scholarly journals Impact of precipitation and land biophysical variables on the simulated discharge of European and Mediterranean rivers

2012 ◽  
Vol 16 (9) ◽  
pp. 3351-3370 ◽  
Author(s):  
C. Szczypta ◽  
B. Decharme ◽  
D. Carrer ◽  
J.-C. Calvet ◽  
S. Lafont ◽  
...  
Keyword(s):  
Land Surface ◽  
River Discharge ◽  
Land Surface Model ◽  
Water Levels ◽  
Surface Model ◽  
Area Index ◽  
Total Runoff ◽  
Global Precipitation Climatology Centre ◽  
Hydrological System ◽  
The Impact

Abstract. This study investigates the impact on river discharge simulations of errors in the precipitation forcing, together with changes in the representation of vegetation variables and of plant transpiration. The most recent European Centre for Medium-Range Weather Forecasts reanalysis (ERA-Interim) is used to drive the Interactions between Soil, Biosphere, and Atmosphere–Total Runoff Integrating Pathways (ISBA-TRIP) continental hydrological system over Europe and the Mediterranean basin over the 1991–2008 period. As ERA-Interim tends to underestimate precipitation, a number of precipitation corrections are proposed. In particular, the monthly Global Precipitation Climatology Centre (GPCC) precipitation product is used to bias-correct the 3-hourly ERA-Interim estimates. This correction markedly improves the match between the ISBA-TRIP simulations and the river discharge observations from the Global Runoff Data Centre (GRDC), at 150 gauging stations. The impact on TRIP river discharge simulations of various representations of the evapotranspiration in the ISBA land surface model is investigated as well: ISBA is used together with its upgraded carbon flux version (ISBA-A-gs). The latter is either driven by the satellite-derived climatology of the Leaf Area Index (LAI) used by ISBA, or performs prognostic LAI simulations. The ISBA-A-gs model, with or without dynamically simulated LAI, allows a better representation of river discharge at low water levels. On the other hand, ISBA-A-gs does not perform as well as the original ISBA model at springtime.

scholarly journals Impact of precipitation and land biophysical variables on the simulated discharge of European and Mediterranean rivers

2012 ◽  
Vol 9 (4) ◽  
pp. 5437-5486
Author(s):  
C. Szczypta ◽  
B. Decharme ◽  
D. Carrer ◽  
J.-C. Calvet ◽  
S. Lafont ◽  
...  
Keyword(s):  
Land Surface ◽  
Carbon Flux ◽  
River Discharge ◽  
Land Surface Model ◽  
Water Levels ◽  
Surface Model ◽  
Mediterranean Rivers ◽  
Area Index ◽  
Hydrological System ◽  
The Impact

Abstract. This study investigates the use of the ERA-Interim 3-hourly atmospheric reanalysis over Europe and the Mediterranean basin, to drive the ISBA-TRIP continental hydrological system, at a spatial resolution of 0.5°, over the 1991–2008 period. Several versions of the representation of evapotranspiration in the ISBA land surface model are used to simulate the runoff which is converted into river discharge by the TRIP river routing model. In particular, the impact of using contrasting representations of the vegetation variables is investigated: ISBA is used together with its upgraded carbon flux version (ISBA-A-gs). The latter is either driven by a satellite-derived climatology of the Leaf Area Index (LAI) or performs prognostic LAI simulations. As ERA-Interim tends to underestimate precipitation, a number of precipitation corrections are proposed. In particular, the monthly GPCC precipitation product is used to un-bias the 3-hourly ERA-Interim estimates. This correction markedly improves the match between the ISBA-TRIP simulations and the river discharge observations of the Global Runoff Data Centre (GRDC), at 150 gauging stations. The interactive LAI version of ISBA-A-gs does not perform as well as the original ISBA model at springtime. On the other hand, the use of the ISBA-A-gs model allows a better representation of river discharge at low water levels. Constraining the ISBA-A-gs LAI with satellite-derived LAI data improves the simulations at springtime.

scholarly journals The effect of satellite-derived surface soil moisture and leaf area index land data assimilation on streamflow simulations over France

