An exhaustive search and examination of all literature on phytoplankton PP in Chinese reservoirs from 1980 to 2023 was conducted using the China Knowledge Network Database and Web of Science. Information such as phytoplankton PP, geographical coordinates (latitude and longitude), sunshine hours, Secchi depth (SD), reservoir age, storage capacity, mean water depth, reservoir area, annual average temperature, annual average rainfall, total nitrogen (TN), and total phosphorus (TP) was extracted. In addition, the nitrogen-to-phosphorus ratio, water retention time and light limitation index were calculated. A comprehensive database comprising 165 reservoirs was ultimately established (Table 1 and Supplementary Material S1). Reservoir distributions and river basin divisions are shown in Figure 1.

The light limitation was derived from either photosynthetically active radiation or in situ light profile data. The light limitation index was calculated as the ratio of euphotic zone depth (Z_eu) and mean water depth Formulas 1,2: L i g h t   l i m i t a t i o n   i n d e x = Z e u w a t e r   d e p t h (1) Z e u = 2.5 × S D (2) where SD was Secchi depth, the coefficient of 2.5 follows established practice for converting transparency measurements to euphotic depth (Kirk, 1994).

The total phytoplankton PP in Chinese reservoirs was calculated using a simple extrapolation method (Formulas 3,4). Using the photosynthetic principle that 1 mg of O₂ release corresponds to 0.30 mg of organic carbon production, and the carbon sequestration capacity (CSC) formula is as follows (Shiying, 1981): T p p = ∑ T b a s i n P × S × 365 × 10 − 9 (3) CSC = T p p × 0.30 (4) where T p p is the aggregate phytoplankton PP in reservoirs nationwide (expressed in Tg O₂·a⁻¹), while P is the median phytoplankton PP within individual basins (in g O₂·m⁻²·d⁻¹). In addition, S is the surface area of reservoirs within each basin (km²). The country’s total phytoplankton PP is derived by aggregating the individual PP values from all basins. Reservoir surface area data, derived from Ran et al. (2021)and Zhou et al. (2024), were obtained by integrating satellite imagery with national inventories to estimate reservoir surface area and reduce associated uncertainties.

The statistical analyses and data plotting were performed using SPSS 18.0, Origin 2019, and ArcGIS 10.7, with a significance threshold of P < 0.05 for both difference and correlation evaluations. Descriptive statistics, presented as Mean ± SE, were employed to assess seasonal fluctuations in key indicators and discern differences across reservoirs within various basins. A one-way ANOVA, complemented by the Duncan test, was applied to evaluate variations in phytoplankton PP and seasons. Crucial indicators over distinct decades (1980s, 1990s, 2000s, 2010s, and 2020s) was used Mann-Kendall test. Pearson correlation analysis was conducted to clarify associations between phytoplankton PP and environmental factors. In addition, stepwise multiple regression analysis was utilized to identify the predominant factors influencing phytoplankton PP.

Data on phytoplankton PP in Chinese reservoirs is predominantly concentrated in the eastern region, with limited observations from the northwest and southwest (Figures 1, 2). The mean phytoplankton PP in these reservoirs is 2.42 ± 1.65 g O₂·m⁻²·d⁻¹, ranging from 0.13 to 10.22 g O₂·m⁻²·d⁻¹, with a median of 2.15 g O₂·m⁻²·d⁻¹, indicating significant spatial variability. The YR basin exhibited the highest phytoplankton PP (3.54 ± 0.78 g O₂·m⁻²·d⁻¹), followed by the S/LR basin (3.25 ± 0.26 g O₂·m⁻²·d⁻¹) and the H/HR basin (3.07 ± 0.34 g O₂·m⁻²·d⁻¹) (Figure 3A). In contrast, the lowest phytoplankton PP values were observed in the NIR basin (0.45 ± 0. 16 g O₂·m⁻²·d⁻¹) and the SWR basin (0.99 ± 0.29 g O₂·m⁻²·d⁻¹). The YTZ basin and the PR basin recorded phytoplankton PP values of 2.18 ± 0.17 g O₂·m⁻²·d⁻¹ and 1.17 ± 0.28 g O₂·m⁻²·d⁻¹, respectively. The phytoplankton PP in the YR basin, S/LR basin, and H/HR basin was significantly higher than that in the PR basin, NIR basin, and SWR basin (P < 0.05).

