The study analyzes the hydromorphological impacts of barrage construction and operation on the Yamuna River over 25 years, employing the River Flow Health Index (RFHI). The RFHI methodology includes segregating flow data into different periods, identifying key parameters, assessing flow alterations, and developing an index from 0 to 1. Results indicate moderate alterations in the flow regime, with RFHI of 0.379 and 0.328 for different periods. Geomorphological analysis revealed variations in river sinuosity, peaking at 1.232 in 1999 before reducing to 1.206 by 2018, and braid-channel ratio decreasing from 1.864 in 1999 to 1.508 in 2018. Channel width narrowed in 1999 to 0.082 km and slightly expanded to 0.093 km by 2018. The channel area reduced from 6.418 km² in 1993 to 5.632 km² in 2018, with the bar area significantly decreasing and bar density dropping from 3.28 to 0.83, indicating changes in the channel belt. Construction phases showed increased fluvial bars and channel multiplicity and decreased channel area and width. Post-Hathnikund barrage, withdrawals during lean seasons left the channel dry, as seen in 2003, 2008, and 2013. These findings emphasize integrated river basin management to harmonize development with ecological impacts.

  • Use of the River Flow Health Index on Yamuna, assessing hydrological changes.

  • Advanced GIS remote sensing for analyzing barrage-induced geomorphological impacts.

  • Fills research gap on barrages’ effects on flow and morphology.

  • Guides sustainable water management, aligning ecological conservation with human demands.

  • Influences policies for river restoration, applicable to global river systems.

Barrages impact sediment transport, disrupt fish migration, and alter habitat conditions (Agarwal & Narain 1997; Misra 2010; Sharma & Kansal 2011; Joshi et al. 2016; Sharma et al. 2017; Yadav et al. 2023). For instance, the Farakka barrage significantly changes the Ganges River's flow (Gain & Giupponi 2014). Recognizing these impacts is crucial for formulating strategies to maintain a sustainable water supply amidst growing demand and climate change (Jain 2012; Shahid et al. 2018).

The present work focuses on studying the impact of barrages in India's upper segment of the Yamuna River, crucial for water supply in North India. The section of the river is interrupted by a major barrage, the Tajewala barrage, replaced by the Hathnikund barrage in 1999. An analysis of the planform characteristics of the Yamuna River stretch would provide us with a great deal of information on the morphological alterations in the river basin.

The oldest commonly used method for assessing hydrological alterations was developed by Richter et al. (1996), which demonstrated the use of the ‘range of variability approach’ (RVA). The method computed the 25th and 75th percentiles of 33 hydrological parameters (indicators of hydrological alterations, IHAs) used in the RVA based on the pre-dam and post-dam discharge records. Although this technique is widely used, it has some likely impediments. Specifically, in the RVA technique, the values beyond the interquartile range are neglected, thus underestimating the overall alteration value (Olden & Poff 2003; Mathews & Richter 2007). The River Flow Health Index (RFHI) method, developed by Mohanty and Tare in 2022, is a powerful tool to quantify the hydrological allegations on a 0–1 scale. The main advantage of this method is that it constructs the confidence intervals with which the results are reported, thereby addressing the uncertainties associated with large hydrologic data.

Due to the limited research on quantifying changes in different components of flow regime and planform caused by barrages (Shih et al. 2022), as well as the lack of study on the effect of construction works on river planform (Lawrence 2001; Bellmore et al. 2017), our objective is to investigate (i) the significant changes in the flow regime brought by decommissioning and constructing barrages using the RFHI method and (ii) the resultant alterations in channel planform, using remote sensing imagery. This methodology is significant as it provides a structured framework for evaluating the impacts of barrages on river hydrology and morphology, aiding in decision-making processes vital for ecological sustainability and effective water resource management.

Yamuna, a major tributary of the river Ganga, originates from the Yamunotri glacier at 6,320 m above mean sea level. It has a total length of around 1,376 km and a catchment area of 366,000 km2. In the basin, annual rainfall averages around 1,200 mm, most falling during the monsoon season (June to September). The stream travels through the Shivalik mountain range in the Indian states of Himachal Pradesh and Uttarakhand. It runs past the well-known Sikh shrine Poanta Sahib after leaving Dak Pathar. After passing through Poanta Sahib, the river enters the Yamuna Nagar district of Haryana and reaches Hathnikund, where it is once again diverted for irrigation into the Western Yamuna Canal and the Eastern Yamuna Canal. The Hathnikund barrage was constructed in June 1999 for irrigation purposes. It replaced the Tajewala barrage, built in 1873, and is no longer used. It is located 3 km upstream. The Hathnikund barrage on the Yamuna River primarily provides water to the Indian states of Haryana and Delhi. The barrage diverts water into the western and eastern Yamuna canals, significantly impacting the water supply to these densely populated and agriculturally intensive areas. The socio-economic imperatives for constructing the Hathnikund barrage included improved water distribution efficiency, better flood management, and enhanced capacity for water storage.

The study area is delimited downstream of the Hathnikund barrage and a flow monitoring station at Kalanaur (coordinates: 30°04′10″ N, 77°21′52″ E) located in the Saharanpur district of Uttar Pradesh (Figure 1). No tributaries join the river in the study region. The reach is studied to find the alterations in the flow regime when one barrage is decommissioned and the other becomes operational. Important keystone species, mahseer, and economically important Indian major carp (IMC) were found in the study reach (Mishra et al. 2007).
Figure 1

Study area showing the barrages across the Yamuna River in India.

Figure 1

Study area showing the barrages across the Yamuna River in India.

Close modal

The geological formation of the study area in the Yamuna basin consists of alluvium of the Quaternary period of the Cenozoic era (Singh 2012). The soils in the study area have been classified as brownhill soils (Devi 1992), formed from the weathering of granite, gneiss, and schist.

Data collection

Hydrological data

Numerous hydrological stations are located along the Yamuna River and its main tributaries. These stations are operated by the Central Water Commission (CWC), a department of the Ministry of Jal Shakti, Government of India. Discharge characteristics such as river level and velocity are monitored once daily (Jain et al. 2007) or three times daily (CWC 2017). The discharge is then determined by multiplying the cross-sectional area derived from the river stage by the velocity. Data on daily water discharge are collected from the CWC maintained at Kalanaur gauging station with coordinates 30°04′10″ E, 77°21′52″ N for 40 years (1976–2015).

