Abstract
The study focused on analysing the eutrophication indicators of the Krishnagiri Reservoir Project (KRP) using the Water Quality Analysis Simulation Program (WASP). The reservoir was divided into six segments to simulate the indicators, and field measurements from 2015 to 2018 were used to calibrate the model while 2019 to 2022 were used to validate it. The results showed that the model predictions were in good agreement with the measured values, indicating the reliability of the model. The study assessed the impact of nutrient loads on dissolved oxygen, nitrate, phosphate, and chlorophyll-a. Four nutrient loading scenarios were simulated, and the most effective scenario (Scenario 4) involved a 75% reduction in nutrient load, which increased the reaeration rate by 1.47% and decreased chlorophyll-a concentrations by 88%. The study concluded that maintaining nitrates below 2.5 mg/L and phosphates below 0.75 mg/L could help restore the KRP reservoir's trophic status from hypereutrophic to mesotrophic. Overall, the study demonstrated the use of the WASP model in developing nutrient loading scenarios to manage reservoir water quality effectively. The findings could help policymakers and managers make informed decisions about reducing nutrient loads and restoring the trophic status of eutrophicated reservoirs.
HIGHLIGHTS
The study focused on the eutrophication management of the Krishnagiri Reservoir Project (KRP) using the Water Quality Analysis Stimulation Program (WASP) model with different nutrient load scenarios.
WASP is used to simulate the eutrophication indicators such as dissolved oxygen, chlorophyll-a, nitrate, and phosphate concentrations in the KRP reservoir.
Calibrated Eutro-model was used to develop scenarios for eutrophication management.
INTRODUCTION
Water quality models are important tools in the management of water resources. Since a wide variety of models are available, it is necessary to choose the appropriate one for the intended use. A ScoRE approach has been proposed (Ejigu 2021) to select the most appropriate model based on the scope, record, and user experience among the eight water quality models (CE-QUAL-W2, MIKE HYDRO River, MOHID Water, SIMCAT, SisBaHIA, TOMCAT, QUAL2KW, and WASP7) for the selected problem. The Water Quality Analysis Simulation Program (WASP) model ranked as the second-best model with a ScoRE value of 3.6 (Mateus et al. 2018). Another comparison among the multiple regression, remote sensing methods and WASP model for water quality prediction (salinity) in Akkulam-Veli Lake (Albert Moses et al. 2016) found that the WASP model is the most efficient model among them with an efficiency of 0.83.
WASP (version 8) is an open-source dynamic compartment model for aquatic systems that can simulate both the water column and the underlying benthos. WASP's eutrophication sub-model is a process-based, mechanistic or deterministic water quality model that predicts phytoplankton growth patterns. Nutrients, phytoplankton, and dissolved oxygen (DO) concentrations are the key water quality elements studied in eutrophication modelling research. The availability of nutrients, temperature, and light all influence phytoplankton development in the WASP model (Moses et al. 2015).
WASP has been of interest to several researchers in the recent past. For example, the WASP model has been applied in El Pañe Reservoir (Mamani Larico & Zúñiga Medina 2019) to assess the eutrophication process by dividing the reservoir into 11 superficial segments representing one-dimensional horizontal distribution and found that aquaculture activities and the benthic flux are the major nutrient sources to control the phytoplankton community. WASP is also capable of capturing the seasonality and spatial variability of under-ice coupled river-lake system's water quality (Akomeah et al. 2020).
A similar study with WASP in Lake Obili showed that improved DO concentration levels in the reservoir can improve aquatic life (Ajeagah et al. 2017). There have been several studies (Narasimhan et al. 2010; Park et al. 2013; Mbuh et al. 2019; Obin et al. 2021; Shabani et al. 2021; Wimordi et al. 2021; Żelazny et al. 2023) that have investigated the effectiveness of WASP in water quality assessment and eutrophication control.
