Benchmarking global land surface models against the observed mean annual runoff from 150 large basins

2012 ◽  
Vol 470-471 ◽  
pp. 269-279 ◽  
Author(s):  
Xinyao Zhou ◽  
Yongqiang Zhang ◽  
Yingping Wang ◽  
Huqiang Zhang ◽  
Jai Vaze ◽  
...  
Keyword(s):  
Land Surface ◽  
Annual Runoff ◽  
Land Surface Models ◽  
Surface Models ◽  
Global Land

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

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1362 ◽  
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.

Parameter optimization for carbon and water fluxes in two global land surface models based on surrogate modelling

2018 ◽  
Vol 38 ◽  
pp. e1016-e1031 ◽  
Author(s):  
Jianduo Li ◽  
Qingyun Duan ◽  
Ying-Ping Wang ◽  
Wei Gong ◽  
Yanjun Gan ◽  
...  
Keyword(s):  
Parameter Optimization ◽  
Land Surface ◽  
Water Fluxes ◽  
Land Surface Models ◽  
Surrogate Modelling ◽  
Surface Models ◽  
Global Land

scholarly journals Global terrestrial water storage connectivity revealed using complex climate network analyses

2015 ◽  
Vol 22 (4) ◽  
pp. 433-446 ◽  
Author(s):  
A. Y. Sun ◽  
J. Chen ◽  
J. Donges
Keyword(s):  
Spatial Patterns ◽  
Land Surface ◽  
Water Storage ◽  
Data Sets ◽  
Terrestrial Water Storage ◽  
Land Surface Models ◽  
Data Set ◽  
Surface Models ◽  
Global Land ◽  
Terrestrial Water

Abstract. Terrestrial water storage (TWS) exerts a key control in global water, energy, and biogeochemical cycles. Although certain causal relationship exists between precipitation and TWS, the latter quantity also reflects impacts of anthropogenic activities. Thus, quantification of the spatial patterns of TWS will not only help to understand feedbacks between climate dynamics and the hydrologic cycle, but also provide new insights and model calibration constraints for improving the current land surface models. This work is the first attempt to quantify the spatial connectivity of TWS using the complex network theory, which has received broad attention in the climate modeling community in recent years. Complex networks of TWS anomalies are built using two global TWS data sets, a remote sensing product that is obtained from the Gravity Recovery and Climate Experiment (GRACE) satellite mission, and a model-generated data set from the global land data assimilation system's NOAH model (GLDAS-NOAH). Both data sets have 1° × 1° grid resolutions and cover most global land areas except for permafrost regions. TWS networks are built by first quantifying pairwise correlation among all valid TWS anomaly time series, and then applying a cutoff threshold derived from the edge-density function to retain only the most important features in the network. Basinwise network connectivity maps are used to illuminate connectivity of individual river basins with other regions. The constructed network degree centrality maps show the TWS anomaly hotspots around the globe and the patterns are consistent with recent GRACE studies. Parallel analyses of networks constructed using the two data sets reveal that the GLDAS-NOAH model captures many of the spatial patterns shown by GRACE, although significant discrepancies exist in some regions. Thus, our results provide further measures for constraining the current land surface models, especially in data sparse regions.

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

2021 ◽  
Vol 13 (21) ◽  
pp. 4460
Author(s):  
Dayang Wang ◽  
Dagang Wang ◽  
Chongxun Mo
Keyword(s):  
Remote Sensing ◽  
Water Supply ◽  
Bias Correction ◽  
Land Surface ◽  
Land Surface Models ◽  
Community Land Model ◽  
Model Version ◽  
Correction Effect ◽  
Surface Models ◽  
Global Land

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.

scholarly journals Forest biomass allometry in global land surface models

2011 ◽  
Vol 25 (3) ◽  
pp. n/a-n/a ◽  
Author(s):  
Adam Wolf ◽  
Philippe Ciais ◽  
Valentin Bellassen ◽  
Nicolas Delbart ◽  
Christopher B. Field ◽  
...  
Keyword(s):  
Land Surface ◽  
Forest Biomass ◽  
Land Surface Models ◽  
Surface Models ◽  
Global Land
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