Phytoplankton PP increased from small I-type (S1) to large I-type (L1) reservoirs but decreased in large Ⅱ-type (L2) reservoirs (Figure 3B). The order of phytoplankton PP was as follows: L1 (3.46 ± 0.53 g O₂·m⁻²·d⁻¹) > M (2.36 ± 0.22 g O₂·m⁻²·d⁻¹) > S2 (2.18 ± 0.19 g O₂·m⁻²·d⁻¹) > S1 (2.12 ± 0.29 g O₂·m⁻²·d⁻¹) > L2 (2.10 ± 0.34 g O₂·m⁻²·d⁻¹). No significant difference in phytoplankton PP across reservoirs of various sizes.

The long-term series showed peak phytoplankton PP in the 1990s, subsequent decline, and recent recovery in the 2020s (Figure 4A). Mann-Kendall tests indicated no significant temporal trends (Figure 4B). The overall ranking of phytoplankton PP was as follows: 1990s (3.18 ± 0.33 g O₂·m⁻²·d⁻¹) >2020s (2.82 ± 0.54 g O₂·m⁻²·d⁻¹) >2000s (2.40 ± 0.16 g O₂·m⁻²·d⁻¹) >1980s (2.38 ± 0.20 g O₂·m⁻²·d⁻¹) >2010s (2.20 ± 0.61 g O₂·m⁻²·d⁻¹). Seasonally (Figure 4C), phytoplankton PP was significantly higher in summer (4.65 ± 0.52 g O₂·m⁻²·d⁻¹) compared to winter (1.41 ± 0.44 g O₂·m⁻²·d⁻¹) (P < 0.05), approximately 3.3 times that of winter. Phytoplankton PP slightly increased in spring (2.94 ± 0.50 g O₂·m⁻²·d⁻¹) compared to autumn (2.89 ± 0.66 g O₂·m⁻²·d⁻¹), but this difference was not statistically significant (P > 0.05).

On the national scale, phytoplankton PP showed a weakly positive correlation with reservoir characteristics (Figure 5). None of the reservoir characteristics analyzed (including Secchi depth, total storage capacity, annual discharge, reservoir area, reservoir age, and retention time) were significantly correlated with phytoplankton PP (P > 0.05). Water depth was the only exception with a significant positive correlation with phytoplankton PP (R = 0.17, P < 0.05).

In basin-specific analyses (Table 2), phytoplankton PP demonstrated positive correlations with water depth in the PR basin (R = 0.90, P < 0.01) and the YTZ basin (R = 0.28, P < 0.01). Within the H/HR basin, there was a significant positive correlation between phytoplankton PP and reservoir area (R = 0.68, P < 0.01). However, no discernible correlations were observed between phytoplankton PP and parameters such as reservoir age, retention time, or annual discharge (P > 0.05).

Correlation analysis (Figure 6) reveals a significant positive correlation between phytoplankton PP and latitude alongside a significant negative correlation with average annual temperature and average annual rainfall (P < 0.01). Phytoplankton PP increases significantly with longitude, decreasing gradually from the east coast to the interior.

Phytoplankton PP demonstrated a significant positive correlation with TN and sunshine hours (P < 0.01) (Figure 6) but did not significantly correlate with TP (Supplementary Figure S1) or the N:P ratio (P > 0.05). However, at the basin-specific scale, this significant positive correlation between phytoplankton PP and TN was only observed in the YTZ basin (P < 0.01). In contrast, phytoplankton PP exhibited a significant positive correlation with the N:P ratio in the H/HR basin (P < 0.01). No significant correlation was identified between phytoplankton PP and TP across the river basins.

The total phytoplankton PP in Chinese reservoirs was quantified as 22.08 Tg·O₂·a⁻¹. The YTZ basin leads in contribution, representing over 43% (9.55 Tg·O₂·a⁻¹) (Figure 7). The YR basin and the S/LR basin follow, contributing 21.67% (4.79 Tg·O₂·a⁻¹) and 21.46% (4.74 Tg·O₂·a⁻¹), respectively. The PR basin contributes 8.93% (1.97 Tg·O₂·a⁻¹). The NIR basin and SWR basin make more modest contributions of 1.30% (0.29 Tg·O₂·a⁻¹) and 0.37% (0.08 Tg·O₂·a⁻¹), respectively.

