Global evaluation of the nutrient-enabled version of the land surface model ORCHIDEE-CNP v1.2 (r5986)

Abstract. The availability of phosphorus (P) and nitrogen (N) constrains the ability of ecosystems to use resources such as light, water and carbon. In turn, nutrients impact the distribution of productivity, ecosystem carbon turnovers and their net exchange of CO2 with the atmosphere in response to variation of environmental conditions in both space and time. In this study, we evaluated the performance of the global version of the land surface model ORCHIDEE-CNP (v1.2), which explicitly simulates N and P biogeochemistry in terrestrial ecosystems coupled with carbon, water and energy transfers. We used data from remote sensing, ground-based measurement networks and ecological databases. Components of the N and P cycle at different levels of aggregation (from local to global) are in good agreement with data-driven estimates. When integrated for the period 1850 to 2017 forced with variable climate, rising CO2 and land use change, we show that ORCHIDEE-CNP underestimates the land carbon sink in the Northern Hemisphere (NH) during recent decades despite an a priori realistic gross primary productivity (GPP) response to rising CO2. This result suggests either that processes other than CO2 fertilization, which are omitted in ORCHIDEE-CNP such as changes in biomass turnover, are predominant drivers of the northern land sink and/or that the model parameterizations produce emerging nutrient limitations on biomass growth that are too strict in northern areas. In line with the latter, we identified biases in the simulated large-scale patterns of leaf and soil stoichiometry as well as plant P use efficiency, pointing towards P limitations that are too severe towards the poles. Based on our analysis of ecosystem resource use efficiencies and nutrient cycling, we propose ways to address the model biases by giving priority to better representing processes of soil organic P mineralization and soil inorganic P transformation, followed by refining the biomass production efficiency under increasing atmospheric CO2, phenology dynamics and canopy light absorption.

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Global evaluation of nutrient enabled version land surface model ORCHIDEE-CNP v1.2 (r5986)

10.5194/gmd-2020-93 ◽

2020 ◽

Author(s):

Yan Sun ◽

Daniel S. Goll ◽

Jinfeng Chang ◽

Philippe Ciais ◽

Betrand Guenet ◽

...

Keyword(s):

Land Surface ◽

Large Scale ◽

Production Efficiency ◽

Carbon Sink ◽

A Priori ◽

Land Surface Model ◽

Terrestrial Ecosystems ◽

Surface Model ◽

Global Evaluation ◽

The North

Abstract. The availability of phosphorus (P) and nitrogen (N) constrain the ability of ecosystems to use resources such as light, water and carbon. In turn, nutrients impact the distribution of productivity, ecosystem carbon turnovers and their net exchange of CO2 with the atmosphere in response to variation of environmental conditions both in space and in time. In this study, we evaluated the performance of the global version of the land surface model ORCHIDEE-CNP (v1.2) which explicitly simulates N and P biogeochemistry in terrestrial ecosystems coupled with carbon, water and energy transfers. We used data from remote-sensing, ground-based measurement networks and ecological databases. Components of the N and P cycle at different levels of aggregation (from local to global) are in good agreement with data-driven estimates. When integrated for the period 1850 to 2017 forced with variable climate, rising CO2 and land use change, we show that ORCHIDEE-CNP underestimates the land carbon sink in the North Hemisphere (NH) during the recent decades, despite an a priori realistic GPP response to rising CO2. This result suggests either that other processes than CO2 fertilization which are omitted in ORCHIDEE-CNP, such as changes in biomass turnover, are predominant drivers of the northern land sink, and/or that the model parameterizations produce too strict emerging nutrient limitations on biomass growth in northern areas. In line with the latter, we identified biases in the simulated large-scale patterns of leaf and soil stoichiometry and plant P use efficiency pointing towards a too severe P limitations towards the poles. Based on our analysis of ecosystem resource use efficiencies and nutrient cycling, we propose ways to address the model biases by giving priority to better representing processes of soil organic P mineralization and soil inorganic P transformation, followed by refining the biomass production efficiency under increasing atmospheric CO2, phenology dynamics and canopy light absorption.

