Application of Different Indices to Assess the Trophic Status of a Warm Monomictic Reservoir in the Lesotho Highlands, Southern Africa

Abstract

The sustainable management of water supply reservoirs requires analysis of spatiotemporal variations in nutrient levels, phytoplankton composition, and trophic status. The Katse Dam (KD) is a strategic raw water supply source that generates hydropower and sustains aquaculture. However, it is exposed to nutrient enrichment from mining and aquaculture, whose impact on its trophic status necessitates monitoring. This study applies the organic pollution index (OPI), the modified pollution index (MPI), and Carlson’s trophic state index (CTSI) to assess the trophic status of KD. The results from the first decade (FD) (2003–2013), when the intensity of mining and aquaculture activities was minimal, were compared with the results from the second decade (SD) (2014–2024) when there was higher activity. The MPI revealed that KD transitioned from a contaminated status during the FD to a greatly contaminated status during the SD. KD shifted from mesotrophic to eutrophic in the transitional zone and from eutrophic to hypereutrophic in the lacustrine zone. The cyanobacteria Radiocystis sp. replaced Asterionella sp. and became the most abundant algae in the SD, followed by the diatom Flagilaria sp. Principal component analysis (PCA) indicated stronger correlations between NH₄, PO₄, NO₃, and NO₂, while canonical correspondence analysis (CCA) indicated a strong correlation between PO₄ and Fragilaria sp. in the SD. The OPI classified KD water quality as excellent, with the exception of the lacustrine zone, where the water quality was polluted in 2016 and 2021. The data analysis revealed how long-term variations in KD water chemistry and phytoplankton influenced trophic status. This study thus provides water managers with a template for assessing water quality to secure the strategic value of the KD.

1. Introduction

Water is essential for driving the economy and supporting social development through the maintenance of livelihoods, human health, environmental functioning and industrial activities [1]. Yet, the quality and quantity of water available in freshwater ecosystems is rapidly declining due in part to nutrient enrichment exacerbated by anthropogenic activities [2]. Nutrient enrichment causes eutrophication of aquatic ecosystems, which leads to the proliferation of phytoplankton and water quality degradation [3]. Human activities such as mining, aquaculture, livestock rearing, and sewage discharge release nutrients and accelerate eutrophication in aquatic ecosystems [3]. Consequently, one quarter of the Earth’s population lacks access to clean water due in part to the effects of eutrophication [4]. Katse Dam (KD) is a raw water supply source that drives the economy of the Gauteng region of South Africa [5]. It generates hydropower for Lesotho, provides a resource for aquaculture, and hosts boating tours for tourists. However, KD is exposed to point and non-point pollution sources whose impact on its trophic status necessitates monitoring.

Mining and aquaculture are two major human activities that influence the trophic status of the Katse Dam (KD). The opening of the Liqhobong and Kao mines in the Lesotho highlands in 2011 [6] marked the commencement of prolonged and intense mining upstream of the KD. Mining introduces nitrates into the environment via ammonium nitrates embedded in the explosives used for blasting the ore [7,8]. The KD rural catchment is exposed to mining, livestock rearing, and household waste such as detergents, which bring an influx of nutrients through inflow rivers and rainfall runoff [9,10]. In addition, aquaculture causes organic pollution from wasted fish feed, which increases phytoplankton biomass and affects trophic status [11]. This sequence of events indicates that the decomposition of organic matter eutrophication and phytoplankton diversity are interrelated [11]. The noted water quality challenges necessitate the development of monitoring tools to evaluate the trophic status and prevent sudden and unexpected eutrophication. However, the trophic status of KD has not been adequately monitored in the past despite its exposure to mining and aquaculture activities.

Roos [5] classified the trophic status of the Katse Dam (KD) as mesotrophic using descriptive statistics. The application of modern indices to characterize the trophic status using phytoplankton as biological pollution indicators thus remains unexplored. In particular, the interaction between phytoplankton as biological indicator and chemical parameters to understand the dynamics leading to eutrophication has not been studied in the context of KD. Consequently, this study represents a vital and valuable research direction for this strategic raw water supply source, similar to studies by Oberholster et al. [12] at Loskop Dam, South Africa, and Akter et al. [13], at Nokhoali ponds in Bangladesh. This research seeks to close the existing knowledge gap by exploring scientific evidence that links anthropogenic activities to the water quality status using biological, physical and chemical parameters. It introduces a novel approach in the context of KD, by using more than one index, each focusing on a specific type of pollution as suggested by Yadav et al. [14].

Researchers worldwide have developed different indices to assess the water quality and pollution status of various aquatic ecosystems [15,16]. These include the organic pollution index [17], the eutrophication index [18], the comprehensive diatom index [19], the pollution index developed by Jiang and Shen [20], and Carlson’s trophic state index [21]. Various researchers modified these indices to fit different environmental conditions. While phytoplankton have been used extensively to assess pollution in aquatic systems in Europe [22], the development of indices that incorporate biological indicators has not been explored adequately worldwide [23]. According to Oberholster et al. [12], an effective index uses a single value on a numerical scale to represent the complex interaction between phytoplankton and physical and chemical parameters. It further indicates the tolerance or sensitivity of biological parameters towards pollution in ecosystems of various types and complexities. The Katse Dam is a large, deep, and extensive reservoir that therefore necessitates the application of different indices to evaluate its trophic status.

The Katse Dam’s thermal characteristics reveal it to be a warm monomictic reservoir that stratifies in summer and mixes only once a year in winter [5]. Stratification and mixing affect nutrient cycling, phytoplankton vertical migration, and biological processes, which selectively cause dominance of certain phytoplankton species in the photic zone [24]. Furthermore, Paris, J.R.; King, R.A.; Obiol, J.F.; Shaw, S.; Lange, A.; Bourret, V.; Hamilton, P.B.; Rowe, D.; Laing, L.V.; Farbos, A et al. [25] argue that adaptive responses to physical and chemical parameters by phytoplankton drive their succession, diversity, and tolerance to pollutants. Phytoplankton such as Fragilaria sp. adapt and thrive in polluted environments and replace sensitive species [25]. The complex interaction between physical and chemical parameters and phytoplankton necessitated the application of advanced statistical methods to elucidate their temporal and spatial trends.

Canonical correspondence analysis (CCA) was applied to discover the association between physical and chemical parameters and phytoplankton [12,13]. Principal component analysis (PCA) reduces data complexity, and it was employed to uncover pollutants that influence the water quality of the KD [26]. Hence, the novel contribution of this study lies in its deviation from the application of descriptive statistics in previous studies to represent ecosystem integrity [5,9]. The study advances by considering the time-varying nature of aquatic ecosystems, where the trophic status depends on patterns of environmental variations over time, as suggested by Bain et al. [15]. This study applied different indices to assess organic nutrient enrichment and determine the presence of harmful algae that can accelerate eutrophication and are toxic to fish and humans.

This study applied various indices to determine spatial and temporal trends in the trophic status, phytoplankton composition, and organic pollution status, thereby facilitating plans to secure the sustainable water supply, economic, and societal value of the KD. The aim of the study was achieved through the following objectives: (1) determine the change in trophic status of the KD using Carlson’s trophic state index (CTSI); (2) investigate the succession in phytoplankton composition and pollution index classification of the KD using modified pollution index over two decades from 2003 to 2024; (3) and assess the pollution status of the KD using the organic pollution index (OPI).

