Interannual variability of evapotranspiration and vegetation productivity

Fatichi, Simone; Ivanov, V. Y.

doi:10.1002/2013wr015044

Cited by 78 publications

(108 citation statements)

References 97 publications

(172 reference statements)

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“…These processes, and their interactions with changes to canopy structure and species composition could also accumulate to affect total transpiration over longer (daily-seasonal-annual) time scales by affecting water use efficiency and the effective degree of daily stomata-closing stresses the trees experience post disturbance. By analyzing and understanding these fast daily dynamics rather than longer-term periods (monthly) or periods of drought stress, it may be possible to improve modeling of transpiration at the subdaily time step [Thompson et al, 2011] as well as to show more realistic coupling between hydrodynamic phenomena and gross primary productivity in land surface models [Fatichi and Ivanov, 2014].…”

Section: Introductionmentioning

confidence: 99%

Species‐specific transpiration responses to intermediate disturbance in a northern hardwood forest

et al. 2014

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Intermediate disturbances shape forest structure and composition, which may in turn alter carbon, nitrogen, and water cycling. We used a large-scale experiment in a forest in northern lower Michigan where we prescribed an intermediate disturbance by stem girdling all canopy-dominant early successional trees to simulate an accelerated age-related senescence associated with natural succession. Using 3 years of eddy covariance and sap flux measurements in the disturbed area and an adjacent control plot, we analyzed disturbance-induced changes to plot level and species-specific transpiration and stomatal conductance. We found transpiration to be~15% lower in disturbed plots than in unmanipulated control plots. However, species-specific responses to changes in microclimate varied. While red oak and white pine showed increases in stomatal conductance during postdisturbance (62.5 and 132.2%, respectively), red maple reduced stomatal conductance by 36.8%. We used the hysteresis between sap flux and vapor pressure deficit to quantify diurnal hydraulic stress incurred by each species in both plots. Red oak, a ring porous anisohydric species, demonstrated the largest mean relative hysteresis, while red maple, bigtooth aspen, and paper birch, all diffuse porous species, had the lowest relative hysteresis. We employed the Penman-Monteith model for LE to demonstrate that these species-specific responses to disturbance are not well captured using current modeling strategies and that accounting for changes to leaf area index and plot microclimate are insufficient to fully describe the effects of disturbance on transpiration.

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Section: Introductionmentioning

confidence: 99%

Species‐specific transpiration responses to intermediate disturbance in a northern hardwood forest

et al. 2014

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“…; e.g., Higgins et al, 1999;Barlow et al, 2001), the strength and persistence of seasonality (e.g., Fatichi et al, 2012), and stochasticity in weather and precipitation formation. Interannual variation in precipitation is an important descriptor of the climatic environment which directly impacts the occurrence of droughts (e.g., Dai et al, 2004;Dai, 2011), vegetation productivity in water-limited ecosystems (e.g., Knapp and Smith, 2001;Reyer et al, 2013;Fatichi and Ivanov, 2014), as well as the distribution of rainfall extremes (e.g., Groisman et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Describing the interannual variability of precipitation with the derived distribution approach: effects of record length and resolution

Meier

Moraga

Pranzini

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Interannual variability of precipitation is traditionally described by fitting a probability model to yearly precipitation totals. There are three potential problems with this approach: a long record (at least 25-30 years) is required in order to fit the model, years with missing rainfall data cannot be used, and the data need to be homogeneous, i.e., one has to assume stationarity. To overcome some of these limitations, we test an alternative methodology proposed by Eagleson (1978), based on the derived distribution (DD) approach. It allows estimation of the probability density function (pdf) of annual rainfall without requiring long records, provided that continuously gauged precipitation data are available to derive external storm properties. The DD approach combines marginal pdfs for storm depths and inter-arrival times to obtain an analytical formulation of the distribution of annual precipitation, under the simplifying assumptions of independence between events and independence between storm depth and time to the next storm. Because it is based on information about storms and not on annual totals, the DD can make use of information from years with incomplete data; more importantly, only a few years of rainfall measurements should suffice to estimate the parameters of the marginal pdfs, at least at locations where it rains with some regularity.For two temperate locations in different climates (Concepción, Chile, and Lugano, Switzerland), we randomly resample shortened time series to evaluate in detail the effects of record length on the DD, comparing the results with the traditional approach of fitting a normal (or lognormal) distribution. Then, at the same two stations, we assess the biases introduced in the DD when using daily totalized rainfall, instead of continuously gauged data. Finally, for randomly selected periods between 3 and 15 years in length, we conduct full blind tests at 52 high-quality gauging stations in Switzerland, analyzing the ability of the DD to estimate the longterm standard deviation of annual rainfall, as compared to direct computation from the sample of annual totals.Our results show that, as compared to the fitting of a normal or lognormal distribution (or equivalently, direct estimation of the sample moments), the DD approach reduces the uncertainty in annual precipitation estimates (especially interannual variability) when only short records (below 6-8 years) are available. In such cases, it also reduces the bias in annual precipitation quantiles with high return periods. We demonstrate that using precipitation data aggregated every 24 h, as commonly available at most weather stations, introduces a noticeable bias in the DD. These results point to the tangible benefits of installing high-resolution (hourly, at least) precipitation gauges, next to the customary, manual rain-measuring instrument, at previously ungauged locations. We propose that the DD approach is a suitable tool for the statistical description and study of annual rainfall, not only when short records ...

