scholarly journals Improving vegetation phenological parameterization of a land surface model

2016 ◽  
Author(s):  
Baozhang Chen ◽  
Mingliang Che
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Growing Season ◽  
Surface Model ◽  
Observation Data ◽  
Mean Square ◽  
Surface Models ◽  
Absolute Bias ◽  
Index Of Agreement ◽  
Coordinate Data

Abstract. The growing degree day (GDD) model and the growing season index (GSI) model are two common approaches used in various land surface models (LSMs) for simulating phenophases. The capacity of these two models for simulating phenolphases was evaluated by coupling them to a LSM (DLM: Dynamic Land Model) and validated by observation data from the 22 selected eddy covariance flux towers representing six typical plant functional types. The main findings are threefold: (i) the simulated phenophases using DLM-GSI were much closer to the observations derived from the green chromatic coordinate data than using DLM-GDD. The start of the growing season (SGS) was estimated to be earlier by DLM-GSI and later by DLM-GDD. Meanwhile, the end of growing season (EGS) was estimated to be later by DLM-GSI and earlier by DLM-GDD; (ii) compared to the GDD model, the GSI model significantly decreased the absolute bias of the phenophases simulated by DLM for all sites. The DLM-GSI model simulated biases for SGS and EGS decreased by 48.2 % and by 39 % on average, respectively; and (iii) the accuracy of modeled GPP using the DLM-GSI model is much higher than using the DLM-GDD model for all sites. The DLM-GSI model reduced the root mean square error of simulated GPP by 8.0 % and increased the corresponding index of agreement by 7.5 %.

Developing a triple tree-ring constraint for tree growth and physiology in a global land surface model

2021 ◽  
Author(s):  
Jonathan Barichivich ◽  
Philippe Peylin ◽  
Valérie Daux ◽  
Camille Risi ◽  
Jina Jeong ◽  
...  
Keyword(s):  
Tree Ring ◽  
Land Surface ◽  
Forest Ecosystems ◽  
Ring Width ◽  
Land Surface Model ◽  
Surface Model ◽  
Growth Variability ◽  
Tree Ring Data ◽  
Surface Models ◽  
Global Land

<p>Gradual anthropogenic warming and parallel changes in the major global biogeochemical cycles are slowly pushing forest ecosystems into novel growing conditions, with uncertain consequences for ecosystem dynamics and climate. Short-term forest responses (i.e., years to a decade) to global change factors are relatively well understood and skilfully simulated by land surface models (LSMs). However, confidence on model projections weaken towards longer time scales and to the future, mainly because the long-term responses (i.e., decade to century) of these models remain unconstrained. This issue limits confidence on climate model projections. Annually-resolved tree-ring records, extending back to pre-industrial conditions, have the potential to constrain model responses at interannual to centennial time scales. Here, we constrain the representation of tree growth and physiology in the ORCHIDEE global land surface model using the simulated interannual variability of tree-ring width and carbon (Δ<sup>13</sup>C) and oxygen (δ<sup>18</sup>O) stable isotopes in six sites in boreal and temperate Europe.  The model simulates Δ<sup>13</sup>C (r = 0.31-0.80) and δ<sup>18</sup>O (r = 0.36-0.74) variability better than tree-ring width variability (r < 0.55), with an overall skill similar to that of other state-of-the-art models such as MAIDENiso and LPX-Bern. These results show that growth variability is not well represented, and that the parameterization of leaf-level physiological responses to drought stress in the temperate region can be improved with tree-ring data. The representation of carbon storage and remobilization dynamics is critical to improve the realism of simulated growth variability, temporal carrying over and recovery of forest ecosystems after climate extremes. The simulated physiological response to rising CO2 over the 20th century is consistent with tree-ring data in the temperate region, despite an overestimation of seasonal drought stress and stomatal control on photosynthesis. Photosynthesis correlates directly with isotopic variability, but correlations with δ<sup>18</sup>O combine physiological effects and climate variability impacts on source water signatures. The integration of tree-ring data (i.e. the triple constraint: width, Δ<sup>13</sup>C and δ<sup>18</sup>O) and land surface models as demonstrated here should contribute towards reducing current uncertainties in forest carbon and water cycling.</p>

scholarly journals Using Cosmic-Ray Neutron Probes in Validating Satellite Soil Moisture Products and Land Surface Models