2. Materials and Methods

2.1. Study Area Description and Selection of Water Sampling Sites

The Katse Dam (KD) (29.336605 S, 28.506544 E) is located in the Lesotho highlands, 2 km downstream of the Bokong River and the Malibamatšo River confluence. The map of the study area was generated using ArcGIS Desktop (ArcMap) version 10.8.2 (Esri, Redlands, CA USA) (Figure 1). The study area is characterized by a temperate subtropical highland climate with an average rainfall of 710 mm [27]. Summer reaches high temperatures of around 35 °C, while winter is dominated by temperatures as low as −12 °C, commonly associated with snowfall [9,27]. Two diamond mining companies, Liqhobong mine and Kao mine, operate upstream of the Malibamatšo River catchment and potentially bring an influx of nitrates and metals into KD, as shown in Figure 1 [6,7,8,9,10]. In addition, cattle kraals and sheep shearing sheds are located close to inflowing rivers. Two aquaculture companies, Katse Fish Farm and Sanlei Trout, operate in the KD (Figure 1). These anthropogenic activities contribute nutrients and organic matter from livestock waste, fish feed, excreta, and mortality [9,28].

The Lesotho Highlands Water Project (LHWP) is an inter-basin water transfer project abstracting water from the Senqu River basin in Lesotho for delivery to the Gauteng region of South Africa [5]. The Katse Reservoir has a total surface area of 35.8 km² within a catchment area of 1869 km². During maximum supply, it has a volume of 1950 × 10⁶ m³ [5,29]. The morphometric characteristics of the KD, originally compiled by Roos [5] and adapted by Moahloli et al. [29], are shown in

Table 1. Shows the morphometric characteristics of the Katse Dam (adapted from [5,29]).

Morphometric Characteristics	Magnitude
Catchment area	1869 km2
Crest length	710 m
Maximum reservoir width	900 m
Surface area at full supply level	35.8 km2
Intake position from the Damwall	18 km
Total longitudinal reservoir length	Approximately 35 km
Damwall height	185 m
Capacity at full supply level	1950 × 106 m3

.

Water quality samples for this study were collected from four water sampling sites (WSSs) shown in

Table 2. The location of the four water sampling sites at Katse Dam.

Site Code	Katse Dam Site Name	Longitudinal Zone	GPS Coordinates
			Latitude	Longitude
KD-A	Dam wall	lacustrine	−29.332604	28.504942
KD-B	Island	lacustrine	−29.240807	28.473924
KD-C	Intake	transitional	−29.173980	28.483194
KD-D	Upstream	riverine	−29.093910	28.498472

: Damwall (KD-A), located at the lacustrine zone; Island (KD-B), located at the lacustrine zone; Intake (KD-C), located at the transitional zone; Upstream (KD-D), located at the riverine zone. At each WSS, geographic coordinates were taken using a Garmin eTrex^® 10 GPS device (Garmin Asia Corporation, New Taipei City, Taiwan). The zones influence the selection of WSSs due to differences in hydrodynamics, morphometric features, and chemical and biological composition of the water [30]. These biotic and abiotic factors, in turn, influence water quality and trophic status along the longitudinal zones of the KD. The riverine zone has elevated metals and nutrient input from human and geological activities in the catchment. The transitional zone is characterized by higher light availability, while the lacustrine zone has relatively low chlorophyll-a and low total suspended solids (TSS) [30].

2.2. Methods

The methodological approach employed in this study followed the steps shown in Figure 2: (1) identifying pollution sources in the KD catchment; (2) designing the conceptual framework based on the objectives; (2) site selection and data collection; (3) and data analysis using the selected indices to (4) assess the spatial and temporal trends in the trophic status of Katse Dam.

2.2.1. Selection of Water Quality Parameters

The parameters used in this study were based on identified environmental pressures in the catchment, namely, the cattle kraals, the sheep shearing sheds, aquaculture, and mining. Maleri et al. [28] adopted parameters related to aquaculture to characterize the water quality status of Nietvoorbij Dam in the Western Cape, South Africa. Oberholster et al. [12] incorporated parameters associated with mining and sewage to study their association with phytoplankton assemblage at Loskop Dam, South Africa.

The selected parameters used in the computation of pollution index (PI) were Secchi disk depth (SDD), dissolved oxygen (DO), electrical conductivity (EC), potential of hydrogen (pH), calcium (Ca), manganese (Mn), sodium (Na), zinc (Zn), cadmium (Cd), copper (Cu), magnesium (Mg), iron (Fe), total phosphorus (TP), orthophosphates (PO₄), ammonium nitrogen (NH₄), nitrate (NO₃, nitrite (NO₂), total organic carbon (TOC), chloride (Cl), total suspended solids (TSS), total dissolved solids (TDS), chemical oxygen demand (COD), and chlorophyll-a (Chl-a), based on their association with aquaculture [28] and with effluent from mining [12]. Physical and chemical parameters for the computation of the organic pollution index were selected based on the index applied by Son et al. [17], namely, COD, DO, dissolved inorganic nitrogen (DIN), and dissolved inorganic phosphates (DIP). DIN and DIP were estimated by summing the dissolved and particulate measurements of each nutrient in milligrams per liter to obtain minimum estimates for nutrients [14,17]. DIN is the total limiting concentration of NO₃, NO₂, and NH₄, while DIP is the concentration of orthophosphate [17].

2.2.2. Data Collection and Laboratory Analysis

The water transparency was measured using a round 20 cm diameter black and white Secchi disk (Hach, Manchester, UK) suspended from a 15 m rope graduated at one-meter intervals. DO, EC, and pH and temperature were measured in situ according to the Randwater Method numbers 1.1.2.16.1, 2.1.3.01.2, and 1.1.2.15.1 using a portable multiparameter probe instrument (YSI Sonde Model 6600, YSI Incorporated, Yellow Springs, OH, USA) [9,31,32]. A Von Dorn sampler (KC Denmark A/S, Silkeborg, Denmark) was used to collect water from a depth of 1.5 m below the surface. The water sample was distributed into two brown labeled 1 L polypropylene bottles for analysis of chlorophyll-a and phytoplankton. The remaining sample was distributed into two 1 L polyethylene bottles pre-rinsed with distilled water for analysis of nutrients and metals [33].

All water samples were analyzed at the chemistry and hydrobiology sections of the Randwater Laboratory, which is accredited by the South African National Accredited System (SANAS). Chlorophyll-a was analyzed using Thermo Scientific Orion AquaMate 8000 UV-Vis spectrophotometry (Thermo Fisher Scientific Incorporated, Berlin, Germany) based on the methods described by Satory [34]. The water samples were cold-preserved in darkness at 4 °C in a SnoMaster SMDZ-LP96D Freezer (SnoMaster, Johannesburg, South Africa) during transport to the laboratory until analysis. Automated analytical techniques, namely UV-visible spectrophotometry (Thermo Scientific Evolution 300, Thermo Fisher Scientific incorporated, Berlin, Germany) for nutrients (TP, PO₄, NH₄, NO₃, NO₂) and inductively coupled plasma-optical emission spectrometry (ICP-OES) (Thermo Sceintific iCAP 7000 Series Thermo Fisher Scientific incorporated, Berlin, Germany) [9] for Ca, Mn, Na, Mg, Fe, Zn, Cd, and Cu, were based on methods described by the American Public Health Association (APHA) [35]. Total organic carbon (TOC) was determined using standard methods outlined by the APHA [35]. Chemical oxygen demand (COD) was determined using the spectroscopy method 410.4 using the Thermo Scientific GENESYS 30 VIS Spectrophotometer (Thermo Fisher Scientific Incorporated, Berlin, Germany) [36]. Water samples were taken in January, April, July, and October for the study period from 2003 to 2024 as suggested by Pasztaleniec [23] for a monomictic reservoir like the KD.

