A Global High‐Resolution Data Set of Soil Hydraulic and Thermal Properties for Land Surface Modeling

Dai, Yongjiu; Xin, Qinchuan; Wei, Nan; Zhang, Yonggen; Shangguan, Wei; Yuan, Hua; Zhang, Shupeng; Liu, Shaofeng; Lu, Xingjie

doi:10.1029/2019ms001784

Cited by 131 publications

(121 citation statements)

References 77 publications

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“…The level of agreement between model simulations and field observations is usually measured using three statistics (Brovkin et al ., 2013; Frydrychowicz‐Jastrzebska and Bugala, 2015; Dai et al ., 2019): the Pearson correlation coefficient (R), root mean square error (RMSE), and mean bias error (MBE), which are calculated as follows:

normalR = \frac{\sum_{i = 1}^{N} ((), M_{i} - \overset{M}{true}) ((), O_{i} - \overset{O}{true})}{\sqrt{\sum_{i = 1}^{N} {(M_{i} - \overset{false¯}{M})}^{2}} \sqrt{\sum_{i = 1}^{N} {(O_{i} - \overset{false¯}{O})}^{2}}},

MBE = \frac{1}{N} \sum_{i = 1}^{N} ((), M_{i} - O_{i}),

RMSE = \sqrt{\frac{\sum_{i = 1}^{N} {((), M_{i} - O_{i})}^{2}}{N}},

where M i and O i are the model simulated and field observed values for the same variable, respectively;

\overset{M}{true}

and

\overset{O}{true}

are the means of the simulations and observations, respectively, and; N is the number of days.…”

Section: Methodsmentioning

confidence: 99%

Assessment of surface exchange coefficients in the Noah‐MP land surface model for different land‐cover types in China

Xia

Chen

et al. 2021

Intl Journal of Climatology

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The parameterization of surface exchange coefficients (Ch) representing land–atmosphere coupling strength plays a key role in land surface modelling. Previous studies have found that land–atmosphere coupling in land surface models (LSMs) is largely overestimated, which affects the predictability of weather and climate evolution. To improve the representation of land–atmosphere interactions in LSMs, this study investigated the impact of dynamic canopy‐height‐dependent coupling strength in an offline Noah LSM with multi‐parameterization options (Noah‐MP) when applied to China. Comparison with the default Noah‐MP LSM showed that the dynamic scheme significantly improved the Ch calculations and realistically reduced the biases of simulated surface energy and water components against observations. It is noteworthy that the improvements brought about by the dynamic scheme differed across land‐cover types. The scheme was superior in reproducing the observed Ch as well as surface energy and water variables for short vegetation (grass, crops, and shrubs), whereas the improvement for tall canopy vegetation (forest) was not significant although the estimations were reasonable. The improved version benefits from the treatment of the roughness length for heat. Further, the scheme can better reproduce the observed surface thermal components, while the improvement in hydraulic variables (such as soil moisture) remains limited. Overall, the dynamic coupling scheme markedly affects the simulation of land–atmosphere interactions, and altering the dynamics of surface coupling has the potential to improve the representation of land–atmosphere interactions and, thus, the further development of LSM.

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normalR = \frac{\sum_{i = 1}^{N} ((), M_{i} - \overset{M}{true}) ((), O_{i} - \overset{O}{true})}{\sqrt{\sum_{i = 1}^{N} {(M_{i} - \overset{false¯}{M})}^{2}} \sqrt{\sum_{i = 1}^{N} {(O_{i} - \overset{false¯}{O})}^{2}}},

MBE = \frac{1}{N} \sum_{i = 1}^{N} ((), M_{i} - O_{i}),

RMSE = \sqrt{\frac{\sum_{i = 1}^{N} {((), M_{i} - O_{i})}^{2}}{N}},

where M i and O i are the model simulated and field observed values for the same variable, respectively;

\overset{M}{true}

and

\overset{O}{true}

are the means of the simulations and observations, respectively, and; N is the number of days.…”

Section: Methodsmentioning

confidence: 99%

Assessment of surface exchange coefficients in the Noah‐MP land surface model for different land‐cover types in China

Xia

Chen

et al. 2021

Intl Journal of Climatology

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“…Large uncertainties exist in the reanalysis data, surface meteorology, surface and soil datasets in the Tibetan Plateau region, which are required for model inputs and parameterization, limiting a comprehensive evaluation of model processes and structure across the region (Dai et al, 2019). Ground-based meteorological data are particularly sparse in the Tibetan Plateau, with current stations mainly located in the central plateau and along the Qinghai-Tibet Railway/Highways, and complex topography adds additional challenges to upscaling local meteorological data to regional scale.…”

Section: Challengesmentioning

confidence: 99%

Progress and Challenges in Studying Regional Permafrost in the Tibetan Plateau Using Satellite Remote Sensing and Models

Jiang

Zheng

et al. 2020

Front. Earth Sci.