Phytoplankton primary production (PP) shows significant latitudinal gradients across China’s major basins, with higher values in the Yellow River (YR), Songhua River and Liaohe River (S/LR), and Haihe and Huaihe River (H/HR) basins compared to the Pearl River (PR), Northwest Internal Rivers (NIR), and Southwest Rivers (SWR) basins (Figure 3A). This south-north increasing trend reflects spatial variation in nutrient availability, light condition, and anthropogenic impacts (Wang et al., 2019b; Zhong et al., 2021; Jung et al., 2023). Eutrophic reservoirs in the eastern and northeastern plains sustain elevated PP through nutrient enrichment, whereas oligotrophic southwestern reservoirs with superior water quality limit growth due to nutrient scarcity (Yu et al., 2022; Hu et al., 2024). Extended growing seasons and favorable photoperiods enhance productivity in northern regions (Sun et al., 2023), while cloud cover and shorter photoperiods reduce output in PR and Yangtze (YTZ) basins (Ran et al., 2013a). Industrial activities in eastern/northeastern plains further alter productivity patterns via pollution loading (Cao et al., 2020). Topographic and climatic constraints render southwestern and Qinghai-Tibetan Plateau reservoirs particularly sensitive to temperature/precipitation variability (Ma et al., 2010).

Elevated phytoplankton PP in eastern plain reservoirs confirms established spatial eutrophication gradients, consistent with prior findings (Sun et al., 2023; Hu et al., 2024). Conversely, western regions (particularly Qinghai-Tibet Plateau and southwestern areas) exhibit nutrient-limited systems with suppressed PP (Deng et al., 2020).

Seasonal PP variations generally follow summer > spring > autumn > winter. While Sun et al. (2023) reported peak PP in spring/autumn for northern China (YTZ/SWR basins), this discrepancy likely stems from their predominant lake (vs. reservoir) sampling focus. Our reservoir data align with southern studies showing summer maxima > spring/autumn > winter (Ran et al., 2013a; Wang et al., 2019a; Yi et al., 2022). Dry-season reductions in nutrients and temperature constrain biomass and PP relative to wet periods (Lu et al., 2016). Notably, pronounced seasonal variability in downstream Yalongjiang and Three Gorges reservoirs underscore hydrological controls on PP dynamics (Ran et al., 2017a; 2017b; Deng et al., 2020).

Sixteen variables were analyzed to identify key drivers of phytoplankton PP in Chinese reservoirs. Sunshine hours, water depth, and rainfall emerged as primary predictors, though the stepwise regression model explained 28.9% of PP variation (Table 3). Regionally, the Yellow River (YR) and Songhua River and Liaohe River (S/LR) basins showed non-significant regression results. Significant drivers included: Sunshine hours and Reservoir capacity (explaining 70.0% variation) in the Haihe and Huaihe River (H/HR) basin; Sunshine hours and average rainfall (41.1%) in the Yangtze (YTZ) basin; water depth and reservoir area (89.5%) in Pearl River (PR) basin.

Phytoplankton photosynthetic rates exhibit a significant positive correlation with light radiation (Kirk, 1994). At the national scale, phytoplankton PP shows a significant positive correlation with sunshine hours (r = 0.43, P < 0.01), with stronger regional correlations (r = 0.43–0.76, P < 0.01). Rainfall constrains phytoplankton PP by reducing light availability, evidenced by significant negative correlations between light duration and rainfall, consistent with lower light duration in southwestern regions. Furthermore, turbidity and net radiation collectively regulate underwater light conditions (Sun et al., 2023): light intensity increases with radiation but decreases with turbidity (Tanabe et al., 2019). We used a light limitation index integrating Secchi depth and water depth, yet it showed no significant correlation with phytoplankton PP (Supplementary Figure S1, P > 0.05). This contrasts with typical patterns where turbidity suppresses phytoplankton PP and light radiation enhances it. Consequently, the index’s reliability as a phytoplankton PP indicator requires further validation, potentially influenced by limited sample size.

