Ozone-induced gross primary productivity reductions over European forests inferred from satellite observations

Abstract. Tropospheric O3 damages leaves and directly inhibits photosynthesis, posing a threat to terrestrial carbon sinks. Previous investigations have mostly relied on sparse in-situ data or simulations using land surface models. This work is the first to use satellite data to quantify the effect of O3 exposure on gross primary productivity (GPP). O3-induced GPP reductions were estimated to vary between 0.36–9.55% across European forests along a North-South transect between 2003–2015, in line with prior estimates. No significant temporal trend could be determined over most of Europe, while Random Forest analysis (RFA) shows that soil moisture is a significant variable governing GPP reductions over the Mediterranean. Comparisons between this work and GPP reductions simulated by the Yale Interactive Biosphere (YIBs) model suggest that satellite-based estimates over the Mediterranean region may be biased by +12%, potentially because of differences in modelling stomatal sensitivity to soil moisture and prior O3 exposure. This work has demonstrated for the first time that satellite-based datasets can be leveraged to assess the impact of O3 on the terrestrial carbon sink, which are comparable with in-situ or model-based analyses.

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SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

10.20944/preprints201904.0216.v1 ◽

2019 ◽

Author(s):

Nemesio Rodriguez-Fernandez ◽

Patricia de Rosnay ◽

Clement Albergel ◽

Philippe Richaume ◽

Filipe Aires ◽

...

Keyword(s):

Neural Network ◽

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Positive Impact ◽

Land Surface Model ◽

Surface Model ◽

Northern Asia ◽

The Impact

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised - Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if regional differences can exist. Experiments performing joint data assimilation (DA) of NNSM, 2 metre air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although, NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m, but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April-September, while NNSM alone has a significant positive effect in July-September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 hours lead time.

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A bare ground evaporation revision in the ECMWF land-surface scheme: evaluation of its impact using ground soil moisture and satellite microwave data

Hydrology and Earth System Sciences ◽

10.5194/hess-16-3607-2012 ◽

2012 ◽

Vol 16 (10) ◽

pp. 3607-3620 ◽

Cited By ~ 30

Author(s):

C. Albergel ◽

G. Balsamo ◽

P. de Rosnay ◽

J. Muñoz-Sabater ◽

S. Boussetta

Keyword(s):

Soil Moisture ◽

Brightness Temperature ◽

Land Surface ◽

The United States ◽

Microwave Emission ◽

Bare Ground ◽

New Formulation ◽

The Impact ◽

Surface Fields

Abstract. In situ soil moisture data from 122 stations across the United States are used to evaluate the impact of a new bare ground evaporation formulation at ECMWF. In November 2010, the bare ground evaporation used in ECMWF's operational Integrated Forecasting System (IFS) was enhanced by adopting a lower stress threshold than for the vegetation, allowing a higher evaporation. It results in more realistic soil moisture values when compared to in situ data, particularly over dry areas. Use was made of the operational IFS and offline experiments for the evaluation. The latter are based on a fixed version of the IFS and make it possible to assess the impact of a single modification, while the operational analysis is based on a continuous effort to improve the analysis and modelling systems, resulting in frequent updates (a few times a year). Considering the field sites with a fraction of bare ground greater than 0.2, the root mean square difference (RMSD) of soil moisture is shown to decrease from 0.118 m3 m−3 to 0.087 m3 m−3 when using the new formulation in offline experiments, and from 0.110 m3 m−3 to 0.088 m3 m−3 in operations. It also improves correlations. Additionally, the impact of the new formulation on the terrestrial microwave emission at a global scale is investigated. Realistic and dynamically consistent fields of brightness temperature as a function of the land surface conditions are required for the assimilation of the SMOS data. Brightness temperature simulated from surface fields from two offline experiments with the Community Microwave Emission Modelling (CMEM) platform present monthly mean differences up to 7 K. Offline experiments with the new formulation present drier soil moisture, hence simulated brightness temperature with its surface fields are larger. They are also closer to SMOS remotely sensed brightness temperature.