2.2.3. Temperature Profile

Temperatures were taken using the YSI 6600 multiparameter sonde fitted with temperature probes. The sonde was programmed to take measurements every 1 m down the water column up to 2 m above the bottom sediments at each WSS. The maximum depths obtained were corrected using meters above sea level (MASL) readings at the time of sampling. The temperature readings were plotted against corresponding MASL depths on an Excel spreadsheet to illustrate mixing in winter and the three distinct layers of stratification in summer: epilimnion, thermocline, hypolimnion.

2.2.4. Phytoplankton Analysis

The water samples for the analysis of phytoplankton were fixed and preserved with 1% Lugol’s iodine solution before centrifugation and sedimentation at the Randwater laboratory according to the methods described by Van Vuuren et al. [37]. After settling, all algal cells were counted with an inverted light microscope (Carl Zeiss, Oberkochen, Germany) at a magnification of 250× using a technique described by Lund et al. [38]. The concentration of identified phytoplankton species was expressed as cells per milliliter (cells mL⁻¹). All phytoplankton counts were used to determine the presence, abundance, and dominance of species in each sample [12]. A technique used by Oberholster et al. [12] was employed to measure the dominance of each algal species using the Berger–Paker Index (BPI) [39] using the following equation:

$$\mathbf{D}={\displaystyle \frac{\mathbf{N}\mathbf{m}\mathbf{a}\mathbf{x}}{\mathbf{N}}}$$

(1)

where D is the dominance of each species and Nmax refers to the count of the most abundant species present in each sample. N refers to the sum of abundances of all species sampled at each WSS. This study focused on calculating the pollution value of taxa (PVT) only for identified dominant phytoplankton species per site for each year.

Table 3. Categories of phytoplankton diversity based on the Berger–Parker index (BPI) (Oberholster et al. [12].

BPI	Degree of Dominance	Ecosystem Status
0.8–1.0	Extreme dominance	Ecosystem potentially at risk
0.5–0.7	High dominance	Warrants investigation
0.3–0.4	Moderate dominance	Typical in various ecosystems
0–0.2	Low dominance	Indicates high diversity

provides the BPI scale to gauge the ecosystem risk profile based on the extent of dominance by phytoplankton.

2.3. Data Analysis

2.3.1. Statistical Analysis

Both the CCA and PCA biplots were created using CANOCO V 4.5 software (Agricultural Mathematics Group, Wageningen, The Netherlands). The CCA was utilized to evaluate the degree of association between selected water quality parameters and identified phytoplankton species in the FD and in the SD. In the resulting ordinations, parameters are correlated when their pointers are at (90°) angle, show a strong positive correlation if their pointers delimit an acute angle, and show a strong negative correlation if their pointers are in opposite directions subtending towards (180°) angle [40]. CCA reduces the dimensionality of data and highlights the correlations between phytoplankton species and selected water quality parameters to identify potential sources of pollution [40]. PCA and CCA were performed on the combined data for all four WSSs, and for each WSS separately. Due to their similarity, only the combined PCA and CCA results from all four WSSs were used to reflect the conditions of the entire reservoir. PCA minimizes data complexity and reveals sources of variability, which can assist environmental managers in identifying sources of pollution [26,41].

2.3.2. Modified Pollution Index

The pollution index developed by Jiang and Shen [20] and modified by Oberholster et al. [12] for the assessment of Loskop Dam’s pollution status was applied by following these three steps:

• The initial step was to calculate the pollution index (PI) values of the physical and chemical parameters of the KD to represent each season across three months using the following equation:

$$\mathrm{P}\mathrm{I}={\sum }_{i=1}^n{\displaystyle \frac{\mathrm{C}}{\mathrm{C}\mathrm{L}}}$$

(2)

where C is the concentration or measurement of a parameter in the KD, and CL is the limiting value of the parameter. In this case, the WHO [42] water quality guidelines and Department of Water Affairs and Forestry (DWAF) [43] guidelines for aquaculture were used. The PI values obtained for selected parameters for each year were summed to obtain a total PI value for the decade.

2.

The second step was to evaluate the PVT for each dominant phytoplankton species in the FD and SD at each WSS. The equation is as follows:

$$\mathrm{P}\mathrm{V}\mathrm{T}=({\sum }_{i=1}^n({\displaystyle \frac{\frac{\mathrm{P}\mathrm{I}}{\mathrm{s}}}{\mathrm{n}}}))/\mathrm{N}$$

(3)

where n is number of physical and chemical parameters; PI is the value calculated in step 1 above; N is the number of WSSs in this study based on the methods applied by Castro-Roa and Pinilla-Agudelo [22]; and s is the total number of years for each decade.

3.

The last step was to calculate the pollution index of KD (PIKD) value for the algae community for each quarter, according to the following equation:

$$\mathrm{P}\mathrm{I}\mathrm{K}\mathrm{D}=({\sum }_{i=1}^n\left(\mathrm{P}\mathrm{V}\mathrm{T}\right)i)/{\mathrm{n}}_5$$

(4)

where n_s is the total number of species, PVT is the pollution value per taxa determined for each species, and i represents the presence of the species at the KD. A high PVT value indicates the high resilience to contamination of an ecosystem at high risk of pollution [12]. Based on the method applied by Castro-Roa and Pinilla-Agudelo [22], the PIKD was converted to a percentage, where 100% represented the highest value from which the phytoplankton values were subtracted.

Table 4. Categories of contamination based on pollution index of Katse Dam (PIKD) (Oberholster et al. [12]).

PIKD %	Degree of Contamination	Limnological Condition
>85	slightly contaminated	Significant phytoplankton diversity. Limnological conditions of the dam are good to acceptable.
65–85	moderately contaminated	Signs of nutrient enrichment. Limnological conditions of the dam are intermediate.
33–65	contaminated	Only pollution-resistant species are abundant. Sensitive species reduced. Limnological conditions of the dam are insufficient.
<33	greatly contaminated	Significantly reduced phytoplankton diversity. A limited number of tolerant phytoplankton species are dominant. Limnological conditions of the dam are poor.

shows the characteristics of the dam based on the percentage PKID obtained by applying Equations (2)–(4).

The modified pollution index method is consistent with Indicator 6.3.2 of the SDG 6, which requires methods focusing on physico-chemical characteristics of water, including nutrient enrichment as well as biological indicators, including measurement of algae [2].

