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Title Investigating short-term response of soil moisture-temperature-CO2 interactions to weather anomalies

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Author Anjileli, Hassan

Publication Date 2016

Peer reviewed|Thesis/dissertation

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UNIVERSITY OF CALIFORNIA, IRVINE

“Investigating short-term response of soil moisture-temperature-CO2 interactions to weather anomalies”

THESIS

submitted in partial satisfaction of the requirements for the degree of

MASTER OF SCIENCE

in Civil Engineering

Hassan Anjileli

Thesis Committee: Professor Amir AghaKouchak, Chair Professor Kuolin Hsu Dr. Peter A. Bowler

2016

Table of contents

List of figures ...... iii

List of tables ...... iv

Acknowledgements ...... v

Abstract of the thesis ...... vi

1 Introduction ...... 1

2 Materials and Methods ...... 4

2.1 Site description ...... 4

2.2 Soil CO2 flux and additional measurements ...... 6

2.3 Methodology ...... 8

3 Result and Discussion ...... 10

4 Conclusion ...... 26

5 References ...... 29

List of figures

Figure 1: Temperature shift in mean and variance which results in more extremes (AghaKouchak et al., 2013 CH. 1))...... 2

Figure 2: Study area (blue solid line) at Seasonal Marsh (red solid line) at the San Joaquin Marsh

Reserve (SJMR)...... 5

Figure 3: Device LI 8100A-104C and three solar panels in Seasonal Marsh at SMJR...... 6

Figure 4: daily patterns of: CO2 flux, air temperature, soil temperature and soil moisture of five randomly consecutive days ...... 11

Figure 5. Correlation coefficient between each parameters ...... 12

Figure 6: Time-lagged daily patterns of: CO2 flux, air temperature, soil temperature and soil moisture of five randomly consecutive days ...... 13

Figure 7. Time-lagged correlation coefficient between each parameters ...... 14

Figure 8: Field study measurements from February to June ...... 16

Figure 9. Time series of precipitation and CO2 flux on hot and non-hot days ...... 19

Figure 10: Relationship between CO2 flux and precipitation and VWC on hot and non-hot days

...... 20

Figure 11: Relationship between soil temperature and CO2 flux on hot and non-hot days ...... 22

Figure 12: Relationship between soil temperature and CO2 flux on hot and non-hot days ...... 23

Figure 13: Relationship between soil temperature and soil moisture ...... 25

iii

List of tables

Table 1: Correlation of CO2 flux with other collected parameters ...... 12

Table 2: Time-lagged correlation of CO2 flux with other collected parameters ...... 14

Table 3: Amount of warm days with three warm spells durations (3-day, 5-day and 7-day) ...... 18

Acknowledgements

Firstly, I would like to express my sincere appreciation and gratitude to Prof. Amir

AghaKouchak for his guidance during my research. His support and inspiring suggestions have been precious for the development of this thesis.

I am also indebted to Prof. Hamidreza Norouzi (New York City College of Technology), who provided us the soil respiration measurement equipment, for the fruitful and pleasant collaboration. In addition Dr. Peter Bowler, who granted us permission to enter the San Joaquin

Marsh Reserve and provided us with useful information, deserves special thanks.

Particular thanks go to Drs. Hamed Moftakhari and Alireza Farahmand. Although they were very busy with their daily tasks and research, they were available to clear any doubts I had and patiently revised and corrected my thesis. I also appreciate Samaneh Ashraf for her support in discussing new ideas and accompanying me to the San Joaquin Marsh Reserve at any time of the day.

Abstract of the thesis

“Investigating short-term response of soil moisture-temperature-CO2 interactions to weather anomalies”

Hassan Anjileli

Master of Science in Civil Engineering

University of California, Irvine, 2016

Professor Amir AghaKouchak, Chair

Global warming, caused by the increase of CO2 emissions, has increased frequency and intensity of climate extremes. Extreme events have large impacts on the terrestrial ecosystem carbon.

Therefore, it is fundamental to investigate the subterrestrial processes of producing carbon dioxide.

Carbon dioxide flux is affiliated with heterotroph and autotroph and they are influenced by abiotic and biotic factors. In this study we investigated the soil moisture-soil temperature-soil respiration relationships on bare soil and the impact of hot days and warm spells on soil respiration.

