Organizing Carbon And Hydrology In Dynamic Ecosystems Research Articles

Validation of a new global irrigation scheme in the land surface model ORCHIDEE v2.2

Abstract. Irrigation activities are important for sustaining food production and account for 70 % of total global water withdrawals. In addition, due to increased evapotranspiration (ET) and changes in the leaf area index (LAI), these activities have an impact on hydrology and climate. In this paper, we present a new irrigation scheme within the land surface model ORCHIDEE (ORganising Carbon and Hydrology in Dynamic EcosystEms)). It restrains actual irrigation according to available freshwater by including a simple environmental limit and using allocation rules that depend on local infrastructure. We perform a simple sensitivity analysis and parameter tuning to set the parameter values and match the observed irrigation amounts against reported values, assuming uniform parameter values over land. Our scheme matches irrigation withdrawals amounts at global scale, but we identify some areas in India, China, and the USA (some of the most intensively irrigated regions worldwide), where irrigation is underestimated. In all irrigated areas, the scheme reduces the negative bias of ET. It also exacerbates the positive bias of the leaf area index (LAI), except for the very intensively irrigated areas, where irrigation reduces a negative LAI bias. The increase in the ET decreases river discharge values, in some cases significantly, although this does not necessarily lead to a better representation of discharge dynamics. Irrigation, however, does not have a large impact on the simulated total water storage anomalies (TWSAs) and its trends. This may be partly explained by the absence of nonrenewable groundwater use, and its inclusion could increase irrigation estimates in arid and semiarid regions by increasing the supply. Correlation of irrigation biases with landscape descriptors suggests that the inclusion of irrigated rice and dam management could improve the irrigation estimates as well. Regardless of this complexity, our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, which is important to explore the joint evolution of climate, water resources, and irrigation activities.

Introducing a new floodplain scheme in ORCHIDEE (version 7885): validation and evaluation over the Pantanal wetlands

Abstract. Adapting and improving the hydrological processes in land surface models are crucial given the increase in the resolution of the climate models to correctly represent the hydrological cycle. The present paper introduces a floodplain scheme adapted to the higher-resolution river routing of the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model. The scheme is based on a sub-tile parameterisation of the hydrological units – a hydrological transfer unit (HTU) concept – based on high-resolution hydrologically coherent digital elevation models, which can be used for all types of resolutions and projections. The floodplain scheme was developed and evaluated for different atmospheric forcings and resolutions (0.5∘ and 25 km) over one of the world's largest floodplains: the Pantanal, located in central South America. The floodplain scheme is validated based on the river discharge at the outflow of the Pantanal which represents the hydrological cycle over the basin, the temporal evolution of the water mass over the region assessed by the anomaly of total water storage in the Gravity Recovery And Climate Experiment (GRACE), and the temporal evaluation of the flooded areas compared to the Global Inundation Extent from Multi-Satellites version 2 (GIEMS-2) dataset. The hydrological cycle is satisfactorily simulated; however, the base flow may be underestimated. The temporal evolution of the flooded area is coherent with the observations, although the size of the area is underestimated in comparison to GIEMS-2. The presence of floodplains increases the soil moisture up to 50 % and decreases average temperature by 3 ∘C and by 6 ∘C during the dry season. The higher soil moisture increases the vegetation density, and, along with the presence of open-water surfaces due to the floodplains, it affects the surface energy budget by increasing the latent flux at the expense of the sensible flux. This is linked to the increase in the evapotranspiration related to the increased water availability. The effect of the floodplain scheme on the land surface conditions highlights that coupled simulations using the floodplain scheme may influence local and regional precipitation and regional circulation.

Improving modelled albedo over the Greenland ice sheet through parameter optimisation and MODIS snow albedo retrievals

Abstract. Greenland ice sheet mass loss continues to accelerate as global temperatures increase. The surface albedo of the ice sheet determines the amount of absorbed solar energy, which is a key factor in driving surface snow and ice melting. Satellite-retrieved snow albedo allows us to compare and optimise modelled albedo over the entirety of the ice sheet. We optimise the parameters of the albedo scheme in the ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) land surface model for 3 random years taken over the 2000–2017 period and validate over the remaining years. In particular, we want to improve the albedo at the edges of the ice sheet, since they correspond to ablation areas and show the greatest variations in runoff and surface mass balance. By giving a larger weight to points at the ice sheet's edge, we improve the model–data fit by reducing the root-mean-square deviation by over 25 % for the whole ice sheet for the summer months. This improvement is consistent for all years, even those not used in the calibration step. We also show the optimisation successfully improves the model–data fit at 87.5 % of in situ sites from the PROMICE (Programme for Monitoring of the Greenland Ice Sheet) network. We conclude by showing which additional model outputs are impacted by changes to the albedo parameters, encouraging future work using multiple data streams when optimising these parameters.

