Spatial heterogeneity and distribution patterns of dissolved carbons and total ammonia nitrogen in the upper Changjiang River under 2022 summer extreme drought conditions

Abstract

The biogeochemical cycling of carbon in aquatic systems is profoundly regulated by extreme hydrological events, particularly through their impacts on dissolved carbon species (DCs) and total ammonia nitrogen (TAN). Despite growing recognition of these interactions, the spatial correlations and environmental linkages between DCs and TAN during meteorological extremes remain poorly constrained in large river systems. To address this critical uncertainty, we conducted a field campaign during the unprecedented summer drought (June–September 2022) in the Changjiang River Basin, collecting 24 water samples across three lateral positions along the upper Changjiang River mainstem. Our analyses revealed three key findings: First, dissolved inorganic carbon (DIC) constituted the predominant DC component (> 75%), while dissolved organic carbon (DOC) exhibited marked spatial variability (coefficient of variation > 35%). Second, bank-specific correlations emerged between carbon fractions and TAN, with DCs-TAN relationships showing strong correlations along river banks but no significant association in the river center. Third, spatial autocorrelation analyses using univariate and bivariate Moran’s I indices quantified these heterogeneities, particularly revealing a striking positive association between DOC and TAN in the right bank (Moran’s I = 0.64). This spatial variability suggests synergistic controls by drought-induced hydrological forcing, land-use derived inputs, and water quality parameters. Our findings establish a mechanistic framework linking extreme drought conditions to lateral carbon-nutrient coupling patterns, providing critical baseline data for modeling climate-driven biogeochemical shifts in monsoon-regulated river systems.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13021-025-00357-6.

Introduction

Accelerated climate warming has markedly increased the frequency and magnitude of extreme hydrological events, with intensification documented across major river basins [37, 51]. These disturbances fundamentally alter hydrological connectivity by modifying runoff regimes. Extreme hydrologic events are typically accompanied by changes in surface runoff and river flow [25], which in turn affect organic matter decomposition and the export of dissolved carbon (DC) from soils to rivers [3, 9, 11, 42]. Given the hydrodynamic coupling between DC fluxes and flow regimes [14], such climate-driven disturbances are restructuring aquatic carbon cycling architectures across spatial scales, particularly through anomalies in dissolved organic carbon (DOC) concentrations during hydrological extremes [14, 31, 55]. As the frequency of extreme events continues to rise [7, 17, 37, 47], their impacts on DOC have become an increasing concern [5, 34, 39, 45].

Emerging evidence reveals that the occurrence and distribution of dissolved carbon vary among river systems [8, 32, 40]. Dissolved carbon in streams and catchments shows varying sensitivity to temperature and discharge, often linked to vertical differences in soil solution DOC [49]. Soil erosion alters carbon export from the terrestrial biosphere, affecting both carbon burial and sequestration rates [15]. Anthropogenic disturbances also strongly influence dissolved carbon biogeochemistry in rivers [18]. For instance, land use change and urbanization enhance DC export into rivers [16], which may trigger unexpected shifts in DCs [33].

Among these drivers, total ammonia nitrogen (TAN) has been identified as a key factor influencing DC supersaturation in rivers [30]. Extreme weather events, including droughts and floods, can alter the processes that regulate solute production and transport, such as DC [39]. With the increasing frequency and severity of extreme events, research interest in their effects on DC has grown. To elucidate these effects during extreme weather, researchers have investigated the distribution of DC within river systems affected by various events, including drought [51], flood [50], precipitation [45], and snowmelt [39]. Notably, the effects of extreme droughts on dissolved organic carbon (DOC) in aquatic ecosystems have garnered considerable attention [39, 52]. Studies indicate that DOC exhibits varying spatial distribution characteristics across different stages of drought [51]. Furthermore, carbon fluxes in water bodies fluctuate during prolonged drought conditions, with intensified droughts leading to increased carbon retention [6]. These studies underscore that extreme climate events can induce substantial changes in the spatial–temporal distribution of DCs, thereby contributing valuable insights into the dynamics of carbon cycling under such conditions.

