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Unravelling the Effects of Climate Extremes and Land Use on Greenhouse Gas Emissions in the Yangtze River Riparian: Soil Columns Experiments

Ecohydrology

April 2025
18(3)

DOI:10.1002/eco.70033

Authors:

Kemal Adem Abdela

Nanjing University of Information Science and Technology

Giri Kattel

University of Melbourne

Show all 7 authorsHide

River riparian basins play a crucial role in mitigating greenhouse gas (GHG) emissions through carbon sequestration and nitrogen sinks. However, increased ecological stresses led to the release of CO 2 , CH 4 and N 2 O. This study aimed to investigate how extreme temperatures, water levels, moisture content, land use changes and soil composition influence GHG emissions in the riparian corridor and to recommend mitigation techniques. It was carried out at the Yangtze River Riparian zone, China, using soil column testing. It used soil column testing. The results showed that extreme temperatures caused the highest emissions of CO₂ (29–45%), CH₄ (24–43%) and N₂O (27–33%). This was due to increased soil temperatures and accelerated organic carbon/nitrogen decomposition. Conversely, control and wet–dry cycles absorbed CO 2 (1–3%), CH 4 (3–10%) and N 2 O (1–21%) by improving soil aeration, increased oxygen availability, soil structure, stable water table and low temperature change. Grasses in riparian areas also improved carbon sinks. Highest water levels had lowest gas concentrations and emissions due to low oxygen level. Adaptive wet‐dry cycles, grass cover and better water table management can restore riparian areas, maintain soil moisture, balance soil carbon/nitrogen levels and mitigate climate change by improving soil quality. Dissolved organic matter fluorescence (DOMFluor) components are essential for soil carbon dynamics, aquatic biome safety, nutrient cycling and ecological balance in riparian zones. The study recommends implementing restoration practices, managing soil moisture, afforestation, regulating temperature and monitoring water tables to mitigate GHG emissions and address climate change. Future policies should focus on promoting resilient land use and ecosystems.

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Ecohydrology, 2025; 18:e70033

https://doi.org/10.1002/eco.70033

Ecohydrology

RESEARCH ARTICLE

Unravelling the Effects of Climate Extremes and Land

Use on Greenhouse Gas Emissions in the Yangtze River

Riparian: Soil Columns Experiments

1School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing, China | 2Ethiopian Ministr y of

Agriculture, Addis Ababa, Ethiopia | 3State Key Laboratory of Microbial Technology, Shandong University, Qingdao, China | 4Department of Ocean

Science, The Hong Kong University of Science and Technology, Hong Kong, China | 5Department of Infrastructure Engineering, The University of

Melbourne, Melbourne, Australia | 6Department of Hydrology BayCEER, University of Bayreuth, Bay reuth,Germany

Correspondence: Zhi- Guo Yu (zhiguo.yu@nuist.edu.cn)

Received: 14 July 2024 | Revised: 10 December 2024 | Accepted: 28 March 2025

Funding: This paper was supported by the National Natural Science Foundation of China (grant numbers: 32471702 and 41877337).

ABSTRACT

River riparian basins play a crucial role in mitigating greenhouse gas (GHG) emissions through carbon sequestration and nitro-

gen sinks. However, increased ecological stresses led to the release of CO2, CH4 and N2O. This study aimed to investigate how

extreme temperatures, water levels, moisture content, land use changes and soil composition influence GHG emissions in the

riparian corridor and to recommend mitigation techniques. It was carried out at the Yangtze River Riparian zone, China, using

soil column testing. It used soil column testing. The results showed that extreme temperatures caused the highest emissions of

CO₂ (29–45%), CH₄ (24–43%) and N₂O (27–33%). This was due to increased soil temperatures and accelerated organic carbon/

nitrogen decomposition. Conversely, control and wet–dry cycles absorbed CO2 (1–3%), CH4 (3–10%) and N2O (1–21%) by improv-

ing soil aeration, increased oxygen availability, soil structure, stable water table and low temperature change. Grasses in riparian

areas also improved carbon sinks. Highest water levels had lowest gas concentrations and emissions due to low oxygen level.

Adaptive wet- dry cycles, grass cover and better water table management can restore riparian areas, maintain soil moisture, bal-

ance soil carbon/nitrogen levels and mitigate climate change by improving soil quality. Dissolved organic matter fluorescence

(DOMFluor) components are essential for soil carbon dynamics, aquatic biome safety, nutrient cycling and ecological balance

in riparian zones. The study recommends implementing restoration practices, managing soil moisture, afforestation, regulating

temperature and monitoring water tables to mitigate GHG emissions and address climate change. Future policies should focus

on promoting resilient land use and ecosystems.

1 | Introduction

Riparian zones in global river basins are crucial for reducing

greenhouse gas (GHG) emissions. These areas are ecologically

resilient, with extended thermal regimes and nitrogen sink capa-

bilities, and have the potential to sequester significant amounts

of carbon, ranging from 68 to 158 Mg C/ha annually (Arifanti

et al. 2022). They also contribute to biodiversity, ecosystem

health and wildlife habitats worldwide. The loss of riparian

zones can disrupt ecosystem functions and services, including

carbon regulation (Upadhyay etal. 2023). Therefore, restoring

riparian forest zones enhances carbon sequestration and re-

duces GHG emissions globally (Daba and Dejene2018; Dybala

et al. 2019). Planting trees in riparian zones is one effective

2 of 22 Ecohydrology, 2025

method, as it can double biomass carbon accumulation com-

pared to naturally regenerated forests (Bustamante etal.2019).

For example, active tree plantations in Brazil have improved for-

est recovery by up to 75% over 20 years, significantly increasing

carbon sinks and supporting climate change mitigation efforts

(Aubrey etal.2019; Steven etal.2023).

Among global river basins, the Yangtze River riparian basin

(YR RB) represents a unique ecosystem with c omplex GHG emis-

sion dynamics (Yang etal.2024). The YRRB can potentially ab-

sorb substantial amounts of carbon from the earth's surface and

atmosphere (Jin etal.2023). For instance, the Three Gorges Dam

(TGD) within the YRRB significantly reduces the river's annual

CO2, CH4 and N2O emissions (Leng etal.2023). However, the

specific amount of GHGs absorbed by the riparian zones in the

TGD has yet to be quantified (Shi etal.2021). The study of car-

bon dynamics in the YRRB is still in its early stages, with reports

indicating that the riverbed in the middle- lower Yangtze River

has shifted from a carbon sink to a source, impacting its ability

to absorb GHGs (Tomczyk etal. 2022). Research suggests that

the Yangtze basin contributes to 16% of China's 2019 GHG emis-

sions, approximately 1.7 billion tons of atmospheric CO2 (Tan

etal.2020; Zhu and Zhang2021). This significant carbon source

results from increased human activities, changes in hydrolog-

ical conditions and vegetation types and extreme temperature

fluctuations in the basin (Qiao etal.2023).

Riparian hydrology is strongly influenced by factors such as

land use changes, soil organic carbon content and extremely

high surface temperatures. Along with hydrological degrada-

tion, riparian zones face alterations in chemical compositions

and microbial communities (Mishra 2017). Changes in micro-

bial dynamics can affect water tables, biogeochemical cycling

and GHG emissions (Bertolet etal.2018). For instance, deeper

water table levels in aquifers can inf luence soil organic mat-

ter dynamics and increase GHG exchange in riparian zones

(Tiemeyer etal.2016; Wilson etal.2016).

Waterlogging in riparian zones, agricultural activities and

natural floodplain flooding can all have an impact on carbon

sequestration (Talbot et al. 2018). Riparian system responses

to changes in GHG emissions remain challenging (Salimi

et al. 2021). Variations in water levels and temperatures con-

stantly affect floodplain systems, especially during excessive

precipitation, flooding and high temperatures (Li et al. 2 017).

Effective monitoring of riparian basins is essential to regulate

pressure responses and adapt to changes in GHG emissions.

The riparian zones of the Yangtze River floodplain system face

significant threats, including habitat loss, hydrological changes,

water pollution and overexploitation of biological resources

(Wang etal.2016). Studies on the effects of severe temperatures

on GHG emissions in the Yangtze River's riparian zones indi-

cate that rising temperatures have made it increasingly difficult

to sustain plant community dynamics (Lu, Tang, et al. 2020;

Jin et al. 2023), impacting net primary productivity (NPP)

and the basin's carbon sequestration potential (Wang, Delang,

etal.2021). More research is needed to better understand GHG

dynamics and climate impacts in the YRRB to develop effective

mitigation and adaptation measures and assess carbon absorp-

tion capacity (Ma etal.2023).

Our study examines the effects of climate change and land use

on GHG emissions in the YRRB under various scenarios, in-

cluding control, wet–dry cycles, grass cover and extreme tem-

perature stress. We hypothesize that soil columns exposed to

extreme temperatures will exhibit higher GHG flux and concen-

trations at shallow water depths, whereas grass cover and wet–

dry cycles will reduce emissions. Additionally, we propose that

grass cover will decrease CO2 and N2O emissions. Study aimed

to investigate how extreme temperatures, water levels, moisture

content, land use changes and soil composition influence GHG

emissions in the riparian corridor. The findings of this study

could inform comprehensive strategies to minimize soil GHG

emissions and guide field- based climate change research. This

will advance our understanding of riparian basin ecology and

lead to the development of novel GHG monitoring systems ca-

pable of improving climate change over wider river basin areas.

2 | Materials and Methods

2.1 | Sampling Site

Closed- design samples were collected from moist soil near the

river's riparian basin. They were collected from Yangtze River

Riparian at Pukou, Nanjing, China (32°03′ N, 118°40′ E). The

area has a humid subtropical climate, which has a relative hu-

midity of 70%. The average annual precipitation is 1090.4 mm,

with average rains for 117 days. It has an average air tempera-

ture of 15.4°C, the lowest yearly extreme temperature of −14°C

(13 July 1934), and the maximum of 43°C (6 January 1955). The

average lowest temperature is ~1.6°C in January and the average

highest temperature is ~30.6°C in July.

2.2 | Experiment Design

Soil column testing has proven to be an effective method for

assessing the impacts of climate change on ecosystems (Peng

etal.2020). Conducted in controlled environments, these exper-

iments offer more reliable and consistent results than field stud-

ies, revealing trends and mechanisms that may more accurately

reflect true conditions (Stewart etal.2013).

