Improving the Noah‐MP Model for Simulating Hydrothermal Regime of the Active Layer in the Permafrost Regions of the Qinghai‐Tibet Plateau

Permafrost degradation may induce soil carbon (C) loss, critical for global C cycling, and be mediated by microbes. Despite larger C stored within the active layer of permafrost regions, which are more affected by warming, and the critical roles of Qinghai-Tibet Plateau in C cycling, most previous studies focused on the permafrost layer and in high-latitude areas. We demonstrate in situ that permafrost degradation alters the diversity and potentially decreases the stability of active layer microbial communities. These changes are associated with soil C loss and potentially a positive C feedback. This study provides insights into microbial-mediated mechanisms responsible for C loss within the active layer in degraded permafrost, aiding in the modeling of C emission under future scenarios.

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Assessing the simulated soil hydrothermal regime of the active layer from the Noah-MP land surface model (v1.1) in the permafrost regions of the Qinghai–Tibet Plateau

Geoscientific Model Development ◽

10.5194/gmd-14-1753-2021 ◽

2021 ◽

Vol 14 (3) ◽

pp. 1753-1771

Author(s):

Xiangfei Li ◽

Tonghua Wu ◽

Xiaodong Wu ◽

Jie Chen ◽

Xiaofan Zhu ◽

...

Keyword(s):

Soil Temperature ◽

Snow Cover ◽

Land Surface ◽

Tibet Plateau ◽

Cold Bias ◽

Surface Model ◽

Model Parameters ◽

Hydrothermal Regime ◽

Qinghai Tibet Plateau ◽

Permafrost Regions

Abstract. Extensive and rigorous model intercomparison is of great importance before model application due to the uncertainties in current land surface models (LSMs). Without considering the uncertainties in forcing data and model parameters, this study designed an ensemble of 55 296 experiments to evaluate the Noah LSM with multi-parameterization (Noah-MP) for snow cover events (SCEs), soil temperature (ST) and soil liquid water (SLW) simulation, and investigated the sensitivity of parameterization schemes at a typical permafrost site on the Qinghai–Tibet Plateau (QTP). The results showed that Noah-MP systematically overestimates snow cover, which could be greatly resolved when adopting the sublimation from wind and a semi-implicit snow/soil temperature time scheme. As a result of the overestimated snow, Noah-MP generally underestimates ST, which is mostly influenced by the snow process. A systematic cold bias and large uncertainties in soil temperature remain after eliminating the effects of snow, particularly in the deep layers and during the cold season. The combination of roughness length for heat and under-canopy (below-canopy) aerodynamic resistance contributes to resolving the cold bias in soil temperature. In addition, Noah-MP generally underestimates top SLW. The runoff and groundwater (RUN) process dominates the SLW simulation in comparison to the very limited impacts of all other physical processes. The analysis of the model structural uncertainties and characteristics of each scheme would be constructive to a better understanding of the land surface processes in the permafrost regions of the QTP as well as to further model improvements towards soil hydrothermal regime modeling using LSMs.

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Exploring the contribution of precipitation to water within the active layer during the thawing period in the permafrost regions of central Qinghai-Tibet Plateau by stable isotopic tracing

The Science of The Total Environment ◽

10.1016/j.scitotenv.2019.01.064 ◽

2019 ◽

Vol 661 ◽

pp. 630-644 ◽

Cited By ~ 11

Author(s):

Xiaofan Zhu ◽

Tonghua Wu ◽

Lin Zhao ◽

Chengsong Yang ◽

Huiwen Zhang ◽

...

Keyword(s):

Active Layer ◽

Tibet Plateau ◽

Isotopic Tracing ◽

Stable Isotopic ◽

Qinghai Tibet Plateau ◽

Permafrost Regions

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Soil Moisture Calibration Equations for Active Layer GPR Detection—a Case Study Specially for the Qinghai–Tibet Plateau Permafrost Regions

Remote Sensing ◽

10.3390/rs12040605 ◽

2020 ◽

Vol 12 (4) ◽

pp. 605

Author(s):

Erji Du ◽

Lin Zhao ◽

Defu Zou ◽

Ren Li ◽

Zhiwei Wang ◽

...

Keyword(s):

Dielectric Constant ◽

Soil Moisture ◽

Active Layer ◽

Complex Refractive Index ◽

Tibet Plateau ◽

Data Set ◽

Calibration Equation ◽

Qinghai Tibet Plateau ◽

Moisture Estimation ◽

Permafrost Regions

Ground-penetrating radar (GPR) is a convenient geophysical technique for active-layer soil moisture detection in permafrost regions, which is theoretically based on the petrophysical relationship between soil moisture (θ) and the soil dielectric constant (ε). The θ–ε relationship varies with soil type and thus must be calibrated for a specific region or soil type. At present, there is lack of such a relationship for active-layer soil moisture estimation for the Qinghai–Tibet plateau permafrost regions. In this paper, we utilize the Complex Refractive Index Model to establish such a calibration equation that is suitable for active-layer soil moisture estimation with GPR velocity. Based on the relationship between liquid water, temperature, and salinity, the soil water dielectric constant was determined, which varied from 84 to 88, with an average value of 86 within the active layer for our research regions. Based on the calculated soil-water dielectric constant variation range, and the exponent value range within the Complex Refractive Index Model, the exponent value was determined as 0.26 with our field-investigated active-layer soil moisture and dielectric data set. By neglecting the influence of the soil matrix dielectric constant and soil porosity variations on soil moisture estimation at the regional scale, a simple active-layer soil moisture calibration curve, named CRIM, which is suitable for the Qinghai–Tibet plateau permafrost regions, was established. The main shortage of the CRIM calibration equation is that its calculated soil-moisture error will gradually increase with a decreasing GPR velocity and an increasing GPR velocity interpretation error. To avoid this shortage, a direct linear fitting calibration equation, named as υ-fitting, was acquired based on the statistical relationship between the active-layer soil moisture and GPR velocity with our field-investigated data set. When the GPR velocity interpretation error is within ±0.004 m/ns, the maximum moisture error calculated by CRIM is within 0.08 m3/m3. While when the GPR velocity interpretation error is larger than ±0.004 m/ns, a piecewise formula calculation method, combined with the υ-fitting equation when the GPR velocity is lower than 0.07 m/ns and the CRIM equation when the GPR velocity is larger than 0.07 m/ns, was recommended for the active-layer moisture estimation with GPR detection in the Qinghai–Tibet plateau permafrost regions.

