Under warming conditions and with increasing human perturbations, rivers across the globe are facing drastic shifts in their hydrologic regime, resulting in fragmentation and disconnection from the catchment. Subsequently, a dependency on in situ primary productivity as the source of organic matter increases and warrants detailed investigation of the nature of primary production in urbanized river systems. In this study, primary productivity was estimated at multiple locations along the continuum of an engineered (Sabarmati) and a free flowing (Mahi) river systems in India using ¹³C tracer incubation method. Significantly enhanced primary productivity in the riverfront (engineered construction along the Sabarmati that holds water supplied by a canal) and polluted downstream of the Sabarmati compared to free flowing Mahi was observed. It was also observed that water stagnancy, temperature, and nutrient availability were the key factors regulating the rates of primary productivity in the urban river system. The study highlights the salient features of riverine primary productivity associated with engineered modifications, which needs to be considered for future river development projects.

1 INTRODUCTION

Rivers play a significant role in the global carbon (C) cycle, as they act as active reactors of organic matter and conduits of greenhouse gases to the atmosphere (Aufdenkampe et al., 2011; Cole et al., 2007; Drake et al., 2018). Rivers as bioreactors are sustained by the availability and degradation of organic matter (Machado-Silva et al., 2023). Apart from allochthonous inputs, in situ primary productivity is a major contributor of organic matter in rivers by fixing inorganic C into biomass. The organic matter formed, acts as substrate and fuels several biogeochemical processes like methanogenesis and drive the lateral export of C along with the evasion of CH₄ and CO₂ from the system (Bertuzzo et al., 2022; Webb et al., 2019). Riverine primary productivity is governed by several factors including light availability, water temperature, nutrient availability, and the residence time of water (Jia et al., 2019; Lee et al., 2015; Li et al., 2014; Lürling et al., 2013). Landcover and catchment characteristics also affect riverine biogeochemistry by regulating the inputs of organic matter and nutrients along with occasional light availability (Blaszczak et al., 2019). The net role of rivers as source or sink of CO₂ depends on the balance between primary productivity and respiration. In many steams, it is observed that gross primary productivity (GPP) is less than ecosystem respiration resulting in net heterotrophy (Cole et al., 2007; Duarte & Prairie, 2005). Studies have estimated stream GPP and ecosystem respiration in the range of 1–2500 and 15–14,000 mg C m⁻² d⁻¹, respectively, indicating the dominance of respiration over primary production (Battin et al., 2008; Bernhardt et al., 2022; Demars et al., 2011; Gagne-Maynard et al., 2017; Neely & Wetzel, 1995; Odum, 1956; Ward et al., 2018; Wissmar et al., 1981).

Over the last few decades, rivers across the globe have faced the direct consequences of climate change and anthropogenic interventions, resulting in drastic shifts in their flow regimes. Currently, only 37% of rivers that are longer than 1000 km remain free flowing over their entire length and 51%–60% of rivers do not flow for at least 1 day per year (Grill et al., 2019; Messager et al., 2021). The flow intermittency and obstruction has serious consequences on the riverine biogeochemistry affecting the nature of C cycling (Han et al., 2018; Silverthorn et al., 2023; Wang et al., 2018, 2022). Fragmentation and disintegration of river systems from the catchments limit the availability of allochthonous organic matter and increases the dependency on in situ primary production (Collins et al., 2016; Jacquet et al., 2022). Furthermore, urbanization and land use change result in excessive inputs of nutrients that further affects primary productivity (Jia et al., 2019; Yoshikawa et al., 2017). Therefore, it is timely to assess the nature of primary productivity in urbanized river systems and decipher various environmental controls on the same. In this regard, the present study aims to understand the role of water stagnancy caused due to human perturbations of river channels and wastewater inputs on the riverine primary productivity by quantifying the rates of primary productivity along the river continuum.

2 MATERIALS AND METHODS

2.1 Study area

To address the above objectives, primary productivity experiments were performed along the continuum of two rivers (Sabarmati and Mahi) situated in semi-arid climate zone of the Indian state of Gujarat. Sabarmati is highly perturbed by engineering modifications, whereas the Mahi is relatively free flowing (Figure 1). Draining a catchment of 21,674 km², the Sabarmati River has its headwaters in the Aravalli hills and flows ~371 km to ultimately drain into the Arabian Sea. Twin cities, Gandhinagar and Ahmedabad (population > 6 million), are located along the banks of the river and the latter is responsible for discharge of large amounts of wastewater and industrial effluents into the river (Figure 1) (Mohanty et al., 2021). As the river passes through Ahmedabad, a riverfront (lined on both sides) is constructed that holds water in stagnancy over prolonged periods. The water in the riverfront is largely supplied by a canal system (Narmada Canal) fed by the Narmada River, a major river in the central India (Figure 1). The downstream reach of Sabarmati is highly polluted and runs ~70 km before draining into the Gulf of Khambhat. The Mahi River rises near the Mahi Kanta hills (Vindhyachal range) in the western part of the state of Madhya Pradesh. It is bound by the Aravalli hills and the Vindhayas in the north and south, respectively. The Mahi rivers traverses a length of 583 km before draining into the Arabian Sea.

Details are in the caption following the image — **Figure 1**
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Map (left) showing sampling sites along the Sabarmati River, Mahi River, and Narmada Canal along with a Google Earth image (right) showing the sampling sites in the riverfront (within the city of Ahmedabad) and the Narmada canal.

