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Journal ArticleDOI

Hourly and daily rainfall intensification causes opposing effects on C and N emissions, storage, and leaching in dry and wet grasslands

30 Jul 2019-Biogeochemistry (Springer International Publishing)-Vol. 144, Iss: 2, pp 197-214
TL;DR: In this paper, the authors investigate the effects of expected twenty-first century changes in hourly and daily rainfall on soil carbon and nitrogen emissions, soil organic matter (SOM) stocks, and leaching using a coupled mechanistic soil biogeochemical model (BAMS2).
Abstract: Climate change is expected to alter hourly and daily rainfall regimes and, in turn, the dynamics of ecosystem processes controlling greenhouse gas emissions that affect climate. Here, we investigate the effects of expected twenty-first century changes in hourly and daily rainfall on soil carbon and nitrogen emissions, soil organic matter (SOM) stocks, and leaching using a coupled mechanistic carbon and nitrogen soil biogeochemical model (BAMS2). The model represents various abiotic and biotic processes involving 11 SOM pools. These processes include fungal depolymerization, heterotrophic bacterial mineralization, nitrification, denitrification, microbial mortality, necromass decomposition, microbial response to water stress, protection, aqueous advection and diffusion, aqueous complexation, and gaseous dissolution. Multi-decadal modeling with varying rainfall patterns was conducted on nine Australian grasslands in tropical, temperate, and semi-arid regions. Our results show that annual $${\text{CO}}_2$$ emissions in the semi-arid grasslands increase by more than 20% with a 20% increase in annual rainfall (with no changes in the rainfall timing), but the tropical grasslands have opposite trends. A 20% increase in annual rainfall also increases annual $${\text{N}}_2{\text{O}}$$ and NO emissions in the semi-arid grasslands by more than 10% but decreases emissions by at least 25% in the temperate grasslands. When subjected to low frequency and high magnitude daily rainfall events with unchanged annual totals, the semi-arid grasslands are the most sensitive, but changes in annual $${\text{CO}}_2$$ emissions and SOM stocks are less than $$5\%$$ . Intensification of hourly rainfall did not significantly alter $${\text{CO}}_2$$ emissions and SOM stocks but changed annual $${\text{NH}}_3$$ emissions in the tropical grasslands by more than 300%.

Summary (4 min read)

Introduction

  • Studies based on single and multiple cycles of drying-rewetting experiments have arrived at very different conclusions regarding the carbon sources and mechanisms contributing to the observed CO 2 pulses (Schimel 2018) .
  • To this end, the authors aim to quantify the long-term impacts of hourly and daily rainfall variations on carbon and nitrogen emissions, leaching, and storage in grasslands with different seasonal rainfall regimes using a mechanistic model.

BAMS2 reaction network

  • To account for the control of nitrogen availability on SOM dynamics, the BAMS1 carbon model described in Riley et al. (2014) was coupled to the nitrogen cycle model developed in Maggi et al. (2008) .
  • All microbial functional groups assimilate both carbon and nitrogen for growth, with fungi and bacteria having a C:N ratio of 8 and 5, respectively (Mouginot et al. 2014 ).
  • The original stoichiometric parameters of SOM decomposition reactions in BAMS1 (Riley et al. 2014) were recalculated to account for the nitrogen immobilization into microbial biomass (Supplementary Information Table S .1).
  • SOM polymers are considered to be non-soluble (in solid phases) organic carbon and do not undergo protection processes.

Biogeochemical and transport solver

  • The BAMS2 reaction network (Fig. 1 ) was solved in the general-purpose multi-phase and multi-component bioreactive transport simulator BRTSim-v3.1a (Maggi 2019) .
  • Equations used to model the transport of fluids and compounds in aqueous, gaseous, and biological phases are described in detail in Maggi (2019) .
  • The function f(S L ) in Eq. 5 describes the reduction of microbial activity as a result of changes in water saturation to account for processes not explicitly modeled, such as physiological stress and substrate diffusion within a soil layer; note that chemical transport across soil layers is explicitly modeled as described above.
  • Descriptions of mathematical equations, numerical methods, and solution convergence criteria used in BRTSim-v3.1a are detailed in Maggi (2019) .

