Seasonal and interannual variation in water vapor and energy exchange over a typical steppe in Inner Mongolia, China

In this study, we conducted eddy covariance (EC) measurements of water vapor exchange over a typical steppe in a semi-arid area of the Inner Mongolia Plateau, China. Measurement sites were located within a 25-year-old enclosure with a relatively low leaf area index (similar to 1. 5 m(2) m(-2)) and dominated by Leymus chinensis. Energy balance closure was (H + LE) = 17.09 + 0.69 x (Rn - G) (W/m(2); r(2) = 0.95, n = 6596). Precipitation during the two growing seasons of the study period was similar to the long-term average. The peak evapotranspiration in 2004 was 4 mm d(-1), and 3.5 mm d(-1) in 2003. The maximum latent heat flux was higher than the sensible heat flux, and the sensible heat flux dominated the energy budget at midday during the entire growing season in 2003; latent heat flux was the main consumption component for net radiation during the 2004 growing season. During periods of frozen soil in 2003 and 2004, the sensible heat flux was the primary consumption component for net radiation. The soil heat flux component was similar in 2003 and 2004. The decoupling coefficient (between 0.5 and 0.1) indicates that evapotranspiration was strongly controlled by saturation water vapor pressure deficit (VPD) in this grassland. The results of this research suggest that energy exchange and evapotranspiration were controlled by the phenology of the vegetation and soil water content. In addition, the amount and frequency of rainfall significantly affect energy exchange and evapotranspiration upon the Inner Mongolia plateau. (c) 2007 Published by Elsevier B.V.

Depression of net ecosystem CO2 exchange in semi-arid Leymus chinensis steppe and alpine shrub

Uptake and release of carbon in grassland ecosystems is very critical to the global carbon balance and carbon storage. In this study, the dynamics of net ecosystem CO2 exchange (FNEE) of two grassland ecosystems were observed continuously using the eddy covariance technique during the growing season of 2003. One is the alpine shrub on the Tibet Plateau, and the other is the sem-arid Leymus chinensis steppe in Inner Mongolia of China. It was found that the FNEE of both ecosystems was significantly depressed under high solar radiation. Comprehensive analysis indicates that the depression of FNEE in the L. chinensis steppe was the results of decreased plant photosynthesis and increased ecosystem respiration (R-eco) under high temperature. Soil water stress in addition to the high atmospheric demand under the strong radiation was the primary factor limiting the stomatal conductance. In contrast, the depression of FNEE in the alpine shrub was closely related to the effects of temperature on both photosynthesis and ecosystem respiration, coupled with the reduction of plant photosynthesis due to partial stomatal closure under high temperature at mid-day. The R,c of the alpine shrub was sensitive to soil temperature during high turbulence (u* > 0.2 m s(-1)) but its FNEE decreased markedly when the temperature was higher than the optimal value of about 12 degrees C. Such low optimal temperature contrasted the optimal value (about 20 degrees C) for the steppe, and was likely due to the acclimation of most alpine plants to the long-term low temperature on the Tibet Plateau. We inferred that water stress was the primary factor causing depression of the FNEE in the semi-arid steppe ecosystem, while relative high temperature under strong solar radiation was the main reason for the decrease of FNEE in the alpine shrub. This study implies that different grassland ecosystems may respond differently to climate change in the future. (c) 2006 Elsevier B.V All rights reserved.