2.2 Sample collection and incubation experiments

Sampling was conducted along the mainstem and selected tributaries of the Sabarmati and the mainstem of the Mahi River in February 2021 (Figure 1). To observe the effect of human perturbations, sampling locations in the Sabarmati mainstem was classified into upstream, riverfront, and downstream reaches. Additionally, to observe the effect of water stagnancy, samples were collected from the Narmada Canal (source of water for the riverfront) for comparison. Water temperature, electrical conductivity (EC), and pH were measured in situ using handheld probes (HANNA). Dissolved oxygen (DO) was estimated using the Winkler titration method (Grasshoff et al., 1983). Samples for particulate matter were collected in Nalgene bottles and kept in the dark until incubation and filtration. Water samples for dissolved inorganic carbon (DIC) were collected in airtight serum glass bottles with butyl rubber septa and aluminum caps and poisoned using saturated HgCl₂. Water samples for dissolved inorganic nitrogen (DIN: nitrite (NO₂⁻), nitrate (NO₃⁻), and ammonium (NH₄⁺)) were collected in 60 mL high density polyethylene (HDPE) bottles and kept frozen (at −20°C) until analysis. For particulate organic matter (POM), sample water was filtered onto 0.7 µm GF/F filters (precombusted at 400°C for 4 h). For primary productivity estimation, samples were incubated in 1 L polycarbonate Nalgene bottles (in duplicates) and spiked with 98+ atom % enriched ¹³C-NaHCO₃ prepared using ultrapure water (Choi et al., 2020; Molinari et al., 2021). The tracer volume was selected such that it was <10% of the ambient DIC concentration. The samples were incubated for 4 h in sunlight (approximately between 10:00 a.m. and 2:00 p.m. local time). Similar to POM, postincubation samples were also filtered onto GF/F filters and dried in an oven overnight at 60°C for mass spectrometric analysis. For C content and its isotopic composition, a portion of filters were fumigated with HCl to remove the inorganic C fractions, whereas N content and its isotopic compositions were measured in unfumigated portions.

2.3 Sample analysis

Carbon (natural POM and incubated samples) and Nitrogen (N) (natural POM) contents and their isotopic compositions were measured using an elemental analyser (EA) interfaced to an isotope ratio mass spectrometer (IRMS) (Flash 2000-Delta V Plus; Thermo Fisher Scientific). International Atomic Energy Agency (IAEA) standards of cellulose (IAEA -CH-3; δ¹³C = –24.72‰ and C content = 44.4%) and ammonium sulfate (IAEA-N-2; δ¹⁵N = 20.3‰ and N content = 21.2%) were used as standards for C and N analyses, respectively. The analytical precisions for repeat measurements of standards during sample analysis were better than 0.1‰ and 0.3‰ for C and N isotopic compositions, respectively, whereas it was better than 10% for C and N contents. DIC concentration and isotopic composition was measured using GasBench–IRMS (Mat 253; Thermo Fisher Scientific) with analytical precision better than 0.1‰ and 5% for δ¹³C and DIC, respectively. NO₃⁻, NO₂⁻ and NH₄⁺ was measured using a continuous flow autoanalyzer (Skalar) with analytical precision of ±0.01 mg L⁻¹.

The calculation for uptake rates were performed using the following equation (Slawyk et al., 1977):

<math display="block" wiley:location="equation/rvr288-math-0001.png" xmlns="http://www.w3.org/1998/Math/MathML" altimg="urn:x-wiley:27504867:media:rvr288:rvr288-math-0001"><mrow><mrow><mtable columnalign="left"><mtr><mtd><mtext>Uptake</mtext><mspace width="0.25em"/><mtext>rate</mtext><mo>(</mo><mo>\unicode{x000B5}</mo><mtext>mol</mtext><mspace width="0.25em"/><mi mathvariant="normal">C</mi><mspace width="0.25em"/><msup><mi mathvariant="normal">L</mi><mrow><mo>\unicode{x02013}</mo><mn>1</mn></mrow></msup><msup><mi mathvariant="normal">h</mi><mrow><mo>\unicode{x02013}</mo><mn>1</mn></mrow></msup><mo>)</mo><mo>\unicode{x0003D}</mo><mi>P</mi><mo>\unicode{x0002A}</mo><mi>\unicode{x00394}</mi><msub><mi>I</mi><mi mathvariant="normal">P</mi></msub></mtd></mtr><mtr><mtd><mo>\unicode{x02007}</mo><mo>/</mo><mo>[</mo><mi>T</mi><mo>\unicode{x0002A}</mo><mo>[</mo><msub><mi>I</mi><mn>0</mn></msub><msub><mi>S</mi><mi mathvariant="normal">a</mi></msub><mo>\unicode{x0002B}</mo><msub><mi>I</mi><mi mathvariant="normal">r</mi></msub><msub><mi>S</mi><mi mathvariant="normal">t</mi></msub><mo>)</mo><mo>/</mo><msub><mi>S</mi><mi mathvariant="normal">a</mi></msub><mo>\unicode{x0002B}</mo><msub><mi>S</mi><mi mathvariant="normal">t</mi></msub><mo>)</mo><mo>\unicode{x02013}</mo><msub><mi>I</mi><mn>0</mn></msub><mo>]</mo><mo>]</mo><mo>,</mo></mtd></mtr></mtable></mrow></mrow></math>

where, P = particulate organic carbon (POC) content; ΔI_P = increase in ¹³C atom % during incubation; S_a and S_t are the ambient DIC and tracer concentration, respectively. I_r and I₀ are the ¹³C atom % of added tracer and natural sample DIC, and T = incubation time.

2.4 Statistical analysis

SigmaPlot 14.0 and RStudio (version 2021.09.2) were used to run various statistical tests on the data. Analysis of variance (ANOVA) was run to detect significant variation in the rates of primary productivity among different sample classes. Spearman rank correlation was used to check for significant relationship among measured parameters.

