Harmonized World Soil Database Research Articles

Ecological factors impacting genetic characteristics and metabolite accumulations of Gastrodia elata

ObjectiveThe investigation of the correlation between ecological factors and the genetic characteristics or metabolites of plants offers valuable insights into the regional causes of genetic and metabolic diversity. Here, Gastrodia elata, a medicinal plant, is employed as a model to explore the environmental factors that influence its genetic characteristics and metabolic accumulations. MethodsA total of 23 G. elata populations from six cultispecies and 11 cultivated regions were selected based on the predictions of the global geographic information system. The genetic characteristics of these populations were evaluated using highly polymorphic simple sequence repeat markers. Additionally, the metabolic accumulations and antioxidant capacity of mature tubers were measured employing colorimetry and high performance liquid chromatography (HPLC). Ecological data of each region were obtained from the WorldClim-global climate database and harmonized world soil database. To assess the influence of ecological factors on the genetic characteristics and metabolic profiles of G. elata, Pearson’s correlation analysis was conducted. ResultsGenetic variation among G. elata populations exceeded that within populations. Genetic diverisity, distance and structure manifested regional and species-specific patterns. Metabolic profiling and antioxidant capacity exhibited regional variations. Notably, the Lueyang region demonstrated that a content range of total polysaccharide, total protein, and phenolic glycosides was 9.34 %−189.67 % higher than the average. Similarly, in the Hubei region, total phenolic content, p-hydroxybenzyl alcohol content, and antioxidant indicators were observed to be higher than the average levels, by 106.57 %, 136.47 % and 12.50 %−91.14 %, respectively. Furthermore, ecological factors had a significant comprehensive impact on G. elata genetic characteristics (r > 0.256 and P<0.05). Multivariate metabolite accumulations in G. elata were influenced by dominant ecological factors. Temperature notably impacted the accumulation of total protein (|r| > 0.528 and P<0.05). Moisture, encompassing precipitation and soil content, significantly affected the production of phenolic glycosides (|r| > 0.503 and P<0.05). ConclusionThe genetic characteristics of G. elata manifested regional and species-specific patterns, with the metabolic accumulations and antioxidant capacity of mature tubers exhibited regional variations. Specifically, multivariate ecological factors comprehensively influenced genetic characteristics. Temperature and moisture played pivotal roles in regulating the accumulations of proteins and phenolic glycosides, respectively. These findings underscore the significant impact of ecological factors on the shaping of G. elata, highlighting their crucial role in enhancing the quality of Chinese medicinal materials.

Assessment of the Vulnerability of Aquifers in Basement Areas to Pollution from Agriculture: The Case of the Boulbi Rice Plain in Burkina Faso

Groundwater is a major source of water, meeting the domestic water needs of more than 70% of Africa&amp;apos;s population. Although prized for its relatively good quality compared with surface water, groundwater is increasingly subjected to multiple sources of pollution. Long thought to be the solution to increasing agricultural production and achieving food self-sufficiency, agricultural inputs are now being pointed out in Burkina Faso as a major source of water pollution. However, few studies exist showing the contribution of agricultural inputs to groundwater pollution. The aim of this study is to show the impact of the use of agricultural inputs on groundwater quality: the case of the Boulbi valley rice-growing area in Burkina Faso, West Africa. Soil properties were measured using a double-ring infiltrometer and Harmonized World Soil Database. Groundwater recharge was assessed by Thornthwaite’s equation. The DRASTIC, GOD and SI methods were applied to map the valley’s vulnerability. Fertilizers and phytochemicals were recorded by surveys. A sampling of surface and groundwater was done in 32 locations and the chemical characteristics (pH, EC, NO&amp;lt;sup&amp;gt;3-&amp;lt;/sup&amp;gt;, SO&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt;&amp;lt;sup&amp;gt;2-&amp;lt;/sup&amp;gt;, PO&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt;&amp;lt;sup&amp;gt;2-&amp;lt;/sup&amp;gt; and K&amp;lt;sup&amp;gt;+&amp;lt;/sup&amp;gt;) confronted with the vulnerability indices. Results show that the soils were predominantly clay (41%), silt (37%) and silty sand (22%). Twenty types of phytochemicals were used, among which 35% were composed of the controversial glyphosate (denounced as carcinogenic) and 30% made with paraquat chloride also accused of being responsible for several self-poisoning. All the three methods pointed to a low vulnerability risk, partly because of the purification role of clay. The average pH is 8.2 ± 0.4, explaining the low-rice yield (&amp;lt;4.0 tons/ha), in spite of fertilizer use. Although the risk assessment rendered non-alarming situation, preventive measures about health and environment need to be taken.

