scholarly journals Evaluation and improvement of the default soil hydraulic parameters for the Noah Land Surface Model

Geoderma ◽  
2017 ◽  
Vol 285 ◽  
pp. 247-259 ◽  
Author(s):  
Andrea Sz. Kishné ◽  
Yohannes Tadesse Yimam ◽  
Cristine L.S. Morgan ◽  
Bright C. Dornblaser
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Hydraulic Parameters ◽  
Soil Hydraulic Parameters ◽  
Noah Land Surface Model ◽  
Soil Hydraulic

scholarly journals Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction

2017 ◽  
Vol 21 (5) ◽  
pp. 2509-2530 ◽  
Author(s):  
Roland Baatz ◽  
Harrie-Jan Hendricks Franssen ◽  
Xujun Han ◽  
Tim Hoar ◽  
Heye Reemt Bogena ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Surface Model ◽  
Hydraulic Parameters ◽  
Soil Map ◽  
Soil Hydraulic Parameters ◽  
Soil Hydraulic

Abstract. In situ soil moisture sensors provide highly accurate but very local soil moisture measurements, while remotely sensed soil moisture is strongly affected by vegetation and surface roughness. In contrast, cosmic-ray neutron sensors (CRNSs) allow highly accurate soil moisture estimation on the field scale which could be valuable to improve land surface model predictions. In this study, the potential of a network of CRNSs installed in the 2354 km2 Rur catchment (Germany) for estimating soil hydraulic parameters and improving soil moisture states was tested. Data measured by the CRNSs were assimilated with the local ensemble transform Kalman filter in the Community Land Model version 4.5. Data of four, eight and nine CRNSs were assimilated for the years 2011 and 2012 (with and without soil hydraulic parameter estimation), followed by a verification year 2013 without data assimilation. This was done using (i) a regional high-resolution soil map, (ii) the FAO soil map and (iii) an erroneous, biased soil map as input information for the simulations. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the FAO soil map and the biased soil map, soil moisture predictions improved strongly to a root mean square error of 0.03 cm3 cm−3 for the assimilation period and 0.05 cm3 cm−3 for the evaluation period. Improvements were limited by the measurement error of CRNSs (0.03 cm3 cm−3). The positive results obtained with data assimilation of nine CRNSs were confirmed by the jackknife experiments with four and eight CRNSs used for assimilation. The results demonstrate that assimilated data of a CRNS network can improve the characterization of soil moisture content on the catchment scale by updating spatially distributed soil hydraulic parameters of a land surface model.

Stone Content Influence on Land Surface Model Simulation of Soil Moisture and Evapotranspiration at Reynolds Creek Watershed

2020 ◽  
Vol 21 (8) ◽  
pp. 1889-1904
Author(s):  
Kshitij Parajuli ◽  
Scott B. Jones ◽  
David G. Tarboton ◽  
Lawrence E. Hipps ◽  
Lin Zhao ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Profile ◽  
Land Surface ◽  
Model Simulation ◽  
Land Surface Model ◽  
Surface Model ◽  
Hydraulic Parameters ◽  
Reynolds Creek ◽  
Soil Hydraulic Parameters ◽  
Soil Hydraulic

AbstractConsiderable advancement in spatiotemporal resolution of remote sensing and ground-based measurements has enabled refinement of parameters used in land surface models for simulating surface water fluxes. However, land surface modeling capabilities are still inadequate for accurate representation of subsurface properties and processes, which continue to limit the accuracy of land surface model simulation. Our objective in this study was to examine the performance of the variously parameterized Noah land surface model with multiphysics option (Noah-MP) in simulating evapotranspiration (ET) and soil moisture dynamics in stony soils using verification from eddy covariance ET and in situ soil moisture data during the growing season of year 2015, obtained from the Lower Sheep subcatchment within the Reynolds Creek Experimental Watershed in southwestern Idaho. We evaluated the performance of Noah-MP considering four different scenarios with 1) a one-layer soil profile with Noah-MP default soil hydraulic parameters and three more five-layer soil profiles using 2) Noah-MP default soil hydraulic parameters; 3) soil hydraulic parameters derived from a pedotransfer function using field observations; and 4) hydraulic parameters from scenario 3, which also accounted for stone content in each layer. Each modeling experiment was forced with the same set of initial conditions, atmospheric input, and vegetation parameters. Our results indicate that enhanced representation of soil profile properties and stone content information noticeably improve the Noah-MP land surface model simulation of soil moisture content and evapotranspiration.