On 3 March 2022, soil from the Yangtze River sediment was

sampled and used to fill 10 soil columns. Each column was

packed with 50 cm of soil, leaving a 10 cm headspace for air and

water at varying depths. Measurements were taken at six differ-

ent water table levels (0, 10, 20, 30, 40 and 50 cm), allowing for

a comprehensive study of the soil's properties and behaviours

under different conditions (Figure1).

The soil columns, constructed from robust acrylic material

(with a wall thickness of 0.6 cm, an inner diameter of 30 cm, and

a length of 60 cm), were equipped with evenly distributed water

and air sampling ports. Ceramic samplers, 5 cm in diameter and

2.5 mm in thickness, were strategically placed at five ports for

air and water sampling purposes. Gas and water samples were

collected every 15 days and monthly for chemical analysis, re-

spectively. A 40- day watering period was implemented to es-

tablish equilibrium before subjecting the columns to different

stress conditions. The study used a control setup, a full water

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3 of 22

column, regular watering, and five sampling holes in the water

table to understand hydrological and ecological dynamics, eval-

uating soil characteristics and functions under consistent satu-

ration (Figure1a).

The wet–dry cycle treatment, which used half as much water

as the control, resulted in a lower water table level in the col-

umns. It involved more frequent irrigation and featured short

periods of saturation followed by dryness. These fluctuations

affected the soil's physical properties, including strength,

stiffness and water retention capacity. The study aimed to un-

derstand how varying water levels and wet–dry cycles impact

soil structure and water dynamics in riparian delta environ-

ments (Figure1b).

The grass cover group had grass grown in the columns. Grass

cover influences soil structure, water table connection and

water content, improving soil quality by retaining nutrients,

increasing fertility and suppressing weeds through fibrous root

systems (Figure1c).

In the heat- treated group, a water- filled tube connected with a

heater was inserted into the soil columns. The temperature of

the water was raised by 8°C from the control condition (room

temperature 24°C) to 32°C, which is near the average maximum

temperature of the study area. The warm water was distributed

across all portions of the water- filled tube to raise the tempera-

ture of the soil columns (Figure1d).

In the cold- stressed treatment group, the heater was replaced

with a cooler, lowering the temperature to 16°C, the average

temperature of the study area (Figure1e). Temperature sensors

were inserted at a depth of 10 cm in the columns to monitor

the temperature throughout the experiments (Figure 1) (Lim

etal.2016; Gavili etal.2018).

At each column's five water tables, a levelling hole sealed

with a syringe- operated valve was used to collect f lux data

every 15 days, as well as gas concentration and freshwater

sample data every month, for one year from October 2022 to

October 2023.

2.3 | GHGs Measurements

To compare the GHG emissions of the control groups with the

stressed groups, gas samples were collected from October 2022

to October 202 3. Seven gas samples were collected at 5- min inter-

vals over a 30- min period using 10 mL polypropylene syringes.

Each air sample was collected over a period of 30 min. The air

samples were then incubated in a box, 21 cm high, placed on the

head of each soil column. The soil columns gas f luxes were mea-

sured every 15 days by collecting samples at each interval. The

air from the headspace above the soil was circulated, containing

the gases emitted from the soil.

The flux of CO2, CH4 and N2O was then calculated using the

Beijing Normal University Flux Formula (Tian et al. 2011)

(Equations1 and 2). This method allowed for a comprehensive

and accurate comparison of GHGs emissions between the con-

trol and stressed groups.

Where S is the slope, h is the height of the air box in-

cubator, Pa is the constant air pressure (101,325 Pa),

r = 8.3143 m3 Pa mol−1 K−1, K is the temperature in kelvin

(K = 273.15) and MW is the molecular weight of the GHGs

emission, P is the atmospheric pressure, V (m3) is the com-

bined volume of the headspace and the sampling loop,

through which the head space gas flows, R is the gas constant

(8.314 Pa m3 K−1 mol−1), T is the absolute temperature (K) and

S (m2) is the surface area of the exposed soil.

The composition (concentrations) of the gases CO2, CH4 and

N2O were monitored once a month at different depths (0, 10,

20, 30, 40 and 50 cm). The soil column concentrations of CO2,

CH4 and N2O (expressed in mmol L−1) following isolation from

the outside environment. Samples were collected at five differ-

ent water table levels for gas chromatography examination to

(1)

Flux

r×K×10

−6

×60 ×24 ×1000 ×MW

(2)

FGHG =PV

RTS

GHG

FIGUR E  | Experiment design. (a) Control water level was controlled about 10 cm over soil. (b) Wet–dry cycle had low water level it was kept as

half of the control and frequently irrigated. (c) Grass stress column: we grow grass over head of column keeping water the same with control. (d) Heat

stressed column: we kept temperature at 32°C and (e) cold stressed kept at 16°C.

4 of 22 Ecohydrology, 2025

determine gas concentration (Itodo and Arowojolu2018). This

comprehensive method resulted in a complete investigation

of gas composition and concentration at various depths and

water table levels. These samples were taken at five various

water table levels for GC analysis to measure gas concentra-

tions analysis (Gonzalez- Benecke et al. 2 017). The monthly

measurement of gas concentrations in the soil column in-

volved collecting samples at each water level within the col-

umn using lateral ports. The port allowing gas concentration

to samples the columns were temporarily closed to ensure

it was solely from the soil columns. The samples were then

injected into pre- evacuated airtight gas sampling vials and

examined in the lab using gas chromatography (GC; Agilent

Technologies 7890B, USA) to measure the quantities of CO2,

CH4 and N2O in the different samples.

We assessed the concentration of CO2, CH4 and N2O in soil cores

by calculating the number of moles evolved per molar mass of C,

H2, N2 and O2. In this context, we calculated GHGs by compar-

ing the moles of each gas generated or consumed to the moles

of its constituents in the soil columns (Costagliola etal. 2018)

(Equation3).

Where P is the atmospheric pressure, V (m3) is the com-

bined volume of the headspace and the sampling loop,

through which the head space gas flows, R is the gas constant

(8.314 Pa m3 K−1 mol−1), T is the absolute temperature (K) and

S (m2) is the surface area of the exposed soil (Rezanezhad

etal.2014).

2.4 | Crucial Components in Freshwater

Dynamics.

Total carbon (TC), total nitrogen (TN), dissolved organic

carbon (DOC) and dissolved oxygen (DO) enable precise

measurement of these critical water quality indicators using

specialized analytical methods (Li etal.2018). TOC analysers

are used to monitor water quality by measuring TC, TN, DOC

and DO. High- temperature combustion is used in the study to

convert carbon species to CO2, which is then measured with

infrared gas detectors (Shetty and Goyal 2022). DO is mea-

sured using luminous DO sensors, electrochemical electrodes

or a manual, high- precision process known as Winkler titra-

tion (Wei etal. 2019). Its consumed represents the amount

of oxygen that has reacted or been consumed in the sample.

TN comprises all nitrogen forms in a sample, while TC is

the sum of inorganic and organic carbon in soil (Batjes2014)

(Equation4).

Following isopore membrane filtration (Millipore) and preser-

vation with mercuric chloride to halt biological activity, samples

for dissolved inorganic nutrients were obtained. A total organic

carbon analyser (TOC- VCSH) and an ASI- V auto sampler were

utilized in a high- temperature catalytic oxidation procedure

to measure DOC and total dissolved nitrogen (TDN) (Krishna

etal.2015). This technique is particularly effective in oxidizing

dissolved organic matter in water. Water samples were placed

into acid- cleaned glass vials before being run through pre-

combusted (300°C; 6 h) inline filters. Non- dispersive infrared

and chemiluminescence detectors were used to measure DOC

and TN (Halewood etal.2022).

The concentrations of ammonia (NH₄+) and nitrate (NO₃−

)

were measured using spectrophotometry (Hach DR6000,

USA) (Equations 5 and 6). Prior to analysis, water samples

were filtered through a 0.45- μm needle filter membrane to

eliminate particulate matter (Planquette and Sherrell 2012).

For nitrate analysis, a 0.8% sulfuric acid solution was prepared

by dissolving 0.16 g of H₂SO₄ in 200 mL of distilled water. A

1- g solution of KNO₃ was prepared in a beaker at room tem-

perature, and t hen 1 mol L−1 of HCl was added. A calibration

curve was created with standard NO₃−- N solutions ranging

from 0.5 to 4 mL. Samples were run with 0.2 mL of HCl and

H₂SO₄, and absorbance was measured at a wavelength suit-

able for nitrate detection at 220 nm.

Beer's Law used to correlate the absorbance (AA) observed

during the analysis with the concentration of nitrate (Singh

et al. 2022). To measure NH₄+, we utilized 1.91 g of NH₄Cl

and 100 g of CHKNa to create KNaC₄H₄O₆·4H₂O in 200 mL.

Then, 10 mL samples were run with 0.2 mL of CHKNa and

0.3 mL Nessler's reagent. Ammonium ions are neutralized to

ammonia, which reacts with salicylate and hypochlorite, pro-

ducing a blue colour detected at 630 nm. This colour change,

due to the alteration of salicylic acid substituents, follows the

Berthelot Reaction. iGEM protocols were used to understand

the reasons behind the blue colour. We calculated the slope

of nitrate (NO₃−

) and ammonium (NH₄+) concentrations

(Equation7).

Where: A = absorbance (no units); ε = molar absorptivit y

(L·mol−1·cm−1); c = concentration of the analyses (mol·L−1);

l = path length of the sample cell (cm).

Nitrification; oxidation of nitrate (NO₃- ) or ammonium (NH₄+)

to nitrite (NO₂−

) and ultimately to nitrate (NO₃−

)

Where: y = absorbance, m = slope of the line (change in absor-

bance per unit concentration), x = concentration of nitrate nitro-

gen (mg/L) and b = y- int ercept.

2.5 | Fluorescence PARAFAC Analysis

To investigate the luminous properties of dissolved organic

matter released from sediments and soils, we employed three-

dimensional fluorescence, parallel factor analysis (PARAFAC)

and UV–visible spectroscopy. PARAFAC modelling was used to

compare DOM components (de Souza etal.2022). Fluorescence

Excitation- Emission Matrix (EEM) is essential tools for

(3)

Conc

.GHG =

PVdC

GHG

RTSdt

(4)

A=𝜀⋅c⋅l

(5)

NH3

−orNH4

→

NO2

−

2H2O

H+

(6)

NO2

−

→

NO3

−

(7)

5 of 22

analysing the fluorescence properties of organic compounds

in water samples, providing information about the excitation

and emission wavelengths at which substances fluoresce (Liu

etal. 2019).

