scholarly journals Scale Impact of Soil Moisture Observations to Noah-MP Land Surface Model Simulations

2020 ◽  
Vol 12 (7) ◽  
pp. 1169
Author(s):  
Jifu Yin ◽  
Xiwu Zhan
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Synthetic Experiment ◽  
Model Simulations ◽  
Coarse Spatial Resolution ◽  
Satellite Microwave ◽  
National Water

Due to the limitations of satellite antenna technology, current operational microwave soil moisture (SM) data products are typically at tens of kilometers spatial resolutions. Many approaches have thus been proposed to generate finer resolution SM data using ancillary information, but it is still unknown if assimilation of the finer spatial resolution SM data has beneficial impacts on model skills. In this paper, a synthetic experiment is thus conducted to identify the benefits of SM observations at a finer spatial resolution on the Noah-MP land surface model. Results of this study show that the performance of the Noah-MP model is significantly improved with the benefits of assimilating 1 km SM observations in comparison with the assimilation of SM data at coarser resolutions. Downscaling satellite microwave SM observations from coarse spatial resolution to 1 km resolution is recommended, and the assimilation of 1 km remotely sensed SM retrievals is suggested for NOAA National Weather Service and National Water Center.

scholarly journals Development and evaluation of 0.05° terrestrial water storage estimates using CABLE land surface model and GRACE data assimilation

2021 ◽  
Author(s):  
Natthachet Tangdamrongsub ◽  
Michael F. Jasinski ◽  
Peter Shellito
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Parameters ◽  
Terrestrial Water Storage ◽  
Grace Data ◽  
Terrestrial Water

Abstract. Accurate estimation of terrestrial water storage (TWS) at a meaningful spatiotemporal resolution is important for reliable assessments of regional water resources and climate variability. Individual components of TWS include soil moisture, snow, groundwater, and canopy storage and can be estimated from the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. The spatial resolution of CABLE is currently limited to 0.5° by the resolution of soil and vegetation datasets that underlie model parameterizations, posing a challenge to using CABLE for hydrological applications at a local scale. This study aims to improve the spatial detail (from 0.5° to 0.05°) and timespan (1981–2012) of CABLE TWS estimates using rederived model parameters and high-resolution meteorological forcing. In addition, TWS observations derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are assimilated into CABLE to improve TWS accuracy. The success of the approach is demonstrated in Australia, where multiple ground observation networks are available for validation. The evaluation process is conducted using four different case studies that employ different model spatial resolutions and include or omit GRACE data assimilation (DA). We find that the CABLE 0.05° developed here improves TWS estimates in terms of accuracy, spatial resolution, and long-term water resource assessment reliability. The inclusion of GRACE DA increases the accuracy of groundwater storage (GWS) estimates and has little impact on surface soil moisture or evapotranspiration. The use of improved model parameters and improved state estimations (via GRACE DA) together is recommended to achieve the best GWS accuracy. The workflow elaborated in this paper relies only on publicly accessible global datasets, allowing reproduction of the 0.05° TWS estimates in any study region.

scholarly journals Assimilating satellite-based soil moisture observations in a land surface model: The effect of spatial resolution

2021 ◽  
Vol 13 ◽  
pp. 100105
Author(s):  
Tasnuva Rouf ◽  
Manuela Girotto ◽  
Paul Houser ◽  
Viviana Maggioni
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model

scholarly journals ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?

2018 ◽  
Vol 22 (6) ◽  
pp. 3515-3532 ◽  
Author(s):  
Clement Albergel ◽  
Emanuel Dutra ◽  
Simon Munier ◽  
Jean-Christophe Calvet ◽  
Joaquin Munoz-Sabater ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Snow Depth ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Model Simulations ◽  
In Situ Observations

