Harmonized World Soil Database Research Articles

Factors controlling soil organic carbon content in wetlands at multiple scales and assessment of the universality of estimation equations: A mega-data study

The factors controlling soil organic carbon (SOC) content in wetlands need to be identified to estimate the global stores of SOC. Although there have been a large number of small-scale studies of the local patterns of SOC content, global studies are still required. We used a random forest algorithm and other statistical approaches to determine the controls on the SOC content in wetlands at global, continental, and national scales based on the Harmonized World Soil Database and field data. The results showed that, at the three scales explored, the soil cation exchange capacity and bulk density were the main controls on the SOC content in wetlands. Moreover, equations for estimating global SOC content were established. To assess the universality of SOC content estimation equations, the soil properties were considered as a “community” and the normalized stochasticity ratio (NST) was used to assess the stochasticity in the assembly of soil “communities”. The results showed that, globally, the interaction of these factors was stochastic in the “community” composed of the controllers and SOC. The reason for this result might be that microbes were not considered in the equation. Therefore, the weighted abundance of related microbes (WARM) was therefore recommended in the estimation of SOC. With NST and WARM factors, we found that microbes play a key role in increasing the determinacy of SOC estimation equations in wetlands with less anthropogenic contamination. Our findings show that when microbial impacts are taken into account, the patterns of SOC content in pristine wetlands are more universal. Our newly established equations for estimating global SOC content are crucial in projecting changes in wetland SOC, and the two factors indicated in this study favor the universality for SOC content estimation.

Flood Modeling on Koya Catchment Area Using Hyfran, Web Map Service, and HEC-RAS Software

In this research, The boundaries of the Koya catchment area have been delineated, and valley paths in the region were drawn by using the Water Modeling System (WMS) software, Analysis of the morphometric information indicated that the morphometric characteristics of watersheds contribute to the floods. The average surface runoff depth depends on the curve number values that are determined based on the types of soil cover and soil class according to Harmonized World Soil Database HWSD software that indicates the soil class in the study area are Group B silt loam, The results obtained also show that the potential for surface runoff varies with land use and soil characteristics. Also, the value of the curve number (CN) was determined to be 71. The hydrological modeling was performed by the HEC-HMS program that simulates the process of rainfall to runoff using the SCS curve number model. A flood hydrograph was constructed at the catchment area outlet and the floodplain delineation was verified by the HEC-RAS software. The results indicated that the 100-year return period flood could Reach critical areas such as the urban area, agricultural area, residential areas. the results of this study indicate that there are suitable sites in the catchment areas for constructing small dams and ponds for water harvesting.

Analysis of the consequences of land-use changes and soil types on organic carbon storage in the Tarim River Basin from 2000 to 2020

With fast development of industry and agriculture in last two decades, land use changed greatly on specific soil types of intrinsic low quality with C loss potential and necessitates avoiding. Although researchers studied the effect of land-use changes on soil carbon storage, that on some soil types had rarely been reported, which led to relatively more C loss. We explored the impact of land-use changes and soil types on soil organic carbon (SOC) sequestration in the top 30 cm in the Tarim River Basin (the second largest inland river in the world) from 2000 to 2020. The land-use images with a one-kilometer spatial resolution and soil data from the Harmonized World Soil Database were analysed. The groundwater levels were monitored by 11 wells along the river from 2012 to 2017. Land-use changes were dominated by increases in the areas of cultivated land (1843–4099 km2) and woodland (5055–5433 km2) and decreases in grassland area ( 19076–12634 km2). The increase in cultivated land area was acquired from grassland (54%), woodland (32%) and unused land (14%), which was dominated by Phaeozems, Solonchaks and Fluvisols (84%). The converted land use to woodland was dominated by Fluvisols, Phaeozems, Arenosols and Solonchaks (98%). The converted land use from grassland to other land-use types was dominated by Arenosols, Fluvisols and Phaeozems (85%). The SOC was reduced by 9.83 Tg (+8.04 in cultivated land, +0.06 in woodland, −17.93 in grassland). The SOC stock change efficiency (SOCE kg C m−2) was SOC change divided by area change. The increase in SOC (Tg) of cultivated land was 5.04 from grassland (SOCE 3.76) and 2.9 from woodland (SOCE 3.68). The SOC of woodland increased by 8.66 Tg at cost of losing grassland (SOCE 3.35). Moreover, land-use changes affected local ecological environment. The cultivated land along the river increased 298 km2, and the desertification advanced towards oasis by decreasing 950 km2 of grassland in transition zone. The average groundwater table increased in the upstream (−3.12 to −2.33 m) and midstream regions (−1.84 to −1.71 m) monthly from 2012 to 2017 and in the downstream region (−7 to −2.84 m) annually from 2009 to 2017. This was attributed to expanded cultivated land and salt-washing cultivation on Solonchaks. In conclusion, the land-use conversion from grassland to woodland and cultivated land, mainly for Fluvisols, Phaeozems and Solonchaks, caused negative SOC storage, especially in riverbanks and oasis-desert transition zones facing climate change.