Acquisition of satellite images

Satellite images encompassing several Landsat satellites are obtained from the United States Geological Survey (https://earthexplorer.usgs.gov/) for the year 1993, prior to the start of construction of the Hathnikund barrage, 1999 during the construction of the barrage, and different periods spanning 19 years after the barrage is operational. Details of the remote sensing images obtained are tabulated in Table 1.

Table 1

Specifics about remote sensing images

PeriodImage typeDate of acquisitionPath/row numberResolution
Altered – 1 Landsat-TM 27-04-1993 147/039 30 m 
Intermediate 28-04-1999 
Altered – 2 Landsat ETM 10-02-2003 
20-04-2008 
Landsat 8 18-04-2013 
Landsat 8 31-03-2018 
PeriodImage typeDate of acquisitionPath/row numberResolution
Altered – 1 Landsat-TM 27-04-1993 147/039 30 m 
Intermediate 28-04-1999 
Altered – 2 Landsat ETM 10-02-2003 
20-04-2008 
Landsat 8 18-04-2013 
Landsat 8 31-03-2018 

Methods

The methodology can be divided into two main components: (1) hydrologic analysis utilizing the RFHI and (2) geomorphic mapping of satellite images. The detailed methodology is outlined in Figure 2.
Figure 2

Flowchart showing the methodology used in the study.

Figure 2

Flowchart showing the methodology used in the study.

Close modal

Calculation of the RFHI

Mohanty & Tare (2022) proposed a powerful tool to estimate the hydrological alterations induced by anthropogenic interventions. The approach for calculating the RFHI comprises four steps:

  • (a) Segregation of flow data into different periods;

  • (b) Determining significant hydrological parameters;

  • (c) Evaluation of flow alterations; and

  • (d) Quantifying the health of the river flows during altered and intermediate periods.

The flowchart outlining the methodology for computing RFHI is presented in Figure 2.

Reference and altered periods

Reference conditions (near-natural) refer to the flow state before any anthropogenic intervention on the studied river. Here, the river has already been altered; hence, we do not have any reference period. Altered conditions are considered to have occurred after the barrage across the river became operational. We have two altered periods, i.e., 1976–1993 during the operation of the Tajewala barrage and 2001–2015 during the operation of the Hathnikund barrage. The period of construction (1995–2000) of the Hathnikund barrage in the Yamuna River is considered the intermediate condition.

Hydrologic parameters

Mohanty & Tare (2022) developed a powerful methodology to evaluate the hydrological alterations that account for the uncertainties in hydrologic modeling. The RFHI method includes 171 hydrologically relevant parameters published by different authors (Supplementary Table S1). These parameters are divided into seven components of the flow regime (Richter et al. 1996; Magilligan & Nislow 2005):

  • (i) Magnitude (n, number of parameters = 76): The amount of water flowing past a specific location in a time.

  • (ii) Variability (n = 52): The traits that demonstrate how the flow changes throughout time.

  • (iii) Duration (n = 12): The amount of time corresponding to a certain flow condition.

  • (iv) Frequency (n = 12): The number of times a certain amount of flow happens over a given time interval.

  • (v) Timing (n = 50): The prediction of a particular magnitude of flows.

  • (vi) Rate of change (n = 6): It describes how quickly flow changes from one value to another.

  • (vii) Other (n = 17): This category includes the flow regime's distribution, shape, and range.

The hydrological parameters are estimated using a mix of computer programs developed in the MATLAB programming language from the daily flows (Henriksen et al. 2006) for different periods in the river. The value of each parameter is calculated for each water year of the study period.

Assessment of hydrological alteration

Principal component analysis (PCA) is employed to identify key subsets of hydrological parameters that represent natural flow patterns at specific locations, reducing data redundancy. However, traditional PCA methods often overlook the uncertainties inherent in hydrological data (Babamoradi et al. 2013). To mitigate this issue, a bootstrapping approach is integrated with PCA (Erfon & Tibshirani 1986). This method enhances the analysis by accounting for variability and uncertainty, using random sampling with replacement to generate multiple data samples.

The methodology comprises several steps:

  • (a) PCA on Original Dataset: Initially, PCA is applied to the hydrological data to diminish its dimensionality. This step isolates principal components that capture critical patterns in the flow regime. However, this traditional PCA might not fully account for data uncertainties.

  • (b) Bootstrapping for Eigenvalue/Eigenvector Analysis: Bootstrapping is paired with PCA to address this limitation. Multiple sample sets are created from the original data, and PCA is performed on each set. This repetition, perhaps hundreds of times, aids in understanding the data's variability and uncertainty, thus strengthening the analysis.

  • (c) Variance Calculation with Confidence Intervals (CIs): This involves calculating the variance explained by the top eigenvalues, their median percentages, and 95% CIs. The CIs gauge the reliability of the eigenvalues, reflecting their potential variability across samples.

  • (d) Differentiation of Eigenvalues Based on CIs: Eigenvalues are differentiated based on their CIs. Non-overlapping intervals indicate distinct eigenvalues, whereas overlapping intervals suggest similarities. This step is crucial for identifying consistent data patterns despite inherent variability.

  • (e) Identification of Significant Hydrological Parameters: The analysis is refined by examining eigenvector loadings to pinpoint significant hydrological parameters in each component. This step emphasizes the flow regime's key features and validates their significance across different samples, adding a layer of uncertainty analysis.

  • (f) Projection of Original Data onto Feature Vectors: The original data is mapped onto the identified feature vectors (principal components). This projection aids in understanding the temporal variation of these components while considering data uncertainty.

  • (g) Statistical Comparison Across Periods: Utilizing a two-sample Kolmogorov–Smirnov test (Kroll et al. 2015), the principal components are compared across different periods. This comparison detects significant changes in the flow regime, offering insights into its stability and variability.

  • (h) River Flow Health Index: The RFHI is formulated using the geometric mean of the D-statistics from the Kolmogorov–Smirnov test. This index, reflecting the cumulative probability distribution difference between principal component (PC) sets, indicates the flow regime's alteration degree. A D-value near 1 implies significant changes, while a value close to 0 indicates minimal changes. This index is a comprehensive measure of the river flow's overall health and variability, encapsulating the impact of uncertainties.

