scholarly journals Development of a land surface model with coupled snow and frozen soil physics

2017 ◽  
Vol 53 (6) ◽  
pp. 5085-5103 ◽  
Author(s):  
Lei Wang ◽  
Jing Zhou ◽  
Jia Qi ◽  
Litao Sun ◽  
Kun Yang ◽  
...  
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Frozen Soil ◽  
Surface Model ◽  
Soil Physics

Improving estimates of UK soil moisture using the JULES land surface model with COSMOS-UK measurements in the LaVEnDAR data assimilation framework

2020 ◽  
Author(s):  
Elizabeth Cooper ◽  
Ewan Pinnington ◽  
Richard Ellis ◽  
Eleanor Blyth ◽  
Simon Dadson ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Soil Texture ◽  
Land Surface ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Surface Model ◽  
Soil Physics ◽  
Soil Moisture Sensors ◽  
The Uk

<p>Soil moisture predictions are increasingly important in hydrological, ecological and agricultural applications. In recent years the availability of wide-area assessments of current and future soil-moisture states has grown, yet few studies have combined model-based assessments with observations beyond the point scale. Here we use the JULES land surface model together with COSMOS-UK data to evaluate the extent to which data assimilation can improve predictions of soil moisture across the United Kingdom.</p><p>COSMOS-UK is a network of soil moisture sensors run by UKCEH. The network provides soil moisture measurements at around 50 sites throughout the UK using innovative Cosmic Ray Neutron Sensors (CRNS). Half hourly measurements of the meteorological variables that the Joint UK Land Environment Simulator (JULES) requires as driving data are also recorded at COSMOS-UK sites, allowing us to run JULES at observation locations. This provides a unique opportunity to compare soil moisture outputs from JULES with CRNS observations; these measurements have a footprint of up to 12 ha (approx 30 acres) and are therefore better scale matched with JULES outputs than those from point sensors.</p><p>We have used the Land Variational Ensemble Data Assimilation Framework (LaVEnDAR) to combine soil moisture estimates from JULES with daily CRNS observations from one year at a number of COSMOS-UK sites. We show that this results in improved soil moisture predictions from JULES over several years. This has been achieved by optimising parameters in the pedo-transfer function used to derive JULES soil physics parameters from soil texture information. Using data assimilation with LaVEnDAR in this way allows us to explore the relationships between soil moisture estimates, soil physics parameters and soil texture, as well as improving the agreement between JULES model outputs and observations.</p>

scholarly journals Using data assimilation to optimize pedotransfer functions using field-scale in situ soil moisture observations

2021 ◽  
Vol 25 (5) ◽  
pp. 2445-2458
Author(s):  
Elizabeth Cooper ◽  
Eleanor Blyth ◽  
Hollie Cooper ◽  
Rich Ellis ◽  
Ewan Pinnington ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Land Surface Model ◽  
Pedotransfer Functions ◽  
Surface Model ◽  
Climate Projections ◽  
Soil Physics ◽  
Field Scale

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological, and meteorological applications. In recent years, the availability of wide-area soil moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the Joint UK Land Environment Simulator (JULES) land surface model using field-scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way, we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way improves the soil moisture predictions of a land surface model at 16 UK sites, leading to the potential for better flood, drought, and climate projections.

Treatment of frozen soil and snow cover in the land surface model SEWAB

2001 ◽  
Vol 69 (1-2) ◽  
pp. 23-37 ◽  
Author(s):  
K. Warrach ◽  
H.-T. Mengelkamp ◽  
E. Raschke
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Land Surface Model ◽  
Frozen Soil ◽  
Surface Model

scholarly journals Atmosphere-Cryosphere Coupled Model for Regional Climate Applications

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Ki-Hong Min ◽  
Wen-Yih Sun
Keyword(s):  
Land Surface ◽  
Transition Period ◽  
Hydrological Cycle ◽  
Regional Climate ◽  
Land Surface Model ◽  
Coupled Model ◽  
Feedback Mechanism ◽  
Frozen Soil ◽  
Surface Model ◽  
Soil Frost