Coupling the Community Land Model 5.0 with the Parallel Data Assimilation Framework

2021 ◽  
Author(s):  
Lukas Strebel ◽  
Heye Bogena ◽  
Harry Vereecken ◽  
Harrie-Jan Hendricks Franssen
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
System Modeling ◽  
Main Memory ◽  
Surface Model ◽  
Hydraulic Parameters ◽  
Community Land Model ◽  
Parallel Data ◽  
Soil Hydraulic Parameters ◽  
Soil Hydraulic

<p>Land surface models are important tools to improve our understanding of interacting ecosystem processes, but their predictions are associated with uncertainties related to model forcings, parameters and process simplifications. As high-quality observations become more and more available, they can be used to constrain the uncertainty of land surface model predictions. In this study, we use data assimilation for the fusion of data into the Community Land Model 5.0 (CLM5). CLM5 simulates a broad variety of important land surface processes including moisture and energy partitioning, surface runoff, subsurface runoff, photosynthesis and carbon and nitrogen storage in vegetation and soil. Here, we focus on water movement in soils and related soil hydraulic parameters and assimilate in-situ soil moisture data into CLM5 to improve the estimate of model states and soil hydraulic parameters. To do this, we have coupled the Parallel Data Assimilation Framework (PDAF) with CLM5. This coupling is based on the online variant of PDAF, i.e., data assimilation occurs during simulation runtime in the main memory and not via input/output files. Online coupling requires modification of the model source code, but we aim to keep the modifications to the CLM5 code minimal so that maintenance of the ongoing CLM5 developments remains straightforward. To this end, our approach reuses the existing CLM5 ensemble mode with only necessary adjustments to connect the PDAF parallel communicators. Furthermore, we developed the coupling in the framework of the Terrestrial System Modeling Platform (TSMP). TSMP is a highly modular modeling system for the fully integrated soil-vegetation-atmosphere system. To illustrate the potential of this coupling, we use the ensemble Kalman Filter to perform simultaneous state and parameter updates in a forest headwater catchment.</p>

Improvement of the Noah land surface model for warm season processes: evaluation of water and energy flux simulation

2012 ◽  
Vol 27 (2) ◽  
pp. 297-303 ◽  
Author(s):  
Helin Wei ◽  
Youlong Xia ◽  
Kenneth E. Mitchell ◽  
Michael B. Ek
Keyword(s):  
Energy Flux ◽  
Land Surface ◽  
Land Surface Model ◽  
Warm Season ◽  
Surface Model ◽  
Noah Land Surface Model

scholarly journals Assessment of Roughness Length Schemes Implemented within the Noah Land Surface Model for High-Altitude Regions

2014 ◽  
Vol 15 (3) ◽  
pp. 921-937 ◽  
Author(s):  
Donghai Zheng ◽  
Rogier van der Velde ◽  
Zhongbo Su ◽  
Martijn J. Booij ◽  
Arjen Y. Hoekstra ◽  
...  
Keyword(s):  
Heat Flux ◽  
High Altitude ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Heat ◽  
Heat Fluxes ◽  
Surface Model ◽  
Surface Heat Fluxes ◽  
Noah Land Surface Model ◽  
Better Than

