ECOSTRESS: NASA's next generation mission to measure evapotranspiration from the International Space Station

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station ECOSTRESS) was launched to the International Space Station on June 29, 2018. The primary science focus of ECOSTRESS is centered on evapotranspiration (ET), which is produced as level‐3 (L3) latent heat flux (LE) data products. These data are generated from the level‐2 land surface temperature and emissivity product (L2_LSTE), in conjunction with ancillary surface and atmospheric data. Here, we provide the first validation (Stage 1, preliminary) of the global ECOSTRESS clear‐sky ET product (L3_ET_PT‐JPL, version 6.0) against LE measurements at 82 eddy covariance sites around the world. Overall, the ECOSTRESS ET product performs well against the site measurements (clear‐sky instantaneous/time of overpass: r2 = 0.88; overall bias = 8%; normalized RMSE = 6%). ET uncertainty was generally consistent across climate zones, biome types, and times of day (ECOSTRESS samples the diurnal cycle), though temperate sites are over‐represented. The 70 m high spatial resolution of ECOSTRESS improved correlations by 85%, and RMSE by 62%, relative to 1 km pixels. This paper serves as a reference for the ECOSTRESS L3 ET accuracy and Stage 1 validation status for subsequent science that follows using these data

Trh stavebního spoření v České republice

Import 20/04/2006Prezenční výpůjčkaVŠB - Technická univerzita Ostrava. Ekonomická fakulta. Katedra (115) podnikatelství a management

Improved global estimations of gross primary productivity of natural vegetation types by incorporating plant functional type

Satellite-based light use efficiency (LUE) models are important tools for estimating regional and global vegetation gross primary productivity (GPP). However, all LUE models assume a constant value of maximum LUE at canopy scale (LUEmaxcanopy) over a given vegetation type. This assumption is not supported by observed plant traits regulating LUEmaxcanopy, which varies greatly even within the same ecosystem type. In this study, we developed an improved satellite data driven GPP model by identifying the potential maximal GPP (GPPPOT) and their dominant climate control factor in various plant functional types (PFT), which takes into account both plant trait and climatic control inter-dependence. We selected 161 sites from the FLUXNET2015 dataset with eddy covariance CO2 flux data and continuous meteorology to derive GPPPOT and their dominant climate control factor of vegetation growth for 42 natural PFTs. Results showed that (1) under the same phenology and incident photosynthetic active radiation, the maximal variance of GPPPOT is found in different PFTs of forests (10.9 g C m−2 day−1) and in different climatic zones of grasslands (>10 g C m−2 day−1); (2) intra-annual change of GPP in tropical and arid climate zones is mostly driven by vapor pressure deficit (VPD) changes, while temperature is the dominant climate control factor in temperate, boreal and polar climate zones; even under the same climate condition, physiological stress in photosynthesis is different across PFTs; (3) the model that takes into account the plant trait difference across PFTs had a higher agreement with flux tower-based GPP data (GPPflux) than the GPP products that omit PFT differences. Such agreement was highest for natural vegetation cover sites (R2 = 0.77, RMSE = 1.79 g C m−2 day−1). These results suggest that global scale GPP models should incorporate both plant traits and their dominant climate control factor variance in various PFT to reduce the uncertainties in terrestrial carbon assessments

Improved global estimations of gross primary productivity of natural vegetation types by incorporating plant functional type

Satellite-based light use efficiency (LUE) models are important tools for estimating regional and global vegetation gross primary productivity (GPP). However, all LUE models assume a constant value of maximum LUE at canopy scale (LUEmaxcanopy) over a given vegetation type. This assumption is not supported by observed plant traits regulating LUEmaxcanopy, which varies greatly even within the same ecosystem type. In this study, we developed an improved satellite data driven GPP model by identifying the potential maximal GPP (GPPPOT) and their dominant climate control factor in various plant functional types (PFT), which takes into account both plant trait and climatic control inter-dependence. We selected 161 sites from the FLUXNET2015 dataset with eddy covariance CO2 flux data and continuous meteorology to derive GPPPOT and their dominant climate control factor of vegetation growth for 42 natural PFTs. Results showed that (1) under the same phenology and incident photosynthetic active radiation, the maximal variance of GPPPOT is found in different PFTs of forests (10.9 g C m−2 day−1) and in different climatic zones of grasslands (>10 g C m−2 day−1); (2) intra-annual change of GPP in tropical and arid climate zones is mostly driven by vapor pressure deficit (VPD) changes, while temperature is the dominant climate control factor in temperate, boreal and polar climate zones; even under the same climate condition, physiological stress in photosynthesis is different across PFTs; (3) the model that takes into account the plant trait difference across PFTs had a higher agreement with flux tower-based GPP data (GPPflux) than the GPP products that omit PFT differences. Such agreement was highest for natural vegetation cover sites (R2 = 0.77, RMSE = 1.79 g C m−2 day−1). These results suggest that global scale GPP models should incorporate both plant traits and their dominant climate control factor variance in various PFT to reduce the uncertainties in terrestrial carbon assessments.ISSN:0303-2434ISSN:1872-826XISSN:1569-843