Energy exchange between the atmosphere and a meadow ecosystem on the Qinghai-Tibetan Plateau

To reveal the potential contribution of grassland ecosystems to climate change, we examined the energy exchange over an alpine Kobresia meadow on the northeastern Qinghai-Tibetan Plateau. The annual pattern of energy exchange showed a clear distinction between periods of frozen soil with the daily mean soil temperature at 5 cm (T-s5 ≤ 0 ° C) and non-frozen soil (T-s5 > 0 ° C). More than 80% of net radiation was converted to sensible heat (H) during the frozen soil period, but H varied considerably with the change in vegetation during the non-frozen soil period. Three different sub-periods were further distinguished for the later period: (1) the pre-growth period with Bowen ratio (β) > 1 was characterized by a high β of 3.0 in average and the rapid increase of net radiation associated with the increases of H, latent heat (LE) and soil heat; (2) during the Growth period when β ≤ 1, the LE was high but H fluxes was low with β changing between 0.3 and 0.4; (3) the post-growth period with average β of 3.6 when H increased again and reached a second maximum around early October. The seasonal pattern suggests that the phenology of the vegetation and the soil water content were the major factors affecting the energy partitioning in the alpine meadow ecosystem. © 2005 Elsevier B.V. All rights reserved.

Carbon dioxide exchange between the atmosphere and an alpine meadow ecosystem on the Qinghai-Tibetan Plateau, China

We used the eddy covariance method to measure the M exchange between the atmosphere and an alpine meadow ecosystem (37degrees29-45'N, 101degrees12-23'E, 3250m a.s.l.) on the Qinghai-Tibetan Plateau, China in the 2001 and 2002 growing seasons. The maximum rates Of CO2 uptake and release derived from the diurnal course Of CO2 flux (FCO2) were -10.8 and 4.4 mumol m(-2) s(-1), respectively, indicating a relatively high net carbon sequestration potential as compared to subalpine coniferous forest at similar elevation and latitude. The largest daily CO2 uptake was 3.9 g cm(-2) per day on 7 July 2002, which is less than half of those reported for lowland grassland and forest at similar latitudes. The daily CO2 uptake during the measurement period indicated that the alpine ecosystem might behave as a sink of atmospheric M during the growing season if the carbon lost due to grazing is not significant. The daytime CO2 uptake was linearly correlated with the daily photosynthetic photon flux density each month. The nighttime averaged F-CO2 showed a positive exponential correlation with the soil temperature, but apparently negative correlation with the soil water content. (C) 2004 Elsevier B.V. All rights reserved.

Crop yield reduction in the tropics under climate change: Processes and uncertainties

Many modelling studies examine the impacts of climate change on crop yield, but few explore either the underlying bio-physical processes, or the uncertainty inherent in the parameterisation of crop growth and development. We used a perturbed-parameter crop modelling method together with a regional climate model (PRECIS) driven by the 2071-2100 SRES A2 emissions scenario in order to examine processes and uncertainties in yield simulation. Crop simulations used the groundnut (i.e. peanut; Arachis hypogaea L.) version of the General Large-Area Model for annual crops (GLAM). Two sets of GLAM simulations were carried out: control simulations and fixed-duration simulations, where the impact of mean temperature on crop development rate was removed. Model results were compared to sensitivity tests using two other crop models of differing levels of complexity: CROPGRO, and the groundnut model of Hammer et al. [Hammer, G.L., Sinclair, T.R., Boote, K.J., Wright, G.C., Meinke, H., and Bell, M.J., 1995, A peanut simulation model: I. Model development and testing. Agron. J. 87, 1085-1093]. GLAM simulations were particularly sensitive to two processes. First, elevated vapour pressure deficit (VPD) consistently reduced yield. The same result was seen in some simulations using both other crop models. Second, GLAM crop duration was longer, and yield greater, when the optimal temperature for the rate of development was exceeded. Yield increases were also seen in one other crop model. Overall, the models differed in their response to super-optimal temperatures, and that difference increased with mean temperature; percentage changes in yield between current and future climates were as diverse as -50% and over +30% for the same input data. The first process has been observed in many crop experiments, whilst the second has not. Thus, we conclude that there is a need for: (i) more process-based modelling studies of the impact of VPD on assimilation, and (ii) more experimental studies at super-optimal temperatures. Using the GLAM results, central values and uncertainty ranges were projected for mean 2071-2100 crop yields in India. In the fixed-duration simulations, ensemble mean yields mostly rose by 10-30%. The full ensemble range was greater than this mean change (20-60% over most of India). In the control simulations, yield stimulation by elevated CO2 was more than offset by other processes-principally accelerated crop development rates at elevated, but sub-optimal, mean temperatures. Hence, the quantification of uncertainty can facilitate relatively robust indications of the likely sign of crop yield changes in future climates. (C) 2007 Elsevier B.V. All rights reserved.