3 RESULTS

3.1 Environmental and biogeochemical parameters

Among the various reaches sampled, except for the riverfront (28.45 ± 1.82°C), water temperature was similar in the Narmada Canal, Sabarmati, and Mahi (24.27 ± 1.94°C). The river water was mostly alkaline with pH varying from 7.62 to 9.24 (8.45 ± 0.39). Compared to other reaches of the river, relatively low pH in the downstream reach of the Sabarmati and high pH in the riverfront was observed (Table 1). The riverfront was well oxygenated (DO: 9.39 ± 4.09 mg L⁻¹) compared to the Narmada Canal, Mahi, and the upstream reaches of Sabarmati (DO: 5.94 ± 1.11 mg L⁻¹). The downstream reach of Sabarmati had low DO (2.84 ± 2.83 mg L⁻¹) and two locations just downstream of the Ahmedabad city (SD 1 and SD 2) had DO below the detection limit (Table 1). NO₂⁻ was low in all reaches (7.50 ± 4.90 µgN L⁻¹), except the downstream reach of the Sabarmati (20.41 ± 18.83 µgN L⁻¹) and the riverfront (42.74 ± 8.85 µgN L⁻¹). NO₃⁼ was low in the upstream (18.76 ± 17.31 µgN L⁻¹) and downstream (77.70 ± 52.67 µgN L⁻¹) reaches of Sabarmati compared to the other reaches (346.15 ± 241.15 µgN L⁻¹). NH₄⁺ was manifold higher in the downstream reach of the Sabarmati (3975.90 ± 410.05 µgN L⁻¹) compared to the riverfront (238.46 ± 98.19 µgN L⁻¹) and other reaches (56.24 ± 38.34 µgN L⁻¹) (Table 1). The DIC concentration was also the highest in the downstream reach of Sabarmati (7.38 ± 2.43 mM) with lowest δ¹³C_DIC (−10.0 ± 1.0‰) compared to other reaches (DIC = 2.8 ± 1.1 mM; δ¹³C_DIC = 7.2 ± 1.3‰) (Table 1). Biomass (POC and particulate nitrogen [PN]) was higher in the downstream reach (1622.35 ± 1246.66 and 202.72 ± 147.78 µM, respectively) and riverfront (420.70 ± 232.96 and 71.59 ± 45.01 µM, respectively), compared to the upstream reaches (62.41 ± 46.37 and 7.61 ± 6.31 µM, respectively) and the Narmada Canal (71.71 ± 37.29 and 10.32 ± 5.88 µM, respectively) (Table 1). δ¹³C_POC averaged around −27.5 ± 2.9‰ among all sampling locations with the riverfront and the Narmada Canal showing relatively lower values (−29.6 ± 2.1 and −30.4 ± 3.9‰). The δ¹⁵N_PN was the lowest in the downstream reach of Sabarmati (1.2 ± 1.5‰) compared to the riverfront (5.6 ± 2.0‰), upstream reaches (5.6 ± 0.3‰), Narmada Canal (7.2 ± 3.5‰), and Mahi (8.8 ± 2.4‰) (Table 1).

Table 1. Variations in environmental and biogeochemical parameters along the Sabarmati, Mahi, and Narmada Canal.

Site	pH	T (°C)	EC (μS/cm)	DO (mg L⁻¹)	NO₂⁻ (µgN L⁻¹)	NO₃⁻ (µgN L⁻¹)	NH₄⁺ (µgN L⁻¹)	DIC (mM)	δ¹³C_DIC (‰)	POC (µM)	δ¹³C_POC (‰)	PN (µM)	δ¹⁵N_PN (‰)
SU 1	8.48	22.7	560	7.01	3.22	36.54	30.38	2.78	−7.2	33.97	−24.7	3.5	5.3
SU 2	8.57	21.9	470	6.01	2.24	1.96	0.03	1.42	−5.1	37.34	−23.0	4.5	5.5
SU 3	8.36	22.5	430	6.51	2.94	17.78	22.82	2.25	−5.7	115.91	−29.8	14.9	5.8
RF 1	9.22	28.4	310	15.35	49.28	253.40	148.12	2.39	−5.3	764.37	−30.8	138.4	7.4
RF 2	8.71	28.7	350	8.51	51.38	387.10	355.88	2.11	−7.7	295.42	−31.9	46.2	4.5
RF 3	8.51	27.5	350	6.17	34.16	389.76	282.38	1.92	−7.9	260.73	−27.9	43.2	3.3
RF 4	9.24	29.2	330	7.51	36.12	261.66	167.44	2.71	−7.1	362.28	−27.9	58.6	7.1
SD 1	7.68	27.6	2550	BDL	11.76	8.12	4359.88	9.90	−10.7	2475.78	−25.5	230.2	−0.1
SD 2	7.70	-	2290	BDL	7.98	81.06	4270.00	9.01	−10.8	2843.02	−25.3	385.5	0.7
SD 3	8.29	28.3	1510	4.84	48.44	136.08	3494.40	5.49	−8.7	968.17	−26.1	165.5	3.4
SD 4	7.85	24.8	1550	0.83	13.44	85.54	3779.30	5.10	−9.8	202.44	−25.4	29.7	0.6
ST 1	8.36	24.6	390	3.67	3.78	53.06	51.24	1.97	−5.8	202.90	−30.1	24.2	5.6
ST 2	8.34	24.4	700	4.17	18.76	799.12	48.16	4.95	−8.3	49.29	−27.1	9.6	6.8
ST 3	8.28	22.5	600	3.67	11.76	113.26	96.18	4.95	−8.3	48.17	−23.3	8.1	9.3
NC 1	8.45	23.5	320	7.68	5.18	477.68	52.92	1.81	−9.4	45.35	−33.1	6.2	4.7
NC 2	8.51	23.5	320	5.51	5.60	472.92	149.52	1.88	−9.6	98.08	−27.6	14.5	9.7
M 1	8.50	23.2	380	5.67	3.92	129.78	18.34	1.97	−5.8	55.20	−28.7	6.1	12.0
M 2	8.45	23.4	410	4.01	4.76	126.98	71.82	3.49	−7.2	84.73	−25.9	10.3	10.4
M 3	8.49	23.3	490	6.84	5.04	85.12	74.34	2.58	−7.9	122.52	−23.2	9.9	8.0
M 4	8.67	24.6	470	5.51	12.88	727.58	45.64	3.16	−8.0	114.61	−28.3	12.4	7.2
M 5	8.76	27.5	470	4.67	12.74	568.68	69.72	4.47	−6.9	125.71	−31.2	14.4	6.3

Abbreviations: BDL, below detection limit; DO, dissolved oxygen; DIC, dissolved inorganic carbon; EC, electrical conductivity; M, Mahi; NC, Narmada Canal; PN, particulate nitrogen; POC, particulate organic carbon; RF, Riverfront; SD, Sabarmati Downstream; ST, Sabarmati Tributaries; SU, Sabarmati Upstream.