USING SPATIAL DATA TO SIMULATE RAIN RUNOFF

The paper presents the main stages of spatial data preparation for computer models of runoff formation by the example of the Charysh River basin. DEM was employed to construct a system of sub-basins and a hydrological graph as well as to calculate morphometric characteristics of its elements. Sources of data on vegetation and mechanical composition of soils are given Using the Harmonized World Soil Database and the soil map of Altai Krai, a map of soil texture (grain-size composition) of the Charysh River basin was created. Two ways of precipitation accounting (the weather station data; the Persiann-CDR data set) were compared. Calculations of runoff from the the Charysh catchment were made for two models.

Projecting the urbanization effect on soil organic carbon stocks in polar and steppe areas of European Russia by remote sensing

This paper describes the effect of urbanization on soil organic carbon (SOC) stocks by projecting the main urban land cover classes over the initial pre-urban soil maps. Two cities different in climate and soil conditions as well as in availability of SOC data were chosen as the case studies. Rostov-on-Don is the center of Russian South, where croplands and natural steppes have been conventionally thoroughly studied by soil scientists. In contrast, soils of Murmansk located in Russian Arctics have always been overlooked due to low suitability for agriculture. Global, national and regional soil maps and databases were used to estimate pre-urban SOC stocks in the areas. The outcomes based on Harmonized World Soil Database were highly uncertain, underestimating 0-100 cm SOC stocks in the polar region and overestimating them in the steppe region, whereas the results based on Digital Soil Map of Russia and regional maps were comparable. Land cover structures of Rostov-on-Don and Murmansk were mapped based on the stepwise per-pixel and sub-pixel classification algorithms applied to Sentinel-2 and included the following classes: sealed soils, green lawns, trees and shrubs, bare soils and water. Murmansk was dominated by trees and shrubs (58.1%) with the proportion of area 17.5% covered by sealed soils. In Rostov-on-Don, less than 30% of the total area was covered by trees and shrubs which is also comparable with bare soils (19.6%)_and lawns (23.4%), whereas almost one third of the territory was sealed (27.6%). These land cover structures had a different impact on the topsoil SOC stocks: a 30-50% increase in Murmansk compared to the 18% decrease in Rostov-on-Don. An increase of the 0-100 cm SOC stocks was shown for both regions, however in the polar conditions it was two times higher compared to the steppe. In polar conditions, conversion of natural soils into urban non-sealed soils increased SOC stocks from 30% to more than 4 times in 0-10 cm layer and from 47% to almost 3 times in the 0-100 cm layer. The highest increase was reported for the lawns, whereas SOC under trees and shrublands were considerably lower. In Rostov-on-Don , sealed and bare soils stored less SOC compared to the initial natural soils. The conversion of natural areas into urban green infrastructure increased SOC up to 50-70%. Although the absolute SOC values based on the global and national legacy data are highly uncertain, especially for the polar areas, the research outcomes clearly reveal possible patterns in SOC changes induced by different urbanization pathways in contrast climatic conditions and highlight the complexity of the urbanization effect on soils

Uncertainty of modelled bioenergy with carbon capture and storage due to variability of input data