2.3.3. Organic Pollution Index

Researchers have applied the OPI to monitor and classify water quality of various ecosystems using four parameters, namely, COD, DO, DIP, and DIN [14,17,44]. The OPI was used in this study to compare the levels of organic nutrients in the KD for the period from 2003 to 2013 and for the period from 2014 to 2024. It is expressed as follows [14,17]:

$$\mathrm{OPI} = {\displaystyle \frac{\mathbf{C}\mathbf{O}\mathbf{D}}{\mathbf{C}\mathbf{O}\mathbf{D}\mathbf{s}}}+ {\displaystyle \frac{\mathbf{D}\mathbf{I}\mathbf{N}}{\mathbf{D}\mathbf{I}\mathbf{N}\mathbf{s}}}+ {\displaystyle \frac{\mathbf{D}\mathbf{I}\mathbf{P}}{\mathbf{D}\mathbf{I}\mathbf{P}\mathbf{s}}}- {\displaystyle \frac{\mathbf{D}\mathbf{O}}{\mathbf{D}\mathbf{O}\mathbf{s}}}$$

(5)

The numerators in Equation (5) represent measured concentrations after analyzing water samples. DIN is the total concentration of nitrate, nitrite, and ammonium; DIP is the concentration of orthophosphates [17]. COD_s, DIN_s, DIP_s, and DO_s are Ref [42] standards for COD, DIN, DIP, and DO [14]. According to Son et al. [17], the OPI classifies water quality as excellent (<0), good (0–1), polluted (1–4), or extremely polluted (4–5).

2.3.4. Carlson’s Trophic State Index

Calrson’s trophic state index (TSI) is based on water transparency (measured as Secchi disk depth), nutrient concentration (measured as concentration of total phosphorus (TP)), and the algal biomass (measured as Chl-a), which are the major components that influence eutrophication state [45]. The trophic state index ranges from 0 to 100, as shown in

Table 5. Carlson’s trophic state index classification (Opiyo et al. [43]).

TSI	Trophic Status	Secchi Disk Depth	Total Phosphorus (TP)	Chlorophyll-a (Chl-a)
0–40	Oligotrophic	>8–4	0–12	0–2.6
40–50	Mesotrophic	4–2	12–24	2.6–7.3
50–70	Eutrophic	2–0.5	24–96	7.3–56
70–100	Hypereutrophic	0.5–<0.25	96–384	56–155+

[45]. This study applied the TSI to characterize the eutrophication state of the Katse Dam using the following equations developed by Carlson [45]:

TSI for water transparency:

TSI (SDD) = 60 − 14.41 ln Secchi disk depth (m)

(6)

TSI for total phosphorus:

TSI (TP) = 14.42 ln Total phosphorus (ug/L)

(7)

TSI for chlorophyll-a:

TSI (Chl-a) = 9.81 ln chlorophyll-a (ug/L) + 30.6

(8)

Trophic state index:

TSI = [TSI(TP) + TSI(Chl-a) + TSI (SDD)]/3

(9)

The trophic state index (TSI) ranges from 0 to 100 (Table 2).

Despite Karpowicz et al. [46] arguing that TSI components lack reliability during mixing of large reservoirs, this study used the TSI to classify the KD’s trophic state according to Table 5. The trophic state of aquatic ecosystems is characterized by their state of nutrient enrichment and depends on the concentrations of total nitrogen, phosphates, and chlorophyll-a and Secchi disk depth [47].

3. Results

3.1. Analysis of Physical, Chemical, and Biological Parameters

The average values and standard deviations of selected parameters are shown in

Table 6. Average physical and chemical parameters from 2003 to 2013 at Katse Dam (* guideline values not stipulated; ^min.—minimum; ^max.—maximum).

2003–2013		Water Quality Monitoring Sites						Guidelines
Variable	Units	KD-A	KD-B	KD-C	KD-D	^Min.	^Max.	WHO [42]	DWAF [43]
Chemical parameters
NH4	mg/L	0.07 ± 0.04	0.07 ± 0.05	0.08 ± 0.04	0.07 ± 0.04	0.005	0.25	*	<0.025
NO3	mg/L	0.13 ± 0.08	0.13 ± 0.06	0.16 ± 0.10	0.17 ± 0.06	0.0	0.96	0–50	<300
NO2	mg/L	0.03 ± 0.03	0.04 ± 0.03	0.03 ± 0.02	0.04 ± 0.02	0.0	0.62	0–3	<50
TP	mg/L	0.07 ± 0.05	0.07 ± 0.03	0.07 ± 0.04	0.07 ± 0.04	0.01	0.63	*	<1
PO4	mg/L	0.04 ± 0.01	0.03 ± 0.02	0.04 ± 0.02	0.04 ± 0.03	0.00	0.65	*	*
DIN	mg/L	0.34 ± 0.23	0.36 ± 0.25	0.49 ± 058	0.57 ± 0.7	0.03	1.07	10	*
DIP	mg/L	0.093 ± 0.06	0.07 ± 0.03	0.04 ± 0.03	0.078 + 0.05	0.02	0.63	5	*
Physical parameters
EC	mS/m	7.09 ± 0.64	7.17 ± 0.39	7.10 ± 0.20	6.88 ± 0.41	0.054	24.0	*	*
TSSs	mg/L	5.19 ± 0.90	6.15 ± 2.23	6.25 ± 3.18	8.22 ± 0.17	0.0	115	*	*
Secchi	m	6.6 ± 0.7	5.6 ± 0.5	4.5 ± 0.6	3.0 ± 0.7	0.5	11.9	*	*
COD	mg/L	6.00 ± 1.17	5.62 ± 1.09	5.96 ± 1.03	6.01 ± 1.18	5	15	5 *	*
DO	mg/L	7.99 ± 0.48	7.96 ± 0.49	7.97 ± 0.53	8.25 ± 0.61	3.36	12.7	5	5.0–8.0
Biological Variable
Chl-a	μg/L	5.00 ± 1.03	4.50 ± 1.56	5.08 ± 2.49	7.95 ± 4.68	0	98	0–30	*

and

Table 7. Average physical and chemical parameters from 2014 to 2024 at Katse Dam (* guideline value not stipulated, ^min.—minimum, ^max.—maximum).

2014–2024		Water Quality Monitoring Sites						Guidelines
Variable	Unit	KD-A	KD-B	KD-C	KD-D	^Min.	^Max.	WHO [42]	DWAF [43]
Chemical parameters
NH4	mg/L	0.09 ± 0.06	0.08 ± 0.07	0.08 ± 0.04	0.13 ± 0.09	0.00	0.54	*	<0.025
NO3	mg/L	0.25 ± 0.12	0.31 ± 0.11	0.34 ± 0.30	0.60 ± 0.50	0.01	3.1	0–50	<300
NO2	mg/L	0.02 ± 0.01	0.02 ± 0.02	0.04 ± 0.07	0.02 ± 0.03	0.00	0.36	0–3	<50
TP	mg/L	0.26 ± 0.08	0.46 ± 0.46	0.18 ± 0.13	0.30 ± 0.09	0.03	2.5	*	<1
PO4	mg/L	0.10 ± 0.05	0.12 ± 0.15	0.06 ± 0.03	0.09 ± 0.04	0.00	0.35	*	*
DIN	mg/L	0.2 ± 0.16	0.12 ± 0.15	0.23 ± 0.14	0.24 ± 0.13	0.07	1.27	10	*
DIP	mg/L	0.04 ± 0.04	0.04 ± 0.1	0.04 ± 0.03	0.04 ± 0.05	0.25	0.30	5	*
Physical parameters
EC	mS/m	7.44 ± 2.34	8.31 ± 1.2	7.63 ± 0.68	8.46 ± 0.98	0.80	12.6	*	*
TSSs	mg/L	10.22 ± 2.3	9.65 ± 4.14	10.90 ± 9.8	12.03 ± 5.44	0.00	94.0	*	*
Secchi	m	5.0 ± 1.4	4.3 ± 1.4	3.8 ± 1.1	2.8 ± 1.1	0.30	9.40	*	*
COD	mg/L	7.81 ± 0.53	9.67 ± 6.58	7.85 ± 0.50	7.93 ± 0.50	5.00	55.0	10 *	*
DO	mg/L	8.04 ± 6.38	7.30 ± 3.59	6.09 ± 1.56	7.85 ± 3.86	5.8	10.9	5	5.0–8.0
Biological parameters
Chl-a	μg/L	2.75 ± 0.77	1.0 ± 2.20	4.01 ± 1.44	9.11 ± 7.31	0.001	23.0	0–30	*