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Recent climate change has induced widespread soil thawing and permafrost degradation in the Tibetan Plateau. Significant advances have been made in better characterizing Tibetan Plateau soil freeze/thaw dynamics, and their interaction with local-scale ecohydrological processes. However, factors such as sparse networks of in-situ sites and short observational period still limit our understanding of the Tibetan Plateau permafrost. Satellite-based optical and infrared remote sensing can provide information on land surface conditions at high spatial resolution, allowing for better representation of spatial heterogeneity in the Tibetan Plateau and further infer the related permafrost states. Being able to operate at “all-weather” conditions, microwave remote sensing has been widely used to retrieve surface soil moisture, freeze/thaw state, and surface deformation, that are critical to understand the Tibetan Plateau permafrost state and changes. However, coarse resolution (>10 km) of current passive microwave sensors can add large uncertainties to the above retrievals in the Tibetan Plateau area with high topographic relief. In addition, current microwave remote sensing methods are limited to detections in the upper soil layer within a few centimetres. On the other hand, algorithms that can link surface properties and soil freeze/thaw indices to permafrost properties at regional scale still need improvements. For example, most methods using InSAR (interferometric synthetic aperture radar) derived surface deformation to estimate active layer thickness either ignore the effects of vertical variability of soil water content and soil properties, or use site-specific soil moisture profiles. This can introduce non-negligible errors when upscaled to the broader Tibetan Plateau area. Integrating satellite remote sensing retrievals with process models will allow for more accurate representation of Tibetan Plateau permafrost conditions. However, such applications are still limiting due to a number of factors, including large uncertainties in current satellite products in the Tibetan Plateau area, and mismatch between model input data needs and information provided by current satellite sensors. Novel approaches to combine diverse datasets with models through model initialization, parameterization and data assimilation are needed to address the above challenges. Finally, we call for expansion of local-scale observational network, to obtain more information on deep soil temperature and moisture, soil organic carbon content, and ground ice content.

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“…edu.cn/research/data (last access: 1 November 2020). These datasets include (i) a soil particle-size distribution dataset of China (Shangguan et al, 2012), (ii) a China dataset of soil properties (Shangguan et al, 2013), (iii) a China dataset of soil hydraulic parameters (Dai et al, 2013), (iv) a Global Soil Dataset for use in Earth System Models (GSDE) (Shangguan et al, 2014), (v) a global depth to bedrock dataset (Shangguan et al, 2017), (vi) a global dataset of soil hydraulic and thermal parameters (Dai et al, 2019), and (vii) a reprocessed global leaf area index dataset (Yuan et al, 2011). Among these datasets, the GSDE has been widely used, which provides soil information including soil texture, organic carbon, and bulk density at a spatial resolution of 30 arcsec for eight vertical soil layers to a depth of 2.3 m. Figures 6 and 7 show respectively the spatial distribution of bulk density and clay content on the TP derived from the GSDE and two other widely used soil datasets, i.e., the HWSD and SoilGrid provided by the ISRIC (International Soil Reference and Information Centre).…”

Section: Improvement Of Static Land Parameter Datamentioning

confidence: 99%

“…Moreover, the LSM is a powerful tool to provide a better understanding of the interactions among hydrological, ecological, and biogeochemical processes (Pan et al, 2012). Over the past decades, the LSMs have evolved from a simple bucket model (Manabe, 1969) to more sophisticated model systems (Dai et al, 2003;Dickinson et al, 1993;Oleson et al, 2010;Sellers et al, 1986Sellers et al, , 1996 that have incorporated many physical and physiological processes occurring in the atmosphere-snow-vegetationsoil-aquifer system. Meanwhile, several offline multi-model intercomparison projects have been conducted to identify the strength and weakness of LSMs, such as the Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS) (Henderson-Sellers et al, 1995, the Global Soil Wetness Project (GSWP) (Dirmeyer et al, 1999;Dirmeyer, 2011), and the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project (ALMIP) (Boone et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Last-decade progress in understanding and modeling the land surface processes on the Tibetan Plateau

Lu¹,

Zheng²,

Yang³

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. Land surface models (LSMs) that simulate water and energy exchanges at the land–atmosphere interface are a key component of Earth system models. The Tibetan Plateau (TP) drives the Asian monsoon through surface heating and thus plays a key role in regulating the climate system in the Northern Hemisphere. Therefore, it is vital to understand and represent well the land surface processes on the TP. After an early review that identified key issues in the understanding and modeling of land surface processes on the TP in 2009, much progress has been made in the last decade in developing new land surface schemes and supporting datasets. This review summarizes the major advances. (i) An enthalpy-based approach was adopted to enhance the description of cryosphere processes such as glacier and snow mass balance and soil freeze–thaw transition. (ii) Parameterization of the vertical mixing process was improved in lake models to ensure reasonable heat transfer to the deep water and to the near-surface atmosphere. (iii) New schemes were proposed for modeling water flow and heat transfer in soils accounting for the effects of vertical soil heterogeneity due to the presence of high soil organic matter content and dense vegetation roots in surface soils or gravel in soil columns. (iv) Supporting datasets of meteorological forcing and soil parameters were developed by integrating multi-source datasets including ground-based observations. Perspectives on the further improvement of land surface modeling on the TP are provided, including the continuous updating of supporting datasets, parameter estimation through assimilation of satellite observations, improvement of snow and lake processes, adoption of data-driven and artificial intelligence methods, and the development of an integrated LSM for the TP.

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A Global High‐Resolution Data Set of Soil Hydraulic and Thermal Properties for Land Surface Modeling

Cited by 131 publications

References 77 publications

Assessment of surface exchange coefficients in the Noah‐MP land surface model for different land‐cover types in China

Assessment of surface exchange coefficients in the Noah‐MP land surface model for different land‐cover types in China

Progress and Challenges in Studying Regional Permafrost in the Tibetan Plateau Using Satellite Remote Sensing and Models

Last-decade progress in understanding and modeling the land surface processes on the Tibetan Plateau

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