Dynamic Phenology Research Articles

Regulating the time of the crop model clock: A data assimilation framework for regions with high phenological heterogeneity

in crop growth data assimilation systems, the mismatch between simulated and observed phenology significantly deteriorates the performance of crop growth modeling. This situation may be more severe for smallholder farmers-managed fields, where the phenological heterogeneity was high even when climate condition was relatively uniform. Previous studies investigated the non-sequential methods to retrospectively assimilate historical phenology observations. However, approaches to dynamically assimilating phenological measurements through sequential data assimilation methods remain unexplored one of the most intractable challenges of dynamic phenology assimilation is that a considerable proportion of model parameters and variables are entangled with phenology, therefore simply assimilating phenological measurements could disturb the model clock. This study aims to establish a robust crop data assimilation framework capable of assimilating phenological measurements in real time without disturbing the model clock the framework used an open-source version of the AquaCrop model to simulate crop growth and used the ensemble Kalman filter (EnKF) to assimilate observations sequentially. A parameter refresh method was proposed to restore the phenological consistency of model parameters after updating the phenology state. Assimilation strategies with different observation types and compositions of state vectors were designed after a global sensitivity analysis of model parameters. These strategies were evaluated through the Observing System Simulation Experiments (OSSE), and the selected strategies were tested in a real-world case. the results of the OSS Experiments show that the phenological mismatch problem greatly affects crop growth simulation, and this mismatch could not be narrowed effectively by assimilating non-phenological observations. Assimilating phenological measurements with the proposed parameters refresh method and assimilation strategies closed this mismatch and produced better performance compared to the Restart-EnKF method. In the real-world paddy rice case, assimilating phenology with the proposed strategies significantly improved yield estimation in low-yield plots (less than 4 ton/ha) compared to assimilating canopy cover (CC) alone, with an R2 increase from 0.07 to 0.48. Assimilating CC, biomass and phenology simultaneously produced the best yield estimation for all plots, with R2 = 0.57 and RMSE = 1.00 ton/ha. assimilating phenology under a consistent model clock significantly improved yield estimation when the phenological heterogeneity of plots was high. the results highlight the effectiveness and robustness of the established data assimilation framework for dynamic crop growth simulation, indicating the potentials of the proposed data assimilation framework for regional in-season crop modeling and yield forecasting.

Prediction of Grassland Biodiversity Using Measures of Spectral Variance: A Meta-Analytical Review

Over the last 20 years, there has been a surge of interest in the use of reflectance data collected using satellites and aerial vehicles to monitor vegetation diversity. One methodological option to monitor these systems involves developing empirical relationships between spectral heterogeneity in space (spectral variation) and plant or habitat diversity. This approach is commonly termed the ‘Spectral Variation Hypothesis’. Although increasingly used, it is controversial and can be unreliable in some contexts. Here, we review the literature and apply three-level meta-analytical models to assess the test results of the hypothesis across studies using several moderating variables relating to the botanical and spectral sampling strategies and the types of sites evaluated. We focus on the literature relating to grasslands, which are less well studied compared to forests and are likely to require separate treatments due to their dynamic phenology and the taxonomic complexity of their canopies on a small scale. Across studies, the results suggest an overall positive relationship between spectral variation and species diversity (mean correlation coefficient = 0.36). However, high levels of both within-study and between-study heterogeneity were found. Whether data was collected at the leaf or canopy level had the most impact on the mean effect size, with leaf-level studies displaying a stronger relationship compared to canopy-level studies. We highlight the challenges facing the synthesis of these kinds of experiments, the lack of studies carried out in arid or tropical systems and the need for scalable, multitemporal assessments to resolve the controversy in this field.

Impacts of climate change on winter wheat and summer maize dual-cropping system in the North China Plain

