Improper use of water resources has caused significant changes in Natural River Flow (NRF), which indicates the need to assess changes in Hydrological Indicators (HI) and Environmental Flow Components (EFC). Therefore, the present study was conducted to identify critical watersheds using HI and EFC under the influence of the dam and without the impact of the dam using the Borda Scoring Algorithm (BSA) based on Game Theory (GT) in Nir-Chay, Balkhlou-Chay and Qareh-Sou Rivers as consequent stations in northern Iran. Indicators of Hydrological Alteration (IHA) software was used to quantify river flow indices. 26 HI and 21 EFC have been quantified and were used to identify critical watersheds. Principal Component Analysis (PCA) was used to determine the most significant indicators. After the data reduction, the initial matrix and comparative preferences of Game Theory (GT) were used, and the scores were assigned to watersheds based on Borda Scoring Algorithm (BSA). Based on the results, it can be said that in the Doost-Beiglou watershed, the small flood peak in the pre-dam period was 14.08, which was reduced to 3.5 in the post-dam period. The scoring of the studied watersheds based on HI and EFC GT theory showed that the watersheds affected by the dam with the scores of 216 and 174 were ranked first and were selected as the most critical watersheds in the study area. As a concluding remark, in the watersheds of Pol-e-Almas and Doost-Beiglou, which were affected by Yamchi and Sabalan dams, significant changes were observed in HI and EFCs. Therefore, in terms of NRF, Balkhlou-Chay and Qareh-Sou Rivers were categorized as critical conditions, which highlights the importance of these watersheds for managers and decision-makers in making optimal decisions.
Research Article
Using Game Theory Algorithm to Identify Critical Watersheds based on Environmental Flow Components and Hydrological Indicators
https://doi.org/10.21203/rs.3.rs-1760717/v1
This work is licensed under a CC BY 4.0 License
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Borda Scoring Algorithm
Dam effect
Flow regime
Hydrological changes
Water resources management
Natural River Flow (NRF) is a primary factor in determining the structure and function of aquatic ecosystems and riverbanks (Mathews and Richter, 2007; Poff et al., 2010). Natural river flow is vital in the preservation of aquatic ecosystems and is recognized as a pattern for ecological conservation and restoration by water managers (Amini et al., 2019; Gao et al., 2009; Ripl, 2003). NRF is also a significant driver of biodiversity and ecological systems (Annear et al., 2002; George et al., 2021), and its changes have adverse effects on the aquatic ecosystem and its functions (Bunn and Arthington, 2002; Maskey et al., 2022; Poff and Zimmerman, 2010). The utilization of water resources has historically been an essential factor in human life (Busico et al., 2021; Kourgialas and Karatzas, 2011; Long et al., 2019; Monico et al., 2022). Rivers provide various services to humans, including drinking, industry, agriculture, and recreation/tourism. Moreover, the hydrological regime of the river is the driving force and the main factor of the river system dynamics (Zuo and Liang, 2015). Maintaining Environmental Flow (EF) in rivers is essential in order to preserve biodiversity as well as meet the water needs of human communities in many riverine ecosystems (Annear et al., 2002; Pérez-Blanco et al., 2021). Indeed, in recent years, water abstraction from the river, flow diversion, and water transfer has had significant effects on the river environment (Behboudian et al., 2021; McGinn et al., 2021; Song et al., 2021; Zhang et al., 2019). Therefore, limitations should be considered for the extent of the changes based on tipping thresholds. These constraints are considered by examining the EF in terms of value and timing in river ecosystems (Kapetas et al., 2019; Poff and Matthews, 2013; Wang et al., 2015).
Improper use of water resources has caused significant changes in NRF, which indicates the need to assess changes in Hydrological Indicators (HI) due to human activities (Pal and Talukdar, 2020) and requires more research on the environmental consequences of changes in NRF (Chen et al., 2010). On the other hand, hydrological regimes play a significant role in changing the composition, structure, and function of living organisms and aquatic environment, wetlands, and river ecosystems (Ripl, 2003). Furthermore, hydrological changes due to dam construction have caused changes in the NRF (Graf, 2006, 1999; López-Moreno et al., 2009; Magilligan and Nislow, 2005; Zhang et al., 2017). Although human manipulation of NRF has social benefits, it destroys the natural services of the ecosystem and threatens biodiversity by changing NRF (Gao et al., 2009; Ripl, 2003). The increase in the construction of dams, water diversion, groundwater exploitation, canalization and, inter-basin water transfer in the world, has caused large-scale hydrological changes in the water bodies and environment (Richter et al., 2003; Zhang et al., 2015, 2012, 2009).
The complexity and multidimensionality of natural systems make the decision-making process difficult for managers, especially in human-induced altered conditions. Watersheds are, therefore, one of the best natural systems for planning and management (Sudia et al., 2021). On the contrary, the inability of conventional optimization methods, the multiplicity of factors influencing decision-making, and the internal dependencies of these factors make it difficult for planners to understand the problem. Therefore, it is necessary to use Multi-Criteria Decision-Making (MCDM) methods to make decisions and combine criteria and alternatives (Narendra et al., 2021). One of the most optimal methods for MCDM is Game Theory (GT) (Avand et al., 2021; Nasiri Khiavi, 2022). GT is a mathematical method for solving problems of allocating common resources and confronting the stakeholders' goals. This method solves the problem by identifying modes between different players and various alternatives. Actors can be different decision-makers and alternatives are the criteria considered by decision makers. In this way, the best alternative can be selected and then implemented using different decision criteria (Lee, 2012). The purpose of this method is to achieve a condition in which the maximum demand of each player is met provided that the maximum needs of other players are met (Skardi et al., 2013; Üçler et al., 2015).