However, limited knowledge exists regarding the spatial patterns of DC, TAN concentrations, and their correlations in the upper reaches of the Changjiang River during extreme droughts. From June to September 2022, the Changjiang River Basin experienced an extreme drought, characterized by its longest duration and highest intensity since records began in 1961 [43]. During this period, the temperature increased 2–4 °C in this region, and its rain reduced 80% than the 30-year average [28]. Despite this significant event, limited research has been conducted to investigate the effects of this record-breaking drought on the spatial distribution and interrelationship between DC and TAN in the upper Changjiang River.

In this study, we aimed to clarify the spatial patterns of DC concentrations during a record-breaking extreme drought by analyzing 24 water samples collected from the upper Changjiang River in September 2022. The laboratory analyses focused on DOC, DIC, and TAN. Specifically, our objectives were to: (1) analyze the spatial distribution of DCs and TAN of the upper reaches of the Changjiang River when occurring extreme drought; (2) investigate the relationship between DCs and TAN under this extreme drought conditions; and (3) reveal the spatial correlations between DCs and TAN during this period.

Methods and materials

Study area

The study area (Fig. 1) is located in the northern part of Yulong Naxi Autonomous County, within Lijiang City, Yunnan Province. It lies at the northern foothills of the Jade Dragon Snow Mountain, adjacent to Haba Snow Mountain to the west. Its coordinates range from 27° 10″ N to 27° 23″ N latitude and 100° 10″ E to 100° 20″ E longitude. This region is situated in the lower reaches of the Tiger Leaping Gorge, characterized by an annual rainfall of 600–900 mm and an average annual temperature of about 15°. The Changjiang River flows through this area from south to north, covering a total length of about 20 km. Notably, environmental differences exist between the two banks: the right bank supports a larger agricultural area and features more extensive vegetation cover adjacent to the river than the left bank (Fig. 1).

Fig. 1.

Study area

Water samples collection and chemical analysis

In this area, we randomly set up a total of 24 monitoring points, including 13 in the center of the river, 5 on the left bank, and 6 on the right bank. One 400 mL pre-rinsed polyethylene bottle was used to collect water samples from each monitoring point in the running rivers. Before collecting, each bottle was rinsed three times with river water, then filled, capped, and taken back immediately to the laboratory. In the laboratory, water samples were first filtered through 25 mm Whatman GF/F filters, and then were kept in the LDPE bottles at 4 °C to weaken biological activities. An Aurora 1030 W TOC Analyzer (OI Analytical, USA) was conducted to determine the DOC concentration [44]. The DIC concentration was calculated from alkalinity, which was measured by titrating samples with 0.02 mol L⁻¹ hydrochloric acid within 12 h using a titrimeter (Brand 4760161, Germany) [36, 38]. Each water sample was measured three times repeatedly, and their average values are used as the final DOC and DIC concentration at each sampling point.

Spatial distribution of dissolved carbon and its spatial heterogeneity

The analyzed data for DOC and DIC were imported into ArcGIS for visualization and spatial analysis. TAN affects the biological processes in the water, which is closely related to the occurrence characteristics of dissolved carbons [27, 54].

Person’s correlation analysis was used to calculate the linear relationship between dissolved carbons and TAN. The coefficient is calculated as follows [56]:

where is the Person’s correlation coefficient, ranging from − 1 (completely negative correlation) to 1 (completely positive correlation). Higher absolute values of implies stronger linear relationship. and denote the measured values of DOC, DIC, and TAN, and and are their respective mean values.

To further reveal the spatial heterogeneity of dissolved carbons, univariate Moran’s I and bivariate Moran’s I were conducted to investigate the spatial autocorrelation and spatial correlation between dissolved carbons and TAN. The univariate Moran’s I measures the local spatial cluster patterns and outliers of dissolved carbon distribution [1], while the bivariate Moran’s I considers the spatial relationship between two different variables across spatial units and statistically validates visually identified patterns [41]. Unlike Pearson’s correlation, Moran’s I is capable of revealing better spatial clustering and spatial dispersion across geographical space [2]. Univariate Moran’s I can be defined as:

here, n represents the total spatial units, and donates the observed values of the i-th and j-th spatial units. is the mean of all observed values. is the spatial weight matrix, and is the sum of the spatial weights.

Bivariate Moran’s I involves two observed indicators, x and y, and is calculated as follows:

Y and y donate the observed values and mean of y observed indicator. The meanings of the other letters are the same as those in the univariate Moran’s I formula.

