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Hydrological alterations due to anthropogenic activities in Krishna River Basin, India
Ecological Indicators 108 (2020) 105663
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Original Articles
Hydrological alterations due to anthropogenic activities in Krishna River Basin, India
T
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A. Uday Kumar , K.V. Jayakumar Department of Civil Engineering, National Institute of Technology, Warangal 506004, India
A R T I C LE I N FO
A B S T R A C T
Keywords: Hydrological alteration Krishna River Range of Variability Approach (RVA) Ecosystem
The present study has been taken up to quantify the impacts of the anthropogenic activities on the hydrology of the middle and lower stretches of the Krishna River over the past sixty years. The Flow Health (FH) method which is based on the Range of Variability Approach (RVA) is used to quantify the hydrological alteration (flow changes) of different flow characteristics. The flow characteristics of pre- and post-dam impact periods are compared and evaluated to understand the ecologically sensitive streamflow parameters. The study is primarily focussed on the impact of human activities such as dam constructions. The wet and dry periods are excluded as they are impacted by climate variability. Results of the study confirmed that the impact of the Nagarjuna Sagar on the Krishna River Basin is the highest among the five dams studied, with an average FH score of 0.54 and that of PD Jurala dam is the lowest, with an average FH score of 0.65. This study will be beneficial to help restore regional water resources and eco-environmental system in the middle and lower Krishna River Basin.
1. Introduction Rivers provide basic natural needs for all life activities and ecological processes in nature. It is widely acknowledged that the maintenance of the natural flow in a river is needed in order to maintain the natural structure and function of a river (Bradford and Heinonen, 2008). Naturally, hydrological process and functioning of a river are extremely important to maintain a healthy aquatic environment and promote human well-being through ecosystem services (Acreman and Ferguson, 2010; Poff et al., 2010). Hydrologic processes include surface water storage and hydrodynamic balance processes. Surface water storage processes provide attenuation of high flow events, backwater areas and base flow (Boodoo et al., 2014). High flow is important for aquatic ecosystems as it provides relief from the physical stress. Backwater areas are important to provide low-velocity habitat, and they provide refuge areas during high flow periods. They also increase the contact time for biochemical processes. Maintenance of base flow is important for aquatic ecosystems as it helps to sustain longitudinal connectivity in a river and makes pathways available for organisms to migrate. The base flow provides in-stream habitat during dry periods and can maintain soil moisture during dry periods (Poff et al., 2010). Hydrodynamic process shapes the stream channel, which depends on the flow regimen character, supply, and transport of the sedimentation in the river channel (De Nooij et al., 2006).
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Rivers play an important role in the transfer and distribution of water resources from nature to the human needs. For example, the rich diversity of species in freshwater ecosystems supports economic productivity such as fisheries. Rivers are valuable sources of genetic information and promote cleaning of water (Dudgeon et al., 2006; Zhang et al., 2017). Biologically complex and functionally intact freshwater ecosystems provide essential goods and services like food supply, purification of industrial and human wastes, and flood control. Rivers help in increasing the capacity of an ecosystem to adapt to any future environmental alterations like climate change (Baron et al., 2002; Peres and Cancelliere, 2016, Pfeiffer and Ionita, 2017; Belmar et al., 2018). The ecological integrity of a riverine ecosystem depends on the natural dynamic character of the streamflow captured by the five components of the flow regime: magnitude, frequency, duration, timing, and rates of change. Thus, the flow has been identified as a master variable that controls the physical and ecological processes of the rivers. Ecological processes include activities such as nutrient cycling, movement of sediment and water. These processes interact within a system to form unique ecological characteristics such as stream morphology, stream temperature, the composition of biological communities and sedimentation. It is, therefore, necessary to protect the ecological functions, as the biotic communities within a given system depend on the processes and characteristics of flow to carry out different phases of their lives.
Corresponding author. E-mail addresses: [email protected] (A. Uday Kumar), [email protected] (K.V. Jayakumar).
https://doi.org/10.1016/j.ecolind.2019.105663 Received 11 March 2019; Received in revised form 26 July 2019; Accepted 20 August 2019 1470-160X/ © 2019 Elsevier Ltd. All rights reserved.
Ecological Indicators 108 (2020) 105663
A. Uday Kumar and K.V. Jayakumar
2. Study area
Globally, there is a drastic increase in the utilization of water (for industries, agriculture, power generation etc.,) which leads to decrease in the availability of fresh water sources (Penas et al., 2016; Wurbs and Hoffpauir, 2017). In the 20th century, the human population grew fourfold around the world which lead to increased consumption of water, as much as eight times than earlier. At present more than half of the world’s accessible surface water is used by humans, and by 2025 the magnitude is likely to increase by 70% (Jain and Kumar, 2014). Diverse flow regimes such as (magnitude, duration, timing, and frequency) among the rivers have been observed due to human activities. It was reported from various studies that the anthropogenic activities significantly affect the ecosystem and extensive modifications had occurred in the river systems and biodiversity (Yang et al., 2017; Abe and Joseph, 2015; Dudgeon, 2010; Arthington et al., 2012; ButchartKuhlmann et al., 2018; Uday Kumar and Jayakumar, 2018a). Freshwater ecosystems are the most endangered ecosystems in the world. This is because biodiversity changes in the freshwater ecosystems are greater than those of terrestrial ecosystems. In the recent years, due to huge pressure on freshwater ecosystems, more than 20% of the world’s freshwater species have become extinct. Construction of barriers such as dams and diversion weirs along the rivers are the major contributors for the exploitation of biodiversity. More than 40,000 large dams are affecting the natural flows of 60% rivers in the world which lead to a sustained threat to the ecological stability of the rivers and their associated flood wetlands (Nilsson et al., 2005; Hua and Cui, 2018; Huang et al., 2018). In providing water for humans, the freshwater requirement for species and ecosystems are largely neglected (Liu et al., 2016; Zhang et al., 2019; Liu et al., 2018). Thus, understanding and analysing the flow regime changes are important to protect the ecosystem along the rivers by improving the water resource management (Caiola et al., 2014).
2.1. Overview of Krishna River The Krishna River, which has been chosen for the current study, is the fourth-longest river in India, with a length of 1400 km and having a catchment area of 2, 60,000 km2. Its catchment extends over four political states, viz., Maharashtra, Karnataka, Telangana, and Andhra Pradesh. Over 9 million people live in the Krishna River basin and more than 6 million of them depend on the river for their drinking water needs. Droughts and floods often affect the economy of the states lying in the river basin along with the loss of human lives. Large number of dams and reservoirs were built in the Krishna River region between 1963 and 1999. The Narayanapur dam, constructed in the year 1983 and located in the middle of Krishna River, has a drainage area of 47,850 km2. It was envisaged as a single purpose project meant for irrigation, but electrical generation and drinking water considerations in the downstream have complicated its management. The PD Jurala dam constructed in 1996 is located 185 km downstream from Narayanapur dam and is used mainly for irrigation purposes. The major dams along the lower Krishna River are the Srisailam and Nagarjuna Sagar Project (NSP), constructed in the years 1984 and 1968 respectively, and these dams play vital roles in flood control as well as reduction of sediment deposition in the downstream area. Srisailam dam is located 210 Km downstream of PD Jurala and 122 Km upstream of NSP. One of the main tributaries of Krishna River is the Bhima River, across which lies the Ujjani dam built during the year 1980, to control flooding from tributaries and to provide water for irrigation. Due to the construction of a large number of dams on the main river and also across the main tributaries, the natural flow regime of the river significantly changed and zero flow conditions were observed during many years in the lower Krishna River because of the rapidly increasing water used in the upstream.
1.1. Water management issues in India Being a tropical monsoon season country, many river valley projects have been constructed for irrigation, flood control, and hydropower generation in India. Floodplains have been cut out by embankments along rivers. Land-based infrastructure development activities continue to increase sedimentation. In addition to urbanisation, industrialisation and agricultural intensification, rivers are affected with discharges of domestic, industrial effluents, fertilizers and pesticides. Due to these, rivers exist as dirty polluted streams and acquiring a substantial flow only during the short span of rainy days. Out of the 30 river basins from all over the world that were identified as world-class priorities for the protection of water biodiversity, 9 are in India, and Krishna is one such river (Jain and Kumar, 2014; Uday Kumar and Jayakumar, 2018b). Despite the growing awareness on environmental flow (EF), developing countries like India do not yet apply the concept of EF and there are significant barriers in merging with present water management practices. Planning and management of water resources in India treat the water which flows into the sea as ‘wasted’ and more importance is given to control the river water through the dams and other structures for social development. India is facing a challenge of managing its limited water resources with high spatial and temporal variability. Similarly, many countries have the lowest importance for the water needs of an aquatic atmosphere. Even in the new National Water Policy, priority for water allocation to “ecology” is listed as the fourth item. There are no studies on the relations between the flow and functioning of river ecosystems in India. Most of the studies focus on various organisms and water quality without reference to flow regimes. The objective of this study is to determine the changes in flow regime characteristics of Krishna River. These changes were identified by comparing hydrologic regime characteristics of pre-dam conditions, with the current (postdam) characteristics. This is done by removing the impacts of the climate in the basin.
2.2. Data collection and analysis 2.2.1. Data available To calculate the hydrological alteration, monthly streamflow of 5 gauge stations in the middle and lower Krishna River basin were available in the present study. These records are collected from the Central Water Commission (CWC) and separated into reference (Preconstruction) and test (Post-construction) impact periods based on the timing of the construction of the projects. The length of the monthly mean streamflow records of the reference and test periods varied among gauging stations. The locations of the dams are shown in Fig. 1, and complete information about the data is given in Table 1. 2.3. Removing of the climate variability impacts on hydrological process In the process of analysis to determine hydrological alteration, streamflow data for two different periods i.e., before and after dam construction were used. Streamflow data has inherent variability due to the impacts of dam construction and climate variability. The impact of climate variability on streamflow in the study area needs to be removed before analysing hydrological alteration. Generally, the magnitude of climate variability is characterised by high and low flows which are described as wet and dry years respectively. If the annual precipitation is more than PMean + PStandard Deviation, then the year can be called as a wet year, whereas, if the annual precipitation is less than PMean − PStandard Deviation, then the year can be called as a dry year (Chulsang, 2006). Periods with annual basin precipitation in between PMean + Standard Deviation and PMean − Standard Deviation are considered as the normal years. Here PMean is the annual precipitation average and PStandard Deviation is the standard deviation of annual precipitation. Thus, the streamflow records corresponding to the normal years are considered in the hydrological alteration assessment as shown in Fig. 2. 2
Ecological Indicators 108 (2020) 105663
A. Uday Kumar and K.V. Jayakumar
Fig. 1. Study area and location of dams.