Remote estimation of thermal inertia and soil heat flux for bare soil

A method is presented which allows thermal inertia (the soil heat capacity times the square root of the soil thermal diffusivity, C(h)rootD(h)), to be estimated remotely from micrometeorological observations. The method uses the drop in surface temperature, T-s, between sunset and sunrise, and the average night-time net radiation during that period, for clear, still nights. A Fourier series analysis was applied to analyse the time series of T-s . The Fourier series constants, together with the remote estimate of thermal inertia, were used in an analytical expression to calculate diurnal estimates of the soil heat flux, G. These remote estimates of C(h)rootD(h) and G compared well with values derived from in situ sensors. The remote and in situ estimates of C(h)rootD(h) both correlated well with topsoil moisture content. This method potentially allows area-average estimates of thermal inertia and soil heat flux to be derived from remote sensing, e.g. METEOSAT Second Generation, where the area is determined by the sensor's height and viewing angle. (C) 2003 Elsevier B.V. All rights reserved.

Use of a crop model ensemble to quantify CO2 stimulation of water-stressed and well-watered crops

Increased atmospheric concentrations of carbon dioxide (CO2) will benefit the yield of most crops. Two free air CO2 enrichment (FACE) meta-analyses have shown increases in yield of between 0 and 73% for C3 crops. Despite this large range, few crop modelling studies quantify the uncertainty inherent in the parameterisation of crop growth and development. We present a novel perturbed-parameter method of crop model simulation, which uses some constraints from observations, that does this. The model used is the groundnut (i.e. peanut; Arachis hypogaea L.) version of the general large-area model for annual crops (GLAM). The conclusions are of relevance to C3 crops in general. The increases in yield simulated by GLAM for doubled CO2 were between 16 and 62%. The difference in mean percentage increase between well-watered and water-stressed simulations was 6.8. These results were compared to FACE and controlled environment studies, and to sensitivity tests on two other crop models of differing levels of complexity: CROPGRO, and the groundnut model of Hammer et al. [Hammer, G.L., Sinclair, T.R., Boote, K.J., Wright, G.C., Meinke, H., Bell, M.J., 1995. A peanut simulation model. I. Model development and testing. Agron. J. 87, 1085-1093]. The relationship between CO2 and water stress in the experiments and in the models was examined. From a physiological perspective, water-stressed crops are expected to show greater CO2 stimulation than well-watered crops. This expectation has been cited in literature. However, this result is not seen consistently in either the FACE studies or in the crop models. In contrast, leaf-level models of assimilation do consistently show this result. An analysis of the evidence from these models and from the data suggests that scale (canopy versus leaf), model calibration, and model complexity are factors in determining the sign and magnitude of the interaction between CO2 and water stress. We conclude from our study that the statement that 'water-stressed crops show greater CO2 stimulation than well-watered crops' cannot be held to be universally true. We also conclude, preliminarily, that the relationship between water stress and assimilation varies with scale. Accordingly, we provide some suggestions on how studies of a similar nature, using crop models of a range of complexity, could contribute further to understanding the roles of model calibration, model complexity and scale. (C) 2008 Elsevier B.V. All rights reserved.