AbstractUncertainty is inherent in modelled projections of bioenergy with carbon capture and storage (BECCS), yet sometimes treated peripherally. One source of uncertainty comes from different climate and soil inputs. We investigated variations in 70‐year UK projections of Miscanthus × giganteus (M × g), BECCS and environmental impacts with input data. We used cohort datasets of UKCP18 RCP8.5 climate projections and Harmonized World Soil Database (HWSD) soil sequences, as inputs to the MiscanFor bioenergy model. Low annual yield occurred 1 in 10 years as a UK‐average but yield uncertainty varied regionally, especially south and east England. BECCS projections were similar among cohorts, with variation resulting from climate cohorts of the same database ensemble (3.99 ± 0.14 t C ha−1 year−1) larger than uncertainty resulting from soil sequences in each grid block (3.96 ± 0.03 t C ha−1 year−1). This is supported by annual time series, displaying variable annual climate and a close yield–BECCS–climate relationship but partial correspondence of yield and BECCS with maximal soil variability. Each HWSD soil grid square contains up to 10 ranked soil types. Predominant soil commonly has over 50% coverage, indicating why BECCS from combined soil sequences were not significantly different from BECCS using the dominant soil type. Mean BECCS from the full climate ensemble combined with the full soil sequences, over the current area of cropping limits in England and Wales, is 3.98 ± 0.14 t C ha−1 year−1. The bioenergy crop has a mean seasonal soil water deficit of 65.79 ± 4.27 mm and associated soil carbon gain of 0.22 ± 0.03 t C ha−1 year−1, with bioenergy feedstock calculated at 131 GJ t−1 y−1. The uncertainty is specific to the input datasets and model used. The message of this study is to ensure that uncertainty is accounted for when interpreting modelled projections of land use impacts.

New Insight on Soil Loss Estimation in the Northwestern Region of the Zagros Fold and Thrust Belt

Soil loss is one of the most important causes of land degradation. It is an inevitable environmental and socio-economic problem that exists in many physiographic regions of the world, which, besides other impacts, has a direct bearing on agricultural productivity. A reliable estimate of soil loss is critical for designing and implementing any mitigation measures. We applied the widely used Revised Universal Soil Loss Equation (RUSLE) in the Khabur River Basin (KhRB) within the NW part of the Zagros Fold and Thrust Belt (ZFTB). The areas such as the NW Zagros range, characterized by rugged topography, steep slope, high rainfall, and sparse vegetation, are most susceptible to soil erosion. We used the Digital Elevation Model (DEM) of the Shuttle Radar Topography Mission (SRTM), Tropical Rainfall Measuring Mission (TRMM), Harmonized World Soil Database (HWSD), and Landsat imagery to estimate annual soil loss using the RUSLE model. In addition, we estimated sediment yield (SY) at sub-basin scale, in the KhRB where a number of dams are planned, and where basic studies on soil erosion are lacking. Estimation of SY will be useful in mitigation of excessive sedimentation affecting dam performance and watershed management in this region. We determined the average annual soil loss and the SY in the KhRB to be 11.16 t.ha−1.y−1 and 57.79 t.ha−1.y−1, respectively. The rainfall and runoff erosivity (R factor), slope length (L factor), and slope steepness (S factor), are the three main factors controlling soil loss in the region. This is the first study to determine soil loss at the sub-basin scale along with identifying suitable locations for check dams to trap the sediment before it enters downstream reservoirs. The study provides valuable input data for design of the dams to prevent excessive siltation. This study also aims at offering a new approach in relating potential soil erosion to the actual erosion and hypsometric integrals.

Measuring land losses caused by water erosion using the SWAT model in the Ourika watershed in the High Atlas of Morocco

In Morocco, the phenomena of water erosion cause significant economic losses mainly linked to the silting up of dams, the degradation of equipment and socio-economic infrastructures, the loss of soil productivity and the insecurity of the population. The SWAT (Soil and Water Assessment Tool) model was used to estimate the quantities of sediments generated by the various erosive processes at the level of the Ourika watershed. The SWAT modeling, which is done with daily time steps, used as basic data; a Digital Elevation Model GDEM-ASTER (Global Digital Elevation-Advanced Space borne Thermal Emission and Reflection Radiometer) with 30 m of resolution, a land cover map developed from the Landsat 8 OLI (Operational Land Imager) satellite image of 2017 with 30 m of resolution and a soil map published by FAO (Harmonized World Soil Database). Also, daily meteorological data from the Tensift Water Basin Agency over a period from 1992 to 2001 were used. The results obtained showed that soil losses due to water erosion in the Ourika watershed reached an average of 9.18 t.ha-1.year-1. The model was calibrated and validated using the SWAT-CUP (SWAT Calibration and Uncertainty Procedures) software SUFI-2 (Sequential Uncertainty Fitting) and after several simulations and iterations a determination coefficient R2 of 0.76 was obtained.

