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Map of Köppen–Geiger climate regions across North America (Kottek et al., 2006). Six locations are selected representing the following: (a) tundra – ET (51.8 @BULLET N, 116.5 @BULLET W; 1383 m a.s.l.); (b) continental with warm summers – Dfb (44.7 @BULLET N, 73.8 @BULLET W; 383 m.a.s.l.); (c) temperate with dry summers – Csb (37.8 @BULLET N, 122.4 @BULLET W; 16 m a.s.l.); (d) hot arid desert – BWh (32.7 @BULLET N, 114.6 @BULLET W; 43 m a.s.l.); (e) equatorial monsoon – Am (26.0 @BULLET N, 80.3 @BULLET W; 2 m a.s.l.); (f) cold arid steppe – BSk (22.2 @BULLET N, 101.0 @BULLET W; 1850 m a.s.l.).
Source publication
Bioclimatic indices for use in studies of ecosystem function,
species distribution, and vegetation dynamics under changing climate
scenarios depend on estimates of surface fluxes and other quantities, such as
radiation, evapotranspiration and soil moisture, for which direct
observations are sparse. These quantities can be derived indirectly from
me...
Contexts in source publication
Context 1
... SPLASH model was run at six locations across North America (see Fig. 3), representing six distinct climate re- gions across latitudinal and elevational gradients. A total of 10 years (i.e., 1991-2000) of monthly CRU TS3.23 data (i.e., precipitation, air temperature, and cloudiness fraction) were extracted from the 0.5 • × 0.5 • pixel located over each site. Air temperature and cloudiness fraction were ...
Context 2
... (2000 CE), including monthly pre- cipitation (mm mo −1 ), monthly mean daily air temperature Figure 5. Monthly SPLASH results of evapotranspiration, E m (potential in solid black, actual in dashed red, and equilibrium in dotted black), climatic water deficit, E m , and Priestley-Taylor coefficient, α m , for the six climate regions defined in Fig. 3: (a) tundra, (b) continental with warm summers, (c) temperate with dry summers, (d) hot arid desert, (e) equatorial monsoon, and (f) cold arid steppe. Months of the year are represented along the x axis. Results are of 1 year (2000 CE). ( • C), and monthly cloudiness fraction. Monthly precipita- tion was converted to daily precipitation by ...
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... Leaching losses are modelled to scale with the ratio of daily soil water runoff to total 390 root zone water storage. The soil water balance is modelled following Davis et al. (2017). 391 N dep is a model forcing. ...
Reducing uncertainty in carbon cycle projections relies on reliable representations of interactions between the carbon and nutrient cycles. Here, we build on a set of principles and hypotheses, funded in established theoretical understanding and supported by empirical evidence, to formulate and implement a dynamic model of carbon-nitrogen cycle (C-N) interactions in terrestrial ecosystems. The model combines a representation of photosynthetic acclimation to the atmospheric environment with implications for canopy and leaf N, a representation of functional balance for modelling C allocation and growth different tissues, and a representation of plant N uptake based on a first principles-informed, simplified description of solute transport in the soil. Here, we provide a comprehensive description of the model and its implementation and demonstrate the stability and functionality of the model for simulating seasonal variations of ecosystem C and N fluxes and pools. This may provide a way forward for representing key ecosystem processes related to C and N cycling based on established theoretical concepts with strong empirical support.
... From AWAP, we calculated the monthly sums of precipitation (P), mean monthly daily temperature maximum (T max ), minimum (T min ), and vapor pressure deficit (VPD) calculated from the 3:00 p.m. vapor pressure measurement and T max . Shortwave radiation and mean air temperature was used to calculate potential evapotranspiration (PET) using a variant of the Priestley-Taylor method (Davis et al., 2017). The monthly aggregate of AWAP data was then used to calculate moving window (3-12 months) estimates of pre-and post-fire meteorological anomalies corresponding to the location and timing of every burned pixel observation. ...