3.2 Primary productivity rates

The rates of primary productivity varied across and among different reaches of the Sabarmati along with Mahi and the Narmada Canal. The highest uptake rate (p < 0.05; ANOVA) was observed in the riverfront (630.41 ± 112.72 µM C d⁻¹) followed by the downstream reach of Sabarmati (390.71 ± 425.46 µM C d⁻¹) (Figure 2). The upstream reaches of Sabarmati and its tributaries along with the Narmada Canal and the Mahi River showed relatively lower uptake rates of 17.29 ± 13.87, 43.50 ± 3.27, 18.09 ± 5.51, and 37.18 ± 49.39 µM C d⁻¹, respectively (Figure 2).

4 DISCUSSION

4.1 Spatial variability in primary productivity

Primary productivity varies across biomes, climate regimes, and stream ecosystems (Lamberti & Steinman, 1997; Minshall, 1978) and higher rates of gross primary productivity is generally observed in middle-order streams (with less canopy and comparatively less light limitation) as compared to lower- (with dense canopy) and higher-order streams (with high turbidity and enhanced light attenuation) (Lamberti & Steinman, 1997). The river continuum concept (Vannote et al., 1980) justly incorporates this variation of primary productivity with stream order, which is further validated by studies elsewhere (Bott et al., 1985; Naiman et al., 1987). In this study, primary productivity varied along the continuum of Sabarmati and Mahi with lower rates in the upstream reaches of the Sabarmati as compared to its downstream reaches (Figure 2). These rates of primary productivity were similar to the rates (dark carbon fixation) estimated in streams using ¹⁴C labeled tracer (Machado-Silva et al., 2023). Considering an euphotic layer of 10 cm and recalculating our rates to compare with literature data, the primary productivity varied from 0.22 to 147.53 mg C m⁻² d⁻¹, which was comparable to the range of values of stream GPP (1–2500 mg C m⁻² d⁻¹) reported by studies elsewhere (Battin et al., 2008; Bernhardt et al., 2022; Gagne-Maynard et al., 2017; Neely & Wetzel, 1995; Odum, 1956; Wissmar et al., 1981). Streams that receive allochthonous inputs of organic matter has higher rates of respiration than could otherwise be sustained by in situ production (Fisher & Likens, 1973; Marcarelli et al., 2011). However, not all streams are connected to the catchment and receive little or no input of terrestrial organic matter, therefore increasing the dependency on primary productivity. In this study, higher primary productivity was observed in the riverfront compared to the Narmada Canal (Figure 2), indicating that water stagnancy is favorable to phytoplankton growth due to relatively low turbidity and higher water residence time (di Persia & Neiff, 1986; Dudgeon, 1995; Lévêque, 1995). Rivers downstream of dams and reservoirs are usually cut off from the catchment (dams holding back most of the allochthonous organic matter), and therefore show very high rates of GPP compared to respiration (i.e., net autotrophy) (Davis et al., 2012; Genzoli & Hall, 2016). A meta-analysis showed that GPP was higher in human altered small streams, which was not reflected in larger rivers (Finlay, 2011). In agreement with the above, the downstream reaches of the Sabarmati showed higher rates of primary productivity, which were comparable with the riverfront (Figure 2). High nutrient inputs through wastewater discharge supported high rates of primary productivity in the Sabarmati downstream as evident from the high DIN concentrations and a strong positive correlation between primary productivity and DIN (Table 1 and Figure 3a,b). Rates of primary production can go as high as 6000 mg C m⁻² d⁻¹ in polluted waters and are predominantly due to higher nutrient availability (Gücker et al., 2006; Rao, 1979). In the Mahi, increasing primary productivity from upstream to downstream reaches was observed and the rates were comparable to that of the tributaries of Sabarmati but lower than the highly productive riverfront and the polluted downstream reaches of Sabarmati (Figure 2).

4.2 Environmental controls on primary productivity

Water temperature and nutrient availability showed strong correlation with primary productivity (Figure 3a,b). Enhanced primary productivity at higher water temperature might be related to increased autotrophic respiration and simultaneously GPP or temperature could just be an indicator of insolation, which directly affects primary productivity (Lamberti & Steinman, 1997). Light availability (a function of shading, turbidity, and insolation) and canopy cover play a dominant role in regulating rates of stream GPP (Acuña et al., 2004; Beaulieu et al., 2013; Bernot et al., 2010; Hall et al., 2015; Hunt et al., 2012; Mulholland et al., 2001). Temperature is also known to control stream metabolism directly (enzyme kinetics) and indirectly (altering community structure); however, it is difficult to decouple its effects from the effect of light (Demars et al., 2011; Huryn et al., 2014; Rasmussen et al., 2011; Yvon-Durocher et al., 2012). In this study, the effect of water stagnancy on primary productivity was evident from the higher uptake rates in the riverfront (Figures 2 and 3a). Increased water residence time results in increased water temperature as observed in Nakdong River (Choi et al., 2020; Park et al., 2018), which ultimately increases primary production. Yu et al. (2019) observed enhanced primary productivity in the Yellow River during spring and summer due to less turbidity and suitable water temperature. Similar increase in in situ productivity was inferred during low flow conditions in the Ganges, Mekong, and Yellow River by Sarkar et al. (2023). Primary productivity in the river could be associated with dinitrogen fixation, which is reflected in the negative relationship between primary productivity and δ¹⁵N_PN (Figure 3b) due to negligible isotopic fractionation during the process of N₂ fixation (Fry, 2006). Increasing primary productivity also resulted in a decrease in δ¹³C_POC, which is typical of freshwater plankton (δ¹³C_POC = − 42‰ to −24‰; Kendall et al., 2001). Low flow conditions (clear and less turbid) are usually favorable for phytoplankton and algal growths, which under well-oxygenated and noneutrophic conditions can have highly depleted δ¹³C_POC (Leng & Melanie, 2006). Among inorganic nitrogen species, NH₄⁺ is preferably assimilated during primary production due to lower energy costs and the positive correlation between NH₄⁺ and primary productivity observed in this study indicates that nutrient availability controls the rates of primary production in rivers. Bernot et al. (2010) observed similar control of nutrients (NO₃⁻) on GPP in streams.