Measurements were taken from February to June 2016 in Southern California (33° 39ʹ32.7ʺ N,

117° 50ʹ55.9ʺ W) where several hot spells occurred. Bias corrected maximum air temperature was used to calculate the warm spell threshold. Land and atmospheric parameters respond with dealt to soil respiration. Association metrics were employed to explore the relationship between soil respiration and both collected and time-lagged data. Soil moisture and soil temperature were the two most important factors affecting soil respiration whereas soil temperature was the ascendant factor. The results show that soil with temperatures ranging from 20 to 25 °C displays the highest average CO2 flux value (~ 1.12 μmol CO2 m-2 s-1). By deviating from the soil temperature range

of 20 to 25 °C the average CO2 flux decreases. Soil respiration increases by around 50% when soil temperature increases from 15 to 20 °C to 20 to 25 °C and decreases by 48% by deviating from the soil temperature range of 20 to 25 °C to 25 to 30 °C. Soil respiration decreased when both soil moisture and soil temperature were out of the optimum range (0.225 to 0.250 m3/m3 and 20 to

25 °C, respectively) of microorganism. In other words, soil respiration decreased when soil moisture and or soil temperature were too low or too high. Hot days or warm spells intensified soil temperature, leading to low soil respiration compared to non-hot days or warm spells. A net flux

-2 of 0.60 grams of CO2 per m was released during the study period.

vii

1 Introduction

The increase of atmospheric carbon dioxide (CO2) through human activities, since the beginning of the Industrial Revolution, has led to global warming (Scharlemann et al., 2014, IPCC

2013). The Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change

(IPCC) confirmed that climate change not only results in increasing mean temperature globally, but also shifts the frequency, magnitude and the intensity of climate extremes. (IPCC 2012; Perkins et al., 2012)) (Figure 1). Given these shifts, a marginal change in the variance of climate variables have unforeseen changes on the key characteristics of extremes (Lu et al., 2013; Nicholls and

Alexander, 2007).

There is no universal definition of what constitutes an extreme event and they are by current definition inherently hard to discover and tough to determine reliably (AghaKouchak et al., 2013).

The Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate

Change Adaptation (SREX) has confirmed that the number and length of extreme events has increased since the mid-20th century (IPCC 2012). Extreme climatic events have significant impacts on many environmental and socio-economic systems (Erwan Monier and Xiang Gao,

2013; Mazdiyasni and AghaKouchak, 2015). Climate extremes have a large influence on the terrestrial ecosystem carbon stocks (Reichstein et al., 2013). However, there is still a significant lack of knowledge about the distribution of carbon and carbon dioxide fluxes of the soil and the way they responds to such extreme events (Graf Pannatier et al., 2012).

The estimated quantity of carbon emitted and stored in from soil including peatlands, wetlands and permafrost have been recently studied (Davidson and Janssens, 2006; Scharlemann et al., 2014). Global warming can change the characteristics of soil over the next several decades

(Maier et al., 2011).The soil will be a net source if the influx of carbon from plant growth dominates the carbon flux balance, and will be a net sink if the outflux of carbon, such as soil respiration, dominates (Maier et al., 2011). The volume of soil carbon pool in form of organic

Figure 1: Temperature shift in mean and variance which results in more extremes (AghaKouchak et al., 2013 CH. 1)) matter, which is twice as much as the atmosphere, could potentially influence the atmospheric CO2 concentration (Lu et al., 2013; Post et al., 1982). Thus, any small change in the underground carbon pools might have large impacts on carbon dioxide flux to the atmosphere (Fabianek et al., 2015;

Hirano, 2003; Ryan and Law, 2005). Therefore, the subterrestrial processes that cause the changes in underground carbon pools still need to be studied to help us achieve a deeper understanding of the way that global warming affects the soil carbon flux (Bond-Lamberty and Thomson, 2010;

Han et al., 2007).

Soil respiration is a major contributor to the global CO2 flux, and is indeed seven times larger than the CO2 emissions from fossil fuel combustion (Giardina et al., 2014; Merbold et al.,

2012). CO2 flux is associated with the metabolic activity of organisms found in the soil, including heterotrophic (decomposers) and autotrophic (roots and their microbes) (Cable et al., 2008;

Janssens et al., 2001). Heterotrophic respiration represents the loss of stored carbon via decomposition of soil organic matter (SOM) and autotrophic respiration describes the loss of carbon which has been fixed during photosynthesis (Cable et al., 2008). CO2 flux is a function of terrain level, vertical depth of soil, spatial characteristics of the local site and soil and vegetation type (Cannone et al., 2012; Maier et al., 2011). Abiotic and biotic factors also play important roles in determining soil respiration. The major abiotic components influencing soil respiration are soil temperature and soil moisture (Martin and Bolstad, 2005). However, abiotic and biotic factors are somehow related. Abiotic factors are a determinant for soil carbon dioxide flux through biotic factors such as root growth and microbial life.

Soil respiration is not well documented for bare soil and semi-arid terrestrial ecosystems, due to their low productivity, which has resulted in less knowledge about their impact (Rey et al.,

2011). In this study, we report continuous measurements of CO2 flux at a bare soil study area located in Southern California, and we aim to understand how land and atmospheric conditions including soil moisture (VWC), air temperature (Air Temp.), soil temperature (Soil Temp.) and precipitation affect soil respiration.

We also investigate the impact of hot days and warm spells, as a representative of climate extreme events, on land-atmosphere interactions. The definition of a warm spell may differ by region. However, they are generally characterized as a consecutive spell of excessive warm days

(Perkins et al., 2012).

