scholarly journals Assessing the simulated soil thermal regime from Noah-MPLSM v1.1 for near-surface permafrost modeling on the Qinghai-Tibet Plateau

2020 ◽  
Author(s):  
Xiangfei Li ◽  
Tonghua Wu ◽  
Xiaodong Wu ◽  
Xiaofan Zhu ◽  
Guojie Hu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Land Surface ◽  
Stomatal Resistance ◽  
Cold Season ◽  
Canopy Gap ◽  
Tibet Plateau ◽  
Surface Model ◽  
Near Surface ◽  
Qinghai Tibet Plateau

Abstract. Land surface models (LSMs) are effective tools for near-surface permafrost modeling. Extensive and rigorous model inter-comparison is of great importance before application due to the uncertainties in current LSMs. This study designed an ensemble of 6912 experiments to evaluate the Noah land surface model with multi-parameterization (Noah-MP) for soil temperature (ST) simulation, and investigate the sensitivity of parameterization schemes at a typical permafrost site on the Qinghai-Tibet Plateau. The results showed that Noah-MP generally underestimates ST, especially that during the cold season. In addition, the simulation uncertainty is greater in the cold season (October-April) and for the deep soil layers. ST is most sensitive to surface layer drag coefficient (SFC) while largely influenced by runoff and groundwater (RUN). By contrast, the influence of canopy stomatal resistance (CRS) and soil moisture factor for stomatal resistance (BTR) on ST is negligible. With limited impacts on ST simulation, vegetation model (VEG), canopy gap for radiation transfer (RAD) and snow/soil temperature time scheme (STC) are more influential on shallow ST, while super-cooled liquid water (FRZ), frozen soil permeability (INF) and lower boundary of soil temperature (TBOT) have greater impacts on deep ST. Furthermore, an optimal configuration of Noah-MP for permafrost modeling were extracted based on the connectivity between schemes, and they are: table leaf area index with calculated vegetation fraction, Jarvis scheme for CRS, Noah scheme for BTR, BATS model for RUN, Chen97 for SFC, zero canopy gap for RAD, variant freezing-point depression for FRZ, hydraulic parameters defined by soil moisture for INF, ST at 8 m for TBOT, and semi-implicit method for STC. The analysis of the model structural uncertainties and characteristics of each scheme would be constructive to a better understanding of the land surface processes on the QTP and further model improvements towards near-surface permafrost modeling using the LSMs.

scholarly journals 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

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.

Adjoint retrieval of prognostic land surface model variables for an NWP model: Assimilation of ground surface temperature

2010 ◽  
Vol 2 (2) ◽  
Author(s):  
Diandong Ren
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Land Surface Model ◽  
Initial Guess ◽  
Surface Model ◽  
Soil Moisture Condition ◽  
Wide Range ◽  
Surface Soil Temperature

AbstractBased on a 2-layer land surface model, a rather general variational data assimilation framework for estimating model state variables is developed. The method minimizes the error of surface soil temperature predictions subject to constraints imposed by the prediction model. Retrieval experiments for soil prognostic variables are performed and the results verified against model simulated data as well as real observations for the Oklahoma Atmospheric Surface layer Instrumentation System (OASIS). The optimization scheme is robust with respect to a wide range of initial guess errors in surface soil temperature (as large as 30 K) and deep soil moisture (within the range between wilting point and saturation). When assimilating OASIS data, the scheme can reduce the initial guess error by more than 90%, while for Observing Simulation System Experiments (OSSEs), the initial guess error is usually reduced by over four orders of magnitude.Using synthetic data, the robustness of the retrieval scheme as related to information content of the data and the physical meaning of the adjoint variables and their use in sensitivity studies are investigated. Through sensitivity analysis, it is confirmed that the vegetation coverage and growth condition determine whether or not the optimally estimated initial soil moisture condition leads to an optimal estimation of the surface fluxes. This reconciles two recent studies.With the real data experiments, it is shown that observations during the daytime period are the most effective for the retrieval. Longer assimilation windows result in more accurate initial condition retrieval, underlining the importance of information quantity, especially for schemes assimilating noisy observations.

scholarly journals Effects of Air Temperature and Precipitation on Soil Moisture on the Qinghai-Tibet Plateau during the 2015 Growing Season