5 CONCLUSION

The present study explores the variation in primary productivity at multiple locations along the continuum of two rivers, a highly engineered Sabarmati and relatively free flowing Mahi. High rates of primary productivity was observed in the riverfront and the downstream reaches of the Sabarmati. Water stagnancy, high water temperature, and nutrient availability were the primary factors responsible for higher rates of primary productivity in the riverfront and polluted downstream reaches of the Sabarmati. This study indicates that, in a future scenario of a warming world and with increased human perturbations of river channels, fragmented and urbanized rivers would likely become more productive, resulting in high organic matter production that might ultimately fuel processes like methanogenesis and carbon burial. Extensive studies accessing the shift in productivity of river channels in regions witnessing changes in hydrological regimes and in data sparse regions is highly warranted, especially using robust techniques such as the isotopically enriched tracer uptake incubation experiments.

ACKNOWLEDGMENTS

The funding for this study was provided by the Department of Space, Government of India.

ETHICS STATEMENT

None declared.

Open Research

DATA AVAILABILITY STATEMENT

Data are available on request from the authors.

REFERENCES

Acuña, V., Giorgi, A., Muñoz, I., Uehlinger, U., & Sabater, S. (2004). Flow extremes and benthic organic matter shape the metabolism of a headwater Mediterranean stream. Freshwater Biology, 49(7), 960–971.
10.1111/j.1365-2427.2004.01239.x
Web of Science® Google Scholar
Aufdenkampe, A. K., Mayorga, E., Raymond, P. A., Melack, J. M., Doney, S. C., Alin, S. R., Aalto, R. E., & Yoo, K. (2011). Riverine coupling of biogeochemical cycles between land, oceans, and atmosphere. Frontiers in Ecology and the Environment, 9(1), 53–60.
10.1890/100014
Web of Science® Google Scholar
Battin, T. J., Kaplan, L. A., Findlay, S., Hopkinson, C. S., Marti, E., Packman, A. I., Newbold, J. D., & Sabater, F. (2008). Biophysical controls on organic carbon fluxes in fluvial networks. Nature Geoscience, 1(2), 95–100.
10.1038/ngeo101
CAS Web of Science® Google Scholar
Beaulieu, J. J., Arango, C. P., Balz, D. A., & Shuster, W. D. (2013). Continuous monitoring reveals multiple controls on ecosystem metabolism in a suburban stream. Freshwater Biology, 58(5), 918–937.
10.1111/fwb.12097
CAS Web of Science® Google Scholar
Bernhardt, E. S., Savoy, P., Vlah, M. J., Appling, A. P., Koenig, L. E., Hall, Jr., R. O., Arroita, M., Blaszczak, J. R., Carter, A. M., Cohen, M., Harvey, J. W., Heffernan, J. B., Helton, A. M., Hosen, J. D., Kirk, L., McDowell, W. H., Stanley, E. H., Yackulic, C. B., & Grimm, N. B. (2022). Light and flow regimes regulate the metabolism of rivers. Proceedings of the National Academy of Sciences, 119(8):e2121976119.
10.1073/pnas.2121976119
CAS PubMed Web of Science® Google Scholar
Bernot, M. J., Sobota, D. J., Hall, Jr., R. O., Mulholland, P. J., Dodds, W. K., Webster, J. R., Tank, J. L., Ashkenas, L. R., Cooper, L. W., Dahm, C. N., Gregory, S. V., Grimm, N. B., Hamilton, S. K., Johnson, S. L., McDowell, W. H., Meyer, J. L., Peterson, B., Poole, G. C., Valett, H. M., … Wilson, K. (2010). Inter-regional comparison of land-use effects on stream metabolism. Freshwater Biology, 55(9), 1874–1890.
10.1111/j.1365-2427.2010.02422.x
Web of Science® Google Scholar
Bertuzzo, E., Hotchkiss, E. R., Argerich, A., Kominoski, J. S., Oviedo-Vargas, D., Savoy, P., Scarlett, R., von Schiller, D., & Heffernan, J. B. (2022). Respiration regimes in rivers: Partitioning source-specific respiration from metabolism time series. Limnology and Oceanography, 67(11), 2374–2388.
10.1002/lno.12207
CAS Web of Science® Google Scholar
Blaszczak, J. R., Delesantro, J. M., Urban, D. L., Doyle, M. W., & Bernhardt, E. S. (2019). Scoured or suffocated: Urban stream ecosystems oscillate between hydrologic and dissolved oxygen extremes. Limnology and Oceanography, 64(3), 877–894.
10.1002/lno.11081
CAS Web of Science® Google Scholar
Bott, T. L., Brock, J. T., Dunn, C. S., Naiman, R. J., Ovink, R. W., & Petersen, R. C. (1985). Benthic community metabolism in four temperate stream systems: An inter-biome comparison and evaluation of the river continuum concept. Hydrobiologia, 123, 3–45.
10.1007/BF00006613
Web of Science® Google Scholar
Choi, J., Min, J. O., Choi, B., Kim, D., Kang, J. J., Lee, S. H., Choi, K., Lee, H., Jung, J., & Shin, K. H. (2020). Key factors controlling primary production and cyanobacterial harmful algal blooms (cHABs) in a continuous weir system in the Nakdong River, Korea. Sustainability, 12(15), 6224.
10.3390/su12156224
CAS Web of Science® Google Scholar