2 Materials and Methods

2.1 Site description

The study area of this research is located at the San Joaquin Marsh Reserve (SJMR) (Phase

2, Ponds and Seasonal Marsh), which is close to the University of California, Irvine (UCI) (Figure

2). It is adjacent to the UCI main campus and bordered to the south by the San Diego Creek (SDC).

The region experiences a Mediterranean climate with an average annual temperature of 17 °C and a mean annual precipitation of 300 mm (P. Bowler, 2007).

The SJMR is one of the reserves of the University of California Natural Reserve System

(UCNRS) with an area of 817,500 m2. It is subdivided into different sections, including man-made areas, i.e. ponds (shown by the green solid line in Figure 2), Phase II (shown by yellow solid line), and untouched, i.e. Seasonal Marsh (shown by red solid line). The measurements gauge is located in the Seasonal Marsh (33° 39ʹ32.7ʺ N latitude, 117° 50ʹ55.9ʺ W longitude) at an elevation of 2 m above sea level (Figure 2 solid red line).

Plant species in the studied area include curly dock (Rumex crispus), annual saltmarsh aster

(Aster subulatus var. ligulatus), rabbitfoot grass (Polypogon monspeliensis), bristly ox-tongue

(Picris echioides), alkali mallow (Malvella leprosa), spearscale (Atriplex triangularis) and seaside heliotrope (Heliotropium curassavicum) (P. Bowler, 2007). The soil at the experimental site is characterized as drained Omni clay (P. Bowler, 2007). The typical profile of Omni clay from 0 to

30cm is defined as gray silty clay, very dark gray moist; moderate very coarse prismatic structure; many very fine and medium and common coarse roots; many very fine and medium tubular pores;

strongly effervescent with lime segregated in a few fine soft masses; moderately alkaline with a potential of Hydrogen (pH) of 8.2 (John K. Wachtell, 1978).

Figure 2: Study area (blue solid line) at Seasonal Marsh (red solid line) at the San Joaquin Marsh Reserve (SJMR).

2.2 Soil CO2 flux and additional measurements

Field measurements were taken at a bare soil area in the Seasonal Marsh. One polyvinyl chloride (PVC) collar with a diameter of 20 cm and height of 11 cm was inserted into the soil to a depth of five to six cm one week before measuring soil respiration. The collar is left in place until the end of the study to limit soil disturbance and to allow repeated measurements. The vegetation within the collar was cleared off to make sure that the soil remains bare over the entire observation period.

LI-8100A

LI-8100A- 104C

60 Watt

Figure 3: Device LI 8100A-104C and three solar panels in Seasonal Marsh at SMJR.

Diurnal soil respiration was measured from February 5th to June 30, 2016 using an automated soil CO2 flux system (LI-8100A, LI-COR, Inc., Lincoln, Nebraska, USA) with one LI-

8100A-104C long-term transparent chamber (Figure 3). The measurement device was mounted on a collar and five measurements with an observation period of two minutes were made every hour. Before each flux measurement, the chamber was automatically vented for 65 seconds (45 sec. pre-purge and 20 sec. post-purge). An umbrella was installed above the analyzer control unit to protect the analyzer from sunlight and avoid over-heating. It is worth mentioning that the data collection continues through the automated soil CO2 flux system.

Simultaneously, soil temperature and soil volumetric water content near the chamber at 5 cm depth below ground were observed using an auxiliary soil temperature thermistor (LI-COR,

Inc., Lincoln, NE, USA) and an ECH2O model EC-5 (Decagon Devices, Inc., Pullman, WA, USA), respectively. Soil temperature and soil moisture sensors were attached to the LI-8100A analyzer control unit. The EC-5 determines the volumetric water content by measuring the dielectric constant media using capacitance domain technology (Kočárek and Kodešová, 2012).

The system was powered with a 55 Ah/12 Volt battery and a 60 Watt solar panel in the beginning, during which the system ran out of power for a few short periods (i.e. during long cloudy days). Thus, we decided to upgrade the system to a 180 Ah/12 Volt battery recharged with two 100 Watt and one 60 Watt solar panels (Figure 3). In addition, two weeks of data were not captured during early April (04.01.2016 to 04.15.2016) due to a bite on the power cable by an animal.

In order to calculate the warm spell threshold, longterm daily data (01.01.1969 to

06.30.2016) of temperature and precipitation were obtained from www.weathersource.com for the

weather station in John Wayne Airport (SNA), Santa Ana, CA, (yellow solid line in Figure 2 (upper left)) located within a distance of three kilometers from the study area (Figure 2).

2.3 Methodology

To investigate the relationship between CO2 flux and land and atmospheric conditions such as air temperature, soil temperature, soil moisture, Pressure, relative humidity and CO2 concentration in the atmosphere, three types of association assessment methods were employed to the collected data: Pearson correlation coefficient (푟), Spearman’s rank correlation coefficient 휌푠

(rho) and Kendall’s tau (τ). In all three association assessment methods we used CO2 flux as the first variable (dependent) and land and atmospheric conditions as the second variable

(independent).

