A global soil data set for earth system modeling Shangguan, Wei; Dai, Yongjiu; Duan, Qingyun ...
Journal of advances in modeling earth systems,
March 2014, 2014-03-00, 20140301, Letnik:
6, Številka:
1
Journal Article
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We developed a comprehensive, gridded Global Soil Dataset for use in Earth System Models (GSDE) and other applications. The GSDE provides soil information, such as soil particle‐size distribution, ...organic carbon, and nutrients, and quality control information in terms of confidence level at 30″ × 30″ horizontal resolution and for eight vertical layers to a depth of 2.3 m. The GSDE is based on the Soil Map of the World and various regional and national soil databases, including soil attribute data and soil maps. We used a standardized data structure and data processing procedures to harmonize the data collected from various sources. We then used a soil type linkage method (i.e., taxotransfer rules) and a polygon linkage method to derive the spatial distribution of the soil properties. To aggregate the attributes of different compositions of a mapping unit, we used three mapping approaches: the area‐weighting method, the dominant soil type method, and the dominant binned soil attribute method. The data set can also be aggregated to a lower resolution. In this paper, we only show the vertical and horizontal variations of sand, silt and clay contents, bulk density, and soil organic carbon as examples of the GSDE. The GSDE estimates of global soil organic carbon stock to the depths of 2.3, 1, and 0.3 m are 1922.7, 1455.4, and 720.1 Gt, respectively. This newly developed data set provides more accurate soil information and represents a step forward to advance earth system modeling.
Key Points
A global soil data set was developed for earth system modeling
Various data sources were harmonized using consistent processes
Examples of the data set were given show the vertical and horizontal variations
Depth to bedrock serves as the lower boundary of land surface models, which controls hydrologic and biogeochemical processes. This paper presents a framework for global estimation of depth to bedrock ...(DTB). Observations were extracted from a global compilation of soil profile data (ca. 1,30,000 locations) and borehole data (ca. 1.6 million locations). Additional pseudo‐observations generated by expert knowledge were added to fill in large sampling gaps. The model training points were then overlaid on a stack of 155 covariates including DEM‐based hydrological and morphological derivatives, lithologic units, MODIS surface reflectance bands and vegetation indices derived from the MODIS land products. Global spatial prediction models were developed using random forest and Gradient Boosting Tree algorithms. The final predictions were generated at the spatial resolution of 250 m as an ensemble prediction of the two independently fitted models. The 10–fold cross‐validation shows that the models explain 59% for absolute DTB and 34% for censored DTB (depths deep than 200 cm are predicted as 200 cm). The model for occurrence of R horizon (bedrock) within 200 cm does a good job. Visual comparisons of predictions in the study areas where more detailed maps of depth to bedrock exist show that there is a general match with spatial patterns from similar local studies. Limitation of the data set and extrapolation in data spare areas should not be ignored in applications. To improve accuracy of spatial prediction, more borehole drilling logs will need to be added to supplement the existing training points in under‐represented areas.
Key Points
Observations from soil and geological surveys are combined for developing global spatial prediction models of depth to bedrock
Machine learning explains 59% of variation in spatial distribution of depth to bedrock for interpolation but much less for extrapolation
The framework proposed can be used to gradually improve accuracy by adding more ground observations
•Interdependencies enhance performance in covariate analysis.•The proposed model outperforms existing methods.•The proposed model expands DSM application possibilities.
The accurate and ...cost-effective mapping of soil texture is essential for agricultural development and environmental activities. Soil texture exhibits high spatial heterogeneity which poses challenges for recent Digital Soil Mapping (DSM) methods in achieving accurate predictions. Feature engineering methods, extensively used to capture complex soil-forming relationships and enhance prediction accuracy, often involve labor-intensive processes. Additionally, the engineered “discrete” feature cannot reflect interactions between environmental covariates or dependencies. To address the challenges, this study proposes a novel Local-Global Dependency Long Short-Term Memory model (LGD-LSTM) to enhance soil texture predictions at various soil depths. Firstly,a covariate reorganization method hasbeen devised to generate multiple sets of input. Subsequently, several Long Short-Term Memory models (LSTM)have beenemployed to extract the interdependencies among the covariates.Finally, predictions are generated using a fully-connected layer. Cross-validation was conducted within this experiment to analyze prediction accuracy: the average explained variation (R2) ranged from 0.66 to 0.73, and the root mean square error (RMSE) ranged from 6.52% to 10.89%. The results indicated that the LGD-LSTM model offers distinct advantages over other digital soil mapping methods, including Random Forests (RF), Convolutional Neural Network (CNN), and the standard Long Short-Term Memory model (LSTM). In summary, this LGD-LSTM method demonstrates superior performance with relatively high accuracy, ensuring its applicability in effectively representing spatial variations in soil texture. Furthermore, it presents a novel option for DSM applications, enhancing the field's methodology and potential impact.