Results

Statistical analysis

The summary statistics for DOC, DIC, and TAN are detailed for the river center, left bank, and right bank, as shown in Fig. 2. For the river center (Fig. 2a), the DOC concentration ranged from 2.39 mg/L to 44.21 mg/L, with a mean of 8.91 mg/L, a standard deviation (SD) of 10.81%, and a coefficient of variation (CV) of 1.21. DIC values ranged from 23.71 mg/L to 37.25 mg/L, with a mean of 32.10 mg/L, an SD of 3.48%, and a CV of 0.11. TAN concentrations ranged from 0.99 mg/L to 1.38 mg/L, yielding a mean of 1.18 mg/L and an SD of 0.12 mg/L. Skewness analysis showed that DIC exhibited a left-skewness pattern (skewness value: − 0.78), DOC was right-skewness (skewness value: 2.97), and TAN was close to a normal distribution pattern.

Fig. 2.

Statistics of dissolved carbons and total ammonia nitrogen in river center (a), left bank (b), and right bank (c)

On the left bank (Fig. 2b), DOC ranged from 0.57 mg/L to 10.23 mg/L, while DIC varied between 31.91 mg/L and 40.66 mg/L. The mean values were 5.38 mg/L for DOC and 35.39 mg/L for DIC. TAN concentrations ranged from 0.96 mg/L to 1.28 mg/L, with a mean of 1.17 mg/L. On the right bank (Fig. 2c), DOC concentrations were between 1.55 mg/L and 9.34 mg/L (mean: 5.46 mg/L), and DIC ranged from 30.29 mg/L to 36.36 mg/L(mean: 32.40 mg/L). The mean TAN concentration was 1.34 mg/L, with a range from 0.86 mg/L to 1.82 mg/L. For the left bank, both DIC and DOC showed a right-skewness pattern, with DOC being close to a normal distribution. TAN on the left bank showed a left-skewness pattern (skewness value: − 1.11).

The CVs of DOC in the river center, left bank and right bank were all greater than 35%, indicating high variability under extreme drought events according to the CV values reported by Wilding [46]. Additionally, the varying CVs for TAN further demonstrate differences in variability, likely attributable to variations in land use and distance from the riverbank. DIC on the right bank was right-skewness. Both DIC and TAN were close to a normal distribution pattern, with skewness values of − 0.02 and − 0.06, respectively.

Spatial patterns of dissolved carbons and total ammonia nitrogen

In the upper reaches of the Changjiang River, DIC was the predominant form of dissolved carbon, with notable concentration differences observed between the river center, left bank, and right bank (Fig. 3). The DOC in the river center ranged from 23.50 mg/L to 37.50 mg/L, showing a tendency to decrease with the river flow direction. Although no distinct pattern emerged, significant variability was noted, particularly the unusually high values at monitoring point RC10. On the left bank, DIC concentrations were relatively stable, ranging from 31.50 mg/L to 41.00 mg/L, while DOC displayed considerable variability and similarly declined with river flow. The DIC concentrations on the right bank showed minor fluctuations within a range of 30.00 mg/L to 36.50 mg/L. In contrast, DOC on the right bank ranged from 1.50 mg/L to 9.50 mg/L, increasing as it progressed downstream, which is an opposite trend compared to the left bank. The spatial distribution differences and the proportion of DOC and DIC could be displayed clearly in Fig. 4. Except for RC10, the DOC percentage at all monitoring points exceeded 75%.

Fig. 3.

Spatial distributions of dissolved organic carbon and dissolved inorganic carbon

Fig. 4.

Spatial patterns of dissolved carbons and total ammonia nitrogen

The spatial patterns of TAN were also displayed in Fig. 4. Overall, TAN concentrations remained relatively stable without significant variation along the flow direction, although pronounced fluctuations occurred on the right bank. (Fig. 4). The maximum and minimum values of TAN on the left bank showed little difference. In the river center, TAN concentrations ranged from 0.99 mg/L to 1.38 mg/L, while those on the left bank varied between 0.96 mg/L and 1.28 mg/L. Notably, TAN concentrations on the right bank demonstrated greater variability with values spanning from 0.86 mg/L to 1.82 mg/L.

