scholarly journals Spatio-Temporal Variations and Uncertainty in Land Surface Modelling for High Latitudes: Univariate Response Analysis

2019 ◽  
Author(s):  
Didier G. Leibovici ◽  
Shaun Quegan ◽  
Edward Comyn-Platt ◽  
Gary Hayman ◽  
Maria Val Martin ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Temporal Pattern ◽  
Temporal Variations ◽  
The Other ◽  
Surface Modelling ◽  
Land Surface Modelling ◽  
Spatio Temporal ◽  
The Times ◽  
The Impact

Abstract. A range of applications analysing the impact of environmental changes due to climate change, e.g. geographical spread of climate sensitive infections (CSIs), agriculture crop modelling, etc., make use of Land Surface Modelling (LSM) to predict future land surface conditions. There are multiple LSMs to choose from that account for land processes in different ways and, depending on the application, the choice of LSM and its sensitivity will have different impacts. For useful predictions for a specific application, one must therefore understand the inherent uncertainties in the LSMs and the variations between them, as well as uncertainties arising from variation in the climate data driving the LSMs. This requires methods to analyse multivariate spatio-temporal variations and differences. A methodology is proposed based on multi-way data analysis, which extends Singular Value Decomposition (SVD) to multi-dimensional tables, and provides spatio-temporal descriptions of agreements and disagreements between LSMs for both historical simulations and future predictions. The application underlying this paper is prediction of how climate change will affect the spread of CSIs in the Fenno-Scandinavian and north-west Russian regions, and the approach is explored by comparing Net Primary Production (NPP) estimates over the period 1998–2013 from versions of leading LSMs (JULES, CLM5 and two versions of ORCHIDEE) that are adapted to high latitude processes, as well as variations in JULES up to 2100 when driven by 34 global circulation models (GCMs). A single optimal spatio-temporal pattern, with slightly different weights for the four LSMs (up to 14 % maximum difference), provides a good approximation to all their estimates of NPP, capturing between 87 % and 93 % of the variability in the individual models, as well as around 90 % of the variability in the combined LSM dataset. The next best adjustment to this pattern, capturing an extra 4 % of the overall variability, is essentially a spatial correction applied to ORCHIDEE-HLveg that significantly improves the fit to this LSM, with only small improvements for the other LSMs. Subsequent correction terms gradually improve the overall and individual LSM fits, but capture at most 1.7 % of the overall variability. Analysis of differences between LSMs provides information on the times and places where the LSMs differ and by how much, but in this case no single spatio-temporal pattern strongly dominates the variability. Hence interpretation of the analysis requires the summation of several such patterns. Nonetheless, the three best principal tensors capture around 76 % of the variability in the LSM differences, and to a first approximation successively indicate the times and places where ORCHIDEE-HLveg, CLM5 and ORCHIDEE-MICT respectively differ from the other LSMs. Differences between the climate forcing GCMs had a marginal effect up to 6 % on NPP predictions out to 2100 without specific spatio-temporal GCM interaction.

scholarly journals Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis

2020 ◽  
Vol 17 (7) ◽  
pp. 1821-1844
Author(s):  
Didier G. Leibovici ◽  
Shaun Quegan ◽  
Edward Comyn-Platt ◽  
Garry Hayman ◽  
Maria Val Martin ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Temporal Pattern ◽  
Temporal Variations ◽  
The Other ◽  
Surface Modelling ◽  
Land Surface Modelling ◽  
Spatio Temporal ◽  
The Times ◽  
The Impact