Land surface and climate modelling requires continuous and consistent Leaf Area Index (LAI). High spatiotemporal resolution and long-time record data are more in demand nowadays and will continue to ...be in the future. MODIS LAI products meet these requirements to some degree. However, due to the presence of cloud and seasonal snow cover, the instrument problems and the uncertainties of retrieval algorithm, the current MODIS LAI products are spatially and temporally discontinuous and inconsistent, which limits their application in land surface and climate modelling. To improve the MODIS LAI products on a global scale, we considered the characteristics of the MODIS LAI data and made the best use of quality control (QC) information, and developed an integrated two-step method to derive the improved MODIS LAI products effectively and efficiently on a global scale. First, we used the modified temporal spatial filter (mTSF) method taking advantage of background values and QC information at each pixel to do a simple data assimilation for relatively low quality data. Then we applied the post processing-TIMESAT (A software package to analyze time-series of satellite sensor data) Savitzky–Golay (SG) filter to get the final result. We implemented the method to 10
years of the MODIS Collection 5 LAI data. In comparison with the LAI reference maps and the MODIS LAI data, our results showed that the improved MODIS LAI data are closer to the LAI reference maps in magnitude and also more continuous and consistent in both time-series and spatial domains. In addition, simple statistics were used to evaluate the differences between the MODIS LAI and the improved MODIS LAI.
► The MODIS LAI products are not continuous and consistent in space and time-series. ► We develop an integrated two-step method to reprocess the MODIS LAI products. ► We generate 10-year improved MODIS LAI data for land surface and climate modeling.
The objective of this study is to develop a dataset of the soil hydraulic parameters associated with two empirical soil functions (i.e., a water retention curve and hydraulic conductivity) using ...multiple pedotransfer functions (PTFs). The dataset is designed specifically for regional land surface modeling for China. The authors selected 5 PTFs to derive the parameters in the Clapp and Hornberger functions and the van Genuchten and Mualem functions and 10 PTFs for soil water contents at capillary pressures of 33 and 1500 kPa. The inputs into the PTFs include soil particle size distribution, bulk density, and soil organic matter. The dataset provides 12 estimated parameters and their associated statistical values. The dataset is available at a 30 × 30 arc second geographical spatial resolution and with seven vertical layers to the depth of 1.38 m. The dataset has several distinct advantages even though the accuracy is unknown for lack of in situ and regional measurements. First, this dataset utilizes the best available soil characteristics dataset for China. The Chinese soil characteristics dataset was derived by using the 1:1 000 000 Soil Map of China and 8595 representative soil profiles. Second, this dataset represents the first attempt to estimate soil hydraulic parameters using PTFs directly for continental China at a high spatial resolution. Therefore, this dataset should capture spatial heterogeneity better than existing estimates based on lookup tables according to soil texture classes. Third, the authors derived soil hydraulic parameters using multiple PTFs to allow flexibility for data users to use the soil hydraulic parameters most preferable to or suitable for their applications.
We developed a multi-layer soil particle-size distribution dataset (sand, silt and clay content), based on USDA (United States Department of Agriculture) standard for regional land and climate ...modelling in China. The 1:1,000,000 scale soil map of China and 8595 soil profiles from the Second National Soil Survey served as the starting point for this work. We reclassified the inconsistent soil profiles into the proper soil type of the map as much as possible because the soil classification names of the map units and profiles were not quite the same. The sand, silt and clay maps were derived using the polygon linkage method, which linked soil profiles and map polygons considering the distance between them, the sample sizes of the profiles, and soil classification information. For comparison, a soil type linkage was also generated by linking the map units and soil profiles with the same soil type. The quality of the derived soil fractions was reliable. Overall, the map polygon linkage offered better results than the soil type linkage or the Harmonized World Soil Database. The dataset, with a 1-km resolution, can be applied to land and climate modelling at a regional scale.
► The polygon linkage method was developed to derive soil PSD map. ► A representative value was given for each soil polygon instead of map unit. ► Distance, profile ample sizes and soil classification were considered. ► Soil polygon linkage offered better results than type linkage or HWSD.
Observations show that the global mean surface temperature has increased steadily since the 1950s and this warming trend is particularly strong and linear over land after 1979. This paper analyzes ...the relationship between surface temperature trends observed over land for the period 1979-2012 and enhanced vegetation index (EVI), a satellite measured vegetation greenness index, by large-scale ecoregion. The land areas between 50°S and 50°N are classified into various large-scale ecoregions based on the climatological EVI values. The regional mean temperature trends exhibit significant spatial dependence on the regional mean EVI. In general, the warming rate increases dramatically with decreasing EVI, with the strongest warming rate seen over the driest ecoregions. When anthropogenic and natural forcings are included, climate models are generally able to reproduce observed major features of the spatial dependence. When only natural forcings are used, none of the observed features are simulated. Furthermore, the simulated temperature changes in the latter are mostly far outside the range of those in the former. These results suggest stronger warming amplification over drier ecoregions in the context of global warming, pointing mainly to human influence.