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1362 ◽  
Author(s):  
Mustafa Berk Duygu ◽  
Zuhal Akyürek
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Surface Model ◽  
High Temporal Resolution ◽  
Land Surface Models ◽  
Statistical Measures ◽  
Surface Models ◽  
Global Land

Soil moisture content is one of the most important parameters of hydrological studies. Cosmic-ray neutron sensing is a promising proximal soil moisture sensing technique at intermediate scale and high temporal resolution. In this study, we validate satellite soil moisture products for the period of March 2015 and December 2018 by using several existing Cosmic Ray Neutron Probe (CRNP) stations of the COSMOS database and a CRNP station that was installed in the south part of Turkey in October 2016. Soil moisture values, which were inferred from the CRNP station in Turkey, are also validated using a time domain reflectometer (TDR) installed at the same location and soil water content values obtained from a land surface model (Noah LSM) at various depths (0.1 m, 0.3 m, 0.6 m and 1.0 m). The CRNP has a very good correlation with TDR where both measurements show consistent changes in soil moisture due to storm events. Satellite soil moisture products obtained from the Soil Moisture and Ocean Salinity (SMOS), the METOP-A/B Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer 2 (AMSR2), Climate Change Initiative (CCI) and a global land surface model Global Land Data Assimilation System (GLDAS) are compared with the soil moisture values obtained from CRNP stations. Coefficient of determination ( r 2 ) and unbiased root mean square error (ubRMSE) are used as the statistical measures. Triple Collocation (TC) was also performed by considering soil moisture values obtained from different soil moisture products and the CRNPs. The validation results are mainly influenced by the location of the sensor and the soil moisture retrieval algorithm of satellite products. The SMAP surface product produces the highest correlations and lowest errors especially in semi-arid areas whereas the ASCAT product provides better results in vegetated areas. Both global and local land surface models’ outputs are highly compatible with the CRNP soil moisture values.

scholarly journals Evaluating a land surface model at a water-limited site: implications for land surface contributions to droughts and heatwaves

2021 ◽  
Author(s):  
Mengyuan Mu ◽  
Martin De Kauwe ◽  
Anna Ukkola ◽  
Andy Pitman ◽  
Teresa Gimeno ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Heat Fluxes ◽  
Surface Model ◽  
Strong Impact ◽  
Water Fluxes ◽  
Land Surface Models ◽  
Groundwater Aquifer ◽  
Surface Models

<p>Land surface models underpin coupled climate model projections of droughts and heatwaves. However, the lack of simultaneous observations of individual components of evapotranspiration, concurrent with root-zone soil moisture, has limited previous model evaluations. Here, we use a comprehensive set of observations from a water-limited site in southeastern Australia including both evapotranspiration and soil moisture to a depth of 4.5 m to evaluate the Community Atmosphere-Biosphere Land Exchange (CABLE) land surface model. We demonstrate that alternative process representations within CABLE had the capacity to improve simulated evapotranspiration, but not necessarily soil moisture dynamics - highlighting problems of model evaluations against water fluxes alone. Our best simulation was achieved by resolving a soil evaporation bias; a more realistic initialisation of the groundwater aquifer state; higher vertical soil resolution informed by observed soil properties; and further calibrating soil hydraulic conductivity. Despite these improvements, the role of the empirical soil moisture stress function in influencing the simulated water fluxes remained important: using a site calibrated function reduced the soil water stress on plants by 36 % during drought and 23 % at other times. These changes in CABLE not only improve the seasonal cycle of evapotranspiration, but also affect the latent and sensible heat fluxes during droughts and heatwaves. The range of parameterisations tested led to differences of ~150 W m<sup>-2</sup> in the simulated latent heat flux during a heatwave, implying a strong impact of parameterisations on the capacity for evaporative cooling and feedbacks to the boundary layer (when coupled). Overall, our results highlight the opportunity to advance the capability of land surface models to capture water cycle processes, particularly during meteorological extremes, when sufficient observations of both evapotranspiration fluxes and soil moisture profiles are available.</p>

scholarly journals Modeling Snow Surface Spectral Reflectance in a Land Surface Model Targeting Satellite Remote Sensing Observations