3. Methodology The Flow Health (FH) method, which is conceptually based on the Range of Variability Approach (RVA), is used to calculate the degree of departure from the natural flow regime due to the dams (Richter et al., 1997). FH calculates the ecological importance in the form of nine hydrological indicators, which are explained in Sections 3.1–3.7. These indicators are calculated by comparing the flow of the reference period with the flow of the test period, and the FH scores are calculated for each of the nine indicators. The FH score values of the indicators lie between ‘1’ and ‘0’, where a score of ‘1’ represents conditions close to the reference period, while a score of 0 represents a farther condition with reference to the reference period. Ranges of FH score and their alteration condition are shown in Table 2. An overall FH score is calculated by combining scores of all the nine indicators. The reference period is the benchmark and its flow data are unimpaired through regulation. FH components are assessed as dimensionless parameters which can be globally applied to any river system. These indicators are developed based on the flow duration curve, which is the graphical representation of discharge versus exceedance probability. To calculate the FH score for all the indicators, FDC of the reference period and test period are compared from the top to bottom. Based on the importance of the indicators, the threshold percentile for each indicator is defined differently. The methodology and concept of using the FH score follow Gippel et al. (2009).
Fig. 2. Water year separation of the streamflow time-series for Krishna River in the middle and lower basin (in terms of the results and recommendation for threshold of wet/dry year by Chulsang (2006)). Table 2 Flow health Score range and its alteration condition.
3.1. Low flow (LF) and lowest monthly (LM) flow
Sl. No
Flow Health Score
Alteration Condition
1 2 3 4 5
0.8–1 0.6–0.8 0.4–0.6 0.2–0.4 0.0–0.2
Very small Small Moderate High Very high
above 75th percentile are considered as high flows and flow below the 25th percentile are considered as extreme low flows. Therefore, if the sum of six low flow months’ values in a test year exceeds the 75th percentile value in the reference period, then the score is calculated by using Eq. (1). If it is less than the 25th percentile value in the reference
LF indicator score of the test year is calculated based on the sum of six low flow months’ values (January–June) and its percentile lies in the reference period. The threshold percentile for LF and LM are taken in between 25th to 75th percentile. The flows in the low flow season Table 1 Major dams in Krishna River. S. No
Dam
River
1 2 3 4 5
Narayanapur Dam Ujjani Dam P D Jurala Srisailam Dam Nagarjuna Sagar Project (NSP)
Krishna Bhima Krishna Krishna Krishna
upper basin Middle Sub-basin Middle Sub-basin Lower Sub-basin
Down Stream Gauge station location (Data Available)
Reference Period
Test Period
DE Sugar (1966–2014) Yadagir (1965–2014) K. Agraharam (1971–2010) Nagarjuna Sagar Project (NSP) (1968–2014) Vijayawada (1960–2014)
1966–1983 1965–1980 1971–1996 1968–1984 1960–1970
1984–2014 1981–2014 1997–2010 1985–2014 1971–2014
3
Ecological Indicators 108 (2020) 105663
A. Uday Kumar and K.V. Jayakumar
Fig. 3. Working flow chart of Low flow, lowest monthly flow, high flow, highest monthly flow.
respectively
period then the score is calculated by using Eq. (2). If the value of the percentile reference distribution is in between the 25th and 75th percentiles, then the score will be ‘1’. The LM flow score is calculated in a similar way to the LF score but based on lowest monthly flow value of the year and its percentile lies in the reference period (Gippel et al., 2009). The flow chart given in Fig. 3 explains the working procedure for LF and LM periods. If the flow is greater than 75th percentile in low flow season
Low flow Season score = 1.75 −
Percentile 100
Score = 4 ×
Percentile 100
(2)
3.3. Persistently lower (PL) flow The PL flow index is used to calculate the low flow effects for the entire year (12 months). The threshold percentile for PL is taken as the 25th percentile because flows below the 25% percentile are considered as low flows. During the reference period, if the flow in the test year exceeds the 25th percentile, then a value ‘0’ is assigned on a specific month. If the magnitude of flow is below the 25th percentile, then a value of ‘1’ is assigned for that month. The score is ‘0’ if the cumulative sum of the test year is 12 (if 12 months flow value falls below < 25th percentile, it means then the cumulative sum will be 12). If the cumulative total is less than or equal to ‘1’, then the test year score is ‘1’. For the cumulative sum lying between 1 and 12, the score is calculated by using Eq. (3). The flow chart given in Fig. 4 explains the working procedure of PL flow (Gippel et al., 2009). For PL, 1 > Cumulative total < 12
(1)
3.2. High flow (HF) and highest monthly (HM) flow HF volume test year score is calculated based on the sum of the six high flow months’ values (July–December) and its percentile lies in the reference period. The threshold percentile for HF and HM is taken as the 25th percentile because flows above the 25% percentile in high flow season are considered as low flows. If the sum of six high flow months’ values in the test year exceeds the 25th percentile value in the reference period, then the score is ‘1’ and if it is less than the 25th percentile value of the reference period, then the score is calculated by using Eq. (2). The HM flow score is calculated in a similar way to the HF score but based on the highest monthly value of the year and its percentile lies in the reference period. The flow chart given in Fig. 3 explains the working procedure for HF and HM flow conditions (Gippel et al., 2009). If the flow is less than 25th percentile in low and high flow season
Score = 1.0909 − 0.0909 × Cumulative total
(3)
3.4. Persistently higher (PH) flow PH indicator is used to calculate high flow effects during the low flow season (6 months) January–June. The thresholds percentile for PH 4
Ecological Indicators 108 (2020) 105663
A. Uday Kumar and K.V. Jayakumar
Fig. 4. Working flow chart of persistently lower flow, persistently higher flow, persistently very low flow.
is taken as the 75th percentile because flows above the 75th percentile occur mostly in the high flow season only; if they occur in the low flow season, they will damage the ecology. During the reference period, if the test year monthly mean flow value in the low flow seasons exceeds the 75th percentile, then assign a value of 1 for that month. If the value of flow is below the 75th percentile, then assign a value 0 for that month. The positive score across the low flow season (6 months) is counted and if the cumulative total value of the test year is 6, then the score is ‘0’. If the cumulative total is less than or equal to 1 then the test year score is ‘1’. If the cumulative total is greater than 1 and up to 6, then the score is calculated by using Eq. (4). The flow chart given in Fig. 4 explains the working procedure of PH flow (Gippel et al., 2009). PH, 1 > Cumulative total ≤ 6
Score = 1.2 − −0.2 × (Cumulative total)
less than or equal to the 10th percentile in the reference period. Below the 10th percentile, flows are the minimum flows which can maintain minimum water quality and oxygen levels in the river. If the monthly mean flow value in the test period is greater than the 10th percentile in the reference period, then a value of 0 is assigned, and if it is less than the 10th percentile, a value of 1 is assigned. The score is ‘1’ if the cumulative sum of the test year is 1. If the cumulative total is greater than or equal to 6, then the test year score is ‘0’. For seasons having cumulative sum between 1 and 6, the score is calculated by using Eq. (5). The flow chart given in Fig. 4 explains the working procedure for PVL flow (Gippel et al., 2009). For PV, 1 > Cumulative total < 6
Score = 1 −
(4)
3.5. Persistently very low (PVL) flow
Cumulative Total 6
(5)
3.6. Seasonality flow shift (SFS)
PVL score of the test year is calculated by assuming that the flow is
SFS indicator is used to calculate the shifting of flow during a season 5
Ecological Indicators 108 (2020) 105663
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4.2. Highest monthly flow (HM)
to other times of the year. By controlling the flow of rivers with barriers, seasonality of flow changes significantly. The score is calculated by using Eqs. (6) and (7). If the flow is less than the 75th percentile
The mean score value of HM decreased from 0.83 (very small impact) in the reference period to 0.40 (large impact) in the test period. The scores of HM for all dams in the test period ranged from 0.30 to 0.47 which is less than that of the reference period range from 0.81 to 0.85. This indicates that large floods totally disappeared and small floods decreased in terms of the magnitude and occurrence. Large alterations were observed in the case of Narayanapur, Srisailam and NSP dams with HM scores of 0.30, 0.39 and 0.38 respectively. Moderate alterations were observed for PD Jurala and Ujjani dams with HM scores of 0.47 and 0.46 respectively. In the case of Srisailam and NSP, flow health score of HM is high because these dams are located in the lower basin. Most of the flood water gets collected on the upstream basin. Due to this, major useful ions travelling from upstream to downstream decrease and heavy metals deposit increases, which further leads to deterioration of the aquatic body. In the reference impact period, the sediment along Srisailam and NSP sections were basically transported from upstream runoff. Due to the construction of reservoirs on the upstream, the sediment load on the downstream side has decreased. The sedimentation in the downstream river channel after the construction of a reservoir was mainly the silt scoured off from the riverbed. Due to less sedimentation, the physical characteristic of the channel has changed corresponding to the change in the relationship between water level and river flow.
(6)
Score = 1 If the flow is greater than the 75th percentile
Percentile ⎞ Score = 4 − 4 ∗ ⎛ ⎠ ⎝ 100
(7)
3.7. Flood flow interval (FFI) The FFI is used to calculate the 6 years return intervals for both periods and compared their values. The default threshold for FFI is a return interval of 6 years which is generally accepted as bank full flow. The score is calculated by using Eqs. (8)–(10). FFI case 1: If N < 48 (8)
Score = 1 FFI case 2: If N > 96
(9)
Score = 0 FFI case 3: If 48 < N < 96
N Score = 2 − ⎛ ⎞ ⎝ 48 ⎠
4.3. Low flow (LF) and lowest monthly (LM) flow
(10)
N is the floods’ frequency in months and by default it is 48 months.