Hydrological modelling on atmospheric grids: using graphs of sub-grid elements to transport energy and water

Abstract. Land surface models (LSMs) use the atmospheric grid as their basic spatial decomposition because their main objective is to provide the lower boundary conditions to the atmosphere. Lateral water flows at the surface on the other hand require a much higher spatial discretization as they are closely linked to topographic details. We propose here a methodology to automatically tile the atmospheric grid into hydrological coherent units which are connected through a graph. As water is transported on sub-grids of the LSM, land variables can easily be transferred to the routing network and advected if needed. This is demonstrated here for temperature. The quality of the river networks generated, as represented by the connected hydrological transfer units, are compared to the original data in order to quantify the degradation introduced by the discretization method. The conditions the sub-grid elements impose on the time step of the water transport scheme are evaluated, and a methodology is proposed to find an optimal value. Finally the scheme is applied in an off-line version of the ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) LSM over Europe to show that realistic river discharge and temperatures are predicted over the major catchments of the region. The simulated solutions are largely independent of the atmospheric grid used thanks to the proposed sub-grid approach.

A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models

Abstract. The existing medium-resolution land cover time series produced under the European Space Agency's Climate Change Initiative provides 29 years (1992–2020) of annual land cover maps at 300 m resolution, allowing for a detailed study of land change dynamics over the contemporary era. Because models need two-dimensional parameters rather than two-dimensional land cover information, the land cover classes must be converted into model-appropriate plant functional types (PFTs) to apply this time series to Earth system and land surface models. The first-generation cross-walking table that was presented with the land cover product prescribed pixel-level PFT fractional compositions that varied by land cover class but that lacked spatial variability. Here we describe a new ready-to-use data product for climate modelling: spatially explicit annual maps of PFT fractional composition at 300 m resolution for 1992–2020, created by fusing the 300 m medium-resolution land cover product with several existing high-resolution datasets using a globally consistent method. In the resulting data product, which has 14 layers for each of the 29 years, pixel values at 300 m resolution indicate the percentage cover (0 %–100 %) for each of 14 PFTs, with pixel-level PFT composition exhibiting significant intra-class spatial variability at the global scale. We additionally present an updated version of the user tool that allows users to modify the baseline product (e.g. re-mapping, re-projection, PFT conversion, and spatial sub-setting) to meet individual needs. Finally, these new PFT maps have been used in two land surface models – Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) and the Joint UK Land Environment Simulator (JULES) – to demonstrate their benefit over the conventional maps based on a generic cross-walking table. Regional changes in the fractions of trees, short vegetation, and bare-soil cover induce changes in surface properties, such as the albedo, leading to significant changes in surface turbulent fluxes, temperature, and vegetation carbon stocks. The dataset is accessible at https://doi.org/10.5285/26a0f46c95ee4c29b5c650b129aab788 (Harper et al., 2023).

Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model

Abstract. Ammonia (NH3) is an important atmospheric constituent. It plays a role in air quality and climate through the formation of ammonium sulfate and ammonium nitrate particles. It has also an impact on ecosystems through deposition processes. About 85 % of NH3 global anthropogenic emissions are related to food and feed production and, in particular, to the use of mineral fertilizers and manure management. Most global chemistry transport models (CTMs) rely on bottom-up emission inventories, which are subject to significant uncertainties. In this study, we estimate emissions from livestock by developing a new module to calculate ammonia emissions from the whole agricultural sector (from housing and storage to grazing and fertilizer application) within the ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) global land surface model. We detail the approach used for quantifying livestock feed management, manure application, and indoor and soil emissions and subsequently evaluate the model performance. Our results reflect China, India, Africa, Latin America, the USA, and Europe as the main contributors to global NH3 emissions, accounting for 80 % of the total budget. The global calculated emissions reach 44 Tg N yr−1 over the 2005–2015 period, which is within the range estimated by previous work. Key parameters (e.g., the pH of the manure, timing of N application, and atmospheric NH3 surface concentration) that drive the soil emissions have also been tested in order to assess the sensitivity of our model. Manure pH is the parameter to which modeled emissions are the most sensitive, with a 10 % change in emissions per percent change in pH. Even though we found an underestimation in our emissions over Europe (−26 %) and an overestimation in the USA (+56 %) compared with previous work, other hot spot regions are consistent. The calculated emission seasonality is in very good agreement with satellite-based emissions. These encouraging results prove the potential of coupling ORCHIDEE land-based emissions to CTMs, which are currently forced by bottom-up anthropogenic-centered inventories such as the CEDS (Community Emissions Data System).