Abstract. The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released the first 7-year segment of its latest atmospheric reanalysis: ERA-5 over the period 2010–2016. ERA-5 has important changes relative to the former ERA-Interim atmospheric reanalysis including higher spatial and temporal resolutions as well as a more recent model and data assimilation system. ERA-5 is foreseen to replace ERA-Interim reanalysis and one of the main goals of this study is to assess whether ERA-5 can enhance the simulation performances with respect to ERA-Interim when it is used to force a land surface model (LSM). To that end, both ERA-5 and ERA-Interim are used to force the ISBA (Interactions between Soil, Biosphere, and Atmosphere) LSM fully coupled with the Total Runoff Integrating Pathways (TRIP) scheme adapted for the CNRM (Centre National de Recherches Météorologiques) continental hydrological system within the SURFEX (SURFace Externalisée) modelling platform of Météo-France. Simulations cover the 2010–2016 period at half a degree spatial resolution. The ERA-5 impact on ISBA LSM relative to ERA-Interim is evaluated using remote sensing and in situ observations covering a substantial part of the land surface storage and fluxes over the continental US domain. The remote sensing observations include (i) satellite-driven model estimates of land evapotranspiration, (ii) upscaled ground-based observations of gross primary production, (iii) satellite-derived estimates of surface soil moisture and (iv) satellite-derived estimates of leaf area index (LAI). The in situ observations cover (i) soil moisture, (ii) turbulent heat fluxes, (iii) river discharges and (iv) snow depth. ERA-5 leads to a consistent improvement over ERA-Interim as verified by the use of these eight independent observations of different land status and of the model simulations forced by ERA-5 when compared with ERA-Interim. This is particularly evident for the land surface variables linked to the terrestrial hydrological cycle, while variables linked to vegetation are less impacted. Results also indicate that while precipitation provides, to a large extent, improvements in surface fields (e.g. large improvement in the representation of river discharge and snow depth), the other atmospheric variables play an important role, contributing to the overall improvements. These results highlight the importance of enhanced meteorological forcing quality provided by the new ERA-5 reanalysis, which will pave the way for a new generation of land-surface developments and applications.

An offline reanalysis of land surface variables with LDAS-Monde forced by a kilometric scale NWP system

2020 ◽  
Author(s):  
Bertrand Bonan ◽  
Clément Albergel ◽  
Adrien Napoly ◽  
Yongjun Zheng ◽  
Jean-Christophe Calvet
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Surface Soil ◽  
Weather Prediction ◽  
Land Surface Model ◽  
Research Centre ◽  
Small Scale ◽  
Surface Model ◽  
Surface Soil Moisture

<p>LDAS-Monde is the offline land data assimilation system (LDAS) developed by Météo-France’s research centre (CNRM) aiming to monitor the evolution of land surface variables (LSVs) at various scales, from regional to global. It combines numerical simulations from the multilayer and interactive vegetation ISBA land surface model and satellite-derived observations of surface soil moisture and leaf area index (LAI). LDAS-Monde has been successfully validated over the globe.</p><p>In this work, we study the possibility to set up LDAS-Monde to the context of the kilometric spatial resolution. In this context, we assimilate satellite observations of LAI from the Copernicus Global Land Service (CGLS) into the ISBA land surface model forced with Météo-France’s small scale numerical weather prediction system AROME. We produce a reanalysis of LSVs at 2.5-km spatial resolution over the AROME domain centred on France starting from 2017. The quality of this reanalysis is assessed by comparing the obtained reanalysis with satellite products of LAI and surface soil moisture from e.g. CGLS and in-situ measurements of soil moisture from various networks (SMOSMANIA, …). We also show the ability of our system to monitor the evolution of LSVs in the context of the severe drought that France suffered during the summer 2018. LDAS-Monde at 2.5-km spatial resolution displays a great potential for agricultural monitoring at high resolution. We also plan to adapt our framework to 1.0-km spatial resolution.</p>

scholarly journals Global evaluation of gross primary productivity in the JULES land surface model v3.4.1

2017 ◽  
Vol 10 (7) ◽  
pp. 2651-2670 ◽  
Author(s):  
Darren Slevin ◽  
Simon F. B. Tett ◽  
Jean-François Exbrayat ◽  
A. Anthony Bloom ◽  
Mathew Williams
Keyword(s):  
Spatial Resolution ◽  
Primary Productivity ◽  
Land Surface ◽  
Land Surface Model ◽  
Gross Primary Productivity ◽  
Surface Model ◽  
Global Evaluation ◽  
Annual Average ◽  
Model Simulations ◽  
The Tropics