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Global evaluation of the nutrient enabled version of land surface model ORCHIDEE-CNP (v1.2)

10.5194/egusphere-egu2020-9609 ◽

2020 ◽

Author(s):

Yan Sun ◽

Daniel S Goll ◽

Jinfeng Chang ◽

Philippe Ciais ◽

Betrand Guenet ◽

...

Keyword(s):

Land Surface ◽

Large Scale ◽

Comprehensive Evaluation ◽

Large Fraction ◽

Land Surface Model ◽

P Uptake ◽

Surface Model ◽

Global Evaluation ◽

Total N ◽

P Fluxes

Future land carbon (C) uptake under climate changes and rising atmospheric CO2 is influenced by nitrogen (N) and phosphorus (P) constraints. A few existing land surface models (LSMs) account for both N and P dynamics, but lack comprehensive evaluation. This will lead to large uncertainty in estimating the P effect on terrestrial C cycles. With the increasing number of measurements and data-driven products for N- and P- related variables, comprehensive model evaluations on large scale is becoming feasible.In this study, we evaluated the performance of ORCHIDEE-CNP (v1.2) which explicitly simulates N and P cycles in plant and soil, in four aspects: 1) terrestrial C fluxes, 2) N and P fluxes and budget, 3) leaf and soil stoichiometry and 4) resource use efficiencies. We found that ORCHIDEE-CNP improves the simulation of the magnitude of gross primary productivity (GPP) due to more realistic strength of the CO2 fertilization effect of GPP than the without-nutrient-version ORCHIDEE. However, ORCHIDEE-CNP cannot capture the positive and increasing C sink in North Hemisphere over past decades, which is mainly due to that a large fraction of N and P &#8216;locked&#8217; in soil organic matter cannot be re-allocated into vegetation and leads to a strong N and P limitation on plant growth. ORCHIDEE-CNP generally simulates comparable global total N and P fluxes (e.g. N biofixation, P weathering, N and P uptake etc.) for both natural and agricultural biomes. Overall, ORCHIDEE-CNP doesn&#8217;t performance worse in C fluxes than ORCHIDEE, and gives reasonable N and P cycles, which is acceptable in simulating the coupling relationships between C, N and P cycles can be used to explore the nutrient limitations on land C sink from present to the future.&#160;

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Large-scale groundwater modeling using global datasets: a test case for the Rhine-Meuse basin

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-8-2555-2011 ◽

2011 ◽

Vol 8 (2) ◽

pp. 2555-2608 ◽

Cited By ~ 2

Author(s):

E. H. Sutanudjaja ◽

L. P. H. van Beek ◽

S. M. de Jong ◽

F. C. van Geer ◽

M. F. P. Bierkens

Keyword(s):

Sensitivity Analysis ◽

Land Surface ◽

Large Scale ◽

Groundwater Modeling ◽

Land Surface Model ◽

Water Levels ◽

Surface Model ◽

Groundwater Model ◽

Model Output ◽

Global Datasets

Abstract. Large-scale groundwater models involving aquifers and basins of multiple countries are still rare due to a lack of hydrogeological data which are usually only available in developed countries. In this study, we propose a novel approach to construct large-scale groundwater models by using global datasets that are readily available. As the test-bed, we use the combined Rhine-Meuse basin that contains groundwater head data used to verify the model output. We start by building a distributed land surface model (30 arc-second resolution) to estimate groundwater recharge and river discharge. Subsequently, a MODFLOW transient groundwater model is built and forced by the recharge and surface water levels calculated by the land surface model. Although the method that we used to couple the land surface and MODFLOW groundwater model is considered as an offline-coupling procedure (i.e. the simulations of both models were performed separately), results are promising. The simulated river discharges compare well to the observations. Moreover, based on our sensitivity analysis, in which we run several groundwater model scenarios with various hydrogeological parameter settings, we observe that the model can reproduce the observed groundwater head time series reasonably well. However, we note that there are still some limitations in the current approach, specifically because the current offline-coupling technique simplifies dynamic feedbacks between surface water levels and groundwater heads, and between soil moisture states and groundwater heads. Also the current sensitivity analysis ignores the uncertainty of the land surface model output. Despite these limitations, we argue that the results of the current model show a promise for large-scale groundwater modeling practices, including for data-poor environments and at the global scale.