2017 ◽  
Vol 21 (4) ◽  
pp. 2015-2033 ◽  
Author(s):  
David Fairbairn ◽  
Alina Lavinia Barbu ◽  
Adrien Napoly ◽  
Clément Albergel ◽  
Jean-François Mahfouf ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Land Surface ◽  
River Discharge ◽  
Surface Soil ◽  
Land Surface Model ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Area Index

Abstract. This study evaluates the impact of assimilating surface soil moisture (SSM) and leaf area index (LAI) observations into a land surface model using the SAFRAN–ISBA–MODCOU (SIM) hydrological suite. SIM consists of three stages: (1) an atmospheric reanalysis (SAFRAN) over France, which forces (2) the three-layer ISBA land surface model, which then provides drainage and runoff inputs to (3) the MODCOU hydro-geological model. The drainage and runoff outputs from ISBA are validated by comparing the simulated river discharge from MODCOU with over 500 river-gauge observations over France and with a subset of stations with low-anthropogenic influence, over several years. This study makes use of the A-gs version of ISBA that allows for physiological processes. The atmospheric forcing for the ISBA-A-gs model underestimates direct shortwave and long-wave radiation by approximately 5 % averaged over France. The ISBA-A-gs model also substantially underestimates the grassland LAI compared with satellite retrievals during winter dormancy. These differences result in an underestimation (overestimation) of evapotranspiration (drainage and runoff). The excess runoff flowing into the rivers and aquifers contributes to an overestimation of the SIM river discharge. Two experiments attempted to resolve these problems: (i) a correction of the minimum LAI model parameter for grasslands and (ii) a bias-correction of the model radiative forcing. Two data assimilation experiments were also performed, which are designed to correct random errors in the initial conditions: (iii) the assimilation of LAI observations and (iv) the assimilation of SSM and LAI observations. The data assimilation for (iii) and (iv) was done with a simplified extended Kalman filter (SEKF), which uses finite differences in the observation operator Jacobians to relate the observations to the model variables. Experiments (i) and (ii) improved the median SIM Nash scores by about 9 % and 18 % respectively. Experiment (iii) reduced the LAI phase errors in ISBA-A-gs but had little impact on the discharge Nash efficiency of SIM. In contrast, experiment (iv) resulted in spurious increases in drainage and runoff, which degraded the median discharge Nash efficiency by about 7 %. The poor performance of the SEKF originates from the observation operator Jacobians. These Jacobians are dampened when the soil is saturated and when the vegetation is dormant, which leads to positive biases in drainage and/or runoff and to insufficient corrections during winter, respectively. Possible ways to improve the model are discussed, including a new multi-layer diffusion model and a more realistic response of photosynthesis to temperature in mountainous regions. The data assimilation should be advanced by accounting for model and forcing uncertainties.

scholarly journals JULES-crop: a parametrisation of crops in the Joint UK Land Environment Simulator

2014 ◽  
Vol 7 (5) ◽  
pp. 6773-6809
Author(s):  
T. Osborne ◽  
J. Gornall ◽  
J. Hooker ◽  
K. Williams ◽  
A. Wiltshire ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Land Surface Model ◽  
Water Security ◽  
Climate Change Impacts ◽  
Heat Fluxes ◽  
Surface Model ◽  
Yield Model ◽  
Area Index ◽  
The Impact

Abstract. Studies of climate change impacts on the terrestrial biosphere have been completed without recognition of the integrated nature of the biosphere. Improved assessment of the impacts of climate change on food and water security requires the development and use of models not only representing each component but also their interactions. To meet this requirement the Joint UK Land Environment Simulator (JULES) land surface model has been modified to include a generic parametrisation of annual crops. The new model, JULES-crop, is described and evaluation at global and site levels for the four globally important crops; wheat, soy bean, maize and rice is presented. JULES-crop demonstrates skill in simulating the inter-annual variations of yield for maize and soy bean at the global level, and for wheat for major spring wheat producing countries. The impact of the new parametrisation, compared to the standard configuration, on the simulation of surface heat fluxes is largely an alteration of the partitioning between latent and sensible heat fluxes during the later part of the growing season. Further evaluation at the site level shows the model captures the seasonality of leaf area index and canopy height better than in standard JULES. However, this does not lead to an improvement in the simulation of sensible and latent heat fluxes. The performance of JULES-crop from both an earth system and crop yield model perspective is encouraging however, more effort is needed to develop the parameterisation of the model for specific applications. Key future model developments identified include the specification of the yield gap to enable better representation of the spatial variability in yield.