Freshwater samples were filtered via a 0.45- μm filter to assure

purity and maintained pure by capturing particles and microor-

ganisms (Chauhan etal.2015).

Fluorescence EEM data were acquired using a Cary Eclipse

Fluorescence Spectrometer (Agilent Cary Eclipse Fluorescence

Spectrophotometer, USA), with 5 nm increments covering ex-

citation and emission ranges of 200–450 nm and 250–600 nm,

respectively. Subsequently, PARAFAC analysis was performed

on the reshaped EEMs to identify possible fluorophores in the

DOM (de Souza etal.2022). The Fluorescence Index, Freshness

Index, Humification Index (HIX), and Biological Index (BIX)

were calculated following the methodology outlined by Chuang

etal.(2021). We analysed 345 water samples using the dissolved

organic matter fluorescence (DOMFluor) toolkit, discovering

DOM components and peak list of FluI, FrI, HIX and BIX were

computed using Chuang etal.(2021) model.

We compared MATLAB (2023b) results with the online

OpenFluor database (https:// openf luor. labli cate. com/ ), which

showed a 95% similarity of components with other experimen-

tal results, aiding in the identification of DOM components. To

compare MATLAB findings with OpenFluor, we collected fluo-

rescence spectra data, conducted Principal Component Analysis

(PCA) using MATLAB, uploaded the data to OpenFluor, com-

pared components identified by MATLAB PCA analysis with

those identified by OpenFluor, and assessed any inconsisten-

cies between the results. This systematic comparison method

ensures accurate identification of components in dissolved or-

ganic matter.

2.6 | Data Analysis

DOM Flour (version 1.7), MATLAB (version 2023b, MathWorks,

USA), surfer (version 2022 surfer golden) and OriginPro (ver-

sion 2023b) were used for data analysis. Statistical approaches

such as Q- Q plots, the Shapiro test, two- way ANOVA and lin-

ear regression were used to compare soil column CO2, CH4 and

N2O fluxes and concentrations between treatments. The result is

considered statistically significant when the p value is less than

0.05 (FigureS1).

3 | Results

3.1 | Temperature, Vegetation Coverage and GHG

Emission

We investigated the inf luence of water table levels on CO₂,

CH₄ and N₂O fluxes in soil columns under various condi-

tions: control, wet–dry cycle, grass cover, heat stress and cold

treatment (Figure 1a–e). GHG fluxes were calculated using

the flux formula (Equation1). During the first phase (March–

May), CO₂ flux decreased, then rose to a maximum in August

and September. The highest seasonal mean CO₂ fluxes were

observed in the heat stress (109.360 mmol·m−2·day−1) and

cold stress groups (59.29 mmol·m−2·day−1) in autumn. FCO₂

exchange rates significantly changed over the study period

(Figure2, Table1). Annual mean FCO₂ in the control and wet–

dry cycle groups were 1.28 ± 3.88 and 0.9 ± 1.07 mmol·m−2·day−1,

FIGUR E  | GHG emission flux in columns over time in 2022/2023; (a) CO2 flux; (b) CH4 flux; (c) N2O flux over time; (d) temperature change in-

side columns at 10 cm depth. The results demonstrate that CO2, CH4 and N2O fluxes and Temp vary significantly with each variable and time chang-

es, (p < 0. 05) (n = 48), 15 October 2022–30 October 2023.

6 of 22 Ecohydrology, 2025

respectively. The highest emitted fluxes were in the heat-

treated (43.32 ± 1.75 mmol·m−2·day−1) and cold- stressed groups

(32.64 ± 2.17 mmol·m−2·day−1) (Figure2a, TablesS1, S2).

The mean diffusive surface CH₄ f luxes were 2.8 mmol·m−2·-

day−1 in the control and 0.9 mmol·m−2·day−1 in the wet–dry

cycle. In the grass- covered, heat- stressed and cold- stressed

conditions, the diffused amounts were 16.06 mmol·m−2·day−1,

8.14 mmol·m −2·day−1 and 4.02 mmol·m−2·day−1, respectively.

During phase 1 (October to April), methane exchange sig-

nificantly decreased, but it increased during phase 2 (May to

September). The grass- covered columns emitted more CH₄

during phase 2. In this phase, CH₄ flux was sequestered in the

control and wet–dry conditions, while emissions increased in

the grass- covered, heat and cold conditions, peaking in autumn

(24 .72 mmo l·L −1) (Figure2b).

The most significant N₂O flux sink was the grass- covered

group, with FN₂O measured at 8.8 × 10−3 ± 1.1 × 10−3 nmol·m −2·-

day−1. The heat- treated group emitted an average N₂O flux of

9.89 × 10−3 ± 5.2 × 10−3 nmol ·m−2·day−1. The highest emissions

were observed in the wet–dry cycle in September, with an an-

nual flux of 4.3 × 10−2 ± 1.6 nmol·m−2·day−1 due to soil moisture

dynamics affecting microbial activity. The wet–dry cycle had a

maximum flux of 0.0272 nmol·m−2·day−1 in autumn. Overall,

the heat and cold- stressed groups showed slight emission in-

creases from March to September (Figure2c).

Temperature sensors at a depth of 10 cm regularly recorded tem-

perature changes. Annual mean temperatures varied: control

(15.6°C), wet–dry cycle (16.1°C), grass- covered (17.08°C), heat-

treated (20.32°C) and cold- stressed (12.93°C) (Table S1). The

maximum temperature was recorded in the heat- treated group

in summer (25.7°C) and the minimum in the control group in

spring (7.2°C). These temperature changes provide useful in-

sights into GHG emission dynamics under various environ-

mental conditions. Extreme temperature stress had the highest

variation (1.8°C–4.2°C), potentially causing high flux genera-

tion (Figure1d).

The results indicate that the control and wet–dry cycle had neg-

ative average CO₂ flux emissions, suggesting possible carbon

absorption or reduced emissions in these groups. Average CO₂

emissions from the grass treatment were moderate, while those

from the heat and cold treatments were significantly higher.

The heat- treated group produced the highest CO₂ emissions, fol-

lowed by the cold- stressed group (Tables1, S1, FigureS2). The

control group had the lowest average CH₄ emissions, whereas

the wet–dry and grass- covered groups had much higher CH₄

emissions. The control group also had relatively low N₂O emis-

sion levels, but the wet–dry cycle and grass- covered groups had

higher N₂O emissions. Temperature was the most critical factor

for GHG emissions, while vegetation significantly affected CH₄

flux (Table1, FigureS2).

3.2 | Water Table and GHGs Emission

Evaluating the concentration of GHGs in soil provides insights

into the total GHG concentration, including carbon and nitro-

gen sequestration (Smith et al. 2020) (Equation 2). Under dif-

ferent conditions, CO₂ concentrations decreased with depth

and peaked at 10 cm. At this depth, CO₂ concentrations were

14.1 mmol·L−1 in the control, 18.02 mmol·L−1 in the wet–dry

TABLE  | Seasonal mean flux at head of columns.

Control Error

Wet–

dry Error Grass Error Heat Error Cold Error

CO2 flux

(mmol·m−2 L−1)

Autumn 22.059 5.770 17.85 0 1.781 34.853 5.409 109.360 1.801 59.294 3.511

Winter 0.296 1.748 3.838 0.581 1.913 1.871 22.423 1.121 21.763 2.764

Spring −38.008 6.267 −31.274 1.118 −21.998 3.543 9.273 1.464 18.188 2.695

Summer 10.533 1.732 5.976 0.801 2.798 2.791 32.228 2.611 31.299 2.695

Ch4 Flux

(mmol·m−2 L−1)

Autumn 1.442 1.039 −0.519 0.612 24.724 4.144 16.120 1.339 7.848 2.040

Winter −0.371 0.019 −0.087 0.007 7.6 68 1.427 3.834 1.133 1.917 0.324

Spring −2.225 0.111 −0. 217 0.055 3.799 0.520 1.900 0.141 0.950 0.234

Summer −9.874 0.444 −2.779 0.211 18.053 2.514 10.693 1.768 5.347 1.315

N2o Flux

(nmol·m−2 L−1)

Autumn 0.0113 0.0008 0.0272 0.001 0.0299 0.0043 0.0175 0.0027 0.0155 0.0028

Winter −0.0 014 0.0004 0.0031 0.0012 0.0070 0.0046 0.0085 0.0027 0.0102 0.0027

Spring −0.0004 0.0003 −0.0018 0.001 −0.006 0.0036 0.0052 0.0018 0.0064 0.0035

Summer −0.0024 0.0001 0.0159 0.0012 0.0136 0.0019 0.0115 0.0030 0.0059 0.0019

Temperature

(°C)

Autumn 20.6 0.7 20.3 1.9 23.7 2.6 24.7 0.8 17.3 1.7

Winter 12.6 0.9 10.0 1.0 11.9 0.9 15.0 0.9 9.1 0.9

Spring 7. 2 0.7 10.9 1.1 10.1 0.5 15.9 1.5 6.5 1.0

Summer 21.7 1.6 23.2 2.1 22.6 2.4 25.7 0.9 18.8 1.9

7 of 22

cycle, 10.8 m mol·L−1 in grass- covered, 26.84 mmol·L−1 in cold-

stressed and 39.48 mmol·L−1 in heat- treated groups (Table S3).

Extreme temperatures produced the highest CO₂ concentra-

tions, while grass- covered conditions had the lowest . The annual

mean CO₂ concentrations were 4.92 mmol·L−1 in the control,

6. 39 mm ol·L−1 in the wet–dry cycle, 3.17 mmol·L−1 in grass-

covered, 18. 21 mmol·L−1 in heat- stressed and 17.26 mmol·L−1 in

cold- treated groups (Figure3a). CO₂ flux and concentration de-

creased with water table depth. The highest concentrations were

found at shallow depths (10 cm), likely discharging into the eco-

system, except for vegetation at 30 cm depth. The highest CO₂

concentrations observed were 25.34 mmol·L−1 in the control,

42. 02 m mol·L −1 in the wet–dry cycle, 10.8 mmol·L−1 in grass-

covered, 2 9.21 mmol·L−1 in heat- stressed and 96.84 mmol·L−1

under cold treatment. Local maximum concentrations were

observed under extreme temperatures, while lower concentra-

tions were seen in grass- covered conditions. Concentrations re-

mained higher from Day 90 (December) to 300 (July) in the heat

treatment (Figure3b).

