The Land surface Data Toolkit (LDT v7.2) – a data fusion environment for land data assimilation systems

Arsenault, Kristi R.; Kumar, Sujay V.; Geiger, J.; Wang, Shugong; Kemp, Eric M.; Mocko, David M.; Beaudoing, H. K.; Getirana, Augusto; Navari, Mahdi; Li, Bailing; Jacob, Jossy P.; Wegiel, Jerry; Peters‐Lidard, Christa D.

doi:10.5194/gmd-11-3605-2018

Cited by 49 publications

(27 citation statements)

References 79 publications

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“…The approach presented in this study is a new and effective way to validate the processes implemented in TBMs, to provide a better definition of vegetation response to climate (Liang et al, ), and could help to improve existing data assimilation frameworks (Arsenault et al, ; Kaminski et al, ; LeBauer, Wang, Richter, Davidson, & Dietze, ) by providing ecological constraints. The availability of continuous observations from eddy‐covariance flux measurements gives a unique opportunity to resolve the different components of the short‐ and long‐term variability of traits through this approach.…”

Section: Concluding Remarks and Recommendationsmentioning

confidence: 99%

Covariations between plant functional traits emerge from constraining parameterization of a terrestrial biosphere model

Peaucelle

Bacour

Ciais

et al. 2019

Global Ecol Biogeogr

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Aim The mechanisms of plant trait adaptation and acclimation are still poorly understood and, consequently, lack a consistent representation in terrestrial biosphere models (TBMs). Despite the increasing availability of geo‐referenced trait observations, current databases are still insufficient to cover all vegetation types and environmental conditions. In parallel, the growing number of continuous eddy‐covariance observations of energy and CO2 fluxes has enabled modellers to optimize TBMs with these data. Past attempts to optimize TBM parameters mostly focused on model performance, overlooking the ecological properties of ecosystems. The aim of this study was to assess the ecological consistency of optimized trait‐related parameters while improving the model performances for gross primary productivity (GPP) at sites. Location Worldwide. Time period 1992–2012. Major taxa studied Trees and C3 grasses. Methods We optimized parameters of the ORCHIDEE model against 371 site‐years of GPP estimates from the FLUXNET network, and we looked at global covariation among parameters and with climate. Results The optimized parameter values were shown to be consistent with leaf‐scale traits, in particular, with well‐known trade‐offs observed at the leaf level, echoing the leaf economic spectrum theory. Results showed a marked sensitivity of trait‐related parameters to local bioclimatic variables and reproduced the observed relationships between traits and climate. Main conclusions Our approach validates some biological processes implemented in the model and enables us to study ecological properties of vegetation at the canopy level, in addition to some traits that are difficult to observe experimentally. This study stresses the need for: (a) implementing explicit trade‐offs and acclimation processes in TBMs; (b) improving the representation of processes to avoid model‐specific parameterization; and (c) performing systematic measurements of traits at FLUXNET sites in order to gather information on plant ecophysiology and plant diversity, together with micro‐meteorological conditions.

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Section: Concluding Remarks and Recommendationsmentioning

confidence: 99%

Covariations between plant functional traits emerge from constraining parameterization of a terrestrial biosphere model

Peaucelle

Bacour

Ciais

et al. 2019

Global Ecol Biogeogr

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“…Reanalysis products are carefully designed and tested before release. In addition, many studies have evaluated their performance by intercomparison, by comparison with observations (e.g., Jiang et al, 2015) and by applying them to model simulations (e.g., Albergel et al, 2018;Beck et al, 2019). For this reason, we focus on the application of GlobSim for demonstration rather than the direct testing of reanalysis variables or their interpolated products.…”