Site descriptions

  • The BAMS2 reaction network was applied in nine Australian grasslands in tropical, temperate, and semi-arid regions that have distinct seasonal rainfall regimes.
  • Site locations were determined based on the Dynamic Land Cover Dataset (Lymburner et al. 2011 ) and the modified KOppen climate classification of the Bureau of Meteorology, Australia (Stern and Dahni 2013) (Table 1 ).
  • In contrast, the wet season in the temperate region starts from May to September with lower annual rainfall but a higher number of wet days than the tropical region.
  • Historical daily rainfall and temperature data (from 1979 to 2017) at each site were obtained from the CPC US Unified Precipitation data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA (Xie et al. 2010) , and the Global Historical Climatology Network-Daily dataset (Menne et al. 2012) , respectively.
  • Plant water uptake , plant nitrogen uptake (R20-R21), and root exudation (R27) were allocated over the soil depth according to the root distribution.

Rainfall scenarios

  • Numerical experiments were conducted with three rainfall scenarios.
  • The weather generator in Chen et al. ( 2010) was modified to generate rainfall time series with varying statistical properties specific for each scenario, whereas no modification was applied to the evapotranspiration time series.
  • The authors discuss the possible implication of this simplification below.
  • Change in annual cumulative rainfall amount, also known as Scenario 1.
  • Rainfall time series were modified so that the annual cumulative rainfall amount (P cum ) ranged within +/-20% of the historical value, while the annual number of wet days (D wet ) remained constant.

Analyses and benchmarking

  • Prior to the numerical experiments, baseline simulations (using historical rainfalls) were initialized with SOM concentrations close to the organic carbon content reported in the SoilGrids database (Hengl T et al. 2017 ) and the microbial biomass close to zero.
  • The simulations were run for 2000 years for biochemical reactions in the root zone to reach a steady state and to develop a steady microbial biomass profile.
  • The outputs of the 2000-year simulations were then used as initial conditions in the numerical experiments.
  • Because BAMS2 includes only microbial heterotrophic respiration, CO 2 emissions in the baseline simulations were compared against heterotrophic soil respiration flux (R H ) of 353 natural and unmanaged grasslands reported in the Soil Respiration Database Version 4.0 (SRDB-V4 Bond-Lamberty and Thomson 2018).
  • The lag time between two time series was quantified using cross-correlation analysis (function xcorr in Matlab2017a).

Benchmarking of baseline simulations

  • In baseline simulations, the semi-arid grasslands, which received the lowest amount and least frequent rainfall, had the lowest CO 2 emissions and SOM inputs (Fig. 2 ).
  • CO 2 emissions in these sites were slightly lower than those in the temperate grasslands.
  • Other studies argued that a wetter soil would have higher anaerobicity, and therefore should have higher N 2 O emissions (Skiba and Smith 2000) .
  • In BAMS2, is the only source of inorganic nitrogen to the soil, mainly coming from N 2 fixation (R19) and mineralization of N-containing monomers (R9-R11).
  • The authors note however that, in wet soils that have low concentrations, the nitrifiers may have adapted to a K M value lower than that applied in BAMS2, which was calibrated against temperate soils (Maggi et al. 2008) .

Controls of soil moisture dynamics on C and N emissions

  • In all grasslands, the correlation R(S, P) was relatively weak with slightly higher values observed in the tropical grasslands in the wet season.
  • Impacts of annual rainfall amount Contrary to the general expectation that increasing annual rainfall (P cum ) would have a larger impact on drier lands, their simulations suggested that both dry and wet grasslands are very sensitive to changes in P cum , and they have distinctive responses (Fig. 4 ), also known as Scenario 1.
  • Together with increased water advection at high P cum , the increased biological activity also led to a substantial increase in DOC and DIC leaching to soils below the root zone (Supplementary Fig. S.7a, b) .
  • Nitrification and denitrification rates in the temperate grasslands decreased substantially with increasing P cum , leading to the reduction in N 2 O and NO emissions (Fig. 4b, c ).
  • Increased water content also decreased the volatilization of ammonia (Fig. 4d ).