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“…The P-ANPP sensitivities obtained from spatial relationships are usually higher than those obtained by temporal relationships (Estiarte et al, 2016;Fatichi and Ivanov, 2014;Sala et al, 2012). Possible mechanisms behind the steeper spatial relationship may be (1) a 'vegetation constraint' reflecting the adaptation of plant communities over long time scales in such a way that grasslands make the best use of the water received from rainfall for growth, and (2) the spatial variation in structural and functional traits of ecosystems (soil properties, nutrient pools, plant and 15 microbial community composition) that constrain local P-ANPP sensitivities (Lauenroth and Sala, 1992;Smith et al, 2009;Wilcox et al, 2016).…”

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confidence: 93%

“…Only a few previous model studies have directly linked the responses of grassland primary productivity to altered precipitation at field observation sites (Peng et al, 2013;Zhou et al, 2008;Fatichi and Ivanov, 2014). Peng et al (2013) conducted an analysis with a process-based model (ORCHIDEE 2-layer version) to address how precipitation changes regulate carbon cycling in a semi-arid grassland ecosystem in northern China.…”

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confidence: 99%

Asymmetric Responses of Primary Productivity to Altered Precipitation Simulated by Ecosystem Models across Three Longterm Grassland Sites

Wu¹,

Ciais²,

Viovy³

et al. 2018

Preprint

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Changes in precipitation variability are known to influence grassland growth. Field measurements of aboveground net primary productivity (ANPP) in temperate grasslands suggest that both positive and negative asymmetric responses to changes in precipitation may occur. Under normally variable precipitation regimes, wet years typically result in ANPP gains being larger 5 than ANPP declines in dry years (positive asymmetry), whereas increases in ANPP are lower in magnitude in extreme wet years compared to reductions during extreme drought (negative asymmetry). Whether ecosystem models that couple carbonwater system in grasslands are capable of simulating these non-symmetrical ANPP responses is an unresolved question. In this study, we evaluated the simulated responses of temperate grassland primary productivity to scenarios of altered precipitation with fourteen ecosystem models at three sites, Shortgrass Steppe (SGS), Konza Prairie (KNZ) and Stubai Valley meadow 10 (STU), spanning a rainfall gradient from dry to moist. We found that: (1) Gross primary productivity (GPP), NPP, ANPP and belowground NPP (BNPP) showed concave-down nonlinear response curves to altered precipitation in all the models, but with different curvatures and mean values. (2) The slopes of spatial relationships (across sites) between modeled primary productivity and precipitation were steeper than the temporal slopes obtained from inter-annual variations, consistent with empirical data. (3) The asymmetry of the responses of modeled primary productivity under normal inter-annual precipitation 15 variability differed among models, and the median of the model-ensemble suggested a negative asymmetry across the three sites, in contrast to empirical studies. (4) The median sensitivity of modeled productivity to rainfall consistently suggested greater negative impacts with reduced precipitation than positive effects with increased precipitation under extreme conditions. This study indicates that most models overestimate the extent of negative drought effects and/or underestimate the impacts of increased precipitation on primary productivity under normal climate conditions, highlighting the need for improving eco- 20hydrological processes in models. IntroductionPrecipitation is a key climatic determinant of ecosystem productivity, especially in grasslands which limits productivity over the majority of the globe (Lambers et al., 2008;Sala et al., 1988;Hsu et al., 2012;Beer et al., 2010). Climate models project substantial changes in amounts and frequencies of precipitation regimes worldwide, and this is supported by observational 25 data (Karl and Trenberth, 2003; Donat et al., 2016;Fischer and Knutti, 2016). Potential for increasing occurrence and severity of droughts and increased heavy rainfall events related to global warming will likely affect grassland growth Gherardi and Sala, 2015;Lau et al., 2013;Reichstein et al., 2013). As a consequence, better understanding of the responses of grassland productivity to altered precipitation is needed ...

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Interannual variability of evapotranspiration and vegetation productivity

Cited by 78 publications

References 97 publications

Species‐specific transpiration responses to intermediate disturbance in a northern hardwood forest

Species‐specific transpiration responses to intermediate disturbance in a northern hardwood forest

Describing the interannual variability of precipitation with the derived distribution approach: effects of record length and resolution

Asymmetric Responses of Primary Productivity to Altered Precipitation Simulated by Ecosystem Models across Three Longterm Grassland Sites

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