The Use of Remote Sensing-Based ET Estimates to Improve Global Hydrological Simulations in the Community Land Model Version 5.0

Terrestrial evapotranspiration (ET) is a critical component of water and energy cycles, and improving global land evapotranspiration is one of the challenging works in the development of land surface models (LSMs). In this study, we apply a bias correction approach into the Community Land Model version 5.0 (CLM5) globally by utilizing the remote sensing-based ET dataset. Results reveal that the correction approach can alleviate both overestimation and underestimation of ET by CLM5 over the globe. The adjustment to overestimation is generally effective, whereas the effectiveness for underestimation is determined by the ET regime, namely water-limited or energy-limited. In the areas with abundant precipitation, the underestimation is effectively corrected by increasing ET without the water supply limit. In areas with rare precipitation, however, increasing ET is limited by water supply, which leads to an undesirable correction effect. Compared with the ET simulated by CLM5, the bias correction approach can reduce the global-averaged relative bias (RB) and the root mean square error (RMSE) by 51.8% and 65.9% against Global Land Evaporation Amsterdam Model (GLEAM) ET data, respectively. Meanwhile, the correlation coefficient (CC) can also be improved from 0.93 to 0.98. Continentally, the most substantial ET improvement occurs in Asia, with the RB and RMSE decreased by 69.7% (from 7.04% to 2.14%) and 70.2% (from 0.312 mm day−1 to 0.093 mm day−1, equivalent to from 114 mm year−1 to 34 mm year−1), and the CC increased from 0.92 to 0.99, respectively. Consequently, benefiting from the improvement of ET, the simulations of runoff and soil moisture are also improved over the globe and each of the six continents, and the improvement varies with region. This study demonstrates that the use of satellite-based ET products is beneficial to hydrological simulations in land surface models over the globe.

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Incorporating remote sensing-based ET estimates into the Community Land Model version 4.5

Hydrology and Earth System Sciences ◽

10.5194/hess-21-3557-2017 ◽

2017 ◽

Vol 21 (7) ◽

pp. 3557-3577 ◽

Cited By ~ 4

Author(s):

Dagang Wang ◽

Guiling Wang ◽

Dana T. Parr ◽

Weilin Liao ◽

Youlong Xia ◽

...

Keyword(s):

Bias Correction ◽

Land Surface ◽

Correction Method ◽

Limiting Factor ◽

Development Effort ◽

Land Surface Models ◽

Community Land Model ◽

Bias Correction Method ◽

Model Version ◽

Surface Models

Abstract. Land surface models bear substantial biases in simulating surface water and energy budgets despite the continuous development and improvement of model parameterizations. To reduce model biases, Parr et al. (2015) proposed a method incorporating satellite-based evapotranspiration (ET) products into land surface models. Here we apply this bias correction method to the Community Land Model version 4.5 (CLM4.5) and test its performance over the conterminous US (CONUS). We first calibrate a relationship between the observational ET from the Global Land Evaporation Amsterdam Model (GLEAM) product and the model ET from CLM4.5, and assume that this relationship holds beyond the calibration period. During the validation or application period, a simulation using the default CLM4.5 (CLM) is conducted first, and its output is combined with the calibrated observational-vs.-model ET relationship to derive a corrected ET; an experiment (CLMET) is then conducted in which the model-generated ET is overwritten with the corrected ET. Using the observations of ET, runoff, and soil moisture content as benchmarks, we demonstrate that CLMET greatly improves the hydrological simulations over most of the CONUS, and the improvement is stronger in the eastern CONUS than the western CONUS and is strongest over the Southeast CONUS. For any specific region, the degree of the improvement depends on whether the relationship between observational and model ET remains time-invariant (a fundamental hypothesis of the Parr et al. (2015) method) and whether water is the limiting factor in places where ET is underestimated. While the bias correction method improves hydrological estimates without improving the physical parameterization of land surface models, results from this study do provide guidance for physically based model development effort.

