Soil organic matter dominates the magnitude of porosity and bulk density in temperate soils, with important implications for land surface models

2021 ◽  
Author(s):  
Amy Thomas ◽  
Fiona Seaton ◽  
Jack Cosby ◽  
Bridget Emmett ◽  
Sabine Reinsch ◽  
...  
Keyword(s):  
Climate Change ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Land Surface ◽  
Pedotransfer Functions ◽  
Soil Porosity ◽  
Land Surface Models ◽  
Surface Models ◽  
Global Land ◽  
Temperate Soils

<p>Soil porosity controls the flow of mass and energy through soil, and thus plays a fundamental role in regulating hydrological and biochemical cycling at the land surface. Global land surface and earth system models commonly derive porosity from soil texture using pedotransfer functions. This does not allow for response to change in environment or management, or potentially important climate feedbacks. Furthermore, the approach does not fully represent the baseline spatial variation in this important soil property. Here we show that porosity, and bulk density (BD), depend on SOM in temperate soils, using two comprehensive national data sets, covering the full range of soil organic matter (SOM) (n=1385 & n=2570). Our novel use of analytical models with machine learning (ML) algorithms opens up new physical insight into controls on porosity and BD, while generalized additive mixed models (GAMMs) provide further insights and opportunities for prediction. Our models allow us to consider influence of management on soil compaction and recent observations that soil porosity responds to climate change. The dependence of soil porosity on SOM, more so than texture, indicates the need for a paradigm shift in the conceptualization and modelling of these soil physical properties. Broad habitat was also an important control, and explained some of the variance in the relationship between SOM and porosity. This highlights that changes in soil porosity may occur due to land use or climate change, and will create feedbacks to hydrological and biogeochemical cycling which should be represented in Global land surface models. This will also be important for other pedotransfer functions, e.g. the use of BD to determine carbon stock from concentration.  In addition, we found opportunities for improved representation of the spatial pattern of porosity, even in the absence of measured data on SOM, based on climate and earth observation data.</p>

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.

scholarly journals Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

2020 ◽  
Author(s):  
Elizabeth Cooper ◽  
Eleanor Blyth ◽  
Hollie Cooper ◽  
Rich Ellis ◽  
Ewan Pinnington ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Large Scale ◽  
Pedotransfer Functions ◽  
Surface Model ◽  
Climate Projections ◽  
Land Surface Models ◽  
Surface Models

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological and meteorological applications. In recent years the availability of wide-area soil-moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in-situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the JULES land surface model using field scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way can improve the performance of land surface models, leading to the potential for better flood, drought and climate projections.

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 Deriving pedotransfer functions for soil quartz fraction in southern France from reverse modeling

SOIL ◽  
2016 ◽  
Vol 2 (4) ◽  
pp. 615-629 ◽  
Author(s):  
Jean-Christophe Calvet ◽  
Noureddine Fritz ◽  
Christine Berne ◽  
Bruno Piguet ◽  
William Maurel ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Land Surface ◽  
Geometric Mean ◽  
Sensible Heat Flux ◽  
Pedotransfer Functions ◽  
Quartz Content ◽  
Southern France ◽  
Weather Stations

Abstract. The quartz fraction in soils is a key parameter of soil thermal conductivity models. Because it is difficult to measure the quartz fraction in soils, this information is usually unavailable. This source of uncertainty impacts the simulation of sensible heat flux, evapotranspiration and land surface temperature in numerical simulations of the Earth system. Improving the estimation of soil quartz fraction is needed for practical applications in meteorology, hydrology and climate modeling. This paper investigates the use of long time series of routine ground observations made in weather stations to retrieve the soil quartz fraction. Profile soil temperature and water content were monitored at 21 weather stations in southern France. Soil thermal diffusivity was derived from the temperature profiles. Using observations of bulk density, soil texture, and fractions of gravel and soil organic matter, soil heat capacity and thermal conductivity were estimated. The quartz fraction was inversely estimated using an empirical geometric mean thermal conductivity model. Several pedotransfer functions for estimating quartz content from gravimetric or volumetric fractions of soil particles (e.g., sand) were analyzed. The soil volumetric fraction of quartz (fq) was systematically better correlated with soil characteristics than the gravimetric fraction of quartz. More than 60 % of the variance of fq could be explained using indicators based on the sand fraction. It was shown that soil organic matter and/or gravels may have a marked impact on thermal conductivity values depending on which predictor of fq is used. For the grassland soils examined in this study, the ratio of sand-to-soil organic matter fractions was the best predictor of fq, followed by the gravimetric fraction of sand. An error propagation analysis and a comparison with independent data from other tested models showed that the gravimetric fraction of sand is the best predictor of fq when a larger variety of soil types is considered.

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 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.

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