for the FD and SD, respectively. The parameters were compared with the World Health Organization [42] and the South African Department of Water Affairs and Forestry [43] guidelines for aquaculture. An inverse relationship was observed between a concentration of TSSs of 12.02 mg/L and a Secchi disk depth of 2.82 m in the riverine zone (KD-D), and between a concentration of TSSs of 10.22 mg/L and a Secchi disk depth of 5.04 m in the lacustrine zone (KD-A) (Table 7). The average concentrations of nitrates ranged from 0.13 mg/L to 0.17 mg/L in the FD and were relatively lower than the range of 0.25 mg/L to 0.6 mg/L in the SD. EC was higher in the SD, ranging from 7.44 mS/m to 8.46 mS/m (KD-D), compared to the FD, which ranged from 6.88 mS/m/to 7.09 mS/m. There was a significant increase in orthophosphate concentration in the SD, with a range of 0.09 mg/L to 0.12 mg/L, relative to the FD, with a lower range of 0.03 mg/L to 0.04 mg/L. DO, which supports aquatic life, was higher in the FD, ranging from 7.8 mg/L to 8.3 mg/L (Table 6).

Based on Ref. [5], while most parameters complied with the guidelines, copper (Table S2) and ammonium (Table 7) exceeded the guideline limits. The concentration of ammonium (NH₄) was noncompliant with the DWAF [43] throughout the study period at all monitoring sites. The average concentrations of most parameters were higher in the SD (Table S2) compared to the FD (Table S1). An exception was DO, which was relatively lower in the SD, with a range of 6.1 mg/L to 8.0 mg/L (Table 7).

3.2. Spatial and Temporal Variations in Phytoplankton Distribution

The KD’s phytoplankton composition, diversity, and succession during the FD and SD were revealed by the BPI results, as shown in

Table 8. Longitudinal distribution of dominant phytoplankton species at the Katse Dam in the first decade (2003–2013) and the second decade (2014–2014).

Year	KD-A	BPI	KD-B	BPI	KD-C	BPI	KD-D	BPI
2003	Cosmarium sp.	0.71	Cosmarium sp.	0.95	Cosmarium sp.	0.32	Cosmarium sp.	0.38
2004	Chlamydomonas sp.	0.35	Oocystis sp.	0.47	Asterionella sp.	0.69	Asterionella sp.	0.79
2005	Asterionella sp.	0.34	Asterionella sp.	0.28	Asterionella sp.	0.43	Asterionella sp.	0.64
2006	Asterionella sp.	0.26	Asterionella sp.	0.15	Radiocystis sp.	0.69	Asterionella sp.	0.54
2007	Radiocystis sp.	0.33	Radiocystis sp.	0.41	Radiocystis sp.	0.66	Fragilaria sp.	0.48
2008	Radiocystis sp.	0.72	Radiocystis sp.	0.79	Radiocystis sp.	0.33	Radiocystis sp.	0.46
2009	Monoraphidium sp.	0.33	Radiocystis sp.	0.32	Radiocystis sp.	0.43	Radiocystis sp.	0.44
2010	Quadrigula sp.	0.50	Radiocystis sp.	0.90	Radiocystis sp.	0.62	Fragilaria sp.	0.74
2011	Radiocystis sp.	0.97	Cosmarium sp.	0.38	Radiocystis sp.	0.49	Radiocystis sp.	0.77
2012	Radiocystis sp.	0.60	Cosmarium sp.	1.00	Radiocystis sp.	0.74	Pennate diatoms	0.90
2013	Microcystis sp.	0.95	Fragilaria sp.	0.33	Radiocystis sp.	0.63	Radiocystis sp.	0.69
2014	Fragilaria sp.	0.59	Fragilaria sp.	0.53	Radiocystis sp.	0.72	Fragilaria sp.	1.00
2015	Cryptomonas minor	0.50	Fragilaria sp.	0.73	Fragilaria sp.	0.70	Fragilaria sp.	0.59
2016	Radiocystis sp	0.96	Radiocystis sp.	0.93	Radiocystis sp.	0.97	Radiocystis sp.	0.53
2017	Dynobryon sp.	0.50	Fragilaria sp.	0.81	Fragilaria sp.	0.91	Fragilaria sp.	0.60
2018	Cosmarium sp.	0.43	Fragilaria sp.	0.90	Fragilaria sp.	0.87	Fragilaria sp.	0.66
2019	Radiocystis sp.	0.53	Centric diatoms	0.89	Radiocystis sp.	0.41	Fragilaria sp.	0.47
2020	Radiocystis sp.	0.22	Centric diatoms	0.31	Nitzschia sp.	0.33	Fragilaria sp.	0.50
2021	Fragilaria sp.	0.70	Fragilaria sp.	0.67	Fragilaria sp.	0.78	Fragilaria sp.	0.64
2022	Fragilaria sp.	0.39	Fragilaria sp.	0.47	Fragilaria sp.	0.86	Fragilaria sp.	0.77
2023	Radiocystis sp.	0.92	Radiocystis sp.	0.95	Radiocystis sp.	0.75	Radiocystis sp.	0.82
2024	Radiocystis sp.	0.65	Fragilaria sp.	0.61	Fragilaria sp.	0.60	Radiocystis sp.	0.44

. Cosmarium sp. showed extreme dominance in 2003 at KD-B in the lacustrine zone with a BPI of 0.95. Cosmarium sp. was replaced by Asterionella sp., which showed moderate to high dominance in 2005 with the BPI in the range of 0.28 to 0.64. Notably, Radiocystis sp. became dominant between 2006 and 2014, but it was later succeeded by Fragilaria sp. between 2015 and 2018 in the transitional zone (KD-C). A BPI of 1 for Fragilaria sp. in 2014 indicated extreme dominance within an ecosystem potentially at risk of point source pollution, such as mining, based on the interpretation shown in Table 3.

Phytoplankton species were identified at varying counts at 1.5 m below the surface of the photic zone. This study calculated the PVT values (Table S4) only for dominant phytoplankton based on the application of the BPI per site for each year (Table 8).

3.3. Phytoplankton Relationship with Physical and Chemical Parameters

In the SD, the highest PVT of 37.7 for Radiocystis sp. in 2023 (Table S4) was consistent with the highest PI of 19.8 in 2023 for physical and chemical parameters (Table S3). As shown in Table 8, ten different species dominated the less degraded ecosystem in the FD, while seven different species dominated the more degraded ecosystem in the SD.