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Analysis of Raindrop Size Distribution Characteristics in Permafrost Regions of the Qinghai–Tibet Plateau Based on New Quality Control Scheme

Water ◽

10.3390/w11112265 ◽

2019 ◽

Vol 11 (11) ◽

pp. 2265 ◽

Cited By ~ 1

Author(s):

Ma ◽

Zhao ◽

Yang ◽

Xiao ◽

Zhang ◽

...

Keyword(s):

Particle Size ◽

Quality Control ◽

Rain Rate ◽

Tibet Plateau ◽

Frozen Ground ◽

Fall Velocity ◽

Raindrop Size Distribution ◽

Raindrop Size ◽

Qinghai Tibet Plateau ◽

Permafrost Regions

Raindrop size distribution (DSD) can reflect the fundamental microphysics of precipitation and provide an accurate estimation of its amount and characteristics; however, there are few observations and investigations of DSD in cold, mountainous regions. We used the second-generation particle size and velocity disdrometer Parsivel2 to establish a quality control scheme for raindrop spectral data obtained for the Qinghai–Tibet Plateau in 2015. This scheme included the elimination of particles in the lowest two size classes, particles >10 mm in diameter and rain rates <0.01 mm∙h−1. We analyzed the DSD characteristics for different types of precipitation and rain rates in both permafrost regions and regions with seasonally frozen ground. The precipitation in the permafrost regions during the summer were mainly solid with a large particle size and slow fall velocity, whereas the precipitation in the regions with seasonally frozen ground were mainly liquid. The DSD of snow had a broader drop spectrum, the largest particle size, the slowest fall velocity, and the largest number of particles, followed by hail. Rain and sleet shared similar DSD characteristics, with a smaller particle size, slower velocity, and smaller number of particles. The particle concentration for different classes of rain rate decreased with an increase in particle size and decreased gradually with an increase in rain rate. Precipitation with a rain rate >2 mm∙h−1 was the main contributor to the annual precipitation. The dewpoint thresholds for snow and rain in permafrost regions were 0 and 1.5 °C, respectively. The dewpoint range 0–1.5 °C was characterized by mixed precipitation with a large proportion of hail. This study provides valuable DSD information on the Qinghai–Tibet Plateau and can be used as an important reference for the quality control of raindrop spectral data in regions dominated by solid precipitation.

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Small-Baseline Approach for Monitoring the Freezing and Thawing Deformation of Permafrost on the Beiluhe Basin, Tibetan Plateau Using TerraSAR-X and Sentinel-1 Data

Sensors ◽

10.3390/s20164464 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4464

Author(s):

Jing Wang ◽

Chao Wang ◽

Hong Zhang ◽

Yixian Tang ◽

Xuefei Zhang ◽

...

Keyword(s):

Active Layer ◽

Surface Deformation ◽

Residual Deformation ◽

Dynamic Monitoring ◽

Tibet Plateau ◽

Time Lags ◽

Freezing And Thawing ◽

Linear Deformation ◽

Small Baseline ◽

Qinghai Tibet Plateau

The dynamic changes of the thawing and freezing processes of the active layer cause seasonal subsidence and uplift over a large area on the Qinghai–Tibet Plateau due to ongoing climate warming. To analyze and investigate the seasonal freeze–thaw process of the active layer, we employ the new small baseline subset (NSBAS) technique based on a piecewise displacement model, including seasonal deformation, as well as linear and residual deformation trends, to retrieve the surface deformation of the Beiluhe basin. We collect 35 Sentinel-1 images with a 12 days revisit time and 9 TerraSAR-X images with less-than two month revisit time from 2018 to 2019 to analyze the type of the amplitude of seasonal oscillation of different ground targets on the Beiluhe basin in detail. The Sentinel-1 results show that the amplitude of seasonal deformation is between −62.50 mm and 11.50 mm, and the linear deformation rate ranges from −24.50 mm/yr to 5.00 mm/yr (2018–2019) in the study area. The deformation trends in the Qinghai–Tibet Railway (QTR) and Qinghai–Tibet Highway (QTH) regions are stable, ranging from −18.00 mm to 6 mm. The InSAR results of Sentinel-1 and TerraSAR-X data show that seasonal deformation trends are consistent, exhibiting good correlations 0.78 and 0.84, and the seasonal and linear deformation rates of different ground targets are clearly different on the Beiluhe basin. Additionally, there are different time lags between the maximum freezing uplift or thawing subsidence and the maximum or minimum temperature for the different ground target areas. The deformation values of the alpine meadow and floodplain areas are higher compared with the alpine desert and barren areas, and the time lags of the freezing and thawing periods based on the Sentinel-1 results are longest in the alpine desert area, that is, 86 days and 65 days, respectively. Our research has important reference significance for the seasonal dynamic monitoring of different types of seasonal deformation and the extensive investigations of permafrost in Qinghai Tibet Plateau.

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