Krishnagiri Reservoir Project (KRP), located in South India is hyper-eutrophicated for the past two decades (Elangovan & Murali 2020). The research study (Sudha & Ambujam 2012; Ambujam & Sudha 2016) on the reservoir found that the nutrient loads resulting from the catchment processes significantly alter the trophic status of the reservoir. Total phosphorus from the cropland is the major contributor to the KRP (Sasitharan & Arunbabu 2020) leading to the present eutrophic condition of the reservoir. Remote sensing techniques deployed for the rapid assessment of reservoir trophic status (Elangovan & Murali 2020; Abdul Wahid & Arunbabu 2022) mapped the concentration of chlorophyll-a (chl-a), one of the indicators for eutrophication assessment emphasized the need for monitoring and managing the reservoir before it reaches an anoxic condition and loses its storage capacity. However, earlier work in Krishnagiri Reservoir does not attempt to develop process-based water quality models. The present study focused on the eutrophication management of the KRP using a process-based mechanistic WASP model with different nutrient load scenarios. To achieve this objective, WASP is used to simulate the eutrophication indicators such as DO, chl-a, nitrate, and phosphate concentrations in the KRP reservoir. The calibrated model was then used to develop scenarios for eutrophication management.
MATERIALS AND METHODS
Study area
WASP model governing equations
The WASP model is based on the conservation of mass, which accounts for all material entering and exiting the water body through direct material addition (runoff and loading), advective and dispersive transport modes, as well as physical, chemical, and biological changes.
Constituent kinetic reactions (SK)
Dissolved oxygen
Nitrate-nitrogen
Inorganic phosphorus
The parameters in Equations (b) and (c) are explained below.
S is dissolved inorganic nutrient concentration (M/L3), S′ is another inorganic form of the nutrient which decays to the form S (e.g., NH3 NO3), (M/L3), Sorg is the dissolved organic nutrient concentration (M/L3), Sdet is the suspended particulate organic nutrient concentration (M/L3), Ssed is the organic sediment nutrient concentration (M/L3), K1 is the transformation rate of S′ into S (T−1), K2 is the transformation rate of S into some other dissolved inorganic form of the nutrient (T−1), Korg is the hydrolysis rate of dissolved organic nutrient (T−1), Kdet is the decomposition rate of particulate organic nutrient (T−1), Ksed is the decomposition rate of organic sediment nutrient (T−1), Ks is the settling rate for particulate organic nutrient (T−1), Vs is the photosynthetic uptake rate for nutrient (M/L3 T), es is the soluble excretion rate of nutrient by all organisms (M/L3 T), f1 is the fraction of soluble excretions which are inorganic, f2 is the fraction of detritus decomposition products which are immediately available for algal uptake, and f3 is the fraction of sediment decomposition products which are immediately available for algal uptake.
Phytoplankton
Model segmentation
The geometry of reservoir segments
Segment . | Volume (Mm3) . | Length (m) . | Breadth (m) . | Depth (m) . | Average velocity (m/s) . | Slope . |
---|---|---|---|---|---|---|
S6 | 0.55 | 1,452 | 766 | 0.5 | 0.2064 | 0.00048 |
S5 | 3.39 | 3,175 | 883 | 1.21 | 0.028 | 0.00137 |
S4 | 7.871 | 3,172 | 880 | 2.82 | 0.012 | 0.00320 |
S3 | 9.171 | 2,990 | 676 | 4.537 | 0.008 | 0.00671 |
S2 | 9.45 | 1,774 | 932 | 5.72 | 0.010 | 0.00610 |
S1 | 4.31 | 1,074 | 696 | 5.767 | 0.017 | – |
Segment . | Volume (Mm3) . | Length (m) . | Breadth (m) . | Depth (m) . | Average velocity (m/s) . | Slope . |
---|---|---|---|---|---|---|
S6 | 0.55 | 1,452 | 766 | 0.5 | 0.2064 | 0.00048 |
S5 | 3.39 | 3,175 | 883 | 1.21 | 0.028 | 0.00137 |
S4 | 7.871 | 3,172 | 880 | 2.82 | 0.012 | 0.00320 |
S3 | 9.171 | 2,990 | 676 | 4.537 | 0.008 | 0.00671 |
S2 | 9.45 | 1,774 | 932 | 5.72 | 0.010 | 0.00610 |
S1 | 4.31 | 1,074 | 696 | 5.767 | 0.017 | – |
DATA SOURCES
For eutrophication management in the Krishnagiri Reservoir, the EUTRO sub-model has been selected to simulate the water quality state variables. WASP requires datasets such as (i) time series data of meteorological parameters, (ii) inflow data, (iii) initial and boundary conditions of water quality parameters, (iv) environmental constant, and (v) channel geometry of reservoir segment.