To augment the BPI results shown in Table 8, CCA and PCA were performed. The PCA was performed to illustrate the associations between physical and chemical parameters over the FD and SD, as shown in Figure 3a,b. In contrast to their weak correlations in the FD (Figure 3a), Na, Ca, Fe, Mg, and pH showed strong positive correlations in the SD (Figure 3b), indicating point source pollution potentially from mining activity in the upper catchment in the SD.

The CCA biplots illustrating the associations of dominant phytoplankton genera and species with physical and chemical parameters were plotted for the FD (Figure 4a) and the SD (Figure 4b). The eigenvalue of axis 1 (0.94) indicated a 29.2% correlation in the FD, while the eigenvalue of axis 1 (0.84) indicated a higher 56.2% correlation in the SD (Table S5). This result reflected an increasing trend in the influence of nutrients, PO₄, and NO₃ on Fragilaria sp. in the SD. The influence of nutrient enrichment by mining effluent is made evident by the dominance of Fragilaria sp. from 2014 to 2022 at the riverine zone in the SD (Table 8). The eigenvalue of axis 2 (0.80) indicated a 54.1% correlation in the FD, while an eigenvalue of axis 2 (0.34) indicated a 79.2% correlation in the SD between the parameters and dominant phytoplankton (Table S5). This observation is attributed to point-source pollution such as mining [12] and is consistent with other previous studies [15]. There were strong correlations between the parameters COD, P and Cosmarium sp. in the FD. Radiocystis sp. correlated strongly with the parameters NO₂, NH₄, and pH in the FD (Figure 4a). In the SD, there were significant correlations between EC, chlorophyll-a, Ca and Fragilaria sp., which further supports the latter’s dominance in the SD (Figure 4b). There were also significant positive correlations between Radiocystis sp., Secchi disk depth, and COD (Figure 4b).

3.4. Phytoplankton Succession and Pollution Index Classification of the Katse Dam

Table 8 shows that pollution-tolerant phytoplankton replaced pollution-sensitive phytoplankton during the SD. Cosmarium sp., Asterionella sp., Monoraphidium sp., Quadrigula sp., and Pennate diatoms dominated the FD at varying frequencies and abundance, as shown in Table 8. Notably, this study characterized the KD as greatly contaminated with poor limnological conditions (Table 4), as evidenced by PIKD % that ranged between 1% in 2021 to 7% in 2014 (

Table 9. General classification of the Katse Dam according to the pollution index of Katse Dam (PIKD) in the first and second decades.

Year	PIKD %	Interpretation	Year	PIKD %	Interpretation
2003	29	greatly contaminated	2014	7	greatly contaminated
2004	63	contaminated	2015	3	greatly contaminated
2005	66	contaminated	2016	8	greatly contaminated
2006	72	contaminated	2017	4	greatly contaminated
2007	60	contaminated	2018	2	greatly contaminated
2008	40	contaminated	2019	3	greatly contaminated
2009	58	contaminated	2020	68	moderately contaminated
2010	30	greatly contaminated	2021	1	greatly contaminated
2011	6	greatly contaminated	2022	2	greatly contaminated
2012	6	greatly contaminated	2023	4	greatly contaminated
2013	55	contaminated	2024	6	greatly contaminated

). The pollution index of Katse Dam (PIKD) data classified it as greatly contaminated in the SD, being worse than the contaminated status from 2004 to 2009 and in 2013 in the FD (Table 9).

3.5. Thermal Stratification and Turnover

The thermal stratification phase of the Katse Dam is stable from October to April (Figure 5b,c), while the turnover phase occurs between May and September (Figure 5a,d). The two phases in 2003 and 2024 illustrated in Figure 5 are attributable to the warm monomictic characteristic of the KD.

3.6. Organic Pollution Analysis of Katse Dam

The relatively higher OPI values at KD-A and KD-B in the lacustrine zone in 2003 were attributed to the high COD (Figure 6). All the WSSs displayed excellent OPI status with values below zero in the FD (Figure 6).

The results in Figure 7 show that the Katse Dam reached a polluted status in 2016, 2019, 2020, and 2021 in the lacustrine zone. The KD-A site showed an elevated OPI in 2016 and 2021 with a COD of 55 mg/L and 32 mg/L, respectively. Overall, Figure 6 and Figure 7 show that the KD’s pollution status remained excellent with low DIP and DIN levels.

3.7. Contribution of Trophic State Components to Carlson’s Trophic State Index

The radar diagrams in Figure 8a–d show the sequence TSI_SDD > TSI_TP > TSI_Chl-a at the lacustrine zone (KD-A) in both decades and TSI_TP > TSI_SDD > TSI_Chl-a at the riverine zone (KD-D) in the SD. The transitional zone (KD-C) showed higher TSI_TP in the SD than in the FD (Figure S1). Figure 8c (KD-D in the shallow riverine zone) indicates that TSI_Chl-a increased as TSI_TP increased, whereas Figure 8a (in the deeper lacustrine zone) shows that TSI_Chl-a decreased as TSI_TP increased. The total phosphorus contribution to TSI (TSI_TP) was higher in the SD relative to the FD.

The KD exhibited mesotrophic conditions in 2009, 2010, and 2012, as illustrated in Figure 9.

Comparison of Figure 9 and Figure 10 indicated a marked transition from eutrophic to hypereutrophic state at KD-A (lacustrine zone) in 2015, 2016, 2019, and 2023. The Katse Dam maintained a eutrophic state at most of the WSSs in the SD, as shown in Figure 10.

4. Discussion

4.1. Spatial and Temporal Variations in Physical and Chemical Parameters

The observed tendency of water transparency (measured as Secchi disk depth) to decrease as TSSs increase is supported by Kim et al. [48], who also associated increasing TSSs with increasing EC. The relatively high nitrate concentrations and reduced water transparency due to high TSS in the SD suggest that mining effluent and rainfall runoff degrade water quality in the riverine zone at KD-D (Figure 1) [9,10]. Reservoir mixing has the potential to release orthophosphates that accumulate in the sediments over the years [46]. DO was higher in the FD relative to the SD due to respiration by fish and bacterial decomposition of organic matter, including wasted fish feed [15,28]. Trujillo-Rogel et al. [49] assert that the metabolic processes of caged fish exert a high dissolved oxygen demand. Ammonium originates from feed residues and fish feces and has toxicity implications for fish in aquaculture cages [50]. A major source of copper is the excessive use of copper components in mining activities [6]. Overall, most parameters were found to be within guideline limits yet remained higher in the SD (Table 7) compared to the FD (Table 6), indicating that significant point source pollution affected the Katse Dam in the SD.

4.2. Phytoplankton Distribution and Succession

Physical and chemical parameters drive the selective succession and diversity of phytoplankton [13,51]. According to Pasztaleniec [23], diatoms tend to dominate mesotrophic reservoirs, while blue-green algae dominate hypereutrophic reservoirs. Conversely, Cosmarium sp. is normally found in clean oligotrophic and alkaline organic-rich reservoirs [52]. While Asterionella sp. is indicative of low phosphate and moderate to high nitrate concentrations in temperate oligotrophic reservoirs [4,53], Radiocystis sp. and Fragilaria sp. are indicative of eutrophic conditions and phosphate loading in semi-mesotrophic reservoirs [54].