Sensitivity of joint atmospheric-terrestrial water balance simulations to soil representation: Convection-permitting coupled WRF-Hydro simulations for southern Africa

Regional weather and climate models play a crucial role in understanding and representing the regional water cycle, yet the accuracy of soil data significantly affects their reliability. In this study, we employ the fully coupled Weather Research and Forecasting Hydrological Modeling system (WRF-Hydro) to assess how soil hydrophysical properties influence regional land-atmosphere coupling and the water cycle over the southern Africa region. We utilize four widely-used global soil datasets, including default soil data for model from the Food and Agriculture Organization, and alternative datasets from the Harmonized World Soil Database, Global Soil Dataset for Earth System Model, and global gridded soil information system SoilGrids. By conducting convection-permitting coupled WRF-Hydro simulations with the Noah-MP land surface model using each of the aforementioned soil datasets, our benchmark analysis reveals substantial differences in soil hydrophysical properties and their significant impact on the simulated regional water cycle during the austral summer. Alterations in soil datasets lead to both spatial and temporal variations in surface water and energy fluxes, which in turn profoundly influence the atmospheric thermodynamic structure. Reduced soil water-holding capacity leads to subsequent reduction in soil moisture and latent heat, resulting in significant decreases in convective available potential energy and convective inhibition, signaling potential effects on precipitation distributions. In arid interior regions of southern Africa, shifts towards drier and warmer surface conditions due to soil data discrepancies are found to enhance atmospheric moisture convergence, suggesting a possible localized negative feedback of soil moisture on precipitation. Overall, the results for southern Africa indicate that soil data discrepancies exert more pronounced impact on terrestrial fields in dry subregions and on atmospheric fields in temperate subregions, highlighting the broad uncertainties in the regional water cycle reproduced within the model.

From field soil sampling to watershed model: Upscaling by integrating information entropy and interpolation method

Soil property data plays a crucial role in watershed hydrology and non-point source (H/NPS) modeling, but how to improve modeling accuracy with affordable soil samplings and the effects of sampling information on H/NPS modeling remains to be further explored. In this study, the number of sampling points and soil properties were optimized by the information entropy and the spatial interpolation method. Then the sampled properties were parameterized and the effects of different parameterization schemes on H/NPS modeling were tested using the Soil and Water Assessment Tool (SWAT). The results indicated that the required sampling points increased successively for soil bulk density (SOL_BD), soil saturated hydraulic conductivity (SOL_K) and soil available water capacity (SOL_AWC). Compared to the traditional database (Harmonized world soil database), the NSE and R2 performance by new scheme increased by 22.8% and 10.5%, respectively. The entropy-based optimization reduced the sampling points by 13.2%, indicating a more cost-effective scheme. Compared to hydrological simulation, sampled properties showed greater effects on NPS modeling, especially for nitrogen. This proposed method/framework can be generalized to other watersheds by upscaling field soil sampling information to the watershed scale, thus improving H/NPS simulation.

