. Water stress is deemed strong when the WSI is more than 1.0.

Table 2

Interpretation of Water Stress Index
Category	Index
Slightly Exploited Moderately Exploited Heavily Exploited Overexploited	< 0.3 0.3–0.6 0.6–1.0 > 1.0

The key advantage of WSI is that it can be calculated in grided form to provide spatial distribution of water stress in a study area. The value of withdrawals can readily be determined for each grid based on GIS data, whereas the Q value can be apportioned to the grids.

2.3 Water Resources Index

Water Resources Index (WRI) (Lim et al., 2023) is an index calculated to reflect the water resources availability. It is defined as the ratio of the actual water availability (WRA) to the average water resources capacity (WRC) for a consecutive duration of T (day):

$$\begin{array}{c}Water Resources Index \left(WRI\right) = \frac{{WRA}^{Net}}{{WRC}^{Net}}\left(3\right)\end{array}$$

where:

WRA^Net = net value of total upstream WRA^Net

+ current active storage

+ estimated inflow discharge volume

+ current abstractable groundwater storage

– projected abstraction volume

– planned outflow discharge volume

and,

WRC^Net = net value of total upstream WRC^Net

+ maximum active storage at full supply level

+ average inflow discharge volume

+ total abstractable groundwater storage

– full abstraction volume

– average outflow discharge volume

The points of interest (POIs) for the calculation of WRI can be either reservoir or water treatment plant intake (abstraction) point. The water resources availability and capacity at the POIs is cumulative in the downriver direction as given by the following equations:

$$\begin{array}{c}{WRA}_{B}^{Net}= {WRA}_{A}^{Net}+ {\sum }_{i=1}^{i=n}{WRA}_{B,i}^{+}- {WRA}_{B,i}^{-}\left(4\right)\end{array}$$

$$\begin{array}{c}{WRC}_{B}^{Net}= {WRC}_{A}^{Net}+ {\sum }_{i=1}^{i=n}{WRC}_{B,i}^{+}- {WRC}_{B,i}^{-}\left(5\right)\end{array}$$

Here A and B are 2 successive POIs, where B is located downstream of A. Note that WRI takes into considerations the basin water storage and the regular water usage. The duration T can be conveniently selected based on the data interval, or a suitable averaging period (e.g. 7 days).

The value of WRI ranges from 0 to 1. Deviation from the past averages indicates excess or deficit of water availability much similar to the concept of Standard Precipitation Index (SPI). Table 2 shows the interpretation of the WRI values. WRI range between 0.8 and 1.0 considered “Average” and 0.6 to 0.8 is “Below Average”. When WRI falls below 0.6, 0.4 and 0.2, the water availability may be interpreted as “Severely Low”, “Extremely Low” and “Emergency” respectively.

Table 3

Interpretation of Water Resources Index
Definition	Climatic Condition	Water Resources Conditions
WRI ≥ 1	Flooding	Wet season in river and reservoir overspilling flood water
0.6 ≤ WRI < 1	Normal Weather	In-stream and reservoir storage enough for consumption and utilization
0. 4 ≤ WRI < 0.6	Moderate Drought	In-stream and reservoir storage just barely enough for consumption and utilization
0.2 ≤ WRI < 0.4	Severe Drought	In-stream and reservoir storage has a risk of not enough for consumption and utilization
0 ≤ WRI < 0.2	Extreme Drought	In-stream and reservoir storage not enough for consumption and utilization

The main advantages of WRI is the rich temporal information in the time-series format. Therefore, it can identify the changes in water availability at different time scale, ranging from seasons, months to weeks. Furthermore, the consideration of impoundment storage means WRI is less sensitive to meteorological drought unless the storage is considerably depleted.

Malaysia is experiencing rising water stress due to increased water demand particularly in urbanized and industrialized regions such as the Klang Valley. There have been several events related to the El Nino phenomenon resulting in prolonged drought in year 1998, 2014 and 2016 (Ideris et al., 2020). Therefore, achieving sustainable water management has become an urgent priority including for the country.

Klang basin is the most developed region in Malaysia and it falls under the administrative jurisdiction of the Selangor State and two (2) Federal Territories (Kuala Lumpur and Putrajaya). It has a total area of 1,297 sq.km, 82% of which is in Selangor (Fig. 1). Klang River originates from the main range of Peninsular Malaysia and runs westward towards the Straits of Malacca with an approximate length of 120 km. It has two (2) major tributaries i.e. Batu and Gombak rivers which flow through Kuala Lumpur.

