Biosphere Energy Transfer Hydrology Research Articles

Wide discrepancies in the magnitude and direction of modeled solar-induced chlorophyll fluorescence in response to light conditions

Abstract. Recent successes in passive remote sensing of far-red solar-induced chlorophyll fluorescence (SIF) have spurred the development and integration of canopy-level fluorescence models in global terrestrial biosphere models (TBMs) for climate and carbon cycle research. The interaction of fluorescence with photochemistry at the leaf and canopy scales provides opportunities to diagnose and constrain model simulations of photosynthesis and related processes, through direct comparison to and assimilation of tower, airborne, and satellite data. TBMs describe key processes related to the absorption of sunlight, leaf-level fluorescence emission, scattering, and reabsorption throughout the canopy. Here, we analyze simulations from an ensemble of process-based TBM–SIF models (SiB3 – Simple Biosphere Model, SiB4, CLM4.5 – Community Land Model, CLM5.0, BETHY – Biosphere Energy Transfer Hydrology, ORCHIDEE – Organizing Carbon and Hydrology In Dynamic Ecosystems, and BEPS – Boreal Ecosystems Productivity Simulator) and the SCOPE (Soil Canopy Observation Photosynthesis Energy) canopy radiation and vegetation model at a subalpine evergreen needleleaf forest near Niwot Ridge, Colorado. These models are forced with local meteorology and analyzed against tower-based continuous far-red SIF and gross-primary-productivity-partitioned (GPP) eddy covariance data at diurnal and synoptic scales during the growing season (July–August 2017). Our primary objective is to summarize the site-level state of the art in TBM–SIF modeling over a relatively short time period (summer) when light, canopy structure, and pigments are similar, setting the stage for regional- to global-scale analyses. We find that these models are generally well constrained in simulating photosynthetic yield but show strongly divergent patterns in the simulation of absorbed photosynthetic active radiation (PAR), absolute GPP and fluorescence, quantum yields, and light response at the leaf and canopy scales. This study highlights the need for mechanistic modeling of nonphotochemical quenching in stressed and unstressed environments and improved the representation of light absorption (APAR), distribution of light across sunlit and shaded leaves, and radiative transfer from the leaf to the canopy scale.

Global NPP and straw bioenergy trends for 2000–2014

The ability of terrestrial ecosystems to sequestrate carbon and the use of cultivated land for food and bioenergy are both affected by large scale meteorological phenomena and climate change. In this study we used the Biosphere Energy Transfer Hydrology (BETHY/DLR) model to compute global Net Primary Productivity (NPP) on 1 km2 resolution for the fifteen years 2000–2014. We found a global average of 60.2 Pg carbon per year, with the main contribution from the Tropics. Extending pre-existing studies with our results revealed a surprisingly low positive global trend in NPP of 0.04 Pg y−1 for the past 30 years. To bring NPP into application we used it to derive straw bioenergy potentials. For agricultural areas we found a global average bioenergy potential of 35.9 EJ y−1. On a regional level as e.g. the “Wheat belt” in Western-Australia we found an interannual variability of up to ±40%, due to climate events (i.e. ENSO).

Net primary productivity in Kazakhstan, its spatio-temporal patterns and relation to meteorological variables

Arid and semiarid environments are susceptible to environmental degradation and desertification. Modelling net primary productivity (NPP) and analysis of spatio-temporal patterns help to understand ecological functioning especially in these areas. In this study, we apply the Biosphere Energy Transfer Hydrology Model (BETHY/DLR) to derive NPP for Kazakhstan for 2003–2011. Results are analyzed regarding spatial, monthly, and inter-annual variations. Mean annual NPP for Kazakhstan is 143 g C m−2 and maximum productivity is reached in June. Most monthly NPP anomalies occur in semiarid North of Kazakhstan. These regions seem to be most strongly affected by changes in meteorology and are likely to be vulnerable to changing climate. Arid ecosystems show lower inter-annual NPP variability than semiarid lands. Correlations between NPP and meteorological parameters reveal variable influence of temperature, PAR, and precipitation on vegetation productivity during the year. Reaction of vegetation growth to precipitation is delayed 1–2 months. Temperature is most critical in spring and precipitation in summer affects NPP in August–October. The results presented in this study help to identify regions that are vulnerable to global change. They allow predictions on possible effects of expected future climate change on vegetation productivity in arid and semiarid Kazakhstan and support sustainable land management.

