Evaluation of streamflow simulation results of land surface models in GLDAS on the Tibetan plateau

Abstract. We perform a land-surface model intercomparison to investigate how the simulation of permafrost area on the Tibetan Plateau (TP) varies among six modern stand-alone land-surface models (CLM4.5, CoLM, ISBA, JULES, LPJ-GUESS, UVic). We also examine the variability in simulated permafrost area and distribution introduced by five different methods of diagnosing permafrost (from modeled monthly ground temperature, mean annual ground and air temperatures, air and surface frost indexes). There is good agreement (99 to 135  ×  104 km2) between the two diagnostic methods based on air temperature which are also consistent with the observation-based estimate of actual permafrost area (101  × 104 km2). However the uncertainty (1 to 128  ×  104 km2) using the three methods that require simulation of ground temperature is much greater. Moreover simulated permafrost distribution on the TP is generally only fair to poor for these three methods (diagnosis of permafrost from monthly, and mean annual ground temperature, and surface frost index), while permafrost distribution using air-temperature-based methods is generally good. Model evaluation at field sites highlights specific problems in process simulations likely related to soil texture specification, vegetation types and snow cover. Models are particularly poor at simulating permafrost distribution using the definition that soil temperature remains at or below 0 °C for 24 consecutive months, which requires reliable simulation of both mean annual ground temperatures and seasonal cycle, and hence is relatively demanding. Although models can produce better permafrost maps using mean annual ground temperature and surface frost index, analysis of simulated soil temperature profiles reveals substantial biases. The current generation of land-surface models need to reduce biases in simulated soil temperature profiles before reliable contemporary permafrost maps and predictions of changes in future permafrost distribution can be made for the Tibetan Plateau.

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Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area

The Cryosphere Discussions ◽

10.5194/tcd-9-1769-2015 ◽

2015 ◽

Vol 9 (2) ◽

pp. 1769-1810 ◽

Cited By ~ 6

Author(s):

W. Wang ◽

A. Rinke ◽

J. C. Moore ◽

X. Cui ◽

D. Ji ◽

...

Keyword(s):

Tibetan Plateau ◽

Soil Temperature ◽

Air Temperature ◽

Land Surface ◽

Ground Temperature ◽

Diagnostic Methods ◽

The Tibetan Plateau ◽

Temperature Profiles ◽

Land Surface Models ◽

Surface Models

Abstract. We perform a land surface model intercomparison to investigate how the simulation of permafrost area on the Tibetan Plateau (TP) varies between 6 modern stand-alone land surface models (CLM4.5, CoLM, ISBA, JULES, LPJ-GUESS, UVic). We also examine the variability in simulated permafrost area and distribution introduced by 5 different methods of diagnosing permafrost (from modeled monthly ground temperature, mean annual ground and air temperatures, air and surface frost indexes). There is good agreement (99–135 x 104 km2) between the two diagnostic methods based on air temperature which are also consistent with the best current observation-based estimate of actual permafrost area (101 x 104 km2). However the uncertainty (1–128 x 104 km2) using the three methods that require simulation of ground temperature is much greater. Moreover simulated permafrost distribution on TP is generally only fair to poor for these three methods (diagnosis of permafrost from monthly, and mean annual ground temperature, and surface frost index), while permafrost distribution using air temperature based methods is generally good. Model evaluation at field sites highlights specific problems in process simulations likely related to soil texture specification and snow cover. Models are particularly poor at simulating permafrost distribution using definition that soil temperature remains at or below 0°C for 24 consecutive months, which requires reliable simulation of both mean annual ground temperatures and seasonal cycle, and hence is relatively demanding. Although models can produce better permafrost maps using mean annual ground temperature and surface frost index, analysis of simulated soil temperature profiles reveals substantial biases. The current generation of land surface models need to reduce biases in simulated soil temperature profiles before reliable contemporary permafrost maps and predictions of changes in permafrost distribution can be made for the Tibetan Plateau.

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Modeling Evapotranspiration over China’s Landmass from 1979 to 2012 Using Multiple Land Surface Models: Evaluations and Analyses

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0212.1 ◽

2017 ◽

Vol 18 (4) ◽

pp. 1185-1203 ◽

Cited By ~ 14

Author(s):

Shaobo Sun ◽

Baozhang Chen ◽

Quanqin Shao ◽

Jing Chen ◽

Jiyuan Liu ◽

...

Keyword(s):

Land Surface ◽

Regional Scale ◽

Temporal Variations ◽

The Tibetan Plateau ◽

Land Surface Models ◽

Infiltration Capacity ◽

Community Land Model ◽

Variable Infiltration Capacity Model ◽

Capacity Model ◽

Surface Models

Abstract Land surface models (LSMs) are useful tools to estimate land evapotranspiration at a grid scale and for long-term applications. Here, the Community Land Model, version 4.0 (CLM4.0); Dynamic Land Model (DLM); and Variable Infiltration Capacity model (VIC) were driven with observation-based forcing datasets, and a multiple-LSM ensemble-averaged evapotranspiration (ET) product (LSMs-ET) was developed and its spatial–temporal variations were analyzed for the China landmass over the period 1979–2012. Evaluations against measurements from nine flux towers at site scale and surface water budget–based ET at regional scale showed that the LSMs-ET had good performance in most areas of China’s landmass. The intercomparisons between the ET estimates and the independent ET products from remote sensing and upscaling methods suggested that there were fairly consistent patterns between each dataset. The LSMs-ET produced a mean annual ET of 351.24 ± 10.7 mm yr−1 over 1979–2012, and its spatial–temporal variation analyses showed that (i) there was an overall significant ET increasing trend, with a value of 0.72 mm yr−1 (p < 0.01), and (ii) 36.01% of Chinese land had significant increasing trends, ranging from 1 to 9 mm yr−1, while only 6.41% of the area showed significant decreasing trends, ranging from −6.28 to −0.08 mm yr−1. Analyses of ET variations in each climate region clearly showed that the Tibetan Plateau areas were the main contributors to the overall increasing ET trends of China.