Abstract. A range of applications analysing the impact of environmental changes due to climate change, e.g. geographical spread of climate-sensitive infections (CSIs) and agriculture crop modelling, make use of land surface modelling (LSM) to predict future land surface conditions. There are multiple LSMs to choose from that account for land processes in different ways and this may introduce predictive uncertainty when LSM outputs are used as inputs to inform a given application. For useful predictions for a specific application, one must therefore understand the inherent uncertainties in the LSMs and the variations between them, as well as uncertainties arising from variation in the climate data driving the LSMs. This requires methods to analyse multivariate spatio-temporal variations and differences. A methodology is proposed based on multiway data analysis, which extends singular value decomposition (SVD) to multidimensional tables and provides spatio-temporal descriptions of agreements and disagreements between LSMs for both historical simulations and future predictions. The application underlying this paper is prediction of how climate change will affect the spread of CSIs in the Fennoscandian and north-west Russian regions, and the approach is explored by comparing net primary production (NPP) estimates over the period 1998–2013 from versions of leading LSMs (JULES, CLM5 and two versions of ORCHIDEE) that are adapted to high-latitude processes, as well as variations in JULES up to 2100 when driven by 34 global circulation models (GCMs). A single optimal spatio-temporal pattern, with slightly different weights for the four LSMs (up to 14 % maximum difference), provides a good approximation to all their estimates of NPP, capturing between 87 % and 93 % of the variability in the individual models, as well as around 90 % of the variability in the combined LSM dataset. The next best adjustment to this pattern, capturing an extra 4 % of the overall variability, is essentially a spatial correction applied to ORCHIDEE-HLveg that significantly improves the fit to this LSM, with only small improvements for the other LSMs. Subsequent correction terms gradually improve the overall and individual LSM fits but capture at most 1.7 % of the overall variability. Analysis of differences between LSMs provides information on the times and places where the LSMs differ and by how much, but in this case no single spatio-temporal pattern strongly dominates the variability. Hence interpretation of the analysis requires the summation of several such patterns. Nonetheless, the three best principal tensors capture around 76 % of the variability in the LSM differences and to a first approximation successively indicate the times and places where ORCHIDEE-HLveg, CLM5 and ORCHIDEE-MICT differ from the other LSMs. Differences between the climate forcing GCMs had a marginal effect up to 6 % on NPP predictions out to 2100 without specific spatio-temporal GCM interaction.

scholarly journals Spatio-temporal Variability and Trends of Mean and Extreme Rainfall Events in the Sudano-sahelian Region of Cameroon

2021 ◽  
Author(s):  
Ibrahim NJOUENWET ◽  
Lucie A. Djiotang Tchotchou ◽  
Brian Odhiambo Ayugi ◽  
Guy Merlin Guenang ◽  
Derbetini A. Vondou ◽  
...  
Keyword(s):  
Climate Change ◽  
Extreme Rainfall ◽  
Temporal Variations ◽  
Annual Rainfall ◽  
Drought Risk ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Precipitation Concentration ◽  
Spatio Temporal ◽  
The Impact

Abstract The Sudano-Sahelian region of Cameroon is mainly drained by the Benue, Chari and Logone rivers, which are very useful for water resources, especially for irrigation, hydropower generation, and navigation. Long-term changes in mean and extreme rainfall events in the region may be of crucial importance in understanding the impact of climate change. Daily and monthly rainfall data from twenty-five synoptic stations in the study area from 1980 to 2019 and extreme indices from the Expert Team on Climate Change Detection and Indices (ETCCDI) measurements were estimated using the non-parametric Modified Mann-Kendall test and the Sen slope estimator. The precipitation concentration index (PCI), the precipitation concentration degree (PCD), and the precipitation concentration period (PCP) were used to explore the spatio-temporal variations in the characteristics of rainfall concentrations. An increase in extreme rainfall events was observed, leading to an upward trend in mean annual. Trends in consecutive dry days (CDD) are significantly increasing in most parts of the study area. This could mean that the prevalence of drought risk is higher in the study area. Overall, the increase in annual rainfall could benefit the hydro-power sector, agricultural irrigation, the availability of potable water sources, and food security.

scholarly journals Modelling LAI, surface water and carbon fluxes at high-resolution over France: comparison of ISBA-A-gs and ORCHIDEE

2012 ◽  
Vol 9 (1) ◽  
pp. 439-456 ◽  
Author(s):  
S. Lafont ◽  
Y. Zhao ◽  
J.-C. Calvet ◽  
P. Peylin ◽  
P. Ciais ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Land Surface ◽  
Satellite Remote Sensing ◽  
Carbon Fluxes ◽  
Canopy Conductance ◽  
Area Index ◽  
Surface Modelling ◽  
Land Cover Map ◽  
Land Surface Modelling ◽  
The Impact