The water supply network in Klang River basin is highly complex with inter-basin connection from the neighbouring Selangor and Langat River basins. Figure 2 shows the schematic of the combined network of the three river basins. For this study, only Batu, Klang Gates and Tasik Subang dams (Table 4) and the WTPs in the basin (Table 5) are considered. The schematic is thus simplified as shown in Fig. 3.

Batu and Klang Gates dams are located at the upstream of Klang River basin. These two dams are built for the purpose of flood mitigation and water supply. Tasik Subang that serves as direct water sources for the lower Klang river basin but its catchment area is located in the neighbouring basin. There is a total of 8 water treatment plants (WTPs) within the basin, 4 of which are run-of-river (ROR) and the other 4 have direct transfer from the dam. Only Wangsa Maju WTP combines both river water source and dam transfer (see Table 5).

Note that not only the water supply for Klang River basin come partly from the neighbouring basins, water resource from Klang River basin is also partly supplied to the neighbouring basins as well. The service areas of the 8 WTPs are demarcated based on the information from the water company and is as shown in Fig. 1.

Table 4

Summary of Dams
Dam	Catchment Area (sq.km)	Gross Storage (MCM)	Active Storage (MCM)
Klang Gates	77.16	32.00	22.6
Batu	50.00	33.60	27.50
Tasik Subang	10.28	3.50	3.45

Table 5

Summary of Water Treatment Plants
No.	Water Treatment Plant	Supply Source	Design Capacity (MLD)
1	Gombak	Gombak River	22.5
2	Kepong	Keroh River	4.5
3	Sg. Rumput	Rumput river	2.3
4	Ampang Intake	Ampang River	18.0
5	Wangsa Maju	Klang Gates Dam, Gombak River	45.0
6	Bukit Nanas	Klang Gates Dam	145.0
7	Sg. Batu	Batu Dam	113.7
8	North Hummock	Tasik Subang Dam	22.5

For the purpose of the study, MIKE HYDRO Basin (MHB) is used to simulate the water availability and water allocation. Data required include time series of rainfall, evaporation, streamflow, dam water level, dam inflow, dam release and spill, WTP abstraction and production. In addition, information related to the dam and intake structures are also pertinent, including: height-volume-area (HVA), characteristic levels, flood control level (FCL), minimum operating level (MOL). The POIs are selected at the WTPs.

From MHB model simulation, the mean annual runoff of the respective POIs is determined to calculate the annual FI and WSI, using additional data on the basin population and the water demand. The WRI time series at the POIs is averaged to obtain the annual WRI value. Comparison of the three indices is then performed for the year from 2016 to 2018. According to Hasan et al. (2021), Peninsular Malaysia experienced a critical drought which affected more than 48% of the entire basin from year 2016 to 2018.

4.1 Falkenmark Indicator (FI)

Table 6 shows the mean annual rainfall (MAR) and the simulated mean annual runoff adopted at the POIs for the calculation of Falkenmark Indicator (FI). Note that the mean annual runoffs are only applicable to POIs with ROR schemes such as Sg Kepong, Sg Rumput, Sg Gombak and Sg Ampang WTPs. For POIs with direct transfer from the dam, the mean annual rainfall is used. For Wangsa Maju WTP, which has combined equal input from ROR intake at Gombak River and direct dam transfer from Klang Gates, the mean annual rainfall and mean annual runoff are summed as the denominator in the FI.

Table 6

Mean annual rainfall and simulated mean annual runoff
POI	Source (River/Dam)	Simulated Mean Annual Runoff (MCM)			Mean Annual Rainfall (MCM)
POI	Source (River/Dam)	2016	2017	2018	2016	2017	2018
Sg Batu	Batu Dam	n.a.	n.a.	n.a.	108.082	133.871	128.996
Sg Kepong	Kepong River	28.283	30.419	32.205	n.a.	n.a.	n.a.
Sg Rumput	Rumput River	50.064	70.099	44.714	n.a.	n.a.	n.a.
Sg Gombak	Gombak River	388.899	637.337	536.729	n.a.	n.a.	n.a.
Wangsa Maju	Gombak River	829.603	1269.866	967.030	n.a.	n.a.	n.a.
Wangsa Maju	Klang Gates Dam	n.a.	n.a.	n.a.	35.007	47.156	39.015
Bukit Nanas	Klang Gates Dam	n.a.	n.a.	n.a.	112.801	151.948	125.715
Ampang	Ampang River	222.429	378.718	247.323	n.a.	n.a.	n.a.
North Hummock	Tasik Subang Dam	n.a.	n.a.	n.a.	21.922	23.136	27.305

Population data obtained from the Department of Statistics is by district and does not coincide with the basin boundary. Hence, it is first assumed that the population is uniformly distributed in the built-up area. The population estimate for the service area of the respective POI is then derived as shown in Table 7.