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ON THE ROLE OF INTEGRATED GROUNDWATER-LAND SURFACE MODELS FOR GROUNDWATER SUSTAINABILITY ASSESSMENTS

10.1130/abs/2019am-338177 ◽

2019 ◽

Author(s):

Hoori Ajami ◽

◽

Matthew F Mccabe ◽

Jason P. Evans ◽

Simon Stisen

Keyword(s):

Land Surface ◽

Land Surface Models ◽

Groundwater Sustainability ◽

Surface Models ◽

Sustainability Assessments

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Trends of land surface heat fluxes on the Tibetan Plateau from 2001 to 2012

International Journal of Climatology ◽

10.1002/joc.5119 ◽

2017 ◽

Vol 37 (14) ◽

pp. 4757-4767 ◽

Cited By ~ 16

Author(s):

Cunbo Han ◽

Yaoming Ma ◽

Xuelong Chen ◽

Zhongbo Su

Keyword(s):

Tibetan Plateau ◽

Land Surface ◽

Surface Heat ◽

Heat Fluxes ◽

The Tibetan Plateau ◽

Surface Heat Fluxes ◽

Land Surface Heat Fluxes

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Eco‐evolutionary optimality as a means to improve vegetation and land‐surface models

New Phytologist ◽

10.1111/nph.17558 ◽

2021 ◽

Author(s):

Sandy P. Harrison ◽

Wolfgang Cramer ◽

Oskar Franklin ◽

Iain Colin Prentice ◽

Han Wang ◽

...

Keyword(s):

Land Surface ◽

Land Surface Models ◽

Surface Models ◽

Evolutionary Optimality

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The CarboEurope Regional Experiment Strategy

Bulletin of the American Meteorological Society ◽

10.1175/bams-87-10-1367 ◽

2006 ◽

Vol 87 (10) ◽

pp. 1367-1380 ◽

Cited By ~ 92

Author(s):

A. J. Dolman ◽

J. Noilhan ◽

P. Durand ◽

C. Sarrat ◽

A. Brut ◽

...

Keyword(s):

Land Surface ◽

Major Product ◽

State Variables ◽

Land Surface Models ◽

Quality Metric ◽

Key Characteristics ◽

Surface Models ◽

Global Soil ◽

Standard Deviations ◽

Model Agreement

The Second Global Soil Wetness Project (GSWP-2) is an initiative to compare and evaluate 10-year simulations by a broad range of land surface models under controlled conditions. A major product of GSWP-2 is the first global gridded multimodel analysis of land surface state variables and fluxes for use by meteorologists, hydrologists, engineers, biogeochemists, agronomists, botanists, ecologists, geographers, climatologists, and educators. Simulations by 13 land models from five nations have gone into production of the analysis. The models are driven by forcing data derived from a combination of gridded atmospheric reanalyses and observations. The resulting analysis consists of multimodel means and standard deviations on the monthly time scale, including profiles of soil moisture and temperature at six levels, as well as daily and climatological (mean annual cycle) fields for over 50 land surface variables. The monthly standard deviations provide a measure of model agreement that may be used as a quality metric. An overview of key characteristics of the analysis is presented here, along with information on obtaining the data.

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On the utility of land surface models for agricultural drought monitoring

Hydrology and Earth System Sciences ◽

10.5194/hess-16-3451-2012 ◽

2012 ◽

Vol 16 (9) ◽

pp. 3451-3460 ◽

Cited By ~ 42

Author(s):

W. T. Crow ◽

S. V. Kumar ◽

J. D. Bolten

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Cross Correlation ◽

Root Zone ◽

Vegetation Indices ◽

Agricultural Drought ◽

Drought Monitoring ◽

Global Evaluation ◽

Land Surface Models ◽

Surface Models

Abstract. The lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely sensed vegetation indices (VI) is examined between January 2000 and December 2010 to quantify the skill of various soil moisture models for agricultural drought monitoring. Examined modeling strategies range from a simple antecedent precipitation index to the application of modern land surface models (LSMs) based on complex water and energy balance formulations. A quasi-global evaluation of lagged VI/soil moisture cross-correlation suggests, when globally averaged across the entire annual cycle, soil moisture estimates obtained from complex LSMs provide little added skill (< 5% in relative terms) in anticipating variations in vegetation condition relative to a simplified water accounting procedure based solely on observed precipitation. However, larger amounts of added skill (5–15% in relative terms) can be identified when focusing exclusively on the extra-tropical growing season and/or utilizing soil moisture values acquired by averaging across a multi-model ensemble.

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