Water use efficiency of China\u27s terrestrial ecosystems and responses to drought

Water use efficiency (WUE) measures the trade-off between carbon gain and water loss of terrestrial ecosystems, and better understanding its dynamics and controlling factors is essential for predicting ecosystem responses to climate change. We assessed the magnitude, spatial patterns, and trends of WUE of China’s terrestrial ecosystems and its responses to drought using a process-based ecosystem model. During the period from 2000 to 2011, the national average annual WUE (net primary productivity (NPP)/evapotranspiration (ET)) of China was 0.79 g C kg−1 H2O. Annual WUE decreased in the southern regions because of the decrease in NPP and the increase in ET and increased in most northern regions mainly because of the increase in NPP. Droughts usually increased annual WUE in Northeast China and central Inner Mongolia but decreased annual WUE in central China. “Turning-points” were observed for southern China where moderate and extreme droughts reduced annual WUE and severe drought slightly increased annual WUE. The cumulative lagged effect of drought on monthly WUE varied by region. Our findings have implications for ecosystem management and climate policy making. WUE is expected to continue to change under future climate change particularly as drought is projected to increase in both frequency and severity

Upscaling key ecosystem functions across the conterminous United States by a water-centric ecosystem model

We developed a water-centric monthly scale simulation model (WaSSI-C) by integrating empirical water and carbon flux measurements from the FLUXNET network and an existing water supply and demand accounting model (WaSSI). The WaSSI-C model was evaluated with basin-scale evapotranspiration (ET), gross ecosystem productivity (GEP), and net ecosystem exchange (NEE) estimates by multiple independent methods across 2103 eight-digit Hydrologic Unit Code watersheds in the conterminous United States from 2001 to 2006. Our results indicate that WaSSI-C captured the spatial and temporal variability and the effects of large droughts on key ecosystem fluxes. Our modeled mean (±standard deviation in space) ET (556 ± 228 mm yr−1) compared well to Moderate Resolution Imaging Spectroradiometer (MODIS) based (527 ± 251 mm yr−1) and watershed water balance based ET (571 ± 242 mm yr−1). Our mean annual GEP estimates (1362 ± 688 g C m−2 yr−1) compared well (R2 = 0.83) to estimates (1194 ± 649 g C m−2 yr−1) by eddy flux-based EC-MOD model, but both methods led significantly higher (25–30%) values than the standard MODIS product (904 ± 467 g C m−2 yr−1). Among the 18 water resource regions, the southeast ranked the highest in terms of its water yield and carbon sequestration capacity. When all ecosystems were considered, the mean NEE (−353 ± 298 g C m−2 yr−1) predicted by this study was 60% higher than EC-MOD\u27s estimate (−220 ± 225 g C m−2 yr−1) in absolute magnitude, suggesting overall high uncertainty in quantifying NEE at a large scale. Our water-centric model offers a new tool for examining the trade-offs between regional water and carbon resources under a changing environment

Impacts of Climate Extremes on Terrestrial Productivity

Terrestrial biosphere absorbs approximately 28% of anthropogenic CO2 emissions. This terrestrial carbon sink might become saturated in a future climate regime. To explore the issues associated with this topic, an accurate estimate of gross primary production (GPP) of global terrestrial ecosystems is needed. A major uncertainty in modeling global terrestrial GPP is the parameter of light use efficiency (LUE). Most LUE estimates in global models are satellite-based and coarsely measured with emphasis on environmental variables. Others are from eddy covariance towers with much greater spatial and temporal data quality and emphasis on mechanistic processes, but in a limited number of sites. In this study, we conducted a comprehensive global study of tower-based LUE from 237 FLUXNET towers, and scaled up LUEs from in-situ tower level to global biome level. We integrated the tower-based LUE estimates with key environmental and biological variables at 0.5º × 0.5º grid-cell resolutions, using a random forest regression (RFR) approach. Then we developed a RFR-LUE-GPP model using the grid-cell LUE data. In order to calibrate the LUE model, we developed a data-driven RFR-GPP model using random forest regression method only. Our results showed LUE varies largely with latitude. We estimated a global area-weighted average of LUE at 1.23±0.03 gC m-2 MJ-1 APAR, which led to an estimate of global gross primary production (GPP) of 107.5±2.5 Gt C /year from 2001 to 2005. Large uncertainties existed in GPP estimations over sparsely vegetated areas covered by savannas and woody savannas at middle to low latitude (i.e. 20ºS to 40ºS and 5ºN to 40ºN) due to the lack of available data. Model results were improved by incorporating Köppen climate types to represent climate/meteorological information in machine learning modeling. This brought a new understanding to the recognized problem of climate-dependence of spring onset of photosynthesis and the challenges in accurately modeling the biome GPP of evergreen broad leaf forests (EBF). The divergent responses of GPP to temperature and precipitation at mid-high latitudes and at mid-low latitudes echo the necessity of modeling GPP separately by latitudes. We also used a perfect-deficit approach to identify forest canopy photosynthetic capacity (CPC) deficits and analyze how they correlate to climate extremes, based on observational data measured by the eddy covariance method at 27 forest sites over 146 site-years. We found that droughts severely affect the carbon assimilation capacities of evergreen broadleaf forest and deciduous broadleaf forest. The carbon assimilation capacities of Mediterranean forests were highly sensitive to climate extremes, while marine forest climates tended to be insensitive to climate extremes. Our estimates suggest an average global reduction of forest canopy photosynthetic capacity due to unfavorable climate extremes of 6.3 Pg C (~5.2% of global gross primary production) per growing season over 2001-2010, with evergreen broadleaf forests contributing 52% of the total reduction. At biome-scale, terrestrial carbon uptake is controlled mainly by weather variability. Observational data from a global monitoring network indicate that the sensitivity of terrestrial carbon sequestration to mean annual temperature (T) breaks down at a threshold value of 16oC, above which terrestrial CO2 fluxes are controlled by dryness rather than temperature. Here we show that since 1948 warming climate has moved the 16oC T latitudinal belt poleward. Land surface area with T \u3e16oC and now subject to dryness control rather than temperature as the regulator of carbon uptake has increased by 6% and is expected to increase by at least another 8% by 2050