Evaluating the vegetation–atmosphere coupling strength of ORCHIDEE land surface model (v7266)

Abstract. Plant transpiration dominates terrestrial latent heat fluxes (LE) and plays a central role in regulating the water cycle and land surface energy budget. However, Earth system models (ESMs) currently disagree strongly on the amount of transpiration, and thus LE, leading to large uncertainties in simulating future climate. Therefore, it is crucial to correctly represent the mechanisms controlling the transpiration in models. At the leaf scale, transpiration is controlled by stomatal regulation, and at the canopy scale, through turbulence, which is a function of canopy structure and wind. The coupling of vegetation to the atmosphere can be characterized by the coefficient Ω. A value of Ω→0 implies a strong coupling of vegetation and the atmosphere, leaving a dominant role to stomatal conductance in regulating water (H2O) and carbon dioxide (CO2) fluxes, while Ω→1 implies a complete decoupling of leaves from the atmosphere, i.e., the transfer of H2O and CO2 is limited by aerodynamic transport. In this study, we investigated how well the land surface model (LSM) Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) (v7266) simulates the coupling of vegetation to the atmosphere by using empirical daily estimates of Ω derived from flux measurements from 90 FLUXNET sites. Our results show that ORCHIDEE generally captures the Ω in forest vegetation types (0.27 ± 0.12) compared with observation (0.26 ± 0.09) but underestimates Ω in grasslands (GRA) and croplands (CRO) (0.25 ± 0.15 for model, 0.33 ± 0.17 for observation). The good model performance in forests is due to compensation of biases in surface conductance (Gs) and aerodynamic conductance (Ga). Calibration of key parameters controlling the dependence of the stomatal conductance to the water vapor deficit (VPD) improves the simulated Gs and Ω estimates in grasslands and croplands (0.28 ± 0.20). To assess the underlying controls of Ω, we applied random forest (RF) models to both simulated and observation-based Ω. We found that large observed Ω are associated with periods of low wind speed, high temperature and low VPD; it is also related to sites with large leaf area index (LAI) and/or short vegetation. The RF models applied to ORCHIDEE output generally agree with this pattern. However, we found that the ORCHIDEE underestimated the sensitivity of Ω to VPD when the VPD is high, overestimated the impact of the LAI on Ω, and did not correctly simulate the temperature dependence of Ω when temperature is high. Our results highlight the importance of observational constraints on simulating the vegetation–atmosphere coupling strength, which can help to improve predictive accuracy of water fluxes in Earth system models.

