scholarly journals Advances in Land Surface Modelling

2021 ◽  
Author(s):  
Eleanor M. Blyth ◽  
Vivek K. Arora ◽  
Douglas B. Clark ◽  
Simon J. Dadson ◽  
Martin G. De Kauwe ◽  
...  
Keyword(s):  
Land Use ◽  
Water Use ◽  
Land Surface ◽  
Climate Prediction ◽  
Data Availability ◽  
Land Surface Models ◽  
Surface Modelling ◽  
Surface Models ◽  
Weather And Climate ◽  
Land Surface Modelling

AbstractLand surface models have an increasing scope. Initially designed to capture the feedbacks between the land and the atmosphere as part of weather and climate prediction, they are now used as a critical tool in the urgent need to inform policy about land-use and water-use management in a world that is changing physically and economically. This paper outlines the way that models have evolved through this change of purpose and what might the future hold. It highlights the importance of distinguishing between advances in the science within the modelling components, with the advances of how to represent their interaction. This latter aspect of modelling is often overlooked but will increasingly manifest as an issue as the complexity of the system, the time and space scales of the system being modelled increase. These increases are due to technology, data availability and the urgency and range of the problems being studied.

scholarly journals Inter-comparison of two land-surface models applied at different scales and their feedbacks while coupled with a regional climate model

2012 ◽  
Vol 16 (3) ◽  
pp. 1017-1031 ◽  
Author(s):  
F. Zabel ◽  
W. Mauser ◽  
T. Marke ◽  
A. Pfeiffer ◽  
G. Zängl ◽  
...  
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Regional Climate ◽  
Moderate Increase ◽  
Net Radiation ◽  
Land Surface Models ◽  
Near Surface ◽  
Temporal Behaviour ◽  
Surface Models ◽  
The Alps

Abstract. Downstream models are often used in order to study regional impacts of climate and climate change on the land surface. For this purpose, they are usually driven offline (i.e., 1-way) with results from regional climate models (RCMs). However, the offline approach does not allow for feedbacks between these models. Thereby, the land surface of the downstream model is usually completely different to the land surface which is used within the RCM. Thus, this study aims at investigating the inconsistencies that arise when driving a downstream model offline instead of interactively coupled with the RCM, due to different feedbacks from the use of different land surface models (LSM). Therefore, two physically based LSMs which developed from different disciplinary backgrounds are compared in our study: while the NOAH-LSM was developed for the use within RCMs, PROMET was originally developed to answer hydrological questions on the local to regional scale. Thereby, the models use different physical formulations on different spatial scales and different parameterizations of the same land surface processes that lead to inconsistencies when driving PROMET offline with RCM output. Processes that contribute to these inconsistencies are, as described in this study, net radiation due to land use related albedo and emissivity differences, the redistribution of this net radiation over sensible and latent heat, for example, due to different assumptions about land use impermeability or soil hydraulic reasons caused by different plant and soil parameterizations. As a result, simulated evapotranspiration, e.g., shows considerable differences of max. 280 mm yr−1. For a full interactive coupling (i.e., 2-way) between PROMET and the atmospheric part of the RCM, PROMET returns the land surface energy fluxes to the RCM and, thus, provides the lower boundary conditions for the RCM subsequently. Accordingly, the RCM responses to the replacement of the LSM with overall increased annual mean near surface air temperature (+1 K) and less annual precipitation (−56 mm) with different spatial and temporal behaviour. Finally, feedbacks can set up positive and negative effects on simulated evapotranspiration, resulting in a decrease of evapotranspiration South of the Alps a moderate increase North of the Alps. The inconsistencies are quantified and account for up to 30% from July to Semptember when focused to an area around Milan, Italy.