Abstract. The Leaf Area Index (LAI) is a measure of the amount of photosynthetic leaves and governs the canopy conductance to water vapor and carbon dioxide. Four different estimates of LAI were compared over France: two LAI products derived from satellite remote sensing, and two LAI simulations derived from land surface modelling. The simulated LAI was produced by the ISBA-A-gs model and by the ORCHIDEE model (developed by CNRM-GAME and by IPSL, respectively), for the 1994–2007 period. The two models were driven by the same atmospheric variables and used the same land cover map (SAFRAN and ECOCLIMAP-II, respectively). The MODIS and CYCLOPES satellite LAI products were used. Both products were available from 2000 to 2007 and this relatively long period allowed to investigate the interannual and the seasonal variability of monthly LAI values. In particular the impact of the 2003 and 2005 droughts were analyzed. The two models presented contrasting results, with a difference of one month between the average leaf onset dates simulated by the two models, and a maximum interannual variability of LAI simulated at springtime by ORCHIDEE and at summertime by ISBA-A-gs. The comparison with the satellite LAI products showed that, in general, the seasonality was better represented by ORCHIDEE, while ISBA-A-gs tended to better represent the interannual variability, especially for grasslands. While the two models presented comparable values of net carbon fluxes, ORCHIDEE simulated much higher photosynthesis rates than ISBA-A-gs (+70%), while providing lower transpiration estimates (−8%).

scholarly journals Modelling LAI, surface water and carbon fluxes at high-resolution over France: comparison of ISBA-A-gs and ORCHIDEE

2011 ◽  
Vol 8 (4) ◽  
pp. 7399-7439 ◽  
Author(s):  
S. Lafont ◽  
Y. Zhao ◽  
J.-C. Calvet ◽  
P. Peylin ◽  
P. Ciais ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Land Surface ◽  
Satellite Remote Sensing ◽  
Carbon Fluxes ◽  
Canopy Conductance ◽  
Area Index ◽  
Surface Modelling ◽  
Land Cover Map ◽  
Land Surface Modelling ◽  
The Impact

Abstract. The Leaf Area Index (LAI) is a measure of the amount of photosynthetic leaves and governs the canopy conductance to water vapor and carbon dioxide. Four different estimates of LAI were compared over France: two LAI products derived from satellite remote sensing, and two LAI simulations derived from land surface modelling. The simulated LAI was produced by the ISBA-A-gs model and by the ORCHIDEE model (developed by CNRM-GAME and by IPSL, respectively), for the 1994–2007 period. The two models were driven by the same atmospheric variables and used the same land cover map (SAFRAN and ECOCLIMAP-II, respectively). The MODIS and CYCLOPES satellite LAI products were used. Both products were available from 2000 to 2007 and this relatively long period allowed to investigate the interannual and the seasonal variability of monthly LAI values. In particular the impact of the 2003 and 2005 droughts were analyzed. The two models presented contrasting results, with a difference of one month between the average leaf onset dates simulated by the two models, and a maximum interannual variability of LAI simulated at springtime by ORCHIDEE and at summertime by ISBA-A-gs. The comparison with the satellite LAI products showed that, in general, the seasonality was better represented by ORCHIDEE, while ISBA-A-gs tended to better represent the interannual variability, especially for grasslands. While the two models presented comparable values of net carbon fluxes, ORCHIDEE simulated much higher photosynthesis rates than ISBA-A-gs (+70 %), while providing lower transpiration estimates (−8 %).

Application of an optimality-based model to operate at half-hourly timestep to implement plant acclimation within a land-surface modelling framework

2021 ◽  
Author(s):  
Giulia Mengoli ◽  
Anna Agustí-Panareda ◽  
Souhail Boussetta ◽  
Sandy P. Harrison ◽  
Carlo Trotta ◽  
...  
Keyword(s):  
Time Scales ◽  
Land Surface ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Extended Model ◽  
Test Model ◽  
Area Index ◽  
Surface Modelling ◽  
Plant Acclimation ◽  
Land Surface Modelling