Assimilation of vegetation conditions improves the representation of drought over agricultural areas

2021 ◽  
Author(s):  
David M. Mocko ◽  
Sujay V. Kumar ◽  
Christa D. Peters-Lidard ◽  
Shugong Wang
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Agricultural Drought ◽  
False Alarms ◽  
Surface Model ◽  
Model Errors ◽  
Physical Processes ◽  
Area Index ◽  
Agricultural Areas ◽  
The Impact

AbstractThis study presents an evaluation of the impact of vegetation conditions on a land-surface model (LSM) simulation of agricultural drought. The Noah-MP LSM is used to simulate water and energy fluxes and states, which are transformed into drought categories using percentiles over the continental U.S. from 1979 to 2017. Leaf Area Index (LAI) observations are assimilated into the dynamic vegetation scheme of Noah-MP. A weekly operational drought monitor (the U.S. Drought Monitor) is used for the evaluation. The results show that LAI assimilation into Noah-MP’s dynamic vegetation scheme improves the model's ability to represent drought, particularly over cropland areas. LAI assimilation improves the simulation of the drought category, detection of drought conditions, and reduces the instances of drought false alarms. The assimilation of LAI in these locations not only corrects model errors in the simulation of vegetation, but also can help to represent unmodeled physical processes such as irrigation towards improved simulation of agricultural drought.

scholarly journals Global Assimilation of Remotely Sensed Leaf Area Index: The Impact of Updating More State Variables Within a Land Surface Model

2022 ◽  
Vol 3 ◽  
Author(s):  
Azbina Rahman ◽  
Xinxuan Zhang ◽  
Paul Houser ◽  
Timothy Sauer ◽  
Viviana Maggioni
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Land Surface Model ◽  
Net Ecosystem Exchange ◽  
Surface Model ◽  
Random Errors ◽  
Area Index ◽  
Leaf Mass ◽  
The Impact

As vegetation regulates water, carbon, and energy cycles from the local to the global scale, its accurate representation in land surface models is crucial. The assimilation of satellite-based vegetation observations in a land surface model has the potential to improve the estimation of global carbon and energy cycles, which in turn can enhance our ability to monitor and forecast extreme hydroclimatic events, ecosystem dynamics, and crop production. This work proposes the assimilation of a remotely sensed vegetation product (Leaf Area Index, LAI) within the Noah Multi-Parameterization land surface model using an Ensemble Kalman Filter technique. The impact of updating leaf mass along with LAI is also investigated. Results show that assimilating LAI data improves the estimation of transpiration and net ecosystem exchange, which is further enhanced by also updating the leaf mass. Specifically, transpiration anomaly correlation coefficients improve in about 77 and 66% of the global land area thanks to the assimilation of leaf area index with and without updating leaf mass, respectively. Random errors in transpiration are also reduced, with an improvement of the unbiased root mean square error in 70% (74%) of the total area without the update of leaf mass (with the update of leaf mass). Similarly, net ecosystem exchange anomaly correlation coefficients improve from 52 to 75% and random errors improve from 49 to 62% of the total pixels after the update of leaf mass. Better performances for both transpiration and net ecosystem exchange are observed across croplands, but the largest improvement is shown over forests and woodland. The global scope of this work makes it particularly important in data poor regions (e.g., Africa, South Asia), where ground observations are sparse or not available altogether but where an accurate estimation of carbon and energy variables can be critical to improve ecosystem and crop management.

scholarly journals Interpretation of ASCAT Radar Scatterometer Observations Over Land: A Case Study Over Southwestern France

2019 ◽  
Vol 11 (23) ◽  
pp. 2842 ◽  
Author(s):  
Daniel Shamambo ◽  
Bertrand Bonan ◽  
Jean-Christophe Calvet ◽  
Clément Albergel ◽  
Sebastian Hahn
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Extreme Event ◽  
Cloud Model ◽  
Large Fraction ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
The Difference ◽  
The Impact