Climate change exacerbates the frequency of agricultural-relevant climate extremes, which could threaten crop growth and food production. The North China Plain (NCP), as one of the most important food production bases in China, is dominated by winter wheat and summer maize. The response of this dual-cropping system to climate change has not been thoroughly evaluated. In this study, the remote sensing normalized difference vegetation index (NDVI) was used to extract the dynamic phenology of winter wheat and summer maize and characterize crop growth status. The meteorological data from 1982 to 2015 were used to examine the mean climate factors and four typical climate extremes (including heat stress, spring frost, meteorological drought, and extreme wet events) associated with phenology shifts. Then, the effects of climate changes on winter wheat and summer maize growth were explored by a panel regression model. In the NCP during 1982–2015, the winter wheat growth exhibited no significant sensitivity to the four climate extremes, and only extreme wet event exerted a significant impact on summer maize growth. The insensitivity of crop growth to climate extremes may benefit from widespread irrigation, improved cultivars and agricultural management (e.g., topdressing and insect pests control). In addition, over the last 34 years, mean climate conditions, especially average temperature, solar radiation and vapor pressure deficit, generally made more contributions to the variations of wheat and maize growth than climate extremes, indicating that mean climate conditions dominated crop growth changes in the NCP. Our findings highlight the possible effects of climate change on crop growth of regional dual-cropping system and provide a critical foundation for future effective measures to ensure regional food production.

Advances in Land Surface Models and Indicators for Drought Monitoring and Prediction

AbstractMillions of people across the globe are affected by droughts every year, and recent droughts have highlighted the considerable agricultural impacts and economic costs of these events. Monitoring the state of droughts depends on integrating multiple indicators that each capture particular aspects of hydrologic impact and various types and phases of drought. As the capabilities of land surface models and remote sensing have improved, important physical processes such as dynamic, interactive vegetation phenology, groundwater, and snowpack evolution now support a range of drought indicators that better reflect coupled water, energy, and carbon cycle processes. In this work, we discuss these advances, including newer classes of indicators that can be applied to improve the characterization of drought onset, severity, and duration. We utilize a new model-based drought reconstruction to illustrate the role of dynamic phenology and groundwater in drought assessment. Further, through case studies on flash droughts, snow droughts, and drought recovery, we illustrate the potential advantages of advanced model physics and observational capabilities, especially from remote sensing, in characterizing droughts.

Understanding water and energy fluxes in the Amazonia: Lessons from an observation-model intercomparison.

Tropical forests are an important part of global water and energy cycles, but the mechanisms that drive seasonality of their land-atmosphere exchanges have proven challenging to capture in models. Here, we (1) report the seasonality of fluxes of latent heat (LE), sensible heat (H), and outgoing short and longwave radiation at four diverse tropical forest sites across Amazonia-along the equator from the Caxiuanã and Tapajós National Forests in the eastern Amazon to a forest near Manaus, and from the equatorial zone to the southern forest in Reserva Jaru; (2) investigate how vegetation and climate influence these fluxes; and (3) evaluate land surface model performance by comparing simulations to observations. We found that previously identified failure of models to capture observed dry-season increases in evapotranspiration (ET) was associated with model overestimations of (1) magnitude and seasonality of Bowen ratios (relative to aseasonal observations in which sensible was only 20%-30% of the latent heat flux) indicating model exaggerated water limitation, (2) canopy emissivity and reflectance (albedo was only 10%-15% of incoming solar radiation, compared to 0.15%-0.22% simulated), and (3) vegetation temperatures (due to underestimation of dry-season ET and associated cooling). These partially compensating model-observation discrepancies (e.g., higher temperatures expected from excess Bowen ratios were partially ameliorated by brighter leaves and more interception/evaporation) significantly biased seasonal model estimates of net radiation (Rn ), the key driver of water and energy fluxes (LE~0.6 Rn and H~0.15 Rn ), though these biases varied among sites and models. A better representation of energy-related parameters associated with dynamic phenology (e.g., leaf optical properties, canopy interception, and skin temperature) could improve simulations and benchmarking of current vegetation-atmosphere exchange and reduce uncertainty of regional and global biogeochemical models.

The 2019–2020 Australian Drought and Bushfires Altered the Partitioning of Hydrological Fluxes

AbstractThough coarse in spatial resolution, the nearly all weather measurements from passive microwave sensors can help in improving the spatio‐temporal coverage of optical and thermal infrared sensors for monitoring vegetation changes on the land surface. This study demonstrates the use of vegetation optical depth (VOD) retrievals from the Soil Moisture Active Passive mission for capturing the vegetation alterations from the recent 2019 to 2020 Australian bushfires and drought. The impact of vegetation disturbances on terrestrial water budget is examined by assimilating the VOD retrievals into a dynamic phenology model. The results demonstrate that assimilating VOD observations lead to improved simulation of evapotranspiration, runoff, and soil moisture states. The study also demonstrates that the vegetation changes from the 2019 to 2020 Australian drought and fires led to significant modifications in the partitioning of evaporative and runoff fluxes, resulting in increased bare soil evaporation, reduced transpiration, and higher runoff.