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Impact of Urbanization on Heavy Convective Precipitation under Strong Large-Scale Forcing: A Case Study over the Milwaukee–Lake Michigan Region

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-020.1 ◽

2014 ◽

Vol 15 (1) ◽

pp. 261-278 ◽

Cited By ~ 41

Author(s):

Long Yang ◽

James A. Smith ◽

Mary Lynn Baeck ◽

Elie Bou-Zeid ◽

Stephen M. Jessup ◽

...

Keyword(s):

Land Surface ◽

Large Scale ◽

Lake Michigan ◽

Land Surface Model ◽

Urban Land ◽

Surface Model ◽

Urban Representations ◽

Simulation Results ◽

Impact Of Urbanization ◽

The City

Abstract In this study, observational and numerical modeling analyses based on the Weather Research and Forecasting Model (WRF) are used to investigate the impact of urbanization on heavy rainfall over the Milwaukee–Lake Michigan region. The authors examine urban modification of rainfall for a storm system with continental-scale moisture transport, strong large-scale forcing, and extreme rainfall over a large area of the upper Midwest of the United States. WRF simulations were carried out to examine the sensitivity of the rainfall distribution in and around the urban area to different urban land surface model representations and urban land-use scenarios. Simulation results suggest that urbanization plays an important role in precipitation distribution, even in settings characterized by strong large-scale forcing. For the Milwaukee–Lake Michigan region, the thermodynamic perturbations produced by urbanization on the temperature and surface pressure fields enhance the intrusion of the lake breeze and facilitate the formation of a convergence zone, which create favorable conditions for deep convection over the city. Analyses of model and observed vertical profiles of reflectivity using contoured frequency by altitude displays (CFADs) suggest that cloud dynamics over the city do not change significantly with urbanization. Simulation results also suggest that the large-scale rainfall pattern is not sensitive to different urban representations in the model. Both urban representations, the Noah land surface model with urban land categories and the single-layer urban canopy model, adequately capture the dominant features of this storm over the urban region.

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Modelling river runoff and estimating its weather-related uncertainty for 11 large-scale rivers located in different regions of the globe

Hydrology Research ◽

10.2166/nh.2017.015 ◽

2017 ◽

Vol 49 (4) ◽

pp. 1072-1087 ◽

Cited By ~ 9

Author(s):

Yeugeniy M. Gusev ◽

Olga N. Nasonova ◽

Evgeny E. Kovalev ◽

Georgii V. Aizel

Keyword(s):

River Runoff ◽

Land Surface ◽

Input Data ◽

Large Scale ◽

Land Surface Model ◽

Grid Cell ◽

Surface Model ◽

Model Parameters ◽

Data Sets ◽

Natural Conditions

Abstract In order to study the possibility of reproducing river runoff with making use of the land surface model Soil Water–Atmosphere–Plants (SWAP) and information based on global data sets 11 river basins suggested within the framework of the Inter-Sectoral Impact Model Intercomparison Project and located in various regions of the globe under a wide variety of natural conditions were used. Schematization of each basin as a set of 0.5° × 0.5° computational grid cells connected by a river network was carried out. Input data including atmospheric forcing data and land surface parameters based, respectively, on the global WATCH and ECOCLIMAP data sets were prepared for each grid cell. Simulations of river runoff performed by SWAP with a priori input data showed poor agreement with observations. Optimization of a number of model parameters substantially improved the results. The obtained results confirm the universal character of SWAP. Natural uncertainty of river runoff caused by weather noise was estimated and analysed. It can be treated as the lowest limit of predictability of river runoff. It was shown that differences in runoff uncertainties obtained for different rivers depend greatly on natural conditions of a river basin, in particular, on the ratio of deterministic and random components of the river runoff.