Climate change has accelerated the frequency of catastrophic wildfires; however, the drivers that control the time‐to‐recover of forests are poorly understood. We integrated remotely sensed data, climate records, and landscape features to identify the causes of variability in the time‐to‐recover of canopy leaf area in southeast Australian eucalypt forests. Approximately 97% of all observed burns between 2001 and 2014 recovered to a pre‐fire leaf area index (±0.25 sd) within six years. Time‐to‐recover was highly variable within individual wildfires (ranging between ≤1 and ≥5 years), across burn seasons (90% longer January to September), and year of fire (median time‐to‐recover varying four‐fold across fire years). We used the logistic growth function to estimate the leaf area recovery rate, burn severity, and the long‐term carrying capacity of leaf area. Time‐to‐recover was most correlated with the leaf area recovery rate. The leaf area recovery rate was largest in areas that experienced high burn severity, and smallest in areas of intermediate to low burn severity. The leaf area recovery rate was also strongly accelerated by anomalously high post‐fire precipitation, and delayed by post‐fire drought. Finally we developed a predictive machine‐learning model of time‐to‐recover (R²: 0.68). Despite the exceptionally high burn severity of the 2019–2020 Australian megafires, we forecast the time‐to‐recover to be only 15% longer than the average of previous fire years.
... Compared to version 4.0, version 5.0 of the midden database has been expanded to include more precise midden-sample location data, calibration and evaluation of midden-sample age data, and assignment of plant functional types (PFTs) for the taxa in each midden sample. We have also assigned midden-sample sites to World Wildlife Fund (WWF) ecoregions and major habitat types (MHTs) 17 and interpolated modern climate and bioclimate data 18,19 to each midden-sample site location. ...
... The same approach was used to interpolate World WeatherDisc absolute minimum and maximum temperature ) data 52 to the midden-sample site locations. The interpolated monthly climate data (1961-1990 30-year mean) were used to calculate a set of bioclimatic variables (Table 12) for each midden site location using SPLASH 19 (SPLASH code is available from https:// bitbucket.org/labprentice/splash/src/master/). The monthly climate data were interpolated to a daily time step using a mean-preserving interpolation method 53,54 . ...
Plant macrofossils from packrat (Neotoma spp.) middens provide direct evidence of past vegetation changes in arid regions of North America. Here we describe the newest version (version 5.0) of the U.S. Geological Survey (USGS) North American Packrat Midden Database. The database contains published and contributed data from 3,331 midden samples collected in southwest Canada, the western United States, and northern Mexico, with samples ranging in age from 48 ka to the present. The database includes original midden-sample macrofossil counts and relative-abundance data along with a standardized relative-abundance scheme that makes it easier to compare macrofossil data across midden-sample sites. In addition to the midden-sample data, this version of the midden database includes calibrated radiocarbon (¹⁴C) ages for the midden samples and plant functional type (PFT) assignments for the midden taxa. We also provide World Wildlife Fund ecoregion assignments and climate and bioclimate data for each midden-sample site location. The data are provided in tabular (.xlsx), comma-separated values (.csv), and relational database (.mdb) files.
... 04 temperature and precipitation variables were derived from CHELSA climate data at a 30 arcsec spatial resolution (c. 1 km). MI was calculated using the Priestly-Taylor formulation provided by the SPLASH algorithm of (Davis et al., 2017), because it has been shown to perform substantially better in reproducing eddy-flux measurements of actual evapotranspiration under unstressed conditions compared to Penman-Monteith formula (Maes et al., 2018). MI values lower than one indicate there is less precipitation than evapotranspiration, resulting in drought stress for plants thereby providing more information than values solely based on precipitation. ...