CH₄ concentrations varied with water level and time under

different conditions. The average CH₄ concentrations

were 14.17 mmol·L−1 in control, 38.63 mmol·L−1 in wet–dry

cycle, 1.65 mmol·L−1 in grass- covered, 40.74 mmol·L−1 in

heat- stressed and 71.28 mmol·L−1 in cold- treated groups

(Figure 3c). The maximum CH₄ concentration in the control

was 39.9 9 mmol·L−1 at 20 cm depth on Day 270. In the grass-

covered group, it was 3.18 mmol·L−1 at 30 cm depth. In the

wet–dry cycle, it was 63.68 mmol·L−1 at 10 cm depth on Day

30. In heat- stressed conditions, it was 108.65 mmol·L−1 on Day

180, and in cold treatment, it was 121.35 mmol·L−1 in March.

The highest CH₄ concentration was observed in heat- treated

conditions, while the lowest was in grass- covered areas

(Figure3d).

The concentration of nitrous oxide (N₂O) was inf luenced by

environmental factors such as moisture, temperature, land use

and agricultural activities. The highest average concentration

was under the wet–dry cycle, with a value of 1.26 nmol·L−1 on

Day 180 (in March). This was followed by control conditions

with a concentration of 1.08 nmol·L−1 on Day 150 (February),

heat conditions with 0.77 nmol·L−1 at 30 days (October),

grassy conditions with 0.95 nmol·L−1 on Day 90 (December)

and cold conditions with 0.76 nmol·L−1 on Day 30 (Figure3e).

N₂O concentrations generally decreased with time and depth

but were dependent on conditions. After 30 days of the wet–

dry cycle, the maximum concentration of 1.78 nmol·L−1 was

found at a depth of 10 cm. After 30 days of heat stress, a peak

concentration of 1.88 nmol·L−1 was observed at 30 cm. After

60 days (November) in grassy regions, a peak concentration of

1.5 7 nmol·L −1 was discovered at 30 cm (Figure3f, Tables2, S3

and FigureS2).

3.3 | Differences in Soil Characteristics and GHG

Emissions Across Environmental Conditions

The study reveals significant variations in TC, DOC, DO,

nitrate (NO₃−

), ammonia (NH₄+) and TN across different

environmental conditions, highlighting the importance of

FIGUR E  | Net average concentration of GHG with depth and turnover time, (a) carbon dioxide (CO2); (b) methane (CH4); (c) nitrous oxide

(N2O), as evidenced by the p- values, p < 0.05. The results show significant variations in CO2, CH4 and N2O levels with water level and every factor,

30 October 2022–30 October 2023.

8 of 22 Ecohydrology, 2025

understanding these differences when assessing ecosystem

dynamics, nutrient cycling and environmental health, as

well as developing conservation or management strategies

(TableS2, FigureS3). Soil characteristics, including TC, DOC,

TN, NO₃−, NH₄+ and DO, play crucial roles in GHG emissions.

Understanding the carbon and nitrogen cycles is essential for

TABLE  | Annual mean concentration of GHG in soil columns at various water levels.

Control Error WT Error Grass Error Heat Error Cold Error

CO2 (mmol·L −1) 0 3.42 1.79 4.91 1.89 3.60 1.05 15.58 1.89 9.22 2.88

−10 10.05 2 .17 13.88 3.86 8.35 1.86 25.64 2.59 23.52 5.06

−20 4.52 1.29 5.01 1.03 2.84 0.72 18.30 3.28 13.63 2.21

−30 2.21 0.34 1.17 0.51 4.24 5.07 12.74 1.82 16.53 4.50

−40 1.44 0.52 7.61 1.70 0.01 0.00 14.28 4.47 16.94 7.74

−50 7.88 2.23 5.78 1.23 0.01 0.00 13.90 3.23 17.75 2.23

CH4(mmol·L −1) 0 5.83 1.91 26.71 2.90 1.28 17.18 53.56 21.11 30.51 14.28

−10 38.57 3.97 48.40 3.73 3.72 13.01 68.33 21.71 49.0 0 11.82

−20 31.71 2.37 36.84 3.23 2.72 17.31 28.54 12.67 26.37 13.14

−30 2.87 1.97 9.42 3.71 2.47 13.47 21.05 14.51 10.98 10.4 6

−40 3.01 1.87 15.11 3.33 0.51 11.07 28.54 15.34 25.46 11.51

−50 23.05 4.77 25.29 8.90 1.65 0.50 42.45 11.0 0 16.47 4.50

NO2 (mmol·L−1) 0 1.09 0.01 1.24 0.02 0.69 0.02 0.64 0.04 0.62 0.08

−10 0.93 0.02 0.96 0.04 0.75 0.02 0.66 0.02 0.81 0.07

−20 0.90 0.03 0.83 0.03 0.81 0.02 0.62 0.02 0.55 0.07

−30 1.12 0.03 0.98 0.05 0.96 0.03 0.67 0.03 0.86 0.06

−40 0.97 0.02 0.94 0.05 0.93 0.03 0.64 0.02 0.71 0.05

−50 1.12 0.03 0.91 0.05 0.90 0.05 0.64 0.08 0.67 0.05

FIGUR E  | Freshwater dynamics components (carbon cycle). (a) Total carbon (TC), (b) dissolved organic carbon (DOC), (c) dissolved oxygen

(O2), (d) overtime interchange of total carbon and (e) temporal exchange of dissolved organic carbon f. ongoing exchange of dissolved oxygen where

p < 0.05 (n = 150) (Time (day) where 30 October 2022–30 October 2023).

9 of 22

assessing ecosystem functioning, nutrient cycling, GHG emis-

sions and the health of river riparian ecosystems (Figures4,

5, Tables3, S4).

The average TC concentrations varied across conditions: control

(17.6 5 m mo l·L −1), wet–dry cycle (16.73 mmol·L−1), grass- covered

(17.95 mmol·L−1), heat- treated (13.04 mmol·L−1) and cold- treated

(11. 78 m mol ·L−1). Maximum TC concentrations were observed

at different water table depths: control at 50 cm (22.6 mmol·L−1),

grass- covered at 30 cm (21.24 mmol·L−1) and wet–dry cycle at

20 cm (20.0 9 mmol·L−1). For heat- treated and cold- treated con-

ditions, maximum TC concentrations were 15.55 mmol·L−1 at

10 cm and 18.97 mmol·L−1 at 30 cm, respectively. This indicates

that river riparian CO₂ flux and water table levels are directly

related, with riparian areas being significant carbon stores

(Figure4a).

The turnover of TC decreased over time. The maximum

TC concentration was observed in grass at 25.6 mmol·L−1

after Day 24 (April), while the minimum was found in heat-

stressed conditions at 15.55 mmol·L−1 after Day 30 (October).

Carbon turnover in soil is inf luenced by water table depth and

extreme temperature, with deeper water tables and higher

temperatures enhancing soil respiration. High water levels

store carbon in river riparian areas, and changes can impact

the carbon cycle. Riparian carbon storage is primarily due

to slow decomposition, but climate change can promote CO₂

emissions (Figure4b).

DOC concentration increased with the increasing depth of the

water table. The average DOC concentrations were the follow-

ing: control (7.69 mmol·L−1), wet–dry cycle (7.76 mmol·L−1),

grass- covered (8.59 mmol·L−1), heat- treated (4.72 mmol·L−1) and

cold- treated (4.07 mmol·L−1). Long- term patterns of DOC in soil

solutions reflect local impacts, while surface water DOC dynam-

ics mimic soil solution dynamics (Figure 4c). The maximum

DOC concentration was observed at a 50 cm water table in grass-

covered areas (17.92 mmol·L−1), in the control (15.81 mmol·L−1)

and at 40 cm in the wet–dry cycle (16.18 mmol·L−1). Heat and

cold stress resulted in maximum concentrations at shallow

depths of 10 cm and 30 cm (8.42 mmol·L−1 and 7.57 mmol·L−1,

respectively). The maximum DOC concentration occurred in

grass at Day 60 (November), while the minimum was found in

cold stress at Day 90 (December) (Figure4d).

DO content decreased with depth across all conditions, rang-

ing from 0.16 to 0.31 nmol·L−1. Heat- treated and cold- stressed

settings exhibited the lowest yearly DO concentrations

(0.16 nmol·L−1 and 0.18 nmol·L−1, respectively), while control

(0.27 nmol·L−1) and wet–dry cycle (0.26 nmol·L−1) conditions

had the highest annual mean DO concentrations. DO dynam-

ics in soils are regulated by water table depth, seasonal fluc-

tuations, and temperature (Figure 4e). During phase 1, from

Days 60 to 120 (November to March), DO turnover peaked at a

maximum of 0.31 nmol·L−1 in the control, 0.29 nmol·L−1 in the

wet–dry cycle, 0.22 nmol·L−1 in grass- covered, 0.16 nmol·L−1

in heat- stressed and 0.18 nmol·L−1 in cold- stressed conditions

at Day 60 (November), and then decreased to a minimum after

Day 270 (April). DO distribution in river riparian areas were sig-

nificantly influenced by water table depth, soil temperature, and

microbial activity levels (Figure4f).

TN significantly influences GHG emissions in river riparian

basins, acting as a barrier to nitrate pollution and sustaining

FIGUR E  | Freshwater dynamics components (nitrogen cycle), (a) total nitrogen, (b) nitrate nitrogen (NO3

−- N), (c) mean ammonium- nitrogen

(NH4+- N), (d) overtime exchange of total nitrogen, (e) overtime exchange nitrate nitrogen, (f) overtime exchange ammonium- nitrogen, where

(p < 0.05) and (n = 150). (Time (day) where 30 October 2022–30 October 2023).

10 of 22 Ecohydrology, 2025

TABLE  | Seasonal mean concentration of freshwater chemistr y.