Cole, J. J., Prairie, Y. T., Caraco, N. F., McDowell, W. H., Tranvik, L. J., Striegl, R. G., Duarte, C. M., Kortelainen, P., Downing, J. A., Middelburg, J. J., & Melack, J. (2007). Plumbing the global carbon cycle: Integrating inland waters into the terrestrial carbon budget. Ecosystems, 10, 172–185.
10.1007/s10021-006-9013-8
CAS Web of Science® Google Scholar
Collins, S. M., Kohler, T. J., Thomas, S. A., Fetzer, W. W., & Flecker, A. S. (2016). The importance of terrestrial subsidies in stream food webs varies along a stream size gradient. Oikos, 125(5), 674–685.
10.1111/oik.02713
Web of Science® Google Scholar
Davis, C. J., Fritsen, C. H., Wirthlin, E. D., & Memmott, J. C. (2012). High rates of primary productivity in a semi-arid tailwater: Implications for self-regulated production. River Research and Applications, 28(10), 1820–1829.
10.1002/rra.1573
Web of Science® Google Scholar
Demars, B. O. L., Russell Manson, J., Ólafsson, J. S., Gíslason, G. M., Gudmundsdóttir, R., Woodward, G., Reiss, J., Pichler, D. E., Rasmussen, J. J., & Friberg, N. (2011). Temperature and the metabolic balance of streams. Freshwater Biology, 56(6), 1106–1121.
10.1111/j.1365-2427.2010.02554.x
Web of Science® Google Scholar
Di Persia, D. H., Neiff, J. J., & Olazarri, J. (1986). The Uruguay River system. Monographiae Biologicae, 599–629. https://doi.org/10.1007/978-94-017-3290-1_12
10.1007/978-94-017-3290-1_12
Google Scholar
Drake, T. W., Raymond, P. A., & Spencer, R. G. M. (2018). Terrestrial carbon inputs to inland waters: A current synthesis of estimates and uncertainty. Limnology and Oceanography Letters, 3, 132–142.
10.1002/lol2.10055
CAS Web of Science® Google Scholar
Duarte, C. M., & Prairie, Y. T. (2005). Prevalence of heterotrophy and atmospheric CO₂ emissions from aquatic ecosystems. Ecosystems, 8, 862–870.
10.1007/s10021-005-0177-4
CAS Web of Science® Google Scholar
Dudgeon, D. (1995). The ecology of rivers and streams in tropical Asia. In C. E. Cushing, K. W. Cummins & G. W. Minshall, Eds., River and stream ecosystems (pp. 615–657). Elsevier Science B.V.
Google Scholar
Finlay, J. C. (2011). Stream size and human influences on ecosystem production in river networks. Ecosphere, 2(8), art87.
10.1890/ES11-00071.1
Web of Science® Google Scholar
Fisher, S. G., & Likens, G. E. (1973). Energy flow in Bear Brook, New Hampshire: An integrative approach to stream ecosystem metabolism. Ecological Monographs, 43(4), 421–439.
10.2307/1942301
Web of Science® Google Scholar
Fry, B. (2006). Stable isotope ecology ( 521, p. 318). Springer.
10.1007/0-387-33745-8
Google Scholar
Gagne-Maynard, W. C., Ward, N. D., Keil, R. G., Sawakuchi, H. O., Da Cunha, A. C., Neu, V., Brito, D. C., Da Silva Less, D. F., Diniz, J. E. M., De Matos Valerio, A., Kampel, M., Krusche, A. V., & Richey, J. E. (2017). Evaluation of primary production in the lower Amazon River based on a dissolved oxygen stable isotopic mass balance. Frontiers in Marine Science, 4, 26.
10.3389/fmars.2017.00026
Web of Science® Google Scholar
Genzoli, L., & Hall, Jr., R. O. (2016). Shifts in Klamath River metabolism following a reservoir cyanobacterial bloom. Freshwater Science, 35(3), 795–809.
10.1086/687752
Web of Science® Google Scholar
Grasshoff, K., Ehrharft, M., & Kremling, K. (1983). Methods of seawater analysis ( 2nd Edn.). Verlag Chemie.
Google Scholar
Grill, G., Lehner, B., Thieme, M., Geenen, B., Tickner, D., Antonelli, F., Babu, S., Borrelli, P., Cheng, L., Crochetiere, H., Ehalt Macedo, H., Filgueiras, R., Goichot, M., Higgins, J., Hogan, Z., Lip, B., McClain, M. E., Meng, J., Mulligan, M., & Zarfl, C. (2019). Mapping the world's free-flowing rivers. Nature, 569(7755), 215–221. https://doi.org/10.1038/s41586-019-1111-9
10.1038/s41586-019-1111-9
CAS PubMed Web of Science® Google Scholar
Gücker, B., Brauns, M., & Pusch, M. T. (2006). Effects of wastewater treatment plant discharge on ecosystem structure and function of lowland streams. Journal of the North American Benthological Society, 25(2), 313–329.
10.1899/0887-3593(2006)25[313:EOWTPD]2.0.CO;2
Web of Science® Google Scholar
Hall, Jr., R. O., Yackulic, C. B., Kennedy, T. A., Yard, M. D., Rosi-Marshall, E. J., Voichick, N., & Behn, K. E. (2015). Turbidity, light, temperature, and hydropeaking control primary productivity in the Colorado river, grand canyon. Limnology and Oceanography, 60(2), 512–526.
10.1002/lno.10031
Web of Science® Google Scholar
Han, Q., Wang, B., Liu, C. Q., Wang, F., Peng, X., & Liu, X. L. (2018). Carbon biogeochemical cycle is enhanced by damming in a karst river. Science of the Total Environment, 616-617, 1181–1189.
10.1016/j.scitotenv.2017.10.202
CAS PubMed Web of Science® Google Scholar
Hunt, R. J., Jardine, T. D., Hamilton, S. K., & Bunn, S. E., TRopical Rivers and Coastal Knowledge Research Hub. (2012). Temporal and spatial variation in ecosystem metabolism and food web carbon transfer in a wet-dry tropical river. Freshwater Biology, 57(3), 435–450.
