Search

The maximum carboxylation rate (Vcmax) is an important parameter for the coupled simulation of gross primary production (GPP) and evapotranspiration (ET) in terrestrial biosphere models (TBMs) such as the Canadian Land Surface Scheme Including biogeochemical Cycles (CLASSIC). Observations of Vcmax show it to vary both spatially and temporally, but it is often prescribed as constant in time and space for plant functional types (PFTs) in TBMs, which introduces large errors over North America's boreal biome. To reduce this uncertainty, we used a Bayesian algorithm to optimize Vcmax25 (Vcmax at 25 °C) in CLASSIC against eddy covariance observations at eight mature boreal forest stands in North America for six representative PFTs (two trees, two shrubs, and two herbs). As expected, the simulated GPP and ET using the optimized parameters generally obtained reduced root mean square deviation values compared with eddy covariance observations and corresponding stand-level estimates obtained from gridded global data products. The optimized Vcmax25 values for each PFT compared reasonably well with reported estimates derived from leaf-level gas exchange measurements. However, a large spatial variability of Vcmax25 was identified, especially for the shrub and herb PFTs. We found that the site characteristics, particularly latitude for the shrub PFTs and air temperature for evergreen needleleaf tree, explained much of the spatial variability, providing a basis to improve Vcmax25 parameterizations in TBMs at regional scales. [ABSTRACT FROM AUTHOR]

Carbon (C) and greenhouse gas (GHG) research has traditionally required data collection and analysis using advanced and often expensive instruments, complex and proprietary software, and skilled technicians. Partly as a result, relatively little C and GHG research has been conducted in resource-constrained developing countries. At the same time, these are the same countries and regions in which climate-change impacts will likely be strongest, and in which major science uncertainties are centred, given the importance of dryland and tropical systems to the global C cycle. Increasingly, scientific communities have adopted appropriate technology and approach (AT&A) for C and GHG research, which focuses on low-cost and low-technology instruments, open source software and data, and participatory and networking-based research approaches. Adopting AT&A can mean acquiring data with fewer technical constraints and lower economic burden and is thus a strategy for enhancing C and GHG research in developing countries. However, AT&A can be characterized by higher uncertainties; these can often be mitigated by carefully designing experiments, providing clear protocols for data collection, and monitoring and validating the quality of obtained data. For implementing this approach in developing countries, it is first necessary to recognize the scientific and moral importance of AT&A. At the same time, new AT&A techniques should be identified and further developed. All these processes should be promoted in collaboration with local researchers and through training local staff and encouraged for wide use and further innovation in developing countries. [ABSTRACT FROM AUTHOR]

With increasing crop water demands and drought threats, mapping and monitoring of cropland evapotranspiration (ET) at high spatial and temporal resolutions becomes increasingly critical for water management and sustainability. However, estimating ET from satellite for precise water resources management is still challenging due to the limitations in both existing ET models and satellite input data. Specifically, the process of ET is complex and difficult to model, and existing satellite remote sensing data could not fulfill high resolutions in both space and time. To address the above two issues, this study presented a new high spatiotemporal resolution ET mapping framework, i.e., BESS-STAIR, which integrates a satellite-driven water-carbon-energy coupled biophysical model BESS (Breathing Earth System Simulator) with a generic and fully-automated fusion algorithm STAIR (SaTallite dAta IntegRation). In this framework, STAIR provides daily 30-meter multispectral surface reflectance by fusing Landsat and MODIS satellite data to derive fine-resolution leaf area index and visible/near-infrared albedo, all of which, along with coarse-resolution meteorological and CO2 data, are used to drive BESS to estimate gap-free 30-m resolution daily ET. We applied BESS-STAIR from 2000 through 2017 in six areas across the U.S. Corn Belt, and validated BESS-STAIR ET estimations using flux tower measurements over 12 sites (85 site-years). Results showed that BESS-STAIR daily ET achieved an overall R2 = 0.75, with RMSE = 0.93 mm d-1 and relative error = 27.9 % when benchmarked with the flux measurements. In addition, BESS-STAIR ET estimations well captured the spatial patterns, seasonal cycles, and interannual dynamics in different sub-regions. The high performance of the BESS-STAIR framework is primarily resulted from: (1) the implementation of coupled constraints on water, carbon, and energy in BESS, (2) high-quality daily 30-m data from STAIR fusion algorithm, and (3) BESS's applicability under all-sky conditions. BESS-STAIR is calibration-free and has great potentials to be a reliable tool for water resources management and precision agriculture applications for the U.S. Corn Belt, and even for worldwide given the global coverage of its input data. [ABSTRACT FROM AUTHOR]