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Coupling the Community Land Model version 5.0 to the parallel data assimilation framework PDAF: Description and applications

10.5194/gmd-2021-38 ◽

2021 ◽

Author(s):

Lukas Strebel ◽

Heye Bogena ◽

Harry Vereecken ◽

Harrie-Jan Hendricks Franssen

Keyword(s):

Data Assimilation ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Land Surface ◽

Land Surface Models ◽

Community Land Model ◽

Model Version ◽

Parallel Data ◽

Surface Models

Abstract. Land surface models are important for improving our understanding of the earth system. They are continuously improving and becoming more accurate in describing the varied surface processes, e.g. the Community Land Model version 5 (CLM5). Similarly, observational networks and remote sensing operations are increasingly providing more and higher quality data. For the optimal combination of land surface models and observation data, data assimilation techniques have been developed in the past decades that incorporate observations to update modeled states and parameters. The Parallel Data Assimilation Framework (PDAF) is a software environment that enables ensemble data assimilation and simplifies the implementation of data assimilation systems in numerical models. In this paper, we present the further development of the PDAF to enable its application in combination with CLM5. This novel coupling adapts the optional CLM5 ensemble mode to enable integration of PDAF filter routines while keeping changes to the pre-existing parallel communication infrastructure to a minimum. Soil water content observations from an extensive in-situ measurement network in the Wüstebach catchment in Germany are used to illustrate the application of the coupled CLM5+PDAF system. The results show overall reductions in root mean square error of soil water content from 7 % up to 35 % compared to simulations without data assimilation. We expect the coupled CLM5+PDAF system to provide a basis for improved regional to global land surface modelling by enabling the assimilation of globally available observational data.

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Incorporating remote sensing ET into Community Land Model version 4.5

10.5194/hess-2016-696 ◽

2017 ◽

Author(s):

Dagang Wang ◽

Guiling Wang ◽

Dana T. Parr ◽

Weilin Liao ◽

Youlong Xia ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Correction Method ◽

Energy Budgets ◽

Land Surface Models ◽

Community Land Model ◽

Model Version ◽

Surface Models ◽

Time Invariant ◽

The Relationship

Abstract. Land surface models bear substantial biases in simulating surface water and energy budgets despite of the continuous development and improvement of model parameterizations. To reduce model biases, Parr et al. (2015) proposed a method incorporating satellite-based evapotranspiration (ET) products into land surface models. Here we apply this method to the Community Land Model version 4.5 (CLM4.5) and test its performance over the conterminous US (CONUS). We first calibrate a relationship between the observational ET from the Global Land Evaporation Amsterdam Model (GLEAM) product and the model ET from CLM4.5, and assume that this relationship holds beyond the calibration period. During the validation or application period, a simulation using the default CLM4.5 (CLM) is conducted first, and its output is combined with the calibrated observational-vs-model ET relationship to derive a corrected ET; an experiment (CLMET) is then conducted in which the model-generated ET is overwritten using the corrected ET. Using the observations of ET, runoff, and soil moisture content as benchmarks, we demonstrate that CLMET greatly reduces the biases existing in CLM. The improvement differs with region, being more significant in eastern CONUS than western CONUS, with the most striking improvement over the southeast CONUS. This regional dependence reflects primarily the regional dependence in the degree to which the relationship between observational and model ET remains time-invariant (a fundamental hypothesis of the Parr et al. method). The bias correction method provides an alternative way to improve the performance of land surface models, which could lead to more realistic drought evaluations with improved ET and soil moisture estimates.

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An Evaluation of the Community Land Model (Version 3.5) and Noah Land Surface Models for Temperature and Precipitation Over Nebraska (Central Great Plains): Implications for Agriculture in Simulations of Future Climate Change and Adaptation

Climate Change Management - Climate Change Adaptation, Resilience and Hazards ◽

10.1007/978-3-319-39880-8_2 ◽

2016 ◽

pp. 21-34 ◽

Cited By ~ 3

Author(s):

Jane A. Okalebo ◽

Robert J. Oglesby ◽

Song Feng ◽

Kenneth Hubbard ◽

Ayse Kilic ◽

...