Global modelling of soil carbonyl sulfide exchanges

Abstract. Carbonyl sulfide (COS) is an atmospheric trace gas of interest for C cycle research because COS uptake by continental vegetation is strongly related to terrestrial gross primary productivity (GPP), the largest and most uncertain flux in atmospheric CO2 budgets. However, to use atmospheric COS as an additional tracer of GPP, an accurate quantification of COS exchange by soils is also needed. At present, the atmospheric COS budget is unbalanced globally, with total COS flux estimates from oxic and anoxic soils that vary between −409 and −89 GgS yr−1. This uncertainty hampers the use of atmospheric COS concentrations to constrain GPP estimates through atmospheric transport inversions. In this study we implemented a mechanistic soil COS model in the ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) land surface model to simulate COS fluxes in oxic and anoxic soils. Evaluation of the model against flux measurements at seven sites yields a mean root mean square deviation of 1.6 pmol m−2 s−1, instead of 2 pmol m−2 s−1 when using a previous empirical approach that links soil COS uptake to soil heterotrophic respiration. However, soil COS model evaluation is still limited by the scarcity of observation sites and long-term measurement periods, with all sites located in a latitudinal band between 39 and 62∘ N and no observations during wintertime in this study. The new model predicts that, globally and over the 2009–2016 period, oxic soils act as a net uptake of −126 GgS yr−1 and anoxic soils are a source of +96 GgS yr−1, leading to a global net soil sink of only −30 GgS yr−1, i.e. much smaller than previous estimates. The small magnitude of the soil fluxes suggests that the error in the COS budget is dominated by the much larger fluxes from plants, oceans, and industrial activities. The predicted spatial distribution of soil COS fluxes, with large emissions from oxic (up to 68.2 pmol COS m−2 s−1) and anoxic (up to 36.8 pmol COS m−2 s−1) soils in the tropics, especially in India and in the Sahel region, marginally improves the latitudinal gradient of atmospheric COS concentrations, after transport by the LMDZ (Laboratoire de Météorologie Dynamique) atmospheric transport model. The impact of different soil COS flux representations on the latitudinal gradient of the atmospheric COS concentrations is strongest in the Northern Hemisphere. We also implemented spatiotemporal variations in near-ground atmospheric COS concentrations in the modelling of biospheric COS fluxes, which helped reduce the imbalance of the atmospheric COS budget by lowering soil COS uptake by 10 % and plant COS uptake by 8 % globally (with a revised mean vegetation budget of −576 GgS yr−1 over 2009–2016). Sensitivity analyses highlighted the different parameters to which each soil COS flux model is the most responsive, selected in a parameter optimization framework. Having both vegetation and soil COS fluxes modelled within ORCHIDEE opens the way for using observed ecosystem COS fluxes and larger-scale atmospheric COS mixing ratios to improve the simulated GPP, through data assimilation techniques.

Constraining a land cover map with satellite-based aboveground biomass estimates over Africa

Abstract. Most land surface models can, depending on the simulation experiment, calculate the vegetation distribution and dynamics internally by making use of biogeographical principles or use vegetation maps to prescribe spatial and temporal changes in vegetation distribution. Irrespective of whether vegetation dynamics are simulated or prescribed, it is not practical to represent vegetation across the globe at the species level because of its daunting diversity. This issue can be circumvented by making use of 5 to 20 plant functional types (PFTs) by assuming that all species within a single functional type show identical land–atmosphere interactions irrespective of their geographical location. In this study, we hypothesize that remote-sensing-based assessments of aboveground biomass can be used to constrain the process in which real-world vegetation is discretized in PFT maps. Remotely sensed biomass estimates for Africa were used in a Bayesian framework to estimate the probability density distributions of woody, herbaceous and bare soil fractions for the 15 land cover classes, according to the United Nations Land Cover Classification System (UN-LCCS) typology, present in Africa. Subsequently, the 2.5th and 97.5th percentiles of the probability density distributions were used to create 2.5 % and 97.5 % credible interval PFT maps. Finally, the original and constrained PFT maps were used to drive biomass and albedo simulations with the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) model. This study demonstrates that remotely sensed biomass data can be used to better constrain the share of dense forest PFTs but that additional information on bare soil fraction is required to constrain the share of herbaceous PFTs. Even though considerable uncertainties remain, using remotely sensed biomass data enhances the objectivity and reproducibility of the process by reducing the dependency on expert knowledge and allows assessing and reporting the credible interval of the PFT maps which could be used to benchmark future developments.

Variance Based Sensitivity Analysis of FLake Lake Model for Global Land Surface Modeling

AbstractGiven the ever increasing spatial resolution of climate models and the significant role of lakes on the regional climate, it becomes important to represent water bodies in climate models. Such developments have started in the IPSL (Institut Pierre Simon Laplace) climate model and its land surface component, ORganizing Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE), with the Freshwater Lake model, FLake. To answer the questions raised by these new developments, such as the lake differentiation and related model parameters, we analyze spatial distributions of lake characteristics in the whole world to perform a global sensitivity analysis of the FLake parameters. As a result, three different climates and four lake depth configurations were selected as test cases. The Sobol method as sensitivity analysis based on variance decomposition was chosen to rank parameters impact on the model output, that is, lake surface water temperature, latent and sensible heat fluxes. We focus on the 11 parameters of the FLake model, which are the lake depth, the albedo and light extinction coefficient of water, snow, and ice respectively, the fetch, and the relaxation coefficient of the thermocline shape factor. The results show different sensitivity features according to the lake type and climate. The dominant role and time varying contribution of the lake depth, radiative parameters (albedo, light extinction coefficient) and thermocline relaxation coefficient linked to the atmospheric conditions, were clearly highlighted. These findings will lead us to distinguish between different lake categories in each grid cell of ORCHIDEE in the future implementation.