2020 ◽  
Vol 12 (18) ◽  
pp. 3101
Author(s):  
Donghang Shao ◽  
Wenbo Xu ◽  
Hongyi Li ◽  
Jian Wang ◽  
Xiaohua Hao
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Spectral Reflectance ◽  
Satellite Remote Sensing ◽  
Land Surface Model ◽  
Integrated Model ◽  
Surface Model ◽  
Snow Surface ◽  
Snow Albedo ◽  
Surface Models

Snow surface spectral reflectance is very important in the Earth’s climate system. Traditional land surface models with parameterized schemes can simulate broadband snow surface albedo but cannot accurately simulate snow surface spectral reflectance with continuous and fine spectral wavebands, which constitute the major observations of current satellite sensors; consequently, there is an obvious gap between land surface model simulations and remote sensing observations. Here, we suggest a new integrated scheme that couples a radiative transfer model with a land surface model to simulate high spectral resolution snow surface reflectance information specifically targeting multisource satellite remote sensing observations. Our results indicate that the new integrated model can accurately simulate snow surface reflectance information over a large spatial scale and continuous time series. The integrated model extends the range of snow spectral reflectance simulation to the whole shortwave band and can predict snow spectral reflectance changes in the solar spectrum region based on meteorological element data. The kappa coefficients (K) of both the narrowband snow albedo targeting Moderate Resolution Imaging Spectroradiometer (MODIS) data simulated by the new integrated model and the retrieved snow albedo based on MODIS reflectance data are 0.5, and both exhibit good spatial consistency. Our proposed narrowband snow albedo simulation scheme targeting satellite remote sensing observations is consistent with remote sensing satellite observations in time series and can predict narrowband snow albedo even during periods of missing remote sensing observations. This new integrated model is a significant improvement over traditional land surface models for the direct spectral observations of satellite remote sensing. The proposed model could contribute to the effective combination of snow surface reflectance information from multisource remote sensing observations with land surface models.

Assessment of the uncertainty related to irrigation modeling by a land surface model across India

2020 ◽  
Author(s):  
Dazhi Li ◽  
Xujun Han ◽  
Dhanya C.t. ◽  
Stefan Siebert ◽  
Harry Vereecken ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Monsoon Season ◽  
Land Surface Model ◽  
Surface Fluxes ◽  
Spatiotemporal Pattern ◽  
Surface Model ◽  
Irrigation Requirement ◽  
Community Land Model ◽  
Surface Models

<p>Irrigation is very important for maintaining the agricultural production and sustaining the increasing population of India. The irrigation requirement can be estimated with land surface models by modeling water storage changes but the estimates are affected by various uncertainties such as regarding the spatiotemporal distribution of areas where and when irrigation is potentially applied. In the present work, this uncertainty is analyzed for the whole Indian domain. The irrigation requirements and hydrological fluxes over India were reconstructed by multiple simulation experiments with the Community Land Model (CLM) version 4.5 for the year of 2010.</p><p>These multiple simulation scenarios showed that the modeled irrigation requirement and the land surface fluxes differed between the scenarios, representing the spatiotemporal uncertainty of the irrigation maps. Using a season-specific irrigation map resulted in a higher transpiration-evapotranspiration ratio (T/ET) in the pre-monsoon season compared to the application of a static irrigation map, which implies a higher irrigation efficiency. The remote sensing based evapotranspiration products GLEAM and MODIS ET were used for comparison, showing a similar increasing ET-trend in the pre-monsoon season as the irrigation induced land surface modeling. The correspondence is better if the seasonal irrigation map is used as basis for simulations with CLM. We conclude that more accurate temporal information on irrigation results in modeled evapotranspiration closer to the spatiotemporal pattern of evapotranspiration deduced from remote sensing. Another conclusion is that irrigation modeling should consider the sub-grid heterogeneity to improve the estimation of soil water deficit and irrigation requirement.</p>