Numerous studies have been conducted in relation to dam and hydrological alteration. Based on this, we can refer to dam and hydrological alteration (Chen et al., 2010; Sojka et al., 2016; Wang et al., 2016; Zhang et al., 2015); IHA and human activities (BAEZA-SANZ and GARCÍA-DEL-JALÓN, n.d.); dam and precipitation effects on hydrological alteration (Zhao et al., 2012); the hydrological response of a coastal watershed to climate change and human activities (Huang et al., 2013). Also, in relation to Indicators of Hydrological Alteration (IHA) and Environmental Flow Components (EFC), we can mention the studies of Principal Components Analysis (PCA) and HI (Olden and Poff, 2003; Yang et al., 2008); climate change impact on river hydrology (Alexandre, 2008); IHA and the description of river discharge change (Gao et al., 2009; Pfeiffer and Ionita, 2017); climate change impact on streamflow (Roudier et al., 2014); IHA and climate change (Eum et al., 2017; Lee et al., 2014); IHA and water quality and quantity modeling (Cakir et al., 2020); HI and biodiversity change (Sun et al., 2019; Zolfagharpour et al., 2022); IHA and ecological alteration (George et al., 2021); EF and reliability of water demands (Monico et al., 2022); health of hydrological systems (Pardo-loaiza et al., 2021); HI and potential eco-systemic implications (Usman et al., 2021); assessing HI caused by climate change and reservoir operations (Maskey et al., 2022).
According to the literature review, that HI have been studied in previous studies using different tools and approaches. While in most studies only IHA and EFCs have been quantified, but the prioritization of the watershed system and also the critical phenomena due to the lack of EFC using optimal MCDM have not been reported. Nevertheless, in the present study, Borda Scoring Algorithm (BSA) based on (GT) has been used, which is one of the most optimal algorithms related to MCDM.
Balkhlou-Chay and Qareh-Sou rivers are of great importance water resources in Ardabil province. The Balkhlou-Chay River is a permanent habitat for aquatic mammals and a seasonal habitat for a variety of migratory birds, and because it passes through the center of Ardabil, it is more susceptible to environmental threats. Also, Qareh-Sou River, although it is one of the permanent rivers, but due to the withdrawal of water for irrigation purposes, most of its bed dries up in low flow summer periods. In general, considering the importance and behavior of rivers in natural ecosystems and its significant role in human life and meeting basic needs, in the present study, the effects of negative changes in NRF in some watersheds of Ardabil province were evaluated. Therefore, this study was conducted to quantify 26 HI and 21 EFC in Nir, Pol-e-Almas (under the influence of Yamchi Dam), Arbab-Kandi and Doost-Beiglou (under the influence of Sabalan Dam) river gauge station and to identify the vulnerability degree of these watersheds using BSA based on GT in Ardabil province.
2.1 Description of the study area
Ardabil province is located in the northwest of Iran and is roughly circumscribed by a rectangle at 37°09' and 39°42' N and 47°15' and 48°56' E. The area of Ardabil province is 17953 Km2, which is about 1.09% of the total area of the country. The general altitude of the province varies from 40 to 4811 m amsl, and this difference in height causes a difference of about \({20}^{^\circ }\)C in the average annual temperature. West of Ardabil province has the highest total annual rainfall. In these areas, the average annual rainfall is estimated to be 400 to 500 mm. The southern regions of the province have an annual rainfall of about 350 mm. The northern regions of the province have the lowest annual rainfall (210 to 240 mm). The average annual temperature is between 7.9 and \({15.2}^{^\circ }\)C (Nasiri Khiavi et al., 2019a, b).
The watersheds studied in this research included Nir, Pol-e-Almas, Arbab-Kandi and Doost-Beiglou. Important rivers of these watersheds include Nir-Chay, Balkhlou-Chay and Qareh-Sou. The upland area of Nir, Pol-e-Almas, Arbab-Kandi and Doost-Beiglou watersheds are 162.47, 1035.53, 4775.20, and 7486.45 Km2, respectively. In this study, the river gauge stations of Pol-e-Almas and Doost-Beiglou are affected by Yamchi and Sabalan dams. Yamchi Dam is a type of earth storage dam and its approximate capacity is about 80 million cubic meters, which was put into operation in 2004. This dam provides drinking water, water needs for agriculture, and environmental flow requirement of Shorabil lake in Ardabil. Regarding Sabalan Dam, it can be said that this dam is also of the reservoir type and the construction site of Sabalan Dam is located at a height of 77 meters from the horizontal bed of Qareh-Sou River and at a distance of 40 km from Meshgin Shahr city. This dam was put into operation in 2006. The purpose of constructing Sabalan Dam is to store 105 million cubic meters, regulate about 115 million cubic meters of Qareh-Sou River runoff, irrigate 15,000 hectares of agricultural lands and gardens along the river, supply drinking water to Meshgin Shahr city and flood control purposes. Figures (1) shows the geographical location of the studied watersheds in Ardabil province.
Figure 1.
2.2 Research methodology
In the present study, PCA, IHA software and BSA based on GT were used to identify critical watersheds based on HI and EFC. Toward this, first daily discharge values were received from Ardabil Regional Water Company. Nir River Gauge Station (NRGS), Pol-e-Almas River Gauge Station (PRGS), Arbab-Kandi River Gauge Station (ARGS) and Doost-Beiglou River Gauge Station (DRGS) were selected to study the watersheds. General characteristics of river gauge stations and the period of available data in each station were presented in Table 1.
|
Station name |
River name |
Latitude |
Longitude |
Statistical period |
|---|---|---|---|---|
|
NRGS |
Nir-Chay |
38-02-02 |
47-59-37 |
2002–2018 |
|
PRGS |
Balkhlou-Chay |
38-08-16 |
48-11-19 |
1969–2018 |
|
ARGS |
Qareh-Sou |
38-29-41 |
48-01-58 |
1999–2018 |
|
DRGS |
Qareh-Sou |
38-33-02 |
47-32-18 |
1973–2018 |
| Note: NRGS (Nir River Gauge Station), PRGS (Pol-e-Almas River Gauge Station), ARGS (Arbab-Kandi River Gauge Station) and DRGS (Doost-Beiglou River Gauge Station) | ||||
Table 1.