Analysis and Design of a Box Culvert Using Bentley Culvert Master Software

Box culverts are utilized in situations where natural stream flow intersects with roads and railway lines. This research utilizes a digital elevation model and the water Modeling System software to assess the catchment area of the primary valley and identify the factors contributing to flooding in Qoshtapa City. The study involves an analysis of the existing culvert and generated the necessary data for the design of a new culvert. Despite the presence of the existing culvert, floodwater levels rose to over 1 m above the roadway elevation of Erbil-Kirkuk during the last flood event in 2021–2022. The research collected hydrological and climatic data for the study area, conducted soil type analysis using the Harmonized World Soil Database software, and performed hydraulic calculations to estimate the maximum flood discharge of the valley using the Hydrological Engineering Center-Hydrological Modeling System software for flood return periods of 50, 100, and 200 years, for design, to select the best economic alternative. The new culvert design was executed using Bentley Culvert Master software to ensure that floodwaters can flow through the culvert without rising to street level. The results indicated that the new culvert design surpasses the capacity of the existing one. The results show that the best economic alternative hydraulic design is the first alternative capacity of 201 m3/s of a 100-year return period; the new design cross-section area of the culvert is 52.5 m2.

A machine learning and experimental-based model for prediction of soil sorption capacity toward phenanthrene

Sorption by soil is the fundamental basis for environment fate of hydrophobic organic contaminants (HOCs), which varies significantly depending on diverse properties of soils. Therefore, a generalized approach to predict HOC sorption by soils is required. In this study, 488 data points were extracted from references and adopted to develop models for estimating the sorption capacities of phenanthrene in soils using six different machine learning (ML) approaches. The extreme gradient boosting (XGBT) model demonstrated the most favorable performance, achieving a coefficient of determination of 0.91 and root-mean-square errors of 0.24 for the testing dataset. The XGBT model's performance was further demonstrated by comparing with experimental data from batch sorption tests conducted on 20 soil samples collected from 17 provinces of China. The differences between the predicted values and the experimental values were statistically equal to zero (p = 0.14). Leveraging the XBGT model together with soil properties from the Harmonized World Soil Database, the distribution of sorption capacities in Chinese soils was successfully depicted on a national scale. This research is expected to contribute to a deeper understanding of the migration of persistent organic pollutants in terrestrial system. Furthermore, the established model holds implications for more precise and scientific soil environmental management.

ESTABLISHING THE FIRST DATABASE OF SOIL HYDRAULIC PROPERTIES IN TUNISIA BASED ON PEDOTRANSFER FUNCTIONS

Soil hydraulic properties play a crucial role in the unsaturated soil water supply process. Currently, Tunisia lacks a database containing essential values for soil retention properties and soil saturated hydraulic conductivity. The main objective of this study is to create the inaugural soil hydraulic properties database through the utilization of open-access data and pedotransfer functions (PTFs). To achieve this goal, the harmonized world soil database (FAO) was employed to identify 752 measurement points across Tunisia. Subsequently, the soil texture, organic carbon content, and bulk density were determined at each point. These acquired values were then entered into the CalcPTF software to estimate the van Genuchten soil retention parameters. The calculation of soil saturated hydraulic conductivity was accomplished using two widely recognized pedotransfer functions (Saxton and Rosetta). The outcomes facilitated the creation of a catalog containing soil hydraulic parameter value for each soil texture. Significance of discrepancies between values obtained from the PTFs was assessed using a Tukey test. The spatial variability of each soil hydraulic property was studied using the simple kriging. In conclusion, the establishment of this significant soil hydraulic properties database holds diverse applications in agricultural, hydrological, and environmental studies in Tunisia.

Upscaling Soil Organic Carbon Measurements at the Continental Scale Using Multivariate Clustering Analysis and Machine Learning