The Pearson correlation coefficient (푟) in equation 1 is a measure of the linear correlation between two variables and ranges between -1 and 1 (1, 0 and -1 means completely positive correlation, no correlation and completely negative correlation, respectively) (Guo and Shen,

2015).

(Σ푋)(ΣY) Σ푋푌 − 푛 푟 = (1) 2 ( )2 2 √ 2 Σ푋 2 (Σ푌) (Σ푋 − 푛 )(Σ푌 − 푛 )

Where 푟, 푋, 푌 and 푛 represent the Pearson correlation coefficient, the first variable, the second variable and the number of observations, respectively (Chok, 2010).

The Spearman correlation coefficient 휌푠 (rho) in equation 2 is a non-parametric estimator of the rank correlation. If two variables are related by monotonic functions, the Spearman shows a perfect correlation between variables.

6Σ푑2 휌 = 1 − 푖 (2) 푠 푛(푛2 − 1)

Where 휌푠 (rho), 푑푖and 푛 denotes the Spearman rank correlation coefficient, differences between the ranks of two variables and the number of observations, respectively (de Siqueira Santos et al.,

2014).

Kendall’s tau (τ) association coefficient in equation 3, also a measure of rank correlation, is an estimator of the ordinal association between two quantities, ranging between -1 and 1. If the two rankings are in total agreement or disagreement, the Kendall tau coefficient will be 1 or -1, respectively. If the two variables are independent then the coefficient is expected to be approximately 0.

퐶 − 퐷 휏 = 1 (3) 2 푛(푛 − 1)

Where 휏, 퐶 and 퐷 is Kendall’s tau coefficient correlation, concordant pairs (when the rank of second variable is greater than the rank of the first variable) and discordant pairs (when the rank is equal to or less than the rank of the first variable), respectively (Stevenson, 2012).

In addition, three types of association assessment methods were employed by considering time lag since time lag of land and atmospheric parameters respond to CO2 flux with a time delay.

In order to investigate the effects of the warm spell on land-atmosphere interactions, we used the long-term daily maximum air temperature from John the Wayne Airport weather station

(SNA) to calculate the hot days or warm spell threshold for the study area. Here, we defined warm spells as the 85th percentile of the maximum air temperature of the long-term climatology for each month. For example, a 3-day warm spell in a given month is detected as three or more consecutive days of air temperature exceeding the 85th percentile of the maximum air temperature for that month. The air temperature values derived from SNA were lower compared to that derived from the chamber. The possible reasons for this systematic difference are: 1. sunlight shines directly onto the temperature sensor of the transparent chamber, and 2. our sensor is placed just above the ground, so exposed to less wind flow compared to SNA. Therefore, the calculated warm spell threshold based on SNA data were bias corrected to be compatible with the maximum air temperature of the chamber.

3 Result and Discussion

A total of 15,555 data points of soil respiration (CO2 flux), air temperature (Air Temp.), soil temperature (Soil Temp.), soil moisture (VWC), Pressure, relative humidity (RH) and CO2 concentration in the atmosphere (CO2) was collected from February to June 2016.

Figure 4 shows the diurnal pattern of: CO2 flux, air temperature, soil temperature and soil moisture over five randomly chosen consecutive days in February. The y-axis is separated into a right and left side. The right axis shows air temperature (°C), soil temperature (°C) and soil moisture (%)

and the left axis shows CO2 flux. The x-axis represents the time at which the field measurements

were taken. The diurnal pattern of CO2 flux (dashed dark red line) during the day is characterized

with a sharp increase from sunrise until 11or 12, where it reaches its highest respiration rate.

Figure 4: daily patterns of: CO2 flux, air temperature, soil temperature and soil moisture of five randomly consecutive days Afterwards it decreases sharply and reaches approximately zero around 6 PM. During the night,

-2 -1 however, CO2 flux fluctuates on average from -0.2 to 0.5 μmol CO2 m s . Similar to CO2 flux,

air temperature (dashed purple circle line) rises sharply from sunrise and reaches the highest

temperature around 12 PM, and decreases sharply until 6 PM. Afterwards, it decreases gradually

until the following morning. Soil temperature (orange circle) increases sharply from 9 AM in the

morning and reaches its highest point at approximately 3 to 4 PM, after which it very slowly

decreases until the following morning. The oscillation of soil moisture (brown triangle line) is

similar to soil temperature, however, it exhibits less variability. The observed soil moisture

fluctuations could be due to the fact that the EC-5 soil moisture sensor is influenced by soil

temperature and this issue is mentioned by many researches such as (Chanzy et al., 2012; Fares et al., 2016; Kizito et al., 2008; Kočárek and Kodešová, 2012).

Table 1 shows the correlation between CO2 flux and the collected data based on three types of coefficient. As shown in Table 1, Spearman shows higher correlation values compared to Pearson and Kendall tau (τ).