Scenario 2: impacts of daily rainfall amount and frequency

  • The authors investigated the response of C and N dynamics to variations in daily rainfall amount and frequency by changing the number of wet days D wet in a year while keeping the total annual rainfall constant; that is, a time-series with a smaller D wet value has fewer but larger rainfall events.
  • The balance between increased SOM inputs and decomposition caused a slight increase in SOM stocks (<2%, Fig. 5e ) and a substantial increase in DOC and DIC leaching to below the root zone (Supplementary Fig. S.8a, b) .
  • The effects of increased rainfall intensity and reduced frequency on nitrogen emissions in the semi-arid grasslands matched relatively well with the numerical-experiments tested in Gu and Riley (2010) .
  • Gu and Riley (2010) also found that, when applied with a low total rainfall amount, high intensity and low frequency rainfall events reduced N 2 O emissions in sandy loams soils, but increased NO emissions.
  • Big pulses of water diluted and transported inorganic nitrogen out of the root zones, and hence decreased the nitrification and denitrification rates.

Discussion

  • The BAMS2 model represents the highly complex interplay between many biotic and abiotic mechanisms hypothesized to be important for carbon and nitrogen cycles, including depolymerization, SOM mineralization, microbial mortality, necromass decomposition, N 2 fixation, nitrification, denitrification, protection, advection, and diffusion.
  • These mechanisms have different responses to soil water content, and therefore a detailed description of their interactions is pivotal to this study that explicitly aims at assessing the impact of rainfall variability on soil carbon and nitrogen dynamics.
  • The authors note however that the determination of model parameter values can be difficult for a model with high complexity, and this can introduce additional uncertainties.
  • The authors note that the parameter sensitivity may change after coupling the two models, and therefore a global sensitivity analysis of BAMS2 is needed, and it is the target of their next work.
  • Hence, field studies that spanned across time-scales of months may capture only the transient effects.

Conclusions

  • The authors present a C-N coupled mechanistic SOM model (BAMS2) to investigate the effects of hourly and daily rainfall variations on soil carbon and nitrogen emissions, stocks, and leaching in grasslands with different seasonal rainfall regimes.
  • BAMS2 captured relatively well the Birch effect and the carbon and nitrogen dynamics observed in grasslands, with model outputs falling within the range of field observations compiled in various published databases.
  • Dry and wet grasslands responded differently to variations in rainfall patterns and rainfall variability had a different impact on carbon and nitrogen emissions.
  • The balance between SOM inputs and decomposition, however, always resulted in increasing SOM stocks with increasing annual rainfall in all grasslands.
  • High rainfall amounts can dilute concentrations to below optimal values for nitrification, thus reducing N 2 O and NO emissions in the temperate grasslands.

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Lawrence Berkeley National Laboratory
Recent Work
Title
Hourly and daily rainfall intensification causes opposing effects on C and N emissions,
storage, and leaching in dry and wet grasslands
Permalink
https://escholarship.org/uc/item/0zs3r5jb
Journal
Biogeochemistry, 144(2)
ISSN
0168-2563
Authors
Tang, FHM
Riley, WJ
Maggi, F
Publication Date
2019-07-30
DOI
10.1007/s10533-019-00580-7
Peer reviewed
eScholarship.org Powered by the California Digital Library
University of California

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$-,>
$?=&
/&
$9
-
(93,>
@9
-

(93$
A
$
A++>
B"($$(9
C
@

&$
=&
&8'-+++1D-+;A:%

8%-+;-:/
$$&
82-+;A1B-+;5:
8E-+;F:7
&3&
&8C-++A1G
-+;61?-+;6:8G-++61(
-+;F:$82/*=<-+;A1
*-+;,:89
-

5
9
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1
-++,1B-+;-:<7
&$$
C8(9:
7$
9(9
<<$
8(-++6:$8<-+;-:
9
-
97
=/9
-
$383
HBirch eectI*;4,F1#-+;+1J-+;+:(
$/7&
&&$
$$&9
-
8(-+;F:
$$$
8B;4F6:$<$$8&/EK-+;-:
$8-+;5:$
8C-+;-:7&
/L7
$&//$
@$&
(9&
829:$82@:8#-+;F:9
-
M7$
$$8**-+++:1
=2@
<$7
3&$
8*-+;-13*;4441