Outlook from the soil perspective of urban expansion and food security

The use of soil as support for built-up areas represents only one of its several functions. Farmlands at the fringe of conurbations have more chance of being converted into built-up areas due to the favourable topography and the accessibility to existing infrastructure, being in the vicinity of urban areas. We analysed the global land-take during the period 2000–2014. The data are based on a global dataset describing the spatial evolution of human settlements using the Global Human Settlement Layer, which was derived from Landsat images collected in 1975, 1990, 2000 and 2014. Although the global land-take represents roughly 0.1% of the global terrestrial Earth, it affects 1% of the naturally fertile soils, according to the proposed Soil Productivity Indexes (SPI), based upon the potential soil productivity, calculated on the basis of the Harmonized World Soil Database. We have found that, few large conurbations develop on potentially high productive soil, while scarcely productive soils sustain the expansion of several megalopolises. On a global scale and through the centuries, considered comparatively as individual overall age of settlements, a trend between the intrinsic quality of the soils and its use for settlement purposes as major competitor, was not observed.

Using Apparent Electrical Conductivity as Indicator for Investigating Potential Spatial Variation of Soil Salinity across Seven Oases along Tarim River in Southern Xinjiang, China

Soil salinization is a major soil health issue globally. Over the past 40 years, extreme weather and increasing human activity have profoundly changed the spatial distribution of land use and water resources across seven oases in southern Xinjiang, China. However, knowledge of the spatial distribution of soil salinization in this region has not been updated since a land survey in the 1970s to 1980s (the harmonized world soil database, HWSD) due to scarce observational data. Now, given the uncertainty raised by near future climate change, it is important to develop quick, reliable and accurate estimates of soil salinity at larger scales for a better manage strategy to the local fragile ecosystem that with limited land and water resources. This study collected electromagnetic induction (EMI) readings near surface soil to update on the spatial distribution and changes of water and salt in the region and to map apparent electrical conductivity (ECa, mS·m−1), in four coil configurations: vertical dipole in 1.50 m (ECav01) and 0.75 m (ECav05), so as the horizontal dipole in 0.75 m (ECah01) and 0.37 m (ECah05), then all the ECa coil configurations were modeled with random forest algorithm. The validation results showed an R2 range of 0.77–0.84 and an RMSE range of 115.17–142.76 mS·m−1. The validation accuracy of deep ECa dipole (ECah01, ECav05, and ECav01) was greater than that of shallow ECa (ECah05), as the former integrated a thicker portion of the subsurface. The range of EC spatial variability that can be explained by ECa is 0.19–0.36 (farmland, mean value is 0.28), grassland is 0.16–0.49 (shrub/grassland, mean value is 0.34), and bare land is 0.28–0.70 (bare land, mean value is 0.56). Among them, ECav01 has the best predictive ability. As the depth increased, the influence of soil-related variables decreased, and the contribution of climate-related variables increased. The main factor affecting ECa variation was climate-related variables, followed by vegetation-related variables and soil-related variables. Scatter plot show ECa was significantly correlated with ECe_HWSD_030 (0–30 cm, r = 0.482, p < 0.01) and ECe_HWSD_30100 (30–100 cm, r = 0.556, p < 0.01). The predicted spatial ECa maps were similar to the ECe values from HWSD, but also implies that the distribution of soil water and salt has undergone tremendous changes since 1980s. The study demonstrates that EMI data provide a reliable and cost-effective tool for obtaining high-resolution soil maps that can be used for better land evaluation and soil improvement at larger scales.