Cropland mapping with L-band UAVSAR and development of NISAR products

Planned satellite launches will provide open access and operational L-band radar data streams at space-time resolutions not previously available. To further prepare, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) platform was used to observe cropland sites across the southern United States to support the development of L-band (24 cm) prototype science products. Time series flights flew over four independent areas during the growing season of 2019 while crop ground measurements were collected. Major crops include corn, cotton, pasture, peanut, rice, and soybean. A suite of cropland classification experiments applied a set of machine learning (ML) algorithms (random forest, feedforward fully connected neural network, support vector machine), the recently developed Multi-temporal Binary Tree Classification (MBTC), and a phenology (Coefficient of Variation; CoV) approach to synergistically assess performance, scattering mechanisms, and limitations. Specific objectives of this research application included 1.) evaluation of L-band mapping performance across multiple independent agricultural production areas with field scale training data, and 2.) assessment of the CoV approach for the generation of prototype NISAR Level 2 science products. Collectively, SAR terms with sensitivity to volume scattering performed well and consistently across CoV mapping experiments achieving accuracy greater than 80% for cropland vs not cropland. Dynamic phenology classes, such as herbaceous wetlands, had some confusion with CoV agriculture requiring further regionalized training optimization. Volume scattering and cross-pol terms were most useful across the different ML techniques with overall accuracy and Kappa consistently over 90% and 0.85, respectively, for crop type by late growth stages for L-band observations. As expected, time series information was more valuable compared to any single ML technique, site, or crop schema. Ultimately, as more SAR platforms launch, the user community should leverage physical contributions of different wavelengths and polarizations along with growing open access time series for efficient and meaningful agricultural products.

Modelling the interaction of climate, forest ecosystem, and hydrology to estimate catchment dissolved organic carbon export

AbstractThe potential for increased loads of dissolved organic carbon (DOC) in streams and rivers is a concern for regulating the water quality in water supply watersheds. With increasing hydroclimatic variability related to global warming and shifts in forest ecosystem community and structure, understanding and predicting the magnitude and variability of watershed supply and transport of DOC over multiple time scales have become important research and management goals. In this study, we use a distributed process‐based ecohydrological model (Regional Hydro‐Ecological Simulation System [RHESSys]) to explore controls and predict streamflow DOC loads in Biscuit Brook. Biscuit Brook is a forested headwater catchment of the Neversink Reservoir, part of the New York City water supply system in the Catskill Mountains. Three different model structures of RHESSys were proposed to explore and evaluate hypotheses addressing how vegetation phenology and hydrologic connectivity between deep groundwater and riparian zones influence streamflow and DOC loads. Model results showed that incorporating dynamic phenology improved model agreement with measured streamflow in spring, summer, and fall and fall DOC concentration, compared with a static phenology. Additionally, the connectivity of deep groundwater flux through riparian zones with dynamic phenology improved streamflow and DOC flux in low flow conditions. Therefore, this study suggests the importance of inter‐annual vegetation phenology and the connectivity of deep groundwater drainage through riparian zones in the hydrology and stream DOC loading in this forested watershed and the ability of process‐based ecohydrological models to simulate these dynamics. The advantage of a process‐based modelling approach is specifically seen in the sensitivity to forest ecosystem dynamics and the interactions of hydroclimate variability with ecosystem processes controlling the supply and distribution of DOC. These models will be useful to evaluate different forest management approaches toward mitigating water quality concerns.

Impact of dynamic vegetation phenology on the simulated pan-Arctic land surface state

The pan-Arctic land surface is undergoing rapid changes in a warming climate, with near-surface permafrost projected to degrade significantly during the twenty-first century. Vegetation-related feedbacks have the potential to influence the rate of degradation of permafrost. In this study, the impact of dynamic phenology on the pan-Arctic land surface state, particularly near-surface permafrost, for the 1961–2100 period, is assessed by comparing two simulations of the Canadian Land Surface Scheme (CLASS)—one with dynamic phenology, modelled using the Canadian Terrestrial Ecosystem Model (CTEM), and the other with prescribed phenology. These simulations are forced by atmospheric data from a transient climate change simulation of the 5th generation Canadian Regional Climate Model (CRCM5) for the Representative Concentration Pathway 8.5 (RCP8.5). Comparison of the CLASS coupled to CTEM simulation to available observational estimates of plant area index, spatial distribution of permafrost and active layer thickness suggests that the model captures reasonably well the overall distribution of vegetation and permafrost. It is shown that the most important impact of dynamic phenology on the land surface occurs through albedo and it is demonstrated for the first time that vegetation control on albedo during late spring and early summer has the highest potential to impact the degradation of permafrost. While both simulations show extensive near-surface permafrost degradation by the end of the twenty-first century, the strong projected response of vegetation to climate warming and increasing CO2 concentrations in the coupled simulation results in accelerated permafrost degradation in the northernmost continuous permafrost regions.