Keyword(s):

Climate Change ◽

Great Plains ◽

Land Surface ◽

Future Climate ◽

Future Climate Change ◽

Land Surface Models ◽

Community Land Model ◽

Model Version ◽

Temperature And Precipitation ◽

Surface Models

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Using Cosmic-Ray Neutron Probes in Validating Satellite Soil Moisture Products and Land Surface Models

Water ◽

10.3390/w11071362 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1362 ◽

Cited By ~ 2

Author(s):

Mustafa Berk Duygu ◽

Zuhal Akyürek

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Cosmic Ray ◽

Surface Model ◽

High Temporal Resolution ◽

Land Surface Models ◽

Statistical Measures ◽

Surface Models ◽

Global Land

Soil moisture content is one of the most important parameters of hydrological studies. Cosmic-ray neutron sensing is a promising proximal soil moisture sensing technique at intermediate scale and high temporal resolution. In this study, we validate satellite soil moisture products for the period of March 2015 and December 2018 by using several existing Cosmic Ray Neutron Probe (CRNP) stations of the COSMOS database and a CRNP station that was installed in the south part of Turkey in October 2016. Soil moisture values, which were inferred from the CRNP station in Turkey, are also validated using a time domain reflectometer (TDR) installed at the same location and soil water content values obtained from a land surface model (Noah LSM) at various depths (0.1 m, 0.3 m, 0.6 m and 1.0 m). The CRNP has a very good correlation with TDR where both measurements show consistent changes in soil moisture due to storm events. Satellite soil moisture products obtained from the Soil Moisture and Ocean Salinity (SMOS), the METOP-A/B Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer 2 (AMSR2), Climate Change Initiative (CCI) and a global land surface model Global Land Data Assimilation System (GLDAS) are compared with the soil moisture values obtained from CRNP stations. Coefficient of determination ( r 2 ) and unbiased root mean square error (ubRMSE) are used as the statistical measures. Triple Collocation (TC) was also performed by considering soil moisture values obtained from different soil moisture products and the CRNPs. The validation results are mainly influenced by the location of the sensor and the soil moisture retrieval algorithm of satellite products. The SMAP surface product produces the highest correlations and lowest errors especially in semi-arid areas whereas the ASCAT product provides better results in vegetated areas. Both global and local land surface models’ outputs are highly compatible with the CRNP soil moisture values.

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Evaluating and improving the Community Land Model's sensitivity to land cover

Biogeosciences ◽

10.5194/bg-15-4731-2018 ◽

2018 ◽

Vol 15 (15) ◽

pp. 4731-4757 ◽

Cited By ~ 13

Author(s):

Ronny Meier ◽

Edouard L. Davin ◽

Quentin Lejeune ◽

Mathias Hauser ◽

Yan Li ◽

...

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Land Surface ◽

Soil Column ◽

Plant Functional Type ◽

Model Parameters ◽

Functional Type ◽

Land Surface Models ◽

Surface Models ◽

Open Land

Abstract. Modeling studies have shown the importance of biogeophysical effects of deforestation on local climate conditions but have also highlighted the lack of agreement across different models. Recently, remote-sensing observations have been used to assess the contrast in albedo, evapotranspiration (ET), and land surface temperature (LST) between forest and nearby open land on a global scale. These observations provide an unprecedented opportunity to evaluate the ability of land surface models to simulate the biogeophysical effects of forests. Here, we evaluate the representation of the difference of forest minus open land (i.e., grassland and cropland) in albedo, ET, and LST in the Community Land Model version 4.5 (CLM4.5) using various remote-sensing and in situ data sources. To extract the local sensitivity to land cover, we analyze plant functional type level output from global CLM4.5 simulations, using a model configuration that attributes a separate soil column to each plant functional type. Using the separated soil column configuration, CLM4.5 is able to realistically reproduce the biogeophysical contrast between forest and open land in terms of albedo, daily mean LST, and daily maximum LST, while the effect on daily minimum LST is not well captured by the model. Furthermore, we identify that the ET contrast between forests and open land is underestimated in CLM4.5 compared to observation-based products and even reversed in sign for some regions, even when considering uncertainties in these products. We then show that these biases can be partly alleviated by modifying several model parameters, such as the root distribution, the formulation of plant water uptake, the light limitation of photosynthesis, and the maximum rate of carboxylation. Furthermore, the ET contrast between forest and open land needs to be better constrained by observations to foster convergence amongst different land surface models on the biogeophysical effects of forests. Overall, this study demonstrates the potential of comparing subgrid model output to local observations to improve current land surface models' ability to simulate land cover change effects, which is a promising approach to reduce uncertainties in future assessments of land use impacts on climate.

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