Table 1: Correlation of CO2 flux with other collected parameters

VARIABLES / COEFFICIENT TYPES PEARSON KENDALL SPEARMAN PRESSURE -0.16 -016 -0.16 RH -0.44 -0.31 -0.45 VWC -0.03 0.06 0.08 AIR TEMP. 0.60 0.48 0.69 SOIL TEMP. 0.16 0.12 0.18

CO2 -0.42 -0.32 -0.47

Figure 5. Correlation coefficient between each parameters

Figures 5 displays the correlation coefficients (cc) of all collected parameters with each other using

Spearman rank correlation. In Figure 5, the first column shows the cc of CO2 flux with other parameters: pressure (-0.17), relative humidity (-0.45), VWC (0.08), air temperature (0.69), soil temperature (0.18) and CO2 concentration (-0.47). The highest cc exists with air temperature (0.69) compared to CO2 flux. VWC and soil temperature have a strong correlation with CO2 flux, however, an opposite pattern is seen here. This is due to the reason that the shift or time delay of

VWC and soil temperature distorted the correlation (Phillips et al., 2011). .

Generally, CO2 flux responds to the collected land and atmospheric variables with some time delay; therefore, it is essential to consider the lag of the variables for calculating the correlation between them and CO2 flux.

Figure 6: Time-lagged daily patterns of: CO2 flux, air temperature, soil temperature and soil moisture of five randomly consecutive days

In addition, shows Figure 6 the diurnal pattern of: CO2 flux, air temperature, soil temperature and

soil moisture over five randomly chosen consecutive days in February by considering the time lag.

The time lag between CO2 flux and each parameter was removed through the shift of the values,

so that each parameter was in phase with CO2 flux. Here, we can see that all parameters reach their

highest and lowest point approximately at the same time.

Table 2 shows time lagged correlation coefficients of CO2 flux and the collected parameters.

Spearman shows in general an increase of the correlation coefficients compared to Table 1.

Table 2: Time-lagged correlation of CO2 flux with other collected parameters

VARIABLES / COEFFICIENT TYPES PEARSON KENDALL SPEARMAN PRESSURE -0.22 -0.15 -0.22 RH -0.50 -0.36 -0.52 VWC -0.03 0.10 0.14 AIR TEMP. 0.61 0.50 0.71 SOIL TEMP. 0.35 0.26 0.39 CO2 -0.43 -0.33 -0.49

Figure 7. Time-lagged correlation coefficient between each parameters

Additionally, Figure 7 represents the cc of all collected parameters with each other considering the lag time. Here, we see higher cc values in the first column relative to Figure 6. The cc of Pressure,

RH, VWC, Air Temp., Soil Temp., and CO2 concentration of atmosphere with CO2 flux show an increase of 37.5%, 15.5%, 75%, 2.9%, 117% and 4.3% respectively when we consider the time lag. The two highest increases were seen in cc of VWC (from 0.08 to 0.14) and soil temperature

(from 0.18 to 0.39) with CO2 flux. This shows that soil temperature and VWC are two main contributors to CO2 flux. A higher cc of soil temperature with CO2 flux compared to VWC reveals that soil temperature contributes to CO2 flux more than VWC does. Specifically, the relationship between soil temperature and CO2 flux as well as their lag has also been mentioned by Philips

(2011) and Chen (2013). Since CO2 flux is mainly associated with VWC and soil temperature

(Hashimoto and Komatsu, 2006; Janssens et al., 2001; Maier et al., 2011), we will focus more on these parameters and neglect Pressure, RH, and CO2 concentration of the atmosphere in this study.

In addition to VWC and soil temperature, air temperature is also considered for investigating the effects of warm spells on CO2 flux.

Figure 7 also shows the duration of lag time between CO2 flux and other parameters. This time lag is calculated and marked in the first column. Lag time depends on the phase shift between two parameters. The higher the phase shift, the higher the time delay duration. The collected parameters relative to CO2 flux have minimum of 0.15 hours (air temperature) to a maximum of 4.15 hours

(VWC) time delay. It can be concluded that CO2 flux responds to air temperature changes in a fast rate. Soil temperature and VWC, which have a strong influence on CO2 flux, show the highest time lag of 4 to 4.15 hours respectively. Wang et al., (2013) also shows a lag time of 3 to 4 hours between soil temperature and CO2 flux.

Figure 8: Field study measurements from February to June

Figure 8 shows the results of the daily field experiment time series (February to June) of the maximum, minimum and average values of the parameters: air temperature, precipitation, soil temperature, VWC and CO2 flux. The time series are all based on bias corrected threshold and the time lag is also considered.

The first row shows air temperature values. Air temperature ranges between -1.7 and 46.0 °C and has increased 9 °C from February to June on average. The biased corrected warm spells threshold, which is by definition the 85th percentile of maximum air temperature, is shown as a horizontal black line in the air temperature graphs. The threshold of warm spells for February, March, April,

May and June are 30.14, 30.10, 36.23, 38.44 and 36.50 °C respectively. Hot days are illustrated with red areas. Warm spells are red areas with at least three consecutive hot days. Figure 8 displays

46 hot days in which it includes seven warm spells with different durations and intensities. Warm spells have appeared in all months from February to June except May.