ABSTRACT Current land surface models still have difficulties with producing reliable surface heat fluxes and skin temperature (Tsfc) estimates for high-altitude regions, which may be addressed via adequate parameterization of the roughness lengths for momentum (z0m) and heat (z0h) transfer. In this study, the performance of various z0h and z0m schemes developed for the Noah land surface model is assessed for a high-altitude site (3430 m) on the northeastern part of the Tibetan Plateau. Based on the in situ surface heat fluxes and profile measurements of wind and temperature, monthly variations of z0m and diurnal variations of z0h are derived through application of the Monin–Obukhov similarity theory. These derived values together with the measured heat fluxes are utilized to assess the performance of those z0m and z0h schemes for different seasons. The analyses show that the z0m dynamics are related to vegetation dynamics and soil water freeze–thaw state, which are reproduced satisfactorily with current z0m schemes. Further, it is demonstrated that the heat flux simulations are very sensitive to the diurnal variations of z0h. The newly developed z0h schemes all capture, at least over the sparse vegetated surfaces during the winter season, the observed diurnal variability much better than the original one. It should, however, be noted that for the dense vegetated surfaces during the spring and monsoon seasons, not all newly developed schemes perform consistently better than the original one. With the most promising schemes, the Noah simulated sensible heat flux, latent heat flux, Tsfc, and soil temperature improved for the monsoon season by about 29%, 79%, 75%, and 81%, respectively. In addition, the impact of Tsfc calculation and energy balance closure associated with measurement uncertainties on the above findings are discussed, and the selection of the appropriate z0h scheme for applications is addressed.

scholarly journals Evaluation of a Photosynthesis-Based Canopy Resistance Formulation in the Noah Land-Surface Model

2010 ◽  
Vol 138 (2) ◽  
pp. 263-284 ◽  
Author(s):  
Anil Kumar ◽  
Fei Chen ◽  
Dev Niyogi ◽  
Joseph G. Alfieri ◽  
Michael Ek ◽  
...  
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Canopy Resistance ◽  
Noah Land Surface Model

Impact of GVF Derivation Methods on Noah Land Surface Model Simulations and WRF Model Forecasts

2018 ◽  
Vol 19 (12) ◽  
pp. 1917-1933 ◽  
Author(s):  
Li Fang ◽  
Xiwu Zhan ◽  
Christopher R. Hain ◽  
Jifu Yin ◽  
Jicheng Liu
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Weather Prediction ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Vegetation Fraction ◽  
Data Products ◽  
Noah Land Surface Model

Abstract Green vegetation fraction (GVF) plays a crucial role in the atmosphere–land water and energy exchanges. It is one of the essential parameters in the Noah land surface model (LSM) that serves as the land component of a number of operational numerical weather prediction models at the National Centers for Environmental Prediction (NCEP) of NOAA. The satellite GVF products used in NCEP models are derived from a simple linear conversion of either the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) currently or the enhanced vegetation index (EVI) from the Visible Infrared Imaging Radiometer Suite (VIIRS) planned for the near future. Since the NDVI or EVI is a simple spectral index of vegetation cover, GVFs derived from them may lack the biophysical meaning required in the Noah LSM. Moreover, the NDVI- or EVI-based GVF data products may be systematically biased over densely vegetated regions resulting from the saturation issue associated with spectral vegetation indices. On the other hand, the GVF is physically related to the leaf area index (LAI), and thus it could be beneficial to derive GVF from LAI data products. In this paper, the EVI-based and the LAI-based GVF derivation methods are mathematically analyzed and are found to be significantly different from each other. Impacts of GVF differences on the Noah LSM simulations and on weather forecasts of the Weather Research and Forecasting (WRF) Model are further assessed. Results indicate that LAI-based GVF outperforms the EVI-based one when used in both the offline Noah LSM and WRF Model.

scholarly journals Evaluating a New Deposition Velocity Module in the Noah Land-Surface Model

2010 ◽  
Vol 137 (2) ◽  
pp. 271-290 ◽  
Author(s):  
U. Charusombat ◽  
D. Niyogi ◽  
A. Kumar ◽  
X. Wang ◽  
F. Chen ◽  
...  
Keyword(s):  
Land Surface ◽  
Deposition Velocity ◽  
Land Surface Model ◽  
Surface Model ◽  
Noah Land Surface Model
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