This methodology effectively utilizes bootstrapping with PCA to address uncertainties inherent in hydrological data. It ensures that the identified patterns and changes in the flow regime are statistically reliable and truly reflect the underlying variability in the data. The overall index is computed at Kalanaur station in Yamuna using the above approach.

For dam operators, this metric is valuable in decision-making processes. By understanding the degree of change in the flow regime (as indicated by the D-value), operators can assess the impact of their activities on river health. A higher D-value would signal a need for more careful management strategies to minimize hydrological alterations, whereas a lower value would suggest that current practices are not significantly impacting the flow regime.

Geomorphic mapping and calculation of planform parameters

Morphological alterations are evaluated using a geographic information system (GIS) using ArcMap 10.4. The geomorphic units are classified using the procedure outlined in Wheaton et al. (2015).

Planform parameters (Table 2) are calculated from the satellite-generated maps. The framework for image processing and geomorphic mapping is outlined in Figure 2. To find the sinuosity, the length of the central line (centerline of the stream) and the straight line connecting the endpoints of the reach are calculated. The ratio between central line length (Lcmax) and straight length (Lr) is recorded as sinuosity (Friend & Sinha 1993). All mid-channel and main channel lengths are generated for braid-channel ratio calculation. Then, the braid-channel ratio is calculated by dividing the lengths of all mid-channel lengths (Lctot) by the main channel length (Lcmax) for the reach (Friend & Sinha 1993). Perpendiculars are drawn to the central line of the stream to generate the average channel width. The average width of these perpendiculars in the reach is the average channel width. In multi-channel reach, the width of the bars is excluded to compute channel width. The area of all the bars is generated and recorded as bar area in aggregate. The channel belt area, excluding the bar area, is reported as the channel area. The number of bars per kilometer of river reach determines bar density. The study considered different periods: the first altered period, 1993, during construction (1999), and the second altered period, 2003, 2008, 2013, and 2013.

Table 2

Description of the planform parameters used in the study

Planform parameterDescription
Sinuosity Sinuosity is the ratio of valley gradient to channel gradient over a stream reach 
Braid-channel ratio It differentiates between rivers with a single thalweg and rivers with several thalwegs. A higher value implies that the stream contains a network of small channels divided by small deposits of silt known as bars 
Average channel width The average width of the primary channel 
Channel area The area is occupied by water in the stream 
Bar area The area is occupied by different depositional units like mid-channel bar, side bar, point bar, alluvial island, and confluence bar 
Bar density Bar density comprises almost all channel belt attributes which include side bar, point bar, alluvial island, and mid-channel bar along with the channel area 
Planform parameterDescription
Sinuosity Sinuosity is the ratio of valley gradient to channel gradient over a stream reach 
Braid-channel ratio It differentiates between rivers with a single thalweg and rivers with several thalwegs. A higher value implies that the stream contains a network of small channels divided by small deposits of silt known as bars 
Average channel width The average width of the primary channel 
Channel area The area is occupied by water in the stream 
Bar area The area is occupied by different depositional units like mid-channel bar, side bar, point bar, alluvial island, and confluence bar 
Bar density Bar density comprises almost all channel belt attributes which include side bar, point bar, alluvial island, and mid-channel bar along with the channel area 

Temporal variation in river flow regime

Several essential steps are involved in deriving findings from evaluating alterations in the river's hydrological regime. These steps encompass the choice of non-redundant hydrological parameters and the subsequent computation of the flow health index based on these selected parameters. Uncertainty in the proposed model is quantified to develop the lower and upper bounds of the CI using the bootstrap resampling method. The CIs constructed using the bootstrap PCA model, with a CI of 95%, are displayed in Figure 3, along with the width and coverage of the CI. Within this figure, the primary component is depicted along the X-axis, and the extent of variance captured by the leading component is shown on the Y-axis. Moreover, Supplementary Table S2 furnishes a comprehensive listing of the selected parameter values about different aspects of the flow regime components at Kalanaur, the downstream station across the Yamuna River.
Figure 3

Eigenspectra of different flow regime components. (a) Magnitude, (b) Variability, (c) Duration, (d) Frequency, (e) Timing, (f) Rate of change, and (g) Other at Kalanaur.

Figure 3

Eigenspectra of different flow regime components. (a) Magnitude, (b) Variability, (c) Duration, (d) Frequency, (e) Timing, (f) Rate of change, and (g) Other at Kalanaur.

Close modal

The results of the analysis for each group of hydrological parameters are as follows:

Group 1: Magnitude: The first eigenvalue in this group is highly significant, contributing to 41.07% of the total variance in the first PC within the magnitude category (Figure 3(a)). The top five variables that exert the most influence on this primary axis are high peak flow (MH 26), high flow volume (MH 22 and MH 23), low flow index (ML 15), and baseflow index (ML 19).

Group 2: Variability: This group's first eigenvalue is also substantial, accounting for 30.51% of the overall variance (Figure 3(b)). The five most influential factors contributing to this component are variability across monthly flow (MA 36, MA 38, and MA 29), variability across maximum monthly flows (MH 13), and standard deviation of the percentiles of the logs of the entire flow record divided by the mean of percentiles of the logs (MA 4).

Group 3: Duration: The first PC in this group explains 35.97% of the total variance (Figure 3(c)). It is primarily influenced by parameters related to high flow duration (DH 20, DH 15, DH 18, and DH 19) and high flood pulse count (FH 4).

Group 4: Frequency: Within the frequency component, the first PC, selected for its significance, contributes 37.72% of the variance (Figure 3(d)). It is mainly associated with high flood pulse count (FH 1), flood frequency (FH 7, FH 8, and FH 9), and low flood pulse count (FL 1).

Group 5: Timing: In the timing component, the first PC accounts for 26.94% of the variance (Figure 3(e)). Significant parameters in this component include mean maximum October (MH 10) and January (MH 1) monthly flows, mean minimum November (ML 11) and May (ML 5) monthly flows, and mean monthly flow in October (MA 21).