Control Error Wet– dry Error Grass Error Heat Error Cold Error

TC (mmol·L−1) 0 8.49 1.48 9.00 1.20 11.96 1.77 8.06 0.92 8.13 1.20

−10 12.63 2.20 14.99 2.00 16.73 2.48 14.62 1.67 8.39 1.24

−20 18.93 3.29 20.09 2.68 19.02 2.82 14.03 1.60 12.63 1.87

−30 20.72 3.60 19.40 2.59 21.24 3.15 14.4 0 1.65 15.27 2.26

−40 22.52 3.92 20.01 2.67 18.10 2.68 14.18 1.62 13.97 2.07

−50 22.60 3.93 16.88 2.25 20.62 3.05 12.94 1.48 12.29 1.82

DOC (mmol·L−1) 0 4.14 0.55 3.91 0.58 4.63 0.80 3.46 0.69 2.85 0.57

−10 5.90 0.79 5.88 0.87 7.85 1.37 5.54 1.11 3.28 0.66

−20 8.34 1.11 9.21 1.37 9.70 1.69 4.82 0.96 4.46 0.89

−30 8.34 1.11 9.30 1.38 9.15 1.59 5.25 1.05 5.18 1.04

−40 9.38 1.25 9.56 1.42 8.97 1.56 4.62 0.92 4.56 0.91

−50 10.03 1.34 8.71 1.29 11.26 1.96 4.62 0.92 4.09 0.82

DO (mmol·L−1) 0 0.31 0.09 0.29 0.02 0.28 0.02 0.17 0.02 0.18 0.02

−10 0.26 0.07 0.25 0.02 0.20 0.02 0.17 0.02 0.17 0.02

−20 0.27 0.07 0.23 0.02 0.21 0.02 0.16 0.02 0.16 0.02

−30 0.25 0.07 0.25 0.02 0.20 0.02 0.15 0.02 0.16 0.00

−40 0.27 0.07 0.26 0.02 0.21 0.02 0.15 0.02 0.16 0.00

−50 0.27 0.07 0.26 0.02 0.19 0.02 0.14 0.02 0.15 0.02

TN (mmol·L−1) 0 28.70 2.87 29.71 3.50 28.54 4.08 28.17 3.76 29.00 3.74

−10 42.67 4.27 56.94 6.70 39.65 5.66 21.42 2.46 30.00 3.87

−20 47.52 4.75 54.09 6.36 31.26 4.47 42.70 7.03 47.17 6.09

−30 31.12 3.11 34.16 4.02 36.42 5.20 32.78 4.37 46.32 5.98

−40 34.90 3.49 44.67 5.26 28.05 4.01 23.41 2.59 36.26 4.68

−50 26.02 2.60 33.80 3.98 21.72 3.10 24.70 3.29 31.03 4.00

NO3

− (mmol·L −1) 0 1.48 0.07 1.01 0.05 1.65 0.07 0.99 0.04 0.75 0.02

−10 2.11 0.10 0.54 0.05 1.45 0.07 0.75 0.05 0.59 0.03

−20 0.98 0.07 1.51 0.05 0.56 0.04 0.69 0.05 0.64 0.04

−30 0.40 0.03 0.75 0.04 0.66 0.04 0.50 0.04 0.75 0.05

−40 0.53 0.04 1.09 0.05 0.44 0.04 0.50 0.04 0.56 0.04

−50 0.88 0.05 1.12 0.06 0.80 0.05 0.48 0.04 0.51 0.05

NH4+ (mmol·L−1) 0 2.29 0.16 4.02 0.25 3.58 0.22 3.53 0.17 4.51 0.25

−10 7.14 0.31 7.12 0.36 9.21 0.46 6.98 0.41 6.91 0.29

−20 8.76 0.46 8.43 0.42 13.13 1.38 10.04 0.53 8.80 0.55

−30 8.51 0.50 8.66 0.56 5.74 0.29 6.53 0.29 9.27 0.46

−40 7.6 6 0.54 9.60 0.54 7.38 0.51 8.42 0.50 8.76 0.49

−50 5.89 0.28 8.67 0.42 6.48 0.41 5.67 0.30 5.60 0.28

(Continues)

11 of 22

reservoir nitrogen levels via nitrogen fixation. TN concentrations

changed with water table depth, with average values: control

(35.16 nmol·L−1), wet–dry cycle (42.23 nmol·L−1), grass- covered

(30.94 n mol·L−1), cold stress (36.63 nmol·L−1), while heat- treated

columns had the lowest value (29.36 nmol·L−1). TN concentration

in the wet–dry cycle column had the greatest mean concentration

at 10 cm (56.94 nmol·L−1), while the grass- covered column had

the lowest at this depth (39.65 nmol·L−1) (Figure5a,b).

Nitrogen absorption and methane dissimilation are related, with

NO₃− influencing the nitrogen cycle and potentially boosting

N₂O emissions (Hu et al. 2023). Nitrogen levels can influence

both processe s, emphasizi ng the possible role of nitrogen absorp-

tion. The mean NO₃− concentration decreased with increasing

water table depth, with average values: control (1.06 nmol·L−1),

wet–dry cycle (1 nmol·L−1), grass- covered (0.93 nmol·L−1),

cold- stressed (0.63 nmol·L−1) and heat- treated (0.65 nmol·L−1)

(Figure 5c). In the wet–dry cycle, NO₃− concentration reached

its maximum on Day 240 (June), with a value of 4.23 nmol·L−1

at 10 cm depth. Changes in water table depth significantly influ-

ence NO₃−- N release from river riparian areas (Figure5d). The

mean NH₄+ content increased with water table depth. Average

values were as follows: control (6.71 nmol·L−1), wet–dry cycle

(7.75 nm ol· L−1), grass- covered (7.59 nmol·L−1), cold- stressed

(7. 31 n mo l·L −1) and heat- treated (6.86 nmol·L−1) (Figure5e). The

NH₄+ concentration in grass exchange peaked at 13.13 nmol·L−1

at a depth of 30 cm after Day 210 (April) (Figure5f). The pH val-

ues in soil were increased with depth where surface layer of soil

columns testing had a lower pH compared to deeper layers. Heat

and cold stressed had minimum pH value (6.9) compared to

control (7.2), where wet–dry cycle had highest annual mean pH

value (7.4) followed by grass covered (7.3) (Table3, FigureS4).

3.4 | Pore Water DOM Chemistry Variation

Dissolved organic matter significantly impacts GHG emissions

and the health of aquatic environments, including lakes, rivers,

ponds and wetlands (Xenopoulos etal.2021). Factors such as ex-

treme climatic conditions, land use patterns, grass cover, and bi-

ological changes play a crucial role in determining the quantity

and quality of DOM (Singh etal.2017). The f luorescence- parallel

factor analysis of DOM identifies three carbon components: two

resembling humic substances and one similar to fluorescent

protein, each with distinct excitation and emission wavelengths

(Figure6, Table4).

Component C1, a humic- like protein, is found in terrestrial

plants and soil. It shares properties with protein- like fluoro-

phores, humic substances, terrestrial humic compounds, and

benzoic acid/monolignol- like molecules, containing high-

molecular- weight UVA- humic chemicals. It has an excitation

wavelength of 230/325 nm and an emission wavelength of

420 nm (Figu re6a,b).

Component C2 is similar to tyrosine, a compound found in bio-

logical production. It has excitation wavelengths of 265/205 nm

and 380 nm, and an emission wavelength of 490 nm. It shares

properties with tyrosine and humic compounds, deriving from

sediments and phenolic chemicals, and is linked to terrigenous

biomarkers (Figure6c,d).

Component C3 comprises humic and fulvic acids, commonly

associated with allochthonous origins. It has excitation wave-

lengths of 220 or 280 nm and an emission wavelength of 346 nm.

Its composition is inf luenced by landform, size, plant cover ty pe,

and protein- like DOM content. Seasonal f luctuations in humic-

like DOM composition, DOC, and nutrient concentrations af-

fect CO₂ and CH₄ levels, indicating high biological activity

(Figure6e,f).

These three components are proposed as potential indicators for

microbial processing and the composition of humic- like DOM,

with sources traceable to allochthonous or terrestrial origins

(Table4). The components (C1, C2 and C3) fluctuate over time

and respond to different conditions (Table5). Under control con-

ditions, annual mean proportions were 183.6 for C1, 31.7 for C2,

and 40.9 for C3. Variations over time were minimal, with the

maximum concentration (84%) observed on Day 270 (June) and

the minimum (60%) on Day 210 (April). This indicates stability

in the absence of external forces, with maximum C1 in the con-

trol at 267.6 (Figure7a).

In the wet–dry cycle, C1 varied from 58% to 87%, C2 from 6%

to 20% and C3 from 4% to 23%, with maximum C1 at 302.6 and

minimum C3 (Figure 7b). Grass- covered conditions showed

annual means of 183 for C1, 36.8 for C2 and 46.8 for C3, with

more C1 and C2 than the control and wet–dry cycles. C1 ranged

from 62% to 82% (Figure7c). Heat stress resulted in the highest

annual C1 (83%–58%), C2 (5%–18%) and C3 (12%–23%), with no-

table changes over time (Figure7d). Cold treatment showed the

lowest C1 (44%–76%) and the highest C2 (169.1 or 32%) and C3

(126.1 or 13%–24%), with frequent changes (Figure7e).

Control Error Wet– dry Error Grass Error Heat Error Cold Error

pH Va lues 07.0 2 0.05 7.19 0.08 7.00 0.02 6.70 0.02 6.36 0.01

−10 7.02 0.06 7.25 0.03 7. 32 0.03 6.77 0.05 6.62 0.03

−20 7.01 0.04 7.24 0.05 7.2 3 0.01 6.85 0.00 6.64 0.01

−30 7.09 0.00 7.39 0.10 7.27 0.06 6.87 0.01 6.87 0.02

−40 7.37 0.03 7.52 0.01 7.37 0.06 7.05 0.04 7.2 2 0.01

−50 7.82 0.04 7.80 0.03 7.78 0.03 7.40 0.07 7.71 0.01

TABLE  | (Continued)

12 of 22 Ecohydrology, 2025

FIGUR E  | The fluorescence organic components of PARAFAC A. (a, b). C1 component, B. (c, d). C2 component C. (e–f ) C3 component represent

the three organic components (C1–C3) and their fluctuation with emissions and excitation wavelengths (Em and Ex) 30 October 2022–30 October

2023.

TABLE  | Column test DOC comparison result 95% where Ex is extension and Em is emission.

NMax wavelength Description Sources References

C1 Ex = 230/325, Em = 420 Protein- like DOM fluorophores,

Humic- like compounds,

terrestrial humic- like compounds

humic compounds, benzoic

acid/monolignol- like C1

Terrestrial plants

and soils

(Garcia etal.2015,

Ryan etal.2022)

C2 Ex = 265/205/380,

Em = 490

Tyrosine- like component humic-

like, Terrestrial humic- like, humic-

like, tyrosine- like component

Sediments and/or

phenolic compounds

(Wang etal.2015,

Chen etal.2018)

C3 Ex = 220 (280), Em = 34 6 Traced terrigenous biomarkers and

two components are introduced as

potential indicators for microbial

processing, humic- like dissolved

organic matter humic- like DOM

composition, humic- like components

Terrestrial origins (Walker etal.2013,

Li etal.2016)

TABLE  | Distribution of PARA FAC components of DOM in column- test soils of different treatments.