Many reservoirs, which serve as potable water sources, have been assessed for the presence of toxic cyanobacteria, which tend to dominate the phytoplankton community structure [54]. Microcystis sp., with 0.95 BPI, showed extreme dominance (0.8 to 1.0 BPI) at the lacustrine zone (KD-A) in 2013. The results in Table 3 and Table 9 show an ecosystem at risk, which is attributable to increased pollution due to the commencement of mining and aquaculture in 2011 [6]. This is concerning because Microcystis sp. is a toxic cyanobacterium that disrupts water treatment efficiency and poses health risks to humans and animals [28,55,56].

4.3. Effects of Environment Factors on Phytoplankton Composition

In addition to hydrodynamics such as thermal stratification and mixing, phytoplankton composition is influenced by changes in trophic conditions and concentrations of physical and chemical parameters [13,56]. This premise was confirmed by Ladera et al. [57], who suggested that Fragilaria sp. dominance in aquatic ecosystems was influenced by iron concentration. The results revealed a trend indicating that higher pollution value per taxa (PVT) values represented higher phytoplankton tolerance to degraded water quality, while lower PVT values represented sensitivity [57,58]. According to Jones et al. [59], the replacement of Asterionella sp. by Radiocytis sp. and Fragillaria sp. is attributed to the formation of cadmium (Cd) complexes with enzymes responsible for the growth and proliferation of Asterionella sp. Furthermore, research by Saros et al. [53] supports the observation that higher levels of nitrates in the SD favored the thriving of Fragilaria sp. over Asterionella sp. These observations were supported by advanced statistical analysis such as CCA and PCA.

CCA ordination suggests that Radiocystis sp. and Fragilaria sp. are tolerant to most environmental conditions, including degraded ecosystems, being located near the intersection of axis 1 and axis 2 and having higher PVT values [57]. Conversely, Nitzschia sp. and Centric and Pennate diatoms are sensitive to changes in physical and chemical parameters, as illustrated by their location farther from the intersection of axis 1 and axis 2 [57]. Axis 1 of the CCA in the SD (Figure 4b) explains 52.1% of the variance, indicating that the KD was characterized by nutrient-rich parameters such as NO₃, NH₄, and PO₄. Nitzschia sp. was located at a higher value of axis 1, indicating that its abundance was influenced by the nutrient-rich parameters that shape axis 1 [57]. The PCA biplot in Figure 3b shows that Secchi depth, NH₄, PO₄³, NO₃, and NO₂ were strongly correlated, which indicated increased nutrient loading in the SD from mining activity [8,9] and bacterial decomposition of particulate organic matter from aquaculture [28].

4.4. Pollution Index Classification of Katse Dam

Saros et al. [53] associated Asterionella sp. with oligotrophic state, which confirms that the KD had better water quality in 2005 to 2006 when it was a dominant species. Despite this finding, green algae were replaced by Radiocystis sp. cyanobacteria between 2010 and 2013. The successive changes in composition from sensitive genera to tolerant genera indicated increased nutrient loading and a transition from good to poor water quality conditions over the two study periods (Table 7) [51,54]. The succession is attributed to the decomposition of organic matter from wasted fish feed, mortality, and influx of nitrates from mining [7,8,28]. Cyanobacteria accumulate large amounts of nutrients to survive the poor water quality conditions and eventually replace the more sensitive chlorophyceae species [37,55]. Oligotrophic systems are thus characterized by low phytoplankton biomass and high species diversity, relative to eutrophic and hypereutrophic systems, which allow few taxa to thrive and dominate at high algal biomass [14,56,60].

4.5. Effects of Thermal Stratification and Mixing on the Trophic Status of Katse Dam

The thermocline layer of deep reservoirs like KD inhibits nutrient cycling from the hypolimnion layer during stratification in summer [61]. Hence, the stable thermocline during stratification selectively encourages Fragilaria sp., which thrives in ecosystems with high nitrates and low phosphates [62]. This is consistent with an observation by Sirunda et al. [55] that higher temperature causes dominance by pollution-tolerant phytoplankton genera. Conversely, turnover releases hypolimnetic nutrients from sediments to the phytoplankton community in the upper epilimnion layer, which promotes rapid algal growth [4,24]. Nevertheless, Harding and Paxton [56] argue that, following winter turnover, low temperatures inhibit algal growth despite high nutrient levels.

4.6. Analysis of Dissolved Inorganic Nutrients

The eutrophication process increases algae proliferation, which in turn increases primary productivity and reduces the dissolved oxygen concentration [10,51,56]. The higher levels of organic matter increase turbidity and reduce light penetration [48]. These events suggest that the organic nutrient enrichment and eutrophication processes are highly correlated and exhibit a cyclic tendency [11,62]. There was high organic nutrient availability at the lacustrine zone (KD-A and KD-B) (Figure 6) in 2003, which was attributed to the gradual decomposition of a large amount of submerged plant shrubs, tree leaves, and branches, similar to the findings reported by Van Vuuren et al. [37] following the impoundment of Mohale Dam, Lesotho highlands. Furthermore, except for the high COD, which contributed to the high OPI in 2003, all the WSSs displayed excellent OPI status with values below zero in the FD (Figure 6).

The high COD levels are attributable to low reservoir levels at the KD during drought years in 2016 and 2021, as supported by the literature [63]. A significant decline in DO in the SD (Table 7) relative to the FD (Table 6) is associated with bacterial decomposition of organic matter from aquaculture [28]. The observed decline in DO is explained by Trujillo-Rogel et al. [49], who assert that the metabolic processes of caged fish exert a high dissolved oxygen demand on aquatic ecosystems. Reinl, K.L.; Harris, T.D.; Elfferich, I.; Coker, A.; Zhan, Q.; Domis, L.N.D.S.; Morales-Williams, A.M.; Bhattacharya, R.; Grossart, H.-P.; North, R.L.; et al. [62] suggest that particulate organic matter (POM) contributed by livestock and aquaculture waste is not available as DIN and DIP until it is decomposed by bacteria. POM is therefore not available for utilization by most phytoplankton, except a few that possess appropriate enzymes [62]. The presence of POM in the catchment and KD was thus not directly reflected by the organic pollution index components, DIP, and DIN. Phytoplankton utilizes DIP and increases biomass to accelerate eutrophication [62]. However, TP is released from sediments during reservoir mixing and made available for phytoplankton in the form of DIP, following decomposition by bacteria [62,64,65]. Even though the processes that lead to the eutrophication of reservoirs take decades, it is essential to manage external loading of nutrients into reservoirs to delay and manage the effects of eutrophication [56].

4.7. Trophic State Classification of Katse Dam

The trophic state depends on the concentrations of total nitrogen, phosphates, and chlorophyll-a (Chl-a) and water transparency (Secchi disk depth) [52]. The KD is classified as a system with high water clarity and is phosphate-limited because the Secchi disk depth and total phosphorus are relatively higher than Chl-a [44,45].

