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Seasonal precipitation distribution determines ecosystem CO₂ and H₂O exchange by regulating spring soil water-salt dynamics in a brackish wetland

Cite this dataset

Huang, Wanxin et al. (2024). Seasonal precipitation distribution determines ecosystem CO₂ and H₂O exchange by regulating spring soil water-salt dynamics in a brackish wetland [Dataset]. Dryad. https://doi.org/10.5061/dryad.pc866t1z3

Abstract

The intensification of the global hydrological cycle is anticipated to increase the variability of precipitation patterns. Brackish wetlands respond to changes in precipitation patterns by regulating the absorption and release of CO2 and H2O to maintain the stability of ecosystem functions. However, there is limited understanding of how the inter-seasonal precipitation distribution affects ecosystem CO2 and H2O exchange compared to annual precipitation totals. Here, we conducted four consecutive years of field experiments in a brackish wetland, manipulating the proportion of precipitation across different seasons while maintaining a constant annual precipitation total. We utilized five inter-seasonal precipitation distribution proportions (+73%, +56%, control (CK), -56%, and -73%) to examine the effects of seasonal precipitation distribution (SPD) on ecosystem CO2 and H2O exchange. Our findings revealed that the ecosystem CO2 and H2O fluxes showed a trend of decreasing with the decrease of spring precipitation distribution. Among them, the annual net ecosystem CO2 exchange (NEE), evapotranspiration (ET), carbon use efficiency (CUE), and water use efficiency (WUE) were shown to be more sensitive to decrease in spring precipitation distribution and increase in summer and autumn precipitation distribution. This negative asymmetric response pattern suggests that annual ecosystem CO2 and H2O exchange is primarily governed by seasonal precipitation variability, with spring soil water-salt dynamics identified as the key driver. Therefore, this association can be explained by the fact that drought of the early growth stage exacerbates soil salinization and inhibits vegetation colonization and growth, thereby greatly impairing the annual CO2-H2O exchange capacity of brackish wetlands. Our results emphasized that the spring's extreme precipitation-induced soil water-salt conditions will greatly influence CO2 and H2O exchange in brackish wetlands in the future. These findings are crucial for improving predictions of the carbon sequestration and water-holding capacity of brackish wetlands.

README: Seasonal precipitation distribution determines ecosystem CO₂ and H₂O exchange by regulating spring soil water-salt dynamics in a brackish wetland

https://doi.org/10.5061/dryad.pc866t1z3

Description of the data and file structure

Data usage bootstrap:

See abiotic factors data at: Huang et al._ abiotic factors

See biotic factors data at: Huang et al._ biotic factors

See ecosystem CO₂ and H₂O exchange data at: Huang et al._ ecosystem CO₂ and H₂O exchange

See structural equation model fitting at: Huang et al._ SEM

See simulated precipitation at: Huang et al._ Precipitation

DATA-SPECIFIC INFORMATION FOR: Huang et al._ abiotic factors

  1. Number of variables: 3
  2. Number of sheets: 3
  3. Number of cases/rows: 6 columns 366 rows/sheet
Variable List:
  • EC: soil electrical conductivity ds/m
    • "Soil electrical conductivity" refers to the ability of ions dissolved in soil solution to conduct current. It is a measure of the soluble salt content of soil and is commonly used to assess the salinity or salinization of soil. Soil conductivity is proportional to the soluble salt content in the soil, so it can be used as a basis for judging soil salt status.
  • SM: soil moisture %
    • "Soil moisture" refers specifically to soil volumetric moisture content. The proportion of water in a given volume of soil is usually expressed as a percentage. This index is used to describe the content of water in the soil and is very important for understanding soil water status and plant water supply.
  • ST: soil temperature °C
    • "Soil temperature" refers to the temperature inside the soil, which has an important impact on many processes in the soil ecosystem, including microbial activity, decomposition of organic matter, plant growth, and water evaporation. The change in soil temperature can reflect the change in climate conditions.
Other relevant information:

The five inter-seasonal precipitation distribution proportions are +73%, +56%, CK, -56%, and -73%.