Table 7

Population estimation for the WTP service area
Service Area of POI	Total Built-Up Area (km²)	Population ('000)
Sg Batu	31.28	201.370
Sg Kepong	4.26	24.348
Sg Rumput	0.51	1.504
Sg Gombak	5.39	16.195
Wangsa Maju	16.75	128.023
Bukit Nanas	27.66	279.671
Ampang	18.27	80.000
North Hummock	75.61	213.617
Total	179.715	944.727

4.2 Water Stress Index (WSI)

For Water Stress Index (WSI), the water withdrawal data at each POI is obtained from the state water company (Table 8). The mean annual rainfall (MAR) value for each WTP is proportioned based on the MAR of Klang River basin according to the catchment size (Table 9).

Table 8

WTP withdrawals (2016–2018)
Water Treatment Plant (WTP)	Withdrawals (MCM)			Average Withdrawals (2016–2018)
Water Treatment Plant (WTP)	2016	2017	2018	Average Withdrawals (2016–2018)
Sg Batu	51.92	42.61	44.11	46.22
Sg Kepong	0.64	0.95	0.89	0.83
Sg Rumput	0.39	0.43	0.43	0.42
Sg Gombak	11.07	13.14	12.02	12.08
Wangsa Maju	14.34	15.16	16.83	15.44
Bukit Nanas	51.51	46.05	43.54	47.03
Ampang	6.33	6.69	6.94	6.65
North Hummock	4.73	4.42	5.03	4.73

Table 9

Mean Annual Rainfall (MAR) (2016–2018)
POI	Source (River/Dam)	Catchment Area (km²)	Mean Annual Rainfall (MCM)			Average Mean Annual Rainfall (2016–2018)
			2016	2017	2018
			Basin total = 2193.14	Basin total = 2624.46	Basin total = 2694.93
Sg Batu	Batu Dam	51.48	112.91	135.11	138.74	128.92
Sg Kepong	Kepong River	2.16	4.73	5.66	5.81	5.40
Sg Rumput	Rumput River	4.97	10.90	13.05	13.40	12.45
Sg Gombak	Gombak River	41.54	91.09	109.01	111.93	104.01
Wangsa Maju	Gombak River	64.21	140.83	168.53	173.05	160.80
Wangsa Maju	Klang Gates Dam	75.67	39.30	47.03	48.30	44.88
Bukit Nanas	Klang Gates Dam	75.67	126.65	151.55	155.62	144.61
Ampang	Ampang River	17.59	38.57	46.16	47.39	44.04
North Hummock	Tasik Subang Dam	9.69	21.24	25.42	26.10	24.25

4.3 Water Resources Index (WRI)

The first step of WRI calculation is to obtain the water resources capacity (WRC) of the POIs based on historical averages. The WRC is associated with the total capacity of an intake, which comprises average flow, WTP design capacity and storage (if any). The WRC is tabulated in Table 10.

The water resources availability (WRA) for the POIs is calculated for every 7-day period. Hence, the WRI is a time-series. The annual value is then derived by averaging for the purpose of comparison with the other indices.