Design and optimisation of a large-area process-based model for annual crops

The formulation of a new process-based crop model, the general large-area model (GLAM) for annual crops is presented. The model has been designed to operate on spatial scales commensurate with those of global and regional climate models. It aims to simulate the impact of climate on crop yield. Procedures for model parameter determination and optimisation are described, and demonstrated for the prediction of groundnut (i.e. peanut; Arachis hypogaea L.) yields across India for the period 1966-1989. Optimal parameters (e.g. extinction coefficient, transpiration efficiency, rate of change of harvest index) were stable over space and time, provided the estimate of the yield technology trend was based on the full 24-year period. The model has two location-specific parameters, the planting date, and the yield gap parameter. The latter varies spatially and is determined by calibration. The optimal value varies slightly when different input data are used. The model was tested using a historical data set on a 2.5degrees x 2.5degrees grid to simulate yields. Three sites are examined in detail-grid cells from Gujarat in the west, Andhra Pradesh towards the south, and Uttar Pradesh in the north. Agreement between observed and modelled yield was variable, with correlation coefficients of 0.74, 0.42 and 0, respectively. Skill was highest where the climate signal was greatest, and correlations were comparable to or greater than correlations with seasonal mean rainfall. Yields from all 35 cells were aggregated to simulate all-India yield. The correlation coefficient between observed and simulated yields was 0.76, and the root mean square error was 8.4% of the mean yield. The model can be easily extended to any annual crop for the investigation of the impacts of climate variability (or change) on crop yield over large areas. (C) 2004 Elsevier B.V. All rights reserved.

The observed relationships between wheat and climate in China

Recent changes in climate have had a measurable impact on crop yield in China. The objective of this study is to investigate how climate variability affects wheat yield in China at different spatial scales. First the response of wheat yield to the climate at the provincial level from 1978 to 1995 for China was analysed. Wheat yield variability was only correlated with climate variability in some regions of China. At the provincial level, the variability of precipitation had a negative impact on wheat yield in parts of southeast China, but the seasonal mean temperature had a negative impact on wheat yield in only a few provinces, where significant variability in precipitation explained about 23–60% of yield variability, and temperature variability accounted for 37–41% of yield variability from 1978 to 1995. The correlation between wheat yield and climate for the whole of China from 1985 to 2000 was investigated at five spatial scales using climate data. The Climate Research Unit (CRU) and National Centers for Environmental Prediction (NCEP) proportions of the grid cells with a significant yield–precipitation correlation declined progressively from 14.6% at 0.5° to 0% at 5° scale. In contrast, the proportion of grid cells significant for the yield–temperature correlation increased progressively from 1.9% at 0.5° scale to 16% at 5° scale. This indicates that the variability of precipitation has a higher association with wheat yield at small scales (0.5°, 2°/2.5°) than at larger scales (4°/5.0°); but wheat yield has a good association with temperature at all levels of aggregation. The precipitation variable at the smaller scales (0.5°, 2°/2.5°) is a dominant factor in determining inter-annual wheat yield variability more so than at the larger scales (4°/5°). We conclude that in the current climate the relationship between wheat yield and each of precipitation and temperature becomes weaker and stronger, respectively, with an increase in spatial scale.

Simulating dynamic crop growth with an adapted land surface model – JULES-SUCROS: model development and validation

The increasing demand for ecosystem services, in conjunction with climate change, is expected to signif- icantly alter terrestrial ecosystems. In order to evaluate the sustainability of land and water resources, there is a need for a better understanding of the relationships between crop production, land surface characteristics and the energy and water cycles. These relationships are analysed using the Joint UK Land Environment Simulator (JULES). JULES includes the full hydrological cycle and vegetation effects on the energy, water, and carbon fluxes. However, this model currently only simulates land surface processes in natural ecosystems. An adapted version of JULES for agricultural ecosystems, called JULES-SUCROS has therefore been developed. In addition to overall model improvements, JULES-SUCROS includes a dynamic crop growth structure that fully fits within and builds upon the biogeochemical modelling framework for natural vegetation. Specific agro-ecosystem features such as the development of yield-bearing organs and the phenological cycle from sowing till harvest have been included in the model. This paper describes the structure of JULES-SUCROS and evaluates the fluxes simulated with this model against FLUXNET measurements at 6 European sites. We show that JULES-SUCROS significantly improves the correlation between simulated and observed fluxes over cropland and captures well the spatial and temporal vari- ability of the growth conditions in Europe. Simulations with JULES-SUCROS highlight the importance of vegetation structure and phenology, and the impact they have on land–atmosphere interactions.