DATA-SPECIFIC INFORMATION FOR: Huang et al._ biotic factors

  1. Number of variables: 3
  2. Number of sheets: 3
  3. Number of cases/rows: 2 columns 21 rows/sheet (aboveground biomass (AGB)); 2 columns 21 rows/sheet (belowground biomass (BGB)); 5 columns 21 rows/sheet (coverage)
Variable List:
  • AGB: aboveground biomass g/cm²
    • "Aboveground biomass" refers to the sum of the biomass of all plants living above ground in an ecosystem, usually including tree trunks, branches, leaves, and herbs. This indicator is an important parameter for measuring ecosystem productivity and carbon storage capacity and has important implications for ecology, forestry, and agricultural research.
  • BGB: belowground biomass g/cm²
    • "Belowground biomass" refers to the total of plant root biomass. Subsurface biomass is an important component of terrestrial ecosystems and is crucial to understanding the structure and function of ecosystems.
  • Coverage: %
    • "Coverage" refers to the projected area of vegetation on the ground as a percentage of the total area. It is a measure of the density of vegetation in an area, reflecting the coverage of vegetation on the ground.
Other relevant information:

The aboveground biomass under -73% treatment is missing two data points due to sample loss, which has been interpolated with the mean and marked in red.

Coverage was analyzed and plotted using annual mean values.

DATA-SPECIFIC INFORMATION FOR: Huang et al._ ecosystem CO₂ and H₂O exchange

  1. Number of variables: 6
  2. Number of sheets: 6
  3. Number of cases/rows: 14 columns 21 rows/sheet
Variable List:
  • NEE: net ecosystem CO₂ exchange µmol m﹣² s﹣¹
    • "Net ecosystem CO₂ exchange" refers to the difference between gross primary productivity (GPP) and ecosystem respiration (ER), reflecting the capacity of ecosystems to absorb or release carbon.
  • ER: ecosystem respiration µmol m﹣² s﹣¹
    • "Ecosystem respiration" refers to the total amount of carbon dioxide released by all living organisms (including plants, animals, and microorganisms) in a given ecosystem through respiration. It is an important part of the ecosystem's carbon cycle and reflects the metabolic activity of organisms in the ecosystem.
  • GPP: gross primary productivity µmol m﹣² s﹣¹
    • "Gross primary productivity" refers to the total amount of energy fixed or organic matter produced by all primary producers (mainly plants) in an ecosystem through photosynthesis over a given period. It is the basis of the energy and material circulation of the ecosystem and is a key indicator to measure the productive capacity of the ecosystem.
  • ET: evapotranspiration mmol m﹣² s﹣¹
    • "Evapotranspiration" refers to the total amount of water lost from the surface to the atmosphere through evaporation and plant transpiration over a given period.
  • WUE: water use efficiency µmol CO₂ mmol﹣¹ H₂O
    • "Water use efficiency" refers to the amount of water required by plants to fix each unit mass of carbon dioxide through photosynthesis. It is an indicator of the efficiency of plants in water acquisition and use.
  • CUE: carbon use efficiency (no units)
    • "Carbon use efficiency" refers to the use efficiency of each unit of carbon fixed by plants through photosynthesis, usually expressed as the ratio of carbon fixed to carbon absorbed.
Other relevant information:

Because the error of the CUE = -NEE/GPP calculation result is too large, which seriously affects the result, four abnormal CUE values in January and February are excluded, which have been interpolated with the mean and marked in red.

DATA-SPECIFIC INFORMATION FOR: Huang et al._ SEM

  1. Number of variables: 11
  2. Number of sheets: 1
  3. Number of cases/rows: 12 columns 21 rows
Variable List:

As defined above. 

SPD: seasonal precipitation distribution

Other relevant information:

The SEM model fitting is analyzed by using the simulated precipitation in spring, soil water and salt in spring, and the annual mean value of biotic factors and ecosystem CO₂ and H₂O fluxes.