Control Wet–dry cycle Grass Heat Cold

C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3

Autumn 138.67 24.18 32.94 163.87 24.99 28.05 187.02 4 4.47 55.60 157. 52 26.79 36.96 141.21 34.96 41.44

Winter 230.12 36.19 46.36 229.88 33.32 42.96 199.19 36.76 45.93 170.91 36.34 46 .71 109.93 20.76 29.28

Spring 135.44 28.22 4 0.53 194.90 49.40 50.60 165.08 36.32 50.65 179.51 50.20 61.52 133.51 25.32 34.14

Summer 230.34 38.07 43.76 139.14 44.58 47.32 18 0.78 29.69 34.97 2 41.12 49.14 61.25 224.62 93.94 82.56

13 of 22

On average, C1 peaked at 87% on Days 30 and 120 (Oct. and

March) in the wet–dry cycle, reaching its lowest at 44% on Day

270 (June). The highest C2 concentration (32%) was observed on

Day 270 (June) and the lowest (150 days) in February under cold

treatment. Component C3 reached its maximum (23%) on Day

210 (April) in control conditions and its lowest (5%) on Day 30

(Oct.) in the wet–dry cycle (Tables5 and S5).

The characteristics of DOM fluctuate with environmental con-

ditions ( Table6). The study presents the peak list of relative com-

ponents of DOC described by the fluorescence indicator (FluI),

Freshness Index (FrI), Biological Index (BIX), and Humification

Index (HIX) under various conditions in a river riparian basin

(de Souza etal.2022).

The annual mean FluI changed over time, with values of 2.5 in

control, 2.3 in the wet–dry cycle, 2.6 in grass, 2.1 in heat and

2.8 in cold- treated conditions. The maximum FluI (5.9) was ob-

served in January under cold conditions, while the lowest mean

(2.09) was found in heat stress. The minimum FluI in the wet–

dry cycle was 1.54 in May (Figure S5a). The annual mean FrI

was 0.73 in control, 0.76 in the wet–dry cycle, 0.74 in grass, 0.73

in heat and 0.74 in cold conditions, peaking at 0.88 in November

and reaching a minimum of 0.612 in January in the wet–dry

cycle (FigureS5b).

The BIX mean was 0.77 in control, 0.79 in the wet–dry cycle,

0.78 in grass, 0.77 in heat and 0.79 in cold conditions. Its maxi-

mum value was 0.94 in November, with a minimum of 0.65 in

January in the wet–dry cycle. The graph shows varying index

values over time, with BIX having the largest peaks. Grass-

covered groups displayed cyclical patterns with peaks in June

and December and troughs in March and September, showing

mild swings with noticeable peaks of 0.89 (FigureS5c).

FIGUR E  | Distribution of PARAFAC Average components of DOM (C1, C2 and C3) columns test soils of different treatments: (a) control, (b)

wet–dry cycle, (c) grass- covered, (d) heat stressed, (e) cold- treated from 30 October 2022–30 October 2023.

14 of 22 Ecohydrology, 2025

The mean annual HIX was 0.9 in control, 0.88 in the wet–dry

cycle, 0.89 in grass, 0.88 in heat,\ and 0.87 in cold conditions.

HIX peaked at 0.99 in January in the wet–dry cycle and signifi-

cantly decreased to a minimum of 0.8 in March. In t he absence of

specific treatment, HIX reached a maximum of 0.95 in February

and March, with a minimum of 0.83 in June. Under heat stress,

HIX decreased from 0.95 in October to 0.82 in February during

phase 1, increasing in phase 2. Low values were observed under

heat stress, while cold conditions showed peak HIX values, in-

creasing in phase 1 and decreasing in phase 2, with a minimum

in June (0.83) (FigureS5d).

These results illustrate how different climatic conditions alter

DOC components in river riparian basins, aiding the under-

standing of biological dynamics and water quality management

(Tables6, S6).

4 | Discussion

4.1 | Effects of Environmental Variables on Soil

Columns' GHG Emissions

This study investigated the effects of various environmental

conditions on CO₂, CH₄ and N₂O f luxes into soil columns by

designing experiments across five water table levels and five

variables (Figure 1a–e). The control provided moderate GHG

concentrations and minimum GHG flux, while extreme tem-

peratures had highest concentration that might increase GHG

emissions probably microbial activities increased (Sirohi

etal.2023). Thus, variations in water table and soil temperature

changes also impact GHG emissions (Figure2, Tables1, S1, S2).

CO₂ exchange rates varied significantly under different condi-

tions, with extreme temperatures having the greatest environ-

mental impact, indicating increased CO₂ exchange (Dusenge

etal.2019). Heat- treated columns emitted the highest CO₂ flux

due to the impact of soil water concentrations of total organic

carbon/nitrogen decomposition and extreme temperature im-

pact on GHG fluxes. CO₂ fluxes initially decreased but grad-

ually increased due to declining organic carbon content, with

seasonal temperature increased resulting in a 52% in CO₂ flux in

heat- treated columns (Filaček etal.2022) (Figure2a).

Cold stress can slow down organic matter decomposition, ac-

cumulating plant residues, which could increase soil CO₂ f lux

and contribute to higher GHG emissions. It resulted in a 40%

increase in CO₂ retention in the soil. Cold stress also could in-

creases soil CO₂ flux by limiting microbial activity and root

respiration, resulting in the buildup of organic molecules due

to continued cellular respiration despite reduced photosynthesis

(Yang etal.2019) (Figure2a). It altered riparian soil respiration

and increased plant stress, slowing down organic matter break-

down, which could affect photosynthesis and result in the ac-

cumulation of plant residues (Sutfin etal.2016). These residues

should contribute to CO2 emissions once conditions are favour-

able for decomposition (Stegarescu etal.2020).

Extreme temperatures significantly influenced CO₂ flux emis-

sions, with higher mean concentrations of CO₂ in both hot and

cold conditions. Heat- treated soil columns produced more CO₂

due to oxidation processes, methanogen activity, and nitrogen

mineralization rates. CO₂ flux decreased from October to May

but increased in the second phase with rising temperatures

(Panahi etal.2020; Edwing etal.2024) (Figure2a,b).

TABLE  | Peak list of relative components of DOC of FluI, FrI, BIX and HIX.

Control Error Wet– dry Error Grass Error Heat Error Cold Error

FluI Autumn 3.139 0.272 2.339 0.195 2.496 0.238 2.389 0.455 1.976 0.226

Winter 2.033 0.145 2.792 0.233 3.199 0.305 1.967 0.264 3.834 0.514

Spring 2 .755 0.197 1.940 0.162 1.949 0.186 1.771 0.238 3.648 0.417

Summer 1.912 0.137 1.941 0.161 2.746 0.262 2.242 0.301 1.852 0.211

FrI Autumn 0.680 0.030 0.773 0.037 0.775 0.039 0.726 0.041 0.777 0.041

Winter 0.760 0.033 0.731 0.035 0.738 0.037 0.747 0.042 0.708 0.038

Spring 0.72 4 0.032 0.723 0.034 0.765 0.038 0.746 0.042 0.709 0.038

Summer 0.760 0.033 0.791 0.038 0.701 0.035 0.72 4 0.041 0.782 0.042

BIX Autumn 0.731 0.037 0.815 0.057 0.817 0.043 0.761 0.041 0.813 0.041

Winter 0.796 0.040 0.775 0.040 0.779 0.041 0.778 0.041 0.749 0.038

Spring 0.752 0.038 0.751 0.038 0.802 0.042 0.773 0.041 0.756 0.038

Summer 0.794 0.040 0.831 0.043 0.736 0.039 0.752 0.040 0.822 0.042

HIX Autumn 0.932 0.031 0.892 0.029 0.885 0.035 0.933 0.032 0.853 0.041

Winter 0.883 0.029 0.912 0.029 0.901 0.036 0.862 0.029 0.894 0.043

Spring 0.938 0.031 0.869 0.028 0.861 0.034 0.836 0.028 0.900 0.043

Summer 0.853 0.028 0.858 0.028 0.918 0.037 0.888 0.030 0.847 0.040

15 of 22

In contrast, the control and wet–dry cycles showed reduced CO₂

fluxes (1% and 2%, respectively) due to the lowest concentrations

and carbon absor ption by saturated soil and aquatic plants (D ybala

etal.2019; Kauffman etal.2022). In the control scenario, soil col-

umns maintained a stable environment with balanced CO₂ fluxes

due to moderate organic matter decomposition. The wet–dry cy-

cles regulated by moisture content impacted soil oxygen levels and

soil properties, promoting CO₂ sequestration during wet periods

and CO₂ release during dry periods (De Carlo etal.2019).

Grass- covered areas had the lowest CO₂ emissions (5%), which

varied depending on climatic conditions. Grass sequestered

carbon through photosynthesis, lowering emissions during

wet and green seasons when plants absorbed more CO₂, but

emissions increased during the dry season (Tang et al. 2019).

Healthy grass- covered areas served as carbon sinks, absorbing

CO₂ through photosynthesis and preserving soil structure, thus

reducing net CO₂ emissions (Lorenz etal. 2018) (Tables1, S1,

Figures2a, S2, S3).

Methane levels in grass- covered columns significantly increased

due to temperature effects, root system interactions, and growth

conditions. The varied microbial community in grass- covered

soil might control methane levels, with warmer temperatures

could accelerating microbial metabolism and increased meth-

ane consumption. Improved root- microbe interactions also

boosted methane oxidation. Methane oxidation increased by

25% in heat- treated groups, demonstrating the temperature's

impact on methane dynamics in river riparian zones, espe-

cially during warm seasons (August and September) (Waldo

etal.2019) (Figure2b).

CH₄ concentrations were highest under cold (43%) conditions

due to cold- seep habitats, gas hydrates, and methane bubbles

releasing methane- rich fluids (Peketi etal. 2021). Heat con-

ditions significantly impacted methanogenesis, with extreme

heat could increase methanogen activity and leading to higher

CH₄ production (Figure 2b); however, heat probably inhib-

its methane consumption by methanotrophic bacteria (He

etal.2023).

N₂O fluxes showed distinct phases: the first (October–February/

March) and the second (April–September). During the initial

phase, FN₂O levels declined due to low soil temperatures, de-

creased plant activity, and restricted oxygen availability. During

the second phase, FN₂O levels rose due to higher soil tempera-

tures, increased plant growth, and improved soil aeration. Soil

health and environmental factors like organic matter, drainage,

and nutrient balance inf luenced these oscillations. Higher NO₃−

concentrations accelerated CO₂ and N₂O fluxes, while higher

nitrate concentrations favoured CH₄ oxidation, reducing CH₄

production (Zaman etal.2012).