Relative Importance of Land Use and Climate Change on Hydrology in Agricultural Watershed of Southern China

Quantitative assessment of the impact of land use and climate change on hydrological processes is of great importance to water resources planning and management. The main objective of this study was to quantitatively assess the response of runoff to land use and climate change in the Zhengshui River Basin of Southern China, a heavily used agricultural basin. The Soil and Water Assessment Tool (SWAT) was used to simulate the river runoff for the Zhengshui River Basin. Specifically, a soil database was constructed based on field work and laboratory experiments as input data for the SWAT model. Following SWAT calibration, simulated results were compared with observed runoff data for the period 2006 to 2013. The Nash-Sutcliffe Efficiency Coefficient (NSE) and the correlation coefficient (R2) for the comparisons were greater than 0.80, indicating close agreement. The calibrated models were applied to simulate monthly runoff in 1990 and 2010 for four scenarios with different land use and climate conditions. Climate change played a dominant role affecting runoff of this basin, with climate change decreasing simulated runoff by −100.22% in 2010 compared to that of 1990, land use change increasing runoff in this basin by 0.20% and the combination of climate change and land use change decreasing runoff by 60.8m3/s. The decrease of forestland area and the corresponding increase of developed land and cultivated land area led to the small increase in runoff associated with land use change. The influence of precipitation on runoff was greater than temperature. The soil database used to model runoff with the SWAT model for the basin was constructed using a combination of field investigation and laboratory experiments, and simulations of runoff based on that new soil database more closely matched observations of runoff than simulations based on the generic Harmonized World Soil Database (HWSD). This study may provide an important reference to guide management decisions for this and similar watersheds.

River basin salinization as a form of aridity

Soil-salinization affects, to a different extent, more than one-third of terrestrial river basins (estimate based on the Food and Agriculture Organization Harmonized World Soil Database, 2012). Among these, many are endorheic and ephemeral systems already encompassing different degrees of aridity, land degradation, and vulnerability to climate change. The primary effect of salinization is to limit plant water uptake and evapotranspiration, thereby reducing available soil moisture and impairing soil fertility. In this, salinization resembles aridity and-similarly to aridity-may impose significant controls on hydrological partitioning and the strength of land-vegetation-atmosphere interactions at the catchment scale. However, the long-term impacts of salinization on the terrestrial water balance are still largely unquantified. Here, we introduce a modified Budyko's framework explicitly accounting for catchment-scale salinization and species-specific plant salt tolerance. The proposed framework is used to interpret the water-budget data of 237 Australian catchments-29% of which are already severely salt-affected-from the Australian Water Availability Project (AWAP). Our results provide theoretical and experimental evidence that salinization does influence the hydrological partitioning of salt-affected watersheds, imposing significant constraints on water availability and enhancing aridity. The same approach can be applied to estimate salinization level and vegetation salt tolerance at the basin scale, which would be difficult to assess through classical observational techniques. We also demonstrate that plant salt tolerance has a preeminent role in regulating the feedback of vegetation on the soil water budget of salt-affected basins.

Self-organizing map of soil properties in the context of hydrological modeling

One of the most relevant inputs for hydrological modeling is the soil map. The soil sources and scales for the soil properties are diverse, and the quality of soil mapping is increasing, but soil surveying is time-consuming and large area campaigns are expensive. The taxonomic unit approach for soil mapping is common and limited to one layer of data. This limitation causes errors in simulated water fluxes through the soil when taxonomic units approach is implemented during hydrological modeling analysis. Some strategies using geostatistics and machine learning algorithms such as Kriging and Self-Organizing maps (SOM) are improving the taxonomic units’ approach and could serve as an alternative for soil mapping for hydrological purposes. The aim of this work is to study the influence of different soil maps and resolutions on the main hydrological components of a sub-arid watershed in central Spain. For this, the Soil Water and Assessment Tool (SWAT) was parameterized with three different soil maps. A first one was based on Harmonized World Soil database from FAO, at scale 1:1,000,000 (HWSD). The other two were based on a Kriging interpolation at 100 × 100 m from soil samples. To obtain soil properties map from it, two strategies were applied: one was to average the soil properties following the official taxonomic soil units at 1:400,000 scale (Agricultural Technological Institute of Castilla and Leon - ITACyL) and the other was to applied Self-organizing map (SOM) to create the soil units (SOMM).The results suggest that scale and soil properties mapping influence HRU definition, which in turn affects water flow through the soils. Statistical metrics of model performance were improved from R2 =0.62 and NSE=0.46 with HWSD soil map to R2 =0.86 and NSE=0.84 with SOM and similar values were achieved during validation. Thus, the SOM is presented as an innovative algorithm applied for hydrological modeling with SWAT, significantly increasing the level of model accuracy to stream flow in sub-arid watersheds.