Group 6: Rate of change: The main parameters in the rate of change component capture 42.35% of the variance and are related to reversals (RA 8), no. day rises (RA 5), fall rate (RA 3), change of flow (RA 7), and rise rate (RA 1).

Group 7: Other: In the other component, the first PC is highly significant, explaining 61.55% of the variance (Figure 3). It is primarily linked to the skewness in daily flows (MA 5), spread in daily flows (MA 9 and MA 10), and skewness in monthly flows (MA 40).

Combining the identification of principal components and the selection of a representative subset of parameters with the physical and biological understanding of the targeted streamflow regimes can enhance analysis (Olden & Poff 2003). To support the choice of a specific subset, it is essential to demonstrate the ecological significance of these parameters. The comprehensive analysis of combining bootstrapping with PCA, as outlined in this section, forms the foundation for the next valuation phase.

Evaluation of the RFHI

Valuing the hydrological alterations during the construction of the Hathnikund barrage

During the barrage construction at the Kalanaur gauging station, significant alterations were observed in various flow regime components. Table 3 summarizes the observed alterations in each component.

Table 3

Alterations in different flow regime components at Kalanaur gauging station during the barrage construction

ComponentAlteration value
Magnitude 0.444 
Variability 0.444 
Duration 0.333 
Frequency 0.500 
Timing 0.278 
Rate of change 0.278 
Other 0.444 
Overall 0.379 
ComponentAlteration value
Magnitude 0.444 
Variability 0.444 
Duration 0.333 
Frequency 0.500 
Timing 0.278 
Rate of change 0.278 
Other 0.444 
Overall 0.379 

These results indicate that the most significant alteration was in the frequency component (0.500), suggesting a notable change in the occurrence rate of specific flow events. Conversely, the timing and rate of change components experienced the least alteration, each with a value of 0.278. The overall alteration value, which represents an aggregate measure of all components, was calculated to be 0.379, denoting a moderate change in the flow regime due to the barrage construction.

Valuing the hydrological alterations after the operation of the Hathnikund barrage

This section deals with comparing hydrological alterations prior to the construction and after the operation of the Hathnikund barrage (Table 4).

Table 4

Alterations in different flow regime components at Kalanaur gauging station after the operation of the Hathnikund barrage

ComponentAlteration value
Magnitude 0.378 
Variability 0.267 
Duration 0.378 
Frequency 0.222 
Timing 0.311 
Rate of change 0.500 
Other 0.311 
Overall 0.328 
ComponentAlteration value
Magnitude 0.378 
Variability 0.267 
Duration 0.378 
Frequency 0.222 
Timing 0.311 
Rate of change 0.500 
Other 0.311 
Overall 0.328 

The flow regime at Kalanaur, influenced by the Hathnikund barrage, displayed moderate alterations across various components. The magnitude of flow changed with an alteration value of 0.378, reflecting moderate changes in water volume. Flow variability saw a lower alteration value of 0.267, indicating minor inconsistencies in flow. Duration changes were also moderate, with a value of 0.378. Frequency alterations were less pronounced at 0.222, suggesting minimal impact on the occurrence of flow conditions. The timing of flow events was moderately altered at 0.311. The most significant change was in the rate of flow alterations, marked by a value of 0.500, pointing to a substantial increase in the speed of flow changes. Other unspecified flow regime changes were also moderate at 0.311. Overall, the cumulative impact of the barrage on the flow regime was moderate, with a total alteration value of 0.328.

Temporal variations in planform parameters

The planform changes are studied in three stages: prior to the construction (1993), during construction works of the barrages (1999), and different periods after the operation of the Hathnikund barrage (Table 5).

Table 5

Planform parameters due to the construction and operation of barrages across the Yamuna River

Planform parameters199319992003200820132018
Sinuosity 1.190 1.232 The river is left dry 1.206 
Braid-channel ratio 1.785 1.864 1.508 
Average channel width (km) 0.106 0.082 0.093 
Channel area (km26.418 5.639 5.632 
Bar area 27.405 29.935 17.957 
Bar density 3.28 2.71 0.83 
Planform parameters199319992003200820132018
Sinuosity 1.190 1.232 The river is left dry 1.206 
Braid-channel ratio 1.785 1.864 1.508 
Average channel width (km) 0.106 0.082 0.093 
Channel area (km26.418 5.639 5.632 
Bar area 27.405 29.935 17.957 
Bar density 3.28 2.71 0.83 

The satellite images of the Yamuna River from 1993 to 2018 reveal a dynamic fluvial system subject to considerable morphological changes, likely influenced by the construction and operation of barrages. Over the 25 years, sinuosity experienced an overall increment from 1.190 to 1.206, suggesting increased river meandering initially during the construction phase, followed by a slight straightening post-Hathnikund period. The braid-channel ratio, indicative of the river's tendency to split into multiple channels, increased from 1.785 to 1.864, reflecting a greater degree of channel bifurcation, which later decreased to 1.508 in 2018, implying a reduction in channel fragmentation and a trend toward a more singular flow path. The average channel width decreased from 0.106 to 0.082 km, then slightly increased to 0.093 km, evidencing the narrowing in 1999 and subsequent minor river widening in 2018. A similar trend was observed in the channel area, decreasing from 6.418 to 5.639 km² in 1999 and maintaining a near-steady value of 5.632 km² in 2018. The bar area initially increased from 27.405 to 29.935 km², subsequently diminishing to 17.957 km², while bar density decreased from 3.28 to 2.71 and then significantly to 0.83. These alterations highlight the evolving sediment deposition patterns and suggest adjustments to sediment load and river hydraulics. The river's morphology (Figure 4) underscores the progressive and sometimes abrupt geomorphological changes influenced by human interventions and natural sediment dynamics. The drying of the channel (Figure 4(c)–4(e)) following the barrage construction suggests a substantial modification in the river's flow regime, likely due to the diversion of water for irrigation, consumption, or other uses.
Figure 4

Geomorphological map representing the major geomorphic features of the study area in the Yamuna River reach during (a) 1993 (during operation of Tajewala barrage), (b) 1999 (during various construction activities), (c) 2003, (d) 2008, (e) 2013, and (f) 2018 (post-operation of Hathnikund barrage).