AbstractEstimates of soil organic carbon (SOC) stocks are essential for many environmental applications. However, significant inconsistencies exist in SOC stock estimates for the U.S. across current SOC maps. We propose a framework that combines unsupervised multivariate geographic clustering (MGC) and supervised Random Forests regression, improving SOC maps by capturing heterogeneous relationships with SOC drivers. We first used MGC to divide the U.S. into 20 SOC regions based on the similarity of covariates (soil biogeochemical, bioclimatic, biological, and physiographic variables). Subsequently, separate Random Forests models were trained for each SOC region, utilizing environmental covariates and SOC observations. Our estimated SOC stocks for the U.S. (52.6 ± 3.2 Pg for 0–30 cm and 108.3 ± 8.2 Pg for 0–100 cm depth) were within the range estimated by existing products like Harmonized World Soil Database, HWSD (46.7 Pg for 0–30 cm and 90.7 Pg for 0–100 cm depth) and SoilGrids 2.0 (45.7 Pg for 0–30 cm and 133.0 Pg for 0–100 cm depth). However, independent validation with soil profile data from the National Ecological Observatory Network showed that our approach (R2 = 0.51) outperformed the estimates obtained from Harmonized World Soil Database (R2 = 0.23) and SoilGrids 2.0 (R2 = 0.39) for the topsoil (0–30 cm). Uncertainty analysis (e.g., low representativeness and high coefficients of variation) identified regions requiring more measurements, such as Alaska and the deserts of the U.S. Southwest. Our approach effectively captures the heterogeneous relationships between widely available predictors and the current SOC baseline across regions, offering reliable SOC estimates at 1 km resolution for benchmarking Earth system models.

Enhanced Understanding of Key Soil Properties in Northern Xinjiang Using Water-Heat-Spectral Datasets Based on Bioclimatic Guidelines

Current digital soil mapping of soil properties (soil organic carbon, SOC; electrical conductivity, EC; and pH) is mainly based on transfer learning, which is inadequate in terms of accuracy for the northern plain area of Xinjiang. To address this issue, establishing a new model is urgently required that can improve our understanding of the soil properties in this region. To this end, based on the global bioclimatic variables and surface dry–wet and wet–dry transitions, The study developed a spectral–water–heat database (SWHD). The study then incorporated this database and background data into machine learning algorithms (XGBoost, LightGBM, and random forest) to establish models applicable to the study area and draw spatial changes in the key soil properties. Our findings revealed that the organic carbon content was the highest in grasslands, whereas shrublands had high soil salinity. The pH value indicated overall alkalinity in the study area. Additionally, the SWHD-based predictions outperformed the mean or maximum value datasets, with LightGBM showing superior performance among all models. Furthermore, the validation accuracy obtained through our optimal algorithm was significantly higher than that obtained by other products, such as Harmonized World Soil Database (HWSD) and SoilGrid250, likely because of the limitations of these datasets, which may represent historical soil properties rather than current variations in the soil properties in the region. The study also observed that the mean SOC and EC values significantly decreased compared to the historical data, while the decrease in pH was smaller but not significant. Structural equation modeling and variable importance analysis revealed that the variables with the greatest influence on modeling SOC, EC, and pH were BIO10, DTW2021_406-426_B3 (Surface reflectance acquired in spring), and land use type. Our improved model developed based on the SWHD dataset offers important scientific evidence and decision support for land use management and provides a solid foundation for future research in this field.

Effect of climate and habitat on morphological characteristics and fruit production of Picralima nitida (Stapf) in West Africa

Picralima nitida is an important African medicinal plant species used in pharmaceutical industries and traditional medicine to treat several diseases. Despite its importance, the species faces conservation problem. This study evaluated the effect of climate and habitat on morphological characteristics and fruit production of P. nitida. A total of 132 fruiting trees was randomly sampled from three habitat types (home gardens, botanical garden and natural forest) and three soils (Nitisols, Arenosols and Acrisols) in Dahomey Gap and Guineo-Congolese zone. A total of 5 morphological traits and fruit production were recorded per tree to describe the relationship between morphological determinism of the species and climate. Pearson correlation was performed to identify the suitable habitat type in each climatic zone. Harmonized World Soil Database was used to determine physicochemical characteristics of the soil types on which the species occurred. Results revealed that the climatic zones (p&lt;0.001) and habitat types (p&lt;0.0001) influenced significantly the morphological characteristics and fruit production of P. nitida. High trees with more number of fruits were observed in home gardens on acid soils (Nitisols (pH: 5) and Arenosols (pH: 5.3)) in Dahomey Gap. In Guineo-Congolese zone, the species was more present in natural forest on acid Acrisols (pH: 5).