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Comparing Assimilation of Soil Moisture and C-band Backscatter in High Resolution Land Surface Model

10.5194/egusphere-egu2020-10277 ◽

2020 ◽

Author(s):

Leqiang Sun ◽

Stéphane Belair ◽

Marco Carrera ◽

Bernard Bilodeau

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Open Loop ◽

In Situ Measurements ◽

Lookup Table ◽

Surface Model ◽

Loop Model ◽

The Impact

Canadian Space Agency (CSA) has recently started receiving and processing the images from the recently launched C-band RADARSAT Constellation Mission (RCM). The backscatter and soil moisture retrievals products from the previously launched RADARSAT-2 agree well with both in-situ measurements and surface soil moisture modeled with land surface model Soil, Vegetation, and Snow (SVS). RCM will provide those products at an even better spatial coverage and temporal resolution. In preparation of the potential operational application of RCM products in Canadian Meteorological Center (CMC), this paper presents the scenarios of assimilating either soil moisture retrieval or outright backscatter signal in a 100-meter resolution version of the Canadian Land Data Assimilation System (CaLDAS) on field scale with time interval of three hours. The soil moisture retrieval map was synthesized by extrapolating the regression relationship between in-situ measurements and open loop model output based on soil texture lookup table. Based on this, the backscatter map was then generated with the surface roughness retrieved from RADARSAT-2 images using a modified Integral Equation Model (IEM) model. Bias correction was applied to the Ensemble Kalman filter (EnKF) to mitigate the impact of nonlinear errors introduced by multi-sourced perturbations. Initial results show that the assimilation of backscatter is as effective as assimilating soil moisture retrievals. Compared to open loop, both can improve the analysis of surface moisture, particularly in terms of reducing bias. &#160;

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Simulating Gross Primary Productivity of vegetation under soil water stress using in-situ and reanalysis soil moisture data inputs

10.5194/egusphere-egu2020-21397 ◽

2020 ◽

Author(s):

Timothy Lam ◽

Amos P. K. Tai

Keyword(s):

Soil Moisture ◽

Water Stress ◽

Soil Water ◽

Primary Productivity ◽

Gross Primary Productivity ◽

Efficiency Coefficient ◽

Community Land Model ◽

Soil Water Stress ◽

Soil Moisture Data

This study utilises in-situ and reanalysis soil moisture data inputs from various sources to evaluate the effect of soil water stress on Gross Primary Productivity (GPP) of different Plant Functional Types (PFTs) using Terrestrial Ecosystem Model in R (TEMIR), which is under development by Tai Group of Atmosphere-Biosphere Interactions (Tai et al. in prep.). An empirical soil water stress function with reference to Community Land Model (CLM) Version 4.5 is employed to quantify water stress experienced by vegetation which hinders stomatal conductance and thus carboxylation rate. The model results are compared against observations at FLUXNET sites in semi-arid regions across the globe at daily timescale where in-situ GPP data is available and water stress inhibits plant functions to some extent. By dividing the soil into two layers (topsoil and root zone), GPP simulation improves significantly comparing with using single layer bulk soil (Modified Nash-Sutcliffe Model Efficiency Coefficient N increases from -0.686 to -0.586). Such upgrade is particularly substantial for vegetation with shallow roots such as grass PFTs. Despite this improvement, the model is characterised by an overall overestimation of GPP when water stress occurs, and inconsistency of accuracy subject to PFTs and degree of water stress experienced. This study informs responses of various PFTs to soil water stress, capacity of TEMIR in simulating the responses, and possible drawbacks of empirical soil water stress functions, and highlights the importance of topsoil moisture data input for vegetation drought monitoring.Keywords: Soil water stress, Terrestrial model representation, Photosynthesis, In-situ data, Reanalysis data, FLUXNET

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Anatomy of the 2018 agricultural drought in The Netherlands using in situ soil moisture and satellite vegetation indices

10.5194/hess-2020-358 ◽

2020 ◽

Cited By ~ 1

Author(s):

Joost Buitink ◽

Anne M. Swank ◽

Martine van der Ploeg ◽

Naomi E. Smith ◽

Harm-Jan F. Benninga ◽

...