10.1111/j.1365-2427.2011.02708.x
CAS Web of Science® Google Scholar
Huryn, A. D., Benstead, J. P., & Parker, S. M. (2014). Seasonal changes in light availability modify the temperature dependence of ecosystem metabolism in an Arctic stream. Ecology, 95(10), 2826–2839.
10.1890/13-1963.1
Web of Science® Google Scholar
Jacquet, C., Carraro, L., & Altermatt, F. (2022). Meta-ecosystem dynamics drive the spatial distribution of functional groups in river networks. Oikos, 2022(11), e09372.
10.1111/oik.09372
Web of Science® Google Scholar
Jia, J., Gao, Y., Song, X., & Chen, S. (2019). Characteristics of phytoplankton community and water net primary productivity response to the nutrient status of the Poyang Lake and Gan River, China. Ecohydrology, 12(7), e2136.
10.1002/eco.2136
CAS Web of Science® Google Scholar
Kendall, C., Silva, S. R., & Kelly, V. J. (2001). Carbon and nitrogen isotopic compositions of particulate organic matter in four large river systems across the United States. Hydrological Processes, 15(7), 1301–1346.
10.1002/hyp.216
Web of Science® Google Scholar
Lamberti, G. A., & Steinman, A. D. (1997). A comparison of primary production in stream ecosystems. Journal of the North American Benthological Society, 16(1), 95–104.
10.2307/1468241
Google Scholar
Lee, Y., Ha, S. Y., Park, H. K., Han, M. S., & Shin, K. H. (2015). Identification of key factors influencing primary productivity in two river-type reservoirs by using principal component regression analysis. Environmental Monitoring and Assessment, 187, 213.
10.1007/s10661-015-4438-1
PubMed Web of Science® Google Scholar
M. J. Leng & J. Melanie, eds. (2006). Isotopes in palaeoenvironmental research ( 10, p. 307). Springer.
10.1007/1-4020-2504-1
Google Scholar
Lévêque, C. (1995). River and stream ecosystems of northwestern Africa. In C. E. Cushing, K. W. Cummins, & G. W. Minshall (Eds.) River and stream ecosystems (pp. 519–536). Elsevier Science B.V.
Google Scholar
Li, H. M., Tang, H. J., Shi, X. Y., Zhang, C. S., & Wang, X. L. (2014). Increased nutrient loads from the Changjiang (Yangtze) River have led to increased harmful algal blooms. Harmful algae, 39, 92–101.
10.1016/j.hal.2014.07.002
CAS Web of Science® Google Scholar
Lürling, M., Eshetu, F., Faassen, E. J., Kosten, S., & Huszar, V. L. M. (2013). Comparison of cyanobacterial and green algal growth rates at different temperatures. Freshwater Biology, 58(3), 552–559.
10.1111/j.1365-2427.2012.02866.x
Web of Science® Google Scholar
Machado-Silva, F., Bastviken, D., Miranda, M., Peixoto, R. B., Marotta, H., & Enrich-Prast, A. (2023). Dark carbon fixation in stream carbon cycling. Limnology and Oceanography. https://doi.org/10.1002/lno.12430
10.1002/lno.12430
Web of Science® Google Scholar
Marcarelli, A. M., Baxter, C. V., Mineau, M. M., & Hall, Jr., R. O. (2011). Quantity and quality: Unifying food web and ecosystem perspectives on the role of resource subsidies in freshwaters. Ecology, 92(6), 1215–1225.
10.1890/10-2240.1
PubMed Web of Science® Google Scholar
Messager, M. L., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H., Snelder, T., Tockner, K., Trautmann, T., Watt, C., & Datry, T. (2021). Global prevalence of non-perennial rivers and streams. Nature, 594(7863), 391–397. https://doi.org/10.1038/s41586-021-03565-5
10.1038/s41586-021-03565-5
CAS PubMed Web of Science® Google Scholar
Minshall, G. W. (1978). Autotrophy in stream ecosystems. Bioscience, 28(12), 767–771.
10.2307/1307250
Web of Science® Google Scholar
Mohanty, B., Das, A., Mandal, R., Banerji, U., & Acharyya, S. (2021). Heavy metals in soils and vegetation from wastewater irrigated croplands near Ahmedabad, Gujarat: Risk to human health. Nature Environment & Pollution Technology, 20(1), 163–175.
10.46488/NEPT.2021.v20i01.017
CAS Google Scholar
Molinari, B., Stewart-Koster, B., Adame, M. F., Campbell, M. D., McGregor, G., Schulz, C., Malthus, T. J., & Bunn, S. (2021). Relationships between algal primary productivity and environmental variables in tropical floodplain wetlands. Inland Waters, 11(2), 180–190.
10.1080/20442041.2020.1843932
Web of Science® Google Scholar
Mulholland, P. J., Fellows, C. S., Tank, J. L., Grimm, N. B., Webster, J. R., Hamilton, S. K., Martí, E., Ashkenas, L., Bowden, W. B., Dodds, W. K., McDowell, W. H., Paul, M. J., & Peterson, B. J. (2001). Inter-biome comparison of factors controlling stream metabolism. Freshwater Biology, 46(11), 1503–1517.
10.1046/j.1365-2427.2001.00773.x
CAS Web of Science® Google Scholar
Naiman, R. J., Melillo, J. M., Lock, M. A., Ford, T. E., & Reice, S. R. (1987). Longitudinal patterns of ecosystem processes and community structure in a subarctic river continuum. Ecology, 68(5), 1139–1156.
10.2307/1939199
Web of Science® Google Scholar