2.2.1 Description of IHA and EFC
In the IHA (Belay et al., 2010; Monico et al., 2022) group, 26 indicators were selected. These indicators included average monthly flow (12 months), minimum flow 1, 3, 7, 30 and 90 days, maximum flow 1, 3, 7, 30 and 90 days, number of days with zero flow, base flow index, rise rate and fall rate. Also in the EFC (Belay et al., 2010; Tegos et al., 2018) group, 21 components were used, including minimum monthly flows (12 months), extreme low frequency, high flow frequency, small flood peak, small flood frequency, small flood rise rate, small flood fall rate, large flood peak, large flood frequency and large flood rise rate. IHA software was used to quantify HI and EFC (George et al., 2021). This software was provided by Natural Sciences and Technology Center (NSTC), which used this software and daily discharge data to calculate all the effective parameters in changing the flow. There were several options in this software to control how hydrological parameters are calculated. Output tables and graphs depended on the options used and user requirement and assumptions (The Nature Conservancy, 2009).
2.2.2 PCA and selection of effective indicators and components
Using IHA software, the values of HI and EFC were quantified. Then, FA and PCA (Bro and Smilde, 2014; Bucherie et al., 2022; Foody et al., 2004) were used to select the influential hydrological and environmental indicators affecting the determination of critical watersheds. PCA is one of the most important approaches to identify effective factors and determine the minimum number of influential factors with maximum effectiveness (Adhami and Sadeghi, 2016). This method is a data analysis tool that uses a large number of related variables to reduce the number of variables, while retaining as much information as possible (Wu, 2021). Statistical analyzes were performed in SPSS software. Based on cumulative variance (Adhami and Sadeghi, 2016) and scree plot, the main components were determined, which explains a large part of the variance in the data. Then in each component, effective indicators were selected based on significance of the correlation coefficient.
2.2.3 Application of BSA based on GT for weighting HI and EFC
After quantification of hydrological indicators and environmental flow and selection of effective indicators using PCA, the Borda algorithm was used to weigh the indicators and determine critical watersheds,. First, the effect level of each indicator in each hydrometry station was determined. Then the initial matrix of game theory and comparative preferences of Borda algorithm was formed. Then, watersheds were prioritized based on three approaches (1) based on the values of hydrological indicators (2) based on the values of environmental flow components and (3) a combination of hydro and environmental methods.
BSA was a ranking approach to group decision making and was used to score indicators. The Borda score was determined for each candidate. It was the sum of the individual scores of each indicator (Avand et al., 2021). The indicators were arranged in descending order by their Borda score. For every n representative, there will be n ranks. Accordingly, n-1 points were awarded to the representative in the first rank and n-2 to the representative with the second priority, and so on (Nasiri Khiavi, 2022). In this algorithm, the score of each representative was denoted by \(BS \left(A\right)\) and was expressed as (Eqs. 6, 7, 8, and 9):
Where, #{…} is the number factors in the set. Finally, each option with the highest score is recognized as the first priority (Adhami et al., 2019; Adhami and Sadeghi, 2016; Balinski and Laraki, 2007).
Finally, using this algorithm, NRGS, PRGS, ARGS and DRGS were weighted based on the degree of vulnerability in the values of HI and EFC. PRGS and DRGS were affected by the Yamchi and Sabalan dams, respectively. Therefore, in these watersheds, prioritization was done based on BSA in two stages. First, the data before the construction of the dam in the PRGSa and DRGSa were entered into IHA software. Then, in order to determine the effects of dams in PRGSb and DRGSb, data after dam construction were entered into IHA software and critical watershed grading was performed without dam effect and under dam effect. Also, the correlation matrix related to the monthly flow values, and box plot of final scores of HI and EFC based on BSA was presented in R software. Finally, spatial changes of all three approaches (HI, EFC and Combined) were presented in each river gauge station using ArcGIS10.1 software. Figure 2. shows the flow chart of the research methodology.
Figure 2.
The results of variance values in PCA based on HI and EFC are presented in Table 2. The arrangement of 47 studied indicators in each component was listed in Table 3. Also, Fig. 3. shows the scree plot and how each component is positioned based on Eigenvalue.