Table 10

Water Resources Capacity (WRC) of the POIs
POI	Source (River/Dam)	Design Capacity (MLD)	Design Capacity (MCM)	7-day Capacity (MCM)	Simulated Average Flow (2009–2018) (MCM)	7-day Average Flow (MCM)	Active + Inactive (if any) storage (MCM)	WRC (MCM)
Sg Batu	Batu Dam	113.7	0.1137	0.7959	0.203	1.423	30.869	31.496
Sg Kepong	Kepong River	4.5	0.0045	0.0315	0.007	0.046	NA	0.014
Sg Rumput	Rumput River	2.8	0.0028	0.0196	0.017	0.118	NA	0.098
Sg Gombak	Gombak River	23.0	0.0230	0.1610	0.154	1.080	NA	0.919
Wangsa Maju	Gombak River	45.0	0.0450	0.3150	0.308	0.961	NA	6.407
Wangsa Maju	Klang Gates Dam	45.0	0.0450	0.3150	0.236	1.654	3.090	6.407
Bukit Nanas	Klang Gates Dam	145.0	0.1450	1.0150	0.236	1.654	17.719	18.358
Ampang	Ampang River	18.0	0.0180	0.1260	0.072	0.506	NA	0.380
North Hummock	Tasik Subang Dam	22.5	0.0225	0.1575	0.029	0.204	2.936	2.982

The comparison is made between Falkenmark Indicator (FI), Water Stress Index (WSI) and Water Resources Index (WRI) based on the results from year 2016 to 2018 to identify the interpretation that can be derived from these indicator/ indices. However, all 3 approaches need to be normalized to a uniform range of value for direct comparison. For the present purpose, the range of 0 to 1 is adopted, where 0 indicates absolute scarcity, whereas value ≥ 1 indicates ‘no stress’. For FI values, the range from 0 to 1700 is mapped proportionally to the range from 0 to 1. For WSI, the adjusted values are obtained by calculating (1 – WSI). For WRI, there is no adjustment. Note that both FI and WSI do not consider the basin storage and the results are available only on annual basis without temporal resolution. Hence, they omit the seasonal water availability and is not able to identify water stress at smaller time scale. Note that groundwater resources of the respective POIs are not covered in the study.

5.1 Long-term (Annual) Water Availability

The results obtained are as plotted in Fig. 4. The plots are organized such that the ROR schemes (Sg Kepong, Sg Rumput, Sg Gombak, Ampang) are on the left and WTPs with direct dam transfer (Bukit Nanas, North Hummock, Sg Batu) are on the right. Wangsa Maju WTP has both dam direct transfer and river abstraction and is placed between the 2 groups.

5.1.1 WTPs with Direct Dam Transfer

The results show distinct behaviour for WTPs with direct transfer from the dams (Batu, North Hummock and Bukit Nanas). For these POIs, the WRI have relatively higher values because the reservoir impoundment has a dominating effect over annual surface runoff on the actual water availability in the basin. The WSI value is governed by the withdrawal. Since the withdrawal forms only a fraction of the total runoff and storage combined at these POI, the adjusted WSI values are always lower than the WRI values. The WRI values drop significantly for Sg Batu in year 2017 suggesting a higher than usual water use in the service area.

Meanwhile, the adjusted FI values which are calculated using the mean annual runoff as the numerator show the lowest values for these POIs, and thus are not representative of the augmented actual basin water availability condition. Note that if water availability is assessed based on the FI value, all these POIs (especially North Hummock WTP) would be in absolute water scarcity, which is not true. The limitation of Falkenmark Indicator (FI) approach is mainly due to the fact that it does not take into account the storage such as dams and off-river storage which increase the amount of water available. It also omits the seasonal water availability variations at a smaller scale. FI ignores the availability or quality of the water. It may show that there are enough water resources, but the water may be contaminated or not accessible.

The WRI values at WTPs with direct dam transfer are compared to storage capacity to its respective dams. Consequently, the WRI illustrates a similar pattern to storage capacity that gives us the indication that WRI value changes based on dam storage. Note that, Wangsa Maju and Bukit Nanas WTPs have different values of WRI because they were calculated based on a ratio that is affected by their design capacities.

5.1.2 ROR schemes

For the ROR schemes, the absence of storage means the residual flows at the POIs are generally higher and thus resulted in relatively higher adjusted FI values compared to the WRI. The WSI value for ROR scheme is governed by water withdrawal, which forms a relatively higher portion of the total river runoff compared to the WTPs with direct dam transfer. The WSI values are thus lower and the adjusted WSI values higher. This behaviour contrasts the WTPs with direct dam transfer in the preceding section and is most evident for Sg Rumput WTP (year 2016, 2018) and Sg Gombak WTP (year 2016).