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 Land-surface modelling in hydrological perspective – a review

2006 ◽  
Vol 3 (2) ◽  
pp. 229-241 ◽  
Author(s):  
J. Overgaard ◽  
D. Rosbjerg ◽  
M. B. Butts
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Potential Evapotranspiration ◽  
Land Surface Models ◽  
Atmospheric Models ◽  
Dynamic Coupling ◽  
Evaluation Procedures ◽  
Surface Models ◽  
Different Types ◽  
Land Surface Modelling

Abstract. The purpose of this paper is to provide a review of the different types of energy-based land-surface models (LSMs) and discuss some of the new possibilities that will arise when energy-based LSMs are combined with distributed hydrological modelling. 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. Finally, the dynamic coupling of hydrological and atmospheric models is explored, and the perspectives of such efforts are discussed.

Forest structure and vegetation dynamics as a driver of global carbon uptake

2020 ◽  
Author(s):  
Ben Poulter ◽  
Leo Calle ◽  
Thomas Pugh ◽  
Nathan McDowell ◽  
Philippe Ciais ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover Change ◽  
Forest Structure ◽  
Land Surface ◽  
Vegetation Dynamics ◽  
Carbon Uptake ◽  
Forest Age ◽  
Land Surface Models ◽  
Surface Models ◽  
Global Carbon

<p>The drivers for terrestrial carbon uptake remain unclear despite a clear signal that the land removes the equivalent of up to 25-30% of fossil fuel CO2 emissions each year. Recent work has confirmed sustained carbon uptake by the land that is proportional to anthropogenic emissions, meaning that the land 'sink' has strengthened over the past five decades, and with interannual variability driven by climate. Drivers responsible for sustained uptake include hypotheses related to lengthening growing season length, increasing nitrogen deposition, changes in the ratio of diffuse to direct radiation, and land-use and land cover change. More recently, land-use and land-cover change has been investigated as a driver of land carbon uptake owing to an emergence of global-scale datasets related to canopy disturbance, land use, and forest age. At the same time, land-surface models have increased their realism in terms of moving beyond 'big-leaf' model representation of ecosystems to including vertical structure and horizontal heteorogeneity via size-and-age structured approaches. This presentation will address recent work identified forest structure and vegetation dynamics as a driver for global carbon uptake and provide examples of how remote sensing observations have led to new datasets for initialization land-surface models. Compared to inventory-based approaches, land-surface models initialized with forest age show a lessor role in explaining net terrestrial carbon uptake at global scales, but at regional scales, vegetation structure is a key determinant of carbon exchange. New satellite missions improving forest structure observations are expected to reduce uncertainties and contribute substantially to ongoing land-surface model development.</p>

scholarly journals FluxnetLSM R package (v1.0): A community tool for processing FLUXNET data for use in land surface modelling

2017 ◽  
Author(s):  
Anna M. Ukkola ◽  
Ned Haughton ◽  
Martin G. De Kauwe ◽  
Gab Abramowitz ◽  
Andy J. Pitman
Keyword(s):  
Land Surface ◽  
R Package ◽  
File Format ◽  
Gap Filling ◽  
Surface Modelling ◽  
Flux Tower ◽  
Surface Models ◽  
Land Surface Modelling ◽  
Scale Surface ◽  
Community Standard

Abstract. Flux towers measure ecosystem-scale surface-atmosphere exchanges of energy, carbon dioxide and water vapour. The network of flux towers now encompasses ~ 900 sites, spread across every continent. Consequently, these data have become an essential benchmarking tool for land surface models (LSMs). However, these data as released are not immediately usable for driving, evaluating and benchmarking LSMs. Flux tower data must first be transformed into a LSM-readable file format, a process which involves changing units, screening missing data and varying degrees of additional gap- filling. All of this often leads to an under-utilisation of these data in model benchmarking. To resolve some of these issues, and to help make flux tower measurements more widely used, we present a reproducible, open-source R package that transforms the latest FLUXNET2015 release into community standard NetCDF files that are directly usable by LSMs.