| Component | 1 | 2 | 3 | 4 | |
|---|---|---|---|---|---|
| Initial Eigenvalues | Total | 35.17 | 8.94 | 1.40 | 1.11 |
| Variance % | 74.84 | 19.02 | 2.99 | 2.37 | |
| Cumulative % | 74.84 | 93.86 | 96.85 | 99.23 | |
| Extraction Sums of Squared | Total | 35.17 | 8.94 | 1.40 | 1.11 |
| Variance % | 74.84 | 19.02 | 2.99 | 2.37 | |
| Cumulative % | 74.84 | 93.86 | 96.85 | 99.23 | |
| Rotation Sums of Squared | Total | 28.18 | 15.04 | 2.08 | 1.33 |
| Variance % | 59.96 | 32.00 | 4.43 | 2.83 | |
| Cumulative % | 59.96 | 91.96 | 96.39 | 99.23 | |
| Indicators | Component | |||
|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |
| Small flood-RR | 0.979 | |||
| High flow-F | 0.978 | |||
| Small flood-P | 0.973 | |||
| Jul-LF | 0.971 | |||
| May-LF | 0.965 | |||
| Jan-LF | 0.963 | |||
| Aug-LF | 0.963 | |||
| Apr-LF | 0.962 | |||
| Feb-LF | 0.962 | |||
| Dec-LF | 0.961 | |||
| Small flood-FR | -0.960 | |||
| Large flood-RR | 0.959 | |||
| Mar-LF | 0.959 | |||
| Nov-LF | 0.956 | |||
| Oct-LF | 0.954 | |||
| Sep-LF | 0.946 | |||
| Oct | 0.942 | 0.325 | ||
| Jun-LF | 0.942 | |||
| 1 day-max | 0.938 | 0.327 | ||
| 3 day-max | 0.937 | 0.330 | ||
| 7 day-max | 0.935 | 0.335 | ||
| 30 day-max | 0.926 | 0.361 | ||
| Nov | 0.903 | 0.417 | ||
| 90 day-max | 0.887 | 0.450 | ||
| Fall rate | -0.871 | -0.405 | ||
| Dec | 0.856 | 0.500 | ||
| Sep | 0.833 | 0.537 | ||
| Rise rate | 0.813 | 0.507 | ||
| Jan | 0.792 | 0.591 | ||
| Jun | 0.986 | |||
| May | 0.984 | |||
| Jul | 0.975 | |||
| Apr | 0.972 | |||
| 90 day-min | 0.958 | |||
| Mar | 0.357 | 0.933 | ||
| 30 day-min | 0.383 | 0.920 | ||
| 7 day-min | 0.470 | 0.874 | ||
| 3 day-min | 0.491 | 0.861 | ||
| 1 day-min | 0.496 | 0.847 | ||
| Feb | 0.600 | 0.786 | ||
| BFI | 0.783 | -0.397 | 0.454 | |
| Aug | 0.674 | 0.729 | ||
| Extreme low-F | -0.710 | 0.642 | ||
| Large flood-P | 0.681 | 0.705 | ||
| Small flood-F | 0.431 | -0.564 | 0.552 | |
| Large flood-F | 0.359 | 0.911 | ||
| N-zero | -0.415 | -0.620 | -0.651 | |
| Note: LF (Low Flow), F (Frequency), P (Peak), RR (Rise Rate), FR (Fall Rate), N-zero (Number of days with zero flow) and BFI (Base Flow Index); Gray cells show selected indicators in each component. | ||||
Table 2.
Table 3.
Figure 3.
The results of quantitative values of HI and EFC in NRGS, PRGSa, PRGSb, ARGS, DRGSb and DRGSb were presented in Tables 4 and 5, respectively. Also, the correlation matrix between 12 monthly flow indices in each watershed was shown in Fig. 4.
| Indicators | Unit | River gauge station | |||||
|---|---|---|---|---|---|---|---|
| NRGS | PRGSa | PRGSb | ARGS | DRGSa | DRGSb | ||
| Oct | CMS | 6.31 | 5.34 | 1.73 | 3.49 | 30.91 | 0.56 |
| Nov | CMS | 8.62 | 5.83 | 1.67 | 4.64 | 28.08 | 0.44 |
| Dec | CMS | 10.92 | 6.68 | 1.64 | 5.67 | 27.06 | 0.32 |
| Jan | CMS | 12.67 | 8.03 | 1.30 | 5.87 | 24.96 | 0.28 |
| Feb | CMS | 12.97 | 6.21 | 1.02 | 4.34 | 16.37 | 0.42 |
| Mar | CMS | 13.28 | 5.22 | 1.05 | 2.29 | 11.30 | 0.61 |
| Apr | CMS | 13.67 | 5.09 | 1.13 | 1.00 | 9.42 | 0.16 |
| May | CMS | 14.05 | 4.75 | 1.21 | 0.72 | 8.71 | 0.23 |
| Jun | CMS | 14.44 | 3.21 | 1.29 | 0.68 | 8.12 | 0.38 |
| Jul | CMS | 14.24 | 3.67 | 1.36 | 0.89 | 9.28 | 0.45 |
| Aug | CMS | 13.34 | 4.33 | 1.41 | 1.33 | 19.84 | 0.50 |
| Sep | CMS | 13.14 | 4.87 | 1.44 | 2.14 | 31.76 | 0.54 |
| 1 day-min | CMS | 4.13 | 2.36 | 0.68 | 0.47 | 4.45 | 0.07 |
| 3 day-min | CMS | 4.23 | 2.36 | 0.68 | 0.47 | 4.47 | 0.07 |
| 7 day-min | CMS | 4.42 | 2.38 | 0.69 | 0.47 | 4.51 | 0.07 |
| 30 day-min | CMS | 5.50 | 2.47 | 0.73 | 0.48 | 4.86 | 0.08 |
| 90 day-min | CMS | 8.29 | 2.81 | 0.85 | 0.56 | 6.21 | 0.11 |
| 1 day-max | CMS | 15.6 | 13.66 | 2.32 | 10.62 | 74.65 | 1.95 |
| 3 day-max | CMS | 15.59 | 13.26 | 2.32 | 10.25 | 73.73 | 1.89 |
| 7 day-max | CMS | 15.56 | 12.62 | 2.31 | 9.78 | 72.11 | 1.80 |
| 30 day-max | CMS | 15.4 | 11.04 | 2.26 | 8.53 | 64.32 | 1.29 |
| 90 day-max | CMS | 14.96 | 9.041 | 2.17 | 6.52 | 44.61 | 0.83 |
| N-zero | Number | 33.23 | 0.033 | 82.11 | 170.50 | 12.30 | 181.20 |