Terrestrial ecosystems are an important part of Earth systems, and they are undergoing remarkable changes in response to global warming. This study investigates the response of the terrestrial ...vegetation distribution and carbon fluxes to global warming by using the new dynamic global vegetation model in the second version of the Chinese Academy of Sciences (CAS) Earth System Model (CAS-ESM2). We conducted two sets of simulations, a present-day simulation and a future simulation, which were forced by the present-day climate during 1981–2000 and the future climate during 2081–2100, respectively, as derived from RCP8.5 outputs in CMIP5. CO
2
concentration is kept constant in all simulations to isolate CO
2
-fertilization effects. The results show an overall increase in vegetation coverage in response to global warming, which is the net result of the greening in the mid-high latitudes and the browning in the tropics. The results also show an enhancement in carbon fluxes in response to global warming, including gross primary productivity, net primary productivity, and autotrophic respiration. We found that the changes in vegetation coverage were significantly correlated with changes in surface air temperature, reflecting the dominant role of temperature, while the changes in carbon fluxes were caused by the combined effects of leaf area index, temperature, and precipitation. This study applies the CAS-ESM2 to investigate the response of terrestrial ecosystems to climate warming. Even though the interpretation of the results is limited by isolating CO
2
-fertilization effects, this application is still beneficial for adding to our understanding of vegetation processes and to further improve upon model parameterizations.
To represent the physical processes at hillslope scales for hyper‐resolution land surface modeling, we propose a hierarchical, catchment‐based spatial tessellation method. The land surface is divided ...into a hierarchical structure: catchments, height bands along hillslopes within a catchment, and land cover patches within a height band. This catchment‐based structure explicitly represents hillslope drainage networks and can be applied at various resolutions determined by a pre‐defined maximum height band size. The proposed tessellation method is superior to the conventional grid‐based structure in representing land surface heterogeneity, resulting in a higher aggregation skill through the height band representation. The spatial variations in air temperature, leaf area index, saturated soil hydraulic conductivity, and soil porosity are generally lower within a height band than those in a conventional rectangular grid, reflecting the nature of topographic control on climate, vegetation, and soil distribution. The improvement in aggregation skill depends on resolutions and terrain slope angle, more pronounced at 1/6° model resolution and over steeper terrains. Finally, we demonstrate that our proposed catchment‐based structure performs better than the grid‐based structure through modeling tests over the Columbia River basin at resolutions of 1/2°, 1/6°, and 1/20° and a global test at 1/2° using the ILAMB model evaluation metrics.
Plain Language Summary
This paper develops a catchment‐based spacing approach for high‐resolution land surface modeling. The land surface is divided into a hierarchical structure: catchments, height bands along hillslopes within a catchment, and land cover patches within a height band. We demonstrates that the catchment‐based approach can better represent the heterogeneous distributions of water, soils, plants, and climates, especially over mountainous regions than does the conventional, rectangular grid‐based approach. When used in a land surface model, the catchment‐based approach also performs better than the grid‐based approach through modeling tests over the Columbia River basin at resolutions of 1/2°, 1/6°, and 1/20° and a global test at 1/2°.
Key Points
A catchment‐based hierarchical spatial tessellation: catchments, height bands, and land cover patches, is proposed
The catchment‐based tessellation method is superior to the conventional grid‐based structure in representing land surface heterogeneities
The proposed catchment‐based structure performs better than the grid‐based structure in land surface modeling
Previous research found that the warming rate observed for the period 1979–2012 increases dramatically with decreasing vegetation greenness over land between 50°S and 50°N, with the strongest warming ...rate seen over the driest regions such as the Sahara desert and the Arabian Peninsula, suggesting warming amplification over deserts. To further this finding, this paper explores possible mechanisms for this amplification by analyzing observations, reanalysis data and historical simulations of global coupled atmosphere–ocean general circulation models. We examine various variables, related to surface radiative forcing, land surface properties, and surface energy and radiation budget, that control the warming patterns in terms of large-scale ecoregions. Our results indicate that desert amplification is likely attributable primarily to enhanced longwave radiative forcing associated with a stronger water vapor feedback over drier ecoregions in response to the positive global-scale greenhouse gas forcing. This warming amplification and associated downward longwave radiation at the surface are reproduced by historical simulations with anthropogenic and natural forcings, but are absent if only natural forcings are considered, pointing to new potential fingerprints of anthropogenic warming. These results suggest a fundamental pattern of global warming over land that depend on the dryness of ecosystems in mid- and low- latitudes, likely reflecting primarily the first order large-scale thermodynamic component of global warming linked to changes in the water and energy cycles over different ecosystems. This finding may have important implications in interpreting global warming patterns and assessing climate change impacts.
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