A Simple Reclassification Method for Correcting Uncertainty in Land Use/Land Cover Data Sets Used with Land Surface Models

2007 ◽  
Vol 164 (8-9) ◽  
pp. 1789-1809 ◽  
Author(s):  
Joseph G. Alfieri ◽  
Dev Niyogi ◽  
Margaret A. LeMone ◽  
Fei Chen ◽  
Souleymane Fall
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Data Sets ◽  
Land Use Land Cover ◽  
Land Surface Models ◽  
Land Cover Data ◽  
Surface Models

scholarly journals FluxnetLSM R package (v1.0): a community tool for processing FLUXNET data for use in land surface modelling

2017 ◽  
Vol 10 (9) ◽  
pp. 3379-3390 ◽  
Author(s):  
Anna M. Ukkola ◽  
Ned Haughton ◽  
Martin G. De Kauwe ◽  
Gab Abramowitz ◽  
Andy J. Pitman
Keyword(s):  
Land Surface ◽  
R Package ◽  
File Format ◽  
Gap Filling ◽  
Surface Modelling ◽  
Flux Tower ◽  
Surface Models ◽  
Land Surface Modelling ◽  
Scale Surface ◽  
Community Standard

Abstract. Flux towers measure ecosystem-scale surface–atmosphere exchanges of energy, carbon dioxide and water vapour. The network of flux towers now encompasses ∼ 900 sites, spread across every continent. Consequently, these data have become an essential benchmarking tool for land surface models (LSMs). However, these data as released are not immediately usable for driving, evaluating and benchmarking LSMs. Flux tower data must first be transformed into a LSM-readable file format, a process which involves changing units, screening missing data and varying degrees of additional gap-filling. All of this often leads to an under-utilisation of these data in model benchmarking. To resolve some of these issues, and to help make flux tower measurements more widely used, we present a reproducible, open-source R package that transforms the FLUXNET2015 and La Thuile data releases into community standard NetCDF files that are directly usable by LSMs. We note that these data would also be useful for any other user or community seeking to independently quality control, gap-fill or use the FLUXNET data.

scholarly journals Inferring past land-use induced changes in surface albedo from satellite observations: a useful tool to evaluate model simulations

2012 ◽  
Vol 9 (9) ◽  
pp. 12505-12542
Author(s):  
J. P. Boisier ◽  
N. de Noblet-Ducoudré ◽  
P. Ciais
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Surface Albedo ◽  
Vegetation Type ◽  
Satellite Observations ◽  
Land Surface Models ◽  
Historical Changes ◽  
Surface Models ◽  
Past Land Use

Abstract. Cooling resulting from increases in surface albedo has been identified in several studies as the main biogeophysical effect of past land-use induced land cover changes (LCC) on climate. However, the amplitude of this effect remains quite uncertain due to, among other factors, (a) uncertainties in the magnitude of historical LCC and, (b) differences in the way various models simulate surface albedo and more specifically its dependency on vegetation type and snow cover. We have derived monthly albedo climatologies for croplands and four other land-cover types from MODIS satellite observations. We have then estimated the changes in surface albedo since preindustrial times by combining these climatologies with the land-cover maps of 1870 and 1992 used by modelers in the context of the LUCID intercomparison project. These reconstructions show surface albedo increases larger than 10% (absolute) in winter and 2% in summer between 1870 and 1992 over areas that have experienced intense deforestation in the northern temperate regions. The MODIS-based reconstructions of historical changes in surface albedo were then compared to those simulated by the various models participating to LUCID. The inter-model mean albedo response to LCC shows a similar spatial and seasonal pattern to the one resulting from the reconstructions, that is larger increases in winter than in summer driven by the presence of snow. However, individual models show significant differences with the satellite-based reconstructions, despite the fact that land-cover change maps are the same. Our analyses suggest that the primary reason for those discrepancies is how land-surface models parameterize albedo. Another reason, of secondary importance, results from differences in the simulated snowpack. Our methodology is a useful tool not only to infer observations-based historical changes in land surface variables impacted by LCC, but also to point to major deficiencies within the models; we therefore suggest that it could be more widely developed and used in conjunction with other tools in order to evaluate global land-surface models.

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