Multivariable evaluation of land surface processes in forced and coupled modes reveals new error sources to the simulated water cycle in the IPSL (Institute Pierre Simon Laplace) climate model

Abstract. Evaluating land surface models (LSMs) using available observations is important for understanding the potential and limitations of current Earth system models in simulating water- and carbon-related variables. To reveal the error sources of a LSM, five essential climate variables have been evaluated in this paper (i.e., surface soil moisture, evapotranspiration, leaf area index, surface albedo, and precipitation) via simulations with the IPSL (Institute Pierre Simon Laplace) LSM ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) model, particularly focusing on the difference between (i) forced simulations with atmospheric forcing data (WATCH Forcing Data ERA-Interim – WFDEI) and (ii) coupled simulations with the IPSL atmospheric general circulation model. Results from statistical evaluation, using satellite- and ground-based reference data, show that ORCHIDEE is well equipped to represent spatiotemporal patterns of all variables in general. However, further analysis against various landscape and meteorological factors (e.g., plant functional type, slope, precipitation, and irrigation) suggests potential uncertainty relating to freezing and/or snowmelt, temperate plant phenology, irrigation, and contrasted responses between forced and coupled mode simulations. The biases in the simulated variables are amplified in the coupled mode via surface–atmosphere interactions, indicating a strong link between irrigation–precipitation and a relatively complex link between precipitation–evapotranspiration that reflects the hydrometeorological regime of the region (energy limited or water limited) and snow albedo feedback in mountainous and boreal regions. The different results between forced and coupled modes imply the importance of model evaluation under both modes to isolate potential sources of uncertainty in the model.

Irrigation, damming, and streamflow fluctuations of the Yellow River

Abstract. The streamflow of the Yellow River (YR) is strongly affected by human activities like irrigation and dam operation. Many attribution studies have focused on the long-term trends of streamflows, yet the contributions of these anthropogenic factors to streamflow fluctuations have not been well quantified with fully mechanistic models. This study aims to (1) demonstrate whether the mechanistic global land surface model ORCHIDEE (ORganizing Carbon and Hydrology in Dynamic EcosystEms) is able to simulate the streamflows of this complex rivers with human activities using a generic parameterization for human activities and (2) preliminarily quantify the roles of irrigation and dam operation in monthly streamflow fluctuations of the YR from 1982 to 2014 with a newly developed irrigation module and an offline dam operation model. Validations with observed streamflows near the outlet of the YR demonstrated that model performances improved notably with incrementally considering irrigation (mean square error (MSE) decreased by 56.9 %) and dam operation (MSE decreased by another 30.5 %). Irrigation withdrawals were found to substantially reduce the river streamflows by approximately 242.8±27.8×108 m3 yr−1 in line with independent census data (231.4±31.6×108 m3 yr−1). Dam operation does not change the mean streamflows in our model, but it impacts streamflow seasonality, more than the seasonal change of precipitation. By only considering generic operation schemes, our dam model is able to reproduce the water storage changes of the two large reservoirs, LongYangXia and LiuJiaXia (correlation coefficient of ∼ 0.9). Moreover, other commonly neglected factors, such as the large operation contribution from multiple medium/small reservoirs, the dominance of large irrigation districts for streamflows (e.g., the Hetao Plateau), and special management policies during extreme years, are highlighted in this study. Related processes should be integrated into models to better project future YR water resources under climate change and optimize adaption strategies.