It is seen that there is about 8% of the variation in the mean score of LF indicator when compared to the test and reference period (score change from 0.81 to 0.75). The observed variation of LF for Narayanapur changed positively, with the FH scores varying from 0.80 to 0.88 in reference and test impact periods respectively. The flows coming from the Narayanapur dam during 2000–2014 in low flow season was more flat with a relatively smaller volume and with fewer fluctuations. Flat flows are more favourable for those low flow-enduring aquatic plants and small fluctuation-appetite plants. But information regarding the water level fluctuation is also very important with regard to its impact on the function of the river ecosystem, especially in the growth and distribution of aquatic plants. Ujjani has high alteration among all the dams. LF score of Ujjani decreased by 28% from the reference impact period. Low alteration score values are observed in remaining dams, due to the release of water for irrigation and hydro-power during low flow season. Lowest monthly (LM) flow rate of Ujjani has a score of 0.57 and 0.80 during the test impact period and the reference period respectively, which indicate that more low flow frequencies are irregular at these places. During the test period, LM scores of remaining stations range from 0.81 to 0.84, which indicates that continuous hydropower operations are taking place in the river during the dry season. The information regarding the low flows is helpful for
4. Results 4.1. High flow (HF) In all five stations, the flow health scores of HF in the test period show comparatively lesser values than that in the reference period. The HF scores for all stations in the test period range from 0.24 to 0.4, which are lower than that of the reference period range which is from 0.82 to 0.85, indicating that high flow months are strongly influenced by the fluctuations in reservoir operations in the test period. The HF scores of NSP and Srisailam are 0.82 and 0.85 respectively in the reference period and 0.24 and 0.34 respectively in the test period, as seen from Table 3. This large alteration is mainly attributed to the obstruction caused by dams for irrigation and drinking water supply in high flow seasons. The HF scores of NSP were highly altered after 1999, most of which were having values less than 0.3 (large alteration) resulting in a long-term average score value of 0.24. In addition, the hydrological indicator scores of HF for Srisailam, PD Jurala, Narayanapur and Ujjani were altered to FH scores of 0.34, 0.37, 0.30 and 0.40 respectively. The overall mean score value of five stations decreased by 60%.
Table 3 Comparison of results of Flow health score card for five stations along the Krishna River. Indicator/Dam
High flow (HF) Highest monthly (HM) Low flow (LF) Lowest monthly (LM) Persistently higher (PH) Persistently lower (PL) Persistently very low (PVL) Seasonality flow shift (SFS) Flood flow interval (FFI) Flow health score (FH)
Narayanapur Dam
PD Jurala
Ujjani
Ref
Test
Ref
Test
Ref
Test
Ref
0.83 0.83 0.80 0.80 0.79 0.82 0.96 0.76 1 0.84
0.30 0.30 0.88 0.82 0.59 0.63 0.94 0.40 0.77 0.62
0.84 0.84 0.80 0.80 0.82 0.83 0.97 0.79 0.97 0.85
0.37 0.47 0.79 0.82 0.54 0.72 0.98 0.36 0.88 0.65
0.84 0.84 0.81 0.80 0.78 0.84 0.97 0.80 0.97 0.85
0.40 0.46 0.58 0.57 0.59 0.56 0.99 0.47 0.76 0.59
0.85 0.85 0.81 0.81 0.85 0.80 0.97 0.78 0.92 0.85
6
Srisailam
Nagarjuna Sagar Dam
Mean
Test
Ref
Test
Ref
Test
0.34 0.39 0.76 0.81 0.30 0.76 0.95 0.07 0.71 0.56
0.82 0.81 0.86 0.84 0.88 0.86 0.98 0.74 0.94 0.86
0.24 0.38 0.74 0.84 0.31 0.64 0.94 0.1 0.69 0.54
0.84 0.83 0.81 0.81 0.82 0.83 0.97 0.77 0.96 0.85
0.33 0.40 0.75 0.77 0.47 0.66 0.96 0.28 0.76 0.59
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by greater than 100% of that of the reference-impact period. The occurrence of high flows during the low flow season can adversely affect the ecology of the river and those species that are seasonal. In addition, the overall monthly streamflow changed more significantly in the lower reach of the river than in the upper reach. This is because of releasing of water from upstream for irrigation and hydro-power in the low flow seasons. Among the 12 months, December can be considered as a transition month, the change of flood period to dry period in this region. Due to the construction and operation of dams along the Krishna River, the mean monthly streamflow got alter. Decreasing river flow affects the flushing property and increases sediment deposit at one place. As a result, fish population decreases, which leads to loss of breeding and nursery grounds. Seasonal flow shift represents the shift in flow from one month to another month. Analyses show that moderate alteration was observed for Ujjani with SFS score of 0.47 FH, large alteration for Narayanapur and PD Jurala with SFS score of 0.4 FH and 0.36 FH respectively. Very large alteration was observed for Srisailam and NSP with FH scores of 0.07 and 0.1 respectively. The largest decrease in mean annual flow (MAF) was observed for PD Jurala with 46.6%, while the lowest decrease in mean annual flow was observed for the Narayanapur with 28.3%. For remaining dams, the flow decreased by 36.2%, 31.93%, and 32.29% for Ujjani, Srisailam and NSP respectively. This indicates that MAF in Krishna River decreases by more than 30%. The most important driving force for streamflow regime changes in the Krishna River is the construction of the dams to meet the growing demands of the increasing population.
satisfied the many processes in the riverine ecosystem functioning. If the low flow gets altered to extremely low levels, ecological communities will get damaged. 4.4. Persistently higher (PH), persistently lower (PL) and persistently very low (PVL) flows There is a significant change in the mean value of PH from the reference period to the test period, and the FH score changed from 0.82 (very small) to 0.47 (moderate). It shows that in the low flow season, high flow frequencies (greater than75% per cent flow) are increased due to hydroelectric activity. This can lead to waterlogging problems and obstruction the enrolment of exposed soils. PH scores of Srisailam and NSP alter largely as 0.30 and 0.31 respectively. Narayanapur, PD Jurala, and Ujjani have alterations in moderate condition with FH scores of 0.59, 0.54 and 0.59 respectively. This explains that continuous high flows are occurring in low flow seasons. It changes the river hydrological system affecting the lives adapted to a particular season. Mean of persistently lower (PL) indicator of five dams have altered from very small FH (0.83) to small FH (0.66). For Ujjani, the PL score decreased by 33.33% of FH from the reference impact period and altered into moderate condition. For remaining stations, the score ranges from 0.63 to 0.76. This type of flows is very important in maintaining water quality and dissolved oxygen in a river. The overall PL analysis shows that the river loses its water quality in the entire basin which affects not only the environment but also human beings who are depending on the river. For all stations, PVL (< 10% percentile flows) altered very slightly which ranged from 0.94 to 0.99 in the test period. These type of flows can be more helpful at extremely low flow condition to many organisms by maintaining minimum oxygen levels at the highly stressful condition.
4.6. Flood flow interval (FFI) The natural hydrologic regime of most rivers is characterized by regular floods, which can strongly influence the distribution and abundance of aquatic organisms. Floods can provide a chance for fish and other organisms to move from rivers to flood plains to access additional habitats. It is seen from the present study that the overall FFI mean score of 5 dams decreased by 26% of FH, which indicates a reduction in fish and other organisms. For each hydrological station, the overall FH score was determined by calculating the average of 9 hydrological indicators. The results show that the hydrological regime was least affected in the middle reach, moderately affected in Bhima and lower Krishna basins. The analysis shows that FH increases with an increase in the distance from the dam. The mean FH score ranged from 0.85 to 0.86 in reference impact period from 0.54 to 0.62 in the test period. The overall average FH score of reference and test impact period was 0.84 and 0.59 respectively. This indicated that even in the reference period, flow
4.5. Seasonality flow shift (SFS) and mean monthly flow Monthly average flow and deviation factor for the reference period and test period are presented in Table 4 which shows that the monthly streamflow had decreased during June to December and has increased from January to May. Similar flow variability was observed in both the reaches. The mean monthly flow in the low flow season showed significantly upward trend than in the reference period. The largest positive relative alteration rate was observed in the low flow season months such as February, March, April and May. For Ujjani, Srisailam and NSP dams, flows are increased during these months. The mean annual flow (MAF) of Srisailam and NSP for February and March have increased by more than 250% and for January and April have increased Table 4 Mean monthly flow and Deviation factor (DF). Month
January February March April May June July August September October November December
Narayanapur
Ujjani
PD Jurala
Srisailam
Nagarjuna Sagar Dam
Ref m3/s
Test m3/s
DF
Ref m3/s
Test m3/s
DF
Ref m3/s
Test m3/s
DF
Ref m3/s
Test m3/s
DF
Ref m3/s
Test m3/s
DF
40 46 33 12 27 383 2285 3116 1279 433 130 65
50 48 42 32 26 142 1338 2128 1014 634 109 59
25 4 27 167 −4 −63 −41 −32 −21 46 −16 −9
45 27 15 7 15 184 636 1119 1105 955 255 100
32 49 30 54 110 249 342 643 704 367 117 114
−30 82 100 700 626 35 −46 −43 −36 −62 −54 14
59 50 41 28 26 398 2653 3647 2194 1409 292 90
72 80 70 56 53 94 732 2276 1092 1046 153 80
22 60 71 100 104 −76 −72 −38 −50 −26 −48 −11
161 122 91 83 86 480 2807 4944 3404 2533 701 263
382 465 440 239 109 169 1038 2795 2354 1759 549 370
137 282 384 187 27 −65 −63 −43 −31 −31 −22 41
161 122 91 83 86 491 2693 4781 3386 2453 686 253
359 437 417 231 105 160 1042 2706 2211 1693 528 359
123 258 358 178 22 −67 −61 −43 −35 −31 −23 42
DF (Deviation Factor) = ((Test-Ref)/Ref)*100. Ref = Reference impact period (Before dam construction). Test = Test impact period (After dam construction). 7
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regimes do not attain the complete FH score due to the changes in the flow regimes. This study recommends to restore the natural flow regime characteristics in order to meet future water demands, both instream flow requirements and water development should be considered simultaneously.