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Toward a Surface Soil Moisture Product at High Spatiotemporal Resolution: Temporally Interpolated, Spatially Disaggregated SMOS Data

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0280.1 ◽

2018 ◽

Vol 19 (1) ◽

pp. 183-200 ◽

Cited By ~ 15

Author(s):

Y. Malbéteau ◽

O. Merlin ◽

G. Balsamo ◽

S. Er-Raki ◽

S. Khabba ◽

...

Keyword(s):

Soil Moisture ◽

Temporal Resolution ◽

Land Surface ◽

Large Scale ◽

Surface Soil ◽

Land Surface Model ◽

Open Loop ◽

Surface Model ◽

Surface Soil Moisture ◽

Spatiotemporal Resolution

Abstract High spatial and temporal resolution surface soil moisture is required for most hydrological and agricultural applications. The recently developed Disaggregation based on Physical and Theoretical Scale Change (DisPATCh) algorithm provides 1-km-resolution surface soil moisture by downscaling the 40-km Soil Moisture Ocean Salinity (SMOS) soil moisture using Moderate Resolution Imaging Spectroradiometer (MODIS) data. However, the temporal resolution of DisPATCh data is constrained by the temporal resolution of SMOS (a global coverage every 3 days) and further limited by gaps in MODIS images due to cloud cover. This paper proposes an approach to overcome these limitations based on the assimilation of the 1-km-resolution DisPATCh data into a simple dynamic soil model forced by (inaccurate) precipitation data. The performance of the approach was assessed using ground measurements of surface soil moisture in the Yanco area in Australia and the Tensift-Haouz region in Morocco during 2014. It was found that the analyzed daily 1-km-resolution surface soil moisture compared slightly better to in situ data for all sites than the original disaggregated soil moisture products. Over the entire year, assimilation increased the correlation coefficient between estimated soil moisture and ground measurements from 0.53 to 0.70, whereas the mean unbiased RMSE (ubRMSE) slightly decreased from 0.07 to 0.06 m3 m−3 compared to the open-loop force–restore model. The proposed assimilation scheme has significant potential for large-scale applications over semiarid areas, since the method is based on data available at the global scale together with a parsimonious land surface model.

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Assimilation of SMOS brightness temperature into a large-scale distributed conceptual hydrological model

10.5194/hess-2019-414 ◽

2019 ◽

Author(s):

Renaud Hostache ◽

Dominik Rains ◽

Kaniska Mallick ◽

Marco Chini ◽

Ramona Pelich ◽

...

Keyword(s):

Soil Moisture ◽

Brightness Temperature ◽

Land Surface ◽

Large Scale ◽

Hydrological Model ◽

Land Surface Model ◽

Surface Model ◽

Meteorological Model ◽

Temperature Observations

Abstract. The main objective of this study is to investigate how brightness temperature observations from satellite microwave sensors may help in reducing errors and uncertainties in soil moisture simulations with a large-scale conceptual hydro-meteorological model. In particular, we use as forcings the ERA-Interim public dataset and we couple the CMEM radiative transfer model with a hydro-meteorological model enabling therefore soil moisture and SMOS-like brightness temperature simulations. The hydro-meteorological model is configured using recent developments of the SUPERFLEX framework, which enables tailoring the model structure to the specific needs of the application as well as to data availability and computational requirements. In this case, the model spatial resolution is adapted to the spatial grid of the satellite data, and the soil stratification is tailored to the satellite datasets to be assimilated and the forcing data. The hydrological model is first calibrated using a sample of SMOS brightness temperature observations (period 2010–2011). Next, SMOS-derived brightness temperature observations are sequentially assimilated into the coupled SUPERFLEX-CMEM model (period 2010–2015). For this experiment, a Local Ensemble Transform Kalman Filter is used and the meteorological forcings (ERA interim-based rainfall, air and soil temperature) are perturbed to generate a background ensemble. Each time a SMOS observation is available, the SUPERFLEX state variables related to the water content in the various soil layers are updated and the model simulations are resumed until the next SMOS observation becomes available. Our empirical results show that the SUPERFLEX-CMEM modelling chain is capable of predicting soil moisture at a performance level similar to that obtained for the same study area and with a quasi-identical experimental set up using the CLM land surface model. This shows that a simple model, when carefully calibrated, can yield performance level similar to that of a much more complex model. The correlation between simulated and in situ observed soil moisture ranges from 0.62 to 0.72. The assimilation of SMOS brightness temperature observation into the SUPERFLEX-CMEM modelling chain improves the correlation between predicted and in situ observed soil moisture by 0.03 on average showing improvements similar to those obtained using the CLM land surface model.