Figure 4

Geomorphological map representing the major geomorphic features of the study area in the Yamuna River reach during (a) 1993 (during operation of Tajewala barrage), (b) 1999 (during various construction activities), (c) 2003, (d) 2008, (e) 2013, and (f) 2018 (post-operation of Hathnikund barrage).

Close modal

Multivariate hydrological analysis

The multivariate analysis underscores the complexity of river flow characteristics across different hydrological groups, revealing their critical ecological roles. Group 1 emphasizes the significance of peak flow magnitudes in shaping river ecosystems, highlighting their dominant impact on ecological integrity (Poff et al. 1997). Group 2 identifies flow variability as essential for maintaining aquatic biodiversity (Richter et al. 1996), while Group 3 focuses on the ecological importance of prolonged high flows for nutrient cycling and habitat sustainability (Junk et al. 1989). In Group 4, the frequency of floods is linked to impacts on riverine landscapes and biota, influencing sediment transport and species diversity (Tockner & Ward 1999). Group 5's analysis of timing stresses the importance of seasonal flow variations for triggering critical life-cycle events in riverine species (Thoms & Sheldon 2000). Group 6 explores how rapid changes in river flows can disrupt ecological balances, affecting physical and biological river processes (Kuriqi et al. 2021). Lastly, the ‘Other’ group examines flow skewness, a less studied but significant factor in predicting the extremity and predictability of flow events, impacting flood risk management and ecological resilience (Doyle 2005).

Hydrological alterations due to the construction and operations of barrages

The RFHI offers a method to assess the impact of human interventions on river systems, highlighting significant modifications in flow regime components due to the construction and subsequent operation of the Hathnikund barrage across the Yamuna River. The flow magnitude was altered during construction by draining water stored behind the Tajewala barrage, using a rapid-release approach (Stroud 2012). The variability component was most affected during construction as the structures were not used for water abstraction, irrigation, or domestic use, leading to significant alterations. Additionally, the frequency component saw a substantial change, with a rating of 0.500, indicating a notable shift in the occurrence rate of specific flow events. This finding aligns with research by Lee et al. (2023), who reported similar frequency alterations in river systems following infrastructural developments.

The moderate overall alteration values (0.379 during construction and 0.328 post-operation) suggest that significant changes do not constitute extreme modifications to the flow regime. However, moderate changes can accumulate over time, leading to substantial ecological and geomorphological impacts (Poff et al. 1997). These alterations were largely due to the increased discharge capacity of the Eastern Yamuna Canal from 800 cusecs in 1,830 to 4,000 cusecs to address water scarcity (Irrigation and Water Resources Department, Uttar Pradesh). Additionally, the construction of Powerhouse D at the Western Yamuna Canal hydroelectric station in 2004 in Haryana was aimed at addressing power shortages (Haryana Power Generation Corporation Limited). Increased urbanization in Haryana and Uttar Pradesh has led to a significant gap between water availability and demand, resulting in extensive water extraction from Hathnikund during lean seasons to meet agricultural needs (Kumar et al. 2019). Post-operation, the rate of change component showed the most notable alteration (0.500), highlighting risks of rapid flow changes to ecological balance and sediment dynamics (Acreman et al. 2014). Moderate changes in duration (0.378) and timing (0.311) indicate shifts in seasonal river patterns. Maintaining natural flow regimes is important, as frequency deviations can disrupt the reproductive cycles of aquatic life and habitat integrity (Richter et al. 1996) and impact species dependent on seasonal flows (Lytle & Poff 2004). The reduced variability (0.267) and moderate changes in magnitude (0.378) could influence sediment deposition patterns, potentially leading to habitat degradation for riverine species (Chen & Olden 2020). The significant rate of change could disrupt ecological balances, affecting species reliant on stable flow conditions for breeding and feeding (Iqbal & Dutta 2022).

Geomorphological changes and satellite imagery insights

The observed morphological changes in the Yamuna River from 1993 to 2018, detailed through planform parameters such as sinuosity, braid-channel ratio, channel width, channel area, bar area, and bar density, reflect significant influence from barrage construction and operation. The initial increase in sinuosity and braid-channel ratio suggests enhanced meandering and bifurcation, likely due to changes in flow velocity and sediment transport facilitated by barrage construction (Gurnell et al. 2016). Over time, a reduction in these parameters indicates a stabilization effect from the barrages, aligning with findings that link river engineering to reduced river fragmentation (Surian & Rinaldi 2003). Fluctuations in bar area and density highlight changes in sediment deposition patterns, initially increased by sediment accumulation upstream of barrages, then decreased due to regulated flows and sediment capture by the barrage (Major et al. 2012). Such alterations in sediment dynamics can significantly impact river morphology, influencing channel form and habitat structures (Magilligan & Nislow 2005).

The decommissioning of the Tajewala barrage introduces substantial sediment loads back into the river system, potentially leading to channel aggradation and increased braiding (Pearson et al. 2011; Major et al. 2012; East et al. 2015). This reintroduction of sediments can lead to significant geomorphological changes downstream, including altered sediment regimes and increased deposition (Doyle et al. 2003; Cheng & Granata 2007; Riggsbee et al. 2007; Major et al. 2012; Wilcox et al. 2014).

The observed drying of the river channel following barrage operations poses serious ecological risks, disrupting river continuity and affecting aquatic habitats (Palmer et al. 2009; Grill et al. 2015). Such conditions can lead to a cascade of ecological consequences, from altered sediment transport to the loss of habitats essential for aquatic species (Kumar et al. 2019).

Examinations of other barrages like Farakka reveal similar hydrological and ecological disruption patterns, including altered flow patterns, increased salinity, and impacts on aquatic and riparian ecosystems (Gain & Giupponi 2014). These findings emphasize the need for comprehensive river basin management strategies that consider human needs and ecological sustainability. The RFHI and the Environmental Flows Assessment will help examine historical flow data, current ecological conditions, and the life history requirements of important species (Richter et al. 1996; Poff et al. 1997).

The method presented has limitations. For example, in our case study, variations in extreme events within the Rate of change, Frequency, and Other flow regime categories are challenging to interpret, possibly influenced by climate changes (Sillmann et al. 2017) or data insufficiency. Additionally, our results were derived by equally weighting all flow regime categories, but adjusting weights could better capture critical extreme events tied to specific processes.