Keyword(s):

Soil Moisture ◽

The Netherlands ◽

Moisture Content ◽

Land Surface ◽

Soil Moisture Content ◽

Vegetation Indices ◽

Agricultural Drought ◽

Summer Drought ◽

The Impact

Abstract. The soil moisture status near the land surface is a key determinant of vegetation productivity. The critical soil moisture content determines the transition from an energy-limited to a water-limited evapotranspiration regime. This study quantifies the critical soil moisture content by comparison of in situ soil moisture profile measurements of the Raam and Twenthe networks in the Netherlands, with two satellite derived vegetation indices (NIRv and VOD) during the 2018 summer drought. The critical soil moisture content is obtained through a piece-wise linear correlation of the NIRv and VOD anomalies with soil moisture on different depths of the profile. This nonlinear relation reflects the observation that negative soil moisture anomalies develop weeks before the first reduction in vegetation indices. Furthermore, the inferred critical soil moisture content was found to increase with observation depth and this relationship is shown to be linear and distinctive per area, reflecting the tendency of roots to take up water from deeper layers when drought progresses. The relations of non-stressed towards water-stressed vegetation conditions on distinct depths are derived using Remote Sensing, enabling the parameterization of reduced evapotranspiration and its effect on GPP in models to study the impact of a drought on the carbon cycle.

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Enhancing Model Skill by Assimilating SMOPS Blended Soil Moisture Product into Noah Land Surface Model

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0070.1 ◽

2015 ◽

Vol 16 (2) ◽

pp. 917-931 ◽

Cited By ~ 25

Author(s):

Jifu Yin ◽

Xiwu Zhan ◽

Youfei Zheng ◽

Jicheng Liu ◽

Li Fang ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Surface Radiation ◽

Radiation Budget ◽

Shortwave Radiation ◽

Surface Model ◽

Spatial Coverage ◽

In Situ Observations ◽

The Impact

Abstract Many studies that have assimilated remotely sensed soil moisture into land surface models have generally focused on retrievals from a single satellite sensor. However, few studies have evaluated the merits of assimilating ensemble products that are merged soil moisture retrievals from several different sensors. In this study, the assimilation of the Soil Moisture Operational Products System (SMOPS) blended soil moisture (SBSM) product, which is a combination of soil moisture products from WindSat, Advanced Scatterometer (ASCAT), and Soil Moisture and Ocean Salinity (SMOS) satellite sensors is examined. Using the ensemble Kalman filter (EnKF), a synthetic experiment is performed on the global domain at 25-km resolution to assess the impact of assimilating the SBSM product. The benefit of assimilating SBSM is assessed by comparing it with in situ observations from U.S. Department of Agriculture Soil Climate Analysis Network (SCAN) and the Surface Radiation Budget Network (SURFRAD). Time-averaged surface-layer soil moisture fields from SBSM have a higher spatial coverage and generally agree with model simulations in the global patterns of wet and dry regions. The impacts of assimilating SMOPS blended data on model soil moisture and soil temperature are evident in both sparsely and densely vegetated areas. Temporal correlations between in situ observations and net shortwave radiation and net longwave radiation are higher with assimilating SMOPS blended product than without the data assimilation.

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The Impact of the 20–50-Day Atmospheric Intraseasonal Oscillation on the Gross Primary Productivity between the Yangtze and Yellow Rivers

Journal of Climate ◽

10.1175/jcli-d-19-0575.1 ◽

2020 ◽

Vol 33 (8) ◽

pp. 2967-2984

Author(s):

Jianying Li ◽

Jin-Soo Kim ◽

Jong-Seong Kug

Keyword(s):