Correlations and spatial heterogeneity between dissolved carbons and ammonia nitrogen

To investigate the correlation and spatial heterogeneity among DOC, DIC and TAN, we calculated Pearson’s correlation coefficients and Moran’s I.

Significant differences in Pearson’s correlation coefficients were observed among the river center, left bank, and right bank (Fig. 5). In the river center, the relationship between DCs and TAN was weakly correlated (below 0.20), while a stronger correlation (exceeding 0.40) was evident on both the left and right banks. Furthermore, the correlations exhibited opposing trends: (1) DOC in the river center showed a positive correlation with TAN, while the DOC values on the left and right banks were negatively correlated, with a coefficient of − 0.43 and − 0.69, respectively. (2) DIC in the river center demonstrated a negative correlation with TAN, contrasting with the left and right banks, where positive correlations of 0.61 and 0.62 were recorded.

Fig. 5.

Correlation coefficients between dissolved carbons and total ammonia nitrogen

The spatial autocorrelation of DOC, DIC, and TAN was assessed using univariate Moran’s I, as illustrated by the diagonals of the heat maps in Fig. 6. DOC consistently exhibited negative spatial autocorrelation across all sections. The lowest spatial autocorrelation was observed at the river center (Moran’s I = − 0.03), compared to − 0.05 on the left bank and − 0.74 on the right bank. DIC of the river center exhibited a positive spatial autocorrelation (Moran’s I = 0.33), while the left and right banks showed negative correlations (Moran’s I of − 0.08 and − 0.32, respectively). The spatial autocorrelation of TAN on the left and right banks was opposite to that at the river center. TAN of left and right bank exhibited negative spatial autocorrelation with a Moran's I of − 0.24 and − 0.1 respectively, whereas TAN of the river center showed positive spatial autocorrelation with a Moran's I of 0.03.

Fig. 6.

Spatial correlation between dissolved carbons and total ammonia nitrogen: a left bank, b river center, c right bank

Bivariate Moran’s I further elucidated the spatial relationships between DOC/DIC and TAN. The spatial correlation between DOC-TAN and DIC-TAN was consistent, both displaying negative spatial correlation on the left bank and river center, and positive spatial correlation on the right bank. The DOC-TAN and DIC-TAN spatial correlations were consistent, showing negative spatial correlation on the left bank and river center, and positive spatial correlation on the right bank. The DOC-TAN spatial correlation was lowest on the left bank with a Moran’s I of − 0.04, and it was highest on the right bank, reaching 0.64. Similarly, the DIC-TAN spatial correlation was also highest on the right bank with a Moran’s I of 0.42. In contrast, the DIC-TAN spatial correlations on the left bank and river center were similar, with a Moran’s I of − 0.24 and − 0.17 respectively. Additionally, DOC and DIC in the river center and left bank were positively spatially correlated, with Moran’s I values of 0.40 and 0.35 respectively, whereas the right bank showed a negative correlation with a Moran’s I of − 0.30.

Discussions

The drought directly affected the dissolved carbon fluxes from soils to rivers [12]. Extreme drought is often accompanied by reduced precipitation, which significantly disrupts river hydrodynamics and reduces river discharge, thereby reducing the input of terrestrial carbon from runoffs [13]. Precipitation and discharge are essential driving factors for the export of soil DOC to rivers [10]. Research has demonstrated that rainfall contributes 42.4–98.2% of the total runoff discharge, which in turn modulates DC transportation from terrestrial ecosystems to rivers [35]. Complex water recharge dynamics influenced by heat waves also directly impacted the characteristics of DCs [51, 58]. This interplay led to changes in concentration and high variability in DOC within the river center. Dissolved organic matter (DOM) derived from terrestrial ecosystems can transport enriched organic matter upstream, thereby contributing to elevated DOC levels in rivers [57, 59]. This is evident in our results. For example, DOC levels at LB01 exceeded those at RC02 and RB01 because LB01 is located at the confluence of a tributary, which extends over 10 km and introduces substantial terrestrial carbon inputs. Another notable example involves the confluence of the Jiangtai River and the Changjiang River (at monitoring points RB02, RC04, and LB02), where the DOC contents of RB02 and RC04 were higher than that at LB02. The Jiangtai River receives meltwater from the Yulong Snow Mountain upstream and flows through the irrigated farmlands of the Daju Township. Despite the Jiangtai River’s inlet being potentially blocked by bridges and other structures, the DOC of the right bank and river center was higher than that of the left bank.