This paper investigates to what extent soil moisture and vegetation density information can be extracted from the Advanced Scatterometer (ASCAT) satellite-derived radar backscatter (σ°) in a data assimilation context. The impact of independent estimates of the surface soil moisture (SSM) and leaf area index (LAI) of diverse vegetation types on ASCAT σ° observations is simulated over southwestern France using the water cloud model (WCM). The LAI and SSM variables used by the WCM are derived from satellite observations and from the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model, respectively. They permit the calibration of the four parameters of the WCM describing static soil and vegetation characteristics. A seasonal analysis of the model scores shows that the WCM has shortcomings over karstic areas and wheat croplands. In the studied area, the Klaus windstorm in January 2009 damaged a large fraction of the Landes forest. The ability of the WCM to represent the impact of Klaus and to simulate ASCAT σ° observations in contrasting land-cover conditions is explored. The difference in σ° observations between the forest zone affected by the storm and the bordering agricultural areas presents a marked seasonality before the storm. The difference is small in the springtime (from March to May) and large in the autumn (September to November) and wintertime (December to February). After the storm, hardly any seasonality was observed over four years. This study shows that the WCM is able to simulate this extreme event. It is concluded that the WCM could be used as an observation operator for the assimilation of ASCAT σ° observations into the ISBA land surface model.

scholarly journals Improving evapotranspiration in a land surface model using biophysical variables derived from MSG/SEVIRI satellite

2012 ◽  
Vol 16 (8) ◽  
pp. 2567-2583 ◽  
Author(s):  
N. Ghilain ◽  
A. Arboleda ◽  
G. Sepulcre-Cantò ◽  
O. Batelaan ◽  
J. Ardö ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Continental Scale ◽  
Western Africa ◽  
Large Gradient ◽  
The Impact

Abstract. Monitoring evapotranspiration over land is highly dependent on the surface state and vegetation dynamics. Data from spaceborn platforms are desirable to complement estimations from land surface models. The success of daily evapotranspiration monitoring at continental scale relies on the availability, quality and continuity of such data. The biophysical variables derived from SEVIRI on board the geostationary satellite Meteosat Second Generation (MSG) and distributed by the Satellite Application Facility on Land surface Analysis (LSA-SAF) are particularly interesting for such applications, as they aimed at providing continuous and consistent daily time series in near-real time over Africa, Europe and South America. In this paper, we compare them to monthly vegetation parameters from a database commonly used in numerical weather predictions (ECOCLIMAP-I), showing the benefits of the new daily products in detecting the spatial and temporal (seasonal and inter-annual) variability of the vegetation, especially relevant over Africa. We propose a method to handle Leaf Area Index (LAI) and Fractional Vegetation Cover (FVC) products for evapotranspiration monitoring with a land surface model at 3–5 km spatial resolution. The method is conceived to be applicable for near-real time processes at continental scale and relies on the use of a land cover map. We assess the impact of using LSA-SAF biophysical variables compared to ECOCLIMAP-I on evapotranspiration estimated by the land surface model H-TESSEL. Comparison with in-situ observations in Europe and Africa shows an improved estimation of the evapotranspiration, especially in semi-arid climates. Finally, the impact on the land surface modelled evapotranspiration is compared over a north–south transect with a large gradient of vegetation and climate in Western Africa using LSA-SAF radiation forcing derived from remote sensing. Differences are highlighted. An evaluation against remote sensing derived land surface temperature shows an improvement of the evapotranspiration simulations.

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

2019 ◽  
Author(s):  
Salma Tafasca ◽  
Agnès Ducharne ◽  
Christian Valentin
Keyword(s):  
Soil Water ◽  
Soil Texture ◽  
Land Surface ◽  
Large Scale ◽  
Land Surface Model ◽  
Global Scale ◽  
Added Value ◽  
Surface Model ◽  
Total Runoff ◽  
The Impact

Abstract. Soil physical properties play an important role for 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 global scale using the ORCHIDEE LSM, forced by several complex or globally-uniform soil texture maps. The model shows a realistic sensitivity of runoff processes and soil moisture to soil texture, and reveals that medium textures give the highest evapotranspiration and lowest total runoff rates. The three tested complex soil texture maps being rather similar by construction, especially when upscaled at the 0.5° resolution used here, they result in similar water budgets at all scales, compared to the uncertainties of observation-based products and meteorological forcing datasets. A useful outcome is that the choice of the input soil texture map is not crucial for large-scale modelling. The added-value of more detailed soil information (horizontal and vertical resolution, soil composition) deserves further studies.