Section: Demonstrator Applicationmentioning

confidence: 99%

GlobSim (v1.0): deriving meteorological time series for point locations from multiple global reanalyses

et al. 2019

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Abstract. Simulations of land-surface processes and phenomena often require driving time series of meteorological variables. Corresponding observations, however, are unavailable in most locations, even more so, when considering the duration, continuity and data quality required. Atmospheric reanalyses provide global coverage of relevant meteorological variables, but their use is largely restricted to grid-based studies. This is because technical challenges limit the ease with which reanalysis data can be applied to models at the site scale. We present the software toolkit GlobSim, which automates the downloading, interpolation and scaling of different reanalyses – currently ERA5, ERA-Interim, JRA-55 and MERRA-2 – to produce meteorological time series for user-defined point locations. The resulting data have consistent structure and units to efficiently support ensemble simulation. The utility of GlobSim is demonstrated using an application in permafrost research. We perform ensemble simulations of ground-surface temperature for 10 terrain types in a remote tundra area in northern Canada and compare the results with observations. Simulation results reproduced seasonal cycles and variation between terrain types well, demonstrating that GlobSim can support efficient land-surface simulations. Ensemble means often yielded better accuracy than individual simulations and ensemble ranges additionally provide indications of uncertainty arising from uncertain input. By improving the usability of reanalyses for research requiring time series of climate variables for point locations, GlobSim can enable a wide range of simulation studies and model evaluations that previously were impeded by technical hurdles in obtaining suitable data.

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“…Agriculture is one of the most weather-dependent human activities (Hatfield et al, 2011), and smallholder systems are particularly vulnerable to weather variability, including extreme events such as drought. South and Southeast Asia has been experiencing anthropogenic warming since the 1950s (Sivakumar and Stefanski, 2010), and the warming is projected to continue in the near future (Barros and Field, 2014). The frequency of extreme weather events, including droughts, has also been increasing under this warming trend, with implications for food security and social stability in a conflict-prone region that already includes extensive marginal agriculture on semi-arid lands (Samaniego et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Developing a hydrological monitoring and sub-seasonal to seasonal forecasting system for South and Southeast Asian river basins

Zhou

Zaitchik

Kumar

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. South and Southeast Asia is subject to significant hydrometeorological extremes, including drought. Under rising temperatures, growing populations, and an apparent weakening of the South Asian monsoon in recent decades, concerns regarding drought and its potential impacts on water and food security are on the rise. Reliable sub-seasonal to seasonal (S2S) hydrological forecasts could, in principle, help governments and international organizations to better assess risk and act in the face of an oncoming drought. Here, we leverage recent improvements in S2S meteorological forecasts and the growing power of Earth observations to provide more accurate monitoring of hydrological states for forecast initialization. Information from both sources is merged in a South and Southeast Asia sub-seasonal to seasonal hydrological forecasting system (SAHFS-S2S), developed collaboratively with the NASA SERVIR program and end users across the region. This system applies the Noah-Multiparameterization (NoahMP) Land Surface Model (LSM) in the NASA Land Information System (LIS), driven by downscaled meteorological fields from the Global Data Assimilation System (GDAS) and Climate Hazards InfraRed Precipitation products (CHIRP and CHIRPS) to optimize initial conditions. The NASA Goddard Earth Observing System Model sub-seasonal to seasonal (GEOS-S2S) forecasts, downscaled using the National Center for Atmospheric Research (NCAR) General Analog Regression Downscaling (GARD) tool and quantile mapping, are then applied to drive 5 km resolution hydrological forecasts to a 9-month forecast time horizon. Results show that the skillful predictions of root zone soil moisture can be made 1 to 2 months in advance for forecasts initialized in rainy seasons and up to 8 months when initialized in dry seasons. The memory of accurate initial conditions can positively contribute to forecast skills throughout the entire 9-month prediction period in areas with limited precipitation. This SAHFS-S2S has been operationalized at the International Centre for Integrated Mountain Development (ICIMOD) to support drought monitoring and warning needs in the region.

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The Land surface Data Toolkit (LDT v7.2) – a data fusion environment for land data assimilation systems

Cited by 49 publications

References 79 publications

Covariations between plant functional traits emerge from constraining parameterization of a terrestrial biosphere model

Covariations between plant functional traits emerge from constraining parameterization of a terrestrial biosphere model

GlobSim (v1.0): deriving meteorological time series for point locations from multiple global reanalyses

Developing a hydrological monitoring and sub-seasonal to seasonal forecasting system for South and Southeast Asian river basins

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