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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

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

  • Climate change is predicted to increase rainfall temporal variability, with a consensus of a shift towards a higher frequency of droughts and heavier rainfall events (Easterling et al. 2000; Zhang et al. 2013) .
  • 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) .
  • The available inorganic nitrogen produced by increased SOM mineralization after a rainfall pulse 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.
  • 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).
  • 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.
  • 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) .
  • Chemical (R30-R39) is described using Langmuir kinetics to account for the protective capacity of soil, such that (Atkins and De Paula 2005) , where [X(p)] and [X(aq)] are the concentrations of chemical X in protected (p) and aqueous (aq) phases, respectively; k a and k d are the forward and reverse (un-) rate constants, respectively; and Q max is the maximum soil protective capacity.
  • 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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&%-+>
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99/$;+>$
$-,>
$?=&
/&
$9
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(93,>
@9
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
(93$
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:$82/*=<-+;A1
*-+;,:89
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5
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1
-++,1B-+;-:<7
&$$
C8(9:
7$
9(9
<<$
8(-++6:$8<-+;-:
9
-
97
=/9
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$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
-
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Citations
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Journal ArticleDOI
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.

27 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…...

    [...]

  • ...…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…...

    [...]

  • ...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.)...

    [...]

  • ...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-
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


Erich M. Fischer1, Reto Knutti1Institutions (1)
01 May 2014-
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.

17 citations


Journal ArticleDOI
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.

15 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 Jan 2013-
Abstract: The biogeochemical cycles of carbon (C), nitrogen (N) and phosphorus (P) are interlinked by primary production, respiration and decomposition in terrestrial ecosystems. It has been suggested that the C, N and P cycles could become uncoupled under rapid climate change because of the different degrees of control exerted on the supply of these elements by biological and geochemical processes. Climatic controls on biogeochemical cycles are particularly relevant in arid, semi-arid and dry sub-humid ecosystems (drylands) because their biological activity is mainly driven by water availability. The increase in aridity predicted for the twenty-first century in many drylands worldwide may therefore threaten the balance between these cycles, differentially affecting the availability of essential nutrients. Here we evaluate how aridity affects the balance between C, N and P in soils collected from 224 dryland sites from all continents except Antarctica. We find a negative effect of aridity on the concentration of soil organic C and total N, but a positive effect on the concentration of inorganic P. Aridity is negatively related to plant cover, which may favour the dominance of physical processes such as rock weathering, a major source of P to ecosystems, over biological processes that provide more C and N, such as litter decomposition. Our findings suggest that any predicted increase in aridity with climate change will probably reduce the concentrations of N and C in global drylands, but increase that of P. These changes would uncouple the C, N and P cycles in drylands and could negatively affect the provision of key services provided by these ecosystems.

13 citations


References
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Book
01 Jan 1998-
Abstract: (First edition: 1998, this reprint: 2004). This publication presents an updated procedure for calculating reference and crop evapotranspiration from meteorological data and crop coefficients. The procedure, first presented in FAO Irrigation and Drainage Paper No. 24, Crop water requirements, in 1977, allows estimation of the amount of water used by a crop, taking into account the effect of the climate and the crop characteristics. The publication incorporates advances in research and more accurate procedures for determining crop water use as recommended by a panel of high-level experts organised by FAO in May 1990. The first part of the guidelines includes procedures for determining reference crop evapotranspiration according to the FAO Penman-Monteith method. These are followed by updated procedures for estimating the evapotranspiration of different crops for different growth stages and ecological conditions.

20,634 citations


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

  • ...evapotranspiration ET0 estimated using the FAO ETO calculator (Allen et al. 1998)....

    [...]

  • ...Plant actual evapotranspiration (ET) is calculated as ET=kc×ET0 with the plant coefficient kc=0.8 (Allen et al. 2005) and the potential evapotranspiration ET0 estimated using the FAO ETO calculator (Allen et al. 1998)....

    [...]