High temperatures and cold conditions resulted in the largest

N₂O emissions (35% and 25%, respectively), while the control

and wet–dry cycle columns had the lowest emissions (2% and

3%). Temperatures influence increased organic matter decom-

position and reducing oxygen solubility (Hall and Silver 2013).

Cold conditions may favour N₂O- producing microorganisms,

while alternating wet and dry conditions may support di-

verse microbial communities and efficient nitrogen cycling

(Wanithunga 2024). Summer saw the highest N₂O emissions

in the wet–dry cycle and grass- covered columns, with grass-

cover exposure to higher temperatures being the major driver

of increased N₂O emissions in the river riparian area (Schaufler

etal.2010) (Figure2c). Seasonal fluctuations influenced nitro-

gen mineralization and N₂O concentrations, with the wet–dry

cycle's impact amplified by rising temperatures in the river's ri-

parian zone (Figure2, FiguresS2, S3).

Soil temperature variations had distinct phases that di-

rectly impacted GHG emissions in our study. The first phase

(October–February) involved decreased soil temperatures, and

reducing GHG emissions. The second phase (March–August)

involved increased soil temperatures, could accelerating mi-

crobial activity and leading to higher GHG emissions. Soil

warming enhanced CO₂ emissions owing to increased de-

composition of organic matter, increased N₂O emissions due

to higher temperatures promoting nitrification and denitri-

fication, and influenced CH₄ production and consumption

by methanogenic microbes (Valenzuela and Cervantes 2021)

(Figure2d, FiguresS2, S3).

The results suggest that regular irrigation in riparian zones can

effectively reduce CO₂ and CH₄ emissions, while extreme tem-

peratures may cause higher emissions due to increased chemical

reactions and plant material degradation. Heat and cold signifi-

cantly impact GHG emissions, nitrogen breakdown, productiv-

ity, and organic waste processes. Heat stimulates oxidation of

carbon and nitrogen, causing higher emissions, while cold de-

creases emissions but releases stored carbon and enhances or-

ganic matter decomposition. Temperature control is essential

for sustainable waste management and ecosystem health (Chen

et al. 2016; Valenzuela and Cervantes 2021) (Tables1, S1, S2,

FigureS2).

4.2 | Impacts of Water Table on GHGs

Concentration

The carbon and nitrogen cycles regulate the total amounts of

these elements by balancing respiration, photosynthesis, and

sequestration processes (Raimi etal.2021). Maintaining water

table levels and supplying oxygen for the breakdown of or-

ganic materials depend on soil aeration (Thomson etal. 2022).

Research indicates that GHG concentrations decrease with

depth (Chetri etal.2022) (Figures3, 4, 5, Tables2, S3).

This study identified the highest concentrations of GHGs at

various soil depths. Specifically, the maximum CO₂ concentra-

tions were observed at 10 cm depth- 10.05 mmol·L−1 in control

conditions , 13. 88 mmol·L−1 in wet–dry cycles, 8.35 mmol·L−1 in

grass- covered areas, 25.64 mmol·L−1 in heated conditions, and

23.52 mmol·L−1 in cold conditions. Factors such as surface tem-

perature, oxygen availability, root respiration, and microbial

activity could have contributed to the decline in CO₂ concen-

trations as water table depth increased (Riedel2019). At greater

depths, reduced oxygen availability limits root and microbial ac-

tivity, leading to decreased CO₂ generation (Figure3a, b). Roots

systems significantly impact the plant carbon cycle, influencing

soil CO₂ levels through respiration and organic matter break-

down (Stuart Chapin etal. 2009).

16 of 22 Ecohydrology, 2025

Furthermore, the rise in water table levels fosters anaerobic con-

ditions, suppressing microbial activity and thus CO₂ production

(Ebrahimi and Or2016). Conversely, a decrease in water table

levels exposes more soil organic carbon to aerobic conditions,

enhancing decomposition and subsequent CO₂ release (Mueller

etal.2016).

The exchange turnover in heat and cold- stressed conditions

increased CO₂ concentrations by 45% and 29%, respectively,

facilitating greater dissolution of CO₂ in the soil water and

indicating a high rate of CO₂ exchange between the soil and

atmosphere on Day 270 (June) (Tfaily etal.2014) (Figure3b).

CH₄ concentrations in soil columns also varied with depth, in-

fluenced by factors associated with river riparian areas, rice

paddies, and soil quality (Zhang etal.2016). Grass- covered col-

umns had the lowest CH₄ concentrations compared to control,

wet–dry cycle, heat, and cold columns due to the consistent

habitat for methane- consuming bacteria, resulting in steadier

CH₄ concentrations (Figure 3c). The maximum CH₄ concen-

tration was observed at 10–20 cm, attributed to water table

fluctuations, anaerobic conditions, and organic matter de-

composition (Zhao etal.2020). These conditions favour meth-

anogenesis by archaea in water- saturated, oxygen- depleted

environments (Figure3c,d).

Temperature fluctuations further modulate CH₄ emissions, as

vascular plants can transport CH₄ across aerobic layers, poten-

tially enhancing emissions. High temperatures may increase

CH₄ concentrations by 27%–42% due to intensified microbial

activity and organic matter breakdown (Minick et al. 2021).

Methanotrophs, which use methane as an energy source, also

play a crucial role in regulating methane levels, influenced by

factors such as pH, heterotrophic richness, and microbial inter-

actions (Guerrero- Cruz etal.2021).

This study underscores the dynamic interplay between environ-

mental conditions and GHG dynamics in riparian ecosystems,

highlighting the critical role of the water table in regulating

GHG concentrations and emissions. Managing water table fluc-

tuations can mitigate climate change impacts, with strategies

such as riparian restoration and reforestation proving effective

in regulating GHG emissions (Bass etal.2014).

4.3 | Impacts of Water Levels on Soil Organic

Carbon and Nitrogen Characteristics

The study discovered that soil carbon and nitrogen dynamics in

riparian environments have a major impact on GHG emissions,

which were influenced by seasonal f luctuations, water levels,

soil texture, moisture and stress control (Zhang et al. 2023)

(Figures4, 5). Seasonal fluctuations in TC, DOC and DO con-

centrations reveal the impact of temperature, water table, and

moisture on soil carbon dynamics. Understanding these rela-

tionships is crucial for assessing ecosystem functioning, nutri-

ent cycling, and GHG emissions in river riparian ecosystems.

The depth of the water table effects soil carbon distribution,

with the highest amounts found in deep soil in grass- covered

areas (Figure 4a,b). Deeper water tables improve plant perfor-

mance, presumably altering carbon distribution in soil (Leakey

etal.2009).

The concentrations of TC and DOC generally increased with

depth in the soil and water interface, influenced by enhanced

physical and chemical retention, decomposition, organic carbon

transport, microbial activity, and factors such as water stratifi-

cation, soil retention, and changes due to climate and land use

(Kopáček etal.2018; Fu etal.2019) (Figure4). As water depth

increases, dissolved gases disperse, leading to reduced concen-

trations near the bott om. Thi s stratification, often caus ed by tem-

perature or density differences, restricts gas exchange between

layers, thereby reducing GHG concentrations at greater depths.

Additionally, conditions at deeper levels may favour certain mi-

crobial activities, potentially increasing carbon concentrations.

TC production in the soil increased with the depth of the water

table that would affect microbe- organic matter interactions

(Figure 4a,b). Extreme temperatures in riparian environments

result in low TC concentrations, which impact GHG emissions

owing to soil organic matter decomposition, increased carbon

loss, lower plant production, CO2 emissions and nitrogen cycling

(Oelbermann and Raimbault2015). Soil treatments and water

table depths affect DOC concentrations, with grass- covered soil

having higher average DOC due to root exudates and improved

soil structure (Redmile- Gordon etal.2020) (Figure4c,d). Lower

temperatures typically inhibit the decomposition of TC and

DOC in groundwater, while leaching processes transport these

carbons further down into the soil profile (Figure4a–d) (Ofiti

etal.2021). Factors such as soil moisture, extreme temperatures,

vegetation cover, and land use changes significantly impact TC

and DOC concentrations. Notably, wet and grass- covered soils

have higher amounts of TC and DOC due to increased organic

matter content. Conversely, heat treatment can decrease TC and

DOC by enhancing carbon release and accelerating decompo-

sition, yet it also increases CO₂ levels in soil layers (Schaufler

etal.2010; Tezza etal.2019). Changes in water table depth sig-

nificantly impact DOC release from wet soils, particularly in

disturbed catchments such as urban development, deforestation,

or natural events like glaciation (Figure4c,d).

We noted that wet–dry cycles and grass- covered conditions ex-

hibited higher TC, DOC, and DO levels compared to those under

heat and cold stress, and consequently emitted the lowest CO₂.

This phenomenon is attributed to the slow warming of wet soils,

which promotes organic matter formation and thus increases TC

and DOC levels (Ritson etal.2017; Qu etal.2021). Grass cover

typically enhances soil organic matter, reduces compaction, and

promotes microbial activity, which is crucial for effective car-

bon cycling (Karlen and Cambardella2020). The temperature of

the soil could primarily regulates microbial activity and organic

matter decomposition rates; for instance, cold- stressed soils ex-

hibit slower soil carbon breakdown (Górniak2017).

DO content decreases with depth across all conditions, with

heat- treated and cold- stressed had the lowest annual DO con-

centrations (Figure4e,f) (Singh et al. 2014; Zhang etal.2015).

DO concentrations in riparian soils significantly impact GHG

emissions, with higher levels increasing aerobic respiration

and methane oxidation, while lower levels reduce these effects

(Baskerville et al. 2021). Heat treatments not only reduce or-

ganic waste and lower carbon levels by breaking down organic

compounds but also can remove oxygen- producing organ-

isms, decreasing DO levels in the soil (Mani et al.2020). The

dynamics of soil organic carbon, water table depth, moisture,

17 of 22

temperature, and agricultural practices significantly affect GHG

emissions. Decomposition of organic matter increases GHG

concentrations, elevated soil nitrogen levels boost nitrous oxide

emissions, and sufficient DO levels facilitate methane oxidation

(Wang etal.2017).