Towards an improved and more flexible representation of water stress in coupled photosynthesis–stomatal conductance models

Coupled photosynthesis–stomatal conductance (A–gs) models are commonly used in ecosystem models to represent the exchange rate of CO2 and H2O between vegetation and the atmosphere. The ways these models account for water stress differ greatly among modelling schemes. This study provides insight into the impact of contrasting model configurations of water stress on the simulated leaf-level values of net photosynthesis (A), stomatal conductance (gs), the functional relationship among them and their ratio, the intrinsic water use efficiency (A/gs), as soil dries. A simple, yet versatile, normalized soil moisture dependent function was used to account for the effects of water stress on gs, on mesophyll conductance (gm) and on the biochemical capacity. Model output was compared to leaf-level values obtained from the literature. The sensitivity analyses emphasized the necessity to combine both stomatal and non-stomatal limitations of A in coupled A–gs models to accurately capture the observed functional relationships A vs. gs and A/gsvs. gs in response to drought. Accounting for water stress in coupled A–gs models by imposing either stomatal or biochemical limitations of A, as commonly practiced in most ecosystem models, failed to reproduce the observed functional relationship between key leaf gas exchange attributes. A quantitative limitation analysis revealed that the general pattern of C3 photosynthetic response to water stress may be well represented in coupled A–gs models by imposing the highest limitation strength to gm, then to gs and finally to the biochemical capacity.

Determination of turbulent heat fluxes using a large aperture scintillometer over undulating mixed agricultural terrain

Scintillometry is an established technique for determining large areal average sensible heat fluxes. The scintillometer measurement is related to sensible heat flux via Monin–Obukhov similarity theory, which was developed for ideal homogeneous land surfaces. In this study it is shown that judicious application of scintillometry over heterogeneous mixed agriculture on undulating topography yields valid results when compared to eddy covariance (EC). A large aperture scintillometer (LAS) over a 2.4 km path was compared with four EC stations measuring sensible (H) and latent (LvE) heat fluxes over different vegetation (cereals and grass) which when aggregated were representative of the LAS source area. The partitioning of available energy into H and LvE varied strongly for different vegetation types, with H varying by a factor of three between senesced winter wheat and grass pasture. The LAS derived H agrees (one-to-one within the experimental uncertainty) with H aggregated from EC with a high coefficient of determination of 0.94. Chronological analysis shows individual fields may have a varying contribution to the areal average sensible heat flux on short (weekly) time scales due to phenological development and changing soil moisture conditions. Using spatially aggregated measurements of net radiation and soil heat flux with H from the LAS, the areal averaged latent heat flux (LvELAS) was calculated as the residual of the surface energy balance. The regression of LvELAS against aggregated LvE from the EC stations has a slope of 0.94, close to ideal, and demonstrates that this is an accurate method for the landscape-scale estimation of evaporation over heterogeneous complex topography.

Calibration and bias correction of climate projections for crop modelling: an idealised case study over Europe

Producing projections of future crop yields requires careful thought about the appropriate use of atmosphere-ocean global climate model (AOGCM) simulations. Here we describe and demonstrate multiple methods for ‘calibrating’ climate projections using an ensemble of AOGCM simulations in a ‘perfect sibling’ framework. Crucially, this type of analysis assesses the ability of each calibration methodology to produce reliable estimates of future climate, which is not possible just using historical observations. This type of approach could be more widely adopted for assessing calibration methodologies for crop modelling. The calibration methods assessed include the commonly used ‘delta’ (change factor) and ‘nudging’ (bias correction) approaches. We focus on daily maximum temperature in summer over Europe for this idealised case study, but the methods can be generalised to other variables and other regions. The calibration methods, which are relatively easy to implement given appropriate observations, produce more robust projections of future daily maximum temperatures and heat stress than using raw model output. The choice over which calibration method to use will likely depend on the situation, but change factor approaches tend to perform best in our examples. Finally, we demonstrate that the uncertainty due to the choice of calibration methodology is a significant contributor to the total uncertainty in future climate projections for impact studies. We conclude that utilising a variety of calibration methods on output from a wide range of AOGCMs is essential to produce climate data that will ensure robust and reliable crop yield projections.