Contrary to the above, Sg Kepong WTP shows an opposite trend where the indices indicate high to low water availability from WRI to WSI to FI, suggesting the low streamflow at the POI (7-day total = 0.046 MCM, lowest amongst all POIs). In other instances, both WRI and FI shows high water availability except the WSI due to high withdrawal, e.g. Sg Gombak WTP (23 MLD) and Ampang WTP (18 MLD) compared to Sg Kepong WTP (4.5 MLD) and Sg Rumput WTP (2.8 MLD).

For Ampang WTP, the indices show little difference over the 3-year period suggesting a highly matured area and resilience to climatic variation.

5.1.3 WTP With Combined Dam Transfer and River Abstraction

For Wangsa Maju WTP, the water shortage in reservoir impoundment reflected in the lower WRI values are observed for year 2016 and 2018. Nevertheless, its alternative river water source at Sg Gombak remained high during these periods and thus supplemented the water availability. The lower water availability based on the adjusted WSI value compared to the adjusted FI value is due to the high withdrawal of 45 MLD.

5.2 Monthly Water Availability

Comparison of the indices in the preceding section is based on annual total which are not practical for operational decision-making. In this section, the monthly WRI and WSI for POIs with direct dam transfer, i.e., Sg. Baru, Wangsa Maju, Bukit Nanas and North Hummock WTPs are compared (Fig. 5).

Results show that contrary to WRI values, the WSI gives indication of water stress in a number of occasions, namely, for Sg. Batu WTP: February 2016 and July 2018, for Bukit Nanas WTPs: January and March 2016, and April 2018, for North Hummock WTPs: August 2016. At these times, the magnitude of withdrawals may approach the net rainfall (MAR-EWR) (see Eq. 2), indicating dry spell condition. However, the basin impoundment storage provides necessary buffer against severe water resource shortage as indicated by the WRI values.

Low WRI values (≤ 0.4) are observed in the following time period, namely, for Sg Batu WTP: August – October 2016, for Bukit Nanas WTP: March 2016, for Wangsa Maju WTP: February to March and September 2016. The analysis is in good agreement with the peak of the extreme drought due to El-Nino effect in year 2016 (Razak and Mahmud, 2021).

In general, the fluctuation of WRI and WSI follows similar trend, but WRI is less volatile due to the storage effective which cushioned the transition process between water resource depletion and recovery. The more drastic rise and fall of the monthly WSI values are not accurate depiction of the actual water resource condition and may cause undue alarm. Note that the reason Wangsa Maju WTP shows more pronounced WRI changes compared to WSI is because the WTP abstract equal quantity from the river what it receives via direct transfer from the dam for water production. In this case, the WRI values reflects the magnitude of the available river flow at the POI. Meanwhile, the WRI values for North Hummock WTP remain high throughout the said period, indicating the storage is not significantly affected by the drought event. However, the WRI values follow a similar pattern to storage capacity (%) as illustrated in all four (4) figures.

The adjusted monthly WSI value is always below unity, whereas the WRI values may at times exceed 1, indicating flood or water excess, notably in November of 2017 for Bukit Nanas and Wangsa Maju WTPs, both of which are connected to the Klang Gates dam. Cross examination with the model output shows that Klang Gates dam has significant dam release for the said month and in fact the dam continues to release until the following month December, as illustrated in Fig. 6

5.3 Daily Water Availability

The WRI values are calculated based on 7-day total inflow, outflow and storage change at the POI. This allows the water availability to be monitored closely for timely decision-making on day-to-day basis.

Figure 7 shows the daily WRI values at the WTPs in comparison with the respective dam storage capacity (%). For WTPs which receive direct transfer from the dam, namely, Sg Batu WTP from Batu Dam, Bukit Nanas WTP from Klang Gates dam, and North Hummock WTP from Tasik Subang, the WRI at the WTP follows closely the water level at the dam. The discrepancy is attributed to the actual 7-day inflow and withdrawal, which are negligible compared to the instantaneous available impoundment storage. For North Hummock WTP, various events where WRI exceeds 1 leading to dam water release is observed.

For Wangsa Maju WTP which abstract equal volume from Sg Batu as it receives direct transfer from Klang Gates dam, the WRI values are significantly affected by the streamflow at the intake point.