DATA-SPECIFIC INFORMATION FOR: Huang et al._ Precipitation

  1. Number of variables: 0
  2. Number of sheets: 1
  3. Number of cases/rows: 6 columns 6 rows
Other relevant information:

CK represented the average precipitation (mm) of each quarter from 1988 to 2018; +73% and +56% indicated that the spring precipitation was 73% and 56% higher while summer and autumn precipitation was 73% and 56% lower than the control; -56% and -73% indicated that a 56% and 73% decrease in spring precipitation and a 56% and 73% increase in summer and autumn precipitation compared to the control. Notably, the annual precipitation total and winter precipitation remained constant across the five treatments.

Sharing/Access information

NA

Code/Software

NA

Methods

Abiotic factors measurements

From April 2022 to March 2023, a soil three-parameter sensor (HdyraProbe Lite Soil Moisture sensor, Stevens Water, USA) was used to measure surface (0-10 cm) soil volumetric moisture content (SM), soil electrical conductivity (EC), and soil temperature (ST). Data were logged once a day throughout the experimental period using a data logger (CR1000, Campbell Science, Inc., USA).

Biotic factors measurements

In May 2019, a 1 × 1 m quadrat was selected in each plot as a permanent vegetation survey area by diagonal crossing. Vegetation surveys were conducted on a clear morning of every quarter in 2022 to record indicators such as coverage. At the end of the 2022 growing season, aboveground biomass (AGB) was obtained by the cutting method, and belowground biomass (BGB) was obtained by root drilling on the 1 × 1 m quadrate. Subsequently, AGB and BGB were placed in an oven, first baked at 105 ℃ for 1 h, then converted to 70 ℃ for 48 h, and finally weighed to obtain dry weight.

Ecosystem CO2 and H2O exchange measurements

The ecosystem CO2 and H2O fluxes were quantified using a Li-6400 portable photosynthesis system (Li-Cor, Inc., Lincoln, NE, USA) in conjunction with the closed chamber method. From April 2022 to March 2023, measurements were carried out at 9:00-11:00 AM on sunny days, twice a month, before and after precipitation. A 0.25 × 0.25 × 0.03 m3 acrylic plexiglass base was permanently installed within each plot, with one end inserted into the soil at a depth of 3 cm and the other end flush with the ground. Before measurements, a 0.25 × 0.25 × 1.2 m3 acrylic plexiglass chamber was placed on the base to ensure an airtight environment throughout the entire space. Two small fans were positioned at the top corners of the chamber to ensure complete air mixing during the measurement process and reduce errors. The rate of CO2 concentration change under light conditions was denoted by net ecosystem CO2 exchange (NEE), with the ecosystem net carbon uptake and release represented by NEE of "-" and "+", respectively. After lighting conditions were measured, the chamber gas was evenly mixed with the outside air, and the chamber was covered with a black cloth. At this time, the concentration changes rate of CO2 represented ecosystem respiration (ER). Gross primary productivity (GPP), evapotranspiration (ET), water use efficiency (WUE), and carbon use efficiency (CUE) were calculated as follows: GPP=-NEE+ER; WUE=GPP/ET; CUE=1-ER/GPP=-NEE/GPP.

In addition, their sensitivities were calculated as: |∆X|=|(XT-XC)/XC|. Among them, XT represents CO2 and H2O fluxes under ±SPD treatments, and XC represents CK. ΔX absolute value (|ΔX|) represents the sensitivity of fluxes. A positive asymmetric response is defined when |ΔX| under +SPD treatments is greater than under -SPD treatments, and a negative asymmetric response is defined when |ΔX| under +SPD treatments is less than under -SPD treatments.

Funding

National Key Research and Development Program of China, Award: 2022YFF0802101

Natural Science Foundation of China, Award: 42101117

National Natural Science Foundation of China, Award: 42071126

Natural Science Foundation of China, Award: U2106209

Natural Science Foundation of China, Award: U1906220

Chinese Academy of Sciences, Award: 121311KYSB20190029, International Science Partnership Program

Forestry Science and Technology Innovation Project of Shandong Province, Award: 2019LY006

Natural Science Foundation of Shandong Province, Award: ZR2019BC106