Water quality data
DO, nitrate, phosphate, and chlorophyll-a are the water quality parameters modelled in WASP. The historical information on the KRP reservoir's water quality parameters from 2015 to 2020, as reported by researchers (Table 2) (Ambujam & Sudha 2016; Elangovan & Murali 2020; Abdul Wahid & Arunbabu 2022), was compiled into a database in the wet chemistry laboratory of the Centre for Water Resources and made available for model set-up and calibration. From 2021 to 2022, a field programme was set up to collect data monthly frequency on the water quality of the KRP reservoir. Chlorophyll sondes and multiparameter probes were employed to make in-situ measurements on chl-a and DO. Surface water samples were collected, tested in a laboratory for nitrate and phosphate, and used to validate the model.
Model set-up . | Dataset period . | Work from Wet Chemistry Laboratory CWR . | State variables simulated . |
---|---|---|---|
Initial conditions | 2015 | Ambujam & Sudha (2016) | DO, chl-a, nitrate, and phosphate |
Boundary condition and calibration | 2016 | ||
2018 | Elangovan & Murali (2020) | ||
2019 | Abdul Wahid & Arunbabu (2022) | ||
2020 | |||
Validation | 2021 | In-situ measurements and sample testing in the laboratory | |
2022 |
Model set-up . | Dataset period . | Work from Wet Chemistry Laboratory CWR . | State variables simulated . |
---|---|---|---|
Initial conditions | 2015 | Ambujam & Sudha (2016) | DO, chl-a, nitrate, and phosphate |
Boundary condition and calibration | 2016 | ||
2018 | Elangovan & Murali (2020) | ||
2019 | Abdul Wahid & Arunbabu (2022) | ||
2020 | |||
Validation | 2021 | In-situ measurements and sample testing in the laboratory | |
2022 |
Meteorological data
Eutro-model in WASP requires temperature, precipitation, wind speed, relative humidity, and solar radiation to characterize the weather conditions of the study region. Solar radiation was acquired from NASA's POWER Data Access View while meteorological data such as temperature, precipitation, wind speed, and relative humidity were obtained from the State Surface and Ground Data Centre, Chennai.
Inflow data
The transport of the pollutant is dependent of the inflow into the reservoir. The inflow data was obtained from the Krishnagiri Reservoir, Water Resources Department.