cwrj3302165. Butchart-Kuhlmann, D., Kralisch, S., Fleischer, M., Meinhardt, M., Brenning, A., 2018. Multicriteria decision analysis framework for hydrological decision support using environmental flow components. Ecol. Ind. 93, 470–480. https://doi.org/10.1016/j. ecolind.2018.04.057. Caiola, N., Ibanez, C., Verdu, J., Munne, A., 2014. Effects of flow regulation on the establishment of alien fish species: a community structure approach to biological validation of environmental flows. Ecol. Ind. 45, 598–604. https://doi.org/10.1016/j. ecolind.2014.05.012. Chulsang, Yoo, 2006. Long term analysis of wet and dry years in Seoul, Korea. J. Hydrol. 318 (1–4), 24–36. https://doi.org/10.1016/j.jhydrol.2005.06.002. De Nooij, R.J.W., Verberk, W.C.E.P., Lenders, H.J.R., Leuven, R.S.E.W., Nienhuis, P.H., 2006. The importance of hydrodynamics for protected and endangered biodiversity of lowland rivers. Hydrobiologia 565 (1), 153–162. https://doi.org/10.1007/s10750005-1911-9. Dudgeon, D., 2010. Prospects for sustaining freshwater biodiversity in the 21st century: linking ecosystem structure and function. Curr. Opin. Environ. Sustainab. 2 (5–6), 422–430. https://doi.org/10.1016/j.cosust.2010.09.001. Dudgeon, D., Arthington, A.H., Gessner, M.O., Kawabata, Z.I., Knowler, D.J., Leveque, C., Naiman, R.J., Prieur-Richard, A.H., Soto, D., Stiassny, M.L., Sullivan, C.A., 2006. Freshwater biodiversity: importance, threats, status and conservation challenges. Biol. Rev. 81 (2), 163–182. https://doi.org/10.1017/S1464793105006950. Gippel, C.J., Cosier, M., Markar, S., Liu, C., 2009. Balancing environmental flows needs and water supply reliability. Water Resour. Develop. 25 (2), 331–353. https://doi. org/10.1080/07900620902868802. Hua, Y., Cui, B., 2018. Environmental flows and its satisfaction degree forecasting in the Yellow River. Ecol. Ind. 92, 207–220. https://doi.org/10.1016/j.ecolind.2017.02. 017. Huang, W., Liu, X., Peng, W., Wu, L., Yano, S., Zhang, J., Zhao, F., 2018. Periphyton and ecosystem metabolism as indicators of river ecosystem response to environmental flow restoration in a flow-reduced river. Ecol. Ind. 92, 394–401. https://doi.org/10. 1016/j.ecolind.2017.11.025. Jain, S.K., Kumar, P., 2014. Environmental flows in India: towards sustainable water management. Hydrol. Sci. J. 59 (3–4), 751–769. https://doi.org/10.1080/02626667. 2014.896996. Liu, J., Liu, Q., Yang, H., 2016. Assessing water scarcity by simultaneously considering environmental flow requirements, water quantity, and water quality. Ecol. Ind. 60, 434–441. https://doi.org/10.1016/j.ecolind.2015.07.019. Liu, Y., Cao, S., Yang, Y., Zhang, X., 2018. Assessment of hydrologic regime considering the distribution of hydrologic parameters. Water Sci. Technol. Water Supply 18 (3), 875–885. https://doi.org/10.2166/ws.2017.161. Nilsson, C., Reidy, C.A., Dynesius, M., Revenga, C., 2005. Fragmentation and flow regulation of the world’s large river systems. Science 308 (5720), 405–408. https://doi. org/10.1126/science.1107887. Penas, F.J., Barquin, J., Alvarez, C., 2016. Assessing hydrologic alteration: evaluation of different alternatives according to data availability. Ecol. Ind. 60, 470–482. https:// doi.org/10.1016/j.ecolind.2015.07.021. Peres, D.J., Cancelliere, A., 2016. Environmental flow assessment based on different metrics of hydrological alteration. Water Resour. Manage. 30 (15), 5799–5817. https://doi.org/10.1007/s11269-016-1394-7. Pfeiffer, M., Ionita, M., 2017. Assessment of hydrologic alterations in Elbe and Rhine Rivers, Germany. Water 9 (9), 684–694. https://doi.org/10.3390/w9090684. Poff, N.L., Richter, B.D., Arthington, A.H., Bunn, S.E., Naiman, R.J., Kendy, E., Acreman, M., Apse, C., Bledsoe, B.P., Freeman, M.C., Henriksen, J., 2010. The ecological limits of hydrologic alteration (ELOHA): a new framework for developing regional environmental flow standards. Freshw. Biol. 55 (1), 147–170. https://doi.org/10.1111/ j.1365-2427.2009.02204.x. Richter, B., Baumgartner, J., Wigington, R., Braun, D., 1997. How much water does a river need? Freshw. Biol. 37 (1), 231–249. https://doi.org/10.1046/j.1365-2427. 1997.00153.x. Uday Kumar, A., Jayakumar, K.V., 2018a. Assessment of hydrological alteration and environmental flow requirements for Srisailam dam on Krishna River, India. Water Policy 20 (6), 1176–1190. https://doi.org/10.2166/wp.2018.203. Uday Kumar, A., Jayakumar, K.V., 2018b. Assessment of environmental flows using hydrological methods for Krishna River, India. Adv. Environ. Res. 7 (3), 161–175. https://doi.org/10.12989/aer.2019.7.3.161. Wurbs, R.A., Hoffpauir, R.J., 2017. Environmental flow requirements in a water availability modelling system. Sustainability Water Qual. Ecol. 9, 9–21. https://doi.org/ 10.1016/j.swaqe.2016.05.003. Yang, N., Zhang, Y., Duan, K., 2017. Effect of Hydrologic Alteration on the Community Succession of Macrophytes at Xiangyang Site. Scientifica, Hanjiang River, China https://doi.org/10.1155/2017/4083696. Zhang, H., Sun, T., Xue, S., Yang, W., Yin, X., 2017. Environmental flow assessment in estuaries taking into consideration species dispersal in fragmented potential habitats. Ecol. Ind. 78, 541–548. https://doi.org/10.1016/j.ecolind.2017.03.042. Zhang, W., Jia, Y., Ge, J., Huang, X., Ni, G., Hou, J., Wang, H., 2019. Multi-index data dimension reduction approach and its applicability in the calculation of indicators of hydrological alteration. Hydrol. Res. 50 (1), 231–243. https://doi.org/10.2166/nh. 2018.068.
5. Conclusion The present study assessed hydrological alterations in the form of nine hydrological indicators based on the monthly average flows, timing, duration and frequency. The results indicate that hydrological alterations in the Krishna River, one of the largest rivers in South India, are consequences of human activities. Low flow seasons show a significant positive change and high flow seasons show negative changes from the natural flow condition. Such changes impact aquatic and riparian habitat and adversely affect the key species in the river and surrounding environment. The mean monthly flows of the Srisailam and NSP dams have decreased significantly during the months of July, August and September. The mean annual flow in the Krishna River decreased by more than 30%. Reduction in the overall flow from the dam for providing irrigation and domestic water demands was observed and this may be worsened by climate changes and population growth in future. The dams are having a great influence on hydrological variability and the seasonality of river flow. The impact of PD Jurala dam on the hydrological regime is comparatively small, with an average mean FH value of 0.65, the last position among the five dams. The average value of FH score for the Srisailam and NSP reservoirs are 0.56 and 0.54, ranking in first and second among the five dams in the study area. Sediment deposition in the lower reaches of the basin has significantly changed the natural flow regimes in the downstream of the river reach. Future other changes such as sediment yield, water temperature, nutrient influx and water quality parameter are also needed to be monitored for a better understanding of the ecosystem impacts due to human activates in a river. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecolind.2019.105663. References Abe, G., Joseph, J.E., 2015. Changes in streamflow regime due to anthropogenic regulations in the humid tropical Western Ghats, Kerala State, India. J. Mountain Sci. 12 (2), 456–470. https://doi.org/10.1007/s11629-013-2764-8. Acreman, M.C., Ferguson, A.J.D., 2010. Environmental flows and the European water framework directive. Freshw. Biol. 55 (1), 32–48. https://doi.org/10.1111/j.13652427.2009.02181.x. Arthington, A.H., Zhang, Y., Bunn, S.E., Mackay, S., Xia, J., Kennard, M., 2012. Classification of flow regimes for environmental flow assessment in regulated rivers: the Huai River Basin, China. River Res. Appl. 28 (7), 989–1005. https://doi.org/10. 1002/rra.1483. Baron, J.S., Poff, N.L., Angermeier, P.L., Dahm, C.N., Gleick, P.H., Hairston, N.G., Jackson, R.B., Johnston, C.A., Richter, B.D., Steinman, A.D., 2002. Meeting ecological and societal needs for freshwater. Ecol. Appl. 12 (5), 1247–1260. https://doi.org/10. 1890/1051-0761(2002) 012[1247:MEASNF]2.0.CO;2. Belmar, O., Vila-Martinez, N., Ibanez, C., Caiola, N., 2018. Linking fish-based biological indicators with hydrological dynamics in a Mediterranean river: relevance for environmental flow regimes. Ecol. Ind. 95, 492–501. https://doi.org/10.1016/j. ecolind.2018.06.073. Boodoo, K.S., McClain, M.E., Upegui, J.J.V., Lopez, O.L.O., 2014. Impacts of implementation of Colombian environmental flow methodologies on the flow regime and hydropower production of the Chinchiná River, Colombia. Ecohydrol. Hydrobiol. 14 (4), 267–284. https://doi.org/10.1016/j.ecohyd.2014.07.001. Bradford, M.J., Heinonen, J.S., 2008. Low flows, instream flow needs and fish ecology in small streams. Canadian Water Resour. J. 33 (2), 65–180. https://doi.org/10.4296/
8
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Original Articles
Hydrological alterations due to anthropogenic activities in Krishna River Basin, India
T
⁎
A. Uday Kumar , K.V. Jayakumar Department of Civil Engineering, National Institute of Technology, Warangal 506004, India
A R T I C LE I N FO
A B S T R A C T
Keywords: Hydrological alteration Krishna River Range of Variability Approach (RVA) Ecosystem
The present study has been taken up to quantify the impacts of the anthropogenic activities on the hydrology of the middle and lower stretches of the Krishna River over the past sixty years. The Flow Health (FH) method which is based on the Range of Variability Approach (RVA) is used to quantify the hydrological alteration (flow changes) of different flow characteristics. The flow characteristics of pre- and post-dam impact periods are compared and evaluated to understand the ecologically sensitive streamflow parameters. The study is primarily focussed on the impact of human activities such as dam constructions. The wet and dry periods are excluded as they are impacted by climate variability. Results of the study confirmed that the impact of the Nagarjuna Sagar on the Krishna River Basin is the highest among the five dams studied, with an average FH score of 0.54 and that of PD Jurala dam is the lowest, with an average FH score of 0.65. This study will be beneficial to help restore regional water resources and eco-environmental system in the middle and lower Krishna River Basin.