scholarly journals Sensitivity of Land Surface Simulations to the Treatment of Vegetation Properties and the Implications for Seasonal Climate Prediction

2008 ◽  
Vol 9 (3) ◽  
pp. 348-366 ◽  
Author(s):  
Xiang Gao ◽  
Paul A. Dirmeyer ◽  
Zhichang Guo ◽  
Mei Zhao
Keyword(s):  
Land Surface ◽  
Climate Model ◽  
Positive Impact ◽  
Land Surface Model ◽  
Surface Fluxes ◽  
Surface Model ◽  
Canopy Interception ◽  
Area Index ◽  
Temperature And Precipitation ◽  
The Impact

Abstract A coupled land–atmosphere climate model is used to investigate the impact of vegetation parameters (leaf area index, absorbed radiation, and greenness fraction) on the simulation of surface fluxes and their potential role in improving climate forecasts. Ensemble simulations for 1986–95 have been conducted with specified observed sea surface temperatures. The vegetation impact is analyzed by comparing integrations with two different ways of specifying vegetation boundary conditions: observed interannually varying vegetation versus the climatological annual cycle. Parallel integrations are also implemented and analyzed for the land surface model in an uncoupled mode within the framework of the Second Global Soil Wetness Project (GSWP-2) for the same period. The sensitivity to vegetation anomalies in the coupled simulations appears to be relatively small. There appears to be only episodic and localized favorable impacts of vegetation variations on the skill of precipitation and temperature simulations. Impacts are sometimes manifested strictly through changes in land surface fluxes, and in other cases involve clear interactions with atmospheric processes. In general, interannual variations of vegetation tend to increase the temporal variability of radiation fluxes, soil evaporation, and canopy interception loss in terms of both spatial frequency and global mean. Over cohesive regions of significant and persistent vegetation anomalies, cumulative statistics do show a net response of surface fluxes, temperature, and precipitation with vegetation anomalies of ±20% corresponding to a precipitation response of about ±6%. However, in about half of these cases no significant response was found. The results presented here suggest that vegetation may be a useful element of the land surface for enhancing seasonal predictability, but its role in this model appears to be relatively minor. Improvement does not occur in all circumstances, and strong anomalies have the best chance of a positive impact on the simulation.

scholarly journals JULES-crop: a parametrisation of crops in the Joint UK Land Environment Simulator

2015 ◽  
Vol 8 (4) ◽  
pp. 1139-1155 ◽  
Author(s):  
T. Osborne ◽  
J. Gornall ◽  
J. Hooker ◽  
K. Williams ◽  
A. Wiltshire ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Gross Primary Production ◽  
Land Surface Model ◽  
Water Security ◽  
Heat Fluxes ◽  
Surface Model ◽  
Yield Model ◽  
Area Index ◽  
The Impact

Abstract. Studies of climate change impacts on the terrestrial biosphere have been completed without recognition of the integrated nature of the biosphere. Improved assessment of the impacts of climate change on food and water security requires the development and use of models not only representing each component but also their interactions. To meet this requirement the Joint UK Land Environment Simulator (JULES) land surface model has been modified to include a generic parametrisation of annual crops. The new model, JULES-crop, is described and evaluation at global and site levels for the four globally important crops; wheat, soybean, maize and rice. JULES-crop demonstrates skill in simulating the inter-annual variations of yield for maize and soybean at the global and country levels, and for wheat for major spring wheat producing countries. The impact of the new parametrisation, compared to the standard configuration, on the simulation of surface heat fluxes is largely an alteration of the partitioning between latent and sensible heat fluxes during the later part of the growing season. Further evaluation at the site level shows the model captures the seasonality of leaf area index, gross primary production and canopy height better than in the standard JULES. However, this does not lead to an improvement in the simulation of sensible and latent heat fluxes. The performance of JULES-crop from both an Earth system and crop yield model perspective is encouraging. However, more effort is needed to develop the parametrisation of the model for specific applications. Key future model developments identified include the introduction of processes such as irrigation and nitrogen limitation which will enable better representation of the spatial variability in yield.

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