Nitrogen significantly influences PP in inland waters (Gao et al., 2019). In this study, the correlation between phytoplankton PP and TN is considerably stronger than between TP and phytoplankton PP, indicating nitrogen’s critical limiting role. Phytoplankton PP significantly correlates with TN in the YTZ basin (P < 0.05). These findings contradict prior studies (Jia et al., 2022; Sun et al., 2023). Methodological differences may explain the divergence: model estimates versus field measurements, or combined lake-reservoir sampling increasing system heterogeneity. Among reservoir characteristics, only water depth shows a significant correlation with phytoplankton PP. This is because water depth critically influences light penetration, nutrient distribution, and thermal stratification, as established by previous studies (Ran et al., 2016; Wang et al., 2019a; Zhong et al., 2021; Jung et al., 2023). However, the regression model’s limited predictive power suggests that key variables affecting phytoplankton PP in Chinese reservoirs remain unquantified or underexplored due to research constraints.

In addition to understanding the dynamics of PP, assessing the carbon sink capacity of reservoirs is critical (Figure 8). The total phytoplankton PP in Chinese reservoirs is 22.08 Tg⋅O₂⋅a⁻¹. Using the photosynthetic principle that 1 mg of O₂ release corresponds to 0.30 mg of organic carbon production (Shiying, 1981), the total carbon sequestration by phytoplankton in these reservoirs is estimated at 6.62 Tg⋅C⋅a⁻¹.

Previous studies estimate phytoplankton photosynthetic carbon sequestration in Chinese terrestrial aquatic ecosystems at approximately 18.69 and 19.41 Tg⋅C⋅a⁻¹, respectively (Gao et al., 2019; Sun et al., 2023). However, these studies did not differentiate between lakes and reservoirs. Using the lake (75,703 km²) and reservoir (25,616 km²) area data from Sun et al. (2023), we recalculated the reservoir-specific carbon sequestration. This yielded estimates of 6.32 Tg⋅C⋅a⁻¹ based on Sun et al.'s total and 6.56 Tg⋅C⋅a⁻¹ based on Gao et al.'s total (Gao et al., 2019; Sun et al., 2023). These recalculated values closely align with this study’s finding of 6.62 Tg⋅C⋅a⁻¹. Furthermore, compared to the global estimate for reservoir phytoplankton carbon sequestration (36 Tg·C·a⁻¹) (Maavara et al., 2017), the Chinese reservoir contribution represents 18.40% of the global total.

Phytoplankton primary production estimation methods include light-dark bottle incubation, VGPM modeling, and carbon isotope techniques (¹³C/¹⁴C), deriving PP from dissolved oxygen changes, chlorophyll/environmental parameters, and carbonate isotopes, respectively (Pratt and Berkson, 1959; Cox et al., 2015; Lan et al., 2020; Lu et al., 2023). Methodological limitations and environmental factors cause significant PP discrepancies across methods or sites. Thus, this study exclusively uses light-dark bottle-sourced PP data to minimize methodological errors.

The light-dark bottle method exhibits broad applicability to river, reservoir, and estuary environments, with particular effectiveness in eutrophic waters. Key limitations include: (1) static incubation impeding nutrient replenishment in flowing waters, potentially causing PP underestimation; (2) exclusion of benthic metabolism, preventing comprehensive ecosystem assessment; and (3) reduced sensitivity in oligotrophic or high-bacterial-concentration waters (Hs et al., 2019; Loken et al., 2021; Molinari et al., 2021). This study employed the light-dark bottle method to measure reservoir phytoplankton PP. The method revealed low PP measurements in oligotrophic and mesotrophic reservoirs (Lu et al., 2023). Southern China’s oligotrophic reservoirs, experiencing long-term nutrient deficiency, light limitation, thermal stratification, and other constraints, inherently exhibit low phytoplankton PP (Chen et al., 2020; Deng et al., 2020; Li et al., 2022). Consequently, the light-dark bottle method may underestimate PP and carbon sequestration potential in such systems. Furthermore, severe data gaps exist in some regions (e.g., only three reservoirs sampled each in each of the southwest river’s basin and the northwest internal rivers basin), potentially limiting the model’s explanatory power regarding driving factors. Seasonal data may also be biased since not all reservoirs include seasonal data.

This study investigates phytoplankton PP characteristics and carbon sequestration potential in Chinese reservoirs, revealing significant impacts of regional variations, seasonal fluctuations, and environmental factors (Sun et al., 2023; Wei et al., 2024). To advance the understanding of carbon balance dynamics in these ecosystems, future research must resolve these uncertainties. Implementing a comprehensive long-term monitoring system is therefore essential for systematic assessment of reservoir phytoplankton PP.