1. Introduction Rivers provide basic natural needs for all life activities and ecological processes in nature. It is widely acknowledged that the maintenance of the natural flow in a river is needed in order to maintain the natural structure and function of a river (Bradford and Heinonen, 2008). Naturally, hydrological process and functioning of a river are extremely important to maintain a healthy aquatic environment and promote human well-being through ecosystem services (Acreman and Ferguson, 2010; Poff et al., 2010). Hydrologic processes include surface water storage and hydrodynamic balance processes. Surface water storage processes provide attenuation of high flow events, backwater areas and base flow (Boodoo et al., 2014). High flow is important for aquatic ecosystems as it provides relief from the physical stress. Backwater areas are important to provide low-velocity habitat, and they provide refuge areas during high flow periods. They also increase the contact time for biochemical processes. Maintenance of base flow is important for aquatic ecosystems as it helps to sustain longitudinal connectivity in a river and makes pathways available for organisms to migrate. The base flow provides in-stream habitat during dry periods and can maintain soil moisture during dry periods (Poff et al., 2010). Hydrodynamic process shapes the stream channel, which depends on the flow regimen character, supply, and transport of the sedimentation in the river channel (De Nooij et al., 2006).
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Rivers play an important role in the transfer and distribution of water resources from nature to the human needs. For example, the rich diversity of species in freshwater ecosystems supports economic productivity such as fisheries. Rivers are valuable sources of genetic information and promote cleaning of water (Dudgeon et al., 2006; Zhang et al., 2017). Biologically complex and functionally intact freshwater ecosystems provide essential goods and services like food supply, purification of industrial and human wastes, and flood control. Rivers help in increasing the capacity of an ecosystem to adapt to any future environmental alterations like climate change (Baron et al., 2002; Peres and Cancelliere, 2016, Pfeiffer and Ionita, 2017; Belmar et al., 2018). The ecological integrity of a riverine ecosystem depends on the natural dynamic character of the streamflow captured by the five components of the flow regime: magnitude, frequency, duration, timing, and rates of change. Thus, the flow has been identified as a master variable that controls the physical and ecological processes of the rivers. Ecological processes include activities such as nutrient cycling, movement of sediment and water. These processes interact within a system to form unique ecological characteristics such as stream morphology, stream temperature, the composition of biological communities and sedimentation. It is, therefore, necessary to protect the ecological functions, as the biotic communities within a given system depend on the processes and characteristics of flow to carry out different phases of their lives.
Corresponding author. E-mail addresses: [email protected] (A. Uday Kumar), [email protected] (K.V. Jayakumar).
https://doi.org/10.1016/j.ecolind.2019.105663 Received 11 March 2019; Received in revised form 26 July 2019; Accepted 20 August 2019 1470-160X/ © 2019 Elsevier Ltd. All rights reserved.
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2. Study area
Globally, there is a drastic increase in the utilization of water (for industries, agriculture, power generation etc.,) which leads to decrease in the availability of fresh water sources (Penas et al., 2016; Wurbs and Hoffpauir, 2017). In the 20th century, the human population grew fourfold around the world which lead to increased consumption of water, as much as eight times than earlier. At present more than half of the world’s accessible surface water is used by humans, and by 2025 the magnitude is likely to increase by 70% (Jain and Kumar, 2014). Diverse flow regimes such as (magnitude, duration, timing, and frequency) among the rivers have been observed due to human activities. It was reported from various studies that the anthropogenic activities significantly affect the ecosystem and extensive modifications had occurred in the river systems and biodiversity (Yang et al., 2017; Abe and Joseph, 2015; Dudgeon, 2010; Arthington et al., 2012; ButchartKuhlmann et al., 2018; Uday Kumar and Jayakumar, 2018a). Freshwater ecosystems are the most endangered ecosystems in the world. This is because biodiversity changes in the freshwater ecosystems are greater than those of terrestrial ecosystems. In the recent years, due to huge pressure on freshwater ecosystems, more than 20% of the world’s freshwater species have become extinct. Construction of barriers such as dams and diversion weirs along the rivers are the major contributors for the exploitation of biodiversity. More than 40,000 large dams are affecting the natural flows of 60% rivers in the world which lead to a sustained threat to the ecological stability of the rivers and their associated flood wetlands (Nilsson et al., 2005; Hua and Cui, 2018; Huang et al., 2018). In providing water for humans, the freshwater requirement for species and ecosystems are largely neglected (Liu et al., 2016; Zhang et al., 2019; Liu et al., 2018). Thus, understanding and analysing the flow regime changes are important to protect the ecosystem along the rivers by improving the water resource management (Caiola et al., 2014).
2.1. Overview of Krishna River The Krishna River, which has been chosen for the current study, is the fourth-longest river in India, with a length of 1400 km and having a catchment area of 2, 60,000 km2. Its catchment extends over four political states, viz., Maharashtra, Karnataka, Telangana, and Andhra Pradesh. Over 9 million people live in the Krishna River basin and more than 6 million of them depend on the river for their drinking water needs. Droughts and floods often affect the economy of the states lying in the river basin along with the loss of human lives. Large number of dams and reservoirs were built in the Krishna River region between 1963 and 1999. The Narayanapur dam, constructed in the year 1983 and located in the middle of Krishna River, has a drainage area of 47,850 km2. It was envisaged as a single purpose project meant for irrigation, but electrical generation and drinking water considerations in the downstream have complicated its management. The PD Jurala dam constructed in 1996 is located 185 km downstream from Narayanapur dam and is used mainly for irrigation purposes. The major dams along the lower Krishna River are the Srisailam and Nagarjuna Sagar Project (NSP), constructed in the years 1984 and 1968 respectively, and these dams play vital roles in flood control as well as reduction of sediment deposition in the downstream area. Srisailam dam is located 210 Km downstream of PD Jurala and 122 Km upstream of NSP. One of the main tributaries of Krishna River is the Bhima River, across which lies the Ujjani dam built during the year 1980, to control flooding from tributaries and to provide water for irrigation. Due to the construction of a large number of dams on the main river and also across the main tributaries, the natural flow regime of the river significantly changed and zero flow conditions were observed during many years in the lower Krishna River because of the rapidly increasing water used in the upstream.
1.1. Water management issues in India Being a tropical monsoon season country, many river valley projects have been constructed for irrigation, flood control, and hydropower generation in India. Floodplains have been cut out by embankments along rivers. Land-based infrastructure development activities continue to increase sedimentation. In addition to urbanisation, industrialisation and agricultural intensification, rivers are affected with discharges of domestic, industrial effluents, fertilizers and pesticides. Due to these, rivers exist as dirty polluted streams and acquiring a substantial flow only during the short span of rainy days. Out of the 30 river basins from all over the world that were identified as world-class priorities for the protection of water biodiversity, 9 are in India, and Krishna is one such river (Jain and Kumar, 2014; Uday Kumar and Jayakumar, 2018b). Despite the growing awareness on environmental flow (EF), developing countries like India do not yet apply the concept of EF and there are significant barriers in merging with present water management practices. Planning and management of water resources in India treat the water which flows into the sea as ‘wasted’ and more importance is given to control the river water through the dams and other structures for social development. India is facing a challenge of managing its limited water resources with high spatial and temporal variability. Similarly, many countries have the lowest importance for the water needs of an aquatic atmosphere. Even in the new National Water Policy, priority for water allocation to “ecology” is listed as the fourth item. There are no studies on the relations between the flow and functioning of river ecosystems in India. Most of the studies focus on various organisms and water quality without reference to flow regimes. The objective of this study is to determine the changes in flow regime characteristics of Krishna River. These changes were identified by comparing hydrologic regime characteristics of pre-dam conditions, with the current (postdam) characteristics. This is done by removing the impacts of the climate in the basin.
2.2. Data collection and analysis 2.2.1. Data available To calculate the hydrological alteration, monthly streamflow of 5 gauge stations in the middle and lower Krishna River basin were available in the present study. These records are collected from the Central Water Commission (CWC) and separated into reference (Preconstruction) and test (Post-construction) impact periods based on the timing of the construction of the projects. The length of the monthly mean streamflow records of the reference and test periods varied among gauging stations. The locations of the dams are shown in Fig. 1, and complete information about the data is given in Table 1. 2.3. Removing of the climate variability impacts on hydrological process In the process of analysis to determine hydrological alteration, streamflow data for two different periods i.e., before and after dam construction were used. Streamflow data has inherent variability due to the impacts of dam construction and climate variability. The impact of climate variability on streamflow in the study area needs to be removed before analysing hydrological alteration. Generally, the magnitude of climate variability is characterised by high and low flows which are described as wet and dry years respectively. If the annual precipitation is more than PMean + PStandard Deviation, then the year can be called as a wet year, whereas, if the annual precipitation is less than PMean − PStandard Deviation, then the year can be called as a dry year (Chulsang, 2006). Periods with annual basin precipitation in between PMean + Standard Deviation and PMean − Standard Deviation are considered as the normal years. Here PMean is the annual precipitation average and PStandard Deviation is the standard deviation of annual precipitation. Thus, the streamflow records corresponding to the normal years are considered in the hydrological alteration assessment as shown in Fig. 2. 2
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Fig. 1. Study area and location of dams.