Quantifying the constraint of biospheric process parameters by CO<sub>2</sub> concentration and flux measurement networks through a carbon cycle data assimilation system

Abstract. The sensitivity of the process parameters of the Biosphere Energy Transfer HYdrology (BETHY) model to choices of atmospheric concentration network, high frequency terrestrial fluxes, and the choice of flux measurement network is investigated by using a carbon cycle data assimilation system. We use BETHY-generated fluxes as a proxy of flux measurements. Results show that monthly mean or low-frequency observations of CO2 concentration provide strong constraints on parameters relevant for net flux (NEP) but only weak constraints for parameters controlling gross fluxes. The use of high-frequency CO2 concentration observations, which has led to great refinement of spatial scales in inversions of net flux, adds little to the observing system in the Carbon Cycle Data Assimilation System (CCDAS) case. This unexpected result is explained by the fact that the stations of the CO2 concentration network we use are not well placed to measure such high frequency signals. Indeed, CO2 concentration sensitivities relevant for such high frequency fluxes are found to be largely confined in the vicinity of the corresponding fluxes, and are therefore not well observed by background monitoring stations. In contrast, our results clearly show the potential of flux measurements to better constrain the model parameters relevant for gross primary productivity (GPP) and net primary productivity (NPP). Given uncertainties in the spatial description of ecosystem functions, we recommend a combined observing strategy.

Quantifying the carbon uptake by vegetation for Europe on a 1 km<sup>2</sup> resolution using a remote sensing driven vegetation model

Abstract. In this study we compare monthly gross primary productivity (GPP) time series (2000–2007), computed for Europe with the Biosphere Energy Transfer Hydrology (BETHY/DLR) model with monthly data from the eddy covariance measurements network FLUXNET. BETHY/DLR with a spatial resolution of 1 km2 is designed for regional and continental applications (here Europe) and operated at the German Aerospace Center (DLR). It was adapted from the BETHY scheme to be driven by remote sensing data (leaf area index (LAI) and land cover information) and meteorology. Time series of LAI obtained from the CYCLOPES database are used to control the phenology of vegetation. Meteorological time series from the European Centre for Medium-Range Weather Forecasts (ECMWF) are used as driver. These comprise daily information on temperature, precipitation, wind speed and radiation. Additionally, static maps such as land cover, elevation, and soil type are used. To validate our model results we used eddy covariance measurements from the FLUXNET network of 74 towers across Europe. For forest sites we found that our model predicts between 20 and 40% higher annual GPP sums. In contrast, for cropland sites BETHY/DLR results show about 18% less GPP than eddy covariance measurements. For grassland sites, between 10% more and 16% less GPP was calculated with BETHY/DLR. A mean total carbon uptake of 2.5 PgC a−1 (±0.17 PgC a−1) was found for Europe. In addition, this study reports on risks that arise from the comparison of modelled data to FLUXNET measurements and their interpretation width. Furthermore we investigate reasons for uncertainties in model results and focus here on Vmax values, and finally embed our results into a broader context of model validation studies published during the last years in order to evaluate differences or similarities in analysed error sources.

Consistent assimilation of MERIS FAPAR and atmospheric CO<sub>2</sub> into a terrestrial vegetation model and interactive mission benefit analysis

Abstract. The terrestrial biosphere is currently a strong sink for anthropogenic CO2 emissions. Through the radiative properties of CO2, the strength of this sink has a direct influence on the radiative budget of the global climate system. The accurate assessment of this sink and its evolution under a changing climate is, hence, paramount for any efficient management strategies of the terrestrial carbon sink to avoid dangerous climate change. Unfortunately, simulations of carbon and water fluxes with terrestrial biosphere models exhibit large uncertainties. A considerable fraction of this uncertainty reflects uncertainty in the parameter values of the process formulations within the models. This paper describes the systematic calibration of the process parameters of a terrestrial biosphere model against two observational data streams: remotely sensed FAPAR (fraction of absorbed photosynthetically active radiation) provided by the MERIS (ESA's Medium Resolution Imaging Spectrometer) sensor and in situ measurements of atmospheric CO2 provided by the GLOBALVIEW flask sampling network. We use the Carbon Cycle Data Assimilation System (CCDAS) to systematically calibrate some 70 parameters of the terrestrial BETHY (Biosphere Energy Transfer Hydrology) model. The simultaneous assimilation of all observations provides parameter estimates and uncertainty ranges that are consistent with the observational information. In a subsequent step these parameter uncertainties are propagated through the model to uncertainty ranges for predicted carbon fluxes. We demonstrate the consistent assimilation at global scale, where the global MERIS FAPAR product and atmospheric CO2 are used simultaneously. The assimilation improves the match to independent observations. We quantify how MERIS data improve the accuracy of the current and future (net and gross) carbon flux estimates (within and beyond the assimilation period). We further demonstrate the use of an interactive mission benefit analysis tool built around CCDAS to support the design of future space missions. We find that, for long-term averages, the benefit of FAPAR data is most pronounced for hydrological quantities, and moderate for quantities related to carbon fluxes from ecosystems. The benefit for hydrological quantities is highest for semi-arid tropical or sub-tropical regions. Length of mission or sensor resolution is of minor importance.