Environmental constants
Each water quality metric has a predefined set of constants from which appropriate, and constants have to be chosen. For moderately deep to deep channels with heights 0–10 m and stream velocity 0.15–0.5 m/s, WASP simulates DO with a reaeration coefficient derived using O'Connor & Dobbins (1958) with depth-averaged values ranging from 0 to 10 per day. Temperature coefficient values between 1.022 and 1.024 are commonly employed in most modelling applications to represent the effects of temperature on reaeration (Downing & Truesdale 2007). Abiotic and biotic nutrients are both present in the aquatic system in different ways. The abiotic nutrient concentrations of dissolved inorganic nutrient NO3, dissolved organic nutrient PO4, and biotic nutrient phytoplankton with chlorophyll as its algal unit are all simulated using the WASP model. To model nutrients, Thomann & Fitzpatrick's (1982) equations are used to fix the denitrification and phosphorus transformation rate coefficient values. Phytoplankton dynamics were modelled using a maximum growth rate constant of 0.05 per day at 20 °C and a growth temperature coefficient that ranged from 1.02 to 1.1. Additional environmental constants, such as the phosphorus-to-carbon ratio, nitrogen-to-carbon ratio, carbon and chlorophyll ratio, nitrogen-to-chlorophyll ratio, and saturating light intensity, are needed to model the dynamics of phytoplankton (O'Connor & Dobbins 1958). The model is calibrated by adjusting the constants using the trial-and-error approach to acquire site-specific constants for the model. The environment constants used in the present study are presented in Table 4.
Model performance indicators
The degree of linear association between observed and simulated data is indicated by the correlation coefficient, which has a range of −1 to +1. There is no linear connection if r is equal to zero. A complete positive or negative linear connection is obtained when r is −1 or 1, respectively (Moriasi et al. 2007). Similarly, R2 represents the fraction of the variation in measured data that the model explains. R2 has a value between 0 and 1, with higher values suggesting less error variation; normally, values above 0.5 are regarded as acceptable (Shalabh & Dhar 2021).
Eutrophication control scenarios
Various scenarios have been considered to control the eutrophication problem in the reservoir. Nutrients enter water bodies via point and non-point source pollution. Scenarios are defined in a way to control this pollution by adapting to better management practices and controlling the loads entering the reservoir. The scenarios for which the model has been run are defined below.
Restricting livestock access to the reservoir, improving riparian buffer zones, and establishing built wetlands are the best management strategies for addressing pollution from both point and non-point sources (Zamparas & Zacharias 2014). To limit sediment yield, stone barriers, field bunding, mulching, and bio-fencing can be used in the catchment region (Arunbabu et al. 2014). Table 3 depicts situations that combine several best management techniques to minimize load from both point and non-point sources.
Scenario . | Stock access to the reservoir (1) . | Riparian buffer zone (2) . | Wetlands (3) . | Stonewall, field bunds, mulching, bio-fencing (4) . | Percentage of organic farming in the catchment area (5) . | Nutrient loading in percent (1 + 2 + 3 + 4 + 5) . |