Analysis of Soil Moisture Changes in Europe during a Single Growing Season in a New ECMWF Soil Moisture Assimilation System

2008 ◽  
Vol 9 (1) ◽  
pp. 116-131 ◽  
Author(s):  
Bart van den Hurk ◽  
Janneke Ettema ◽  
Pedro Viterbo
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Land Surface Model ◽  
Growing Season ◽  
Atmospheric Model ◽  
Surface Model ◽  
Soil Moisture Data ◽  
The Impact ◽  
Assimilation System

Abstract This study aims at stimulating the development of soil moisture data assimilation systems in a direction where they can provide both the necessary control of slow drift in operational NWP applications and support the physical insight in the performance of the land surface component. It addresses four topics concerning the systematic nature of soil moisture data assimilation experiments over Europe during the growing season of 2000 involving the European Centre for Medium-Range Weather Forecasts (ECMWF) model infrastructure. In the first topic the effect of the (spinup related) bias in 40-yr ECMWF Re-Analysis (ERA-40) precipitation on the data assimilation is analyzed. From results averaged over 36 European locations, it appears that about half of the soil moisture increments in the 2000 growing season are attributable to the precipitation bias. A second topic considers a new soil moisture data assimilation system, demonstrated in a coupled single-column model (SCM) setup, where precipitation and radiation are derived from observations instead of from atmospheric model fields. For many of the considered locations in this new system, the accumulated soil moisture increments still exceed the interannual variability estimated from a multiyear offline land surface model run. A third topic examines the soil water budget in response to these systematic increments. For a number of Mediterranean locations the increments successfully increase the surface evaporation, as is expected from the fact that atmospheric moisture deficit information is the key driver of soil moisture adjustment. In many other locations, however, evaporation is constrained by the experimental SCM setup and is hardly affected by the data assimilation. Instead, a major portion of the increments eventually leave the soil as runoff. In the fourth topic observed evaporation is used to evaluate the impact of the data assimilation on the forecast quality. In most cases, the difference between the control and data assimilation runs is considerably smaller than the (positive) difference between any of the simulations and the observations.

scholarly journals Observed nighttime conductance alters modeled global hydrology and carbon budgets

2015 ◽  
Vol 8 (12) ◽  
pp. 10339-10363 ◽  
Author(s):  
D. L. Lombardozzi ◽  
M. J. B. Zeppel ◽  
R. A. Fisher ◽  
A. Tawfik
Keyword(s):  
Stomatal Conductance ◽  
Land Surface ◽  
Arid Regions ◽  
Land Surface Model ◽  
Carbon Gain ◽  
Surface Model ◽  
Land Surface Models ◽  
Climate Simulations ◽  
Surface Models ◽  
Semi Arid

Abstract. The terrestrial biosphere regulates climate through carbon, water, and energy exchanges with the atmosphere. Land surface models estimate plant transpiration, which is actively regulated by stomatal pores, and provide projections essential for understanding Earth's carbon and water resources. Empirical evidence from 204 species suggests that significant amounts of water are lost through leaves at night, though land surface models typically reduce stomatal conductance to nearly zero at night. Here, we apply observed nighttime stomatal conductance values to a global land surface model, to better constrain carbon and water budgets. We find that our modifications increase transpiration up to 5 % globally, reduce modeled available soil moisture by up to 50 % in semi-arid regions, and increase the importance of the land surface on modulating energy fluxes. Carbon gain declines up to ~ 4 % globally and > 25 % in semi-arid regions. We advocate for realistic constraints of minimum stomatal conductance in future climate simulations, and widespread field observations to improve parameterizations.

scholarly journals Evaluating a land surface model at a water-limited site: implications for land surface contributions to droughts and heatwaves