The evolution of geophytes in response to different environmental stressors is poorly understood largely due to the great morphological variation in underground plant organs, which includes species with rhizomatous structures or underground storage organs (USOs). Here we compare the evolution and ecological niche patterns of different geophytic organs in Solanum L., classified based on a functional definition and using a clade-based approach with an expert-verified specimen occurrence dataset. Results from PERMANOVA and Phylogenetic ANOVAs indicate that geophytic species occupy drier areas, with rhizomatous species found in the hottest areas whereas species with USOs are restricted to cooler areas in the montane tropics. In addition, rhizomatous species appear to be adapted to fire-driven disturbance, in contrast to species with USOs that appear to be adapted to prolonged climatic disturbance such as unfavorable growing conditions due to drought and cold. We also show that the evolution of rhizome-like structures leads to changes in the relationship between range size and niche breadth. Ancestral state reconstruction shows that in Solanum rhizomatous species are evolutionarily more labile compared to species with USOs. Our results suggest that underground organs enable plants to shift their niches towards distinct extreme environmental conditions and have different evolutionary constraints.
... The key difference between DNN PET and DNN ET is that DNN PET was trained using data from days with high soil moisture only, whereas DNN ET was trained using all available data (see the Estimating potential ET section). We used either observational soil moisture from FLUXNET2015 or modelled soil moisture from a bucket-type soil water balance model (Davis et al., 2017). We preferred modelled data for the many sites where the quality of the observational data was poor, as described in the Soil moisture data section. ...
... For many FLUX-NET2015 sites, we found that observational soil moisture data were unavailable, incomplete or inconsistent with ET observations, as described in Section 1 of Methods S1. For these sites, we simulated soil moisture with SPLASH, a bucket-type soil water balance model (Davis et al., 2017). This model was based on a Priestley-Taylor formulation for ET estimation. ...
... This model was based on a Priestley-Taylor formulation for ET estimation. We set the water-holding capacity ('bucket depth') to 220 mm (Orth et al., 2013;Davis et al., 2017). Given that we used both modelled soil moisture and observational soil moisture across sites, the variable was normalized between 0 and 1 on a site-by-site basis for better comparison across sites. ...
Accounting for water limitation is key to determining vegetation sensitivity to drought. Quantifying water limitation effects on evapotranspiration (ET) is challenged by the heterogeneity of vegetation types, climate zones and vertically along the rooting zone.
Here, we train deep neural networks using flux measurements to study ET responses to progressing drought conditions. We determine a water stress factor (fET) that isolates ET reductions from effects of atmospheric aridity and other covarying drivers. We regress fET against the cumulative water deficit, which reveals the control of whole‐column moisture availability.
We find a variety of ET responses to water stress. Responses range from rapid declines of fET to 10% of its water‐unlimited rate at several savannah and grassland sites, to mild fET reductions in most forests, despite substantial water deficits. Most sensitive responses are found at the most arid and warm sites.
A combination of regulation of stomatal and hydraulic conductance and access to belowground water reservoirs, whether in groundwater or deep soil moisture, could explain the different behaviors observed across sites. This variety of responses is not captured by a standard land surface model, likely reflecting simplifications in its representation of belowground water storage.
... We characterized DI on a pixel-by-pixel basis with absolute and standardized climatological water balance (WB) anomalies as follows. The climatological WB was calculated as the difference between precipitation (P) and potential evapotranspiration (PET) that was estimated using the Priestley- Taylor Method (see Equation 22 in Davis et al. (2017)) with inputs of mean monthly total daily shortwave radiation and mean monthly daily mean air temperature from the ANUCLIM products (Hutchinson et al., 2014) generated from observed Bureau of Meteorology data (Table 1). The Priestley-Taylor Method estimates potential evapotranspiration over saturated land surface using an empirical coefficient times the water-equivalence of net surface radiation, ignoring the soil heat flux (Lhomme, 1997;Priestley & Taylor, 1972). ...