The study distinguished the outcomes of water availability investigation using 3 indicator/indices. Difference in results interpretation stems from the varied contributing factors considered in these approaches. The Falkenmark Indicator and Water Stress Index are easy to implement because the input data required is readily available and no water resource modelling is required. However, both methods only provide spatial estimation without temporal resolution. Reservoir impoundment storage such as dams or off-river storage (ORS) plays a significant role in augmentation of basin water availability. Results show that omission of the storage effect leads to less accurate representation of the basin water resource condition. The WRI value derived from water allocation modelling at basin POIs is shown to give rich spatial and temporal information of the water availability. This approach has great potential to be used by water managers for both planning and operation time scale in decision-making.

Author Contribution

Ishak, Ahmad Fakhri – investigation, formal analysis, writing - original draft preparationLee, Wei-Koon - methodology, writing – review and editing, supervisionLim, Foo-Hoat – conceptualization, resources

Acknowledgement

The data used in the present study is extracted from the National Water Balance Study (NAWABS) for Klang River basin. The authors acknowledge the colleagues at Angkasa Consulting Services Sdn Bhd. (ACSSB) for the model simulation works and the review and feedback given by the Department of Irrigation and Drainage (DID) Malaysia.

Falkenmark M 1989 The massive water scarcity now threatening Africa -- why isn't it being addressed? Ambio 18 112–8 Online: http://www.jstor.org/stable/4313541
Frizzone, J. A., Lima, S. C. R. V., Lacerda, C. F., & Mateos, L. (2021). Socio-economic indexes for water use in irrigation in a representative basin of the tropical semiarid region. Water (Switzerland), 13(19), 1–20.
Hoekstra, A. Y., Mekonnen, M. M., Chapagain, A. K., Mathews, R. E., & Richter, B. D. (2012). Global monthly water scarcity: Blue water footprints versus blue water availability. PLoS ONE, 7(2).
Ideris, M. M., Cai, G. Y., Zainol, Z., & Amin, M. Z. M. (2020). Development of Water Stress Index for Water Resources Under Changing. Malaysia Water Research Journal, June, 1–11.
Javadinejad, S., Dara, R., & Jafary, F. (2020). Evaluation of hydro-meteorological drought indices for characterizing historical and future droughts and their impact on groundwater. Resources Environment and Information Engineering, 2(1), 71–83.
Khalid, H. W., Khalil, R. M. Z., & Qureshi, M. A. (2021). Evaluating spectral indices for water bodies extraction in western Tibetan Plateau. Egyptian Journal of Remote Sensing and Space Science, 24(3), 619–634.
Lim, F. H., Lee, W. K., Ishak, A. M., Hasan, A. A., Khor, J. W. S., Ahmad Sulaiman, M. N. I., Ishak, A. F., & Liew, J. (2023). Multi-criteria evaluation for long-term water resources augmentation planning with consideration of global change. Environmental Advances, 12(April), 100375.
Liu, J., Yang, H., Gosling, S. N., Kummu, M., Flörke, M., Pfister, S., Hanasaki, N., Wada, Y., Zhang, X., Zheng, C., Alcamo, J., & Oki, T. (2017). Water scarcity assessments in the past, present, and future. Earth’s Future, 5(6), 545–559.
Nepomilueva, D. (2017). Water scarcity indexes: Water availability to satisfy human needs. Bachelor’s Degree, DP in Env. Eng. Thesis, Helsinki Metropolia Univ. of Applied Sciences.
Smakhtin V, Revenga C and Döll P 2005 Taking into Account Environmental Water Requirements in Global-scale Water Resources Assessments. Comprehensive assessment of water management in agriculture. Research Report 2. Colombo, Sri Lanka: Comprehensive Assessment Secretariat.
Tidwell V C, Kobos P H, Malczynski L A, Klise G and Castillo C R 2012 Exploring the WaterThermoelectric Power Nexus J. Water Resour. Plan. Manag. 138 491–501
Vörösmarty C J, Douglas E M, Green P A and Revenga C 2005 Geospatial Indicators of Emerging Water Stress: An application to Africa. AMBIO A J. Hum. Environ. 34 230–6
Xu, H., & Wu, M. M. (2017). Water Availability Indices – A Literature Review. Report ANL/ESD-17/5. Energy Systems Division, Argonne National Laboratory.

No competing interests reported.

Comparative Study on the Application of Water Resources Index for a Highly-Regulated Urbanized Basin

Status:

Version 1

Abstract

Figures

1 Introduction

2 Review of Water Indices

2.1 Falkenmark Indicator

2.2 Water Stress Index (WSI)

2.3 Water Resources Index

3 Study Area

4 Materials and Methods