The TN significantly impacts GHG emissions in river ripar-

ian basins (Figure 5). TN acts as a barrier to nitrate pollution

and helps sustain reservoir nitrogen levels through nitrogen

fixation. TN concentrations vary with water table depth, with

the highest average values observed in the wet–dry cycle and

the lowest in heat- treated columns (Nguyen etal.2023). Water

table depth plays a crucial role in regulating TN levels and GHG

emissions (Figure5a,b). The drop in NO₃−- N concentrations in

riparian zones is due to increased water table depth, which re-

duces plants' capacity to take nitrogen from the soil and affects

the nitrogen cycle, including nitrification and denitrification.

Environmental conditions such as temperature and moisture

content affect nitrogen absorption and methane dissimilation

(Lu, Li, etal.2020) (Figure5c,d).

Ammonium content increases with water table depth, influ-

enced by hydrological impacts, temperature, vegetation, and

seasonal variations (Figure 5e,f). Deeper water tables create

favourable conditions for ammonium accumulation due to re-

duced oxygen levels in deeper soil layers (Cameron etal.2013).

The relationship between nitrogen absorption and methane

dissimilation is also evident, with nitrate inf luencing the nitro-

gen cycle and potentially boosting N₂O emissions (Wang, Hou,

etal.2021). Managing water table depth and nitrogen levels in

riparian habitats in order to minimize GHG emissions and im-

prove water quality (Welsh etal.2021).

The pH values in riparian soil rose with depth, with the surface

layer had a lower pH than the deeper layers. Extreme tempera-

ture had minimum pH value compered to control, wet–dry cycle

and grass covered which may increase GHG emission (Tables3,

S4, Figure S4) (Vithana etal. 2019). It was influenced by vege-

tation coverage, hydrological conditions, soil type, climate, and

seasonal f luctuations (Özkan and Gökbulak2017). Soil pH can

signif icantly impact GHG emissions, part icularly N2O, by increas-

ing its neutrality (Hénault etal.2019). Extreme temperatures can

drop soil pH and cause N2O emissions (Wang etal.2018).

Ultimately, the study founds a significant link between the car-

bon and nitrogen cycles, pH values, temperature extremes, water

table and their impact on GHG dynamics within river riparian

basins. These cycles are crucial for managing the balance and

emissions of GHGs, where changes in land use, temperature,

and other environmental variables play a pivotal role (Ansari

etal.2024). This comprehensive understanding can aid in the

development of strategies to mitigate climate change impacts

through effective management of riparian zones.

4.4 | Implications of Soil DOM Components

for GHG Emission

Our study found that environmental variables such as extreme

climate, land use changes, and dissolved organic matter sig-

nificantly influenced the measurement of components C1–C3,

with negligible variability observed in the control condition

(Figures6, 7, Tables4, 5). Matthews etal. (Matthews etal.2012)

similarly noted that soil conditions—including control settings,

wet–dry cycles, grass cover, heat treatments, and cold stress—

profoundly affect GHG emissions.

The DOMFluor toolset has proven effective in understanding

the complex nature of organic matter in marine environments by

analysing the three components of DOM, which we have identi-

fied as C1, C2 and C3 in this study. Component C1 is crucial in

terrestrial plants and soils that are influenced by environmental

conditions (Garcia etal. 2015; Ryan et al. 2022) (Figure 6a,b).

Component C2, resembling tyrosine, is may be associated with

onsite biological production in river riparian basins, where it

originates from tyrosine- like compounds produced by bacteria

and algae (Wang et al. 2015; Chen et al. 2018) (Figure 6c,d).

Component C3, resembling humic compounds, contains humic

and fulvic acids typically associated with allochthonous inputs

in river basins such as the Yangtze River. These are predomi-

nantly derived from terrestrial plants and soils washed into

the river during rain events or runoff, and are largely formed

through plant and carbon/nitrogen degradation (Li et al. 2016;

Weigelhofer etal.2020) (Figure6e,f, Table4).

Fluorescence- parallel factor analysis (PARAFAC) is a pivotal

technique for exploring the storage, cycling, and export of DOM

in aquatic ecosystems (Broder etal.2017; Weigelhofer etal.2020;

Wang, Kong, etal. 2021). This method assesses the impacts of

DOM on freshwater ecosystems and the carbon cycle, offering

insights that are vital for climate change predictions (Broder

etal.2 017; Laglera etal.2019). Humic and fluorescent protein- li ke

components play significant roles in affecting freshwater ecosys-

tems, aquatic life, and the global carbon cycle through complex

mechanisms where DOM is paramount (Pan etal.2024).

The three components—C1 (Humic- like compounds), C2

(Tyrosine- like components), and C3 (Humic- like dissolved or-

ganic matter)—significantly impact GHG emissions in river

riparian basins. High concentrations of C1 can reduce net soil

GHG emissions as humic- like substances s uch as ar tificial humic

acids effectively reduce nitrous oxide emissions. High concen-

trations of C2 can increase organic matter produced by plants

and microbes, potentially inf luencing the carbon cycle and GHG

emissions in conditions like the wet–dry cycle and cold stress

(Tables 5, S5). Conversely, high concentrations of C3 can de-

crease potential carbon dioxide emissions, playing a crucial role

in carbon sequestration in the hydrosphere, particularly under

control (24%) and grass- covered (23%) conditions (Figure7a,b).

However, the actual impact on GHG emissions can vary depend-

ing on environmental conditions and the interactions between

these components and other ecosystem elements.

This study reveals a complex relationship between environ-

mental conditions and the characteristics of dissolved organic

matter, which could significantly impact GHG emissions.

Understanding the complexity and environmental sensitivity of

DOM is crucial for developing effective GHG emission manage-

ment and reduction strategies (Solomon etal.2015).

FluI, a fluorescence indicator, varies in riparian areas due to

environmental factors such as temperature. Cold treatment,

18 of 22 Ecohydrology, 2025

for example, results in higher FluI concentrations, indicating

temperature- sensitive changes in microbial activity or organic

matter decomposition rates (Frank etal. 2018). Seasonal vari-

ations suggest that GHG mitigation potential in riparian areas

may fluctuate throughout the year, emphasizing the need for

adaptive management strategies that take into account seasonal

changes in DOM composition, DOC, and nutrient concentra-

tions (Emerson etal.2021) (FigureS5a).

The Freshness Index (FrI) in riparian areas varies across treat-

ments and seasons, with moisture fluctuations potentially af-

fecting organic matter freshness (Li etal. 2021). The highest

FrI value was found in the wet–dry cycle treatment, with max-

imum FrI values occurring in November and January. This

could lead to increased carbon sequestration in riparian soils,

as fresher organic matter is more stable and stored long- term.

Higher FrI values could also influence methane emissions,

which can be mitigated by wet–dry cycles. Understanding

these factors can inform restoration efforts (Küsel etal.2016)

(FigureS5b).

The Biological Index (BIX) in riparian areas is influenced by

seasonal changes, with higher mean values in the wet–dry

cycle and cold treatments suggesting a preference for freshly

produced organic matter. Vegetation impact in riparian areas is

cyclical, with peak periods around June and December. Higher

BIX values indicate a greater proportion of microbially derived

organic matter, potentially influencing GHG emissions and

mitigation potential (Xenopoulos etal.2021). Seasonal manage-

ment strategies should be tailored to account for these changes,

such as enhancing carbon sequestration during high microbial

activity and implementing water management strategies to re-

duce methane emissions. Understanding the grass growth cycle

can help manage the carbon cycle more efficiently. Maintaining

healthy grass cover in the Yangtze River riparian basin may

be an important method for reducing GHGs emissions (Zhang

etal.2016) (FigureS5c).

The Humification Index (HIX) results indicate that riparian

areas have potential for carbon sequestration and GHG miti-

gation. Seasonal variation and heat stress sensitivity highlight

the need for adaptive management strategies (Dmuchowski

etal.2022). The highest HIX values in January indicate that or-

ganic matter humification is highest during colder months and

decreases in warmer periods. Treatment effects and heat stress

impact also play a role. Higher HIX values indicate more humi-

fied, stable organic matter, which has positive implications for

carbon sequestration (Font etal.2021) (FigureS5d).

Seasonal management strategies can be used to maximize

carbon sequestration during high humification periods and

protect soil organic matter during periods more susceptible

to decomposition (de Souza etal.2022). Vegetation manage-

ment can enhance CO2 mitigation by maintaining or enhanc-

ing riparian vegetation cover. Climate change adaptation is

crucial due to soil organic matter vulnerability under heat

stress. Nutrient management can be optimized by balancing

ecosystem services in riparian zones (Pandey and Ghosh2023)

(Figures6–7).

5 | Summary

Despite their significant role as a sink for GHGs, river ripar-

ian basins are currently emitting more GHGs due to extreme

climatic changes and other environmental factors such as soil

chemistry, land use, extreme heat, dissolved organic matter, and

water table variations. This study found that sequestration de-

creased CO₂ and CH₄ fluxes in wet–dry cycle and grass- covered

columns, while photosynthesis lowered CO₂ and N₂O f luxes in

grass- covered columns. Leaching and the disintegration of or-

ganic matter increased total and DOC at deeper water tables.

Nitrous oxide emissions were highest under wet–dry cycle con-

ditions, driven by soil moisture and organic nitrogen. Anaerobic

decomposition released more CO₂ at low water levels, whereas

methane was released at high water levels. Extreme climatic

conditions resulted in increased GHG emissions due to rapid

soil organic matter decomposition and high chemical reaction

rates. The carbon and nitrogen contents in river riparian zones

are critical to biological cycles, inf luencing both storage and

emissions within the ecosystem.

The study confirmed that regulating wet–dry cycles and grass

coverage can help restore river basins, conserve soil moisture,

and manage land use changes and water tables. These practices

could help prevent climate change and reduce GHG emissions

in river riparian zones. Enhancing soil composition, increas-

ing carbon storage, and promoting vegetation growth can all

contribute to reducing GHG emissions in the Yangtze River

basin. Finally, the study encourages further research to better

understand the role of sustainable land management in climate

change mitigation.

Author Contributions

Kemal Adem contributed to the conceptualization, methodology, for-

mal analysis, investigation, writing of the original draft, and reviewing

and editing. Shun Li was responsible for data curation, visualization,

and reviewing and editing. Qiong Zhang Giri Kattel Jun- Ming Wu and

Xiaoqiao Tang all contributed to the reviewing and editing of the man-

uscript. Zhi- Guo Yu played a role in conceptualization, methodology,

formal analysis, investigation, as well as funding acquisition and super-

vision. All authors have read and approved the final manuscript.

Data Availability Statement

Research data are not shared.

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