| BFI | CMS | 0.72 | 0.46 | 0.45 | 0.05 | 0.32 | 0.07 |
| Rise rate | CMS | 0.28 | 0.27 | 0.01 | 0.11 | 0.62 | 0.13 |
| Fall rate | CMS | -0.25 | -0.11 | -0.07 | -0.17 | -0.64 | -0.12 |
| Note: NRGS (Nir River Gauge Station), PRGSa (Pol-e-Almas River Gauge Station; Without the dam impact), PRGSb (Pol-e-Almas River Gauge Station; Under the dam influence); ARGS (Arbab-Kandi River Gauge Station); DRGSa (Doost-Beiglou River Gauge Station; Without the dam impact), DRGSb (Doost-Beiglou River Gauge Station; Under the dam influence), N-zero (Number of days with zero flow) and BFI (Base Flow Index). | |||||||
| Components | Unit | River gauge station | |||||
|---|---|---|---|---|---|---|---|
| NRGS | PRGSa | PRGSb | ARGS | DRGSa | DRGSb | ||
| Oct-LF | CMS | 1.17 | 4.55 | 1.10 | 1.93 | 10.07 | 0.45 |
| Nov-LF | CMS | 1.21 | 4.24 | 0.96 | 1.53 | 10.04 | 0.39 |
| Dec-LF | CMS | 1.12 | 4.14 | 0.98 | 1.31 | 10.55 | 0.28 |
| Jan-LF | CMS | 1.14 | 4.09 | 1.09 | 1.53 | 10.29 | 0.29 |
| Feb-LF | CMS | 1.12 | 3.65 | 0.87 | 1.60 | 9.51 | 0.32 |
| Mar-LF | CMS | 1.05 | 3.06 | 0.87 | 1.45 | 7.60 | 0.15 |
| Apr-LF | CMS | 1.07 | 3.22 | 0.93 | 1.49 | 8.19 | 0.19 |
| May-LF | CMS | 1.09 | 3.31 | 1.00 | 1.93 | 8.31 | 0.35 |
| Jun-LF | CMS | 1.16 | 2.42 | 1.05 | 0.95 | 8.28 | 0.60 |
| Jul-LF | CMS | 1.16 | 2.99 | 1.10 | 1.22 | 8.40 | 0.52 |
| Aug-LF | CMS | 1.08 | 3.80 | 1.14 | 1.31 | 9.03 | 0.62 |
| Sep-LF | CMS | 1.08 | 4.58 | 1.06 | 2.04 | 9.24 | 0.57 |
| Extreme low-F | Number | 0.04 | 0.50 | 0.22 | 0.73 | 0.48 | 0.76 |
| High flow-F | Number | 0.00 | 0.03 | 0.00 | 0.00 | 0.30 | 0.00 |
| Small Flood-P | CMS | 3.00 | 14.08 | 3.50 | 11.42 | 42.21 | 1.33 |
| Small Flood-F | Number | 0.09 | 0.46 | 0.22 | 0.36 | 0.51 | 0.61 |
| Small Flood-RR | CMS | 0.01 | 0.25 | 0.00 | 0.17 | 2.68 | 0.12 |
| Small Flood-FR | CMS | -0.10 | -0.19 | -0.02 | -0.22 | -0.88 | -0.01 |
| Large flood-P | CMS | 203.00 | 47.67 | - | 40.00 | 313.30 | 11.00 |
| Large flood-F | Number | 0.04 | 0.10 | 0 | 0.10 | 0.09 | 0.07 |
| Large flood-RR | CMS | 0.288.00 | 0.34 | - | 0.36 | 6.00 | 0.69 |
| Note: NRGS (Nir River Gauge Station), PRGSa (Pol-e-Almas River Gauge Station; Without the dam impact), PRGSb (Pol-e-Almas River Gauge Station; Under the dam influence); ARGS (Arbab-Kandi River Gauge Station); DRGSa (Doost-Beiglou River Gauge Station; Without the dam impact), DRGSb (Doost-Beiglou River Gauge Station; Under the dam influence), LF (Low Flow), F (Frequency), P (Peak), RR (Rise Rate) and FR (Fall Rate). | |||||||
Table 4.
Table 5.
Figure 4.
The initial matrix and comparative preferences of HI and EFC in each watershed were presented using BSA based on GT in Tables 6 and 7. The final weighted scores of the river flow indices as well as the scores of each watershed (without dam impact and under dam impact) based on these indicators were entered in Table 8.
| Indicators | River gauge station | |||||
|---|---|---|---|---|---|---|
| NRGS | PRGSa | PRGSb | ARGS | DRGSa | DRGSb | |
| Oct | DRGSb | PRGSb | ARGS | PRGSa | NRGS | DRGSa |
| Nov | DRGSb | PRGSb | ARGS | PRGSa | NRGS | DRGSa |
| Dec | DRGSb | PRGSb | ARGS | PRGSa | NRGS | DRGSa |
| Jan | DRGSb | PRGSb | ARGS | PRGSa | NRGS | DRGSa |
| Feb | DRGSb | PRGSb | ARGS | PRGSa | NRGS | DRGSa |
| Mar | DRGSb | PRGSb | ARGS | PRGSa | NRGS | DRGSa |
| Apr | DRGSb | ARGS | PRGSb | PRGSa | DRGSa | NRGS |
| May | DRGSb | ARGS | PRGSb | PRGSa | DRGSa | NRGS |
| Jun | DRGSb | ARGS | PRGSb | PRGSa | DRGSa | NRGS |
| Jul | DRGSb | ARGS | PRGSb | PRGSa | DRGSa | NRGS |
| Aug | DRGSb | ARGS | PRGSb | PRGSa | NRGS | DRGSa |
| Sep | DRGSb | PRGSb | ARGS | PRGSa | NRGS | DRGSa |
| 1 day-min | DRGSb | ARGS | PRGSb | PRGSa | NRGS | DRGSa |
| 3 day-min | DRGSb | ARGS | PRGSb | PRGSa | NRGS | DRGSa |
| 7 day-min | DRGSb | ARGS | PRGSb | PRGSa | NRGS | DRGSa |
| 30 day-min | DRGSb | ARGS | PRGSb | PRGSa | DRGSa | NRGS |
| 90 day-min | DRGSb | ARGS | PRGSb | PRGSa | DRGSa | NRGS |
| 1 day-max | DRGSb | PRGSb | ARGS | PRGSa | NRGS | DRGSa |
| 3 day-max | DRGSb | PRGSb | ARGS | PRGSa | NRGS | DRGSa |