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Qingyan Xie ◽  
Jianping Li ◽  
Yufei Zhao
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Hydrological Cycle ◽  
Critical Role ◽  
Growing Season ◽  
Tibet Plateau ◽  
Ecosystem Stability ◽  
Near Surface ◽  
Temperature And Precipitation ◽  
Qinghai Tibet Plateau

The Qinghai-Tibet Plateau (QTP) holds massive freshwater resources and is one of the most active regions in the world with respect to the hydrological cycle. Soil moisture (SM) plays a critical role in hydrological processes and is important for plant growth and ecosystem stability. To investigate the relationship between climatic factors (air temperature and precipitation) and SM during the growing season in various climate zones on the QTP, data from three observational stations were analyzed. The results showed that the daily average (Tave) and minimum air temperatures (Tmin) significantly influenced SM levels at all depths analyzed (i.e., 10, 20, 30, 40, and 50 cm deep) at the three stations, and Tmin had a stronger effect on SM than did Tave. However, the daily maximum air temperature (Tmax) generally had little effect on SM, although it had showed some effects on SM in the middle and deeper layers at the Jiali station. Precipitation was an important factor that significantly influenced the SM at all depths at the three stations, but the influence on SM in the middle and deep layers lagged the direct effect on near-surface SM by 5–7 days. These results suggest that environment characterized by lower temperatures and higher precipitation may promote SM conservation during the growing season and in turn support ecosystem stability on the QTP.

scholarly journals Combined Assimilation of Satellite Precipitation and Soil Moisture: A Case Study Using TRMM and SMOS Data

2017 ◽  
Vol 145 (12) ◽  
pp. 4997-5014 ◽  
Author(s):  
Liao-Fan Lin ◽  
Ardeshir M. Ebtehaj ◽  
Alejandro N. Flores ◽  
Satish Bastola ◽  
Rafael L. Bras
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Specific Humidity ◽  
Surface Model ◽  
Precipitation Data ◽  
Near Surface ◽  
Satellite Precipitation ◽  
Soil Moisture Data ◽  
Mean Square Errors

This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).

scholarly journals Application of satellite microwave remote sensed brightness temperature in the regional soil moisture simulation

2009 ◽  
Vol 6 (1) ◽  
pp. 1233-1260 ◽  
Author(s):  
X. K. Shi ◽  
J. Wen ◽  
L. Wang ◽  
T. T. Zhang ◽  
H. Tian ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Microwave Remote Sensing ◽  
Temperature Data ◽  
Fine Tuning ◽  
Tibet Plateau ◽  
Surface Model ◽  
Satellite Microwave

Abstract. As the satellite microwave remote sensed brightness temperature is sensitive to land surface soil moisture (SM) and SM is a basic output variable in model simulation, it is of great significance to use the brightness temperature data to improve SM numerical simulation. In this paper, the theory developed by Yan et al. (2004) about the relationship between satellite microwave remote sensing polarization index and SM was used to estimate the land surface SM from AMSR-E (Advanced Microwave Scanning Radiometer – Earth Observing System) brightness temperature data. With consideration of land surface soil texture, surface roughness, vegetation optical thickness, and the AMSR-E monthly SM products, the regional daily land surface SM was estimated over the eastern part of the Qinghai-Tibet Plateau. The results show that the estimated SM is lower than the ground measurements and the NCEP (American National Centers for Environmental Prediction) reanalysis data at the Maqu Station (33.85° N, 102.57° E) and the Tanglha Station (33.07° N, 91.94° E), but its regional distribution is reasonable and somewhat better than that from the daily AMSR-E SM product, and its temporal variation shows a quick response to the ground daily precipitations. Furthermore, in order to improve the simulating ability of the WRF (Weather Research and Forecasting) model to land surface SM, the estimated SM was assimilated into the Noah land surface model by the Newtonian relaxation (NR) method. The results indicate that, by fine tuning of the quality factor in NR method, the simulated SM values are improved most in desert area, followed by grassland, shrub and grass mixed zone. At temporal scale, Root Mean Square Error (RMSE) values between simulated and observed SM are decreased 0.03 and 0.07 m3/m3 by using the NR method in the Maqu Station and the Tanglha Station, respectively.