The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data

We describe a model-data fusion (MDF) inter-comparison project (REFLEX), which compared various algorithms for estimating carbon (C) model parameters consistent with both measured carbon fluxes and states and a simple C model. Participants were provided with the model and with both synthetic net ecosystem exchange (NEE) of CO2 and leaf area index (LAI) data, generated from the model with added noise, and observed NEE and LAI data from two eddy covariance sites. Participants endeavoured to estimate model parameters and states consistent with the model for all cases over the two years for which data were provided, and generate predictions for one additional year without observations. Nine participants contributed results using Metropolis algorithms, Kalman filters and a genetic algorithm. For the synthetic data case, parameter estimates compared well with the true values. The results of the analyses indicated that parameters linked directly to gross primary production (GPP) and ecosystem respiration, such as those related to foliage allocation and turnover, or temperature sensitivity of heterotrophic respiration, were best constrained and characterised. Poorly estimated parameters were those related to the allocation to and turnover of fine root/wood pools. Estimates of confidence intervals varied among algorithms, but several algorithms successfully located the true values of annual fluxes from synthetic experiments within relatively narrow 90% confidence intervals, achieving >80% success rate and mean NEE confidence intervals <110 gC m−2 year−1 for the synthetic case. Annual C flux estimates generated by participants generally agreed with gap-filling approaches using half-hourly data. The estimation of ecosystem respiration and GPP through MDF agreed well with outputs from partitioning studies using half-hourly data. Confidence limits on annual NEE increased by an average of 88% in the prediction year compared to the previous year, when data were available. Confidence intervals on annual NEE increased by 30% when observed data were used instead of synthetic data, reflecting and quantifying the addition of model error. Finally, our analyses indicated that incorporating additional constraints, using data on C pools (wood, soil and fine roots) would help to reduce uncertainties for model parameters poorly served by eddy covariance data.

Variation in the global-scale impacts of climate change on crop productivity due to climate model uncertainty and adaptation

Crop production is inherently sensitive to fluctuations in weather and climate and is expected to be impacted by climate change. To understand how this impact may vary across the globe many studies have been conducted to determine the change in yield of several crops to expected changes in climate. Changes in climate are typically derived from a single to no more than a few General Circulation Models (GCMs). This study examines the uncertainty introduced to a crop impact assessment when 14 GCMs are used to determine future climate. The General Large Area Model for annual crops (GLAM) was applied over a global domain to simulate the productivity of soybean and spring wheat under baseline climate conditions and under climate conditions consistent with the 2050s under the A1B SRES emissions scenario as simulated by 14 GCMs. Baseline yield simulations were evaluated against global country-level yield statistics to determine the model's ability to capture observed variability in production. The impact of climate change varied between crops, regions, and by GCM. The spread in yield projections due to GCM varied between no change and a reduction of 50%. Without adaptation yield response was linearly related to the magnitude of local temperature change. Therefore, impacts were greatest for countries at northernmost latitudes where warming is predicted to be greatest. However, these countries also exhibited the greatest potential for adaptation to offset yield losses by shifting the crop growing season to a cooler part of the year and/or switching crop variety to take advantage of an extended growing season. The relative magnitude of impacts as simulated by each GCM was not consistent across countries and between crops. It is important, therefore, for crop impact assessments to fully account for GCM uncertainty in estimating future climates and to be explicit about assumptions regarding adaptation.