Predicting canopy biophysical properties and sensitivity of plant carbon uptake to water limitations with a coupled eco-hydrological framework

Variations in soil water availability and atmospheric water demand impact seasonal canopy dynamics are often represented by the fraction of photosynthetically active radiation (FPAR) and leaf area index (LAI) under a coupled eco-hydrologic framework. Changes in FPAR and LAI lead to changes in evapotranspiration (ET) and gross primary productivity (GPP), coupling the water and carbon cycles. In this study, a predictive Dynamic Canopy Biophysical Properties (DCBP) model is adapted to predict daily FPAR and LAI forced by observed and modeled meteorological and root-zone soil moisture conditions, respectively. Vegetation green-up and die-off responses to temperature (T), vapor pressure deficit (VPD), soil water potential (ψsoil), and photoperiod (Pht) are modeled through a modified form of the growing season index (GSI). The DCBP model parameterizations of seasonality (T and Pht) and intraseasonal water stress (VPD and ψsoil) are calibrated separately for distinct plant functional types (PFTs) and soil types using a Bayesian estimator. To investigate the impact of dynamic phenology on modeled GPP and hydrologic processes, phenology predicted by the DCBP model is input to the Duke Coupled Hydrology Model with Prognostic Vegetation (DCHM-PV), and hydrologic conditions are input to the DCBP model to specify water availability constraints. The coupled model framework was evaluated against AmeriFlux tower data and remotely sensed FPAR and LAI products. Sensitivity analysis of the predicted daily FPAR, LAI, and GPP to the diurnal cycle of root zone water indicates that mid-day soil water availability is the primary control on seasonality across different PFTs and soil textures in the DCBP model. Further, calibrated parameters describing plant-water relations change significantly depending on whether the inference period used in the data assimilation includes persistent meteorological drought, thus effectively resulting in distinct plant water use strategies in the DCHM-PV. The dynamics of water stress recovery are examined by mapping seasonal phenology into the phase space of soil water stress and carbon uptake. In the Southeast U.S., simulated annual differences in GPP can be as high as 350 g C/m2/year with ET increases up to 125 mm/year during wet years. These values represent first order estimates of the dynamics of plant-water use feedbacks on the water and carbon budgets, and highlight the need to incorporate vegetation-specific phenology responses to water availability in order to accurately estimate the terrestrial carbon storage component of the global carbon budget at local and regional scales.

A model inter-comparison study to examine limiting factors in modelling Australian tropical savannas

Abstract. 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.

Predicting landscape-scale CO<sub>2</sub> flux at a pasture and rice paddy with long-term hyperspectral canopy reflectance measurements

Abstract. Measurements of hyperspectral canopy reflectance provide a detailed snapshot of information regarding canopy biochemistry, structure and physiology. In this study, we collected 5 years of repeated canopy hyperspectral reflectance measurements for a total of over 100 site visits within the flux footprints of two eddy covariance towers at a pasture and rice paddy in northern California. The vegetation at both sites exhibited dynamic phenology, with significant interannual variability in the timing of seasonal patterns that propagated into interannual variability in measured hyperspectral reflectance. We used partial least-squares regression (PLSR) modeling to leverage the information contained within the entire canopy reflectance spectra (400–900 nm) in order to investigate questions regarding the connection between measured hyperspectral reflectance and landscape-scale fluxes of net ecosystem exchange (NEE) and gross primary productivity (GPP) across multiple timescales, from instantaneous flux to monthly integrated flux. With the PLSR models developed from this large data set we achieved a high level of predictability for both NEE and GPP flux in these two ecosystems, where the R2 of prediction with an independent validation data set ranged from 0.24 to 0.69. The PLSR models achieved the highest skill at predicting the integrated GPP flux for the week prior to the hyperspectral canopy reflectance collection, whereas the NEE flux often achieved the same high predictive power at daily to monthly integrated flux timescales. The high level of predictability achieved by PLSR in this study demonstrated the potential for using repeated hyperspectral canopy reflectance measurements to help partition NEE into its component fluxes, GPP and ecosystem respiration, and for using quasi-continuous hyperspectral reflectance measurements to model regional carbon flux in future analyses.