Soil Moisture ◽

East Asia ◽

Primary Productivity ◽

Meteorological Data ◽

Intraseasonal Oscillation ◽

Terrestrial Ecosystems ◽

Gross Primary Productivity ◽

Mean Power ◽

Vegetation Growth ◽

The Impact

AbstractGiven their high carbon uptake, the terrestrial ecosystems in the East Asia summer monsoon (EASM) region play an irreplaceable role in the global carbon cycle. Because the rich vegetation growth over East Asia benefits mainly from the sufficient water supply brought by the EASM, which is characterized by a strong intraseasonal oscillation (ISO), the intraseasonal spatiotemporal variations and underlying drivers of photosynthesis activity over East Asia have been comprehensively investigated using the daily gross primary productivity (GPP) and meteorological data. Strong intraseasonal fluctuations of GPP have been identified over the area between the Yangtze and Yellow Rivers (YYR) with a magnitude of 0.4 gC m−2 day−1. The mean power spectrum suggests that 20–50-day variation is the major component of the intraseasonal GPP anomalies over the YYR during the summers of 1980–2013. The 20–50-day ISO of YYR GPP anomalies is modulated by the local 20–50-day precipitation variation via soil moisture, with precipitation (soil moisture) leading GPP by 10 (7) days. The 20–50-day YYR precipitation anomalies are in turn controlled by tropical ISO signals, particularly the convective activity over the western North Pacific. This leading relationship between the 20–50-day atmospheric ISO and GPP suggests a potential for extended-range predictability of vegetation growth.

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L-Band Soil Moisture Retrievals Using Microwave Based Temperature and Filtering. Towards Model-Independent Climate Data Records

Remote Sensing ◽

10.3390/rs13132480 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2480

Author(s):

Robin van der Schalie ◽

Mendy van der Vliet ◽

Nemesio Rodríguez-Fernández ◽

Wouter A. Dorigo ◽

Tracy Scanlon ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Passive Microwave ◽

Climate Data ◽

Land Surface Models ◽

Surface Models ◽

L Band ◽

The Impact ◽

Microwave Sensors

The CCI Soil Moisture dataset (CCI SM) is the most extensive climate data record of satellite soil moisture to date. To maximize its function as a climate benchmark, both long-term consistency and (model-) independence are high priorities. Two unique L-band missions integrated into the CCI SM are SMOS and SMAP. However, they lack the high-frequency microwave sensors needed to determine the effective temperature and snow/frozen flagging, and therefore use input from (varying) land surface models. In this study, the impact of replacing this model input by temperature and filtering based on passive microwave observations is evaluated. This is derived from an inter-calibrated dataset (ICTB) based on six passive microwave sensors. Generally, this leads to an expected increase in revisit time, which goes up by about 0.5 days (~15% loss). Only the boreal regions have an increased coverage due to more accurate freeze/thaw detection. The boreal regions become wetter with an increased dynamic range, while the tropics are dryer with decreased dynamics. Other regions show only small differences. The skill was evaluated against ERA5-Land and in situ observations. The average correlation against ERA5-Land increased by 0.05 for SMAP ascending/descending and SMOS ascending, whereas SMOS descending decreased by 0.01. For in situ sensors, the difference is less pronounced, with only a significant change in correlation of 0.04 for SM SMOS ascending. The results indicate that the use of microwave-based input for temperature and filtering is a viable and preferred alternative to the use of land surface models in soil moisture climate data records from passive microwave sensors.

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Irrigation Amounts and Timing Retrieval through Data Assimilation of Surface Soil Moisture into the FAO-56 Approach in the South Mediterranean Region

Remote Sensing ◽

10.3390/rs13142667 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2667

Author(s):

Nadia Ouaadi ◽

Lionel Jarlan ◽

Saïd Khabba ◽

Jamal Ezzahar ◽

Michel Le Page ◽

...