Assessing the ability of MODIS EVI to estimate terrestrial ecosystem gross primary production of multiple land cover types

© 2016 Elsevier Ltd Terrestrial ecosystem gross primary production (GPP) is the largest component in the global carbon cycle. The enhanced vegetation index (EVI) has been proven to be strongly correlated with annual GPP within several biomes. However, the annual GPP-EVI relationship and associated environmental regulations have not yet been comprehensively investigated across biomes at the global scale. Here we explored relationships between annual integrated EVI (iEVI) and annual GPP observed at 155 flux sites, where GPP was predicted with a log-log model: ln(GPP)=a×ln(iEVI)+b. iEVI was computed from MODIS monthly EVI products following removal of values affected by snow or cold temperature and without calculating growing season duration. Through categorisation of flux sites into 12 land cover types, the ability of iEVI to estimate GPP was considerably improved (R2 from 0.62 to 0.74, RMSE from 454.7 to 368.2 g C m−2 yr−1). The biome-specific GPP-iEVI formulae generally showed a consistent performance in comparison to a global benchmarking dataset (R2 = 0.79, RMSE = 387.8 g C m−2 yr−1). Specifically, iEVI performed better in cropland regions with high productivity but poorer in forests. The ability of iEVI in estimating GPP was better in deciduous biomes (except deciduous broadleaf forest) than in evergreen due to the large seasonal signal in iEVI in deciduous biomes. Likewise, GPP estimated from iEVI was in a closer agreement to global benchmarks at mid and high-latitudes, where deciduous biomes are more common and cloud cover has a smaller effect on remote sensing retrievals. Across biomes, a significant and negative correlation (R2 = 0.37, p < 0.05) was observed between the strength (R2) of GPP-iEVI relationships and mean annual maximum leaf area index (LAImax), and the relationship between the strength and mean annual precipitation followed a similar trend. LAImax also revealed a scaling effect on GPP-iEVI relationships. Our results suggest that iEVI provides a very simple but robust approach to estimate spatial patterns of global annual GPP whereas its effect is comparable to various light-use-efficiency and data-driven models. The impact of vegetation structure on accuracy and sensitivity of EVI in estimating spatial GPP provides valuable clues to improve EVI-based models

Comparison of MODIS-Algorithms for Estimating Gross Primary Production from Satellite Data in semi-arid Africa