---|---|---|---|---|---|---|
1 | Not restricted | Not enhanced | Not constructed | Not practised | 0 | 100 |
2 | Not restricted | Not enhanced | Not constructed | Practised | 25 | 75 |
3 | Restricted | Enhanced | Constructed | Not practised | 50 | 50 |
4 | Restricted | Enhanced | Constructed | Practised | 75 | 25 |
Scenario . | Stock access to the reservoir (1) . | Riparian buffer zone (2) . | Wetlands (3) . | Stonewall, field bunds, mulching, bio-fencing (4) . | Percentage of organic farming in the catchment area (5) . | Nutrient loading in percent (1 + 2 + 3 + 4 + 5) . |
---|---|---|---|---|---|---|
1 | Not restricted | Not enhanced | Not constructed | Not practised | 0 | 100 |
2 | Not restricted | Not enhanced | Not constructed | Practised | 25 | 75 |
3 | Restricted | Enhanced | Constructed | Not practised | 50 | 50 |
4 | Restricted | Enhanced | Constructed | Practised | 75 | 25 |
Constants . | Min . | Max . | Trial 1 . | Trial 2 . | Trial 3 (value fitted) . |
---|---|---|---|---|---|
Phytoplankton maximum growth rate constant @20 °C (1/day) | 0 | 3 | 0 | 0.01 | 0.05 |
Phytoplankton growth temperature coefficient | 0 | 1.07 | 0.02 | 0.5 | 1 |
Phytoplankton carbon to chlorophyll ratio (mg C/mg Chl) | 0 | 200 | 12 | 24 | 50 |
Optimal temperature for growth (C) | 0 | 1 | 8 | 16 | 20 |
Shape parameter for below optimal temperatures | 0 | 1 | 0.001 | 0.025 | 0.05 |
Shape parameter for above optimal temperatures | 0 | 1 | 0.002 | 0.03 | 0.05 |
Phytoplankton respiration rate constant @20 °C (1/day) | 0 | 0.5 | 0.01 | 0.05 | 0.1 |
Phytoplankton respiration temperature coefficient | 0 | 1.08 | 0.01 | 0.5 | 0.1 |
Phytoplankton death rate constant (non-zoo predation) (1/day) | 0 | 1.07 | 0.5 | 1 | 1.07 |
Phytoplankton death rate due to salinity toxicity (1/day) | 0 | 1 | 0.5 | 0.8 | 0 |
Salinity at which algal mortality is half maximum value (g/L) | 0 | 100 | 0.2 | 0.4 | 0 |
Phytoplankton zooplankton grazing rate constant (1/day) | 0 | 30 | 12 | 18 | 30 |
Grazability (0 to 1) | 0 | 1 | 0.5 | 0.8 | 0 |
Nitrogen fixation option (0 = no, 1 = yes) | 0 | 1 | 0.4 | 0.9 | 0 |
Phytoplankton optimal light saturation as PAR (Watts/m2) | 0 | 350 | 120 | 230 | 300 |
Phytoplankton half-sat. for mineralization rate (mg Phyt C/L) | 0 | 1 | 0 | 1.5 × 10−6 | 1.0 × 10−6 |
Phytoplankton half-saturation constant for N uptake (mg N/L) | 0 | 0.05 | 0 | 1.0 × 10−6 | 1.0 × 10−6 |
Phytoplankton half-saturation constant for P uptake (mg P/L) | 0 | 0.05 | 0.01 | 0.015 | 0.02 |
Phytoplankton half-saturation constant for Si uptake (mg Si/L) | 0 | 0.2 | 0 | 0.02 | 0.04 |
Phytoplankton nitrogen to carbon ratio (mg N/mg C) | 0 | 0.43 | 0.15 | 0.2 | 0.18 |
Phytoplankton phosphorus to carbon ratio (mg P/mg C) | 0 | 0.24 | 0 | 0.02 | 0.05 |
Phytoplankton silica to carbon ratio (mg Si/mg C) | 0 | 1 | 0.3 | 0.5 | 0.8 |
Global reaeration rate constant @20 °C (1/day) | 0 | 10 | 0 | 5 | 0.26 |
Calc Reaeration Option (0 = Covar, 1 = O'Connor, 2 = Owens, 3 = Churchill, 4 = Tsivoglou) | 0 | 4 | 0 | 2 | 1 |
Minimum reaeration velocity (m/day) | 0 | 24 | 0 | ||
Maximum allowable calculated reaeration rate per day | 0 | 100 | 24 | 24 | 24 |
Use the total depth of water column for reaeration | 0 | 1 | 0 | 1 | 0.5 |
Waterbody type used for wind-driven reaeration rate | 0 | 3 | 0 | 2 | 1 |
Elevation above sea level (m) | 0 | 15,000 | 492 | 492 | 492 |
Oxygen to carbon stoichiometric ratio | 0 | 2.67 | 2.5 | 2.55 | 2.67 |