The intensity of human activities indirectly influences the spatial distribution of DCs and TAN [18, 19, 26]. The unusually high DOC concentration at RC10-exceeding that of DIC-can be linked to agricultural contributions. Numerous terraced fields are situated on the right bank near this monitoring point, where residual substances from fertilizers and pesticides are likely washed into the river via irrigation, resulting in elevated DOC concentrations. There is a marked contrast in vegetation cover between the two banks: the left bank is characterized by sparse vegetation and increased bare soil, while the right bank boasts a relatively high density of vegetation with less exposed soil. These differences contributed to the significant variations in the concentrations of DCs and TAN among the river center, left bank and right bank during the extreme drought (Figs. 3 and 4).

Furthermore, river flow and the distance from the shore also play a crucial role in determining the spatial distribution of DCs and TAN [19, 48, 49]. In the study area, the river initially receives meltwater from the Jade Dragon Snow Mountain and Haba Snow Mountain, subsequently flowing through irrigated farmland in Daju Township before continuing downstream. The intensity and prolonged duration of the extreme drought restricted water recharge along the river, leading to decreased runoffs and water discharges in this region. Consequently, the river’s carrying capacity was diminished, causing most nutrients to settle into sediments rather than being transported downstream [60]. This may explain the lack of significant changes in DOC and TAN concentrations in the river center. Pearson’s correlation coefficients between DCs and TAN were higher on the left and right banks compared to the river center. Variations in the correlations further support the rationale for segmenting the river into three distinct parts: the river center, left bank, and right bank, as shown in Figs. 5 and 6. Future research should consider distance from the shore and the intensity of human activities when analyzing the occurrence characteristics of rivers.

To further investigate the spatial distribution and influencing factors of river DCs under extreme climate events, we reviewed and summarized related studies in Table 1, focusing mainly on the impact of extreme climate change, including drought and rainfall, on the spatial pattern of DCs [6, 22]. These study areas span the tropical [6], subtropical [23], and frigid zones [52], are concentrated in diverse environments such as estuaries, rivers, agricultural catchments, and tidal marshes [20, 21, 24]. Extreme weather events, particularly droughts and floods, notably influence dissolved carbon characteristics. Droughts have been shown to affect DOC across different temperature zones [6, 52]. Most studies have shown that DOC concentrations decreased during droughts [6], and varied in the different propagation stages of drought [50, 51]. However, some studies reported the opposite. For example, a study in a lake catchment in the Frigid zone indicated that DOC concentration rose with increasing drought duration and severity [52]. These differences were related to the geographical settings and the drought duration. In contrast, rainfall or flooding that increased discharges also impacts DC concentrations [4, 53]. The relationship between DCs and discharges varies with total rainfall, peak discharge and flood intensity [20, 23]. For example, a study of the Mississippi River reported that DOC was greater than the previously reported annual averages [53]. Another study argued that DOC was positively correlated with discharges regardless of rainfall patterns and intensities [24]. Peak concentrations would increase during rainfall or flooding [39]. Under extreme rainfall conditions, DOC increased by 40–174% [22]. These findings underscore the complex interactions between hydrological dynamics and the occurrence of DCs in river systems, particularly under extreme climatic conditions. Understanding these relationships is critical for predicting how future climate variability may alter DCs cycles in freshwater ecosystems.

Table 1.

Occurrence characteristics of dissolved carbons and its affecting factors of related studies