The climatic patterns of the world are changing and with them the spatial distribution of global terrestrial carbon; the food and fiber of the world and in itself an important factor in the changing climate. Knowledge of how the terrestrial carbon stock is changing, its distribution and quantity, is important in understanding how the patterns of the world are changing and large scale models using remotely sensed data have emerged for this purpose. This study compares four vegetation related MODIS (Moderate Resolution Imaging Spectroradiometer) products, derived from MODIS satellite data using algorithms which calculates the two vegetation indices, Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), and the two biophysical factors, Leaf Area Index (LAI) and absorbed Fraction of Photosynthetically Active Radiation (FPAR). The comparison is in their ability to estimate intra-annual variations of Gross Primary Production (GPP); this is done using the time-series data of quality screened eddy covariance (EC) Flux Tower stations from the Carbo Africa network as truth data. The results show a modest agreement between the different vegetation metrics and EC Flux Tower derived GPP, with an overall average coefficient of determination (R2) of 0.63 for LAI, R2 of 0.51 for NDVI, R2 of 0.52 FPAR and a R2 of 0.49 for EVI, using all stations and years of data. When each station received the same weight, i.e. using the correlation of all observation for each station and then calculating the average, the overall correlation improved, still showing LAI as the best predictor of Flux Tower GPP with a R2 of 0.62, but with an improved EVI with a R2 of 0.61, while NDVI and FPAR had an R2 of 0.57 and 0.59 respectively. This result and the observed large variation in between stations, e.g. NDVI between an R2 of 0.62 and 0.83 for the station Demokeya compared to an R2 of 0.32 and 0.49 of NDVI for the station Tchizalamou, may indicate a site specific proficiency of the vegetation metrics. When the observations within the growing period were tested separately a strong decrease in correlation was observed, with an average R2 between 0.41 – 0.56 for all station and years and an average R2 between 0.36 – 0.45 for all sites using all observations for each station regardless of year, lending strength to the assumption that the non-vegetation period observations affect the correlation greatly. The study concludes that up scaling of an intra-annual standardized major axis regression model based solely on the relationship between any of these metrics and Flux Tower estimated GPP is inadvisable due to the modest overall intra-annual agreement between the metrics and GPP. It is also concluded that since the vegetation metrics display site specific proficiency, models of GPP would benefit from site specific ancillary data that describes vegetation-limiting factors, e.g. water availability.Klimatmönstren världen över förändras och med dessa den globala distributionen och mängden av landbundet kol, dvs vegetationen som bland annat nyttjas som mat och fiber. Också I sig självt en viktig faktor i klimatets utveckling genom dess roll i energi- och vattenkretsloppen. Vetskap om kvantitet och distribution av landbundet kol och hur detta förändras är en viktigt del av arbetet i att förstå hur de globala mönster förändras, och för denna avsikt har bredskaliga modeller som nyttjar satellit data framtagits. Denna studie jämför fyra vegetations relaterade MODIS (Moderate Resolution Imaging Spectroradiometer) produkter, som erhålls från MODIS satellit data genom algoritmer som kalkylerar de två vegetation indexen, Normalized Vegetation Index (NDVI) och Enhanced Vegetation Index (EVI), och de två biofysiska faktorerna, Leaf Area Index (LAI) och absorbed Fraction of Photosynthetically Active Radiation (FPAR). Deras förmåga att uppskatta variationen av den totala primär produktionen (Gross Primary Production, GPP) över året jämförs, genom tidsserier av eddy kovarians (EC) data från flux torn ur Carbo Africa nätverket, vars tidsserie-utveckling används som sanningspunkter varmot variationen från de motsvarande tidsserierna av algoritmerna jämförs. Resultatet visar en blygsam korrelation mellan de olika vegetations algoritmernas reslutat och EC flux torn uppskattat GPP, med ett medel av determinationskoefficienter (R2) på 0.49 för EVI, 0.51 för NDVI,0.52 för FPAR och ett R2 på 0.63 för LAI, då data från alla stationer och år användes. När var station erhöll lika stor vikt, dvs då korrelationen kalkylerades för samtliga observationer från var station, varpå medel togs fram, förbättrades korrelationen över lag. fLAI visades fortfarande som den bästa prediktorn av flux-torns uppskattad GPP med ett R2 på 0.62, ett starkt förbättrat R2 för EVI på 0.61erhölls, medans NDVI och FPAR visa ett R2 på 0.57 respektive 0.59. Detta resultat och en stundtals stor variation mellan stationer, t.ex. NDVI med ett R2 mellan 0.62 och 0.83 för stationen Demokeya jämfört med ett R2 mellan 0.32 och 0.49 för NDVI och stationen Tchizalamou, visar kanske på plats specifika förmågor hos vegetations algoritmerna. När observationer inom vegetationsperioden testades separat observerades en starkt minskad korrelation, med ett medel R2 mellan 0.41 – 0.56 för alla stationer och år, och ett R2 mellan 0.36 – 0.45 för alla platser vid användning av samtliga observationer för varje station oberoende av år, vilket indikerar att observationerna utanför växtperioden har stort inflytande på korrelationen. En slutsats av studien är att uppskalning av en standrardiserad storaxels regressions modell för inom annuell variation baserad endast på relationen mellan en av dessa vegetations algoritmer och flux-torns uppskattad GPP ej är att rekommendera med tanke på den blygsamma överrensstämmelsen mellan vegetationsalgoritmerna och flux torn uppskattat GPP. En annan slutsats är att eftersom dessa vegetations algoritmer uppvisar plats specifika förmågor skulle modeller av GPP ha fördel av plats specifik stöd-data som beskriver faktorer som begränsar vegetation, t.ex. vattentillgänglighet