Drought‐induced vegetation declines have been reported across the globe and may have widespread implications for ecosystem composition, structure, and functions. Thus, it is critical to maximizing our understanding of how vegetation has responded to recent drought extremes. To date, most drought assessments emphasized the importance of drought intensity for vegetation responses. However, drought timing, duration, and repeat exposure all may be important aspects of ecosystem response with the potential for non‐linear effects. Cumulative effects are one such phenomenon, representing the additional decline due to repeated exposure to drought, and indicating gradual loss of ecosystem resistance. This study quantifies the frequency and magnitude of cumulative effects among Australian ecosystems as they responded to the Millennium Drought. Three distinct biophysical variables derived from satellite remote sensing were analyzed, including fraction of photosynthetically absorbed radiation, photosynthetic vegetation cover, and canopy density derived from passive microwave data. Cumulative effects were detected in only 8%–20% of the fire‐free landscape exposed to repeat or long‐duration drought, and could be a statistical artifact. In those limited cases, they approximately doubled drought impacts on leaf abundance, canopy cover, and vegetation density. Cultivated lands and grasslands were the most susceptible to cumulative effects, losing resistance to recurrent droughts, but could be false discovery. Despite being relatively infrequent in forests and savannas, cumulative effects caused larger additional declines in these ecosystems. Overall, our study demonstrates that repeated exposure appears to have limited influence on the magnitude of drought impacts on canopy structure affecting only a few areas.
... D 0 was calculated from CRU vapour pressure and mean daily maximum and minimum temperatures. I 0 was calculated from latitude, month and CRU cloud cover data using SPLASH (Davis et al., 2017). PFT-weighted trait values were estimated using proportions of evergreen, deciduous and grassland vegetation derived from the ESA-CCI land cover data set at 0.5° resolution (Li et al., 2018). ...
Aim
Leaf traits are central to plant function, and key variables in ecosystem models. However recently published global trait maps, made by applying statistical or machine-learning techniques to large compilations of trait and environmental data, differ substantially from one another. This paper aims to demonstrate the potential of an alternative approach, based on eco-evolutionary optimality theory, to yield predictions of spatio-temporal patterns in leaf traits that can be independently evaluated.
Innovation
Global patterns of community-mean specific leaf area (SLA) and photosynthetic capacity (Vcmax) are predicted from climate via existing optimality models. Then leaf nitrogen per unit area (Narea) and mass (Nmass) are inferred using their (previously derived) empirical relationships to SLA and Vcmax. Trait data are thus reserved for testing model predictions across sites. Temporal trends can also be predicted, as consequences of environmental change, and compared to those inferred from leaf-level measurements and/or remote-sensing methods, which are an increasingly important source of information on spatio-temporal variation in plant traits.
Main conclusions
Model predictions evaluated against site-mean trait data from > 2,000 sites in the Plant Trait database yielded R2 = 73% for SLA, 38% for Nmass and 28% for Narea. Declining species-level Nmass, and increasing community-level SLA, have both been recently reported and were both correctly predicted. Leaf-trait mapping via optimality theory holds promise for macroecological applications, including an improved understanding of community leaf-trait responses to environmental change.
... Values in energy units of the latent heat flux are converted to mass units accounting for the temperature and air-pressure dependence of the latent heat of vaporization following ref. 46 . The P in is based on daily reanalysis data of P in the form of rain and snow from WATCH-WFDEI 47 . ...
... Examples, visualizing the diagnosing of S 0 from EF, are given in Supplementary Fig. 8. We performed additional tests of the method's reliability in estimating S 0 by deriving S dEF from simulations of the ecosystem water balance and ET, where S 0 was prescribed, using the SPLASH (Simple Process-Led Algorithms for Simulating Habitats) model 46 . This demonstrates that the method applied for S dSIF and S dEF yields accurate estimates of S 0 across all climatic conditions and independent of the size of S 0 (Supplementary Text 2 and Supplementary Fig. 5). ...