Figure 8c (shallow riverine zone) indicates that Chl-a increases as TP increases as supported by the literature [64], whereas Figure 8a (deeper lacustrine zone) shows that Chl-a decreases as TP increases. These rare results are consistent with the findings of Soto [65] showing that some temperate monomictic reservoirs are characterized by a low Chl-a concentration and higher water transparency even when the concentration of phosphorus is elevated. The same unexpectedly low Chl-a concentration was observed in Chilean reservoirs, showing a deviation from similar reservoirs in the Northern Hemisphere [65]. This is attributable to potential N-limitation and deeper epilimnion, like in the KD, which contrasts with the suggestion of P-limitation in the literature [66]. The results shown in Figure 8c,d for the shallow riverine zone in both cases reveal that phosphate contributes significantly to the eutrophication of the KD. These results align with the findings of Mnyango et al. [45] that the Roodepoort Dam is phosphate-limited. In such a case, Mnyango et al. [26] suggest that authorities should derive strategies that reduce the influx of phosphates into water resources like the KD.

An assessment of phosphate loads in the ten largest global reservoirs indicated that Lake Victoria experiences significant phosphorus loading due to anthropogenic pollution such as sewage effluent [67]. Similarly, mining camps contribute to the high population densities in the rural catchment of KD, where there is no running water and adequate sewage treatment facilities. Erlangga et al. [11] assert that effluent from inefficient sewage treatment facilities increases the eutrophication rate and leads to toxic cyanobacterial blooms. In addition, aquaculture potentially contributes to phosphate loads into the KD through fish waste and fish feed [28]. Besides the noted phosphorus-limited nature of eutrophication of ecosystems, Omoregie et al. [58] highlight the need to manage the influx of nitrates from their source to slow down the eutrophication rate. Notably, the observed relationship between TSI components, TSI_TP, TSI_SDD and TSI_Chl-a over the two decades and along the longitudinal zones revealed the impact of increased mining and aquaculture activity as measured through the application of CTSI (Figure S1).

The CTSI rates aquatic ecosystems according to the amount of biological productivity they sustain [45]. It provides a simplified way of assessing the influence of biotic and abiotic factors, including thermal stratification, nutrient availability, light intensity, photosynthetic rate, and phytoplankton composition, on the trophic state of reservoirs [45,46]. Karpowicz [46] asserts that the CTSI is more accurate for monomictic reservoirs than for dimictic and polymictic reservoirs, which experience mixing more than once a year and show a higher variability related to Secchi disk depth (TSI_SDD), the total phosphorus (TSI_TP), and the chlorophyll-a (TSI_Chl-a). Although Rossouw et al. [66] support the use of the CSTI for assessing the trophic state of reservoirs, they caution that such indices do not scale the extent of productivity and dynamics thereof. Roos [5] previously characterized the KD as oligo-mesotrophic with the potential of shifting to a eutrophic state due to increased P-loading, which mainly comes from aquaculture.

This study’s results contrast with the literature findings that eutrophication exhibits a cyclic tendency with organic nutrient enrichment. The CTSI shows a deteriorating trophic status while the organic pollution index reveals an excellent OPI state in most WSSs in the SD. The results obtained from the application of different indices provide water managers with (1) an indication of the excellent organic pollution index status of the KD; (2) a structured and simple presentation of the data to both technical and non-technical stakeholders; and (3) a clear indication of the transition of the KD from mesotrophic in the FD to hypereutrophic status in the SD in the lacustrine zone.

5. Limitations and Future Considerations

Using the modified pollution index to investigate changes in phytoplankton community structure as indicators of ecosystem status has some limitations in a deep reservoir that experiences stratification and mixing, as observed by Bai et al. [24]. Furthermore, vertical migration along the water column causes irregular distribution of phytoplankton [14,53]. Consequently, the results in Table 9 were affected by the vertical migration preferences of phytoplankton that thrive on nutrients present several meters below the surface [23,53]. These phytoplankton were excluded by sampling near the surface (1.5 m) of the photic zone. The results in Table 9 are thus not completely representative of all phytoplankton in the entire water column. It is therefore recommended that samples should be taken at the surface, middle, and bottom of the water column. The data from integrated sampling would be suitable and representative enough to be analyzed using phytoplankton taxonomic diversity indices such as the Shannon–Wiener index, the Margalef index, and the Pielou evenness index, similarly to a study by Cai et al. [54]. Additionally, Pearson correlation, which is best applied on integrated samples from the water column, was not used in this study to assess the correlations between the indices [54].

Despite these observations, this study only took water samples from a depth of 1.5 m in the photic zone, and did not consider phytoplankton that prefer low-light conditions that exist several meters below the surface of the photic zone. The literature reveals that the horizontal and vertical heterogeneity of phytoplankton distribution is influenced by thermal stratification and mixing [23]. Future studies should therefore consider the thermal stratification and mixing effects of the KD reservoir by comparing integrated samples along the vertical column during mixing as well as water samples taken during stratification, similarly to a study by Bai et al. [24].

Carlson’s trophic state index is limited in that it overestimates trophic levels [45], and its components (TSI_SDD, TSI_TP, TSI_Chl-a) do not conform during turnover (mixing) of reservoirs [44], this study acknowledges these limitations. Future studies may consider the application of modified trophic state indices, such as the Van Ginkel trophic state index [47] and the comprehensive trophic level index [51].

Although the use of different indices has highlighted the eutrophication trends in the KD, future research should explore integrating advanced real-time monitoring tools, remote sensing, and satellite imagery to elucidate the reservoir’s spatial and temporal trends to predict algal blooms [66]. Machine learning algorithms can be applied to model phosphate and nitrate inputs using historical datasets to predict the KD’s carrying capacity based on P-loading rates, reservoir capacity, flushing rates, and current and future aquaculture production rates [5,68].

Authorities should engage with existing stakeholders in the KD catchment, namely aquaculture and mining companies, to develop innovative policy frameworks and regulatory instruments to reduce the influx of nitrates and phosphates from identified sources. Initiatives such as wetland restorations, construction of artificial wetlands, awareness-raising campaigns about sustainable cultivation methods, and relocation of livestock posts to minimize localized pollution should be considered. Addressing the negative trajectory in the KD’s trophic status requires an integrated approach that seeks to optimize and allocate resources where they are most needed to mitigate and deter the eutrophication of this strategic raw water supply source.

6. Conclusions

This research classified the KD as hypereutrophic in the lacustrine zone and eutrophic from the transitional zone to the riverine zone. The KD is classified as greatly contaminated in the SD relative to the contaminated status in the FD. The OPI classified the KD’s water quality as excellent in the FD and SD, with the exception of the lacustrine zone, where the water quality was polluted in 2016 and 2021. The CTSI and the modified pollution index of PIKD revealed that the KD is hypereutrophic in the lacustrine zone and was greatly contaminated in the SD. Overall, the excellent OPI classification contrasts with the eutrophic status classification at the riverine and transitional zones in the SD. The dominance of Cosmarium sp. and Asterionella sp. in 2003 and 2005, respectively, is indicative of the oligotrophic conditions and low phosphate availability of the Katse Dam in the FD. Pollution-sensitive species were replaced by Radiocystis sp. and Fragilaria sp. in the SD.

This study provides a template for the application of different indices that can be adapted to monitor water quality in other warm monomictic mountainous reservoirs with catchments that are exposed to mining and aquaculture. The hypereutrophic condition in the lacustrine zone is an alert trigger indicating deteriorating water quality and algal bloom formation in the Katse Dam. Environmental managers can use the results to direct resources to mitigate environmental impacts and prevent further water quality degradation. Water managers can apply the methods used in this study in future water quality and trophic status assessments to sustain the water supply and economic and societal value of the Katse Dam.