Assessing countrywide soil organic carbon stock using hybrid machine learning modelling and legacy soil data in Cameroon

Countrywide estimates of soil organic carbon stock (SOCS) are useful to set up national strategies for sustainable land use management as well as to enhance the accuracy of global SOCS inventories. We appraised the spatial distribution of SOCS at five depth layers (0–15 cm, 15–30 cm, 30–100 cm, 0–30 cm and 0–100 cm) in Cameroon at 100 m spatial resolution, using a national harmonized legacy soil database (Camsodat 0.1) with 1432 georeferenced soil profiles. We assessed the prediction performances of random forest (RF) and generalized boosted regression (GBR), combined with two hybridization approaches of spatial interpolation of the residuals using ordinary kriging (OK) and inverse distance weighting (IDW). The estimates were compared to two global estimates derived from the Harmonized World Soil Database (HWSD) and SoilGrids250m. The SOCS distribution across the country showed a moderate spatial heterogeneity at all depth layers with coefficients of variation between 35% and 47%, and values ranging from 6 to 108 Mg C ha−1 at 0–15 cm, from 4 to 107 Mg C ha−1 at 15–30 cm, from 10 to 276 Mg C ha−1 at 30–100 cm, from 11 to 210 Mg C ha−1 at 0–30 cm and from 21 to 468 Mg C ha−1 at the 0–100 cm layer. Of the selected environmental covariates, terrain and climate attributes were the most relevant to predict the SOCS spatial distribution at country level. The RF model outperformed the GBR model, with about 10% improvement on prediction performance (R2) for most soil depths. The hybridization further slightly improved performance. However, OK was only slightly better than IDW in the overall assessment. Compared to national estimates, SoilGrids overestimated the SOCS by 15% at 0–30 cm depth, while HWSD underestimated SOCS by 26% at the same depth. Overall, about 5.7 Pg C are stored in the top 1 m of soils in Cameroon, with about 50% of that in the top 30 cm. The national distribution of SOCS is consistent with the pattern of agro-ecological zones. Our assessment provides baseline information for sustainable land management and climate change mitigation, as well as for improving the understanding of the spatial distribution of SOCS in Cameroon.

Soil acidity challenges and the significance of liming and organic amendments in tropical agricultural lands with reference to Ethiopia

Globally, about 30% of the ice-free land, comprising large proportion of agricultural land, is affected by soil acidity which is potentially limiting agricultural productivity and causing environmental challenges. On the other hand, it is well documented that liming can curb the situation and boost agricultural production when appropriately applied. However, it is not well understood why the tendency of farmers toward liming is generally poor in tropical smallholder farming systems. Thus, this review is aimed to critically evaluate the effect of liming on crop yield, analyze the significance of organic amendment as an alternative to liming to treat acid soils and identify the socioeconomic challenges obstructing the implementation of soil acidity treatment. With this review, the possible link of land management and soil acidity is also evaluated to showcase if approaches to any other land management could have negative or positive effects on soil acidity. To understand the effect of management on soil acidity, a logistic regression was run using soil data from the Harmonized World Soil Database, taking soil organic carbon (SOC) as an explanatory variable and pH as a dependent variable. With this review, studies from science, socioeconomics and policy focusing on soil acidity and its management were assessed. The literature showed liming can increase crop yield up to 0.5 t ha−1. The regression analysis to support this review also revealed a positive relationship between SOC and pH, implying the strong effect of land management on soil acidity. This further reveals that a bad management of soil organic matter can lead to an exacerbation of soil acidity. In other words, effective soil acidity management in the region requires an integrated approach that involves combined application of organic amendments, liming and applying optimum inorganic fertilizers. In short, the principle of integrated soil fertility management can be applicable to mitigate soil acidity problems in the region. This, however, needs to be complemented with improved policies that incorporate effective incentive mechanisms.