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Impact of ozone on the carbon, heat and water fluxes in a land-surface model

10.5194/egusphere-egu21-16300 ◽

2021 ◽

Author(s):

Noel Clancy ◽

William Collins ◽

Pier Luigi Vidale ◽

Gerd Folberth

Keyword(s):

Land Surface ◽

Hydrological Cycle ◽

Carbon Sink ◽

Land Surface Model ◽

Surface Model ◽

Water Fluxes ◽

Ozone Sensitivity ◽

Energy Cycle ◽

Earth’S Climate ◽

Quantitative Impact

Carbon uptake by land ecosystems is a hugely important carbon sink for the Earth's climate. Plants uptake carbon dioxide from the atmosphere via pores on the surface of their leaves called stomata. However, ozone can also be taken up by plants in this way leading to damage to the plant, a decrease in its growth rate and an impact on the carbon cycle. Ozone damage to plants also modifies other processes within the ecosystem such as transpiration and respiration rates, thereby effecting the hydrological cycle and energy cycle. The Joint UK Land and Environment Simulator (JULES) land-surface model includes ozone sensitivity parameters for all its vegetation cover (plant functional types). Our recent results from JULES experiments at FLUXNET sites show that ozone reduces photosynthesis and suppresses transpiration, thereby impacting the carbon, heat and water fluxes in JULES. Furthermore, we identify differences in a quantitative impact on leaf phenology.

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Coupling an individual-based boreal forest model with a permafrost land-surface model to forecast biomass development in boreal larch forests at the Siberian treeline

10.5194/egusphere-egu2020-14992 ◽

2020 ◽

Author(s):

Simone Stünzi ◽

Stefan Kruse ◽

Julia Boike ◽

Ulrike Herzschuh ◽

Moritz Langer

Keyword(s):

Land Surface ◽

Boreal Forests ◽

Environmental Changes ◽

Carbon Sink ◽

Land Surface Model ◽

Radiation Budget ◽

Surface Model ◽

Data Set ◽

Forest Model ◽

Larch Forests

The fate of boreal forests under global warming and forced rapid environmental changes is still highly uncertain, in terms of remaining a carbon sink or becoming a future carbon source. Forest dynamics and resulting ecosystem services are strongly interlinked in the vast permafrost-covered regions of the Siberian treeline ecotone. Consequently, understanding the role of current and future active layer dynamics is crucial for the prediction of aboveground biomass and thus carbon stock developments.We present a coupled model version combining CryoGrid, a sophisticated one-dimensional permafrost land surface model adapted for the use in forest ecosystems, with LAVESI, a detailed, individual-based and spatially explicit larch forest model. Subsequently, parameterizing against an extensive field data set of >100 forest inventories conducted along the treeline of larch-dominated boreal forests in Siberia (97-169&#176; E), we run simulations covering the upcoming decades under contrasting climatic change scenarios.The model setup can reproduce the energy transfer and thermal regime in permafrost ground as well as the radiation budget, nitrogen and photosynthetic profiles, canopy turbulence and leaf fluxes and predict the expected establishment, die-off and treeline movements of larch forests. Our results will show vegetation and permafrost dynamics, quantify the magnitudes of different feedback processes between permafrost, vegetation, and climate and reveal their impact on carbon stocks in Northern Siberia.

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