Modeling the impacts of diffuse light fraction on photosynthesis in ORCHIDEE (v5453) land surface model

Abstract. Aerosol- and cloud-induced changes in diffuse light have important impacts on the global land carbon cycle, as they alter light distribution and photosynthesis in vegetation canopies. However, this effect remains poorly represented or evaluated in current land surface models. Here, we add a light partitioning module and a new canopy light transmission module to the ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) land surface model (trunk version, v5453) and use the revised model, ORCHIDEE_DF, to estimate the fraction of diffuse light and its effect on gross primary production (GPP) in a multilayer canopy. We evaluate the new parameterizations using flux observations from 159 eddy covariance sites over the globe. Our results show that, compared with the original model, ORCHIDEE_DF improves the GPP simulation under sunny conditions and captures the observed higher photosynthesis under cloudier conditions in most plant functional types (PFTs). Our results also indicate that the larger GPP under cloudy conditions compared with sunny conditions is mainly driven by increased diffuse light in the morning and in the afternoon as well as by a decreased vapor pressure deficit (VPD) and decreased air temperature at midday. The observations show that the strongest positive effects of diffuse light on photosynthesis are found in the range from 5 to 20 ∘C and at a VPD < 1 kPa. This effect is found to decrease when the VPD becomes too large or the temperature falls outside of the abovementioned range, which is likely due to the increasing stomatal resistance to leaf CO2 uptake. ORCHIDEE_DF underestimates the diffuse light effect at low temperature in all PFTs and overestimates this effect at high temperature and at a high VPD in grasslands and croplands. The new model has the potential to better investigate the impact of large-scale aerosol changes and long-term changes in cloudiness on the terrestrial carbon budget, both in the historical period and in the context of future air quality policies and/or climate engineering.

How Simulations of the Land Carbon Sink Are Biased by Ignoring Fluvial Carbon Transfers: A Case Study for the Amazon Basin

Summary Land-surface models are important tools for simulation of the past, present, and future capacity of terrestrial ecosystems to absorb anthropogenic CO2 emissions. However, fluvial carbon (C) transfers are presently neglected in these models. Using the Amazon basin as a case study, we show that this negligence leads to significant underestimation of the net uptake of atmospheric C while terrestrial C storage changes are overestimated. These biases arise from the fact that C—in reality, leached from soils and exported through the river network—is instead represented as partly being respired and partly being stored in soils. Moreover, these biases scale mainly to the fluvial C export to the coast, despite aquatic CO2 emission to the atmosphere being the major pathway of riverine C exports. We further show that fluvial C transfers may change significantly in response to changes in either hydrology or in atmospheric C uptake by vegetation.

Weak sensitivity of the terrestrial water budget to global soil texture maps in the ORCHIDEE land surface model

Abstract. Soil physical properties play an important role in estimating soil water and energy fluxes. Many hydrological and land surface models (LSMs) use soil texture maps to infer these properties. Here, we investigate the impact of soil texture on soil water fluxes and storage at different scales using the ORCHIDEE (ORganizing Carbon and Hydrology in Dynamic EcosystEms) LSM, forced by several complex or globally uniform soil texture maps. At the point scale, the model shows a realistic sensitivity of runoff processes and soil moisture to soil texture and reveals that loamy textures give the highest evapotranspiration and lowest total runoff rates. The three tested complex soil texture maps result in similar water budgets at all scales, compared to the uncertainties of observation-based products and meteorological forcing datasets, although important differences can be found at the regional scale, particularly in areas where the different maps disagree on the prevalence of clay soils. The three tested soil texture maps are also found to be similar by construction, with a shared prevalence of loamy textures, and have a spatial overlap over 40 % between each pair of maps, which explains the overall weak impact of soil texture map change. A useful outcome is that the choice of the input soil texture map is not crucial for large-scale modelling, but the added value of more detailed soil information (horizontal and vertical resolution, soil composition) deserves further studies.

CE-DYNAM (v1): a spatially explicit process-based carbon erosion scheme for use in Earth system models