Increased Lateral Transfer of Soil Organic Carbon Induced by Climate and Vegetation Changes Over the Southeast Coastal Region of China

AbstractSoil erosion and the subsequent lateral movement of soil carbon profoundly affect the spatial pattern of carbon sources and sinks in terrestrial ecosystems. The eight river basins of Fujian Province considered in this study are an important part of the southeastern coastal river basin of the ninth largest river system in China. In recent years, the erosion of soil and soil organic carbon in the southeast coastal region of China is not ideal owing to the high and concentrated rainfall, natural vegetation destruction, and other reasons. In this study, the soil organic carbon erosion and deposition rates related to erosion in the eight river basins of Fujian Province are quantified, and their spatial distribution characteristics are extracted. Based on the soil erosion rate from two national soil erosion surveys and topsoil carbon content from the Harmonized World Soil Database, we estimated that the lateral soil organic carbon erosion increased from 7.53 to 26.75 g C m−2 yr−1 in eight river basins of Fujian Province from 1995–1996 to 2010–2012. The soil organic carbon erosion rate in the eight river basins increased by 2.6 times, while the soil organic carbon erosion rate for the whole of China decreased by 44% during the same period. The increase in torrential rain and decrease in vegetation cover in the southeastern coastal areas largely contributed to the erosion of soil and soil organic carbon. The lateral transfer rate of soil organic carbon may be further exacerbated by the increase of torrential rain in the southeastern coastal areas of China.

Updated information on soil salinity in a typical oasis agroecosystem and desert-oasis ecotone: Case study conducted along the Tarim River, China.

Precise and spatially explicit regional estimates of soil salinity are necessary to efficiently management and utilise limited land and water resources. Despite advances achieved in remote sensing over the past century, knowledge about the distribution and severity of soil salinization in economically important areas, such as oasis agroecosystems and desert-oasis ecotones (OADoE), is currently limited. An example of an area is southern Xinjiang, where the OADoE has a high anthropogenic influence. This study was conducted with the aim of mapping soil salinity in typical OADoE using remote sensing and machine learning techniques (Cubist and Random Forest, RF). A range of covariates was obtained from the multi-temporal Landsat-8 operational land imager (OLI) satellite for the period from 2013 to 2018. The values of coefficients of determination (R2), Lin's concordance correlation coefficient, root mean square error, and relative root mean squared error values, were 0.78, 0.87, 9.59, and 0.76, respectively, for the Cubist and 0.78, 0.86, 9.79, and 0.78, respectively, for RF models. The slope of the linear fitting equation was higher for the Cubist model (0.75) than for RF (0.69). The explanatory power of Cubist and RF for soil salinity variation were 33.22% and 31.41% in the agroecosystem, and 72.25% and 71.66% in desert-oasis ecotone, respectively. For the agroecosystem, the range of the predicted values for 89.13% (Cubist) and 84.78% (RF) of sample was controlled within the same observational range at an interval of 0-5 dS m-1. Compared to single-year data (from 2013 to 2018), the ability to account for model spatial variability in soil salinity based on multi-year Landsat images was increased by 16%-35%. According to the variable importance evaluation, soil-related indices are the most important predictor variables, followed by vegetation, topography, landform, and land use, with relative importance values of 60%, 21%, 16%, and 3%, respectively. The predicted map was also broadly consistent with those obtained for Xinjiang in the Harmonized World Soil Database (HWSD) from the second national soil survey of China conducted from 1984 to 1997. The results also showed that the average value of the study area is 8.10 dS m-1 based on the Cubist-based map whereas that of the HWSD is 10.60 dS m-1, this implied that the overall salinity level has reduced by 23.58%. The methodological framework presented covers all prediction process steps and has considerable potential to be used in future soil salinity mapping at large scales for other similar region as OADoEs. The map derived from the Cubist/RF model revealed more detailed variation information about spatial distribution of the soil salinity compared to HWSD, and can further assist with decision-making when planning and utilising on existing soil and water resources in OADoEs.