Modeling Climate Change Indicates Potential Shifts in the Global Distribution of Orchardgrass

Orchardgrass (Dactylis glomerata L.) is highly tolerant of shade, cold, and overwintering, making it an ideal species for grassland ecological restoration and livestock production. However, the genetic diversity of orchardgrass may be threatened by climate change. Using a Maximum Entropy (MaxEnt) model with the BCC-CSM2-MR global climate database and the Harmonized World Soil Database, we projected the current and future distribution of orchardgrass suitable areas globally. The predicted ecological thresholds for vital environmental factors were determined to be a temperature seasonality range of 411.50–1034.37 °C, a mean diurnal range of −0.88–10.69 °C, a maximum temperature of the warmest month of 22.21–35.45 °C, and precipitation of the coldest quarter of 116.56–825.40 mm. A range of AUC values from 0.914 to 0.922, indicating the accuracy of the prediction model. Our results indicate that the total area of current suitable habitats for orchardgrass was estimated to be 2133.01 × 104 km2, it is dispersed unevenly over six continents. Additionally, the suitable areas of habitats increased in higher latitudes while decreasing in lower latitudes as greenhouse gas emissions increased. Therefore, efforts should be made to save places in the southern hemisphere that are in danger of becoming unsuitable, with the possibility of using northern America, China, and Europe in the future for conservation and extensive farming.

Impact of alternative soil data sources on the uncertainties in simulated land-atmosphere interactions

Numerical weather- and climate prediction models rely on soil data to accurately model land surface processes. However, as soil data are produced using soil profiles and maps with multiple sources of uncertainty, wide discrepancies prevail in global soil datasets. Comparison of four commonly used soil datasets in Earth system climate models, i.e., Food and Agriculture Organization soil data, Harmonized World Soil Database, Global Soil Dataset for Earth System Model, and global gridded soil information system SoilGrids, yields widespread differences in southern Africa. This study investigates the simulated land-atmosphere interactions in southern Africa in the context of the uncertainties from applying different global soil datasets. We conducted ensemble simulations using the fully coupled Weather Research and Forecasting Hydrological Modeling system (WRF-Hydro) incorporated with each of the global soil datasets mentioned above. Model simulations were performed at 4-km convection-permitting scale from January 2015 to June 2016. By quantifying model's internal variability and comparing the modeling results, results show that the simulated temperature, soil moisture, and surface energy fluxes are largely impacted by soil texture differences. For instance, changes in soil texture and associated hydrophysical parameters result in large differences in air temperature up to 1.7°C and surface heat flux up to 25 W/m2, and disparities in averaged surface soil moisture differ up to 0.1 m3/m3 in austral summer months. Differences in soil texture characteristics also regulate local climatic conditions differently in the wet and dry seasons as well as in different climatic regions. Furthermore, the thermodynamic differences in surface energy fluxes caused by soil texture demonstrate physical feedback perspective on atmospheric processes, resulting in distinct changes in planetary boundary layer height. This study demonstrates the non-negligible impact of soil data on land surface-atmosphere coupled modeling and highlights the need for consistent consideration of modeling uncertainties from soil data in modeling applications.