Environmental Effects on Normalized Gross Primary Productivity in Beech and Norway Spruce Forests

The strong effects of climate change are expected to negatively impact the long-term resilience and function of forest ecosystems, which could lead to changes in forest carbon balance and productivity. However, these forest responses may vary with local conditions and forest types. Accordingly, this study was carried out to determine gross primary productivity (GPP) sensitivity to changes in environmental parameters. Central European beech (at Štítná) and spruce species (at Bílý Kr̆íz̆ and Rájec), growing under contrasting climatic conditions, were studied. The comparative analyses of GPP were based on a five-year-long dataset of eddy covariance fluxes during the main growing season (2012–2016). Results of forest GPP responses with changes in environmental factors from a traditional Stepwise multiple linear regression model (SMLR) were used and compared with Random forest (RF) analyses. To demonstrate how actual GPP trends compare to potential GPP (GPPpot) courses expected under near-optimal environmental conditions, we computed normalized GPP (GPPnorm) with values between 0 and 1 as the ratio of the estimated daily sum of GPP to GPPpot. The study confirmed the well-known effect of total intensity of the photosynthetically active radiation and its diffuse fraction on GPPnorm across all the forest types. However, the study also showed the secondary effects of other environmental variables on forest productivity depending on the species and local climatic conditions. The reduction in forest productivity at the beech forest in Štítná was presumed to be mainly induced by edaphic drought (anisohydric behaviour). In contrast, reduced forest productivity at the spruce forest sites was presumably induced by both meteorological and hydrological drought events, especially at the moderately dry climate in Rájec. Overall, our analyses call for more studies on forest productivity across different forest types and contrasting climatic conditions, as this productivity is strongly dependent on species type and site-specific environmental conditions

Národohospodářské modely dopadů opatření politiky životního prostředí na makroekonomické agregáty v České republice

Cílem projektu je na základě ex-post analýzy časových řad identifikovat vliv zaváděných administrativních a ekonomických nástrojů ochrany životního prostředí na makroekonomické agregáty a tyto poznatky využít ke konstrukci makroekonomického modelu, který umožní v budoucnu provádět ex-ante analýzy připravovaných změn. Řešení je rozděleno do osmi částí. V první je proveden rozbor právních norem z oblasti životního prostředí, které byly přijaty během transformace a měly vliv na ekonomické chování firem a domácností. Druhá část je zaměřena na sektor domácností a analyzuje faktory, které ovlivňují výdaje domácností na energie a dopravu. Předmětem třetí části je analýza dopadů změn cen vodného a stočného na spotřebu vody v ČR. Čtvrtá kapitola analyzuje vliv poplatků na odpadové hospodářství. Pátá část je zaměřena na firemní sektor a podrobně zkoumá dopady environmentální regulace na konkurenceschopnost. Cílem šesté kapitoly je objasnit, jaké faktory stály za snížením znečištění vzduchu v období transformace. Sedmá kapitola řeší problematiku dopadů liberalizace zahraničního obchodu na obchod s environmentálním zbožím. Poslední kapitola obsahuje popis makroekonomického modelu a ukázky simulací

Non-stomatal processes reduce gross primary productivity in temperate forest ecosystems during severe edaphic drought

Severe drought events are known to cause important reductions of gross primary productivity (GPP) in forest ecosystems. However, it is still unclear whether this reduction originates from stomatal closure (Stomatal Origin Limitation) and/or non-stomatal limitations (Non-SOL). In this study, we investigated the impact of edaphic drought in 2018 on GPP and its origin (SOL, NSOL) using a data set of 10 European forest ecosystem flux towers. In all stations where GPP reductions were observed during the drought, these were largely explained by declines in VCMAX (NSOL) when the soil relative extractable water content (REW) dropped below around 0.4. Concurrently, we found that the stomatal slope parameter (G1, related to SOL) of the Medlyn et al. unified optimization model linking vegetation conductance and GPP remained relatively constant. This result was unexpected as it implies that NSOL (instead of stomatal closure) was the main process limiting GPP during drought.JRC.C.5-Air and Climat

Non-stomatal processes reduce gross primary productivity in temperate forest ecosystems during severe edaphic drought

Severe drought events are known to cause important reductions of gross primary productivity (GPP) in forest ecosystems. However, it is still unclear whether this reduction originates from stomatal closure (Stomatal Origin Limitation) and/or non-stomatal limitations (Non-SOL). In this study, we investigated the impact of edaphic drought in 2018 on GPP and its origin (SOL, NSOL) using a data set of 10 European forest ecosystem flux towers. In all stations where GPP reductions were observed during the drought, these were largely explained by declines in the maximum apparent canopy scale carboxylation rate VCMAX,APP (NSOL) when the soil relative extractable water content dropped below around 0.4. Concurrently, we found that the stomatal slope parameter (G1, related to SOL) of the Medlyn et al. unified optimization model linking vegetation conductance and GPP remained relatively constant. This result was unexpected as it implies that NSOL (instead of stomatal closure) was the main process limiting GPP during drough