Impact on ecology

Mishra et al. (2007) observed a decline in the catch of IMCs from 2002 to 2004, which they attributed to water withdrawals from the Hathnikund barrage and changes in physical habitat. Specifically, the percentage of IMC and large catfish catches dropped significantly from a high of 5.4% (1997–1998) to 2.45% (2003–2004) and from 10.5% (1997–1998) to 6.53% (2003–2004), respectively. This decrease coincides with the start of increased water diversion around 2000–2001, suggesting a direct link to habitat alterations from the barrage operations. Although mahseer populations showed stability, likely due to fishery interventions like seed induction, the barrage threatens their long-term sustainability, which impedes migration necessary for breeding (Mathur et al. 2022).

Barrages often disrupt sediment transport and hydrological flows, crucial for maintaining aquatic habitats (Graf 2006). For example, changes in flow regimes can severely impact fish migration patterns and spawning sites, directly affecting biodiversity (Bhatt et al. 2017). Furthermore, alterations in river morphology, such as changes in channel width and depth due to barrage operation, can lead to habitat loss for various aquatic and riparian species, altering the ecosystem structure and function (Bunn & Arthington 2002). The Hathnikund barrage could similarly influence the Yamuna River's ecological balance, affecting its biota and ability to support the surrounding biodiversity. By examining these impacts, the paper could offer insights into sustainable water management that balances human needs with ecological preservation (Poff et al. 1997; Palmer et al. 2009).

The study on the hydromorphological shifts of the Yamuna River due to barrage construction provides significant insights that can guide future river management and policymaking:

  • 1. The RFHI revealed moderate alterations in flow frequency and rate of change due to barrage operations. This demonstrates the barrages' significant role in altering the natural hydrological regime, which can have lasting impacts on river health and downstream water availability.

  • 2. Analysis through satellite imagery confirmed that barrages have contributed to changes in river sinuosity, braid-channel ratio, and other planform parameters. Such physical transformations suggest that barrage operations are a major driver of river morphology changes, impacting sediment transport and river stability.

  • 3. The alterations in flow and morphology due to barrages likely lead to significant ecological impacts, including changes in habitat quality and biodiversity. The study underscores the importance of maintaining ecological integrity through sustainable flow management practices.

  • 4. The findings advocate for an integrated approach to river basin management that balances developmental needs with ecological sustainability.

The financial support for this study was received from cGanga, NMCG, Department of Water Resources, River Development & Ganga Rejuvenation, Ministry of Jal Shakti, India.

M.M.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, review, and editing. V.T.: Conceptualization, Funding acquisition, Supervision, Writing – review and editing.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