scholarly journals The ERA5-Land soil temperature bias in permafrost regions

2020 ◽  
Vol 14 (8) ◽  
pp. 2581-2595 ◽  
Author(s):  
Bin Cao ◽  
Stephan Gruber ◽  
Donghai Zheng ◽  
Xin Li
Keyword(s):  
Soil Temperature ◽  
Land Surface ◽  
Soil Column ◽  
Land Surface Model ◽  
Surface Model ◽  
Active Layer Thickness ◽  
Near Surface ◽  
Warm Bias ◽  
Temperature Bias ◽  
Permafrost Regions

Abstract. ERA5-Land (ERA5L) is a reanalysis product derived by running the land component of ERA5 at increased resolution. This study evaluates ERA5L soil temperature in permafrost regions based on observations and published permafrost products. We find that ERA5L overestimates soil temperature in northern Canada and Alaska but underestimates it in mid–low latitudes, leading to an average bias of −0.08 ∘C. The warm bias of ERA5L soil is stronger in winter than in other seasons. As calculated from its soil temperature, ERA5L overestimates active-layer thickness and underestimates near-surface (<1.89 m) permafrost area. This is thought to be due in part to the shallow soil column and coarse vertical discretization of the land surface model and to warmer simulated soil. The soil temperature bias in permafrost regions correlates well with the bias in air temperature and with maximum snow height. A review of the ERA5L snow parameterization and a simulation example both point to a low bias in ERA5L snow density as a possible cause for the warm bias in soil temperature. The apparent disagreement of station-based and areal evaluation techniques highlights challenges in our ability to test permafrost simulation models. While global reanalyses are important drivers for permafrost simulation, we conclude that ERA5L soil data are not well suited for informing permafrost research and decision making directly. To address this, future soil temperature products in reanalyses will require permafrost-specific alterations to their land surface models.

scholarly journals Using MODIS Land Surface Temperatures for Permafrost Thermal Modeling in Beiluhe Basin on the Qinghai-Tibet Plateau

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4200 ◽  
Author(s):  
Anyuan Li ◽  
Caichu Xia ◽  
Chunyan Bao ◽  
Guoan Yin
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Ground Surface ◽  
Tibet Plateau ◽  
Active Layer Thickness ◽  
Permafrost Region ◽  
Thermal Dynamics ◽  
Near Surface ◽  
Warm Bias ◽  
Qinghai Tibet Plateau

It is essential to monitor the ground temperature over large areas to understand and predict the effects of climate change on permafrost due to its rapid warming on the Qinghai-Tibet Plateau (QTP). Land surface temperature (LST) is an important parameter for the energy budget of permafrost environments. Moderate Resolution Imaging Spectroradiometer (MODIS) LST products are especially valuable for detecting permafrost thermal dynamics across the QTP. This study presents a comparison of MODIS-LST values with in situ near-surface air temperature (Ta), and ground surface temperature (GST) obtained from 2014 to 2016 at five sites in Beiluhe basin, a representative permafrost region on the QTP. Furthermore, the performance of the thermal permafrost model forced by MODIS-LSTs was studied. Averaged LSTs are found to strongly correlated with Ta and GST with R2 values being around 0.9. There is a significant warm bias (4.43–4.67 °C) between averaged LST and Ta, and a slight warm bias (0.67–2.66 °C) between averaged LST and GST. This study indicates that averaged MODIS-LST is supposed to be a useful data source for permafrost monitoring. The modeled ground temperatures and active-layer thickness have a good agreement with the measurements, with a difference of less than 1.0 °C and 0.4 m, respectively.

scholarly journals Developing a drought-monitoring index for the contiguous US using SMAP

2018 ◽  
Vol 22 (12) ◽  
pp. 6611-6626 ◽  
Author(s):  
Sara Sadri ◽  
Eric F. Wood ◽  
Ming Pan
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Drought Index ◽  
Surface Soil ◽  
Root Zone ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Short Term ◽  
Near Surface ◽  
Index Maps