<p>Vegetation and atmosphere are linked through the perpetual exchange of water, carbon and energy. An accurate representation of the processes involved in these exchanges is crucial in forecasting Earth system states. Although vegetation has become an undisputed key component in land-surface modelling (LSMs), the current generation of models differ in terms of how key processes are formulated. Plant processes react to environmental changes on multiple time scales. Here we differentiate a fast (minutes) and a slower (acclimated – weeks to months) response. Some current LSMs include plant acclimation, even though they require additional parameters to represent this response, but the majority of them represent only the fast response and assume that this also applies at longer time scales. Ignoring acclimation in this way could be the cause of inconsistent future projections. Our proposition is to include plant acclimation in a LSM schema, without having to include new plant-functional-type-dependent parameters. This is possible by using an alternative model development strategy based on eco-evolutionary theory, which explicitly predicts the acclimation of photosynthetic capacities and stomatal behaviour to environmental variations. So far, this theory has been tested only at weekly to monthly timescales. Here we develop and test an approach to apply an existing optimality-based model of gross primary production (GPP), the P model, at the sub-daily timestep necessary for use in an LSM, making an explicit differentiation between the fast and slow responses of photosynthesis and stomatal conductance. We test model performance in reproducing the diurnal cycle of GPP as recorded by flux tower measurements across different biomes, including boreal and tropical forests. The extended model requires only a few meteorological inputs, and a satellite-derived product for leaf area index or green vegetation cover. It is able to manage both timescales of acclimation without PFT-dependent photosynthetic parameters and has shown to operate with very good performance at all sites so far investigated. The model structure avoids the need to store past climate and vegetation states. These findings therefore suggest a simple way to include both instantaneous and acclimated responses within a LSM framework, and to do so in a robust way that does not require the specification of multiple parameters for different plant functional types.</p>

ECLand: an ECMWF land surface modelling platform

2021 ◽  
Author(s):  
Gianpaolo Balsamo ◽  
Souhail Boussetta
Keyword(s):  
Land Surface ◽  
Urban Areas ◽  
Land Surface Model ◽  
Open Water ◽  
Surface Fluxes ◽  
Surface Model ◽  
New Developments ◽  
Surface Modelling ◽  
Land Surface Modelling ◽  
New Research

<p>The ECMWF operational land surface model, based on the Carbon-Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (CHTESSEL) is the baseline for global weather, climate and environmental applications at ECMWF. In order to expedite its progress and benefit from international collaboration, an ECLand platform has been designed to host advanced and modular schemes. ECLand is paving the way toward a land model that could support a wider range of modelling applications, facilitating global kilometer scales testing as envisaged in the Copernicus and Destination Earth programmes. This presentation introduces the CHTESSEL and its recent new developments that aims at hosting new research applications.</p><p>These new improvements touch upon different components of the model: (i) vegetation, (ii) snow, (iii) soil hydrology, (iv) open water/lakes (v) rivers and (vi) urban areas. The developments are evaluated separately with either offline simulations or coupled experiments, depending on their level of operational readiness, illustrating the benchmarking criteria for assessing process fidelity with regards to land surface fluxes and reservoirs involved in water-energy-carbon exchange, and within the Earth system prediction framework, as foreseen to enter upcoming ECMWF operational cycles.</p><p>Reference: Souhail Boussetta, Gianpaolo Balsamo*, Anna Agustì-Panareda, Gabriele Arduini, Anton Beljaars, Emanuel Dutra, Glenn Carver, Margarita Choulga, Ioan Hadade, Cinzia Mazzetti, Joaquìn Munõz-Sabater, Joe McNorton, Christel Prudhomme, Patricia De Rosnay, Irina Sandu, Nils Wedi, Dai Yamazaki, Ervin Zsoter, 2021: ECLand: an ECMWF land surface modelling platform, MDPI Atmosphere, (in prep).</p>

scholarly journals Land-surface modelling in hydrological perspective

2005 ◽  
Vol 2 (6) ◽  
pp. 1815-1848 ◽  
Author(s):  
J. Overgaard ◽  
D. Rosbjerg ◽  
M. B. Butts
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Potential Evapotranspiration ◽  
Distributed Data ◽  
Atmospheric Models ◽  
Dynamic Coupling ◽  
Surface Modelling ◽  
Evaluation Procedures ◽  
Surface Models ◽  
Land Surface Modelling