| 7 day-max | DRGSb | PRGSb | ARGS | PRGSa | NRGS | DRGSa |
| 30 day-max | DRGSb | PRGSb | ARGS | PRGSa | NRGS | DRGSa |
| 90 day-max | DRGSb | PRGSb | ARGS | PRGSa | NRGS | DRGSa |
| N-zero | DRGSb | ARGS | PRGSb | NRGS | DRGSa | PRGSa |
| BFI | ARGS | DRGSb | DRGSa | DRGSb | PRGSa | NRGS |
| Rise-R | PRGSb | ARGS | DRGSb | NRGS | PRGSa | DRGSa |
| Fall-R | DRGSb | PRGSa | DRGSb | ARGS | NRGS | DRGSa |
| Note: See Table 2 for river gauge station and indicator specifications | ||||||
| Components | River gauge station | |||||
|---|---|---|---|---|---|---|
| NRGS | PRGSa | PRGSb | ARGS | DRGSa | DRGSb | |
| Oct-LF | DRGSb | PRGSb | NRGS | ARGS | PRGSa | DRGSa |
| Nov-LF | DRGSb | PRGSb | NRGS | ARGS | PRGSa | DRGSa |
| Dec-LF | DRGSb | PRGSb | NRGS | ARGS | PRGSa | DRGSa |
| Jan-LF | DRGSb | PRGSb | NRGS | ARGS | PRGSa | DRGSa |
| Feb-LF | DRGSb | PRGSb | NRGS | ARGS | PRGSa | DRGSa |
| Mar-LF | DRGSb | PRGSb | NRGS | ARGS | PRGSa | DRGSa |
| Apr-LF | DRGSb | PRGSb | NRGS | ARGS | PRGSa | DRGSa |
| May-LF | DRGSb | PRGSb | NRGS | ARGS | PRGSa | DRGSa |
| Jun-LF | DRGSb | PRGSb | PRGSb | NRGS | PRGSa | DRGSa |
| Jul-LF | DRGSb | PRGSb | NRGS | ARGS | PRGSa | DRGSa |
| Aug-LF | DRGSb | NRGS | PRGSb | ARGS | PRGSa | DRGSa |
| Sep-LF | DRGSb | PRGSb | NRGS | ARGS | PRGSa | DRGSa |
| Extreme low-F | DRGSb | ARGS | PRGSb | DRGSa | PRGSb | NRGS |
| High flow-F | DRGSb | PRGSb | ARGS | NRGS | PRGSa | DRGSa |
| Small Flood-P | DRGSb | NRGS | PRGSb | ARGS | PRGSa | DRGSa |
| Small Flood-F | NRGS | PRGSb | ARGS | PRGSa | PRGSa | DRGSb |
| Small Flood-RR | PRGSb | NRGS | DRGSb | ARGS | PRGSa | DRGSa |
| Small Flood-FR | PRGSb | PRGSb | NRGS | PRGSa | ARGS | DRGSa |
| Large flood-P | PRGSb | DRGSb | ARGS | PRGSa | NRGS | DRGSa |
| Large flood-F | PRGSb | NRGS | DRGSb | DRGSa | PRGSa | ARGS |
| Large flood-RR | PRGSb | NRGS | PRGSa | ARGS | DRGSb | DRGSa |
| Note: See Table 3 for river gauge station and component specifications | ||||||
| Indicators | River gauge station | |||||
|---|---|---|---|---|---|---|
| NRGS | PRGSa | PRGSb | ARGS | DRGSa | DRGSb | |
| Oct | 1 | 2 | 4 | 3 | 0 | 5 |
| Nov | 1 | 2 | 4 | 3 | 0 | 5 |
| Dec | 1 | 2 | 4 | 3 | 0 | 5 |
| Jan | 1 | 2 | 4 | 3 | 0 | 5 |
| Feb | 1 | 2 | 4 | 3 | 0 | 5 |
| Mar | 1 | 2 | 4 | 3 | 0 | 5 |
| Apr | 0 | 2 | 3 | 4 | 1 | 5 |
| May | 0 | 2 | 3 | 4 | 1 | 5 |
| Jun | 0 | 2 | 3 | 4 | 1 | 5 |
| Jul | 0 | 2 | 3 | 4 | 1 | 5 |
| Aug | 1 | 2 | 3 | 4 | 0 | 5 |
| Sep | 1 | 2 | 4 | 3 | 0 | 5 |
| 1 day-min | 1 | 2 | 3 | 4 | 0 | 5 |
| 3 day-min | 1 | 2 | 3 | 4 | 0 | 5 |
| 7 day-min | 1 | 2 | 3 | 4 | 0 | 5 |
| 30 day-min | 0 | 2 | 3 | 4 | 1 | 5 |
| 90 day-min | 0 | 2 | 3 | 4 | 1 | 5 |
| 1 day-max | 1 | 2 | 4 | 3 | 0 | 5 |
| 3 day-max | 1 | 2 | 4 | 3 | 0 | 5 |
| 7 day-max | 1 | 2 | 4 | 3 | 0 | 5 |
| 30 day-max | 1 | 2 | 4 | 3 | 0 | 5 |
| 90 day-max | 1 | 2 | 4 | 3 | 0 | 5 |
| N-zero | 2 | 0 | 3 | 4 | 0 | 5 |
| BFI | 0 | 1 | 2 | 5 | 3 | 4 |
| Rise-R | 2 | 1 | 5 | 4 | 0 | 3 |
| Fall-R | 1 | 4 | 3 | 2 | 0 | 3 |
| Oct-LF | 3 | 1 | 4 | 2 | 0 | 5 |
| Nov-LF | 3 | 1 | 4 | 2 | 0 | 5 |
| Dec-LF | 3 | 1 | 4 | 2 | 0 | 5 |
| Jan-LF | 3 | 1 | 4 | 2 | 0 | 5 |
| Feb-LF | 3 | 1 | 4 | 2 | 0 | 5 |
| Mar-LF | 3 | 1 | 4 | 2 | 0 | 5 |
| Apr-LF | 3 | 1 | 4 | 2 | 0 | 5 |
| May-LF | 3 | 1 | 4 | 2 | 0 | 5 |
| Jun-LF | 2 | 1 | 3 | 4 | 0 | 5 |
| Jul-LF | 3 | 1 | 4 | 2 | 0 | 5 |
| Aug-LF | 4 | 1 | 3 | 2 | 0 | 5 |
| Sep-LF | 3 | 1 | 4 | 2 | 0 | 5 |
| Extreme low-F | 0 | 3 | 1 | 4 | 2 | 5 |
| High flow-F | 2 | 1 | 4 | 3 | 0 | 5 |
| Small Flood-P | 4 | 1 | 3 | 2 | 0 | 5 |
| Small Flood-F | 5 | 2 | 4 | 3 | 1 | 0 |
| Small Flood-RR | 4 | 1 | 6 | 2 | 0 | 3 |
| Small Flood-FR | 3 | 2 | 4 | 1 | 0 | 5 |
| Large flood-P | 1 | 2 | 5 | 3 | 0 | 4 |
| Large flood-F | 4 | 1 | 5 | 0 | 2 | 3 |
| Large flood-RR | 4 | 3 | 5 | 2 | 0 | 1 |
| Total weight | 84 | 78 | 174 | 137 | 14 | 216 |
| Note: See Table 2 for river gauge station specifications; Blue indicates the very high priority and yellow indicates the very low priority. | ||||||
Table 6.