Global and regional soil organic carbon estimates: Magnitudes and uncertainties

Globally, soil is the largest terrestrial carbon (C) reservoir. Robust quantification of soil organic C (SOC) stocks in existing global observation-based estimates avails accurate predictions in carbon-climate feedbacks and future climate trends. We investigated the magnitudes and distributions of global and regional SOC estimates (i.e., density and stocks) based on five widely used global gridded SOC datasets, a regional permafrost dataset developed in 2021 (UM2021), and a global-scale soil profile database (World Soil Information Service) reporting measurements of a series of physical and chemical edaphic attributes. The five global gridded SOC datasets were the Harmonized World Soil Database (HWSD), World Inventory of Soil Emission Potentials at 30 arc-second resolution (WISE30sec), Global Soil Dataset for Earth System Models (GSDE), Global Gridded Soil Information at 250-m resolution (SoilGrids250m), and Global Soil Organic Carbon Map (GSOCmap). Our analyses showed that the magnitude and distribution of SOC varied widely among datasets, with certain datasets showing region-specific robustness. At the global scale, SOC stocks at the top 30 and 100 cm were estimated to be 828 (range: 577–1 171) and 1 873 (range: 1 086–2 678) Pg C, respectively. The estimates from GSDE, GSOCmap, and WISE30sec were comparable, and those of SoilGrids250m and HWSD were at the upper and lower ends. The spatial SOC distribution varied greatly among datasets, especially in the northern circumpolar and Tibetan Plateau permafrost regions. Regionally, UM2021 and WISE30sec performed well in the northern circumpolar permafrost regions, and GSDE performed well in China. The estimates of SOC by different datasets also showed large variabilities across different soil layers and biomes. The discrepancies were generally smaller for the 0–30 cm soil than the 0–100 cm soil. The datasets demonstrated relatively higher agreement in grasslands, croplands, and shrublands/savannas than in other biomes (e.g., wetlands). The users should be mindful of the gaps between regions and biomes while choosing the most appropriate SOC dataset for specific uses. Large uncertainties in existing global gridded SOC estimates were generally derived from soil sampling density, different sources, and various mapping methods for soil datasets. We call for future efforts for standardizing soil sampling efforts, cross-dataset comparison, proper validation, and overall global collaboration to improve SOC estimates.

Strategy for Deriving Sacramento Model Parameters Using Soil Properties to Improve Its Runoff Simulation Performances

Physically-based parameter estimations are essential to improve the simulation performance of a hydrologic model and to produce physically reasonable parameters with spatial consistency. This study proposed a parameter derivation strategy to improve the Sacramento Soil Moisture Accounting (SAC-SMA) model simulation performance based on the publicly accessible Harmonized World Soil Database (HWSD). The HWSD soil properties were used to estimate the soil moisture characteristics, and the HWSD soil texture classifications and International Geosphere-Biosphere Programme (IGBP) land cover types were used to identify the Soil Conservation Service (SCS) runoff curve number (CN). After the soil moisture characteristics and CNs were identified, the major parameters of the SAC-SMA model were derived. The simulation results were evaluated using the Nash efficiency coefficient (NSEC), and Free Search (FS) algorithm was used to further adjust and calibrate the parameters. Compared with the simulation accuracy (NSEC = 0.66~0.88) and parameter transferability (NSEC = 0.22~0.83) obtained for the SAC-SMA model using directly calibrated parameters, the HWSD data-derived parameters allowed the SAC-SMA model to achieve a similar simulation accuracy (NSEC = 0.65~0.86) and a better transferability (NSEC = 0.61~0.85).

Data Preprocessing of Soil Attributes for Ecohydrological Applications Using SWAT Model at Xin'anjiang Upstream Watershed, China

Ecohydrological (EH) models have been widely applied worldwide to investigate hydrological and environmental issues. One obstacle for EH model application in China is the input data preparation, especially the soil data. This study aims to develop two sets of methods respectively for soil data preprocessing with two different soil database sources (Harmonized World Soil Database (HWSD) and domestic Chinese Soil Databases (CSD)) for a typical EH model of SWAT in China. With the two sets of developed methods, we have resulted two sets of SWAT soil datasets for the study region of a real Chinese landscape respectively. Both sets of methods with different database sources can be generalized for other landscapes in China. We further compare the two sets of methods to give suggestions for the selection of the two database sources when applying SWAT in Chinese landscapes. With HWSD, soil data preprocessing requires relatively less work, while there are relatively more soil types being resulted. With CSD, although soil data preprocessing require relatively more work and there are relatively less soil types, the attribute information for each soil type is more detailed.