How sensitive are estimates of carbon fixation in agricultural models to input data?

BackgroundProcess based vegetation models are central to understand the hydrological and carbon cycle. To achieve useful results at regional to global scales, such models require various input data from a wide range of earth observations. Since the geographical extent of these datasets varies from local to global scale, data quality and validity is of major interest when they are chosen for use. It is important to assess the effect of different input datasets in terms of quality to model outputs. In this article, we reflect on both: the uncertainty in input data and the reliability of model results. For our case study analysis we selected the Marchfeld region in Austria. We used independent meteorological datasets from the Central Institute for Meteorology and Geodynamics and the European Centre for Medium-Range Weather Forecasts (ECMWF). Land cover / land use information was taken from the GLC2000 and the CORINE 2000 products.ResultsFor our case study analysis we selected two different process based models: the Environmental Policy Integrated Climate (EPIC) and the Biosphere Energy Transfer Hydrology (BETHY/DLR) model. Both process models show a congruent pattern to changes in input data. The annual variability of NPP reaches 36% for BETHY/DLR and 39% for EPIC when changing major input datasets. However, EPIC is less sensitive to meteorological input data than BETHY/DLR. The ECMWF maximum temperatures show a systematic pattern. Temperatures above 20°C are overestimated, whereas temperatures below 20°C are underestimated, resulting in an overall underestimation of NPP in both models. Besides, BETHY/DLR is sensitive to the choice and accuracy of the land cover product.DiscussionThis study shows that the impact of input data uncertainty on modelling results need to be assessed: whenever the models are applied under new conditions, local data should be used for both input and result comparison.

Validating modelled NPP using statistical yield data

The German Remote Sensing Data Center operates the Biosphere Energy Transfer Hydrology Model, a process model that estimates the net primary productivity of agricultural areas. The model is driven by remote sensing data and meteorological data. Remotely sensed datasets including a time series of the leaf area index, which describes vegetation condition, and a land cover classification, which provides information about land use, are needed. Currently leaf area indices and land cover data derived from the sensor vegetation are used. Both datasets have spatial resolutions of about 1 km × 1 km and are freely available for the area of investigation (Germany and Austria). The meteorological input parameters are air temperature (at 2 m height), precipitation, cloud cover, wind speed (at 10 m height) and soil water content (in the four uppermost soil layers); these are obtained from the European Centre for Medium-Range Weather Forecasts, with a spatial resolution of about 0.25° × 0.25° and a temporal resolution up to four times daily. The output of the model, the gross primary productivity, is calculated at daily resolution. By subtracting the cumulative plant maintenance and growth respiration, the net primary productivity is then determined. In order to validate the modelled net primary productivity, crop yield estimates derived from the national statistics of Germany and Austria are used. After estimating above-ground biomass using plant-specific above- to below-ground ratios, conversion factors (corn-to-straw and leaf-to-beet relations) are applied to estimate total biomass. Finally the carbon content of dry matter is estimated. To correlate model results with these statistical data, the modelled data are aggregated to net primary productivity per administrative district. The results show that a process model using remote sensing data as input can deliver reliable estimates of agricultural biomass potential which are highly correlated with statistically derived estimates of actual biomass produced. ► Using the vegetation model BETHY/DLR yearly NPP is modelled (1 km spatial resolution). ► Validation of biophysical model revealed accuracy of about 8–17% for agriculture. ► Slight overestimation in comparison to calibrated model results (15%). ► ECMWF does not represent drought events as happened in 2001 in Austria.