There have been significant advances in our understanding of the climate system, but two major problems still exist in modeling atmospheric response during cold seasons: (a) lack of detailed physical description of snow and frozen soil in the land-surface schemes and (b) insufficient understanding of regional climate response from the cryosphere. A multilayer snow land-surface model based on the conservations of heat and water substance inside the soil and snow is coupled to an atmospheric RCM, to investigate the effect of snow, snowmelt, and soil frost on the atmosphere during cold seasons. The coupled RCM shows much improvement in moisture and temperature simulation for March-April of 1997 compared to simple parameterizations used in GCMs. The importance of such processes in RCM simulation is more pronounced in mid-to-high latitudes during the transition period (winter–spring) affected by changes in surface energy and the hydrological cycle. The effect of including cryosphere physics through snow-albedo feedback mechanism changes the meridional temperature gradients and in turn changes the location of weather systems passing over the region. The implications from our study suggest that, to reduce the uncertainties and better assess the impacts of climate change, RCM simulations should include the detailed snow and frozen soil processes.

scholarly journals Improved modelling of Siberian river flow through the use of an alternative frozen soil hydrology scheme in a land surface model

2012 ◽  
Vol 6 (1) ◽  
pp. 309-340
Author(s):  
D. L. Finney ◽  
E. Blyth ◽  
R. Ellis
Keyword(s):  
Land Surface ◽  
River Flow ◽  
Land Surface Model ◽  
Control Model ◽  
Frozen Soil ◽  
Surface Model ◽  
Soil Hydrology ◽  
Siberian River ◽  
Flow Through ◽  
Surface Environment

Abstract. A parameterisation to incorporate the effects of frozen soil on modelled hydrology is described and implemented within a land surface model, the Joint UK Land Surface Environment Simulator. It is shown to generally improve the modelled flow of Siberian rivers compared to observations, specifically in seasons of freezing or thawing soil. Most noticeably, the revised model increases the snowmelt flow peak by 26–100% compared to the control model thereby better matching observed flows. The model physics resulting in the changes to river flow are discussed and attention is given to the effect of inaccuracies in snowfall driving data which can hinder the comparison of new model processes.

scholarly journals Improved modelling of Siberian river flow through the use of an alternative frozen soil hydrology scheme in a land surface model

2012 ◽  
Vol 6 (4) ◽  
pp. 859-870 ◽  
Author(s):  
D. L. Finney ◽  
E. Blyth ◽  
R. Ellis
Keyword(s):  
Land Surface ◽  
River Flow ◽  
Land Surface Model ◽  
Control Model ◽  
Frozen Soil ◽  
Surface Model ◽  
Soil Hydrology ◽  
Siberian River ◽  
Flow Through ◽  
Surface Environment

Abstract. A parameterisation to incorporate the effects of frozen soil on modelled hydrology is described and implemented within a land surface model, the Joint UK Land Surface Environment Simulator. It is shown to generally improve the modelled flow of Siberian rivers compared to observations, specifically in seasons of freezing or thawing soil. Most noticeably, the revised model increases the snowmelt flow peak by 26–100% compared to the control model, thereby better matching observed flows. The model physics resulting in the changes to river flow are discussed and attention is given to the effect of inaccuracies in snowfall driving data which can hinder the comparison of new model processes.

L'apport de la télédétection spatiale à la modélisation des surfaces continentales

2020 ◽  
pp. 052
Author(s):  
Jean-Christophe Calvet ◽  
Jean-Louis Champeaux
Keyword(s):  
Satellite Data ◽  
Land Surface ◽  
Land Surface Model ◽  
Static Model ◽  
Geographic Information ◽  
Surface Model ◽  
Model Parameters

Cet article présente les différentes étapes des développements réalisés au CNRM des années 1990 à nos jours pour spatialiser à diverses échelles les simulations du modèle Isba des surfaces terrestres. Une attention particulière est portée sur l'intégration, dans le modèle, de données satellitaires permettant de caractériser la végétation. Deux façons complémentaires d'introduire de l'information géographique dans Isba sont présentées : cartographie de paramètres statiques et intégration au fil de l'eau dans le modèle de variables observables depuis l'espace. This paper presents successive steps in developments made at CNRM from the 1990s to the present-day in order to spatialize the simulations of the Isba land surface model at various scales. The focus is on the integration in the model of satellite data informative about vegetation. Two complementary ways to integrate geographic information in Isba are presented: mapping of static model parameters and sequential assimilation of variables observable from space.

Sign in / Sign up

Export Citation Format

Share Document