POTENTIAL CONTRASTS IN CO2 AND CH4 FLUX RESPONSE UNDER CHANGING CLIMATE CONDITIONS: A SATELLITE REMOTE SENSING DRIVEN ANALYSIS OF THE NET ECOSYSTEM CARBON BUDGET FOR ARCTIC AND BOREAL REGIONS

The impact of warming on the net ecosystem carbon budget (NECB) in Arctic-boreal regions remains highly uncertain. Heightened CH4 emissions from Arctic-boreal ecosystems could shift the northern NECB from an annual carbon sink further towards net carbon source. Northern wetland CH4 fluxes may be particularly sensitive to climate warming, increased soil temperatures and duration of the soil non-frozen period. Changes in northern high latitude surface hydrology will also impact the NECB, with surface and soil wetting resulting from thawing permafrost landscapes and shifts in precipitation patterns; summer drought conditions can potentially reduce vegetation productivity and land sink of atmospheric CO2 but also moderate the magnitude of CH4 increase. The first component of this work develops methods to assess seasonal variability and longer term trends in Arctic-boreal surface water inundation from satellite microwave observations, and quantifies estimate uncertainty. The second component of this work uses this information to improve understanding of impacts associated with changing environmental conditions on high latitude wetland CH4 emissions. The third component focuses on the development of a satellite remote sensing data informed Terrestrial Carbon Flux (TCF) model for northern wetland regions to quantify daily CH4 emissions and the NECB, in addition to vegetation productivity and landscape CO2 respiration loss. Finally, the fourth component of this work features further enhancement of the TCF model by improving representation of diverse tundra and boreal wetland ecosystem land cover types. A comprehensive database for tower eddy covariance CO2 and CH4 flux observations for the Arctic-boreal region was developed to support these efforts, providing an assessment of the TCF model ability to accurately quantify contemporary changes in regional terrestrial carbon sink/source strength

Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites:TL-LUE Parameterization and Validation

Light use efficiency (LUE) models are widely used to simulate gross primary production (GPP). However, the treatment of the plant canopy as a big leaf by these models can introduce large uncertainties in simulated GPP. Recently, a two-leaf light use efficiency (TL-LUE) model was developed to simulate GPP separately for sunlit and shaded leaves and has been shown to outperform the big-leaf MOD17 model at six FLUX sites in China. In this study we investigated the performance of the TL-LUE model for a wider range of biomes. For this we optimized the parameters and tested the TL-LUE model using data from 98 FLUXNET sites which are distributed across the globe. The results showed that the TL-LUE model performed in general better than the MOD17 model in simulating 8 day GPP. Optimized maximum light use efficiency of shaded leaves (εmsh) was 2.63 to 4.59 times that of sunlit leaves (εmsu). Generally, the relationships of εmsh and εmsu with εmax were well described by linear equations, indicating the existence of general patterns across biomes. GPP simulated by the TL-LUE model was much less sensitive to biases in the photosynthetically active radiation (PAR) input than the MOD17 model. The results of this study suggest that the proposed TL-LUE model has the potential for simulating regional and global GPP of terrestrial ecosystems, and it is more robust with regard to usual biases in input data than existing approaches which neglect the bimodal within-canopy distribution of PAR

Remote Sensing of Land Surface Phenology

Land surface phenology (LSP) uses remote sensing to monitor seasonal dynamics in vegetated land surfaces and retrieve phenological metrics (transition dates, rate of change, annual integrals, etc.). LSP has developed rapidly in the last few decades. Both regional and global LSP products have been routinely generated and play prominent roles in modeling crop yield, ecological surveillance, identifying invasive species, modeling the terrestrial biosphere, and assessing impacts on urban and natural ecosystems. Recent advances in field and spaceborne sensor technologies, as well as data fusion techniques, have enabled novel LSP retrieval algorithms that refine retrievals at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. Meanwhile, rigorous assessment of the uncertainties in LSP retrievals is ongoing, and efforts to reduce these uncertainties represent an active research area. Open source software and hardware are in development, and have greatly facilitated the use of LSP metrics by scientists outside the remote sensing community. This reprint covers the latest developments in sensor technologies, LSP retrieval algorithms and validation strategies, and the use of LSP products in a variety of fields. It aims to summarize the ongoing diverse LSP developments and boost discussions on future research prospects