<C-++4:J
$&&
$$8E!-+;+1(
-+;F:&$$(9
<$$<$
$82?3-+;-:3$8##N-+;5:
8-+;6:8**-++,1(3
;44,:8#;44-1
(7;4F,1!-+++:$&
3&/
8-++F1((-++5:<
82?3-+;-:@L
$
$3$$&
97$
8#=;4441#-++5:$
$&&8C
-+;-1*-+;,:=
7&(9
&=/
&$

*%(;&!
8-+;5:&8-++F:$
&(9
O8*%(-1*%$(9
&-:;;(98&
:&$8$
77:3
$=
&=7
*%(-$3/$&
1
-
991$1
3
7
&%
/

*%(-3

&$(9
*%(;$$!8-+;5:
&8-++F:O
38*%(-;:(98
:1&(9
8
:1&8
A
 
9
-
9
-
:1&$
8F
DEP
:$8B
HET
:/7<
$8B
AOB
:/7<$8B
NOB
:$
8B
DEN
:
@*%(- $(98!;O!F
;:%$$$
$&"F,&
8-+;5:@</8!4O
!;;:<$$
& 
$$F
DEP
B
HET
(97<$B
AOB
 
3$(9
*%(;8!-+;5:
$<$$
8(@$(;:
(8!;-O!;A:
8!;5O!;6:8-++F:
$$
$$8($(;:@
*%(-
-
7 8!;4:%!;4
$7
-
7$
7$
-
7$&
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Citations
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Journal ArticleDOI
TL;DR: In this paper, the authors argue that a far better strategy revolves around the effect of climate change on functions/services that soils render, since climatologists forecast less frequent but more intense rainfall events in the future, which may lead to food shortages, catastrophic flooding and soil erosion if soils are not able to cope.
Abstract: Over the last two decades, the sequestration of carbon in soils has often been presented as a possible way to mitigate the steady increase in the concentration of CO2 in the atmosphere, one of the most commonly mentioned causes of climate change. A large body of literature, as well as sustained efforts to attract funding for the research on soil organic matter, have focused on the soil carbon sequestration – climate change nexus. However, because CO2 is not the only greenhouse gas released by soils, and given the fact that the feasibility of large-scale carbon sequestration remains controversial, this approach does not appear optimal to convince policy makers. In this perspective article, we argue that a far better strategy revolves around the effect of climate change on functions/services that soils render. In particular, since climatologists forecast less frequent but more intense rainfall events in the future, which may lead to food shortages, catastrophic flooding, and soil erosion if soils are not able to cope, a more suitable focus of the research would be to increase soil organic matter content so as to strengthen the water regulation function of soils. The different conceptual and methodological shifts that this new focus will require are discussed in detail.

60 citations


Cites background from "Hourly and daily rainfall intensifi..."

  • ...One of the consistent predictions climate modelers have made over the last decade is that climate change will result in less frequent but more intense rainfall events in many parts of the world (e.g., Trenberth et al., 2003; Sun et al., 2007; Min et al., 2011; Ipcc, 2013; Intergovernmental Panel on Climate Change, 2014; Kendon et al., 2014; Berghuijs et al., 2017; Tang et al., 2019; Hess et al., 2020; Morán-Ordóñez et al., 2020; O’Donnell and Thorne, 2020)....