Table 7.
Table 8.
The box plot related to the changes in the scores obtained for each HI and EFC by each watershed was presented using BSA in Fig. 5. Also, changes and spatial arrangement of Borda scores with three approaches of HI, EFC and combination of these two approaches in each watershed were presented as histograms in Figs. 6 and 7.
River flow conditions affect its biodiversity and can be examined with hydrological and environmental indicators (Jiang et al., 2014; Sun et al., 2019) and it evaluates many physical and ecological activities (Petts et al., 2006; Roy et al., 2006; Usman et al., 2021). (Monico et al., 2022) stated that the use of HI for optimal decision making and planning is common in water resources management. However, river flow components play an important role in the exploitation of water resources, biochemical processes, environment and maintaining the integrity of the natural ecosystem (Arthington, 2012; Kennard et al., 2010). Therefore, the assessment of water resources related to dams should include the assessment of the total flow regime in order to determine the more accurate range of changes in water resources and provide a proper understanding of the mechanisms of change in rivers and environmental systems (Zhang et al., 2017). IHA and EFC have been developed to assess the reflection of human manipulations in NRF and the influence of dam construction (Mathews and Richter, 2007). Quantification of IHA parameters is a common approach for assessing NRF change that involves comparing flow regimes (The Nature Conservancy, 2009). Therefore, based on the findings of (Cardoso, 2013), which stated that using these indicators, the basic characteristics of river flow including magnitude, duration, timing, frequency and rate can be examined.
The process of selecting effective indicators in HI and EFC to identify critical watersheds using the main component showed that four groups of components were effective in selecting the most influential indicators. The proportion of the first and second components with variance values of 59.96 and 32.00% was much higher than the third and fourth components with variance values of 4.43 and 2.83%. The eigenvalues of components (Adhami and Sadeghi, 2016; Bro and Smilde, 2014) 1, 2, 3 and 4 based on their total variance explained in the main components were 74.84, 19.02, 2.99 and 2.37%, respectively (Table 2 and Fig. 3). Based on the results of the rotated component matrix (Bucherie et al., 2022; Foody et al., 2004; Olden and Poff, 2003; Yang et al., 2008), 40 hydrologic indices were selected in the first components. Therefore, 40 indices, the correlation coefficient was significant in 30 indices, the highest correlation coefficient was related to small flood rise rate, high flow frequency, small flood peak and July-low flow with the value of 0.98 (α < 0.01) (Table 3).
In the second components, 29 hydrological indicators were selected in the main components. Accordingly, 19 indices had an acceptable correlation coefficient, with the June and May indices ranking first with a value of 0.99. In general, based on the results of the main components, it can be said that among the 47 hydrological indicators studied, only small flood fall rate, fall rate, small flood frequency and N-zero indicators had not an acceptable correlation and other indicators were selected as effective parameters in the study of susceptible watersheds. Consequently, according to reliable sources, these four physical indicators are the most important parameters in the study of NRF, and therefore these indicators were used for the further analysis. One of the main reasons for selecting these indicators in this study was that according to (Belay et al., 2010; Greco et al., 2021; Tegos et al., 2018), with the occurrence of small floods, aquatic animals and other living organisms are able to move downstream and establish new habitats in floodplain areas. (Mathews and Richter, 2007) also stated that large floods affected the physical and biological structure of rivers, creating competition between species, and contributing to the formation of key habitats.
Quantitative results of HI and EFC showed that in most indicators, small values in DRGSa were the maximum and confirmed that among the studied watersheds, the condition of this watershed was appropriate. Hence, this condition in this watershed was related to the pre-dam period and the post-dam period (DRGSb), the values of all indicators changed and had a decreasing trend (Ely et al., 2020; Villablanca et al., 2022; Yan et al., 2010; Zolfagharpour et al., 2022). In PRGS watershed, which was affected by Yamchi dam, N-zero index in the pre-dam period was 0.033, but in the post-dam period, it increased to 82.11. Also in DRGS watershed, the values of this index in the post-dam periods, had a large upward trend (Nasiri Khiavi et al., 2019b; Zuo and Liang, 2015). Based on Table 4, BFI also had a decreasing trend in the stations affected by the dam (Nasiri Khiavi et al., 2019a). The small flood peak in PRGSa was 14.08 CMS, which was reduced to 3.5 CMS in the post-dam period (Marcinkowski and Grygoruk, 2017; Mathews and Richter, 2007; Mathias Kondolf and Batalla, 2005; Nasiri Khiavi et al., 2019a; Sojka et al., 2016; Villablanca et al., 2022; Zhang et al., 2015).