3. Methodology The Flow Health (FH) method, which is conceptually based on the Range of Variability Approach (RVA), is used to calculate the degree of departure from the natural flow regime due to the dams (Richter et al., 1997). FH calculates the ecological importance in the form of nine hydrological indicators, which are explained in Sections 3.1–3.7. These indicators are calculated by comparing the flow of the reference period with the flow of the test period, and the FH scores are calculated for each of the nine indicators. The FH score values of the indicators lie between ‘1’ and ‘0’, where a score of ‘1’ represents conditions close to the reference period, while a score of 0 represents a farther condition with reference to the reference period. Ranges of FH score and their alteration condition are shown in Table 2. An overall FH score is calculated by combining scores of all the nine indicators. The reference period is the benchmark and its flow data are unimpaired through regulation. FH components are assessed as dimensionless parameters which can be globally applied to any river system. These indicators are developed based on the flow duration curve, which is the graphical representation of discharge versus exceedance probability. To calculate the FH score for all the indicators, FDC of the reference period and test period are compared from the top to bottom. Based on the importance of the indicators, the threshold percentile for each indicator is defined differently. The methodology and concept of using the FH score follow Gippel et al. (2009).
Fig. 2. Water year separation of the streamflow time-series for Krishna River in the middle and lower basin (in terms of the results and recommendation for threshold of wet/dry year by Chulsang (2006)). Table 2 Flow health Score range and its alteration condition.
3.1. Low flow (LF) and lowest monthly (LM) flow
Sl. No
Flow Health Score
Alteration Condition
1 2 3 4 5
0.8–1 0.6–0.8 0.4–0.6 0.2–0.4 0.0–0.2
Very small Small Moderate High Very high
above 75th percentile are considered as high flows and flow below the 25th percentile are considered as extreme low flows. Therefore, if the sum of six low flow months’ values in a test year exceeds the 75th percentile value in the reference period, then the score is calculated by using Eq. (1). If it is less than the 25th percentile value in the reference
LF indicator score of the test year is calculated based on the sum of six low flow months’ values (January–June) and its percentile lies in the reference period. The threshold percentile for LF and LM are taken in between 25th to 75th percentile. The flows in the low flow season Table 1 Major dams in Krishna River. S. No
Dam
River
1 2 3 4 5
Narayanapur Dam Ujjani Dam P D Jurala Srisailam Dam Nagarjuna Sagar Project (NSP)
Krishna Bhima Krishna Krishna Krishna
upper basin Middle Sub-basin Middle Sub-basin Lower Sub-basin
Down Stream Gauge station location (Data Available)
Reference Period
Test Period
DE Sugar (1966–2014) Yadagir (1965–2014) K. Agraharam (1971–2010) Nagarjuna Sagar Project (NSP) (1968–2014) Vijayawada (1960–2014)
1966–1983 1965–1980 1971–1996 1968–1984 1960–1970
1984–2014 1981–2014 1997–2010 1985–2014 1971–2014
3
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Fig. 3. Working flow chart of Low flow, lowest monthly flow, high flow, highest monthly flow.
respectively
period then the score is calculated by using Eq. (2). If the value of the percentile reference distribution is in between the 25th and 75th percentiles, then the score will be ‘1’. The LM flow score is calculated in a similar way to the LF score but based on lowest monthly flow value of the year and its percentile lies in the reference period (Gippel et al., 2009). The flow chart given in Fig. 3 explains the working procedure for LF and LM periods. If the flow is greater than 75th percentile in low flow season
Low flow Season score = 1.75 −
Percentile 100
Score = 4 ×
Percentile 100
(2)
3.3. Persistently lower (PL) flow The PL flow index is used to calculate the low flow effects for the entire year (12 months). The threshold percentile for PL is taken as the 25th percentile because flows below the 25% percentile are considered as low flows. During the reference period, if the flow in the test year exceeds the 25th percentile, then a value ‘0’ is assigned on a specific month. If the magnitude of flow is below the 25th percentile, then a value of ‘1’ is assigned for that month. The score is ‘0’ if the cumulative sum of the test year is 12 (if 12 months flow value falls below < 25th percentile, it means then the cumulative sum will be 12). If the cumulative total is less than or equal to ‘1’, then the test year score is ‘1’. For the cumulative sum lying between 1 and 12, the score is calculated by using Eq. (3). The flow chart given in Fig. 4 explains the working procedure of PL flow (Gippel et al., 2009). For PL, 1 > Cumulative total < 12
(1)
3.2. High flow (HF) and highest monthly (HM) flow HF volume test year score is calculated based on the sum of the six high flow months’ values (July–December) and its percentile lies in the reference period. The threshold percentile for HF and HM is taken as the 25th percentile because flows above the 25% percentile in high flow season are considered as low flows. If the sum of six high flow months’ values in the test year exceeds the 25th percentile value in the reference period, then the score is ‘1’ and if it is less than the 25th percentile value of the reference period, then the score is calculated by using Eq. (2). The HM flow score is calculated in a similar way to the HF score but based on the highest monthly value of the year and its percentile lies in the reference period. The flow chart given in Fig. 3 explains the working procedure for HF and HM flow conditions (Gippel et al., 2009). If the flow is less than 25th percentile in low and high flow season
Score = 1.0909 − 0.0909 × Cumulative total
(3)
3.4. Persistently higher (PH) flow PH indicator is used to calculate high flow effects during the low flow season (6 months) January–June. The thresholds percentile for PH 4
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Fig. 4. Working flow chart of persistently lower flow, persistently higher flow, persistently very low flow.
is taken as the 75th percentile because flows above the 75th percentile occur mostly in the high flow season only; if they occur in the low flow season, they will damage the ecology. During the reference period, if the test year monthly mean flow value in the low flow seasons exceeds the 75th percentile, then assign a value of 1 for that month. If the value of flow is below the 75th percentile, then assign a value 0 for that month. The positive score across the low flow season (6 months) is counted and if the cumulative total value of the test year is 6, then the score is ‘0’. If the cumulative total is less than or equal to 1 then the test year score is ‘1’. If the cumulative total is greater than 1 and up to 6, then the score is calculated by using Eq. (4). The flow chart given in Fig. 4 explains the working procedure of PH flow (Gippel et al., 2009). PH, 1 > Cumulative total ≤ 6
Score = 1.2 − −0.2 × (Cumulative total)
less than or equal to the 10th percentile in the reference period. Below the 10th percentile, flows are the minimum flows which can maintain minimum water quality and oxygen levels in the river. If the monthly mean flow value in the test period is greater than the 10th percentile in the reference period, then a value of 0 is assigned, and if it is less than the 10th percentile, a value of 1 is assigned. The score is ‘1’ if the cumulative sum of the test year is 1. If the cumulative total is greater than or equal to 6, then the test year score is ‘0’. For seasons having cumulative sum between 1 and 6, the score is calculated by using Eq. (5). The flow chart given in Fig. 4 explains the working procedure for PVL flow (Gippel et al., 2009). For PV, 1 > Cumulative total < 6
Score = 1 −
(4)
3.5. Persistently very low (PVL) flow
Cumulative Total 6
(5)
3.6. Seasonality flow shift (SFS)
PVL score of the test year is calculated by assuming that the flow is
SFS indicator is used to calculate the shifting of flow during a season 5
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4.2. Highest monthly flow (HM)
to other times of the year. By controlling the flow of rivers with barriers, seasonality of flow changes significantly. The score is calculated by using Eqs. (6) and (7). If the flow is less than the 75th percentile
The mean score value of HM decreased from 0.83 (very small impact) in the reference period to 0.40 (large impact) in the test period. The scores of HM for all dams in the test period ranged from 0.30 to 0.47 which is less than that of the reference period range from 0.81 to 0.85. This indicates that large floods totally disappeared and small floods decreased in terms of the magnitude and occurrence. Large alterations were observed in the case of Narayanapur, Srisailam and NSP dams with HM scores of 0.30, 0.39 and 0.38 respectively. Moderate alterations were observed for PD Jurala and Ujjani dams with HM scores of 0.47 and 0.46 respectively. In the case of Srisailam and NSP, flow health score of HM is high because these dams are located in the lower basin. Most of the flood water gets collected on the upstream basin. Due to this, major useful ions travelling from upstream to downstream decrease and heavy metals deposit increases, which further leads to deterioration of the aquatic body. In the reference impact period, the sediment along Srisailam and NSP sections were basically transported from upstream runoff. Due to the construction of reservoirs on the upstream, the sediment load on the downstream side has decreased. The sedimentation in the downstream river channel after the construction of a reservoir was mainly the silt scoured off from the riverbed. Due to less sedimentation, the physical characteristic of the channel has changed corresponding to the change in the relationship between water level and river flow.
(6)
Score = 1 If the flow is greater than the 75th percentile
Percentile ⎞ Score = 4 − 4 ∗ ⎛ ⎠ ⎝ 100
(7)
3.7. Flood flow interval (FFI) The FFI is used to calculate the 6 years return intervals for both periods and compared their values. The default threshold for FFI is a return interval of 6 years which is generally accepted as bank full flow. The score is calculated by using Eqs. (8)–(10). FFI case 1: If N < 48 (8)
Score = 1 FFI case 2: If N > 96
(9)
Score = 0 FFI case 3: If 48 < N < 96
N Score = 2 − ⎛ ⎞ ⎝ 48 ⎠
4.3. Low flow (LF) and lowest monthly (LM) flow
(10)
N is the floods’ frequency in months and by default it is 48 months.