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01 May 2014
TL;DR: In this article, the authors used a spatially aggregated perspective to detect a distinct intensification of heavy precipitation events and hot extremes and showed that much of the local to regional differences in trends of extremes can be explained by internal variability, which can regionally mask or amplify the forced long-term trends.
Abstract: Observed trends in the intensity of hot and cold extremes as well as in dry spell length and heavy precipitation intensity are often not significant at local scales. However, using a spatially aggregated perspective, we demonstrate that the probability distribution of observed local trends across the globe for the period 1960–2010 is clearly different to what would be expected from internal variability. We detect a distinct intensification of heavy precipitation events and hot extremes. We show that CMIP5 models generally capture the observed shift in the trend distribution but tend to underestimate the intensification of heavy precipitation and cold extremes and overestimate the intensification in hot extremes. Using an initial condition experiment sampling internal variability, we demonstrate that much of the local to regional differences in trends of extremes can be explained by internal variability, which can regionally mask or amplify the forced long-term trends for many decades.

42 citations

Journal ArticleDOI
TL;DR: In this article, the conundrum as to why some SOM decomposes rapidly, while other thermodynamically unstable SOM can persist on centennial time scales leads to substantial uncertainty in model structures, as well as uncertainty in the predictability of the land carbon sink trajectory.
Abstract: Soil organic matter (SOM) represents the single largest actively cycling reservoir of terrestrial organic carbon, accounting for more than three times as much carbon as that present in the atmosphere or terrestrial vegetation (Schmidt et al. 2011; Lehmann and Kleber 2015). SOM is vulnerable to decomposition to either CO2 or CH4, which can increase atmospheric greenhouse gas concentrations (GHGs) and serve as a positive feedback to climate change. Conversely, the formation and stabilization of SOM within aggregates or associated with soil minerals can lead to carbon sequestration, representing a negative feedback to climate change. However, the conundrum as to why some SOM decomposes rapidly, while other thermodynamically unstable SOM can persist on centennial time scales (Hedges et al. 2000), leads to substantial uncertainty in model structures, as well as uncertainty in the predictability of the land carbon sink trajectory.

41 citations


Cites background or methods from "Hourly and daily rainfall intensifi..."

  • ...BAMS also represented a reduced number of SOM molecular structures (Riley et al. 2014; Dwivedi et al. 2017; Tang et al. 2019)....

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  • ...…1998, 2010; Jenkinson et al. 2008) to more complex models such as the Biotic and Abiotic Model of SOM (BAMS; Riley et al. 2014; Dwivedi et al. 2017; Tang et al. 2019) and the COntinuous representation of SOC in the organic layer and the mineral soil, Microbial Interactions and Sorptive…...

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  • ...…Gu et al. 2009), PFLOTRAN (Hammond et al. 2014), CRUNCH (Steefel et al. 2015), ecosys (Grant 2013), BAMS (Riley et al. 2014; Dwivedi et al. 2017a; Tang et al. 2019), and BeTR (Tang et al. 2013; Tang and Riley 2018)—that are available to describe and can represent the interaction of various…...

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  • ...Overall, we argue that there is a need to represent SOM compounds using their molecular structures in reactive transport models along with physical protection mechanisms (e.g., MAOM) and different functional groups of microbes (e.g., Riley et al. 2014; Dwivedi et al. 2017; Tang et al. 2019.)...

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  • ...2015), ecosys (Grant 2013), BAMS (Riley et al. 2014; Dwivedi et al. 2017a; Tang et al. 2019), and BeTR (Tang et al....

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01 Dec 2006
TL;DR: In this article, three determinant factors in decomposition patterns of soil organic matter (SOM): temperature, water and carbon (C) inputs were studied. But the authors focused on the role of the above-defined environmental factors on the variability of soil C dynamics.
Abstract: This experiment was designed to study three determinant factors in decomposition patterns of soil organic matter (SOM): temperature, water and carbon (C) inputs. The study combined field measurements with soil lab incubations and ends with a modelling framework based on the results obtained. Soil respiration was periodically measured at an oak savanna woodland and a ponderosa pine plantation. Intact soils cores were collected at both ecosystems, including soils with most labile C burnt off, soils with some labile C gone and soils with fresh inputs of labile C. Two treatments, dry-field condition and field capacity, were applied to an incubation that lasted 111 days. Short-term temperature changes were applied to the soils periodically to quantify temperature responses. This was done to prevent confounding results associated with different pools of C that would result by exposing treatments chronically to different temperature regimes. This paper discusses the role of the above-defined environmental factors on the variability of soil C dynamics. At the seasonal scale, temperature and water were, respectively, the main limiting factors controlling soil CO2 efflux for the ponderosa pine and the oak savanna ecosystems. Spatial and seasonal variations in plant activity (root respiration and exudates production) exerted a strong influence over the seasonal and spatial variation of soil metabolic activity. Mean residence times of bulk SOM were significantly lower at the Nitrogen (N)-rich deciduous savanna than at the N-limited evergreen dominated pine ecosystem. At shorter time scales (daily), SOM decomposition was controlled primarily by temperature during wet periods and by the combined effect of water and temperature during dry periods. Secondary control was provided by the presence/absence of plant derived C inputs (exudation). Further analyses of SOM decomposition suggest that factors such as changes in the decomposer community, stress-induced changes in the metabolic activity of decomposers or SOM stabilization patterns remain unresolved, but should also be considered in future SOM decomposition studies. Observations and confounding factors associated with SOM decomposition patterns and its temperature sensitivity are summarized in the modeling framework.