Accurate estimation of terrestrial photosynthesis has broad scientific and societal impacts. Measurements of photosynthesis can be used to assess plant health, quantify crop yield, and determine the largest CO2 flux in the carbon cycle. Long-term and continuous monitoring of vegetation optical properties can provide valuable information about plant physiology. Recent developments of the remote sensing of solar-induced chlorophyll fluorescence (SIF) and vegetation spectroscopy have shown promising results in using this information to quantify plant photosynthetic activities and stresses at the ecosystem scale. However, there are few automated systems that allow for unattended observations over months to years. Here we present FluoSpec 2, an automated system for collecting irradiance and canopy radiance that has been deployed in various ecosystems in the past years. The instrument design, calibration, and tests are recorded in detail. We discuss the future directions of this field spectroscopy system. A network of SIF sensors, FluoNet, is established to measure the diurnal and seasonal variations of SIF in several ecosystems. Automated systems such as FluoSpec 2 can provide unique information on ecosystem functioning and provide important support to the satellite remote sensing of canopy photosynthesis. [ABSTRACT FROM AUTHOR]

The savanna complex is a highly diverse global biome that occurs within the seasonally dry tropical to subtropical equatorial latitudes and are structurally and functionally distinct from grasslands and forests. Savannas are open-canopy environments that encompass a broad demographic continuum, often characterised by a changing dominance between C3-tree and C4-grass vegetation, where frequent environmental disturbances such as fire modulates the balance between ephemeral and perennial life forms. Climate change is projected to result in significant changes to the savanna floristic structure, with increases to woody biomass expected through CO2 fertilisation in mesic savannas and increased tree mortality expected through increased rainfall interannual variability in xeric savannas. The complex interaction between vegetation and climate that occurs in savannas has traditionally challenged terrestrial biosphere models (TBMs), which aim to simulate the interaction between the atmosphere and the land surface to predict responses of vegetation to changing in environmental forcing. In this review, we examine whether TBMs are able to adequately represent savanna fluxes and what implications potential deficiencies may have for climate change projection scenarios that rely on these models. We start by highlighting the defining characteristic traits and behaviours of savannas, how these differ across continents and how this information is (or is not) represented in the structural framework of many TBMs. We highlight three dynamic processes that we believe directly affect the water use and productivity of the savanna system: phenology, root-water access and fire dynamics. Following this, we discuss how these processes are represented in many current-generation TBMs and whether they are suitable for simulating savanna fluxes. Finally, we give an overview of how eddy-covariance observations in combination with other data sources can be used in model benchmarking and intercomparison frameworks to diagnose the performance of TBMs in this environment and formulate road maps for future development. Our investigation reveals that many TBMs systematically misrepresent phenology, the effects of fire and root-water access (if they are considered at all) and that these should be critical areas for future development. Furthermore, such processes must not be static (i.e. prescribed behaviour) but be capable of responding to the changing environmental conditions in order to emulate the dynamic behaviour of savannas. Without such developments, however, TBMs will have limited predictive capability in making the critical projections needed to understand how savannas will respond to future global change. [ABSTRACT FROM AUTHOR]

The savanna ecosystem is one of the most dominant and complex terrestrial biomes, deriving from a distinct vegetative surface comprised of co-dominant tree and grass populations. While these two vegetation types co-exist functionally, demographically they are not static but are dynamically changing in response to environmental forces such as annual fire events and rainfall variability. Modelling savanna environments with the current generation of terrestrial biosphere models (TBMs) has presented many problems, particularly describing fire frequency and intensity, phenology, leaf biochemistry of C3 and C4 photosynthesis vegetation, and root-water uptake. In order to better understand why TBMs perform so poorly in savannas, we conducted a model inter-comparison of six TBMs and assessed their performance at simulating latent energy (LE) and gross primary productivity (GPP) for five savanna sites along a rainfall gradient in northern Australia. Performance in predicting LE and GPP was measured using an empirical benchmarking system, which ranks models by their ability to utilise meteorological driving information to predict the fluxes. On average, the TBMs performed as well as a multi-linear regression of the fluxes against solar radiation, temperature and vapour pressure deficit but were outperformed by a more complicated nonlinear response model that also included the leaf area index (LAI). This identified that the TBMs are not fully utilising their input information effectively in determining savanna LE and GPP and highlights that savanna dynamics cannot be calibrated into models and that there are problems in underlying model processes. We identified key weaknesses in a model's ability to simulate savanna fluxes and their seasonal variation, related to the representation of vegetation by the models and root-water uptake. We underline these weaknesses in terms of three critical areas for development. First, prescribed tree-rooting depths must be deep enough, enabling the extraction of deep soil-water stores to maintain photosynthesis and transpiration during the dry season. Second, models must treat grasses as a co-dominant interface for water and carbon exchange rather than a secondary one to trees. Third, models need a dynamic representation of LAI that encompasses the dynamic phenology of savanna vegetation and its response to rainfall interannual variability. We believe that this study is the first to assess how well TBMs simulate savanna ecosystems and that these results will be used to improve the representation of savannas ecosystems in future global climate model studies. [ABSTRACT FROM AUTHOR]