Neely, R. K., & Wetzel, R. G. (1995). Simultaneous use of 14 C and 3 H to determine autotrophic production and bacterial protein production in periphyton. Microbial Ecology, 30, 227–237.
10.1007/BF00171931
CAS PubMed Web of Science® Google Scholar
Odum, H. T. (1956). Primary production in flowing waters 1. Limnology and Oceanography, 1(2), 102–117.
10.4319/lo.1956.1.2.0102
Web of Science® Google Scholar
Park, J., Wang, D., & Lee, W. H. (2018). Evaluation of weir construction on water quality related to algal blooms in the Nakdong River. Environmental Earth Sciences, 77, 408.
10.1007/s12665-018-7590-4
Web of Science® Google Scholar
Rao, S. R. (1979). The effect of sewage and industrial waste discharges on the primary production of a shallow turbulent river. Water Research, 13(10), 1017–1021.
10.1016/0043-1354(79)90197-0
CAS Web of Science® Google Scholar
Rasmussen, J. J., Baattrup-Pedersen, A., Riis, T., & Friberg, N. (2011). Stream ecosystem properties and processes along a temperature gradient. Aquatic Ecology, 45, 231–242.
10.1007/s10452-010-9349-1
Web of Science® Google Scholar
Sarkar, S., Verma, S., Begum, M. S., Park, J. H., & Kumar, S. (2023). Sources, supply, and seasonality of total suspended matter and associated organic carbon and total nitrogen in three large Asian rivers—Ganges, Mekong, and Yellow. Frontiers in Earth Science, 11, 1067744.
10.3389/feart.2023.1067744
Web of Science® Google Scholar
Silverthorn, T., López-Rojo, N., Foulquier, A., Chanudet, V., & Datry, T. (2023). Greenhouse gas dynamics in river networks fragmented by drying and damming. Freshwater Biology, 68(12), 2027–2041.
10.1111/fwb.14172
CAS Web of Science® Google Scholar
Slawyk, G., Collos, Y., & Auclair, J. C. (1977). The use of the ¹³C and ¹⁵N isotopes for the simultaneous measurement of carbon and nitrogen turnover rates in marine phytoplankton 1. Limnology and Oceanography, 22(5), 925–932.
10.4319/lo.1977.22.5.0925
CAS Web of Science® Google Scholar
Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R., & Cushing, C. E. (1980). The river continuum concept. Canadian Journal of Fisheries and Aquatic Sciences, 37(1), 130–137.
10.1139/f80-017
Web of Science® Google Scholar
Wang, F., Maberly, S. C., Wang, B., & Liang, X. (2018). Effects of dams on riverine biogeochemical cycling and ecology. Inland Waters, 8(2), 130–140.
10.1080/20442041.2018.1469335
CAS Web of Science® Google Scholar
Wang, H., Ran, X., Bouwman, A. F., Wang, J., Xu, B., Song, Z., Sun, S., Yao, Q., & Yu, Z. (2022). Damming alters the particulate organic carbon sources, burial, export and estuarine biogeochemistry of rivers. Journal of Hydrology, 607, 127525.
10.1016/j.jhydrol.2022.127525
CAS Web of Science® Google Scholar
Ward, N. D., Sawakuchi, H. O., Neu, V., Less, D. F. S., Valerio, A. M., Cunha, A. C., Kampel, M., Bianchi, T. S., Krusche, A. V., Richey, J. E., & Keil, R. G. (2018). Velocity-amplified microbial respiration rates in the lower Amazon River. Limnology and Oceanography Letters, 3(3), 265–274.
10.1002/lol2.10062
Web of Science® Google Scholar
Webb, J. R., Santos, I. R., Maher, D. T., & Finlay, K. (2019). The importance of aquatic carbon fluxes in net ecosystem carbon budgets: A catchment-scale review. Ecosystems, 22, 508–527.
10.1007/s10021-018-0284-7
CAS Web of Science® Google Scholar
Wissmar, R. C., Richey, J. E., Stallard, R. F., & Edmond, J. M. (1981). Plankton metabolism and carbon processes in the Amazon River, its tributaries, and floodplain waters, Peru-Brazil, May-June 1977. Ecology, 62(6), 1622–1633.
10.2307/1941517
CAS Web of Science® Google Scholar
Yoshikawa, T., Tomizawa, K., Okamoto, Y., Watanabe, K., Salaenoi, J., Hayashizaki, K., Kurokura, H., & Ishikawa, S. (2017). Nutrients, light and phytoplankton production in the shallow, tropical coastal waters of Bandon Bay, Southern Thailand. Marine Ecology, 38(6), e12475.
10.1111/maec.12475
CAS Web of Science® Google Scholar
Yu, M., Eglinton, T. I., Haghipour, N., Montluçon, D. B., Wacker, L., Hou, P., Zhang, H., & Zhao, M. (2019). Impacts of natural and human-induced hydrological variability on particulate organic carbon dynamics in the Yellow River. Environmental Science & Technology, 53(3), 1119–1129.
10.1021/acs.est.8b04705
CAS PubMed Web of Science® Google Scholar
Yvon-Durocher, G., Caffrey, J. M., Cescatti, A., Dossena, M., Giorgio, P., Gasol, J. M., Montoya, J. M., Pumpanen, J., Staehr, P. A., Trimmer, M., Woodward, G., & Allen, A. P. (2012). Reconciling the temperature dependence of respiration across timescales and ecosystem types. Nature, 487(7408), 472–476.
10.1038/nature11205
CAS PubMed Web of Science® Google Scholar

Citing Literature

Volume3, Issue2

May 2024

Pages 191-198

Water stagnancy and wastewater input enhance primary productivity in an engineered river system

Abstract

1 INTRODUCTION