25 citations

Journal ArticleDOI
TL;DR: In this article, a stochastic model for soil heterotrophic respiration rates was developed, which analytically links the statistical properties of respiration to those of precipitation, and showed that both the mean rewetting pulse respiration and the mean respiration during drying increase with increasing mean precipitation.
Abstract: . Soil drying and wetting cycles promote carbon (C) release through large heterotrophic respiration pulses at rewetting, known as the “Birch” effect. Empirical evidence shows that drier conditions before rewetting and larger changes in soil moisture at rewetting cause larger respiration pulses. Because soil moisture varies in response to rainfall, these respiration pulses also depend on the random timing and intensity of precipitation. In addition to rewetting pulses, heterotrophic respiration continues during soil drying, eventually ceasing when soils are too dry to sustain microbial activity. The importance of respiration pulses in contributing to the overall soil heterotrophic respiration flux has been demonstrated empirically, but no theoretical investigation has so far evaluated how the relative contribution of these pulses may change along climatic gradients or as precipitation regimes shift in a given location. To fill this gap, we start by assuming that heterotrophic respiration rates during soil drying and pulses at rewetting can be treated as random variables dependent on soil moisture fluctuations, and we develop a stochastic model for soil heterotrophic respiration rates that analytically links the statistical properties of respiration to those of precipitation. Model results show that both the mean rewetting pulse respiration and the mean respiration during drying increase with increasing mean precipitation. However, the contribution of respiration pulses to the total heterotrophic respiration increases with decreasing precipitation frequency and to a lesser degree with decreasing precipitation depth, leading to an overall higher contribution of respiration pulses under future more intermittent and intense precipitation. Specifically, higher rainfall intermittency at constant total rainfall can increase the contribution of respiration pulses up to ∼10 % or 20 % of the total heterotrophic respiration in mineral and organic soils, respectively. Moreover, the variability of both components of soil heterotrophic respiration is also predicted to increase under these conditions. Therefore, with future more intermittent precipitation, respiration pulses and the associated nutrient release will intensify and become more variable, contributing more to soil biogeochemical cycling.

19 citations


Cites background or methods from "Hourly and daily rainfall intensifi..."

  • ...Numerical process-based models have also been driven by randomly generated rainfall time series (e.g., Tang et al., 2019)....

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  • ...For scenarios of constant total rainfall and variable rain event frequency, Tang et al. (2019) found that rainfall intensification increased heterotrophic respiration in a semi-arid grassland, even though in their simulations soil organic C stocks also slightly increased due to higher plant…...

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  • ...To capture these dynamics, a more complex model describing the changes in substrate and microbial compartments would be needed (e.g., Brangarí et al., 2018; Lawrence et al., 2009; Tang et al., 2019) at the cost of losing the analytical tractability....