Theta – reaeration temperature correction | 0 | 1.03 | 1 | 1.01 | 1.024 |
Theta – SOD temperature correction | 0 | 1.1 | 0 | 0.6 | 1 |
Denitrification rate constant @20 °C (1/day) | 0 | 0.09 | 0 | 0.08 | 0.09 |
Denitrification temperature coefficient | 0 | 1.04 | 0 | 1.04 | 1 |
Half-saturation constant for denitrification oxygen limit (mg O2/L) | 0 | 0 | 0 | 0 | 0 |
Dissolved organic phosphorus mineralization rate constant @20 °C (1/day) | 0 | 0.22 | 0 | 0.1 | 0.19 |
Dissolved organic phosphorus mineralization temperature coefficient | 0 | 1.08 | 0 | 1 | 1 |
Constants . | Min . | Max . | Trial 1 . | Trial 2 . | Trial 3 (value fitted) . |
---|---|---|---|---|---|
Phytoplankton maximum growth rate constant @20 °C (1/day) | 0 | 3 | 0 | 0.01 | 0.05 |
Phytoplankton growth temperature coefficient | 0 | 1.07 | 0.02 | 0.5 | 1 |
Phytoplankton carbon to chlorophyll ratio (mg C/mg Chl) | 0 | 200 | 12 | 24 | 50 |
Optimal temperature for growth (C) | 0 | 1 | 8 | 16 | 20 |
Shape parameter for below optimal temperatures | 0 | 1 | 0.001 | 0.025 | 0.05 |
Shape parameter for above optimal temperatures | 0 | 1 | 0.002 | 0.03 | 0.05 |
Phytoplankton respiration rate constant @20 °C (1/day) | 0 | 0.5 | 0.01 | 0.05 | 0.1 |
Phytoplankton respiration temperature coefficient | 0 | 1.08 | 0.01 | 0.5 | 0.1 |
Phytoplankton death rate constant (non-zoo predation) (1/day) | 0 | 1.07 | 0.5 | 1 | 1.07 |
Phytoplankton death rate due to salinity toxicity (1/day) | 0 | 1 | 0.5 | 0.8 | 0 |
Salinity at which algal mortality is half maximum value (g/L) | 0 | 100 | 0.2 | 0.4 | 0 |
Phytoplankton zooplankton grazing rate constant (1/day) | 0 | 30 | 12 | 18 | 30 |
Grazability (0 to 1) | 0 | 1 | 0.5 | 0.8 | 0 |
Nitrogen fixation option (0 = no, 1 = yes) | 0 | 1 | 0.4 | 0.9 | 0 |
Phytoplankton optimal light saturation as PAR (Watts/m2) | 0 | 350 | 120 | 230 | 300 |
Phytoplankton half-sat. for mineralization rate (mg Phyt C/L) | 0 | 1 | 0 | 1.5 × 10−6 | 1.0 × 10−6 |
Phytoplankton half-saturation constant for N uptake (mg N/L) | 0 | 0.05 | 0 | 1.0 × 10−6 | 1.0 × 10−6 |
Phytoplankton half-saturation constant for P uptake (mg P/L) | 0 | 0.05 | 0.01 | 0.015 | 0.02 |
Phytoplankton half-saturation constant for Si uptake (mg Si/L) | 0 | 0.2 | 0 | 0.02 | 0.04 |
Phytoplankton nitrogen to carbon ratio (mg N/mg C) | 0 | 0.43 | 0.15 | 0.2 | 0.18 |
Phytoplankton phosphorus to carbon ratio (mg P/mg C) | 0 | 0.24 | 0 | 0.02 | 0.05 |
Phytoplankton silica to carbon ratio (mg Si/mg C) | 0 | 1 | 0.3 | 0.5 | 0.8 |
Global reaeration rate constant @20 °C (1/day) | 0 | 10 | 0 | 5 | 0.26 |
Calc Reaeration Option (0 = Covar, 1 = O'Connor, 2 = Owens, 3 = Churchill, 4 = Tsivoglou) | 0 | 4 | 0 | 2 | 1 |
Minimum reaeration velocity (m/day) | 0 | 24 | 0 | ||
Maximum allowable calculated reaeration rate per day | 0 | 100 | 24 | 24 | 24 |
Use the total depth of water column for reaeration | 0 | 1 | 0 | 1 | 0.5 |
Waterbody type used for wind-driven reaeration rate | 0 | 3 | 0 | 2 | 1 |
Elevation above sea level (m) | 0 | 15,000 | 492 | 492 | 492 |
Oxygen to carbon stoichiometric ratio | 0 | 2.67 | 2.5 | 2.55 | 2.67 |
Theta – reaeration temperature correction | 0 | 1.03 | 1 | 1.01 | 1.024 |
Theta – SOD temperature correction | 0 | 1.1 | 0 | 0.6 | 1 |
Denitrification rate constant @20 °C (1/day) | 0 | 0.09 | 0 | 0.08 | 0.09 |
Denitrification temperature coefficient | 0 | 1.04 | 0 | 1.04 | 1 |
Half-saturation constant for denitrification oxygen limit (mg O2/L) | 0 | 0 | 0 | 0 | 0 |