The rooting-zone water-storage capacity—the amount of water accessible to plants—controls the sensitivity of land–atmosphere exchange of water and carbon during dry periods. How the rooting-zone water-storage capacity varies spatially is largely unknown and not directly observable. Here we estimate rooting-zone water-storage capacity globally from the relationship between remotely sensed vegetation activity, measured by combining evapotranspiration, sun-induced fluorescence and radiation estimates, and the cumulative water deficit calculated from daily time series of precipitation and evapotranspiration. Our findings indicate plant-available water stores that exceed the storage capacity of 2-m-deep soils across 37% of Earth’s vegetated surface. We find that biome-level variations of rooting-zone water-storage capacities correlate with observed rooting-zone depth distributions and reflect the influence of hydroclimate, as measured by the magnitude of annual cumulative water-deficit extremes. Smaller-scale variations are linked to topography and land use. Our findings document large spatial variations in the effective root-zone water-storage capacity and illustrate a tight link among the climatology of water deficits, rooting depth of vegetation and its sensitivity to water stress.
... We calculated site-specific bioclimatic variables from monthly temperature, precipitation, and sunshine hours with the Simple Process-Led Algorithms for Simulating Habitats (SPLASH) model (41). This model has been validated at both site and global scales (41) and has been extensively used to calculate bioclimatic variables. ...
... We calculated site-specific bioclimatic variables from monthly temperature, precipitation, and sunshine hours with the Simple Process-Led Algorithms for Simulating Habitats (SPLASH) model (41). This model has been validated at both site and global scales (41) and has been extensively used to calculate bioclimatic variables. Defining the (thermal) growing season as the period when mean quasi-daily temperature (interpolated from monthly data) is above 0°C, we calculated the ratio of growing-season length to the number of days in the year and the mean values of temperature and PPFD during growing season, as estimates of site-mean f, T g , and PPFD. ...
The life span of leaves increases with their mass per unit area (LMA). It is unclear why. Here, we show that this empirical generalization (the foundation of the worldwide leaf economics spectrum) is a consequence of natural selection, maximizing average net carbon gain over the leaf life cycle. Analyzing two large leaf trait datasets, we show that evergreen and deciduous species with diverse construction costs (assumed proportional to LMA) are selected by light, temperature, and growing-season length in different, but predictable, ways. We quantitatively explain the observed divergent latitudinal trends in evergreen and deciduous LMA and show how local distributions of LMA arise by selection under different environmental conditions acting on the species pool. These results illustrate how optimality principles can underpin a new theory for plant geography and terrestrial carbon dynamics.
... The model performed poor at high latitudes with cold climate. In fact, currently simulated and remotely sensed soil moisture still has large uncertainties at high latitudes (Brocca et al., 2014;Davis et al., 2017;Yi et al., 2011). The uncertainties of soil moisture might be a reason for the poor performance of the P model at high latitudes (Stocker et al., 2020). ...
Accurate estimation of terrestrial gross primary productivity (GPP) is essential for quantifying the net carbon exchange between the atmosphere and biosphere. Light use efficiency (LUE) models are widely used to estimate GPP at different spatial scales. However, difficulties in proper determination of maximum LUE (LUEmax) and downregulation of LUEmax into actual LUE result in uncertainties in GPP estimated by LUE models. The recently developed P model, as a LUE-like model, captures the deep mechanism of photosynthesis and simplifies parameterization. Site level studies have proved the outperformance of P model over LUE models. However, the global application of the P model is still lacking. Thus, the effectiveness of 5 water stress factors integrated into the P model was compared. The optimal P model was used to generate a new long-term (1981–2020) global monthly GPP dataset at a spatial resolution of 0.1° × 0.1°, called PGPP. Validation at globally distributed 109 FLUXNET sites indicated that PGPP is better than three widely-used GPP products. R² between PGPP and observed GPP equals to 0.75, the corresponding root mean squared error (RMSE) and mean absolute error (MAE) equal to 1.77 g C m⁻² d⁻¹ and 1.28 g C m⁻² d⁻¹. During the period from 1981 to 2020, PGPP significantly increased in 69.02% of global vegetated regions (p < 0.05). Overall, PGPP provides a new GPP product choice for global ecology studies and the comparison of various water stress factors provides a new idea for the improvement of GPP model in the future.