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References
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Journal ArticleDOI
TL;DR: In this paper, the authors presented the collation and analysis of the gridded land-based dataset of indices of temperature and precipitation extremes: HadEX2, which was calculated based on station data using a consistent approach recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices.
Abstract: [1] In this study, we present the collation and analysis of the gridded land-based dataset of indices of temperature and precipitation extremes: HadEX2. Indices were calculated based on station data using a consistent approach recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices, resulting in the production of 17 temperature and 12 precipitation indices derived from daily maximum and minimum temperature and precipitation observations. High-quality in situ observations from over 7000 temperature and 11,000 precipitation meteorological stations across the globe were obtained to calculate the indices over the period of record available for each station. Monthly and annual indices were then interpolated onto a 3.75° × 2.5° longitude-latitude grid over the period 1901–2010. Linear trends in the gridded fields were computed and tested for statistical significance. Overall there was very good agreement with the previous HadEX dataset during the overlapping data period. Results showed widespread significant changes in temperature extremes consistent with warming, especially for those indices derived from daily minimum temperature over the whole 110 years of record but with stronger trends in more recent decades. Seasonal results showed significant warming in all seasons but more so in the colder months. Precipitation indices also showed widespread and significant trends, but the changes were much more spatially heterogeneous compared with temperature changes. However, results indicated more areas with significant increasing trends in extreme precipitation amounts, intensity, and frequency than areas with decreasing trends.

1,055 citations


"Hourly and daily rainfall intensifi..." refers background in this paper

  • ...…and the predicted changes are spatially heterogeneous (Maslin and Austin 2012), trend-detection studies based on global and regional rainfall datasets have consistently reported an intensification in daily (Donat et al. 2013; Fischer and Knutti 2014) and hourly (Guerreiro et al. 2018) extremes....

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Journal ArticleDOI
TL;DR: A combination of stable isotope and acetylene (0.01% v/v) inhibition techniques were used for the first time to determine N2O production during denitrification, autotrophic nitrification and heterotrophic Nitrification in a silt loam soil at contrasting (20-70%) water-filled pore space (WFPS) as mentioned in this paper.
Abstract: A combination of stable isotope and acetylene (0.01% v/v) inhibition techniques were used for the first time to determine N2O production during denitrification, autotrophic nitrification and heterotrophic nitrification in a fertilised (200 kg N ha−1) silt loam soil at contrasting (20–70%) water-filled pore space (WFPS). 15N-N2O emissions from 14NH415NO3 replicates were attributed to denitrification and 15N-N2O from 15NH415NO3 minus that from 14NH415NO3 replicates was attributed to nitrification and heterotrophic nitrification in the presence of acetylene, as there was no dissimilatory nitrate reduction to ammonium or immobilisation and remineralisation of 15N-NO3−. All of the N2O emitted at 70% WFPS (31.6 mg N2O-N m−2 over 24 days; 1.12 μg N2O-N g dry soil−1; 0.16% of N applied) was produced during denitrification, but at 35–60% WFPS nitrification was the main process producing N2O, accounting for 81% of 15N-N2O emitted at 60% WFPS, and 7.9 μg 15N-N2O m−2 (0.28 ng 15N-N2O g dry soil−1) was estimated to be emitted over 7 days during heterotrophic nitrification in the 50% WFPS treatment and accounted for 20% of 15N-N2O from this treatment. Denitrification was the predominant N2O-producing process at 20% WFPS (2.6 μg 15N-N2O m−2 over 7 days; 0.09 ng 15N-N2O g dry soil−1; 85% of 15N-N2O from this treatment) and may have been due to the occurrence of aerobic denitrification at this WFPS. Our results demonstrate the usefulness of a combined stable isotope and acetylene approach to quantify N2O emissions from different processes and to show that several processes may contribute to N2O emission from agricultural soils depending on soil WFPS.

1,031 citations


"Hourly and daily rainfall intensifi..." refers background in this paper

  • ...…may be immobilized into microbial biomass (Dijkstra et al. 2012), taken up by plants (LLü et al. 2014), leached (Neilen et al. 2017), nitrified (Bateman and Baggs 2005; Stark and Firestone 1995), or lost as nitrogen gases through denitrification (Li et al. 1992; Sexstone et al. 1985; Riley and…...

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Journal ArticleDOI
TL;DR: It is demonstrated that root exudates are an important component of carbon loss from plants and that they may have a more important role in nutrient acquisition and plant growth than previously thought.