Improving the predictability of global CO2assimilation rates under climate change

Feedbacks between the terrestrial carbon cycle and the atmosphere have the potential to greatly modify expected rates of future climate change. This makes it all the more urgent to exploit all existing data for the purpose of accurate modelling of the underlying processes. Here we use a Bayesian random sampling method to constrain parameters of the Farquhar model of leaf photosynthesis and a model of leaf respiration against a comprehensive set of plant trait data at the leaf level. The resulting probability density function (PDF) of model parameters is contrasted with a PDF derived using a conventional expert knowledge approach. When running the Biosphere Energy Transfer Hydrology (BETHY) scheme with a 1000- member sub-sample of each of the two PDFs for present climate and a climate scenario, we find that the use of plant trait data is able to reduce the uncertainty range of simulated net leaf assimilation (NLA) by more than a factor of two. Most of the remaining variability is caused by only four parameters, associated with the acclimation of photosynthesis to plant growth temperature and to how leaf stomata react to atmospheric CO2 concentration. We suggest that this method should be used extensively to parameterize Earth system models, given that data bases on plant traits are increasingly being made available to the modelling community. Citation: Ziehn, T., J. Kattge, W. Knorr, and M. Scholze (2011), Improving the predictability of global CO2 assimilation rates under climate change, Geophys. Res. Lett., 38, L10404, doi:10.1029/2011GL047182. (Less)

Propagating uncertainty through prognostic carbon cycle data assimilation system simulations

One of the major advantages of carbon cycle data assimilation is the possibility to estimate carbon fluxes with uncertainties in a prognostic mode, that is beyond the time period of carbon dioxide (CO2) observations. The carbon cycle data assimilation system is built around the Biosphere Energy Transfer Hydrology Scheme (BETHY) model, coupled to the atmospheric transport model TM2. It uses about 2 decades of observations of the atmospheric carbon dioxide concentration from a global network to constrain 57 process parameters via an adjoint approach. The model's Hessian matrix of second derivatives provides uncertainty estimates for the optimized process parameters that are consistent with the assumed uncertainties in the observations and the model. With those estimated parameter values, the model can predict the response of the terrestrial biosphere to prescribed climate forcing beyond the assimilation period. We develop a methodological framework that is able to propagate parameter uncertainties through such a prognostic simulation and provide uncertainty estimates for the simulation results. We demonstrate the concept for a 4‐year hindcast simulation from 2000 to 2003 following a 21‐year assimilation period from 1979 to 1999. We discuss prognostic uncertainties for surface fluxes and atmospheric carbon dioxide.

Two decades of terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS)

This paper presents the space‐time distribution of terrestrial carbon fluxes for the period 1979–1999 generated by a terrestrial carbon cycle data assimilation system (CCDAS). CCDAS is based around the Biosphere Energy Transfer Hydrology model. We assimilate satellite observations of photosynthetically active radiation and atmospheric CO2 concentration observations in a two‐step process. The control variables for the assimilation are the parameters of the carbon cycle model. The optimized model produces a moderate fit to the seasonal cycle of atmospheric CO2 concentration and a good fit to its interannual variability. Long‐term mean fluxes show large uptakes over the northern midlatitudes and uptakes over tropical continents partly offsetting the prescribed efflux due to land use change. Interannual variability is dominated by the tropics. On interannual timescales the controlling process is net primary productivity (NPP) while for decadal changes the main driver is changes in soil respiration. An adjoint sensitivity analysis reveals that uncertainty in long‐term storage efficiency of soil carbon is the largest contributor to uncertainty in net flux.

Uncertainties in global terrestrial biosphere modeling: 1. A comprehensive sensitivity analysis with a new photosynthesis and energy balance scheme

Modeling the terrestrial biosphere's carbon exchanges constitutes a key tool for investigation of the global carbon cycle, which has lead to the recent development of numerous terrestrial biosphere models. However, as demonstrated by recent intercomparison studies, results of plant carbon uptake, expressed as net primary productivity (NPP), still diverge to a large degree. Here, we address the question of uncertainty by conducting a series of sensitivity tests with a single, process‐based model, the Biosphere Energy‐Transfer Hydrology (BETHY) scheme. We calculate NPP globally for a standard model setup and various alternative model setups representing either changes in modeling strategy or approximate uncertainties of the most important model parameters. The results show that estimated uncertainties of many process parameters are still too large for reliable predictions of global NPP. The largest uncertainties come from plant respiration, photosynthesis and soil water storage. The surface radiation balance and day‐to‐day variations in weather, often not included into terrestrial vegetation models, are also found to contribute significantly to overall uncertainties, while stomatal behavior, the aerodynamic coupling of vegetation and atmosphere, and the choice of the vegetation map turn out to be relatively unimportant. A further comparison with field measurements of NPP suggests that such data are too unreliable for validating biosphere model predictions. We conclude that the inherent uncertainties in process‐oriented biosphere modeling are able to explain the discrepancies that have occurred when comparing the results of different models.