Acreman
M. C.
,
Overton
I. C.
,
King
J.
,
Wood
P. J.
,
Cowx
I. G.
,
Dunbar
M. J.
,
Kendy
E.
&
Young
W. J.
2014
L’évolution du rôle de l’éco-hydrologie dans la détermination des débits environnementaux
.
Hydrological Sciences Journal
59
(
3–4
),
433
450
.
https://doi.org/10.1080/02626667.2014.886019
.
Agarwal
A. K.
&
Narain
S.
1997
Dying Wisdom: Rise, Fall and Potential of India's Traditional Water Harvesting Systems
.
Center for Science and Environment, New Delhi, India
.
Babamoradi
H.
,
Van Den Berg
F.
&
Rinnan
Å
.
2013
Bootstrap based confidence limits in principal component analysis – A case study
.
Chemometrics and Intelligent Laboratory Systems
120
,
97
105
.
https://doi.org/10.1016/j.chemolab.2012.10.007
.
Bellmore
J. R.
,
Duda
J. J.
,
Craig
L. S.
,
Greene
S. L.
,
Torgersen
C. E.
,
Collins
M. J.
&
Vittum
K.
2017
Status and trends of dam removal research in the United States
.
Wiley Interdisciplinary Reviews: Water
4
(
2
).
https://doi.org/10.1002/WAT2.1164
.
Bunn
S. E.
&
Arthington
A. H.
2002
Basic principles and ecological consequences of altered flow regimes for aquatic biodiversity
.
Environmental Management
30
(
4
),
492
507
.
https://doi.org/10.1007/s00267-002-2737-0
.
Central Water Commission
2017
Handbook for Hydrometeorological Observations
. .
Cheng
F.
&
Granata
T.
2007
Sediment transport and channel adjustments associated with dam removal: Field observations
.
Water Resources Research
43
(
3
),
1
14
.
https://doi.org/10.1029/2005WR004271
.
Devi
L.
1992
Climatic Characteristics and Water Balance (A Study of Uttar Pradesh)
.
Concept Publishing Company
,
New Delhi
.
Doyle
M. W.
2005
Incorporating hydrologic variability into nutrient spiraling
.
Journal of Geophysical Research: Biogeosciences
110
(
G1
),
1
11
.
https://doi.org/10.1029/2005jg000015
.
Doyle
M. W.
,
Stanley
E. H.
&
Harbor
J. M.
2003
Channel adjustments following two dam removals in Wisconsin
.
Water Resources Research
39
(
1
),
1
15
.
https://doi.org/10.1029/2002WR001714
.
East
A. E.
,
Pess
G. R.
,
Bountry
J. A.
,
Magirl
C. S.
,
Ritchie
A. C.
,
Logan
J. B.
,
Randle
T. J.
,
Mastin
M. C.
,
Minear
J. T.
,
Duda
J. J.
,
Liermann
M. C.
,
McHenry
M. L.
,
Beechie
T. J.
&
Shafroth
P. B.
2015
Large-scale dam removal on the Elwha River, Washington, USA: River channel and floodplain geomorphic change
.
Geomorphology
228
,
765
786
.
https://doi.org/10.1016/j.geomorph.2014.08.028
.
Erfon
B.
&
Tibshirani
R.
1986
Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy
.
Statistical Science
1
(
1
),
54
77
.
https://doi.org/10.2307/2246134
.
Friend
P. F.
&
Sinha
R.
1993
Braiding and meandering parameters
.
Geological Society Special Publication
75
(
December
),
105
111
.
https://doi.org/10.1144/GSL.SP.1993.075.01.05
.
Graf
W. L.
2006
Downstream hydrologic and geomorphic effects of large dams on American rivers
.
Geomorphology
79
(
3–4
),
336
360
.
https://doi.org/10.1016/j.geomorph.2006.06.022
.
Grill
G.
,
Lehner
B.
,
Lumsdon
A. E.
,
Macdonald
G. K.
,
Zarfl
C.
&
Reidy Liermann
C.
2015
An index-based framework for assessing patterns and trends in river fragmentation and flow regulation by global dams at multiple scales
.
Environmental Research Letters
10
(
1
).
https://doi.org/10.1088/1748-9326/10/1/015001
.
Gurnell
A. M.
,
Corenblit
D.
,
García de Jalón
D.
,
González del Tánago
M.
,
Grabowski
R. C.
,
O'Hare
M. T.
&
Szewczyk
M.
2016
A conceptual model of vegetation–hydrogeomorphology interactions within river corridors
.
River Research and Applications
32
,
142
163
.
https://doi.org/10.1002/rra.2928
.
Haryana Power Generation Corporation Limited. Available from: https://hpgcl.org.in/installed-capacity (accessed 26 December 2022)
.
Henriksen
J. A.
,
Heasley
J.
,
Kennen
J.
&
Nieswand
S.
2006
Users Manual for the Hydroecological Integrity Assessment Process Software (including the New Jersey Assessment Tools). Open File Report 2006-1093. Biological Resources Discipline, US Geological Survey, Reston, Virginia
.
Iqbal
K.
&
Dutta
V.
2022
Effect of flow alteration on river ecology: State of the art
. In:
Environmental Studies and Climate Change
. CRC Press, pp.
47
79
. eBook ISBN 9781003220824.
Irrigation & Water Resources Department, Uttar Pradesh. Available from: https://idup.gov.in/en/article/yamuna (accessed 15 December 2022)
.
Jain
S. K.
2012
Sustainable water management in India considering likely climate and other changes
.
Current Science
102
(
2
),
177
188
.
Jain
S. K.
,
Agarwal
P. K.
&
Singh
V. P.
2007
Major uses of water in India
. In:
Hydrology and Water Resources of India
. Water Science and Technology Library, vol. 57.
(Singh, V. P. & Singh, P., eds.). Springer, Dordrecht. https://doi.org/10.1007/1-4020-5180-8_17.
Joshi
K. D.
,
Alam
A.
,
Jha
D. N.
,
Srivastava
S. K.
&
Kumar
V.
2016
Fish diversity, composition and invasion of exotic fishes in river Yamuna under altered water quality conditions
.
Indian Journal of Animal Sciences
86
(
8
),
957
963
.
https://doi.org/10.56093/ijans.v86i8.60837
.
Junk
W. J.
,
Bayley
P. B.
&
Sparks
R. E.
1989
The flood pulse concept in river-floodplain
.
Canadian Special Publication of Fisheries and Aquatic Sciences
106
(
September 1989
),
110
127
.
Kroll
C. N.
,
Croteau
K. E.
&
Vogel
R. M.
2015
Hypothesis tests for hydrologic alteration
.
Journal of Hydrology
530
,
117
126
.
https://doi.org/10.1016/j.jhydrol.2015.09.057
.
Kumar
M.
,
Sharif
M.
&
Ahmed
S.
2019
Impact of urbanization on the river Yamuna basin
.
International Journal of River Basin Management
18
(
4
),
461
475
.
https://doi.org/10.1080/15715124.2019.1613412
.
Kuriqi
A.
,
Pinheiro
A. N.
,
Sordo-Ward
A.
,
Bejarano
M. D.
&
Garrote
L.
2021
Ecological impacts of run-of-river hydropower plants – Current status and future prospects on the brink of energy transition
.
Renewable and Sustainable Energy Reviews
142
(
March
).
https://doi.org/10.1016/j.rser.2021.110833
.
Lawrence
R. E.
2001
The impacts of hydroelectric construction works on the hydrology of a subalpine area in Australia
.
Environmental Geology
40
(
4–5
),
612
621
.
https://doi.org/10.1007/s002540000223
.
Lee
C. J.
,
Choi
H.
,
Kim
D.
,
van Oorschot
M.
,
Penning
E.
&
Geerling
G. W.
2023
Bio-geomorphic alteration through shifting flow regime in a modified monsoonal river system in Korea
.
River Research and Applications