It is seen that there is about 8% of the variation in the mean score of LF indicator when compared to the test and reference period (score change from 0.81 to 0.75). The observed variation of LF for Narayanapur changed positively, with the FH scores varying from 0.80 to 0.88 in reference and test impact periods respectively. The flows coming from the Narayanapur dam during 2000–2014 in low flow season was more flat with a relatively smaller volume and with fewer fluctuations. Flat flows are more favourable for those low flow-enduring aquatic plants and small fluctuation-appetite plants. But information regarding the water level fluctuation is also very important with regard to its impact on the function of the river ecosystem, especially in the growth and distribution of aquatic plants. Ujjani has high alteration among all the dams. LF score of Ujjani decreased by 28% from the reference impact period. Low alteration score values are observed in remaining dams, due to the release of water for irrigation and hydro-power during low flow season. Lowest monthly (LM) flow rate of Ujjani has a score of 0.57 and 0.80 during the test impact period and the reference period respectively, which indicate that more low flow frequencies are irregular at these places. During the test period, LM scores of remaining stations range from 0.81 to 0.84, which indicates that continuous hydropower operations are taking place in the river during the dry season. The information regarding the low flows is helpful for
4. Results 4.1. High flow (HF) In all five stations, the flow health scores of HF in the test period show comparatively lesser values than that in the reference period. The HF scores for all stations in the test period range from 0.24 to 0.4, which are lower than that of the reference period range which is from 0.82 to 0.85, indicating that high flow months are strongly influenced by the fluctuations in reservoir operations in the test period. The HF scores of NSP and Srisailam are 0.82 and 0.85 respectively in the reference period and 0.24 and 0.34 respectively in the test period, as seen from Table 3. This large alteration is mainly attributed to the obstruction caused by dams for irrigation and drinking water supply in high flow seasons. The HF scores of NSP were highly altered after 1999, most of which were having values less than 0.3 (large alteration) resulting in a long-term average score value of 0.24. In addition, the hydrological indicator scores of HF for Srisailam, PD Jurala, Narayanapur and Ujjani were altered to FH scores of 0.34, 0.37, 0.30 and 0.40 respectively. The overall mean score value of five stations decreased by 60%.
Table 3 Comparison of results of Flow health score card for five stations along the Krishna River. Indicator/Dam
High flow (HF) Highest monthly (HM) Low flow (LF) Lowest monthly (LM) Persistently higher (PH) Persistently lower (PL) Persistently very low (PVL) Seasonality flow shift (SFS) Flood flow interval (FFI) Flow health score (FH)
Narayanapur Dam
PD Jurala
Ujjani
Ref
Test
Ref
Test
Ref
Test
Ref
0.83 0.83 0.80 0.80 0.79 0.82 0.96 0.76 1 0.84
0.30 0.30 0.88 0.82 0.59 0.63 0.94 0.40 0.77 0.62
0.84 0.84 0.80 0.80 0.82 0.83 0.97 0.79 0.97 0.85
0.37 0.47 0.79 0.82 0.54 0.72 0.98 0.36 0.88 0.65
0.84 0.84 0.81 0.80 0.78 0.84 0.97 0.80 0.97 0.85
0.40 0.46 0.58 0.57 0.59 0.56 0.99 0.47 0.76 0.59
0.85 0.85 0.81 0.81 0.85 0.80 0.97 0.78 0.92 0.85
6
Srisailam
Nagarjuna Sagar Dam
Mean
Test
Ref
Test
Ref
Test
0.34 0.39 0.76 0.81 0.30 0.76 0.95 0.07 0.71 0.56
0.82 0.81 0.86 0.84 0.88 0.86 0.98 0.74 0.94 0.86
0.24 0.38 0.74 0.84 0.31 0.64 0.94 0.1 0.69 0.54
0.84 0.83 0.81 0.81 0.82 0.83 0.97 0.77 0.96 0.85
0.33 0.40 0.75 0.77 0.47 0.66 0.96 0.28 0.76 0.59
Ecological Indicators 108 (2020) 105663
A. Uday Kumar and K.V. Jayakumar
by greater than 100% of that of the reference-impact period. The occurrence of high flows during the low flow season can adversely affect the ecology of the river and those species that are seasonal. In addition, the overall monthly streamflow changed more significantly in the lower reach of the river than in the upper reach. This is because of releasing of water from upstream for irrigation and hydro-power in the low flow seasons. Among the 12 months, December can be considered as a transition month, the change of flood period to dry period in this region. Due to the construction and operation of dams along the Krishna River, the mean monthly streamflow got alter. Decreasing river flow affects the flushing property and increases sediment deposit at one place. As a result, fish population decreases, which leads to loss of breeding and nursery grounds. Seasonal flow shift represents the shift in flow from one month to another month. Analyses show that moderate alteration was observed for Ujjani with SFS score of 0.47 FH, large alteration for Narayanapur and PD Jurala with SFS score of 0.4 FH and 0.36 FH respectively. Very large alteration was observed for Srisailam and NSP with FH scores of 0.07 and 0.1 respectively. The largest decrease in mean annual flow (MAF) was observed for PD Jurala with 46.6%, while the lowest decrease in mean annual flow was observed for the Narayanapur with 28.3%. For remaining dams, the flow decreased by 36.2%, 31.93%, and 32.29% for Ujjani, Srisailam and NSP respectively. This indicates that MAF in Krishna River decreases by more than 30%. The most important driving force for streamflow regime changes in the Krishna River is the construction of the dams to meet the growing demands of the increasing population.
satisfied the many processes in the riverine ecosystem functioning. If the low flow gets altered to extremely low levels, ecological communities will get damaged. 4.4. Persistently higher (PH), persistently lower (PL) and persistently very low (PVL) flows There is a significant change in the mean value of PH from the reference period to the test period, and the FH score changed from 0.82 (very small) to 0.47 (moderate). It shows that in the low flow season, high flow frequencies (greater than75% per cent flow) are increased due to hydroelectric activity. This can lead to waterlogging problems and obstruction the enrolment of exposed soils. PH scores of Srisailam and NSP alter largely as 0.30 and 0.31 respectively. Narayanapur, PD Jurala, and Ujjani have alterations in moderate condition with FH scores of 0.59, 0.54 and 0.59 respectively. This explains that continuous high flows are occurring in low flow seasons. It changes the river hydrological system affecting the lives adapted to a particular season. Mean of persistently lower (PL) indicator of five dams have altered from very small FH (0.83) to small FH (0.66). For Ujjani, the PL score decreased by 33.33% of FH from the reference impact period and altered into moderate condition. For remaining stations, the score ranges from 0.63 to 0.76. This type of flows is very important in maintaining water quality and dissolved oxygen in a river. The overall PL analysis shows that the river loses its water quality in the entire basin which affects not only the environment but also human beings who are depending on the river. For all stations, PVL (< 10% percentile flows) altered very slightly which ranged from 0.94 to 0.99 in the test period. These type of flows can be more helpful at extremely low flow condition to many organisms by maintaining minimum oxygen levels at the highly stressful condition.
4.6. Flood flow interval (FFI) The natural hydrologic regime of most rivers is characterized by regular floods, which can strongly influence the distribution and abundance of aquatic organisms. Floods can provide a chance for fish and other organisms to move from rivers to flood plains to access additional habitats. It is seen from the present study that the overall FFI mean score of 5 dams decreased by 26% of FH, which indicates a reduction in fish and other organisms. For each hydrological station, the overall FH score was determined by calculating the average of 9 hydrological indicators. The results show that the hydrological regime was least affected in the middle reach, moderately affected in Bhima and lower Krishna basins. The analysis shows that FH increases with an increase in the distance from the dam. The mean FH score ranged from 0.85 to 0.86 in reference impact period from 0.54 to 0.62 in the test period. The overall average FH score of reference and test impact period was 0.84 and 0.59 respectively. This indicated that even in the reference period, flow
4.5. Seasonality flow shift (SFS) and mean monthly flow Monthly average flow and deviation factor for the reference period and test period are presented in Table 4 which shows that the monthly streamflow had decreased during June to December and has increased from January to May. Similar flow variability was observed in both the reaches. The mean monthly flow in the low flow season showed significantly upward trend than in the reference period. The largest positive relative alteration rate was observed in the low flow season months such as February, March, April and May. For Ujjani, Srisailam and NSP dams, flows are increased during these months. The mean annual flow (MAF) of Srisailam and NSP for February and March have increased by more than 250% and for January and April have increased Table 4 Mean monthly flow and Deviation factor (DF). Month
January February March April May June July August September October November December
Narayanapur
Ujjani
PD Jurala
Srisailam
Nagarjuna Sagar Dam
Ref m3/s
Test m3/s
DF
Ref m3/s
Test m3/s
DF
Ref m3/s
Test m3/s
DF
Ref m3/s
Test m3/s
DF
Ref m3/s
Test m3/s
DF
40 46 33 12 27 383 2285 3116 1279 433 130 65
50 48 42 32 26 142 1338 2128 1014 634 109 59
25 4 27 167 −4 −63 −41 −32 −21 46 −16 −9
45 27 15 7 15 184 636 1119 1105 955 255 100
32 49 30 54 110 249 342 643 704 367 117 114
−30 82 100 700 626 35 −46 −43 −36 −62 −54 14
59 50 41 28 26 398 2653 3647 2194 1409 292 90
72 80 70 56 53 94 732 2276 1092 1046 153 80
22 60 71 100 104 −76 −72 −38 −50 −26 −48 −11
161 122 91 83 86 480 2807 4944 3404 2533 701 263
382 465 440 239 109 169 1038 2795 2354 1759 549 370
137 282 384 187 27 −65 −63 −43 −31 −31 −22 41
161 122 91 83 86 491 2693 4781 3386 2453 686 253
359 437 417 231 105 160 1042 2706 2211 1693 528 359
123 258 358 178 22 −67 −61 −43 −35 −31 −23 42
DF (Deviation Factor) = ((Test-Ref)/Ref)*100. Ref = Reference impact period (Before dam construction). Test = Test impact period (After dam construction). 7
Ecological Indicators 108 (2020) 105663
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regimes do not attain the complete FH score due to the changes in the flow regimes. This study recommends to restore the natural flow regime characteristics in order to meet future water demands, both instream flow requirements and water development should be considered simultaneously.