Abstract. Soil erosion by rainfall and runoff is an important process behind the redistribution of soil organic carbon (SOC) over land, thereby impacting the exchange of carbon (C) between land, atmosphere, and rivers. However, the net role of soil erosion in the global C cycle is still unclear as it involves small-scale SOC removal, transport, and redeposition processes that can only be addressed over selected small regions with complex models and measurements. This leads to uncertainties in future projections of SOC stocks and complicates the evaluation of strategies to mitigate climate change through increased SOC sequestration. In this study we present the parsimonious process-based Carbon Erosion DYNAMics model (CE-DYNAM) that links sediment dynamics resulting from water erosion with the C cycle along a cascade of hillslopes, floodplains, and rivers. The model simulates horizontal soil and C transfers triggered by erosion across landscapes and the resulting changes in land–atmosphere CO2 fluxes at a resolution of about 8 km at the catchment scale. CE-DYNAM is the result of the coupling of a previously developed coarse-resolution sediment budget model and the ecosystem C cycle and erosion removal model derived from the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model. CE-DYNAM is driven by spatially explicit historical land use change, climate forcing, and global atmospheric CO2 concentrations, affecting ecosystem productivity, erosion rates, and residence times of sediment and C in deposition sites. The main features of CE-DYNAM are (1) the spatially explicit simulation of sediment and C fluxes linking hillslopes and floodplains, (2) the relatively low number of parameters that allow for running the model at large spatial scales and over long timescales, and (3) its compatibility with global land surface models, thereby providing opportunities to study the effect of soil erosion under global changes. We present the model structure, concepts, limitations, and evaluation at the scale of the Rhine catchment for the period 1850–2005 CE (Common Era). Model results are validated against independent estimates of gross and net soil and C erosion rates and the spatial variability of SOC stocks from high-resolution modeling studies and observational datasets. We show that despite local differences, the resulting soil and C erosion rates, as well as SOC stocks from CE-DYNAM, are comparable to high-resolution estimates and observations at subbasin level. We find that soil erosion mobilized around 66±28 Tg (1012 g) of C under changing climate and land use over the non-Alpine region of the Rhine catchment over the entire period, assuming that the erosion loop of the C cycle was nearly steady state by 1850. This caused a net C sink equal to 2.1 %–2.7 % of the net primary productivity of the non-Alpine region over 1850–2005 CE. This sink is a result of the dynamic replacement of C on eroding sites that increases in this period due to rising atmospheric CO2 concentrations enhancing the litter C input to the soil from primary production.

Improving Estimates of Gross Primary Productivity by Assimilating Solar‐Induced Fluorescence Satellite Retrievals in a Terrestrial Biosphere Model Using a Process‐Based SIF Model

AbstractOver the last few years, solar‐induced chlorophyll fluorescence (SIF) observations from space have emerged as a promising resource for evaluating the spatio‐temporal distribution of gross primary productivity (GPP) simulated by global terrestrial biosphere models. SIF can be used to improve GPP simulations by optimizing critical model parameters through statistical Bayesian data assimilation techniques. A prerequisite is the availability of a functional link between GPP and SIF in terrestrial biosphere models. Here we present the development of a mechanistic SIF observation operator in the ORCHIDEE (Organizing Carbon and Hydrology In Dynamic Ecosystems) terrestrial biosphere model. It simulates the regulation of photosystem II fluorescence quantum yield at the leaf level thanks to a novel parameterization of non‐photochemical quenching as a function of temperature, photosynthetically active radiation, and normalized quantum yield of photochemistry. It emulates the radiative transfer of chlorophyll fluorescence to the top of the canopy using a parametric simplification of the SCOPE (Soil Canopy Observation Photosynthesis Energy) model. We assimilate two years of monthly OCO‐2 (Orbiting Carbon Observatory‐2) SIF product at 0.5° (2015–2016) to optimize ORCHIDEE photosynthesis and phenological parameters over an ensemble of grid points for all plant functional types. The impact on the simulated GPP is considerable with a large decrease of the global scale budget by 28 GtC/year over the period 1990–2009. The optimized GPP budget (134/136 GtC/year over 1990–2009/2001–2009) remarkably agrees with independent GPP estimates, FLUXSAT (137 GtC/year over 2001–2009) in particular and FLUXCOM (121 GtC/year over 1990–2009). Our results also suggest a biome dependency of the SIF‐GPP relationship that needs to be improved for some plant functional types.

Assessment of precipitation error propagation in multi-model global water resource reanalysis