39
,
1639
1651
.
Lytle
D. A.
&
Poff
N. L. R.
2004
Adaptation to natural flow regimes
.
Trends in Ecology and Evolution
19
(
2
),
94
100
.
https://doi.org/10.1016/j.tree.2003.10.002
.
Magilligan
F. J.
&
Nislow
K. H.
2005
Changes in hydrologic regime by dams
.
Geomorphology
71
(
1–2
),
61
78
.
https://doi.org/10.1016/j.geomorph.2004.08.017
.
Major
J. J.
,
O'Connor
J. E.
,
Podolak
C. J.
,
Keith
M. K.
,
Grant
G. E.
,
Spicer
K. R.
,
Pittman
S.
,
Bragg
H. M.
,
Wallick
J. R.
,
Tanner
D. Q.
,
Rhode
A.
&
Wilcock
P. R.
2012
Geomorphic Response of the Sandy River, Oregon, to Removal of Marmot Dam. US Geological Survey Professional Paper 1792, pp. 1–76
.
Mathews
R.
&
Richter
B. D.
2007
Application of the indicators of hydrologic alteration software in environmental flow setting
.
Journal of the American Water Resources Association
43
(
6
),
1400
1413
.
https://doi.org/10.1111/j.1752-1688.2007.00099.x
.
Mathur
R. P.
,
Kapoor
V.
&
Behera
S. K.
2022
Ecological profiling of focal species
. In:
Compendium of Biodiversity in Ganga River System
(Tare, V. & Mathur, R. P., eds.).
Lambert Academic Publishing
,
Mauritius
, pp.
179
224
.
Mishra
D. N.
,
Moza
U.
,
Lakra
C.
&
Kumar
S.
2007
Time scale changes in fisheries of river Yamuna
.
Journal of the Inland Fisheries Society of India
32
(
2
),
48
52
.
Misra
A. K.
2010
A river about to die: Yamuna
.
Journal of Water Resource and Protection
02
(
05
),
489
500
.
https://doi.org/10.4236/jwarp.2010.25056
.
Mohanty
M.
&
Tare
V.
2022
Anthropogenic interventions in watersheds on river flow health: Assessment using bootstrapped principal component analysis
.
Journal of Water Resources Planning and Management
148
(
1
),
1
11
.
https://doi.org/10.1061/(asce)wr.1943-5452.0001499
.
Olden
J. D.
&
Poff
N. L.
2003
Redundancy and the choice of hydrologic indices for characterizing streamflow regimes
.
River Research and Applications
19
(
2
),
101
121
.
https://doi.org/10.1002/rra.700
.
Palmer
M. A.
,
Lettenmaier
D. P.
,
Poff
N. L.
,
Postel
S. L.
,
Richter
B.
&
Warner
R.
2009
Climate change and river ecosystems: Protection and adaptation options
.
Environmental Management
44
(
6
),
1053
1068
.
https://doi.org/10.1007/s00267-009-9329-1
.
Pearson
A. J.
,
Synder
N. J.
&
Collins
M. J.
2011
Rates and processes of channel response to dam removal with a sand-filled impoundment
.
47
(
W08504
).
https://doi.org/10.1029/2010WR009733
.
Poff
N. L.
,
Allan
J. D.
,
Bain
M. B.
,
Karr
J. R.
,
Prestegaard
K. L.
,
Richter
B. D.
,
Sparks
R. E.
&
Stromberg
J. C.
1997
The natural flow regime: A paradigm for river conservation and restoration
.
BioScience
47
(
11
),
769
784
.
https://doi.org/10.2307/1313099
.
Richter
B. D.
,
Baumgartner
J. V.
,
Powell
J.
&
Braun
D. P.
1996
A method for assessing hydrologic alteration within ecosystems
.
Conservation Biology
10
(
4
),
1163
1174
.
https://doi.org/10.2307/2387152
.
Riggsbee
J. A.
,
Julian
J. P.
,
Doyle
M. W.
&
Wetzel
R. G.
2007
Suspended sediment, dissolved organic carbon, and dissolved nitrogen export during the dam removal process
.
Water Resources Research
43
(
9
),
1
16
.
https://doi.org/10.1029/2006WR005318
.
Shahid
M.
,
Cong
Z.
&
Zhang
D.
2018
Understanding the impacts of climate change and human activities on streamflow: A case study of the Soan River basin, Pakistan
.
Theoretical and Applied Climatology
134
,
205
219
.
https://doi.org/10.1007/s00704-017-2269-4
.
Sharma
D.
&
Kansal
A.
2011
Water quality analysis of River Yamuna using water quality index in the national capital territory, India (2000–2009)
.
Applied Water Science
1
(
3–4
),
147
157
.
https://doi.org/10.1007/s13201-011-0011-4
.
Sharma
P.
,
Das
M. K.
,
Vass
K. K.
&
Tyagi
R. K.
2017
Patterns of fish diversity, community structure and ecological integrity of River Yamuna, India
.
Aquatic Ecosystem Health & Management
20
(
1–2
),
30
42
.
https://doi.org/10.1080/14634988.2017.1265879
.
Shih
S. S.
,
Liu
C. H.
&
Ning
J. H.
2022
In-river weir effects on the alteration of flow regime and regarding structural stream habitat
.
Journal of Hydrology
615
(
PA
),
128670
.
https://doi.org/10.1016/j.jhydrol.2022.128670
.
Sillmann
J.
,
Thorarinsdottir
T.
,
Keenlyside
N.
,
Schaller
N.
,
Alexander
L. V.
,
Hegerl
G.
,
Seneviratne
S. I.
,
Vautard
R.
,
Zhang
X.
&
Zwiers
F. W.
2017
Understanding, modeling and predicting weather and climate extremes: Challenges and opportunities
.
Weather and Climate Extremes
18
(
Apr
),
65
74
.
https://doi.org/10.1016/j.wace.2017.10.003
.
Singh
V. P.
2012
The Yamuna River Basin: Water resources and environment
. In:
Water Science and Technology Library
, Vol. 57
(Rai, R. K., Upadhyay, A., Ojha, C. S. P. & Singh, V. P., eds.). Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2001-5
.
Stroud
P. L.
2012
Sediment Strategies: Choosing a Sediment Management Option for Dam Removal
.
Available from: www.hydroworld.com (accessed 14 December 2022)
.
Surian
N.
&
Rinaldi
M.
2003
Morphological response to river engineering and management in alluvial channels in Italy
.
Geomorphology
50
(
4
),
307
326
.
https://doi.org/10.1016/S0169-555X(02)00219-2
.
Thoms
M. C.
&
Sheldon
F.
2000
Water resource development and hydrological change in a large dryland river: The Barwon-Darling River, Australia
.
Journal of Hydrology
228
(
1–2
),
10
21
.
https://doi.org/10.1016/S0022-1694(99)00191-2
.
Tockner
K.
&
Ward
J. V.
1999
Biodiversity along riparian corridors
.
Large Rivers
11
(
3
),
293
310
.
https://doi.org/10.1127/lr/11/1999/293
.
Wheaton
J. M.
,
Fryirs
K. A.
,
Brierley
G.
,
Bangen
S. G.
,
Bouwes
N.
&
O'Brien
G.
2015
Geomorphic mapping and taxonomy of fluvial landforms
.
Geomorphology
248
,
273
295
.
https://doi.org/10.1016/j.geomorph.2015.07.010
.
Wilcox
A. C.
,
O'Connor
J. E.
&
Major
J. J.
2014
Rapid reservoir erosion, hyperconcentrated flow, and downstream deposition triggered by breaching of 38 m tall Condit Dam, White Salmon River, Washington
.
Journal of Geophysical Research: Earth Surface
119
,
1376
1394
.
https://doi.org/10.1002/2013JF003073
.
Yadav
S. R.
,
Jatav
S. K.
&
Jangra
L.
2023
Assessment of bird diversity along Yamuna River, Haryana, India
.
Indian Journal of Ecology
50
(
6
),
2110
2113
.
https://doi.org/https://doi.org/10.55362/IJE/2023/4183
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).

Supplementary data