The second row show precipitation values, obtained from the SNA weather station. Precipitation has occurred during the study time on 10 days. The lowest rainfall amount was in June with 0.25 mm and the highest was in March with 11.43 mm. We can see when precipitation happens air and soil temperature decreases and CO2 flux and VWC increases. The influence of precipitation on air, soil temperature and VWC was mentioned by Anders and Rockel, 2009. It should be noted that a consecutive precipitation event of four days happened during the missing data period in the middle of the first half of April. This is shown in the time series in Figure 9.

Soil temperature time series are shown in the third row. Soil temperature is measured at 5cm depth and ranges between 8.70 and 37.0 °C and has increased on average from February to June by 12.8

°C. Soil temperature exhibits an increase over time and follows the same pattern of air temperature.

In the fourth row, we can see the VWC time series. VWC ranges from 0.18 to 0.38 m3/m3 and has decreased during the study period by 0.13 m3/m3. Soil moisture generally declined during the study period with the exception of rainy events. The highest VWC values are in March, which correspond to high precipitation events. The close relationship between VWC and precipitation was also recognized by Zhao et al., 2016.

-2 The last row indicates the CO2 flux time series. CO2 flux varies from –0.9 to 6.35 μmol CO2 m s-1. CO2 flux increases with precipitation events, which is compatible with Deng et al., 2012; and

-2 Yan et al., 2014. On average 4.3 milligram CO2 m per day was emitted from the soil. Negative

CO2 fluxes occur partly due to changes in the balance between soil respiration and CO2 consumption in the soil. In other words, carbon dioxide enters, rather than exits the soil surface

(Ball et al., 2009; Ma et al., 2013). The negative CO2 flux mechanism is not very well understood in the literature (Iain C. Prentice et al., 2015). However, it has been described in other research studies (Han et al., 2014; Ma et al., 2013).

Table 3: Amount of warm days with three warm spells durations (3-day, 5-day and 7-day)

MONTH/WARM SPELL HOT DAYS 3-DAY 5-DAY 7-DAY FEBRUARY 17 17 13 8 MARCH 10 5 5 0 APRIL 7 6 6 0 MAY 0 0 0 0 JUNE 12 11 7 7

In Table 3, the number of hot days as well as 3-day, 5-day and 7-day warm spells are shown for each month. The bias corrected warm spell threshold for each month was calculated from the longterm data of SNA. Hot days are then counted when the maximum air temperature exceeded the threshold. A 3-day warm spell in a given month is detected as three or more consecutive days of air temperature exceeding the 85th percentile of the maximum air temperature for that month.

The same pattern can be applied for 5-day and 7-day warm-spells. In total 46 hot days has been occurred. February 2016 shows the longest warm spell duration relative to the 48 year base period

(1969 to 2016) and therefore, it can be interpreted that February 2016 was the warmest month in this base period. This result was not only found in our case study, but also globally. February 2016 on average was the warmest February in the 137 year period (1880 to 2016) mentioned by National

Oceanic and Atmospheric Administration (NOAA). Extremes in winter warm spells have increased notably in the 20th century and are mentioned by Beniston, (2005). The warm spell in

February which happened in the winter season is considered as a statistically more extreme event compared to warm spells in other seasons.

Figure 9. Time series of precipitation and CO2 flux on hot and non-hot days

Figure 9 shows the time series of CO2 fluxes and precipitation. Precipitation is shown with blue bars, and dark red dashed line shows CO2 flux time series. Yellow points represent CO2 flux at hot days. In most cases it can been seen that CO2 flux rises when precipitation happens. The CO2 flux

production is almost entirely dependent to root respiration and microbial decomposition of organic matter (Davidson et al., 2006). After a rainfall event, CO2 flux is released through the soil pores by stimulating microbes, fungi and roots through waterdrops from rainfall. However, it should be noted that higher rainfall doesn’t always lead to higher CO2 flux. CO2 flux increases with precipitation according to the amount of VWC content, by exceeding the certain level of VWC content CO2 flux will start to decrease. By looking at the CO2 flux values in hot and non hot days

(yellow and dark red points respectively), no general pattern between CO2 flux and hot days can be interpreted.

Figure 10: Relationship between CO2 flux and precipitation and VWC on hot and non-hot days

Figure 10 displays the relationship between CO2 flux, precipitation and VWC on hot and non-hot days. Time series shows February, March and May since most of the precipitation happened in these months. Blue bars show precipitation and dark red line shows the CO2 flux time series.

Brown bars show VWC and the yellow bars show VWC on hot days. VWC has an average value

of 0.32, 0.33 and 0.20 m3/m3 on February, March and May, respectively. VWC generally decreases from February to May since soil temperature increases due to seasonality. Precipitation is the main driver of VWC (Whan et al., 2015). It can be seen that when precipitation occurs, soil moisture rises correspondingly and decreases gradually during the following one to two weeks.