Interactive Vegetation Phenology, Soil Moisture, and Monthly Temperature Forecasts

AbstractThe time scales that characterize the variations of vegetation phenology are generally much longer than those that characterize atmospheric processes. The explicit modeling of phenological processes in an atmospheric forecast system thus has the potential to provide skill to subseasonal or seasonal forecasts. We examine this possibility here using a forecast system fitted with a dynamic vegetation phenology model. We perform three experiments, each consisting of 128 independent warm-season monthly forecasts: 1) an experiment in which both soil moisture states and carbon states (e.g., those determining leaf area index) are initialized realistically, 2) an experiment in which the carbon states are prescribed to climatology throughout the forecasts, and 3) an experiment in which both the carbon and soil moisture states are prescribed to climatology throughout the forecasts. Evaluating the monthly forecasts of air temperature in each ensemble against observations—as well as quantifying the inherent predictability of temperature within each ensemble—shows that dynamic phenology can indeed contribute positively to subseasonal forecasts, though only to a small extent, with an impact dwarfed by that of soil moisture.

Implementation of a dynamic rooting depth and phenology into a land surface model: Evaluation of carbon, water, and energy fluxes in the high latitude ecosystems

Recent studies and observations have shown that northern high latitude ecosystems (NHLE) are strongly responsive to environmental changes, particularly warming temperature. Ecosystem models are important tools that help us to understand and assess the impact of environmental changes in the NHLE. However, models lack processes that are essential for modeling ecosystem dynamics for the NHLEs. In this study, NHLE-specific dynamic phenology and dynamic rooting distribution and depth parameterizations was implemented in a land surface model, the Integrated Science Assessment Model (ISAM), to improve the estimated carbon, water, and energy fluxes in the NHLs. These parameterizations account for light, water, and nutrient stresses while allocating the assimilated carbon to leaf, stem, and root pools. The model parameters related to these processes were calibrated and evaluated using measured data from 16 sites (12 fluxnet sites and 4 non-flux net sites) representative of the dominant NHLEs. By including these dynamic processes, ISAM was able to capture the measured seasonal variability in leaf area index (LAI) and root distribution in the soil layers. The evaluation of the model results suggested that without including the dynamic processes, the modeled growing season length (GSL) in the NHLE was almost two times higher, as compared to measurements. To quantify the implication of these processes on the C, water, and energy fluxes, we compared the results of two different versions of ISAM, a dynamic version that includes dynamic processes (ISAMDYN) and a static version that does not include dynamic processes (ISAMBC), with measurements from 12 eddy covariance flux sites. The results showed that ISAMDYN, unlike ISAMBC, was more capable to capture the flux site-based seasonal variability in GPP, water, and energy fluxes. Regional analysis revealed that the growing season length increased on average by about 5 days in the NHLs in the 2000s compared to 1990s.

Calibration of the phenology sub-model of APSIM-Oryza: Going beyond goodness of fit

Parameterizing the phenology of new crop varieties is a major challenge in crop modeling. Here we consider calibration of the phenology sub-model of the widely used crop model APSIM-Oryza, using commonly available varietal data. We show that the dynamic phenology sub-model can be well approximated by a static model, with three equations. It is then straightforward to estimate the parameters using any standard statistical software package. The approach is applied to four rice varieties from Sri Lanka. The software provides not only the best-fit parameters, but also uncertainty information about those parameters. This is essential for understanding how well the model will predict out of sample. Here the photoperiod sensitivity coefficient has large uncertainty, and so predictions for day lengths outside the data set are very unreliable. The uncertainty information is also used to show that in our case, doing more field trials would have very little effect on uncertainty.