cwrj3302165. Butchart-Kuhlmann, D., Kralisch, S., Fleischer, M., Meinhardt, M., Brenning, A., 2018. Multicriteria decision analysis framework for hydrological decision support using environmental flow components. Ecol. Ind. 93, 470–480. https://doi.org/10.1016/j. ecolind.2018.04.057. Caiola, N., Ibanez, C., Verdu, J., Munne, A., 2014. Effects of flow regulation on the establishment of alien fish species: a community structure approach to biological validation of environmental flows. Ecol. Ind. 45, 598–604. https://doi.org/10.1016/j. ecolind.2014.05.012. Chulsang, Yoo, 2006. Long term analysis of wet and dry years in Seoul, Korea. J. Hydrol. 318 (1–4), 24–36. https://doi.org/10.1016/j.jhydrol.2005.06.002. De Nooij, R.J.W., Verberk, W.C.E.P., Lenders, H.J.R., Leuven, R.S.E.W., Nienhuis, P.H., 2006. The importance of hydrodynamics for protected and endangered biodiversity of lowland rivers. Hydrobiologia 565 (1), 153–162. https://doi.org/10.1007/s10750005-1911-9. Dudgeon, D., 2010. Prospects for sustaining freshwater biodiversity in the 21st century: linking ecosystem structure and function. Curr. Opin. Environ. Sustainab. 2 (5–6), 422–430. https://doi.org/10.1016/j.cosust.2010.09.001. Dudgeon, D., Arthington, A.H., Gessner, M.O., Kawabata, Z.I., Knowler, D.J., Leveque, C., Naiman, R.J., Prieur-Richard, A.H., Soto, D., Stiassny, M.L., Sullivan, C.A., 2006. Freshwater biodiversity: importance, threats, status and conservation challenges. Biol. Rev. 81 (2), 163–182. https://doi.org/10.1017/S1464793105006950. Gippel, C.J., Cosier, M., Markar, S., Liu, C., 2009. Balancing environmental flows needs and water supply reliability. Water Resour. Develop. 25 (2), 331–353. https://doi. org/10.1080/07900620902868802. Hua, Y., Cui, B., 2018. Environmental flows and its satisfaction degree forecasting in the Yellow River. Ecol. Ind. 92, 207–220. https://doi.org/10.1016/j.ecolind.2017.02. 017. Huang, W., Liu, X., Peng, W., Wu, L., Yano, S., Zhang, J., Zhao, F., 2018. Periphyton and ecosystem metabolism as indicators of river ecosystem response to environmental flow restoration in a flow-reduced river. Ecol. Ind. 92, 394–401. https://doi.org/10. 1016/j.ecolind.2017.11.025. Jain, S.K., Kumar, P., 2014. Environmental flows in India: towards sustainable water management. Hydrol. Sci. J. 59 (3–4), 751–769. https://doi.org/10.1080/02626667. 2014.896996. Liu, J., Liu, Q., Yang, H., 2016. Assessing water scarcity by simultaneously considering environmental flow requirements, water quantity, and water quality. Ecol. Ind. 60, 434–441. https://doi.org/10.1016/j.ecolind.2015.07.019. Liu, Y., Cao, S., Yang, Y., Zhang, X., 2018. Assessment of hydrologic regime considering the distribution of hydrologic parameters. Water Sci. Technol. Water Supply 18 (3), 875–885. https://doi.org/10.2166/ws.2017.161. Nilsson, C., Reidy, C.A., Dynesius, M., Revenga, C., 2005. Fragmentation and flow regulation of the world’s large river systems. Science 308 (5720), 405–408. https://doi. org/10.1126/science.1107887. Penas, F.J., Barquin, J., Alvarez, C., 2016. Assessing hydrologic alteration: evaluation of different alternatives according to data availability. Ecol. Ind. 60, 470–482. https:// doi.org/10.1016/j.ecolind.2015.07.021. Peres, D.J., Cancelliere, A., 2016. Environmental flow assessment based on different metrics of hydrological alteration. Water Resour. Manage. 30 (15), 5799–5817. https://doi.org/10.1007/s11269-016-1394-7. Pfeiffer, M., Ionita, M., 2017. Assessment of hydrologic alterations in Elbe and Rhine Rivers, Germany. Water 9 (9), 684–694. https://doi.org/10.3390/w9090684. Poff, N.L., Richter, B.D., Arthington, A.H., Bunn, S.E., Naiman, R.J., Kendy, E., Acreman, M., Apse, C., Bledsoe, B.P., Freeman, M.C., Henriksen, J., 2010. The ecological limits of hydrologic alteration (ELOHA): a new framework for developing regional environmental flow standards. Freshw. Biol. 55 (1), 147–170. https://doi.org/10.1111/ j.1365-2427.2009.02204.x. Richter, B., Baumgartner, J., Wigington, R., Braun, D., 1997. How much water does a river need? Freshw. Biol. 37 (1), 231–249. https://doi.org/10.1046/j.1365-2427. 1997.00153.x. Uday Kumar, A., Jayakumar, K.V., 2018a. Assessment of hydrological alteration and environmental flow requirements for Srisailam dam on Krishna River, India. Water Policy 20 (6), 1176–1190. https://doi.org/10.2166/wp.2018.203. Uday Kumar, A., Jayakumar, K.V., 2018b. Assessment of environmental flows using hydrological methods for Krishna River, India. Adv. Environ. Res. 7 (3), 161–175. https://doi.org/10.12989/aer.2019.7.3.161. Wurbs, R.A., Hoffpauir, R.J., 2017. Environmental flow requirements in a water availability modelling system. Sustainability Water Qual. Ecol. 9, 9–21. https://doi.org/ 10.1016/j.swaqe.2016.05.003. Yang, N., Zhang, Y., Duan, K., 2017. Effect of Hydrologic Alteration on the Community Succession of Macrophytes at Xiangyang Site. Scientifica, Hanjiang River, China https://doi.org/10.1155/2017/4083696. Zhang, H., Sun, T., Xue, S., Yang, W., Yin, X., 2017. Environmental flow assessment in estuaries taking into consideration species dispersal in fragmented potential habitats. Ecol. Ind. 78, 541–548. https://doi.org/10.1016/j.ecolind.2017.03.042. Zhang, W., Jia, Y., Ge, J., Huang, X., Ni, G., Hou, J., Wang, H., 2019. Multi-index data dimension reduction approach and its applicability in the calculation of indicators of hydrological alteration. Hydrol. Res. 50 (1), 231–243. https://doi.org/10.2166/nh. 2018.068.
5. Conclusion The present study assessed hydrological alterations in the form of nine hydrological indicators based on the monthly average flows, timing, duration and frequency. The results indicate that hydrological alterations in the Krishna River, one of the largest rivers in South India, are consequences of human activities. Low flow seasons show a significant positive change and high flow seasons show negative changes from the natural flow condition. Such changes impact aquatic and riparian habitat and adversely affect the key species in the river and surrounding environment. The mean monthly flows of the Srisailam and NSP dams have decreased significantly during the months of July, August and September. The mean annual flow in the Krishna River decreased by more than 30%. Reduction in the overall flow from the dam for providing irrigation and domestic water demands was observed and this may be worsened by climate changes and population growth in future. The dams are having a great influence on hydrological variability and the seasonality of river flow. The impact of PD Jurala dam on the hydrological regime is comparatively small, with an average mean FH value of 0.65, the last position among the five dams. The average value of FH score for the Srisailam and NSP reservoirs are 0.56 and 0.54, ranking in first and second among the five dams in the study area. Sediment deposition in the lower reaches of the basin has significantly changed the natural flow regimes in the downstream of the river reach. Future other changes such as sediment yield, water temperature, nutrient influx and water quality parameter are also needed to be monitored for a better understanding of the ecosystem impacts due to human activates in a river. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecolind.2019.105663. References Abe, G., Joseph, J.E., 2015. Changes in streamflow regime due to anthropogenic regulations in the humid tropical Western Ghats, Kerala State, India. J. Mountain Sci. 12 (2), 456–470. https://doi.org/10.1007/s11629-013-2764-8. Acreman, M.C., Ferguson, A.J.D., 2010. Environmental flows and the European water framework directive. Freshw. Biol. 55 (1), 32–48. https://doi.org/10.1111/j.13652427.2009.02181.x. Arthington, A.H., Zhang, Y., Bunn, S.E., Mackay, S., Xia, J., Kennard, M., 2012. Classification of flow regimes for environmental flow assessment in regulated rivers: the Huai River Basin, China. River Res. Appl. 28 (7), 989–1005. https://doi.org/10. 1002/rra.1483. Baron, J.S., Poff, N.L., Angermeier, P.L., Dahm, C.N., Gleick, P.H., Hairston, N.G., Jackson, R.B., Johnston, C.A., Richter, B.D., Steinman, A.D., 2002. Meeting ecological and societal needs for freshwater. Ecol. Appl. 12 (5), 1247–1260. https://doi.org/10. 1890/1051-0761(2002) 012[1247:MEASNF]2.0.CO;2. Belmar, O., Vila-Martinez, N., Ibanez, C., Caiola, N., 2018. Linking fish-based biological indicators with hydrological dynamics in a Mediterranean river: relevance for environmental flow regimes. Ecol. Ind. 95, 492–501. https://doi.org/10.1016/j. ecolind.2018.06.073. Boodoo, K.S., McClain, M.E., Upegui, J.J.V., Lopez, O.L.O., 2014. Impacts of implementation of Colombian environmental flow methodologies on the flow regime and hydropower production of the Chinchiná River, Colombia. Ecohydrol. Hydrobiol. 14 (4), 267–284. https://doi.org/10.1016/j.ecohyd.2014.07.001. Bradford, M.J., Heinonen, J.S., 2008. Low flows, instream flow needs and fish ecology in small streams. Canadian Water Resour. J. 33 (2), 65–180. https://doi.org/10.4296/
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