country	Study area	Zone	Affecting factors	Characteristics	References
Brazil	Jaguaribe River estuary	Intertropical Convergence Zone	Extreme drought	DOC decreases, DIC increases	Cavalcante et al. [6]
Australia	Lower Lakes	Subtropical to semiarid	Extreme drought	Eutrophic and higher nutrient	Li et al. [21]
Canada	Harp Lake catchment	Frigid zone	Hydrological drought	DOC increases	Wu et al. [52]
Canada	Harp Lake	Frigid zone	Drought	DOC decreased	Wu et al. [50]
Canada	Harp Lake	Frigid zone	Hydrological drought	DOC reduces	Wu et al. [51]
USA	Mississippi River	North Temperate Zone	Flood	DOC/DIC ≥ the annual averages	Xu and Xu [53]
Sweden	a headwater basin	North Temperate Zone	Precipitation	Peak concentrations varied	Tiwari et al. [39]
China	Luojiang River	Subtropical Zone	Extreme rainfall	DOC increased	Li et al. [22]
China	Agriculture-intensive catchment	Subtropical Zone	Total rainfall, peak discharge	The DC-discharge relationship varied	Li et al. [23]
China	Agricultural catchment	Subtropical area	Rainfall patterns and intensities	DOC was correlated with discharge	Liang et al. [24]
USA	Tidal marsh	The north temperate zone	Precipitation	DC flux change	Lebrasse et al. [20]

The extreme drought observed in the study area, where temperatures were 2–4 °C higher than the average [28], was a historically rare event, having not occurred in the Changjiang River basin in 60 years [43]. Monitoring DCs under such historically rare circumstances provides a basis for analyzing the long-term evolution of DC in the upper reaches of the Changjiang River. Although the spatial patterns of DCs during this single extreme drought event remain inadequately understood due to limited data and the shorter monitoring periods, investigating the spatial distribution under a record-breaking drought still provides valuable data support for future comparisons. However, the drought is an evolving process that progresses through different propagation stages: pre-drought, intensification, recovery and post-drought [51]. Each stage exhibits different intensities and durations, which affect DCs and the river discharges. Previous studies indicated that various water quality parameters, such as water temperature, pH, and SO4, significantly influence dissolved carbon concentrations [29, 49, 51]. Accumulating evidence also demonstrates that climate change, anthropogenic activities, and water chemistry jointly affect dissolved carbon contents in Chinese rivers [42]. To further reveal the impact of the drought on the spatial distribution of DCs in the upper reaches of the Changjiang River, future research should synchronously collect hydrological, water quality, and meteorological data across different drought stages and anthropogenic activities to systematically reveal the full impact of drought on DCs.

Conclusions

This study explored the spatial distributions of dissolved organic carbons (DOC), dissolved inorganic carbon (DIC), and total ammonia nitrogen (TAN) concentrations, along with their correlations and spatial heterogeneity, during a record-breaking extreme heat event from mid-June to late September 2022 in the upper reaches of the Changjiang River. Chemical analyses were conducted on water samples collected from 24 monitoring points. The data revealed that dissolved carbon predominantly existed as DIC, constituting over 75% of the total, while DOC exhibited high variability, with CVs exceeding 35%. Notably, significant differences were observed in the correlations between DOC/DIC and TAN across the river center, left bank, and right bank. DOC displayed a low positive correlation with TAN in the river center but a strong negative correlation on both banks, whereas DIC showed a low negative correlation in the river center but a strong positive correlation on both banks. Furthermore, univariate and bivariate Moran’s I analysis demonstrated distinct spatial heterogeneity, with the spatial correlation between DOC and TAN being notably higher on the right bank (Moran’s I = 0.64) compared to the river center and left bank, indicating distinct regional coupling influenced by factors such as land use, human activities, and water quality. Consequently, future research should integrate the collection of these parameters to systematically analyze the spatial patterns and driving mechanisms of dissolved carbon during extreme drought.

Author contributions

Shuangyin Zhang: Writing—original draft, visualization, methodology, formal analysis, conceptualization, funding acquisition. Siying Wang: Writing—review and editing, validation. Chongqing Fu: Investigation, data curation. Baocheng Zhao: Writing—review and editing, data curation. Jian Xu: Writing–review and editing, Investigation. Junlin Fu: Investigation. Yiyun Chen: Writing—review and editing. Lyuzhou Gao: Supervision, funding acquisition.

Funding

Research grants from National Institute of Natural Hazards, Ministry of Emergency Management of China (Grant Number: ZDJ2024-17) , the National Natural Science Foundation of China (Grant No. 42501500), the Natural Science Foundation of Hubei Province (2024AFB009), and the Civil Aerospace Technology Advance Research Project of China (D040306).

Data availability

Data cannot be shared openly because the authors have unpublished articles using the data.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.