Keyword(s):

Soil Moisture ◽

Water Use ◽

Land Surface ◽

Surface Soil ◽

Surface Soil Moisture ◽

Field Scale ◽

Method Performance ◽

The Impact ◽

Statistical Metrics

Agricultural water use represents more than 70% of the world’s freshwater through irrigation water inputs that are poorly known at the field scale. Irrigation monitoring is thus an important issue for optimizing water use in particular with regards to the water scarcity that the semi-arid regions are already facing. In this context, the aim of this study is to develop and evaluate a new approach to predict seasonal to daily irrigation timing and amounts at the field scale. The method is based on surface soil moisture (SSM) data assimilated into a simple land surface (FAO-56) model through a particle filter technique based on an ensemble of irrigation scenarios. The approach is implemented in three steps. First, synthetic experiments are designed to assess the impact of the frequency of observation, the errors on SSM and the a priori constraints on the irrigation scenarios for different irrigation techniques (flooding and drip). In a second step, the method is evaluated using in situ SSM measurements with different revisit times (3, 6 and 12 days) to mimic the available SSM product derived from remote sensing observation. Finally, SSM estimates from Sentinel-1 are used. Data are collected on different wheat fields grown in Morocco, for both flood and drip irrigation techniques in addition to rainfed fields used for an indirect evaluation of the method performance. Using in situ data, accurate results are obtained. With an observation every 6 days to mimic the Sentinel-1 revisit time, the seasonal amounts are retrieved with R > 0.98, RMSE < 32 mm and bias < 2.5 mm. Likewise, a good agreement is observed at the daily scale for flood irrigation as more than 70% of the detected irrigation events have a time difference from actual irrigation events shorter than 4 days. Over the drip irrigated fields, the statistical metrics are R = 0.74, RMSE = 24.8 mm and bias = 2.3 mm for irrigation amounts cumulated over 15 days. When using SSM products derived from Sentinel-1 data, the statistical metrics on 15-day cumulated amounts slightly dropped to R = 0.64, RMSE = 28.7 mm and bias = 1.9 mm. The metrics on the seasonal amount retrievals are close to assimilating in situ observations with R = 0.99, RMSE = 33.5 mm and bias = −18.8 mm. Finally, among four rainfed seasons, only one false event was detected. This study opens perspectives for the regional retrieval of irrigation amounts and timing at the field scale and for mapping irrigated/non irrigated areas.

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Evaluating and Optimizing Surface Soil Moisture Drydowns in the ORCHIDEE Land Surface Model at In Situ Locations

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0115.1 ◽

2021 ◽

Vol 22 (4) ◽

pp. 1025-1043

Author(s):

Nina Raoult ◽

Catherine Ottlé ◽

Philippe Peylin ◽

Vladislav Bastrikov ◽

Pascal Maugis

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Surface Soil ◽

Temporal Dynamics ◽

Gross Primary Production ◽

Bayesian Optimization ◽

Surface Model ◽

Surface Soil Moisture ◽

The Impact

AbstractThe rate at which land surface soils dry following rain events is an important feature of terrestrial models. It determines, for example, the water availability for vegetation, the occurrences of droughts, and the surface heat exchanges. As such, surface soil moisture (SSM) “drydowns,” i.e., the SSM temporal dynamics following a significant rainfall event, are of particular interest when evaluating and calibrating land surface models (LSMs). By investigating drydowns, characterized by an exponential decay time scale τ, we aim to improve the representation of SSM in the ORCHIDEE global LSM. We consider τ calculated over 18 International Soil Moisture Network sites found within the footprint of FLUXNET towers, covering different vegetation types and climates. Using the ORCHIDEE LSM, we compare τ from the modeled SSM time series to values computed from in situ SSM measurements. We then assess the potential of using τ observations to constrain some water, carbon, and energy parameters of ORCHIDEE, selected using a sensitivity analysis, through a standard Bayesian optimization procedure. The impact of the SSM optimization is evaluated using FLUXNET evapotranspiration and gross primary production (GPP) data. We find that the relative drydowns of SSM can be well calibrated using observation-based τ estimates, when there is no need to match the absolute observed and modeled SSM values. When evaluated using independent data, τ-calibration parameters were able to improve drydowns for 73% of the sites. Furthermore, the fit of the model to independent fluxes was only minutely changed. We conclude by considering the potential of global satellite products to scale up the experiment to a global-scale optimization.

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