2021 ◽  
Vol 25 (1) ◽  
pp. 447-471
Author(s):  
Mengyuan Mu ◽  
Martin G. De Kauwe ◽  
Anna M. Ukkola ◽  
Andy J. Pitman ◽  
Teresa E. Gimeno ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Heat Fluxes ◽  
Surface Model ◽  
Strong Impact ◽  
Water Fluxes ◽  
Land Surface Models ◽  
Groundwater Aquifer ◽  
Surface Models

Abstract. Land surface models underpin coupled climate model projections of droughts and heatwaves. However, the lack of simultaneous observations of individual components of evapotranspiration, concurrent with root-zone soil moisture, has limited previous model evaluations. Here, we use a comprehensive set of observations from a water-limited site in southeastern Australia including both evapotranspiration and soil moisture to a depth of 4.5 m to evaluate the Community Atmosphere-Biosphere Land Exchange (CABLE) land surface model. We demonstrate that alternative process representations within CABLE had the capacity to improve simulated evapotranspiration, but not necessarily soil moisture dynamics–highlighting problems of model evaluations against water fluxes alone. Our best simulation was achieved by resolving a soil evaporation bias, using a more realistic initialisation of the groundwater aquifer state and higher vertical soil resolution informed by observed soil properties, and further calibrating soil hydraulic conductivity. Despite these improvements, the role of the empirical soil moisture stress function in influencing the simulated water fluxes remained important: using a site-calibrated function reduced the soil water stress on plants by 36 % during drought and 23 % at other times. These changes in CABLE not only improve the seasonal cycle of evapotranspiration but also affect the latent and sensible heat fluxes during droughts and heatwaves. The range of parameterisations tested led to differences of ∼150 W m−2 in the simulated latent heat flux during a heatwave, implying a strong impact of parameterisations on the capacity for evaporative cooling and feedbacks to the boundary layer (when coupled). Overall, our results highlight the opportunity to advance the capability of land surface models to capture water cycle processes, particularly during meteorological extremes, when sufficient observations of both evapotranspiration fluxes and soil moisture profiles are available.

Response of the surface climate to different land surface models: WRF sensitivity to surface model options

2021 ◽  
Author(s):  
Daniela C.A. Lima ◽  
Rita M. Cardoso ◽  
Pedro M.M. Soares
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Wrf Model ◽  
Land Surface Model ◽  
Heat Fluxes ◽  
Surface Processes ◽  
Surface Model ◽  
Land Surface Models ◽  
Land Surface Processes ◽  
Surface Models

<p>The Weather Research and Forecasting (WRF) model version 4.2 includes different land surface schemes, allowing a better representation of the land surface processes. Four simulations with the WRF model differing in land surface models and options were investigated as a sensitivity study over the European domain. These experiments span from 2004-2006 with a one-month spin-up and were performed at 0.11<sup>o</sup> horizontal resolution with 50 vertical levels, following the CORDEX guidelines. The lateral boundary conditions were driven by ERA5 reanalysis from European Centre for Medium-Range Weather Forecasts. For the first experiment, the Noah land surface model was used. For the remaining simulations, the Noah-MP (multi-physics) land surface model was used with different runoff and groundwater options: (1) original surface and subsurface runoff (free drainage), (2) TOPMODEL with groundwater and (3) Miguez-Macho & Fan groundwater scheme. The physical parameterizations options are the same for all simulations. These experiments allow the analysis of the sensitivity of different land surface options and to understand how the representation of land surface processes impacts on the atmosphere properties. This study focusses on the investigation of land-atmosphere feedbacks trough the analysis of the soil moisture – temperature and soil moisture – precipitation interactions, latent and sensible heat fluxes, and moisture fluxes. The influence of different surface model options on atmospheric boundary layer is also explored.</p><p>Acknowledgements. The authors wish to acknowledge the LEADING (PTDC/CTA-MET/28914/2017) project funded by FCT. The authors would like to acknowledge the financial support FCT through project UIDB/50019/2020 – Instituto Dom Luiz.</p>

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