Abstract. This study evaluates the ability of the JULES land surface model (LSM) to simulate gross primary productivity (GPP) on regional and global scales for 2001–2010. Model simulations, performed at various spatial resolutions and driven with a variety of meteorological datasets (WFDEI-GPCC, WFDEI-CRU and PRINCETON), were compared to the MODIS GPP product, spatially gridded estimates of upscaled GPP from the FLUXNET network (FLUXNET-MTE) and the CARDAMOM terrestrial carbon cycle analysis. Firstly, when JULES was driven with the WFDEI-GPCC dataset (at 0. 5° × 0. 5° spatial resolution), the annual average global GPP simulated by JULES for 2001–2010 was higher than the observation-based estimates (MODIS and FLUXNET-MTE), by 25 and 8 %, respectively, and CARDAMOM estimates by 23 %. JULES was able to simulate the standard deviation of monthly GPP fluxes compared to CARDAMOM and the observation-based estimates on global scales. Secondly, GPP simulated by JULES for various biomes (forests, grasslands and shrubs) on global and regional scales were compared. Differences among JULES, MODIS, FLUXNET-MTE and CARDAMOM on global scales were due to differences in simulated GPP in the tropics. Thirdly, it was shown that spatial resolution (0. 5° × 0. 5°, 1° × 1° and 2° × 2°) had little impact on simulated GPP on these large scales, with global GPP ranging from 140 to 142 PgC year−1. Finally, the sensitivity of JULES to meteorological driving data, a major source of model uncertainty, was examined. Estimates of annual average global GPP were higher when JULES was driven with the PRINCETON meteorological dataset than when driven with the WFDEI-GPCC dataset by 3 PgC year−1. On regional scales, differences between the two were observed, with the WFDEI-GPCC-driven model simulations estimating higher GPP in the tropics (5° N–5° S) and the PRINCETON-driven model simulations estimating higher GPP in the extratropics (30–60° N).

scholarly journals Toward High-Resolution Soil Moisture Monitoring by Combining Active-Passive Microwave and Optical Vegetation Remote Sensing Products with Land Surface Model

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3924 ◽  
Author(s):  
Toride ◽  
Sawada ◽  
Aida ◽  
Koike
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Low Cost ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Parameters ◽  
Area Index ◽  
Coarse Resolution ◽  
Fine Resolution

The assimilation of radiometer and synthetic aperture radar (SAR) data is a promising recent technique to downscale soil moisture products, yet it requires land surface parameters and meteorological forcing data at a high spatial resolution. In this study, we propose a new downscaling approach, named integrated passive and active downscaling (I-PAD), to achieve high spatial and temporal resolution soil moisture datasets over regions without detailed soil data. The Advanced Microwave Scanning Radiometer (AMSR-E) and Phased Array-type L-band SAR (PALSAR) data are combined through a dual-pass land data assimilation system to obtain soil moisture at 1 km resolution. In the first step, fine resolution model parameters are optimized based on fine resolution PALSAR soil moisture and moderate-resolution imaging spectroradiometer (MODIS) leaf area index data, and coarse resolution AMSR-E brightness temperature data. Then, the 25 km AMSR-E observations are assimilated into a land surface model at 1 km resolution with a simple but computationally low-cost algorithm that considers the spatial resolution difference. Precipitation data are used as the only inputs from ground measurements. The evaluations at the two lightly vegetated sites in Mongolia and the Little Washita basin show that the time series of soil moisture are improved at most of the observation by the assimilation scheme. The analyses reveal that I-PAD can capture overall spatial trends of soil moisture within the coarse resolution radiometer footprints, demonstrating the potential of the algorithm to be applied over data-sparse regions. The capability and limitation are discussed based on the simple optimization and assimilation schemes used in the algorithm.

scholarly journals Optimal averaging of soil moisture predictions from ensemble land surface model simulations

2015 ◽  
Vol 51 (11) ◽  
pp. 9273-9289 ◽  
Author(s):  
W. T. Crow ◽  
C.‐H. Su ◽  
D. Ryu ◽  
M. T. Yilmaz
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Simulations

scholarly journals The Importance of Subsurface Processes in Land Surface Modeling over a Temperate Region: An Analysis with SMAP, Cosmic Ray Neutron Sensing and Triple Collocation Analysis