Dissolved organic phosphorus mineralization rate constant @20 °C (1/day) | 0 | 0.22 | 0 | 0.1 | 0.19 |
Dissolved organic phosphorus mineralization temperature coefficient | 0 | 1.08 | 0 | 1 | 1 |
Scenario 1
Scenario 1 depicts that there is no management practice carried out to control the loads entering the reservoir. Farmers use commercial fertilizers and no other alternative practices like organic farming or the construction of check dams to control sediment yield. The load entering the reservoir is 100%, without any conservation or management practices.
Scenario 2
In this scenario, the load entering the reservoir is reduced by 25%, due to the management practices mentioned above to reduce the point and non-point source pollution. A certain number of farmers in the upper catchment are also opting for organic farming which also helps in controlling the nutrients entering the reservoir. The amount of load entering the reservoir is 75%.
Scenario 3
In this scenario, the load entering the reservoir is reduced by 50%, due to the increase in the number of farmers practising organic farming compared to Scenario 2 which also helps in controlling the nutrients entering the reservoir. The amount of load entering the reservoir is 50%.
Scenario 4
In this scenario, the load entering the reservoir is reduced by 75%, if the majority of the farmers opted for organic farming, which helped in reducing the nutrients coming into the reservoir. The amount of load entering the reservoir is 25%, which is reduced due to the management practices and if a wastewater treatment system is also implemented to reduce the effluent load.
RESULTS AND DISCUSSION
Model calibration and validation
The WASP model was calibrated for the water quality parameters such as DO, nitrate, phosphate, and chl-a. The model period has been set from 1/1/2015 to 31/12/2022 with daily time steps for simulation. Measured water quality data from the KRP reservoir from 2015 to 2020 was used for calibration and the measured data from 2021 to 2022 were used for validating the model. The environmental constants and global kinetic constants for each parameter were adjusted to match the measured data during the calibration process (Table 4).
Model parameter . | Calibration . | Validation . | ||
---|---|---|---|---|
R2 . | NSE . | R2 . | NSE . | |
DO | 0.794 | 0.900 | 0.904 | 0.860 |
Chl-a | 0.770 | 0.636 | 0.774 | 0.712 |
Nitrate | 0.894 | 0.909 | 0.890 | 0.891 |
Phosphate | 0.826 | 0.864 | 0.843 | 0.760 |
Model parameter . | Calibration . | Validation . | ||
---|---|---|---|---|
R2 . | NSE . | R2 . | NSE . | |
DO | 0.794 | 0.900 | 0.904 | 0.860 |
Chl-a | 0.770 | 0.636 | 0.774 | 0.712 |
Nitrate | 0.894 | 0.909 | 0.890 | 0.891 |
Phosphate | 0.826 | 0.864 | 0.843 | 0.760 |
Dissolved oxygen
Chlorophyll-a
Nitrate and phosphate
Model simulated scenarios for eutrophication management
The WASP model's extensive spatiotemporal analysis of water quality metrics such as DO, chl--a, nitrate, and phosphate showed that nutrient content entering the reservoir serves as a source of eutrophication. Eutrophication at the surface causes the active breakdown of organic materials, which influences the benthic zone's DO depletion and improves the surface DO through photosynthesis and aeration. While nutrient loading is the ineluctable cause of the process of water quality degradation, this study proposes the creation of control scenarios for eutrophication management under various loading situations.