Insights for Estimating and Predicting Reservoir Sedimentation Using the RUSLE-SDR Approach: A Case of Darbandikhan Lake Basin, Iraq–Iran

Soil loss (SL) and its related sedimentation in mountainous areas affect the lifetime and functionality of dams. Darbandikhan Lake is one example of a dam lake in the Zagros region that was filled in late 1961. Since then, the lake has received a considerable amount of sediments from the upstream area of the basin. Interestingly, a series of dams have been constructed (13 dams), leading to a change in the sedimentation rate arriving at the main reservoir. This motivated us to evaluate a different combination of equations to estimate the Revised Universal Soil Loss Equation (RUSLE), Sediment Delivery Ratio (SDR), and Reservoir Sedimentation (RSed). Sets of Digital Elevation Model (DEM) gathered by the Shuttle Radar Topography Mission (SRTM), Tropical Rainfall Measuring Mission (TRMM), Harmonized World Soil Database (HWSD), AQUA eMODIS NDVI V6 data, in situ surveys by echo-sounding bathymetry, and other ancillary data were employed for this purpose. In this research, to estimate the RSed, five models of the SDR and the two most sensitive factors affecting soil-loss estimation were tested (i.e., rainfall erosivity (R) and cover management factor (C)) to propose a proper RUSLE-SDR model suitable for RSed modeling in mountainous areas. Thereafter, the proper RSed using field measurement of the bathymetric survey in Darbandikhan Lake Basin (DLB) was validated. The results show that six of the ninety scenarios tested have errors &lt;20%. The best scenario out of the ninety is Scenario #18, which has an error of &lt;1%, and its RSed is 0.46458 km3·yr−1. Moreover, this study advises using the Modified Fournier index (MIF) equations to estimate the R factor. Avoiding the combination of the Index of Connectivity (IC) model for calculating SDR and land cover for calculating the C factor to obtain better estimates is highly recommended.

Errors in soil maps: The need for better on-site estimates and soil map predictions.

High-quality soil maps are urgently needed by diverse stakeholders, but errors in existing soil maps are often unknown, particularly in countries with limited soil surveys. To address this issue, we used field soil data to assess the accuracy of seven spatial soil databases (Digital Soil Map of the World, Namibian Soil and Terrain Digital Database, Soil and Terrain Database for Southern Africa, Harmonized World Soil Database, SoilGrids1km, SoilGrids250m, and World Inventory of Soil Property Estimates) using topsoil texture as an example soil property and Namibia as a case study area. In addition, we visually compared topsoil texture maps derived from these databases. We found that the maps showed the correct topsoil texture in only 13% to 42% of all test sites, with substantial confusion occurring among all texture categories, not just those in close proximity in the soil texture triangle. Visual comparisons of the maps moreover showed that the maps differ greatly with respect to the number, types, and spatial distribution of texture classes. The topsoil texture information provided by the maps is thus sufficiently inaccurate that it would result in significant errors in a number of applications, including irrigation system design and predictions of potential forage and crop productivity, water runoff, and soil erosion. Clearly, the use of these existing maps for policy- and decision-making is highly questionable and there is a critical need for better on-site estimates and soil map predictions. We propose that mobile apps, citizen science, and crowdsourcing can help meet this need.

Validation of SoilGrids 2.0 in an Arid Region of India using In Situ Measurements