The correlation matrix between monthly flows in the studied watersheds showed that the values of these variables in PRGSa and ARGS watersheds had a correlation coefficient of 0.91. PRGSb watershed was also ranked second with DRGSa watershed with a correlation coefficient of 0.74 (α < 0.01) (Fig. 4). Rise rate index in watersheds affected by the dam also showed a decreasing trend. In PRGS and DRGS watersheds, the percentage difference of these indicators in the pre- and post-dam periods decreased by -62.96 and − 79.03%, respectively, which showed the effect of dam construction (Nasiri Khiavi et al., 2019b; Zuo and Liang, 2015). However, in the stations without the effect of the dam, the values of this index were in the optimal state (Table 4). The grading of the studied watersheds based on HI and EFC using BSA based on GT showed that the watersheds affected by the dam won the game. DRGSb and PRGSb watersheds with 216 and 174 points, respectively, gained the first ranks and were selected as the most critical watersheds in study area. However, in these watersheds in the pre-dam periods, BSA based on GT showed the lowest score and no critical condition was observed in these watersheds (Table 8). One of the reasons for choosing this algorithm in the present study was that the BSA is one of the most optimal methods of GT (Avand et al., 2021; Mahjouri and Bizhani-Manzar, 2013; Sheikhmohammady et al., 2010) and this algorithm is based on linear comparison and scoring. (Avand et al., 2021; Madani, 2010) confirmed that BSA best reflected the behavior of the actors involved in the decision.
The box plot related to BSA points also showed that the highest range of changes (BSA = 5) was observed in the watersheds affected by the dam construction (Du et al., 2020; Villablanca et al., 2022) and even in DRGSb, the range of changes in BSA points was very low and confirmed the critical condition in this watershed. However, in the stations without the effect of the dam (DRGSa and PRGSa), the range of changes in BSA scores was observed in low scores (1 to 2) (Fig. 5). The spatial arrangement of HI-EFC approaches and the combination of these two approaches based on BSA scores also showed that in all approaches, the watersheds affected by the dam had the highest value and were in critical condition (Fig. 7). However, according to Fig. 6, it can be seen that the condition was good in watersheds without the impact of the dam. Although the construction of Yamchi and Sabalan dams was effective in controlling floods and reducing small and large floods and had social benefits, but they caused the destruction of natural ecological services and threatened biodiversity in the Balkhlou-Chay and Qareh-Sou Rivers (Gao et al., 2009; Ripl, 2003). (Zhang et al., 2015) confirmed that the hydrological changes caused by the construction of the dam and its effect on EF caused great concern for hydrologists and ecologists. In this regard, (Chen et al., 2010) stated that the construction of dams caused significant hydrological changes that severely upset the balance of river flow. In general, it can be concluded that in the study watersheds in Ardabil province, unfortunately, the construction of Yamchi and Sabalan dams has a negative impact on HI and EFC (Nasiri Khiavi et al., 2019a, 2019b; Wang et al., 2016; Zuo and Liang, 2015) and in terms of NRF, Balkhlou-Chay and Qareh-Sou Rivers were in a critical state in the post-dam period.
This study was conducted with the aim of identifying critical watersheds in terms of Hydrological Indicators (HI) and Environmental Flow Components (EFC) under the influence of the dam using Borda Scoring Algorithm (BSA) based on Game Theory (GT) in some watersheds of Ardabil province, Iran. This study was conducted in Nir River Gauge Station (NRGS), Pol-e-Almas River Gauge Station (PRGS), Arbab-Kandi River Gauge Station (ARGS) and Doost-Beiglou River Gauge Station (DRGS). 26 HI and 21 EFC were selected and PCA was used as data reduction technique. The quantified values of these indicators in each watershed were arranged in order to form the initial matrix of GT and comparative preferences based on the type of their impact on the river flow. Finally, using BSA based on GT, HI and EFC were ranked and critical watersheds were identified. In general, it can be said that the construction of dams in the studied watersheds had a negative impact on HI and EFC.
In the watersheds affected by the dam, the values of the indicators had a decreasing trend. The number of flow zero days in these watersheds increased significantly in the post-dam period. Also, Base Flow Index (BFI) had a decreasing trend in the stations affected by the dam. The small flood peak in the post-dam period decreased compared to the pre-dam period, which showed the regulatory effect of Yamchi and Sabalan dams in Balkhlou-Chay and Qareh-Sou Rivers. The grading of the studied watersheds based on HI and EFC using GT showed that the watersheds affected by the dam were in a critical condition. However, in these watersheds and in the pre-dam period, the lowest score of BSA was observed and no critical condition was observed in these watersheds. Spatial variations of BSA scores in HI and EFC flow showed that in all approaches, the watersheds affected by the dam (PRGSb and DRGSb) had the highest value and were in critical condition. In general, it can be said that in PRGS and DRGS watersheds, which were under Yamchi and Sabalan dams, significant changes were observed in the flow regime of Balkhlou-Chay and Qareh-Sou Rivers, and managers of water resources in this province should identify critical watersheds and make optimal decisions. The results of the present study and the identification of critical watersheds based on parameters related to river flow can help watershed planners to apply mechanical and biological watershed management operations and measures. It is suggested that in future studies, in addition to hydrological and environmental indicators, ecological indicators related to river flow be used and also the impact of changes in various climatic parameters on water resources be investigated. Also, with the involvement of local stakeholders and the implementation of co-management, with more complete and comprehensive data to examine the trend of river flow changes.
Authors’ contributions
Ali Nasiri Khiavi: Conceptualization, Methodology, Writing;
Raoof Mostafazadeh: Supervision, Writing–original draft and Validation;
Fatemeh Ghanbari Talouki: Methodology, Writing.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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