Furthermore, CO2 flux increases sharply after the rainfall and starts to decrease immediately on the following day after precipitation ends (February and May). However, the increase of total precipitation amount is not always proportional to CO2 fluxes. This fact can be seen in Figure 10

th th th (March 6 and 7 ). Initially, precipitation on March 6 yielded to an increase in CO2 flux.

However, the second precipitation event on the following day leads to a decrease in CO2 flux. Soil respiration initially increases with soil moisture up to the level where pores are filled with water, limiting oxygen for soil organism to respire. Therefore, lower CO2 fluxes are directly related to

VWC surplus or deficits (Otieno et al., 2010).

Here, we can see that warm spells had no visible effect on VWC and CO2 flux in February. On the other side, we see that from 23th March VWC continuously decreases and CO2 flux gradually increases. The third row in Figure 8 shows that soil temperature increases on the same time period as VWC decreases. Therefore, we can conclude that decreasing VWC is due to higher soil temperature and the 5-day warm spell shown in yellow bars could intensify the natural soil temperature increase, leading microorganism to respire more CO2 flux.

Figures 11 and 13 represent the relationship between two collected parameters. The vertical red dashed lines divide the x-axis into multiple sections. The yellow and orange dots represent the daily-averaged values on hot and non-hot days, respectively. In each section, the average value of the y-axis parameter for hot day and non-hot day, is shown in bars.

Figure 11: Relationship between soil temperature and CO2 flux on hot and non-hot days

Figure 11 shows the relationship between soil temperature and CO2 flux. Non-hot day points are shown in orange and hot day points are shown in yellow. The average of CO2 flux for non-hot days and hot days is shown in the dark red and yellow bars, respectively. CO2 flux is highly correlated with shallow soil temperature (Wang et al., 2013). It is important to measure soil temperature in the surface layers because they are arguably the most dynamic part of the soil (Cable et al., 2011). It can be seen that soil temperature within the range of 20 to 25 °C shows the highest

-2 -1 average CO2 flux value for hot and non-hot days (0.98 and 1.95 μmol CO2 m s respectively).

CO2 flux averages of hot and non-hot days decreases as we deviate from soil temperature in the

range of 20 and 25 °C. Hot days show on average lower CO2 flux compared with non-hot days

with the exception of 20 and 25 °C soil temperature range. In addition, hot days can accelerate the

evaporation of soil moisture. Hot days heat up the soil temperature in addition to the solar radiation

which results in low respiration of microorganisms. Warm spells perpetuate the drought conditions

when evaporation decreases and moisture availability is lessened (Brabson, 2005).

17 % 50 % -48 % 10 %

Figure 12: Relationship between soil temperature and CO2 flux on hot and non-hot days

Figure 12 indicates the relationship between soil temperature and CO2 flux. The figure presents

the average CO2 flux for different soil temperature range. Soil with temperatures ranging from 20

-2 -1 to 25 °C shows the highest average CO2 flux value (~1.12 μmol CO2 m s ) As we deviate from

the soil temperature range of 20 to 25 °C the average CO2 flux decreases. As shown, soil respiration

decreases by around 50 % when soil temperature increases from 15 to 20 °C to 20 to 25 °C. One

reason for the lower CO2 flux values below 20 °C could be that not all microorganisms in the lower ground are not energized enough to respire due to the limitation in the activation energy. Activation energy (Ae) is the energy, which microorganisms need to decompose organic carbon; the activation energies of the microorganisms are related to the ambient temperature. The Ae for each type of carbon substrates is different. Microorganisms therefore respire different amounts of CO2 flux based on their ambient temperature and Ae (Davidson et al., 2006). Karhu et al., 2014 mentioned that the microbial soil respiration, in different short-term experiments, increases exponentially with temperature. Here, in our short experiment soil respiration shows an opposite pattern, respiration rate above 25 °C decreases. Soil respiration decreases by 48% and increases by 10% by deviating from the soil temperature range of 20 to 25 °C to the range of 25 to 30 °C and 30 to 35 °C, respectively. The reason for the lower CO2 flux values above 25 °C could be related to the moisture content. The high soil temperature in the shallow ground let the still remaining moisture evaporate faster and microorganism are in need of moisture to respire. The low soil moisture concentration leads to dormancy and/or death of microorganism by limiting the contact to available substrate

(Jassal et al., 2008). Due to the rapid soil moisture decrease, microorganism and roots are then in water stress which result to less CO2 flux.