As one of the Earth’s most important natural resources, soil plays a prominent role in regulating ecosystem services, human food production systems and in facilitating a region’s sustainable development. Of late, due recognition has been given to soil sciences and soil information systems as they act as a core to achieve the targets of land degradation neutrality and help in fostering soil governance. In this regard, the availability of global soil databases paves the way for implementing successful soil information systems. Currently, harmonized world soil database from the Food and Agriculture Organization and SoilGrids from International Soil References and Information Centre serve various global soil data products in a geospatial-ready format for the scientific fraternity. In this study, SoilGrids 2.0 is validated with in situ measurements in the arid region of the Thar Desert. Soil fractions and pH at the top surface (0–5 cm) and subsurface (5–15 cm) were measured through soil sample analysis collected from the study area and compared with the values retrieved from SoilGrids 2.0 for the same location. This comparison shows that the SoilGrids 2.0 has underestimated the sand fragments up to ~28% and overestimated ~14% for silt and clay fragments. Deviation of pH in SoilGrids 2.0 was also observed with the root mean square error of one unit. However, in the comparison of soil texture classes from the field and the one given by SoilGrids 2.0, a systematic shift was found, indicating the robustness of SoilGrids prediction algorithm that can be fine-tuned by incorporating additional soil profiles (from contributing agencies) as the current snapshot of the soil database lacks dense and well-distributed soil profiles in this arid region.

The effects of late Cenozoic climate change on the global distribution of frost cracking

Abstract. Frost cracking is a dominant mechanical weathering phenomenon facilitating the breakdown of bedrock in periglacial regions. Despite recent advances in understanding frost cracking processes, few studies have addressed how global climate change over the late Cenozoic may have impacted spatial variations in frost cracking intensity. In this study, we estimate global changes in frost cracking intensity (FCI) by segregation ice growth. Existing process-based models of FCI are applied in combination with soil thickness data from the Harmonized World Soil Database. Temporal and spatial variations in FCI are predicted using surface temperature changes obtained from ECHAM5 general circulation model simulations conducted for four different paleoclimate time slices. Time slices considered include pre-industrial (∼ 1850 CE, PI), mid-Holocene (∼ 6 ka, MH), Last Glacial Maximum (∼ 21 ka, LGM), and Pliocene (∼ 3 Ma, PLIO) times. Results indicate for all paleoclimate time slices that frost cracking was most prevalent (relative to PI times) in the middle- to high-latitude regions, as well as high-elevation lower-latitude areas such the Himalayas, Tibet, the European Alps, the Japanese Alps, the US Rocky Mountains, and the Andes Mountains. The smallest deviations in frost cracking (relative to PI conditions) were observed in the MH simulation, which yielded slightly higher FCI values in most of the areas. In contrast, larger deviations were observed in the simulations of the colder climate (LGM) and warmer climate (PLIO). Our results indicate that the impact of climate change on frost cracking was most severe during the PI–LGM period due to higher differences in temperatures and glaciation at higher latitudes. The PLIO results indicate low FCI in the Andes and higher values of FCI in Greenland and Canada due to the diminished extent of glaciation in the warmer PLIO climate.

Evaluation of global and continental scale soil maps for southern Africa using selected soil properties

Earth-systems modelling of the critical zone often require soil maps that represent in-field conditions. Some developing regions, such as southern Africa, do not have detailed soil data for all areas and are often reliant on data from global soil maps and data sets. This raises the question of global and regional soil map and soil data accuracy at local levels. Although previous studies have indicated that global soil maps do not always provide an accurate representation of in-field soil conditions, it is not clear how inaccurate these maps and data are for southern Africa. This study assessed the accuracy of widely used global and continental-scale soil maps for southern Africa. The accuracy of the soil maps was assessed by comparing profile and mapped soil data. Three soil maps of global, continental, and regional scale (Harmonized World Soil Database version 1.2, SOTER for southern African, and AfSoilGrids250m) were compared to observation data from two profile data sets (Africa Soil Profile Database and South African Soil Profile Database). Furthermore, maps of selected soil properties of the AfSoilGrids250m rasters were compared to the Harmonized World Soil Database map.Soil properties were statistically compared through Lin’s Concordance Correlation Coefficient, Root Mean Square Error, and Mean Absolute Error. Investigated maps and point data generally correlated poorly and large Root Mean Square Error and Mean Absolute Error values were observed for most of the properties. Of the investigated soil maps, the digital AfSoilGrids250m soil property raster layers provided the most accurate estimate of in-field soil conditions for selected soil properties in the study region.