Understanding the effects of soil data quality on SWAT model performance and hydrological processes in Tamedroust watershed (Morocco)

Hydrological modeling remains an indispensable tool for water resources research in order to understand the watershed functioning and to effectively manage water supplies. The study of the hydrologic functioning of a semi-arid watershed is a real challenge in the lack of reliable and regular data. In this work, we propose (1) to evaluate the performance of Soil and Water Assessment Tool (SWAT) on the TAMEDROUST watershed (Morocco) under extremely contrasted climatic conditions during the period 1998–2002 and (2) to test the effects of the soil data resolution on the watershed response. For this second purpose, two soil databases are compared: (i) HWSD-2L, a low-resolution soil database with three soil types obtained from the Harmonized World Soil Database produced by FAO and (ii) TAMED-SOL, a refined database with eleven soil types, performed from field measurements and laboratory analyses.Results before calibration showed a considerable variability and a significant effect of the soil characteristics on the different components of the hydrological cycle such as SW (soil water content) and GWQ groundwater discharge into reach). Flows obtained by HWSD-2L soil database were consistently lower than those simulated by TAMED-SOIL but both databases overpredicted streamflow if compared to observed data. To harmonize the data of the two databases, we proceeded to a harmonization of the soil depth by considering in HWSD-2L only the surface layer, thus creating a third database HWSD-1L which was also considered in the evaluation of the model response. After the calibration period, both soils databases (TAMED-SOIL and HWSD-2L) improved the model performance in terms of streamflow, with values of R2 and NSE between 0.64 and 0.65. Model validation was acceptable and similar for both databases with R2 and NSE values of 0.76 and 0.57.We can conclude that the quality and resolution of soil data affects all hydrological cycle components, mainly SW and water yield (WYLD), but do not lead to significant discrepancies in streamflow simulation, especially after the model calibration.

Rare and endangered soils in the Taihang Mountain region, North China

AbstractLoss of pedodiversity is a worsening land degradation issue worldwide. Knowledge of rare and endangered soils is critical for the protection of pedodiversity. However, such knowledge remains limited, especially for mountain regions with intensive human disturbance. In this paper, rare and endangered soils were identified using the Harmonized World Soil Database (HWSD), and their spatial distributions were determined for Taihang Mountain region, North China. Furthermore, the effects of natural (elevation, precipitation, temperature, slope gradient, soil parent, and vegetation) and human (farming and population density) factors on rare and endangered soils were analyzed. The results showed that based on the second‐level HWSD classification, a total of 94 soil types were identified in Taihang Mountain region, 36 of which were rare and 7 endangered soils. It implied that there was the need to protect soil resources in this region. Cluster analysis showed that Dystric Podzoluvisols and Mollic Gleysols were the most endangered soils in terms of both area and distribution, suggesting that these soil types needed to be protected. Most of the rare and endangered soils were distributed in the northern of Taihang Mountain, indicating that the northern part needed more attention. Correlation analysis showed that the effect of human factors on rare and endangered soils was more important than that of natural factors, confirming that human activities were largely responsible for the occurrence of rare and endangered soils. Such results provided not only further understanding of pedodiversity but also useful information for protection of soil resources and control of land degradation.

Global mapping of soil salinity change

Soil salinity increase is a serious and global threat to agricultural production. The only database that currently provides soil salinity data with global coverage is the Harmonized World Soil Database, but it has several limitations when it comes to soil salinity assessment. Therefore, a new assessment is required. We hypothesized that combining soil properties maps with thermal infrared imagery and a large set of field observations within a machine learning framework will yield a global soil salinity map. The thermal infrared imagery acts as a dynamic variable and allows us to characterize the soil salinity change. For this purpose we used Google Earth Engine computational environment. The random forest classifier was trained using 7 soil properties maps, thermal infrared imagery and the ECe point data from the WoSIS database. In total, six maps were produced for 1986, 2000, 2002, 2005, 2009, 2016. The validation accuracy of the resulting maps was in the range of 67–70%. The total area of salt affected lands by our assessment is around 1 billion hectares, with a clear increasing trend. Comparison with 3 studies investigating local trends of soil salinity change showed that our assessment was in correspondence with 2 of these studies. The global map of soil salinity change between 1986 and 2016 was produced to characterize the spatial distribution of the change. We conclude that combining soil properties maps and thermal infrared imagery allows mapping of soil salinity development in space and time on a global scale.