The breakdown of organic matter from the benthic to the surface, which depletes DO, and reaeration during photosynthetic activity both have an impact on the processes of nutrient loading and eutrophication.
The average chl-a value was found to be 39 g/L in scenario 2, which still maintains the reservoir in the hypereutrophic condition (>22 μg/L) (Carlson 1977), even when watershed conservation activities are successfully implemented to prevent erosion in the catchment region. Under scenario 3 (25 μg/L), there may be a modest shift from a hypereutrophic to a eutrophic condition when the livestock access to the reservoir is controlled and a wetland is created at the inflow zone. However, scenario 4 with stonewalls, mulching, bio-fence, and field bunds used in the agricultural fields in the catchment together with the riparian buffer zone, limited livestock access, and constructed wetlands leads to a reservoir that is a near mesotrophic state as the average chl-a concentration is lowered to 6 μg/L. According to research on water quality forecasting conducted at the Krishnagiri Reservoir (Abdul Wahid & Arunbabu 2022), the reservoir is degrading quickly, and it is predicted that the water quality will deteriorate to a level which cannot be used for agriculture after 10 years. In general, a rising trend with DO and a lowering trend with chl-a are shown when the native nutrient load is restricted to 75%. For effective management of the KRP reservoir using WASP, scenario 4 with optimal management techniques to control point and non-point sources is recommended.
CONCLUSION
The investigation of the water quality metrics showed that the current eutrophic condition of the reservoir is what is changing the DO value. The nutritional load and chl-a value both grow in response to an increase in intake. The average concentration of water quality parameters for the years 2015 to 2022 is relatively high using the USEPA criteria (0.1 mg/L for phosphate, 10 mg/L for nitrate) to limit eutrophication. There is a direct correlation between the high chl-a level and the high phosphate and nitrate levels in the reservoir. The reservoir's average chl-a level is over 22 μg/L, which denotes hypereutrophic conditions that harm the water quality and kill fish. The WASP water quality model successfully predicted the water quality variables and offered the ideal loading scenario for the eutrophication issue. Out of all the scenarios put out, scenario 4 with 75% load reduction, resulted in the appropriate concentrations of DO and chl-a. The dissolved oxygen levels were found to be relatively greater than the other loading situations, and the chl-a concentrations were reduced by 88.2%. The construction of check dams in the appropriate location along the river lengths and a gradual transition to organic farming are the watershed management strategies suggested in the current study. As a result, the nutrient loads (nitrate and phosphate) entering the reservoir will be reduced, which will raise the reservoir's DO levels. Through this work, it has been shown that the WASP model is a reliable tool for forecasting monthly water quality variables and that it can successfully simulate eutrophication control factors. This information aids environmental managers and policymakers in developing better strategies for managing and monitoring water quality.
ACKNOWLEDGEMENTS
The authors thank the Officials of the Water Resources Department and Fisheries Department at KRP dam for their support during the field measurement campaign. The authors also acknowledge the technical support rendered by Dr Sudha in the Wet Chemistry Laboratory, Centre for Water Resources, Anna University.
AUTHORS CONTRIBUTION
A.B.E. conceived the original idea of the project and planned the experiments. D.V.K.C. and A.W.A. carried out the field campaign, collected water quality samples, and performed laboratory tests. A.K.J. carried out data curation and reviewed the draft manuscript. P.N. also performed data curation. D.V.K.C. performed WASP simulations. A.W.A. helped in the interpretation of the results. All authors contributed to writing the manuscript. A.B.E. provided critical feedback and helped shape the research, analysis, and manuscript.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper.
CONFLICT OF INTEREST
The authors declare there is no conflict.