Abstract. Since April 2015, NASA's Soil Moisture Active Passive (SMAP) mission has monitored near-surface soil moisture, mapping the globe (between 85.044∘ N/S) using an L-band (1.4 GHz) microwave radiometer in 2–3 days depending on location. Of particular interest to SMAP-based agricultural applications is a monitoring product that assesses the SMAP near-surface soil moisture in terms of probability percentiles for dry and wet conditions. However, the short SMAP record length poses a statistical challenge for meaningful assessment of its indices. This study presents initial insights about using SMAP for monitoring drought and pluvial regions with a first application over the contiguous United States (CONUS). SMAP soil moisture data from April 2015 to December 2017 at both near-surface (5 cm) SPL3SMP, or Level 3, at ∼36 km resolution, and root-zone SPL4SMAU, or Level 4, at ∼9 km resolution, were fitted to beta distributions and were used to construct probability distributions for warm (May–October) and cold (November–April) seasons. To assess the data adequacy and have confidence in using short-term SMAP for a drought index estimate, we analyzed individual grids by defining two filters and a combination of them, which could separate the 5815 grids covering CONUS into passed and failed grids. The two filters were (1) the Kolmogorov–Smirnov (KS) test for beta-fitted long-term and the short-term variable infiltration capacity (VIC) land surface model (LSM) with 95 % confidence and (2) good correlation (≥0.4) between beta-fitted VIC and beta-fitted SPL3SMP. To evaluate which filter is the best, we defined a mean distance (MD) metric, assuming a VIC index at 36 km resolution as the ground truth. For both warm and cold seasons, the union of the filters – which also gives the best coverage of the grids throughout CONUS – was chosen to be the most reliable filter. We visually compared our SMAP-based drought index maps with metrics such as the U.S. Drought Monitor (from D0–D4), 1-month Standard Precipitation Index (SPI) and near-surface VIC from Princeton University. The root-zone drought index maps were shown to be similar to those produced by the root-zone VIC, 3-month SPI, and the Gravity Recovery and Climate Experiment (GRACE). This study is a step forward towards building a national and international soil moisture monitoring system without which quantitative measures of drought and pluvial conditions will remain difficult to judge.

Evaluation of the Noah Land Surface Model Using Data from a Fair-Weather IHOP_2002 Day with Heterogeneous Surface Fluxes

2008 ◽  
Vol 136 (12) ◽  
pp. 4915-4941 ◽  
Author(s):  
Margaret A. LeMone ◽  
Mukul Tewari ◽  
Fei Chen ◽  
Joseph G. Alfieri ◽  
Dev Niyogi
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Minor Effect ◽  
Near Surface ◽  
Vegetation Fraction ◽  
A Value ◽  
Green Vegetation ◽  
Noah Land Surface Model

Abstract Sources of differences between observations and simulations for a case study using the Noah land surface model–based High-Resolution Land Data Assimilation System (HRLDAS) are examined for sensible and latent heat fluxes H and LE, respectively; surface temperature Ts; and vertical temperature difference T0 − Ts, where T0 is at 2 m. The observational data were collected on 29 May 2002, using the University of Wyoming King Air and four surface towers placed along a sparsely vegetated 60-km north–south flight track in the Oklahoma Panhandle. This day had nearly clear skies and a strong north–south soil-moisture gradient, with wet soils and widespread puddles at the south end of the track and drier soils to the north. Relative amplitudes of H and LE horizontal variation were estimated by taking the slope of the least squares best-fit straight line ΔLE/ΔH on plots of time-averaged LE as a function of time-averaged H for values along the track. It is argued that observed H and LE values departing significantly from their slope line are not associated with surface processes and, hence, need not be replicated by HRLDAS. Reasonable agreement between HRLDAS results and observed data was found only after adjusting the coefficient C in the Zilitinkevich equation relating the roughness lengths for momentum and heat in HRLDAS from its default value of 0.1 to a new value of 0.5. Using C = 0.1 and adjusting soil moisture to match the observed near-surface values increased horizontal variability in the right sense, raising LE and lowering H over the moist south end. However, both the magnitude of H and the amplitude of its horizontal variability relative to LE remained too large; adjustment of the green vegetation fraction had only a minor effect. With C = 0.5, model-input green vegetation fraction, and our best-estimate soil moisture, H, LE, ΔLE/ΔH, and T0 − Ts, were all close to observed values. The remaining inconsistency between model and observations—too high a value of H and too low a value of LE over the wet southern end of the track—could be due to HRLDAS ignoring the effect of open water. Neglecting the effect of moist soils on the albedo could also have contributed.

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