31 Dec 1963-

5,111 citations


Journal ArticleDOI
01 Nov 1931-Physics
Abstract: The flow of liquids in unsaturated porous mediums follows the ordinary laws of hydrodynamics, the motion being produced by gravity and the pressure gradient force acting in the liquid. By making use of Darcey's law, that flow is proportional to the forces producing flow, the equation K∇2ψ+∇K·∇ψ+g∂K/∂z=−ρsA∂ψ/∂t may be derived for the capillary conduction of liquids in porous mediums. It is possible experimentally to determine the capillary potential ψ=∫dp/ρ, the capillary conductivity K, which is defined by the flow equation q=K(g−▿ψ), and the capillary capacity A, which is the rate of change of the liquid content of the medium with respect to ψ. These variables are analogous, respectively, to the temperature, thermal conductivity, and thermal capacity in the case of heat flow. Data are presented and application of the equations is made for the capillary conduction of water through soil and clay but the mathematical formulations and the experimental methods developed may be used to express capillary flow ...

4,766 citations


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

  • ...The water flow along a one-dimensional variably saturated soil column is modeled using the Richards equation (Richards 1931) in conjunction with the empirical relative permeability-potential-saturation relationship of the Brooks-Corey model (Brooks and Corey 1964)....

    [...]


Journal ArticleDOI
22 Sep 2000-Science
TL;DR: Results of observational studies suggest that in many areas that have been analyzed, changes in total precipitation are amplified at the tails, and changes in some temperature extremes have been observed.
Abstract: One of the major concerns with a potential change in climate is that an increase in extreme events will occur. Results of observational studies suggest that in many areas that have been analyzed, changes in total precipitation are amplified at the tails, and changes in some temperature extremes have been observed. Model output has been analyzed that shows changes in extreme events for future climates, such as increases in extreme high temperatures, decreases in extreme low temperatures, and increases in intense precipitation events. In addition, the societal infrastructure is becoming more sensitive to weather and climate extremes, which would be exacerbated by climate change. In wild plants and animals, climate-induced extinctions, distributional and phenological changes, and species' range shifts are being documented at an increasing rate. Several apparently gradual biological changes are linked to responses to extreme weather and climate events.

4,008 citations


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

  • ...…Soil organic carbon, Carbon cycle, Nitrogen cycle, SOM model, Precipitation Introduction Climate change is predicted to increase rainfall temporal variability, with a consensus of a shift towards a higher frequency of droughts and heavier rainfall events (Easterling et al. 2000; Zhang et al. 2013)....

    [...]


Journal ArticleDOI
TL;DR: Bacterial growth is considered as a method for the study of bacterial physiology and biochemistry, with the interpretation of quantitative data referring to bacterial growth limited to populations considered genetically homogeneous.
Abstract: The study of the growth of bacterial cultures does not constitute a specialized subject or branch of research: it is the basic method of Microbiology. It would be a foolish enterprise, and doomed to failure, to attempt reviewing briefly a \"subject\" which covers actually our whole discipline. Unless, of course, we considered the formal laws of growth for their own sake, an approach which has repeatedly proved sterile. In the present review we shall consider bacterial growth as a method for the study of bacterial physiology and biochemistry. More precisely, we shall concern ourselves with the quantitative aspects of the method, with the interpretation of quantitative data referring to bacterial growth. Furthermore, we shall considerz exclusively the positive phases of growth, since the study of bacterial \"death,\" i.e., of the negative phases of growth, involves distinct problems and methods. The discussion will be limited to populations considered genetically homogeneous. The problems of mutation and selection in growing cultures have been excellently dealt with in recent review articles by Delbriick (1) and Luria (2). No attempt is made at reviewing the literature on a subject which, as we have just seen, is not really a subject at all. The papers and results quoted have been selected as illustrations of the points discussed.

3,787 citations


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

  • ...Microbial dynamics is described using Monod kinetics (Monod 1949), where δ is the microbial mortality rate constant....

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