Biosphere‐climate interactions in a changing climate over North America

AbstractThis study focuses on projected changes to vegetation characteristics and their interactions with the atmosphere under future climatic conditions over North America, using four transient climate change simulations of the Canadian Regional Climate Model (CRCM5). Here CRCM5 performs dynamical downscaling of the Canadian Earth System Model (CanESM2) simulated data, for Representative Concentration Pathways (RCPs) 4.5 and 8.5. For each RCP, two CRCM5 simulations are performed—one with static vegetation phenology and the other with dynamic vegetation phenology—for the 1950–2100 period over North America. The dynamic vegetation model used here is the Canadian Terrestrial Ecosystem Model. Results show that the extension of the growing season under future climatic conditions in the dynamic phenology simulations leads to higher annual vegetation productivity and biomass. In comparison with projected changes based on CRCM5 with static phenology, CRCM5 with phenology dynamics leads to an albedo‐mediated warming enhancement across most of North America in spring. In summer, results suggest a warming enhancement in the northern latitudes and an attenuation of warming for more southern regions due to hydrological feedbacks. Furthermore, results suggest that vegetation enhances its water‐use efficiency with rising atmospheric CO2 concentrations. Over southeastern United States, in the dynamic phenology simulation corresponding to the RCP8.5 scenario, the adverse effects of the projected increase in temperatures and decrease in precipitation on vegetation dominate the CO2 fertilization effect, leading to decreasing trends in productivity during the 2071–2100 period. This study thus clearly demonstrates that phenology dynamics modulate greenhouse gas‐mediated warming through various biophysical feedbacks.

Quantifying parameter sensitivity, interaction, and transferability in hydrologically enhanced versions of the Noah land surface model over transition zones during the warm season

We use sensitivity analysis to identify the parameters that are most responsible for controlling land surface model (LSM) simulations and to understand complex parameter interactions in three versions of the Noah LSM: the standard version (STD), a version enhanced with a simple groundwater module (GW), and version augmented by a dynamic phenology module (DV). We use warm season, high‐frequency, near‐surface states and turbulent fluxes collected over nine sites in the U.S. Southern Great Plains. We quantify changes in the pattern of sensitive parameters, the amount and nature of the interaction between parameters, and the covariance structure of the distribution of behavioral parameter sets. Using Sobol′'s total and first‐order sensitivity indexes, we show that few parameters directly control the variance of the model response. Significant parameter interaction occurs. Optimal parameter values differ between models, and the relationships between parameters also change. GW decreases unwarranted parameter interaction and appears to improve model realism, especially at wetter study sites. DV increases parameter interaction and decreases identifiability, implying it is overparameterized and/or underconstrained. At a wet site, GW has two functional modes: one that mimics STD and a second in which GW improves model function by decoupling direct evaporation and base flow. Unsupervised classification of the posterior distributions of behavioral parameter sets cannot group similar sites based solely on soil or vegetation type, helping to explain why transferability between sites and models is not straightforward. Our results suggest that the a priori assignment of parameters should also consider the climatic conditions of a study location.

Interannual variation in biogenic emissions on a regional scale

Interannual variation in biogenic emissions is not well quantified, especially on regional scales. We use a land surface model augmented with a short‐term dynamic phenology scheme to estimate the interannual variation in the emission of biogenic volatile organic compounds (BVOCs) between 1982 and 2004. We use North American Regional Reanalysis data to drive two versions of the National Center for Atmospheric Research Community Land Model (CLM) on a 0.1° grid over eastern Texas. The first version is the standard CLM with prescribed leaf area index (LAI) (i.e., LAI varies seasonally but not interannually); the second version is the standard CLM augmented with a dynamic phenology scheme (CLM‐DP) that allows LAI to respond to environmental variation. We calibrate CLM‐DP using satellite‐derived LAI as our visual constraint. When phenology is prescribed, the domain‐mean (domain‐maximum) average absolute departure from the monthly mean BVOC flux is 11.7% (70.6%); when phenology is allowed to vary with environmental conditions, it is 22.4% (137.7%). The domain‐mean (domain‐maximum) average absolute departure from the monthly mean flux is lower during summer: using CLM‐DP, it is 15.7% (35.3%); using the standard CLM, it is 7.0% (23.0%). The domain‐average, mean‐normalized standard deviation of the June‐July‐August mean BVOC flux is 0.0619 when LAI is prescribed and 0.183 when LAI varies with environmental conditions. Our results imply that interannual variation of leaf biomass density, which is primarily driven by interannual variability of precipitation, is a significant contributor to year‐to‐year differences in BVOC flux on a regional scale, of at least equal importance to interannual variation of temperature and shortwave radiation. Phenology‐driven biogenic emission variability is most pronounced in regions with relatively low emissions: as a grid cell's mean BVOC flux decreases, the mean‐normalized standard deviation of BVOC flux tends to increase. BVOC flux is most variable between years in subhumid, sparsely wooded regions where interannual variability of precipitation is relatively large.