Abstract. This study focuses on the Iberian Peninsula and investigates the propagation of precipitation uncertainty, and its interaction with hydrologic modeling, in global water resource reanalysis. Analysis is based on ensemble hydrologic simulations for a period spanning 11 years (2000–2010). To simulate the hydrological variables of surface runoff, subsurface runoff, and evapotranspiration, we used four land surface models (LSMs) – JULES (Joint UK Land Environment Simulator), ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems), SURFEX (Surface Externalisée), and HTESSEL (Hydrology – Tiled European Centre for Medium-Range Weather Forecasts – ECMWF – Scheme for Surface Exchanges over Land) – and one global hydrological model, WaterGAP3 (Water – a Global Assessment and Prognosis). Simulations were carried out for five precipitation products – CMORPH (the Climate Prediction Center Morphing technique of the National Oceanic and Atmospheric Administration, or NOAA), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), 3B42V(7), ECMWF reanalysis, and a machine-learning-based blended product. As a reference, we used a ground-based observation-driven precipitation dataset, named SAFRAN, available at 5 km, 1 h resolution. We present relative performances of hydrologic variables for the different multi-model and multi-forcing scenarios. Overall, results reveal the complexity of the interaction between precipitation characteristics and different modeling schemes and show that uncertainties in the model simulations are attributed to both uncertainty in precipitation forcing and the model structure. Surface runoff is strongly sensitive to precipitation uncertainty, and the degree of sensitivity depends significantly on the runoff generation scheme of each model examined. Evapotranspiration fluxes are comparatively less sensitive for this study region. Finally, our results suggest that there is no single model–forcing combination that can outperform all others consistently for all variables examined and thus reinforce the fact that there are significant benefits to exploring different model structures as part of the overall modeling approaches used for water resource applications.

Increased Global Land Carbon Sink Due to Aerosol‐Induced Cooling

AbstractAnthropogenic aerosols have contributed to historical climate change through their interactions with radiation and clouds. In turn, climate change due to aerosols has impacted the C cycle. Here we use a set of offline simulations made with the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model driven by bias‐corrected climate fields from simulations of three Coupled Model Intercomparison Project Phase 5 (CMIP5) Earth system models (ESMs; IPSL‐CM5A‐LR, CSIRO‐Mk3.6.0, and GISS‐E2‐R) to quantify the climate‐related impacts of aerosols on land carbon fluxes during 1860–2005. We found that climate change from anthropogenic aerosols (CCAA) globally cooled the climate, and increased land carbon storage, or cumulative net biome production (NBP), by 11.6–41.8 PgC between 1860 and 2005. The increase in NBP from CCAA mainly occurs in the tropics and northern midlatitudes, primarily due to aerosol‐induced cooling. At high latitudes, cooling caused stronger decrease in gross primary production (GPP) than in total ecosystem respiration (TER), leading to lower NBP. At midlatitudes, cooling‐induced decrease in TER is stronger than that of GPP, resulting in NBP increase. At low latitudes, NBP was also enhanced due to the cooling‐induced GPP increase, but precipitation decline from CCAA may negate the effect of temperature. The three ESMs show large divergence in low‐latitude CCAA precipitation response to aerosols, which results in considerable uncertainties in regional estimations of CCAA effects on carbon fluxes. Our results suggest that better understanding and simulation of how anthropogenic aerosols affect precipitation in ESMs is required for a more accurate attribution of aerosol effects on the terrestrial carbon cycle.

ORCHIDEE-ROUTING: revising the river routing scheme using a high-resolution hydrological database

Abstract. The river routing scheme (RRS) in the Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) land surface model is a valuable tool for closing the water cycle in a coupled environment and for validating the model performance. This study presents a revision of the RRS of the ORCHIDEE model that aims to benefit from the high-resolution topography provided by the Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales (HydroSHEDS), which is processed to a resolution of approximately 1 km. Adapting a new algorithm to construct river networks, the new RRS in ORCHIDEE allows for the preservation of as much of the hydrological information from HydroSHEDS as the user requires. The evaluation focuses on 12 rivers of contrasting size and climate which contribute freshwater to the Mediterranean Sea. First, the numerical aspect of the new RRS is investigated, in order to identify the practical configuration offering the best trade-off between computational cost and simulation quality for ensuing validations. Second, the performance of the new scheme is evaluated against observations at both monthly and daily timescales. The new RRS satisfactorily captures the seasonal variability of river discharge, although important biases stem from the water budget simulated by the ORCHIDEE model. The results highlight that realistic streamflow simulations require accurate precipitation forcing data and a precise river catchment description over a wide range of scales, as permitted by the new RRS. Detailed analyses at the daily timescale show the promising performance of this high-resolution RRS with respect to replicating river flow variation at various frequencies. Furthermore, this RRS may also eventually be well adapted for further developments in the ORCHIDEE land surface model to assess anthropogenic impacts on river processes (e.g. damming for irrigation operation).