Abstract. A comprehensive review of energy-based land-surface modelling, as seen from a hydrological perspective, is provided. We choose to focus on energy-based approaches, because in comparison to the traditional potential evapotranspiration models, these approaches allow for a stronger link to remote sensing and atmospheric modelling. New opportunities for evaluation of distributed land-surface models through application of remote sensing are discussed in detail, and the difficulties inherent in various evaluation procedures are presented. Remote sensing is the only source of distributed data at scales that correspond to hydrological modelling scales. Finally, the dynamic coupling of hydrological and atmospheric models is explored, and the future perspectives of such efforts are discussed.

scholarly journals Comparing Assimilation of Synthetic Soil Moisture versus C‐band Backscatter for Hyper‐resolution Land Surface Modelling

2021 ◽  
Author(s):  
Leqiang Sun ◽  
Stephane Belair ◽  
Marco L. Carrera ◽  
Bernard Bilodeau ◽  
Mohammed Dabboor
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Modelling ◽  
Land Surface Modelling

Open areas in patchy ecosystems: key spaces for vegetation survival.

2021 ◽  
Author(s):  
Borja Rodríguez Lozano ◽  
Emilio Rodriguez-Caballero ◽  
Yolanda Cantón
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Vegetation Coverage ◽  
Runoff Water ◽  
Water Losses ◽  
Vegetation Productivity ◽  
Aridity Gradient ◽  
Water Redistribution ◽  
The Impact ◽  
Vegetation Patches

<p>Drylands are one of the largest biomes over the Earth, covering around 40% of land surface. These are water limited ecosystems where vegetation occupies the most favourable positions over the landscape. Less favourable areas are frequently covered by other biotic and abiotic components such as biological soil crusts, bare soil, or stones. During most rainfall events, runoff is generated in open areas (runoff sources) and redistributed through vegetation patches (runoff sinks), therefore increasing water and nutrient availability for plants. Water redistribution feedbacks determine vegetation coverage and productivity, modulate changes in its spatial distribution, and could ameliorate the predicted negative effects of climate change over these ecosystems.</p><p>The principal aim of this study was to quantify the impact of water redistribution processes on vegetation performance, and to evaluate how this effect varies in response to aridity. To achieve it, we analysed the relationships between runoff redistribution from open areas and vegetation productivity, by combining satellite information on vegetation state and topography. More precisely, we calculated Normalized Difference Vegetation Index (NDVI) dynamics during three hydrological years in 17 study sites along an aridity gradient in the SE of the Iberian Peninsula using SENTINEL 2 images. Then we used a DEM and a high spatial resolution vegetation map to derive a water redistribution index that simulate source-sinks interactions between vegetation and open areas. Finally, we analyse the relationship between, potential water redistribution and vegetation dynamics and how it varies along the aridity gradient.</p><p>We found a non-linear relationship between potential water redistribution and vegetation productivity. Overall, vegetation NDVI increases as potential water redistribution did, which demonstrated the importance of water redistribution processes on drylands vegetation performance. However, vegetation capacity to retain runoff water is limited and there is a clear threshold above which increased potential water redistribution does not promote vegetation productivity. Thresholds are caused by the limit capacity of vegetation to infiltrate run off when preferential flows are forming, increasing ecosystem connectivity, and involving local water losses for vegetation.  Therefore, an increase in open areas between vegetation patches could have a positive effect over vegetation through hydrological connectivity but until to a certain point in which global connectivity supposed water losses for plants. This process could have important effects under climate change, by controlling the resistance and resilience of vegetation in drylands ecosystems.</p><p>Acknowledgements. This research was supported by the FPU predoctoral fellowship from the Educational, Culture and Sports Ministry of Spain (FPU17/01886) REBIOARID (RTI2018-101921-B-I00) projects, funded by the FEDER/Science and Innovation Ministry-National Research Agency, and the RH2O-ARID (P18-RT-5130) funded by Junta de Andalucía and the European Union for Regional Development.</p>

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