955 citations


"Hourly and daily rainfall intensifi..." refers background in this paper

  • ...Plants uptake both and (R20–R21) and produce aboveground (R28–R29, leaf and wood litter with C:N ratio of 35, Moretto et al. 2001; Thomas and Asakawa 1993) and belowground (R27, root exudates with C:N ratio of 12, Grayston et al. 1997; Mench and Martin 1991) SOM inputs....

    [...]

  • ...Litter decomposes into simpler organic polymers and monomers through implicit exoenzyme activity (Riley et al. 2014), while root exudates contain only organic monomers such as monosaccharide, fatty acids, organic acids, and amino acids (Grayston et al. 1997)....

    [...]

Journal ArticleDOI
TL;DR: In this article, the proportion of microbial biomass-C released into the soil environment following rapid water potential increase was quantified in two soils using a modified chloroform-fumigation biomass assay.
Abstract: The proportion of microbial biomass-C released into the soil environment following rapid water potential increase was quantified in two soils using a modified chloroform-fumigation biomass assay. Dry samples were isopiestically equilibrated to −2.8 and − 6.9 M Pa and then wetted to field capacity with either H2O or KCl solutions. The KC1 solutions wetted the soils without altering total soil water potential. The biomass-C released by water potential increase ranged from 17 to 70% of total, depending on the soil, the magnitude of the increase, and the method of calculation. In both soils, a greater proportion of biomass-C was released following a 6.9 MPa than a 2.8 M Pa increase. Biomass-C release was also demonstrated by an increase in soluble organic C in leachates of soils subjected to rapid wetting. Respiration of biomass-C mobilized by water potential increase exceeded respiration of biomass-C made available by preceding desiccation, thereby comprising a significant component of the pulse of respiration observed following wetting of dry soil. Water potential increases associated with the wetting of dry soil may be a major catalyst for soil C turnover.

814 citations

Journal ArticleDOI
TL;DR: The idea that there exists a hierarchy of soil moisture pulse events with a corresponding hierarchy of ecological responses is developed, such that small pulses only trigger a small number of relatively minor ecological events, and larger pulses trigger a more inclusive set and some larger ecological events.
Abstract: In arid/semi-arid ecosystems, biological resources, such as water, soil nutrients, and plant biomass, typically go through periods of high and low abundance. Short periods of high resource abundance are usually triggered by rainfall events, which, despite of the overall scarcity of rain, can saturate the resource demand of some biological processes for a time. This review develops the idea that there exists a hierarchy of soil moisture pulse events with a corresponding hierarchy of ecological responses, such that small pulses only trigger a small number of relatively minor ecological events, and larger pulses trigger a more inclusive set and some larger ecological events. This framework hinges on the observation that many biological state changes, where organisms transition from a state of lower to higher physiological activity, require a minimal triggering event size. Response thresholds are often determined by the ability of organisms to utilize soil moisture pulses of different infiltration depth or duration. For example, brief, shallow pulses can only affect surface dwelling organisms with fast response times and high tolerance for low resource levels, such as some species of the soil micro-fauna and -flora, while it takes more water and deeper infiltration to affect the physiology, growth or reproduction of higher plants. This review first discusses how precipitation, climate and site factors translate into soil moisture pulses of varying magnitude and duration. Next, the idea of the response hierarchy for ecosystem processes is developed, followed by an exploration of the possible evolutionary background for the existence of response thresholds to resource pulses. The review concludes with an outlook on global change: does the hierarchical view of precipitation effects in ecosystems provide new perspectives on the future of arid/semiarid lands?

807 citations

Frequently Asked Questions (1)
Q1. What are the contributions in "Hourly and daily rainfall intensification causes opposing effects on c and n emissions, storage, and leaching in dry and wet grasslands" ?

Here, the authors investigate the effects of expected twenty-first century changes in hourly and daily rainfall on soil carbon and nitrogen emissions, soil organic matter ( SOM ) stocks, and leaching using a coupled mechanistic carbon and nitrogen soil biogeochemical model ( BAMS2 ).