2021 ◽  
Vol 13 (16) ◽  
pp. 3068
Author(s):  
Haojin Zhao ◽  
Carsten Montzka ◽  
Roland Baatz ◽  
Harry Vereecken ◽  
Harrie-Jan Hendricks Franssen
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Water Movement ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Surface Modeling ◽  
Temperate Climate ◽  
Surface Model ◽  
Model Simulations ◽  
Land Surface Modeling

Land surface models (LSMs) simulate water and energy cycles at the atmosphere–soil interface, however, the physical processes in the subsurface are typically oversimplified and lateral water movement is neglected. Here, a cross-evaluation of land surface model results (with and without lateral flow processes), the National Aeronautics and Space Administration (NASA) Soil Moisture Active/Passive (SMAP) mission soil moisture product, and cosmic-ray neutron sensor (CRNS) measurements is carried out over a temperate climate region with cropland and forests over western Germany. Besides a traditional land surface model (the Community Land Model (CLM) version 3.5), a coupled land surface-subsurface model (CLM-ParFlow) is applied. Compared to CLM stand-alone simulations, the coupled CLM-ParFlow model considered both vertical and lateral water movement. In addition to standard validation metrics, a triple collocation (TC) analysis has been performed to help understanding the random error variances of different soil moisture datasets. In this study, it is found that the three soil moisture datasets are consistent. The coupled and uncoupled model simulations were evaluated at CRNS sites and the coupled model simulations showed less bias than the CLM-standalone model (−0.02 cm3 cm−3 vs. 0.07 cm3 cm−3), similar random errors, but a slightly smaller correlation with the measurements (0.67 vs. 0.71). The TC-analysis showed that CLM-ParFlow reproduced better soil moisture dynamics than CLM stand alone and with a higher signal-to-noise ratio. This suggests that the representation of subsurface physics is of major importance in land surface modeling and that coupled land surface-subsurface modeling is of high interest.

scholarly journals Global Evaluation of Gross Primary Productivity in the JULES Land Surface Model

2016 ◽  
Author(s):  
Darren Slevin ◽  
Simon F. B. Tett ◽  
Jean-François Exbrayat ◽  
A. Anthony Bloom ◽  
Mathew Williams
Keyword(s):  
Spatial Resolution ◽  
Primary Productivity ◽  
Land Surface ◽  
Land Surface Model ◽  
Gross Primary Productivity ◽  
Surface Model ◽  
Global Evaluation ◽  
Annual Average ◽  
Model Simulations ◽  
The Tropics

Abstract. This study evaluates the ability of the JULES Land Surface Model (LSM) to simulate Gross Primary Productivity (GPP) at regional and global scales for 2001–2010. Model simulations, performed at various spatial resolutions and driven with a variety of meteorological datasets (WFDEI-GPCC, WFDEI-CRU and PRINCETON), were compared to the MODIS GPP product, spatially gridded estimates of upscaled GPP from the FLUXNET network (FLUXNET-MTE) and the CARDAMOM terrestrial carbon cycle analysis. Firstly, JULES was found to simulate interannual variability (IAV) at global scales. When JULES was driven with the WFDEI-GPCC dataset (at 0.5º × 0.5º spatial resolution), it was found that the annual average global GPP simulated by JULES for 2001–2010 was higher than the observation-based estimates (MODIS and FLUXNET-MTE), by 25 % and 8 %, respectively, and CARDAMOM estimates by 23 %. Secondly, GPP fluxes simulated by JULES for various biomes (forests, grasslands and shrubs) at global and regional scales were compared. It was found that differences between JULES, FLUXNET-MTE, MODIS and CARDAMOM at global scales were mostly due to differences in the tropics with CARDAMOM performing better than JULES in this region. Thirdly, it was shown that spatial resolution (0.5º × 0.5º, 1º × 1º and 2º × 2º) had no impact on simulated GPP on these large scales. Finally, the sensitivity of JULES to meteorological driving data, a major source of model uncertainty, was examined. Estimates of annual average global GPP were higher when JULES was driven with the PRINCETON meteorological dataset than when driven with the WFDEI-GPCC dataset by 4 PgC year−1. At regional scales, differences between two were observed with the WFDEI-GPCC driven model simulations estimating higher GPP in the tropics (at 5º N–5º S) and the PRINCETON driven model simulations estimating higher GPP in the extratropics (at 30º N–60º N).

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