Figure 13: Relationship between soil temperature and soil moisture

Figure 13 displays the strong relationship between soil temperature and VWC. Hot day points are shown in yellow and non-hot day points are shown in orange. The average of VWC, for each section, for hot and non-hot days is shown in brown bars. As exhibited earlier in Figure 7, the cc between soil temperature and soil moisture shows a negative correlation (-0.66). Soil temperature increases as soil moisture decreases. This is also mentioned by Davidson and Jansen (2006) and

Lakshmi et al. (2003). Soil temperature increases is comparable with the time line of the measurements (i.e. temperature range 10 to 15°C and 30 to 35 °C belongs to February and June respectively). In the range of 10 to 20 °C, soil moisture shows on average an amount of 0.325 m3/m3 for hot and non-hot days. A 5-day warm spell occurred (mid-April) in the range of 20 to 25

°C and increased, additionally to the warmer getting soil temperature, the evaporation. Therefore,

VWC decreased from 0.26 to 0.23 m3/m3 in a faster rate. From 25 °C above VWC stays stable in the depth of 5 cm even though soil temperature increases. The fact for the constant VWC could be

the condensation water. Condensation water happens on the ground, if soil temperature is in the morning lower that dew-point temperature (Wang et al., 2013).

4 Conclusion

Atmospheric CO2 has increased substantially since the Industrial Revolution, leading to global warming. Due to global warming, frequency and intensity of extreme events have increased over the past decades. Hot days and warm spells are two of important types of extremes, which have a large influence on the terrestrial ecosystem carbon dioxide. The effects of climate extremes on carbon dioxide are still unclear. Recent studies have discussed that soil can become a net source or net sink in the future. Big changes in the carbon dioxide cycle might result from small changes in the below ground. Therefore, understanding the processes in the below ground is essential. Soil respiration is an important component of the subterrestrial underground and is associated with heterotroph and autotroph respirations. Other important factors contributing to soil respiration include abiotic and biotic factors. However, the response of bare soil on soil respiration is poor due to insufficient experimental data in literature. The goal of this study was to understand the soil moisture-soil temperature-soil respiration relationships on bare soil. In addition, we investigate the influence of hot days and warm spells on soil respiration. In this study, diurnal measurements were taken from February 5th to June 30, 2016 in the Seasonal Marsh (33° 39ʹ32.7ʺ N latitude, 117°

50ʹ55.9ʺ W longitude) at the San Joaquin Marsh Reserve (SJMR), CA, USA with an automated

th soil CO2 flux system (LI-8100A). In addition, the 85 percentile of the bias corrected maximum air temperature of the long-term climatology (weather station SNA) was used to calculate the warm spell threshold for the study area. Land and atmospheric parameters were collected at the study

area. Land and atmospheric parameters respond to CO2 flux with time delay. Association metrics were applied to investigate the relationship between CO2 flux and land and atmospheric parameters collected as well as time-lagged data. The results showed that the association assessment methods of CO2 flux with soil moisture and soil temperature increased from 0.08 to 0.14 (75%) and 0.18 to

0.39 (117%) respectively when considering the time lag. CO2 flux production is mostly dependent on root respiration and microbial decomposition of organic matter. CO2 flux is influenced by precipitation in the sense that CO2 flux increases when precipitation occurs until it reaches an certain amount. It then decreases gradually. Additionally, soil moisture and soil temperature have a strong influence on CO2 flux. Soil temperature is the dominant factor compared to soil moisture.

The results show that soil with temperatures ranging from 20 to 25 °C shows the highest average

CO2 flux value (~ 1.12 μmol CO2 m-2 s-1). By deviating from the soil temperature range of 20 to

25 °C the average CO2 flux decreases. Soil respiration increases by around 50% when soil temperature increases from 15 to 20 °C to 20 to 25 °C. On the other hand, soil respiration decreases by 48% by deviating from the soil temperature range of 20 to 25 °C to 25 to 30 °C. Two conditions must be fulfilled for a decrease in soil respiration. Soil moisture and soil temperature must be out of the optimum range (0.225 to .250 m3/m3 and 20 to 25 °C, respectively) so that autotroph and heterotroph can’t perform and respire CO2 and that microorganisms can’t reach the activation energy, respectively. Expressed in a different way, soil moisture have to be too low or too high, to be water limited or oxygen limited, respectively. On the other side, soil temperature have to be also too low or too high, to be energy limited or water limited (due to higher evaporation), respectively. If one or both conditions are limited, soil respiration will decrease. In order to make soil a net sink, either soil moisture or soil temperature has to be out of the optimum range for microorganisms.

The influence of warm spell on soil respiration compared to non-hot days was visible.

Warm spells exacerbate the soil temperature, which led to higher evaporation and therefore, lower respiration compared to non-hot days.

Field measurements were faced with some limitations. The amount of the automated soil

CO2 flux system limited our ability to make comparisons between different measurement points in the area. In addition, measuring soil moisture, soil respiration and other gases (Methane) in the different soil depth was not possible due to the lack of equipment’s. The other limitation of this study included lack of information about soil characteristic and initial conditions which restricted us to model the site. We suggest to gain more information about the study area and simulate soil respiration, soil moisture and soil temperature. Finally, we recommend measuring with additional automated soil CO2 flux systems simultaneously over a longer period of time on various locations at the study area.

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